ObjectiveTo analyze the infection status of main respiratory pathogens in influenza-like illness (ILI) cases in Anji County, Huzhou City, Zhejiang Province, and to provide a reference for the prevention, diagnosis, and treatment of respiratory infections. MethodsThroat swab samples were collected from 520 ILI cases in an influenza sentinel surveillance hospital in Anji County of Zhejiang Province from December 2023 to November 2024. Multiplex real-time fluorescence quantitative polymerase chain reaction (mRT-PCR) was used to detect 18 pathogens and their subtypes, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (Flu A), influenza A (H1N1) virus, influenza A (H3N2) virus, influenza B virus (Flu B), influenza B virus Victoria lineage (BV), influenza B virus Yamagata lineage (BY), coronavirus (CoV), human parainfluenza virus (HPIV), respiratory syncytial virus (RSV), human metapneumovirus (HMPV), adenovirus (ADV), human bocavirus (HBoV), enterovirus (EV), rhinovirus (RV), Mycoplasma pneumoniae (MP), Chlamydia pneumoniae (CP), and Streptococcus pneumoniae (SP). ResultsThe overall positivity rate of pathogens in 520 samples was 33.65%, among which the detection rates of Flu (9.14%), ADV (7.50%), SARS-CoV-2 (6.15%), and EV (3.65%) were relatively high. There were statistically significant differences in the overall positivity rate of pathogens by age and season (all P<0.05). The highest overall positivity rate was observed in the 5‒14 years old group (42.77%), and the overall positivity rate in winter (53.08%) was significantly higher than that in other seasons. ConclusionFrom 2023 to 2024, the main respiratory pathogens detected in ILI cases in Anji County were Flu, ADV, SARS-CoV-2, and EV. The epidemic characteristics showed age and seasonal specificity, so it is necessary to strengthen prevention and control for high-risk populations and epidemic seasons in a targeted manner.

ObjectiveTo investigate the optimal termination time for acupuncture in treating patients with acute peripheral facial paralysis and its cost-effectiveness. MethodsA total of 120 eligible patients with acute-stage peri-pheral facial paralysis were randomly assigned to either the mild dysfunction termination group and the complete recovery termination group， with 60 patients in each group. Both groups received the standard acupuncture treatment protocol. Treatment in the mild dysfunction termination group was terminated when the Sunnybrook facial grade scale （SFGS） score first reached or exceeded 83 points， while that in the complete recovery termination group was terminated when the SFGS score first reached or exceeded 95 points. Assessments were conducted before treatment， 6 and 12 months after onset. SFGS， facial disability index （FDI） including physical function （FDIp） and social function （FDIs）， self-rating anxiety scale （SAS）， and self-rating depression scale （SDS） scores were assessed before treatment， and 6 and 12 months after onset. Any acupuncture-related adverse events during treatment were recorded for safety evaluation. Treatment sessions and medical costs including direct costs， indirect costs， insurance coverage， total societal costs， and patient out-of-pocket expenses were also recorded， and an economic evaluation was conducted including cost-effectiveness ratio （CER） and incremental cost-effectiveness ratio （ICER）. ResultsUltimately， 56 patients in the mild dysfunction termination group and 55 in the complete recovery termination group completed the follow-up. At 6 and 12 months after onset， SFGS and FDIp scores in both groups improved significantly while FDIs， SAS and SDS scores decreased （P＜0.05）. Comparison of scores between groups 6 months and 12 months after onset showed no statistically significant differences （P＞0.05）. During the trial， the incidence of adverse events was 13.3% （8/60） in the mild dysfunction termination group and 18.3% （11/60） in the complete recovery termination group， with no statistically significant difference （P＞0.05）. The number of treatment sessions， total social costs， and out-of-pocket expenses in the mild dysfunction termination group were significantly lower than those in the complete recovery termination group （P＜0.05）. The CER of the mild dysfunction termination group in SFGS， FDIp， FDIs， SAS， and SDS scores was lower than that of the complete recovery termination group. The ICER analysis showed that continuing treatment until full recovery incurred an additional cost of 573.30 CNY/point in SFGS improvement， whereas 1-point improvement in FDIp， FDIs， SAS， and SDS required 21,355.25 CNY， 1779.60 CNY， 3713.96 CNY， and 2755.52 CNY， respectively. ConclusionFor acupuncture in treating acute peripheral facial palsy， terminating treatment when mild dysfunction is achieved yields long-term efficacy comparable to that of continuing treatment until complete recovery， while significantly reducing medical costs and socioeconomic burden.

Objective: To explore the effect of different obesity indicators in predicting cardiovascular and cerebrovascular diseases (CVD) risk among patients with type 2 diabetes mellitus (T2DM), so as to provide the evidence for the early identification of CVD risk among T2DM patients. Methods: The patients with T2DM under community management in Qingpu District, Shanghai Municipality were selected as the study subjects in January 2025. Basic information such as gender, age, and blood glucose control status were collected through the Shanghai Chronic Disease Information Management System, while history of CVD were obtained from residents' electronic health records and the Shanghai Disease Control Information Platform. Obesity was assessed using body mass index (BMI), waist circumference (WC), BMI combined with WC, waist-to-height ratio (WHtR), and triglyceride (TG) combined with WC indicators. The association between obesity and CVD was analyzed using multivariable logistic regression models. The predictive effect of each obesity indicators for CVD was evaluated using the area under the receiver operating characteristic curve (AUC). Results: A total of 4 367 patients with T2DM were included, including 2 121 males (48.57%) and 2 246 females (51.43%). The average age was (68.71±8.05) years. The prevalence of CVD was 44.49%. Multivariable logistic regression analysis showed that after adjusting for age, education level, history of hypertension, duration of T2DM, use of glucose-lowering medications, renal function, and blood glucose control status, obese T2DM patients had a 389.4% increased risk of CVD compared to those with normal BMI; centrally obese T2DM patients had a 100.4% increased risk compared to those with normal WC; T2DM patients with isolated general obesity and compound obesity had 161.0% and 241.1% increased risks of CVD, respectively, compared to those with normal BMI and WC; centrally obese T2DM patients had a 100.4% increased risk compared to those with normal WHtR; T2DM patients with normal TG-high WC and high TG-high WC phenotypes had 83.1% and 68.8% increased risks of CVD, respectively, compared to those with normal TG and normal WC (all P<0.05). BMI had the highest AUC, at 0.714, with sensitivity and specificity of 0.675 and 0.642, respectively. This was followed by BMI combined with WC, which had an AUC of 0.707, with sensitivity and specificity of 0.635 and 0.679, respectively. Conclusions Obesity defined by BMI, WC, BMI combined with WC, WHtR, and TG combined with WC increases the risk of CVD among patients with T2DM. BMI and BMI combined with WC have better predictive effect in predicting CVD risk among patients with T2DM, and can be used as the primary obesity indicators for CVD risk screening.

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

To explore the effect and mechanism of sodium propionate and mixed short-chain fatty acids on colitis induced by Citrobacter rodentium (C.r.) in mice. Mice were induced by oral gavage of C.r. The C.r. growth monitoring, histopathological analysis, qPCR analysis, intestinal permeability test and flow cytometry was used to study the effects of sodium propionate and mixed short-chain fatty acids on intestinal infection. The results showed that sodium propionate could inhibit the growth of C.r. more effectively than mixed short-chain fatty acids. Results of animal experiments showed that sodium propionate significantly reduced the weight loss and intestinal bacterial output in mice. Meanwhile, compared with mixed short chain fatty acids, sodium propionate effectively alleviated the pathological manifestations of colonic inflammatory infiltration, destruction of epithelial cell structure and decrease of goblet cell caused by C.r. infection, also increased the levels of antimicrobial peptides like interleukin-17 (IL-17) and regenerated islet derived protein 3γ (Reg3γ). In addition, sodium propionate decreased intestinal permeability better than mixed short-chain fatty acids, and sodium propionate significantly induced T helper cells 17 (Th17) and regulatory T cells (Treg) differentiation. The results showed that sodium propionate significantly alleviated colitis induced by C.r. infection compared with mixed short-chain fatty acids, which may be related to its inhibition of C.r. growth and enhancement of intestinal anti-infective function. The expected results can provide a safer and effective treatment strategy and scientific basis for colitis.

Electrical impedance tomography (EIT) is a technique that uses an array of electrodes to deliver safe stimulating currents and measures the boundary voltages between adjacent electrode pairs in the array in sequence. Subsequently, it reconstructs the impedance distribution in all or part of the tissue using reconstruction algorithms to achieve structural and functional imaging. Lung EIT technology features continuity, being radiation-free and non-invasive, and it can be used for real-time dynamic monitoring of the lungs in critically ill patients. This paper introduces the basic principles of lung EIT, analyzes the research progress and existing problems of the technology from the perspectives of hardware systems, imaging algorithms, and clinical applications (such as lung ventilation, lung perfusion, and lung function assessment), and discusses the development direction to provide ideas for expanding the clinical application of lung EIT.
Electric Impedance ; Humans ; Tomography/methods* ; Lung Diseases/therapy* ; Algorithms

Due to its synergistic effects and reduced side effects, combination therapy has become an important strategy for treating complex diseases. In traditional Chinese medicine (TCM), the "monarch, minister, assistant, envoy" compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas. However, due to the complex compositions and diverse mechanisms of action of TCM, it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods. Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM. Compared to resource-intensive traditional experimental methods, artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data, providing an efficient means for modeling and optimizing TCM combinations. This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships, thereby contributing to the modernization of TCM theory and methodological innovation.
Artificial Intelligence ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Drug Synergism

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Dendritic cells (DCs) serve as the primary antigen-presenting cells in autoimmune diseases, like rheumatoid arthritis (RA), and exhibit distinct signaling profiles due to antigenic diversity. Type II collagen (CII) has been recognized as an RA-specific antigen; however, little is known about CII-stimulated DCs, limiting the development of RA-specific therapeutic interventions. In this study, we show that CII-stimulated DCs display a preferential gene expression profile associated with migration, offering a new perspective for targeting DC migration in RA treatment. Then, saikosaponin D (SSD) was identified as a compound capable of blocking CII-induced DC migration and effectively ameliorating arthritis. Optineurin (OPTN) is further revealed as a potential SSD target, with Optn deletion impairing CII-pulsed DC migration without affecting maturation. Function analyses uncover that OPTN prevents the proteasomal transport and ubiquitin-dependent degradation of C-C chemokine receptor 7 (CCR7), a pivotal chemokine receptor in DC migration. Optn-deficient DCs exhibit reduced CCR7 expression, leading to slower migration in CII-surrounded environment, thus alleviating arthritis progression. Our findings underscore the significance of antigen-specific DC activation in RA and suggest OPTN is a crucial regulator of CII-specific DC migration. OPTN emerges as a promising drug target for RA, potentially offering significant value for the therapeutic management of RA.

Monotropein is a compound classified into iridoid which is found in herbaceous plants Morindae officinalis. It possesses anti-inflammatory, antioxidant, and anti-osteoarthritic activities. Previous study indicates that monotropein may have the potential to combat cardiovascular disease, although the related mechanism remains unclear. In this study, we constructed the model of atherosclerosis by oxidized low density lipoprotein-induced vascular smooth muscle cells and LDLR –/–mice given high-fat diet to investigate the effects of monotropein on atherosclerosis.Our results showed that monotropein treatment significantly reduced the area of atherosclerotic plaques and necrotic cores in mice, inhibited the proliferation and migration of vascular smooth muscle cells, and reduced inflammatory responses and oxidative stress, which in turn alleviated atherosclerosis. In addition, we found that monotropein reduced the expression levels of P-NF-κB and P-AP-1. In conclusion, our data suggest that monotropein inhibited the proliferation and migration of vascular smooth muscle cells by mediating the activity of NF-κB, AP-1, reducing the level of inflammation and oxidative stress, and thus resisting the development of atherosclerosis. These findings demonstrate the efficacious therapeutic impact of monotropein on atherosclerosis and elucidate its specific target.