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.

Curzerene, a sesquiterpene compound isolated from Curcuma Radix, exhibits various therapeutic effects, such as anti-tumor and anti-hyperlipidemic properties. However, its neuroprotective effects have not yet been reported. This study focused on exploring the neuroprotective effect of curzerene and elucidating its potential mechanism by combining molecular biotechnology with multi-omics approaches. Curzerene was orally administered to LPS-induced depressive-like behaviors and cognitive impairment in mice for 14 days, and the related biochemical parameters were evaluated. The possible mechanisms were elucidated using qRT-PCR, Western Blot, immunofluorescence, untargeted metabolomics, GC-MS and 16S rDNA comprehensively. Curzerene ameliorated depression symptoms and cognitive impairment by increasing the preference for sucrose in SPT and the central area and total distance traveled in OFT, reducing the immobility time in TST and FST, as well as rising the spontaneous alternation ratio in Y maze. Multiple molecular biology techniques analyses indicated the ameliorative effect of curzerene via HMGB1/RAGE/ TLR4 pathway. Moreover, curzerene primarily regulates purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, pyrimidine metabolism, etc. Furthermore, intervention increased the relative abundance of Parabacteroides, Clostridia_UCG-014_unclassified, and Rhodospirillales_unclassified, and enhanced the production of SCFAs. This work demonstrated that curzerene effectively protects against LPS-induced neurological damage, potentially by inhibiting the HMGB1/ RAGE/TLR4 pathway through the restoration of gut microbiota homeostasis, modulation of metabolites, and enhancement of SCFAs. In conclusion, this study offers new perspectives on the therapeutic possibilities of curzerene in mitigating depressive-like behaviors and cognitive impairment.

Objective To address the need for rapid review of automated organ-at-risk (OAR) contouring for head-and-neck cancer in online adaptive radiotherapy, this study used dosimetric indices as the reference standard to evaluate the suitability of geometric similarity metrics and to develop a rapid screening model that balances the risk of missed errors with review efficiency. Methods This retrospective study included 29 patients with head-and-neck radiotherapy, yielding 243 pairs of OAR contours (auto-generated vs. manual). Geometric similarity metrics, including the Dice similarity coefficient (Dice), the 95th percentile Hausdorff distance (HD95), and the maximum Hausdorff distance (HD100), were computed between the two contour sets. Using the dose distributions from clinical treatment plans, dosimetric differences between the two contour sets were calculated for key indices, including mean dose (D_mean) and maximum dose (D_max). Correlations between geometric metrics and dosimetric differences were analyzed. Dosimetric discrepancy events were defined as Y_3Gy using a threshold of 3 Gy. Within a univariable logistic regression triage framework, each geometric metric was used as an independent variable to estimate the probability of Y_3Gy. Discriminative performance was assessed using receiver operating characteristic (ROC) curves and precision-recall (PR) curves, with the area under the curve (AUC) including ROC-AUC and PR-AUC. Practical operating thresholds were determined via threshold sweeping. Results Geometric similarity metrics showed weak-to-moderate correlations with dosimetric differences; compared with maximum dose, correlations with mean dose differences were more consistent. Among the evaluated metrics, HD95 achieved the best classification performance for Y_3Gy. Threshold sweeping suggested that an HD95 threshold in the range of 4-9 mm can balance the risk of missed discrepancies with the efficiency of automated contour quality assurance (QA). Conclusion Several geometric similarity metrics demonstrated only weak-to-moderate associations with dosimetric differences. For head-and-neck OAR contour QA in this study, HD95 provided the best discriminative performance and can support rapid triage of automated contours, with a practical operating threshold range of 4-9 mm, potentially improving the efficiency of adaptive radiotherapy workflows.

Objective To address the need for rapid review of automated organ-at-risk (OAR) contouring for head-and-neck cancer in online adaptive radiotherapy, this study used dosimetric indices as the reference standard to evaluate the suitability of geometric similarity metrics and to develop a rapid screening model that balances the risk of missed errors with review efficiency. Methods This retrospective study included 29 patients with head-and-neck radiotherapy, yielding 243 pairs of OAR contours (auto-generated vs. manual). Geometric similarity metrics, including the Dice similarity coefficient (Dice), the 95th percentile Hausdorff distance (HD95), and the maximum Hausdorff distance (HD100), were computed between the two contour sets. Using the dose distributions from clinical treatment plans, dosimetric differences between the two contour sets were calculated for key indices, including mean dose (D_mean) and maximum dose (D_max). Correlations between geometric metrics and dosimetric differences were analyzed. Dosimetric discrepancy events were defined as Y_3Gy using a threshold of 3 Gy. Within a univariable logistic regression triage framework, each geometric metric was used as an independent variable to estimate the probability of Y_3Gy. Discriminative performance was assessed using receiver operating characteristic (ROC) curves and precision-recall (PR) curves, with the area under the curve (AUC) including ROC-AUC and PR-AUC. Practical operating thresholds were determined via threshold sweeping. Results Geometric similarity metrics showed weak-to-moderate correlations with dosimetric differences; compared with maximum dose, correlations with mean dose differences were more consistent. Among the evaluated metrics, HD95 achieved the best classification performance for Y_3Gy. Threshold sweeping suggested that an HD95 threshold in the range of 4-9 mm can balance the risk of missed discrepancies with the efficiency of automated contour quality assurance (QA). Conclusion Several geometric similarity metrics demonstrated only weak-to-moderate associations with dosimetric differences. For head-and-neck OAR contour QA in this study, HD95 provided the best discriminative performance and can support rapid triage of automated contours, with a practical operating threshold range of 4-9 mm, potentially improving the efficiency of adaptive radiotherapy workflows.

Objective To address the need for rapid review of automated organ-at-risk (OAR) contouring for head-and-neck cancer in online adaptive radiotherapy, this study used dosimetric indices as the reference standard to evaluate the suitability of geometric similarity metrics and to develop a rapid screening model that balances the risk of missed errors with review efficiency. Methods This retrospective study included 29 patients with head-and-neck radiotherapy, yielding 243 pairs of OAR contours (auto-generated vs. manual). Geometric similarity metrics, including the Dice similarity coefficient (Dice), the 95th percentile Hausdorff distance (HD95), and the maximum Hausdorff distance (HD100), were computed between the two contour sets. Using the dose distributions from clinical treatment plans, dosimetric differences between the two contour sets were calculated for key indices, including mean dose (D_mean) and maximum dose (D_max). Correlations between geometric metrics and dosimetric differences were analyzed. Dosimetric discrepancy events were defined as Y_3Gy using a threshold of 3 Gy. Within a univariable logistic regression triage framework, each geometric metric was used as an independent variable to estimate the probability of Y_3Gy. Discriminative performance was assessed using receiver operating characteristic (ROC) curves and precision-recall (PR) curves, with the area under the curve (AUC) including ROC-AUC and PR-AUC. Practical operating thresholds were determined via threshold sweeping. Results Geometric similarity metrics showed weak-to-moderate correlations with dosimetric differences; compared with maximum dose, correlations with mean dose differences were more consistent. Among the evaluated metrics, HD95 achieved the best classification performance for Y_3Gy. Threshold sweeping suggested that an HD95 threshold in the range of 4-9 mm can balance the risk of missed discrepancies with the efficiency of automated contour quality assurance (QA). Conclusion Several geometric similarity metrics demonstrated only weak-to-moderate associations with dosimetric differences. For head-and-neck OAR contour QA in this study, HD95 provided the best discriminative performance and can support rapid triage of automated contours, with a practical operating threshold range of 4-9 mm, potentially improving the efficiency of adaptive radiotherapy workflows.

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.

Objective:To assess the effects of recognition function of four-lead electrocardiogram(ECG)synchronization technique and multi-parameter fusion technique in reducing the number of false alarms and improving the quality of alarms in intensive care units(ICU).Methods:Four-lead ECG synchronization technique and multi-parameter fusion technique were used to strengthen the monitoring and assessment for the alarm of clinical monitors,and reduce the false alarm rate of monitors.The clinical alarm data of bed units corresponding to 48 monitors in clinical use of ICU,cardiovascular intensive care unit(CCU)and neurosurgery intensive care unit(NICU)of Hainan General Hospital from October 14 to December 27,2024 were selected.According to the opening and close of the four-lead ECG synchronization and multi-parameter fusion technique algorithm of the monitors,they were divided into group A(opened four-lead ECG synchronization and multi-parameter fusion),group B(opened four-lead ECG synchronization,but closed multi-parameter fusion),group C(closed four-lead ECG synchronization,but opened multi-parameter fusion)and group D(closed four-lead ECG synchronization and multi-parameter fusion),with 12 units in each group.The numbers of total alarms and false alarms generated by monitor of each bed unit among different optimization strategies were compared.Results:The numbers of average daily alarm of the monitors in groups A,B and C were respectively(134.2±32.4)cases,(392.5±68.2)cases and(583.4±126.5)cases,which were lower than those in group D(1 073.2±168.6),with statistically significant differences(Z=3.45,2.94,2.52,P<0.05).The optimization rates of the alarm numbers in groups A,B and C were increased by 87.51%,63.47%and 45.67%,respectively.The rates of average false alarm of the monitors in groups A,B and C were respectively(1.04±0.15)%,(1.73±0.12)%and(2.07±0.08)%,which were lower than(3.76±0.2)%in group D,with statistically significant differences(Z=3.45,2.94,2.52,P<0.05).Conclusion:Four-lead ECG synchronization technique and multi-parameter fusion technique can effectively optimize the number of alarms in ICU,and reduce the proportion of false alarms of monitors in department,and decrease fatigue of medical staffs for alarm.

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:We aimed to investigate the relationship between CYP2D6*10 gene polymorphisms and adverse reactions associated with tamoxifen treatment in breast cancer patients, and assess the value of CYP2D6*10 gene polymorphism testing in guiding the use of medications in endocrine therapy for breast cancer.Methods:177 breast cancer patients with HR-positive and postoperative tamoxifen were admitted to the First Affiliated Hospital of Nanjing Medical University from November 2012 to December 2021. Their clinicopathologic data were collected for follow-up observation of adverse reactions related to tamoxifen treatment. After two years of tamoxifen treatment, finger blood of these patients was taken for CYP2D6 gene polymorphism detection. Moreover, databases including RNAfold, QTLbase, 3DSNP v2.0, RegulomeDB 2.2, and HaploReg v4.2 were used to predict the annotation of proximal and distal interactions of CYP2D6 polymorphic sites between genes and regulatory elements.Results:Genotyping analysis revealed 40 patients (22.6%) with the CC genotype, 79 (44.6%) with the CT genotype, and 58 (32.8%) with the TT genotype. Common adverse reactions to tamoxifen included abnormal liver function (58), fatty liver (81), uterine fibroids (12), and endometrial surgery for endometrial thickening (17). Univariate and multivariate logistic regression analyses showed that there were no significant statistical differences between the CC and CT+TT genotypes in terms of liver damage, new-onset fatty liver, uterine fibroids, or tumor recurrence and metastasis ( P＞0.05). Notably, endometrial thickening was more significant in patients with the CT+TT genotype (4.37±3.82 mm) than in those with the CC genotype (2.43±2.96 mm), with a statistically significant difference between them ( P＜0.01). Bioinformatic analysis suggested that in breast tissues, the CYP2D6*10 polymorphic locus had a significant expression quantitative trait locus (eQTL) effect with CYP2D6, and its genetic variations could affect the binding of CYP2D6 to transcription factors, which might modulate the expression of CYP2D6 through changes in secondary structure and chromatin modifications, etc., and thus affect the tamoxifen drug sensitivity. Further eQTL analysis showed significant correlation between CYP2D6 expression levels with different genotypes of the CYP2D6 rs1065852 polymorphism in breast tissues ( P＜0.01). Conclusion:Tamoxifen remains a primary therapeutic agent for premenopausal HR-positive breast cancer patients, and its efficacy is influenced by polymorphisms in the CYP2D6*10. It is recommended that for breast cancer patients carrying the CYP2D6 CT and TT genotypes, endometrial monitoring should be strengthened during treatment with tamoxifen, and the medication should be adjusted in a timely manner.

Objective:To assess the effects of recognition function of four-lead electrocardiogram(ECG)synchronization technique and multi-parameter fusion technique in reducing the number of false alarms and improving the quality of alarms in intensive care units(ICU).Methods:Four-lead ECG synchronization technique and multi-parameter fusion technique were used to strengthen the monitoring and assessment for the alarm of clinical monitors,and reduce the false alarm rate of monitors.The clinical alarm data of bed units corresponding to 48 monitors in clinical use of ICU,cardiovascular intensive care unit(CCU)and neurosurgery intensive care unit(NICU)of Hainan General Hospital from October 14 to December 27,2024 were selected.According to the opening and close of the four-lead ECG synchronization and multi-parameter fusion technique algorithm of the monitors,they were divided into group A(opened four-lead ECG synchronization and multi-parameter fusion),group B(opened four-lead ECG synchronization,but closed multi-parameter fusion),group C(closed four-lead ECG synchronization,but opened multi-parameter fusion)and group D(closed four-lead ECG synchronization and multi-parameter fusion),with 12 units in each group.The numbers of total alarms and false alarms generated by monitor of each bed unit among different optimization strategies were compared.Results:The numbers of average daily alarm of the monitors in groups A,B and C were respectively(134.2±32.4)cases,(392.5±68.2)cases and(583.4±126.5)cases,which were lower than those in group D(1 073.2±168.6),with statistically significant differences(Z=3.45,2.94,2.52,P<0.05).The optimization rates of the alarm numbers in groups A,B and C were increased by 87.51%,63.47%and 45.67%,respectively.The rates of average false alarm of the monitors in groups A,B and C were respectively(1.04±0.15)%,(1.73±0.12)%and(2.07±0.08)%,which were lower than(3.76±0.2)%in group D,with statistically significant differences(Z=3.45,2.94,2.52,P<0.05).Conclusion:Four-lead ECG synchronization technique and multi-parameter fusion technique can effectively optimize the number of alarms in ICU,and reduce the proportion of false alarms of monitors in department,and decrease fatigue of medical staffs for alarm.