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
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.
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.
Pancreatic cancer has a 5-year overall survival lower than 8%. Pancreatic adenocarcinoma (PAAD) is the most common type. This study attempted to explore novel molecular subtypes and a prognostic ...model through analyzing tumor microenvironment (TME).
Single-cell RNA sequencing (scRNA-seq) data and expression profiles from public databases were downloaded. Three PAAD samples with single-cell data and 566 samples with gene expression data were included. Seurat was used to identify cell subsets. SVA merged and removed batch effects from multichip datasets. CIBERSORT was used to evaluate the components of different cells in transcriptome, ConsensusClusterPlus was used to identify molecular subtypes, and gene set enrichment analysis was used for functional enrichment analysis. LASSO Cox was performed to construct dimensionality reduction and prognosis model.
Memory B cells (MBCs) were identified to be significantly with PAAD prognosis. Two immune subtypes (IS1 and IS2) with distinct overall survival were constructed. Forty-one DEGs were identified between IS1 and IS2. Four prognostic genes (ANLN, ARNTL2, SERPINB5, and DKK1) were screened to develop a prognostic model. The model was effective in classifying samples into high-risk and low-risk groups with distinct prognosis. Three subgroups of MBCs were identified, where MBC_0 and MBC_1 were differentially distributed between IS1 and IS2, high-risk and low-risk groups.
MBCs were closely involved in PAAD progression, especially MBC_0 and MBC_1 subgroups. The four-gene prognostic model was predictive of overall survival and could guide immunotherapy for patients with PAAD.
Abstract
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.
Given the significant burden of upper digestive diseases, there has been a substantial increase in the utilization of esophagogastroduodenoscopy (EGD) in China from 2012 to 2019. The objective of ...this study is to investigate the development, practice, and factors influencing the widespread use of EGD during this period.BackgroundGiven the significant burden of upper digestive diseases, there has been a substantial increase in the utilization of esophagogastroduodenoscopy (EGD) in China from 2012 to 2019. The objective of this study is to investigate the development, practice, and factors influencing the widespread use of EGD during this period.Two national censuses were conducted among all hospitals in mainland China that perform gastrointestinal endoscopy. These censuses aimed to extract information on the infrastructure, volume, and quality of EGD. The analysis of potential factors influencing EGD practice was based on real-world data from open access sources.MethodsTwo national censuses were conducted among all hospitals in mainland China that perform gastrointestinal endoscopy. These censuses aimed to extract information on the infrastructure, volume, and quality of EGD. The analysis of potential factors influencing EGD practice was based on real-world data from open access sources.From 2012 to 2019, the number of hospitals performing EGD in mainland China increased from 1,518 to 2,265 (1.49-fold) in tertiary hospitals and from 3,633 to 4,097 (1.12-fold) in secondary hospitals, respectively. The national utilization rate of EGD also increased from 1,643.53 to 2,018.06 per 100,000 inhabitants, indicating a 1.23-fold increase. Regions with more endoscopists per 100,000 inhabitants (OR 9.61, P<0.001), more tertiary hospitals performing EGD per million inhabitants (OR 2.43, P<0.001), higher incidence of esophageal and gastric cancer (OR 2.09, P=0 016), and higher number of hospitals performing EGD per million inhabitants (OR 1.77, P=0.01) tended to provided more numerous and qualitied EGD. And hospital grading, regional GDP, incidence of esophageal and gastric cancer and the volume of EGD were observed as the significantly relevant factors of malignant dictation rate (MDR) (P<0.05), but not the number and educational background of endoscopists.ResultsFrom 2012 to 2019, the number of hospitals performing EGD in mainland China increased from 1,518 to 2,265 (1.49-fold) in tertiary hospitals and from 3,633 to 4,097 (1.12-fold) in secondary hospitals, respectively. The national utilization rate of EGD also increased from 1,643.53 to 2,018.06 per 100,000 inhabitants, indicating a 1.23-fold increase. Regions with more endoscopists per 100,000 inhabitants (OR 9.61, P<0.001), more tertiary hospitals performing EGD per million inhabitants (OR 2.43, P<0.001), higher incidence of esophageal and gastric cancer (OR 2.09, P=0 016), and higher number of hospitals performing EGD per million inhabitants (OR 1.77, P=0.01) tended to provided more numerous and qualitied EGD. And hospital grading, regional GDP, incidence of esophageal and gastric cancer and the volume of EGD were observed as the significantly relevant factors of malignant dictation rate (MDR) (P<0.05), but not the number and educational background of endoscopists.Over the past seven years, China has made significant progress in EGD. However, challenges persist in terms of quality and inequality.ConclusionOver the past seven years, China has made significant progress in EGD. However, challenges persist in terms of quality and inequality.
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.
Monoterpenoids are the main components of plant essential oils and the active components of some traditional Chinese medicinal herbs like
Briq
Briq
(L.) Britt
(Blanco) Benth. Pulegone reductase is ...the key enzyme in the biosynthesis of menthol and is required for the stereoselective reduction of the Δ
double bond of pulegone to produce the major intermediate menthone, thus determining the stereochemistry of menthol. However, the structural basis and mechanism underlying the stereoselectivity of pulegone reductase remain poorly understood. In this study, we characterized a novel (-)-pulegone reductase from
(
PR), which can catalyze (-)-pulegone to (+)-menthone and (-)-isomenthone through our RNA-seq, bioinformatic analysis in combination with
enzyme activity assay, and determined the structure of (+)-pulegone reductase from
(
PR) by using X-ray crystallography, molecular modeling and docking, site-directed mutagenesis, molecular dynamics simulations, and biochemical analysis. We identified and validated the critical residues in the crystal structure of
PR involved in the binding of the substrate pulegone. We also further identified that residues Leu56, Val282, and Val284 determine the stereoselectivity of the substrate pulegone, and mainly contributes to the product stereoselectivity. This work not only provides a starting point for the understanding of stereoselectivity of pulegone reductases, but also offers a basis for the engineering of menthone/menthol biosynthetic enzymes to achieve high-titer, industrial-scale production of enantiomerically pure products.
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