Objective:To systematically evaluate the predictive value of the reverse shock index multiplied by the Glasgow coma scale score (rSIG) for mortality of trauma patients.Methods:A comprehensive literature search was conducted to identify studies on the predictive value of rSIG for mortality of trauma patients in the following databases from inception to April 2025, including CNKI, Wanfang Data, SinoMed, PubMed, Cochrane Library, Web of Science, and Embase. Two investigators independently screened the literature, extracted data, and assessed study quality according to predefined inclusion and exclusion criteria. The Quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool was used to evaluate the risk of bias in the included studies. Meta analysis was performed using Stata 17.0 software with a bivariate mixed-effects model. The following metrics were used to assess the predictive value of rSIG for mortality in trauma patients, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (SROC) curve (AUC). The influence of various factors on the predictive performance of rSIG was examined, including injury type, study design, region, sample size, cut-off value, rSIG measurement time, and outcome measures. Additionally, sensitivity analysis, Fagan′s nomogram, and Deeks′ funnel plot were employed to assess the robustness of the findings, clinical applicability, and publication bias.Results:A total of 15 studies involving 710 612 trauma patients were included, 26 105 of whom were deceased. Meta analysis results showed that rSIG had a pooled sensitivity of 0.78(95% CI 0.71, 0.84), a pooled specificity of 0.78(95% CI 0.68, 0.86), a pooled PLR of 3.60(95% CI 2.46, 5.27), a pooled NLR of 0.28(95% CI 0.22, 0.36), a pooled DOR of 12.70(95% CI 8.10, 19.91), and an AUC of 0.85(95% CI 0.81, 0.87) for predicting mortality of trauma patients. Subgroup analysis identified injury type as one of the major sources of heterogeneity, and the predictive specificity of rSIG was significantly higher in patients with multiple trauma (0.82) than in those with isolated traumatic brain injury (0.65) ( P<0.05). Sensitivity analysis indicated that the findings were robust and stable. Fagan′s nomogram showed that when the pre-test probability was 7%, the post-test probability of death increased to 21% in patients with low rSIG and decreased to 2% in those with high rSIG. Deeks′ funnel plots suggested no significant publication bias among the included studies ( P>0.05). Conclusion:Low rSIG has good predictive performance for mortality of trauma patients and can serve as an effective tool for early and rapid prognosis assessment with superior predictive performance in patients with multiple trauma compared to those with traumatic brain injury.

Objective:To investigate the characteristics of the endometrial microbiota in patients with varying degrees of intrauterine adhesion (IUA).Methods:This single-center cross-sectional observational study enrolled 115 patients with IUA who were treated at the Hysteroscopic Center of Fuxing Hospital, Capital Medical University, from May 2022 to October 2023. After quality control and data preprocessing, 81 samples met the inclusion criteria for analysis. Patients were grouped according to an established IUA scoring and grading system into mild IUA ( n=38) and moderate-to-severe IUA ( n=43). Endometrial tissue was collected under sterile conditions. Bacterial genomic DNA was extracted, the 16S rRNA V3-V4 region was amplified, and sequencing was performed on an Illumina platform. Differences in endometrial microbiota diversity and composition were compared between the two groups. Results:Patients with varying degrees of IUA exhibited comparable species richness, evenness and diversity of endometrial microbiota. At the phylum level, the endometrial microbiota across all subjects was predominantly composed of Proteobacteria, Firmicutes, Cyanobacteriota, Bacteroidota, and Actinobacteriota, with Proteobacteria (32.29%) and Firmicutes (23.82%) showing the highest mean relative abundances. At the genus level, Ralstonia (16.67%), Lactobacillus (13.45%), and Streptococcus (7.07%) were the most abundant genera. Group comparisons showed that the abundance of Ralstonia was higher in the mild IUA group, whereas Lactobacillus, Vibrio and Pseudoalteromonas were more abundant in the moderate-to-severe IUA group; however, these differences did not reach statistical significance (all P>0.05). LEfSe analysis further indicated that Lactobacillus, Vibrio, Pseudoalteromonas, Aeromonas, Ureaplasma and Acetobacterium were relatively enriched in the moderate-to-severe IUA group, while Geobacillus, Stomatobaculum and Fusicatenibacter were more abundant in the mild IUA group. Conclusion:The composition of the endometrial microbiota differs among patients with varying IUA severity. IUA progression may be associated with alterations in the endometrial microbiota; however, causal relationships and underlying mechanisms require further investigation.

Objective To establish a method for predicting the risk of endometrial cancer(EC)and endometrial atypical hyperplasia(AH)in women with postmenopausal bleeding(PMB)by collecting clinical data on routine medical history.Methods The clinical data of a total of 408 PMB patients admitted to Fuxing Hospital,Capital Medical University were consecutively collected in this retrospective study from December 2013 to December 2023.According to the results of endometrial pathology,patients were divided into case group and control group.EC and AH were included in the malignant group(case group)and the other endometrial pathologies were included in the non-malignant group(control group).Clinical data,including clinical history,high risk factors,and common gynecological ultrasound measurement indicators,were collected and studied by univariate and multivariate Logistic regression analysis.Results The mean age of 408 patients was(60.4±7.8)years.A total of 74 cases(18.1％)were in case group and 334 cases(81.9％)were in control group.Based on Logistic regression analysis,the best predictors of endometrial malignant lesions were selected to create a"LRDNT"(light bleeding,recurrent bleeding,diabetes,non-uniform echogenicity & thickness)model.LRDNT scores range from 0 to 22.The score of LRDNT ≥15 has the largest Yoden index,and the sensitivity to predict endometrial malignant lesions is 79.73％,the specificity is 80.84％,and the prediction accuracy is 80.64％.Conclusions The risk prediction model LRDNT,which combines clinical information and common gynecological ultrasound measurement indicators of PMB patients,can help clinicians classify patients at high and low risk of endometrial malignant lesions,and optimize the strategy of diagnosis and treatment.

Objective:To analyze and validate the reliability and validity of the social skills improvement system-rating scales Chinese version (parent version) (SSIS-RS-C) in middle school students.Method:A total of 1 486 parents of middle school students were recruited according to the cluster sampling method.The social responsiveness scale and strengths and difficulties questionnaire were used as criterion validity tools.A retest was conducted one month later.SPSS 27.0 was used for descriptive statistics, item analysis, internal consistency test, test-retest reliability test and criterion validity test. AMOS 24.0 was used to perform confirmatory factor analysis .Results:Item analysis indicated significant positive correlations between each item and the subscales ( r=0.293-0.782, all P<0.01), with significant differences in scores between high and low groups ( t=10.079-37.038, all P<0.01).Confirmatory factor analysis supported a seven-factor structure for the social skills subscale(communication, cooperation, assertion, responsibility, empathy, engagement and self control) and a five-factor structure for the problem behavior subscale (externalizing, bullying, hyperactivity/inattention, internalizing and autism spectrum) of the SSIS-RS-C.There was a positive correlation between the social skills subscale and prosocial behavior ( r=0.637, P<0.001), and between the problem behavior subscale and social impairments and difficult behaviors ( r=0.765, 0.688, both P<0.001).The Cronbach's α coefficients for the total scale, social skills subscale and problem behavior subscale were 0.934, 0.972 and 0.963, respectively.The test-retest correlation coefficients for the total score and the two subscales were 0.665, 0.871 and 0.598, respectively (all P<0.001). Conclusion:The SSIS-RS-C demonstrated good reliability and validity in the Chinese adolescent population.

Objective:To analyze and validate the reliability and validity of the social skills improvement system-rating scales Chinese version (parent version) (SSIS-RS-C) in middle school students.Method:A total of 1 486 parents of middle school students were recruited according to the cluster sampling method.The social responsiveness scale and strengths and difficulties questionnaire were used as criterion validity tools.A retest was conducted one month later.SPSS 27.0 was used for descriptive statistics, item analysis, internal consistency test, test-retest reliability test and criterion validity test. AMOS 24.0 was used to perform confirmatory factor analysis .Results:Item analysis indicated significant positive correlations between each item and the subscales ( r=0.293-0.782, all P<0.01), with significant differences in scores between high and low groups ( t=10.079-37.038, all P<0.01).Confirmatory factor analysis supported a seven-factor structure for the social skills subscale(communication, cooperation, assertion, responsibility, empathy, engagement and self control) and a five-factor structure for the problem behavior subscale (externalizing, bullying, hyperactivity/inattention, internalizing and autism spectrum) of the SSIS-RS-C.There was a positive correlation between the social skills subscale and prosocial behavior ( r=0.637, P<0.001), and between the problem behavior subscale and social impairments and difficult behaviors ( r=0.765, 0.688, both P<0.001).The Cronbach's α coefficients for the total scale, social skills subscale and problem behavior subscale were 0.934, 0.972 and 0.963, respectively.The test-retest correlation coefficients for the total score and the two subscales were 0.665, 0.871 and 0.598, respectively (all P<0.001). Conclusion:The SSIS-RS-C demonstrated good reliability and validity in the Chinese adolescent population.

Objective:To systematically evaluate the predictive value of the reverse shock index multiplied by the Glasgow coma scale score (rSIG) for mortality of trauma patients.Methods:A comprehensive literature search was conducted to identify studies on the predictive value of rSIG for mortality of trauma patients in the following databases from inception to April 2025, including CNKI, Wanfang Data, SinoMed, PubMed, Cochrane Library, Web of Science, and Embase. Two investigators independently screened the literature, extracted data, and assessed study quality according to predefined inclusion and exclusion criteria. The Quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool was used to evaluate the risk of bias in the included studies. Meta analysis was performed using Stata 17.0 software with a bivariate mixed-effects model. The following metrics were used to assess the predictive value of rSIG for mortality in trauma patients, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic (SROC) curve (AUC). The influence of various factors on the predictive performance of rSIG was examined, including injury type, study design, region, sample size, cut-off value, rSIG measurement time, and outcome measures. Additionally, sensitivity analysis, Fagan′s nomogram, and Deeks′ funnel plot were employed to assess the robustness of the findings, clinical applicability, and publication bias.Results:A total of 15 studies involving 710 612 trauma patients were included, 26 105 of whom were deceased. Meta analysis results showed that rSIG had a pooled sensitivity of 0.78(95% CI 0.71, 0.84), a pooled specificity of 0.78(95% CI 0.68, 0.86), a pooled PLR of 3.60(95% CI 2.46, 5.27), a pooled NLR of 0.28(95% CI 0.22, 0.36), a pooled DOR of 12.70(95% CI 8.10, 19.91), and an AUC of 0.85(95% CI 0.81, 0.87) for predicting mortality of trauma patients. Subgroup analysis identified injury type as one of the major sources of heterogeneity, and the predictive specificity of rSIG was significantly higher in patients with multiple trauma (0.82) than in those with isolated traumatic brain injury (0.65) ( P<0.05). Sensitivity analysis indicated that the findings were robust and stable. Fagan′s nomogram showed that when the pre-test probability was 7%, the post-test probability of death increased to 21% in patients with low rSIG and decreased to 2% in those with high rSIG. Deeks′ funnel plots suggested no significant publication bias among the included studies ( P>0.05). Conclusion:Low rSIG has good predictive performance for mortality of trauma patients and can serve as an effective tool for early and rapid prognosis assessment with superior predictive performance in patients with multiple trauma compared to those with traumatic brain injury.

Objective:To investigate the characteristics of the endometrial microbiota in patients with varying degrees of intrauterine adhesion (IUA).Methods:This single-center cross-sectional observational study enrolled 115 patients with IUA who were treated at the Hysteroscopic Center of Fuxing Hospital, Capital Medical University, from May 2022 to October 2023. After quality control and data preprocessing, 81 samples met the inclusion criteria for analysis. Patients were grouped according to an established IUA scoring and grading system into mild IUA ( n=38) and moderate-to-severe IUA ( n=43). Endometrial tissue was collected under sterile conditions. Bacterial genomic DNA was extracted, the 16S rRNA V3-V4 region was amplified, and sequencing was performed on an Illumina platform. Differences in endometrial microbiota diversity and composition were compared between the two groups. Results:Patients with varying degrees of IUA exhibited comparable species richness, evenness and diversity of endometrial microbiota. At the phylum level, the endometrial microbiota across all subjects was predominantly composed of Proteobacteria, Firmicutes, Cyanobacteriota, Bacteroidota, and Actinobacteriota, with Proteobacteria (32.29%) and Firmicutes (23.82%) showing the highest mean relative abundances. At the genus level, Ralstonia (16.67%), Lactobacillus (13.45%), and Streptococcus (7.07%) were the most abundant genera. Group comparisons showed that the abundance of Ralstonia was higher in the mild IUA group, whereas Lactobacillus, Vibrio and Pseudoalteromonas were more abundant in the moderate-to-severe IUA group; however, these differences did not reach statistical significance (all P>0.05). LEfSe analysis further indicated that Lactobacillus, Vibrio, Pseudoalteromonas, Aeromonas, Ureaplasma and Acetobacterium were relatively enriched in the moderate-to-severe IUA group, while Geobacillus, Stomatobaculum and Fusicatenibacter were more abundant in the mild IUA group. Conclusion:The composition of the endometrial microbiota differs among patients with varying IUA severity. IUA progression may be associated with alterations in the endometrial microbiota; however, causal relationships and underlying mechanisms require further investigation.

Objective To establish a method for predicting the risk of endometrial cancer(EC)and endometrial atypical hyperplasia(AH)in women with postmenopausal bleeding(PMB)by collecting clinical data on routine medical history.Methods The clinical data of a total of 408 PMB patients admitted to Fuxing Hospital,Capital Medical University were consecutively collected in this retrospective study from December 2013 to December 2023.According to the results of endometrial pathology,patients were divided into case group and control group.EC and AH were included in the malignant group(case group)and the other endometrial pathologies were included in the non-malignant group(control group).Clinical data,including clinical history,high risk factors,and common gynecological ultrasound measurement indicators,were collected and studied by univariate and multivariate Logistic regression analysis.Results The mean age of 408 patients was(60.4±7.8)years.A total of 74 cases(18.1％)were in case group and 334 cases(81.9％)were in control group.Based on Logistic regression analysis,the best predictors of endometrial malignant lesions were selected to create a"LRDNT"(light bleeding,recurrent bleeding,diabetes,non-uniform echogenicity & thickness)model.LRDNT scores range from 0 to 22.The score of LRDNT ≥15 has the largest Yoden index,and the sensitivity to predict endometrial malignant lesions is 79.73％,the specificity is 80.84％,and the prediction accuracy is 80.64％.Conclusions The risk prediction model LRDNT,which combines clinical information and common gynecological ultrasound measurement indicators of PMB patients,can help clinicians classify patients at high and low risk of endometrial malignant lesions,and optimize the strategy of diagnosis and treatment.

Objective:To investigate the risk factors associated with mortality in patients with severe traumatic liver injury (TLI) and to establish and validate an early prediction model for mortality.Methods:A retrospective cohort study was conducted to analyze the clinical data of 273 patients with severe TLI admitted to the ICU from the medical information mart for the intensive care-IV (MIMIC-IV) database. The cohort consisted of 176 males and 97 females, with age ranging from 18 to 83 years [35.6 years(25.7,57.5)years]. The patients were divided into two groups based on in-hospital mortality: the survival group (253 patients, 92.7%) and the death group (20 patients, 7.3%). The two groups were compared with regards to gender, age, cause and type of injury, treatment method, massive blood transfusion, comorbidities as well as vital signs and laboratory tests measured within 24 hours of ICU admission. Univariate analysis was used to screen for risk factors associated with mortality in severe TLI patients. Independent risk factors for mortality were determined using multivariate Logistic regression analysis. Lasso regression was used to screen for predictors of mortality, and a nomogram prognostic model was then established through a multivariate Logistic regression analysis. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the discrimination of the model, while the Hosmer-Lemeshow goodness-of-fit test and calibration curve were used to evaluate the calibration of the model. The model′s clinical applicability was evaluated through decision curve analysis (DCA). Internal validation was performed by the 200 Bootstrap samples, and external validation was performed by using 163 patients with severe TLI from the emergency ICU collaborative research database (eICU-CRD). Finally, the predictive efficacy of the nomogram model was compared to other trauma or severity scores.Results:Univariate analysis showed that the age, cause of injury, massive blood transfusion, chronic liver disease and laboratory tests measured within 24 hours of ICU admission, including temperature, systolic blood pressure, diastolic blood pressure, mean arterial pressure, shock index, platelets, red blood cell distribution width (RDW), mean red blood cell hemoglobin concentration (MCHC), blood glucose, blood urea nitrogen, creatinine, anion gap, bicarbonate, prothrombin time (PT), activated partial thromboplastin time (APTT) and international normalized ratio (INR) were associated with the mortality of severe TLI patients ( P<0.05 or 0.01). Multivariate Logistic regression analysis revealed that age ( OR=1.08, 95% CI 1.03, 1.12, P<0.01), body temperature <36 ℃ ( OR=8.00, 95% CI 2.17, 29.53, P<0.01), shock index ( OR=9.59, 95% CI 1.76, 52.18, P<0.01) and anion gap ( OR=1.32, 95% CI 1.15, 1.53, P<0.01) were significantly associated with mortality in severe TLI patients. Lasso regression analysis selected 7 predictors, including age, body temperature<36 ℃, shock index, anion gap, chronic liver disease, creatinine and APTT. Based on these 7 predictors, a nomogram prediction model was developed. The AUC of the nomogram for predicting mortality was 0.96 (95% CI 0.94, 0.99), and the Hosmer-Lemeshow goodness-of-fit test indicated a good fit ( P>0.05). The calibration curve demonstrated excellent consistency between the predicted and actual probabilities, and DCA demonstrated that the model had good clinical net benefit at all risk threshold probability ranges. Internal validation confirmed the stability of the model ( AUC=0.96, 95% CI 0.92, 0.98), and external validation demonstrated good generalization ability ( AUC=0.95, 95% CI 0.91, 0.98). Moreover, the nomogram exhibited superior predictive efficacy compared with injury severity score (ISS), revised trauma score (RTS), trauma injury severity score (TRISS), sequential organ failure score (SOFA), acute physiological score III (APS III), Logistic organ dysfunction score (LODS), Oxford acute severity of illness score (OASIS) and simplified acute physiological score II (SAPS II). Conclusions:Age, body temperature <36 ℃, shock index and anion gap are independent risk factors for mortality in severe TLI patients. A nomogram prognosis model based on 7 predictors, namely age, body temperature <36 ℃, shock index, anion gap, chronic liver disease, creatinine and APTT exhibits good predictive efficacy and robustness, and is contributive to accurately assess the risk of mortality in severe TLI patients at an early stage.

Objective:To explore the risk factors influencing the prognosis for patients with hepatocellular carcinoma (HCC) recurrence after liver transplantation and summarize the relevant diagnostic and therapeutic experiences.Methods:The clinicopathological features with diagnosis and treatment plan of 102 recurrent HCC patients fulfilling the Fudan Criteria were compared for survival rate (univariate analysis) and independent prognostic indicators were obtained by Cox multivariate analysis.Results:The 1/3/5-year overall survival rates were 92.2%, 48.6% and 34.6% and the 1/3/5-year survival rates with tumor were 63.2%, 31.0% and 16.7% respectively. Cox regression analysis indicated that patient age, whether tumor can be surgically resected or not and personalized diagnostic & therapeutic plan based upon targeted therapy were independent prognostic factors affecting the overall survival rates and survival rates with tumor.Conclusions:Although HCC recurrence and metastasis after liver transplantation seriously influence patient prognosis, satisfactory outcomes may be obtained for some patients through active, effective and precise managements.