Aim To explore the mechanism by which the compound Chaijin Jieyu formula(CCJJY)regulates the TLR4/NLRP3 signaling pathway to inhibit central oxidative stress and improve hippocampal synaptic plasticity damage in depression.Methods SD rats were randomly divided into the control group,chronic unpredictable mild stress group,sleep deprivation group,chronic unpredictable mild stress combined with sleep deprivation group,positive drug group(venlafax-ine+melatonin),low-dose group of CCJJY,medium dose group of CCJJY,and high-dose group of CCJJY,with nine rats in each group.Except for the control group,a rat model of depression complicated with in-somnia was established using chronic unpredictable mild stress combined with sleep deprivation.Depres-sion-like and sleep behaviors in rats were evaluated through weight,food intake,water maze,and pento-barbital sodium tests.ELisa was used to detect ROS,AANAT,and HPLC-EC was used to detect 5-HT con-tent,while Western blot/RT-PCR was used to detect the expression of IL-1β,TLR4,NLRP3,PSD-95,and SYN related proteins and mRNA.HE and Golgic stai-ning were used to observe the pathological changes in the third ventricle,hippocampus,and neuronal synap-ses.Results Compared with the control group,the depression-like behaviors of the model group rats were significant.The expression of IL-1β,TLR4,and NL-RP3 in the hippocampus increased,while the expres-sion of PSD-95 and SYN decreased.Activation of NL-RP3 inflammasomes led to "sleeve like" pathological changes in the third ventricle,with hippocampal neu-rons undergoing apoptosis and significant damage to neuronal synaptic plasticity.Compared with the model group,after intervention with CCJJY,the expression of ROS,IL-1β,TLR4,and NLRP3 decreased,while the expression of AANAT,5-HT,PSD-95,and SYN in-creased.Pathological damage to the third ventricle and hippocampal neurons was repaired.Conclusion The CCJJY improves hippocampal synaptic plasticity dam-age in depression by regulating the TLR4/NLRP3 sig-naling pathway to inhibit central oxidative stress.

Objective:To investigate the intervention effect of five elements music of traditional Chinese medicine and western classical music on the sleep quality of stroke patients, and to compare the difference between the two, to provide a reference for the clinical care measures to improve the sleep quality of stroke patients.Methods:By adopting a randomized controlled trial, 75 stroke patients who were hospitalized in the rehabilitation hospital of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine from July 2022 to July 2023 were selected by convenience sampling method as the study subjects, and the patients were randomly divided the conventional treatment group, the five-element music group, and the classical music group according to the method of randomized numerical table with 25 cases in each group. On the basis of general treatment, the conventional treatment group took sleep health education, the five elements music group were given five-element music intervention based on conventional treatment, and the classical music group were given classical music intervention based on conventional treatment. The changes of Athens Insomnia Scale (AIS), Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) examination parameters in the 3 groups were compared.Results:There were 2 cases of shedding in the conventional treatment group, and 23 cases were finally included, 15 males and 8 females, aged (54.02 ± 7.80) years; there were no cases of shedding in the five-element music group, and 25 cases were finally included, 12 males and 13 females, aged (53.69 ± 6.02) years; and there was 1 case of shedding in the classical music group, and 24 cases were finally included, 10 males and 14 females, aged (52.34 ± 7.08) years. Comparison of AIS, PSQI and PSG scores among the 3 groups of patients before intervention showed no statistically significant differences (all P>0.05). After the intervention, the AIS scores and PSQI scores of the 3 groups were (9.48 ± 1.53) and (12.22 ± 2.94), (6.76 ± 1.36) and (7.64 ± 2.08), (7.46 ± 1.38) and (10.33 ± 2.82), respectively, and the differences were statistically significant ( F=23.21, 18.44, both P<0.05). PSG sleep structure parameters showed that the total sleep time, REM latency time and sleep efficiency of the patients in the five-element music group after the intervention were (399.89 ± 51.76) min, (136.26 ± 25.36) min, (78.87 ± 8.21)%, higher than (368.45 ± 47.88) min, (124.46 ± 26.25) min, (73.36 ± 7.86)% in the classical music group and (345.48 ± 38.69) min, (111.37 ± 23.23) min, (69.44 ± 7.88)% in the conventional treatment group, and the differences between three groups were statistically significant ( F=8.27, 5.93, and 8.49, all P<0.05); the sleep latency time, awakening time, and number of awakenings in the five-element music group were (28.86 ± 17.68) min, (54.37 ± 25.15) min, and (2.72 ± 1.19) times, respectively, which were lower than those in the classical music group (35.67 ± 16.99) min, (64.28 ± 29.34) min, and (3.67 ± 1.12) times and (42.38 ± 18.96) min, (78.38 ± 37.26) min, (4.87 ± 1.46) times in the conventional treatment group, and the differences between three groups were statistically significant ( F=3.51, 3.66, and 17.56, all P<0.05). The results of the PSG sleep progression showed that the duration of the N1 stage of sleep in the five-element music group after the intervention was (95.71 ± 15.23) min, which was higher than (83.20 ± 18.34) min in the classical music group and (80.93 ± 16.47) min in the conventional treatment group, and the difference between three groups was statistically significant ( F=5.53, P<0.01); the N3 stage sleep time and the sleep percentage of the five-element music group after the intervention were respectively (84.23 ± 20.98) min and (23.98 ± 5.89)%, which were higher than (65.33 ± 18.82) min and (18.34 ± 3.78)% in the classical music group and (45.87 ± 18.65) min and (15.03 ± 5.56)% in the conventional treatment group, and the differences between three groups were statistically significant ( F=23.08, 18.50, both P<0.05). Conclusions:Both five elements music and classical music can improve the sleep quality of stroke patients, and the effect of five elements music to improve sleep is more significant.

Objective Advances in synthetic DNA technology have made it much easier to fake human DNA samples.There are literature reports that fake human DNA can be synthesized by different methods and implanted in the field to confuse the investigation or mislead the trial.Therefore,distinguishing authentic human DNA from synthetic DNA and performing individual identification has become a critical scientific challenge.Methods We define a novel composite genetic marker(methylation-microhaplotype)by combining CpG sites stably hypermethylated or hypomethylated in natural human DNA and nearby immediately adjacent microhaplotype sites.A total of 19 locis were obtained according to the screening criteria,and a composite detection system for methylation-microhaplotypes was established using MPS technology.Random volunteer DNA samples were extracted and synthetic DNA samples were prepared based on whole genome amplification techniques.Population DNA samples were analyzed to evaluate forensic parameters and methylation variability of the methylation-microhaplotype markers.Comparative analyses of human and synthetic DNA were conducted to assess the markers'ability to discriminate between the two and to detect/type both components in mixed mixed samples.Results The composite detection system composed of 19 locis demonstrated high individual identification ability,achieving a cumulative individual identification probability of 0.999 999 999 996 86.12 hypermethylated locis and 7 hypomethylated locis had relatively stable methylation levels in 57 human DNA samples.According to the allele methylation rate(Ram)value,the system can effectively identify natural and synthetic DNA samples.Meanwhile,for mixed DNA samples,the presence of human and synthetic DNA samples can be found and genotyped.Conclusion Methylation-microhaplotype genetic markers,which can discover human DNA and synthetic DNA and can detect the presence and genotyping of them from mixed samples,is a potential useful tool for forensic DNA analysis.

Objective To investigate and compare the epidemiological characteristics of common respiratory viruses among influenza-like illness(ILI)and severe acute respiratory infection(SARI)cases in Huzhou,Zhejiang Province before and after the COVID-19 pandemic,so as to provide a basis for formulating and adjusting the prevention and control strategies for viral respiratory infectious diseases.Methods ILI and SARI cases at two influenza surveillance sentinel hospitals in Huzhou and had throat swab samples collected during Nov 2017 to Feb 2020(pre-COVID-19 pandemic period)and Dec 2022 to Apr 2024(post-COVID-19 mitigation phase)were selected as the participants.Seven common viral respiratory pathogens were tested,including influenza A virus(H1N1 and H3N2 subtypes),influenza B virus(Victoria lineage,FluB),respiratory syncytial virus(RSV),rhinovirus(HRV),adenovirus(ADV),and severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).The positive rates of respiratory pathogens before and after the COVID-19 pandemic were compared across different age groups and different time.Results A total of 7 948 ILI samples and 2 294 SARI samples were included.The overall positive rate of ILI samples increased from 33.6%to 47.1%,primarily due to the increase in influenza and COVID-19 infections;the overall positive rate of SARI samples decreased from 31.4%to 24.8%,mainly due to the reduction in HRV and ADV infections.During the post-COVID-19 mitigation phase,SARS-CoV-2(22.1%),H3N2(12.7%),and FluB(6.0%)were the primary pathogens in ILI samples,while RSV(7.1%),H3N2(5.3%),and HRV(4.5%)dominated in SARI samples.During the post-COVID-19 mitigation phase,the influenza virus circulation period was shortened.Before the COVID-19 pandemic,RSV was mainly detected in autumn and winter,while during the post-COVID-19 mitigation phase,out-of-season RSV epidemics were observed in spring and summer.Co-infection rate in ILI cases increased significantly in the post-COVID-19 mitigation phase,predominantly consisting of co-infections of COVID-19 and influenza A virus,while co-infection rate in SARI cases showed a decline.Conclusion We found important epidemiological changes in respiratory viruses in Huzhou during the post-COVID-19 mitigation phase compared to pre-COVID-19 period,including increased positive rates of influenza and COVID-19,and disruptions to the seasonal patterns of influenza and RSV.The prevention and control strategies should be adjusted in a timely manner based on the monitoring data.

Objective:To investigate the prevalence and severity of symptom burden among long-term survivors of oropharyngeal cancer after radiotherapy, to identify core symptom clusters, and to explore their correlation with quality of life.Methods:A previous retrospective study was conducted by the Cancer Hospital, Chinese Academy of Medical Sciences on patients with oropharyngeal cancer who underwent radiotherapy between January 2010 and December 2020. Patients who were still alive as of December 2023 were further followed and analyzed. From December 2023 to August 2024, symptom burden and quality of life were assessed using the Chinese version of the MD Anderson Symptom Inventory–Head and Neck Module (MDASI-HN) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ). Exploratory factor analysis (principal component analysis with Promax rotation) were used to identify symptom clusters. Spearman correlation analysis was performed to explore the relationship between total symptom cluster scores and standardized domain scores of quality of life. Multivariate linear regression analysis was further employed to determine the relationship between identified symptom clusters and overall quality of life.Results:A total of 273 patients were included, with a median follow-up duration of 6.2 years (range: 3.5-14.5 years) and a median age of 61 years (range: 27-88 years) at follow-up. The top 5 incidence rates of symptom reported by patients were mucus problems in the mouth or throat (147 cases, 53.8%), dental or gum issues (143 cases, 52.4%), xerostomia (140 cases, 51.3%), difficulty swallowing or chewing (95 cases, 34.8%), and taste disturbance (79 cases, 28.9%). Among them, xerostomia was the most serious symptom. The most frequently reported interference was impact on work (including household chores) (55 cases, 20.1%). Exploratory factor analysis identified 3 symptom clusters: fatigue-nausea cluster, eating-voice cluster, and xerostomia-sleep cluster, all of which were significantly correlated with lower overall quality of life of patients (all P<0.001). Conclusion:Long-term survivors of oropharyngeal cancer after radiotherapy experience substantial symptom burden. The fatigue-nausea, eating-voice, and xerostomia-sleep clusters are the core symptom clusters impacting quality of life.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective:To analyze the epidemiological characteristics and influencing factors of single-cigarette use and dual cigarette-cigar use in China.Methods:This study was a cross-sectional study that selected 85 638 urban and rural residents who met the inclusion criteria from the 2018 China Health Literacy Survey as research subjects. An analysis was conducted on 21 849 users of cigarettes and cigars among them. Due to the small number of individuals who exclusively used cigars (247 cases), the research subjects were divided into two categories: exclusive cigarette users and dual users of cigarettes and cigars. The groups were categorized by age (18-34 years, 35-54 years, ≥55 years), gender (male, female), education level (primary school and below, junior high school and high school, university and above) and annual household income (<20 000 yuan, 20 000-<80 000 yuan, ≥80 000 yuan) to compare the tobacco usage rate and conduct subgroup analyses for each subgroup. Multivariate logistic regression analysis was employed, incorporating general demographic characteristic information to explore the influencing factors of exclusive cigarette use and dual use of cigarettes and cigars, respectively.Results:The rate of exclusive cigarette use in our country was 24.3%, while the dual use rate of cigarettes and cigars was 0.9%. The exclusive cigarette use rate and the dual use rate of cigarettes and cigars among males were significantly higher than those among females (48.25% vs 2.48%, and 1.84% vs 0.06%) (both P<0.001). For males, the high-risk factors for exclusive cigarette use included living in urban areas ( OR: 1.37, 95% CI: 1.23-1.54), being Han ethnicity ( OR: 1.73, 95% CI: 1.51-1.98), and having an annual household income ≥20 000 yuan ( OR: 1.54, 95% CI: 1.38-1.82) while having a junior high school education or higher was a protective factor ( OR: 0.68, 95% CI: 0.52-0.90). Age≥35 years ( OR: 3.36, 95% CI: 2.62-4.32) and having a junior high school education or higher ( OR: 1.30, 95% CI: 1.02-1.67) were risk factors for dual use of cigarettes and cigars in males. Among females, living in urban areas ( OR: 1.53, 95% CI: 1.19-1.97) and being Han ethnicity ( OR: 5.96, 95% CI: 4.47-7.96) were risk factors for exclusive cigarette use, while having a university education or higher was a protective factor ( OR: 0.28, 95% CI: 0.18-0.42). However, for female dual use of cigarettes and cigars, no significant effects were observed for any demographic characteristics. Conclusions:The use rate of cigarettes alone in China is significantly higher than that of cigarette-cigar dual use, and the rates of cigarette use alone and cigarette-cigar dual use in men are significantly higher than those in women. Tobacco use is being affected by sociodemographic factors, among which place of residence, ethnicity and education level are the main influencing factors of cigarette use alone, and gender, age and education level are the main influencing factors of cigarette-cigar dual use.

Objective:To develop and externally validate a nomogram prediction model for assessing the risk of treatment dropout in allergic rhinitis (AR) patients undergoing sublingual immunotherapy (SLIT).Methods:Between February 2016 and December 2019, data from 358 and 259 AR patients undergoing SLIT were collected from Guizhou Provincial People′s Hospital and Huangshi Central Hospital, respectively. The data included general patient information, dust mite sIgE levels, allergen types, and 22 other clinical variables. Data from Guizhou Provincial People′s Hospital were used as the training set, while data from Huangshi Central Hospital were served as the external validation set. A multivariable Cox regression model was used to identify independent factors associated with SLIT dropout and to develop a nomogram prediction model.Results:Multivariate Cox regression analysis identified several significant factors influencing SLIT dropout, including dust mite sIgE levels (Grade Ⅱ-Ⅳ; HR=1.48, 95% CI: 1.16-1.88), presence of other allergic diseases ( HR=0.47, 95% CI: 0.37-0.61), Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) score ( HR=0.98, 95% CI: 0.97-1.00), WeChat management ( HR=0.77, 95% CI: 0.60-0.98), treatment efficacy ( HR=0.72, 95% CI: 0.56-0.92), age (5-17 years, HR=0.50, 95% CI: 0.36-0.71;≥60 years, HR=1.42, 95% CI: 1.08-1.87), household income (<5 000 CNY, HR=1.44, 95% CI: 1.09-1.90;>20 000 CNY, HR=0.66, 95% CI: 0.44-0.99), allergen types (single dust mite, HR=0.70, 95% CI: 0.49-0.93; and combined pollen or mold, HR=1.45, 95% CI: 1.02-2.04), and time to efficacy <3 months ( HR=0.73, 95% CI: 0.56-0.94), all P<0.05. At the third-year follow-up, the area under curve (AUC) for the nomogram model was 0.913 (95% CI: 0.881-0.943) in the training group and 0.886 (95% CI: 0.838-0.933) in the validation group. Calibration and decision curve analyses demonstrated the model′s consistency with actual dropout rates and clinical benefit in both groups. Additionally, a Brier score of 0.29 further confirmed the model′s predictive accuracy. Conclusion:We successfully develop a nomogram-based prediction model for SLIT dropout in AR patients, which could assist healthcare professionals in effectively identifying high-risk patients and facilitate the development of more personalized and timely treatment plans aimed at enhancing patient compliance.