To explore the correlation between MTHFR C677T gene polymorphism and hypertension, hyperhomocysteinemia(Hcy), and hyperlipidemia in the Tibetan population of Tibet.

Neuropathic pain, a debilitating condition caused by dysfunction of the somatosensory nervous system, remains difficult to treat due to limited understanding of its molecular mechanisms. Bioinformatics analysis identified cerebellin 2 (CBLN2) as highly enriched in human and murine proprioceptive and nociceptive neurons. We found that CBLN2 expression is persistently upregulated in dorsal root ganglia (DRG) following spinal nerve ligation (SNL) in mice. In addition, transcription factor SOX11 binds to 12 cis-regulatory elements within the Cbln2 promoter to enhance its transcription. SNL also induced SOX11 upregulation, with SOX11 and CBLN2 co-localized in nociceptive neurons. The siRNA-mediated knockdown of Sox11 or Cbln2 attenuated SNL-induced mechanical allodynia and thermal hyperalgesia. High-throughput sequencing of DRG following intrathecal injection of CBLN2 revealed widespread gene expression changes, including upregulation of numerous NF-κB downstream targets. Consistently, CBLN2 activated NF-κB signaling, and inhibition with pyrrolidine dithiocarbamate reduced CBLN2-induced pain hypersensitivity, proinflammatory cytokines and chemokines production, and neuronal hyperexcitability. Together, these findings identified the SOX11/CBLN2/NF-κB axis as a critical mediator of neuropathic pain and a promising target for therapeutic intervention.
Animals ; Neuralgia/metabolism* ; Ganglia, Spinal/metabolism* ; Up-Regulation ; Mice ; NF-kappa B/metabolism* ; SOXC Transcription Factors/genetics* ; Male ; Neuroinflammatory Diseases/metabolism* ; Mice, Inbred C57BL ; Nerve Tissue Proteins/genetics* ; Hyperalgesia/metabolism* ; Signal Transduction ; Spinal Nerves

OBJECTIVE: This study investigated the antidepression mechanisms of Xiaoyaosan (XYS), a classic Chinese prescription, from the perspective of energy metabolism in the brain and intestinal tissues. METHODS: Chronic unpredictable mild stress model-a classic depression rat model-was established. Effects of XYS on behaviors and gastrointestinal motility of depressed rats were investigated. Effects of XYS on energetic charge (EC), adenosine triphosphate-related enzymes, and key enzymes of energy metabolism in both hippocampus and jejunum tissues of depressed rats were investigated using high-performance liquid chromatography, biochemical analysis, and real-time quantitative polymerase chain reaction, respectively. Spearman correlation analysis was conducted to construct a correlation network of "behavior-brain energy metabolism-intestinal energy metabolism" of depression. RESULTS: XYS significantly reduced the abnormal behaviors that observed in depressed rats and increased the EC and the activity of Na⁺-K⁺-adenosine triphosphatase (ATPase) and Ca²⁺-Mg²⁺-ATPase in hippocampus and jejunum tissues of depressed rats. XYS restored the key energetic pathways that had been interrupted by depression, including glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation. Furthermore, XYS exhibited antidepressive effects in terms of regulating energy metabolism in tissues of both brain and intestine. CONCLUSION XYS significantly corrected the disturbances in EC and energy metabolism-related enzymes of both brain and intestinal tissues, alleviating both core and concomitant symptoms of depression. The current findings underscore the role of energy metabolism in the antidepressive activity of XYS, providing a fresh perspective on depression, and novel research strategies for revealing the mechanism of actions of traditional Chinese medicines on multi-site and multi-symptom diseases. Please cite this article as: Xiao MT, Wang SY, Wu XL, Zhao ZY, Wang HM, Liu HM, Qin XM, Liu XJ. Antidepressant mechanism of Xiaoyaosan: A perspective from energy metabolism of the brain and intestine. J Integr Med. 2025; 23(6):706-720.
Animals ; Energy Metabolism/drug effects* ; Antidepressive Agents/therapeutic use* ; Drugs, Chinese Herbal/therapeutic use* ; Brain/drug effects* ; Male ; Depression/metabolism* ; Rats ; Rats, Sprague-Dawley ; Intestines/drug effects* ; Hippocampus/drug effects*

OBJECTIVE: Observational studies have found associations between inflammatory bowel disease (IBD) and the risk of dementia, including Alzheimer's dementia (AD) and vascular dementia (VD); however, these findings are inconsistent. It remains unclear whether these associations are causal. METHODS: We conducted a meta-analysis by systematically searching for observational studies on the association between IBD and dementia. Mendelian randomization (MR) analysis based on summary genome-wide association studies (GWASs) was performed. Genetic correlation and Bayesian co-localization analyses were used to provide robust genetic evidence. RESULTS: Ten observational studies involving 80,565,688 participants were included in this meta-analysis. IBD was significantly associated with dementia (risk ratio [ RR] =1.36, 95% CI = 1.04-1.78; I ² = 84.8%) and VD ( RR = 2.60, 95% CI = 1.18-5.70; only one study), but not with AD ( RR = 2.00, 95% CI = 0.96-4.13; I ² = 99.8%). MR analyses did not supported significant causal associations of IBD with dementia (dementia: odds ratio [ OR] = 1.01, 95% CI = 0.98-1.03; AD: OR = 0.98, 95% CI = 0.95-1.01; VD: OR = 1.02, 95% CI = 0.97-1.07). In addition, genetic correlation and co-localization analyses did not reveal any genetic associations between IBD and dementia. CONCLUSION Our study did not provide genetic evidence for a causal association between IBD and dementia risk. The increased risk of dementia observed in observational studies may be attributed to unobserved confounding factors or detection bias.
Humans ; Mendelian Randomization Analysis ; Inflammatory Bowel Diseases/complications* ; Dementia/etiology* ; Observational Studies as Topic ; Genome-Wide Association Study

OBJECTIVE: Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection frequently develop central nervous system damage, yet the mechanisms driving this pathology remain unclear. This study investigated the primary pathways and key factors underlying brain tissue damage induced by the SARS-CoV-2 beta variant (lineage B.1.351). METHODS: K18-hACE2 and C57BL/6 mice were intranasally infected with the SARS-CoV-2 beta variant. Viral replication, pathological phenotypes, and brain transcriptomes were analyzed. Gene Ontology (GO) analysis was performed to identify altered pathways. Expression changes of host genes were verified using reverse transcription-quantitative polymerase chain reaction and Western blot. RESULTS: Pathological alterations were observed in the lungs of both mouse strains. However, only K18-hACE2 mice exhibited elevated viral RNA loads and infectious titers in the brain at 3 days post-infection, accompanied by neuropathological injury and weight loss. GO analysis of infected K18-hACE2 brain tissue revealed significant dysregulation of genes associated with innate immunity and antiviral defense responses, including type I interferons, pro-inflammatory cytokines, Toll-like receptor signaling components, and interferon-stimulated genes. Neuroinflammation was evident, alongside activation of apoptotic and pyroptotic pathways. Furthermore, altered neural cell marker expression suggested viral-induced neuroglial activation, resulting in caspase 4 and lipocalin 2 release and disruption of neuronal molecular networks. CONCLUSION These findings elucidate mechanisms of neuropathogenicity associated with the SARS-CoV-2 beta variant and highlight therapeutic targets to mitigate COVID-19-related neurological dysfunction.
Animals ; COVID-19/genetics* ; Mice ; Brain/metabolism* ; Apoptosis ; Mice, Inbred C57BL ; SARS-CoV-2/physiology* ; Pyroptosis ; Gene Expression Profiling ; Transcriptome ; Male ; Female

Objective: To investigate trends of incidence, mortality, and years of life lost (YLL) rate of lung cancer in cancer registration areas of Guizhou Province from 2017 to 2021, so as to provide references for formulating lung cancer prevention and control strategies and reducing the disease burden of lung cancer. Methods: The qualified lung cancer registration data from cancer registration areas of Guizhou Province from 2017 to 2021 were collected, the crude incidence and mortality of lung cancer were calculated by urban/rural areas, genders and ages. The standardized incidence and standardized mortality was calculated using the age structure of the standard population from the Fifth National Population Census in 2000. YLL was calculated using the standard life table from the Global Burden of Disease Study 2019. The disease burden of lung cancer was assessed using incidence, mortality, and YLL rate, and the trend in the disease burden of lung cancer from 2017 to 2021 was calculated using annual percent change (APC). Results : From 2017 to 2021, the crude incidence, standardized incidence, crude mortality, standardized mortality, YLL and YLL rate in Guizhou Province were 53.13/100 000, 37.58/100 000, 42.77/100 000, 29.44/100 000, 98.19 thousand person-years and 10.95‰, respectively. The standardized incidence and standardized mortality of lung cancer were higher in rural areas than in urban areas (39.45/100 000 vs. 34.23/100 000, 30.68/100 000 vs. 27.18/100 000). The standardized incidence and standardized mortality of lung cancer were higher in males than in females (49.34/100 000 vs. 26.47/100 000, 41.31/100 000 vs. 18.28/100 000). The crude incidence and crude mortality of lung cancer increased with age, peaking in the 80-<85 age group (360.84/100 000) and the ≥85 age group (414.85/100 000), respectively. From 2017 to 2021, the standardized incidence demonstrated downward trends in the total population, urban areas and males (APC=-6.590%, -5.829%, and -6.729%, all P<0.05). The standardized mortality demonstrated downward trends in urban areas and females (APC=-3.710% and -5.378%, both P<0.05). The YLL rate also showed downward trends in urban areas and females (APC=-3.957% and -3.631%, both P<0.05). Conclusions From 2017 to 2021, the overall disease burden of lung cancer in registration areas of Guizhou Province showed a decreasing trend. However, the disease burden remained relatively heavier in rural areas and males, with a relatively gradual change.

Objective To analyze the monitoring data of cardio-cerebrovascular diseases prevention and control system in Yichang in 2022, and to provide data support and experience for the precise prevention and treatment of cardio-cerebrovascular diseases. Methods Acute cardiovascular and cerebrovascular event data were collected from the Yichang Cardio-cerebrovascular Events Monitoring System from January 1, 2022 to December 31, 2022. Descriptive analysis was conducted for the data collected. Statistical analysis was performed using SPSS 20.0 software, and a chi-square test was used to analyze the count data. Results A total of 37,217 cases of cardio-cerebrovascular events were monitored in Yichang in 2022. The crude incidence and the standardized incidence were 983.84/100,000 and 541.55/100,000, respectively. The incidence in males was higher than females (554.93/100,000 vs 428.91/100,000，χ² =464.52，P＜0.05). The top three diseases were cerebral infarction, acute myocardial infarction, and cerebral hemorrhage. The incidence of events increased with age, and 79.80% of the cases were over 60 years old. The main onset time was from May to August. Conclusion The use of the cardio-cerebrovascular events monitoring system in Yichang and the implementation of ^“mandatory reporting card^” monitoring can timely obtain the epidemic characteristics of the diseases, provide support for the precise formulation of prevention and control strategies and measures, reduce underreporting rates, and improve the monitoring system, which is worthy of reference and promotion.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*