Hasil untuk "Medicine"

N. Peppas

Hydrogels in medicine and pharmacy , Hydrogels in medicine and pharmacy , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

R. Bulger

D. Bensky, S. Clavey, Erich Stõger et al.

E. Topol

J. Ahlquist

C. Laine, F. Davidoff

G. Rousseau

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

C. Mason, P. Dunnill

B S Sayan

S. Bent

Jinling Tang, Bao-yan Liu, K. Ma

M. Plebani

Shubham Vatsal, Harsh Dubey, Aditi Singh

Large Language Model (LLM)-based agents that plan, use tools and act has begun to shape healthcare and medicine. Reported studies demonstrate competence on various tasks ranging from EHR analysis and differential diagnosis to treatment planning and research workflows. Yet the literature largely consists of overviews which are either broad surveys or narrow dives into a single capability (e.g., memory, planning, reasoning), leaving healthcare work without a common frame. We address this by reviewing 49 studies using a seven-dimensional taxonomy: Cognitive Capabilities, Knowledge Management, Interaction Patterns, Adaptation & Learning, Safety & Ethics, Framework Typology and Core Tasks & Subtasks with 29 operational sub-dimensions. Using explicit inclusion and exclusion criteria and a labeling rubric (Fully Implemented, Partially Implemented, Not Implemented), we map each study to the taxonomy and report quantitative summaries of capability prevalence and co-occurrence patterns. Our empirical analysis surfaces clear asymmetries. For instance, the External Knowledge Integration sub-dimension under Knowledge Management is commonly realized (~76% Fully Implemented) whereas Event-Triggered Activation sub-dimenison under Interaction Patterns is largely absent (~92% Not Implemented) and Drift Detection & Mitigation sub-dimension under Adaptation & Learning is rare (~98% Not Implemented). Architecturally, Multi-Agent Design sub-dimension under Framework Typology is the dominant pattern (~82% Fully Implemented) while orchestration layers remain mostly partial. Across Core Tasks & Subtasks, information centric capabilities lead e.g., Medical Question Answering & Decision Support and Benchmarking & Simulation, while action and discovery oriented areas such as Treatment Planning & Prescription still show substantial gaps (~59% Not Implemented).

Jihoon Jeong

Small language models (SLMs) in the 100M-10B parameter range increasingly power production systems, yet whether they possess the internal emotion representations recently discovered in frontier models remains unknown. We present the first comparative analysis of emotion vector extraction methods for SLMs, evaluating 9 models across 5 architectural families (GPT-2, Gemma, Qwen, Llama, Mistral) using 20 emotions and two extraction methods (generation-based and comprehension-based). Generation-based extraction produces statistically superior emotion separation (Mann-Whitney p = 0.007; Cohen's d = -107.5), with the advantage modulated by instruction tuning and architecture. Emotion representations localize at middle transformer layers (~50% depth), following a U-shaped curve that is architecture-invariant from 124M to 3B parameters. We validate these findings against representational anisotropy baselines across 4 models and confirm causal behavioral effects through steering experiments, independently verified by an external emotion classifier (92% success rate, 37/40 scenarios). Steering reveals three regimes -- surgical (coherent text transformation), repetitive collapse, and explosive (text degradation) -- quantified by perplexity ratios and separated by model architecture rather than scale. We document cross-lingual emotion entanglement in Qwen, where steering activates semantically aligned Chinese tokens that RLHF does not suppress, raising safety concerns for multilingual deployment. This work provides methodological guidelines for emotion research on open-weight models and contributes to the Model Medicine series by bridging external behavioral profiling with internal representational analysis.

Mehak Pant, Lubna Salman, Anupama V. Betigeri et al.

Background: Students belong to the most significant groups of individuals that use technology. Screen time impacts several factors including health and behavior. It is still mostly unknown how physical activity and screen-grounded programming interact to affect health-related quality of life. Methods: The study had been carried out as a crossed sectional survey. (google form survey circulated to participating university students). The student selection for this cross-sectional survey, included all undergraduate students aged 18–30 years, studying in MRIIRS, Faridabad. Participants-100, inclusion criteria age: 18–30 years university students exclusion criteria age: below 18 years. Results and Conclusion: The findings indicated that the combination of excessive screen time and no physical activity had the biggest detrimental effect on “health-related quality of life”.

A. A. Abdullahi

Prior to the introduction of cosmopolitan medicine, traditional medicine used to be the dominant medical system available to millions of people in Africa in both rural and urban communities. However, the arrival of the Europeans marked a significant turning point in the history of this age-long tradition and culture. This paper examines the trends and challenges of traditional medicine in Africa. The impact of colonialism on African traditional medicine is also examined. Although the paper is on Africa, references are drawn around the world to buttress the growing demand for traditional medicine. The paper concludes that to minimise the current distrust between modern and traditional doctors and to achieve the objective of regulation, standardisation and cooperation, both traditional and modern doctors must acknowledge their areas of strengths and weaknesses from which they operate and be genuinely concerned about the difficult but necessary task of being human.

Konstantinos Sechidis, Cong Zhang, Sophie Sun et al.

Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing important decisions related to drug development. Furthermore, it can lead to personalized medicine by tailoring treatments to individual patient characteristics. This paper introduces novel methodologies for assessing treatment effects using the individual treatment effect as a basis. To estimate this effect, we use a Double Robust (DR) learner to infer a pseudo-outcome that reflects the causal contrast. This pseudo-outcome is then used to perform three objectives: (1) a global test for heterogeneity, (2) ranking covariates based on their influence on effect modification, and (3) providing estimates of the individualized treatment effect. We compare our DR-learner with various alternatives and competing methods in a simulation study, and also use it to assess heterogeneity in a pooled analysis of five Phase III trials in psoriatic arthritis. By integrating these methods with the recently proposed WATCH workflow (Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors), we provide a robust framework for analyzing TEH, offering insights that enable more informed decision-making in this challenging area.

Jack Foxabbott, Arush Tagade, Andrew Cusick et al.

The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.

V. Innao, A.P.M. Barbagallo, O. Bianco et al.

Introduction: Chronic Lymphocytic Leukemia (CLL) is the most common leukemia in Western countries, with age-related incidence and clinical heterogeneity. Evans Syndrome (ES), defined by the concurrent occurrence of immune thrombocytopenia (ITP) and autoimmune hemolytic anemia (AIHA), is a rare immune disorder often associated with lymphoproliferative diseases and potentially fatal outcomes. Management relies on expert consensus due to the lack of randomized trials. Standard CLL therapies include BTK inhibitors (BTKi) and the BCL2 inhibitor Venetoclax, often with anti-CD20 antibodies. When CLL progression is associated with immune cytopenias, immunosuppressive therapy is used, with CLL-directed treatment in refractory cases. To date, no reports have described ITP onset following Venetoclax in a patient with prior AIHA successfully treated with Zanubrutinib. Patients and Methods: We report the case of a 75-year-old man diagnosed in 2019 with asymptomatic CLL who, in 2021, developed steroid- and IVIG-refractory warm AIHA, treated with Bendamustine-Rituximab, achieving partial response. In July 2024, he showed rapid lymphocytosis progression (DT <2 months), splenomegaly (23 cm), thrombocytopenia (PLT 86×10⁹/L), and constitutional symptoms (Rai IV/Binet C). FISH was negative; IGHV unmutated, TP53 wild-type, CLL-IPI 6. Venetoclax-Rituximab (Murano protocol) was started. In week 2 of ramp-up, the patient developed spontaneous ecchymoses and gingival bleeding. Labs showed PLT 0×10⁹/L, Coombs-positive tests, Hb 10.7 g/dL, and a positive SARS-CoV-2 swab. Steroids and IVIG were ineffective. After swab negativization, Rituximab 375 mg/m² was administered weekly ×4 without response. He was referred to our ITP hub. Results: Romiplostim (Rom) was initiated at 3 μg/kg/week and increased by 2 μg/kg/week; Zanubrutinib (Zanu) 160 mg BID was added in week 2. Platelet and hemoglobin normalization occurred within 4 weeks. After 6 months, BTKi was continued at full dose and Rom reduced to 5 μg/kg/week. A 10-day BTKi interruption led to PLT drop (11×10⁹/L), reversed after Zanu reintroduction. The patient has since maintained a complete hematologic response with no adverse events (fig.1). Conclusion: TPO-RAs are effective in ITP. However, when ITP is secondary to CLL, targeting the underlying disease is crucial. BTKi have shown efficacy in ITP, as highlighted in the LUNA3 trial. This case supports the safety and efficacy of combining BTKi and TPO-RAs in refractory secondary ITP.  

Peter N. Robinson

In medical contexts, the word “phenotype” is used to refer to some deviation from normal morphology, physiology, or behavior. The analysis of phenotype plays a key role in clinical practice and medical research, and yet phenotypic descriptions in clinical notes and medical publications are often imprecise. Deep phenotyping can be defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The emerging field of precision medicine aims to provide the best available care for each patient based on stratification into disease subclasses with a common biological basis of disease. The comprehensive discovery of such subclasses, as well as the translation of this knowledge into clinical care, will depend critically upon computational resources to capture, store, and exchange phenotypic data, and upon sophisticated algorithms to integrate it with genomic variation, omics profiles, and other clinical information. This special issue of Human Mutation offers a number of articles describing computational solutions for current challenges in deep phenotyping, including semantic and technical standards for phenotype and disease data, digital imaging for facial phenotype analysis, model organism phenotypes, and databases for correlating phenotypes with genomic variation. Hum Mutat 33:777–780, 2012. © 2012 Wiley Periodicals, Inc.