Objective To explore factors associated with early postoperative hormonal remission in patients with prolactinoma and to develop prediction models based on clinical and radiological features. Methods Data from 107 patients with prolactinoma who underwent transsphenoidal surgery at The First People’s Hospital of Huai’an, Nanjing Medical University between January 2020 and December 2024 was collected, included general clinical characteristics, preoperative laboratory indicators, and imaging features. Based on whether early postoperative prolactin (PRL) levels normalized, patients were divided into a remission group (n=76) and a non-remission group (n=31). Univariate logistic regression was used for preliminary evaluation of candidate variables, followed by LASSO regression for feature selection. Multiple machine learning models were constructed, including logistic regression, random forest, support vector machine, K-nearest neighbors, naive Bayes, decision tree, neural network, and gradient boosting decision tree (GBDT). All models were trained and evaluated using ten-fold cross-validation, with comprehensive assessment of model performance based on the area under the ROC curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score. Results The maximum diameter of tumors in the non remission group was larger than that in the remission group (P=0.019), and the incidence of tumor stroke and preoperative PRL levels were significantly higher than those in the remission group (P＜0.001). Univariate analysis showed that sex, maximum tumor diameter, tumor stroke, and preoperative PRL levels were influencing factors for early postoperative hormone response in patients with prolactinoma (P＜0.05). The comparison results of machine learning models show that the neural network model performs the best (AUC=0.921) and has good clinical application value, followed by the GBDT model (AUC=0.893) and the support vector machine model (AUC=0.884). Other models also show certain predictive ability. Conclusions Sex, preoperative PRL levels, maximum tumor diameter, Hounsfield unit value, and tumor stroke are important factors affecting early hormone response after prolactinoma surgery. The machine learning model constructed based on the above variables has good predictive performance, and performs the best and has good clinical application value.

[Objective] To investigate the factors associated with alanine aminotransferase (ALT) abnormalities in multi-ethnic blood donors across five Zang autonomous prefectures in the plateau regions of Qinghai Province, and to provide evidence for ensuring blood safety and formulating screening strategies. [Methods] A retrospective analysis was performed on the ALT abnormal test results of blood donors in the Zang autonomous prefectures of Qinghai from 2022 to 2024. The correlations between ALT levels and factors including gender, age, altitude, and infectious markers were investigated. [Results] The overall ALT unqualified rate among blood donors in this region was 9.01%. Significant differences in ALT levels were observed across genders and age groups (P<0.05). Variations in ALT abnormality rates were also noted among different plateau regions (P<0.05). Overall, ALT values exhibited an increasing trend with rising altitude. The average ALT unqualified rates were 11.19% in Zang donors, 7.96% in Han donors, and 4.79% in donors from other ethnic groups (P<0.05). No statistically significant association was observed between ALT abnormality and the presence of HBV/HCV infectious markers (P>0.05). [Conclusion] In the plateau areas of Qinghai, multi-ethnic blood donors have a relatively high ALT levels and ALT unqualified rates, showing distinct regional characteristics. ALT elevation in voluntary blood donors is related to non-pathological factors such as gender, age, and dietary habits, but not to infectious indicators.

Objective:To explore the effects of Ziyu Ointment on granulation tissue,TGF-β and downstream Smad protein in the process of wound healing in rats by constructing a rat model of back trauma.Methods:A rat model of back trauma was constructed,and 100 SD male rats were separated into healthy group,back trauma group,Ziyu Ointment treatment group,petrolatum treatment group,Ziyu Ointment+TGF-β1 activation group,with 20 per group.Wound formation surgery was performed,and wound healing of rats in each group was recorded at 5th,10th,and 15th day after operation;the levels of IL-6,IL-1β and TNF-α in the serum of rats in each group were detected;the granulation tissue formed by wound healing at 15th day was selected for HE and Masson staining,the proportion of blood vessel area and collagen area of granulation tissue of rats in each group were analyzed and calculated;the mRNA expression levels of TGF-β1 and Smad3 in granulation tissues of rats in each group were detected by fluorescence quantitative PCR;the expression levels of TGF-β1,Smad3 and p-Smad3 in granulation tissues of rats in each group were detected by Western blot.Results:The healthy group rats had complete epidermal tissue;the levels of serum IL-6,IL-1β,TNF-α,the expression of TGF-β1 mRNA and protein in the wound granulation tissue,and the phosphorylation of Smad3 in the back trauma group were obviously higher than the healthy group(P<0.05);compared with back trauma group,the wound healing rate,proportion of collagen area,and propor-tion of blood vessel area in Ziyu Ointment treatment group and petrolatum treatment group were obviously increased,the serum IL-6,IL-1β,TNF-α contents,TGF-β1 mRNA and protein expression,and Smad3 phosphorylation were obviously decreased(P<0.05);compared with Ziyu Ointment treatment group,the petrolatum treatment group,Ziyu Ointment+TGF-β1 activation group had obviously lower wound healing rate,collagen area percentage,and blood vessel area percentage,and obviously lower serum IL-6,IL-1β,TNF-α contents,TGF-β1 mRNA and protein expression and Smad3 phosphorylation(P<0.05).Conclusion:Ziyu Ointment can inhibit the expression of related genes and proteins by regulating the TGF-β/Smad signal pathway,thereby alleviating inflammation and promoting wound healing.

Interferon regulatory factor 4 (IRF4) is a critical transcription factor that governs the differentiation of cluster of differentiation 4⁺ (CD4⁺) T cells. The pathogenesis and progression of psoriasis are primarily attributed to an immune imbalance stemming from the overproduction of interleukin-17A (IL-17A) by T lymphocytes. However, the role of IRF4 in psoriasis remains unexplored. In this study, we found that IRF4 activity is increased in the cutaneous lesions of patients with psoriasis in response to stimulation by IL-23A and IL-1β. This IRF4 elevation heightens its binding to the E1A binding protein p300 (EP300) promoter, triggering the transcription of downstream retinoic acid receptor-related orphan receptor-γt (RORγt) and increasing the secretion of IL-17A, thereby establishing the IL-1β/IL-23A-IRF4-EP300-RORC-IL-17A inflammatory cascade in psoriasis. The alleviation of imiquimod (IMQ)-induced psoriatic-like symptoms was achieved through the creation of a Irf4 ^-/- gene deletion mouse model and pharmacological inhibition using antisense oligonucleotides targeted for Irf4. This amelioration was accompanied by a decreased number of IL-17A-producing CD4⁺ T cells in the skin. The findings of this study suggest that IRF4 plays a crucial role in the promotion of inflammation and exacerbation of IMQ-induced psoriasiform dermatitis. Consequently, IRF4 targeting could be a promising therapeutic strategy.

OBJECTIVE To explore the effects of meropenem exposure and degradation levels on clinical efficacy in patients with purulent meningitis （PM）. METHODS A total of 131 PM patients treated with meropenem at the Affiliated Suzhou Hospital of Nanjing Medical University from January 2022 to June 2025 were prospectively included. Relevant data were collected and divided into a cured group （91 cases） and a non-cured group （40 cases） based on the efficacy. High-performance liquid chromatography-tandem mass spectrometry was used to determine the concentration of meropenem and its open-loop metabolites. Risk factors that affect efficacy were screened， and their predictive power and correlation were evaluated by univariate analysis， and multivariate Logistic regression analysis， receiver operating characteristic （ROC） curves， and correlation analysis. RESULTS Univariate analysis showed that serum creatinine， creatinine clearance rate， minimum inhibitory concentration of meropenem ≥16 μg/mL， cerebrospinal fluid red blood cell count， cerebrospinal fluid white blood cell count， cerebrospinal fluid glucose content， blood trough concentration， blood open-loop metabolite concentration/trough concentration ratio， and intrathecal injection were all correlated with efficacy （P＜0.05）. The results of multiple Logistic regression analysis showed that serum creatinine blood open-loop metabolite concentration/trough concentration ratio， intrathecal injection， and cerebrospinal fluid glucose content were influencing factors for suboptimal anti-infective ltt efficacy （P＜0.05）. ROC curve analysis showed that when the blood open-loop metabolite concentration/trough concentration ratio was greater than 2.854 （AUC＝0.647）， serum creatinine was less than 59.5 μmol/L （AUC＝0.647）， and cerebrospinal fluid glucose content was less than 3.37 mmol/L （AUC＝0.709）， the risk of treatment failure significantly increased （P＜0.05）. Correlation analysis showed that the blood trough concentration of meropenem was positively correlated with the concentration of its open-loop metabolites （R 2＝0.134 5， P＜0.000 1）. CONCLUSIONS Insufficient exposure level and rapid degradation of meropenem are key mechanisms affecting the anti-infective efficacy of PM. Elevated blood open-loop metabolite concentration/ trough concentration ratio， low serum creatinine level， lack of intrathecal injection， and low cerebrospinal fluid glucose content are independent risk factors for poor efficacy.

Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.

Objective:To investigate the effect of circulating plasma cell(CPC)on the prognosis of multiple myeloma(MM),and to es-tablish and validate a modified CPC-RISS staging system based on CPC and RISS.Methods:A retrospective analysis was performed for the clinical data of 639 treatment-na?ve patients with MM who were treated in Department of Hematology,Xijing Hospital,from January 2006 to June 2023.Peripheral blood smear was used to calculate the percentage of CPC in patients,and the impact of CPC and other related factors on the prognosis of MM patients was analyzed.A CPC-RISS staging system was established based on RISS stage and the percentage of CPC,and the differences in survival and prognosis were analyzed between patients with different stages.Results:Compared with the patients without CPC,detectable CPC was significantly associated with various high-risk factors for MM,and the MM patients with CPC had a lower complete remission rate and shorter overall survival time and progression-free survival time.The modified CPC-RISS staging system was used to classify the patients with MM into four stages,and there were significant differences in median survival time and progression-free survival time between the patients with different stages of MM.Conclusion:The MM pa-tients with the presence of CPC exhibit more aggressive features,worse response to treatment,and a reduction in long-term survival rate.The modified CPC-RISS staging system can effectively predict the prognosis of treatment-na?ve MM patients.

Objective To analyze the clinical and pathogenic characteristics,as well as influencing factors for the prognosis of patients with Candida bloodstream infection(CBSI).Methods Clinical data of 47 CBSI patients in a hospital from January 2015 to September 2024 were collected.Distribution of departments and infection strains,an-timicrobial resistance,and influencing factors for the poor prognosis of patients were analyzed.Results A total of 51 strains of Candida were detected from 47 CBSI patients,mainly from the intensive care unit(ICU;n=25,53.2％),department of intestinal fistula surgery(n=8,17.0％),and department of respiratory medicine(n=4,8.6％),et al.The main detected pathogens were Candida albicans(n=29,56.9％),Candida tropicalis(n=7,13.7％),Candida glabrata(n=6,11.8％),and Candida parapsilosis(n=6,11.8％).Resistance rate of Candida albi-cans to fluconazole was 11.5％(3/26).According to the prognosis results,patients were divided into a good prog-nosis group(n=26,55.3％)and a poor prognosis group(n=21,44.7％).Univariate analysis showed statistically significant differences between patients in the good prognosis group and the poor prognosis group in terms of abso-lute neutrophil count,ICU admission,mechanical ventilation,tracheal intubation,gastrointestinal hemorrhage/per-foration,and surgical treatment(lesion clearance,drainage or unblocking for obstruction)(all P＜0.05).Prelimi-nary multivariate logistic regression analysis showed that gastrointestinal hemorrhage/perforation was a potential risk factor for the poor prognosis in CBSI patients(OR=11.156,95％CI:1.434-86.809,P=0.021).Conclusion The detected CBSI strains are mainly Candida albicans,and gastrointestinal hemorrhage/perforation may be one of the potential risk factors affecting the prognosis of CBSI patients.These patients are generally in critical condition and should be detected and treated as early as possible to improve their prognosis.Due to the small amount of speci-mens,further research is still needed for confirmation.

With the change of infectious disease incidence pattern and the development of related technologies, progresses have been made in the research of infectious disease epidemiology. In recent years, due to the change in the requirements of infectious disease prevention and control, the research focus has expanded from common infectious diseases to diseases which have been eliminated or might be eliminated, as well as emerging and re-emerging infectious diseases. Infectious disease data has been characterized by multiple sources and modalities. Along with the rapid development of pathogen detection methods, infectious disease surveillance has shifted from a single disease-targted one to a comprehensive one. Moreover, novel technologies such as multi-omics and artificial intelligence have been applied in infectious disease epidemiology research. The international cooperation in this field has become increasingly crucial, and the revision of the International Health Regulations and the negotiation of pandemic agreement will have a profound impact. In the future, infectious disease epidemiology research will develop with more powerful tools to improve its capabilities.