Fantasy Football Week 5 Trade Tips: Buy Low, Sell High

Week 5 of the NFL season is upon us, and it might be time to swing for a big trade in your fantasy football league. Whether you’re 4-0, 0-4. or somewhere in between, here’s some helpful advice as we get into trade season with three buy low, sell high candidates in fantasy football.

Sell high candidates:

Raheem Mostert, RB, MIA: We typically don’t want to sell off players who are in one of the league’s best offenses, especially when they’re averaging over 5 yards a carry and have 6 touchdowns through four weeks. But this is the perfect time to sell high on Mostert. Aside from the age (31) and injury concerns with Mostert, we’ve seen this story play out before. Mostert ceded carries last year to Jeff Wilson Jr. halfway through the 2022 season after carrying a big workload early on.

The Dolphins might be wise to run back a similar plan this year with De’von Achane, who is averaging a ridiculous 11.4 yards per attempt on 27 carries in the last two weeks. It wouldn’t be a surprise to see the fresh legs of Achane be leaned on even more going forward, which should make Mostert a sell high candidate while his numbers are still gaudy.

Trade targets for Mostert in season-long leagues: DET RB Jahmyr Gibbs, WAS WR Terry McLaurin

Kenneth Walker, RB, SEA: Kenneth Walker is one of the most fascinating running backs in the league, as he leaves lots of yardage on the table in order to hunt for big plays. While that’s a great thing for fantasy football, you could argue that Zach Charbonnet, who the Seahawks drafted in the second round, has run harder and more effectively over the last two weeks than Walker and is earning a bigger share of the load.

Teams will often make adjustments like that heading into their bye weeks, and with the Seahawks having one in Week 5, this could be the perfect time to cash in on Kenneth Walker’s hot start (4.4 YPA, 5 TD) and sell high with some tough defenses ahead. Seattle plays Cleveland, Baltimore, and San Francisco before the fantasy playoffs, then gets the 49ers again, Philadelphia, and Tennessee in the fantasy football playoffs. That’s a brutal stretch for a running back, even if Charbonnet remains a clear-cut backup.

Trade targets for Kenneth Walker in season-long leagues: NO WR Chris Olave, CIN WR Ja’Marr Chase, DET TE T.J. Hockenson, LAC RB Austin Ekeler

Adam Thielen, WR, CAR: We keep hearing noise about Carolina wanting to trade for a true WR1, so it’s best to move on Thielen in case that actually happens. Thielen has some momentum over the last two weeks, putting up a 11-145-1 line in Week 3 with Andy Dalton and a 7-76 day against his former teammates in Minnesota. That seemed like the two best-case scenarios in a row for Thielen, however, who might be more prone to breaking down at age 33 as the season rolls on. If you can sell high to someone on Bryce Young’s improvement and Thielen being his top target, do it now while that window is open.

Trade targets for Thielen in season-long leagues: DEN WR Marvin Mims Jr., KC WR Rashee Rice, BAL RB Gus Edwards

Buy low candidates:

Jaylen Waddle, WR, MIA: Waddle has been killing fantasy football owners so far this year, as he cost a second-round pick but has yet to catch more than four passes in a game and also missed a week. This is a perfect time to strike for one of the league’s best receivers in a red-hot offense, and the price should be low due to recent performance and injury concerns. If Jaylen Waddle is on a team sinking below .500, throw them a lifesaver-type offer at positions of need and snatch up a massive talent on a discount. This should be as low as Jaylen Waddle’s stock gets all year.

Players to offer for Jaylen Waddle in season-long leagues:TB WR Chris Godwin, LAR RB Kyren Williams, KC RB Isiah Pacheco

Breece Hall, RB, NYJ: The snap count is off, and there’s a good chance Breece Hall goes absolutely nuclear against a Broncos defense that has allowed the most fantasy points to the running back position in the league through four weeks. Make no mistake: this is Breece Hall’s backfield, as Dalvin Cook has been awful and the Jets are going to want to pound the rock with Zach Wilson at quarterback. Don’t be afraid to go over the top to get Breece Hall before he’s impossible to trade for moving forward.

Players to offer for Breece Hall in season-long leagues: DET RB David Montgomery, IND WR Michael Pittman Jr., SF WR Deebo Samuel

Jordan Addison, WR, MIN: KJ Osborn is off to a brutal start (0.77 yards per route, 91st among WR) and should start forfeiting snaps in 2-WR sets to Addison, who has been up-and-down to start the season. One thing we know about the Vikings is that they are going to throw the ball, whether by desire or by necessity, and Addison feels like the kind of late-season bloomer who will benefit massively from teams sending multiple defenders Justin Jefferson’s way. Coming off a game where he didn’t record a catch, this is a perfect time to trade for Addison and hope he torches a favorable playoff schedule (LV, CIN, DET).

Players to offer for Addison in season-long leagues: DEN WR Courtland Sutton, CHI RB Khalil Herbert, JAX TE Evan Engram

The post Fantasy Football Week 5 Trade Tips: Buy Low, Sell High appeared first on ClutchPoints.

Posted 10 Oct 2023 07:31 by | News | 27 comments

MSC-Exos & diabetes-related cognitive impairment. | JIR

Data Sharing Statement

The current study was based on the results of relevant published studies.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

This review was funded by the Innovation Fund (No.CI2021A04803), Scientific research project of Guangdong Provincial Bureau of Traditional Chinese Medicine and Medicine (No.20231290), Shenzhen Pingshan District Health System Research Project (No.202232) and Natural Science Foundation of Jilin Province (No. YDZJ202301ZYTS475).

1. DeFronzo RA, Ferrannini E, Groop L, et al. Type 2 diabetes mellitus. Nat Rev Dis Primers. 2015;1:15019. doi:10.1038/nrdp.2015.19

2. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98. doi:10.1038/nrendo.2017.151

3. Magkos F, Hjorth MF, Astrup A. Diet and exercise in the prevention and treatment of type 2 diabetes mellitus. Nat Rev Endocrinol. 2020;16(10):545–555. doi:10.1038/s41574-020-0381-5

4. Bluestone JA, Herold K, Eisenbarth G. Genetics, pathogenesis and clinical interventions in type 1 diabetes. Nature. 2010;464(7293):1293–1300. doi:10.1038/nature08933

5. Eizirik DL, Pasquali L, Cnop M. Pancreatic beta-cells in type 1 and type 2 diabetes mellitus: different pathways to failure. Nat Rev Endocrinol. 2020;16(7):349–362. doi:10.1038/s41574-020-0355-7

6. Zaccardi F, Webb DR, Yates T, Davies MJ. Pathophysiology of type 1 and type 2 diabetes mellitus: a 90-year perspective. Postgrad Med J. 2016;92(1084):63–69. doi:10.1136/postgradmedj-2015-133281

7. Dayan CM, Korah M, Tatovic D, Bundy BN, Herold KC. Changing the landscape for type 1 diabetes: the first step to prevention. Lancet. 2019;394(10205):1286–1296. doi:10.1016/S0140-6736(19)32127-0

8. Tilg H, Moschen AR, Roden M. NAFLD and diabetes mellitus. Nat Rev Gastroenterol Hepatol. 2017;14(1):32–42. doi:10.1038/nrgastro.2016.147

9. Kirpichnikov D, Sowers JR. Diabetes mellitus and diabetes-associated vascular disease. Trends Endocrinol Metab. 2001;12(5):225–230. doi:10.1016/s1043-2760(01)00391-5

10. Hill MA, Yang Y, Zhang L, et al. Insulin resistance, cardiovascular stiffening and cardiovascular disease. Metabolism. 2021;119:154766. doi:10.1016/j.metabol.2021.154766

11. Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16(7):377–390. doi:10.1038/s41581-020-0278-5

12. Strain WD, Paldanius PM. Diabetes, cardiovascular disease and the microcirculation. Cardiovasc Diabetol. 2018;17(1):57. doi:10.1186/s12933-018-0703-2

13. Viigimaa M, Sachinidis A, Toumpourleka M, Koutsampasopoulos K, Alliksoo S, Titma T. Macrovascular complications of type 2 diabetes mellitus. Curr Vasc Pharmacol. 2020;18(2):110–116. doi:10.2174/1570161117666190405165151

14. Abdel Mageed SS, Doghish AS, Ismail A, et al. The role of miRNAs in insulin resistance and diabetic macrovascular complications – A review. Int J Biol Macromol. 2023;230:123189. doi:10.1016/j.ijbiomac.2023.123189

15. Morsi M, Maher A, Aboelmagd O, Johar D, Bernstein L. A shared comparison of diabetes mellitus and neurodegenerative disorders. J Cell Biochem. 2018;119(2):1249–1256. doi:10.1002/jcb.26261

16. Callisaya ML, Beare R, Moran C, Phan T, Wang W, Srikanth VK. Type 2 diabetes mellitus, brain atrophy and cognitive decline in older people: a longitudinal study. Diabetologia. 2019;62(3):448–458. doi:10.1007/s00125-018-4778-9

17. Hadley G, Zhang J, Harris-Skillman E, Alexopoulou Z, DeLuca GC, Pendlebury ST. Cognitive decline and diabetes: a systematic review of the neuropathological correlates accounting for cognition at death. J Neurol Neurosurg Psychiatry. 2022;93(3):246–253. doi:10.1136/jnnp-2021-328158

18. Koekkoek PS, Kappelle LJ, van den Berg E, Rutten GE, Biessels GJ. Cognitive function in patients with diabetes mellitus: guidance for daily care. Lancet Neurol. 2015;14(3):329–340. doi:10.1016/S1474-4422(14)70249-2

19. Zhang J, Chen C, Hua S, et al. An updated meta-analysis of cohort studies: diabetes and risk of Alzheimer’s disease. Diabetes Res Clin Pract. 2017;124:41–47. doi:10.1016/j.diabres.2016.10.024

20. Kahn SE, Cooper ME, Del prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet. 2014;383(9922):1068–1083. doi:10.1016/S0140-6736(13)62154-6

21. Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement. 2013;9(1):63–75 e2. doi:10.1016/j.jalz.2012.11.007

22. Biessels GJ, Staekenborg S, Brunner E, Brayne C, Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol. 2006;5(1):64–74. doi:10.1016/S1474-4422(05)70284-2

23. Biessels GJ, Despa F. Cognitive decline and dementia in diabetes mellitus: mechanisms and clinical implications. Nat Rev Endocrinol. 2018;14(10):591–604. doi:10.1038/s41574-018-0048-7

24. Uccelli A, Moretta L, Pistoia V. Mesenchymal stem cells in health and disease. Nat Rev Immunol. 2008;8(9):726–736. doi:10.1038/nri2395

25. Shi Y, Wang Y, Li Q, et al. Immunoregulatory mechanisms of mesenchymal stem and stromal cells in inflammatory diseases. Nat Rev Nephrol. 2018;14(8):493–507. doi:10.1038/s41581-018-0023-5

26. Qi K, Li N, Zhang Z, Melino G. Tissue regeneration: the crosstalk between mesenchymal stem cells and immune response. Cell Immunol. 2018;326:86–93. doi:10.1016/j.cellimm.2017.11.010

27. Qiu G, Zheng G, Ge M, et al. Functional proteins of mesenchymal stem cell-derived extracellular vesicles. Stem Cell Res Ther. 2019;10(1):359. doi:10.1186/s13287-019-1484-6

28. Lai RC, Yeo RW, Lim SK. Mesenchymal stem cell exosomes. Semin Cell Dev Biol. 2015;40:82–88. doi:10.1016/j.semcdb.2015.03.001

29. Kalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. Science. 2020;367(6478). doi:10.1126/science.aau6977

30. Lin Z, Wu Y, Xu Y, Li G, Li Z, Liu T. Mesenchymal stem cell-derived exosomes in cancer therapy resistance: recent advances and therapeutic potential. Mol Cancer. 2022;21(1):179. doi:10.1186/s12943-022-01650-5

31. Xunian Z, Kalluri R. Biology and therapeutic potential of mesenchymal stem cell-derived exosomes. Cancer Sci. 2020;111(9):3100–3110. doi:10.1111/cas.14563

32. Batrakova EV, Kim MS. Using exosomes, naturally-equipped nanocarriers, for drug delivery. J Control Release. 2015;219:396–405. doi:10.1016/j.jconrel.2015.07.030

33. Liang Y, Duan L, Lu J, Xia J. Engineering exosomes for targeted drug delivery. Theranostics. 2021;11(7):3183–3195. doi:10.7150/thno.52570

34. Lyu F, Wu D, Wei C, Wu A. Vascular cognitive impairment and dementia in type 2 diabetes mellitus: an overview. Life Sci. 2020;254:117771. doi:10.1016/j.lfs.2020.117771

35. Kodl CT, Seaquist ER. Cognitive dysfunction and diabetes mellitus. Endocr Rev. 2008;29(4):494–511. doi:10.1210/er.2007-0034

36. McCrimmon RJ, Ryan CM, Frier BM. Diabetes and cognitive dysfunction. Lancet. 2012;379(9833):2291–2299. doi:10.1016/S0140-6736(12)60360-2

37. Mendelson SJ, Prabhakaran S. Diagnosis and management of transient ischemic attack and acute ischemic stroke: a review. JAMA. 2021;325(11):1088–1098. doi:10.1001/jama.2020.26867

38. Maida CD, Daidone M, Pacinella G, Norrito RL, Pinto A, Tuttolomondo A. Diabetes and ischemic stroke: an old and new relationship an overview of the close interaction between these diseases. Int J Mol Sci. 2022;23(4):2397. doi:10.3390/ijms23042397

39. Laakso M, Kuusisto J. Insulin resistance and hyperglycaemia in cardiovascular disease development. Nat Rev Endocrinol. 2014;10(5):293–302. doi:10.1038/nrendo.2014.29

40. van Sloten TT, Sedaghat S, Carnethon MR, Launer LJ, Stehouwer CDA. Cerebral microvascular complications of type 2 diabetes: stroke, cognitive dysfunction, and depression. Lancet Diabetes Endocrinol. 2020;8(4):325–336. doi:10.1016/S2213-8587(19)30405-X

41. Janghorbani M, Hu FB, Willett WC, et al. Prospective study of type 1 and type 2 diabetes and risk of stroke subtypes: the Nurses’ Health Study. Diabetes Care. 2007;30(7):1730–1735. doi:10.2337/dc06-2363

42. Sarwar N, Gao P; Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–2222. doi:10.1016/S0140-6736(10)60484-9

43. Hankey GJ, Jamrozik K, Broadhurst RJ, et al. Long-term risk of first recurrent stroke in the Perth Community Stroke Study. Stroke. 1998;29(12):2491–2500. doi:10.1161/01.str.29.12.2491

44. Rawshani A, Rawshani A, Franzen S, et al. Risk Factors, mortality, and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2018;379(7):633–644. doi:10.1056/NEJMoa1800256

45. Joseph JJ, Deedwania P, Acharya T, et al. Comprehensive management of cardiovascular risk factors for adults with type 2 diabetes: a scientific statement from the American Heart Association. Circulation. 2022;145(9):e722–e759. doi:10.1161/CIR.0000000000001040

46. Sprafka JM, Virnig BA, Shahar E, McGovern PG. Trends in diabetes prevalence among stroke patients and the effect of diabetes on stroke survival: the Minnesota Heart Survey. Diabet Med. 1994;11(7):678–684. doi:10.1111/j.1464-5491.1994.tb00332.x

47. Policardo L, Seghieri G, Anichini R, et al. Effect of diabetes on hospitalization for ischemic stroke and related in-hospital mortality: a study in Tuscany, Italy, over years 2004–2011. Diabetes Metab Res Rev. 2015;31(3):280–286. doi:10.1002/dmrr.2607

48. Stevens RJ, Coleman RL, Adler AI, Stratton IM, Matthews DR, Holman RR. Risk factors for myocardial infarction case fatality and stroke case fatality in type 2 diabetes: UKPDS 66. Diabetes Care. 2004;27(1):201–207. doi:10.2337/diacare.27.1.201

49. Sharma M, Hart RG, Connolly SJ, et al. Stroke Outcomes in the COMPASS Trial. Circulation. 2019;139(9):1134–1145. doi:10.1161/CIRCULATIONAHA.118.035864

50. Arboix A, Font A, Garro C, Garcia-Eroles L, Comes E, Massons J. Recurrent lacunar infarction following a previous lacunar stroke: a clinical study of 122 patients. J Neurol Neurosurg Psychiatry. 2007;78(12):1392–1394. doi:10.1136/jnnp.2007.119776

51. Daneman R, Prat A. The blood-brain barrier. Cold Spring Harb Perspect Biol. 2015;7(1):a020412. doi:10.1101/cshperspect.a020412

52. Abbott NJ, Patabendige AA, Dolman DE, Yusof SR, Begley DJ. Structure and function of the blood-brain barrier. Neurobiol Dis. 2010;37(1):13–25. doi:10.1016/j.nbd.2009.07.030

53. Kadry H, Noorani B, Cucullo L. A blood-brain barrier overview on structure, function, impairment, and biomarkers of integrity. Fluids Barriers CNS. 2020;17(1):69. doi:10.1186/s12987-020-00230-3

54. Nielsen LB, Wang C, Sorensen K, et al. Circulating levels of microRNA from children with newly diagnosed type 1 diabetes and healthy controls: evidence that miR-25 associates to residual beta-cell function and glycaemic control during disease progression.; Research Support, Non-U.S. Gov’t. Exp Diabetes Res. 2012;2012:896362. doi:10.1155/2012/896362

55. Bogush M, Heldt NA, Persidsky Y. Blood brain barrier injury in diabetes: unrecognized effects on brain and cognition. J Neuroimmune Pharmacol. 2017;12(4):593–601. doi:10.1007/s11481-017-9752-7

56. Montagne A, Barnes SR, Sweeney MD, et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron. 2015;85(2):296–302. doi:10.1016/j.neuron.2014.12.032

57. Pugazhenthi S, Qin L, Reddy PH. Common neurodegenerative pathways in obesity, diabetes, and Alzheimer’s disease. Biochim Biophys Acta Mol Basis Dis. 2017;1863(5):1037–1045. doi:10.1016/j.bbadis.2016.04.017

58. Tucsek Z, Toth P, Sosnowska D, et al. Obesity in aging exacerbates blood-brain barrier disruption, neuroinflammation, and oxidative stress in the mouse hippocampus: effects on expression of genes involved in beta-amyloid generation and Alzheimer’s disease. J Gerontol a Biol Sci Med Sci. 2014;69(10):1212–1226. doi:10.1093/gerona/glt177

59. Mortuza R, Chen S, Feng B, Sen S, Chakrabarti S. High glucose induced alteration of SIRTs in endothelial cells causes rapid aging in a p300 and FOXO regulated pathway. PLoS One. 2013;8(1):e54514. doi:10.1371/journal.pone.0054514

60. Pallebage-Gamarallage M, Lam V, Takechi R, Galloway S, Clark K, Mamo J. Restoration of dietary-fat induced blood-brain barrier dysfunction by anti-inflammatory lipid-modulating agents. Lipids Health Dis. 2012;11:117. doi:10.1186/1476-511X-11-117

61. van Sloten T, Schram M. Understanding depression in type 2 diabetes: a biological approach in observational studies. F1000Res. 2018;7. doi:10.12688/f1000research.13898.1

62. Najjar S, Pearlman DM, Devinsky O, Najjar A, Zagzag D. Neurovascular unit dysfunction with blood-brain barrier hyperpermeability contributes to major depressive disorder: a review of clinical and experimental evidence. J Neuroinflammation. 2013;10:142. doi:10.1186/1742-2094-10-142

63. Direk N, Koudstaal PJ, Hofman A, Ikram MA, Hoogendijk WJ, Tiemeier H. Cerebral hemodynamics and incident depression: the Rotterdam Study. Biol Psychiatry. 2012;72(4):318–323. doi:10.1016/j.biopsych.2012.01.019

64. Campayo A, Gomez-Biel CH, Lobo A. Diabetes and depression. Curr Psychiatry Rep. 2011;13(1):26–30. doi:10.1007/s11920-010-0165-z

65. Nouwen A, Adriaanse MC, van Dam K, et al. Longitudinal associations between depression and diabetes complications: a systematic review and meta-analysis. Diabet Med. 2019;36(12):1562–1572. doi:10.1111/dme.14054

66. Srikanth V, Sinclair AJ, Hill-Briggs F, Moran C, Biessels GJ. Type 2 diabetes and cognitive dysfunction-towards effective management of both comorbidities. Lancet Diabetes Endocrinol. 2020;8(6):535–545. doi:10.1016/S2213-8587(20)30118-2

67. Rawlings AM, Sharrett AR, Albert MS, et al. The association of late-life diabetes status and hyperglycemia with incident mild cognitive impairment and dementia: the ARIC Study. Diabetes Care. 2019;42(7):1248–1254. doi:10.2337/dc19-0120

68. Esiri MM, Matthews F, Brayne C, et al.; Neuropathology Group. Medical Research Council Cognitive F, Aging S. Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales. Neuropathology Group of the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS). Lancet. 2001;357(9251):169–175. doi:10.1016/s0140-6736(00)03589-3

69. Abner EL, Nelson PT, Kryscio RJ, et al. Diabetes is associated with cerebrovascular but not Alzheimer’s disease neuropathology. Alzheimers Dement. 2016;12(8):882–889. doi:10.1016/j.jalz.2015.12.006

70. Ojo O, Brooke J. Evaluating the association between diabetes, cognitive decline and dementia. Int J Environ Res Public Health. 2015;12(7):8281–8294. doi:10.3390/ijerph120708281

71. Chan DD, Tsou HH, Chang CB, et al. Integrated care for geriatric frailty and sarcopenia: a randomized control trial. J Cachexia Sarcopenia Muscle. 2017;8(1):78–88. doi:10.1002/jcsm.12132

72. Afilalo J, Alexander KP, Mack MJ, et al. Frailty assessment in the cardiovascular care of older adults. J Am Coll Cardiol. 2014;63(8):747–762. doi:10.1016/j.jacc.2013.09.070

73. Sesti G, Antonelli Incalzi R, Bonora E, et al. Management of diabetes in older adults. Nutr Metab Cardiovasc Dis. 2018;28(3):206–218. doi:10.1016/j.numecd.2017.11.007

74. DeCarlo K, Wallia A. Inpatient management of T2DM and hyperglycemia in older adults. Curr Diab Rep. 2019;19(10):104. doi:10.1007/s11892-019-1209-3

75. American Diabetes Association. 12. Older adults: standards of medical care in diabetes-2019. Diabetes Care. 2019;42(Suppl 1):S139–S147. doi:10.2337/dc19-S012

76. Caplan AI. Mesenchymal stem cells. J Orthop Res. 1991;9(5):641–650. doi:10.1002/jor.1100090504

77. Fu X, Liu G, Halim A, Ju Y, Luo Q, Song AG. Mesenchymal stem cell migration and tissue repair. Cells. 2019;8(8):784. doi:10.3390/cells8080784

78. Liu WZ, Ma ZJ, Li JR, Kang XW. Mesenchymal stem cell-derived exosomes: therapeutic opportunities and challenges for spinal cord injury. Stem Cell Res Ther. 2021;12(1):102. doi:10.1186/s13287-021-02153-8

79. Xiao Y, Zheng L, Zou X, Wang J, Zhong J, Zhong T. Extracellular vesicles in type 2 diabetes mellitus: key roles in pathogenesis, complications, and therapy. J Extracell Vesicles. 2019;8(1):1625677. doi:10.1080/20013078.2019.1625677

80. Harrell CR, Jovicic N, Djonov V, Arsenijevic N, Volarevic V. Mesenchymal stem cell-derived exosomes and other extracellular vesicles as new remedies in the therapy of inflammatory diseases. Cells. 2019;8(12):1605. doi:10.3390/cells8121605

81. Zhong J, Xia B, Shan S, et al. High-quality milk exosomes as oral drug delivery system. Biomaterials. 2021;277:121126. doi:10.1016/j.biomaterials.2021.121126

82. Shao J, Zaro J, Shen Y. Advances in exosome-based drug delivery and tumor targeting: from tissue distribution to intracellular fate. Int J Nanomedicine. 2020;15:9355–9371. doi:10.2147/IJN.S281890

83. Zhang J, Li S, Li L, et al. Exosome and exosomal microRNA: trafficking, sorting, and function. Genomics Proteomics Bioinformatics. 2015;13(1):17–24. doi:10.1016/j.gpb.2015.02.001

84. Shen Z, Huang W, Liu J, Tian J, Wang S, Rui K. Effects of mesenchymal stem cell-derived exosomes on autoimmune diseases. Front Immunol. 2021;12:749192. doi:10.3389/fimmu.2021.749192

85. Vidal M, Mangeat P, Hoekstra D. Aggregation reroutes molecules from a recycling to a vesicle-mediated secretion pathway during reticulocyte maturation. J Cell Sci. 1997;110(Pt 16):1867–1877. doi:10.1242/jcs.110.16.1867

86. Nabhan JF, Hu R, Oh RS, Cohen SN, Lu Q. Formation and release of arrestin domain-containing protein 1-mediated microvesicles (ARMMs) at plasma membrane by recruitment of TSG101 protein. Proc Natl Acad Sci U S A. 2012;109(11):4146–4151. doi:10.1073/pnas.1200448109

87. van Niel G, D’Angelo G, Raposo G. Shedding light on the cell biology of extracellular vesicles. Nat Rev Mol Cell Biol. 2018;19(4):213–228. doi:10.1038/nrm.2017.125

88. Larios J, Mercier V, Roux A, Gruenberg J. ALIX- and ESCRT-III-dependent sorting of tetraspanins to exosomes. J Cell Biol. 2020;219(3). doi:10.1083/jcb.201904113

89. Buschow SI, Nolte-’t Hoen EN, van Niel G, et al. MHC II in dendritic cells is targeted to lysosomes or T cell-induced exosomes via distinct multivesicular body pathways. Traffic. 2009;10(10):1528–1542. doi:10.1111/j.1600-0854.2009.00963.x

90. Chairoungdua A, Smith DL, Pochard P, Hull M, Caplan MJ. Exosome release of beta-catenin: a novel mechanism that antagonizes Wnt signaling. J Cell Biol. 2010;190(6):1079–1091. doi:10.1083/jcb.201002049

91. Babuta M, Furi I, Bala S, et al. Dysregulated autophagy and lysosome function are linked to exosome production by micro-RNA 155 in alcoholic liver disease. Hepatology. 2019;70(6):2123–2141. doi:10.1002/hep.30766

92. Buratta S, Tancini B, Sagini K, et al. Lysosomal exocytosis, exosome release and secretory autophagy: the autophagic- and endo-lysosomal systems go extracellular. Int J Mol Sci. 2020;21(7):2576. doi:10.3390/ijms21072576

93. Witwer KW, Buzas EI, Bemis LT, et al. Standardization of sample collection, isolation and analysis methods in extracellular vesicle research. J Extracell Vesicles. 2013;2. doi:10.3402/jev.v2i0.20360

94. Quintana JF, Makepeace BL, Babayan SA, et al. Extracellular Onchocerca-derived small RNAs in host nodules and blood. Parasit Vectors. 2015;8:58. doi:10.1186/s13071-015-0656-1

95. Lobb RJ, Becker M, Wen SW, et al. Optimized exosome isolation protocol for cell culture supernatant and human plasma. J Extracell Vesicles. 2015;4:27031. doi:10.3402/jev.v4.27031

96. Livshits MA, Khomyakova E, Evtushenko EG, et al. Isolation of exosomes by differential centrifugation: theoretical analysis of a commonly used protocol. Sci Rep. 2015;5:17319. doi:10.1038/srep17319

97. Li P, Kaslan M, Lee SH, Yao J, Gao Z. Progress in Exosome Isolation Techniques. Theranostics. 2017;7(3):789–804. doi:10.7150/thno.18133

98. Shu S, Yang Y, Allen CL, et al. Purity and yield of melanoma exosomes are dependent on isolation method. J Extracell Vesicles. 2020;9(1):1692401. doi:10.1080/20013078.2019.1692401

99. Xu Y, Shen L, Li F, Yang J, Wan X, Ouyang M. microRNA-16-5p-containing exosomes derived from bone marrow-derived mesenchymal stem cells inhibit proliferation, migration, and invasion, while promoting apoptosis of colorectal cancer cells by downregulating ITGA2. J Cell Physiol. 2019;234(11):21380–21394. doi:10.1002/jcp.28747

100. Zeringer E, Barta T, Li M, Vlassov AV. Strategies for isolation of exosomes. Cold Spring Harb Protoc. 2015;2015(4):319–323. doi:10.1101/pdb.top074476

101. Doyle LM, Wang MZ. Overview of extracellular vesicles, their origin, composition, purpose, and methods for exosome isolation and analysis. Cells. 2019;8(7):727. doi:10.3390/cells8070727

102. Saeedi S, Israel S, Nagy C, Turecki G. The emerging role of exosomes in mental disorders. Transl Psychiatry. 2019;9(1):122. doi:10.1038/s41398-019-0459-9

103. Ung TH, Madsen HJ, Hellwinkel JE, Lencioni AM, Graner MW. Exosome proteomics reveals transcriptional regulator proteins with potential to mediate downstream pathways. Cancer Sci. 2014;105(11):1384–1392. doi:10.1111/cas.12534

104. Sha S, Shen X, Cao Y, Qu L. Mesenchymal stem cells-derived extracellular vesicles ameliorate Alzheimer’s disease in rat models via the microRNA-29c-3p/BACE1 axis and the Wnt/beta-catenin pathway. Aging. 2021;13(11):15285–15306. doi:10.18632/aging.203088

105. Riancho J, Vazquez-Higuera JL, Pozueta A, et al. MicroRNA profile in patients with Alzheimer’s disease: analysis of miR-9-5p and miR-598 in raw and exosome enriched cerebrospinal fluid samples. J Alzheimers Dis. 2017;57(2):483–491. doi:10.3233/JAD-161179

106. Sarkar S, Jun S, Rellick S, Quintana DD, Cavendish JZ, Simpkins JW. Expression of microRNA-34a in Alzheimer’s disease brain targets genes linked to synaptic plasticity, energy metabolism, and resting state network activity. Brain Res. 2016;1646:139–151. doi:10.1016/j.brainres.2016.05.026

107. Song Z, Qu Y, Xu Y, et al. Microarray microRNA profiling of urinary exosomes in a 5XFAD mouse model of Alzheimer’s disease. Animal Model Exp Med. 2021;4(3):233–242. doi:10.1002/ame2.12175

108. McKeever PM, Schneider R, Taghdiri F, et al. MicroRNA expression levels are altered in the cerebrospinal fluid of patients with young-onset Alzheimer’s disease. Mol Neurobiol. 2018;55(12):8826–8841. doi:10.1007/s12035-018-1032-x

109. Xu H, Zhao G, Zhang Y, et al. Mesenchymal stem cell-derived exosomal microRNA-133b suppresses glioma progression via Wnt/beta-catenin signaling pathway by targeting EZH2. Stem Cell Res Ther. 2019;10(1):381. doi:10.1186/s13287-019-1446-z

110. Venkat P, Zacharek A, Landschoot-Ward J, et al. Exosomes derived from bone marrow mesenchymal stem cells harvested from type two diabetes rats promotes neurorestorative effects after stroke in type two diabetes rats. Exp Neurol. 2020;334:113456. doi:10.1016/j.expneurol.2020.113456

111. Garnier D, Ratcliffe E, Briand J, Cartron PF, Oliver L, Vallette FM. The activation of mesenchymal stem cells by glioblastoma microvesicles alters their exosomal secretion of miR-100-5p, miR-9-5p and let-7d-5p. Biomedicines. 2022;10(1):112.

112. Abner EL, Jicha GA, Shaw LM, Trojanowski JQ, Goetzl EJ. Plasma neuronal exosomal levels of Alzheimer’s disease biomarkers in normal aging. Ann Clin Transl Neurol. 2016;3(5):399–403. doi:10.1002/acn3.309

113. Fiandaca MS, Kapogiannis D, Mapstone M, et al. Identification of preclinical Alzheimer’s disease by a profile of pathogenic proteins in neurally derived blood exosomes: a case-control study. Alzheimers Dement. 2015;11(6):600–7 e1. doi:10.1016/j.jalz.2014.06.008

114. Goetzl EJ, Mustapic M, Kapogiannis D, et al. Cargo proteins of plasma astrocyte-derived exosomes in Alzheimer’s disease. FASEB J. 2016;30(11):3853–3859. doi:10.1096/fj.201600756R

115. Hamlett ED, Ledreux A, Potter H, et al. Exosomal biomarkers in Down syndrome and Alzheimer’s disease. Free Radic Biol Med. 2018;114:110–121. doi:10.1016/j.freeradbiomed.2017.08.028

116. Ahmad W. Glucose enrichment impair neurotransmission and induce Abeta oligomerization that cannot be reversed by manipulating O-beta-GlcNAcylation in the C. elegans model of Alzheimer’s disease. J Nutr Biochem. 2022;108:109100. doi:10.1016/j.jnutbio.2022.109100

117. Chi H, Yao R, Sun C, et al. Blood neuroexosomal mitochondrial proteins predict Alzheimer disease in diabetes. Diabetes. 2022;71(6):1313–1323. doi:10.2337/db21-0969

118. Pietzner M, Wheeler E, Carrasco-Zanini J, et al. Synergistic insights into human health from aptamer- and antibody-based proteomic profiling. Nat Commun. 2021;12(1):6822. doi:10.1038/s41467-021-27164-0

119. Nakano M, Nagaishi K, Konari N, et al. Bone marrow-derived mesenchymal stem cells improve diabetes-induced cognitive impairment by exosome transfer into damaged neurons and astrocytes. Sci Rep. 2016;6:24805. doi:10.1038/srep24805

120. Lu XC, Zheng JY, Tang LJ, et al. MiR-133b Promotes neurite outgrowth by targeting RhoA expression. Cell Physiol Biochem. 2015;35(1):246–258. doi:10.1159/000369692

121. Ma X, Wang Y, Shi Y, et al. Exosomal miR-132-3p from mesenchymal stromal cells improves synaptic dysfunction and cognitive decline in vascular dementia. Stem Cell Res Ther. 2022;13(1):315. doi:10.1186/s13287-022-02995-w

122. Hancock ML, Preitner N, Quan J, Flanagan JG. MicroRNA-132 is enriched in developing axons, locally regulates Rasa1 mRNA, and promotes axon extension. J Neurosci. 2014;34(1):66–78. doi:10.1523/JNEUROSCI.3371-13.2014

123. Pan Q, Kuang X, Cai S, et al. miR-132-3p priming enhances the effects of mesenchymal stromal cell-derived exosomes on ameliorating brain ischemic injury. Stem Cell Res Ther. 2020;11(1):260. doi:10.1186/s13287-020-01761-0

124. Malcangio M. Role of the immune system in neuropathic pain. Scand J Pain. 2019;20(1):33–37. doi:10.1515/sjpain-2019-0138

125. Lang HL, Zhao YZ, Xiao RJ, et al. Small extracellular vesicles secreted by induced pluripotent stem cell-derived mesenchymal stem cells improve postoperative cognitive dysfunction in mice with diabetes. Neural Regen Res. 2023;18(3):609–617. doi:10.4103/1673-5374.350205

126. Liu S, Fan M, Xu JX, et al. Exosomes derived from bone-marrow mesenchymal stem cells alleviate cognitive decline in AD-like mice by improving BDNF-related neuropathology. J Neuroinflammation. 2022;19(1):35. doi:10.1186/s12974-022-02393-2

127. Yin F, Liu Z, Zhang D, Shen Z, Niu Z, Guo L. Identification of key genes involved in neural regeneration and the repairing effect of BDNF-overexpressed BMSCs on spinal cord ischemia-reperfusion injury in rats. Biomed Pharmacother. 2023;160:114293. doi:10.1016/j.biopha.2023.114293

128. Ma W, Wei X, Gu H, et al. Intra-amniotic transplantation of brain-derived neurotrophic factor-modified mesenchymal stem cells treatment for rat fetuses with spina bifida aperta. Stem Cell Res Ther. 2022;13(1):413. doi:10.1186/s13287-022-03105-6

129. Yu X, Qi Y, Zhao T, et al. NGF increases FGF2 expression and promotes endothelial cell migration and tube formation through PI3K/Akt and ERK/MAPK pathways in human chondrocytes. Osteoarthritis Cartilage. 2019;27(3):526–534. doi:10.1016/j.joca.2018.12.007

130. Li R, Li Y, Wu Y, et al. Heparin-poloxamer thermosensitive hydrogel loaded with bFGF and NGF enhances peripheral nerve regeneration in diabetic rats. Biomaterials. 2018;168:24–37. doi:10.1016/j.biomaterials.2018.03.044

131. Kellar D, Craft S. Brain insulin resistance in Alzheimer’s disease and related disorders: mechanisms and therapeutic approaches. Lancet Neurol. 2020;19(9):758–766. doi:10.1016/S1474-4422(20)30231-3

132. Kullmann S, Heni M, Hallschmid M, Fritsche A, Preissl H, Haring HU. Brain insulin resistance at the crossroads of metabolic and cognitive disorders in humans. Physiol Rev. 2016;96(4):1169–1209. doi:10.1152/physrev.00032.2015

133. Liyanagamage D, Martinus RD. Role of mitochondrial stress protein HSP60 in diabetes-induced neuroinflammatIon. Mediators Inflamm. 2020;2020:8073516. doi:10.1155/2020/8073516

134. Lee YS, Olefsky J. Chronic tissue inflammation and metabolic disease. Genes Dev. 2021;35(5–6):307–328. doi:10.1101/gad.346312.120

135. Cui GH, Guo HD, Li H, et al. RVG-modified exosomes derived from mesenchymal stem cells rescue memory deficits by regulating inflammatory responses in a mouse model of Alzheimer’s disease. Immun Ageing. 2019;16:10. doi:10.1186/s12979-019-0150-2

136. Kubota K, Nakano M, Kobayashi E, et al. An enriched environment prevents diabetes-induced cognitive impairment in rats by enhancing exosomal miR-146a secretion from endogenous bone marrow-derived mesenchymal stem cells. PLoS One. 2018;13(9):e0204252. doi:10.1371/journal.pone.0204252

137. Duan S, Wang F, Cao J, Wang C. Exosomes derived from MicroRNA-146a-5p-enriched bone marrow mesenchymal stem cells alleviate intracerebral hemorrhage by inhibiting neuronal apoptosis and microglial M1 polarization. Drug Des Devel Ther. 2020;14:3143–3158. doi:10.2147/DDDT.S255828

138. Nakano M, Kubota K, Kobayashi E, et al. Bone marrow-derived mesenchymal stem cells improve cognitive impairment in an Alzheimer’s disease model by increasing the expression of microRNA-146a in hippocampus. Sci Rep. 2020;10(1):10772. doi:10.1038/s41598-020-67460-1

139. Yin Z, Han Z, Hu T, et al. Neuron-derived exosomes with high miR-21-5p expression promoted polarization of M1 microglia in culture. Brain Behav Immun. 2020;83:270–282. doi:10.1016/j.bbi.2019.11.004

140. Pandey N, Rastogi M, Singh SK. Chandipura virus dysregulates the expression of hsa-miR-21-5p to activate NF-kappaB in human microglial cells. J Biomed Sci. 2021;28(1):52. doi:10.1186/s12929-021-00748-0

141. Ge X, Huang S, Gao H, et al. miR-21-5p alleviates leakage of injured brain microvascular endothelial barrier in vitro through suppressing inflammation and apoptosis. Brain Res. 2016;1650:31–40. doi:10.1016/j.brainres.2016.07.015

142. Ouyang Y, Li D, Wang H, et al. MiR-21-5p/dual-specificity phosphatase 8 signalling mediates the anti-inflammatory effect of haem oxygenase-1 in aged intracerebral haemorrhage rats. Aging Cell. 2019;18(6):e13022. doi:10.1111/acel.13022

143. Gao X, Xiong Y, Li Q, et al. Extracellular vesicle-mediated transfer of miR-21-5p from mesenchymal stromal cells to neurons alleviates early brain injury to improve cognitive function via the PTEN/Akt pathway after subarachnoid hemorrhage. Cell Death Dis. 2020;11(5):363. doi:10.1038/s41419-020-2530-0

144. Pan Q, Wang Y, Lan Q, et al. Exosomes derived from mesenchymal stem cells ameliorate hypoxia/reoxygenation-injured ECs via transferring MicroRNA-126. Stem Cells Int. 2019;2019:2831756. doi:10.1155/2019/2831756

145. Wang X, Zhou Y, Gao Q, et al. The role of exosomal microRNAs and oxidative stress in neurodegenerative diseases. Oxid Med Cell Longev. 2020;2020:3232869. doi:10.1155/2020/3232869

146. Chen P, Chen F, Lei J, Li Q, Zhou B. Activation of the miR-34a-mediated SIRT1/mTOR signaling pathway by urolithin A attenuates D-galactose-induced brain aging in mice. Neurotherapeutics. 2019;16(4):1269–1282. doi:10.1007/s13311-019-00753-0

147. Thal DR, Walter J, Saido TC, Fandrich M. Neuropathology and biochemistry of Abeta and its aggregates in Alzheimer’s disease. Acta Neuropathol. 2015;129(2):167–182. doi:10.1007/s00401-014-1375-y

148. Hur JY. gamma-Secretase in Alzheimer’s disease. Exp Mol Med. 2022;54(4):433–446. doi:10.1038/s12276-022-00754-8

149. Sun Y, Ma C, Sun H, et al. Metabolism: a novel shared link between diabetes mellitus and Alzheimer’s disease. J Diabetes Res. 2020;2020:4981814. doi:10.1155/2020/4981814

150. Chen Z, Zhong C. Decoding Alzheimer’s disease from perturbed cerebral glucose metabolism: implications for diagnostic and therapeutic strategies. Prog Neurobiol. 2013;108:21–43. doi:10.1016/j.pneurobio.2013.06.004

151. Stukas S, Robert J, Wellington CL. High-density lipoproteins and cerebrovascular integrity in Alzheimer’s disease. Cell Metab. 2014;19(4):574–591. doi:10.1016/j.cmet.2014.01.003

152. Dutta BJ, Singh S, Seksaria S, Das Gupta G, Singh A. Inside the diabetic brain: insulin resistance and molecular mechanism associated with cognitive impairment and its possible therapeutic strategies. Pharmacol Res. 2022;182:106358. doi:10.1016/j.phrs.2022.106358

153. Lee M, Ban JJ, Yang S, Im W, Kim M. The exosome of adipose-derived stem cells reduces beta-amyloid pathology and apoptosis of neuronal cells derived from the transgenic mouse model of Alzheimer’s disease. Brain Res. 2018;1691:87–93. doi:10.1016/j.brainres.2018.03.034

154. Jahangard Y, Monfared H, Moradi A, Zare M, Mirnajafi-Zadeh J, Mowla SJ. Therapeutic effects of transplanted exosomes containing miR-29b to a rat model of Alzheimer’s disease. Front Neurosci. 2020;14:564. doi:10.3389/fnins.2020.00564

155. Zhang J, Buller BA, Zhang ZG, et al. Exosomes derived from bone marrow mesenchymal stromal cells promote remyelination and reduce neuroinflammation in the demyelinating central nervous system. Exp Neurol. 2022;347:113895. doi:10.1016/j.expneurol.2021.113895

156. Reza-Zaldivar EE, Hernandez-Sapiens MA, Gutierrez-Mercado YK, et al. Mesenchymal stem cell-derived exosomes promote neurogenesis and cognitive function recovery in a mouse model of Alzheimer’s disease. Neural Regen Res. 2019;14(9):1626–1634. doi:10.4103/1673-5374.255978

157. Wang H, Liu Y, Li J, et al. Tail-vein injection of MSC-derived small extracellular vesicles facilitates the restoration of hippocampal neuronal morphology and function in APP/PS1 mice. Cell Death Discov. 2021;7(1):230. doi:10.1038/s41420-021-00620-y

158. Parsi S, Smith PY, Goupil C, Dorval V, Hebert SS. Preclinical evaluation of miR-15/107 family members as multifactorial drug targets for Alzheimer’s disease. Mol Ther Nucleic Acids. 2015;4(10):e256. doi:10.1038/mtna.2015.33

159. Hebert SS, Horre K, Nicolai L, et al. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer’s disease correlates with increased BACE1/beta-secretase expression. Proc Natl Acad Sci U S A. 2008;105(17):6415–6420. doi:10.1073/pnas.0710263105

160. Wang WX, Rajeev BW, Stromberg AJ, et al. The expression of microRNA miR-107 decreases early in Alzheimer’s disease and may accelerate disease progression through regulation of beta-site amyloid precursor protein-cleaving enzyme 1. J Neurosci. 2008;28(5):1213–1223. doi:10.1523/JNEUROSCI.5065-07.2008

161. Zhang X, Huang X, Fang C, et al. miR-124 regulates the expression of BACE1 in the hippocampus under chronic cerebral hypoperfusion. Mol Neurobiol. 2017;54(4):2498–2506. doi:10.1007/s12035-016-9845-y

162. Yuyama K, Sun H, Mitsutake S, Igarashi Y. Sphingolipid-modulated exosome secretion promotes clearance of amyloid-beta by microglia. J Biol Chem. 2012;287(14):10977–10989. doi:10.1074/jbc.M111.324616

163. Yuyama K, Sun H, Sakai S, et al. Decreased amyloid-beta pathologies by intracerebral loading of glycosphingolipid-enriched exosomes in Alzheimer model mice. J Biol Chem. 2014;289(35):24488–24498. doi:10.1074/jbc.M114.577213

164. Yuyama K, Sun H, Igarashi Y, et al. Immuno-digital invasive cleavage assay for analyzing Alzheimer’s amyloid ss-bound extracellular vesicles. Alzheimers Res Ther. 2022;14(1):140. doi:10.1186/s13195-022-01073-w

165. An K, Klyubin I, Kim Y, et al. Exosomes neutralize synaptic-plasticity-disrupting activity of Abeta assemblies in vivo. Mol Brain. 2013;6:47. doi:10.1186/1756-6606-6-47

166. Perez-Gonzalez R, Kim Y, Miller C, Pacheco-Quinto J, Eckman EA, Levy E. Extracellular vesicles: where the amyloid precursor protein carboxyl-terminal fragments accumulate and amyloid-beta oligomerizes. FASEB J. 2020;34(9):12922–12931. doi:10.1096/fj.202000823R

167. Dinkins MB, Dasgupta S, Wang G, Zhu G, Bieberich E. Exosome reduction in vivo is associated with lower amyloid plaque load in the 5XFAD mouse model of Alzheimer’s disease. Neurobiol Aging. 2014;35(8):1792–1800. doi:10.1016/j.neurobiolaging.2014.02.012

168. Dinkins MB, Wang G, Bieberich E. Sphingolipid-enriched extracellular vesicles and Alzheimer’s disease: a decade of research. J Alzheimers Dis. 2017;60(3):757–768. doi:10.3233/JAD-160567

Posted 10 Oct 2023 05:31 by | News | 1 comment

“A week after the Chiefs defeated the Eagles 38-35, [Isiah] Pacheco had surgery performed by Dr. Mike Guss to repair the broken bone [in his wrist]” – The Kansas City Star Reported – Hand Surgery, PC

The full article can be read at the following link Read more Disclaimer: This content is sourced from Yahoo News. Photo credit: Tammy Ljungblad/[email protected].

Posted 10 Oct 2023 04:52 by | Pachecos | 1 comment

Week 4 Target Share Report – Sports Gambling Podcast

Another week is in the books, and it’s time to review the provided insight on the targets and opportunities coming out of Week 3 of the NFL season. I hope you took my advice to identify trade targets, waiver wire pick-ups, and fades. The results should have helped you in more ways than one. Here were my suggestions:

Suggested Adds or Trades

Suggested Fades

MORE SGPN FANTASY FOOTBALL CONTENT

For the full stat table sorted by team, check the table at the bottom of the article.

Week 3 Absent Players in Week 4

There were a few big hitters that returned in Week 4, with the likes of Brandon Aiyuk, Christian Watson, and Jaylen Waddle. All three returned to what we expected from a route participation perspective and target demand. No major changes here.

Name Team W4 Tgt% W4 Rte% W4 Rush %
Brandon Aiyuk San Francisco 24% 76% 0%
Josh Reynolds Detroit 19% 75% 0%
Nick Westbrook-Ikhine Tennessee 17% 74% 0%
Jalen Tolbert Dallas 10% 23% 0%
Josiah Deguara Green Bay 9% 56% 0%
Christian Watson Green Bay 9% 44% 0%
Jaylen Waddle Miami 9% 66% 0%
Zach Pascal Arizona 8% 18% 0%
Cam Akers Los Angeles Rams 8% 24% 22%
Cedric Tillman Cleveland 7% 17% 0%
David Montgomery Detroit 6% 50% 74%
Josh Whyle Tennessee 6% 14% 0%

Major Route/Target Share Market Movers

For major market movers I want to see a route participation jump of over 30% from week to week or a target share change of over 10%. A couple to recognize in the route participation jump. With the injury to Mike Williams, we were all wondering how the chips would fall.

While we did see Quentin Johnston jump 41% from week to week with a 67% route participation, he only garnered 9% of the targets while Joshua Palmer ran 94% of routes and pulled a 24% target share. With Johnston as a rookie and the high flying offense that Kellen Moore is producing, both of these guys are buys!

Name Team W3 Tgt % W3 Rte % W3-4 Target W3-4 Route W4 Tgt% W4 Rte%
Quentin Johnston Los Angeles Chargers 6% 25% 3% 41% 9% 67%
Devin Duvernay Baltimore 3% 24% 4% 40% 7% 63%
Wan’Dale Robinson New York Giants 13% 23% -1% 39% 12% 62%
Deven Thompkins Tampa Bay 13% 19% -3% 38% 10% 57%
Chris Moore Tennessee 9% 31% -1% 37% 9% 69%
Durham Smythe Miami 3% 24% 5% 35% 9% 60%
Zach Ertz Arizona 6% 50% 14% 34% 20% 84%
Tim Jones Jacksonville 6% 29% -3% 32% 3% 61%
Tim Jones Jacksonville 6% 29% -3% 32% 3% 61%
Luke Schoonmaker Dallas 2% 8% 3% 30% 5% 38%

The Baltimore Ravens Wide Receiver room cannot get healthy. With Bateman and Beckham nursing injuries we have seen Nelson Agholor and Devin Duvernay come into some opportunity but not enough to be relevant. I would stay away and save my waiver priority and FAAB for better options.

A big winner in my mind is Wan’Dale Robinson. I mentioned the Giant’s WR last week and stand by it. While his production was modest, he jumped up 39% to 62% route participation and led the team in target share this week. As he becomes further removed from his ACL injury I expect better days to come. If he is still on your waiver wire, I suggest grabbing him.

Now, let’s take a look at the players that saw a target share jump of over 10%.

Name Team W3 Tgt % W3 Rte % W3-4 Target W3-4 Route W4 Tgt% W4 Rte%
Christian McCaffrey San Francisco 12% 47% 20% 29% 32% 76%
Christian Kirk Jacksonville 12% 86% 17% 6% 29% 92%
Nico Collins Houston 10% 77% 16% -9% 26% 69%
Justin Jefferson Minnesota 21% 91% 15% -7% 36% 84%
Cole Kmet Chicago 7% 72% 15% -11% 21% 62%
Zach Ertz Arizona 6% 50% 14% 34% 20% 84%
Jahmyr Gibbs Detroit 3% 46% 13% -2% 16% 44%
Rashid Shaheed New Orleans 3% 84% 12% -16% 15% 68%
Garrett Wilson New York Jets 19% 95% 11% 0% 30% 95%
Josh Jacobs Las Vegas 9% 54% 11% -15% 20% 39%
Joshua Palmer Los Angeles Chargers 14% 67% 11% 27% 24% 94%
Chris Godwin Tampa Bay 16% 81% 10% 3% 26% 83%

I’ve mentioned it the last two weeks and will give you one last warning: pay attention to Christian Kirk. We saw not only his target share rise but also his route percentage jump for the second week in a row. He is a stud in a stud offense with a stud QB. A couple of tight ends should be mentioned and paid attention to here.

Zach Ertz and Cole Kmet have been flying under the radar for different reasons but are solid options in a scarce tight end landscape. They are players that are running a ton of routes and seeing consistent targets. The fruit will fall at some point. Buy before it’s too late.

Mid-Tier Target Share Market Movers

So, the Mid-tier folks will be the players that have seen a 5% – 10% increase in volume of opportunity. This doesn’t mean they have seen more targets just yet, but their opportunities have risen.

Name Team W3 Tgt % W3 Rte % W3-4 Target W3-4 Route W4 Tgt% W4 Rte%
Jalen Tolbert Dallas 0% 0% 10% 23% 10% 23%
Jaxon Smith-Njigba Seattle 7% 59% 9% 11% 17% 70%
Josiah Deguara Green Bay 0% 0% 9% 56% 9% 56%
Christian Watson Green Bay 0% 0% 9% 44% 9% 44%
Romeo Doubs Green Bay 20% 78% 9% 4% 29% 82%
Terrace Marshall Jr. Carolina 12% 64% 9% -5% 20% 59%
Jaylen Waddle Miami 0% 0% 9% 66% 9% 66%
Emari Demercado Arizona 0% 0% 8% 37% 8% 37%
Zach Pascal Arizona 0% 0% 8% 18% 8% 18%
Dalton Kincaid Buffalo 5% 61% 8% 16% 13% 77%
Michael Wilson Arizona 6% 59% 8% 12% 14% 71%
Cam Akers Los Angeles Rams 0% 0% 8% 24% 8% 24%
Tyler Higbee Los Angeles Rams 12% 84% 8% -1% 19% 83%
Drake London Atlanta 12% 88% 7% -1% 19% 86%
Gus Edwards Baltimore 0% 0% 7% 50% 7% 50%
Isaiah McKenzie Indianapolis 2% 10% 7% -2% 9% 9%
Cedric Tillman Cleveland 0% 0% 7% 17% 7% 17%
David Montgomery Detroit 0% 0% 6% 50% 6% 50%
Kylen Granson Indianapolis 8% 65% 6% -17% 14% 49%
Darnell Mooney Chicago 3% 72% 6% 13% 10% 86%
Isaiah Spiller Los Angeles Chargers 0% 0% 6% 24% 6% 24%
DeVonta Smith Philadelphia 12% 95% 6% -2% 18% 93%
Braxton Berrios Miami 7% 48% 6% -6% 13% 43%
Jerome Ford Cleveland 7% 51% 6% 7% 13% 59%
Najee Harris Pittsburgh 0% 0% 6% 40% 6% 40%
Josh Whyle Tennessee 0% 0% 6% 14% 6% 14%
Ezekiel Elliott New England 3% 26% 6% 18% 9% 44%
Mike Gesicki New England 3% 45% 6% 17% 9% 62%
David Njoku Cleveland 10% 66% 5% 4% 15% 70%
Logan Thomas Washington 0% 0% 5% 73% 5% 73%
JaMycal Hasty Jacksonville 0% 0% 5% 5% 5% 5%
Durham Smythe Miami 3% 24% 5% 35% 9% 60%
JuJu Smith-Schuster New England 10% 58% 5% -20% 15% 38%
Khalil Herbert Chicago 7% 41% 5% 16% 12% 57%

Some young players need to be scooped up and paid attention to moving forward, whether traded for or picked up. Let’s talk about them.

JSN has seen a steady uptick in opportunity and maxed out with a 70% route participation and got a 17% target share. Now, he will never be a primary target over Lockett or Metcalf consistently, but he’s a guy you need to get as the season wears on.

You likely missed the train on Michael Wilson, but you still need to take advantage. This isn’t just a one-week outlier. He has seen consistent route participation and a steady target share. You can see the explosiveness, and with the return of Kyler Murray on the horizon, you should strike while the iron is cool before it really starts to heat up.

Drake London has been a guy that has absolutely disappointed this season. I get it. I was in and still have hope. In the last few weeks, he has been the target leader for the Falcons and is on the field a ton. The numbers will come eventually, whether with Desmond Ridder or a quarterback change to Taylor Heinicke. I like London to be a back-end WR2 for the rest of the season.

Name Team W3 Tgt % W3 Rte % Wk3 Rush % W3-4 Target W3-4 Route W3-4 Rush W4 Tgt% W4 Rte% W4 Rush %
Elijah Higgins Arizona 0% 0% 0% 0% 0% 0% 0% 0% 0%
Greg Dortch Arizona 0% 0% 0% 0% 0% 0% 0% 0% 0%
Keaontay Ingram Arizona 3% 3% 17% -3% -3% -17% 0% 0% 0%
Emari Demercado Arizona 0% 0% 7% 8% 37% -3% 8% 37% 4%
Zach Ertz Arizona 6% 50% 0% 14% 34% 0% 20% 84% 0%
Rondale Moore Arizona 19% 53% 10% -15% 29% -10% 4% 82% 0%
Zach Pascal Arizona 0% 0% 0% 8% 18% 0% 8% 18% 0%
Marquise Brown Arizona 22% 78% 0% -1% 16% 0% 20% 94% 0%
Michael Wilson Arizona 6% 59% 0% 8% 12% 0% 14% 71% 0%
Geoff Swaim Arizona 0% 0% 0% 2% 8% 0% 2% 8% 0%
James Conner Arizona 6% 34% 48% -2% 0% 0% 4% 35% 48%
Trey McBride Arizona 3% 25% 0% -1% -7% 0% 2% 18% 0%
Josh Ali Atlanta 0% 0% 0% 0% 0% 0% 0% 0% 0%
Cordarrelle Patterson Atlanta 0% 0% 0% 0% 0% 0% 0% 0% 0%
Cordarrelle Patterson Atlanta 0% 0% 0% 0% 0% 0% 0% 0% 0%
John FitzPatrick Atlanta 0% 0% 0% 0% 0% 0% 0% 0% 0%
Scotty Miller Atlanta 0% 0% 0% 0% 0% 0% 0% 0% 0%
MyCole Pruitt Atlanta 2% 8% 0% -2% -8% 0% 0% 0% 0%
KhaDarel Hodge Atlanta 2% 14% 0% 3% 10% 0% 5% 24% 0%
Mack Hollins Atlanta 6% 73% 0% 2% 2% 0% 8% 76% 0%
Bijan Robinson Atlanta 12% 67% 56% 1% 0% 11% 14% 68% 67%
Drake London Atlanta 12% 88% 0% 7% -1% 0% 19% 86% 0%
Tyler Allgeier Atlanta 6% 20% 39% -1% -1% -6% 5% 19% 33%
Jonnu Smith Atlanta 16% 65% 0% 0% -3% 0% 16% 62% 0%
Kyle Pitts Atlanta 18% 86% 6% -8% -5% -6% 11% 81% 0%
Tylan Wallace Baltimore 0% 0% 0% 0% 0% 0% 0% 0% 0%
J.K. Dobbins Baltimore 0% 0% 0% 0% 0% 0% 0% 0% 0%
Charlie Kolar Baltimore 0% 0% 0% 0% 0% 0% 0% 0% 0%
Odell Beckham Jr. Baltimore 0% 0% 0% 0% 0% 0% 0% 0% 0%
Justice Hill Baltimore 0% 0% 0% 0% 0% 11% 0% 0% 11%
Isaiah Likely Baltimore 5% 16% 0% -5% -16% 0% 0% 0% 0%
Rashod Bateman Baltimore 8% 74% 0% -8% -74% 0% 0% 0% 0%
Gus Edwards Baltimore 0% 0% 32% 7% 50% 21% 7% 50% 54%
Devin Duvernay Baltimore 3% 24% 0% 4% 40% 0% 7% 63% 0%
Zay Flowers Baltimore 26% 100% 3% -13% -10% 1% 13% 90% 4%
Mark Andrews Baltimore 13% 87% 0% 4% -10% 0% 17% 77% 0%
Nelson Agholor Baltimore 11% 87% 0% -4% -17% 0% 7% 70% 0%
Quintin Morris Buffalo 0% 0% 0% 0% 0% 0% 0% 0% 0%
Damien Harris Buffalo 0% 0% 17% 0% 0% 5% 0% 0% 21%
Trent Sherfield Buffalo 8% 18% 0% 2% 22% 0% 10% 40% 0%
Latavius Murray Buffalo 3% 26% 17% 4% 17% -2% 7% 43% 14%
Dalton Kincaid Buffalo 5% 61% 0% 8% 16% 0% 13% 77% 0%
Deonte Harty Buffalo 8% 24% 7% -1% 10% -7% 7% 33% 0%
Stefon Diggs Buffalo 29% 84% 0% -6% 2% 0% 23% 87% 0%
Khalil Shakir Buffalo 3% 11% 0% 1% -4% 0% 3% 7% 0%
Gabe Davis Buffalo 11% 84% 3% -1% -11% -3% 10% 73% 0%
Dawson Knox Buffalo 5% 63% 0% -2% -16% 0% 3% 47% 0%
James Cook Buffalo 5% 53% 50% -2% -23% -7% 3% 30% 43%
Raheem Blackshear Carolina 0% 0% 0% 0% 0% 0% 0% 0% 0%
Giovanni Ricci Carolina 0% 0% 0% 0% 0% 0% 0% 0% 0%
Ihmir Smith-Marsette Carolina 0% 0% 0% 0% 0% 0% 0% 0% 0%
Tommy Tremble Carolina 1% 22% 0% -1% -22% 0% 0% 0% 0%
Jonathan Mingo Carolina 7% 27% 0% -7% -27% 0% 0% 0% 0%
Ian Thomas Carolina 1% 10% 0% 1% 19% 0% 2% 30% 0%
Laviska Shenault Jr. Carolina 1% 7% 17% 1% 8% -13% 2% 16% 4%
Hayden Hurst Carolina 4% 60% 0% 0% -1% 0% 5% 59% 0%
Chuba Hubbard Carolina 3% 30% 8% 2% -3% 42% 5% 27% 50%
Terrace Marshall Jr. Carolina 12% 64% 0% 9% -5% 0% 20% 59% 0%
Adam Thielen Carolina 21% 88% 0% -3% -6% 4% 18% 82% 4%
DJ Chark Jr. Carolina 15% 93% 0% -8% -8% 0% 7% 84% 0%
Miles Sanders Carolina 13% 52% 75% -7% -27% -29% 7% 25% 46%
Travis Homer Chicago 0% 0% 0% 0% 0% 0% 0% 0% 0%
D’Onta Foreman Chicago 0% 0% 0% 0% 0% 0% 0% 0% 0%
Trent Taylor Chicago 0% 0% 0% 0% 0% 0% 0% 0% 0%
Velus Jones Jr. Chicago 0% 0% 0% 0% 0% 3% 0% 0% 3%
Tyler Scott Chicago 10% 21% 0% -10% -21% 7% 0% 0% 7%
Chase Claypool Chicago 14% 90% 0% -14% -90% 0% 0% 0% 0%
Equanimeous St. Brown Chicago 0% 0% 0% 2% 50% 0% 2% 50% 0%
Robert Tonyan Chicago 0% 0% 0% 5% 26% 0% 5% 26% 0%
Khalil Herbert Chicago 7% 41% 32% 5% 16% 28% 12% 57% 60%
Darnell Mooney Chicago 3% 72% 0% 6% 13% 0% 10% 86% 0%
Marcedes Lewis Chicago 0% 0% 0% 2% 10% 0% 2% 10% 0%
DJ Moore Chicago 21% 100% 0% 3% -10% 0% 24% 90% 0%
Roschon Johnson Chicago 7% 31% 36% -5% -10% -20% 2% 21% 17%
Cole Kmet Chicago 7% 72% 0% 15% -11% 3% 21% 62% 3%
Chris Evans Cincinnati 0% 0% 0% 0% 0% 0% 0% 0% 0%
Andrei Iosivas Cincinnati 0% 0% 0% 0% 0% 0% 0% 0% 0%
Irv Smith Jr. Cincinnati 0% 0% 0% 0% 0% 0% 0% 0% 0%
Trayveon Williams Cincinnati 2% 13% 4% -2% -13% 2% 0% 0% 6%
Charlie Jones Cincinnati 4% 6% 0% -4% -6% 0% 0% 0% 0%
Trenton Irwin Cincinnati 0% 0% 0% 3% 42% 0% 3% 42% 0%
Tyler Boyd Cincinnati 17% 83% 0% 0% 8% 0% 17% 92% 0%
Ja’Marr Chase Cincinnati 28% 94% 0% -6% 3% 0% 22% 97% 0%
Chase Brown Cincinnati 0% 0% 0% 3% 3% 0% 3% 3% 0%
Mitchell Wilcox Cincinnati 4% 22% 0% -1% 0% 0% 3% 22% 0%
Joe Mixon Cincinnati 4% 54% 83% -1% -1% 0% 3% 53% 82%
Drew Sample Cincinnati 2% 20% 0% 1% -6% 0% 3% 14% 0%
Tee Higgins Cincinnati 15% 91% 0% -4% -41% 0% 11% 50% 0%
Pierre Strong Jr. Cleveland 0% 0% 20% 0% 0% 2% 0% 0% 22%
David Bell Cleveland 0% 0% 0% 0% 0% 0% 0% 0% 0%
Nick Chubb Cleveland 0% 0% 0% 0% 0% 0% 0% 0% 0%
Cedric Tillman Cleveland 0% 0% 0% 7% 17% 0% 7% 17% 0%
Jordan Akins Cleveland 0% 0% 0% 2% 15% 0% 2% 15% 0%
Harrison Bryant Cleveland 0% 0% 7% 2% 11% -2% 2% 11% 4%
Marquise Goodwin Cleveland 0% 0% 3% 2% 9% -3% 2% 9% 0%
Jerome Ford Cleveland 7% 51% 33% 6% 7% 6% 13% 59% 39%
Donovan Peoples-Jones Cleveland 10% 78% 0% -1% 5% 0% 9% 83% 0%
David Njoku Cleveland 10% 66% 0% 5% 4% 0% 15% 70% 0%
Elijah Moore Cleveland 22% 80% 10% -15% 0% -6% 7% 80% 4%
Amari Cooper Cleveland 20% 85% 0% -9% -5% 0% 11% 80% 0%
Jalen Brooks Dallas 0% 0% 0% 0% 0% 0% 0% 0% 0%
Peyton Hendershot Dallas 2% 33% 0% -2% -33% 0% 0% 0% 0%
Luke Schoonmaker Dallas 2% 8% 0% 3% 30% 0% 5% 38% 0%
Jalen Tolbert Dallas 0% 0% 0% 10% 23% 0% 10% 23% 0%
KaVontae Turpin Dallas 0% 0% 0% 3% 23% 3% 3% 23% 3%
Deuce Vaughn Dallas 0% 0% 0% 3% 13% 23% 3% 13% 23%
Jake Ferguson Dallas 13% 56% 0% 4% 9% 0% 18% 65% 0%
Brandin Cooks Dallas 13% 75% 0% -3% 0% 0% 10% 75% 0%
Rico Dowdle Dallas 6% 13% 14% -3% -6% -4% 3% 8% 10%
CeeDee Lamb Dallas 13% 85% 7% 2% -10% -4% 15% 75% 3%
Michael Gallup Dallas 13% 73% 0% 2% -11% 0% 15% 63% 0%
Tony Pollard Dallas 6% 71% 79% 2% -34% -44% 8% 38% 35%
Greg Dulcich Denver 0% 0% 0% 0% 0% 0% 0% 0% 0%
Chris Manhertz Denver 2% 2% 0% 1% 17% 0% 3% 19% 0%
Marvin Mims Jr. Denver 12% 26% 5% -5% 13% -5% 6% 39% 0%
Samaje Perine Denver 7% 29% 15% -1% 7% 20% 6% 35% 35%
Courtland Sutton Denver 24% 88% 0% -8% 5% 0% 16% 94% 0%
Jerry Jeudy Denver 17% 76% 0% -1% 1% 0% 16% 77% 0%
Nate Adkins Denver 2% 10% 0% 4% 0% 0% 6% 10% 0%
Adam Trautman Denver 5% 86% 0% -2% -8% 0% 3% 77% 0%
Javonte Williams Denver 5% 29% 55% 5% -12% -43% 10% 16% 12%
Brandon Johnson Denver 7% 52% 0% -4% -14% 0% 3% 39% 0%
Brock Wright Detroit 0% 0% 0% 0% 0% 0% 0% 0% 0%
James Mitchell Detroit 0% 0% 0% 0% 0% 0% 0% 0% 0%
Antoine Green Detroit 0% 0% 0% 0% 0% 0% 0% 0% 0%
Craig Reynolds Detroit 3% 24% 12% -3% -24% -12% 0% 0% 0%
Josh Reynolds Detroit 0% 0% 0% 19% 75% 0% 19% 75% 0%
David Montgomery Detroit 0% 0% 0% 6% 50% 74% 6% 50% 74%
Marvin Jones Jr. Detroit 0% 0% 0% 3% 28% 0% 3% 28% 0%
Amon-Ra St. Brown Detroit 32% 92% 3% -14% 5% -3% 19% 97% 0%
Jahmyr Gibbs Detroit 3% 46% 52% 13% -2% -33% 16% 44% 19%
Sam LaPorta Detroit 30% 73% 0% -14% -4% 0% 16% 69% 0%
Kalif Raymond Detroit 16% 51% 0% -13% -17% 2% 3% 34% 2%
Ben Sims Green Bay 0% 0% 0% 0% 0% 0% 0% 0% 0%
Emanuel Wilson Green Bay 2% 7% 8% -2% -7% -8% 0% 0% 0%
Malik Heath Green Bay 4% 13% 0% -4% -13% 0% 0% 0% 0%
AJ Dillon Green Bay 0% 0% 46% 2% 62% -4% 2% 62% 42%
Josiah Deguara Green Bay 0% 0% 0% 9% 56% 0% 9% 56% 0%
Christian Watson Green Bay 0% 0% 0% 9% 44% 0% 9% 44% 0%
Aaron Jones Green Bay 0% 0% 0% 4% 27% 42% 4% 27% 42%
Tucker Kraft Green Bay 0% 0% 0% 4% 13% 0% 4% 13% 0%
Romeo Doubs Green Bay 20% 78% 0% 9% 4% 8% 29% 82% 8%
Jayden Reed Green Bay 13% 65% 0% -2% -1% 0% 11% 64% 0%
Dontayvion Wicks Green Bay 11% 62% 0% -9% -8% 0% 2% 53% 0%
Samori Toure Green Bay 5% 38% 0% -3% -18% 0% 2% 20% 0%
Luke Musgrave Green Bay 15% 78% 0% -12% -58% 0% 2% 20% 0%
Mike Boone Houston 0% 0% 0% 0% 0% 3% 0% 0% 3%
Noah Brown Houston 0% 0% 0% 0% 0% 0% 0% 0% 0%
Xavier Hutchinson Houston 0% 0% 0% 0% 0% 0% 0% 0% 0%
Dare Ogunbowale Houston 0% 0% 0% 0% 0% 0% 0% 0% 0%
Teagan Quitoriano Houston 3% 6% 0% -3% -6% 0% 0% 0% 0%
Brevin Jordan Houston 6% 16% 0% -6% -16% 0% 0% 0% 0%
John Metchie III Houston 6% 16% 0% -1% 1% 0% 6% 17% 0%
Dameon Pierce Houston 10% 32% 56% -4% -1% 11% 6% 31% 67%
Devin Singletary Houston 6% 39% 36% -4% -4% -17% 3% 34% 19%
Tank Dell Houston 23% 84% 0% -14% -7% 6% 9% 77% 6%
Nico Collins Houston 10% 77% 0% 16% -9% 0% 26% 69% 0%
Robert Woods Houston 19% 90% 0% -5% -13% 0% 14% 77% 0%
Dalton Schultz Houston 6% 68% 0% 2% -28% 0% 9% 40% 0%
Deon Jackson Indianapolis 0% 0% 0% 0% 0% 0% 0% 0% 0%
Evan Hull Indianapolis 0% 0% 0% 0% 0% 0% 0% 0% 0%
Drew Ogletree Indianapolis 0% 0% 0% 0% 0% 0% 0% 0% 0%
Will Mallory Indianapolis 0% 0% 0% 0% 0% 0% 0% 0% 0%
Zack Moss Indianapolis 6% 37% 86% -3% 9% -24% 3% 46% 62%
Mo Alie-Cox Indianapolis 0% 0% 0% 3% 9% 0% 3% 9% 0%
Isaiah McKenzie Indianapolis 2% 10% 0% 7% -2% 0% 9% 9% 0%
Josh Downs Indianapolis 22% 84% 0% -14% -9% 0% 9% 74% 0%
Kylen Granson Indianapolis 8% 65% 0% 6% -17% 0% 14% 49% 0%
Alec Pierce Indianapolis 14% 94% 0% -9% -17% 0% 6% 77% 0%
Michael Pittman Jr. Indianapolis 22% 100% 0% -11% -20% 0% 11% 80% 0%
Elijah Cooks Jacksonville 0% 0% 0% 0% 0% 0% 0% 0% 0%
Parker Washington Jacksonville 0% 0% 0% 0% 0% 0% 0% 0% 0%
Zay Jones Jacksonville 0% 0% 0% 0% 0% 0% 0% 0% 0%
Tank Bigsby Jacksonville 2% 6% 8% -2% -6% 2% 0% 0% 11%
D’Ernest Johnson Jacksonville 4% 16% 13% -4% -16% -9% 0% 0% 4%
Brenton Strange Jacksonville 4% 14% 0% -4% -14% 0% 0% 0% 0%
Jamal Agnew Jacksonville 10% 59% 0% -10% -59% 0% 0% 0% 0%
Tim Jones Jacksonville 6% 29% 0% -3% 32% 0% 3% 61% 0%
Tim Jones Jacksonville 6% 29% 0% -3% 32% 0% 3% 61% 0%
Calvin Ridley Jacksonville 14% 80% 0% -9% 13% 0% 5% 92% 0%
Travis Etienne Jr. Jacksonville 10% 55% 79% -2% 11% -8% 8% 66% 71%
Christian Kirk Jacksonville 12% 86% 0% 17% 6% 0% 29% 92% 0%
JaMycal Hasty Jacksonville 0% 0% 0% 5% 5% 0% 5% 5% 0%
Evan Engram Jacksonville 16% 80% 0% 5% 2% 0% 21% 82% 0%
Luke Farrell Jacksonville 2% 12% 0% 3% 1% 0% 5% 13% 0%
Matt Bushman Kansas City 0% 0% 0% 0% 0% 0% 0% 0% 0%
Blake Bell Kansas City 0% 0% 0% 0% 0% 0% 0% 0% 0%
Richie James Kansas City 0% 0% 0% 0% 0% 0% 0% 0% 0%
Justyn Ross Kansas City 2% 26% 0% -2% -26% 0% 0% 0% 0%
Jerick McKinnon Kansas City 7% 30% 6% -7% -30% 4% 0% 0% 10%
Kadarius Toney Kansas City 2% 5% 0% 3% 26% 0% 5% 31% 0%
Travis Kelce Kansas City 19% 65% 0% 4% 12% 0% 23% 77% 0%
Justin Watson Kansas City 7% 51% 0% -4% 5% 0% 3% 56% 0%
Isiah Pacheco Kansas City 7% 35% 44% 1% 4% 20% 8% 38% 65%
Marquez Valdes-Scantling Kansas City 5% 60% 0% 3% -4% 0% 8% 56% 0%
Rashee Rice Kansas City 16% 49% 0% -6% -5% 0% 10% 44% 0%
Skyy Moore Kansas City 14% 56% 0% -11% -10% 6% 3% 46% 6%
Clyde Edwards-Helaire Kansas City 2% 19% 44% 0% -11% -34% 3% 8% 10%
Noah Gray Kansas City 5% 58% 0% 3% -27% 0% 8% 31% 0%
Kristian Wilkerson Las Vegas 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jesper Horsted Las Vegas 0% 0% 0% 0% 0% 0% 0% 0% 0%
Brandon Bolden Las Vegas 0% 0% 0% 0% 0% 0% 0% 0% 0%
Tre Tucker Las Vegas 0% 0% 0% 0% 0% 0% 0% 0% 0%
Zamir White Las Vegas 0% 0% 0% 0% 0% 13% 0% 0% 13%
Michael Mayer Las Vegas 2% 35% 0% -2% -35% 0% 0% 0% 0%
DeAndre Carter Las Vegas 2% 11% 0% -2% -11% 0% 0% 0% 0%
Austin Hooper Las Vegas 0% 0% 0% 4% 53% 0% 4% 53% 0%
Jakobi Meyers Las Vegas 22% 76% 0% -14% 14% 0% 8% 90% 0%
Ameer Abdullah Las Vegas 6% 17% 0% -1% 12% 0% 4% 29% 0%
Hunter Renfrow Las Vegas 4% 52% 0% 4% 3% 0% 8% 55% 0%
Davante Adams Las Vegas 37% 89% 0% -11% -7% 0% 27% 82% 0%
Josh Jacobs Las Vegas 9% 54% 94% 11% -15% -24% 20% 39% 71%
Austin Ekeler Los Angeles Chargers 0% 0% 0% 0% 0% 0% 0% 0% 0%
Tre’ McKitty Los Angeles Chargers 0% 0% 0% 0% 0% 0% 0% 0% 0%
Elijah Dotson Los Angeles Chargers 0% 0% 0% 0% 0% 0% 0% 0% 0%
Joshua Kelley Los Angeles Chargers 2% 24% 73% -2% -24% -20% 0% 0% 53%
Mike Williams Los Angeles Chargers 16% 73% 0% -16% -73% 0% 0% 0% 0%
Quentin Johnston Los Angeles Chargers 6% 25% 0% 3% 41% 0% 9% 67% 0%
Joshua Palmer Los Angeles Chargers 14% 67% 0% 11% 27% 0% 24% 94% 0%
Isaiah Spiller Los Angeles Chargers 0% 0% 13% 6% 24% 2% 6% 24% 16%
Stone Smartt Los Angeles Chargers 0% 0% 0% 3% 18% 0% 3% 18% 0%
Derius Davis Los Angeles Chargers 2% 8% 0% 4% 10% 9% 6% 18% 9%
Gerald Everett Los Angeles Chargers 12% 49% 0% -6% 9% 0% 6% 58% 0%
Keenan Allen Los Angeles Chargers 39% 90% 0% -27% 4% 0% 12% 94% 0%
Donald Parham Jr. Los Angeles Chargers 4% 31% 0% -1% -10% 0% 3% 21% 0%
Davis Allen Los Angeles Rams 0% 0% 0% 0% 0% 0% 0% 0% 0%
Demarcus Robinson Los Angeles Rams 0% 0% 0% 0% 0% 0% 0% 0% 0%
Zach Evans Los Angeles Rams 0% 0% 0% 0% 0% 0% 0% 0% 0%
Hunter Long Los Angeles Rams 0% 0% 0% 0% 0% 0% 0% 0% 0%
Cooper Kupp Los Angeles Rams 0% 0% 0% 0% 0% 0% 0% 0% 0%
Ben Skowronek Los Angeles Rams 2% 12% 0% -2% -12% 0% 0% 0% 0%
Cam Akers Los Angeles Rams 0% 0% 0% 8% 24% 22% 8% 24% 22%
Ronnie Rivers Los Angeles Rams 0% 0% 0% 4% 17% 26% 4% 17% 26%
Brycen Hopkins Los Angeles Rams 0% 0% 0% 2% 9% 0% 2% 9% 0%
Tyler Higbee Los Angeles Rams 12% 84% 0% 8% -1% 0% 19% 83% 0%
Van Jefferson Los Angeles Rams 7% 86% 8% -1% -1% -8% 6% 85% 0%
Puka Nacua Los Angeles Rams 16% 91% 0% 5% -1% 0% 21% 89% 0%
Tutu Atwell Los Angeles Rams 21% 86% 8% -4% -3% -8% 17% 83% 0%
Kyren Williams Los Angeles Rams 16% 77% 83% -10% -19% -12% 6% 57% 71%
Chris Brooks Miami 0% 0% 20% 0% 0% -20% 0% 0% 0%
Tyler Kroft Miami 0% 0% 0% 0% 0% 0% 0% 0% 0%
Erik Ezukanma Miami 0% 0% 0% 0% 0% 0% 0% 0% 0%
Salvon Ahmed Miami 0% 0% 0% 0% 0% 0% 0% 0% 0%
Julian Hill Miami 3% 66% 0% -3% -66% 0% 0% 0% 0%
River Cracraft Miami 3% 41% 0% -3% -41% 0% 0% 0% 0%
Jaylen Waddle Miami 0% 0% 0% 9% 66% 0% 9% 66% 0%
Durham Smythe Miami 3% 24% 0% 5% 35% 0% 9% 60% 0%
Cedrick Wilson Jr. Miami 0% 0% 0% 4% 26% 0% 4% 26% 0%
De’Von Achane Miami 14% 38% 41% -5% 22% 4% 9% 60% 44%
Tyreek Hill Miami 38% 72% 0% -29% -2% 6% 9% 70% 6%
Braxton Berrios Miami 7% 48% 0% 6% -6% 6% 13% 43% 6%
Raheem Mostert Miami 24% 62% 30% -16% -28% 9% 9% 34% 39%
Nick Muse Minnesota 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jalen Nailor Minnesota 0% 0% 0% 0% 0% 0% 0% 0% 0%
Myles Gaskin Minnesota 0% 0% 0% 0% 0% 0% 0% 0% 0%
Johnny Mundt Minnesota 0% 0% 0% 0% 0% 0% 0% 0% 0%
Brandon Powell Minnesota 2% 7% 0% -2% -7% 0% 0% 0% 0%
Ty Chandler Minnesota 3% 12% 12% -3% -12% -12% 0% 0% 0%
Josh Oliver Minnesota 3% 14% 0% -3% -14% 0% 0% 0% 0%
Justin Jefferson Minnesota 21% 91% 0% 15% -7% 0% 36% 84% 0%
K.J. Osborn Minnesota 5% 90% 0% 3% -14% 0% 8% 76% 0%
Alexander Mattison Minnesota 10% 50% 80% -6% -14% -6% 4% 36% 74%
Jordan Addison Minnesota 14% 76% 0% -10% -16% 0% 4% 60% 0%
T.J. Hockenson Minnesota 19% 86% 0% -7% -18% 0% 12% 68% 0%
Kayshon Boutte New England 0% 0% 0% 0% 0% 0% 0% 0% 0%
Matthew Slater New England 0% 0% 0% 0% 0% 0% 0% 0% 0%
Pharaoh Brown New England 6% 13% 0% -6% -13% 0% 0% 0% 0%
Ezekiel Elliott New England 3% 26% 40% 6% 18% -11% 9% 44% 29%
Mike Gesicki New England 3% 45% 0% 6% 17% 0% 9% 62% 0%
Hunter Henry New England 16% 71% 0% -1% 6% 0% 15% 76% 0%
Demario Douglas New England 10% 39% 3% -1% 2% -3% 9% 41% 0%
DeVante Parker New England 10% 90% 0% 2% -2% 0% 12% 88% 0%
Kendrick Bourne New England 16% 71% 0% -7% -12% 0% 9% 59% 0%
Rhamondre Stevenson New England 10% 68% 48% -1% -18% 19% 9% 50% 67%
JuJu Smith-Schuster New England 10% 58% 0% 5% -20% 0% 15% 38% 0%
Tre’Quan Smith New Orleans 0% 0% 0% 0% 0% 0% 0% 0% 0%
A.T. Perry New Orleans 0% 0% 0% 0% 0% 0% 0% 0% 0%
Kirk Merritt New Orleans 0% 0% 0% 0% 0% 0% 0% 0% 0%
Foster Moreau New Orleans 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jamaal Williams New Orleans 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jimmy Graham New Orleans 3% 24% 0% -3% -24% 0% 0% 0% 0%
Keith Kirkwood New Orleans 3% 5% 0% -3% -5% 0% 0% 0% 0%
Juwan Johnson New Orleans 11% 66% 0% -11% -66% 0% 0% 0% 0%
Taysom Hill New Orleans 3% 21% 17% -1% 17% -1% 2% 38% 17%
Michael Thomas New Orleans 21% 89% 0% -8% -9% 0% 13% 81% 0%
Rashid Shaheed New Orleans 3% 84% 4% 12% -16% -4% 15% 68% 0%
Chris Olave New Orleans 29% 92% 0% -16% -18% 0% 13% 74% 0%
Kendre Miller New Orleans 3% 26% 39% -1% -20% -34% 2% 6% 6%
Eric Gray New York Giants 0% 0% 0% 0% 0% 0% 0% 0% 0%
Lawrence Cager New York Giants 0% 0% 0% 0% 0% 0% 0% 0% 0%
Saquon Barkley New York Giants 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jalin Hyatt New York Giants 0% 0% 0% 4% 58% 0% 4% 58% 0%
Wan’Dale Robinson New York Giants 13% 23% 0% -1% 39% 4% 12% 62% 4%
Darren Waller New York Giants 18% 68% 0% -12% 17% 0% 6% 85% 0%
Gary Brightwell New York Giants 8% 13% 40% -6% 16% -23% 2% 29% 17%
Sterling Shepard New York Giants 0% 0% 0% 2% 10% 0% 2% 10% 0%
Darius Slayton New York Giants 15% 75% 0% -9% 10% 0% 6% 85% 0%
Matt Breida New York Giants 8% 50% 40% 2% 8% 21% 10% 58% 61%
Parris Campbell New York Giants 15% 35% 0% -5% -2% 0% 10% 33% 0%
Isaiah Hodgins New York Giants 3% 55% 0% 3% -15% 0% 6% 40% 0%
Daniel Bellinger New York Giants 3% 25% 0% -1% -23% 0% 2% 2% 0%
Irvin Charles New York Jets 0% 0% 0% 0% 0% 0% 0% 0% 0%
Israel Abanikanda New York Jets 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jason Brownlee New York Jets 0% 0% 0% 0% 0% 0% 0% 0% 0%
Mecole Hardman Jr. New York Jets 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jeremy Ruckert New York Jets 0% 0% 0% 5% 35% 0% 5% 35% 0%
Xavier Gipson New York Jets 0% 0% 0% 2% 14% 13% 2% 14% 13%
Breece Hall New York Jets 5% 26% 57% 2% 13% -20% 7% 40% 38%
Michael Carter New York Jets 2% 14% 0% 5% 9% 6% 7% 23% 6%
Garrett Wilson New York Jets 19% 95% 0% 11% 0% 0% 30% 95% 0%
C.J. Uzomah New York Jets 5% 26% 0% -2% -1% 0% 2% 26% 0%
Dalvin Cook New York Jets 7% 14% 38% -5% -3% -7% 2% 12% 31%
Allen Lazard New York Jets 12% 95% 0% -5% -12% 0% 7% 84% 0%
Tyler Conklin New York Jets 12% 67% 0% 2% -13% 0% 14% 53% 0%
Randall Cobb New York Jets 5% 81% 0% 5% -37% 0% 9% 44% 0%
Albert Okwuegbunam Philadelphia 0% 0% 0% 0% 0% 0% 0% 0% 0%
Quez Watkins Philadelphia 0% 0% 0% 0% 0% 0% 0% 0% 0%
Grant Calcaterra Philadelphia 0% 0% 0% 0% 0% 0% 0% 0% 0%
Rashaad Penny Philadelphia 0% 0% 0% 0% 0% 0% 0% 0% 0%
Boston Scott Philadelphia 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jack Stoll Philadelphia 5% 31% 0% -5% -31% 0% 0% 0% 0%
Olamide Zaccheaus Philadelphia 7% 64% 0% -3% 20% 0% 4% 84% 0%
A.J. Brown Philadelphia 31% 81% 0% -2% 8% 0% 29% 89% 0%
Dallas Goedert Philadelphia 17% 88% 0% -8% 3% 0% 9% 91% 0%
Kenneth Gainwell Philadelphia 5% 40% 37% 0% 0% -21% 4% 40% 15%
DeVonta Smith Philadelphia 12% 95% 0% 6% -2% 0% 18% 93% 0%
D’Andre Swift Philadelphia 5% 48% 42% 4% -3% 12% 9% 44% 54%
Anthony McFarland Jr. Pittsburgh 0% 0% 0% 0% 0% 0% 0% 0% 0%
Diontae Johnson Pittsburgh 0% 0% 0% 0% 0% 0% 0% 0% 0%
Gunner Olszewski Pittsburgh 0% 0% 0% 0% 0% 0% 0% 0% 0%
Miles Boykin Pittsburgh 0% 0% 0% 0% 0% 0% 0% 0% 0%
Najee Harris Pittsburgh 0% 0% 63% 6% 40% -5% 6% 40% 58%
Darnell Washington Pittsburgh 0% 0% 0% 3% 26% 0% 3% 26% 0%
Allen Robinson II Pittsburgh 13% 75% 0% -7% 8% 0% 6% 83% 0%
Jaylen Warren Pittsburgh 13% 44% 27% 5% -1% 7% 17% 43% 33%
Calvin Austin III Pittsburgh 19% 81% 0% -4% -1% 4% 14% 80% 4%
George Pickens Pittsburgh 19% 94% 0% 1% -5% 0% 20% 89% 0%
Pat Freiermuth Pittsburgh 13% 72% 0% -1% -15% 0% 11% 57% 0%
Tyrion Davis-Price San Francisco 0% 0% 0% 0% 0% 0% 0% 0% 0%
Ross Dwelley San Francisco 0% 0% 0% 0% 0% 0% 0% 0% 0%
Brayden Willis San Francisco 0% 0% 0% 0% 0% 0% 0% 0% 0%
Charlie Woerner San Francisco 0% 0% 0% 0% 0% 0% 0% 0% 0%
Elijah Mitchell San Francisco 7% 26% 28% -7% -26% -28% 0% 0% 0%
Jauan Jennings San Francisco 7% 42% 0% -7% -42% 0% 0% 0% 0%
Deebo Samuel San Francisco 26% 84% 3% -26% -84% 8% 0% 0% 10%
Brandon Aiyuk San Francisco 0% 0% 0% 24% 76% 0% 24% 76% 0%
Christian McCaffrey San Francisco 12% 47% 45% 20% 29% 22% 32% 76% 67%
Jordan Mason San Francisco 0% 0% 8% 4% 12% 3% 4% 12% 10%
George Kittle San Francisco 21% 77% 0% -17% 3% 0% 4% 80% 0%
Ronnie Bell San Francisco 5% 26% 0% -1% -14% 0% 4% 12% 0%
Kenny McIntosh Seattle 0% 0% 0% 0% 0% 0% 0% 0% 0%
DK Metcalf Seattle 0% 0% 0% 0% 0% 0% 0% 0% 0%
Will Dissly Seattle 0% 0% 0% 0% 0% 0% 0% 0% 0%
Jake Bobo Seattle 5% 27% 0% -5% -27% 0% 0% 0% 0%
Kenneth Walker III Seattle 7% 24% 53% -7% -24% 15% 0% 0% 68%
Jaxon Smith-Njigba Seattle 7% 59% 0% 9% 11% 0% 17% 70% 0%
Cody Thompson Seattle 0% 0% 0% 3% 7% 0% 3% 7% 0%
DeeJay Dallas Seattle 2% 7% 6% 1% 3% -6% 3% 10% 0%
Tyler Lockett Seattle 17% 85% 0% 0% -5% 0% 17% 80% 0%
Colby Parkinson Seattle 7% 46% 0% -4% -16% 0% 3% 30% 0%
Noah Fant Seattle 12% 49% 0% -6% -22% 0% 7% 27% 0%
Zach Charbonnet Seattle 5% 39% 26% 2% -22% -6% 7% 17% 20%
Payne Durham Tampa Bay 0% 0% 0% 0% 0% 0% 0% 0% 0%
Ko Kieft Tampa Bay 0% 0% 0% 0% 0% 0% 0% 0% 0%
David Wells Tampa Bay 0% 0% 0% 0% 0% 0% 0% 0% 0%
Chase Edmonds Tampa Bay 0% 0% 0% 0% 0% 0% 0% 0% 0%
Sean Tucker Tampa Bay 0% 0% 13% 0% 0% -13% 0% 0% 0%
Deven Thompkins Tampa Bay 13% 19% 0% -3% 38% 3% 10% 57% 3%
Ke’Shawn Vaughn Tampa Bay 0% 0% 0% 2% 17% 31% 2% 17% 31%
Cade Otton Tampa Bay 6% 71% 0% 3% 15% 0% 10% 86% 0%
Rakim Jarrett Tampa Bay 0% 0% 0% 2% 5% 0% 2% 5% 0%
Chris Godwin Tampa Bay 16% 81% 0% 10% 3% 0% 26% 83% 0%
Trey Palmer Tampa Bay 3% 77% 0% 4% -4% 0% 7% 74% 0%
Trey Palmer Tampa Bay 3% 77% 0% 4% -4% 0% 7% 74% 0%
Rachaad White Tampa Bay 10% 81% 88% -3% -14% -36% 7% 67% 52%
Mike Evans Tampa Bay 32% 74% 0% -25% -34% 0% 7% 40% 0%
Kearis Jackson Tennessee 0% 0% 0% 0% 0% 0% 0% 0% 0%
Julius Chestnut Tennessee 0% 0% 0% 0% 0% 0% 0% 0% 0%
Trevon Wesco Tennessee 0% 0% 0% 0% 0% 0% 0% 0% 0%
Treylon Burks Tennessee 19% 72% 0% -19% -72% 0% 0% 0% 0%
Nick Westbrook-Ikhine Tennessee 0% 0% 0% 17% 74% 0% 17% 74% 0%
Chris Moore Tennessee 9% 31% 0% -1% 37% 0% 9% 69% 0%
Derrick Henry Tennessee 0% 0% 73% 3% 37% -2% 3% 37% 71%
Colton Dowell Tennessee 0% 0% 0% 3% 20% 0% 3% 20% 0%
Josh Whyle Tennessee 0% 0% 0% 6% 14% 0% 6% 14% 0%
Chigoziem Okonkwo Tennessee 13% 63% 0% -4% 3% 0% 9% 66% 0%
Tyjae Spears Tennessee 13% 53% 27% -1% -5% -11% 11% 49% 16%
DeAndre Hopkins Tennessee 19% 78% 0% -2% -15% 0% 17% 63% 0%
Curtis Hodges Washington 0% 0% 0% 0% 0% 0% 0% 0% 0%
Chris Rodriguez Jr. Washington 0% 0% 0% 0% 0% 0% 0% 0% 0%
Mitchell Tinsley Washington 0% 0% 0% 0% 0% 0% 0% 0% 0%
Dax Milne Washington 0% 0% 0% 0% 0% 0% 0% 0% 0%
Cole Turner Washington 17% 54% 0% -17% -54% 0% 0% 0% 0%
Logan Thomas Washington 0% 0% 0% 5% 73% 4% 5% 73% 4%
Brian Robinson Washington 0% 0% 77% 4% 27% -16% 4% 27% 61%
Byron Pringle Washington 0% 0% 0% 4% 18% 0% 4% 18% 0%
Dyami Brown Washington 2% 24% 0% 3% 8% 0% 5% 33% 0%
Terry McLaurin Washington 15% 73% 0% 4% 7% 0% 18% 80% 0%
Curtis Samuel Washington 10% 76% 0% 5% -8% 4% 15% 67% 4%
Jahan Dotson Washington 10% 93% 0% 5% -24% 0% 15% 69% 0%
Antonio Gibson Washington 10% 68% 15% -8% -32% 11% 2% 36% 26%
John Bates Washington 5% 46% 0% -3% -34% 0% 2% 13% 0%

Posted 10 Oct 2023 04:31 by | News | 1 comment

Global minerals leaders to attend the Future Minerals Forum

Over 200 speakers, including CEOs from major mineral companies, are set to attend the third edition of the Future Minerals Forum, which is scheduled to take place from 9-11 January 2024 in Riyadh, Saudi Arabia.

The event features speakers from various companies, including Glencore CEO Gary Nagle, Vale CEO Eduardo Bartolomeo, Codelco Chairman Máximo Pacheco, Vale Base Metals Chairman Mark Cutifani, BlackRock’s Evy Hambro, Teck CEO Jonathan Price, and Boliden CEO Mikael Staffas.

The Future Minerals Forum will also feature speakers from various mining companies, including Ivanhoe Mines Founder and Executive Co-Chairman Robert Friedland, Barrick Gold CEO Mark Bristow, KAZ Minerals CEO Andrew Southam, and The Mosaic Company President and CEO Joc O’Rourke. These speakers will discuss investments in South Africa, the Democratic Republic of Congo, gold and copper production, and the role of corporations like Ivanhoe Mines, Barrick Gold, KAZ Minerals, and The Mosaic Company in the global mining industry.

“FMF, a government-led initiative, serves as a vital platform for fostering partnerships and dialogue among global investors, mining firms, and stakeholders. With a focus on the super region covering Africa, Western and Central Asia, FMF amplifies the voices of mineral suppliers, facilitating their pivotal role in the global green transition. In doing so, FMF supports economic development by uniting decision makers, driving investment, and promoting responsible mining, processing and manufacturing industries needed for supplying clean energy and goods.” Vice-Minister of Industry and Mineral Resources for Mining Affairs, Eng. Khalid Al-Mudaifer said.

The third edition of the program aims to develop a global critical minerals strategy, enable investments in the super region, build excellence centres, develop sustainability standards, and create a green metals hub using modern technologies and processing centres, thereby enhancing human capacities and maintaining trust with society.

The post Global minerals leaders to attend the Future Minerals Forum first appeared on Australian Resources.

Posted 10 Oct 2023 03:31 by | News | 1 comment

Guatemala protests intensify, demanding prosecutor resignations

GUATEMALA CITY – Tens of thousands of Guatemalans marched peacefully on Thursday for the fourth consecutive day, demanding the resignation of powerful senior prosecutors accused of working to undermine President-elect Bernardo Arevalo’s ability to take office.

The center-left Arevalo was elected in a landslide win in August, but since then Attorney General Consuelo Porras has intensified efforts to disqualify his anti-graft Movimiento Semilla party and ordered raids on the electoral authority’s offices, seizing ballots.

“We’re going to paralyze the country indefinitely. We demand the resignation of the prosecutor, Consuelo Porras,” said protester Luis Pacheco, head of 48 Cantones, a large Indigenous organization.

Pacheco spoke outside Porras’ offices in Guatemala City, where other groups have been camping out since Monday, waving Guatemalan flags and hoisting signs demanding an end to corruption.

Another sign read: “Get out coup plotters.”

The prosecutor’s office has defended what it describes as lawful actions to investigate Semilla over alleged registration issues and the need to secure evidence via raids.

Arevalo, a previously little-known lawmaker who struck a cord with his campaign pledge to tackle corruption, is fighting a bitter post-election battle with entrenched foes ahead of January’s scheduled inauguration.

After he secured unexpectedly strong support in June’s first-round vote, Porras asked a judge to disqualify Semilla, alleging the six-year-old registration flaws.

Her office’s raids have prompted international criticism while also stoking popular anger. A wide range of protesters, including Indigenous people, rural farmers, and teacher and student groups have taken to the streets to demand the resignations of Porras and one of her top prosecutors, Rafael Curruchiche.

Both have been accused of corruption by the U.S. government.

President Alejandro Giammattei, who in August promised an orderly transition, nominated Porras to her present term as attorney general, and has mostly remained silent on the investigations and raids.

On Wednesday, Giammattei’s government said it was restarting transition talks with Arevalo’s team. REUTERS

Posted 10 Oct 2023 02:31 by | News | No comments

Fantasy Football Week 5 RB Streamers Include Chuba Hubbard

If you missed out on some top RB fantasy waiver targets heading into Week 5, you might be looking for some under-the-radar help heading into your next matchup. Let’s take a look at some potential fantasy football Week 5 RB streamers that you can pick up.

Pick the right fantasy football streamers option to get the win this week and continue down the path of fantasy greatness, which ends with you hoisting your league’s trophy, championship belt or champ chain.

Players must be owned in less than 60 percent of ESPN fantasy leagues to be included in the fantasy football Week 5 RB streamers column. All ownerships for players will be provided and will reflect that of Wednesday morning, once waivers are processed.

Fantasy Football Week 5 RB Streamers

Chuba Hubbard Fantasy Outlook (18%)

With Miles Sanders nursing a groin injury, Chuba Hubbard racked up more carries, snaps and routes than the veteran in Week 4 against the Vikings. Hubbard has been quietly having an efficient year as well, averaging around 4.5 yards per carry. 

Chuba Hubbard doesn’t have a ton of standalone value, but he’s a worthy add if Sanders’ groin injury lingers or forces the veteran to miss time. 

 Latavius Murray Fantasy Outlook (5%)

Latavius Murray had been benefiting as the goal line back for the Bills in Weeks 2 and 3, scoring a touchdown in each game. But Week 4 was a different story, where the Bills opted to give James Cook a crack at finishing drives by the end zone. Nonetheless, Murray should remain the goal-line option for an elite Buffalo offense, as one week isn’t an indicator of his role changing entirely moving forward. 

If you’re in desperate need of a Flex play in Week 5, Murray isn’t a bad fantasy football Week 5 RB streamers option. 

Samaje Perine Fantasy Outlook (54.8%)

At the time of writing, Javonte Williams’ injury isn’t thought to be serious. That being said, if Williams is limited in any capacity (or even misses time) and you missed out on Jaleel McLaughlin, then Perine is another desperation FLEX add for needy rosters. 

The Broncos’ offense has proven to be serviceable, and they’ll welcome a Jets team to Mile High that has been middle of the pack in run defense and just allowed Isiah Pacheco to run for 115 yards and a touchdown. If Williams sits out Week 5, a positive game script and decent matchup could set up Perine for sneaky viability. 

Check out our Week 5 fantasy football rankings to see where Samaje Perine is ranked!

Follow us on all of our social channels! Check out our Twitter, Facebook, YouTube and TikTok for more great FlurrySports content.

Posted 10 Oct 2023 01:31 by | News | No comments

Natrona County Circuit Court arraignments (10/4/23–10/5/23)

CASPER, Wyo. — Provided below are the defendants and charges from appearances in Natrona County Circuit Court on Wednesday and Thursday, Oct. 4–5.

The Honorable Seventh Judicial Circuit Court Judge Brian Christensen presided while Assistant District Attorney Blaine Nelson represented the state.

All persons entering not guilty pleas or charged with felonies are presumed innocent until convicted or pleading guilty. Official charges are subject to change by the Natrona County District Attorney’s Office.

Pleas on felony charges are not entered in circuit court, though bond conditions are set.

A bail bondsman can post a surety bond, typically requiring 10–15%.

A “personal recognizance” or “signature” bond means there is no money required to get out, but the defendant could owe the full amount if they fail to appear.

Factors affecting bond and sentencing include the nature of the crime, the weight of the evidence, the defendant’s criminal history and their ties to the community.

The standard of evidence differs in probation revocations. The state is only required to prove the allegations in an affidavit for revocation “to a preponderance of the evidence.” 

The defendant can admit and have the underlying sentenced imposed or resuspended. The defendant can also deny and seek representation at a contested hearing, and a new bond is set.

For purposes of this document, admissions and denials are represented as guilty and not guilty pleas, respectively. 

Standard fees typically include $150 to the Crime Victims Compensation Fund, $70 in court costs and $50 to Drug Court assessment. Additional fines may be imposed at sentencing. 

Felonies and engrossed misdemeanors (pleas not entered in circuit court)

  • Richard Conner, 52 – Aggravated assault and battery, aggravated burglary with a deadly weapon, unlawful entry to commit battery, stalking, violation of protection order, telephone harassment
    • $50,000 cash only
  • Kyle Pacheco, 33 – Property destruction: over $1,000, domestic battery, interference
    • $3,000 cash or surety bond
  • James Johnson, 56 – Failure to register as a sex offender
    • $5,000 cash or surety bond
  • Mason Cureton, 22 – Possession of methamphetamine, possession of marijuana, possession of clonazepam: third or subsequent upon conviction x3
    • $25,000 cash or surety bond
  • William Hamilton, 39 – Possession of methamphetamine: felony weight, possession of marijuana, DUI: controlled substance
    • $7,500 cash only
  • Tevin Sexton Marquis Taylor, 33 – Possession of methamphetamine: felony weight, interference, possession of fentanyl, possession of marijuana
    • $20,000 cash or surety bond

No contest and guilty pleas

  • Zachary Allen, 34 – Possession of a controlled substance, use of a controlled substance
    • Pleaded no contest to use of controlled substance charge
    • 30 days jail, remaining one year suspended
    • One year supervised probation
  • Clayton Cogdill, 32 – Possession of cocaine, possession of mushrooms
    • One year suspended
    • One year supervised probation
    • $600 fine
  • Nicole Greenleaf, 54 – Possession of methamphetamine, driving under suspension
    • Five days jail, remaining one year suspended
    • One year supervised probation
  • Matthew Riverkamp, 27 – Possession of methamphetamine, use of methamphetamine
    • 18 months suspended
    • Six months supervised probation
  • Alicia Smith, 23 – Domestic battery
    • Six months suspended
    • Six months unsupervised probation
    • $250 fine

Not guilty pleas

  • Joseph Peters, 37 – DUI
    • $2,000 cash or surety bond
  • Arnaldo Perez Fonseca, 32 – Fail to appear for hearing on interference charge
    • $1,000 cash or surety bond
  • Rae Cobert, 20 – Possession of methamphetamine, possession of marijuana, possession of Klonopin
    • $20,000 cash or surety bond
  • Shaun Michael Kiser, 37 – Driving under suspension, no insurance, open container in a moving vehicle, theft, no registration, possession of a controlled substance: fentanyl
    • $10,000 cash or surety bond
  • Rachel Lippett, 35 – Possession of a controlled substance, open container
    • $885 cash or surety bond continued

Posted 10 Oct 2023 00:31 by | News | No comments

20-Minute Vegetable Quinoa Salad Recipe With Creamy Avocado Orange Dressing | Grains | 30Seconds Food

If you haven’t noticed, we love quinoa at our house. The health benefits of quinoa are many, so I’m always looking for new fresh ways to use it (and it’s always so delicious). 

This refreshing and healthy vegetable and quinoa salad recipe with avocado and orange salad dressing is so easy to prepare. Make the quinoa (which is naturally gluten-free and packed with protein, zinc, fiber, folate and antioxidants) and prep your other ingredients while it cooks.

Here is what you need to make the salad: quinoa, fresh tomatoes, carrot, sweet pepper, green onion, parsley, mint, raw pumpkin seeds and raw sunflower seeds. For the salad dressing you need an avocado, orange juice, olive oil and balsamic vinegar. Serve this healthy quinoa
grain salad with your favorite protein for a complete meal!

Note: 30Seconds is a participant in the Amazon affiliate advertising program and this post contains affiliate links, which means we may earn a commission or fees if you make a purchase via those links.

Cuisine: American
Prep Time: 10 minutes
Cook Time: 10 minutes
Total Time: 20 minutes
Servings: 8

Ingredients 

Salad

Dressing

Recipe Notes

Here’s how to make it: 

<Macro 'tip'>

<Macro 'tip'>

<Macro 'tip'>

Nutrition Facts Per Serving

Cholesterol: 0mg

Dietary Fiber: 3.2g

Potassium: 330mg

Recipe cooking times, nutritional information and servings are approximate and provided for your convenience. However, 30Seconds is not responsible for the outcome of any recipe, nor may you have the same results because of variations in ingredients, temperatures, altitude, errors, omissions or cooking/baking abilities. This recipe has been analyzed by VeryWellFit. However, any nutritional information is provided as a courtesy and it is up to the individual to ascertain accuracy. To ensure image quality, we may occasionally use stock photography.

Need to convert cooking and baking measurements? Here are some kitchen conversion charts. Here’s how to submit your recipes to 30Seconds.

Take 30 seconds and join the 30Seconds community, and follow us on Facebook to get recipes in your newsfeed daily.

Related Products on Amazon We Think You May Like:

30Second Mobile, Inc. is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and affiliated sites.

Cutting Boards

$8 & Up

This content was originally published here.

Posted 10 Oct 2023 00:25 by | Vegetables | No comments

SU introduces Vmock, an AI-powered platform, to review student resumes

Beginning this academic year, the College of Arts and Sciences Office of Undergraduate Academic and Career Advising at Syracuse University debuted an initiative using the artificial intelligence software VMock, an online resume review software.

Though VMock has been available to all SU undergraduate students since 2020, this new initiative allows sophomores to choose between using VMock or meeting one-on-one with a career counselor to develop their resume. This initiative only extends to second-year undergraduate students with declared majors in Arts and Sciences or the Maxwell School of Citizenship and Public Affairs, though VMock is a tool available to students in other SU schools and colleges.

SU is not alone in its implementation of VMock as a step in the career advising process. Over 250 higher education institutions in more than 130 countries use VMock’s services, according to its website.

“VMock is intended as a potential, non-required, avenue and tool for all undergraduates as a first pass at their resume,” Steven Schaffling, assistant dean of student success of Arts and Sciences, told The Daily Orange in an email statement.

Sophomore students who have not already met with a career advisor are required to either schedule a 30-minute meeting or upload their resumes to VMock to lift a hold on their MySlice account, which stops students from registering for classes next semester.

If students choose a one-on-one conference, they are still instructed to upload their resumes to VMock prior to the meeting, according to an email sent to Arts and Sciences sophomore students with account holds. The email states that the VMock option takes five minutes.

Sophomores were selected to ensure that students have some version of a resume early on in their academic career, Schaffling wrote. He wrote that the Arts and Science advising office believes starting sophomore year assures that students won’t fall behind in searching for internships or other experiential education opportunities.

“In a post-pandemic age we want to be dynamic and responsive to student needs … allowing fall semester sophomores multiple avenues is the college being responsive to student needs and experience,” Schaffling wrote.

Using VMock, students can receive rapidly-generated, specific feedback, ranging from spell-check to structural issues, by uploading their resume to the VMock website or app.

Hamid Ekbia, a university professor in Maxwell and the director of SU’s Autonomous Systems Policy Institute, said it’s important to keep up human discretion with AI processes like VMock.

“That’s very encouraging to know that the entities, they try to help students, but these are statistical machines and ultimately they find patterns, and patterns are aggregate patterns, they are not individual,” Ekbia said. “As best as we might try to craft a resume that is unique to us, a lot of that might get lost in the process.”

Ekbia is teaching SU’s first AI and Humanity course this fall, listed between the School of Information Studies and Maxwell.

Though VMock’s feedback is unlimited on each resume submission, students can only upload their resumes 10 times per year, according to the SU career services website. VMock also offers community insights about resumes previously uploaded by Syracuse students, from the average number of words to the typical layout.

VMock also includes an option to start a resume from scratch by using community templates from SU’s career services. With formatting pre-determined in the templates, students only have to input their information and experience.

Resumes submitted through VMock are scored based on three categories — impact, presentation and competencies — and receive a score out of 100 points. Impact is worth up to 40 points, while presentation and competencies are both worth up to 30.

Ekbia said filtering software used in hiring practices is designed to find candidates that have similar traits and experiences to what the company is searching for, not what distinguishes candidates. Softwares like VMock can amplify this, which students need to be careful of, he said.

“What matters most when you apply for a job is what makes you different and unique, not what makes you similar to other people,” Ekbia said. “That is the biggest concern that I have with the use of these systems, because they essentially pigeonhole you.”

SU’s College of Engineering and Computer Science has also implemented VMock as part of its career advising process.

ECS Career Services regularly connects with employers to ensure the resume templates used in VMock match up with the industry standards for what a student is pursuing, Sarah Mack, the director of student success and career services for the ECS, wrote in a statement to The Daily Orange.

Dan Pacheco, a professor of practice of magazine, news and digital journalism and the Peter A. Horvitz Endowed Chair in Journalism Innovation at the S.I. Newhouse School of Public Communications, said students’ exposure to and familiarity with programs like VMock is a great way to prepare them to be successful in their job and interview searches.

Although he said services like VMock can be “foresightful,” Pacheco said it’s still important to be aware of artificial intelligence’s inherent flaws. Because artificial intelligence is created by humans — who all have their own biases — and is trained on human data, all AI software also has biases, Pacheco said.

“In the United States, that means that cis, white, heterosexual males have an even bigger advantage when applying for the best jobs because they are already in the majority in the white-collar workforce,” Pacheco wrote in a statement to The Daily Orange.

Both Schaffling and Mack shared the view that VMock is capable of addressing the different resume formats dependent upon a student’s post-graduation goals.

“Advisors and VMock both have the ability to help students put their resume into effective templates to increase their success,” Schaffling wrote.

Mack also wrote that students in the beginning stages of resume creation can use VMock so their individual meetings with advisors are more productive. If VMock has already reviewed the resume, students can focus on discussing how to gain additional experience and receive coaching on their job search when they meet with advisors, she told The D.O.

Even though AI usage can present concerns, Pacheco remains optimistic that when used correctly, the opportunities software like VMock can benefit students.

“As long as humans are using the tools in conjunction with keeping themselves in the equation, and they’re using the tools as a way to enhance what they’re able to do or make themselves more efficient, then we all need to be doing more of that,” Pacheco said.

The post SU introduces Vmock, an AI-powered platform, to review student resumes appeared first on The Daily Orange.

Posted 09 Oct 2023 23:31 by | News | No comments

« Previous Page — Next Page »