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Background: Trifluridine/tipiracil (FTD/TPI) is approved as monotherapy and in combination with bevacizumab for the treatment of patients with refractory metastatic colorectal cancer (mCRC). FTD/TPI plus bevacizumab showed good tolerability in the phase 3 SOLSTICE (first-line) and SUNLIGHT (later-line) trials. This pooled analysis was performed to further characterize the safety of FTD/TPI plus bevacizumab and to compare safety in untreated and previously treated patients with mCRC.

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AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships.

Comput Struct Biotechnol J

January 2025

Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.

Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power.

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Phosphoproteomics profiling of sorafenib-resistant hepatocellular carcinoma patient-derived xenografts reveals potential therapeutic strategies.

iScience

January 2025

Liver Cancer Institute and Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, P.R. China.

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with poor prognosis. Sorafenib, a first-line treatment for advanced HCC, has shown limited clinical benefits due to the onset of drug resistance. Thus, it is imperative to comprehend the mechanisms underlying sorafenib resistance and explore strategies to overcome or delay it.

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Application of nanomaterials in precision treatment of lung cancer.

iScience

January 2025

Department of Thoracic Surgery, Shanghai General Hospital Shanghai Jiao Tong University School of Medicine, No. 100 Haining Road, Hongkou District, Shanghai 200080, China.

Lung cancer remains one of the most prevalent and lethal malignancies worldwide, characterized by high mortality rates due to its aggressive nature, metastatic potential, and drug resistance. Despite advancements in conventional therapies, their efficacy is often limited by systemic toxicity, poor tumor specificity, and the emergence of resistance mechanisms. Nanomedicine has emerged as a promising approach to address these challenges, leveraging the unique physicochemical properties of nanomaterials to enhance drug delivery, reduce off-target effects, and enable combination therapies.

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Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in orthopedics, by enhancing diagnostic accuracy, surgical planning, and personalized treatment. This review explores current applications of AI in orthopedics, focusing on its contributions to diagnostics and surgical procedures. Key methodologies such as artificial neural networks (ANNs), convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble learning have significantly improved diagnostic precision and patient care.

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