Metabolic dysfunction-associated steatotic liver disease (MASLD) and chronic kidney disease (CKD) have shown a significant increase in comorbidity on a global scale due to the prevalence of metabolic syndrome. In 2023, a number of academic societies formally proposed the concept of MASLD, superseding the previous terminology of “non-alcoholic fatty liver disease” and “metabolic dysfunction-associated fatty liver disease”. The diagnostic criteria have been revised to place greater emphasis on the association between hepatic steatosis and cardiometabolic risk factors. MASLD constitutes an independent risk factor for CKD, with this risk potentially increasing in line with the severity of fatty degeneration and the progression of hepatic fibrosis. CKD may represent a potential risk factor for the progression of fibrosis in patients with MASLD. The interaction between the two conditions may accelerate the occurrence of cardiovascular events and increase the risk of all-cause mortality. MASLD and CKD may share core pathophysiological mechanisms, including genetic variants, insulin resistance, lipid metabolism disorders, chronic inflammation, oxidative stress, and gut microbiota dysbiosis. However, the bidirectional causal relationship between the two conditions and the molecular dialogue between organs remains unclear. Furthermore, there are significant gaps in clinical prediction tools and targeted treatment strategies for comorbidities. This paper reviews common pathophysiological mechanisms in MASLD and CKD, the epidemiological and clinical evidence linking MASLD to the risk of CKD, biomarkers and clinical prediction models for coexisting conditions, and potential therapeutic strategies. Our aim is to provide a theoretical basis for early identification, mechanism exploration, and clinical treatment of comorbidities.

Metaplastic breast carcinoma, a rare entity (<1% of breast neoplasms), lacks comprehensive spectroscopic characterization. This study aimed to address this gap by providing a qualitative and quantitative spectroscopic profile of metaplastic carcinoma in comparison to ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC).

A retrospective analysis was conducted on archival tissue blocks of metaplastic carcinoma (n = 10), DCIS (n = 12), and IDC (n = 31). Sections were stained with hematoxylin and eosin for histological confirmation. Attenuated total reflectance Fourier-transform infrared spectroscopy was performed on adjacent unstained sections, with normal breast tissue (n = 10) serving as the control. Spectral data were analyzed using t-tests to identify significant differences in peak intensities and ratios. Hierarchical clustering analysis and receiver operating characteristic curves were generated to assess the diagnostic potential of selected spectral features.

Spectral analysis revealed that mean peak intensities were generally lower in all carcinoma subtypes compared to normal breast tissue. Specific ratios, including A1237/A1080 (phosphate; p < 0.01), A1043/1543 (glycogen; p < 0.01), and A1080/A1632 (nucleocytoplasmic index; p < 0.03), were significantly elevated in carcinomatous tissues. Receiver operating characteristic analysis identified peak 3,280 (area under the curve (AUC) = 0.93–0.96) as highly effective in differentiating normal from carcinomatous tissues. Peak 2,922 showed specificity for distinguishing normal tissue from IDC (AUC ≈ 0.7). Peak 1,744 effectively discriminated between DCIS and metaplastic carcinoma (AUC = 0.7). The ratio 1,080/1,632 (nucleocytoplasmic ratio) demonstrated exceptional diagnostic accuracy, distinguishing normal from carcinomatous tissues (AUC ≈ 1.0), DCIS from IDC (AUC ≈ 0.86), and DCIS from metaplastic carcinoma (AUC ≈ 0.8).

Attenuated total reflectance Fourier-transform infrared spectroscopy, particularly using peak 3,280 (Amide A) and the 1,080/1,632 ratio (nucleocytoplasmic index), offers a promising approach for discriminating between normal breast tissue and carcinoma, as well as differentiating pre-IDC from metaplastic carcinoma. These spectral markers demonstrate both statistical significance and diagnostic potential.

Empirical and theoretical studies can be distinguished among the areas of investigation of the renin-angiotensin-aldosterone system (RAAS) and its relationship with the development of cardiovascular diseases. Theoretical work is based mainly on the bioinformatic analysis of key elements of RAAS (genes, proteins, metabolites), on calculations and predictions of protein interactions, and on mechanisms of RAAS gene expression regulation. An associative gene network based on big data analysis allows us to reveal relationships among the proteins, regulatory pathways, and biological processes acting in RAAS, as well as to identify new diagnostic markers, therapeutic targets, putative molecular mechanisms of the development of RAAS-associated diseases, drug interactions, and drug toxicity.

The reconstruction and analysis of associative gene networks were performed using ANDSystem. The regulation of RAAS-associated gene expression was analyzed by transcription factor (TF) binding sites (TFBSs) prediction in the proximal promoters of these genes and by studying interactions between TFs themselves using the Ensembl Biomart web service and AnimalTFDB 4.0. The recognition of potential TFBSs in RAAS gene promoters was performed using MoLoTool.

According to the centrality criteria of the RAAS associative gene network, the following proteins were identified as exerting a significant influence on information interplay between network components: IL6, EDN1, TNFA, MK01, LEP, and JUN. Analysis of the ten identified TFs and their TFBSs among the genes in the RAAS network under study revealed clusters of three to 26 genes regulated by them.

Components with the highest values of centrality and vertex degrees were identified in the reconstructed associative gene network of the RAAS, and ten TFs supposed to regulate 26 RAAS genes were determined.

Super-enhancers (SEs) are highly enriched clusters of transcriptional regulatory elements within the genome, occupying a central position in tumorigenesis and development. This review aims to synthesize the rapidly expanding body of knowledge on SEs as the central hub of tumor transcriptional regulation.SEs integrate specific transcription factors, dynamic epigenetic modifications (such as H3K27ac), and restructure the three-dimensional spatial architecture of the genome to aberrantly drive the expression of proto-oncogenes and cell identity-related genes. This activity sustains the malignant phenotype, stem cell properties, metabolic reprogramming, and therapy resistance of tumor cells. Their functions involve emerging physical mechanisms such as phase separation forming transcriptional condensates and long-range chromatin looping. The activity of SEs exhibits high tumor-type and tissue specificity. They are activated through unique mechanisms in different cancers, becoming key nodes of “transcriptional addiction” in tumor cells. This characteristic also makes them highly promising therapeutic targets. Inhibitors targeting core SE components (such as the BET protein BRD4 and transcriptional kinases CDK7/9), epigenetic drugs, and strategies aimed at disrupting their phase-separated condensates have shown selective efficacy in various preclinical tumor models. In conclusion, SEs serve as pivotal hubs of transcriptional addiction in cancer by integrating diverse molecular mechanisms to drive oncogenic programs, and their specific components present promising therapeutic targets; future advances in multi-omics and precision strategies will be key to translating these findings into clinical applications.

Bacterial infections (BIs) are common and severe complications in patients with liver cirrhosis, but global data are limited. Here, we aimed to evaluate the global prevalence, temporal changes, and associated mortality risk of BIs in liver cirrhosis.

We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library for eligible studies published without language restrictions until 11 August 2025. A random-effects model was used for meta-analyses, meta-regression by study year, and pooling adjusted hazard ratios.

Fifty-nine studies, including 1,191,421 patients with cirrhosis, were analyzed. The pooled prevalence of BIs (33 studies) was 35.1% (95% confidence interval (CI): 29.2–41.4). The prevalence of Escherichia coli and Streptococcus spp. was 3.8% (95% CI: 2.5–5.2) and 1.5% (95% CI: 0.8–2.6), respectively. The pooled prevalence of multidrug-resistant bacteria was 6.8% (95% CI: 4.0–11.3). The most common BI sites were the gastrointestinal tract, ascites fluid, and urinary tract. The highest prevalence of BIs was reported in Europe (38.2%; 95% CI: 24.8–53.6), followed by South America (37.5%; 95% CI: 29.7–46.1) and Asia (22.8%; 95% CI: 16.3–30.9). Patients with acute-on-chronic liver failure showed the highest prevalence of BIs (44.2%; 95% CI: 29.7–59.8). A modest increasing trend in BIs prevalence was observed over time. BIs were associated with an increased risk of mortality in patients with cirrhosis (adjusted hazard ratios 2.22, 95% CI 1.33–3.71).

BIs are prevalent in cirrhosis, especially in acute-on-chronic liver failure, with a modest upward trend and increased mortality risk.

Colorectal cancer histopathological grading relies on the accurate segmentation of glandular structures. Current deep learning–based methods depend heavily on large-scale pixel-level annotations that are labor-intensive and not amenable to clinical practice. Weakly supervised semantic segmentation offers a promising alternative; yet, existing class activation map–based weakly supervised semantic segmentation approaches often produce incomplete, low-quality pseudo-masks that overemphasize discriminative regions and fail to provide reliable supervision for unannotated glandular structures, limiting their suitability for dense histopathology segmentation under sparse supervision. We propose a novel weakly supervised teacher–student framework that leverages sparse pathologists’ annotations and an Exponential Moving Average–stabilized teacher network to generate refined pseudo-masks.

Our framework integrates confidence-based filtering, adaptive fusion of teacher predictions with limited ground truth, and curriculum-guided refinement, enabling the student network to progressively delineate and accurately segment unannotated glandular regions. We validated our framework on an institutional colorectal cancer cohort from The Ohio State University Wexner Medical Center, consisting of 60 hematoxylin and eosin-stained whole-slide images from independent patients with varying degrees of gland differentiation, as well as on public benchmarks including the Gland Segmentation dataset (derived from stage T3–T4 colorectal adenocarcinomas), TCGA-COAD, TCGA-READ, and SPIDER.

The proposed framework achieved strong performance on the institutional dataset despite limited annotations. On the Gland Segmentation dataset, it demonstrated competitive performance compared to both weakly and fully supervised approaches, achieving a mean Intersection over Union of 80.10% ± 1.52 and a mean Dice coefficient of 89.10% ± 2.10. Moreover, cross-cohort evaluations showed robust generalization on TCGA-COAD and TCGA-READ without requiring additional annotations, while reduced performance on SPIDER reflected pronounced domain shift.

Our framework provides an annotation-efficient and generalizable paradigm for accurate gland segmentation in colorectal histopathology, offering a practical pathway toward significantly reducing annotation burdens while preserving high segmentation fidelity.

Uterine fibroids (UFs) are common hormone-dependent tumors with a complex etiology involving both genetic and environmental factors. This study aimed to investigate, for the first time, the associations between loci from genome-wide association studies (GWAS) and environmental risk factors in UF development, with a particular focus on gene–environment interactions.

DNA samples from 737 women with UF and 451 healthy controls were genotyped for ten UF-associated GWAS single nucleotide polymorphisms (SNPs) using probe-based polymerase chain reaction in this case-control study.

SNP rs66998222 (LOC102723323, G/A) was associated with decreased UF risk in the total sample (odds ratio (OR) = 0.81, p = 0.038) and in patients with a history of induced abortion (OR = 0.70, p = 0.009). SNP rs11031731 (THEM7P, WT1, G/A) increased UF risk overall (OR = 1.39, p = 0.01), and in women with abortion history (OR = 1.60, p = 0.008) or without pelvic inflammatory disease (OR = 1.43, p = 0.02). SNPs rs641760 (PITPNM2, C/T) and rs2553772 (LOC105376626, G/T) showed protective effects depending on abortion history. SNP rs1986649 (FOXO1, C/T) was associated with later UF onset (p = 0.049) and slower growth (p = 0.017). GWAS loci influence UF-related genes involved in proliferation, inflammation, and hormone metabolism, underscoring their pathogenic role.

Induced abortions and inflammation modify the effects of GWAS-identified UF risk loci, with allele-specific impacts on hormonal, inflammatory, and repair pathways. Replication in diverse cohorts is needed to validate these population-specific effects.

Breast cancer (BCA) is one of the most common cancers worldwide, with a high rate of incidence and mortality. This review provides global information on BCA therapy using curcumin. Chemotherapy, as an effective treatment for different stages of BCA, and curcumin, generally regarded as safe compound and an alternative to synthetic drugs, have been described for the treatment of BCA. A few parameters, including nano-curcumin versus bulk curcumin and its encapsulated form versus its corresponding free form, have been discussed. Curcumin, a safe and edible compound with antitumor properties, is a promising medicinal compound for the treatment of BCA. Encapsulation of curcumin enhances its stability and anticancer efficiency. Nano-curcumin exhibits superior properties when compared to its bulk counterparts, leading to notable interactions and effects.