Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.KWF Kankerbestrijding ; Netherlands Organization for Scientific Research (NWO) ; Swedish Research Council European Commission ; Swedish Cancer Society ; Swedish eScience Research Center ; Ake Wiberg Foundation ; Prostatacancerforbundet ; Academy of Finland ; Cancer Foundation Finland ; Google Incorporated ; MICCAI board challenge working group ; Verily Life Sciences ; EIT Health ; Karolinska Institutet ; MICCAI 2020 satellite event team ; ERAPerMe

Acarbose Accelerates Wound Healing via Akt/eNOS Signaling in db/db Mice

Refractory wound is a dreaded complication of diabetes and is highly correlated with EPC dysfunction caused by hyperglycemia. Acarbose is a widely used oral glucose-lowering drug exclusively for T2DM. Previous studies have suggested the beneficial effect of acarbose on improving endothelial dysfunction in patients with T2DM. However, no data have been reported on the beneficial efficacy of acarbose in wound healing impairment caused by diabetes. We herein investigated whether acarbose could improve wound healing in T2DM db/db mice and the possible mechanisms involved. Acarbose hastened wound healing and enhanced angiogenesis, accompanied by increased circulating EPC number in db/db mice. In vitro, a reversed BM-EPC dysfunction was observed after the administration of acarbose in db/db mice, as reflected by tube formation assay. In addition, a significantly increased NO production was also witnessed in BM-EPCs from acarbose treated db/db mice, with decreased O2 levels. Akt inhibitor could abolish the beneficial effect of acarbose on high glucose induced EPC dysfunction in vitro, accompanied by reduced eNOS activation. Acarbose displayed potential effect in promoting wound healing and improving angiogenesis in T2DM mice, which was possibly related to the Akt/eNOS signaling pathway

Priorities identification of habitat restoration for migratory birds under the increased water level during the middle of dry season: A case study of Poyang Lake and Dongting Lake wetlands, China

The distribution of wetland ecosystem restoration sites is uneven and resources are limited, so priority identification is essential for rational resource allocation and reduction of biodiversity loss. Under the influence of human activities and climate variability, the water level during the middle of dry season (MDS) had a significant tendency to increase in Poyang Lake (PYL) and Dongting Lake (DTL), which directly affected the habitat suitability (HS) of migratory birds and threatened their existence. Based on the data of HS and weight of migratory birds, four evaluation indexes, namely, the accuracy of priority identification (API), geographical area equalization (GAE), the spatial autocorrelation consistency (SAC) and comprehensive evaluation index (CEI), were used to evaluate and compare the priorities identification effects of four classification methods, namely, natural breaks method, quantile method, equal interval method, and geometric interval method. The results showed that the geometric interval method and the quantile method are the best methods to identify the priority of habitat restoration of migratory birds in PYL and DTL wetlands, respectively, under the condition of increased water level (IWL) during the MDS. The method of priority identification proposed in this study and its results can provide a scientific basis for the regulation of wetland resources and the conservation of biodiversity (especially migratory birds)

PPARα Agonist Stimulated Angiogenesis by Improving Endothelial Precursor Cell Function Via a NLRP3 Inflammasome Pathway

Background: Impaired wound healing is a common complication of diabetes and is the leading cause of lower extremity amputation. Treatment with fenofibrate, a peroxisome proliferators–activated receptor α (PPARα) agonist, was associated with a lower risk of amputations, particularly minor amputations without known large-vessel diseases, probably through non-lipid mechanisms. The current study aimed to test our hypothesis that fenofibrate stimulates angiogenesis and restores endothelial precursor cell (EPC) function via inhibiting Nod-like receptor protein 3 (NLRP3) inflammasome in streptozotocin (STZ)-induced diabetic mice. Methods: Male C57BL/6 mice were randomly divided into three groups: control, STZ-induced diabetic mice and fenofibrate treated diabetic group. Wound closure was assessed by wound area and CD31 positive capillaries. Both the migration and tube formation capacities of EPCs were measured. Intracellular nitric oxide (NO) and superoxide (O2-) levels were determined. Activity of NLRP3 inflammasome in EPCs was assessed by measuring thioredoxin-interacting protein (TXNIP), NLRP3, and caspase-1 expression. Results: Compared with the untreated diabetic mice, wound closure and capillary densities were significantly increased in fenofibrate treated group. Fenofibrate treatment restored EPC function, increased NO production, and decreased O2- level in EPCs of diabetic mice. Furthermore, fenofibrate deregulated the activity of NLRP3 inflammasome by reducing TXNIP, NLRP3 and caspase-1 expression in EPCs of diabetic mice. In vitro, fenofibrate prevented high glucose induced EPC dysfunction, deregulated NLRP3 inflammasome activity. In addition, fenofibrate inhibited IL-1β expression caused by combination use of high glucose and lipopolysaccharide. Conclusion: Fenofibrate can accelerate wound healing in diabetic mice, which at least in part was mediated by improving the impaired EPC function via a NLRP3 inflammasome pathway, suggesting the significance of PPARα agonists in the treatment of diabetes

Artificial intelligence for diagnosis and Gleason grading of prostate cancer : the PANDA challenge

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840–0.884) and 0.868 (95% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.publishedVersionPeer reviewe