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/OBJECTIVES

The use of complementary and alternative medicine (CAM) has increased over the years. Although the incidence of CAM use among general and disease-specific groups has been researched, little is known about CAM use among Filipino dermatological patients. This study aims to determine the extent and nature of complementary medicine use among patients with dermatologic problems seen in this institution.

This is a descriptive, prospective study that made use of a researcher-created questionnaire to determine the prevalence of complementary and alternative medicine. Six months of research were undertaken at the Dermatology clinic of a tertiary hospital. It utilized convenience sampling technique consisting of patients who visited the Dermatology clinic for an in-person consultation.

Sixty-five percent of the participants had attempted at least one kind of CAM to treat their dermatological condition, with the majority of participants between the ages of 18 and 25 (25.3%). The most prevalent condition treated with CAM was allergies (36.7%) of unknown etiology, followed by fungal infection (17.7%) and eczema (11.4%). Herbal medicine (65.8%) was the most popular method among respondents, followed by folk medicine, which was primarily recommended by family/relatives. The majority of CAM users were influenced by others, and several were financially challenged. Most CAM users reported no or minimal disease improvement, and the majority do not recommend CAM to others.

This is the first study to investigate the use of complementary and alternative medicine in dermatologic diseases in general in the country. Board-certified dermatologist should keep an open mind towards patients who might seek out other type of treatment, either as an adjunct or an alternative, given the high prevalence of CAM users among dermatology patients.

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

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

Objective:To explore the impacts of acupuncture and moxibustion combined with ABCLOVE voice training on voice function and the levels of inflammatory factors in patients who were diagnosed with vocal cord polyp and received operation. Methods:A total of 96 cases who received operation on vocal cord polyp were randomly assigned into two groups. The control group was given ABCLOVE voice training for rehabilitation after the operation. The study group was given acupuncture and moxibustion combined with ABCLOVE voice training. The voice function and the levels of inflammatory factors of the two groups were compared. Results:The effective rate of the study group was higher than that of the control group（P<0.05）. The scores of main symptoms such as hoarseness, dry mouth, and thirst in the study group were lower than those in the control group（P<0.05）. After 4 weeks of treatment, the voice fundamental frequency（F0） of the study group was higher than that of the control group（P<0.05）, the maximum phonation time（MPT） was longer than that of the control group（P<0.05）, and the voice fundamental frequency perturbation value（Jitter） and voice amplitude perturbation value（Shimmer） were lower than those of the control group（P<0.05）. The levels of serum interleukin-6（IL-6） and tumor necrosis factor-α（TNF-α） were lower than those of the control group（P<0.05）. Conclusion:Acupuncture and moxibustion combined with ABCLOVE voice training can obviously alleviate hoarseness and other symptoms, improve voice function and reduce the level of inflammatory factors, thus is an effective treatment for patients who received operation on vocal cord polyp.
Humans ; Moxibustion ; Polyps/therapy* ; Vocal Cords ; Acupuncture Therapy ; Voice Training ; Female ; Male ; Adult ; Middle Aged ; Interleukin-6/blood* ; Tumor Necrosis Factor-alpha/blood* ; Young Adult ; Laryngeal Diseases/therapy*