Renal metastasis of oral adenoid cystic carcinoma is rare. A patient with bilateral renal metastasis secondary to surgery for oral adenoid cystic carcinoma was reported. The left kidney was treated with radiofrequency ablation, and the right kidney was treated with radical nephrectomy. The creatinine was 74 μmol/L at 3 months after surgery. The multidisciplinary comprehensive diagnosis and treatment model adopted in this case provided diagnosis and treatment ideas for patients with bilateral renal secondary malignant tumors.

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:This multicenter retrospective study aimed to analyze the clinicopathological features of duodenal adenocarcinoma (DA) and identify prognostic factors for postoperative survival.Methods:Demographic characteristics, clinicopathological features, treatment outcomes and survival of DA patients undergoing surgical treatment at 18 Chinese medical centers from January 2012 to December 2023 were retrospectively analyzed.Results:Among the 2 056 DA patients included, 46.8% (963) had extra-ampullary DA (EA-DA), and 53.2% (1 093) had peri-ampullary DA (PA-DA). The 1-, 3-, and 5-year overall survival (OS) rates for patients who underwent radical surgery were 93.2%, 71.0%, and 57.2%, respectively. The median overall survival was 76 months, and the median progression-free survival (PFS) was 65 months. No differences in survival were observed between the laparotomy group and minimally invasive surgery (MIS) group either before or after propensity score matching (OS: 76 vs. 75 months before PSM, P=0.986; OS: 75 vs. 75 months after PSM, P=0.602). Furthermore, there were no significant differences between-group in operation time and postoperative complications ( P＞0.05). The MIS group experienced less intraoperative blood loss and shorter hospital stays. The multivariate Cox regression analysis revealed that advanced age ( HR=1.43,95% CI:1.18-1.73), elevated carbohydrate antigen 19-9 levels ( HR=1.24,95% CI:1.02-1.51), perineural invasion ( HR=1.44,95% CI:1.14-1.81), vascular invasion ( HR=1.35,95% CI:1.07-1.71), advanced T stage (T3-4 vs. T1-2: HR=1.86,95% CI:1.49-2.31), regional lymph node metastasis ( HR=1.93,95% CI:1.58-2.36), preoperative biliary drainage ( HR=1.26,95% CI:1.04-1.53), intraoperative blood loss ( HR=1.34,95% CI:1.11-1.62), clinically significant postoperative pancreatic fistulas ( HR=1.53,95% CI:1.12-2.09), and postoperative hemorrhage ( HR=1.62,95% CI:1.14-2.29) were independent risk factors for poor prognosis after surgery (all P＜0.05). Conclusions:Radical surgery is associated with favorable overall survival among DA patients, and no difference in survival is observed between EA-DA and PA-DA patients. MIS is a reliable alternative for DA treatment.

One of the most common adverse reactions associated with cancer and its treatment is sarcopenia,a syndrome primarily caused by the disruption of muscle homeostasis,leading to excessive muscle atrophy and decreased muscle strength.For cancer patients,sarcopenia reflects not only a state of muscle mass loss but also serves as an important factor affecting treatment efficacy and survival rates.However,cancer-related sarcopenia often has a more insidious onset,is not visible to the naked eye,and is easily overlooked.Therefore,it is necessary to recognize the dangers of sarcopenia throughout the cancer treatment process and to identify low muscle mass early.In recent years,as healthcare professionals have increasingly focused on sarcopenia,research on cancer-related sarcopenia has gradually deepened,with new advancements in its assessment,diagnosis,intervention,and impact on anti-cancer treatment.This article provides a review of these key areas to offer a theoretical basis for the clinical practice of cancer-related sarcopenia.

Objective To analyze the research status,hotspots,and trends of wearable devices in nursing applications both domestically and internationally.Methods Using China National Knowledge Infrastructure,Wanfang Data Knowledge Service Platform,and Embase core databases as data sources,VOSviewer was used to visualize and analyze the publication time,keywords,and other relevant literature.Results Total of 428 articles were included,including 196 Chinese articles and 232 English articles.The overall publication volume showed an upward trend.Domestic research focuses on chronic diseases,artificial intelligence,and nursing,with the main research subjects being the elderly;Foreign research focuses on smart devices,self-monitoring,and quality of life,with the main research subjects being adults.Conclusion Currently,the number of publications on the application of wearable devices in nursing is relatively small,but the overall research heat is on the rise,mainly used for chronic diseases and self-monitoring.In the future,the application scope of wearable devices should be expanded and their potential value should be explored to promote the innovation and progress of nursing models.

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.

Renal metastasis of oral adenoid cystic carcinoma is rare. A patient with bilateral renal metastasis secondary to surgery for oral adenoid cystic carcinoma was reported. The left kidney was treated with radiofrequency ablation, and the right kidney was treated with radical nephrectomy. The creatinine was 74 μmol/L at 3 months after surgery. The multidisciplinary comprehensive diagnosis and treatment model adopted in this case provided diagnosis and treatment ideas for patients with bilateral renal secondary malignant tumors.

Objective:To analyze the risk factors for fat liquefaction after surgery for pediatric appendiceal abscess and to construct and validate a predictive model for post-operative fat liquefaction.Methods:The training set collected clinical data from 60 children who developed fat liquefaction after appendiceal abscess surgery and 60 children who did not develop fat liquefaction for comparison.The validation set collected data from 97 children during the same period ac-cording to the same criteria to test the model's efficacy.LASSO logistic regression was used to screen potential diag-nostic factors,and a Logistic regression model was employed for univariate analysis.Collinearity diagnostic tests were conducted among the risk factors.The stepwise regression method using the training dataset was applied to evaluate the importance of each risk factor for fat liquefaction.A multivariate Cox proportional hazards model was used to calcu-late the concordance index of the risk factors in both the training and validation sets.A predictive model was constructed using Logistic regression,and the clinical value of the predictive model was evaluated using ROC curves,calibration curves,and decision curves.Results:Compared with the non-liquefaction group,the liquefaction group had higher val-ues in terms of body weight,heart rate,diameter of the tender mass in the right lower abdomen,degree of appendiceal thickening,body temperature upon admission,fasting plasma glucose(FPG)level,surgical duration,intraoperative blood loss,wound healing time,length of hospital stay,volume of abdominal drainage fluid,and infection status(P<0.05).LASSO logistic regression analysis identified potential factors including body temperature upon admission,heart rate,body weight,diameter of the tender mass in the right lower abdomen,degree of appendiceal thickening,FPG level,sur-gical duration,and intraoperative blood loss.Univariate analysis revealed that body weight,diameter of the tender mass in the right lower abdomen,FPG level,surgical duration,and intraoperative blood loss were influencing factors for fat lique-faction after appendectomy(P<0.05).Body weight and FPG factors were mutually independent with no multicollinearity,while there was multicollinearity among the diameter of the tender mass in the right lower abdomen,surgical duration,and intraoperative blood loss.The risk model combining the diameter of the tender mass in the right lower abdomen,in-traoperative blood loss,FPG,surgical duration,and body weight had the highest concordance index in both the training and validation sets,with values of 0.811 and 0.814,respectively.The Logistic regression model established the predictive model as Logit(P)=-1.136+0.664×(diameter of the tender mass in the right lower abdomen)+0.449×(surgical duration)+0.622×(intraoperative blood loss)+0.200×(body weight)+0.578×(FPG).The area under the ROC curve was 0.920(95%CI:0.869～0.942),with a sensitivity of 93.14%and a specificity of 85.73%.The calibration curve of the predictive model showed good fit with the ideal curve.The predictive model had a high net benefit.Conclusion:Body weight,diameter of the tender mass in the right lower abdomen,FPG level,surgical duration,and intraoperative blood loss are risk factors for fat liquefaction after appendectomy and have good predictive value for the occurrence of fat liquefaction.

Objective:To analyze the risk factors for fat liquefaction after surgery for pediatric appendiceal abscess and to construct and validate a predictive model for post-operative fat liquefaction.Methods:The training set collected clinical data from 60 children who developed fat liquefaction after appendiceal abscess surgery and 60 children who did not develop fat liquefaction for comparison.The validation set collected data from 97 children during the same period ac-cording to the same criteria to test the model's efficacy.LASSO logistic regression was used to screen potential diag-nostic factors,and a Logistic regression model was employed for univariate analysis.Collinearity diagnostic tests were conducted among the risk factors.The stepwise regression method using the training dataset was applied to evaluate the importance of each risk factor for fat liquefaction.A multivariate Cox proportional hazards model was used to calcu-late the concordance index of the risk factors in both the training and validation sets.A predictive model was constructed using Logistic regression,and the clinical value of the predictive model was evaluated using ROC curves,calibration curves,and decision curves.Results:Compared with the non-liquefaction group,the liquefaction group had higher val-ues in terms of body weight,heart rate,diameter of the tender mass in the right lower abdomen,degree of appendiceal thickening,body temperature upon admission,fasting plasma glucose(FPG)level,surgical duration,intraoperative blood loss,wound healing time,length of hospital stay,volume of abdominal drainage fluid,and infection status(P<0.05).LASSO logistic regression analysis identified potential factors including body temperature upon admission,heart rate,body weight,diameter of the tender mass in the right lower abdomen,degree of appendiceal thickening,FPG level,sur-gical duration,and intraoperative blood loss.Univariate analysis revealed that body weight,diameter of the tender mass in the right lower abdomen,FPG level,surgical duration,and intraoperative blood loss were influencing factors for fat lique-faction after appendectomy(P<0.05).Body weight and FPG factors were mutually independent with no multicollinearity,while there was multicollinearity among the diameter of the tender mass in the right lower abdomen,surgical duration,and intraoperative blood loss.The risk model combining the diameter of the tender mass in the right lower abdomen,in-traoperative blood loss,FPG,surgical duration,and body weight had the highest concordance index in both the training and validation sets,with values of 0.811 and 0.814,respectively.The Logistic regression model established the predictive model as Logit(P)=-1.136+0.664×(diameter of the tender mass in the right lower abdomen)+0.449×(surgical duration)+0.622×(intraoperative blood loss)+0.200×(body weight)+0.578×(FPG).The area under the ROC curve was 0.920(95%CI:0.869～0.942),with a sensitivity of 93.14%and a specificity of 85.73%.The calibration curve of the predictive model showed good fit with the ideal curve.The predictive model had a high net benefit.Conclusion:Body weight,diameter of the tender mass in the right lower abdomen,FPG level,surgical duration,and intraoperative blood loss are risk factors for fat liquefaction after appendectomy and have good predictive value for the occurrence of fat liquefaction.

Patients with severe periodontal disease often involve multidisciplinary therapy.This paper reports a case of adult patients with severe periodontitis who was treated by orthodontics,restoration,and periodontics.The space between upper central incisors was closed,aesthetics and periodontal conditions were significantly improved.