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.
.
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.].

convalescents, and to screen for broad-spectrum neutralizing antibodies against the SARS-CoV-2 RBD. Methods Using biotinylated RBD as a molecular probe, flow cytometry was employed to perform single-cell sorting of B cells from peripheral blood mononuclear cells (PBMCs) of convalescents. The obtained B cells were lysed and subjected to reverse transcription, followed by nested PCR amplification of the heavy and light chains of antibodies was conducted using random primers. The amplified products were cloned into corresponding expression vectors, and the respective matched heavy-light chain plasmids were co-transfected into 293F cells for expression. Monoclonal antibodies were then purified using Protein A column chromatography. Neutralization experiments were conducted with the wild-type (WT) pseudovirus, and antibodies with IC50<0.1 μg/mL were selected for further testing of neutralizing breadth and potency against the wild-type (WT), Beta variant (B.1.351), Delta variant (B.1.617.2), and currently prevalent pseudovirus strains (XBB, BA.5, BF.7). Results A total of 21 RBD-specific monoclonal B cells were obtained from two recovered patients, resulting in the isolation of 13 pairs of antibody light/heavy chains. Nine antibodies were successfully expressed, with P1-A1, P1-B6, and P1-B9 exhibiting IC50 values below 0.1 μg/mL against the pseudovirus of the wild-type strain (WT). Specifically, P1-B6 effectively neutralized the wild-type strain (WT), Beta variant (B.1.351), and Delta variant (B.1.617.2), with IC50 values reaching 0.01 μg/mL. P1-B9 demonstrated effective neutralization against the wild-type strain (WT), Beta variant (B.1.351), Delta variant (B.1.617.2), and Gamma variant (P.1) pseudoviruses, with IC50 values of 0.42 μg/mL, 0.63 μg/mL, 0.28 μg/mL, and 2.50 μg/mL, respectively. Additionally, P1-B6 exhibited good neutralization against BA.5 and BF.7 pseudoviruses, with IC50 values of 0.06 μg/mL and 0.09 μg/mL, respectively. Conclusions Infection with the SARS-CoV-2 WT strain can induce the generation of neutralizing antibodies with broad-spectrum activity. Generating these broadly neutralizing antibodies does not require an excessively high somatic hypermutation. The obtained antibodies can be used as candidates for SARS-CoV-2 diagnosis and prevention.

Objective Exploring the effect of Percutaneous angular vertebroplasty(PCVP)on functional recovery in elderly patients with osteoporotic vertebral compression fractures(OVCF).Methods Retrospective selection of clinical data from 108 OVCF patients admitted to our hospital from August 2020 to December 2022.According to the different surgical methods,52 cases were divided into a control group(percutaneous vertebroplasty)and an observation group(percutaneous vertebroplasty)with 56 cases;Compare the recovery of vertebral body function,imaging related parameters,functional impairment and lumbar function,surgical related conditions,gait,and complications between the two groups.Results The intraoperative blood loss in the observation group was less than that in the control group[(10.65±3.52)ml vs.(13.11±3.66)ml,P＜0.05],the number of intraoperative fluoroscopy was less than that of control group[(14.21±4.27)vs.(17.04±4.25),P＜0.05],there was no statistically significant difference in the total clinical effective rate between the observation group of 89.28％and the control group of 86.53％(P＞0.05).After 2 months of surgery,the imaging related parameters,functional impairment,lumbar function,surgical and gait related conditions in the control group were better than those in the control group(P＜0.05).There was no statistically significant difference in the incidence of incision infection,nerve root injury,and recurrent fractures between the two groups(P＞0.05),but the leakage rate of bone cement in the observation group was lower than that in the control group(P＜0.05).Conclusion Both surgeries can effectively improve the level of lumbar vertebral function in OVCF patients.PVCP has advantages in increasing the height of the anterior and posterior edges of the vertebral body,reducing the Cobb angle,and relatively fewer intraoperative fluoroscopy times and bleeding.The incidence of bone cement leakage is lower,making it more safe.

Objective:To analyze the clinical characteristics, imaging, myopathology and outcomes of patients with COVID-19 related autoimmune myopathy.Methods:The clinical features, serum creatine kinase (CK), myositis antibodies, muscle magnetic resonance imaging, myopathology and therapy of 5 patients with COVID-19 related autoimmune myopathy diagnosed in Peking University First Hospital from December 2022 to April 2023 were collected. The effects of the therapy after a short term follow up were analyzed.Results:Among the 5 patients, there were 3 males and 2 females, with onset age of 42-86 years. All patients presented with proximal muscle weakness in the recovery term of COVID-19. Myalgia was noted in 3 cases, dysphagia in 1, skin damage in 2, interstitial lung disease in 1. The serum CK of the 5 patients was 1 663-16 000 IU/L, 1 patient had anti-3-hydroxy-3-methylglutaryl-CoA reductase autoantibodies and 1 patient had anti-signal recognition particle autoantibodies. The electromyography showed myogenic lesions in all patients. Muscle magnetic resonance imaging showed diffuse muscle edema in all patients, myofascial edema in 3 and subcutaneous-tissue edema in 3. The muscle biopsies in 4 patients revealed necrotic myopathy,with high P62 expression in muscle fibers. The electromicroscopy of 2 patients revealed vacuolated mitochondria and intranuclear tubulofilamentous inclusions in muscle fibers. Four patients were treated with glucocorticoids, of whom 2 patients combined with intravenous immunoglobulin, tacrolimus or cyclophosphamide. One case had close monitoring without drug therapy. They showed significant improvement, but the CK was still abnormal in 4 patients.Conclusions:COVID-19 leads to immune mediated myopathy. The manifestation of patients is characterized by proximal predominant weakness and high creatine kinase level. Muscle magnetic resonance imaging shows diffuse muscle edema. The muscle biopsies reveal necrotic myopathy. The effectiveness of immunosuppression needs to be further studied.

A major impedance to neuronal regeneration after peripheral nerve injury(PNI)is the activation of various programmed cell death mechanisms in the dorsal root ganglion.Ferroptosis is a form of pro-grammed cell death distinguished by imbalance in iron and thiol metabolism,leading to lethal lipid peroxidation.However,the molecular mechanisms of ferroptosis in the context of PNI and nerve regeneration remain unclear.Ferroportin(Fpn),the only known mammalian nonheme iron export protein,plays a pivotal part in inhibiting ferroptosis by maintaining intracellular iron homeostasis.Here,we explored in vitro and in vivo the involvement of Fpn in neuronal ferroptosis.We first delineated that reactive oxygen species at the injury site induces neuronal ferroptosis by increasing intracellular iron via accelerated UBA52-driven ubiquitination and degradation of Fpn,and stimulation of lipid peroxidation.Early administration of the potent arterial vasodilator,hydralazine(HYD),decreases the ubiquitination of Fpn after PNI by binding to UBA52,leading to suppression of neuronal cell death and significant ac-celeration of axon regeneration and motor function recovery.HYD targeting of ferroptosis is a promising strategy for clinical management of PNI.

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.