Hasil untuk "Medicine"

W. Osler

J. Richards

P.F.R. Little

The enormous advances in the biological sciences are making a dramatic impact upon clinical medicine. The emerging specialty of molecular medicine applies these theoretical and technological advances to understanding the causes, expression and rational treatment of human disease.

R. Fuller

J. Kassirer

J. Vincent

D. Mader

L. Goodnough, M. Brecher, M. Kanter et al.

K. Thomas, J. Nicholl, P. Coleman

M. Baggiolini

M. Mcgrath

A. Sadeh, C. Acebo

M. Glick

Bo Pan, Peter Zhiping Zhang, Hao-Wei Pang et al.

Matched molecular pairs (MMPs) capture the local chemical edits that medicinal chemists routinely use to design analogs, but existing ML approaches either operate at the whole-molecule level with limited edit controllability or learn MMP-style edits from restricted settings and small models. We propose a variable-to-variable formulation of analog generation and train a foundation model on large-scale MMP transformations (MMPTs) to generate diverse variables conditioned on an input variable. To enable practical control, we develop prompting mechanisms that let the users specify preferred transformation patterns during generation. We further introduce MMPT-RAG, a retrieval-augmented framework that uses external reference analogs as contextual guidance to steer generation and generalize from project-specific series. Experiments on general chemical corpora and patent-specific datasets demonstrate improved diversity, novelty, and controllability, and show that our method recovers realistic analog structures in practical discovery scenarios.

H. Sox, S. Greenfield

Samir K. Gupta, J. Eustace, J. Winston et al.

F. J. D. Lange, Juliane C. Wilcke, Sabine Hoffmann et al.

Empirical substantive research, such as in the life or social sciences, is commonly categorized into the two modes exploratory and confirmatory, both of which are essential to scientific progress. The former is also referred to as hypothesis-generating or data-contingent research, while the latter is also called hypothesis-testing research. In the context of empirical methodological research in statistics, however, the exploratory-confirmatory distinction has received very little attention so far. Our paper aims to fill this gap. First, we revisit the concept of empirical methodological research through the lens of the exploratory-confirmatory distinction. Second, we examine current practice with respect to this distinction through a literature survey including 115 articles from the field of biostatistics. Third, we provide practical recommendations toward a more appropriate design, interpretation, and reporting of empirical methodological research in light of this distinction. In particular, we argue that both modes of research are crucial to methodological progress, but that most published studies -- even if sometimes disguised as confirmatory -- are essentially exploratory in nature. We emphasize that it may be adequate to consider empirical methodological research as a continuum between "pure" exploration and "strict" confirmation, recommend transparently reporting the mode of conducted research within the spectrum between exploratory and confirmatory, and stress the importance of study protocols written before conducting the study, especially in confirmatory methodological research.

Milind Umekar, Anis Ahmad Chaudhary, Monali Manghani et al.

Chronic wounds, including diabetic foot ulcers and pressure sores, are becoming more prevalent due to aging populations and increased metabolic problems. These wounds often persist due to impaired healing, chronic inflammation, oxidative stress, and infections caused by multidrug-resistant pathogens, making conventional treatments—including antibiotics and antiseptics—largely inadequate. This creates an urgent need for advanced, biologically responsive therapies that can both combat infection and promote tissue regeneration. Probiotics have surfaced as a viable option owing to their capacity to regulate immune responses, impede pathogenic biofilms, and generate antibacterial and antioxidant metabolites. However, their clinical application is limited by poor viability, sensitivity to environmental conditions, and short retention at wound sites. Nanotechnology-based delivery systems address these limitations by protecting probiotics from degradation, enhancing site-specific delivery, and enabling controlled, stimuli-responsive release. Encapsulation techniques using materials like chitosan, PLGA, liposomes, nanogels, nanofibers, and microneedles have shown significant success in improving wound healing outcomes in preclinical and clinical models. This review summarizes the current landscape of chronic wound challenges and presents recent advances in probiotic-loaded nanotechnologies. It explores various nano-delivery systems, their mechanisms of action, biological effects, and therapeutic outcomes, highlighting the synergy between probiotics and nanocarriers as a novel, multifaceted strategy for managing chronic wounds.

Wu Huiyong, Zunlong Wang

With the increasing complexity and prosperity of global financial markets, stock market forecasting plays a critical role in investment decision-making, market regulation, and economic planning. This study proposes a hybrid prediction model that integrates Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Long Short-Term Memory (LSTM) neural networks, referred to as the GA-WOA-LSTM model. In this framework, GA is employed to generate the initial population and perform global search for LSTM hyperparameter optimization, while WOA is applied to conduct local refinement of the search space. The LSTM model, known for its superior ability to capture nonlinear dependencies and long-term patterns in time series, is used to model and forecast future stock closing prices. The performance of the proposed model is evaluated on both training and test datasets using key metrics including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). Experimental results demonstrate that the GA-WOA-LSTM model significantly outperforms traditional baseline models in terms of predictive accuracy and generalization capability. This research offers a robust and effective modeling strategy for financial time series forecasting and provides valuable insights for real-world financial applications.

L. Guarente

In this year's Franklin H. Epstein Lecture, Leonard Guarente summarizes the many biologic properties of the sirtuin family of deacetylases and explains why enhancement or inhibition of specific sirtuins may influence many common diseases and longevity.