Hasil untuk "Medicine"

T. Kaptchuk

Beau Norgeot, G. Quer, Brett K. Beaulieu-Jones et al.

J. Myburgh, M. Mythen

Moritz J.S. Jochum, Frédéric S. Singa-Niatou, Crickette Sanz et al.

Transmission of human respiratory pathogens to wild, human-habituated great apes has been repeatedly documented within research and tourism projects. While the implementation of hygiene measures has significantly reduced the risk of pathogen introduction, vigilant surveillance remains essential to evaluate the effectiveness of the adopted measures and identify additional steps for risk reduction. Here, we combined behavioral observations and pathogen genomic surveillance in non-invasive samples to investigate three outbreaks of respiratory disease in human-habituated western lowland gorillas (Gorilla gorilla gorilla) across four sites within the Sangha Trinational Protected Area Network in the northwestern Congo Basin. Clinical signs of respiratory disease were recorded in three groups of monitored gorillas at two neighboring National Parks in the Central African Republic and Republic of Congo. Human respiratory syncytial viruses were identified as the causative agent for all three documented outbreaks. Genomic analyses revealed two distinct viral types suggesting independent introduction rather than intergroup transmission. All symptomatic individuals recovered. These findings highlight the importance of stringent prevention measures at great ape research sites and the need for addressing the burden of respiratory disease in neighboring human communities. The evolving integrated approach centered on the One Health concept in the Sangha Trinational Protected Area Network is proving beneficial to great ape conservation, the preservation of this high-biodiversity landscape and the public health of local communities.

Ibrahim Ayad Jihad, Thekrayat Joodi Jassim, Zainab Naeif Mageed

Background and purpose: Research on the detection of uric acid (URA) and dopamine (DPA) is ongoing because of the difficulties posed by their closely overlapping oxidation potentials. Tungsten disulfide nanostructures have become attractive electrode materials to address this problem due to their low toxicity, low cost, easy production, and strong catalytic activity. Experimental approach: For voltammetric detection of compounds, we present the creation of an electrochemical sensor based on a glassy carbon electrode modified with tungsten disulfide nanostructures. Key results: According to electrochemical analyses, the manufactured sensor performed exceptionally well, having a broad LDR of 0.03 to 600.0 μM and a low LOD of 10 nM for DPA. Conclusion: The effective detection of compounds in real samples, such as injections and urine, with acceptable recovery rates further confirmed the suggested sensor's practical usefulness. In addition to offering a viable method for creating tungsten disulfide-based modified electrodes, this study holds promise for future applications in bioanalytical sensing and clinical diagnostics.

Svetlana S. Konnova, Pavel A. Lepilin, Anastasia A. Zanishevskaya et al.

Biosensor technologies in medicine, as in many other areas, are replacing labor-intensive methods of monitoring human health. This paper presents the results of experimental studies on label-free sensors based on a hollow core microstructured optical waveguide (HC-MOW) for human blood serum analysis. The MOWs with a hollow core of 247.5 µm in diameter were manufactured and used in our work. These parameters allow the hollow core to be filled with high-viscosity solutions due to the capillary properties of the fiber. Calculations of the spectral properties of the HC-MOW fiber were carried out and experimentally confirmed. Twenty-one blood serum samples from volunteers were analyzed using standard photometry (commercial kits) and an experimental biosensor. The obtained transmission spectra were processed by the principal component analysis method and conclusions were drawn about the possibility of using this biosensor in point-of-care medicine. A significant difference was shown between the blood serum of healthy patients and patients with confirmed diagnoses and a long history of cardiovascular system abnormalities. Algorithms for spectra processing using the Origin program are presented.

Anna Carolyna Gianlorenço, Paulo Eduardo Portes Teixeira, Valton Costa et al.

<b>Background/Objectives:</b> Speech and motor control share overlapping neural mechanisms, yet their quantitative relationships in Parkinson’s disease (PD) remain underexplored. This study investigated bidirectional associations between acoustic voice features and objective motor metrics to better understand how vocal and motor systems relate in PD. <b>Methods:</b> Cross-sectional baseline data from participants in a randomized neuromodulation trial were analyzed (n = 13). Motor performance was captured using an Integrated Motion Analysis Suite (IMAS), which enabled quantitative, objective characterization of motor performance during balance, gait, and upper- and lower-limb tasks. Acoustic analyses included harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPS), jitter, shimmer, median fundamental frequency (F0), F0 standard deviation (SD F0), and voice intensity. Univariate linear regressions were conducted in both directions (voice ↔ motor), as well as partial correlations controlling for PD motor symptom severity. <b>Results:</b> When modeling voice outcomes, faster motor performance and shorter movement durations were associated with acoustically clearer voice features (e.g., higher elbow flexion-extension peak speed with higher voice HNR, β = 8.5, R² = 0.56, <i>p</i> = 0.01). Similarly, when modeling motor outcomes, clearer voice measures were linked with faster movement speed and shorter movement durations (e.g., higher voice HNR with higher peak movement speed in elbow flexion/extension, β = 0.07, R² = 0.56, <i>p</i> = 0.01). <b>Conclusions:</b> Voice and motor measures in PD showed significant bidirectional associations, suggesting shared sensorimotor control. These exploratory findings, while limited by sample size, support the feasibility of integrated multimodal assessment for future longitudinal studies.

Selime Beyza Özçevik, Abdullah Dertli

Number sequences with wide-ranging applications in mathematics, physics, medicine, and engineering remain an active research topic. This study examines these sequences through the general framework of Horadam numbers and their special cases associated with Cartan numbers. By defining spinor transformations on the resulting structures, new types of spinors are introduced and their key properties are analyzed. The proposed approach bridges distinct yet contemporary research areas, contributing to a broader interdisciplinary perspective.

Linyan Zou

Major depressive disorder represents one of the most significant global health challenges of the 21st century, affecting millions of people worldwide and creating substantial economic and social burdens. While conventional antidepressant therapies have provided relief for many individuals, their limitations including delayed onset of action, significant side effects, and treatment resistance in a substantial portion of patients have prompted researchers and healthcare providers to explore alternative therapeutic approaches (Kasneci et al.). African traditional medicine, with its rich heritage of plant-based remedies developed over millennia, offers a valuable resource for developing novel antidepressant treatments that may address some of these limitations. This paper examines the integration of large language models with African natural products for depression support, combining traditional knowledge with modern artificial intelligence technology to create accessible, evidence-based mental health support systems. The research presented here encompasses a comprehensive analysis of African medicinal plants with documented antidepressant properties, their pharmacological mechanisms, and the development of an AI-powered support system that leverages DeepSeek's advanced language model capabilities. The system provides evidence-based information about African herbal medicines, their clinical applications, safety considerations, and therapeutic protocols while maintaining scientific rigor and appropriate safety standards. Our findings demonstrate the potential for large language models to serve as bridges between traditional knowledge and modern healthcare, offering personalized, culturally appropriate depression support that honors both traditional wisdom and contemporary medical understanding.

С.В. Баирова, Д.А. Круглова

Сбалансированное питание — одна из главных составляющих здоровья в любом возрасте, но особое значение оно приобретает у детей раннего возраста, ведь чем младше ребенок, тем существеннее влияние питания на его организм. Как недостаточное, так и избыточное поступление основных пищевых веществ и эссенциальных нутриентов может привести к нарушению физического развития, а изначально неправильное осуществление процесса кормления — к нарушению пищевого поведения. По оригинальной анкете проведен опрос родителей 132 детей в возрасте от 9 месяцев до 1 года 3 месяцев с последующим анализом ошибок во введении прикормов. Определены наиболее часто встречающиеся несоответствия и их последствия. Представлена распространенность выявленных у детей отклонений индекса массы тела (ИМТ) от средних значений, выделены конкретные факторы, на которые стоит обращать внимание участковым педиатрам при контроле питания ребенка с тем или иным значением ИМТ. Выполнена оценка сопутствующих нарушений формирования пищевого поведения.

Min Zhang, Ye Bai, Yutong Wang et al.

BackgroundGeneral obesity is a well-established risk factor for gallstone disease (GSD), but whether central obesity contributes additional independent risk remains controversial. We aimed to comprehensively clarify the effect of body fat distribution on GSD.MethodsWe first investigated the observational association of central adiposity, characterized by waist-to-hip ratio (WHR), with GSD risk using data from UK Biobank (N=472,050). We then explored the genetic relationship using summary statistics from the largest genome-wide association study of GSD (ncase=43,639, ncontrol=506,798) as well as WHR, with and without adjusting for body mass index (BMI) (WHR: n=697,734; WHRadjBMI: n=694,649).ResultsObservational analysis demonstrated an increased risk of GSD with one unit increase in WHR (HR=1.18, 95%CI=1.14-1.21). A positive WHR-GSD genetic correlation (rg =0.41, P=1.42×10-52) was observed, driven by yet independent of BMI (WHRadjBMI: rg =0.19, P=6.89×10-16). Cross-trait meta-analysis identified four novel pleiotropic loci underlying WHR and GSD with biological mechanisms outside of BMI. Mendelian randomization confirmed a robust WHR-GSD causal relationship (OR=1.50, 95%CI=1.35-1.65) which attenuated yet remained significant after adjusting for BMI (OR=1.17, 95%CI=1.09-1.26). Furthermore, observational analysis confirmed a positive association between general obesity and GSD, corroborated by a shared genetic basis (rg =0.40, P=2.16×10-43), multiple novel pleiotropic loci (N=11) and a causal relationship (OR=1.67, 95%CI=1.56-1.78).ConclusionBoth observational and genetic analyses consistently provide evidence on an association of central obesity with an increased risk of GSD, independent of general obesity. Our work highlights the need of considering both general and central obesity in the clinical management of GSD.

Alexander P Rockhill, Hao Tan, Christian G Lopez Ramos et al.

The ability to conceptualize numerical quantities is an essential human trait. According to the "Triple Code Model" in numerical cognition, distinct neural substrates encode the processing of visual, auditory, and non-symbolic numerical representations. While our contemporary understanding of human number cognition has benefited greatly from advances in clinical imaging, limited studies have investigated the intracranial electrophysiological correlates of number processing. In this study, 13 subjects undergoing stereotactic electroencephalography for epilepsy participated in a number recognition task. Drawing upon postulates of the Triple Code Model, we presented subjects with numerical stimuli varying in representation type (symbolic vs. non-symbolic) and mode of stimuli delivery (visual vs. auditory). Time-frequency spectrograms were dimensionally reduced with principal component analysis and passed into a linear support vector machine classification algorithm to identify regions associated with number perception compared to inter-trial periods. Across representation formats, the highest classification accuracy was observed in the bilateral parietal lobes. Auditory (spoken and beeps) and visual (Arabic) number formats preferentially engaged the superior temporal cortices and the frontoparietal regions, respectively. The left parietal cortex was found to have the highest classification for number dots. Notably, the putamen exhibited robust classification accuracies in response to numerical stimuli. Analyses of spectral feature maps revealed that non-gamma frequency, below 30 Hz, had greater-than-chance classification value and could be potentially used to characterize format specific number representations. Taken together, our findings obtained from intracranial recordings provide further support and expand on the Triple Code Model for numerical cognition.

Manabu Machida

The area of inverse problems in mathematics is highly interdisciplinary. In various fields of science, engineering, medicine, and industry, there arises a need to reconstruct information about unknown entities that cannot be directly observed. Examples include medical imaging techniques such as X-ray CT and optical tomography. Indeed, the mathematics of inverse problems has often originated from challenges posed by other fields. Inverse problems are often ill-posed and solutions are unstable. In this lecture, we will explore methods to solve such inverse problems.

Youssef Boulaimen, Gabriele Fossi, Leila Outemzabet et al.

The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics and precision medicine. Large Language Models (LLMs) have emerged as transformative tools in this realm. These models can uncover intricate patterns and predictive insights that traditional methods might miss, thus enhancing the predictive accuracy of genetic variant pathogenicity. This study investigates the integration of state-of-the-art LLMs, including GPN-MSA, ESM1b, and AlphaMissense, which leverage DNA and protein sequence data alongside structural insights to form a comprehensive analytical framework for variant classification. Our approach evaluates these integrated models using the well-annotated ProteinGym and ClinVar datasets, setting new benchmarks in classification performance. The models were rigorously tested on a set of challenging variants, demonstrating substantial improvements over existing state-of-the-art tools, especially in handling ambiguous and clinically uncertain variants. The results of this research underline the efficacy of combining multiple modeling approaches to significantly refine the accuracy and reliability of genetic variant classification systems. These findings support the deployment of these advanced computational models in clinical environments, where they can significantly enhance the diagnostic processes for genetic disorders, ultimately pushing the boundaries of personalized medicine by offering more detailed and actionable genetic insights.

Ali Kamrani khodaei, Sina Hajer Ahmadi

Medication nonadherence significantly reduces the effectiveness of therapies, yet it remains prevalent among patients. Nonadherence has been linked to adverse outcomes, including increased risks of mortality and hospitalization. Although various methods exist to help patients track medication schedules, such as the Intelligent Drug Administration System (IDAS) and Smart Blister, these tools often face challenges that hinder their commercial viability. Building on the principles of dosage measurement and information communication in IoT, we introduce the Smart Pill Case a smart health adherence tool that leverages RFID-based data recording and NFC-based data extraction. This system incorporates a load cell for precise dosage measurement and features an Android app to monitor medication intake, offer suggestions, and issue warnings. To enhance the effectiveness and personalization of the Smart Pill Case, we propose integrating federated learning into the system. Federated learning allows the Smart Pill Case to learn from medication adherence patterns across multiple users without compromising individual privacy. By training machine learning models on decentralized data collected from various Smart Pill Cases, the system can continuously improve its recommendations and warnings, adapting to the diverse needs and behaviors of users. This approach not only enhances the tools ability to support medication adherence but also ensures that sensitive user data remains secure and private.

Seungyeon Lee, Ruoqi Liu, Wenyu Song et al.

Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limitation hinders their ability to provide accurate estimations and treatment recommendations for specific subgroups. In this study, we introduce a novel neural network-based framework, named SubgroupTE, which incorporates subgroup identification and treatment effect estimation. SubgroupTE identifies diverse subgroups and simultaneously estimates treatment effects for each subgroup, improving the treatment effect estimation by considering the heterogeneity of treatment responses. Comparative experiments on synthetic data show that SubgroupTE outperforms existing models in treatment effect estimation. Furthermore, experiments on a real-world dataset related to opioid use disorder (OUD) demonstrate the potential of our approach to enhance personalized treatment recommendations for OUD patients.

Chang Wang, Lu Wang

To promote precision medicine, individualized treatment regimes (ITRs) are crucial for optimizing the expected clinical outcome based on patient-specific characteristics. However, existing ITR research has primarily focused on scenarios with categorical treatment options and a single outcome. In reality, clinicians often encounter scenarios with continuous treatment options and multiple, potentially competing outcomes, such as medicine efficacy and unavoidable toxicity. To balance these outcomes, a proper weight is necessary, which should be learned in a data-driven manner that considers both patient preference and clinician expertise. In this paper, we present a novel algorithm for developing individualized treatment regimes (ITRs) that incorporate continuous treatment options and multiple outcomes, utilizing observational data. Our approach assumes that clinicians are optimizing individualized patient utilities with sub-optimal treatment decisions that are at least better than random assignment. Treatment assignment is assumed to directly depend on the true underlying utility of the treatment rather than patient characteristics. The proposed method simultaneously estimates the weighting of composite outcomes and the decision-making process, allowing for construction of individualized treatment regimes with continuous doses. The proposed estimators can be used for inference and variable selection, facilitating the identification of informative treatment assignments and preference-associated variables. We evaluate the finite sample performance of our proposed method via simulation studies and apply it to a real data application of radiation oncology analysis.

Aino Vesikansa, Juha Mehtälä, Katja Mutanen et al.

ABSTRACTBackground: The increasing prevalence of obesity imposes a significant cost burden on individuals and societies worldwide.Objective: In this nationally representative study, the association between body mass index (BMI) groups and the number of metabolic comorbidities (MetC) with total direct costs was investigated in the Finnish population.Study design, setting, and participants: The study cohort included 5,587 adults with BMI ≥18.5 kg/m2 who participated in the cross-sectional FinHealth 2017 health examination survey conducted by the Finnish Institute for Health and Welfare. Data on healthcare resource utilization (HCRU) and drug purchases were collected from national healthcare and drug registers.Main outcome measure: The primary outcome was total direct costs (costs of primary and secondary HCRU and prescription medications).Results: Class I (BMI 30.0–34.9 kg/m2) and class II – III (BMI ≥35.0 kg/m2) obesity were associated with 43% and 40% higher age- and sex-adjusted direct costs, respectively, compared with normal weight, mainly driven by a steeply increased comorbidity in the higher BMI groups. In all BMI groups combined, individuals with ≥2 MetCs comprised 39% of the total study population and 60% of the total costs.Conclusion: To manage the cost burden of obesity, treatment should be given equal consideration as other chronic diseases, and BMIs ≥30.0 kg/m2 should be considered in treatment decisions.

Songhua Yang, Hanjie Zhao, Senbin Zhu et al.

Recent advances in Large Language Models (LLMs) have achieved remarkable breakthroughs in understanding and responding to user intents. However, their performance lag behind general use cases in some expertise domains, such as Chinese medicine. Existing efforts to incorporate Chinese medicine into LLMs rely on Supervised Fine-Tuning (SFT) with single-turn and distilled dialogue data. These models lack the ability for doctor-like proactive inquiry and multi-turn comprehension and cannot align responses with experts' intentions. In this work, we introduce Zhongjing, the first Chinese medical LLaMA-based LLM that implements an entire training pipeline from continuous pre-training, SFT, to Reinforcement Learning from Human Feedback (RLHF). Additionally, we construct a Chinese multi-turn medical dialogue dataset of 70,000 authentic doctor-patient dialogues, CMtMedQA, which significantly enhances the model's capability for complex dialogue and proactive inquiry initiation. We also define a refined annotation rule and evaluation criteria given the unique characteristics of the biomedical domain. Extensive experimental results show that Zhongjing outperforms baselines in various capacities and matches the performance of ChatGPT in some abilities, despite the 100x parameters. Ablation studies also demonstrate the contributions of each component: pre-training enhances medical knowledge, and RLHF further improves instruction-following ability and safety. Our code, datasets, and models are available at https://github.com/SupritYoung/Zhongjing.

Qiao Jin, Robert Leaman, Zhiyong Lu

Biomedical research yields a wealth of information, much of which is only accessible through the literature. Consequently, literature search is an essential tool for building on prior knowledge in clinical and biomedical research. Although recent improvements in artificial intelligence have expanded functionality beyond keyword-based search, these advances may be unfamiliar to clinicians and researchers. In response, we present a survey of literature search tools tailored to both general and specific information needs in biomedicine, with the objective of helping readers efficiently fulfill their information needs. We first examine the widely used PubMed search engine, discussing recent improvements and continued challenges. We then describe literature search tools catering to five specific information needs: 1. Identifying high-quality clinical research for evidence-based medicine. 2. Retrieving gene-related information for precision medicine and genomics. 3. Searching by meaning, including natural language questions. 4. Locating related articles with literature recommendation. 5. Mining literature to discover associations between concepts such as diseases and genetic variants. Additionally, we cover practical considerations and best practices for choosing and using these tools. Finally, we provide a perspective on the future of literature search engines, considering recent breakthroughs in large language models such as ChatGPT. In summary, our survey provides a comprehensive view of biomedical literature search functionalities with 36 publicly available tools.