Perimenopausal depression （PMD） is an affective disorder that occurs in women during the transition from sexual maturity to old age. It can induce various complications， such as insomnia and cognitive decline. The etiology of PMD is complex. Although multiple hypotheses have been proposed， there is still no unified theory that fully explains its pathogenesis. Research into its mechanisms relies heavily on animal experiments， and establishing reliable animal models is crucial for experimental studies. Appropriate animal models can better simulate human pathophysiological states， rapidly evaluate the efficacy and safety of drugs and intervention methods， grasp the essence of the disease， and uncover its intrinsic connections， thereby exploring more advanced intervention strategies. However， there is a lack of systematic review and summarization of literature related to model construction. Additionally， traditional Chinese medicine （TCM）， adhering to the principles of ''syndrome differentiation and treatment'' and ''holistic concept''， has shown significant efficacy in treating PMD. In recent years， research exploring and analyzing its therapeutic mechanisms has been increasing. Therefore， to gain a clearer and more comprehensive understanding of PMD animal modeling methods and the mechanisms of TCM， this paper reviewed Chinese and English literature on PMD animal models and mechanisms of TCM in PMD treatment. It summarized the construction methods of single-factor and multi-factor PMD models， and discussed the advantages and disadvantages of each modeling approach. Furthermore， it delved into the mechanisms of TCM intervention in PMD， revealing that TCM formulas primarily exert their effects by regulating the hypothalamic-pituitary-adrenal axis， hypothalamic-pituitary-ovarian axis， gut-brain axis， cell signaling pathways， neural circuits， hormone levels， and neurotransmitter levels. This review aims to provide a reference for future research in this field. In summary， by summarizing the progress in the methods for PMD animal model construction and the mechanisms of TCM， the paper seeks to offer new insights into the mechanistic research of TCM intervention in PMD.

ObjectiveTo investigate the risk factors for acute kidney injury （AKI） after liver transplantation， and to establish and validate a risk prediction model， and to provide a basis for early identification of high-risk patients and intervention in clinical practice. MethodsA single-center retrospective study was conducted， and clinical data were collected from 162 patients who received liver transplantation in Liver Transplantation Center of The First Hospital of Shanxi Medical University from March 2020 to June 2025. The patients were divided into AKI group with 69 patients and non-AKI group with 93 patients according to the diagnostic criteria for AKI established by the Kidney Disease： Improving Global Outcomes organization and the presence or absence of AKI within 7 days after surgery. The independent-samples t test was used for comparison of normally distributed continuous data between groups， while the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. The univariate differential analysis was used to obtain the factors associated with AKI after liver transplantation， and the multivariate logistic regression analysis was used to identify the independent risk factors and establish a nomogram model； the Bootstrap method with 1 000 repeated samples was used to perform internal validation of the model. The dataset was randomly divided into a training set and a validation set at a ratio of 7∶3， and the receiver operating characteristic （ROC） curve， the calibration curve， and decision curve analysis （DCA） were used to assess the discriminatory ability， calibration， and clinical applicability of the predictive model. ResultsBody mass index （BMI） （odds ratio ［OR］=1.281， 95% confidence interval ［CI］： 1.037　—　1.582， P=0.022）， serum creatinine （OR=1.097， 95%CI： 1.020　—　1.181， P=0.013）， intraoperative blood loss （OR=1.005， 95%CI： 1.002　—　1.009， P=0.004）， and cold ischemia time （OR=0.984， 95%CI： 0.976　—　0.991， P<0.001） were independent risk factors for the development of AKI after liver transplantation. The nomogram prediction model established based on the above factors had an area under the ROC curve （AUC） of 0.964 （95%CI： 0.931　—　0.997）， with an optimal cutoff value of 0.319， a sensitivity of 0.971， and a specificity of 0.903. In the training set （n=113）， the nomogram had an AUC of 0.969 （95% CI： 0.933　—　0.971）， while in the validation set （n=49）， the nomogram had an AUC of 0.941 （95%CI： 0.855　—　0.944）. The calibration curve showed good consistency between the predicted incidence rate and the actual incidence rate， and DCA showed that it had good net clinical benefit. ConclusionBMI， serum creatinine， cold ischemia time， and intraoperative blood loss are independent risk factors for the development of AKI after liver transplantation， and the nomogram prediction model established based on these factors performs well and has a good value in predicting the development of AKI after liver transplantation.

The application of immunosuppressants has significantly reduced the incidence rate of rejection reaction after liver transplantation， but the clinical efficacy of immunosuppressants is greatly affected by individual differences between patients. This article systematically reviews the recent research advances in the interaction between immunosuppressants and gut microbiota， with a focus on the regulatory role and mechanism of intestinal microflora communities on the efficacy of immunosuppressants. Studies have shown that intestinal microbiome is one of the key factors influencing the efficacy of immunosuppressive therapy after liver transplantation. This review aims to provide a theoretical basis for in-depth research in this field and provide new insights for developing individualized immunosuppressive treatment regimens based on the regulation of intestinal microflora.

Meconium is changed from defoliated intestinal villi with apoptosis. A meconium corpuscle is a limited architecture in the meconium at the light microscopic level. In histological and immunohistochemical sections of the abdomen from 40 fetuses at 20–31 weeks of gestation, we found 12 fetuses with meconium and examined the architecture.A solid or loose meconium was seen depending on sites. The solid meconium in the colon and rectum contained numerous spherical or rugby ball-like corpuscles comprising of a core with tightly-packed atrophied cells and a laminar fibrous sheath.The solid meconium surrounded and incorporated villi that maintained the original architecture. The loose meconium was seen not only in the stomach and duodenum but also, depending on specimens, in the colon and rectum. It was composed of a large mucosal fold, a single small villus and/or fragmented epithelia. Notably, these mucosal tissues still contained chromogranin-positive gastrointestinal cells and cytokeratin-positive epithelia. A large ring-like meconium was composed of the entirely circular mucosa or epithelia that seemed to be detached along the muscularis mucosae or the basal lamina.A sheath-like fibrous structure in meconium seemed to originate from the lamina propria and basal lamina of villi. A defoliation of villi with apoptosis seemed difficult to explain a detachment of the entirely circular mucosa or epithelia in large fetuses examined. The latter event might require a specific mechanism such as an in utero intussusception in which a proximal side of the colon was invaginated into the distal lumen.

Objective: To evaluate the diagnostic performance of magnetic resonance spectroscopy (MRS) in discriminating fumarate hydratase-deficient (FH-d) uterine leiomyomas (ULs) from FH-preserved ULs. Materials and Methods: This study consisted of three stages, with independent cohorts recruited for each stage: 1) sample-size estimation was retrospectively performed on UL specimens (diameter ≥3 cm; age, 20–40 years) from our database with immunohistochemistry (IHC) for 2-succinocysteine (2-SC) as the reference, without genetic testing, 2) MRS sequence optimization in confirmed FH germline mutation participants with ultrasound-detected ULs (diameter ≥3 cm), without IHC analysis, and 3) prospective diagnostic test accuracy was evaluated in consecutive participants with ultrasound-detected ULs (diameter ≥3 cm;age, 20–40 years), using IHC for 2-SC for determining the FH status and subsequent genetic testing in those with positive 2-SC results to identify whether FH mutations were germline or somatic in origin. The choline and fumarate peaks in MRS were classified as positive, negative, or technical failure (TF). TFs were analyzed separately and excluded from the primary diagnostic accuracy calculations. T1-, T2-, and diffusion-weighted images were interpreted as hyperintense or hypointense. The enhancement rate and apparent diffusion coefficient were also acquired. Diagnostic performance was compared between MRS and various magnetic resonance imaging (MRI) features. Results: The optimal MRS parameters for the fumarate peak were echo time (TE) = 140 ms and an average of 256. Among the 360 prospective participants, 37 were confirmed to have FH-dULs. MRS showed positive fumarate peaks in 35 of 37 FH-dULs.After excluding six TFs, the positive fumarate peak on MRS showed 94.6% (35/37) sensitivity, 99.7% (316/317) specificity, and 99.2% (351/354) accuracy, all of which were significantly superior to those of other MRI features (P ≤ 0.002). Conclusion A positive fumarate peak on MRS may be a useful imaging biomarker for diagnosing FH-dULs.

Dendritic cells (DCs) serve as a crucial link between innate and adaptive immunity and represent key modulatory nodes in the initiation of adaptive immune responses. Although DC-targeted vaccines and therapeutic strategies show great promise, their development remains in the early stages due to a limited understanding of the regulatory mechanisms governing distinct DC subsets in response to various immunogens and types of immune responses. Recently, a study by Jessica Y. Huang and Michael Y. Gerner published in Cell has uncovered a novel functional dimension of DCs. Beyond their classical roles in antigen presentation and T cell priming, DCs dynamically regulate the spatiotemporal organization of innate and adaptive immune responses within lymph nodes. During early type I immune responses, tissue-resident DC2s recruit innate immune cells and promote their trafficking, effectively limiting pathogen spread; however, this comes at the cost of disrupting lymph node architecture and suppressing the initiation of adaptive immunity. Following effective pathogen restraint, DCs shift their role to mediate the removal of apoptotic neutrophils and facilitate the restoration of lymph node structure, thereby reinstating adaptive immunity. These findings suggest that a deeper understanding of subset-specific regulatory networks of DCs in various immune contexts may enhance the precision and efficacy of DC-targeted immunotherapies.

ObjectiveTo investigate the independent risk factors for decompensation in patients with liver cirrhosis， to construct a nomogram-based risk assessment model， and to assess its risk assessment performance and clinical value by comparing it with Model for End-Stage Liver Disease （MELD）， MELD combined with serum sodium concentration （MELD-Na）， and MELD 3.0 scoring system. MethodsA retrospective analysis was performed for 514 patients with liver cirrhosis who attended The First Hospital of Shanxi Medical University from January 2020 to May 2025， and related data were collected， including demographic data and laboratory markers. According to the presence or absence of decompensation， the patients were divided into compensation group with 275 patients and decompensation group with 239 patients. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator regression analysis was used for screening of variables， and the multivariate logistic regression analysis was used to identify independent risk factors for decompensated liver cirrhosis and construct a nomogram model. The receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis were used to assess the discriminatory ability， calibration， and net clinical benefit of the model. ResultsThe multivariate logistic regression analysis showed that low hemoglobin （odds ratio ［OR］=0.984， 95% confidence interval ［CI］： 0.969　—　0.999， P<0.05）， low lymphocytes （OR=0.564， 95%CI： 0.383　—　0.830， P<0.05）， high international normalized ratio （OR=3.131， 95%CI： 1.242　—　7.891， P<0.05）， and low serum sodium （OR=0.922， 95%CI： 0.872　—　0.975， P<0.05） were independent risk factors for decompensation events in patients with liver cirrhosis. Construct the prediction model equation： Logit（P）=intercept value－0.016×hemoglobin－0.573×lymphocytes+1.141×INR－0.081×serum sodium. The nomogram constructed based on these factors had a good discriminatory ability， with an area under the ROC curve （AUC） of 0.787， a specificity of 77.8%， and a sensitivity of 67.4%， and it had significantly better predictive performance than MELD score （AUC=0.718）， MELD-Na score （AUC=0.719）， and MELD 3.0 score （AUC=0.725）. The calibration curve showed good consistency between the probability of risk assessed by the model and the observed probability. The decision curve analysis showed that compared with the MELD-based scores， this model provided higher net clinical benefit across a wide range of risk thresholds. ConclusionThis study successfully constructed and validated a nomogram risk assessment model incorporating hemoglobin， international normalized ratio， lymphocytes， and serum sodium. This model has good clinical practicability and can help to achieve early identification of high-risk patients with decompensated liver cirrhosis and optimize intervention strategies in clinical practice.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.