Natural Selection (United States)

BackgroundSepsis from infection is a global health priority and clinical trials have failed to deliver effective therapeutic interventions. To address complicating heterogeneity in sepsis pathobiology, and improve outcomes, promising precision medicine approaches are helping identify disease endotypes, however, they require a more complete definition of sepsis subgroups.MethodsHere, we use RNA sequencing from peripheral blood to interrogate the host response to sepsis from participants in a global observational study carried out in West Africa, Southeast Asia, and North America (N = 494).ResultsWe identify four sepsis subtypes differentiated by 28-day mortality. A low mortality immunocompetent group is specified by features that describe the adaptive immune system. In contrast, the three high mortality groups show elevated clinical severity consistent with multiple organ dysfunction. The immunosuppressed group members show signs of a dysfunctional immune response, the acute-inflammation group is set apart by molecular features of the innate immune response, while the immunometabolic group is characterized by metabolic pathways such as heme biosynthesis.ConclusionsOur analysis reveals details of molecular endotypes in sepsis that support immunotherapeutic interventions and identifies biomarkers that predict outcomes in these groups.

Background:Although the adoption of pure laparoscopic donor hepatectomy has expanded driven by considerations of donor cosmesis and function, the criteria for selecting candidates for pure laparoscopic donor right hepatectomy (PLDRH) continue to be debated. This study aimed to delineate the distinctive characteristics of donors and recipients who underwent conventional open-donor right hepatectomy (CDRH) during the era of PLDRH.Materials and methods:The authors conducted a retrospective review of a prospectively collected single-centre database encompassing all right hepatectomies at Seoul National University Hospital from April 2016 to December 2021, a period during which there were no absolute contraindications for PLDRH.Results:During the exclusive PLDRH period, there were still 63 cases of CDRH alongside 362 cases of PLDRH. The CDRH donors were older, had a lower estimated remnant liver volume, and a higher incidence of expected multiple openings in the portal vein and bile duct based on preoperative imaging compared with PLDRH donors. In the subgroup analysis, including only donors meeting two or more criteria (age ≥40 years, estimated remnant liver volume ≥35%, or multiple anticipated vessel openings), recipients in the PLDRH group exhibited significantly more early major complications (P=0.029) compared with those in the CDRH group.Conclusion:As PLDRH gains traction in practice, it is essential to acknowledge that specific donor conditions, such as advanced age, limited remnant liver volume, and anticipation of multiple portal or bile duct openings, may merit contemplating CDRH as a means of optimizing recipient outcomes.

Abstract National ranking systems of the universities in India, and the Open Access initiatives, emphasize the roles of research publications towards enhancing the national research output by achieving higher visibility for the novel works and their citations. The present study insights from the critical analyses of the institutional performances in the top NIRF-ranked universities of the last six years and Scopus-indexed publications. The impacts of constraining factors such as the COVID-19 pandemic on research publications have also been assessed by mapping the annual growth rate (AGR) for each of these universities. Results of the investigation show that the outputs of many of the universities surveyed (n=12 out of 14) had been adversely affected by the pandemic during 2020, regarding the number of publications an affiliated researcher published in SCOPUS-indexed journals. Further, the study discusses the best practices adopted by the top-ranked Indian universities publishing open access (OA) resources. The read and publish agreements with leading publishers, the visibility of the OA publications from deemed-to-be universities like MAHE, Manipal was about 44.79%. Other action plans to boost research outputs, such as providing APC charges by the universities for the OA publications (e.g. VIT, Vellore), boosted the inclination towards open-access publications. The present study illustrates case-specificity in associations between AGR and NIRF-ranking for only two universities (IISc Bangalore and MAHE Manipal) ranked within the top 10 universities. This illustrates the deeper commitment to the best practices within these universities as reasons for higher productivity despite the pandemic scenario.

The inaugural 2023 IEEE Conference on Artificial Intelligence was held June 5-6 at the Hyatt Regency Santa Clara in California's Silicon Valley. This new event was several years in the making and included co-sponsorship by four IEEE societies including the IEEE Computational Intelligence Society (as lead organization for the first two years), IEEE Computer Society, IEEE Signal Processing Society, and IEEE Systems, Man and Cybernetics Society. Given the co-sponsorship, the event is managed by a steering committee with members from all four societies and with a team from one of the societies annually running the event as the lead sponsor for that year. Our initial ideas for the event began in 2019 and developed through 2020 during the covid-19 pandemic. After attending an AI Exposition, we determined that a large event would require cooperation across multiple societies, and starting a new conference series during a pandemic would not be wise. Plans were made to launch the series in 2023 while we solved the co-sponsorship arrangements. We were pleased to work with staff from the IEEE Computer Society in the planning and development of the conference itself, with content focused on the six verticals of interest noted previously in IEEE CIM (AI in transportation, energy, healthcare, earth systems, industrial optimization, and societal implications of these applications). We faced additional headwinds going into 2023 as recession fears loomed across corporate America leading to budget cuts for conference engagement. The poorly timed bankruptcy of Silicon Valley Bank also did not help matters. However, we remained eager to have a fully in-person post-pandemic conference with as much personal interaction as possible.

On the occasion of the 30-year anniversary of the Evolutionary Computation journal, I was invited by Professor Hart to offer some reflections on the article on evolving behaviors in the iterated prisoner's dilemma that I contributed to its first issue in 1993. It's an honor to do so. I would like to thank Professor Ken De Jong, the journal's first editor-in-chief, for his vision in creating the journal, and the editors who have followed and maintained that vision. This article contains some personal reflections on the topic and the field as a whole.

Presents information on IEEE CAI 2023, an Industry-Centered Conference on AI.

Abstract The early detection of incipient dental caries enables preventive treatment, and bitewing radiography is a good diagnostic tool for posterior incipient caries. In the field of medical imaging, the utilization of deep learning with convolutional neural networks (CNNs) to process various types of images has been actively researched and has shown promising performance. In this study, we developed a CNN model using a U-shaped deep CNN (U-Net) for dental caries detection on bitewing radiographs and investigated whether this model can improve clinicians’ performance. In total, 304 bitewing radiographs were used to train the deep learning model and 50 radiographs were used for performance evaluation. The diagnostic performance of the CNN model on the total test dataset was as follows: precision, 63.29%; recall, 65.02%; and F1-score, 64.14%, showing quite accurate performance. When three dentists detected dental caries using the results of the CNN model as reference data, the overall diagnostic performance of all three clinicians significantly improved, as shown by an increased recall ratio (D1, 85.34%; D1', 92.15%; D2, 85.86%; D2', 93.72%; D3, 69.11%; D3', 79.06%). These increases were especially significant in the incipient and moderate caries subgroups. The deep learning model may help clinicians to diagnose dental caries more accurately.