Objective: To develop and validate a clinically actionable prediction model for postoperative pulmonary complications (PPCs) in cardiac surgery patients, focusing on modifiable preoperative risk factors amenable to targeted optimization. Methods: In this prospective observational cohort study, 492 adults undergoing open-chest cardiac surgery between August 15, 2023 and December 31, 2023 were analyzed. Prespecified predictors included gas exchange variables, pulmonary function, inspiratory muscle strength, and physical performance. Univariable and multivariable logistic regression analyses were used to develop the prediction model. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Results: A total of 90 patients (14.1%) developed PPCs after surgery. Five independent predictors were identified: elevated arterial PaCO2 (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.00–1.26), oxygen desaturation (SpO2<93%) (OR 12.47, 95% CI 3.51–48.13), reduced gait speed (OR 0.17, 95% CI 0.04–0.71), lower FEV1/FVC ratio (OR 0.96, 95% CI 0.92–1.00), and diminished inspiratory muscle strength (MIP % predicted) (OR 0.96, 95% CI 0.92–0.99). The model demonstrated good discriminative ability with an AUC of 0.86 (95% CI 0.80–0.93) in the training cohort and 0.87 (95% CI 0.74–0.93) in the validation cohort. Conclusion This parsimonious model achieved high predictive accuracy using five modifiable physiological variables. By targeting abnormalities in gas exchange, pulmonary mechanics, muscle strength, and functional reserve, the model offers a practical tool to guide individualized prehabilitation strategies for reducing PPC risk in cardiac surgery patients.

Objective Heart failure(HF)complicated by acute kidney injury(AKI)significantly impacts patient outcomes,and it is crucial to make early predictions of short-term mortality.This study is focused on developing an interpretable machine learning model to enhance early prediction accuracy in such clinical scenarios.Methods This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care Ⅳ(MIMIC-Ⅳ,version 2.0)database.Data from the first 24 hours after admission to the ICU were extracted and divided into a training set(70％)and a validation set(30％).We utilized the SHapley Additive exPlanation(SHAP)method to interpret the workings of an extreme gradient boosting(XGBoost)model and identify key prognostic factors.The XGBoost model's predictive ability was evaluated against three other machine learning models using the area under the curve(AUC)metric,and its interpretation was enhanced using the SHAP method.Results The study included 8 028 patients with HF complicated by AKI.The XGBoost model outperformed the other models,achieving an AUC of 0.93(95％confidence interval[CI]:0.78-0.94;accuracy=0.89),while neural network model showed the worst performance(AUC=0.79,95％CI:0.77-0.82;accuracy=0.82).Decision curve analysis showed the superior net benefit of the XGBoost model within the 9％to 60％threshold probabilities.SHAP analysis was performed to identify the top 20 predictors,with age(mean SHAP value 1.29)and Glasgow Coma Scale score(mean SHAP value 1.24)emerging as significant factors.Conclusions Our interpretable model offers an enhanced ability to predict mortality risk in HF patients with AKI in ICUs.This model can be used to assist in formulating effective treatment plans and optimizing resource allocation.

Objective: To investigate the effect of intraarticular hemorrhage on extending knee joint contracture model in rats . Methods: 18 mature male SD rats were divided into 3 groups by random number table method . The control group ( group C) was not immobilized and was killed after 4 weeks of feeding . In the simple fixation group( M1 group) , the left lower limb knee joint was immobilized in straight position for 4 weeks . The blood fixationgroup (M2 group) was injected into the knee cavity with body blood and immobilized in a straight position for 4 weeks . The knee joint motion of each group was measured by the joint motion measuring instrument under a stand⁃ard torque . The contracture degree was calculated by the joint range of motion of the knee joint before and after muscles separation . HE staining and Masson staining were used to detect the number of cells and collagen deposi⁃tion in the anterior joint capsule . The protein expressions of transforming growth factor 1 (TGF⁃ β1) , wingless⁃type MMTV integration site family , member 1 ( Wnt1) and beta⁃catenin ( β⁃catenin) in the anterior articular capsule were detected by Western blotting . Results: Compared with group C , total knee contracture and arthrogenic con⁃tracture of rats in M1 and M2 groups increased , and the difference was statistically significant (P < 0. 05) . At the same time , the degree of total contracture and arthrogenic contracture in M2 group was higher than that in M1 group , and the difference was statistically significant (P < 0. 05) . Compared with group C , the number of anterior joint capsule cells and collagen deposition in M1 and M2 groups increased , and the difference was statistically sig⁃group were higher than those in M1 group , and the difference was statistically significant (P < 0. 05) . Compared with group C , the protein expressions of TGF⁃ β1 , Wnt1 and β ⁃catenin in the anterior articular capsule of rats in M1 expressions of TGF⁃ β1 , Wnt1 and β ⁃catenin in the anterior articular capsule of the knee joint in M2 group were sig⁃nificantly higher than those in M1 group , with statistical significance (P < 0. 05) . Conclusion Joint immobiliza⁃ tion can lead to joint contracture , and joint bleeding aggravates the degree of joint capsule fibrosis induced by im⁃mobilization .

Objective To determine the basic and clinical issues to be included in the Guidelines for the Prevention and Treatment of Adenomyosis based on the Delphi method.Methods Based on literature review,preliminary general research,and expert interviews,this study drafted a survey questionnaire for guideline questions.After selecting the expert group,two rounds of Delphi method were conducted to determine the basic and clinical issues included in the guidelines.The questionnaire results were statistically analyzed to evaluate the quality control of Delphi method.Results A total of 15 experts were selected to participate in two rounds of Delphi questionnaire surveys.Finally,39 basic and clinical questions were included,which were condensed into 8 guideline questions.The expert positivity coefficient was 100%,the average coefficient of variation of the two rounds of coordination was 0.23 and 0.30,respectively,and the expert authority coefficient was 0.903.The quality control of the two rounds of Delphi method was good.Conclusion Based on the Delphi method,the basic and clinical issues of the Guidelines for the Prevention and Treatment of Adenomyosis are determined,laying the foundation for the subsequent development of the guidelines.

Objective To identify the basic and clinical questions of the Guidelines for Preventive Treatment of Diseases in Endometriosis based on the Delphi method.Methods Based on the preliminary literature review,preliminary pan-research and expert interviews,the questions of the Guidelines for Preventive Treatment of Diseasesin in Endometriosis were drafted.Totally 15 experts were selected for two rounds of Delphi method expert correspondence and the results were statistically analyzed to finalize the questions for inclusion in the guidelines.Results A total of 65 entries were finally identified for inclusion in the guideline questions and condensed into 8 guideline questions.The quality control of the two rounds of Delphi method was good.The positive coefficient of experts in both rounds was 100%.The coefficient of authority of experts was>0.70.In the degree of Delphi coordination,the mean coefficients of variation were 0.23 and 0.26,and the coefficients of Kendall's concordance were 0.510 and 0.309,respectively(P<0.05).Conclusion This study uses two rounds of Delphi method to determine the issues included in the Guidelines for Preventive Treatment of Diseases in in Endometriosis,laying a solid foundation for the subsequent development of the guidelines.

Antibody-drug conjugates(ADCs)represent a major breakthrough in the treatment of human epidermal growth factor receptor-2(HER-2)-positive breast cancer as they are responsible for significantly improving clinical outcomes.However,their widespread use is accom-panied by severe adverse effects that not only impact the quality of life of patients and treatment adherence but also life-threatening,ulti-mately leading to treatment discontinuation.This article systematically reviews the underlying mechanisms and management strategies for ADC-related toxicities in HER-2-positive breast cancer.Additionally,it focuses on key adverse events,including thrombocytopenia,interstitial lung disease,and cardiotoxicity,that are induced by ADCs such as trastuzumab emtansine(T-DM1)and trastuzumab deruxtecan(T-DXd).Based on the clinical evidence,we proposed early monitoring and standardized intervention measures,emphasizing the importance of timely recognition and systematic management to mitigate risks.Furthermore,we explored future directions for optimizing ADC design to reduce their toxicity and provide valuable insights into their safe clinical application.

Objective To preliminarily explore the potential efficacy of Xuesaitong Soft Capsule(XST)against post-stroke depression(PSD),and to investigate the material basis of XST's anti-PSD effect based on the metabolomics results to analyze its related pharmacokinetic(PK)characteristics and further analyze the pharmacodynamic(PD)equation of representative ingredients.Methods The initial evaluation of drug effica-cy was conducted by detecting the depressive-like behavior and neurotransmitter levels in rats.The Pearson correlation analysis was employed to analyze the correlation between the main metabolites regulated by XST and the saponin components entering the bloodstream.At various time points after drug administration,the blood concentration of ginsenoside Re and the concentration of norepinephrine(NE)in the serum of PSD rats were measured,and the compartment model was fitted accordingly.Furthermore,the liquid chromatography-mass spectrometry was utilized to determine the content of ginsenoside Re in the liver,spleen,kidney,prefron-tal cortex,hippocampus and striatum of PSD rats.Results Ginsenoside Re showed the optimal correlation by the Pearson correlation analysis.Based on its pharmacokinetic parameters,the pharmacodynamic equation with NE was E=160.462 × Ce/(38.663+Ce).The contents of ginsenoside Re in the liver,spleen,kidney,prefron-tal cortex,hippocampus and striatum of rats were(17.23+11.90),(19.05+5.67),(1.95+0.79),(70.13+6.75),(57.03+3.11),and(72.45+5.45)ng/g,respectively.Conclusion XST could improve the depressive-like behaviors in PSD rats by regulating the expression levels of neurotransmitter NE and 5-HT.Ginsenoside Re may be the pharmacodynamical material foundation for XST's preventative treatment of PSD.

Approximately 60% of colorectal cancer (CRC) patients exhibit TP53 mutations, which are strongly associated with tumor progression, chemotherapy resistance, and an unfavorable prognosis. However, targeting p53 has historically been challenging, and currently, there are no approved p53-based therapeutics for clinical use worldwide. In this study, we discovered that ubiquitin carboxyl terminal hydrolase L3 (UCHL3) plays a crucial role in high-level glycolysis, enhanced stem-like properties, and 5-fluorouracil (5-FU) chemoresistance in TP53-mutant CRC by exerting its deubiquitinating enzyme activity to stabilize α-enolase (ENO1) protein. Notably, we identified a newly Food and Drug Administration (FDA)-approved drug, pacritinib, that potently suppresses UCHL3 expression by blocking the janus kinase 2 (JAK2)-signal transducer and activator of transcription 3 (STAT3) pathway in TP53-mutant CRC. Furthermore, Pacritinib was demonstrated to effectively inhibit glycolysis and improve the sensitivity to 5-FU chemotherapy in TP53-mutant CRC. Our findings suggest that targeting the JAK2-STAT3-UCHL3-ENO1 axis is a promising strategy to suppress glycolysis and enhance the efficacy of 5-FU chemotherapy in TP53-mutant CRC. Pacritinib shows potential for clinical application in the treatment of TP53-mutant CRC.

To investigate the protective effects and underlying mechanisms of Qin-Zhu-Liang-Xue decoction (QZLX) in atopic dermatitis (AD) and glucocorticoid resistance, we conducted a single-blinded, randomized controlled clinical trial to evaluate the efficacy and safety of this concoction. Network pharmacology analysis was performed and validated through clinical studies. The efficacy, safety, and mechanism of action of QZLX and glucocorticoid receptor (GR) α recombinant protein were assessed in AD mice induced by 2,4-dinitrofluorobenzene (DNFB). Correlation analysis was performed to determine the clinical relevance of GRα. The trial demonstrated that patients who received QZLX showed considerable improvements in their Scoring Atopic Dermatitis (SCORAD) and Dermatology Life Quality Index (DLQI) scores compared with those who received mizolastine at week 4. Network pharmacological analysis identified GRα as a key target for QZLX in AD treatment. QZLX administration increased the serum GRα expression in AD patients, alleviated AD symptoms in mice, decreased inflammatory cytokine expression, and increased GRα expression without affecting liver or kidney function. In addition, GRα recombinant protein improved AD-like skin lesions in DNFB-induced mice. A negative correlation was observed between GRα expression and clinical parameters, including SCORAD, DLQI, and serum IgE levels. QZLX alleviates AD symptoms through the upregulation of GRα and thus presents a novel therapeutic strategy for the prevention of glucocorticoid resistance in AD management.
Dermatitis, Atopic/drug therapy* ; Animals ; Drugs, Chinese Herbal/administration & dosage* ; Humans ; Mice ; Network Pharmacology ; Male ; Female ; Adult ; Receptors, Glucocorticoid/metabolism* ; Disease Models, Animal ; Single-Blind Method ; Middle Aged ; Young Adult

Objective:To construct and validate a predictive model for pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy.Methods:This retrospective observational study included 595 patients with stage T2-4 and (or) N+M0 LARC diagnosed in the Cancer Hospital of Chinese Academy of Medical Sciences and the Fourth Hospital of Hebei Medical University who had no metastases, tolerated neoadjuvant therapy, completed neoadjuvant therapy, and had undergone radical surgery after neoadjuvant therapy. The training set comprised 299 patients admitted to the Cancer Hospital of Chinese Academy of Medical Sciences from 2013 to 2018, the internal validation set 155 patients admitted from 2019 to 2023, and the external validation set 141 patients admitted to the Fourth Hospital of Hebei Medical University from 2013 to 2021. They were divided into pCR group and non-pCR groups according to postoperative pathology. Among the 299 patients in the training set, 247 were in the non-PCR and 52 in the pCR group; among the 155 patients verified internally, 113 were in the non-PCR and 42 in the pCR group; and among the 141 patients validated externally, 132 were in the non-pCR and nine in the pCR group. Logistic regression was used for univariate and multifactorial analysis to explore the factors associated with pCR and construct a nomogram prediction model. Receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to validate the performance of the predictive model.Results:Univariate and multivariate logistic regression analysis showed that carbohydrate antigen 19-9 ( P=0.040, OR=0.97, 95%CI: 0.93-0.99), neutrophil count ( P<0.001, OR=0.66, 95%CI: 0.52-0.84), tumor T stage: Stage IV ( P=0.011, OR=0.22, 95%CI: 0.07-0.70), tumor N stage: Stage I ( P=0.003, OR=0.22,95%CI:0.08-0.60), Stage II ( P<0.001, OR=0.03, 95%CI: 0.01-0.09) and involvement of mesorectal fascia ( P=0.004, OR=0.09, 95%CI: 0.02-0.47) were independent predictors of pCR. In the training set, the area under the receiver operating characteristic curve of the model was 0.92 (95%CI: 0.87-0.96), whereas in the internal and external validation sets, the AUCs were 0.78 and 0.81, respectively. The calibration curve showed that the prediction model had good prediction efficiency in both the training and verification sets. Decision curve analysis showed that the net benefit of the model was largest when the threshold probability was in the range of 5.2% to 89.7% (in the internal and external validation sets, the threshold probabilities were in the range of 15.7% to 92.3% and 2.2% to 84.1%, respectively). Conclusion:The nomogram model constructed in this study showed efficacy in predicting whether patients with LARC will achieve pCR after receiving neoadjuvant chemoradiotherapy.