Necrotizing fasciitis caused by Pseudomonas aeruginosa: a rare case report and recent concepts in diagnosis and management

Necrotizing fasciitis caused by Pseudomonas aeruginosa is an extremely rare and life threatening bacterial soft tissue infection. Here we report a case study of fully established necrotizing fasciitis associated with monomicrobial pseudomonas infection in a 34 years old male. The patient presented with painful, necrosed areas of skin and soft tissue over right gluteal region which rapidly progressed to right upper back. Aggressive supportive measures and early debridement lead to a full recovery with no functional deficits

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Do futures markets help in price discovery and risk management for commodities in India?

In 2003, trading of commodity futures shifted from single commodity, regional exchanges to national exchanges that trade multiple commodities. This paper examines price discovery and hedging effectiveness of commodity futures after this change and concludes that,on average, futures prices do discover information relatively efficiently,but helps to manage risk less efficiently. The paper uses the viewpoint of the hedger to conjecture what factors may improve hedging effectiveness. These include high settlement costs caused by few and widely dispersed delivery centers and an unreliability of warehouse receipts,a mismatch between the grade specified in the futures contract and what is available for delivery in the market, and disruptions caused by various policy interventions in both commodities spot and futures markets