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Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC. [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in F 1 -scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model's performance, emphasizing the effectiveness of these components in capturing detailed contextual cues. [ABSTRACT FROM AUTHOR]

Primary pulmonary hyalinizing clear cell carcinoma (HCCC) is a very rare lung tumor that accounts for less than 0.09% of all primary lung tumors and has no specific epidemiology. The correct diagnosis requires imaging, laboratory, pathological, immunohistochemical, and molecular examination. The most typical feature of pulmonary HCCC is the clear cell component with clear stroma. In addition, the fusion gene EWSR1::ATF1 due to t(12;22)(q13;q12) is essential for the pathological diagnosis of pulmonary HCCC. The main treatment for pulmonary HCCC is surgery. This review focus on the pathological features, immunohistochemical examination, mutation analysis and treatment of pulmonary HCCC. [ABSTRACT FROM AUTHOR]

Background: Severe community-acquired pneumonia is one of the most lethal forms of CAP with high mortality. For rapid and accurate decisions, we developed a mortality prediction model specifically tailored for elderly SCAP patients. Methods: The retrospective study included 2365 elderly patients. To construct and validate the nomogram, we randomly divided the patients into training and testing cohorts in a 70% versus 30% ratio. The primary outcome was in-hospital mortality. Univariate and multivariate logistic regression analyses were used in the training cohort to identify independent risk factors. The robustness of this model was assessed using the C index, ROC and AUC. DCA was employed to evaluate the predictive accuracy of the model. Results: Six factors were used as independent risk factors for in-hospital mortality to construct the prediction model, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet, and BUN. The C index was 0.743 (95% CI 0.719–0.768) in the training cohort and 0.731 (95% CI 0.694–0.768) in the testing cohort. The ROC curves and AUC for the training cohort and testing cohort (AUC = 0.742 vs. 0.728) indicated a robust discrimination. And the calibration plots showed a consistency between the prediction model probabilities and observed probabilities. Then, the DCA demonstrated great clinical practicality. Conclusions: The nomogram incorporated six risk factors, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet and BUN, which had great predictive accuracy and robustness, while also demonstrating clinical practicality at ICU admission. [ABSTRACT FROM AUTHOR]

Background: Modern medicine emphasizes that medical professionals engage in interprofessional collaboration to better meet the diverse needs of patients from physical, psychological, and social perspectives. As nursing students are the future reserve of the clinical nursing workforce, nursing educators worldwide should pay close attention to nursing students' interprofessional learning attitudes and take responsibility for training qualified interprofessional nursing personnel. However, little is known about the relationship between nursing students' readiness for interprofessional learning and academic self-efficacy. Thus, this study aims to investigate the level of readiness for interprofessional learning and academic self-efficacy among nursing students, and to explore the relationship between the two. Methods: A cross-sectional survey was conducted with a sample of 741 undergraduate nursing students pursuing four-year degrees from a school in Jinan, Shandong Province, China from November to December 2021. The social-demographic questionnaire, Readiness for Interprofessional Learning Scale, and Academic Self-efficacy Scale were used for data collection. Descriptive statistics used to analyze the data included: Cronbach's alpha, t-test, one-way ANOVA, Pearson's correlation, and multiple linear regression analysis. Results: Readiness for interprofessional learning mean score was (3.91 ± 0.44) and mean academic self-efficacy was (3.47 ± 0.42). Significant differences were found in the research variables according to participants' sex, grade, choice of nursing profession, and frequency of communication with health-related major students in studies (p < 0.05, p < 0.001). Pearson correlation analysis showed that academic self-efficacy was positively related to readiness for interprofessional learning (r = 0.316, p < 0.01). The hierarchical regression analysis showed that academic self-efficacy was positively related to readiness for interprofessional learning (β = 0.307, p < 0.001), The model explained 15.6% of the variance in readiness for interprofessional learning (F = 18.038, p < 0.001). Conclusions: Readiness for interprofessional learning and academic self-efficacy were in the middle level among nursing students. Moreover, there was a significant positive correlation between the two. Therefore, it is very important for nursing educators to improve nursing students' academic self-efficacy before improving their readiness for interprofessional learning. [ABSTRACT FROM AUTHOR]

Due to persistent technological impacts on ecological efficiency (eco-efficiency) and variations in economic power and resource endowments among regions, considering regional and temporal heterogeneity becomes imperative. Ecosystems, often divided into economic production and environmental governance stages, necessitate a holistic assessment incorporating regional, temporal heterogeneity and stage distinctions. To address potential issues of a technology gap ratio (TGR) exceeding 1 within a two-stage network structure with dual heterogeneity, we introduce a segmented projection three-layer meta-frontier analysis method. In empirical study, we systematically examined eco-efficiency, emissions inefficiency and technology gaps across management, regional and temporal dimensions in 30 Chinese provinces from 2016 to 2020. Findings reveal disparities in management eco-efficiency, with the central provinces outperforming the east. Regional differences indicate advanced technology in the east, contributing to superior eco-efficiency. Temporal analysis highlights the positive role of scientific and technological development. Emissions inefficiency improvements are noted, necessitating attention toward management and regional technology levels. Eastern provinces exhibit superior emissions efficiency, emphasizing the role of regional and technological development. Recommendations include prioritizing environmental governance, strengthening regional collaborations and implementing policies to bridge technology gaps. [ABSTRACT FROM AUTHOR]

Constructed wetlands use vegetation and microorganisms to remove contaminants like nitrogen and phosphorus from water. For mariculture, the impact of salinity on the efficiency of wastewater treatment of wetlands is unneglectable. However, little is known about their impact on the microbiome in constructed wetlands. Here, we set four salinity levels (15, 22, 29, and 36) in Salicornia constructed wetlands, and the experiment was conducted for a period of 72 days. The 15 group exhibited the highest removal rates of nitrogen compounds and phosphate, compared to the other salinity groups, the nosZ gene exhibited significantly higher expression in the 22 group (p < 0.05), indicated that microorganisms in 22 salinity have higher denitrification abilities. The three dominant phyla identified within the microbiomes were Proteobacteria, known for their diverse metabolic capabilities; Cyanobacteria, important for photosynthesis and nitrogen fixation; and Firmicutes, which include many fermenters. The ecological network analysis revealed a 'small world' model, characterized by high interconnectivity and short path lengths between microbial species, and had higher co-occurrence (45.13%) observed in this study comparing to the Erdös-Réyni random one (32.35%). The genus Microbulbifer emerged as the sole connector taxon, pivotal for integrating different microbial communities involved in nitrogen removal. A negative correlation was observed between salinity levels and network complexity, as assessed by the number of connections and diversity of interactions within the microbial community. Collectively, these findings underscore the critical role of microbial community interactions in optimizing nitrogen removal in constructed wetlands, with potential applications in the design and management of such systems for improved wastewater treatment in mariculture., Competing Interests: Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:, (Copyright © 2024. Published by Elsevier Ltd.)

Background: Tuberculosis (TB) persists as a global health challenge, with its treatment hampered by the side effects of long-term combination drug therapies and the growing issue of drug resistance. Therefore, the development of novel therapeutic strategies is critical. This study focuses on the role of immune checkpoint molecules (ICs) and functions of CD8+ T cells in the search for new potential targets against TB., Methods: We conducted differential expression genes analysis and CD8+ T cell functional gene analysis on 92 TB samples and 61 healthy individual (HI) samples from TB database GSE83456, which contains data on 34,603 genes. The GSE54992 dataset was used to validated the findings. Additionally, a cluster analysis on single-cell data from primates infected with mycobacterium tuberculosis and those vaccinated with BCG was performed., Results: The overexpression of LAG-3 gene was found as a potentially important characteristic of both pulmonary TB (PTB) and extrapulmonary TB (EPTB). Further correlation analysis showed that LAG-3 gene was correlated with GZMB, perforin, IL-2 and IL-12. A significant temporal and spatial variation in LAG-3 expression was observed in T cells and macrophages during TB infection and after BCG vaccination., Conclusion: LAG-3 was overexpressed in TB samples. Targeting LAG-3 may represent a potential therapeutic target for tuberculosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Pan, Yao, Huang, Xu, Chen, Li, Wang, Wu, Li, Liang and Tan.)

Chronic kidney disease (CKD) is a leading public health issue associated with high morbidity worldwide. However, there are only a few effective therapeutic strategies for CKD. Emodin, an anthraquinone compound from rhubarb, can inhibit fibrosis in tissues and cells. Our study aims to investigate the antifibrotic effect of emodin and the underlying molecular mechanism. A unilateral ureteral obstruction (UUO)-induced rat model was established to evaluate the effect of emodin on renal fibrosis development. Hematoxylin and eosin staining, Masson's trichrome staining, and immunohistochemistry staining were performed to analyze histopathological changes and fibrotic features after emodin treatment. Subsequently, a transforming growth factor-beta 1 (TGF-β1)-induced cell model was used to assess the inhibition of emodin on cell fibrosis in vitro. Furthermore, Western blot analysis and real-time quantitative reverse transcription-polymerase chain reaction were performed to validate the regulatory mechanism of emodin on renal fibrosis progression. As a result, emodin significantly improved histopathological abnormalities in rats with UUO. The expression of fibrosis biomarkers and mitochondrial biogenesis-related proteins also decreased after emodin treatment. Moreover, emodin blocked TGF-β1-induced fibrotic phenotype, lipid accumulation, and mitochondrial homeostasis in NRK-52E cells. Conversely, peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1α) silencing significantly reversed these features in emodin-treated cells. Collectively, emodin plays an important role in regulating PGC-1α-mediated mitochondria function and energy homeostasis. This indicates that emodin exhibits great inhibition against renal fibrosis and acts as a promising inhibitor of CKD.