Search

ABSTRACT Streptococcus mitis/oralis can cause invasive diseases, including bloodstream infections. However, existing research primarily focuses on specific populations, and limited studies have been conducted on the prevalence of bloodstream infection caused by S. mitis/oralis across the entire pediatric population. Therefore, clinical data of S. mitis/oralis isolated from blood samples at Children’s Hospital, Zhejiang University School of Medicine, during the period 2019–2023 were collected retrospectively to provide a comprehensive understanding of the clinical characteristics and drug resistance patterns associated with bloodstream infections caused by S. mitis/oralis in pediatric populations. There were 57 (43.5%) instances of contamination across various departments, indicating a relatively dispersed pattern. Bloodstream infections caused by S. mitis/oralis are notably prevalent among pediatric patients with hematological diseases and tumors. The susceptibility rates of the 74 S. mitis/oralis isolates to different antibiotics were as follows: penicillin (23%), ceftriaxone (74.3%), levofloxacin (86.5%), chloramphenicol (89.2%), erythromycin (27%), clindamycin (67.6%), linezolid (100%), and vancomycin (100%). Notably, 21.6% of the isolates exhibited multi-drug resistance (MDR). The predominant mode of MDR in S. mitis/oralis infections was identified as resistance to β-lactams, erythromycin, and clindamycin. The observed low susceptibility rate to penicillin, coupled with the emergence of MDR strains, underscores the imperative for continuous monitoring of the evolving antimicrobial resistance in S. mitis/oralis.IMPORTANCEExisting research primarily focuses on specific populations, such as those with hematopathy or tumors, who experience Streptococcus mitis/oralis bacteremia. Limited studies have been conducted on the prevalence of bloodstream infections caused by S. mitis/oralis across the entire pediatric population. It was found that the contamination rate of S. mitis/oralis isolated from blood cultures was notably high in our study. Therefore, this study evaluated the clinical characteristics and drug resistance patterns of bloodstream infections caused by S. mitis/oralis across the entire pediatric populations, explicitly excluding cases of blood culture contamination. The observed low susceptibility rate to penicillin, coupled with the emergence of multi-drug-resistant strains, underscores the imperative for continuous monitoring of the evolving antimicrobial resistance in S. mitis/oralis.

Vegetation–soil–microbial relationships significantly affect global climatic and environmental changes. Fluctuations in temperature and precipitation caused by climate change are the driving forces of dynamic changes in vegetation–soil–microbial relationships. Our study was conducted in the desert grassland of Ningxia, China, and involved five levels of annual precipitation (33 % (R33), 66 % (R66), 100 % (RCK), 133 % (R133), and 166 % (R166)) and two temperature levels (within and outside the open-top chamber). Our objective was to determine how vegetation–soil–microbial relationships in desert grasslands respond to changes in precipitation and warming. Our results showed that precipitation was positively correlated with above-ground living and root biomass, and soil organic carbon. R166 had the strongest effect on the correlation between vegetation, soil, and microbes, whereas R33 had the weakest effect. Temperature was positively correlated with soil microbial α-diversity, and the effect of warming on the correlation among vegetation, soil, and microbes was less significant than under natural temperature. The combination of precipitation and warming was positively correlated with the above-ground living biomass and soil respiration. The correlation effects of the combination of precipitation and warming on vegetation, soil, and microbes were more substantial than those of precipitation and warming. Our findings provide a theoretical basis for the formulation of reasonable response strategies for desert steppe ecosystems.

In the Internet of Things (IoT) healthcare sector, the wireless body area network (WBAN) is being used to optimize medical results by tracking and treating patients as they go about their daily lives. Health insurance has also been one of the cybercriminal's main goals. The Systematic Review of IoT Healthcare systems particularly wireless body area networks is significant, to reach the benefits and challenges faced by existing methods in the domain. This study provides a systematic survey of WBAN data protection. Various types of devices are used in medical science to detect and diagnose diseases. The network is an integral part of medical science in today's era. In medical sciences, sensors take data from a problematic place like cancerous cells. This research discussed a lot of techniques in the literature review. Most of them are not able to fulfill the requirements. If an unauthorized person reaches the data that can be a severe issue, like the diagnosed disease was blood cancer, and after unauthorized access manipulation can change even the diagnosed issue in the database. A doctor can prescribe the medication based on provided data that has been manipulated by unauthorized persons. Several existing schemes are explored in the literature to determine how the protection of sharing patients' healthcare data can be improved. The systematic literature review (SLR) of multiple security schemes for WBAN is presented in this survey paper.

Abstract This research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images. The model’s performance is benchmarked against conventional CNNs and other recurrent architectures. The research addresses interpretability concerns by employing attention mechanisms that highlight salient features contributing to the model’s decisions. The proposed model attention-gated recurrent units (A-GRU) results show promising results, indicating that the proposed model surpasses the state-of-the-art models in terms of accuracy and obtained 99.32% accuracy. Due to the high predictive capability of the proposed model, we recommend it for the effective diagnosis of Brain tumors in the E-healthcare system.

This paper presents a stochastic expansion planning model for coordinated natural gas and electricity networks, incorporating gas-fired generators, Power-to-Gas facilities, and renewable power sources. The primary objective is to minimize the total cost over the planning horizon, addressing the significant interdependencies between these networks which, if planned independently, can lead to higher overall costs. The originality of this work lies in its comprehensive integration of both systems, leveraging their synergies to optimize infrastructure investment and operational efficiency. Methodologically, the model employs mixed integer linear programming (MILP) within the General Algebraic Modelling System (GAMS), using a Scenario Tree concept to account for the stochastic nature of renewable energy sources (RESs) and load variations. Data from an adapted twenty-node Belgium gas network and a sixteen-bus UK electricity distribution system were utilized. Results demonstrate substantial cost savings and improved system performance with the integrated approach, validating the model’s effectiveness.

Detecting fake news is increasingly crucial in the digital age as social platforms and online news outlets amplify the spread of misinformation. This study proposes a novel approach for fake news detection using a Bi-LSTM neural network with an attention mechanism. The Bi-LSTM model’s ability to discern long-range relationships and sequential patterns in text data is augmented by the attention mechanism, focusing on key segments within the text to enhance discriminative power. The dataset for training comprises a diverse collection of news articles, meticulously annotated by human fact-checkers to include both real and fake news samples. Pre-processing steps, including tokenization, are undertaken to optimize the model’s learning capabilities. The proposed Self-Attention with Bi-Directionals-LSTM (SA-BiLSTM) model demonstrates promising performance in distinguishing real from fake news. The attention mechanism allows the model to highlight critical words and phrases, capturing essential features for accurate predictions. Evaluation through cross-validation reveals competitive results as opposed to cutting-edge fake news identification methods. Ablation studies emphasize the attention mechanism’s significant contribution to identifying relevant textual patterns associated with fake news. The model, incorporating CNN, GRU, Attention-GRU, LSTM, and Bi-LSTM, surpasses contemporary procedures, as validated using the ISOT dataset. The results demonstrate a robust approach to fake news detection using Bi-LSTM with attention, contributing to the fight against misinformation. The proposed model SA-Bi LSTM obtained 99.98% predictive accuracy on test data. The accuracy of the proposed model (SA-BiLSTM) comparatively to baseline models is higher. The model shows potential for preserving information integrity on social media and news outlets, bolstering public trust in reliable sources.

IntroductionIn the evolving healthcare landscape, we aim to integrate hyperspectral imaging into Hybrid Health Care Units to advance the diagnosis of medical diseases through the effective fusion of cutting-edge technology. The scarcity of medical hyperspectral data limits the use of hyperspectral imaging in disease classification.MethodsOur study innovatively integrates hyperspectral imaging to characterize tumor tissues across diverse body locations, employing the Sharpened Cosine Similarity framework for tumor classification and subsequent healthcare recommendation. The efficiency of the proposed model is evaluated using Cohen's kappa, overall accuracy, and f1-score metrics.ResultsThe proposed model demonstrates remarkable efficiency, with kappa of 91.76%, an overall accuracy of 95.60%, and an f1-score of 96%. These metrics indicate superior performance of our proposed model over existing state-of-the-art methods, even in limited training data.ConclusionThis study marks a milestone in hybrid healthcare informatics, improving personalized care and advancing disease classification and recommendations.

Hyperspectral Image Classification (HSIC) is a challenging task due to the high-dimensional nature of Hyperspectral Imaging (HSI) data and the complex relationships between spectral and spatial information. This paper proposes a Graph-Infused Hybrid spatial–spectral Transformer (GFormer) for HSIC. The GFormer combines the power of graph and spatial–spectral transformer to capture both spectral relationships and spatial context. We represent the HSI data as a graph, where nodes represent pixels and edges capture spectral similarities. By incorporating an attention mechanism, the GFormer learns spatial–spectral fusion representations, allowing it to effectively discriminate between different classes. The model can capture long-range dependencies among spectral bands, enabling it to understand complex interactions in the HSI data. Moreover, the GFormer adapts to different spectral resolutions by dynamically adjusting attention weights for each spectral band. Experimental results on benchmark HSI datasets demonstrate that the GFormer outperforms state-of-the-art (SOTA) methods, achieving superior classification accuracy.

IntroductionHeart disease remains a complex and critical health issue, necessitating accurate and timely detection methods.MethodsIn this research, we present an advanced machine learning system designed for efficient and precise diagnosis of cardiac disease. Our approach integrates the power of Random Forest and Ada Boost classifiers, along with incorporating data pre-processing techniques such as standard scaling and Recursive Feature Elimination (RFE) for feature selection. By leveraging the ensemble learning technique of stacking, we enhance the model's predictive performance by combining the strengths of multiple classifiers.ResultsThe evaluation metrics results demonstrate the superior accuracy and obtained the higher performance in terms of accuracy, 99.25%. The effectiveness of our proposed system compared to baseline models.DiscussionFurthermore, the utilization of this system within IoT-enabled healthcare systems shows promising potential for improving heart disease diagnosis and ultimately enhancing patient outcomes.

To address the challenges associated with preparing thin strips of difficult-to-deform materials – such as low processing efficiency and yield, poor shape, and subpar performance – our research team developed an on-line warm rolling experimental machine featuring hydraulic tension. We used the Ti–6Al–4V alloy as our experimental material and conducted warm rolling experiments at various temperatures, thoroughly examining the intricate relationship between microstructure evolution and mechanical properties. Our findings indicate that warm rolling causes a clear refinement of the α phase and β phase microstructures, while intensifying dislocation entanglement. These changes significantly enhance the strength of the Ti–6Al–4V alloy, though Total Elongation Limit (TEL) decreases. After evaluating mechanical properties, surface quality, and energy consumption associated with warm rolling, we determined the optimal warm rolling temperature for the Ti–6Al–4V alloy to be 650 °C. At this temperature, the Ultimate Tensile Strength (UTS), Yield Strength (YS), and TEL were 1325 MPa, 1164 MPa, and 8.0%, respectively, maintaining good ductility. The impressive performance of the Ti–6Al–4V alloy sheet is mainly attributed to the strengthening effects of fine grain and dislocation, while the introduction of some fine recrystallization enhances ductility. High-performance Ti–6Al–4V alloy thin strip was successfully prepared by on-line warm rolling with hydraulic tension, which provided a new idea for the preparation of thin strip of difficult-to-deform materials.

Accurate pre-harvest crop yield estimation is vital for agricultural sustainability and economic stability. The existing yield estimating models exhibit deficiencies in insufficient examination of hyperparameters, lack of robustness, restricted transferability of meta-models, and uncertain generalizability when applied to agricultural data. This study presents a novel meta-knowledge-guided framework that leverages three diverse agricultural datasets and explores meta-knowledge transfer in frequent hyperparameter optimization scenarios. The framework’s approach involves base tasks using LightGBM and Bayesian Optimization, which automates hyperparameter optimization by eliminating the need for manual adjustments. Conducted rigorous experiments to analyze the meta-knowledge transformation of RGPE, SGPR, and TransBO algorithms, achieving impressive R2 values (0.8415, 0.9865, 0.9708) using rgpe_prf meta-knowledge transfer on diverse datasets. Furthermore, the framework yielded excellent results for mean squared error (MSE), mean absolute error (MAE), scaled MSE, and scaled MAE. These results emphasize the method’s significance, offering valuable insights for crop yield estimation, benefiting farmers and the agricultural sector.

Robots are most widely used to replace human contribution with machine generated response. When humans interact with robots, its mandatory for both to forecast actions based on current conditions. Huge efforts have been channelized towards attaining this perfect coordination. To decipher complex environments, the inference of robotic mobility and alteration of random unstructured scenarios is a complicated task in the field of visual processing and imaging. To address this issue, a new Vision-Based Interaction Model based on deep neural networks has been suggested. The proposed model solves the error amplification issue by the application of past inputs through features as reposed by a Deep Belief Network (DBN). In addition, a novel Vision-Based Robotics Learning model is also proposed for scene understanding and recognition using deep neural network understanding. Moreover, a vision theory-based smart learning algorithm is also suggested to decide positive possible outcomes.Therefore, the model is capable of using object motions to extract relevant information used for Turning, Griping and object mobility.To validate the suggested model, a number of experiments have been performed on benchmark datasets and it showed a higher performance as evaluated against some of the niche methods.

Abstract Background Language deficits frequently occur during the prodromal stages of Alzheimer’s disease (AD). However, the characteristics of linguistic impairment and its underlying mechanism(s) remain to be explored for the early diagnosis of AD. Methods The percentage of silence duration (PSD) of 324 subjects was analyzed, including patients with AD, amnestic mild cognitive impairment (aMCI), and normal controls (NC) recruited from the China multi-center cohort, and the diagnostic efficiency was replicated from the Pitt center cohort. Furthermore, the specific language network involved in the fragmented speech was analyzed using task-based functional magnetic resonance. Results In the China cohort, PSD increased significantly in aMCI and AD patients. The area under the curve of the receiver operating characteristic curves is 0.74, 0.84, and 0.80 in the classification of NC/aMCI, NC/AD, and NC/aMCI+AD. In the Pitt center cohort, PSD was verified as a reliable diagnosis biomarker to differentiate mild AD patients from NC. Next, in response to fluency tasks, clusters in the bilateral inferior frontal gyrus, precentral gyrus, left inferior temporal gyrus, and inferior parietal lobule deactivated markedly in the aMCI/AD group (cluster-level P

Abstract The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the accurate detection of brain cancer. However, due to the inaccuracy of artificial diagnostic systems, medical professionals are not effectively incorporating them into the diagnosis process of Brain Cancer. In this research study, we proposed a robust brain tumor classification method using Deep Learning (DL) techniques to address the lack of accuracy issue in existing artificial diagnosis systems. In the design of the proposed approach, an improved convolution neural network (CNN) is used to classify brain tumors employing brain magnetic resonance (MR) image data. The model classification performance has improved by incorporating data augmentation and transfer learning methods. The results confirmed that the model obtained high accuracy compared to the baseline models. Based on high predictive results we suggest the proposed model for brain cancer diagnosis in IoT-healthcare systems.

The study evaluates how simulated climate change affects microorganism communities in the desert grassland of Ningxia Autonomous Region, China. It explores the soil microorganism community and relationships among the soil microorganism community, chemical properties, soil respiration (SR), and plant biomass under climate change. We established a field experiment with five levels of rainfall using rainout shelters and two levels of temperature by the Open-Top Chamber (OTC). The results show that in fungal communities, normal precipitation will promote the number and base number of valid sequences the most, and R66 will significantly promote the mean length of the valid sequence. In the bacterial communities, the interaction of increasing temperature and R133 will promote the number of valid sequences and R166 will promote the length of valid sequences. Neither rainfall nor rising temperature promotes not only the soil community α-diversity but also the soil microorganism community β-diversity. Soil microorganism communities show resistance to rainfall. SR will limit the soil microorganism diversity. Soil organic carbon (SOC), soil total nitrogen (STN), and soil total phosphorus (STP) will promote soil microorganism abundance (SMA) and soil microorganism diversity (SMS). Aboveground living biomass (ALB) and soil temperature (ST) will promote soil α-diversity, whereas the effect of root biomass (RB) on the soil α-diversity is the opposite. HIGHLIGHTS Climate change effects on microorganism communities and relationships in Ningxia Autonomous region are examined.; Field experiments used five levels of rainfall and two of temperature.; Results show normal precipitation best promotes the number and base number of valid sequences.; R66 was found to significantly promote the number of valid sequences.;

Targeted muscle reinnervation (TMR) is a surgical procedure used to transfer residual peripheral nerves from amputated limbs to targeted muscles, which allows the target muscles to become sources of motor control information for function reconstruction. However, the effect of TMR on injured motor neurons is still unclear. In this study, we aimed to explore the effect of hind limb TMR surgery on injured motor neurons in the spinal cord of rats after tibial nerve transection. We found that the reduction in hind limb motor function and atrophy in mice caused by tibial nerve transection improved after TMR. TMR enhanced nerve regeneration by increasing the number of axons and myelin sheath thickness in the tibial nerve, increasing the number of anterior horn motor neurons, and increasing the number of choline acetyltransferase-positive cells and immunofluorescence intensity of synaptophysin in rat spinal cord. Our findings suggest that TMR may enable the reconnection of residual nerve fibers to target muscles, thus restoring hind limb motor function on the injured side.

Insomnia is a common sleep disorder in which patients cannot sleep properly. Accurate detection of insomnia disorder is a crucial step for mental disease analysis in the early stages. The disruption in getting quality sleep is one of the big sources of cardiovascular syndromes such as blood pressure and stroke. The traditional insomnia detection methods are time-consuming, cumbersome, and more expensive because they demand a long time from a trained neurophysiologist, and they are prone to human error, hence, the accuracy of diagnosis gets compromised. Therefore, the automatic insomnia diagnosis from the electrocardiogram (ECG) records is vital for timely detection and cure. In this paper, a novel hybrid artificial intelligence (AI) approach is proposed based on the power spectral density (PSD) of the heart rate variability (HRV) to detect insomnia in three classification scenarios: (1) subject-based classification scenario (normal Vs. insomnia), (2) sleep stage-based classification (REM Vs. W. stage), and (3) the combined classification scenario using both subject-based and sleep stage-based deep features. The ensemble learning of random forest (RF) and decision tree (DT) classifiers are used to perform the first and second classification scenarios, while the linear discriminant analysis (LDA) classifier is used to perform the third combined scenario. The proposed framework includes data collection, investigation of the ECG signals, extraction of the signal HRV, estimation of the PSD, and AI-based classification via hybrid machine learning classifiers. The proposed framework is fine-tuned and evaluated using the free public PhysioNet dataset over fivefold trails cross-validation. For the subject-based classification scenario, the detection performance in terms of sensitivity, specificity, and accuracy is recorded to be 96.0%, 94.0%, and 96.0%, respectively. For the sleep stage-based classification scenario, the detection evaluation results are recorded equally with 96.0% for ceiling level accuracy, sensitivity, and specificity. For the combined classification scenario, the LDA classifier has achieved the best insomnia detection accuracy with 99.0%. In the future, the proposed hybrid AI approach could be applicable for mobile observation schemes to automatically detect insomnia disorders.

Droppers and jumpers are important components of railway overhead line electrification. Their effective maintenance is critical for the railway’s safe and reliable operation; the consequences of their failure can be disproportionally serious. This paper systematically analyses the components’ failure by applying the method of failure modes and effects analysis to the maintenance records and incident statistics of Britain’s East Coast Main Line. The analysis is presented with photographic evidence and a review of the current maintenance strategy and practice that addresses the risk of failure. These results can support improved dropper and jumper design and the development of more effective maintenance strategies to further prevent failure and operational disruption.

Abstract Background Health Related Quality of Life (HRQL) is a multi-dimensional construct that can comprehensively evaluate the patient’s health status, including physical, emotional, mental and social well-being. In this study, we aimed to evaluate the impact of non-alcoholic fatty liver disease (NAFLD) on HRQL in a Chinese population. Methods In this national multicenter cross-sectional survey, patients with NAFLD were enrolled. Chronic Liver Disease Questionnaire (CLDQ)-NAFLD was used to qualify HRQL. Univariate and multivariate analysis were used to identify independent risk factors of HRQL. Results A total of 5181 patients with NAFLD from 90 centers were enrolled in this study (mean age, 43.8 ± 13.3 years; male, 65.8%). The overall CLDQ score was 5.66 ± 0.89. Multivariate logistic regression analysis showed that body mass index (BMI: HR, 1.642; 95% CI, 1.330–2.026), alanine transaminase (ALT: HR, 1.006; 95% CI, 1.001–1.011), triglyceride (HR, 1.184; 95% CI, 1.074–1.305), disease severity (HR, 3.203; 95% CI, 1.418–7.232) and cardiovascular disease (HR, 4.305; 95% CI, 2.074–8.939) were independent risk factors for overall CLDQ score. In the logistic analyses of individual domain, BMI and triglyceride were independent risk factors of all domains. ALT, disease severity, diabetes, depression and cardiovascular disease were influencing factors for the CLDQ score of several domains. Conclusions This national multicenter cross-sectional survey in China indicated that the HRQL in patients with NAFLD was impaired. HRQL was found to be significantly associated with sociodemographic and clinical factors. Attention should be paid to the optimally managing care of patients with NAFLD to improve their HRQL.

In mammals, the liver is the most important organ that plays a vital function in lipid metabolism. Grape seed proanthocyanidin (GSPE) is a kind of natural polyphenolic compound primarily obtained from grape skin and seeds. Recent research found it had high bioavailability in defending against obesity, hyperlipidemia, inflammatory, oxidative stress, and targeting liver tissue. However, the mechanism of GSPE in regulating obesity induced by dietary high-fat (HF) was not fully understood, particularly the influences on liver functions. Therefore, this study aimed to investigate the effects of GSPE supplementation on the liver function and lipid metabolic parameters in rats fed HF diets long-term. A total of 40 healthy female Sprague Dawley rats were selected. After 8 weeks of obesity model feeding, the rats were randomly divided into four treatments: NC, standard diet; NC + GSPE, standard diet + 500 mg/kg body weight GSPE; HF, high-fat diet; HG + GSPE, high fat diet + 500 mg/kg body weight GSPE. Results indicated that long-term HF feeding caused severe liver problems including megalohepatia, steatosis, inflammation, and hepatocyte apoptosis. The supplementation of GSPE alleviated these symptoms. The results of the current experiment confirmed that GSPE addition up-regulated the expression of the Wnt3a/β-catenin signaling pathway, thereby restraining the liver cell endoplasmic reticulum stress and hepatocyte apoptosis. Furthermore, the microRNA-103 may play a role in this signal-regulated pathway. In summary, GSPE had a protective effect on the liver and the current experiment provided a reference for the application of GSPE in animal nutrition as a kind of natural feed additive.

Cytoskeletal microtubule rearrangement and movement are crucial in the repair of spinal cord injury. Spastin plays an important role in the regulation of microtubule severing. Both spastin and collapsin response mediator proteins can regulate neurite growth and branching; however, whether spastin interacts with collapsin response mediator protein 3 (CRMP3) during this process remains unclear, as is the mechanism by which CRMP3 participates in the repair of spinal cord injury. In this study, we used a proteomics approach to identify key proteins associated with spinal cord injury repair. We then employed liquid chromatography-mass spectrometry to identify proteins that were able to interact with glutathione S-transferase-spastin. Then, co-immunoprecipitation and staining approaches were used to evaluate potential interactions between spastin and CRMP3. Finally, we co-transfected primary hippocampal neurons with CRMP3 and spastin to evaluate their role in neurite outgrowth. Mass spectrometry identified the role of CRMP3 in the spinal cord injury repair process. Liquid chromatography-mass spectrometry pulldown assays identified three CRMP3 peptides that were able to interact with spastin. CRMP3 and spastin were co-expressed in the spinal cord and were able to interact with one another in vitro and in vivo. Lastly, CRMP3 overexpression was able to enhance the ability of spastin to promote neurite growth and branching. Therefore, our results confirm that spastin and CRMP3 play roles in spinal cord injury repair by regulating neurite growth and branching. These proteins may therefore be novel targets for spinal cord injury repair. The Institutional Animal Care and Use Committee of Jinan University, China approved this study (approval No. IACUS-20181008-03) on October 8, 2018.

Coronavirus disease 2019 is a major threat to public health globally. Though its pathogenesis has not been fully elucidated, angiotensin-converting enzyme 2 (ACE2) has been recently identified as a receptor for the entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into the cell. Here, we aimed to clarify the potential role of ACE2 in SARS-CoV-2-induced acute lung injury and its underlying mechanism. As a receptor for coronavirus, ACE2 mediates the entry of SARS-CoV-2 into cells in a similar way as for severe acute respiratory syndrome coronavirus (SARS-CoV). The high binding affinity of SARS-CoV-2 to ACE2 correlates with its efficient spread among humans. On the other hand, ACE2 negatively regulates the renin-angiotensin-aldosterone system (RAAS) primarily by converting angiotensin II to angiotensin 1–7, which exerts a beneficial effect on coronavirus-induced acute lung injury. Human recombinant ACE2 has been considered as a potential therapy for SARS-CoV-2 by blocking virus entry and redressing the imbalance of RAAS in SARS-CoV-2 infection. The level of ACE2 expression can be upregulated by treatment with an ACE inhibitor (ACEI) or angiotensin Ⅱ type 1 receptor blocker (ARB). To date, no evidence shows that ACEIs or ARBs increase the susceptibility and mortality of patients infected with SARS-CoV-2, and hence, it is not advisable to discontinue such drugs in patients with cardiovascular disease.

Coumarin and chalcone, two important kinds of natural product skeletons, both exhibit α-glucosidase inhibitory activity. In this work, coumarin-chalcone derivatives 3 (a∼v) were synthesized, and their α-glucosidase inhibitory activity was screened. The results showed that all synthetic derivatives (IC50: 24.09 ± 2.36 to 125.26 ± 1.18 μM) presented better α-glucosidase inhibitory activity than the parent compounds 3-acetylcoumarin (IC50: 1.5 × 105 μM) and the positive control acarbose (IC50: 259.90 ± 1.06 μM). Among them, compound 3t displayed the highest α-glucosidase inhibitory activity (IC50: 24.09 ± 2.36 μM), which was approximately 10 times stronger than that of acarbose. The kinetic assay of 3t (KI = 18.82 μM, KIS = 59.99 μM) revealed that these compounds inhibited α-glucosidase in a mixed-type manner. Molecular docking was used to simulate the interaction between α-glucosidase and compound 3t.

α-Glucosidase inhibitors are known to prevent the digestion of carbohydrates and reduce the impact of carbohydrates on blood glucose. To develop novel α-glucosidase inhibitors, a series of 5-fluoro-2-oxindole derivatives (3a ∼ 3v) were synthesized, and their α-glucosidase inhibitory activities were investigated. Biological assessment results showed that most synthesized compounds presented potential inhibition on α-glucosidase. Among them, compounds 3d, 3f, and 3i exhibited much better inhibitory activity with IC50 values of 49.89 ± 1.16 μM, 35.83 ± 0.98 μM, and 56.87 ± 0.42 μM, respectively, which were about 10 ∼ 15 folds higher than acarbose (IC50 = 569.43 ± 43.72 μM). A kinetic mechanism study revealed that compounds 3d, 3f, and 3i inhibited the α-glucosidase in a reversible and mixed manner. Molecular docking was carried out to simulate the affinity between the compound and α-glucosidase.

Neuronal ceroid lipofuscinosis (NCL) is composed of a group of inherited neurodegenerative diseases, with the hallmark of lipofuscin deposit (a mixture of lipids and proteins with metal materials) inside the lysosomal lumen, which typically emits auto-fluorescence. Adult-onset NCL (ANCL) has been reported to be associated with a mutation in the DNAJC5 gene, including L115R, L116Δ, and the recently identified C124_C133dup mutation. In this study, we reported a novel C128Y mutation in a young Chinese female with ANCL, and this novel mutation caused abnormal palmitoylation and triggered lipofuscin deposits.

Background: Genomic instability of N6-methyladenosine (m6A)–related long noncoding RNAs (lncRNAs) plays a pivotal role in the tumorigenesis of lung adenocarcinoma (LUAD). Our study identified a signature of genomic instability of m6A-associated lncRNA signature and revealed its prognostic role in LUAD.Methods: We downloaded RNA-sequencing data and somatic mutation data for LUAD from The Cancer Genome Atlas (TCGA) and the GSE102287 dataset from the Gene Expression Omnibus (GEO) database. The “Limma” R package was used to identify a network of regulatory m6A-related lncRNAs. We used the Wilcoxon test method to identify genomic-instability–derived m6A-related lncRNAs. A competing endogenous RNA (ceRNA) network was constructed to identify the mechanism of the genomic instability of m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct a prognostic model for internal testing and validation of the prognostic m6A-related lncRNAs using the GEO dataset. Performance analysis was conducted to compare our prognostic model with the previously published lncRNA models. The CIBERSORT algorithm was used to explore the relationship of m6A-related lncRNAs and the immune microenvironment. Prognostic m6A-related lncRNAs in prognosis, the tumor microenvironment, stemness scores, and anticancer drug sensitivity were analyzed to explore the role of prognostic m6A-related lncRNAs in LUAD.Results: A total of 42 genomic instability–derived m6A-related lncRNAs were differentially expressed between the GS (genomic stable) and GU (genomic unstable) groups of LUAD patients. Four differentially expressed lncRNAs, 17 differentially expressed microRNAs, and 75 differentially expressed mRNAs were involved in the genomic-instability–derived m6A-related lncRNA-mediated ceRNA network. A prediction model based on 17 prognostic m6A-associated lncRNAs was constructed based on three TCGA datasets (all, training, and testing) and validated in the GSE102287 dataset. Performance comparison analysis showed that our prediction model (area under the curve [AUC] = 0.746) could better predict the survival of LUAD patients than the previously published lncRNA models (AUC = 0.577, AUC = 0.681). Prognostic m6A-related-lncRNAs have pivotal roles in the tumor microenvironment, stemness scores, and anticancer drug sensitivity of LUAD.Conclusion: A signature of genomic instability of m6A-associated lncRNAs to predict the survival of LUAD patients was validated. The prognostic, immune microenvironment and anticancer drug sensitivity analysis shed new light on the potential novel therapeutic targets in LUAD.

Tumor-associated macrophages (TAMs) have been shown to be an essential component of the tumor microenvironment and facilitate the proliferation and invasion of a variety of malignancies. However, the contribution of TAMs to meningioma progression has not been characterized in detail. In this study, we aimed to discover a novel regulatory pathway by which exosome-mediated M2-polarized macrophages participate in meningioma tumorigenesis and progression. Methods. First, the distribution and functional phenotype of macrophages in meningioma tissues were assessed by immunohistochemistry. Macrophage-derived exosomes (MDEs) were characterized, and further cell coculture experiments were performed to explore the effects of M2-MDEs on the proliferation, migration, and invasion of meningioma cells. RNA sequencing was used to analyze the transcriptomic signatures in meningioma cells treated with M2-MDEs. Three-dimensional tumorspheres and xenograft tumor models were used to evaluate the effects of M2-MDEs on meningioma tumorigenesis and development. Results. We found that M2 macrophages were enriched in meningioma tissue. Coculture with meningioma cells induced the M2 polarization of macrophages. We also found that M2-MDEs were able to significantly promote cell proliferation, cell migration, cell invasion, and tumorigenesis in meningiomas. Bioinformatic analysis suggested that the TGF-β pathway was activated in meningioma cells treated with M2-MDEs. Functional experiments demonstrated that blocking the TGF-β signaling pathway could effectively reverse the tumor-promotive effects mediated by M2-MDEs. Conclusions. Overall, our study showed that M2-MDEs promoted meningioma development and invasion by activating the TGF-β signaling pathway. Targeting exosome-mediated intercellular communication in the tumor microenvironment may be a novel therapeutic strategy for meningioma patients.

The Internet of Things is made of diverse networked things (i.e., smart, intelligent devices) that are consistently interconnected, producing meaningful data across the network without human interaction. Nowadays, the Healthcare system is widely interconnected with IoT environments to facilitate the best possible patient monitoring, efficient diagnosis, and timely operate with existing diseases towards the patients. Concerning the security and privacy of the patient's information. This paper is focused on Secure surveillance mechanism for a medical healthcare system with enabled internet of Things (sensors) by intelligently recorded video summary into the server and keyframes image encryption. This paper is twofold. Firstly, a well-organized keyframe extraction mechanism is called to extract meaningful image frames (detected normal/abnormal activities keyframe) by the visual sensor with an alert sent to the concerned authority in the healthcare system. Secondly, the final decision about the happened activity with extracted keyframes to keep highly secure from any adversary, and we propose an efficient probabilistic and lightweight encryption algorithm. Our proposed mechanism verifies effectiveness through producing results, robustness in nature, minimum execution time, and comparatively secure than other images (keyframes) encryption algorithms. Additionally, this mechanism can reduce storage, bandwidth, required transmission cost, and timely analysis of happened activity from any adversary with protecting the privacy of the patient's information in the IoT enabled healthcare system.

Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

The behavior of peoples' request for a post on online social media is a stochastic process that makes post's ranking highly skewed in nature. We mean peoples interest for a post can grow/decay exponentially or linearly. Considering this nature of the evolutionary peoples' interest, this paper presents a Growth-based Popularity Predictor (GPP) model for predicting and ranking the web-contents. Three different kinds of web-based real datasets namely Movielens, Facebook-wall-post and Digg are used to evaluate the performance of the proposed model. This performance is measured based on four information-retrieval metrics Area Under receiving operating Characteristic (AUC), Novelty, Precision, and Kendal's Tau. The obtained results show that the prediction performance can be further improved if the score is mapped onto a cumulative predicted item's ranking.

A 6063 aluminum alloy thin slab with a cross-section of 260 × 40 mm2 was prepared by a vertical bending continuous casting (VC) process. The effects of homogenization, hot rolling and subsequent heat treatments on the microstructure and properties of the as-cast slabs and as-rolled sheets were investigated by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), field emission electron probe (EPMA), macro- and micro-structure observation and tensile tests. The results show that the as-cast structure of AA 6063 was dominated by fine and uniform equiaxed grains. After homogenization, all Mg2Si phases dissolved back, and the Fe-containing intermetallic phases changed from acicular β-Al5FeSi phase to spherical α-Al8Fe2Si phase. Homogenizing heat treatment before hot rolling can improve the mechanical properties of the alloy. However, in the case of direct rolling without homogenization, the alloy still has good mechanical properties; the strength and plasticity are comparable to that obtained through traditional direct chill (DC) casting, homogenizing and extruding processes, indicating that the VC process has the potential to realize continuous casting and rolling.