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Artificial Intelligence Enhances Breast Cancer Diagnosis Through Deep Learning

Deep learning and convolutional neural networks (CNNs) are transforming breast cancer diagnosis by automating the analysis of histopathological images. Researchers developed a workflow that integrates image preprocessing, hematoxylin–eosin stain separation, K-means clustering, and CNN-based classification to enhance diagnostic accuracy. By filtering poor-quality images and focusing on tumor-rich regions, the model achieved strong classification performance, with 79.0% Top-1 accuracy and 94.5% Top-5 accuracy. Thus, potential use of AI is able to reduce diagnostic variability, support pathologists in clinical decision-making, streamline pathology workflows, and improve access to reliable breast cancer diagnosis, particularly in resource-limited healthcare settings.

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IMOP computational Framework

IMOP-Cancer Predicts Tumor Behaviour Through Mutation Order

IMOP-Cancer, a computational framework identifies how the order of genetic mutations influences cancer progression, prognosis, immune response, and treatment outcomes. Using genomic and transcriptomic data from over 9,000 tumours across 33 cancer types; 106,034 mutation order pairs have been identified that significantly affects cancer phenotypes. The framework integrates mutation timing, cancer cell fraction, phylogenetic analysis, and gene expression profiling to reveal biologically meaningful mutation sequences that influences tumour proliferation, immune activity, drug sensitivity, and patient survival, highlighting its potential in advance precision oncology, prognostic prediction, and personalized cancer therapy.

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GOATOOLS Python Library

GOATOOLS Python Library: Transforming Gene Ontology Research

GOATOOLS is an open-source Python library designed to enhance Gene Ontology Enrichment Analysis (GOEA) by making results more accurate, reproducible, and easier to interpret. It efficiently processes Gene Ontology data, performs statistical enrichment analysis using Fisher’s Exact Test with multiple testing corrections, and organizes complex GO results through a novel grouping approach. The library was validated using simulated datasets and a published Alzheimer’s disease RNA-seq dataset, with performance compared against DAVID and GOstats. GOATOOLS delivers comparable or superior enrichment results while offering greater flexibility, automation, and clearer biological insights for genomic and transcriptomic research.

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A New AI-Based Approach to Personalized Diabetes Care

AI-HEALS: A New AI-Based Approach to Personalized Diabetes Care and Education

The Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) is an innovative mobile health intervention designed to enhance Type 2 diabetes self-management through personalized, AI-driven education. Delivered via WeChat, the platform integrates knowledge-based question answering, lifestyle and physiological monitoring, medication reminders, and tailored health messages. This research protocol outlines a rigorous framework for evaluating whether AI-HEALS can improve HbA1c levels, self-management behaviours, health literacy, psychological well-being, and cost-effectiveness, providing valuable evidence for the future of AI-supported diabetes care.

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Near-Infrared II (NIR-II) imaging technology

NIR-II Nanoprobes are transforming cancer imaging and image-guided surgery

Near-Infrared II (NIR-II) imaging technology is transforming biomedical imaging and disease diagnosis through the use of advanced probes such as quantum dots, lanthanide nanoparticles, carbon nanotubes, and organic dyes. Operating within the 1000–1700 nm wavelength range, NIR-II imaging offers deeper tissue penetration, higher spatial resolution, reduced autofluorescence, and superior signal-to-noise ratios compared with conventional NIR-I imaging (700–900 nm). These advantages have enabled significant progress in cancer detection, image-guided surgery, stem cell tracking, inflammation imaging, targeted drug delivery, and tumor vascular monitoring, resulting in enhanced diagnostic accuracy, improved therapeutic precision, and greater potential for personalized medicine.

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AI based CRISPER screening

AI-Powered CRISPR screening reveals new therapeutic targets for Ebola virus infection

Deep learning models and CRISPR-based gene knockout techniques uncovered 998 host genes involved in Ebola virus replication. To identify potential therapeutic targets, researchers combined artificial intelligence (AI), image-based genome-wide CRISPR screening, and single-cell imaging technologies. AI-powered autoencoders and machine learning algorithms were used to classify different stages of viral infection and identify critical host factors, including UQCRB and STRAP. Notably, inhibition of UQCRB using a small-molecule compound significantly reduced Ebola infection in vitro, highlighting the promising potential of AI-driven precision antiviral drug discovery for emerging infectious diseases.

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Linkage between heavy metal resistant genes and antibiotic resistant genes

Marine E. coli Shows Strong Link Between Heavy Metal and Antibiotic Resistance

About 18 heavy metal resistance genes (HMRGs) associated with arsenic, cadmium, copper, and mercury resistance were identified in 308 E. coli isolates through whole genome sequencing and advanced bioinformatics analysis. Researchers also examined 25 antibiotic-resistant bacterial genomes and discovered important links between HMRGs and antibiotic resistance genes (ARGs). Notably, 100% of the analyzed genomes carried at least one copy of 11 out of the 18 identified HMRGs. These findings suggest that environmental pollution may play a significant role in driving antimicrobial resistance. The study also highlights the potential of using bacterial resistance genes as biomarkers for environmental contamination and emerging public health risks.

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HEDD is an epigenetic drug database for drug discovery

HEDD Database: Advancing Epigenetic Drug Discovery, Cancer Research, and Precision Medicine Through Integrated Biomedical Data

The Human Epigenetic Drug Database (HEDD) is a comprehensive platform developed to organize and integrate epigenetic drug research data, including disease & drug information, clinical trials, molecular targets, high-throughput datasets, and drug-target structures. Data collected from major biomedical databases such as PubChem, DrugBank, GEO, and PDB has been used to create a centralized resource for scientists and clinicians. HEDD contains datasets on 64 epigenetic drugs, 1,606 targets, and 571 disease applications. The database supports flexible searches, 3D molecular visualization, and downloadable datasets, enabling advancements in drug discovery, cancer research, precision medicine, computational biology, and personalized therapeutic development.

HEDD Database: Advancing Epigenetic Drug Discovery, Cancer Research, and Precision Medicine Through Integrated Biomedical Data Read More »

Microbial Electrochemical Technology Treats Nitrate and Arsenic-Contaminated Groundwater

Microbial Electrochemical Technology Treats Nitrate and Arsenic-Contaminated Groundwater

Microbial electrochemical technologies (METs) uses electro-bioremediation systems that can treat groundwater contaminated with nitrate and arsenite simultaneously. A continuous-flow bioelectrochemical reactor has been developed that reduces nitrate into harmless dinitrogen gas while oxidizing toxic arsenite into less harmful arsenate. The system can achieve high nitrate removal rates and over 95% arsenite oxidation efficiency under groundwater-like conditions. This works as the internal recirculation significantly improves treatment performance by enhancing mass transfers and microbial activity of denitrifying and arsenite-oxidizing bacteria like Sideroxydans sp and Achromobacter sp. Thus, electro-bioremediation as a sustainable, low-chemical, and energy-efficient solution for complex groundwater contamination challenges.

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Fetal Body MRI improves the diagnosis of fetal abnormalities

Fetal Body MRI Improves Prenatal Diagnosis and Neonatal Treatment

Fetal body MRI is improving prenatal diagnosis and neonatal treatment planning for congenital abnormalities. The motion-corrected 3D images obtained from MRI enhance the visualisation of fetal anatomy compared to traditional ultrasound alone and aids to determine atypical fetal position, reduced amniotic fluid volume, or high maternal body-mass index. Fetal MRI enables more accurate diagnosis, improves detection of complex abnormalities, supports surgical and delivery planning, and helps clinicians prepare for life-saving neonatal interventions such as airway management procedures, and improves outcomes for newborns with congenital conditions.

Fetal Body MRI Improves Prenatal Diagnosis and Neonatal Treatment Read More »

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