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In the environment of big data of the Internet of Things, smart healthcare is developed in combination with cloud computing. However, with the generation of massive data in smart healthcare systems and the need for real-time data processing, traditional cloud computing is no longer suitable for resources-constrained devices in the Internet of Things. In order to address this issue, we combine the advantages of fog computing and propose a cloud-fog assisted attribute-based signcryption for smart healthcare. In the constructed “cloud-fog-terminal” three-layer model, before the patient (data owner)signcryption, it first offloads some heavy computation burden to fog nodes and the doctor (data user) also outsources some complicated operations to fog nodes before unsigncryption by providing a blinded private key, which greatly reduces the calculation overhead of resource-constrained devices of patient and doctor, improves the calculation efficiency. Thus it implements a lightweight signcryption algorithm. Security analysis confirms that the proposed scheme achieves indistinguishability under chosen ciphertext attack and existential unforgeability under chosen message attack if the computational bilinear Diffie-Hellman problem and the decisional bilinear Diffie-Hellman problem holds. Furthermore, performance analysis demonstrates that our new scheme has less computational overhead for both doctors and patients, so it offers higher computational efficiency and is well-suited for application scenarios of smart healthcare.

The P2X7 receptor (P2X7R), a non-selective cation channel modulated by adenosine triphosphate (ATP), localizes to microglia, astrocytes, oligodendrocytes, and neurons in the central nervous system, with the most incredible abundance in microglia. P2X7R partake in various signaling pathways, engaging in the immune response, the release of neurotransmitters, oxidative stress, cell division, and programmed cell death. When neurodegenerative diseases result in neuronal apoptosis and necrosis, ATP activates the P2X7R. This activation induces the release of biologically active molecules such as pro-inflammatory cytokines, chemokines, proteases, reactive oxygen species, and excitotoxic glutamate/ATP. Subsequently, this leads to neuroinflammation, which exacerbates neuronal involvement. The P2X7R is essential in the development of neurodegenerative diseases. This implies that it has potential as a drug target and could be treated using P2X7R antagonists that are able to cross the blood-brain barrier. This review will comprehensively and objectively discuss recent research breakthroughs on P2X7R genes, their structural features, functional properties, signaling pathways, and their roles in neurodegenerative diseases and possible therapies.

Abstract Background: Evaluating the impact of digestive system diseases is vital for devising effective prevention strategies. However, comprehensive reports on the burden of digestive system diseases in China are lacking. Our study aimed to provide an overview of the burden and trends of digestive system diseases from 1990 to 2019 in China and its provinces. Methods: This cross-sectional study utilized the Global Disease Burden Study 2019 to estimate the incidence, mortality rate, disability-adjusted life years (DALYs), years of life disability, years of life lost, and changes in the burden of digestive diseases across Chinese provinces from 1990 to 2019. The analysis of disease burden primarily examines the characteristics of sub-disease distribution, time trends, age distribution, and sex distribution. Additionally, we compared provincial age-standardized DALYs for digestive diseases with the expected rates based on the socio-demographic index (SDI). Results: In 2019, there were 499.2 million cases of digestive system diseases in China, resulting in 1,557,310 deaths. Stomach cancer, colon and rectal cancer, and esophageal cancer are the top three diseases associated with mortality and DALY related to digestive system diseases. Meanwhile, cirrhosis and other chronic liver diseases, gastroesophageal reflux disease, and gallbladder and biliary diseases are the top three kinds of diseases with the highest prevalence among digestive system diseases. The risk of gastric cancer sharply increases among men after the age of 40 years, leading to a significant disparity in burden between men and women. As the SDI increased, the DALYs associated with digestive system diseases in China and its provinces showed a downward trend. Conclusion: Our study highlights the inverse correlation between DALYs associated with digestive system diseases and the SDI.

Abstract Background: Early detection of esophageal squamous cell carcinoma (ESCC) can considerably improve the prognosis of patients. Aberrant cell-free DNA (cfDNA) methylation signatures are a promising tool for detecting ESCC. However, available markers based on cell-free DNA methylation are still inadequate. This study aimed to identify ESCC-specific cfDNA methylation markers and evaluate the diagnostic performance in the early detection of ESCC. Methods: We performed whole-genome bisulfite sequencing (WGBS) for 24 ESCC tissues and their normal adjacent tissues. Based on the WGBS data, we identified 21,469,837 eligible CpG sites (CpGs). By integrating several methylation datasets, we identified several promising ESCC-specific cell-free DNA methylation markers. Finally, we developed a dual-marker panel based on methylated KCNA3 and OTOP2, and then, we evaluated its performance in our training and validation cohorts. Results: The ESCC diagnostic model constructed based on KCNA3 and OTOP2 had an AUC of 0.91 [95% CI: 0.85-0.95], and an optimal sensitivity and specificity of 84.91% and 94.32%, respectively, in the training cohort. In the independent validation cohort, the AUC was 0.88 [95% CI: 0.83-0.92], along with an optimal sensitivity of 81.5% and specificity of 92.9%. The model sensitivity for stage I-II ESCC was 78.4%, which was slightly lower than the sensitivity of the model (85.7%) for stage III-IV ESCC. Conclusion: The dual-target panel based on cfDNA showed excellent performance for detecting ESCC and might be an alternative strategy for screening ESCC.

Gamma-butyrobetaine hydroxylase 1 antisense RNA 1 (BBOX1-AS1), located on human chromosome 11 p14, emerges as a critical player in tumorigenesis with diverse oncogenic effects. Aberrant expression of BBOX1-AS1 intricately regulates various cellular processes, including cell growth, epithelial–mesenchymal transition, migration, invasion, metastasis, cell death, and stemness. Notably, the expression of BBOX1-AS1 was significantly correlated with clinical-pathological characteristics and tumor prognoses, and it could also be used for the diagnosis of lung and esophageal cancers. Through its involvement in the ceRNA network, BBOX1-AS1 competitively binds to eight miRNAs in ten different cancer types. Additionally, BBOX1-AS1 can directly modulate downstream protein-coding genes or act as an mRNA stabilizer. The implications of BBOX1-AS1 extend to critical signaling pathways, including Hedgehog, Wnt/β-catenin, and MELK/FAK pathways. Moreover, it influences drug resistance in hepatocellular carcinoma. The present study provides a systematic review of the clinical significance of BBOX1-AS1’s aberrant expression in diverse tumor types. It sheds light on the intricate molecular mechanisms through which BBOX1-AS1 influences cancer initiation and progression and outlines potential avenues for future research in this field.

Objective To investigate the clinical features and epidemiology of diarrhea patients and analyze the current distribution of enteropathogens causing diarrhea in a comprehensive hospital in Beijing, China, in 2023. Materials and Methods From April to October 2023, we enrolled patients with diarrheal diseases who visited the gastrointestinal clinic in our hospital. The patients' demographic, epidemiological, and clinical features were obtained via a questionnaire. Stool samples were examined for 20 enteropathogens by multiplex polymerase chain reaction testing. Results We enrolled 260 patients; men and adults accounted for 55.77% and 95.77% of the patients, respectively. The median age was 37 years. Eighty‐four enteropathogens, 72 bacteria and 12 viruses, were identified in 74 patients. Enteroaggregative Escherichia coli was the predominant agent. Patients with and without pathogens detected in stool samples showed no significant differences in age, sex, gastrointestinal symptoms, and stool characteristics. Possible food‐related events were recorded in 57.31% of the patients. Leukocyte counts in patients with bacterial infections were higher than those of patients with viral infections and those with no detected pathogens (p < 0.05). Seasonality of bacterial distribution was observed (p < 0.05). Conclusion Bacteria were predominant pathogens among the diarrhea patients. The incidence of diarrhea was related to hot weather and foodborne illness. Bacterial diarrhea may cause systemic infection. The clinical symptoms of infectious diarrhea were usually non‐specific and unrelated to the type of infection. Timely and comprehensive multi‐pathogen surveillance might be helpful to detect suspected pathogens and promote epidemic prevention and control. (1) We investigated the clinical features and epidemiology of diarrhea patients and analyze the current distribution of 20 enteropathogens causing diarrhea in a comprehensive hospital in Beijing, China, in 2023. (2) The incidence of diarrhea was related to hot weather and foodborne illness. (3) Enteroaggregative Escherichia coli was the predominant agent. (4) Seasonality of bacterial distribution was observed. (5) Bacterial diarrhea may cause systemic infection.

It has been reported that tumor-associated macrophages (TAMs) play a complicated role in cancer occurrence and development, immune escape, and immune checkpoint blockade (ICB) resistance. However, the role of granulin precursor (GRN) highly expressed macrophages (hereafter refer to GRN + macrophages) in hepatocellular carcinoma (HCC) remains poorly understood. Herein, we systematically integrated multiomics analysis of human tumor tissues to illustrate the functional role of GRN + macrophages in HCC. GRN is selectively expressed by TAMs in different type of cancers including HCC, and was significantly associated with poor prognosis in several type of cancer. GRN was closely correlated with infiltration levels of most immune cells, especially the M2 macrophage cells in various cancers. In particular, both mRNA and protein expression level of GRN was significantly upregulated in HCC. Compared with tumor tissue, GRN was more significantly expressed in the stroma area between HCC tissues and adjacent non-tumor tissues. High expression of GRN was significantly correlated with M2-polarization of macrophages and T-cell exhaustion in HCC. GRN + macrophages communicated with intratumoral immune cells, especially CD8 + T cells. Functionally, GRN + macrophages contacted with CD8 + T cells, which inducing T-cell exhaustion. Our study offers a comprehensive understanding of the clinical relevance and immunological role of GRN + macrophages in HCC, indicating its potential role as a promising target for immunotherapeutic strategies.

In the competitive landscape of online learning, developing robust and effective learning resource recommendation systems is paramount, yet the field faces challenges due to high-dimensional, sparse matrices and intricate user–resource interactions. Our study focuses on geometric matrix completion (GMC) and introduces a novel approach, graph-based truncated norm regularization (GBTNR) for problem solving. GBTNR innovatively incorporates truncated Dirichlet norms for both user and item graphs, enhancing the model’s ability to handle complex data structures. This method synergistically combines the benefits of truncated norm regularization with the insightful analysis of user–user and resource–resource graph relationships, leading to a significant improvement in recommendation performance. Our model’s unique application of truncated Dirichlet norms distinctively positions it to address the inherent complexities in user and item data structures more effectively than existing methods. By bridging the gap between theoretical robustness and practical applicability, the GBTNR approach offers a substantial leap forward in the field of learning resource recommendations. This advancement is particularly critical in the realm of online education, where understanding and adapting to diverse and intricate user–resource interactions is key to developing truly personalized learning experiences. Moreover, our work includes a thorough theoretical analysis, complete with proofs, to establish the convergence property of the GMC-GBTNR model, thus reinforcing its reliability and effectiveness in practical applications. Empirical validation through extensive experiments on diverse real-world datasets affirms the model’s superior performance over existing methods, marking a groundbreaking advancement in personalized education and deepening our understanding of the dynamics in learner–resource interactions.

Online learning environments generate vast amounts of student interaction data. While these records capture observable behaviors, they do not directly reveal students’ underlying knowledge states, which are essential for tracking learning progress. Knowledge tracing (KT) addresses this gap by predicting students’ future performance on exercises related to specific concepts, thereby enabling personalized learning and intelligent tutoring. Existing deep learning-based KT methods achieve promising results, but they often overemphasize either the sequential evolution of knowledge or the static structural relationships, which does not reflect the dynamic evolution of student learning. Moreover, they fail to model students’ knowledge state accurately under sparse interactions. To overcome these limitations, we propose DyGAS, a dynamic graph-augmented sequence modeling framework for knowledge tracing. The sequential module captures the dynamics pattern of knowledge acquisition and forgetting, while the structural module employs graph convolutional networks (GCN) to model inter-concept dependencies and knowledge transfer. Additionally, we propose that static knowledge modeling provides semantic priors to stabilize the representation of sparse concepts. Empirical results on three benchmark datasets demonstrate that DyGAS achieves superior performance compared to state-of-the-art methods, offering accurate and robust knowledge tracing across diverse learning scenarios.

Background and objectives Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. Methods The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared. Results A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions. Conclusion The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and  deserves further validation in clinical trials.