BACKGROUND

The Ifontok people are an ethnolinguistic group in the Mountain Province of the Philippines, for whom constructing and occupying a new house symbolizes the fulfillment of personal aspirations and reflects cultural traditions and beliefs that shape community life, worldviews, and social behavior.

This study examined the house construction rituals of the Ifontok people and their influence on health beliefs, highlighting the interconnectedness of culture, environment, and well-being.

A qualitative ethnographic design was employed. Six participants were interviewed between February 2021 and June 2022 until data saturation was reached. Colaizzi’s seven-step method was used for the systematic analysis of cultural experiences.

The Ifontok perform rituals before, during, and after house construction, as well as upon occupying a new home. Rooted in traditional beliefs, these rituals aim to ensure structural stability, invite prosperity, and safeguard holistic health. Beyond their symbolic function, the rituals serve as rites of passage that foster psychological reassurance, emotional resilience, and social cohesion. They frame health as a balance of physical, mental, and spiritual well-being, while adaptive strategies address challenges in sustaining these practices amid modern influences.

Ifontok house construction rituals integrate practical building practices, spiritual beliefs, and communal welfare. They promote mindful living, psychological well-being, and environmental awareness, offering insights for educators and healthcare practitioners working with Indigenous communities.

BACKGROUND AND OBJECTIVES

Many patients have received acupuncture treatment to address various health problems and it has been presented in several studies to be effective. However, stories of patients on the use of acupuncture may present some additional information including contextual factors about acupuncture treatment. This study was conducted to explore experiences of patients in a community-based acupuncture clinic on the impact of acupuncture as a treatment modality. In particular, this study aimed to understand how acupuncture has changed patients’ lives through a life stories approach, describe how the treatment affected their perceptions about their well-being, and to elicit their views of the acupuncture as an alternative or complementary treatment.

A narrative inquiry was done using in-depth interviews individually conducted among eight participants, who were selected purposively based on having had at least 12 consecutive acupuncture sessions at a communitybased acupuncture clinic. All the interviews were audio-recorded and later transcribed by a professional external interviewer following written informed consent from all the participants. The research protocol was submitted and approved by the Ethics Review Committee of the University Research Institute for Health Sciences. The researchers analyzed the interview transcripts using Murray’s narrative analysis approach.

Participants’ stories highlighted their personal experiences of how they had come to know about acupuncture, how their experiences have changed their perceptions of the treatment, and how the outcomes of acupuncture treatment have affected their lives. The findings emphasize four narratives: pain relief, acupuncture being complementary to Western medicine, social support, and advocacy.

This study emphasizes qualitative findings that are essential to be considered in the implementation of community-based acupuncture clinics that may help make acupuncture care accessible to community residents as well as maximize the potential of this treatment modality.

The relationship between gut microbiota and depressive disorder has become a research focus in recent years. Within the microbiota-gut-brain axis, the gut microbiota influences the onset and progression of depressive disorder primarily through the tryptophan metabolic pathway. Tryptophan, an essential amino acid in humans, is subject to dual regulation by intestinal microorganisms, which modulate its metabolic balance via inflammatory stimulation and microbial metabolite production. In depression, excessive activation of the kynurenine branch of tryptophan metabolism leads to the accumulation of proinflammatory and neurotoxic metabolites, thereby exacerbating neuroinflammation in the brain. Intervention studies indicate that the antidepressant-like effects of probiotics and traditional Chinese medicine are associated with remodeling of the gut microbiota, restoration of tryptophan metabolic balance, and alleviation of neuroinflammation. Furthermore, targeted inhibition of kynurenine 3-monooxygenase can mitigate neuroinflammation by regulating microglial activity, thus improving depressive-like behaviors. In summary, the metabolite-inflammation axis represents a central node in the interaction regulation between tryptophan metabolism and the microbiota-gut-brain axis. This provides a theoretical foundation for developing novel therapeutic strategies targeting depression through modulation of gut microbiota-mediated tryptophan metabolism.
Tryptophan/metabolism* ; Gastrointestinal Microbiome/physiology* ; Humans ; Depressive Disorder/microbiology* ; Probiotics/therapeutic use* ; Brain/metabolism* ; Kynurenine/metabolism* ; Metabolic Networks and Pathways ; Animals ; Medicine, Chinese Traditional

OBJECTIVES: Stress urinary incontinence (SUI) is a common condition among women that severely impairs quality of life. Pelvic floor proprioceptive training (PFPT) has attracted increasing attention for its potential to enhance pelvic floor muscle function and alleviate SUI symptoms. This study aims to observe and compare the clinical efficacy of PFPT combined with electroacupuncture, electrical stimulation, and biofeedback therapy versus conventional therapy consisting of electroacupuncture, electrical stimulation, and biofeedback alone in women with SUI, and to explore the role of PFPT in improving symptom and functional outcomes. METHODS: In this randomized controlled trial, 72 women with mild to moderate SUI were recruited from the Department of Rehabilitation Medicine at Third Xiangya Hospital, Central South University, between December 2021 and October 2023. Participants were randomly assigned to an experimental group (n=36) or a control group (n=36). Both groups received health education. The control group underwent electroacupuncture combined with electrical stimulation and biofeedback therapy, while the experimental group additionally received PFPT 3 times per week for 4 weeks. The primary outcome was assessed using the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF). Secondary outcomes included pelvic floor muscle strength, bladder neck mobility, and balance ability. The ICIQ-SF was reassessed at 1, 3, 6, and 12 months post-treatment. RESULTS: Both groups showed statistically significant improvements in all parameters after treatment (all P<0.05). However, there were no statistically significant differences between groups in most measures (all P>0.05). The experimental group demonstrated longer single-leg stance duration with eyes closed than the control group (left leg: P=0.026; right leg: P=0.006), with a significant increase from baseline (P<0.001). At 6 months post-treatment, the cure rate in the experimental group was significantly higher than that in the control group (P=0.037). CONCLUSIONS Conventional therapy effectively improves SUI symptoms, but adding PFPT provides notable additional benefits, including enhanced balance ability and sustained mid-term cure rates. These findings suggest that PFPT is a valuable adjunct to standard SUI management strategies.
Humans ; Female ; Urinary Incontinence, Stress/physiopathology* ; Pelvic Floor/physiopathology* ; Middle Aged ; Biofeedback, Psychology ; Adult ; Exercise Therapy/methods* ; Proprioception ; Electroacupuncture/methods* ; Quality of Life ; Electric Stimulation Therapy/methods* ; Treatment Outcome ; Combined Modality Therapy

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

Traditional Chinese medicine formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.
Artificial Intelligence ; Drugs, Chinese Herbal/pharmacokinetics* ; Humans ; Medicine, Chinese Traditional ; Animals

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery

Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
Artificial Intelligence ; Drug Discovery/methods* ; Humans ; Machine Learning ; Medicine, Chinese Traditional ; Small Molecule Libraries/chemistry*

Due to its synergistic effects and reduced side effects, combination therapy has become an important strategy for treating complex diseases. In traditional Chinese medicine (TCM), the "monarch, minister, assistant, envoy" compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas. However, due to the complex compositions and diverse mechanisms of action of TCM, it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods. Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM. Compared to resource-intensive traditional experimental methods, artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data, providing an efficient means for modeling and optimizing TCM combinations. This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships, thereby contributing to the modernization of TCM theory and methodological innovation.
Artificial Intelligence ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Drug Synergism