Browse Articles

Low power energy balanced clustering routing scheme based on improved SSA and Multi-Hop transmission in IoT

The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease

Bio-inspired computational intelligence metaheuristic-based optimization and sensitivity analysis approach to determine techno-economic feasibility of hydrogen refueling stations for fuel cell vehicles

Association of changes in frailty with mortality in the aged with hypertension

Integrated single cell and bulk RNA sequencing analyses reveal the impact of tryptophan metabolism on prognosis and immunotherapy in colon cancer

Abstract Tryptophan metabolism is intricately associated with the progression of colon cancer. This research endeavored to meticulously analyze tryptophan metabolic characteristics in colon cancer and forecast immunotherapy responses. This study analyzed colon cancer samples from a training cohort of 473 tumors and 41 normal tissues from TCGA, with validation in 902 cancer patients across multiple GEO datasets. Patients were stratified into subtypes through consistent clustering, and a tryptophan metabolic risk score model was constructed using the random forest algorithm. Based on these risk scores, patients were delineated into high and low-risk groups, and their clinicopathologic characteristics, immune cell infiltration, immune checkpoint expression, and signaling pathway disparities were examined. The Oncopredict algorithm facilitated the identification of sensitive chemotherapeutic agents, while the immune escape score was employed to evaluate the immunotherapy response across risk groups. Transcriptomic sequencing findings were corroborated by single-cell sequencing from Shanghai Ruijin Hospital. Two distinct subtypes of colon cancer patients emerged, exhibiting significant prognostic and immune cell infiltration differences. The high-risk group demonstrated a poorer prognosis (p < 0.001), advanced clinical stage (p < 0.001), and elevated immunosuppressive cell expression (p < 0.05). Additionally, three chemotherapeutic drugs showed efficacy in the high-risk cohort, displaying a heightened immune escape potential (p < 0.05) and diminished response to immunotherapy. Single-cell sequencing validated the overexpression of tryptophan-related genes in epithelial cells. In conclusion, tryptophan metabolism significantly influences the colon cancer immune microenvironment, with high-risk patients experiencing adverse prognoses and potentially reduced efficacy of immunotherapy.

Study on the instability characteristics and influencing mechanism of coal-rock parting-coal structure under triaxial loading

Identifying the metabolic profile of Hashimoto’s thyroiditis from the METHAP clinical study

RETRACTED ARTICLE: Minimally invasive robotic ureteral reconstruction using endoscopic submucosal dissection harvested colorectal mucosa graft for ureteral stricture

Immunoinformatics based designing of a multi-epitope cancer vaccine targeting programmed cell death ligand 1

Knowledge, attitude, and practice related to dementia and cognitive impairment among medical specialists with expertise unrelated to dementia

Static magnetic field inhibits epithelial mesenchymal transition and metastasis of glioma

Abstract Gliomas exhibit suboptimal responses to conventional treatments, with tumor cell migration remaining a significant challenge in therapy. Epithelial-mesenchymal transition (EMT) is crucial for glioma cell invasion, and transforming growth factor β1(TGF-β1) is a key factor promoting proliferation, migration, and EMT in glioblastoma (GBM). Although magnetic fields are widely used in the diagnosis and treatment of various diseases, their effects on EMT in glioma cells remain unclear. In this study, we investigated whether a static magnetic field (SMF) could inhibit EMT and metastasis in glioma cells. Cellular functional assays using the U251 and U87 glioma cell lines were performed to investigate their functional and phenotypic changes. Results showed that TGF-β1 treatment increased the invasion and migration capabilities of glioma cells, while simultaneously reducing apoptosis. However, when SMF was combined with TGF-β1 treatment, a significant reduction in cell migration and invasion was observed, along with an increase in apoptosis. Additionally, this combination treatment significantly decreased the protein expression of mesenchymal markers N-cadherin and β-catenin, as well as reduced the levels of the matrix metalloproteinase (MMP)-2. Collectively, these findings suggest that SMFs may attenuate glioma cell metastasis by inhibiting EMT. Therefore, SMFs could represent a promising therapeutic strategy for diminishing glioma metastasis.

Frontispiece: Multicomponent Reaction Integrating Selenium(II)–Nitrogen Exchange (SeNEx) Chemistry and Copper‐Catalyzed Azide–Alkyne Cycloaddition (CuAAC)

Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification

Abstract To address the public health issue of renal failure and the global shortage of nephrologists, an AI-based system has been developed to automatically identify kidney diseases. Recent advancements in machine learning, deep learning (DL), and artificial intelligence (AI) have unlocked new possibilities in healthcare. By harnessing these technologies, we can analyze data to gain insights into symptoms and patterns, ultimately facilitating remote patient care. To create an AI-based diagnosis system for kidney disease, this paper focused on the three major categories of kidney diseases: stones, cysts, and tumors, which were collected and annotated on 12,446 computed tomography (CT) whole abdomen and urogram images. To effectively aid in the automatic identification and diagnosis of kidney diseases, a novel DL model built on the transfer-learning (TL) technology is implemented in this work. DL models are designed to focus on problems, whereas TL uses the knowledge acquired while resolving one issue to another pertinent issue. The proposed model combines multiple DL models to improve overall performance by leveraging the strengths of different architectures, ensembles can enhance accuracy, robustness, and generalization. It enhances the features extracted from MobileNet-V2, ResNet50, and EfficientNet-B0 networks using metaheuristic algorithms and bidirectional long-short-term memory (Bi-LSTM) from the CT image. MobileNetV2, ResNet50, and EfficientNet-B0 hyperparameters have been optimized using a modified grey wolf optimization (GWO) approach for better performance. The suggested model’s performance has been measured using five assessment metrics: accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) and achieved 99.85% accuracy, 99.8% sensitivity, 99.3% specificity, 98.1% precision, and 1.0 AUC.

Mountain flood forecasting in small watershed based on loop multi-step machine learning regression model

MicroRNA profiling reveals novel biomarkers for cardiovascular and psychological health in plateau psycho CVD

Future flood susceptibility mapping under climate and land use change

Frontispiece: The Interplay Between Component Denticity and Flexibility Promotes the Formation of [Ag ^I ⋅⋅⋅Ag ^I ]‐stabilised Links and Knots

Multivalent vaccine candidate from conserved immunogenic peptides in entry or exit proteins of Orthopoxvirus genus

Reducing the clogging of emitters in drip irrigation systems using acid washing and ultrasonic technology

Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model