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ClpXP is a protease complex that plays important roles in protein quality control and cell cycle regulation, but the functions of multiple ClpXs and multiple ClpPs in M. xanthus remain unknown. The genome of Myxococcus xanthus DK1622 contains two clpPs and three clpXs. The clpP1 and clpX1 genes are cotranscribed and are both essential, while the other copies are isolated in the genome and are deletable. The deletion of clpX2 caused the mutant to be deficient in fruiting body development, while the clpX3 gene is involved in resistance to thermal stress. Both ClpPs possess catalytic active sites, but only ClpP1 shows in vitro peptidase activity on the typical substrate Suc-LY-AMC. All of these clpP and clpX genes exhibit strong transcriptional upregulation in the stationary phase, and the transcription of the three clpX genes appears to be coordinated. Our results demonstrated that multiple ClpPs and multiple ClpXs are functionally divergent and may assist in the environmental adaptation and functional diversification of M. xanthus.IMPORTANCEClpXP is an important protease complex of bacteria and is involved in various physiological processes. Myxococcus xanthus DK1622 possesses two ClpPs and three ClpXs with unclear functions. We investigated the functions of these genes and demonstrated the essential roles of clpP1 and clpX1. Only ClpP1 has in vitro peptidase activity on Suc-LY-AMC, and the isolated clpX copies participate in distinct cellular processes. All of these genes exhibited significant transcriptional upregulation in the stationary phase. Divergent functions appear in multiple ClpPs and multiple ClpXs in M. xanthus DK1622.

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BACKGROUND: Using cohort analysis to examine the effects of sleep quality on loneliness among older adults from the life course perspective. METHODS: The hierarchical age-period-cohort growth curve model was used to analyze the data from the 2005-2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). RESULTS: (1) Loneliness has a 'U' curve relationship with age, but with the rate of increase gradually slowing down. (2) There were significant differences in loneliness across birth cohorts, with younger cohorts having higher predicted loneliness than older cohorts at the same age. (3) The influence of different sleep quality on loneliness showed a trend of increasing with age. (4) There were no significant differences in the impact of sleep quality on loneliness in different cohorts. CONCLUSIONS: This study has identified heterogeneity in loneliness, emphasising the need for a diversified intervention approach. Sleep quality has a protective effect on loneliness and is easy to assess, making it an important intervention tool. In addition, it is imperative to account for the influences of age and cohort effects when formulating intervention strategies.

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Chronic hepatitis B virus (HBV) infection is a global health problem that substantially increases the risk of developing liver disease. The development of a novel strategy to induce anti-HB seroconversion and achieve a long-lasting immune response against chronic HBV infection remains challenging. Here, we found that chronic HBV infection affected the signaling pathway involved in STING-mediated induction of host immune responses in dendritic cells (DCs) and then generated a lymph node-targeted nanovaccine that co-delivered hepatitis B surface antigen (HBsAg) and cyclic diguanylate monophosphate (c-di-GMP) (named the PP-SG nanovaccine). The feasibility and efficiency of the PP-SG nanovaccine for CHB treatment were evaluated in HBV-carrier mice. Serum samples were analyzed for HBsAg, anti-HBs, HBV DNA, and alanine aminotransferase levels, and liver samples were evaluated for HBV DNA and RNA and HBcAg, accompanied by an analysis of HBV-specific cellular and humoral immune responses during PP-SG nanovaccine treatment. The PP-SG nanovaccine increased antigen phagocytosis and DC maturation, efficiently and safely eliminated HBV, achieved a long-lasting immune response against HBV reinjection, and disrupted chronic HBV infection-induced immune tolerance, as characterized by the generation and multifunctionality of HBV-specific CD8+ T and CD4+ T cells and the downregulation of immune checkpoint molecules. HBV-carrier mice immunized with the PP-SG nanovaccine achieved partial anti-HBs seroconversion. The PP-SG nanovaccine can induce sufficient and persistent viral suppression and achieve anti-HBs seroconversion, rendering it a promising vaccine candidate for clinical chronic hepatitis B therapy.

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BACKGROUND: This study performs a detailed bioinformatics and machine learning analysis to investigate the genetic foundations of membranous nephropathy (MN) in lung adenocarcinoma (LUAD). METHODS: In this study, the gene expression profiles of MN microarray datasets (GSE99339) and LUAD dataset (GSE43767) were downloaded from the Gene Expression Omnibus database, common differentially expressed genes (DEGs) were obtained using the limma R package. The biological functions were analyzed with R Cluster Profiler package according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Machine learning algorithms, including LASSO regression, support vector machine (SVM), Random Forest, and Boruta analysis, were applied to identify hubgenes linked to LUAD-associated MN. These genes' prognostic values were evaluated in the TCGA-LUAD cohort and validated through immunohistochemistry on renal biopsy specimens. RESULTS: A total of 36 DEGs in common were identified for downstream analyses. Functional enrichment analysis highlighted the involvement of the Toll-like receptor 4 pathway and several immune recognition pathways in LUAD-associated MN. COL3A1, PSENEN, RACGAP1, and TNFRSF10B were identified as hub genes in LUAD-associated MN using machine learning algorithms. ROC analysis demonstrated their effective discrimination of MN with high accuracy. Survival analysis showed that lung adenocarcinoma patients with higher expression of these genes had significantly reduced overall survival. In patients with lung adenocarcinoma-associated MN, RACGAP1, COL3A1, PSENEN, and TNFRSF10B were higher expressed in the glomerular, especially RACGAP1, indicating an important role in the pathogenesis of LUAD-associated membranous nephropathy. CONCLUSIONS: Our study underscores the critical role of RACGAP1, COL3A1, PSENEN, and TNFRSF10B in the development of LUAD-associated MN, providing important insights for future research and the development of potential therapeutic strategies.

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The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

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BACKGROUND AND OBJECTIVES: In clinical practice, some patients are diagnosed with diabetic nephropathy (DN) combined with acute tubulointerstitial nephritis (ATIN) through renal biopsy. There is relatively little research on the treatment and prognosis of such patients, and no consensus exists on the use of glucocorticoid for treatment. Therefore, our study explores the progression of DN combined with ATIN and the renal outcomes after treatment with glucocorticoid. METHODS: This study retrospectively analyzed patients diagnosed with DN combined with ATIN through renal biopsy at our center from January 1, 2015, to December 31, 2021. We collected general patient information, laboratory indicators, renal pathology indicators, and the glucocorticoid usage after kidney biopsy. Follow-up data were collected from medical records. Statistical analysis methods included t-tests, non-parametric tests, and chi-square tests. Univariate and multivariate Cox regression analyses were used to evaluate the risk factors for renal endpoint events in patients. Statistical significance was defined as p-values < 0.05. RESULTS: In this study, a total of 67 patients were included. The subjects were divided into two groups based on whether they received glucocorticoid treatment: 33 patients in the steroid group and 34 in the non-steroid group. In the steroid group, 19 patients reached the renal endpoint event, which was significantly higher than in the non-steroid group (57.58% vs. 29.41%, p = 0.038). Univariate Cox regression analysis showed that serum creatinine (HR = 1.008, p < 0.001), albumin (HR = 0.919, p < 0.001), 24-h urinary protein (HR = 1.093, p = 0.002), hemoglobin (HR = 0.964, p = 0.001), triglycerides (HR = 1.12, p = 0.04), and the use of glucocorticoid (HR = 2.507, p = 0.019) were influencing factors for renal endpoint events in patients with DN combined with ATIN. Multivariate Cox regression analysis showed that albumin (HR = 0.863, p = 0.003) was an independent risk factor for renal endpoint events in patients with DN combined with ATIN. CONCLUSIONS: The use of glucocorticoid in treatment does not improve renal prognosis in patients with DN combined with ATIN. Lower levels of albumin are associated with a worse renal prognosis.

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The Ebola virus (EBOV) is a lethal pathogen causing hemorrhagic fever syndrome which remains a global health challenge. In the EBOV, two multifunctional proteins, VP35 and VP40, have significant roles in replication, virion assembly, and budding from the cell and have been identified as druggable targets. In this study, we employed in silico methods comprising molecular docking, molecular dynamic simulations, and pharmacological properties to identify prospective drugs for inhibiting VP35 and VP40 proteins from the myxobacterial bioactive natural product repertoire. Cystobactamid 934-2, Cystobactamid 919-1, and Cittilin A bound firmly to VP35. Meanwhile, 2-Hydroxysorangiadenosine, Enhypyrazinone B, and Sorangiadenosine showed strong binding to the matrix protein VP40. Molecular dynamic simulations revealed that, among these compounds, Cystobactamid 919-1 and 2-Hydroxysorangiadenosine had stable interactions with their respective targets. Similarly, molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations indicated close-fitting receptor binding with VP35 or VP40. These two compounds also exhibited good pharmacological properties. In conclusion, we identified Cystobactamid 919-1 and 2-Hydroxysorangiadenosine as potential ligands for EBOV that target VP35 and VP40 proteins. These findings signify an essential step in vitro and in vivo to validate their potential for EBOV inhibition.

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Since the origin of the Successful Aging (SA) model by Rowe and Kahn, scholars have been working on enriching the content of SA and taking actions to promote this concept worldwide. While most studies primarily focus on older individuals, only a few scholars have considered the environmental aspect of SA. However, the environment, directly and indirectly, enhances older adults' abilities to achieve SA. To measure SA comprehensively and address inequalities among older adults, this theoretical article aims to challenge current SA models by incorporating both individual and environmental aspects and proposing four measurement dimensions: inclusivity of disadvantaged groups, culture-specific adaptation, balance between physical and social environments, and dynamics of the whole lifecycle. Moreover, this article provides examples to illustrate how environment can support older adults especially those who would be defined as "unsuccessful" under the original SA model. Our proposed model would provide theoretical guidance for future research and spark new ideas for policies and programs that support every older adult in achieving SA.

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Objective: To characterize the value of carotid wall shear stress (WSS) following carotid artery stenting (CAS) in patients with carotid stenosis. Methods: Twenty-eight patients with carotid stenosis treated with CAS between March 2021 to May 2022 in the eighth medical center of the PLA General Hospital were selected for our study. Carotid ultrasound was performed before the operation, one week post-operation, and six months post-operation. Carotid artery WSS was detected by blood flow vector imaging, and the changes in WSS before and after the operation were collected. Genetic testing of drugs was detected for patients with restenosis. Results: Pre-operative WSS of the proximal, narrowest region, and distal carotid arteries in patients with ischemic carotid artery stenosis was 7.88 ± 3.18Pa, 14.36 ± 6.66Pa, and 1.55 ± 1.15Pa, respectively. Comparatively, pre-operative WSS of the proximal, narrowest region and distal carotid arteries in patients without ischemic symptoms was 5.02 ± 1.99Pa, 9.68 ± 4.23Pa, and 1.10 ± 0.68Pa, respectively, with a significant difference between the two groups (p < 0.001). Overall WSS of the proximal, narrowest region, and distal carotid arteries in patients before CAS was 6.68 ± 3.0Pa, 12.47 ± 5.98Pa, and 1.39 ± 0. 96Pa. WSS of the proximal, narrowest region, and distal carotid was 4.15 ± 1.42Pa, 6.71 ± 2.64Pa, and1.86 ± 1.13Pa one week after CAS, compared to 4.44 ± 1.91Pa, 7.90 ± 4.38Pa, and 2. 36 ± 1.09Pa six months after CAS. WSS of the proximal and narrowest region of the carotid artery was reduced after carotid stenting, and the difference was statistically significant (p < 0.001). There was no statistically significant difference in WSS between one week and six months after stenting (P > 0.05). Conclusion: We employed early carotid WSS as a means of evaluating the efficacy of carotid artery stenting. Changes in carotid WSS are closely associated with carotid artery stenosis, providing valuable hemodynamic information for CAS treatment. This technique holds great application value in pre-operative evaluation and long-term follow-up.

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Background: Membranous nephropathy (MN) is an autoimmune disease and represents the most prevalent type of renal pathology in adult patients afflicted with nephrotic syndrome. Despite substantial evidence suggesting a possible link between MN and cancer, the precise underlying mechanisms remain elusive. Methods: In this study, we acquired and integrated two MN datasets (comprising a single-cell dataset and a bulk RNA-seq dataset) from the Gene Expression Omnibus database for differential expression gene (DEG) analysis, hub genes were obtained by LASSO and random forest algorithms, the diagnostic ability of hub genes was assessed using ROC curves, and the degree of immune cell infiltration was evaluated using the ssGSEA function. Concurrently, we gathered pan-cancer-related genes from the TCGA and GTEx databases, to analyze the expression, mutation status, drug sensitivity and prognosis of hub genes in pan-cancer. Results: We conducted intersections between the set of 318 senescence-related genes and the 366 DEGs, resulting in the identification of 13 senescence-related DEGs. Afterwards, we meticulously analyzed these genes using the LASSO and random forest algorithms, which ultimately led to the discovery of six hub genes through intersection (PIK3R1, CCND1, TERF2IP, SLC25A4, CAPN2, and TXN). ROC curves suggest that these hub genes have good recognition of MN. After performing correlation analysis, examining immune infiltration, and conducting a comprehensive pan-cancer investigation, we validated these six hub genes through immunohistochemical analysis using human renal biopsy tissues. The pan-cancer analysis notably accentuates the robust association between these hub genes and the prognoses of individuals afflicted by diverse cancer types, further underscoring the importance of mutations within these hub genes across various cancers. Conclusion: This evidence indicates that these genes could potentially play a pivotal role as a critical link connecting MN and cancer. As a result, they may hold promise as valuable targets for intervention in cases of both MN and cancer.

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Longwave radiation is an important open-air environmental factor that can significantly affect the temperature of concrete, but it has often been ignored in the temperature analysis of open-air concrete structures. In this article, an improved analytical model of concrete temperature was proposed by considering solar radiation, thermal convection, thermal conduction and especially longwave radiation. Temperature monitoring of an open-air concrete block was carried out to verify the proposed model and analyze the heat energy characteristics of open-air concrete. As demonstrated by the open-air experiment, under the influence of longwave radiation, the temperature at the top of the concrete block could decrease rapidly at night and even become lower than the minimum temperature at its bottom. Compared with the analytical model that ignores longwave radiation, the improved model that includes it better matches the measured temperature. According to the energy analysis, although solar radiation controls the transient variation in heat energy, the heat exchange caused by longwave radiation were more than that caused by convection on sunlit surfaces, which indicates the importance of considering longwave radiation.

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Purpose: This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods: In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results: For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion: The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

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BACKGROUND: This study aims to investigate the incidence and prognosis of malignancy in individuals with thrombospondin type-1 domain-containing 7A (THSD7A)-associated membranous nephropathy (MN). METHODS: First, we performed a systematic literature review of prevalence of malignancy in THSD7A-associated MN. Then, we conducted a retrospective analysis of 454 patients diagnosed with MN through renal biopsy at our hospital between January 2016 and December 2020. We assessed the presence of serum anti-THSD7A antibodies and performed immunohistochemical staining of renal tissue for THSD7A. Subsequently, we followed patients with THSD7A-associated MN for a minimum of 3-5 years, collecting their clinical, pathological characteristics, and prognosis. Additionally, we conducted a literature review on patients with THSD7A-associated MN in conjunction with malignancy. RESULTS: We identified a total of nine articles containing comprehensive data on THSD7A-associated MN and malignancy. Among 235 patients with THSD7A-positive MN, 36 individuals had concurrent malignancies, resulting in a malignancy prevalence of 13.3% (95% CI: 8.9-17.7%). In our center, we followed up with 15 patients diagnosed with THSD7A-associated MN and observed three cases of concomitant tumors: two cases of lung adenocarcinoma and one case of small cell lung cancer with multiple metastases. The prevalence of malignancy in our cohort was 20%. Notably, we detected positive THSD7A staining in both renal and lung cancer tissues in one patient with small cell lung cancer. CONCLUSIONS: Patients with THSD7A-associated MN should undergo vigilant follow-up assessments, with a particular focus on actively seeking potential tumorigenic lesions to prevent misdiagnosis or oversight.

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Introduction: Diabetic kidney disease (DKD) necessitates innovative therapeutic strategies. This study delves into the role of DNA damage-inducing transcription factor 4 (DDIT4) within the VDR-mTOR pathway, aiming to identify a novel target for DKD drug discovery. Methods: Transcriptome data from the Gene Expression Omnibus Database were analyzed to assess the expression of mTOR and VDR expression in human renal tissues. Clinical samples from DKD patients and minimal change disease (MCD) controls were examined, and a DKD animal model using 20-week-old db/db mice was established. DDIT4 plasmid transfection was employed to modulate the VDR-mTOR pathway, with its components evaluated using immunohistochemistry, real-time quantitative PCR (qRT-PCR), Western blotting, and enzyme-linked immunosorbent assay (ELISA). Results: Changes in the expression of the VDR-mTOR pathway were observed in both DKD patients and the animal model. Overexpression of DDIT4 increased VDR expression and decreased levels of mTOR, p70s6k, and 4E-BP1. Furthermore, DDIT4 treatment regulated autophagy by upregulating LC3I expression and downregulating LC3II expression. Notably, DDIT4 alleviated oxidative stress by reducing the levels of lipid peroxidation product MDA, while simultaneously increasing the levels of superoxide dismutase (SOD) and glutathione (GSH), underscoring the role of DDIT4 in the pathological process of DKD and its potential as a therapeutic target. Conclusion: Unraveling DDIT4's involvement in the VDR-mTOR pathway provides insights for innovative DKD drug discovery, emphasizing its potential as a therapeutic target for future interventions.

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BACKGROUND: Diabetic kidney disease (DKD) is a primary microvascular complication of diabetes with limited therapeutic effects. Delving into the pathogenic mechanisms of DKD and identifying new therapeutic targets is crucial. Emerging studies reveal the implication of ferroptosis and immune dysregulation in the pathogenesis of DKD, however, the precise relationship between them remains not fully elucidated. Investigating their interplay is pivotal to unraveling the pathogenesis of diabetic kidney disease, offering insights crucial for targeted interventions and improved patient outcomes. METHODS: Integrated analysis, Consensus clustering, Machine learning including Generalized Linear Models (GLM), RandomForest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (xGB), Artificial neural network (ANN) methods of DKD glomerular mRNA sequencing were performed to screen DKD-related ferroptosis genes.CIBERSORT, ESTIMATE and ssGSEA algorithm were used to assess the infiltration of immune cells between DKD and control groups and in two distinct ferroptosis phenotypes. The ferroptosis hub genes were verified in patients with DKD and in the db/db spontaneous type 2 diabetes mouse model via immunohistochemical and Western blotting analyses in mouse podocyte MPC5 and mesangial SV40-MES-13 cells under high-glucose (HG) conditions. RESULTS: We obtained 16 differentially expressed ferroptosis related genes and patients with DKD were clustered into two subgroups by consensus clustering. Five ferroptosis genes (DUSP1,ZFP36,PDK4,CD44 and RGS4) were identified to construct a diagnostic model with a good diagnosis performance in external validation. Analysis of immune infiltration revealed immune heterogeneity between DKD patients and controls.Moreover, a notable differentiation in immune landscape, comprised of Immune cells, ESTIMATE Score, Immune Score and Stromal Score was observed between two FRG clusters. GSVA analysis indicated that autophagy, apoptosis and complement activation can participate in the regulation of ferroptosis phenotypes. Experiment results showed that ZFP36 was significantly overexpressed in both tissue and cells while CD44 was on the contrary.Meanwhile,spearman analysis showed both ZFP36 and CD44 has a strong correlation with different immune cells,especially macrophage. CONCLUSION: The regulation of the immune landscape in DKD is significantly influenced by the focal point on ferroptosis. Newly identified ferroptosis markers, CD44 and ZFP36, are poised to play essential roles through their interactions with macrophages, adding substantial value to this regulatory landscape.

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Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice.

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The absence of bone wall located in the jugular bulb and sigmoid sinus of the temporal bone is one of the important reasons for pulsatile tinnitus. Automatic and accurate detection of these abnormal singes in CT slices has important theoretical significance and clinical value. Due to the shortage of abnormal samples, imbalanced samples, small inter-class differences, and low interpretability, existing deep-learning methods are greatly challenged. In this paper, we proposed a sub-features orthogonal decoupling model, which can effectively disentangle the representation features into class-specific sub-features and class-independent sub-features in a latent space. The former contains the discriminative information, while, the latter preserves information for image reconstruction. In addition, the proposed method can generate image samples using category conversion by combining the different class-specific sub-features and the class-independent sub-features, achieving corresponding mapping between deep features and images of specific classes. The proposed model improves the interpretability of the deep model and provides image synthesis methods for downstream tasks. The effectiveness of the method was verified in the detection of bone wall absence in the temporal bone jugular bulb and sigmoid sinus.

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The chaperone 70 kDa heat shock protein (Hsp70) is important for cells from bacteria to humans to maintain proteostasis, and all eukaryotes and several prokaryotes encode Hsp70 paralogs. Although the mechanisms of Hsp70 function have been clearly illuminated, the function and evolution of Hsp70 paralogs is not well studied. DnaK is a highly conserved bacterial Hsp70 family. Here, we show that dnaK is present in 98.9% of bacterial genomes, and 6.4% of them possess two or more DnaK paralogs. We found that the duplication of dnaK is positively correlated with an increase in proteomic complexity (proteome size, number of domains). We identified the interactomes of the two DnaK paralogs of Myxococcus xanthus DK1622 (MxDnaKs), which revealed that they are mostly nonoverlapping, although both prefer α and ß domain proteins. Consistent with the entire M. xanthus proteome, MxDnaK substrates have both significantly more multi-domain proteins and a higher isoelectric point than that of Escherichia coli, which encodes a single DnaK homolog. MxDnaK1 is transcriptionally upregulated in response to heat shock and prefers to bind cytosolic proteins, while MxDnaK2 is downregulated by heat shock and is more associated with membrane proteins. Using domain swapping, we show that the nucleotide-binding domain and the substrate-binding ß domain are responsible for the significant differences in DnaK interactomes, and the nucleotide binding domain also determines the dimerization of MxDnaK2, but not MxDnaK1. Our work suggests that bacterial DnaK has been duplicated in order to deal with a more complex proteome, and that this allows evolution of distinct domains to deal with different subsets of target proteins.IMPORTANCEAll eukaryotic and ~40% of prokaryotic species encode multiple 70 kDa heat shock protein (Hsp70) homologs with similar but diversified functions. Here, we show that duplication of canonical Hsp70 (DnaK in prokaryotes) correlates with increasing proteomic complexity and evolution of particular regions of the protein. Using the Myxococcus xanthus DnaK duplicates as a case, we found that their substrate spectrums are mostly nonoverlapping, and are both consistent to that of Escherichia coli DnaK in structural and molecular characteristics, but show differential enrichment of membrane proteins. Domain/region swapping demonstrated that the nucleotide-binding domain and the ß substrate-binding domain (SBDß), but not the SBDα or disordered C-terminal tail region, are responsible for this functional divergence. This work provides the first direct evidence for regional evolution of DnaK paralogs.

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Colon polyps in colonoscopy images exhibit significant differences in color, size, shape, appearance, and location, posing significant challenges to accurate polyp segmentation. In this paper, a Weighted Dual-branch Feature Fusion Network is proposed for Polyp Segmentation, named WDFF-Net, which adopts HarDNet68 as the backbone network. First, a dual-branch feature fusion network architecture is constructed, which includes a shared feature extractor and two feature fusion branches, i.e. Progressive Feature Fusion (PFF) branch and Scale-aware Feature Fusion (SFF) branch. The branches fuse the deep features of multiple layers for different purposes and with different fusion ways. The PFF branch is to address the under-segmentation or over-segmentation problems of flat polyps with low-edge contrast by iteratively fusing the features from low, medium, and high layers. The SFF branch is to tackle the the problem of drastic variations in polyp size and shape, especially the missed segmentation problem for small polyps. These two branches are complementary and play different roles, in improving segmentation accuracy. Second, an Object-aware Attention Mechanism (OAM) is proposed to enhance the features of the target regions and suppress those of the background regions, to interfere with the segmentation performance. Third, a weighted dual-branch the segmentation loss function is specifically designed, which dynamically assigns the weight factors of the loss functions for two branches to optimize their collaborative training. Experimental results on five public colon polyp datasets demonstrate that, the proposed WDFF-Net can achieve a superior segmentation performance with lower model complexity and faster inference speed, while maintaining good generalization ability.

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BACKGROUND: This retrospective study aims to investigate the prevalence and immunopathologic characteristics of seropositive and seronegative hepatitis B virus-associated membranous nephropathy (HBV-MN). METHODS: Clinicopathologic and serologic records of 420 patients with histologically confirmed HBV-MN between January 2014 and July 2021 were examined to determine the prevalence of seropositive and seronegative HBV-MN. Serum anti-PLA2R antibody testing was conducted on 280 patients with HBV-associated membranous nephropathy (HBV-MN) from August 2018 to July 2021. Immunopathologic characteristics of HBV-MN patients and anti-PLA2R antibody positivity were analyzed. RESULTS: Among 420 pathologically confirmed HBV-MN patients, 230 (54.8%) were seropositive for HBV. The seropositive group exhibited higher blood creatinine values and incidence of liver function abnormalities than the seronegative group (p < .05). Serum anti-PLA2R antibody testing on 280 HBV-MN patients revealed a total positive rate of 44.6%, with the seronegative group showing a significantly higher rate (62.6%) compared to the seropositive group (32.1%) (p < .01). The anti-PLA2R antibody-positive group displayed higher levels of urine protein (p < .05), serum cholesterol (p < .01), and IgG4 subtypes (p < .05) compared to the negative group. Additionally, the positive group had significantly lower levels of serum albumin and IgG than the negative group (p < .01). CONCLUSIONS: This comprehensive study reveals a significantly higher prevalence of seronegative HBV-MN than previously thought. The blood creatinine values and incidence of liver function abnormalities was higher in the serology-positive group than in the serology-negative group. Notably, the seronegative group displayed a higher positive rate of anti-PLA2R antibodies compared to the seropositive group, indicating distinctive clinical and immunopathologic features.

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