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Abstract Glucagon-like peptide-1 (GLP-1) analogs are important therapeutics for type 2 diabetes and obesity. Ecnoglutide (XW003) is a novel, long-acting GLP-1 analog. We conducted a Phase 2, randomized, double-blind, placebo-controlled study enrolling 145 adults with T2DM. Participants were randomized to 0.4, 0.8, or 1.2 mg ecnoglutide or placebo as once-weekly injections for 20 weeks. The primary objective was to evaluate the efficacy of ecnoglutide, as measured by HbA1c change from baseline at Week 20. Secondary endpoints included body weight, glucose and lipid parameters, as well as safety. We show that, at end of treatment, the 0.4, 0.8, and 1.2 mg groups had statistically significant HbA1c reductions from baseline of −1.81%, −1.90%, and −2.39%, respectively, compared to −0.55% for placebo (P

Abstract Technology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation. Automated crack detection has become very necessary for efficient management and inspection of critical infrastructures. Previous research in crack detection employed classification and localization-based models using Deep Convolutional Neural Networks (DCNNs). This study suggests and compares the effectiveness of transfer learned DCNNs for crack detection as a classification model and as a feature extractor to overcome this restriction. The main objective of this paper is to present various methods of crack detection on surfaces and compare their performance over 3 different datasets. Experiments conducted in this work are threefold: initially, the effectiveness of 12 transfer learned DCNN models for crack detection is analyzed on three publicly available datasets: SDNET, CCIC and BSD. With an accuracy of 53.40%, ResNet101 outperformed other models on the SDNET dataset. EfficientNetB0 was the most accurate (98.8%) model on the BSD dataset, and ResNet50 performed better with an accuracy of 99.8% on the CCIC dataset. Secondly, two image enhancement methods are employed to enhance the images and are transferred learned on the 12 DCNNs in pursuance of improving the performance of the SDNET dataset. The results from the experiments show that the enhanced images improved the accuracy of transfer-learned crack detection models significantly. Furthermore, deep features extracted from the last fully connected layer of the DCNNs are used to train the Support Vector Machine (SVM). The integration of deep features with SVM enhanced the detection accuracy across all the DCNN-dataset combinations, according to analysis in terms of accuracy, precision, recall, and F1-score.

Abstract Background Lymphatic leakage is one of the postoperative complications of neuroblastoma. The purpose of this study is to summarize the clinical characteristics and risk factors of lymphatic leakage and try to find effective prevention and treatment measures. Methods A retrospective study included 186 children with abdominal neuroblastoma, including 32 children of lymphatic leakage and 154 children of non-lymphatic leakage. The clinical information, surgical data, postoperative abdominal drainage, treatment of lymphatic leakage and prognosis of the two groups were collected and analyzed. Results The incidence of lymphatic leakage in this cohort was 14% (32 children). Through univariate analysis of lymphatic leakage group and non-lymphatic leakage group, we found that lymphatic leakage increased the complications, prolonged the time of abdominal drainage and hospitalization, and delayed postoperative chemotherapy (p

Wireless Sensor Networks (WSNs) an energy consumption is a significant problematic due to the limited power resources of sensor nodes. Mobile Sinks (MS) rise the system's flexibility and convenience of data collection. The important role of Load balancing techniques plays in extending network lifetime by uniformly spreading energy usage between sensor nodes. WSN Data Collection for Two-Phase Energy Minimized Load Balancing Scheme (TPEMLB) with Sequential Convex Approximation (SCA), the use of a movable sink, is discovered in this research. The MS collects data from nearby sensors called as sub-sinks along a path as it moves. Data collection throughput is enhanced through efficient data distribution among sub-sinks and a data collection schedule. Geometric programming and SCA methods make an algorithm with guaranteed convergence to meet the problematic task. This research employs a SCA method to discover the best locations for sensor nodes while keeping energy restrictions in mind. Using a series of convex optimization difficulties, this technique repeatedly estimates the optimal sensor node positions that minimize energy consumption while ensuring sufficient coverage of the target region. In the second stage, incorporate a mobile drain that traverses the network intelligently to collect data from sensor nodes. The technique considers sensor node energy levels, data collection rates, and distance from the receptacle to balance network traffic and decrease energy consumption. Subsequently, the proposed model TPEMLB has a higher deployment success rate and efficiency, a more extended network lifetime, lower energy consumption, and better load balancing, and is the preferred solution for the unique challenge.

Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Abstract The challenge of disease relapsed/refractory (R/R) remains a therapeutic hurdle in chimeric antigen receptor (CAR) T‐cell therapy, especially for hematological diseases, with chronic lymphocytic leukemia (CLL) being particularly resistant to CD19 CAR T cells. Currently, there is no approved CAR T‐cell therapy for CLL patients. In this study, we aimed to address this unmet medical need by choosing the B‐cell activating factor receptor (BAFF‐R) as a promising target for CAR design against CLL. BAFF‐R is essential for B‐cell survival and is consistently expressed on CLL tumors. Our research discovered that BAFF‐R CAR T‐cell therapy exerted the cytotoxic effects on both CLL cell lines and primary B cells derived from CLL patients. In addition, the CAR T cells exhibited cytotoxicity against CD19‐knockout CLL cells that are resistant to CD19 CAR T therapy. Furthermore, we were able to generate BAFF‐R CAR T cells from small blood samples collected from CLL patients and then demonstrated the cytotoxic effects of these patient‐derived CAR T cells against autologous tumor cells. Given these promising results, BAFF‐R CAR T‐cell therapy has the potential to meet the long‐standing need for an effective treatment on CLL patients.

Summary: Background: We compared the efficacy and safety profiles of ainuovirine (ANV), a new-generation non-nucleoside reverse transcriptase inhibitor (NNRTI), with boosted elvitegravir (EVG), both coformulated with two nucleoside reverse transcriptase inhibitors (NRTIs), in people living with HIV-1 (PLWH) who had achieved virological suppression on previous NNRTI-based antiretroviral (ARV) regimen. Methods: This study was a multi-centre, randomised, double-blind, active-controlled, non-inferiority trial recruiting PLWH from 10 clinical centres across China. Main inclusion criteria included age of 18–65 years (inclusive), and stably staying on an ARV regimen combining an NNRTI with a two-drug NRTI backbone for at least 12 months. Eligible participants must have maintained plasma HIV-1 ribonucleic acid (RNA) titre below 50 copies per mL confirmed on two successive tests at an interval of at least one month prior to randomisation. Participants were randomly assigned to receive ANV 150 mg plus lamivudine (3TC) 300 mg, and tenofovir disoproxil fumarate (TDF) 300 mg (ANV/3TC/TDF), or cobicistat (Cobi) 150 mg boosted EVG plus emtricitabine (FTC) 200 mg, and tenofovir alafenamide (TAF) 10 mg. The primary efficacy endpoint was the proportion of participants with HIV-1 RNA titre at 50 copies per mL or above at week 48 using the US Food and Drug Administration snapshot algorithm, with a non-inferiority margin of 4 percentage points at a two-side 95% confidence level. This trial is active, but not recruiting, and is registered with Chinese Clinical Trial Registry (ChiCTR), number ChiCTR2100051605. Findings: Between October 2021 and February 2022, 923 patients were screened for eligibility, among whom 762 participants were randomized and had received at least one dose of ANV/3TC/TDF (n = 381) or EVG/Cobi/FTC/TAF (n = 381). At week 48, 7 (1.8%) participants on ANV/3TC/TDF and 6 (1.6%) participants on EVG/Cobi/FTC/TAF had plasma HIV-1 RNA titre at 50 copies per mL or above, including missing virological data within the time window (the Cochran-Mantel-Haenszel method, estimated treatment difference [ETD], 0.3%, 95% CI −1.6 to 2.1), establishing the non-inferiority of ANV/3TC/TDF to EVG/Cobi/FTC/TAF. The proportions of participants experiencing at least one treatment-emergent adverse events (AEs) were comparable between the two arms (97.6% versus 97.6%). A small proportion of participants discontinued study drug due to AEs (0.3% versus 0.3%). Serious AEs occurred in 11 (2.9%) participants on ANV/3TC/TDF and 9 (2.4%) participants on EVG/Cobi/FTC/TAF, respectively, none of which was considered related to study drug at the jurisdiction of the investigator. At week 48, participants on ANV/3TC/TDF showed a significantly less weight gain from baseline compared to those on EVG/Cobi/FTC/TAF (least square mean, 1.16 versus 2.05 kg, ETD −0.90 kg, 95% CI, −1.43 to −0.37). The changes in serum lipids from baseline also favoured ANV/3TC/TDF over EVG/Cobi/FTC/TAF. Interpretation: In virologically suppressed PLWH on previous NNRTI-based ARV regimen, switch to ANV/3TC/TDF resulted in less weight gain, and improved lipid metabolism while maintaining virological suppression non-inferior to that to EVG/Cobi/FTC/TAF. Funding: Jiangsu Aidea Pharmaceutical & the National “Thirteenth Five-year Period” Major Innovative Drugs Research and Development Key Project of the People's Republic of China Ministry of Science and Technology.

Carbon-doped copper ferrite (C–CuFe2O4) was synthesized by a simple two-step hydrothermal method, which showed enhanced tetracycline hydrochloride (TCH) removal efficiency as compared to the pure CuFe2O4 in Fenton-like reaction. A removal efficiency of 94% was achieved with 0.2 g L−1 catalyst and 20 mmol L−1 H2O2 within 90 min. We demonstrated that 5% C–CuFe2O4 catalyst in the presence of H2O2 was significantly efficient for TCH degradation under the near-neutral pH (5–9) without buffer. Multiple techniques, including SEM, TEM, XRD, FTIR, Raman, XPS Mössbauer and so on, were conducted to investigate the structures, morphologies and electronic properties of as-prepared samples. The introduction of carbon can effectively accelerate electron transfer by cooperating with Cu and Fe to activate H2O2 to generate ·OH and ·O2−. Particularly, theoretical calculations display that the p, p, d orbital hybridization of C, O, Cu and Fe can form C–O–Cu and C–O–Fe bonds, and the electrons on carbon can transfer to metal Cu and Fe along the C–O–Fe and C–O–Cu channels, thus forming electron-rich reactive centers around Fe and Cu. This work provides lightful reference for the modification of spinel ferrites in Fenton-like application.

Abstract Scanning electron microscope (SEM) and X-ray diffraction (XRD) were used to discuss the corrosion loss and morphology of the pit and rust layer of carbon steel. It was found that the corrosion process is largely influenced by the cyclic shedding of surface corrosion products, in addition to being controlled by the mechanism of oxide film shedding and pit evolution. A corrosion mechanism (the mechanism of rust layer shedding) is proposed. As a result, in this paper, the corrosion process of the test steel is simulated by the cellular automata. It was set up that the mechanism of oxide film shedding, the mechanism of pit evolution, and the mechanism of rust layer shedding in Cellular Automata Simulation. The optimal time ratio and simulation parameters were found, and a predictable cellular automata corrosion simulation model was built, providing a solution for carbon steel’s service life prediction.

Abstract Ulcerative colitis (UC) is a chronic, recurrent inflammatory bowel disease. UC confronts with severe challenges including the unclear pathogenesis and lack of specific diagnostic markers, demanding for identifying predictive biomarkers for UC diagnosis and treatment. We perform immune infiltration and weighted gene co-expression network analysis on gene expression profiles of active UC, inactive UC, and normal controls to identify UC related immune cell and hub genes. Neutrophils, M1 macrophages, activated dendritic cells, and activated mast cells are significantly enriched in active UC. MMP-9, CHI3L1, CXCL9, CXCL10, CXCR2 and S100A9 are identified as hub genes in active UC. Specifically, S100A9 is significantly overexpressed in mice with colitis. The receiver operating characteristic curve demonstrates the excellent performance of S100A9 expression in diagnosing active UC. Inhibition of S100A9 expression reduces DSS-induced colonic inflammation. These identified biomarkers associated with activity in UC patients enlighten the new insights of UC diagnosis and treatment.

Abstract In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP’s efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases.

Abstract Objective To investigate the clinical features, treatment strategies, and prognosis of neuroblastoma with bilateral blindness. Methods The clinical data of five patients with bilateral blindness neuroblastoma admitted to Beijing Children’s Hospital from April 2018 to September 2020 were retrospectively collected to summarize their clinical characteristics. Results All patients were female and the median age at presentation was 25 (23, 41) months. The median intervention time from the onset of symptoms of bilateral blindness to the start of treatment was 10 (10, 12) days. All five cases were staged as stage M and grouped as high risk. Four cases were MYCN gene amplification and one case was MYCN acquisition. Five children were treated according to a high-risk neuroblastoma treatment protocol. Four children did not recover their vision after treatment, and one case improved to have light perception. All patients were effectively followed up for a median of 20 (12, 31) months, with three deaths, one tumor-free survival, and one recurrent tumor-bearing survival. Conclusion Neuroblastoma with bilateral blindness is rare in the clinic, mostly in children of young age, and is often associated with MYCN amplification and multiple metastases. Early hormone shock therapy and optic nerve decompression are beneficial for preserving the child’s vision. A joint multi-disciplinary treatment may help in the formulation of treatment decisions. Achieving a balance between good visual preservation and survival within the short optic nerve neurotherapeutic window is extremely challenging.

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation not only yields superior predictive accuracy but also bears noteworthy consequences for patient care and accurate cardiac diagnosis. Through the efficient combination of these approaches, our method provides a powerful response to the difficulties presented by complicated cardiac data. The potential to transform illness prediction and prevention techniques and ultimately improve patient outcomes is demonstrated by this integration's success.

Cancer remains one of the leading causes of mortality worldwide, necessitating continuous advancements in early diagnosis and treatment. Deep learning, a subset of artificial intelligence, has emerged as a powerful tool in the field of medical image analysis, revolutionizing the way cancer is detected and diagnosed. The study discusses the various modalities employed in lung cancer diagnosis, such as medical imaging (e.g., radiology and pathology), genomics, and clinical data, highlighting the unique challenges associated with each domain. The proposed Multimodal Fusion Deep Neural Network (MFDNN) architecture design effectively integrates information from different modalities (e.g., medical imaging, genomics, clinical data) to enhance lung cancer diagnostic accuracy. Furthermore, it delves into the integration of clinical data, electronic health records, and multimodal approaches to improve the accuracy and reliability of lung cancer diagnosis. Moreover, we highlight the ethical considerations surrounding the deployment of Artificial Intelligence (AI) in clinical settings and the need for robust validation and regulatory guidelines. The Multimodal Fusion Deep Neural Network (MFDNN) achieves an exceptional accuracy rate of 92.5 %, marking a significant breakthrough in the realm of medical AI. MFDNN excels in precision, with 87.4 % accuracy in predicting cancer cases, and equally impresses in recall, capturing approximately 86.4 % of actual cancerous cases. The F1-score of 86.2 further exemplifies MFDNN's ability to strike a harmonious equilibrium, ensuring both diagnostic accuracy and minimized missed diagnoses. The performance is compared with established methods like CNN, DNN, and ResNet. The results underscore MFDNN's pivotal role in revolutionizing lung cancer diagnosis, promising more accurate and timely identification of this critical condition.

This research presents a process for analyzing a hybrid microgrid's dependability using a fuzzy Markov model. The research initiated an analysis of the various microgrid components, such as wind power systems, solar photovoltaic (PV) systems, and battery storage systems. The states that are induced by component failures are represented using a state-space model. The research continues by suggesting a hybrid microgrid reliability model that analyzes data using a Markov process. Problems arise when trying to estimate reliability metrics for the microgrid using data that is both restricted and imprecise. This is why the study takes uncertainties into account to make microgrid reliability estimations more realistic. The importance of microgrid components concerning their overall availability is evaluated using fuzzy sets and reliability assessments. The study uses numerical analysis and then carefully considers the outcomes. The overall availability of hybrid microgrids is 0.99999.

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

Abstract Learning and inferring underlying motion patterns of captured 2D scenes and then re-creating dynamic evolution consistent with the real-world natural phenomena have high appeal for graphics and animation. To bridge the technical gap between virtual and real environments, we focus on the inverse modeling and reconstruction of visually consistent and property-verifiable oceans, taking advantage of deep learning and differentiable physics to learn geometry and constitute waves in a self-supervised manner. First, we infer hierarchical geometry using two networks, which are optimized via the differentiable renderer. We extract wave components from the sequence of inferred geometry through a network equipped with a differentiable ocean model. Then, ocean dynamics can be evolved using the reconstructed wave components. Through extensive experiments, we verify that our new method yields satisfactory results for both geometry reconstruction and wave estimation. Moreover, the new framework has the inverse modeling potential to facilitate a host of graphics applications, such as the rapid production of physically accurate scene animation and editing guided by real ocean scenes.

Tunicate Swarm Algorithm (TSA) is a novel swarm intelligence algorithm developed in 2020. Though it has shown superior performance in numerical benchmark function optimization and six engineering design problems over its competitive algorithms, it still needs further improvements. This article proposes two improved TSA algorithms using chaos theory, opposition-based learning (OBL) and Cauchy mutation. The proposed algorithms are termed OCSTA and COCSTA. The static and dynamic OBL are used respectively in the initialization and generation jumping phase of OCTSA, whereas centroid opposition-based computing is used, in the same phases, in COCTSA. The proposed algorithms are tested on 30 IEEE CEC2017 benchmark optimization problems consists of unimodal, multimodal, hybrid, and composite functions with 30, 50, and 100 dimensions. The experimental results are compared with the classical TSA, TSA with the local escaping operator (TSA-LEO), Sine Cosine Algorithm (SCA), Giza-Pyramid Construction Algorithm (GPC), Covariance Matrix Adaptation Evolution Strategy (CMAES), Archimedes Optimization Algorithm (AOA), Opposition-Based Arithmetic Optimization Algorithm (OBLAOA), and Opposition-Based Chimp Optimization Algorithm (ChOAOBL). The statistical analysis of experimental results using the Wilcoxon Signed Rank Test establishes that the proposed algorithms outperform TSA and other algorithms for most of the problems. Moreover, high dimensions are used to validate the scalability of OCTSA and COCTSA, and the results show that the modified TSA algorithms are least impacted by larger dimensions. The experimental results with statistical analysis demonstrate the effectiveness of the proposed algorithms in solving global optimization problems.

This study presents the design and implementation of a Fractional Order Proportional, Integral, and Derivative (FOPID) controller intended for the regulation of blood glucose levels in individuals with type 1 diabetes mellitus (T1DM). The efficacy of this controller is evaluated through its application to a nonlinear Stolwijk–Hardy model simulating T1DM patients, accounting for a range of physiological conditions. Utilizing a genetic algorithm, the parameters of the FOPID controller are fine-tuned. By conducting a comparative analysis with previously established control algorithms, the performance of the proposed controller is validated, demonstrating its superior performance. The results underscore the significant improvements achieved by the proposed controller in maintaining blood glucose concentrations within safe thresholds, particularly in scenarios involving disruptions due to meals.

Background and aim: Solid organ transplantation remains a life-saving therapeutic option for patients with end-stage organ dysfunction. Acute cellular rejection (ACR), dominated by dendritic cells (DCs) and CD4+ T cells, is a major cause of post-transplant mortality. Inhibiting DC maturation and directing the differentiation of CD4+ T cells toward immunosuppression are keys to inhibiting ACR. We propose that oxymatrine (OMT), a quinolizidine alkaloid, either alone or in combination with rapamycin (RAPA), attenuates ACR by inhibiting the mTOR–HIF–1α pathway. Methods: Graft damage was assessed using haematoxylin and eosin staining. Intragraft CD11c+ and CD4+ cell infiltrations were detected using immunohistochemical staining. The proportions of mature DCs, T helper (Th) 1, Th17, and Treg cells in the spleen; donor-specific antibody (DSA) secretion in the serum; mTOR–HIF–1α expression in the grafts; and CD4+ cells and bone marrow-derived DCs (BMDCs) were evaluated using flow cytometry. Results: OMT, either alone or in combination with RAPA, significantly alleviated pathological damage; decreased CD4+ and CD11c+ cell infiltration in cardiac allografts; reduced the proportion of mature DCs, Th1 and Th17 cells; increased the proportion of Tregs in recipient spleens; downregulated DSA production; and inhibited mTOR and HIF-1α expression in the grafts. OMT suppresses mTOR and HIF-1α expression in BMDCs and CD4+ T cells in vitro. Conclusions: Our study suggests that OMT-based therapy can significantly attenuate acute cardiac allograft rejection by inhibiting DC maturation and CD4+ T cell responses. This process may be related to the inhibition of the mTOR–HIF–1α signaling pathway by OMT.

Deep learning is a very important technique in clinical diagnosis and therapy in the present world. Convolutional Neural Network (CNN) is a recent development in deep learning that is used in computer vision. Our medical investigation focuses on the identification of brain tumour. To improve the brain tumour classification performance a Balanced binary Tree CNN (BT-CNN) which is framed in a binary tree-like structure is proposed. It has a two distinct modules-the convolution and the depthwise separable convolution group. The usage of convolution group achieves lower time and higher memory, while the opposite is true for the depthwise separable convolution group. This balanced binarty tree inspired CNN balances both the groups to achieve maximum performance in terms of time and space. The proposed model along with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. are experimented on public datasets. Before we feed the data into model the images are pre-processed using CLAHE, denoising, cropping, and scaling. The pre-processed dataset is partitioned into training and testing datasets as per 5 fold cross validation. The proposed model is trained and compared its perforarmance with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. The proposed model reported average training accuracy of 99.61% compared to other models. The proposed model achieved 96.06% test accuracy where as other models achieved 68.86%, 85.8%, 86.88%, and 90.41% respectively. Further, the proposed model obtained lowest standard deviation on training and test accuracies across all folds, making it invariable to dataset.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the “diabetes capital of the world” owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

Background: Primary hepatic lymphoepithelioma-like carcinoma (LELC) is a malignant tumor with a low incidence, but the number of case reports has increased in recent years. The prognosis of hepatic LELC is better than hepatocellular carcinoma (HCC). The differentiation between hepatic LELC and HCC has clinical value during follow-up treatment. The purpose of our study was to compare contrast-enhanced ultrasound (CEUS) imaging features in patients with hepatic LELC and HCC. Methods: Twelve patients with an average age of 60.1±9.5 years and histopathologically confirmed hepatic LELC were included in the study. Forty-three patients with an average age of 57.4±9.0 years and a histopathological diagnosis of HCC were designated as the control group by means of propensity score matching (1:4). The clinical data, B-mode ultrasound (BMUS), and CEUS features were retrospectively analyzed between patients with hepatic LELC and HCC. Results: The serum a-fetoprotein (58.1% [25/43] vs.16.7% [2/12]; p=0.017) and des-gamma-carboxy prothrombin levels (74.4% [32/43] vs.16.7% [2/12]; p=0.001) were not significantly elevated in patients with hepatic LELCs compared to HCCs. LELCs were mainly hypoechoic based on BMUS, while the echogenicity of HCCs varied (p=0.016). A halo sign was less common in patients with hepatic LELCs than HCCs (16.7% [2/12] vs. 58.1% [25/43]; p=0.011). Of hepatic LELCs, 75% (9/12) had homogeneous hyperenhancement based on CEUS, whereas 58.1% (25/43) of HCCs had heterogeneous hyperenhancement (p=0.004). Early washout was noted in 91.7% (11/12) of hepatic LELCs compared to 46.5% (20/43) of HCCs (p=0.005). Furthermore, hepatic LELCs were more likely to exhibit peripheral rim-like hyperenhancement (83.3% [10/12] vs. 11.6% [5/43]; p < 0.001). Conclusion: BMUS and CEUS are helpful in discriminating between hepatic LELC and HCC. A hypoechoic mass, the rare halo sign, homogeneous hyperenhancement in the arterial phase, higher frequencies of early washout, and peripheral rim-like hyperenhancement are useful ultrasound features that can help differentiate hepatic LELCs from HCCs.

Abstract Objective To summarize the clinical characteristics of children with adrenocortical carcinoma (ACC) and preliminarily explore the indications for and efficacy of neoadjuvant chemotherapy in certain patients. Methods The data of 49 children with adrenocortical tumors (ACT) in the past 15 years were retrospectively analyzed, and after pathology assessment using Weiss system grading, 40 children diagnosed with ACC were included. Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and three-dimensional (3D) reconstruction of contrast-enhanced computed tomography data were used to evaluate the response to neoadjuvant chemotherapy. Results Forty patients (17 males, 23 females) with ACC were enrolled. Abnormal hormone levels were common in children with ACC (n = 31), and in terms of clinical presentation, sexual precocity was the most common (n = 14, 35.0%), followed by Cushing’s syndrome (n = 12, 30.0%). Seven of 40 children received neoadjuvant chemotherapy due to a maximum lesion diameter greater than 10 cm (n = 4), invasion of surrounding tissues (n = 2), intravenous tumor thrombus (n = 2), and/or distant metastasis (n = 2); 2 patients achieved partial response, and 5 had stable disease according to the RECIST 1.1 standard. Furthermore, 3D tumor volume reconstruction was performed in 5 children before and after neoadjuvant chemotherapy. Tumor volumes were significantly reduced in all 5 children, with a median volume reduction of 270 (interquartile range, IQR 83, 293) (range: 49–413) ml. After surgery with/without chemotherapy, the 5-year overall survival rate for all children was 90.0% (95% CI-confidence interval 80.0–100.0%), and the 5-year event-free survival rate was 81.5% (95% CI 68.0–97.7%). Conclusion In the diagnosis and treatment of pediatric ACC, a comprehensive endocrine evaluation is necessary to facilitate early diagnosis. Surgery and chemotherapy are important components of ACC treatment, and neoadjuvant chemotherapy should be considered for children with ACC who meet certain criteria, such as a large tumor, distant metastases, or poor general condition.

Abstract Background As a kind of mesenchymal-like stromal cells, endometrial regenerative cells (ERCs) have been demonstrated effective in the treatment of Concanavalin A (Con A)-induced hepatitis. However, the therapeutic mechanism of ERCs is not fully understood. Ecto-5`-nucleotidase (CD73), an enzyme that could convert immune-stimulative adenosine monophosphate (AMP) to immune-suppressive adenosine (ADO), was identified highly expressed on ERCs. The present study was conducted to investigate whether the expression of CD73 on ERCs is critical for its therapeutic effects in Con A-induced hepatitis. Methods ERCs knocking out CD73 were generated with lentivirus-mediated CRISPR-Cas9 technology and identified by flow cytometry, western blot and AMPase activity assay. CD73-mediated immunomodulatory effects of ERCs were investigated by CD4+ T cell co-culture assay in vitro. Besides, Con A-induced hepatitis mice were randomly assigned to the phosphate-buffered saline treated (untreated), ERC-treated, negative lentiviral control ERC (NC-ERC)-treated, and CD73-knockout-ERC (CD73-KO-ERC)-treated groups, and used to assess the CD73-mediated therapeutic efficiency of ERCs. Hepatic histopathological analysis, serum transaminase concentrations, and the proportion of CD4+ T cell subsets in the liver and spleen were performed to assess the progression degree of hepatitis. Results Expression of CD73 on ERCs could effectively metabolize AMP to ADO, thereby inhibiting the activation and function of conventional CD4+ T cells was identified in vitro. In addition, ERCs could markedly reduce levels of serum and liver transaminase and attenuate liver damage, while the deletion of CD73 on ERCs dampens these effects. Furthermore, ERC-based treatment achieved less infiltration of CD4+ T and Th1 cells in the liver and reduced the population of systemic Th1 and Th17 cells and the levels of pro-inflammatory cytokines such as IFN-γ and TNF-α, while promoting the generation of Tregs in the liver and spleen, while deletion of CD73 on ERCs significantly impaired their immunomodulatory effects locally and systemically. Conclusion Taken together, it is concluded that CD73 is critical for the therapeutic efficiency of ERCs in the treatment of Con A-induced hepatitis.

Abstract The cultivation of most crops depends upon the regional weather conditions. So, the analysis of the agro-climatic conditions of a zone contributes significantly to deciding the right crop for the right land in the right season to obtain a better yield. Machine learning algorithms facilitate this process to a great extent for better results. In this paper, the authors proposed an ML-based crop selection model based on the weather conditions and soil parameters, collectively. Weather analysis is done using LSTM RNN and the process of crop selection is completed using Random Forest Classifier. This model gives better results for weather prediction in comparison to ANN. With LSTM RNN, the RMSE observed in Min. Temp. prediction is 5.023%, Max. Temp. Prediction is 7.28%, and Rainfall Prediction is 8.24%. In the second phase, the Random Forest Classifier showed 97.235% accuracy for crop selection, 96.437% accuracy in predicting resource dependency, and 97.647 accuracies in giving the appropriate sowing time for the crop. The model construction time taken with a random forest classifier using mentioned data size is 5.34 s. The authors also suggested the future research direction to further improve this work.

The global effort to combat the COVID-19 pandemic faces ongoing uncertainty with the emergence of Variants of Concern featuring numerous mutations on the Spike (S) protein. In particular, the Omicron Variant is distinguished by 32 mutations, including 10 within its receptor-binding domain (RBD). These mutations significantly impact viral infectivity and the efficacy of vaccines and antibodies currently in use for therapeutic purposes. In our study, we employed structure-based computational saturation mutagenesis approaches to predict the effects of Omicron missense mutations on RBD stability and binding affinity, comparing them to the original Wuhan-Hu-1 strain. Our results predict that mutations such as G431W and P507W induce the most substantial destabilizations in the Wuhan-Hu-1-S/Omicron-S RBD. Notably, we postulate that mutations in the Omicron-S exhibit a higher percentage of enhancing binding affinity compared to Wuhan-S. We found that the mutations at residue positions G447, Y449, F456, F486, and S496 led to significant changes in binding affinity. In summary, our findings may shed light on the widespread prevalence of Omicron mutations in human populations. The Omicron mutations that potentially enhance their affinity for human receptors may facilitate increased viral binding and internalization in infected cells, thereby enhancing infectivity. This informs the development of new neutralizing antibodies capable of targeting Omicron’s immune-evading mutations, potentially aiding in the ongoing battle against the COVID-19 pandemic.

Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth’s unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance in the Southeastern United States, is destroyed to make way for new urban development. This research takes advantage of the extensive temporal archive of multispectral satellite imagery provided by the Landsat program to conduct a 37-year analysis of urban forest canopy cover across the City of Chattanooga. A time series of seven Landsat 5 scenes and three Landsat 8 scenes were acquired between 1984 and 2021 at an interval of five years or less. Each multispectral image was processed digitally and classified into a four-class thematic raster using a supervised hybrid classification scheme with a support vector machine (SVM) algorithm. The obtained results showed a loss of up to 43% of urban forest canopy and a gain of up to 134% urban land area in the city. Analyzing the multidecade spatiotemporal forest canopy in a rapidly expanding metropolitan center, such as Chattanooga, could help direct sustainable development efforts towards areas urbanizing at an above-average rate.

Abstract Breast cancer has emerged as the most life-threatening disease among women around the world. Early detection and treatment of breast cancer are thought to reduce the need for surgery and boost the survival rate. The Magnetic Resonance Imaging (MRI) segmentation techniques for breast cancer diagnosis are investigated in this article. Kapur’s entropy-based multilevel thresholding is used in this study to determine optimal values for breast DCE-MRI lesion segmentation using Gorilla Troops Optimization (GTO). An improved GTO, is developed by incorporating Rotational opposition based-learning (RBL) into GTO called (GTORBL) and applied it to the same problem. The proposed approaches are tested on 20 patients’ T2 Weighted Sagittal (T2 WS) DCE-MRI 100 slices. The proposed approaches are compared with Tunicate Swarm Algorithm (TSA), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Multi-verse Optimization (MVO), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF). The Dice Similarity Coefficient (DSC), sensitivity, and accuracy of the proposed GTO-based approach is achieved $$87.04\%$$ 87.04 % , $$90.96\%$$ 90.96 % , and $$98.13\%$$ 98.13 % respectively. Another proposed GTORBL-based segmentation method achieves accuracy values of $$99.31\%$$ 99.31 % , sensitivity of $$95.45\%$$ 95.45 % , and DSC of $$91.54\%$$ 91.54 % . The one-way ANOVA test followed by Tukey HSD and Wilcoxon Signed Rank Test are used to examine the results. Furthermore, Multi-Criteria Decision Making is used to evaluate overall performance focused on sensitivity, accuracy, false-positive rate, precision, specificity, $$F_1$$ F 1 -score, Geometric-Mean, and DSC. According to both quantitative and qualitative findings, the proposed strategies outperform other compared methodologies.

Breast cancer is a major health concern for women everywhere and a major killer of women. Malignant tumors may be distinguished from benign ones, allowing for early diagnosis of this disease. Therefore, doctors need an accurate method of diagnosing tumors as either malignant or benign. Even if therapy begins immediately after diagnosis, some cancer cells may persist in the body, increasing the risk of a recurrence. Metastasis and recurrence are the leading causes of death from breast cancer. Therefore, detecting a return of breast cancer early has become a pressing medical issue. Evaluating and contrasting various Machine Learning (ML) techniques for breast cancer and recurrence prediction is crucial to choosing the best successful method. Inaccurate forecasts are common when using datasets with a large number of attributes. This study addresses the need for effective feature selection and optimization methods by introducing Recursive Feature Elimination (RFE) and Grey Wolf Optimizer (GWO), in response to the limitations observed in existing approaches. In this research, the performance evaluation of methods is enhanced by employing the RFE and GWO, considering the Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC) datasets taken from the UCI-ML repository. Various preprocessing techniques are applied to raw data, including imputation, scaling, and others. In the second step, relevant feature correlations are used with RFE to narrow down candidate discriminative features. The GWO chooses the best possible combination of attributes for the most accurate result in the next step. We use seven ML classifiers in both datasets to make a binary decision. On the WDBC and WPBC datasets, several experiments have shown accuracies of 98.25% and 93.27%, precisions of 98.13% and 95.56%, sensitivities of 99.06% and 96.63%, specificities of 96.92% and 73.33%, F1-scores of 98.59% and 96.09% and AUCs of 0.982 and 0.936, respectively. The hybrid approach's superior feature selection improved the accuracy of breast cancer performance indicators and recurrence classification.

Ulcerative colitis (UC) is a chronic intestinal inflammatory disease with complex etiology. Interleukin-35 (IL-35), as a cytokine with immunomodulatory function, has been shown to have therapeutic effects on UC, but its mechanism is not yet clear. Therefore, we constructed Pichia pastoris stably expressing IL-35 which enables the cytokines to reach the diseased mucosa, and explored whether upregulation of T-cell protein tyrosine phosphatase (TCPTP) in macrophages is involved in the mechanisms of IL-35-mediated attenuation of UC. After the successful construction of engineered bacteria expressing IL-35, a colitis model was successfully induced by giving BALB/c mice a solution containing 3% dextran sulfate sodium (DSS). Mice were treated with Pichia/IL-35, empty plasmid-transformed Pichia (Pichia/0), or PBS by gavage, respectively. The expression of TCPTP in macrophages (RAW264.7, BMDMs) and intestinal tissues after IL-35 treatment was detected. After administration of Pichia/IL-35, the mice showed significant improvement in weight loss, bloody stools, and shortened colon. Colon pathology also showed that the inflammatory condition of mice in the Pichia/IL-35 treatment group was alleviated. Notably, Pichia/IL-35 treatment not only increases local M2 macrophages but also decreases the expression of inflammatory cytokine IL-6 in the colon. With Pichia/IL-35 treatment, the proportion of M1 macrophages, Th17, and Th1 cells in mouse MLNs were markedly decreased, while Tregs were significantly increased. In vitro experiments, IL-35 significantly promoted the expression of TCPTP in macrophages stimulated with LPS. Similarly, the mice in the Pichia/IL-35 group also expressed more TCPTP than that of the untreated group and the Pichia/0 group.

Summary: An unprecedented strategy for preparing a series of sulfur ylides through electro-oxidative quinylation of sulfides in batch and continuous flow has been developed. Good to excellent yields were obtained with excellent functional group compatibility and good concentration tolerance under exogenous oxidant- and transition metal-free conditions. Advantageously, this electrosynthesis methodology was scalable with higher daily production and steady production was achieved attributing to the use of micro-flow cells.

The proposed hybrid CNN-BiLSTM architecture aims to address the challenge of detecting epileptic seizures systematically from EEG signal analysis. The system consists of several stages, including preprocessing, feature extraction using multi-dimensional CNN, temporal feature processing using BiLSTM, and classification using fully connected layers. The first stage involves preprocessing and normalization of the raw EEG signal to prepare it for further analysis. This step helps in removing noise and standardizing the input for subsequent processing. Next, a multi-dimensional CNN is employed to effectively extract features from the preprocessed EEG sequence data. CNNs are known for their ability to capture spatial features, and in this case, they are utilized to extract relevant features from the EEG data. After the feature extraction stage, the BiLSTM component of the architecture is utilized to process the extracted features and capture temporal dependencies. BiLSTMs are well-suited for sequence modeling tasks and can effectively capture long-range dependencies in the data. By incorporating BiLSTM, the architecture aims to capture important temporal patterns related to epileptic seizures. Finally, the temporal feature values are fed into fully connected layers for classification. The system is designed to detect epileptic seizures and classify them into specific types using a 10-class classification approach. The proposed system reports high detection accuracy with an overall accuracy of 99.53% and an accuracy of 82.95% on the binary classification task. These results suggest that the system performs well in accurately identifying epileptic seizures from EEG signals. Furthermore, the proposed system demonstrates superior performance compared to other existing techniques such as K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) in terms of accuracy. It is important to note that the evaluation and comparison of the proposed system were performed on a publicly available epileptic seizures image database. Overall, the proposed hybrid CNN-BiLSTM architecture shows potential in enhancing the detection and classification of epileptic seizures from EEG signals, potentially improving the efficiency and accuracy of diagnosis and treatment in the early stages of these disorders.

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

Abstract Objective To explore the criteria, safety and efficacy of laparoscopic surgery in pediatric neuroblastoma (NB). Methods A retrospective study of 87 patients with NB without image-defined risk factors (IDRFs) between December 2016 and January 2021 at Beijing Children’s Hospital was conducted. Patients were divided into two groups according to the surgical procedure. Results Between the 87 patients, there were 54 (62.07%) cases in the open surgery group and 33 (37.93%) cases in the laparoscopic surgery group. There were no significant differences between the two groups regarding demographic characteristics, genomic and biological features, operating time or postoperative complications. However, in terms of intraoperative bleeding (p = 0.013) and the time to start postoperative feeding after surgery (p = 0.002), the laparoscopic group was obviously better than the open group. Furthermore, there was no significant difference in the prognosis between the two groups, and no recurrence or death was observed. Conclusion For children with localized NB who have no IDRFs, laparoscopic surgery could be performed safely and effectively. Surgeons who are skilled in this can help children reduce surgical injuries, speed up postoperative recovery, and obtain the same prognosis as open surgery.

Metabolic-associated fatty liver disease (MAFLD), characterized primarily by hepatic steatosis, has become the most prevalent liver disease worldwide, affecting approximately two-fifths of the global population. The pathogenesis of MAFLD is extremely complex, and to date, there are no approved therapeutic drugs for clinical use. Considerable evidence indicates that various metabolic disorders play a pivotal role in the progression of MAFLD, including lipids, carbohydrates, amino acids, and micronutrients. In recent years, the medicinal properties of natural products have attracted widespread attention, and numerous studies have reported their efficacy in ameliorating metabolic disorders and subsequently alleviating MAFLD. This review aims to summarize the metabolic-associated pathological mechanisms of MAFLD, as well as the natural products that regulate metabolic pathways to alleviate MAFLD.

Introduction: Diabetes is considered one of the leading healthcare concerns affecting millions worldwide. Taking appropriate action at the earliest stages of the disease depends on early diabetes prediction and identification. To support healthcare providers for better diagnosis and prognosis of diseases, machine learning has been explored in the healthcare industry in recent years.Methods: To predict diabetes, this research has conducted experiments on five boosting algorithms on the Pima diabetes dataset. The dataset was obtained from the University of California, Irvine (UCI) machine learning repository, which contains several important clinical features. Exploratory data analysis was used to identify the characteristics of the dataset. Moreover, upsampling, normalisation, feature selection, and hyperparameter tuning were employed for predictive analytics.Results: The results were analysed using various statistical/machine learning metrics and k-fold cross-validation techniques. Gradient boosting achieved the greatest accuracy rate of 92.85% among all the classifiers. Precision, recall, f1-score, and receiver operating characteristic (ROC) curves were used to further validate the model.Discussion: The suggested model outperformed the current studies in terms of prediction accuracy, demonstrating its applicability to other diseases with similar predicate indications.

IntroductionThe complexity of the concept of responsibility has led to a relative lack of measuring tools. Meanwhile, the widely used measurement of conscientiousness in the Big Five personality traits suffers from inconsistencies in measuring personal responsibility. Therefore, measuring personal responsibility must be adapted to its cultural context. Spurred by these reasons, we developed a “Chinese College Student Personal Responsibility Scale” (CCSPRS) based on local theoretical foundations. Furthermore, we conducted a preliminary exploration using the new scale, examining the correlations between college students’ responsibility, trust propensity, and prosocial behavior tendencies.MethodsThe initial version of the scale was subjected to item analysis, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) to form the formal version of the scale. A total of 1,038 college students were assembled. The reliability and validity of the scale were also analyzed. We collected data using the proposed CCSPRS, Interpersonal Trust Scale, and Prosocial Tendencies Measure Questionnaire and obtained 301 valid questionnaires.ResultsThe scale’s reliability and validity indicators met the development requirements, and the investigation revealed that women students scored significantly higher in responsibility than men students. Additionally, the responsibility scores were relatively high in the first and fourth years and low in the second and third years, presenting an approximate U-shaped trend. Besides, the college students’ personal responsibility, trust propensity, and prosocial behavior tendencies were positively correlated.DiscussionThe proposed CCSPRS is an effective tool for measuring personal responsibility among Chinese college students. Additionally, this study analyzed the internal beliefs of individuals and concluded that personal responsibility, prosocial behavior, and trust propensity are closely interconnected, especially the relationship between responsibility and prosocial behavior.

BackgroundThis study aimed to explore the characteristics of pediatric upper gastrointestinal (UGI) perforations, focusing on their diagnosis and management.MethodsBetween January 2013 and December 2021, 30 children with confirmed UGI perforations were enrolled, and their clinical data were analyzed. Two groups were compared according to management options, including open surgical repair (OSR) and laparoscopic/gastroscopic repair (LR).ResultsA total of 30 patients with a median age of 36.0 months (1 day–17 years) were included in the study. There were 19 and 11 patients in the LR and OSR groups, respectively. In the LR group, two patients were treated via exploratory laparoscopy and OSR, and the other patients were managed via gastroscopic repair. Ten and three patients presented the duration from symptom onset to diagnosis within 24 h (p = 0.177) and the number of patients with hemodynamically unstable perforations was 4 and 3 in the LR and OSR groups, respectively. Simple suture or clip closure was performed in 27 patients, and laparoscopically pedicled omental patch repair was performed in two patients. There was no significant difference in operative time and length of hospital stay between the LR and OSR groups. Treatment failed in two patients because of severe sepsis and multiple organ dysfunction syndrome, including one with fungal peritonitis.ConclusionSurgery for pediatric UGI perforations should be selected according to the general status of the patient, age of the patient, duration from symptom onset, inflammation, and perforation site and size. Antibiotic administration and surgical closure remain the main strategies for pediatric UGI perforations.

Objective: Glucagon-like peptide (GLP)-1 is an incretin hormone that acts after food intake to stimulate insulin production, enhance satiety, and promote weight loss. Here we describe the discovery and characterization of ecnoglutide (XW003), a novel GLP-1 analog. Methods: We engineered a series of GLP-1 peptide analogs with an alanine to valine substitution (Ala8Val) and a γGlu-2xAEEA linked C18 diacid fatty acid at various positions. Ecnoglutide was selected and characterized in GLP-1 receptor signaling assays in vitro, as well as in db/db mice and a diet induced obese (DIO) rat model. A Phase 1, double-blind, randomized, placebo-controlled, single (SAD) and multiple ascending dose (MAD) study was conducted to evaluate the safety, tolerability, and pharmacokinetics of subcutaneous ecnoglutide injection in healthy participants. SAD doses ranged from 0.03 to 1.0 mg; MAD doses ranged from 0.2 to 0.6 mg once weekly for 6 weeks (ClinicalTrials.gov Identifier: NCT04389775). Results: In vitro, ecnoglutide potently induced cAMP (EC50 = 0.018 nM) but not GLP-1 receptor internalization (EC50 > 10 μM), suggesting a desirable signaling bias. In rodent models, ecnoglutide significantly reduced blood glucose, promoted insulin induction, and led to more pronounced body weight reduction compared to semaglutide. In a Phase 1 trial, ecnoglutide was generally safe and well tolerated as a once-weekly injection for up to 6 weeks. Adverse events included decreased appetite, nausea, and headache. The half-life at steady state ranged from 124 to 138 h, supporting once-weekly dosing. Conclusions: Ecnoglutide showed a favorable potency, pharmacokinetic, and tolerability profile, as well as a simplified manufacturing process. These results support the continued development of ecnoglutide for the treatment of type 2 diabetes and obesity.

The neurodevelopmental Autism Spectrum Disorder (ASD) causes problems in social communication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deep Convolutional Neural Network (CNN) with Dwarf Mongoose optimized Residual Network (DM-ResNet) is proposed for the classification of autism disorder from Magnetic Resonance Imaging (MRI) brain images. Initially, the input brain images are preprocessed to remove the non-brain tissues. The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. FCM-GMM segments the volume into predefined cortical and sub cortical regions. After segmentation, the features are extracted with Visual Geometry Group (VGG)-16 networks which comprised of several tiny kernels with filters for enhancing the depth of network and permit to extract complicated and discriminative features. Region of Interest (ROI) based functional connectivity feature is extracted with VGG-16 and these features are classified with DM optimized ResNet. The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the accuracy of autism detection is improved to 99.83%.

In recent years, blockchain technology has gained significant attention for its potential in various domains. However, the lack of interoperability between different blockchain platforms poses a significant challenge in meeting the demands of the modern world. To address this issue, our research focuses on unlocking blockchain interconnectivity through smart contract-driven cross-chain communication. We aim to contribute to the development of a model that enhances the functionality and usability of blockchain technology. To achieve interoperability, we explore various options and leverage the power of smart contracts. These contracts enable seamless communication and exchange of services between different blockchain platforms, as well as with legacy systems. By implementing our proposed model, we intend to bridge the gap between isolated blockchain systems, enabling them to work together efficiently and effectively. To validate the effectiveness of our model, we have conducted a comparative analysis with existing parent blockchain models for cross-chain communication. We have measured and compared their mean and standard deviation, which indicate the performance improvements achieved by our approach. The results demonstrate that our model outperforms the existing parent blockchain model, offering better cross-chain communication capabilities and showing a 42.27% improvement in efficiency as well. Through our research, we aim to contribute to the advancement of blockchain technology by addressing the critical issue of interoperability. By enabling seamless communication and collaboration between different blockchain platforms, our model has the potential to revolutionize various industries and unlock new opportunities for innovation and growth.

Meta-heuristics are commonly applied to solve various global optimization problems. In order to make the meta-heuristics performing a global search, balancing their exploration and exploration ability is still an open avenue. This manuscript proposes a novel Opposition-based learning scheme, called “PCOBL” (Partial Centroid Opposition-based Learning), inspired by the partial centroid. PCOBL aims to improve meta-heuristics performance through maintaining an effective balance between the exploration and exploitation. PCOBL was incorporated in three different meta-heuristics, and a comparative study was conducted on 28 CEC2013 benchmark problems with 30, 50, and 100 dimensions. In addition, we assessed the PCOBL in the IEEE CEC2011 real-world problems. The empirical results demonstrate that PCOBL balances the exploration and exploitation ability of the meta-heuristics, positively impacting their performance and making them outperform the state-of-the-art algorithms in terms of best-error runs and convergence in most of the optimization problems. Moreover, the computational cost analysis illustrated that the inclusion of PCOBL in the meta-heuristic algorithm has a low impact on its efficiency.

The current review is informed about the effectiveness of plant-derived functional components that aids in the regulation and health of endocrine glands. The endocrine glands include the thyroid, adrenal, hypothalamus, pituitary and pineal, ovaries and testes that play vital functions in our body such as growth and development, metabolism, mood and reproduction controlled by hormones. The abnormalities in the functions of endocrine glands are formed various disorders, some major disorders are diabetes, goiter, kidney problem, brain-related diseases and PCOS. Different parts of plant-based foods (fruits, vegetables, cereals, beans, legumes, herbs and spices) are composed of vitamins, minerals, antioxidants and phenolic compounds that help support different body functions. It is concluded that plant-based foods are a rich source of functional components that play a valuable role in regulating the function of the endocrine glands.

Macrophage-mediated immune suppression contributes to poor outcome in liver metastasis. Here the authors show that CD36-expressing metastasis associated macrophages engulf tumor cell-derived extracellular vesicles enriched in long-chain fatty acids, acquiring a pro-tumorigenic phenotype in a preclinical liver metastasis model.

Usually, polyphenols help address numerous health issues caused by oxidative stress. Tea is a popular beverage (rich in polyphenols) with abundant health promoting and disease prevention with great health-promoting and disease-prevention attributes, originating from the delicate, dried leaves of the Camellia sinensis plant. Tea has been proven to have health-boosting impacts like anti-inflammatory, anti-cancerous, anti-diabetic, and aids in weight loss. Cognitive impairment, also known as cognitive decline caused by aging or other neurological disorders, has become an emerging health concern. Tea polyphenols, especially phenolic acids, havegained enormous attention due to their link to improved cognitive function by preventing cognitive decline. This review summarizes recent studies on the health benefits of polyphenols in tea. Additionally, effective traditional and modern techniques to extract polyphenols and their effects on various diseases have been described.

AimTo summarize systematically our six-year experience in the surgical treatment of postoperative bile leakage after liver tumor surgery in children, and explore its reoperation approach and treatment effect.MethodsThe clinical data of 6 patients with postoperative bile leakage cured by surgery from January 2016 to January 2022 were reviewed retrospectively.ResultsAmong the six pediatric patients with postoperative bile leakage cured by surgery, four were male (67%) and two were female (33%). All patients underwent complex segmentectomy. The median time to bile leakage was 14 days (range, 10 to 32), and the daily drainage volume was stable from 170 ml to 530 ml per day. After conservative treatment failed, four patients received biliary-enteric anastomosis (patients 1, 3, 4, and 6), and two patients received bilio-cholecyst anastomosis (patients 2 and 5). All six patients were successfully treated with reoperation, and five patients were alive and without recurrence, while one patient was lost to follow-up due to abandoned treatment.ConclusionOur study suggests that surgery is a reliable and effective treatment for postoperative intractable bile leakage in children undergoing complex segmentectomy. Bilioenteric anastomosis is the most common technique for bile leakage, and bilio-cholecyst anastomosis is a feasible and effective surgical approach. These findings have important implications for the management of postoperative complications in pediatric patients undergoing complex segmentectomy.

BackgroundIndocyanine Green (ICG) fluorescence imaging has been widely used in the surgical treatment of adult renal cancers, but its application in pediatric renal cancers has rarely been reported. This study aims to summarize the experience of ICG fluorescence imaging in pediatric renal cancers and explores its safety and feasibility.MethodsThe clinical features, surgical information, ICG administration regimen, near infrared radiography data in vivo and ex vivo and pathological results of children with renal cancers using ICG navigation were analyzed and summarized.ResultsThere were 7 cases of renal cancer, including 4 cases of Wilms tumor (WT), 1 case of malignant rhabdoid tumor of the kidney (MRTK) and 2 cases of renal cell carcinoma (RCC). By intraoperative intravenous injection of ICG from 2.5 to 5 mg (0.05–0.67 mg/kg), the tumors were visualized in 6 cases in vivo or ex vivo, and the tumor visualization failed in 1 case due to renal artery embolization before operation. By injecting 5 mg ICG into the normal renal tissue during the operation, 3 patients achieved fluorescent localization of sentinel lymph nodes. No ICG-related adverse reactions were found in any of the patients during or after operation.ConclusionsICG fluorescence imaging is safe and feasible for renal cancers in children. Intraoperative administration can achieve tumor and sentinel lymph node visualization which will facilitate the development of nephron sparing surgery (NSS). However, the technique is affected by ICG dose, anatomical conditions around the tumor, and renal blood flow. A proper dose of ICG and the complete removal of perirenal fat are helpful for the fluorescence imaging of the tumor. It has potential in the operation of renal cancer in children.

Abstract Background Endometrial regenerative cells (ERCs) play an important role in attenuation of acute allograft rejection, while their effects are limited. IL-37, a newly discovered immunoregulatory cytokine of the IL-1 family, can regulate both innate and adaptive immunity. Whether IL-37 overexpression can enhance the therapeutic effects of ERCs in inhibition of acute cardiac allograft rejection remains unknown and will be explored in this study. Methods C57BL/6 mice recipients receiving BALB/c mouse heterotopic heart allografts were randomly divided into the phosphate-buffered saline (untreated), ERC treated, negative lentiviral control ERC (NC-ERC) treated, and IL-37 overexpressing ERC (IL-37-ERC) treated groups. Graft pathological changes were assessed by H&E staining. The intra-graft cell infiltration and splenic immune cell populations were analyzed by immunohistochemistry and flow cytometry, respectively. The stimulatory property of recipient DCs was tested by an MLR assay. Furthermore, serum cytokine profiles of recipients were measured by ELISA assay. Results Mice treated with IL-37-ERCs achieved significantly prolonged allograft survival compared with the ERC-treated group. Compared with all the other control groups, IL-37-ERC-treated group showed mitigated inflammatory response, a significant increase in tolerogenic dendritic cells (Tol-DCs), regulatory T cells (Tregs) in the grafts and spleens, while a reduction of Th1 and Th17 cell population. Additionally, there was a significant upregulation of immunoregulatory IL-10, while a reduction of IFN-γ, IL-17A, IL-12 was detected in the sera of IL-37-ERC-treated recipients. Conclusion IL-37 overexpression can promote the therapeutic effects of ERCs to inhibit acute allograft rejection and further prolong graft survival. This study suggests that gene-modified ERCs overexpressing IL-37 may pave the way for novel therapeutic options in the field of transplantation.