BACKGROUND:The repair process of myocardial injury involves complex cellular and molecular mechanisms,especially mitochondrial calcium homeostasis,macrophage autophagy and pyroptosis pathways.Traditional Chinese medicine(TCM)has shown significant clinical efficacy in improving myocardial injury,but its mechanism of action needs to be thoroughly investigated. OBJECTIVE:To investigate the role of mitochondrial calcium homeostasis-mediated macrophage autophagy and pyroptosis pathways in myocardial injury,and to summarize the progress of TCM in this field. METHODS:A computerized search was performed for relevant literature from the database inception to March 2024 in the Web of Science,PubMed and CNKI.The search terms were"mitochondrial calcium homeostasis,macrophage autophagy,macrophage pyroptosis,traditional Chinese medicine,myocardial injury,myocardial injury reperfusion"in Chinese and English.Through literature review,we analyzed the relationship between mitochondrial calcium homeostasis and macrophage autophagy and pyroptosis,explored the mechanism of their roles in myocardial injury,and summarized the pathways of multi-targeted,multi-pathway effects of TCM. RESULTS AND CONCLUSION:The maintenance of mitochondrial calcium homeostasis has been found to be closely related to the normal function of cardiomyocytes.Macrophages can participate in the repair process of myocardial injury through autophagy and pyroptosis pathways.Autophagy contributes to cell clearance and regulation of inflammatory response,while pyroptosis affects myocardial repair by releasing inflammatory factors.TCM regulates mitochondrial calcium homeostasis and macrophage function through multiple mechanisms.For example,astragalosid regulates calcium homeostasis by lowering mitochondrial membrane potential and inhibiting cytochrome C,and epimedium glycoside plays a role in reducing β-amyloid deposition.In addition,herbal compounds and single drugs promote myocardial repair by activating or inhibiting specific signaling pathways,such as PI3K/AKT and nuclear factor-κB signaling pathways.Future studies should focus on the interactions between mitochondrial calcium homeostasis,autophagy and pyroptosis pathways,as well as how TCM can exert therapeutic effects through these pathways to provide new strategies and drugs for the treatment of myocardial injury.

Lacking therapeutic targets highlights the crucial roles of chemotherapy and radiotherapy in the clinical management of triple-negative breast cancer (TNBC). To relieve the side effects of the chemoradiotherapy combination regimen, we design and develop a self-assembled micelle nanosystem consisting of perfluorocarbon chain-modified cisplatin prodrug. By incorporating perfluorodecalin, this nanosystem can effectively carry ozone and promote irradiation-derived reactive oxygen species (ROS) production. By leveraging the perfluorocarbon sidechain, the nanosystem exhibits efficient internalization by TNBC cells and effectively escapes from lysosomal entrapment. Under X-ray irradiation, ozone-generated ROS disrupts the intracellular redox balance, thereby facilitating the release of cisplatin in a reduction-responsive manner mediated by reduced glutathione. Moreover, oxygen derived from ozone decomposition enhances the efficacy of radiotherapy by alleviating tumor hypoxia. Notably, the combination of irradiation with ozone-loaded cisplatin prodrug nano system synergistically prompts antitumor efficacy and reduces cellular/systemic toxicity in vitro and in vivo. Furthermore, the combo regimen remodels the tumor microenvironment into an immune-favored state by triggering immunogenic cell death and relieving hypoxia, which provides a promising foundation for a combination regimen of immunotherapy. In conclusion, our nanosystem presents a novel strategy for integrating chemotherapy and radiotherapy to optimize the efficacy and safety of TNBC clinical treatment.

OBJECTIVES: To construct a bone tumor classification model based on feature decoupling and fusion for processing modality loss and fusing multimodal information to improve classification accuracy. METHODS: A decoupling completion module was designed to extract local and global bone tumor image features from available modalities. These features were then decomposed into shared and modality-specific features, which were used to complete the missing modality features, thereby reducing completion bias caused by modality differences. To address the challenge of modality differences that hinder multimodal information fusion, a cross-attention-based fusion module was introduced to enhance the model's ability to learn cross-modal information and fully integrate specific features, thereby improving the accuracy of bone tumor classification. RESULTS: The experiment was conducted using a bone tumor dataset collected from the Third Affiliated Hospital of Southern Medical University for training and testing. Among the 7 available modality combinations, the proposed method achieved an average AUC, accuracy, and specificity of 0.766, 0.621, and 0.793, respectively, which represent improvements of 2.6%, 3.5%, and 1.7% over existing methods for handling missing modalities. The best performance was observed when all the modalities were available, resulting in an AUC of 0.837, which still reached 0.826 even with MRI alone. CONCLUSIONS The proposed method can effectively handle missing modalities and successfully integrate multimodal information, and show robust performance in bone tumor classification under various complex missing modality scenarios.
Humans ; Bone Neoplasms/diagnosis* ; Multimodal Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Image Processing, Computer-Assisted/methods* ; Algorithms

Objective:To analyze the exposure level and changes in severe respiratory infection among Chinese residents aged ≥40 years in China and to provide essential data for preventing and controlling chronic respiratory diseases.Methods:The data came from chronic obstructive pulmonary disease surveillance of Chinese residents in 2014-2015 and 2019-2020. The surveillance covers 31 provinces (autonomous regions and municipalities) in China. A multi-stage stratified cluster random sampling method was used to select permanent residents aged ≥40 years. Relevant information about exposure to severe respiratory infections during childhood was collected through investigation. Rigorous complex sampling and weighted analysis were applied to estimate the exposure rate of severe respiratory infections among children with different characteristics among residents aged ≥40 years in China from 2019 to 2020. Additionally, changes in severe respiratory infections during childhood were analyzed over 2014-2015 and 2019-2020.Results:In 2019-2020, the exposure rate of severe respiratory infection before 18 for residents aged ≥40 years in China was 21.58‰ (95% CI: 17.57‰-25.59‰). The exposure rate of severe respiratory infection before the age of 14 was 19.40‰ (95% CI:15.25‰-23.55‰), the difference in the exposure rate between men and women was not statistically significant (both P>0.05), and the exposure rate of urban residents is higher than that of rural residents. The results of multivariate logistic regression analysis showed that in western rural areas, factors including being born by cesarean section, exposure to secondhand smoke before age 14 years old, and a family history of chronic respiratory diseases were associated with severe respiratory infections in childhood. Compared with 2014-2015, the proportion of residents aged 40 and older in China hospitalized for pneumonia or bronchitis before 14 from 2019 to 2020 was slightly lower than five years prior. Conclusions:Among residents in China aged ≥40 years, 21.58‰ experienced severe respiratory infection exposure during childhood, varying exposure levels across different demographic groups. The burden of chronic respiratory diseases in adulthood cannot be ignored. Efforts should be made to expand the coverage of planned immunization and to focus on early-life interventions during childhood to reduce the incidence of severe respiratory infections.

Objective: To investigate the characteristics and trend of stroke incidence in Jining City, Shangdong Province from 2015 to 2022, so as to provide the reference for formulating prevention and control strategies of stroke. Methods: Data of stroke incidence in Jining City from 2015 to 2022 were collected from Shandong Provincial Chronic Disease Surveillance Information Management System. The crude incidence was estimated, standardized using the data of the sixth national population census in 2010, and analyzed by age, gender and subtype. The trend in incidence of stroke was analyzed using annual percent change (APC). Results: A total of 316 267 cases of stroke were reported in Jining City from 2015 to 2022, with a crude incidence of 474.17/105 and a standardized incidence of 371.43/105. The incidence of stroke peaked from March to May (90 409 cases, 28.59%). There were 278 901 cases of ischemic stroke (88.19%) and 37 366 cases of hemorrhagic stroke (11.81%). The crude incidence of stroke was higher in males than in females (525.45/105 vs. 420.16/105, P<0.05). The crude incidence of stroke increased with age (P<0.05), reaching a peak in the age group of 80 years and above (2 764.92/105). From 2015 to 2022, the crude incidence of stroke in the overall population, males, females, the age groups of 0-<30 years and 40-<50 years showed increasing trends (APC=6.142%, 6.992%, 5.054%, 3.693% and 6.587%, all P<0.05); the crude incidence of ischemic stroke in the overall population, males and females showed increasing trends (APC=7.489%, 6.593% and 5.456%, all P<0.05), while the crude incidence rates of hemorrhagic stroke did not show significant trends (APC=3.455%, 2.804% and 1.919%, all P>0.05). Conclusions The crude incidence of stroke increased in Jining City from 2015 to 2022, with ischemic stroke as the predominant subtype. March to May was the peak period for disease onset, and young and middle-aged men should be focused on.

Objective:To determine the relative correction factor of pre-vitamin D and simplify the calculation method of vitamin D assay.Methods:By studying the calculation method of vitamin D content in drug standards of various countries,HPLC was used to determine the relative correction factor of pre-vitamin D,and the influencing factors of determination were investigated.Results:The relative correction factors of pre-vitamin D at 254 nm and 265nm wavelength were determined by statistical analysis of 7 laboratories in China.Conclusion:Using the pre-vi-tamin D relative correction factor method to calculate the total amount of vitamin D simplified the experimental steps can be simplified by the pre-vitamin D relative correction factor method to calculate the total amount of vitamin D and the random operating errors can be avoided.The method is rapid and accurate,and lay a solid foundation for further improving the standard of vitamin D preparations.

METHODS: We retrospectively collected CT scan data from 276 patients with pathologically confirmed primary bone tumors from 4 medical centers in Guangdong Province between January, 2010 and August, 2021. A convolutional neural network (CNN) was employed as the deep learning architecture. The optimal baseline deep learning model (R-Net) was determined through transfer learning, and an optimized model (S-Net) was obtained through algorithmic improvements. Multivariate logistic regression analysis was used to screen the clinical features such as sex, age, mineralization location, and pathological fractures, which were then connected with the imaging features to construct the deep learning fusion model (SC-Net). The diagnostic performance of the SC-Net model and machine learning models were compared with radiologists' diagnoses, and their classification performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score. RESULTS: In the external test set, the fusion model (SC-Net) achieved the best performance with an AUC of 0.901 (95% CI: 0.803-1.00), an accuracy of 83.7% (95% CI: 69.3%-93.2%) and an F1 score of 0.857, and outperformed the S-Net model with an AUC of 0.818 (95% CI: 0.694-0.942), an accuracy of 76.7% (95% CI: 61.4%-88.2%), and an F1 score of 0.828. The overall classification performance of the fusion model (SC-Net) exceeded that of radiologists' diagnoses. CONCLUSIONS The deep learning fusion model based on multi-center CT images and clinical features is capable of accurate classification of osseous and chondroid matrix mineralization and may potentially improve the accuracy of clinical diagnoses of osteogenic versus chondrogenic primary bone tumors.
Humans ; Deep Learning ; Bone Neoplasms/diagnostic imaging* ; Retrospective Studies ; Tomography, X-Ray Computed/methods* ; Neural Networks, Computer ; Male ; Female ; ROC Curve ; Algorithms

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is an underdiagnosed genetic heart disease worldwide. The management and prognosis of obstructive HCM (HOCM) and non-obstructive HCM (HNCM) are quite different, but it also remains challenging to discriminate these two subtypes. HCM is characterized by dysmetabolism, and myocardial amino acid (AA) metabolism is robustly changed. The present study aimed to delineate plasma AA and derivatives profiles, and identify potential biomarkers for HCM. METHODS: Plasma samples from 166 participants, including 57 cases of HOCM, 52 cases of HNCM, and 57 normal controls (NCs), who first visited the International Cooperation Center for HCM, Xijing Hospital between December 2019 and September 2020, were collected and analyzed by high-performance liquid chromatography-mass spectrometry based on targeted AA metabolomics. Three separate classification algorithms, including random forest, support vector machine, and logistic regression, were applied for the identification of specific AA and derivatives compositions for HCM and the development of screening models to discriminate HCM from NC as well as HOCM from HNCM. RESULTS: The univariate analysis showed that the serine, glycine, proline, citrulline, glutamine, cystine, creatinine, cysteine, choline, and aminoadipic acid levels in the HCM group were significantly different from those in the NC group. Four AAs and derivatives (Panel A; proline, glycine, cysteine, and choline) were screened out by multiple feature selection algorithms for discriminating HCM patients from NCs. The receiver operating characteristic (ROC) analysis in Panel A yielded an area under the ROC curve (AUC) of 0.83 (0.75-0.91) in the training set and 0.79 (0.65-0.94) in the validation set. Moreover, among 10 AAs and derivatives (arginine, phenylalanine, tyrosine, proline, alanine, asparagine, creatine, tryptophan, ornithine, and choline) with statistical significance between HOCM and HNCM, 3 AAs (Panel B; arginine, proline, and ornithine) were selected to differentiate the two subgroups. The AUC values in the training and validation sets for Panel B were 0.83 (0.74-0.93) and 0.82 (0.66-0.98), respectively. CONCLUSIONS The plasma AA and derivatives profiles were distinct between the HCM and NC groups. Based on the differential profiles, the two established screening models have potential value in assisting HCM screening and identifying whether it is obstructive.
Humans ; Amino Acids ; Cysteine ; Cardiomyopathy, Hypertrophic/diagnosis* ; Biomarkers ; Proline ; Arginine ; Ornithine ; Glycine ; Choline

Objective To analyze the statistical behavior of plantar pressure distribution, extract the characteristics of foot movement, and provide references for application of gait recognition in medical clinical diagnosis, rehabilitation training and public health. Methods The collected foot pressure data were prepossessed, statistical analysis on the data was performed, the footprint reconstruction was realized, and the pressure distribution rates of the footprints, segmented regions and each region were compared and analyzed, so as to decompose the foot motion characteristics. Results Based on the cross point of pressure peak curve in different regions, the plantar region was divided into toe region, metatarsal region, arch region and heel region, which could accurately extract the foot movement characteristics. Conclusions The peak plantar pressure is used to extract the characteristics of foot movement, which is divided into landing stage, whole foot contact stage, heel tiptoe stage and ground off stage.

The new ultrasound device, automated breast ultrasound (ABUS), has the advantages of repeatable reading of standardized whole breast images, and can separate ultrasound scanning and diagnosis. ABUS is more suitable for breast ultrasound training. This study compared the breast ultrasound imaging training model with ABUS diagnosis practice as the core and the traditional breast ultrasound imaging training model with handhold ultrasound operation and diagnosis as the core, and found that the new training model with ABUS diagnosis practice as the core had a short training period. The diagnostic level of doctors have improved rapidly, and the new training model could efficiently train more technically qualified ultrasound doctors for breast disease.