Cinnamon (Cinnamomum zeylanicum, and Cinnamon cassia), the eternal tree of tropical medicine, belongs to the Lauraceae family. Cinnamon is one of the most important spices used daily by people all over the world. Cinnamon primarily contains vital oils and other derivatives, such as cinnamaldehyde, cinnamic acid, and cinnamate. In addition to being an antioxidant, anti-inflammatory, antidiabetic, antimicrobial, anticancer, lipid-lowering, and cardiovascular-disease-lowering compound, cinnamon has also been reported to have activities against neurological disorders, such as Parkinson's and Alzheimer's diseases. This review illustrates the pharmacological prospective of cinnamon and its use in daily life.
1 Department of Medicine, Section of Infectious Diseases, Baylor College of Medicine, Houston, Texas, United States of America, 2 Department of Medicine, Section of Health Services Research, Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E. DeBakey VA Medical Center, Houston, Texas, United States of America, 3 Department of Pediatrics, National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas, United States of America, 4 Section of Global and Immigrant Health, Department of Pediatrics, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas, United States of America, 5 Center for Vaccine Development, Department of Pediatrics, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas, United States of America, 6 Departments of Pediatrics and Molecular Virology & Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas, United States of America, 7 Department of Biology, Baylor University, Waco, Texas, United States of America
AbstractAs there are increasing reports of fluoroquinolone resistance on use as a first- or second-line treatment for Helicobacter pylori (H pylori), we aimed at evaluation of the efficacy and safety of nitazoxanide-based regimen as a rescue regimen in Egyptian patients whose previous traditional treatment for H pylori infection failed.In total, 100 patients from the outpatient clinic of the Tropical medicine department, Tanta University hospital in whom the standard triple therapy (clarithromycin-based triple therapy) failed were enrolled in the study. Nitazoxanide (500 mg bid), levofloxacin (500 mg once daily), omeprazole (40 mg bid), and doxycyclin (100 mg twice daily) were prescribed for 14 days. Eradication was confirmed by stool antigen for H pylori 6 weeks after the end of treatment. Among the patients enrolled in the study, 44% of patients were men and the mean age for the participants in the study was 46.41 ± 8.05, 13% of patients were smokers, and 4% of patients had a previous history of upper gastro-intestinal bleeding. A total of 94 patients (94%) completed the study with excellent compliance. Only 1 patient (1%) discontinued treatment due to intolerable side effects and 5 patients (5%) did not achieve good compliance or were lost during follow up. However, 83 patients had successful eradication of H pylori with total eradication rates 83% (95 % CI 75.7–90.3%) and 88.30% (95 % CI 81.8–94.8%) according to an intention-to-treat and per-protocol analysis, respectively. Adverse events were reported in 21% of patients: abdominal pain (6%), nausea (9%) and constipation (12%), (2%) headache, and (1%) dizziness. A 2-week nitazoxanide-based regimen is an effective and safe rescue therapy in Egyptian patients whose previous standard triple therapy has failed.
From the Institute for Health Metrics and Evaluation, University of Washington, Seattle (H.J.L., E.G., C.J.L.M.); and the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London (H.J.L.). Prof. Larson can be contacted at heidi . larson@ lshtm . ac . uk or at the Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel St., London WC1E 7HT, United Kingdom.
Background: Retrieval augmented generation (RAG) technology can empower large language models (LLMs) to generate more accurate, professional, and timely responses without fine tuning. However, due to the complex reasoning processes and substantial individual differences involved in traditional Chinese medicine (TCM) clinical diagnosis and treatment, traditional RAG methods often exhibit poor performance in this domain. Objective: To address the limitations of conventional RAG approaches in TCM applications, this study aims to develop an improved RAG framework tailored to the characteristics of TCM reasoning. Methods: We developed TCM-DiffRAG, an innovative RAG framework that integrates knowledge graphs (KG) with chains of thought (CoT). TCM-DiffRAG was evaluated on three distinctive TCM test datasets. Results: The experimental results demonstrated that TCM-DiffRAG achieved significant performance improvements over native LLMs. For example, the qwen-plus model achieved scores of 0.927, 0.361, and 0.038, which were significantly enhanced to 0.952, 0.788, and 0.356 with TCM-DiffRAG. The improvements were even more pronounced for non-Chinese LLMs. Additionally, TCM-DiffRAG outperformed directly supervised fine-tuned (SFT) LLMs and other benchmark RAG methods. Conclusions: TCM-DiffRAG shows that integrating structured TCM knowledge graphs with Chain of Thought based reasoning substantially improves performance in individualized diagnostic tasks. The joint use of universal and personalized knowledge graphs enables effective alignment between general knowledge and clinical reasoning. These results highlight the potential of reasoning-aware RAG frameworks for advancing LLM applications in traditional Chinese medicine.
Retrieval-augmented generation (RAG) promises grounded question answering, yet domain settings with multiple heterogeneous knowledge bases (KBs) remain challenging. In Chinese Tibetan medicine, encyclopedia entries are often dense and easy to match, which can dominate retrieval even when classics or clinical papers provide more authoritative evidence. We study a practical setting with three KBs (encyclopedia, classics, and clinical papers) and a 500-query benchmark (cutoff $K{=}5$) covering both single-KB and cross-KB questions. We propose two complementary methods to improve traceability, reduce hallucinations, and enable cross-KB verification. First, DAKS performs KB routing and budgeted retrieval to mitigate density-driven bias and to prioritize authoritative sources when appropriate. Second, we use an alignment graph to guide evidence fusion and coverage-aware packing, improving cross-KB evidence coverage without relying on naive concatenation. All answers are generated by a lightweight generator, \textsc{openPangu-Embedded-7B}. Experiments show consistent gains in routing quality and cross-KB evidence coverage, with the full system achieving the best CrossEv@5 while maintaining strong faithfulness and citation correctness.
This paper claims that machine learning models deployed in high stakes domains such as medicine must be interpretable, shareable, reproducible and accountable. We argue that these principles should form the foundational design criteria for machine learning algorithms dealing with critical medical data, including survival analysis and risk prediction tasks. Black box models, while often highly accurate, struggle to gain trust and regulatory approval in health care due to a lack of transparency. We discuss how intrinsically interpretable modeling approaches (such as kernel methods with sparsity, prototype-based learning, and deep kernel models) can serve as powerful alternatives to opaque deep networks, providing insight into biomedical predictions. We then examine accountability in model development, calling for rigorous evaluation, fairness, and uncertainty quantification to ensure models reliably support clinical decisions. Finally, we explore how generative AI and collaborative learning paradigms (such as federated learning and diffusion-based data synthesis) enable reproducible research and cross-institutional integration of heterogeneous biomedical data without compromising privacy, hence shareability. By rethinking machine learning foundations along these axes, we can develop medical AI that is not only accurate but also transparent, trustworthy, and translatable to real-world clinical settings.
Natural medicines, particularly Traditional Chinese Medicine (TCM), are gaining global recognition for their therapeutic potential in addressing human symptoms and diseases. TCM, with its systematic theories and extensive practical experience, provides abundant resources for healthcare. However, the effective application of TCM requires precise syndrome diagnosis, determination of treatment principles, and prescription formulation, which demand decades of clinical expertise. Despite advancements in TCM-based decision systems, machine learning, and deep learning research, limitations in data and single-objective constraints hinder their practical application. In recent years, large language models (LLMs) have demonstrated potential in complex tasks, but lack specialization in TCM and face significant challenges, such as too big model scale to deploy and issues with hallucination. To address these challenges, we introduce Tianyi with 7.6-billion-parameter LLM, a model scale proper and specifically designed for TCM, pre-trained and fine-tuned on diverse TCM corpora, including classical texts, expert treatises, clinical records, and knowledge graphs. Tianyi is designed to assimilate interconnected and systematic TCM knowledge through a progressive learning manner. Additionally, we establish TCMEval, a comprehensive evaluation benchmark, to assess LLMs in TCM examinations, clinical tasks, domain-specific question-answering, and real-world trials. The extensive evaluations demonstrate the significant potential of Tianyi as an AI assistant in TCM clinical practice and research, bridging the gap between TCM knowledge and practical application.
Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. Due to the sheer volume and rapid growth of medical literature and the high cost of curation, there is a critical need to investigate Natural Language Processing (NLP) methods to identify, appraise, synthesize, summarize, and disseminate evidence in EBM. This survey presents an in-depth review of 129 research studies on leveraging NLP for EBM, illustrating its pivotal role in enhancing clinical decision-making processes. The paper systematically explores how NLP supports the five fundamental steps of EBM -- Ask, Acquire, Appraise, Apply, and Assess. The review not only identifies current limitations within the field but also proposes directions for future research, emphasizing the potential for NLP to revolutionize EBM by refining evidence extraction, evidence synthesis, appraisal, summarization, enhancing data comprehensibility, and facilitating a more efficient clinical workflow.
J. Dias, Alberto Novaes Ramos Junior, E. D. Gontijo
et al.
Chagas disease is a neglected chronic condition with a high burden of morbidity and mortality. It has considerable psychological, social, and economic impacts. The disease represents a significant public health issue in Brazil, with different regional patterns. This document presents the evidence that resulted in the Brazilian Consensus on Chagas Disease. The objective was to review and standardize strategies for diagnosis, treatment, prevention, and control of Chagas disease in the country, based on the available scientific evidence. The consensus is based on the articulation and strategic contribution of renowned Brazilian experts with knowledge and experience on various aspects of the disease. It is the result of a close collaboration between the Brazilian Society of Tropical Medicine and the Ministry of Health. It is hoped that this document will strengthen the development of integrated actions against Chagas disease in the country, focusing on epidemiology, management, comprehensive care (including families and communities), communication, information, education, and research .
The integration of artificial intelligence [AI] into clinical trials has revolutionized the process of drug development and personalized medicine. Among these advancements, deep learning and predictive modelling have emerged as transformative tools for optimizing clinical trial design, patient recruitment, and real-time monitoring. This study explores the application of deep learning techniques, such as convolutional neural networks [CNNs] and transformerbased models, to stratify patients, forecast adverse events, and personalize treatment plans. Furthermore, predictive modelling approaches, including survival analysis and time-series forecasting, are employed to predict trial outcomes, enhancing efficiency and reducing trial failure rates. To address challenges in analysing unstructured clinical data, such as patient notes and trial protocols, natural language processing [NLP] techniques are utilized for extracting actionable insights. A custom dataset comprising structured patient demographics, genomic data, and unstructured text is curated for training and validating these models. Key metrics, including precision, recall, and F1 scores, are used to evaluate model performance, while trade-offs between accuracy and computational efficiency are examined to identify the optimal model for clinical deployment. This research underscores the potential of AI-driven methods to streamline clinical trial workflows, improve patient-centric outcomes, and reduce costs associated with trial inefficiencies. The findings provide a robust framework for integrating predictive analytics into precision medicine, paving the way for more adaptive and efficient clinical trials. By bridging the gap between technological innovation and real-world applications, this study contributes to advancing the role of AI in healthcare, particularly in fostering personalized care and improving overall trial success rates.
This paper details a technical plan for building a clinical case database for Traditional Chinese Medicine (TCM) using web scraping. Leveraging multiple platforms, including 360doc, we gathered over 5,000 TCM clinical cases, performed data cleaning, and structured the dataset with crucial fields such as patient details, pathogenesis, syndromes, and annotations. Using the $Baidu\_ERNIE\_Speed\_128K$ API, we removed redundant information and generated the final answers through the $DeepSeekv2$ API, outputting results in standard JSON format. We optimized data recall with RAG and rerank techniques during retrieval and developed a hybrid matching scheme. By combining two-stage retrieval method with keyword matching via Jieba, we significantly enhanced the accuracy of model outputs.
With the rapid advance of computer graphics and artificial intelligence technologies, the ways we interact with the world have undergone a transformative shift. Virtual Reality (VR) technology, aided by artificial intelligence (AI), has emerged as a dominant interaction media in multiple application areas, thanks to its advantage of providing users with immersive experiences. Among those applications, medicine is considered one of the most promising areas. In this paper, we present a comprehensive examination of the burgeoning field of AI-enhanced VR applications in medical care and services. By introducing a systematic taxonomy, we meticulously classify the pertinent techniques and applications into three well-defined categories based on different phases of medical diagnosis and treatment: Visualization Enhancement, VR-related Medical Data Processing, and VR-assisted Intervention. This categorization enables a structured exploration of the diverse roles that AI-powered VR plays in the medical domain, providing a framework for a more comprehensive understanding and evaluation of these technologies. To our best knowledge, this is the first systematic survey of AI-powered VR systems in medical settings, laying a foundation for future research in this interdisciplinary domain.
Pedro Zuidberg Dos Martires, Vincent Derkinderen, Luc De Raedt
et al.
Recent developments in AI have reinvigorated pursuits to advance the (life) sciences using AI techniques, thereby creating a renewed opportunity to bridge different fields and find synergies. Headlines for AI and the life sciences have been dominated by data-driven techniques, for instance, to solve protein folding with next to no expert knowledge. In contrast to this, we argue for the necessity of a formal representation of expert knowledge - either to develop explicit scientific theories or to compensate for the lack of data. Specifically, we argue that the fields of knowledge representation (KR) and systems biology (SysBio) exhibit important overlaps that have been largely ignored so far. This, in turn, means that relevant scientific questions are ready to be answered using the right domain knowledge (SysBio), encoded in the right way (SysBio/KR), and by combining it with modern automated reasoning tools (KR). Hence, the formal representation of domain knowledge is a natural meeting place for SysBio and KR. On the one hand, we argue that such an interdisciplinary approach will advance the field SysBio by exposing it to industrial-grade reasoning tools and thereby allowing novel scientific questions to be tackled. On the other hand, we see ample opportunities to move the state-of-the-art in KR by tailoring KR methods to the field of SysBio, which comes with challenging problem characteristics, e.g. scale, partial knowledge, noise, or sub-symbolic data. We stipulate that this proposed interdisciplinary research is necessary to attain a prominent long-term goal in the health sciences: precision medicine.
With the rapid development of artificial intelligence (AI), large language models (LLMs) have shown strong capabilities in natural language understanding, reasoning, and generation, attracting amounts of research interest in applying LLMs to health and medicine. Critical care medicine (CCM) provides diagnosis and treatment for critically ill patients who often require intensive monitoring and interventions in intensive care units (ICUs). Can LLMs be applied to CCM? Are LLMs just like stochastic parrots or ICU experts in assisting clinical decision-making? This scoping review aims to provide a panoramic portrait of the application of LLMs in CCM. Literature in seven databases, including PubMed, Embase, Scopus, Web of Science, CINAHL, IEEE Xplore, and ACM Digital Library, were searched from January 1, 2019, to June 10, 2024. Peer-reviewed journal and conference articles that discussed the application of LLMs in critical care settings were included. From an initial 619 articles, 24 were selected for final review. This review grouped applications of LLMs in CCM into three categories: clinical decision support, medical documentation and reporting, and medical education and doctor-patient communication. LLMs have advantages in handling unstructured data and do not require manual feature engineering. Meanwhile, applying LLMs to CCM faces challenges, including hallucinations, poor interpretability, bias and alignment challenges, and privacy and ethics issues. Future research should enhance model reliability and interpretability, integrate up-to-date medical knowledge, and strengthen privacy and ethical guidelines. As LLMs evolve, they could become key tools in CCM to help improve patient outcomes and optimize healthcare delivery. This study is the first review of LLMs in CCM, aiding researchers, clinicians, and policymakers to understand the current status and future potentials of LLMs in CCM.
Abstract In November 1965, Michel Jouvet accepted me into his laboratory in Lyon as a medical student at a time when sleep research was an adventure. After 4 years of investigations in cats, I obtained my medical doctorate. Being a military physician, I was posted to Antarctica for wintering over and was initiated by Jean Rivolier into the psychology of small isolated human groups. I recorded 180 polysomnographic (PSG) nights in eight of my companions. This was my first contribution to research on human sleep under extreme environments and conditions. I then entered René Hénane’s military thermophysiology laboratory, where I analyzed thermal exchanges during human sleep in the heat. Back to the cold, I spent 2 years in Canada and analyzed sleep during the Arctic winter under the direction of Manny W. Radomski, who headed the Defense and Civil Institute of Environmental Medicine and judged my PhD dissertation along with my first two mentors. Throughout my career, I worked in collaboration with Manny Radomski under the auspices of the Franco-Canadian Accord for Defence Research. We studied sleep and exercise, sleep deprivation, and recovery with and without chemical help. He also gave me support during several investigations in Africa. There, I studied normal sleep under various tropical climates (warm and dry in Niger, warm and humid in Côte d’Ivoire and Congo, temperate mid-mountain in Angola). I determined that human African trypanosomiasis, the ravaging sleeping sickness or tsetse disease, is not a hypersomnia, but a disorder of circadian rhythms, notably in the sleep–wake cycle.
Rochanawan Sootichote, Wilarat Puangmanee, Surachet Benjathummarak
et al.
Due to the lack of an effective therapeutic treatment to flavivirus, dengue virus (DENV) nonstructural protein 1 (NS1) has been considered to develop a vaccine owing to its lack of a role in antibody-dependent enhancement (ADE). However, both NS1 and its antibody have shown cross-reactivity to host molecules and have stimulated anti-DENV NS1 antibody-mediated endothelial damage and platelet dysfunction. To overcome the pathogenic events and reactogenicity, human monoclonal antibodies (HuMAbs) against DENV NS1 were generated from DENV-infected patients. Herein, the four DENV NS1-specific HuMAbs revealed the therapeutic effects in viral neutralization, reduction of viral replication, and enhancement of cell cytolysis of DENV and zika virus (ZIKV) via complement pathway. Furthermore, we demonstrate that DENV and ZIKV NS1 trigger endothelial dysfunction, leading to vascular permeability in vitro. Nevertheless, the pathogenic effects from NS1 were impeded by 2 HuMAbs (D25-4D4C3 and D25-2B11E7) and also protected the massive cytokines stimulation (interleukin [IL-]-1b, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-8, IL-9, IL-13, IL-17, eotaxin, granulocyte colony-stimulating factor, granulocyte-macrophage colony-stimulating factor, Inducible protein-10, monocyte chemoattractant protein-1, macrophage inflammatory protein [MIP]-1 α, MIP-1β, tumor necrosis factor-α, platelet-derived growth factor, and RANTES). Collectively, our findings suggest that the novel protective NS1 monoclonal antibodies generated from humans has multiple therapeutic benefits against DENV and ZIKV infections.