OBJECTIVE: To investigate the efficacy and safety of diagnostic-driven therapy for invasive fungal disease(IFD) in patients with myeloid hematologic malignancies. METHODS: A retrospective analysis was conducted on the clinical data of 91 patients with myeloid hematologic malignancies who received diagnostic-driven therapy for IFD at the Second Hospital of Anhui Medical University from January 1, 2020 to December 31, 2023. The patients were divided into two groups based on medication: 44 patients in the caspofungin group and 47 patients in the voriconazole group. The clinical efficacy and adverse reactions of the two groups were compared and analyzed. RESULTS: The overall response rates in the caspofungin and voriconazole groups were 67.4% and 60.0%, respectively. Among patients who transitioned to diagnostic-driven therapy following prophylactic or empirical treatment with triazole antifungal agents, the response rate of the caspofungin group was significantly higher than that of the voriconazole group (76.9% vs 35.3%, P <0.05). A total of 9 patients in both groups experienced adverse reactions, and no grade III or higher adverse reactions occurred. The incidence of grade I-II adverse reactions in the caspofungin group was lower than in the voriconazole group (2.3% vs 17.0%, P <0.05). CONCLUSION In patients with myeloid hematologic malignancies, caspofungin and voriconazole demonstrate comparable clinical efficacy in diagnostic-driven therapy for IFD, but caspofungin is associated with a lower incidence of adverse reactions. Caspofungin exhibits significant effectiveness when initiating diagnostic-driven therapy after prophylactic or empirical treatment with broad-spectrum triazole antifungal agents.
Humans ; Retrospective Studies ; Hematologic Neoplasms/complications* ; Antifungal Agents/therapeutic use* ; Voriconazole/therapeutic use* ; Caspofungin/therapeutic use* ; Invasive Fungal Infections/diagnosis* ; Male ; Female ; Mycoses/drug therapy* ; Middle Aged ; Treatment Outcome ; Aged ; Adult

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective:To investigate the clinical efficacy and factors influencing treatment of pediatric noninfectious uveitis with Adalimumab (ADA).Methods:A retrospective clinical study. A total of 86 pediatric patients with non-infectious uveitis, diagnosed and treated with ADA at Department of Uveitis Specialist of Xi'an People's Hospital (Xi' an Fourth Hospital) from January 1, 2021 to December 31, 2023, were included in this study. The age of all patients was ≤16 years. Among them, 55 (63.95%, 55/86) patients received ADA combined with one immunosuppressive agent, 28 (32.56%, 28/86) patients received ADA combined with ≥2 immunosuppressive agents, and 3 (3.49%, 3/86) patients received ADA alone without any immunosuppressive agents. All patients underwent best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) examinations. The thickness of the retinal nerve fiber layer (RNFL) in the macular region was measured using an OCT device. The cumulative treatment effectiveness rate at 12 months post-treatment was evaluated using the Kaplan-Meier survival analysis. Multivariate analysis was performed using the Cox proportional hazards regression model, and the optimal predictive model was selected based on the Bayesian information criterion. The association between different treatment regimens and various clinical outcomes was assessed.Results:Among the 86 pediatric patients, 42 were male and 44 were female, with a mean age of (10.47±3.23) years. The distribution of uveitis types was as follows: anterior uveitis in 37 cases, intermediate uveitis in 15 cases, posterior uveitis in 10 cases, and panuveitis in 24 cases. Anterior chamber cells (ACC), keratic precipitates, and synechiae were present in 66, 55, and 38 cases, respectively. The cumulative treatment effectiveness at 12 months was 85.1% [95% confidence interval ( CI) 71.9-92.2], with a median time to treatment effectiveness of 3 months. Compared with baseline, after 6 months of treatment, the BCVA, RNFL thickness ( Z=?6.323, ?8.017), and the grading of ACC and vitreous haze ( χ2= ?6.917, ?5.027) showed significant improvement, with statistically significant differences ( P<0.05). Multivariate analysis revealed that ACC (hazard ratio=22.31, 95% CI 2.43-204.68) and anterior uveitis (hazard ratio=3.88, 95% CI 2.03-7.42) were significantly associated with treatment effectiveness ( P<0.05). Patients with ACC had a median time to treatment effectiveness of 2 months, with a 12-month cumulative treatment effectiveness of 95.5% (95% CI 86.3-98.5). Patients with anterior uveitis had a median time to treatment effectiveness of 2 months, with a 12-month cumulative treatment effectiveness of 97.3% (95% CI 81.3-99.6). Patients without anterior uveitis had a median time to treatment effectiveness of 5 months, with a 12-month cumulative treatment effectiveness of 76.7% (95% CI 54.1-88.2). The cumulative recurrence risk at 12 months was 15.6% (95% CI 6.2-24.1). Conclusion:ADA is safe and effective in treating pediatric non-infectious uveitis, and ACC and anterior uveitis are associated with response rate.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective:To investigate the clinical efficacy and factors influencing treatment of pediatric noninfectious uveitis with Adalimumab (ADA).Methods:A retrospective clinical study. A total of 86 pediatric patients with non-infectious uveitis, diagnosed and treated with ADA at Department of Uveitis Specialist of Xi'an People's Hospital (Xi' an Fourth Hospital) from January 1, 2021 to December 31, 2023, were included in this study. The age of all patients was ≤16 years. Among them, 55 (63.95%, 55/86) patients received ADA combined with one immunosuppressive agent, 28 (32.56%, 28/86) patients received ADA combined with ≥2 immunosuppressive agents, and 3 (3.49%, 3/86) patients received ADA alone without any immunosuppressive agents. All patients underwent best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) examinations. The thickness of the retinal nerve fiber layer (RNFL) in the macular region was measured using an OCT device. The cumulative treatment effectiveness rate at 12 months post-treatment was evaluated using the Kaplan-Meier survival analysis. Multivariate analysis was performed using the Cox proportional hazards regression model, and the optimal predictive model was selected based on the Bayesian information criterion. The association between different treatment regimens and various clinical outcomes was assessed.Results:Among the 86 pediatric patients, 42 were male and 44 were female, with a mean age of (10.47±3.23) years. The distribution of uveitis types was as follows: anterior uveitis in 37 cases, intermediate uveitis in 15 cases, posterior uveitis in 10 cases, and panuveitis in 24 cases. Anterior chamber cells (ACC), keratic precipitates, and synechiae were present in 66, 55, and 38 cases, respectively. The cumulative treatment effectiveness at 12 months was 85.1% [95% confidence interval ( CI) 71.9-92.2], with a median time to treatment effectiveness of 3 months. Compared with baseline, after 6 months of treatment, the BCVA, RNFL thickness ( Z=?6.323, ?8.017), and the grading of ACC and vitreous haze ( χ2= ?6.917, ?5.027) showed significant improvement, with statistically significant differences ( P<0.05). Multivariate analysis revealed that ACC (hazard ratio=22.31, 95% CI 2.43-204.68) and anterior uveitis (hazard ratio=3.88, 95% CI 2.03-7.42) were significantly associated with treatment effectiveness ( P<0.05). Patients with ACC had a median time to treatment effectiveness of 2 months, with a 12-month cumulative treatment effectiveness of 95.5% (95% CI 86.3-98.5). Patients with anterior uveitis had a median time to treatment effectiveness of 2 months, with a 12-month cumulative treatment effectiveness of 97.3% (95% CI 81.3-99.6). Patients without anterior uveitis had a median time to treatment effectiveness of 5 months, with a 12-month cumulative treatment effectiveness of 76.7% (95% CI 54.1-88.2). The cumulative recurrence risk at 12 months was 15.6% (95% CI 6.2-24.1). Conclusion:ADA is safe and effective in treating pediatric non-infectious uveitis, and ACC and anterior uveitis are associated with response rate.

OBJECTIVE: To establish the dynamic treatment strategy of Chinese medicine (CM) for metastatic colorectal cancer (mCRC) by machine learning algorithm, in order to provide a reference for the selection of CM treatment strategies for mCRC. METHODS: From the outpatient cases of mCRC in the Department of Oncology at Xiyuan Hospital, China Academy of Chinese Medical Sciences, 197 cases that met the inclusion criteria were screened. According to different CM intervention strategies, the patients were divided into 3 groups: CM treatment alone, equal emphasis on Chinese and Western medicine treatment (CM combined with local treatment of tumors, oral chemotherapy, or targeted drugs), and CM assisted Western medicine treatment (CM combined with intravenous regimen of Western medicine). The survival time of patients undergoing CM intervention was taken as the final evaluation index. Factors affecting the choice of CM intervention scheme were screened as decision variables. The dynamic CM intervention and treatment strategy for mCRC was explored based on the cost-sensitive classification learning algorithm for survival (CSCLSurv). Patients' survival was estimated using the Kaplan-Meier method, and the survival time of patients who received the model-recommended treatment plan were compared with those who received actual treatment plan. RESULTS: Using the survival time of patients undergoing CM intervention as the evaluation index, a dynamic CM intervention therapy strategy for mCRC was established based on CSCLSurv. Different CM intervention strategies for mCRC can be selected according to dynamic decision variables, such as gender, age, Eastern Cooperative Oncology Group score, tumor site, metastatic site, genotyping, and the stage of Western medicine treatment at the patient's first visit. The median survival time of patients who received the model-recommended treatment plan was 35 months, while those who receive the actual treatment plan was 26.0 months (P=0.06). CONCLUSIONS The dynamic treatment strategy of CM, based on CSCLSurv for mCRC, plays a certain role in providing clinical hints in CM. It can be further improved in future prospective studies with larger sample sizes.
Humans ; Colorectal Neoplasms/therapy* ; Machine Learning ; Female ; Male ; Middle Aged ; Neoplasm Metastasis ; Algorithms ; Medicine, Chinese Traditional/methods* ; Aged ; Adult ; Kaplan-Meier Estimate

Purpose: The association between immune-related adverse events (irAEs) and survival outcomes in non–small cell lung cancer (NSCLC) patients treated with programmed death-(ligand) 1 [PD-(L)1] inhibitors remains controversial, partly due to variations in dealing with immortal-time bias (ITB). Materials and Methods: We retrospectively enrolled 425 advanced NSCLC patients who received anti–PD-(L)1 monotherapy between January 2016 and June 2021, stratifying them into irAE (n=127) and non-irAE (n=298) groups. The primary endpoint was to assess the impact of irAEs on progression-free survival (PFS) and overall survival (OS). Landmark (2-, 3-, 6-, and 9-month) and time-dependent Cox analyses were performed to eliminate ITB. Results: With a median follow-up of 38.8 months, the occurrence of overall irAEs was significantly associated with superior PFS (11.2 vs. 3.4 months, p < 0.001) and OS (31.4 vs. 14.0 months, p < 0.001), which persisted in landmark and time-dependent Cox analyses. For the main organ-specific irAEs, skin, thyroid, and hepatic irAEs, respectively, showed significantly improved survival compared to the non-irAE group, whereas pneumonitis did not. Single-organ irAEs had the best outcomes compared with multi-organ or no irAE, which also held across subgroups of skin, thyroid, and hepatic irAEs. Moreover, severe grade irAEs and immunotherapy discontinuation had a detrimental effect on survival, systemic steroid therapy showed little effect, while immunotherapy resumption had tolerable safety and a trend of improved survival. Conclusion After adequately adjusting ITB, the occurrence of overall irAEs predicts for favorable efficacy of anti–PD-(L)1 monotherapy in NSCLC, with better outcomes observed in patients with skin, thyroid, or hepatic irAEs, particularly those with single-organ involvement.

Objective:To investigate the expression of KCTD8 gene in breast ductal carcinoma and its correlation with clinical factors and prognosis.Methods:Immunohistochemistry technology(IHC)were employed to detect protein expression levels of KCTD8 in 27 pairs of breast ductal carci-noma and its paired adjacent tissues.Analyzing the correlation between changes in KCTD8 expres-sion of protein and clinical factors using statistical techniques.RNA expression and methylation data of breast cancer(including intraductal cancer)were analysed from TCGA database.Result:The pro-tein expression of KCTD8 gene in 27 pairs of breast ductal carcinoma tissues showed a decreasing trend compared to adjacent tissues(P＜0.05),and the decreased expression level of protein was cor-related with the tumor size of patients(P＜0.05).The analysis results of the TCGA database indicate that the expression and hypemethylation of KCTD 8 gene in breast cancer(including intraductal can-cer)tissues affected the prognosis of patients.Conclusion:The reduced protein expression level of KCTD8 gene in breast ductal carcinoma may be involved in the development and affect the prog-nosis of patients.

AIM To study the comprehensive quality of Epimedii Folium from different botanical origins,and to provide a basis for its quality assessment to ensure its further development and utilization.METHODS The analysis was performed on a 30℃ thermostatic Agilent ZORBAX SB-C18 column (4.6 mm×250 mm,5μm),with the mobile phase comprising of acetonitrile-0.1％ phosphate flowing at 1 mL/min in a gradient elution manner,and the detection wavelength was set at 270 nm.With regard to the established HPLC fingerprints,the twenty-nine batches of samples achieved their chemical pattern recognition by using cluster analysis,principal component analysis and orthogonal partial least squares-discriminant analysis.RESULTS Among the twelve common peaks in the fingerprint of twenty-nine batches of samples,five were identified,sharing a similarity within the range of 0.235-0.999.Displaying significant content differences in epimedin A,epimedin B,epimedin C,icariin,and pogostemin I.Each batch of E.Folium of different origins categorized into 4 types was observed with peak 3,epimedin B,epimedin C,and icariin as the main components.CONCLUSION This method of strong specificity,stability and reliability can be applicable in the quick identification of E.Folium from different botanical origins and its quality assessment.

AIM To study the comprehensive quality of Epimedii Folium from different botanical origins,and to provide a basis for its quality assessment to ensure its further development and utilization.METHODS The analysis was performed on a 30℃ thermostatic Agilent ZORBAX SB-C18 column (4.6 mm×250 mm,5μm),with the mobile phase comprising of acetonitrile-0.1％ phosphate flowing at 1 mL/min in a gradient elution manner,and the detection wavelength was set at 270 nm.With regard to the established HPLC fingerprints,the twenty-nine batches of samples achieved their chemical pattern recognition by using cluster analysis,principal component analysis and orthogonal partial least squares-discriminant analysis.RESULTS Among the twelve common peaks in the fingerprint of twenty-nine batches of samples,five were identified,sharing a similarity within the range of 0.235-0.999.Displaying significant content differences in epimedin A,epimedin B,epimedin C,icariin,and pogostemin I.Each batch of E.Folium of different origins categorized into 4 types was observed with peak 3,epimedin B,epimedin C,and icariin as the main components.CONCLUSION This method of strong specificity,stability and reliability can be applicable in the quick identification of E.Folium from different botanical origins and its quality assessment.