Neurodegenerative disorders (NDDs) are prevalent chronic conditions characterized by progressive synaptic loss and pathological protein alterations. Increasing evidence suggested that mitochondrial quality control (MQC) serves as the key cellular process responsible for clearing misfolded proteins and impaired mitochondria. Herein, we provided a comprehensive analysis of the mechanisms through which MQC mediates the onset and progression of NDDs, emphasizing mitochondrial dynamic stability, the clearance of damaged mitochondria, and the generation of new mitochondria. In addition, traditional Chinese medicines (TCMs) and their active monomers targeting MQC in NDD treatment have been demonstrated. Consequently, we compiled the TCMs that show great potential in the treatment of NDDs by targeting MQC, aiming to offer novel insights and a scientific foundation for the use of MQC stabilizers in NDD prevention and treatment.

Neurodegenerative disorders(NDDs)are prevalent chronic conditions characterized by progressive synaptic loss and pathological protein alterations.Increasing evidence suggested that mitochondrial quality control(MQC)serves as the key cellular process responsible for clearing misfolded proteins and impaired mitochondria.Herein,we provided a comprehensive analysis of the mechanisms through which MQC mediates the onset and progression of NDDs,emphasizing mitochondrial dynamic stability,the clearance of damaged mitochondria,and the generation of new mitochondria.In addition,traditional Chinese medicines(TCMs)and their active monomers targeting MQC in NDD treatment have been demonstrated.Consequently,we compiled the TCMs that show great potential in the treatment of NDDs by targeting MQC,aiming to offer novel insights and a scientific foundation for the use of MQC stabilizers in NDD prevention and treatment.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.

Heart failure(HF)is a highly morbid syndrome that seriously affects the physical and mental health of patients and generates an enormous socio-economic burden.In addition to cardiac myocyte oxidative stress and apoptosis,which are considered mechanisms for the development of HF,alterations in cardiac energy metabolism and pathological autophagy also contribute to cardiac abnormalities and ultimately HF.Silent information regulator 1(Sirt1)and adenosine monophosphate-activated protein kinase(AMPK)are nicotinamide adenine dinucleotide(NAD+)-dependent deacetylases and phosphorylated kinases,respectively.They play similar roles in regulating some pathological processes of the heart through regulating targets such as peroxisome proliferator-activated receptor γ coactivator 1α(PGC-1α),protein 38 mitogen-activated protein kinase(p38 MAPK),peroxisome proliferator-activated receptors(PPARs),and mammalian target of rapamycin(mTOR).We summarized the synergistic effects of Sirt1 and AMPK in the heart,and listed the traditional Chinese medicine(TCM)that exhibit cardioprotective properties by modulating the Sirt1/AMPK pathway,to provide a basis for the development of Sirt1/AMPK activators or inhibitors for the treatment of HF and other cardiovascular diseases(CVDs).

Osteoporotic vertebral compression fracture (OVCF) can lead to lower back pain and may be even accompanied by scoliosis, neurological dysfunction and other complications, which will affect the daily activities and life quality of patients. Vertebral augmentation is an effective treatment method for OVCF, but it cannot correct unbalance of bone metabolism or improve the osteoporotic status, causing complications like lower back pain, limited spinal activities and vertebral refracture. The post-operative systematic and standardized rehabilitation treatments can improve curative effect and therapeutic efficacy of anti-osteoporosis, reduce risk of vertebral refracture, increase patient compliance and improve quality of life. Since there still lack relevant clinical treatment guidelines for postoperative rehabilitation treatments following vertebral augmentation for OVCF, the current treatments are varied with uneven therapeutic effect. In order to standardize the postoperative rehabilitation treatment, the Spine Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized relevant experts to refer to relevant literature and develop the "Guideline for postoperative rehabilitation treatment following vertebral augmentation for osteoporotic vertebral compression fracture (2022 version)" based on the clinical guidelines published by the American Academy of Orthopedic Surgeons (AAOS) as well as on the principles of scientificity, practicality and advancement. The guideline provided evidence-based recommendations on 10 important issues related to postoperative rehabilitation treatments of OVCF.

Objective: To explore the association between the exposure to major air pollutants in pre-pregnancy and early pregnancy (peri-conceptional period) and gestational diabetes mellitus (GDM). Methods: From March 2015 to April 2018, 4 817 pregnancies were recruited at three prenatal check-ups hospital in Hefei (Hefei First People′s Hospital, Hefei. Maternal and Child Care Hospital and the First Affiliated Hospital of Anhui Medical University), China. Questionnaire was used to collect the demographic data, the health status and lifestyle of pregnant women. GDM was diagnosed according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2017 Edition). Logistic regression was used to investigate the association of exposure to major air pollutants (PM_2.5, PM₁₀, SO₂, CO and NO₂) during different periods of pre-pregnancy (12 weeks before pregnancy) and first trimester (12 weeks after last menstruation) and duration of exposure to high levels of pollutants with GDM. Results: The mean±SD of the age of subjects was (29.14±4.19) years old and the prevalence of GDM was 21.4% (n=1 030). The results of multivariate logistic regression analysis showed that after adjusting for confounding factors, the risk of GDM increased gradually with the prolonged exposure time of high-concentration pollutants compared with pregnant women who were not exposed to high pollution during the pre-pregnancy (χ²=61.28, P_trend<0.001) with the OR (95%CI) values for exposure time of 1, 2, and 3 months about 1.42 (1.10-1.84), 1.73 (1.29-2.33), and 2.51 (1.75-3.59), respectively. In the pre-pregnancy period, in every 10 μg/m³ increase of PM_2.5 and PM₁₀, the OR (95%CI) values of GDM were 1.14 (1.08-1.20) and 1.13 (1.08-1.19), respectively; for each increase of 1 μg/m³ and 0.10 mg/m³ of SO₂ and CO, the OR (95% CI) values of GDM were 1.03 (1.01-1.05) and 1.07 (1.01-1.13), respectively. For every 1 μg/m³ increase in the average concentration of SO₂ in the first trimester, the OR (95%CI) value of GDM was 1.02 (1.01-1.05). Conclusion PM_2.5, PM₁₀, SO₂ and CO exposure during the pre-pregnancy and SO₂ exposure in first trimester were positively correlated with the risk of GDM.

To explore the association between the exposure to major air pollutants in pre?pregnancy and early pregnancy (peri?conceptional period) and gestational diabetes mellitus (GDM). Methods From March 2015 to April 2018, 4 817 pregnancies were recruited at three prenatal check?ups hospital in Hefei (Hefei First People′s Hospital, Hefei. Maternal and Child Care Hospital and the First Affiliated Hospital of Anhui Medical University), China. Questionnaire was used to collect the demographic data, the health status and lifestyle of pregnant women. GDM was diagnosed according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2017 Edition). Logistic regression was used to investigate the association of exposure to major air pollutants (PM2.5, PM10, SO2, CO and NO2) during different periods of pre?pregnancy (12 weeks before pregnancy) and first trimester (12 weeks after last menstruation) and duration of exposure to high levels of pollutants with GDM. Results The mean±SD of the age of subjects was (29.14±4.19) years old and the prevalence of GDM was 21.4% (n=1 030). The results of multivariate logistic regression analysis showed that after adjusting for confounding factors, the risk of GDM increased gradually with the prolonged exposure time of high?concentration pollutants compared with pregnant women who were not exposed to high pollution during the pre?pregnancy (χ2=61.28, Ptrend<0.001) with the OR (95% CI ) values for exposure time of 1, 2, and 3 months about 1.42 (1.10-1.84), 1.73 (1.29-2.33), and 2.51 (1.75-3.59), respectively. In the pre?pregnancy period, in every 10 μg/m3 increase of PM2.5 and PM10, the OR (95%CI) values of GDM were 1.14 (1.08-1.20) and 1.13 (1.08-1.19), respectively;for each increase of 1 μg/m3 and 0.10 mg/m3 of SO2 and CO, the OR (95% CI) values of GDM were 1.03 (1.01-1.05) and 1.07 (1.01-1.13), respectively. For every 1 μg/m3 increase in the average concentration of SO2 in the first trimester, the OR (95%CI) value of GDM was 1.02 (1.01-1.05). Conclusion PM2.5, PM10, SO2 and CO exposure during the pre?pregnancy and SO2 exposure in first trimester were positively correlated with the risk of GDM.

Objective To explore the relationship between different statuses of vitamin D and parathyroid hormone (PTH) during pregnancy with glucose metabolism and the risk of gestational diabetes mellitus (GDM). Methods A total of 4138 pregnant women who had antenatal care in 3 hospitals of Hefei from March 2015 to December 2017 were recruited during 21-24 weeks. Baseline questionnaires were performed and serum 25-hydroxyvitamin D [25(OH) D] and PTH levels were measured in fasting venous blood. Glucose tolerance tests were performed during 24 to 28 weeks. Multivariate linear regression model and multivariate logistic regression model were applied to analyze the differences of glucose metabolism index and GDM risk among pregnant women at different statusesof25(OH)DandPTH.Results Theaveragelevelof25(OH)Dinthesecondtrimesterwas(39.8±16.6) nmol/L, with the median PTH 10.7 (6.9, 16.7) ng/L and the detection rate of GDM 20.4％. Covariance analysis showed no statistically significant association of vitamin D and PTH levels with glucose metabolism indexes. Pregnant women with high PTH and vitamin D deficiency had higher 1h postprandial plasma glucose ( 1hPG) , the area under the glucose curve ( AUCglu ) levels, and GDM risk compared with pregnant women with middle/lower PTH and vitamin D deficiency ( control group, all P<0.05) , and higher PTH accompanied with vitamin D non-deficiency ( Group 2, all P<0.05) . However, significant changes in glucose metabolism indicators and GDM risk were not observed in low-level PTH-pregnant women with vitamin D deficiency ( group 1) and group 2 pregnant women compared with control group. Conclusion There is an interaction between vitamin D/PTH levels and glucose metabolism during pregnancy. Vitamin D deficiency with elevated PTH level is associated with abnormal glucose metabolism.