ObjectiveTo investigate the current situation of mutual recognition for ethical review outcomes in multicenter clinical research across 10 contracting medical institutions in Guangdong Province， analyze existing issues and propose improvement recommendations， thereby promoting the standardized management of mutual recognition for ethical review outcomes in medical institutions. MethodsData from 381 multicenter clinical studies conducted in these 10 medical institutions from January 24 to October 31， 2024， were collected. Text visualization was performed using Python and the WordCloud library， and statistical analyses were conducted with SPSS 25.0. ResultsOf the 381 studies investigated， industry-sponsored clinical trials （ISTs） accounted for 51.71%， while investigator-initiated clinical trials （IITs） constituted 48.29%. The proportions of ethical committees serving as primary reviewers and collaborative reviewers were 33.33% and 66.67%， respectively. The confirmation methods of mutual recognition outcomes were primarily expedited reviews （50.66%） and meeting reviews （49.34%）， and no cases of “direct confirmation” were found. The Chi-square test revealed statistically significant differences in review confirmation methods based on project type （χ²=14.851， P<0.001） and ethics committee role （χ²= 69.435， P<0.001）. The frequency distribution trend of the contingency table showed that IST projects and primary ethics committees preferred to employ meeting review （58.88% and 79.53% respectively， both higher than the average level of 49.34%）， while IIT projects and the collaborative ethics committees more frequently utilized expedited review （60.87% and 65.75% respectively， both higher than the average level of 50.66%）. ConclusionThe confirmation methods of mutual recognition for ethical review outcomes in multicenter clinical research are significantly associated with the role of the ethics committee and the type of project. It is recommended to improve management systems， enhance information construction and personnel training， as well as clarify mutual recognition responsibilities and strengthen supervision. This aims to ensure review quality while improving mutual recognition efficiency， thereby safeguarding the rights and interests of research participants.

ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

ObjectiveTo describe the prevalence and dynamic pain characteristics of dysmenorrhea among female medical students through a menstrual diary-based observational study. MethodsFrom April to June 2021, female students were recruited from a medical college in Shanghai to complete menstrual diaries and record dysmenorrhea-related characteristics for two menstrual cycles. Dysmenorrhea severity was assessed using the visual analogue scale (VAS) (no pain: 0; mild: 1‒3; moderate: 4‒6; severe: 7‒10). A total of 323 diaries were collected, of which 303 were 93.81% valid (valid response rate: 93.81%), covering 596 complete menstrual cycles. Among them, 293 participants completed diaries for two cycles and were included in the analysis of persistent dysmenorrhea. Period, point, and persistent prevalence of dysmenorrhea were calculated using the number of participants as the denominator. Paired comparisons were performed using Cochran’s Q test and Bonferroni-adjusted McNemar tests. Using menstrual cycles with dysmenorrhea as the unit of analysis, the distributions of pain onset, peak timing, and pain duration across different severities were compared using the χ² tests or Fisher’s precision probability tests. ResultsAmong 303 female college students, 269 experienced dysmenorrhea during the first recorded cycle, and 288 had dysmenorrhea in any cycle. Among 596 complete menstrual cycles recorded, 527 were associated with dysmenorrhea (88.42%), and among 293 students who completed 2 complete recorded cycles, 239 had persistent dysmenorrhea. The period, point, and persistent prevalence of dysmenorrhea among female college students were 95.05% (288/303), 88.78% (269/303), and 81.57% (239/293), respectively (Q=61.61, P<0.001). The prevalence of persistently severe dysmenorrhea was 5.12% (15/293). Among the 527 dysmenorrheic cycles, there were statistically significant differences in the distribution of pain onset time (P=0.003) and pain duration (P<0.001) among different degrees of dysmenorrhea; mild dysmenorrhea mostly started on the first day of menstruation (52.83%), and moderate dysmenorrhea mostly started before menstruation (50.19%). Pain duration increased with severity; the proportions of cycles with pain lasting ≥4 days were 13.20%, 43.03%, and 51.56% for mild, moderate, and severe dysmenorrhea cycles, respectively. For all severity groups, mean pain scores peaked on the first day of menstruation and then decreased day by day to a stable level. ConclusionDysmenorrhea is highly prevalent among female medical students, and differences exist across pain severity levels in onset time, duration, and dynamic changes in pain. Identification and health guidance for those with persistently moderate-to-severe dysmenorrhea should be strengthened, and medical evaluation should be conducted when necessary.

Objective: To investigate the sleep quality in patients with burning mouth syndrome (BMS) and its influencing factors, providing a basis for developing sleep intervention measures to reduce the impact of BMS symptoms. Methods: This study was reviewed and approved by the Medical Ethics Committee, and informed consent was obtained from patients. A total of 150 patients with BMS and 150 healthy volunteers were enrolled as subjects in this study. The Pittsburgh sleep quality index (PSQI) was used to assess the sleep quality of patients with BMS. Visual analog scale (VAS) was used to assess the degree of oral mucosal pain, generalized anxiety disorder 7-item scale (GAD-7) was used to assess the frequency of anxiety symptoms, and the patient health questionnaire depression questionnaire (PHQ-9) was used to assess the frequency of depression symptoms. Univariate analysis was performed to identify potential influencing factors affecting sleep quality in patients with BMS, and multiple linear regression analysis was employed to determine independent risk factors. Results: The PSQI score for patients with BMS was 7.61 ± 4.29, which was significantly higher than that of healthy controls (P = 0.016). In the PSQI subscale analysis, patients with BMS exhibited increased sleep latency, decreased sleep duration, and lower sleep efficiency compared to healthy controls (P<0.05). Patients with BMS and comorbid sleep difficulties had significantly higher scores on GAD-7 and PHQ-9 compared to the patients with BMS without sleep difficulties (P<0.001), but there was no significant difference in pain VAS scores between the two (P = 0.068). Multiple linear regression analysis revealed that longer disease duration (>6 months), the presence of systemic concomitant symptoms (such as headache and mental stress), and higher depression scores were identified as independent risk factors affecting sleep quality in patients with BMS. Conclusion For patients with BMS, long course of illness, presence of headaches, high mental stress, and depressive symptoms may be independent factors affecting their sleep quality.

Multiple system atrophy (MSA) is a rare neurodegenerative disease with complex clinical manifestations, presenting substantial challenges in clinical diagnosis and treatment. Its symptoms and the eight extraordinary meridians are potentially correlated; therefore, this article explores the association between MSA symptom clusters and the eight extraordinary meridians based on their circulation and physiological functions, as well as their treatment strategies. The progression from deficiency to damage in the eight extraordinary meridians aligns with the core pathogenesis of MSA, which is characterized by "the continuous accumulation of impacts from the vital qi deficiency leading to eventual damage". Liver and kidney deficiency and the emptiness of the eight extraordinary meridians are required for the onset of MSA; the stagnation of qi deficiency and the gradual damage to the eight extraordinary meridians are the key stages in the prolonged progression of MSA. The disease often begins with the involvement of the yin and yang qiao mai, governor vessel, thoroughfare vessel, and conception vessel before progressing to multiple meridian involvements, ultimately affecting all eight extraordinary meridians simultaneously. The treatment approach emphasizes that "the direct method may be used for joining battle, but indirect method will be needed in order to secure victory" and focuses on "eliminate pathogenic factors and reinforce healthy qi". Distinguishing the extraordinary meridians and focusing on the primary symptoms are pivotal to improving efficacy. Clinical treatment is aimed at the target, and tailored treatment based on careful clinical observation ensures precision in targeting the disease using the eight extraordinary meridians as the framework and core symptoms as the specific focus. Additionally, combining acupuncture, daoyin therapy, and other method may help prolong survival. This article classifies clinical manifestations based on the theory of the eight extraordinary meridians and explores treatment.

ObjectiveTo investigate the awareness and clinical practice of guidelines for the prevention and treatment of chronic hepatitis B （2022 edition） among clinicians. MethodsFrom July 19 to December 31， 2024， a self-designed electronic questionnaire was distributed via the WeChat mini program to collect related data from 1 588 clinicians nationwide， including their awareness and practice based on 18 questions regarding testing and referral， diagnosis and treatment， and follow-up. ResultsAmong all respondents， only 350 clinicians correctly understood all the updated key points of antiviral indications and treatment for special populations in the 2022 edition of guidelines for the prevention and treatment of chronic hepatitis B， with an overall awareness rate of 22.0%. Only 20% ‍—‍ ‍40% of the patients with positive HBV DNA and an age of >30 years receive antiviral therapy， while 80% ‍—‍ ‍100% of the patients with positive HBV DNA and a family history of hepatitis B cirrhosis or hepatocellular carcinoma receive antiviral therapy. The median follow-up rates at 1 year， 3 years， and 5 years were 67.5% 57.5% and 47.5%，respectively， showing a trend of gradual reduction， which might be associated with the influencing factors such as insufficient time for follow-up management by clinicians， insufficient awareness of the disease among patients， and poor adherence to follow-up. ConclusionThere is a gap between the awareness and practice of guidelines for the prevention and treatment of chronic hepatitis B （2022 edition） among clinicians. It is recommended to further strengthen training and focus on the whole process of “detection， diagnosis， treatment， and management” for patients with chronic hepatitis B in healthcare institutions， in order to promote the implementation of the guidelines.

Objective:To investigate the potential utility of the lymphocyte to monocyte ratio (LMR) and its modified index, albumin-LMR product, as predictive biomarkers for disease activity and presence of lupus nephritis in systemic lupus erythematosus (SLE).Methods:A total of 264 patients with newly diagnosed SLE who were treated at Henan Provincial People′s Hospital between December 2016 and September 2022 were included in this study. Their clinical data were subsequently collected for analysis. Patients were classified into non-active disease group (SLEDAI<5, n=55) and active disease group(SLEDAI≥5, n=209) based on the SLE SLEDAI. The Mann-Whitney U test was employed to compare the differences in clinical parameter levels, LMR and albumin-LMR product between the two groups. Additionally, the active disease group was further stratified into mild(SLEDAI 5~9, n=86), moderate(SLEDAI 10~14, n=96) and severe (SLEDAI≥15, n=27) subgroups to assess differences in LMR and albumin-LMR product. Further more patients were stratified into two groups based on renal involvement: those without lupus nephritis(non-LN) and those with lupus nephritis (LN). For non-parametric comparisons, the Mann-Whitney U test was used for intergroup comparisons between two groups, while the Kruskal-Wallis H test was applied for comparisons among three groups. If the Kruskal-Wallis H test revealed statistically significant differences ( P<0.05), pairwise comparisons were performed using the Mann-Whitney U test, and the significance level was adjusted using the Bonferroni method to account for multiple testing.Correlations between LMR, albumin-LMR product, and disease activity indicators were analyzed using Spearman′s correlation. The diagnostic value of LMR and albumin-LMR product for SLE activity was evaluated using the receiver operating characteristic (ROC) curves. Results:Both LMR and albumin-LMR product were significantly lower in active disease group compared to the non-active group {LMR:3.56 (2.15, 5.00) vs. 5.68 (3.89, 7.00); albumin-LMR product [93.21 (59.50, 143.98)g/L] vs. [187.89 (137.67, 260.90)]g/L, Z=-5.68, -7.05, P<0.001 for all}. Further subgroup analysis revealed that LMR and albumin-LMR product levels in severe, moderate, and mild active disease were also significantly decreased compared to the non-active disease group {LMR:3.83(1.78, 5.09)、3.09(2.06, 4.90)、3.65(2.45, 5.03) vs. 5.68(3.89, 7.00); albumin-LMR product: [95.69(66.57, 121.61)]g/L、[79.82(49.02, 126.91)]g/L、[104.73(69.21, 169.01)]g/L vs. [187.89(137.67, 260.90)]g/L, H=34.27, 58.29, P<0.001 for all}. A significant disparity in the levels of LMR and albumin-LMR product was detected between the non-LN and LN, with statistical significance ( Z=-3.44, P=0.001 and Z=-7.06, P<0.001). Correlation analysis indicated that LMR negatively correlated with SLEDAI( r=-0.31), urea( r=-0.29), creatinine ( r=-0.28) and 24-hour urinary protein level ( r=-0.27), all P<0.001, with no significant correlation to complement C3 or C4. Albumin-LMR product showed stronger negative correlations with SLEDAI ( r=-0.44), urea ( r=-0.40), creatinine ( r=-0.37), and 24-hour urinary protein ( r=-0.55), all P<0.001, and a positive correlation with complement C3 ( r=0.18, P=0.004). The areas under the ROC curves for LMR and LMR combined with complement C3 were 0.749 and 0.795, respectively, while for albumin-LMR product and its combination with complement C3, they were 0.809 and 0.833, indicating superior diagnostic efficacy for the modified albumin-LMR product. Conclusion:LMR and albumin-LMR product levels are significantly associated with SLE disease activity and may serve as potential biomarkers for assessing SLE activity and the degree of lupus nephritis.

Objective:To screen bile acid-related characteristic genes in IgA nephropathy (IgAN) based on the feature gene selection algorithm in the machine learning method, aiming to exploring the molecular biological mechanisms and biomarkers of IgAN.Methods:The gene expression data and sample grouping information of GSE93798, GSE116626 and GSE35487 were downloaded from the Gene Expression Omnibus (GEO). Bile acid-related gene sequences were obtained from the Molecular Signatures Database (MSigDB). R language was used to identify differentially expressed genes between IgAN samples and healthy control samples. Candidate genes were obtained by intersecting differentially expressed genes and bile acid-related genes. The least absolute shrinkage and selection operator (LASSO) algorithm in machine learning was used to screen the feature genes in the candidate genes as biomarkers, and the feature genes in the training set and validation set were analyzed by the rate of change index. Receiver operating characteristic curve (ROC) method was used to evaluate the diagnostic value of identified bile acid related characteristic genes for IgAN. Gene set enrichment analysis (GSEA) was used to analyze the Spearman correlation between the characteristic genes and all other genes and their related metabolic pathways. The expression of disease-characteristic genes in the kidney tissues of IgAN rats was validated by real-time PCR.Results:Gene expression information from kidney tissue samples of 20 IgAN cases and 22 healthy controls were obtained from GEO database. A total of 204 bile acid-related genes including 24 pathways were obtained from MSigDB. The results of gene differential expression analysis showed that 333 genes in the kidney tissues of IgAN patients were differentially expressed compared with those of healthy controls, including 102 up-regulated genes and 231 down-regulated genes, among which 12 differentially expressed genes were related to bile acid genes, as follows: NR1H4,SLC23A1, ALDH8A1, FABP1, ALB, SLC27A2, DIO1, CYP8B1, BBOX1, PIPOX, AKR1C1 and SLC10A2. Five characteristic genes ( NR1H4, SLC23A1, FABP1, ALB and AKR1C1) were screened by LASSO regression algorithm.ROC analysis results showed that in GSE93798 cohort genes, the AUC of NR1H4, SLC23A1, FABP1 and ALB genes with differential expression was >0.95 respectively in diagnosing IgAN, and that of AKR1C1 genes with differential expression was >0.85 in diagnosing IgAN. The gene expression data of SLC23A1 in GSE35487 cohort was missing. ROC analysis results of other four genes showed that the AUC of differential expression of ALB gene for IgAN was >0.95 respectively, that of NR1H4 gene was >0.70, and that of both FABP1 and AKR1C1 gene was >0.60. In the GSE116626 cohort genes, the AUC of five disease characteristic genes ( NR1H4, SLC23A1, FABP1, ALB, AKR1C1) for diagnosing IgAN was >0.60, respectively. These results suggested that 5 characteristic genes have certain distinguishing ability between IgAN group and control group. GSEA results were displayed that the characteristic genes were related to butyric acid metabolism, propionic acid metabolism, arginine and proline metabolism, valine leucine and isoleucine degradation, fatty acid metabolism, etc. These results suggested that five characteristic genes might be related to IgAN through the above metabolic mechanisms. The verification results of five bile acid characteristic genes in the rat model of IgAN in the kidney tissue showed that the expressions of four genes, NR1H4, SLC23A1, FABP1 and ALB, were higher than those of the control group, and there was no statistical significance in the expression of AKR1C1 gene between the two groups. Conclusions:The expression of bile acid-related characteristic genes is abnormal in the kidney tissue of IgAN patients. Four bile acid-related differentially expressed genes, NR1H4, SLC23A1, FABP1 and ALB, are expected to be biomarkers for non-invasive diagnosis and therapeutic targets .

Objective To develop a machine learning-based risk prediction model for postoperative acute kidney injury(AKI)and a model for mortality in obese patients admitted to intensive care unit(ICU)in order to improve early warning and prognostic evaluation to support clinical decision-making.Methods Data of obese postoperative ICU patients were retrospectively retrieved from the MIMIC-Ⅳ and eICU databases for statistical analysis.Ultimately,2 520 patients(670 from MIMIC-Ⅳ and 1 850 from eICU databases)were included to build the risk prediction models for AKI and mortality.The data included demographic information,vital signs,laboratory findings,surgical types,comorbidities,and medication use.After data cleaning and preprocessing,Boruta feature selection was applied,followed by the construction of prediction models using 7 machine learning algorithms,that is,Gradient Boosting Machine(GBM),Generalized Linear Model(GLM),k-Nearest Neighbors(KNN),Na?ve Bayes(NB),Neural Network(NNET),Support Vector Machine(SVM),and XGBoost.Model performance was evaluated through cross-validation and external validation.Results In the risk prediction models of AKI,the SVM model achieved the highest AUC value of 0.80 in the testing set and 0.71 in the external validation test.For the risk prediction models of mortality,the GBM model outperformed others in the prediction,attaining an AUC value of 0.91 in the testing set.Conclusion Risk predictive models for postoperative AKI and mortality in obese ICU patients are successfully constructed,and are valuable tools for clinicians to optimize early intervention and improve clinical outcomes for the patients.

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology