Cell cycle checkpoint-related genes (CCCRGs) are implicated in the development and progression of hepatocellular carcinoma (HCC). However, their precise roles and underlying mechanisms remain insufficiently characterized and require further investigation. This study aimed to explore the prognostic significance of CCCRGs in HCC, and to investigate the mechanism by which they promote the progression of HCC.

HCC datasets from The Cancer Genome Atlas and International Cancer Genome Consortium were analyzed to identify hub genes. A prognostic model was constructed and validated using Kaplan–Meier analysis, nomogram, calibration curves, decision curve analysis, and receiver operating characteristic analysis. Immune infiltration patterns were assessed using single sample gene set enrichment analysis, while pathway activities were evaluated via gene set variation analysis. Single-cell RNA sequencing data from GSE149614 were analyzed with Seurat and CellChat to investigate cell–cell communication. Patient-derived HCC specimens were examined through immunohistological evaluation, HCC cell lines were used for in vitro functional assays, and in vivo tumor growth was assessed through animal experiments.

CCCRGs showed significant associations with prognosis, malignant biological behavior, and immune responses in HCC. Centromere protein (CENP) I was identified as a critical hub gene that markedly promoted HCC proliferation, metastasis, and epithelial–mesenchymal transition, while inhibiting apoptosis. Mechanistically, CENPI suppressed YAP phosphorylation, enhancing its nuclear translocation and thereby driving malignant progression. Additionally, CENPI impaired immune effector cell infiltration, likely by disrupting tumor antigen presentation and chemokine-mediated CD8+ T cell chemotaxis, thereby promoting immune escape.

This study underscores the prognostic significance of CCCRGs in HCC and identifies CENPI as a key driver of tumor progression through the Hippo pathway. Furthermore, it reveals CENPI’s role in promoting immune escape, suggesting novel therapeutic targets for HCC treatment.

Clove essential oil (CEO) derived from Syzygium aromaticum and miswak (Salvadora persica) contains bioactive compounds with antimicrobial properties. Due to the growing interest in alternatives to conventional antibiotics, this study aimed to evaluate the in vitro antimicrobial efficacy of CEO, miswak, and their combination against key peri-implantitis pathogens.

The antimicrobial activities of CEO, miswak, and their combinations were tested against Fusobacterium nucleatum, Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, and Prevotella intermedia. Disc diffusion and serial dilution methods were used to measure the inhibition zones and minimum inhibitory concentrations, respectively. Doxycycline served as a standard antibiotic for comparison, while ethanol was used as a negative control. Data were analyzed using one-way analysis of variance and Tukey’s honestly significant difference test, with significance set at α = 0.05.

CEO exhibited inhibition zones of 10–16 mm, comparable to that of doxycycline (13–16 mm), whereas miswak (6–13 mm vs. 1–14 mm) and the CEO–miswak combination (8–14 mm vs. 0–14 mm) showed lower activity. Mean minimum inhibitory concentration values were lowest for doxycycline (1.73 ± 0.46 µg/mL), followed by CEO (2.37 ± 0.24 µg/mL) and CEO–miswak combination (2.92 ± 0.12 µg/mL). Statistical analysis showed that the CEO–miswak combination was less effective than CEO (p = 0.0326) and doxycycline (p = 0.0001), but not different from miswak (p = 0.9836). CEO showed slightly greater activity than miswak (p = 0.0605).

Among the natural extracts tested, CEO exhibited superior antimicrobial efficacy, whereas miswak was less effective. The combination of CEO with miswak did not enhance antimicrobial efficacy, suggesting antagonistic interactions between their bioactive compounds.

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.

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.

Peginterferon-α treatment exhibits low rates of the serological conversion rate of hepatitis B e antigen (HBeAg) and the negative conversion rate of hepatitis B virus (HBV) DNA, with significant myelosuppression leading to treatment discontinuation in some patients. Traditional Chinese medicine (TCM) may ameliorate liver inflammation and modulate immune responses. This study aims to investigate the efficacy of combining TCM with pegylated-interferon (PEG-IFN) α-2b and its impact on myelosuppression adverse effects.

This study included 117 HBeAg-positive chronic hepatitis B (CHB) patients who started initial antiviral therapy at Xiamen Hospital of TCM between June 2018 and January 2023. According to the treatment regimen, patients were divided into the observation group (n = 56, receiving PEG-IFN α-2b combined with Licorice 15 g, Angelica sinensis 20 g, Poria 20 g, Paeonia lactiflora 20 g, Rhizoma Atractylodis Macrocephalae 20 g, Radix Bupleurum Chinense 20 g, Mentha piperita 3 g, Ginger three slices for more than six months) and the control group (n = 61, receiving PEG-IFN α-2b alone). This study retrospectively analyzed etiological indicators, liver biochemical indicators, and blood routine tests before and after treatment.

After 24 and 48 weeks of treatment, the observation group demonstrated significantly superior outcomes to the control group in quantitative reduction of hepatitis B surface antigen, the serological conversion rate of HBeAg, and the reduction in HBV DNA quantification (P < 0.05). By week 48, the HBV DNA negative conversion rate in the observation group (46.67%) was significantly higher than that in the control group (26.67%) (P < 0.05). Regarding safety, the incidence of myelosuppression in the observation group was significantly lower than that in the control group at both 24 and 48 weeks of treatment (P < 0.05)

Real-world findings demonstrate that adjunctive TCM significantly enhances the antiviral efficacy of peginterferon α-2b in HBeAg-positive CHB patients while concurrently mitigating treatment-limiting myelosuppression. This combination strategy may represent a clinically valuable approach to optimizing interferon-based therapy for CHB.

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.

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.

Primary hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, with curative options still limited for patients with advanced disease. As an emerging modality of cancer immunotherapy, tumor vaccines represent a promising approach that activates the host immune system to recognize and eliminate malignant cells. Multiple vaccine platforms, including peptide vaccines, dendritic-cell vaccines, nucleic-acid vaccines, and viral-vector vaccines, have been explored for HCC. Among these, peptide- and dendritic-cell-based vaccines are supported by the most extensive clinical data, demonstrating favorable safety and immunogenicity profiles. The advent of personalized therapeutic cancer vaccines based on tumor-specific antigens has further refined the precision of vaccine design. Nevertheless, several major challenges persist, including immune suppression within the tumor immune microenvironment, marked tumor heterogeneity, immune-escape mechanisms, and limited vaccine immunogenicity, all of which hinder clinical efficacy. In addition, issues related to standardization, large-scale production, and regulatory oversight remain unresolved. Recent advances in sequencing technology, nanotechnology, and artificial intelligence have opened new avenues for optimizing vaccine platforms and delivery strategies. Combination therapies that integrate cancer vaccines with immune checkpoint inhibitors, chemotherapy, or locoregional treatments are also being actively investigated to improve patient outcomes. In summary, although vaccine-based immunotherapy for HCC is still at an early stage, its integration with personalized medicine and multimodal therapeutic strategies holds great potential for improving the long-term prognosis of patients with HCC. Therefore, this review aims to systematically summarize current advances in tumor vaccine–based immunotherapy for hepatocellular carcinoma, with a particular focus on vaccine platforms, target antigens, clinical trial outcomes, and future challenges for clinical translation.

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.