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Bioequivalence study of two formulations of lurasidone film coated tablets in healthy subjects under fed conditions
Distinct cardiac troponin alterations in patients with cocaine and alcohol use disorders during abstinence for cardiovascular risk assessment
Large language models provide discordant information compared to ophthalmology guidelines
Institutions of public judgment established by social contract and taxation
Indirect reciprocity is a compelling explanation for stable cooperation in a large society: Those who cooperate appropriately earn a good standing, so that others are more likely to cooperate with them. However, this mechanism requires a population to agree on who has good standing and who has bad standing. Consensus can be provided by a central institution that monitors and broadcasts reputations. But how might such an institution be maintained, and how can a population ensure that it is effective and incorruptible? Here, we explore a simple mechanism to sustain an institution for judging reputations: a tax collected from each member of the population. We analyze the possible tax rate that individuals will rationally pay to sustain an institution of judgment, which provides a public good in the form of information, and we derive necessary conditions for individuals to resist the temptation to evade their tax payment. We also consider the possibility that institution members may be corrupt and subject to bribery, and we analyze how strong the incentives against corruption need to be. Our analysis has implications for establishing robust public institutions that provide social information to support cooperation in large populations—and the potential negative consequences associated with wealth or income inequality.
Multidrug-resistant Klebsiella pneumoniae ST70 harboring blaNDM in a migratory Penguin
Abstract The growing prevalence of antimicrobial resistance poses a global threat to human and animal health. In this study, we investigated the occurrence and genetic basis of antimicrobial resistance in a Magellanic Penguin (Spheniscus magellanicus) rescued off the coast of Rio de Janeiro, Brazil. The penguin presented a bacterial infection, identified as Klebsiella pneumoniae. Molecular analysis revealed the presence of several resistance genes, including those that confer resistance to carbapenems, beta-lactams, quinolones, and other classes of antibiotics. The bacterial strain belonged to Sequence Type 70 (ST70), a clone previously associated with human nosocomial infections. This study highlights the potential of migratory penguins as vectors of antimicrobial-resistant microorganisms, emphasizing the need for a One Health approach to address the complex interaction between environmental factors, animal health, and human well-being. The findings underscore the urgency of implementing strategies to mitigate the spread of multidrug-resistant bacteria in natural and urban environments.
Resilient oscillator-based cyberattack detection for distributed secondary control of inverter-interfaced Islanded microgrids
Lightweight identity authentication and key agreement scheme for VANETs based on SSL-PUF
Prognostic significance of acute exacerbations and usual interstitial pneumonia in fibrotic interstitial lung disease
Association between inflammation indicators and albuminuria in US adults: a cross-sectional study
Smart deep learning model for enhanced IoT intrusion detection
Personalized prediction model generated with machine learning for kidney function one year after living kidney donation
Abstract Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies among individuals. This study aimed to develop a machine learning (ML) model to predict serum creatinine (Cre) levels at one year post-donation using preoperative clinical data, including kidney-, fat-, and muscle-volumetry values from computed tomography. A total of 204 living kidney donors were included. Symbolic regression via genetic programming was employed to create an ML-based Cre prediction model using preoperative clinical variables. Validation was conducted using a 7:3 training-to-test data split. The ML model demonstrated a median absolute error of 0.079 mg/dL for predicting Cre. In the validation cohort, it outperformed conventional methods (which assume post-donation eGFR to be 70% of the preoperative value) with higher R 2 (0.58 vs. 0.27), lower root mean squared error (5.27 vs. 6.89), and lower mean absolute error (3.92 vs. 5.8). Key predictive variables included preoperative Cre and remnant kidney volume. The model was deployed as a web application for clinical use. The ML model offers accurate predictions of post-donation kidney function and may assist in monitoring donor outcomes, enhancing personalized care after kidney donation.
Identification of potential COVID-19 Mpro inhibitors through covalent drug docking, molecular dynamics simulation, and MMGBSA calculation
Structural mechanism for the recognition of E2F1 by the ubiquitin ligase adaptor Cyclin F
Cyclin F, a noncanonical member of the cyclin protein family, plays a critical role in regulating transitions in the cell division cycle. Unlike canonical cyclins, which bind and activate cyclin-dependent kinases (CDKs), Cyclin F functions as a substrate receptor protein within the Skp1–Cullin-F-box E3 ubiquitin ligase complex, enabling the ubiquitylation of target proteins. The structural features that distinguish Cyclin F as a ligase adaptor and the mechanisms underlying its selective substrate recruitment over Cyclin A, which functions in complex with CDK2 at a similar time in the cell cycle, remain largely unexplored. We utilized single-particle cryoelectron microscopy to elucidate the structure of a Cyclin F–Skp1 complex bound to an E2F1 peptide. The structure and biochemical analysis reveal important differences in the substrate-binding site of Cyclin F compared to Cyclin A. Our findings expand on the canonical cyclin-binding motif (Cy or RxL) and highlight the importance of electrostatics at the E2F1 binding interface, which varies between Cyclin F and Cyclin A. These results advance our understanding of E2F1 regulation and may inform strategies for selectively targeting Cyclin F in cancer or neurodegeneration.