Deep Learning for Biomarker Discovery in Cancer Genomes.

Background: Genomic data is essential for clinical decision-making in precision oncology. Bioinformatic algorithms are widely used to analyze next-generation sequencing (NGS) data, but they face two major challenges. First, these pipelines are highly complex, involving multiple steps and the integration of various tools.

In-context learning enables multimodal large language models to classify cancer pathology images.

Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processing, in-context learning provides an alternative, where models learn from within prompts, bypassing the need for parameter updates.

Transcriptomic profile in carbendazim-induced developmental defects in zebrafish (Danio rerio) embryos/larvae.

Carbendazim is a widely used fungicide to protect agricultural and horticultural crops against a wide array of fungal species. Published reports have shown that the wide usage of carbendazim resulted in reprotoxicity, carcinogenicity, immunotoxicity, and developmental toxicity in mammalian models. However, studies related to the developmental toxicity of carbendazim in aquatic organisms are not clear.

Antioxidant effects of α-lipoic acid against epididymal oxidative damage in adult offspring rats exposed to maternal hypothyroidism stress.

It is well known that the epididymis promotes post-testicular sperm maturation events. However, its malfunction during congenital hypothyroidism is relatively less understood as compared to the testis. The present study evaluated the probable effect of α-lipoic acid on epididymal oxidative stress parameters in rats exposed to antithyroid drug, carbimazole during fetal period.