Colorectal cancer (CRC) is one of the most prevalent and deadly malignancies globally. Early detection and accurate diagnosis are crucial for improving survival rates and treatment outcomes. Research in oncology and medical informatics has made significant progress. However, conventional screening approaches, such as colonoscopies, fecal occult blood tests, and imaging modalities, have limitations in terms of sensitivity, specificity, and patient adherence. Thus, a literature review was conducted to identify contemporary evidence relevant to the study objectives. Searches were conducted across major scientific databases, including PubMed, EBSCO, and Scopus. The search strategy targeted peer-reviewed publications encompassing original research articles, systematic reviews, and expert commentaries published within the past 5 years. High-impact journals, such as The New England Journal of Medicine and Nature were manually screened to ensure the inclusion of seminal and authoritative works. Notably, advances in artificial intelligence (AI), software detection tools, models, and algorithms have enabled improved screening, rapid detection, risk prediction, and more accurate and consistent diagnosis. Real-time AI-assisted endoscopy has improved detection rates for colorectal neoplasia. The review indicated that the adenoma detection rate during colonoscopy was 54.8% in the computer-aided polyp detection group compared to 40.4% in the control group. The incorporation of AI in the early detection and diagnosis of CRC presents considerable potential. While the use of these tools has potential benefits, challenges remain. Ongoing efforts are focused on overcoming barriers to clinical integration and improving clinical outcomes, ensuring that AI technologies are safe, effective, and accessible.

Gallbladder cancer (GBC) is an aggressive malignancy with limited therapeutic options. The role of neutrophil extracellular traps (NETs) in GBC remains unexplored. This study performed an integrated bioinformatics analysis using two Gene Expression Omnibus datasets (GSE138109 and GSE276931) to investigate NET-associated molecular mechanisms in GBC. After batch correction, differential expression analysis and weighted gene co‑expression network analysis identified phenotype‑associated modules. Intersection with high‑confidence NET‑related genes revealed 54 core genes linking NETs to GBC. Linear mixed model analysis highlighted significant downregulation of ADAMTS1, GPX3, and MYH11 in tumor tissues. Pathway activity analysis showed the upregulation of cell‑cycle pathways (E2F_TARGETS and G2M_CHECKPOINT) and the downregulation of inflammatory pathways (MYOGENESIS and TNFA_SIGNALING_VIA_NFKB). These findings map a regulatory network through which NETs may influence GBC progression by disrupting extracellular matrix integrity and oxidative balance. The study provides a preliminary framework for mechanistic and therapeutic exploration, aligning with the need for molecularly informed strategies in advanced biliary tract cancer.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are neurodegenerative disorders that share clinical, pathological, and genetic overlap, most notably through mutations in the C9orf72 gene. Since its discovery in 2011, this mutation has emerged as a key driver of neurodegeneration through a complex interplay of toxic gain-of-function and loss-of-function mechanisms. The repeat expansion disrupts RNA metabolism, proteostasis, and nucleocytoplasmic transport, while impairing autophagy and endolysosomal trafficking due to reduced C9orf72 protein expression. Concurrently, bidirectional transcription of repeat-containing RNA generates RNA foci and dipeptide repeat proteins via repeat-associated non-AUG translation, sequestering RNA-binding proteins and inducing cellular stress. These processes converge on neuroinflammation, mitochondrial dysfunction, and TDP-43 proteinopathy-culminating in a rapidly progressive clinical phenotype with frequent cognitive involvement. This review synthesizes recent mechanistic, clinical, and therapeutic advances in C9orf72-associated ALS/FTD. It highlights the growing relevance of biomarkers such as neurofilament light chain and poly-GP, the failure of antisense oligonucleotide therapy (BIIB078) to modify TDP-43 pathology despite adequate CNS penetration, and the promise of emerging approaches, including allele-specific CRISPR-Cas9 editing, autophagy modulation, and inflammasome inhibition. Future research must integrate multi-omics datasets, patient-derived organoids, and advanced preclinical models to clarify the relative contributions of gain- and loss-of-function mechanisms. Bridging these insights with precision medicine frameworks offers the most compelling path toward mechanism-driven interventions capable of altering the natural course of C9orf72-related neurodegeneration.

Breast cancer remains the most diagnosed cancer among women worldwide, and its prevalence continues to increase annually. Previously, we reported that ellagic acid (EA), a compound isolated from Terminalia leiocarpa (DC.) Baill., a plant used in Nigerian traditional medicine, inhibited the proliferation of cultured human MCF-7 breast cancer cells. EA is a pharmacologically active polyphenolic compound with antioxidant, anti-inflammatory, and anti-estrogenic activities. In this work, we investigated the effects of EA on the transcriptome of MCF-7 cells using next-generation sequencing. After EA treatment, MCF-7 cells were harvested, and total RNA was isolated and used to prepare RNA sequencing libraries. Sequencing was performed using NovaSeq 6000. The results showed that EA significantly altered gene expression patterns in MCF-7 cells. Bioinformatic analysis of the sequencing data revealed that 4,848 transcripts were differentially expressed, with 2,180 upregulated genes and 2,668 downregulated genes ( q ≤ 0.01). Ingenuity Pathway Analysis indicated that EA-induced expression changes mapped to 98 canonical pathways, including upregulation of apoptosis signaling pathways and downregulation of estrogen signaling and estrogen-dependent breast cancer signaling pathways ( q ≤ 0.05). Furthermore, EA downregulated gene expression in the epithelial-to-mesenchymal transition (EMT) canonical pathway, such as PRRX1/2, TGF, TWIST1, and VIM. These data indicate that EA has multiple mechanisms of action in MCF-7 cells, including the induction of apoptosis and the reduction of both estrogen and EMT signaling. Thus, with further research, EA may serve as an inexpensive, safe, and effective treatment for breast cancer and metastatic diseases.

Natural products derived from cyanobacteria have emerged as a prolific source of structurally diverse and biologically active compounds with significant therapeutic potential. This review summarizes the current literature on the isolation of natural products and their medicinal properties from the cyanobacterial genus Nostoc. It emphasizes the structural diversity of secondary metabolites biosynthesized by Nostoc. Based on the literature on the chemical characterization of natural products, we provide an outlook for harnessing the latent biocatalytic potential of this cyanobacterial genus to discover enzymes directly involved in the production of these diverse natural products. The review provides potential hints for future identification of enzymes with unique biochemistry and/or for the synthesis of novel natural product analogs from the families discussed herein.

Urinary tract infections (UTI) are a recurring issue in women’s health as a result of the associated high economic burden. Our study profiled antibiotic resistance and virulence factors in Proteus species isolated from women with UTI from the University Teaching Hospital, Lafia, Nigeria. A total of 368 women aged 18 and above participated in the study. Urine samples were collected from volunteers and screened for significant bacteriuria using the pour plate technique. The isolates were identified using biochemical tests, and Proteus species were characterized for antibiotic susceptibility using the Kirby–Bauer disk diffusion method. The plasmids of the isolates were extracted and identified using agarose gel electrophoresis. Only 21 participants had significant bacteriuria, and 9 bacterial genera were isolated from their urine samples. Escherichia coli (52.4%), Klebsiella sp. (57.1%), and Proteus sp. (42.9%) were the most occurring species, while Corynebacterium sp. (4.8%) was found in only one patient. The demographic profile revealed that women aged 31–40 with a 40% incidence of Proteus sp. were the most susceptible group, and women who use a water system for sewage disposal and a borehole water source each had an incidence of 80%, respectively. Four Proteus isolates, P3, P5, P6, and P9, were resistant to nine antibiotics, and streptomycin had the best inhibition against the 10 Proteus isolates. Adhesion and hemolysin plasmids were the most commonly identified virulence plasmids, while resistance plasmids included conjugative R-plasmid, extended-spectrum beta-lactamase plasmids, and multi-conjugative plasmids of 50 kb, 40 kb, 80 kb, and 200 kb, respectively. The findings underscore the need for improved surveillance of antibiotic resistance promoters in the community. The co-occurrence of multiple plasmids is of concern as it promotes antibiotic resistance through horizontal gene transfer.