Objective To construct and compare logistic regression and decision tree models for predicting systemic inflammatory response syndrome (SIRS) in patients with type B aortic dissection (TBAD) after interventional surgery. Methods A retrospective analysis was conducted on clinical data of TBAD patients at Peking University Shenzhen Hospital from 2020 to 2024. The patients were divided into a SIRS group and a non SIRS group based on whether SIRS occurred within 24 hours after surgery. Multivariate logistic regression was used to analyze the influencing factors of SIRS occurrence in TBAD intervention patients, and a decision tree model was constructed using SPSS Modeler to compare the predictive performance of the two models. Results A total of 742 patients with TBAD were included, including 579 males and 163 females, aged 27-97 (58.85±10.79) years. Within 24 hours after intervention, a total of 506 patients developed SIRS, with an incidence rate of 68.19%. Logistic regression analysis showed that the extensive involvement of the dissection, the surgical time≥2 hours, PET coated stents implanted, serum creatinine, white blood cell count, C-reactive protein, monocyte (MONO), neutrophil count levels elevated, estimated glomerular filtration rate and decreased albumin levels were independent risk factors for SIRS (P<0.05). The decision tree model selected a total of 10 explanatory variables and 6 layers with 37 nodes, among which MONO was the most important predictor. The area under the decision tree model curve was 0.829 [95%CI (0.800, 0.856)], which was better than the logistic regression model's 0.690 [95%CI (0.655, 0.723)], and the difference was statistically significant (P<0.001). Conclusion The incidence of SIRS after TBAD intervention is high, and the decision tree model has better predictive performance than logistic regression. It can identify high-risk patients with higher accuracy and provide a practical tool for early clinical intervention.

Objective To establish a method for rapid detection of tramadol in urine by liquid-liquid extraction（LLE）-surface-enhanced Raman spectroscopy （SERS）. Methods Tramadol was extracted from urine with chloroform∶isopropyl alcohol （9∶1） extractant and detected in urine samples by enhanced Raman spectroscopy （wavelength 785 nm）. Results The quantitative curve of tramadol was Y=204.35 X−465.62, r=0.9952, and the linear range was 1-100 μg/ml. The detection limit of tramadol by this method （S/N=3） was 0.53 μg/ml. The sensitivity of SERS was higher than that of conventional methods, and it had reasonable reproducibility. Conclusion This method is simple, efficient and economical, and can be used for qualitative and quantitative analysis of tramadol personalized medicine.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

Objective:To examine the interaction between gender and the visceral adiposity index (VAI) in relation to the risk of type 2 diabetes mellitus (T2DM).Methods:This retrospective cohort study utilized data from the public Dryad database derived from the NAGALA (NAFLD in the Gifu Area, Longitudinal Analysis) project (1994-2016). Participants were stratified into quartiles based on VAI levels. A multivariate Cox proportional hazards regression model was employed to evaluate whether VAI independently predicts T2DM risk. Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves were constructed for each VAI quartile. Subgroup analyses were conducted to examine associations across age and body mass index categories. Both multiplicative and additive interaction effects between gender and VAI were assessed. Additionally, gender-specific Cox models were fitted to further explore these associations.Results:A total of 15 453 participants [8 419 males and 7 034 females; mean age, (43.7±8.9) years] were included, with a median follow-up duration of 5.39 years. During follow-up, 373 participants (2.4%) developed T2DM. After adjustment for potential confounders, higher VAI levels were independently associated with increased T2DM risk ( HR=1.16; 95% CI 1.11-1.21), consistent with the results across VAI quartiles. Kaplan-Meier analysis revealed a significant trend of increasing T2DM incidence across VAI quartiles ( P<0.001). The area under the ROC curve for VAI in predicting T2DM at 3, 5, and 10 years was 0.755, 0.735, and 0.696, respectively. Sensitivity analyses showed that elevated VAI was associated with increased T2DM risk across all age and body mass index subgroups (all P<0.05). Regarding interaction analysis, the HR (95% CI) for the multiplicative interaction between VAI and gender was 1.22 (1.19-1.26). The relative excess risk of interaction was -1.08 (95% CI -2.96 to -0.06), the attributable proportion of interaction was -0.54 (95% CI -1.35 to -0.01), and the synergy index was 0.48 (95% CI 0.26-0.91), indicating a negative additive interaction. Using low-VAI women as the reference group, the risk of T2DM in high-VAI women was higher ( HR=2.53, 95% CI 1.59-4.02) compared to high-VAI men ( HR=2.01, 95% CI 1.49-2.72). In gender-specific analyses, increasing VAI remained significantly associated with elevated T2DM risk after adjustment in both females ( HR=1.43, 95% CI 1.21-1.68) and males ( HR=1.16; 95% CI 1.11-1.22), with consistent findings across VAI quartiles. Conclusions:VAI and gender demonstrated multiplicative and additive interaction in relation to T2DM risk. The association between increasing VAI and T2DM risk was more pronounced in women than in men.

Objective:To examine the interaction between gender and the visceral adiposity index (VAI) in relation to the risk of type 2 diabetes mellitus (T2DM).Methods:This retrospective cohort study utilized data from the public Dryad database derived from the NAGALA (NAFLD in the Gifu Area, Longitudinal Analysis) project (1994-2016). Participants were stratified into quartiles based on VAI levels. A multivariate Cox proportional hazards regression model was employed to evaluate whether VAI independently predicts T2DM risk. Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves were constructed for each VAI quartile. Subgroup analyses were conducted to examine associations across age and body mass index categories. Both multiplicative and additive interaction effects between gender and VAI were assessed. Additionally, gender-specific Cox models were fitted to further explore these associations.Results:A total of 15 453 participants [8 419 males and 7 034 females; mean age, (43.7±8.9) years] were included, with a median follow-up duration of 5.39 years. During follow-up, 373 participants (2.4%) developed T2DM. After adjustment for potential confounders, higher VAI levels were independently associated with increased T2DM risk ( HR=1.16; 95% CI 1.11-1.21), consistent with the results across VAI quartiles. Kaplan-Meier analysis revealed a significant trend of increasing T2DM incidence across VAI quartiles ( P<0.001). The area under the ROC curve for VAI in predicting T2DM at 3, 5, and 10 years was 0.755, 0.735, and 0.696, respectively. Sensitivity analyses showed that elevated VAI was associated with increased T2DM risk across all age and body mass index subgroups (all P<0.05). Regarding interaction analysis, the HR (95% CI) for the multiplicative interaction between VAI and gender was 1.22 (1.19-1.26). The relative excess risk of interaction was -1.08 (95% CI -2.96 to -0.06), the attributable proportion of interaction was -0.54 (95% CI -1.35 to -0.01), and the synergy index was 0.48 (95% CI 0.26-0.91), indicating a negative additive interaction. Using low-VAI women as the reference group, the risk of T2DM in high-VAI women was higher ( HR=2.53, 95% CI 1.59-4.02) compared to high-VAI men ( HR=2.01, 95% CI 1.49-2.72). In gender-specific analyses, increasing VAI remained significantly associated with elevated T2DM risk after adjustment in both females ( HR=1.43, 95% CI 1.21-1.68) and males ( HR=1.16; 95% CI 1.11-1.22), with consistent findings across VAI quartiles. Conclusions:VAI and gender demonstrated multiplicative and additive interaction in relation to T2DM risk. The association between increasing VAI and T2DM risk was more pronounced in women than in men.

The primary aim of this study was to conduct a methodical examination and assessment of the prognostic efficacy exhibited by magnetic resonance imaging (MRI)-derived radiomic models concerning the preoperative prediction of lymph-vascular space infiltration (LVSI) in cervical cancer cases. A comprehensive and thorough exploration of pertinent academic literature was undertaken by two investigators, employing the resources of the Embase, PubMed, Web of Science, and Cochrane Library databases. The scope of this research was bounded by a publication cutoff date of May 15, 2023. The inclusion criteria encompassed studies that utilized radiomic models based on MRI to prognosticate the accuracy of preoperative LVSI estimation in instances of cervical cancer. The Diagnostic Accuracy Studies-2 framework and the Radiomic Quality Score metric were employed. This investigation included nine distinct research studies, enrolling a total of 1,406 patients. The diagnostic performance metrics of MRI-based radiomic models in the prediction of preoperative LVSI among cervical cancer patients were determined as follows: sensitivity of 83% (95% confidence interval [CI]=77%–87%), specificity of 74% (95% CI=69%–79%), and a corresponding AUC of summary receiver operating characteristic measuring 0.86 (95% CI=0.82–0.88). The results of the synthesized meta-analysis did not reveal substantial heterogeneity. This meta-analysis suggests the robust diagnostic proficiency of the MRI-based radiomic model in the prognostication of preoperative LVSI within the cohort of cervical cancer patients. In the future, radiomics holds the potential to emerge as a widely applicable noninvasive modality for the early detection of LVSI in the context of cervical cancer.

The application of Raman spectroscopy in the field of laboratory medicine is making continuous progress and development. The biosensor platform based on Raman spectroscopy provides a new means for accurate molecular diagnosis of diseases. In particular, as a fast and non-destructive detection method, surface-enhanced Raman scattering has the advantages of simple sample preparation, little interference from water and real-time detection, and shows great application potential in the field of medical examination. At the same time, with the integration of SERS and other technologies, including electrochemistry, new nano-materials, microfluidic, biochip, DNA nano-machine, artificial intelligence and machine learning, it will play a more and more important role in the field of medical laboratory. With the deepening of SERS research and the cross-integration between multiple disciplines, it will be widely used in biomedical detection and is expected to become an important technology platform for the next generation of precision diagnosis.

Objective:To explore the relationship between insomnia and osteoporosis.Methods:Mendelian randomization (MR) analysis were used in this study. The single nucleotide polymorphisms (SNPs) related to insomnia from genome-wide association analysis research data were selected as the instrumental variables by using inverse variance weighted (IVW), MR-Egger regression, weighted median method, maximum likelihood, penalized weighted median estimator, and Mendelian randomization robust adjusted profile score (MR-RAPS) to determine the causal relationship between insomnia and osteoporosis. Odds ratio ( OR) and 95% confidence interval ( CI) values were used to evaluate the association between insomnia and osteoporosis. Cochran′s Q-test was used to detect heterogeneity of SNPs, MR-Egger regression was used to test for level pleiotropy, and the leave-one-out method was used to test sensitivity, MR pleiotropy residual sum and outlier (MR-PRESSO) method and radial MR were used to detect erroneous outliers. Results:The screening criteria were set based on the three major assumptions of MR; finally, 31 SNPs were included in the MR analysis. The results of MR causal effect analysis using the IVW method showed that insomnia increased the risk of osteoporosis by about 0.7% ( OR=1.007, 95% CI 1.001-1.014, P=0.044); heterogeneity testing showed heterogeneity between SNPs ( Q=57.91, P<0.001); and the MR- Egger intercept test did not indicate horizontal pleiotropy in this study (intercept value=3.807×10 -5, P=0.888). Leave-one-out method showed that no single SNP had a significant impact on the overall results. No abnormal SNP was detected according to the MR-PRESSO results ( P=0.059), and radial MR did not detect any outliers. Conclusion:Mendelian randomization analysis showed that insomnia can increase the risk of osteoporosis.