Mitochondria not only serve as the central hub for cellular energy metabolism,but also participate in crucial physiological processes such as signal transduction and activity regulation.Abnormal mitochondrial quantity,imbalance in mtDNA copy number,accumulation of genetic mutations,and aberrant activation of autophagic pathways can lead to mitochondrial dysfunction or even organelle disorders,while activating mechanisms of cellular damage and death,consequently contributing to the pathogenesis of various diseases.As a highly selective form of autophagy,mitophagy plays a pivotal regulatory role in organelle quality control and homeostasis maintenance.This article focuses on the classic PINK1/Parkin pathway-mediated mitophagy as an entry point,summarizing recent research advances regarding the role of mitophagy in neurodegenerative diseases,aiming to provide novel insights for developing targeted mitophagy regulation strategies to improve prevention and treatment approaches for neurodegenerative disorders.

Objective:To analyze the clinical efficacy of vascular resection and reconstruction during radical resection in patients with hilar cholangiocarcinoma.Methods:A retrospective analysis was conducted on the clinical data of 151 patients with hilar cholangiocarcinoma who underwent radical resection in the Department of Hepatobiliary and Pancreatic Surgery at the First Affiliated Hospital of Anhui Medical University from April 2018 to April 2025. Among them, there were 91 males and 60 females, with an age of (65.4±10.5) years. According to whether radical resection of hilar cholangiocarcinoma was combined with vascular resection, the patients were divided into the vascular resection group ( n=19) and the control group ( n=132). Postoperative complications such as bleeding, biliary fistula, and thrombosis were recorded, along with intraoperative blood loss, R 0 resection rate, perioperative mortality, and recurrence rate at six months postoperatively. Results:The preoperative bilirubin reduction and intraoperative blood loss in the vascular resection group were 6 (31.6) and 200 (200, 200) ml, respectively, while those in the control group were 45 (34.1) and 200 (100, 200) ml, respectively. There were no statistically significant differences between the two groups (all P>0.05). The combined liver resection, Billroth operation type Ⅰ-Ⅱ, and operation time in the vascular resection group were 18 (94.7), 1 (5.3), and 420 (377.5, 512.5) min, respectively, while those in the control group were 79 (59.8), 38 (28.8), and 322.5 (260, 410) min, respectively. There were statistically significant differences between the two groups (all P<0.05). The R 0 resection, perioperative mortality rate, postoperative bleeding, postoperative biliary fistula, postoperative thrombosis, postoperative pathology (adenocarcinoma), and recurrence rate at 6 months after surgery in the vascular resection group were 16 (84.2), 2 (10.5), 2 (10.5), 3 (15.8), 1 (5.3), 18 (94.7), and 1 (5.9), respectively, while those in the control group were 103 (78.0), 5 (3.8), 5 (3.8), 25 (18.9), 2 (1.5), 121 (91.7), and 6 (4.7), respectively. There were no statistically significant differences between the two groups (all P>0.05). The postoperative hospital stay, alanine aminotransferase on the 1st and 3rd day after surgery, and postoperative liver failure in the vascular resection group were 18 (13.5, 21.5) days, 619 (305.4, 1 634.0) U/L, and 1 (5.3), respectively, while those in the control group were 14 (11, 18), 254.5 (139.3, 468.3) U/L, and 3 (2.3), respectively. There were statistically significant differences between the two groups (all P<0.05). Conclusion:Vascular resection and reconstruction during radical resection of hilar cholangiocarcinoma in patients has certain safety and efficacy.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective:To explore the effectiveness of staged pain nursing based on Snyder's hope theory in patients with leukemia.Methods:From January 2023 to December 2024, 124 leukemia patients admitted to Shanxi Provincial People's Hospital were selected as study subjects using convenience sampling. Using a random number table method, subjects were divided into a control group and an observation group, with 62 cases in each group. Control group received routine nursing, while observation group received staged pain nursing based on Snyder's hope theory in addition to routine nursing. Negative emotions, hope levels, and coping styles were compared between the two groups of patients before and after intervention.Results:After intervention, both groups of patients had lower scores on the Hamilton Anxiety Scale, Hamilton Depression Scale, and the avoidance and resignation dimensions of the Medical Coping Modes Questionnaire compared to before intervention, and observation group had lower scores than control group, with statistically significant differences ( P<0.05). After intervention, the Chinese version of the Herth Hope Scale scores and the confrontation dimension scores of the Medical Coping Modes Questionnaire for both groups of patients were higher than before intervention, and observation group had higher scores than control group, with statistically significant differences ( P<0.05) . Conclusions:Staged pain nursing based on Snyder's hope theory effectively improves negative emotions and coping styles of leukemia patients, and increases their levels of hope, demonstrating favorable application effects.

Plant-derived bioactive peptides have become a research hotspot in the fields of food and medicine due to their high source safety,easy absorption and utilization by the human body,and potential edible and medicinal value.Bioactive peptides can be classified into antihypertensive,hypoglycemic,hypolipidemic,anticancer,antioxidant,antimicrobial,and anti-inflammatory peptides according to their functions.Among these,antihypertensive,hypoglycemic,and hypolipidemic peptides are collectively referred to as cardiovascular active peptides,which can be used for the treatment and prevention of cardiovascular diseases and have an important role in the development of modem biomedicine.This review focused on the preparation methods,separation,purification,and identification techniques of bioactive peptides,as well as their mechanisms of action and applications in regulating cardiovascular diseases,aiming to provide a reference for further development and application of plant-derived cardiovascular peptides.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective:To analyze the clinical efficacy of vascular resection and reconstruction during radical resection in patients with hilar cholangiocarcinoma.Methods:A retrospective analysis was conducted on the clinical data of 151 patients with hilar cholangiocarcinoma who underwent radical resection in the Department of Hepatobiliary and Pancreatic Surgery at the First Affiliated Hospital of Anhui Medical University from April 2018 to April 2025. Among them, there were 91 males and 60 females, with an age of (65.4±10.5) years. According to whether radical resection of hilar cholangiocarcinoma was combined with vascular resection, the patients were divided into the vascular resection group ( n=19) and the control group ( n=132). Postoperative complications such as bleeding, biliary fistula, and thrombosis were recorded, along with intraoperative blood loss, R 0 resection rate, perioperative mortality, and recurrence rate at six months postoperatively. Results:The preoperative bilirubin reduction and intraoperative blood loss in the vascular resection group were 6 (31.6) and 200 (200, 200) ml, respectively, while those in the control group were 45 (34.1) and 200 (100, 200) ml, respectively. There were no statistically significant differences between the two groups (all P>0.05). The combined liver resection, Billroth operation type Ⅰ-Ⅱ, and operation time in the vascular resection group were 18 (94.7), 1 (5.3), and 420 (377.5, 512.5) min, respectively, while those in the control group were 79 (59.8), 38 (28.8), and 322.5 (260, 410) min, respectively. There were statistically significant differences between the two groups (all P<0.05). The R 0 resection, perioperative mortality rate, postoperative bleeding, postoperative biliary fistula, postoperative thrombosis, postoperative pathology (adenocarcinoma), and recurrence rate at 6 months after surgery in the vascular resection group were 16 (84.2), 2 (10.5), 2 (10.5), 3 (15.8), 1 (5.3), 18 (94.7), and 1 (5.9), respectively, while those in the control group were 103 (78.0), 5 (3.8), 5 (3.8), 25 (18.9), 2 (1.5), 121 (91.7), and 6 (4.7), respectively. There were no statistically significant differences between the two groups (all P>0.05). The postoperative hospital stay, alanine aminotransferase on the 1st and 3rd day after surgery, and postoperative liver failure in the vascular resection group were 18 (13.5, 21.5) days, 619 (305.4, 1 634.0) U/L, and 1 (5.3), respectively, while those in the control group were 14 (11, 18), 254.5 (139.3, 468.3) U/L, and 3 (2.3), respectively. There were statistically significant differences between the two groups (all P<0.05). Conclusion:Vascular resection and reconstruction during radical resection of hilar cholangiocarcinoma in patients has certain safety and efficacy.

Objective:To explore the effectiveness of staged pain nursing based on Snyder's hope theory in patients with leukemia.Methods:From January 2023 to December 2024, 124 leukemia patients admitted to Shanxi Provincial People's Hospital were selected as study subjects using convenience sampling. Using a random number table method, subjects were divided into a control group and an observation group, with 62 cases in each group. Control group received routine nursing, while observation group received staged pain nursing based on Snyder's hope theory in addition to routine nursing. Negative emotions, hope levels, and coping styles were compared between the two groups of patients before and after intervention.Results:After intervention, both groups of patients had lower scores on the Hamilton Anxiety Scale, Hamilton Depression Scale, and the avoidance and resignation dimensions of the Medical Coping Modes Questionnaire compared to before intervention, and observation group had lower scores than control group, with statistically significant differences ( P<0.05). After intervention, the Chinese version of the Herth Hope Scale scores and the confrontation dimension scores of the Medical Coping Modes Questionnaire for both groups of patients were higher than before intervention, and observation group had higher scores than control group, with statistically significant differences ( P<0.05) . Conclusions:Staged pain nursing based on Snyder's hope theory effectively improves negative emotions and coping styles of leukemia patients, and increases their levels of hope, demonstrating favorable application effects.