Background A diverse cohort of patients and susceptible individuals congregate in healthcare facilities, where exposure to pathogenic microorganisms associated with respiratory infectious diseases constitutes a significant risk factor for cross-infection. Central air-conditioning ventilation systems improve some indoor environment indicators while exacerbating the risk of transmission of respiratory infectious diseases. Objective To investigate the distribution characteristics of microbial communities in the central air-conditioning ventilation systems of hospitals, providing a scientific basis for the selection of microbial indicators in hygiene standards for hospital central air-conditioning ventilation systems and for hospital risk early warning systems. Methods In October 2023, two central air-conditioning ventilation systems were selected from a Grade 3A hospital in Shanghai: one was an all-air air-conditioning system serving the waiting area on the ground floor, and the other was a fan coil plus fresh air system serving the outpatient area on the third floor. Samples from four different components of the ventilation systems—air outlets, filters, surface coolers, and condensate trays—were collected for high-throughput sequencing of the 16S rRNA gene to analyze bacterial communities. Alpha-diversity and beta-diversity analyses were performed to investigate the microbial community composition and diversity characteristics of the hospital central air-conditioning ventilation systems. Functional analysis was conducted to determine the relative abundance of bacterial functions in these systems.Results A total of 528 operational taxonomic units (OTUs) were identified, encompassing 20 bacterial phyla, 37 classes, 79 orders, 123 families, and 240 genera. The analysis revealed that the bacterial community was predominantly composed of Proteobacteria, Gemmatimonadates, Bacteroidetes, and Actinobacteria. The diversity analysis indicated that bacterial community richness and diversity were highest in the condensate trays, while no statistically significant differences (P > 0.05) were observed in the bacterial community composition among the air outlets, filters, and surface coolers. The functional analysis showed that the bacterial communities in the central air-conditioning ventilation systems primarily exhibited chemoheterotrophic, oxidative energy-dependent heterotrophic, and ureolytic functional characteristics. Conclusion The dominance of Proteobacteria suggests that this phylum exhibits strong adaptability in the central air-conditioning ventilation systems, possibly related to its ability to survive and reproduce under varying environmental conditions. The diversity analysis indicates that the condensate tray is a critical area for bacterial proliferation in the central air-conditioning ventilation systems. The similarity in environmental conditions among the air outlets, filters, and surface coolers result in similar bacterial community structures. The functional analysis reveals that the bacterial communities possess robust energy conversion and metabolic capabilities, potentially contributing to processes such as organic matter decomposition and nitrogen cycling within the central air-conditioning ventilation systems.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

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

[This corrects the article DOI: 10.1016/j.jpha.2023.08.006.].

Objective To investigate the expression of calumenin(CALU)in gastric cancer and its effect on metastasis and invasion of gastric cancer,and analyze its relationship with the prognosis of gastric cancer patients.Methods The Cancer Genome Atlas(TCGA)database was used to analyze the expression level of CALU in gastric cancer and its impact on patient prognosis.A total of 102 pairs of gastric cancer and paracancerous tissue samples were collected from 189 gastric cancer patients who underwent partial gastrectomy in First Affiliated Hospital of Army Medical University from January 2018 to December 2022.The expression of CALU in gastric cancer and paracancerous tissues was detected by immunohistochemical assay,and the relationship of its expression with clinicopathological parameters was statistically analyzed.After gastric cancer cells with CALU knockdown and overexpression were constructed,and the efficiencies of knockdown and overexpression were evaluated by Western blotting as well as RT-qPCR.Transwell assay was applied to determine the effect of CALU on the migration and invasion abilities of gastric cancer cells.Results Bioinformation analysis found that CALU was significantly highly expressed in gastric cancer tissues(P<0.05),and its expression level was negatively correlated with the prognosis of patients(P<0.05).Immunohistochemical results showed that the expression level of CALU was obviously highly in gastric cancer tissues than the paracancerous tissues(P<0.01),and its level was positively correlated with the depth of infiltration(P<0.01),lymph node metastasis(P<0.01),and TNM stage(P<0.05).Statistical analysis revealed that the clinical data of 102 patients showed that CALU expression was positively correlated with the TNM stage(P=0.021)and T stage(P<0.001)and N stage(P=0.028).CALU knockdown significantly inhibited the migration and invasion abilities of gastric cancer cells(P<0.01),while over-expression obtained the opposite results.Conclusion CALU is highly expressed in gastric cancer tissues and promotes metastasis and invasion of gastric cancer and thus leads to poor prognosis in patients.

This paper Summarizes the experience of national renowned Chinese medical practitioner professor YANG Jun in treating lumbar pain with acupuncture and moxibustion.In the view of professor YANG Jun,the etiology of lumbar pain is various and the pathogensis is complex,of which mainly related to be invaded by external pathogen,meridians obstructing blockage,kidney deficiency due to elderly,less of moistening due to kidney essence deficiency,sprain caused by fall and strike,and blood stasis obstructing meridians,and the syndrome differentiation of lumbar pain usually involves cold-damp obstructing blockage,kidney essence deficiency,and blood stasis blocking collaterals.The waist is the residence of kidney,kidney meridian runs through the spine and connecting it to the kidney,the bladder meridian locates on both sides of the spine and connecting it to the kidney,the circulation of governor vessel(GV)to the kidney,so the pathogensis of lumbar pain is closely related to kidney,bladder meridian,governor vessel.The basic pathogensis of lumbar pain is meridian qiand blood stasis stagnation,or the meridian less of moistening due to kidney essence deficiency,both of which cause pain.The main therapeutic principle for lumbar pain should be focusing on mind-regulating and meridians-dredging,cold-dispersing and damp-removing,kidney-boosting and blood-activating,as well as collateral-unblocking and pain-relieving,combining the lumbosacral spinal tri-needling,regulating the bladder meridian with moxibustion and electro-acupuncture,thus to achieve the optimal quantitative and effective state.

Objective: To analyze the impact of persistent obesity on their lung function, so as to offer insights for implementing intervention measures to increase lung function in obese school age children. Methods: A total of 335 children from the Sheyang Mini Birth Cohort established in 2009 in Yancheng City, Jiangsu Province, who participated in the follow up at the ages of 7 years (2016) and 10 years (2019), were selected as the study participants. Physical measurements including height, weight, and lung function were recorded. According to the World Health Organization standard, that is, gender and age specific to correct the body mass index to calculate the body mass index Z score, was used to evaluate the obesity status of children at the age of 7 and 10. Children were divided into four groups, including sustained non obesity group, restored obesity group, newly classified obesity group, and persistent obesity group. Meanwhile, the lung function prediction equations recommended by the Global Lung Function Initiative were used to standardize the lung function indexes of children. Pulmonary function differences among these groups were examined, and the relationship between childhood obesity and pulmonary function was longitudinally analyzed using generalized estimating equations. Results: The prevalence of obesity were 9.0% and 16.1% at the age of 7 and 10 years, respectively. The proportion of both newly classified and persistent obesity group were 8.1%, respectively. The forced expiratory volume in one second (FEV 1) and forced vital capacity (FVC) were (1 269.90±202.70) and (1 415.70±230.00) mL, respectively, at the age of 7 years. FEV 1 and FVC at the age of 10 years were (1 440.80±403.20) and (1 555.60±517.60) mL, respectively. Cross sectional analysis at age 7 showed that forced expiratory flow at 75% vital capacity (FEF 75 ) ( β=-0.52, 95%CI =-0.96--0.07) and maximal mid expiratary flow (MMEF) ( β=-0.45, 95%CI =-0.89--0.00) were significantly lower in obese children compared to their non obese peers ( P < 0.05). Longitudinal analysis indicated that obese children had lower levels of lung pulmonary function, with a statistically significant difference in FEV 1 ( β=-0.44, 95%CI=-0.85--0.02, P <0.05). There was no significant difference among the various obesity groups ( P >0.05), while gender stratified results revealed significant reductions in FEV 1/FVC in newly classified obese girls at age 10 years ( β=-1.76, 95%CI =-3.13--0.38) and in MMEF in persistently obese girls at age 10 years ( β=-1.44, 95%CI = -2.79- -0.09) ( P <0.05). Conclusion Obesity may contribute to reduced lung function levels in school aged children, with newly classified and persistent obesity having more pronounced effects on lung function in girls.

Objective To investigate the expression of PLCD3 mRNA in the synovium of osteoarthritis(OA)and its relationship with immune cell infiltration.Methods Based on the differentially expressed genes of OA found in the previous study,the expression of phospholipase Cδ3(PLCD3)mRNA was detected by col-lecting synovial samples from OA group and control group.CIBERSORT algorithm was used to analyze the infiltration pattern of immune cells in OA group and control group,and the correlation between PLCD3 and infiltrating immune cells was further analyzed.Results Compared with the control group,the relative expres-sion level of PLCD3 mRNA was significantly increased in synovial samples of OA group(P<0.05).The pro-portions of B cells naive,NK cells activated,M2 macrophages and mast cells activated in synovial tissues of OA group were relatively high(P<0.05).PLCD3 was positively correlated with the proportion of these four immune cells(P<0.05).Conclusion PLCD3 may be a key biomarker for the diagnosis of OA,which may be involved in the pathogenesis of OA by interacting with infiltrating immune cells.

Objective:To analyze the methylation characteristics of the lymphocyte-specific protein-tyrosine kinase (LCK) promoter region in the peripheral blood circulation of rheumatoid arthritis (RA) patients and its correlation with clinical indicators.Methods:Targeted methylation sequencing was used to compare the methylation levels of 7 CpG sites in the LCK promoter region in the peripheral blood of RA patients with healthy controls (HC) and osteoarthritis (OA) patients. Correlation analysis and ROC curve construction were performed with clinical information.Results:Non-parametric tests revealed that compared with HC [0.53(0.50, 0.57)] and OA patients [0.59(0.54, 0.62), H=47.17, P<0.001], RA patients [0.63(0.59, 0.68)] exhibited an overall increase in methylation levels. Simultaneously, when compared with the HC group [0.38(0.35, 0.41), 0.59(0.55, 0.63), 0.60(0.55, 0.64), 0.59(0.55, 0.63), 0.58(0.53, 0.62), 0.45(0.43, 0.49), 0.57(0.54, 0.61)], the RA group [0.46(0.42, 0.49), 0.70(0.65, 0.75), 0.70(0.66, 0.76), 0.70(0.65, 0.75), 0.69(0.64, 0.74), 0.55(0.51, 0.59), 0.68(0.63, 0.73)] showed a significant elevation in methylation levels at CpG sites cg05350315_60, cg05350315_80, cg05350315_95, cg05350315_101, cg05350315_104, cg05350315_128, and cg05350315_142, with statistically significant differences ( Z=-5.63, -5.89, -5.91, -5.89, -5.98, -5.95, -5.95, all P<0.001). Compared with the OA group [0.65(0.59, 0.69), 0.65(0.60, 0.69), 0.64(0.58, 0.68), 0.50(0.45, 0.54), 0.63(0.58, 0.67)], the RA group [0.70(0.66, 0.76), 0.70(0.65, 0.75), 0.69(0.64, 0.74), 0.55(0.51, 0.59), 0.68(0.63, 0.73)] exhibited a significant increase in methylation levels at CpG sites cg05350315_95, cg05350315_101, cg05350315_104, cg05350315_128, and cg05350315_142, with statistically significant differences ( Z=-3.56, -3.52, -3.60, -3.67, -3.62; P=0.036, 0.042, 0.031, 0.030, 0.030). Furthermore, Pearson correlation coefficient analysis revealed a positive correlation between the overall methylation level in this region and C-reactive protein (CRP) ( r=0.19, P=0.004) and erythrocyte sedimentation rate ( r=0.14, P=0.035). The overall methylation level of the LCK promoter region in the CRP (low) group [0.63 (0.58, 0.68)] was higher than that in the CRP (high) group [0.65(0.61, 0.70)], with statistically significant differences ( Z=2.60, P=0.009). Finally, by constru-cting a ROC curve, the discriminatory efficacy of peripheral blood LCK promoter region methylation levels for identifying RA patients, especially seronegative RA patients, from HC and OA groups was validated, with an AUC value of 0.78 (95% CI: 0.63, 0.93). Conclusion:This study provides insights into the methylation status and methylation haplotype patterns of the LCK promoter region in the peripheral blood of RA patients. The overall methylation level in this region is positively correlated with the level of inflammation and can be used to differentiate seronegative RA patients from the HC and OA patients.