Hasil untuk "Medicine (General)"

Shuai Wang, Lihong Cui

For a (molecular) graph $G$ and any real number $α\ne 0$ , the zero-order general Randić index , denote by $^0R_α$, is defined by the following equation: \begin{align*} {^0R_α} (G) =\sum_{v\in G}d_G (v) ^α (α\in \mathbb{R}-\left\{0\right\}) . \end{align*} In this paper, we use this index to give sufficient conditions for a graph $G$ to satisfy the Hamiltonian (or $k$-Hamiltonian) property, and show that none of these conditions can be dropped. Finally we give similar results for the case when $G$ is a balanced bipartite graph.

Anran Li, Yuanyuan Chen, Wenjun Long et al.

Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis. To enable their use in clinical settings, LLMs are typically further adapted through continued pretraining or post-training using clinical data. However, most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems. Federated learning (FL) is a promising solution for enabling collaborative model development across healthcare institutions. Yet applying FL to LLMs in medicine remains fundamentally limited. First, conventional FL requires transmitting the full model during each communication round, which becomes impractical for multi-billion-parameter LLMs given the limited computational resources. Second, many FL algorithms implicitly assume data homogeneity, whereas real-world clinical data are highly heterogeneous across patients, diseases, and institutional practices. We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications. Fed-MedLoRA transmits only low-rank adapter parameters, reducing communication and computation overhead, while Fed-MedLoRA+ further incorporates adaptive, data-aware aggregation to improve convergence under cross-site heterogeneity. We apply the framework to clinical information extraction (IE), which transforms patient narratives into structured medical entities and relations. Accuracy was assessed across five patient cohorts through comparisons with BERT models, and LLaMA-3 and DeepSeek-R1, GPT-4o models. Evaluation settings included (1) in-domain training and testing, (2) external validation on independent cohorts, and (3) a low-resource new-site adaptation scenario using real-world clinical notes from the Yale New Haven Health System.

Jiatong Han

Traditional Chinese Medicine theory is built on imagistic thinking, in which medical principles and diagnostic and therapeutic logic are structured through metaphor and metonymy. However, existing English translations largely rely on literal rendering, making it difficult for target-language readers to reconstruct the underlying conceptual networks and apply them in clinical practice. This study adopted a human-in-the-loop framework and selected four passages from the medical canon Huangdi Neijing that are fundamental in theory. Through prompt-based cognitive scaffolding, DeepSeek V3.1 was guided to identify metaphor and metonymy in the source text and convey the theory in translation. In the evaluation stage, ChatGPT 5 Pro and Gemini 2.5 Pro were instructed by prompts to simulate three types of real-world readers. Human translations, baseline model translations, and prompt-adjusted translations were scored by the simulated readers across five cognitive dimensions, followed by structured interviews and Interpretative Phenomenological Analysis. Results show that the prompt-adjusted LLM translations perform best across all five dimensions, with high cross-model and cross-role consistency. The interview themes reveal differences between human and machine translation, effective strategies for metaphor and metonymy transfer, and readers' cognitive preferences. This study provides a cognitive, efficient and replicable HITL methodological pathway for translation of ancient, concept-dense texts like TCM.

Seungwook Han, Jyothish Pari, Samuel J. Gershman et al.

Large Language Models (LLMs) have demonstrated impressive real-world utility, exemplifying artificial useful intelligence (AUI). However, their ability to reason adaptively and robustly -- the hallmarks of artificial general intelligence (AGI) -- remains fragile. While LLMs seemingly succeed in commonsense reasoning, programming, and mathematics, they struggle to generalize algorithmic understanding across novel contexts. Our experiments with algorithmic tasks in esoteric programming languages reveal that LLM's reasoning overfits to the training data and is limited in its transferability. We hypothesize that the core issue underlying such limited transferability is the coupling of reasoning and knowledge in LLMs. To transition from AUI to AGI, we propose disentangling knowledge and reasoning through three key directions: (1) pretaining to reason using RL from scratch as an alternative to the widely used next-token prediction pretraining, (2) using a curriculum of synthetic tasks to ease the learning of a reasoning prior for RL that can then be transferred to natural language tasks, and (3) learning more generalizable reasoning functions using a small context window to reduce exploiting spurious correlations between tokens. Such a reasoning system coupled with a trained retrieval system and a large external memory bank as a knowledge store can overcome several limitations of existing architectures at learning to reason in novel scenarios.

Emily A. Prentice, Abby Moler, Amanda Sweeney et al.

Background: The Links to Care Community Grants Project was developed to improve breast cancer outcomes by increasing access to appropriate follow-up care, improving processes for care transitions, and enhancing care coordination between community health centers (CHCs) and hospital partners. Methods: This 24-month multi-pronged project encompasses quality improvement (QI) coaching, technical assistance support, and evaluation. QI coaching follows the Model for Improvement to test and adapt to changes. Local and centralized technical assistance supports the individual needs of the health system. A data collection tool was developed to evaluate implemented interventions and assess changes in breast cancer screening and diagnostic testing completion rates, time between care transitions, and process improvements made throughout the project period. Results: Seven CHCs comprised of 27 clinic sites with 26 255 patients eligible for breast cancer screening agreed to participate. Baseline findings demonstrate an average screening rate of 51.1%. Conclusion: The Links to Care Community Grants Project will evaluate the effectiveness of implemented patient, provider, and/or system-level interventions and care coordination process improvements on reducing delays along the breast cancer care continuum.

Xiaosong Wang, Xiaofan Zhang, Guotai Wang et al.

The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas. However, domain-specific applications of such foundation models (e.g., in medicine) remain untouched or often at their very early stages. It will require an individual set of transfer learning and model adaptation techniques by further expanding and injecting these models with domain knowledge and data. The development of such technologies could be largely accelerated if the bundle of data, algorithms, and pre-trained foundation models were gathered together and open-sourced in an organized manner. In this work, we present OpenMEDLab, an open-source platform for multi-modality foundation models. It encapsulates not only solutions of pioneering attempts in prompting and fine-tuning large language and vision models for frontline clinical and bioinformatic applications but also building domain-specific foundation models with large-scale multi-modal medical data. Importantly, it opens access to a group of pre-trained foundation models for various medical image modalities, clinical text, protein engineering, etc. Inspiring and competitive results are also demonstrated for each collected approach and model in a variety of benchmarks for downstream tasks. We welcome researchers in the field of medical artificial intelligence to continuously contribute cutting-edge methods and models to OpenMEDLab, which can be accessed via https://github.com/openmedlab.

Aoyi Wang, Yingdong Wang, Haoyang Peng et al.

The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput drug-target interaction predicting models and biological profile-based methods have been commonly applied for identifying TCM targets based on the structural information of TCM chemical components and biological information, respectively. However, the existing methods lack the integration of TCM chemical and biological information, resulting in difficulty in the systematic discovery of TCM action pathways. To solve this problem, we propose a novel target identification model NP-TCMtarget to explore the TCM target path by combining the overall chemical and biological profiles. First, NP-TCMtarget infers TCM effect targets by calculating associations between drug/disease inducible gene expression profiles and specific gene signatures for 8,233 targets. Then, NP-TCMtarget utilizes a constructed binary classification model to predict binding targets of herbal ingredients. Finally, we can distinguish TCM direct and indirect targets by comparing the effect targets and binding targets to establish the action pathways of herbal components-direct targets-indirect targets by mapping TCM targets in the biological molecular network. We apply NP-TCMtarget to the formula XiaoKeAn to demonstrate the power of revealing the action pathways of herbal formula. We expect that this novel model could provide a systematic framework for exploring the molecular mechanisms of TCM at the target level. NP-TCMtarget is available at http://www.bcxnfz.top/NP-TCMtarget.

Wanying Zhao, Linlin Ye, Lei Cao et al.

BACKGROUND: Owing to the adverse effects of unilateral neglect (UN) on rehabilitation outcomes, fall risk, and activities of daily living, this field has gradually got considerable interest. Notwithstanding, there is presently an absence of efficient portrayals of the entire research field; hence, the motivation behind this study was to dissect and evaluate the literature published in the field of UN following stroke and other nonprogressive brain injuries to identify hotspots and trends for future research. MATERIALS AND METHODS: Original articles and reviews related to UN from 1970 to 2022 were retrieved from the Science Citation Index Expanded of the Web of Science Core Collection. CiteSpace, VOSviewer, and Bibliometrix software were used to observe publication fields, countries, and authors. RESULTS: A total of 1,202 publications were incorporated, consisting of 92% of original articles, with an overall fluctuating upward trend in the number of publications. Italy, the United Kingdom, and the United States made critical contributions, with Neuropsychologia being the most persuasive academic journal, and Bartolomeo P. ranked first in both the quantity of publications and co-citations. Keywords were divided into four clusters, and burst keyword detection demonstrated that networks and virtual reality might additionally emerge as frontiers of future development and warrant additional attention. CONCLUSIONS: UN is an emerging field, and this study presents the first bibliometric analysis to provide a comprehensive overview of research in the field. The insights and guidance garnered from our research on frontiers, trends, and popular topics could prove highly valuable in facilitating the rapid development of this field while informing future research directions.

Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl et al.

Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates that DP has negative implications on model accuracy and fairness, which are unacceptable in medicine and represent a main barrier to the widespread use of privacy-preserving techniques. In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training. For this, we used two datasets: (1) A large dataset (N=193,311) of high quality clinical chest radiographs, and (2) a dataset (N=1,625) of 3D abdominal computed tomography (CT) images, with the task of classifying the presence of pancreatic ductal adenocarcinoma (PDAC). Both were retrospectively collected and manually labeled by experienced radiologists. We then compared non-private deep convolutional neural networks (CNNs) and privacy-preserving (DP) models with respect to privacy-utility trade-offs measured as area under the receiver-operator-characteristic curve (AUROC), and privacy-fairness trade-offs, measured as Pearson's r or Statistical Parity Difference. We found that, while the privacy-preserving trainings yielded lower accuracy, they did largely not amplify discrimination against age, sex or co-morbidity. Our study shows that -- under the challenging realistic circumstances of a real-life clinical dataset -- the privacy-preserving training of diagnostic deep learning models is possible with excellent diagnostic accuracy and fairness.

Li-Chin Chen, Kuo-Hsuan Hung, Yi-Ju Tseng et al.

The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for TVR detection. The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority (p < 0.01) compared to prior GLP processing. Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise.

Gabriel Conant, Kyle Gannon, James E. Hanson

We prove a number of results relating the concepts of Keisler measures, generic stability, randomizations, and NIP formulas. Among other things, we do the following: (1) We introduce the notion of a Keisler-Morley measure, which plays the role of a Morley sequence for a Keisler measure. We prove that if $μ$ is fim over $M$, then for any Keisler-Morley measure $λ$ in $μ$ over $M$ and any formula $\varphi(x,b)$, $\lim_{i \to \infty} λ(\varphi(x_i,b)) = μ(\varphi(x,b))$. We also show that any measure satisfying this conclusion must be fam. (2) We study the map, defined by Ben Yaacov, taking a definable measure $μ$ to a type $r_μ$ in the randomization. We prove that this map commutes with Morley products, and that if $μ$ is fim then $r_μ$ is generically stable. (3) We characterize when generically stable types are closed under Morley products by means of a variation of ict-patterns. Moreover, we show that NTP$_2$ theories satisfy this property. (4) We prove that if a local measure admits a suitably tame global extension, then it has finite packing numbers with respect to any definable family. We also characterize NIP formulas via the existence of tame extensions for local measures.

Tia G. Sawhney, Angela Dobes, Sirimon O'Charoen

The quality-adjusted life-year (QALY) is a metric widely used when assessing the cost-effectiveness of drugs and other health interventions. The assessments are used in the development of recommendations for pricing, formulary placement decisions, and health policy decisions. A new bill, H.R. 485, the Protecting Health Care for All Patients Act of 2023, was approved by the US House Energy and Commerce Health Subcommittee that will, if passed, end the practice of using QALYs in all federal programs.^1,2^ Proponents of the ban say that QALYs undervalue the positive effects of therapeutics on people with disabilities.^3^ We share their concerns. Furthermore, our review of the mathematical properties of QALYs, including an analysis of quality-of-life utility (QOL utility) data recently collected from patients with inflammatory bowel disease (IBD), has led us to conclude that QALYs are an inappropriate metric of drug and treatment cost-effectiveness for all people, both disabled and nondisabled, and should not be the basis for US healthcare policy decisions.

Zella King, Joseph Farrington, Martin Utley et al.

Abstract Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68–0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.

A. M. Hamed

Abstract Background A design of equally spaced eight-circles placed at equal distances from the origin is suggested. Three models corresponding to the eight-circle design considering conic, linear, and quadratic distributions are investigated. This arrangement is considered for the sake of improving both microscope resolution and image contrast as compared with the pure annular aperture. This design is different compared with other recent work on aperture modulation. Results and discussions The point spread function (PSF) is computed in all the models using the fast Fourier transform (FFT) algorithm that computes the discrete Fourier transform (DFT) corresponding to the models and compared with the corresponding PSF in the case of uniform circular aperture. In addition, the autocorrelation images for the apertures are shown differently. It is shown smooth pattern for the circular arrangement as compared with the deformation and shrinking appeared in the central peak in case of conic model. Finally, the speckle images corresponding to the considered apertures are investigated. Reconstructed apertures are obtained from the speckle images using the FFT algorithm. Conclusions The PSF is computed for the described models, and the autocorrelation corresponding to the apertures showed difference. The reconstructed apertures from the speckle images can be improved using filtering techniques. It is noted that MATLAB codes are constructed in the computations of all images and plots.

Katherine A Rhodes, Man Cheong Ma, María A Rendón et al.

The mechanisms used by human adapted commensal Neisseria to shape and maintain a niche in their host are poorly defined. These organisms are common members of the mucosal microbiota and share many putative host interaction factors with Neisseria meningitidis and Neisseria gonorrhoeae. Evaluating the role of these shared factors during host carriage may provide insight into bacterial mechanisms driving both commensalism and asymptomatic infection across the genus. We identified host interaction factors required for niche development and maintenance through in vivo screening of a transposon mutant library of Neisseria musculi, a commensal of wild-caught mice which persistently and asymptomatically colonizes the oral cavity and gut of CAST/EiJ and A/J mice. Approximately 500 candidate genes involved in long-term host interaction were identified. These included homologs of putative N. meningitidis and N. gonorrhoeae virulence factors which have been shown to modulate host interactions in vitro. Importantly, many candidate genes have no assigned function, illustrating how much remains to be learned about Neisseria persistence. Many genes of unknown function are conserved in human adapted Neisseria species; they are likely to provide a gateway for understanding the mechanisms allowing pathogenic and commensal Neisseria to establish and maintain a niche in their natural hosts. Validation of a subset of candidate genes confirmed a role for a polysaccharide capsule in N. musculi persistence but not colonization. Our findings highlight the potential utility of the Neisseria musculi-mouse model as a tool for studying the pathogenic Neisseria; our work represents a first step towards the identification of novel host interaction factors conserved across the genus.

Ty A. Davis, DO, Thelma Rocio Jimenez Mosquea, MD, Ana C. Belzarena, MD, MPH

Eosinophilic granuloma (EG) is a rare benign tumor-like disorder characterized by abnormal proliferation Langerhans cells. EG frequently presents as a solitary lesion in the axial skeleton and diaphysis long bones. Here we present the case of a 14-year-old male with multifocal EG with a lesion located in the femoral epiphysis mimicking an aneurysmal bone cyst that presented a diagnostic challenge. While the initial presentation of EG patients may appear uncommon, its overlapping features with other benign and malignant etiologies highlight the importance of increased awareness of this condition, as well as the need for an experienced multidisciplinary team in its diagnosis and treatment.

Jimmy Martin‐Delgado, Carla Tovar, Israel Pazmiño et al.

Abstract Introduction Diabetes is the second leading cause of death in Ecuador, as 79% of the indigenous population live in rural areas that are difficult to access and have below‐average health resources. The objective of this study was to define person‐centred indicators to monitor the care received by patients with diabetes in the indigenous population. Method Qualitative research combining three focus groups (with the participation of 10 patients and 18 professionals) to capture relevant information and Delphi to reach a consensus on the pertinence, relevance, and feasibility of a set of indicators was conducted. Two rounds of the Delphi technique were performed, with the participation of 64 professionals in the first round (90% response rate) and 34 in the second round (53% response rate). Results A total of 23 indicators were identified which were distributed in the previously identified six dimensions (cosmovision, accessibility, adaptability to cosmovision, resources, equipment, community care, quality culture and results). Conclusions The consensus on the set of indicators among all the participants in this study strengthened the results obtained. These indicators have considered the feasibility and relevance and aimed to achieve comprehensive person‐centred care for diabetes among the indigenous population in Ecuador and possibly the Andean community. Patient or Public Contribution These indicators’ development included patients and caregivers since its conception. During the qualitative phase of this research, relevant information on cultural and social beliefs was gathered directly from the study population to achieve patient‐centred indicators for adequate diabetes care.

Lifeng Yu, Xin Liu, S. Leng et al.

Michael Timothy Bennett

The following defines intent, an arbitrary task and its solutions, and then argues that an agent which always constructs what is called an Intensional Solution would qualify as artificial general intelligence. We then explain how natural language may emerge and be acquired by such an agent, conferring the ability to model the intent of other individuals labouring under similar compulsions, because an abstract symbol system and the solution to a task are one and the same.

Ravindran Chirukandath , CR Nimisha , PJ Babu et al.

Occurrence of malignancy in the TG cyst has been rarely reported, though rare, and papillary carcinoma predominates the common type but squamous cell carcinomas, anaplastic carcinoma, and medullary have been reported rarely. Mucoepidermoid carcinomas are most commonly seen in salivary glands, and as per the available literature, there was only two cases reported in thyroglossal cyst. We are presenting a 67-year-old lady presented with a 6×8 cm hard swelling below symphysis menti with no thyromegaly and moving on protrusion of tongue, and on MRI, it was found to be thyroglossal cyst with infiltration of strap muscles. Cytological investigation revealed it to be a TG cyst malignancy. The patient underwent total thyroidectomy and radical Sistrunk’s operation. Histopathological and immunohistochemistry revealed it to be a histological examination revealed a low-grade mucoepidermoid carcinoma consistent with origin in a thyroglossal duct remnant it invaded the hyoid bone and adjacent strap muscles. Various diagnostic and treatment dilemmas in the treatment of TG cyst malignancy are discussed with reference to mucoepidermoid carcinoma. We are reporting an usual histological surprise in a thyroglossal cyst malignancy being the only second reported case of TG cyst mucoepidermoid carcinoma this case highlights the importance of removal of thyroglossal duct cysts at an early stage and aggressive surgical approach in high-grade tumors.