CD33 Ameliorates Surgery-Induced Spatial Learning and Memory Impairments Through TREM2.

Neuroinflammation is implicated in the onset of postoperative cognitive dysfunction (POCD), with CD33 and triggering receptor expressed on myeloid cells 2 (TREM2) playing crucial roles in immune response modulation and neuroinflammatory processes. A total of 96 aged male C57/BL6 mice (9-12 months) were randomly assigned to one of four groups, each receiving an siRNA injection into the lateral ventricle. Subsequently, the mice underwent partial hepatectomy under general anesthesia. To assess cognitive function, the Morris water maze tests were conducted both pre- and post-surgery. Following behavioral assessments, hippocampal tissues were swiftly harvested. The regulation of CD33 and TREM2 expression was achieved through siRNA in the BV2 microglia cell line. Expression levels of CD33 and TREM2 were evaluated both in vitro and in vivo using quantitative RT-PCR and western blot analyses. This study explored the impact of CD33 and TREM2 on POCD in aged mice and revealed that surgery and anesthesia increased CD33 expression, leading to spatial learning and memory impairments. Inhibiting CD33 expression via siRNA administration ameliorated cognitive deficits and mitigated the neuroinflammatory response triggered by surgery. Additionally, CD33 inhibition reversed the surgery-induced decrease in synaptic-related proteins, highlighting its role in preserving synaptic integrity. Moreover, our experiments suggest that CD33 may influence neuroinflammation and cognitive function through mechanisms involving TREM2. This is evidenced by the suppression of pro-inflammatory cytokines following CD33 knockdown in microglia and the reversal of these effects when both CD33 and TREM2 are concurrently knocked down. These findings imply that CD33 might promote neuroinflammation by inhibiting TREM2. This study highlights the potential of targeting CD33 as a promising therapeutic strategy for preventing and treating POCD. It provides valuable insights into the intricate mechanisms underlying cognitive dysfunction following surgical procedures.

Clinical features of thymoma with and without myasthenia gravis.

Objective: To explore the clinical features of thymoma with and without myasthenia gravis (MG). Methods: This was a retrospective study. Two hundred and thirty-three patients with mediastinal masses who were initially diagnosed in People's Hospital of Shijiazhuang, China, between January 2014 and June 2022 and had complete clinical data and underwent surgical treatment at People's Hospital of Shijiazhuang were retrospectively analyzed. Result: The age of patients with thymoma alone was significantly older than that of thymoma patients complicated with MG. The number of female patients was slightly more than males for both groups. Proportions of type A, AB, B1, B2, and B3 thymomas in Group-A were 0.77, 11.54, 11.51, 33.85, and 31.54%, respectively, and the proportions in Group-B were 9.68, 22.58, 12.90, 32.26, and 22.58%. The size of tumors in patients with thymoma alone was larger than that of patients with thymoma complicated with MG. The proportion of patients with tumor size of more than 10 cm in the thymoma alone group was significantly higher than that in the MG group. There were no relapses in patients with type A disease and relapses were noted in a few patients with type B1, B2 and B3 diseases. The same survival rates were reported for the two groups. Conclusion: MG rarely occurs in type A and type C diseases. The prognosis of thymoma with MG is similar to that of thymoma alone. The main causes of death may be myasthenia crisis in thymoma patients with MG and advanced tumor stage in patients with thymoma alone.

CagA 3' region polymorphism of Helicobacter pylori and its association with chronic gastritis in the Chinese population.

Introduction. Cytotoxin-associated gene A (CagA) from Helicobacter pylori is highly related to chronic gastritis. Tyrosine phosphorylation of Glu-Pro-Ile-Tyr-Ala (EPIYA) motifs from CagA determines the pathogenicity of H. pylori.Gap statement. The precise amino acid variations surrounding the EPIYA motifs and their correlation with clinical outcomes have been poorly explored.Aim. The purpose of this study was to examine the CagA 3' region polymorphism of H. pylori and its association with chronic gastritis in the Chinese population.Method. A total of 86 cagA-positive H. pylori strains were isolated from patients with chronic gastritis in two different hospitals in Beijing, PR China. Genomic DNA was extracted commercial kits, and the cagA 3' variable region of H. pylori was amplified by polymerase chain reaction (PCR). The PCR products were sequenced and analysed using the CLC Sequence Viewer, BioEdit, and WebLogo 3.Results. Two hundred and fifty-nine EPIYA motifs were identified from cagA-positive H. pylori strains. Notably, EPIYA-B exhibited a higher frequency of variation in comparison to EPIYA-A, EPIYA-C, and EPIYA-D. The prevalent sequences for East-Asian-type CagA were QVNK and TIDF, while KVNK and TIDD were most commonly observed for Western-type CagA. The CRPIA motifs of East-Asian-type CagA and Western-type CagA varied at positions 4, 6, 7, 8, and 10. CagA-ABD (73.2%) was the most prevalent type, followed by CagA-ABC (18.6%) and CagA-AB (3.4%). The ratio of CagA-ABD was observed to be higher in cases of chronic non-atrophic gastritis with erosive (NAGE) or chronic atrophic gastritis (AG) compared to chronic non-atrophic gastritis (NAG), and the difference was found to be statistically significant (χ2=59.000/64.000, P<0.001).Conclusions. The EPIYA segments of Western-type CagA and East-Asian-type CagA differ significantly and the presence of CagA-ABD may be associated with severe chronic gastritis from this study.

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches.

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional in vivo and in vitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

[Correlation of environment temperature with the incidence of testicular torsion].

Objective： To explore the influence of environment temperature on the incidence of testicular torsion. METHODS: We collected the clinical data on 172 cases of testicular torsion diagnosed in the Second Hospital of Hebei Medical University from December 2013 to December 2020. According to the local environment temperature on the day of onset, we divided the patients into groups A (below 0℃), B (0－10℃), C (10－20℃) and D (above 20℃), and compared the incidence rates of testicular torsion among the four groups, followed by correlation analysis. RESULTS: The incidence rate of testicular torsion was 12.8% (n = 22) in group A, 35.5% (n = 61) in B, 34.9% (n = 60) in C and 16.9% (n = 29) in D, the highest at 0－10℃ in group B, with statistically significant difference among the four groups (χ2 = 29.07, P <0.001). Spearman correlation analysis indicated that the incidence of testicular torsion was negatively correlated with the environment temperature (r = －0.261, P <0.01), with no statistically significant difference among different seasons (χ2 = 5.349, P >0.05), but higher in autumn and winter than in the other two seasons. CONCLUSION: The incidence of testicular torsion is negatively correlated with the environment temperature, elevated when the temperature decreases, but has no statistically significant difference among different seasons, though relatively higher in autumn and winter.

Predicting surgical efficacy and diagnosing histological inflammation: the clinical significance of prostate exosome proteins in benign prostatic hyperplasia.

Background: Benign prostatic hyperplasia (BPH) is one of the most common causes of lower urinary tract symptoms (LUTS) among the aging male population. Recent studies have shown that histological inflammation (HI) plays a significant role in BPH, with prostatic exosomal protein (PSEP) identified as a potential biomarker for prostate diseases. Therefore, this study aimed to explore the effect of HI on LUTS in patients with BPH, and to further explore the clinical value of PSEP as a diagnostic biomarker of BPH complicated with HI and whether PSEP could be used as an index to predict the improvement of LUTS after operation. Methods: This study was an open-label, cohort study. The study enrolled all patients who were clinical diagnosed as BPH with LUTS and prepared to receive operation of the prostate at the Department of Urology of the Second Hospital of Hebei Medical University. International Prostate Symptom Score (IPSS) were used to evaluate the LUTS of the BPH. And the enrolled patients were divided into four groups, including none, mild HI, moderate HI, and severe HI, based on postoperative pathological results. Then the relationships between HI and IPSS, the National Institutes of Health-Chronic Prostatitis Symptom Index (NIH-CPSI), as well as PSEP were analyzed. Simple and multiple linear regression analyses were performed on the preoperative IPSS and the difference of IPSS before and after surgery was examined. SPSS software version 26 was used for statistical analysis and Prism 9.0 was used to make violin plots. Results: A total of 69 patients were enrolled in the study. The violin plot results indicated IPSS and NIH-CPSI scores exhibited significant increases in correlation with the severity levels of HI (P<0.001; P<0.001). Among BPH patients with total prostate-specific antigen (t-PSA) levels higher than 4.0 ng/mL, a significant correlation was observed between PSEP levels and HI (P=0.04). Besides, simple and multiple linear regression analysis showed that HI (P<0.001) or PSEP (P=0.03) was significantly associated with IPSS and improvement of LUTS, assessed by postoperative and preoperative IPSS differences. Conclusions: The study indicated that IPSS and PSEP (when t-PSA >4 ng/mL) were correlated with the severity of HI in patients with BPH. PSEP was linearly correlated with IPSS and the degree of reduction in IPSS after surgery. Consequently, PSEP may serve as a promising predictor for assessing surgical efficacy and diagnosing the severity of HI in patients with BPH.

Elucidating Microglial Heterogeneity and Functions in Alzheimer's Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction.

In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional neural network (CNN) model. Focusing on the pivotal role of microglia in AD pathology, our analysis revealed 11 distinct microglial subclusters, with 4 exhibiting obviously alterations in AD and HC groups. The investigation of cell-cell communication networks unveiled intricate interactions between AD-related microglia and various cell types within the central nervous system (CNS). Integration of WGCNA and scRNA-seq facilitated the identification of critical genes associated with AD-related microglia, providing insights into their involvement in processes such as peptide chain elongation, synapse-related functions, and cell adhesion. The identification of 9 hub genes, including USP3, through the least absolute shrinkage and selection operator (LASSO) and COX regression analyses, presents potential therapeutic targets. Furthermore, the development of a CNN-based model showcases the application of deep learning in enhancing diagnostic accuracy for AD. Overall, our findings significantly contribute to unraveling the molecular intricacies of microglial responses in AD, offering promising avenues for targeted therapeutic interventions and improved diagnostic precision.

Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy.

Background: Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD. Method: Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD. Result: Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis. Conclusion: Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.

Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder.

BACKGROUND: Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD. METHODS: Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. FINDINGS: Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability. INTERPRETATION: These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD. FUNDING: National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Nano/micro flexible fiber and paper-based advanced functional packaging materials.

Recently, fiber-based and functional paper food packaging has garnered significant attention for its versatility, excellent performance, and potential to provide sustainable solutions to the food packaging industry. Fiber-based food packaging is characterized by its large surface area, adjustable porosity and customizability, while functional paper-based food packaging typically exhibits good mechanical strength and barrier properties. This review summarizes the latest research progress on food packaging based on fibers and functional paper. Firstly, the raw materials used for preparing fiber and functional paper, along with their physical and chemical properties and roles in food packaging, were discussed. Subsequently, the latest advancements in the application of fiber and paper materials in food packaging were introduced. This paper also discusses future research directions and potential areas for improvement in fiber and functional paper food packaging to further enhance their effectiveness in ensuring food safety, quality, and sustainability.

Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches.

BACKGROUND: Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions. METHODS: Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequent functional analysis of these genes was conducted on the gene set cancer analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy. Additionally, multivariate COX regression analysis highlighted the significant role of POLD1 and examined its association with DNA methylation. Finally, molecular docking and immunohistochemistry experiments were carried out to assess the binding affinity of POLD1 to PCa drugs and its impact on PCa prognosis. RESULTS: The study identified 21 metastasis-related genes using the WGCNA method, which were found to be associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms showed strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results. POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients. CONCLUSIONS: The developed diagnostic and metastasis models provide substantial value for patients with prostate cancer. The discovery of POLD1 as a novel biomarker related to prostate cancer metastasis offers a promising avenue for enhancing treatment of prostate cancer metastasis.

Construction of composite self-assembly coating based on chitosan for enhancing the flame-retardant and antibacterial performances of cotton fabric.

In this work, the step-by-step dip-coating (SBS) method was used to effectively improve the drawback of LBL by reducing the construction of a multilayer polyelectrolyte. Bio-based flame retardants, phytic acid (PA), and chitosan (CS) were further self-assembly on the surface of cotton fabric treated by epichlorohydrin-modified aramid nanofibers (AEP), ionic liquid (IL), and Cu ion. The pure cotton fabric was immersed in each dipping liquid only once, improving fire safety and antibacterial performance. The treated cotton self-extinguished with a 59 mm char length in the vertical flammability test, and the limiting oxygen index (LOI) value increased from 18.5 % to 38.5 %. The result of the cone calorimeter test (CCT) revealed that the fire hazard of flame-retardant cotton noteworthy declined (e.g., ~44.1 % and 55.4 % decline in peak heat release rate (pHRR) and total heat release rate (THR)). Conspicuously, the treated cotton exhibited a remarkably inhibiting effect on E. coli and S. aureus activity. The cotton fabric after flame-retardant finishing exhibited excellent fire safety and antibacterial performance.

Genetically predicted asthma and the risk of abnormal spermatozoa.

Background: Several previous animal and human studies have found a strong association between asthma and spermatozoa quality, but whether these associations are causal or due to bias remains to be elucidated. Methods: We performed a two-sample Mendelian randomization (MR) analysis to assess the causal effect of genetically predicted asthma on the risk of abnormal spermatozoa. Asthma, childhood-onset asthma (COA), and adult-onset asthma (AOA) (sample sizes ranging from 327,670 to 408,442) were included as the exposures. Genetic information for abnormal spermatozoa was obtained from a genome-wide association study (GWAS) comprising 209,921 participants. In univariable MR (UVMR) analysis, the inverse variance weighted (IVW) method was conducted as the primary method, with the MR Egger and weighted median used as supplementary methods for causal inference. Sensitivity analyses, including the Cochran Q test, Egger intercept test, MR-PRESSO, and leave-one-out analysis, were performed to verify the robustness of the MR results. Multivariable MR (MVMR) was conducted to evaluate the direct causal effects of asthma on abnormal spermatozoa risk. Results: UVMR detected causal associations between genetically predicted asthma and an increased risk of abnormal spermatozoa (OR: 1.270, 95% CI: 1.045-1.545, p = 0.017). Moreover, we found that AOA (OR: 1.46, 95% CI: 1.051, 2.018, p = 0.024) has positive causal effects on the risk of abnormal spermatozoa rather than COA (p = 0.558). Sensitivity analysis found little evidence of bias in the current study (p > 0.05). MVMR further confirmed that asthma directly affected the risk of abnormal spermatozoa. Conclusion: Our MR study suggested that genetically predicted asthma could be associated with an increased risk of abnormal spermatozoa, and similar results were obtained in AOA. Further studies are warranted to explain the underlying mechanisms of this association and may provide new avenues for prevention and treatment.

Aging-Associated Metabolite Methylmalonic Acid Increases Susceptibility to Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease characterized by pulmonary fibroblast overactivation, resulting in the accumulation of abnormal extracellular matrix and lung parenchymal damage. Although the pathogenesis of IPF remains unclear, aging was proposed as the most prominent nongenetic risk factor. Propionate metabolism undergoes reprogramming in the aging population, leading to the accumulation of the by-product methylmalonic acid (MMA). This study aimed to explore alterations in propionate metabolism in IPF and the impact of the by-product MMA on pulmonary fibrosis. It revealed alterations in the expression of enzymes involved in propionate metabolism within IPF lung tissues, characterized by an increase in propionyl-CoA carboxylase and methylmalonyl-CoA epimerase expression, and a decrease in methylmalonyl-CoA mutase expression. Knockdown of methylmalonyl-CoA mutase, the key enzyme in propionate metabolism, induced a profibrotic phenotype and activated co-cultured fibroblasts in A549 cells. MMA exacerbated bleomycin-induced mouse lung fibrosis and induced a profibrotic phenotype in both epithelial cells and fibroblasts through activation of the canonical transforming growth factor-ß/Smad pathway. Overall, these findings unveil an alteration of propionate metabolism in IPF, leading to MMA accumulation, thus exacerbating lung fibrosis through promoting profibrotic phenotypic transitions via the canonical transforming growth factor-ß/Smad signaling pathway.

Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches.

Background: Angiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated. Method: This study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was constructed using multiple machine learning techniques, while a prognostic model was developed employing the LASSO algorithm, underscoring the relevance of angiogenesis-related genes in PRAD. Further analysis identified MAP7D3 as the most significant prognostic gene among angiogenesis-related genes using multivariate Cox regression analysis and various machine learning algorithms. The study also investigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the binding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3. Result: Overall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD and its response to immunotherapy. Through molecular docking studies, it was revealed that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermore, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating with a poorer prognosis. Conclusion: Our study confirmed the important role of angiogenesis-related genes in PRAD and identified a new angiogenesis-related target MAP7D3.

Common immunological and prognostic features of lung and bladder cancer via smoking-related genes: PRR11 gene as potential immunotherapeutic target.

Smoking is a well-known risk factor for non-small-cell lung cancer (NSCLC) and bladder urothelial carcinoma (BLCA). Despite this, there has been no investigation into a prognostic marker based on smoking-related genes that could universally predict prognosis in these cancers and correlate with immune checkpoint therapy. This study aimed to identify smoking-related differential genes in NSCLC and BLCA, analyse their roles in patient prognosis and immune checkpoint therapy through subgroup analyses, and shed light on PRR11 as a crucial prognostic gene in both cancers. By examining PRR11 co-expressed genes, a prognostic model was constructed and its impact on immunotherapy for NSCLC and BLCA was evaluated. Molecular docking and tissue microarray analyses were conducted to explore the correlation between PRR11 and its reciprocal gene SPDL1. Additionally, miRNAs associated with PRR11 were analysed. The study confirmed a strong link between smoking-related genes, prognosis, and immune checkpoint therapy in NSCLC and BLCA. PRR11 was identified as a key smoking-associated gene that influences the efficacy of immune checkpoint therapy by modulating the stemness of these cancers. A prognostic model based on PRR11 co-expressed genes in BLCA was established and its prognostic value was validated in NSCLC. Furthermore, it was found that PRR11 regulates PDL1 via SPDL1, impacting immunotherapeutic efficacy in both cancers. The involvement of hsa-miR-200b-3p in the regulation of SPDL1 expression by PRR11 was also highlighted. Overall, the study elucidates that PRR11 modulates patient immunotherapy by influencing PDL1 expression through its interaction with SPDL1, with potential upstream regulation by hsa-miR-200b-3p.

Repair and regeneration of the alveolar epithelium in lung injury.

Considerable progress has been made in understanding the function of alveolar epithelial cells in a quiescent state and regeneration mechanism after lung injury. Lung injury occurs commonly from severe viral and bacterial infections, inhalation lung injury, and indirect injury sepsis. A series of pathological mechanisms caused by excessive injury, such as apoptosis, autophagy, senescence, and ferroptosis, have been studied. Recovery from lung injury requires the integrity of the alveolar epithelial cell barrier and the realization of gas exchange function. Regeneration mechanisms include the participation of epithelial progenitor cells and various niche cells involving several signaling pathways and proteins. While alveoli are damaged, alveolar type II (AT2) cells proliferate and differentiate into alveolar type I (AT1) cells to repair the damaged alveolar epithelial layer. Alveolar epithelial cells are surrounded by various cells, such as fibroblasts, endothelial cells, and various immune cells, which affect the proliferation and differentiation of AT2 cells through paracrine during alveolar regeneration. Besides, airway epithelial cells also contribute to the repair and regeneration process of alveolar epithelium. In this review, we mainly discuss the participation of epithelial progenitor cells and various niche cells involving several signaling pathways and transcription factors.

Gut microbiome changes in mouse, Mongolian gerbil, and hamster models following Clostridioides difficile challenge.

Introduction: Clostridioides difficile infection (CDI), as well as its etiology and pathogenesis, have been extensively investigated. However, the absence of suitable CDI animal models that reflect CDI symptoms and the associated gut microbiome changes in humans has limited research progress in this field. Thus, we aimed to investigate whether Mongolian gerbils, which present a range of human pathological conditions, can been used in studies on CDI. Methods: In this study, we infected Mongolian gerbils and two existing CDI model animals, mice and hamsters, with the hypervirulent ribotype 027 C. difficile strain, and comparatively analyzed changes in their gut microbiome composition via 16S rRNA gene sequencing. Methods: In this study, we infected Mongolian gerbils and two existing CDI model animals, mice and hamsters, with the hypervirulent ribotype 027 C. difficile strain, and comparatively analyzed changes in their gut microbiome composition via 16S rRNA gene sequencing. Results: The results obtained showed that C. difficile colonized the gastrointestinal tracts of the three rodents, and after the C. difficile challenge, C57BL/6J mice did not manifest CDI symptoms and their intestines showed no significant pathological changes. However, the hamsters showed explosive intestinal bleeding and inflammation and the Mongolian gerbils presented diarrhea as well as increased infiltration of inflammatory cells, mucus secretion, and epithelial cell shedding in their intestinal tissue. Further, intestinal microbiome analysis revealed significant differences with respect to intestinal flora abundance and diversity. Specifically, after C. difficile challenge, the Firmicutes/Bacteroidetes ratio decreased for C57BL/6J mice, but increased significantly for Mongolian gerbils and hamsters. Furthermore, the abundance of Proteobacteria increased in all three models, especially in hamsters, while that of Verrucomicrobia only increased significantly in C57BL/6J mice and Mongolian gerbils. Our results also indicated that differences in the relative abundances of Lactobacillaceae and Akkermansia were primarily responsible for the observed differences in response to C. difficile challenge. Conclusion: Based on the observed responses to C. difficile challenge, we concluded for the first time that the Mongolian gerbil could be used as an animal model for CDI. Additionally, the taxa identified in this study may be used as biomarkers for further studies on CDI and to improve understanding regarding changes in gut microbiome in CDI-related diseases.

Fat-soluble Vitamins and Lung Cancer: Where We Are?

Fat-soluble vitamins (vitamins A, D, E, and K) are vital substances for maintaining normal physiological functions in the body. In recent years, scholars have explored the relationship between fat-soluble vitamins and the wasting disease - lung cancer. In this paper, we review recent studies on fat-soluble vitamins and lung cancer to clarify the relevance and molecular mechanisms of various vitamins in lung cancer, and whether the levels of fat-soluble vitamins in the body and vitamin supplementation affect the development of lung cancer. Our review could facilitate the discovery of biomarkers, potential therapeutic targets in lung cancer, and anti-tumor adjuvant drugs, in addition to highlighting other new ideas in the prevention and treatment of lung cancer.