Empathy in undergraduate medical students: a multi-center cross-sectional study in China.

BACKGROUND: Fostering empathy has been continuously emphasized in the global medical education. Empathy is crucial to enhance patient-physician relationships, and is associated with medical students' academic and clinical performance. However, empathy level of medical students in China and related influencing factors are not clear. METHODS: This was a cross-sectional study among medical students in 11 universities. We used the Jefferson Scale of Empathy Student-version of Chinese version to measure empathy level of medical students. Factors associated with empathy were identified by the univariate and multivariate logistic regression analyses. Based on the variables identified above, the nomogram was established to predict high empathy probability of medical students. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate the discrimination, calibration and educational utility of the model. RESULTS: We received 10,901 samples, but a total of 10,576 samples could be used for further analysis (effective response rate of 97.02%). The mean empathy score of undergraduate medical students was 67.38 (standard deviation = 9.39). Six variables including gender, university category, only child or not, self-perception doctor-patient relationship in hospitals, interest of medicine, Kolb learning style showed statistical significance with empathy of medical students (P < 0.05). Then, the nomogram was established based on six variables. The validation suggested the nomogram model was well calibrated and had good utility in education, as well as area under the curve of model prediction was 0.65. CONCLUSIONS: We identify factors influencing empathy of undergraduate medical students. Moreover, increasing manifest and hidden curriculums on cultivating empathy of medical students may be needed among medical universities or schools in China.

Identification of key eRNAs for intervertebral disc degeneration by integrated multinomial bioinformatics analysis.

BACKGROUND: Intervertebral disc degeneration (IVDD) is a common degenerative condition leading to abnormal stress distribution under load, causing intervertebral stenosis, facet joint degeneration, and foraminal stenosis. Very little is known about the molecular mechanism of eRNAs in IVDD. METHODS: Gene expression profiles of 38 annulus disc samples composed of 27 less degenerated discs (LDs) and 11 more degenerated discs (MDs) were retrieved from the GEO database. Then, differentially expressed enhancer RNAs (DEeRNAs), differentially expressed target genes (DETGs), and differentially expressed transcription factors (DETFs), hallmark of cancer signalling pathways according to GSVA; the types and quantity of immune cells according to CIBERSORT; and immune gene sets according to ssGSEA were analysed to construct an IVDD-related eRNA network. Then, multidimensional validation was performed to explore the interactions among DEeRNAs, DETFs and DEGs in space. RESULTS: A total of 53 components, 14 DETGs, 15 DEeRNAs, 3 DETFs, 5 immune cells, 9 hallmarks, and 7 immune gene sets, were selected to construct the regulatory network. After validation by online multidimensional databases, 21 interactive DEeRNA-DEG-DETF axes related to IVDD exacerbation were identified, among which the C1S-CTNNB1-CHD4 axis was the most significant. CONCLUSION: Based upon the results of our study, we theorize that the C1S-CTNNB1-CHD4 axis plays a vital role in IVDD exacerbation. Specifically, C1S recruits CTNNB1 and upregulates the expression of CHD4 in IVDD, and subsequently, CHD4 suppresses glycolysis and activates oxidative phosphorylation, thus generating insoluble collagen fibre deposits and leading to the progression of IVDD. Overall, these DEeRNAs could comprise promising therapeutic targets for IVDD due to their high tissue specificity.

Identification of Bone Metastatic and Prognostic Alternative Splicing Signatures in Prostate Adenocarcinoma.

As the most common nonepithelial malignancy, prostate adenocarcinoma (PRAD) is the fifth chief cause of cancer mortality in men. Distant metastasis often occurs in advanced PRAD and most patients are dying from it. However, the mechanism of PRAD progression and metastasis is still unclear. It's widely reported that more than 94% of genes are selectively splicing in humans and many isoforms are particularly related with cancer progression and metastasis. Spliceosome mutations occur in a mutually exclusive manner in breast cancer, and different components of spliceosomes are targets of somatic mutations in different types of breast cancer. Existing evidence strongly supports the key role of alternative splicing in breast cancer biology, and innovative tools are being developed to use splicing events for diagnostic and therapeutic purposes. In order to identify if the PRAD metastasis is associated with alternative splicing events (ASEs), the RNA sequencing data and ASEs data of 500 PRAD patients were retrieved from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. By Lasso regression, five genes were screened to construct the prediction model, with a good reliability by ROC curve. Additionally, results in both univariate and multivariate Cox regression analysis confirmed the well prognosis efficacy of the prediction model (both P < 0.001). Moreover, a potential splicing regulatory network was established and after multiple-database validation, we supposed that the signaling axis of HSPB1 up-regulating the PIP5K1C - 46,721 - AT (P < 0.001) might mediate the tumorigenesis, progression and metastasis of PRAD via the key members of Alzheimer's disease pathway (SRC, EGFR, MAPT, APP and PRKCA) (P < 0.001).

A multi-center cross-sectional study on identification of influencing factors of medical students' emotional engagement in China.

BACKGROUND: Studies exploring influencing factors of emotional engagement among medical students are scarce. Thus, we aimed to identify influencing factors of medical students' emotional engagement. METHODS: We carried out a multi-center cross-sectional study among 10,901 medical students from 11 universities in China. The Chinese version of Utrecht Work Engagement Scale-Student version (UWES-S) was used to evaluate emotional engagement level of medical students. The predictors related to engagement level were determined by the logistic regression analysis. Furthermore, we constructed a nomogram to predict emotional engagement level of medical students. RESULTS: A total of 10,576 sample were included in this study. The mean emotional engagement score was 74.61(± 16.21). In the multivariate logistic regression model, we found that males showed higher engagement level compared with females [odds ratio (OR) (95% confidence interval (CI)): 1.263 (1.147, 1.392), P < 0.001]. Medical students from the second batches of medical universities had higher engagement level and from "Project 985" universities had lower engagement level compared with 211 project universities [OR (95%CI): 1.376 (1.093, 1.733), P = 0.007; OR (95%CI): 0.682 (0.535, 0.868), P = 0.002]. Medical students in grade 4 and grade 2 presented lower engagement level compared with in grade 1 [OR (95%CI): 0.860 (0.752, 0.983), P = 0.027; OR (95%CI): 0.861 (0.757, 0.980), P = 0.023]. Medical students lived in provincial capital cities had higher engagement level compared with in country [OR (95%CI): 1.176 (1.022, 1.354), P = 0.024]. Compared with eight-year emotional duration, medical students in other emotional duration (three-year and four-year) had lower engagement level [OR (95%CI): 0.762 (0.628, 0.924), P = 0.006]. Medical students' engagement level increased with increases of grade point average and interest in studying medicine. Medical students learned by converging style showed lower engagement level [OR (95%CI): 0.827 (0.722, 0.946), P = 0.006] compared with accommodating style. The model showed good discriminative ability (area under curve = 0.778), calibrating ability and clinical utility. CONCLUSIONS: We identified influencing factors of medical students' emotional engagement and developed a nomogram to predict medical students' emotional engagement level, providing reference and convenience for educators to assess and improve emotional engagement level of medical students. It is crucial for educators to pay more attention to emotional engagement of medical students and adopt effective strategies to improve their engagement level.

The Identification of Prognostic and Metastatic Alternative Splicing in Skin Cutaneous Melanoma.

Skin cutaneous melanoma (SKCM) is a type of highly invasive cancer originated from melanocytes. It is reported that aberrant alternative splicing (AS) plays an important role in the neoplasia and metastasis of many types of cancer. Therefore, we investigated whether ASEs of pre-RNA have such an influence on the prognosis of SKCM and the related mechanism of ASEs in SKCM. The RNA-seq data and ASEs data for SKCM patients were obtained from the TCGA and TCGASpliceSeq database. The univariate Cox regression revealed 1265 overall survival-related splicing events (OS-SEs). Screened by Lasso regression, 4 OS-SEs were identified and used to construct an effective prediction model (AUC: .904), whose risk score was proved to be an independent prognostic factor. Furthermore, Kruskal-Wallis test and Mann-Whitney-Wilcoxon test showed that an aberrant splicing type of aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) regulated by CDC-like kinase 1 (CLK1) was associated with the metastasis and stage of SKCM. Besides, the overlapped signal pathway for AIMP2 was galactose metabolism identified by the co-expression analysis. External database validation also confirmed that AIMP2, CLK1, and the galactose metabolism were associated with the metastasis and stage of SKCM patients. ChIP-seq and ATAC-seq methods further confirmed the transcription regulation of CLK1, AIMP2, and other key genes, whose cellular expression was detected by Single Cell Sequencing. In conclusion, we proposed that CLK1-regulated AIMP2-78704-ES might play a critical role in the tumorigenesis and metastasis of SKCM via galactose metabolism. Besides, we established an effective model with MTMR14-63114-ES, URI1-48867-ES, BATF2-16724-AP, and MED22-88025-AP to predict the metastasis and prognosis of SKCM patients.

Targeting Lymphotoxin Beta and Paired Box 5: a potential therapeutic strategy for soft tissue sarcoma metastasis.

BACKGROUND: Soft tissue sarcomas (STS) has a high rate of early metastasis. In this study, we aimed to uncover the potential metastasis mechanisms and related signaling pathways in STS with differentially expressed genes and tumor-infiltrating cells. METHODS: RNA-sequencing (RNA-seq) of 261 STS samples downloaded from the Cancer Genome Atlas (TCGA) database were used to identify metastasis-related differentially expressed immune genes and transcription factors (TFs), whose relationship was constructed by Pearson correlation analysis. Metastasis-related prediction model was established based on the most significant immune genes. CIBERSORT algorithm was performed to identify significant immune cells co-expressed with key immune genes. The GSVA and GSEA were performed to identify prognosis-related KEGG pathways. Ultimately, we used the Pearson correlation analysis to explore the relationship among immune genes, immune cells, and KEGG pathways. Additionally, key genes and regulatory mechanisms were validated by single-cell RNA sequencing and ChIP sequencing data. RESULTS: A total of 204 immune genes and 12 TFs, were identified. The prediction model achieved a satisfactory effectiveness in distant metastasis with the Area Under Curve (AUC) of 0.808. LTB was significantly correlated with PAX5 (P < 0.001, R = 0.829) and hematopoietic cell lineage pathway (P < 0.001, R = 0.375). The transcriptional regulatory pattern between PAX5 and LTB was validated by ChIP sequencing data. CONCLUSIONS: We hypothesized that down-regulated LTB (immune gene) modulated by PAX5 (TF) in STSs may have the capability of inducing cancer cell metastasis in patients with STS.

The predicting roles of carcinoembryonic antigen and its underlying mechanism in the progression of coronavirus disease 2019.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has induced a worldwide epidemiological event with a high infectivity and mortality. However, the predicting biomarkers and their potential mechanism in the progression of COVID-19 are not well known. OBJECTIVE: The aim of this study is to identify the candidate predictors of COVID-19 and investigate their underlying mechanism. METHODS: The retrospective study was conducted to identify the potential laboratory indicators with prognostic values of COVID-19 disease. Then, the prognostic nomogram was constructed to predict the overall survival of COVID-19 patients. Additionally, the scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of the most important prognostic indicators in lungs and peripherals, respectively. RESULTS: In total, 304 hospitalized adult COVID-19 patients in Wuhan Jinyintan Hospital were included in the retrospective study. CEA was the only laboratory indicator with significant difference in the univariate (P < 0.001) and multivariate analysis (P = 0.020). The scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of CEA in lungs and peripherals, respectively. The results revealed the potential roles of CEA were significantly distributed in type II pneumocytes of BALF and developing neutrophils of PBMCs, participating in the progression of COVID-19 by regulating the cell-cell communication. CONCLUSION: This study identifies the prognostic roles of CEA in COVID-19 patients and implies the potential roles of CEACAM8-CEACAM6 in the progression of COVID-19 by regulating the cell-cell communication of developing neutrophils and type II pneumocyte.

Prognostic Factors for Survival in Patients with Malignant Giant Cell Tumor of Bone: A Risk Nomogram Analysis Based on the Population.

BACKGROUND Malignant giant cell tumor of bone (MGCTB) is a rare histological type of malignant tumor that has a high tendency for local relapse and distant metastasis and ultimately leads to a poor prognosis. The purpose of this study was to describe the epidemiological features, identify the prognostic factors, and construct nomograms for patients with MGCTB. MATERIAL AND METHODS Patients with MGCTB that was histologically diagnosed between 1973 and 2014 were selected from the Surveillance, Epidemiology, and End Results (SEER) database as a training set. Survival analysis, Lasso regression, and random forests were used to identify the prognostic variables and establish the nomograms for patients with MGCTB, while an external cohort of 37 patients from our own institution and an external cohort of 163 patients from the SEER database in 2016 were used to validate the generalization performance of the nomograms. RESULTS In total, univariate and multivariable analysis indicated that age, International Classification of Diseases for Oncology, historical stage, primary site, surgery information, radiotherapy, and chemotherapy were independent prognostic variables for overall survival or cause-specific survival. Nomograms based on the multivariable models were built to predict survival, and we achieved a higher C-index in subsequent multidimensional validation. CONCLUSIONS Age, historical stage, and chemotherapy were independent prognostic variables for overall survival and cause-specific survival of MGCTB patients, and radiotherapy and primary site were independent prognostic variables for overall survival. Nomograms based on significant clinicopathological features and clinical experience can be effective in predicting the probability of survival for MGCTB patients.

Collagen Type III Alpha 1 chain regulated by GATA-Binding Protein 6 affects Type II IFN response and propanoate metabolism in the recurrence of lower grade glioma.

Some studies suggested the prognosis value of immune gene in lower grade glioma (LGG). Recurrence in LGG is a tough clinical problem for many LGG patients. Therefore, prognosis biomarker is required. Multivariate prognosis Cox model was constructed and then calculated the risk score. And differential expressed transcription factors (TFs) and differential expressed immune genes (DEIGs) were co-analysed. Besides, significant immune cells/pathways were identified by single sample gene set enrichment analysis (ssGSEA). Moreover, gene set variation analysis (GSVA) and univariate Cox regression were applied to filter prognostic signalling pathways. Additionally, significant DEIG and immune cells/pathways, and significant DEIG and pathways were co-analysed. Further, differential enriched pathways were identified by GSEA. In sum, a scientific hypothesis for recurrence LGG including TF, immune gene and immune cell/pathway was established. In our study, a total of 536 primary LGG samples, 2,498 immune genes and 318 TFs were acquired. Based on edgeR method, 2,164 DEGs, 2,498 DEIGs and 31 differentials expressed TFs were identified. A total of 106 DEIGs were integrated into multivariate prognostic model. Additionally, the AUC of the ROC curve was 0.860, and P value of Kaplan-Meier curve < 0.001. GATA6 (TF) and COL3A1 (DEIG) were selected (R = 0.900, P < 0.001, positive) as significant TF-immune gene links. Type II IFN response (P < 0.001) was the significant immune pathway. Propanoate metabolism (P < 0.001) was the significant KEGG pathway. We proposed that COL3A1 was positively regulated by GATA6, and by effecting type II IFN response and propanoate metabolism, COL3A1 involved in LGG recurrence.

Nomograms for predicting the overall and cause-specific survival in patients with malignant peripheral nerve sheath tumor: a population-based study.

BACKGROUND: Malignant peripheral nerve sheath tumor (MPNST) is extremely rare in soft tissue sarcoma, with a high rate of recurrence and metastasis. Due to its rarity, the epidemiological features and prognostic factors are still uncertain. Moreover, nomograms for patients with MPNST have not been constructed and validated until now. PATIENTS AND METHODS: Patients diagnosed with MPNST between 1973 and 2014 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Survival analysis, machine learning and Lasso regression were used to identify the prognostic factors for overall survival (OS) and cause-specific survival (CSS). Significant prognostic factors were integrated to construct nomograms and then the nomograms were validated externally with a separate cohort from our own institution. RESULTS: A total of 689 patients were included in the training set and 42 patients in the validation set. Multivariate analysis suggested that age, histology, historic stage and chemotherapy were independent prognostic factors for OS and primary site, surgery, historic stage and chemotherapy for CSS. The nomograms based on multivariate models were developed and validated for predicting 3- and 5-year OS and CSS, with a C-index of 0.686 and 0.707, respectively. In the external validation set, the C-index was 0.700 for OS and 0.722 for CSS. CONCLUSION: ICD-O-3 histology, historic stage and chemotherapy were independent prognostic factors for OS and primary site, surgery, historic stage and chemotherapy for CSS. The constructed nomograms could provide individual prediction for MPNST patients and assist oncologists in making accurate survival evaluation.

Evaluating and Predicting the Probability of Death in Patients with Non-Metastatic Osteosarcoma: A Population-Based Study.

BACKGROUND Osteosarcoma is one of the most common bone tumors, with strong local aggressiveness and early metastasis. The aim of this study was to describe the epidemiological data and evaluate the prognostic factors for overall survival (OS) and cause-specific survival (CSS) in patients with non-metastatic osteosarcoma. MATERIAL AND METHODS Patients histologically diagnosed with non-metastatic osteosarcoma between 2005 and 2014 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Survival analysis, machine learning, and Lasso regression were used to identify the prognostic factors for OS and CSS, and the accuracy of the nomograms was tested and compared with the American Joint Committee on Cancer (AJCC) staging systems. RESULTS The entire cohort comprised 1000 patients with non-metastatic osteosarcoma. The multivariable analysis suggested that age, tumor size, grade, and American Joint Committee on Cancer (AJCC) T staging were independent prognostic factors for OS and CSS. Additionally, the nomograms based on these results could better predict probability of OS (Internal validation C-index, 0.7095) and CSS (0.7100) compared with the sixth (OS: 0.613; CSS: 0.628) and seventh edition AJCC staging systems (0.602, 0.613). CONCLUSIONS Relatively young age and low histopathological grade were favorable factors for both OS and CSS. Nomograms based on multivariable models worked well in predicting the probability of death for patients with non-metastatic osteosarcoma.

Role of Telemedicine Intervention in the Treatment of Patients with Chronic Heart Failure: A Systematic Review and Meta-analysis.

OBJECTIVE: Although telemedicine interventional therapy is an innovative method to reduce public medical burden and improve heart failure, its effectiveness is still controversial. This meta-analysis evaluates the role of telemedicine interventional therapy in the treatment of patients with chronic heart failure. METHODS: Relevant literature on telemedicine in chronic heart failure treatment was screened and extracted based on predefined criteria. Quality assessment used Cochrane Handbook 5.1.0 tool, and meta-analysis was conducted using R 4.2.2 software. RESULTS: Fifteen English-language articles were ultimately included in this meta-analysis. The risk bias evaluation determined that 4 articles were low-risk bias and 11 articles were unclear risk bias. The meta-analysis revealed that, compared to the routine intervention group, the all-cause hospitalization rate of patients in the telemedicine intervention group decreased [OR = 0.63, 95% CI (0.41; 0.96), P =.03], and the hospitalization rate of heart failure also decreased [OR = 0.70, 95% CI (0.48; 0.85), P <.01]. However, there were no differences in mortality [OR = 0.64, 95% CI (0.41; 1.01), P =.05], length of hospitalization [MD = -0.42, 95% CI (-1.22; 0.38), P =.31], number of emergency hospitalizations [MD = -0.09, 95% CI (-0.33; 0.15), P =.45], medication compliance [OR = 1.67, 95% CI (0.92; 3.02), P =.09], or MLHFQ scores [MD = -2.30, 95% CI (-6.16; 1.56), P =.24] among the patients. CONCLUSION: This meta-analysis showed that telemedicine reduced overall and heart failure-related hospitalizations in chronic heart failure patients, suggesting its value in clinical management. However, it did not significantly affect mortality, hospital stay length, emergency visits, medication adherence, or quality of life. This suggests the need to optimize specific aspects of telemedicine, identify key components, and develop strategies for better treatment outcomes.

Multi-omics analysis-based macrophage differentiation-associated papillary thyroid cancer patient classifier.

BACKGROUND: The reclassification of Papillary Thyroid Carcinoma (PTC) is an area of research that warrants attention. The connection between thyroid cancer, inflammation, and immune responses necessitates considering the mechanisms of differential prognosis of thyroid tumors from an immunological perspective. Given the high adaptability of macrophages to environmental stimuli, focusing on the differentiation characteristics of macrophages might offer a novel approach to address the issues related to PTC subtyping. METHODS: Single-cell RNA sequencing data of medullary cells infiltrated by papillary thyroid carcinoma obtained from public databases was subjected to dimensionality reduction clustering analysis. The RunUMAP and FindAllMarkers functions were utilized to identify the gene expression matrix of different clusters. Cell differentiation trajectory analysis was conducted using the Monocle R package. A complex regulatory network for the classification of Immune status and Macrophage differentiation-associated Papillary Thyroid Cancer Classification (IMPTCC) was constructed through quantitative multi-omics analysis. Immunohistochemistry (IHC) staining was utilized for pathological histology validation. RESULTS: Through the integration of single-cell RNA and bulk sequencing data combined with multi-omics analysis, we identified crucial transcription factors, immune cells/immune functions, and signaling pathways. Based on this, regulatory networks for three IMPTCC clusters were established. CONCLUSION: Based on the co-expression network analysis results, we identified three subtypes of IMPTCC: Immune-Suppressive Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (ISMPTCC), Immune-Neutral Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (INMPTCC), and Immune-Activated Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (IAMPTCC). Each subtype exhibits distinct metabolic, immune, and regulatory characteristics corresponding to different states of macrophage differentiation.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications.

Background: Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field. Methods: We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field. Results: 3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field. Conclusion: Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

A 10-year mono-center study on patients with burns ≥70% TBSA: prediction model construction and multi-center validation: retrospective cohort.

BACKGROUND: Burn injuries with ≥70% total body surface area (TBSA) are especially acute and life-threatening, leading to severe complications and terrible prognosis, while a powerful model for prediction of overall survival (OS) is lacked. The objective of this study is to identify prognostic factors for the OS of patients with burn injury ≥70% TBSA, construct and validate a feasible predictive model. MATERIALS AND METHODS: Patients diagnosed with burns ≥70% TBSA admitted and treated between 2010 and 2020 in our hospital were included. A cohort of the patients from the Kunshan explosion were assigned as the validation set. The Chi-square test and K-M survival analysis were conducted to identify potential predictors for OS. Then, multi-variate Cox regression analysis was performed to identify the independent factors. Afterwards, we constructed a nomogram to predict OS probability. Finally, the Kunshan cohort was applied as an external validation set. RESULTS: Gender, the percentage of third- and fourth-degree burn as well as organ dysfunction were identified as significant independent factors. A nomogram only based on the factors of the individuals was built and evidenced to have promising predictive accuracy, accordance, and discrimination by both internal and external validation. CONCLUSIONS: This study recognized significant influencing factors for the OS of patients with burns ≥70% TBSA. Furthermore, our nomogram proved to be an effective tool for doctors to quickly evaluate patients' outcomes and make appropriate clinical decisions at an early stage of treatment.

EVs-mediated delivery of CB2 receptor agonist for Alzheimer's disease therapy.

Alzheimer's disease (AD) is a typical neurodegenerative disease that leads to irreversible neuronal degeneration, and effective treatment remains elusive due to the unclear mechanism. We utilized biocompatible mesenchymal stem cell-derived extracellular vesicles as carriers loaded with the CB2 target medicine AM1241 (EVs-AM1241) to protect against neurodegenerative progression and neuronal function in AD model mice. According to the results, EVs-AM1241 were successfully constructed and exhibited better bioavailability and therapeutic effects than bare AM1241. The Morris water maze (MWM) and fear conditioning tests revealed that the learning and memory of EVs-AM1241-treated model mice were significantly improved. In vivo electrophysiological recording of CA1 neurons indicated enhanced response to an auditory conditioned stimulus following fear learning. Immunostaining and Western blot analysis showed that amyloid plaque deposition and amyloid ß (Aß)-induced neuronal apoptosis were significantly suppressed by EVs-AM1241. Moreover, EVs-AM1241 increased the number of neurons and restored the neuronal cytoskeleton, indicating that they enhanced neuronal regeneration. RNA sequencing revealed that EVs-AM1241 facilitated Aß phagocytosis, promoted neurogenesis and ultimately improved learning and memory through the calcium-Erk signaling pathway. Our study showed that EVs-AM1241 efficiently reversed neurodegenerative pathology and enhanced neurogenesis in model mice, indicating that they are very promising particles for treating AD.

Extracellular Matrix-Mimetic Immunomodulatory Hydrogel for Accelerating Wound Healing.

Macrophages play a crucial role in the complete processes of tissue repair and regeneration, and the activation of M2 polarization is an effective approach to provide a pro-regenerative immune microenvironment. Natural extracellular matrix (ECM) has the capability to modulate macrophage activities via its molecular, physical, and mechanical properties. Inspired by this, an ECM-mimetic hydrogel strategy to modulate macrophages via its dynamic structural characteristics and bioactive cell adhesion sites is proposed. The LZM-SC/SS hydrogel is in situ formed through the amidation reaction between lysozyme (LZM), 4-arm-PEG-SC, and 4-arm-PEG-SS, where LZM provides DGR tripeptide for cell adhesion, 4-arm-PEG-SS provides succinyl ester for dynamic hydrolysis, and 4-arm-PEG-SC balances the stability and dynamics of the network. In vitro and subcutaneous tests indicate the dynamic structural evolution and cell adhesion capacity promotes macrophage movement and M2 polarization synergistically. Comprehensive bioinformatic analysis further confirms the immunomodulatory ability, and reveals a significant correlation between M2 polarization and cell adhesion. A full-thickness wound model is employed to validate the induced M2 polarization, vessel development, and accelerated healing by LZM-SC/SS. This study represents a pioneering exploration of macrophage modulation by biomaterials' structures and components rather than drug or cytokines and provides new strategies to promote tissue repair and regeneration.

Spinal cord injury: molecular mechanisms and therapeutic interventions.

Spinal cord injury (SCI) remains a severe condition with an extremely high disability rate. The challenges of SCI repair include its complex pathological mechanisms and the difficulties of neural regeneration in the central nervous system. In the past few decades, researchers have attempted to completely elucidate the pathological mechanism of SCI and identify effective strategies to promote axon regeneration and neural circuit remodeling, but the results have not been ideal. Recently, new pathological mechanisms of SCI, especially the interactions between immune and neural cell responses, have been revealed by single-cell sequencing and spatial transcriptome analysis. With the development of bioactive materials and stem cells, more attention has been focused on forming intermediate neural networks to promote neural regeneration and neural circuit reconstruction than on promoting axonal regeneration in the corticospinal tract. Furthermore, technologies to control physical parameters such as electricity, magnetism and ultrasound have been constantly innovated and applied in neural cell fate regulation. Among these advanced novel strategies and technologies, stem cell therapy, biomaterial transplantation, and electromagnetic stimulation have entered into the stage of clinical trials, and some of them have already been applied in clinical treatment. In this review, we outline the overall epidemiology and pathophysiology of SCI, expound on the latest research progress related to neural regeneration and circuit reconstruction in detail, and propose future directions for SCI repair and clinical applications.

Global trends in research of fibroblasts associated with rheumatoid diseases in the 21st century: A bibliometric analysis.

Background: Rheumatoid Diseases (RDs) are a group of systemic auto-immune diseases that are characterized by chronic synovitis, and fibroblast-like synoviocytes (FLSs) play an important role in the occurrence and progression of synovitis. Our study is the first to adopt bibliometric analysis to identify the global scientific production and visualize its current distribution in the 21st century, providing insights for future research through the analysis of themes and keywords. Methods: We obtained scientific publications from the core collection of the Web of Science (WoS) database, and the bibliometric analysis and visualization were conducted by Biblioshiny software based on R-bibliometrix. Results: From 2000 to 2022, a total of 3,391 publications were reviewed. China is the most prolific country (n = 2601), and the USA is the most cited country (cited 7225 times). The Center of Experimental Rheumatology at University Hospital Zürich supported the maximum number of articles (n = 40). Steffen Gay published 85 records with 6263 total citations, perhaps making him the most impactful researcher. Arthritis and Rheumatism, Annals of Rheumatic Diseases, and Rheumatology are the top three journals. Conclusion: The current study revealed that rheumatoid disease (RD)-related fibroblast studies are growing. Based on the bibliometric analysis, we summarized three important topics: activation of different subsets of fibroblasts; regulation of fibroblast function; and in vitro validation of existing discoveries. They are all valuable directions, which provide reference and guidance for researchers and clinicians engaged in the research of RDs and fibroblasts.

Pan-cancer analysis reveals signal transducer and activator of transcription (STAT) gene family as biomarkers for prognostic prediction and therapeutic guidance.

Background: The signal transducer and activator of transcription (STAT) gene family have been widely found to regulate cell proliferation, differentiation, apoptosis, and angiogenesis through complex signaling pathways, and thus impacting tumor formation and development in different types of tumor. However, the roles of STATs on prognostic prediction and therapeutic guidance in pan-cancer remain unexplored. Materials and Methods: The dataset of 33 types of TCGA tumor, para-carcinoma and normal tissues, was obtained from the UCSC Xena database, including the gene expression profiles in the formats of FPKM value, demographic characteristics, clinical information, and survival data of STATs. Differential expression and co-expression analyses, WGCNA, clinical relevance analysis, immune subtype analysis, tumor stemness analysis, tumor purity analysis, immune infiltration analysis, immunotherapy related analysis, tumor mutation related analysis, and drug sensitivity analysis were performed by R software. Results: Differential expression of STAT1 was found between normal and BRCA tissues (p < 0.001, log2FC = 0.895). Additionally, the strongest correlation among STATs lied between STAT1 and STAT2 (correlation coefficient = 0.6). Moreover, high expression levels of STAT1 (p = 0.031) were revealed to be notably correlated with poor prognosis in KIRP. In addition, STAT1 expressed the highest value in immune subtypes C1, C2, C3, and C6 in LUAD. What's more, strong negative correlations were demonstrated between expression of STAT6 and mDNAss and mRNAss of TGCT. Additionally, STAT4 expression was characterized to be significantly negatively correlated with tumor purity of the majority of cancer types. Moreover, STAT1 and STAT3 were shown to be generally high-expressed in pan-cancer myeloid cells, and STATs all had positive correlation with the infiltration of the majority of immune cells. In addition, STATs were revealed to be closely linked with immunotherapy response. What's more, STAT4 expression was identified to have a strong negative correlation with TMB value in DLBC. Last but not least, positive correlations were accessed between STAT5 and sensitivity of Nelarabine (cor = 0.600, p < 0.001). Conclusion: In the present study, we identified STATs as biomarkers for prognostic prediction and therapeutic guidance in pan-cancer. Hopefully our findings could provide a valuable reference for future STATs research and clinical applications.