BACKGROUND:Mitochondrial autophagy is closely related to the occurrence and development of steroid-induced osteonecrosis of the femoral head(SONFH),but specific biomarkers and regulatory mechanisms remain unclear. OBJECTIVE:To identify the key biomarkers of mitochondrial autophagy in steroid-induced osteonecrosis of the femoral head using machine learning algorithms and to conduct an immune infiltration analysis. METHODS:The SONFH datasets GSE123568 and GSE74089 were downloaded from the GEO database,serving as the training and validation sets,respectively.Differentially expressed genes between SONFH and control groups were selected,and weighted gene co-expression network analysis was performed.Mitochondrial autophagy-related genes were obtained from MitoCarta3.0 and intersected with differentially expressed genes and module genes.Two machine learning algorithms were utilized to identify key genes of SONFH mitochondrial autophagy,and validated using an external validation set.CIBERSORT and immune infiltration analysis were employed to assess the proportion of immune cells,and ssGSEA was used to analyze the correlation between mitochondrial autophagy genes and immune cells. RESULTS AND CONCLUSION:Differential analysis identified a total of 1 163 differentially expressed genes,including 663 upregulated genes and 500 downregulated genes.Weighted gene co-expression network analysis identified 4 key modules,comprising 1 412 module genes.Intersection with mitochondrial autophagy genes yielded 39 intersecting genes as disease-related mitochondrial autophagy genes.Gene ontology enrichment analysis showed that the biological processes were mainly related to heme metabolism,mitochondrial transport,nucleotide bisphosphate metabolism and thioester metabolism,and the cellular components were mainly related to mitochondrial matrix,mitochondrial outer membrane,organelle outer membrane and mitochondrial inner membrane,and the molecular functions were mainly related to fatty acid ligase activity,iron-sulfur cluster binding,and cofactor A ligase activity.Kyoto Encyclopedia of Genes and Genomes enrichment analysis mapped out a total of six pathways,which were mainly related to fatty acid degradation,mitochondrial autophagy,butyric acid metabolism,fatty acid biosynthesis and cofactor biosynthesis.Through LASSO regression and RFE-SVM algorithm analysis,four intersecting genes(ALDH5A1,FBXL4,MCL1,and STOM)were identified.The receiver operating characteristic curves of the four core genes and the diagnostic column chart validation set were all greater than 0.9.The occurrence and development of SONFH were related to immune cells such as dendritic cells,bone marrow-derived suppressor cells,regulatory T cells,and central memory CD8 T cells.To conclude,the four key mitochondrial autophagy genes ALDH5A1,FBXL4,MCL1,and STOM play a crucial role in the progression of SONFH through osteoclast differentiation and immune mechanisms.Additionally,all four genes have good disease prediction efficacy and can serve as biomarkers for the diagnosis and treatment of SONFH.

Poly(ADP-ribosyl)ation (PARylation) is a specific form of post-translational modification (PTM) predominantly triggered by the activation of poly-ADP-ribose polymerase 1 (PARP1). However, the role and mechanism of PARylation in the advancement of acute kidney injury (AKI) remain undetermined. Here, we demonstrated the significant upregulation of PARP1 and its associated PARylation in murine models of AKI, consistent with renal biopsy findings in patients with AKI. This elevation in PARP1 expression might be attributed to trimethylation of histone H3 lysine 4 (H3K4me3). Furthermore, a reduction in PARylation levels mitigated renal dysfunction in the AKI mouse models. Mechanistically, liquid chromatography-mass spectrometry indicated that PARylation mainly occurred in receptor for activated C kinase 1 (RACK1), thereby facilitating its subsequent phosphorylation. Moreover, the phosphorylation of RACK1 enhanced its dimerization and accelerated the ubiquitination-mediated hypoxia inducible factor-1α (HIF-1α) degradation, thereby exacerbating kidney injury. Additionally, we identified a PARP1 proteolysis-targeting chimera (PROTAC), A19, as a PARP1 degrader that demonstrated superior protective effects against renal injury compared with PJ34, a previously identified PARP1 inhibitor. Collectively, both genetic and drug-based inhibition of PARylation mitigated kidney injury, indicating that the PARylated RACK1/HIF-1α axis could be a promising therapeutic target for AKI treatment.

BACKGROUND:The pathogenesis of rheumatoid arthritis has not yet been fully clarified,and recent studies have shown that mitophagy is associated with rheumatoid arthritis,but the key mechanisms need to be explored in depth.OBJECTIVE:To identify and validate the core interaction genes of mitophagy in rheumatoid arthritis using multiple machine learning algorithms and to analyze its immunoregulatory process.METHODS:The rheumatoid arthritis transcriptome expression dataset GSE15573 was retrieved from the GEO database as an independent training set,with the GSE97779 and GSE55235 collections used as independent validation sets.The differentially expressed genes of rheumatoid arthritis were screened using the training set,and"WGCNA"analysis was also performed.Then we downloaded the mitophagy-related genes from the"MitoCarta3.0"database,and intersected them with the differentially expressed genes of rheumatoid arthritis and the genes in the"WGCNA"analysis module to obtain the rheumatoid arthritis-mitophagy-related genes,and then analyzed the related genes for functional enrichment to clarify the cellular pathways.Feature genes were initially identified using the"Random Forest"and"Lasso"algorithms.The overlapping genes from these two methods were further refined using the"GMM"algorithm to identify the core interaction genes between rheumatoid arthritis and mitophagy.A predictive model was then developed and validated using an external dataset.Finally,"CIBERSORT"was employed to analyze the proportions and interactions of immune cell subsets during immune infiltration,while"ssGSEA"was used to examine the associations between the rheumatoid arthritis-mitophagy core interaction genes and immune cell subsets."ssGSEA"was also utilized to analyze the"GO"and"KEGG"biological pathways of the core interaction genes.RESULTS AND CONCLUSION:(1)Totally 807 differentially expressed genes in rheumatoid arthritis were obtained by differential analysis,1 208 genes were selected from two feature modules by"WGCNA"analysis,1136 genes were sorted out from the MitoCarta 3.0 database,and 53 HUB genes were obtained from the intersection of the three genes as rheumatoid arthritis-mitophagy related genes.(2)The results of functional enrichment analysis of related genes showed that the cellular processes were mainly related to mitophagy,peroxisome metabolism,cellular senescence,and necroptosis.(3)The three machine learning algorithms identified four rheumatoid arthritis-mitophagy core interaction genes(DNAJA3,C12orf65,AKR7A2,and PDHB).The area under the curve of nomoscore was 0.989,and the area under the curve values of rheumatoid arthritis-mitophagy core interaction genes verified by the receiver operating characteristic curve of external patient samples were all greater than 0.7.(5)Immunoregulatory analysis showed that the mitophagy process in rheumatoid arthritis was closely associated with memory B cells,M0 macrophages,activated memory CD4 T cells,and resting memory CD4 T cells.(6)The biological pathway analysis revealed that the core interaction genes were strongly associated with 821"GO"pathways(|cor|＞0.8,P＜0.001)and 48"KEGG"pathways(|cor|＞0.8,P＜0.001).The key biological processes identified were related to mitochondrial DNA metabolic process,mitochondrial DNA repair,mitochondrial DNA replication,mitochondrial genome maintenance,positive regulation of mitochondrial depolarization,and positive regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway.To conclude,DNAJA3,C12orf65,AKR7A2,and PDHB are the core interaction genes of the mitophagy process in rheumatoid arthritis,which play key roles in disease progression by participating in specific immune processes and have precise and predictive effects on the diagnosis of rheumatoid arthritis.

BACKGROUND:The pathogenesis of rheumatoid arthritis has not yet been fully clarified,and recent studies have shown that mitophagy is associated with rheumatoid arthritis,but the key mechanisms need to be explored in depth.OBJECTIVE:To identify and validate the core interaction genes of mitophagy in rheumatoid arthritis using multiple machine learning algorithms and to analyze its immunoregulatory process.METHODS:The rheumatoid arthritis transcriptome expression dataset GSE15573 was retrieved from the GEO database as an independent training set,with the GSE97779 and GSE55235 collections used as independent validation sets.The differentially expressed genes of rheumatoid arthritis were screened using the training set,and"WGCNA"analysis was also performed.Then we downloaded the mitophagy-related genes from the"MitoCarta3.0"database,and intersected them with the differentially expressed genes of rheumatoid arthritis and the genes in the"WGCNA"analysis module to obtain the rheumatoid arthritis-mitophagy-related genes,and then analyzed the related genes for functional enrichment to clarify the cellular pathways.Feature genes were initially identified using the"Random Forest"and"Lasso"algorithms.The overlapping genes from these two methods were further refined using the"GMM"algorithm to identify the core interaction genes between rheumatoid arthritis and mitophagy.A predictive model was then developed and validated using an external dataset.Finally,"CIBERSORT"was employed to analyze the proportions and interactions of immune cell subsets during immune infiltration,while"ssGSEA"was used to examine the associations between the rheumatoid arthritis-mitophagy core interaction genes and immune cell subsets."ssGSEA"was also utilized to analyze the"GO"and"KEGG"biological pathways of the core interaction genes.RESULTS AND CONCLUSION:(1)Totally 807 differentially expressed genes in rheumatoid arthritis were obtained by differential analysis,1 208 genes were selected from two feature modules by"WGCNA"analysis,1136 genes were sorted out from the MitoCarta 3.0 database,and 53 HUB genes were obtained from the intersection of the three genes as rheumatoid arthritis-mitophagy related genes.(2)The results of functional enrichment analysis of related genes showed that the cellular processes were mainly related to mitophagy,peroxisome metabolism,cellular senescence,and necroptosis.(3)The three machine learning algorithms identified four rheumatoid arthritis-mitophagy core interaction genes(DNAJA3,C12orf65,AKR7A2,and PDHB).The area under the curve of nomoscore was 0.989,and the area under the curve values of rheumatoid arthritis-mitophagy core interaction genes verified by the receiver operating characteristic curve of external patient samples were all greater than 0.7.(5)Immunoregulatory analysis showed that the mitophagy process in rheumatoid arthritis was closely associated with memory B cells,M0 macrophages,activated memory CD4 T cells,and resting memory CD4 T cells.(6)The biological pathway analysis revealed that the core interaction genes were strongly associated with 821"GO"pathways(|cor|＞0.8,P＜0.001)and 48"KEGG"pathways(|cor|＞0.8,P＜0.001).The key biological processes identified were related to mitochondrial DNA metabolic process,mitochondrial DNA repair,mitochondrial DNA replication,mitochondrial genome maintenance,positive regulation of mitochondrial depolarization,and positive regulation of mitochondrial outer membrane permeabilization involved in apoptotic signaling pathway.To conclude,DNAJA3,C12orf65,AKR7A2,and PDHB are the core interaction genes of the mitophagy process in rheumatoid arthritis,which play key roles in disease progression by participating in specific immune processes and have precise and predictive effects on the diagnosis of rheumatoid arthritis.

BACKGROUND:The etiology of osteoarthritis is varied and its pathogenesis is still unclear.As bioinformatics has been deepening in recent years,increasing studies have found that the aberrant expression of long non-coding RNAs(lncRNAs)and microRNAs(miRNAs)in joint tissues may mediate the downstream signaling pathways involved in the development of osteoarthritis. OBJECTIVE:To review the mechanism of lncRNA in the development of osteoarthritis and the therapeutic effects of monomers and active compounds derived from traditional Chinese medicine that modulate lncRNA and downstream signaling pathways in osteoarthritis. METHODS:We searched CNKI,WanFang,VIP,and PubMed using the search terms of"long non-coding RNA,knee osteoarthritis,miRNA,chondrocytes,signaling pathway,and traditional Chinese medicine"in Chinese and English,respectively.The search time was from the inception of each database to March 2023.A total of 61 articles were included according to the inclusion and exclusion criteria. RESULTS AND CONCLUSION:The pathogenesis of knee osteoarthritis involves a complex molecular regulatory network,including aberrant expression of lncRNAs and miRNAs in cartilage tissues,which may lead to apoptosis of chondrocytes,degradation of cartilage extracellular matrix,and production of large amounts of pro-inflammatory cytokines.These changes interact with each other to cause degeneration of articular cartilage and progression of osteoarthritis.Therefore,further in-depth studies are needed to reveal the fine mechanisms of the molecular regulatory network.The mechanism of traditional Chinese medicine in the treatment of osteoarthritis mainly focuses on regulating the expression of lncRNA and miRNA,thereby alleviating chondrocyte apoptosis and extracellular matrix degradation,promoting cell proliferation,and slowing down the development of osteoarthritis.

A mixed drug self-delivery system(DSDS)with high drug content(＞50％)was developed to regulate pH-triggered drug release,based on two doxorubicin(DOX)-DOX dimmers:D-DOXADH and D-DOXcar con-jugated with acid-labile dynamic covalent bonds(hydrazone and carbamate,respectively)and stabilized with PEGylated D-DOXADH(D-DOXADH-PEG).Owing to the different stability of the dynamic covalent bonds in the two dimers and the noncovalent interaction between them,pH-triggered drug release could be easily regulated by adjusting the feeding ratios of the two DOX-DOX dimers in the mixed DSDS.Similar in vitro cellular toxicity was achieved with the mixed DSDS nanoparticles prepared with different feeding ratios.The mixed DSDS nanoparticles had a similar DOX content and diameter but different drug releasing rates.The MTT assays revealed that a high anti-tumor efficacy could be achieved with the slow-release mixed DSDS nanoparticles.

Objective:To translate the Decisional Balance Scale for Diabetes Diet (DB-DD) into Chinese, and test its reliability and validity.Methods:After obtaining the authorization of the author of the source scale, the scale was sinicized by translation and back translation method. After expert consultation, cognitive interview and pre-survey for cultural adjustment, the Chinese version of DB-DD scale was formed. From September 2020 to January 2021, a total of 496 patients with type 2 diabetes mellitus in endocrinology department of 4 Class Ⅲ Grade A hospitals in Guangdong province were surveyed by the convenient sampling method to evaluate the reliability and validity of the scale. A total of 496 questionnaires were sent out and 440 were effectively received, with the effective recovery of 88.7%.Results:There were 12 items in the Chinese DB-DD, including 2 dimensions of benefit (6 items) and damage (6 items) . The content validity index of the total scale was 0.98, and the content validity index of the item level was 0.86 to 1.00. The correlation coefficients of the Chinese version of DB-DD total scale, benefit subscale and impairment subscale and diabetes self-management behavior scale were 0.413, 0.222 and -0.221, respectively ( P<0.001) . The Cronbach's α coefficients of the total scale and each subscale were 0.730-0.899 and the split-half reliability coefficients were 0.725-0.856. The test-retest reliability coefficient of the total scale was 0.916 and the test-retest reliability coefficients of the benefit and damage subscales were respectively 0.933 and 0.902. Conclusions:The Chinese version of Decisional Balance Scale for Diabetes Diet has good reliability and validity, which can be used to evaluate the dietary decision balance of diabetic patients in the Chinese cultural context.

Objective: To analyze the causes and correlative factors of high perioperative blood transfusion demand in patients with lung tumor, and to discuss the influence of high blood transfusion demand on patients’ postoperative recovery and its predictive factors. Methods: From November 2007 to October 2017, clinical data of patients who had underwent surgery for lung tumors in the Department of Thoracic Surgery, Cancer Hospital, Chinese Academy of Medical Sciences were collected. A total of 83 cases with perioperative transfusion of red blood cells ≥5U were classified as high transfusion demand group. Another 83 cases were selected from the rest of the patients with transfusion of red blood cells <5U as normal transfusion demand group. Related clinical and transfusion data were summarized to analyze the causes of high blood transfusion demand and its effect on postoperative recovery, univariate and multivariate logistic regressions were used to analyse correlative factors. Results: From November 2007 to October 2017, 23 898 patients with lung tumor underwent surgery in our department and the high blood transfusion demand rate was 0.35%. In the last 10 years, the ratio of high transfusion demand was 0.61%(46/7 503) in the first 5 years versus 0.23%(37/16 395) in the later 5 years(P<0.01). By contrasting high transfusion demand group and normal transfusion demand group, the ratio of thoracoscopic surgery was 42.17%(35/83) vs. 26.51%(22/83)(P<0.05), the ratio of postoperative blood transfusion was 39.76%(33/83) vs. 22.90%(19/83)(P<0.05) and the ratio of left upper lung surgery was 24.10%(20/83) vs. 12.05%(10/83)(P<0.05). The ratio of patients with preoperative comorbidities was 21.69%(18/83) in the high transfusion demand group versus 8.43%(7/83) in the normal transfusion demand group(P<0.05), and the ratio of patients with anemia was 57.83%(48/83) vs. 52.63%(30/83)(P<0.05). The incidence of complications was 39.76%(33/83) in the high transfusion demand group versus 18.07%(15/83) in the normal transfusion demand group(P<0.01), and the incidence of Infection-related complication were 25.30%(21/83) vs. 8.43%(7/83)(P<0.01). Conclusion For perioperative high blood transfusion demand in lung tumors, there were significant differences between different operation approaches, site of operation and phase of blood transfusion. The perioperative high blood transfusion demand may also increase the risk of postoperative complications, comorbidities or anemia were its predictive factors.

Objective: To study the impact of the advance of the times and technological progress on the surgical treatment of lung cancer. Methods: The data of patients with non-small cell lung cancer treated by thoracic surgery at Cancer Hospital of Chinese Academy of Medical Sciences from 2005 to 2015 were retrospectively analyzed. The population distribution, operation methods and treatment results were analyzed retrospectively. Results: 510 patients (in 2005) and 1 235 (in 2015) non-small cell lung cancer patients were included in this study. The proportions of male patients (79.0% vs. 55.8%), smoking (52.9% vs. 30.1%), squamous cell carcinoma (50.2% vs. 22.4%) and video-assisted thoracoscopic surgery (VATS) (0 vs. 61.1%), stage Ⅰ (15.2% vs. 36.8%), the number of lymph node dissection (21.8 vs. 16.6), intraoperative blood transfusion rate (9.6% vs. 1.9%), palliative resection rate (7.5% vs. 2.0%), the average length of stay (10.8 d vs. 7.6 d) were significantly changed. There was no significant difference in the average age of patients and operation time. Conclusion There was a significant change in the distribution of population and surgical techniques in patients undergoing lung cancer surgery in last ten years.

BACKGROUND: Surgeons are the direct decision-makers and performers in the surgical treatment of patients with lung cancer. Whether the differences among doctors affect the survival of patients is unclear. This study analyzed the five-year survival rates of different thoracic surgeries in patients undergoing surgery to assess the physician's impact and impact. METHODS: A retrospective analysis of five years between 2002-2007 in the Department of Thoracic Surgery, Cancer Hospital, Chinese Academy of Medical Sciences, for surgical treatment of lung cancer patients. According to different surgeons grouping doctors to compare the basic information of patients, surgical methods, short-term results and long-term survival differences. RESULTS: A total of 712 patients treated by 11 experienced thoracic surgeons were included in this study. The patients have nosignificant difference with gender, age, smoking, pathological type between groups. There were significant differences in clinical staging, surgery type, operation time, blood transfusion rate, number of lymph node dissection, palliative resection rate, postoperative complications and perioperative mortality. There was a significant difference in five-year survival rates among patients treated by different doctors. This difference can be seen in all clinical stage analyzes with consistency. In the multivariate analysis, it was suggested that surgeon was an independent factor influencing the prognosis of patients. CONCLUSIONS Thoracic surgeon has a significant effect on the therapeutic effect of lung cancer patients.
Adult ; Aged ; Aged, 80 and over ; Female ; Humans ; Lung Neoplasms ; surgery ; Male ; Middle Aged ; Multivariate Analysis ; Postoperative Complications ; etiology ; Retrospective Studies ; Surgeons ; statistics & numerical data ; Survival Analysis ; Thoracic Surgery ; Thoracic Surgical Procedures ; adverse effects