Objective To systematically analyze the spatiotemporal distribution characteristics and epidemiological trends of tracheal, bronchus, and lung cancer (TBL) disease burden attributed to air pollution globally and in China and the United States from 1990 to 2021, and to assess the patterns of disease burden changes from 2022 to 2031 based on predictive models, providing a scientific basis for formulating targeted TBL prevention and control strategies. Methods Based on the Global Burden of Disease (GBD) 2021 database, we analyzed the disease burden data of TBL attributed to air pollution globally and in China and the United States from 1990 to 2021. R Studio 4.3.2 software was used to analyze the corresponding trends and the Bayesian age-period-cohort (BAPC) prediction model was used to predict the status of the disease burden of TBL attributed to air pollution in the world and in China and the United States from 2022 to 2031. Results In 2021, China had the highest number of deaths and disability-adjusted life years attributed to air pollution (211 400 patients and 4.8947 million person-years), followed by the United States (6 000 patients and 124 300 person-years). The age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate (ASDR) of TBL due to air pollution in the world and in China and the United States showed a decreasing trend. From 1990 to 2021, the ASMR and ASDR of TBL in China due to air pollution were much higher than those in the United States and the global average. In terms of gender, from 1990 to 2021, the disease burden of male patients with TBL attributed to air pollution was much higher than that of female patients. The BAPC prediction model showed that from 2022 to 2031, the ASMR and ASDR of TBL attributed to air pollution showed an upward trend globally, while they showed a downward trend in China and the United States. Conclusion Over the past 30 years, the air pollution-related TBL disease burden in the world and in China and the United States has continued to decline, but China's disease burden is still significantly higher than the global average. The disease burden in men far exceeds that in women, with men and the population aged ≥50 years being high-risk groups. In the future, the global disease trend may reverse and rise, while China and the United States are expected to continuously decline. However, precise prevention and control for high-risk groups remains a key challenge.

Objective To integrate and analyze the disease burden of esophageal cancer caused by alcohol consumption in China from 1990 to 2019, along with the differences between genders, and predict the trends in disease burden changes from 2020 to 2029 to improve prevention and treatment strategies. Methods The disease burden of esophageal cancer caused by alcohol consumption in China from 1990 to 2019 was extracted and integrated from the 2019 Global Burden of Disease (GBD) database, and the corresponding trend was analyzed using the Joinpoint regression model with Joinpoint 4.9.1.0 software. The gray prediction model [GM (1, 1) ] was used to forecast the disease burden of alcohol-related esophageal cancer in China from 2020 to 2029. Results In 2019, the leading causes of esophageal cancer in China were tobacco, alcohol, high body mass index, and insufficient fruit and vegetable intake, accounting for the first to fifth positions in esophageal cancer deaths. From a gender perspective, in 2019, the death number and standardized mortality rate for males were 18.97 times and 20.00 times higher than for females, respectively. The disability-adjusted life years (DALYs) and standardized DALYs rate for males were 33.08 times and 24.78 times higher than those for females, respectively, indicating a heavier disease burden of alcohol-related esophageal cancer among Chinese males. From 1990 to 2019, the average annual percentage change (AAPC) in deaths and DALYs due to alcohol-related esophageal cancer in China was 2.08% and 1.63%, respectively, showing a continuous upward trend with statistical significance (P<0.05). The AAPC values for standardized mortality rate and standardized DALYs rate from 1990 to 2019 were –0.92% and –1.23%, respectively, showing a continuous downward trend with statistical significance (P<0.05). The population aged ≥55 years was the main group bearing the disease burden among all age groups from 1990 to 2019. The gray prediction model predicted that by 2029, the overall standardized mortality rate and standardized DALYs rate would decrease to 2.94/100 000and 67.94/100 000, with a greater decline in females than in males. Conclusion Over the past 30 years, the disease burden of alcohol-related esophageal cancer in China has slightly decreased. However, the reduction in disease burden is still lower compared to the overall decline in esophageal cancer burden, and the disease burden for males is significantly higher than for females. Focusing on prevention and treatment for males and the elderly population remains a major issue in addressing alcohol-related esophageal cancer in China.

Piezo1, a mechanosensitive nonselective cation channel characterized by multiple transmembrane domains, plays a critical role intransducing mechanical stimuli at the cellular membrane and participates in various physiological and pathological processes. Recent studies have established a significant association between Piezo1 and the occurrence and development of multiple ophthalmic disorders. Substantial evidence demonstrates that Piezo1 contributes to ocular disease progression by regulating fundamental cellular processes including proliferation, differentiation, apoptosis, and inflammatory responses, with particular relevance to glaucoma, corneal diseases, retinal disorders, and dry eye syndrome. Piezo1 has made rapid progress in ophthalmology, and has been established as an important mechanosensor in the eye, widely involved in intraocular pressure regulation, retinal function maintenance, corneal homeostasis and repair, and ocular development, and its dysfunction is closely related to the pathological mechanisms of many important blinding eye diseases. Consequently, Piezo1 is not only a key molecule for understanding ocular mechanobiology, but also represents a highly promising therapeutic target. Its study offers new perspectives for the development of novel therapeutic strategies against ocular diseases. This review systematically summarizes current research advances regarding Piezo1 channels in ophthalmology, analyzes their mechanistic involvement in disease processes, and evaluates their potential therapeutic value, thereby offering innovative perspectives for the clinical management of ocular diseases.

Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

Objective To analyze the virus subtypes, molecular evolutional and molecular transmission network features of the first confirmed mpox case in Huai’an City, Jiangsu Province, so as to provide insights into understanding of the transmission and evolution dynamics of mpox virus and formulation of the mpox control strategy in the city. Methods Genomic DNA was extracted from swabs of the first confirmed mpox case’s skin lesions in Huai’an City, and the amplicon sequencing library was constructed using the hypersensitive mpox virus whole-genome capture kit. High-throughput sequencing was performed using the GridION X5 nanopore sequencer on the Nanopore sequencing platform, and single nucleotide polymorphism (SNP) analysis of mpox virus genome sequences was performed following sequence assembly. In addition, phylogenetic analysis, genetic genealogy and molecular traceability analysis were performed. Results The virus whole genome sequence of the first confirmed mpox case was successfully obtained by high-throughput sequencing, with a full length of 197 182 bp, and was named hMpxV/China/JS-HA01/2023, which belonged to the clade IIb (West African clade) lineage B.1.3. Compared with the mpox virus reference sequence MPXV-M5312_HM12_Rivers-001 (GenBank accession number: NC_063383), the genome sequence of the Huai’an virus isolate carried 86 SNPs, including 40 SNPs in the coding region as non-synonymous mutations and 73 SNPs as nucleotide mutations caused by APOBEC3 (APOBEC3). Of the 97 mpox virus gene sequences, 79 sequences were included in the molecular network (81.44%), and the threshold of the genetic distance accessed to the network was 0.35/10⁵. There were two large molecular transmission clusters and one scattered cluster in the molecular transmission network of the mpox virus, andthehMpxV/China/JS-HA01/2023 sequence was located in the large cluster. The 97 gene sequences formed 92 haplotypes, including three shared haplotypes Hap_4, Hap_6 and Hap_38, and an exclusive haplotype Hap_1 of hMpxV/China/JS-HA01/2023 generated from mutation of the exclusive haplotype Hap_43, while the exclusive haplotype Hap_43 was generated from mutation of the shared haplotype Hap_38. Conclusions The whole genome sequence of the mpox virus isolated from the first confirmed mpox case in Huai’an City has been successfully obtained, and the molecular evolutionary and molecular transmission network characteristics of the virus have been preliminarily understood.

Objective: To investigate the sleep quality in patients with burning mouth syndrome (BMS) and its influencing factors, providing a basis for developing sleep intervention measures to reduce the impact of BMS symptoms. Methods: This study was reviewed and approved by the Medical Ethics Committee, and informed consent was obtained from patients. A total of 150 patients with BMS and 150 healthy volunteers were enrolled as subjects in this study. The Pittsburgh sleep quality index (PSQI) was used to assess the sleep quality of patients with BMS. Visual analog scale (VAS) was used to assess the degree of oral mucosal pain, generalized anxiety disorder 7-item scale (GAD-7) was used to assess the frequency of anxiety symptoms, and the patient health questionnaire depression questionnaire (PHQ-9) was used to assess the frequency of depression symptoms. Univariate analysis was performed to identify potential influencing factors affecting sleep quality in patients with BMS, and multiple linear regression analysis was employed to determine independent risk factors. Results: The PSQI score for patients with BMS was 7.61 ± 4.29, which was significantly higher than that of healthy controls (P = 0.016). In the PSQI subscale analysis, patients with BMS exhibited increased sleep latency, decreased sleep duration, and lower sleep efficiency compared to healthy controls (P<0.05). Patients with BMS and comorbid sleep difficulties had significantly higher scores on GAD-7 and PHQ-9 compared to the patients with BMS without sleep difficulties (P<0.001), but there was no significant difference in pain VAS scores between the two (P = 0.068). Multiple linear regression analysis revealed that longer disease duration (>6 months), the presence of systemic concomitant symptoms (such as headache and mental stress), and higher depression scores were identified as independent risk factors affecting sleep quality in patients with BMS. Conclusion For patients with BMS, long course of illness, presence of headaches, high mental stress, and depressive symptoms may be independent factors affecting their sleep quality.

BACKGROUND:Psoralen has a strong anti-osteoporotic activity and may have a restorative effect on chemotherapy-induced osteoporosis. OBJECTIVE:To explore the restorative effect of psoralen on the osteogenic differentiation of bone marrow mesenchymal stem cells in mice inhibited by cyclophosphamide and its mechanism. METHODS:C57BL/6 mouse bone marrow mesenchymal stem cells were isolated and cultured.Effect of psoralen on viability of bone marrow mesenchymal stem cells was detected by MTT assay.Osteogenic induction combined with alkaline phosphatase staining was used to determine the optimal dose of psoralen to restore the osteogenic differentiation of bone marrow mesenchymal stem cells inhibited by cyclophosphamide.The mRNA expression levels of Runx2,alkaline phosphatase,Osteocalcin,osteoprotegerin,and Wnt/β-catenin signaling pathway-related genes Wnt1,Wnt4,Wnt10b,β-catenin,and c-MYC were measured by RT-qPCR at different time points under the intervention with psoralen.The protein expression of osteogenic specific transcription factor Runx2 and Wnt/β-catenin signaling pathway related genes Active β-catenin,DKK1,c-MYC,and Cyclin D1 was determined by western blot assay at different time points under the intervention with psoralen. RESULTS AND CONCLUSION:(1)There was no significant effect of different concentrations of psoralen on the viability of bone marrow mesenchymal stem cells.The best recovery of the inhibition of osteogenic differentiation of bone marrow mesenchymal stem cells caused by cyclophosphamide was under the intervention of psoralen at a concentration of 200 μmol/L.(2)Psoralen reversed the reduction in osteogenic differentiation marker genes Runx2,alkaline phosphatase,Osteocalcin and osteoprotegerin mRNA expression and Runx2 protein expression in bone marrow mesenchymal stem cells caused by cyclophosphamide conditioned medium.(3)Psoralen reversed the decrease in Wnt/β-catenin pathway-related genes Wnt4,β-catenin,c-MYC mRNA and Active β-catenin,c-MYC,and Cyclin D1 protein expression and the increase in DKK1 protein expression in bone marrow mesenchymal stem cells caused by cyclophosphamide conditioned medium.(4)The results showed that cyclophosphamide inhibited osteogenic differentiation of bone marrow mesenchymal stem cells in mice,and psoralen had a restorative effect on it.The best intervention effect was achieved at a concentration of 200 μmol/L psoralen,and this protective effect might be related to the activation of Wnt4/β-catenin signaling pathway by psoralen.

Objective To utilize single-cell and peripheral blood transcriptomic data from 3D brain organoids,combined with machine learning,to analyze the role of mitochondrial autophagy genes in schizophrenia(SCZ).Methods By integrating two machine learning algorithms,we identified differentially expressed mitochondrial autophagy-related genes between schizophrenia patients and healthy controls using peripheral blood RNA sequencing data.The relationship between mitophagy gene,immune cells and inflammatory factors was further explored.Comprehensive single-cell analysis was used to explore the signaling pathways and specific transcription factors based on mitophagy genes.Results Using machine learning,seven key mitophagy genes expressed in schizophrenia patients were identified.Based on Mitoscore analysis,at the single-cell level,neurons with high mitochondrial autophagy activity(Mitohigh_Neuron)formed new interactions with endothelial cells via the SPP1 signaling pathway.Conclusion This study identified two subtypes of mitophagy and seven key mitophagy genes in schizophrenia,providing new insights into the pathogenesis of the disease.

Objective To address the personalized differences in motion gait between stroke patients and healthy older adults,as well as the issue of abnormal gait recognition,a deep learning fusion-based approach is proposed to effectively improve the accuracy of abnormal gait recognition.Methods A model fusing convolutional neural networks(CNN)and bidirectional long short-term memory networks(BiLSTM)was adopted,with the introduction of a residual network(ResNet).Unilateral ankle joint movement data at different walking speeds within a comfortable range were collected from healthy older adults and stroke patients.Signals from inertial sensors and electromyography sensors were used as inputs,while gait features were analyzed and gait differences between the two groups were compared.The effectiveness of the model was validated by comparing the classification performance of traditional deep learning models and CNN-ResNet-BiLSTM models with different layer combinations in terms of abnormal gait recognition accuracy.Results The CNN-ResNet-BiLSTM model,which introduced residual connectivity,performed excellently in abnormal gait recognition.Compared with traditional deep learning models such as the gated recurrent unit(GRU)and long short-term memory network(LSTM),its prediction accuracy was improved by 13.6％and 8.36％,respectively.Additionally,compared with other model combinations,this model achieved an overall accuracy of 97.78％.Conclusions The algorithm proposed in this study can be applied to stroke-related abnormal gait detection,providing technique support for the early diagnosis and precise monitoring of such diseases.

Magnetic resonance angiography(MRA)is a non-invasive imaging technique used to observe blood vessels.Quantitative analysis of MRA images enables visualization of vascular pathways,condition,and blood flow dynamics,which is essential for diagnosing vascular diseases such as vascular lesions,stenosis,and occlusions.Vessel segmentation serves as the fundamental basis for quantitative vascular analysis.However,the complex morphology of vessels,difficulties in labeling,and scarcity of accurate 3D vascular annotations pose significant challenges for MRA-based vessel segmentation.A strategy of selectively occluding vessels during model training is proposed to enhance the algorithm's capacity to capture the topological structure of blood vessels,thereby improving the continuity of vessel segmentation results.Additionally,a Refine network is incorporated to refine the binary segmentation results of the segmentation network,thereby further improving segmentation accuracy.Model training and testing are carried out using 42 cases of 3D MRA data from the public MIDAS dataset.For the test set,the 3D U-Net baseline model with vessel occlusion strategy shows a β0 Error of 1.2742±0.2103 and a β1 Error of 0.3393±0.0818,respectively,which are 0.1136 and 0.0280 lower than the baseline.The model integrating vessel occlusion strategy and Refine network achieves an average Dice score of 0.7105±0.0125,which is 0.0028 higher than the baseline.These results demonstrate that the proposed method effectively improves both vascular connectivity and segmentation accuracy.