Despite of the widespread use of hydrogen and methane breath test, the variability in testing protocols, gas measurement techniques, and interpretation criteria continues to challenge the reproducibility and comparability across centers, especially in the Asia-Pacific region. The Asian Neurogastroenterology and Motility Association hence presents the first Asian monograph guiding application and interpretation of breath test. The monograph was formulated according to the framework of indications, preparatory process, performance, and interpretation of results, as well as future direction for research.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and pathological brain changes. While aging is the primary risk factor, circadian rhythm disruption (CRD) is increasingly recognized as a central driver of AD pathology. CRD exacerbates oxidative stress, systemic inflammation, and gut microbiome dysbiosis, impairing sleep-wake cycles, disrupting metabolic homeostasis, and promoting neuroinflammation, ultimately accelerating disease progression. Oxidative stress, a key factor in neuronal damage, is both a cause and consequence of circadian misalignment, while mitochondrial dysfunction further amplifies oxidative damage, impairing synaptic function and cognitive stability. Additionally, gut microbiome dysbiosis contributes to neuroinflammatory processes, worsening neurodegeneration. Given these complex interactions, this review aims to elucidate the role of CRD in AD pathology and explore potential therapeutic interventions targeting circadian dysfunction. Specifically, it examines the efficacy of time-restricted feeding (TRF), a dietary strategy that aligns food intake with circadian rhythms. TRF has shown promise in restoring circadian function, reducing oxidative stress, improving mitochondrial health, and promoting gut microbiome diversity. By addressing CRD, TRF may offer a novel approach to mitigating AD pathologies. This review also identifies current research gaps and future directions for developing circadian-based interventions in AD prevention and treatment.

Background: According to the World Health Organization (WHO), 6.7 million people die annually due to air pollution caused by solid fuel use, with the majority of deaths resulting from respiratory diseases and cardiovascular conditions. In Mongolia, air pollution ranks as the fourth leading risk factor contributing to mortality, following hypertension, diabetes, and other major health risks. Although there have been numerous studies on outdoor air pollution in Mongolia, research linking indoor air pollution at the household level with the health status of residents remains limited. Aim: To compare indoor PM2.5 concentrations in households of Ulaanbaatar and Darkhan and examine their association with hypertension during the winter season. Materials and Methods: The study was conducted during November and December 2023, and January 2024, involving 240 households in Ulaanbaatar and Darkhan. Indoor PM2.5 concentrations were measured using Purple Air real-time sensors continuously for 24 hours over approximately one month. After measuring indoor air pollution, individuals aged 18–60 years living in the selected households were recruited based on specific inclusion criteria. Blood pressure was measured three times and the average value was recorded. Information on respiratory illnesses was collected through structured questionnaires. Statistical analysis was performed using STATA version 19.0. Results: A total of 241 households participated in the study, with 116 from Ulaanbaatar and 125 from Darkhan. Of the participants, 46.5% were male and 53.5% were female. In terms of housing type, 96 households (39.8%) lived in gers, 97 (40.2%) lived in stove-heated houses, and 48 (19.9%) lived in apartments. Among all participants, 66.0% (n=159) had hypertension and 34.0% (n=79) had normal blood pressure. Among participants aged over 40, 69.9–88.5% had hypertension, which is statistically significantly higher compared to younger individuals (p=0.0001). By body mass index, 75.3% (n=72) of overweight individuals and 78.4% (n=58) of obese participants had hypertension, showing a statistically significant difference compared to participants with normal weight (p=0.0001). The 24-hour average concentration of indoor PM2.5 was measured using the Purple Air device, and the levels in gers and stove-heated houses exceeded the limit set by the MNS 4585:2025 standard (37.5 µg/m³) Conclusion This study identified a relationship between environmental factors, such as air pollution and housing type, and the prevalence of hypertension. The indoor PM2.5 concentration in gers and stove-heated houses was above the standard limit, indicating a negative impact on the health of those residents. Furthermore, the high prevalence of hypertension among participants over the age of 40 and those who are overweight suggests a possible link to lifestyle and environmental conditions.

OBJECTIVE: This cross-sectional study assessed the methodological quality of systematic reviews (SRs) of Chinese herbal medicine (CHM) published in Chinese between Jan 2021 and Sep 2022. METHODS: Chinese language CHM SRs were identified through literature searches across 3 international and 4 Chinese databases. Methodological quality was appraised using A MeaSurement Tool to Assess systematic Reviews 2. Logistic regressions were used to explore associations between bibliographical characteristics and quality. RESULTS: Analyses of methodological quality found that among the 213 sampled SRs, 69.5% were of critically low quality, 30.5% were of low quality, and none achieved high or moderate quality. Common shortcomings included the failure to identify the studies excluded from the analysis, failure to disclose funding sources, and limited evaluation of the potential impact of bias on conclusions. Logistic regressions revealed that SRs led by corresponding authors affiliated with universities or academic institutions tended to be of lower quality than SRs led by authors affiliated with hospitals or clinical facilities. CONCLUSION Recent Chinese language CHM SRs exhibited limited methodological quality, making them unlikely to support the development of clinical practice guidelines. Urgent initiatives are needed to enhance training for researchers, peer-reviewers and editors involved in the preparation and publication of SRs. Adoption of Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines in Chinese language journals is crucial to improve the relevance of SRs for Chinese medicine development. Addressing deficiencies in methodology and reporting is essential for promoting evidence-based practices and informed clinical decisions in Chinese medicine. Please cite this article as: Jiang Y, Zhong CC, Wang BH, Xu SS, Ho FF, Kwong MH, Ho L, Zhou JHS, Lam KC, Liu JP, Zhang BT, Chung VCH. Methodological quality of systematic reviews on orally administered Chinese herbal medicine published in Chinese between 2021 and 2022: A cross-sectional study. J Integr Med. 2025; 23(5):492-501.
Cross-Sectional Studies ; Drugs, Chinese Herbal/administration & dosage* ; Systematic Reviews as Topic/standards* ; Humans ; China ; Administration, Oral ; Medicine, Chinese Traditional

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. Methods Patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. Results A total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.