Objective To analyze the distribution status of the end digits of standardized blood pressure measurement recordings in the clinic and the effectiveness of standardized blood pressure measurement for community hypertension screening. Methods The first visit blood pressure measurement data from the Community Health Service Center in Jing'an District, Shanghai from June 2023 to May 2024 were collected and analyzed. According to different measurement methods, the data were divided into two groups: standardized blood pressure measurement and conventional blood pressure measurement. SPSS 19.0 software was used for data analysis. The differences in the distribution balance of the end digits of blood pressure values and the detection rate of blood pressure elevation between the two different groups were analyzed. Results The frequency range of the end digits of blood pressure recorded values in the standardized pressure measurement group was 9.42% to 10.83%, and the detection rate of elevated blood pressure was 24.89%. The conventional pressure measurement group had a preference of the end digit ^"0^", and the detection rate of elevated blood pressure was only 2.12%. The results of multiple logistic regression analysis showed that gender, age, season, and different blood pressure measurement modes were all influencing factors for the detection rate of elevated blood pressure. Conclusion The standardized blood pressure measurement mode in the clinic is suitable for community hypertension screening and pressure measurement, with higher data quality than the conventional pressure measurement mode.

Objective To establish a stable and reliable sepsis model of rat after liver transplantation (LT) for clinical translational research and analyze its characteristics. Methods The "two-sleeve method" was used to establish the in situ LT model of SD rats, and the sepsis model was constructed through cecal ligation and puncture (CLP) at 3 d after the operation. SD rats were randomly divided into 3 groups: sham operation group (Sham group), LT group, and LT + CLP group, with 6 rats in each group. The changes in body weight, rectal temperature and survival rate were compared, and the sepsis score was used for evaluation. The levels of blood biochemical indicators [alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea (Urea), creatinine (Cr), creatine kinase (CK), lactate dehydrogenase (LDH)] and inflammatory factors [interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α] in each group were detected, and the pathological changes and cell apoptosis in different organs were observed. Results Compared with the Sham group, the body weight of the LT group and LT + CLP group decreased (all P<0.05). The rectal temperature of the LT + CLP group showed a continuous downward trend after the operation, the sepsis score increased sharply after the operation, and the survival rate dropped to 16.7%, and the differences between the Sham group, LT group and LT + CLP group were statistically significant (all P<0.05). The levels of ALT, AST, Urea, Cr, CK, LDH, and serum IL-1β, IL-6, IL-10 and TNF-α in the LT + CLP group were higher than those in the Sham group and LT group rats within 72 hours after the operation(all P<0.05). The pathological examination of the LT + CLP group showed severe tissue structure destruction, necrosis and infiltration of inflammatory cells in multiple organs, and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining showed an increased level of cell apoptosis in multiple organs. Conclusions Using liver transplantation combined with CLP, a stable animal model of liver transplantation infection is successfully established, which exhibits a high mortality rate, significant multi-organ damage and intense inflammatory response, providing an ideal animal model for transplantation infection research.

Background/Aims: Dysregulated cholesterol metabolism is a hallmark of hepatocellular carcinoma (HCC) that drives tumor initiation and progression. However, clinical targeting of cholesterol metabolism has yielded limited benefits due to stringent feedback in tumor cells. Identifying a central mediator capable of restoring cholesterol homeostasis within the cell’s intrinsically fine-tuned regulatory framework is urgently needed. Methods: We integrated a proteomic dataset from patients with cholesterol-dysregulated HCC into a global cholesterol metabolic regulatory network to identify potential therapeutic targets for disrupted cholesterol homeostasis. The prognostic significance of the candidate targets was further validated in an independent cohort through immunohistochemistry. Functional and mechanistic studies were conducted in vitro using HCC cell lines and in vivo using mouse models. The pharmacological efficacy of the candidate agent was evaluated in both subcutaneous and orthotopic HCC mouse models. Results: ER lipid raft-associated 1 (ERLIN1), a pivotal regulator of cholesterol metabolism reprogramming, was identified as an independent favorable prognostic indicator in HCC. ERLIN1 constrains HCC progression both in vitro and in vivo by stabilizing the INSIG1–SCAP–SREBP2 axis and maintaining the metabolic balance of intracellular cholesterol. Under hypoxia, impaired factor-inhibiting hypoxia-1-dependent hydroxylation of ASB11 at asparagine residues 90 and 92 enhances ASB11-mediated ERLIN1 degradation. Pharmacological targeting of this axis using zoledronic acid (ZoA) attenuated HCC progression by weakening the ASB11–ERLIN1 interaction and restoring cholesterol homeostasis. Conclusions ERLIN1 represents a druggable metabolic vulnerability in cholesterol-dysregulated HCC. Targeting the ASB11–ERLIN1 axis with the clinically approved ZoA reestablishes cholesterol homeostasis and offers a promising therapeutic strategy to overcome the current limitations of cholesterol-targeted HCC therapies.

Background The contamination of public baths with Legionella pneumophila contamination has become a growing public health concern in recent years. However, research on its association with bacterial community characteristics in water samples remains limited. The integration of 16S rRNA sequencing and random forest modeling provides a new approach to elucidate the bacterial community characteristics of public bath water and their association with Legionella pneumophila contamination. Objective To investigate the bacterial community structure and diversity of public bath water in Shanghai, explore the association between Legionella pneumophila contamination and bacterial community characteristics, and identify key bacterial genera associated with contamination, thereby providing a scientific basis for formulating hygiene management regulations for public bath water. Methods From February to March 2023, water samples were collected from ten public baths in Shanghai which were selected based on business scale, regional distribution, and functional differences. Water quality parameters were evaluated, and the samples were categorized into Legionella-positive and Legionella-negative groups based on the detection results of Legionella pneumophila. The bacterial community structure, α-diversity, and β-diversity were analyzed using 16S rRNA sequencing. Redundancy analysis (RDA) was employed to examine the relationship between physicochemical factors and bacterial community diversity. A random forest model was employed to identify key bacterial genera distinguishing the two groups, with the importance of genera being evaluated based on the mean decrease accuracy (MDA). Results The oxygen consumption in the Legionella-positive group was significantly lower than that in the Legionella-negative group (mean values: 1.85 mg·L⁻¹ vs. 6.81 mg·L⁻¹, P< 0.05), while no significant differences were observed in other physicochemical indicators. The sequencing results revealed a total of 27 bacterial phyla and 454 bacterial genera, with Proteobacteria (63.00%) being the dominant phylum. The dominant genera included Pelomonas (8.50%), Acidovorax (8.13%), Mycobacterium (7.93%), and Acinetobacter (6.59%). The α-diversity analysis indicated that bacterial community richness (Chao1 and ACE indices) was significantly higher in the Legionella-positive group than in the Legionella-negative group (P<0.01). The β-diversity analysis showed no significant difference in the bacterial community structure between the two groups (P>0.05). The RDA analysis demonstrated that the bacterial community diversity was positively correlated with pH and negatively correlated with oxygen consumption and free residual chlorine. The RDA1 and RDA2 explained 23.92% and 21.30% of the bacterial community diversity, respectively. The random forest model identified 20 key genera significantly influencing the microbial community distribution between the two groups, including unclassified_Bradyrhizobiaceae (MDA=2.42), Meiothermus (MDA=2.37), and Flavihumibacter (MDA=2.26). Conclusion The diversity of bacterial communities in public bath water is influenced by pH, oxygen consumption, and free residual chlorine. Samples contaminated with Legionella pneumophila exhibit greater microbial richness and contain characteristic key bacterial genera that contribute to community differences. Machine learning random forest technology helps identify these distinctive key bacterial genera. The findings provide a basis for carrying out risk early warning strategies in such settings.

During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.
Brain-Computer Interfaces ; Humans ; Space Flight ; Astronauts/psychology* ; Neurofeedback ; Cognition ; Electroencephalography ; Man-Machine Systems

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.apsb.2021.07.024.].

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

Liver fibrosis is a vital cause of morbidity in patients with liver diseases and developing novel anti-fibrotic drugs is imperative. Isovalerylspiramycin I (ISP I) as a major component of carrimycin applied to upper respiratory infections, was first found to possess anti-fibrotic potential. The present study aims to evaluate the functions and mechanisms of ISP I in protecting against liver fibrosis. According to our results, ISP I not only reduced the expressions of fibrogenic markers in LX-2 cells but also appeared great protective effects on liver injury and liver fibrosis in bile duct ligation (BDL) rats and carbon tetrachloride (CCl₄) mice. We proved that nucleotide-binding protein 2 (NUBP2) was the direct target of ISP I. ISP I through targeting NUBP2, increased the amount of vascular non-inflammatory molecule-1 (VNN1) on the cell membrane, which will inhibit oxidative stress and fibrosis. Simultaneously, the original carrimycin's protective effect on liver damage and fibrosis was verified. Therefore, our study provides potential agents for patients with liver fibrosis-related diseases, and the clear mechanism supports wide application in the clinic.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms