DNA polymerase epsilon catalytic subunit A (POLE) gene plays a crucial role in DNA repair and chromosomal replication. Mutations in the POLE gene have been linked to cancer, particularly colorectal carcinoma (CRC). However, the genomic landscape and pathological significance of POLE mutant CRC remain underreported. This study aimed to characterize the clinicopathologic features and genomic landscape of CRC harboring POLE mutations and to investigate the implications of co-occurring genetic alterations.

We identified thirty-four CRC cases with POLE mutations from our institution’s database using the next-generation sequencing gene panels including 161-gene panel for the cases of 2016–2021 and the 505-gene panel for the case of 2022–2023. We collected clinicopathologic data (age, sex, tumor site, and grading) and conducted comprehensive next-generation sequencing. Survival outcomes were assessed by reviewing patients’ medical records at the time of data collection, with survival status determined based on the most recent clinical follow-up available with overall survival as the primary endpoint and a median follow-up time of 20.5 months. Statistical analyses, including chi-squared testing and CoMutation plotting, were performed using Python.

The enrolled 34 patients had a median age of 60.5 years (range: 37–84); tumors were in the colon (26 cases, 77%) and rectum (8 cases, 23%), with a mismatch repair deficiency rate of 29%. Next-generation sequencing analysis of a 505-gene panel revealed that POLE mutations were predominantly missense (89%). The mutations were distributed across various domains: 11.4% in the exonuclease domain, 25.7% in the catalytic domain, 20% in an unknown functional domain, and 42.9% in a nonfunctional domain. The average number of genomic mutations per case was 12.1 ± 12.3. CoMutation analysis identified two subsets: genomic mutation high (>5 mutations, range 6–60 mutations, n = 22) and mutation low (. Notably, TP53 mutations occurred in 55% of cases, and defects in double-stranded DNA repair proteins occurred in 47% of cases. POLE mutant CRC with co-occurring DNA repair mutations exhibited a significantly higher total number of genomic mutations (19.9 ± 14.4, range 7–60 mutations; chi-squared = 5.1, p-value = 0.02). Although a survival comparison between TP53 wild-type and TP53 mutant subgroups of POLE-mutant CRC is not statistical significant (p = 0.37), it showed a trend toward better survival in the TP53 wild-type group.

Our findings reveal unique genomic landscapes in POLE mutant CRC, particularly with co-occurring TP53 or double-stranded DNA repair mutations, which are critical in colorectal carcinogenesis. These tumors demonstrate increased genetic instability, highlighting potential for immunotherapy.

Accumulating evidence indicates that fecal syndecan-2 (SDC2) methylation is a promising biomarker for early detection of colorectal cancer. This study aimed to investigate the diagnostic efficacy of fecal SDC2 methylation testing for adenomas and evaluate the risk stratification efficacy of the Asia-Pacific Colorectal Screening Scoring (APCS) combined with SDC2 methylation status.

This was a prospective, multicenter diagnostic study. Adult participants with no history of colonoscopy within the past three years were enrolled. Demographic data were collected, and APCS scores were evaluated. All participants underwent fecal SDC2 methylation testing and colonoscopy. Colonoscopy outcomes and pathological results of any polyps served as reference standards. The fecal SDC2 methylation test and reference standard assessments were conducted in a blinded manner. The APCS-SDC2 scoring system was developed by integrating fecal SDC2 methylation results with APCS scores, and its efficacy was assessed.

In total, 985 participants were enrolled, among whom 62 (6.3%) tested positive for fecal SDC2 methylation. The sensitivity and specificity of fecal SDC2 methylation in detecting advanced adenomas were 31.3% (95% confidence interval (CI): 21.6–42.7%) and 96.1% (95% CI: 94.6–97.2%), respectively. The APCS-SDC2 scoring system demonstrated superior discriminatory performance for advanced adenomas (area under the curve: 0.7032; 95% CI: 0.5869–0.8195). For advanced adenoma screening, the specificity of the APCS-SDC2 score was higher than that of the APCS score (86.7% vs. 66.7%; P < 0.001).

A positive fecal SDC2 methylation test indicated a higher risk of advanced adenoma, and colonoscopy should be prioritized. The APCS-SDC2 scoring system demonstrated superior risk stratification performance for advanced adenomas.

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.

ATP-binding cassette (ABC) transporters are transmembrane proteins involved in the translocation of bilirubin, bile acids, phospholipids, and cholesterol into bile canaliculi. Mutations in particular genes encoding these transporters—including BSEP (ABCB11 gene), MDR3 (ABCB4 gene), sterolin-1 and sterolin-2 (ABCG5/8 genes), and MRP2 (ABCC2 gene)—result in a wide spectrum of liver diseases, ranging from benign conditions such as Dubin-Johnson syndrome to more severe presentations like progressive familial intrahepatic cholestasis. The severity of disease is influenced by many factors, including zygosity, mutation type, and environmental modifiers such as hormones, consanguinity, and founder effects. Homozygous and compound heterozygous mutations typically result in severe and early-onset diseases, while heterozygous single-allelic mutants generally result in milder diseases. Next-generation genetic testing has proven to have high diagnostic value and is important for prognostication. With knowledge of the underlying specific mutations, there is also potential for future targeted therapy for many severe diseases. The aim of this review is to update and discuss the hepatic diseases associated with ABC transporter mutations, the genetic and environmental effects that influence the severity of disease, typical presentations of these cholestatic hepatic diseases, diagnostic considerations, and treatment options.

Human immunodeficiency virus (HIV)/hepatitis C virus (HCV) coinfection leads to severe systemic inflammation, increasing non-AIDS morbidity and mortality risk. CD39 ectoenzyme on T-cells, which catalyzes the conversion of pro-inflammatory purines to immunosuppressive adenosine, plays an important role in inflammation control. The role of CD39+ T-cells in systemic inflammation during HIV/HCV coinfection under antiretroviral therapy (ART) remains unexplored. This study aimed to identify specific patterns of CD39 expression on T-cells in ART-treated HIV/HCV coinfected patients and assess their relationship with systemic inflammation.

We conducted a case-control study that enrolled 41 HIV/HCV coinfected patients on stable ART (cases) and 23 healthy controls. CD39 expression on blood CD4+ and CD8+ T-cells, including CD45RA+ and CD45RA– subsets, was quantified using flow cytometry. Cytokines were assessed using multiplex and enzyme-linked immunosorbent assays.

A significant proportion of CD4+ T-cells expressed CD39 in both groups (cases – 24.0%; controls – 16.1%). That was not true for CD8+ T-cells (cases – 3.2%; controls – 2.8%). CD39 expression was higher on CD45RA+ than CD45RA– CD4+ T-cells (cases – 39.4% vs. 19.0%; controls – 24.6% vs. 9.2%). HIV/HCV coinfected patients exhibited a significantly increased proportion of CD39+ CD4+ T-cells compared to uninfected controls (P < 0.01). A negative correlation was observed between the percentage of CD39+ CD4+ CD45RA– T-cells and levels of pro-inflammatory chemokines monocyte chemoattractant protein 1 (R = –0.392; P < 0.01) and eotaxin (R = –0.325; P < 0.05).

The data suggest a compensatory expansion of cells with regulatory properties that is ultimately insufficient to control systemic immune activation.

Despite rapid advances in computational biology and regulatory reforms encouraging the reduction of animal use, a clear synthesis of how artificial intelligence (AI)-driven polypharmacology can function as a scientific and ethical bridge between traditional in vivo pharmacology and human-relevant drug development remains lacking. The shift from cage-based experimentation to code-based predictive modeling presents both opportunities and unresolved challenges in biological interpretation, regulatory acceptance, and pharmacology education. Therefore, this review aims to critically examine the transition toward AI-enabled, human-centric drug discovery within the framework of the 3R principles (Replacement, Reduction, and Refinement). Specifically, it explores (i) the global regulatory and ethical drivers accelerating non-animal methodologies, (ii) the scientific and educational gaps emerging from reduced dependence on animal models, and (iii) the role of AI and deep learning in reconstructing biological complexity through multi-omics integration and predictive toxicity modeling. By analyzing emerging AI platforms and computational strategies, this review highlights how AI-driven polypharmacology may offer a scalable, ethical, and precision-oriented framework for future pharmacological research.

Medulloblastoma (MB) is a malignant neoplasm that is relatively common in children but rare in young adults, accounting for less than 1% of all intracranial tumors. This study reports a rare case of MB metastasis to the right temporoparietal region in a 42-year-old woman, presenting with focal neurological symptoms such as weakness in the left arm and leg, speech disturbances, and impaired coordination. The patient had a history of cerebellar MB and underwent surgical resection, radiation therapy, and chemotherapy. Despite treatment, metastasis occurred, highlighting the diagnostic and therapeutic challenges in adult MB cases. The article also reviews the literature on MB in young adults, emphasizing the importance of dynamic neuroclinical monitoring and timely instrumental diagnosis for early detection and management of MB metastases.

The history of screening for cervical cancer is rich with implementing cutting-edge ideas and technologies. From the very first “Pap smear” to the semi-automated and computerized systems of today, the way we screen for cervical cancer has changed dramatically in the past 75 years. With the advent of new techniques and more advanced machine learning algorithms, we sought to understand the current and future applications of artificial intelligence in clinical pathology around cervical cancer screening, diagnosis, and treatment.

A structured narrative review was conducted to examine the historical evolution and contemporary advances in cervical cancer screening, diagnosis, excisional management, and artificial intelligence applications. Peer-reviewed articles, consensus guidelines, and global policy documents published between January 1990 and March 2025 were identified through targeted searches of PubMed and review of reference lists from relevant publications. Search terms included combinations of “cervical cancer screening,” “Papanicolaou test,” “liquid-based cytology,” “HPV testing,” “colposcopy,” “loop electrosurgical excision procedure,” “digital pathology,” “deep learning,” and “artificial intelligence.” Emphasis was placed on multi-center validation studies, systematic reviews, regulatory and implementation guidance, and global health frameworks. Publications lacking methodological transparency or direct relevance to clinical or translational practice were excluded.

Through a review of the literature, we describe how innovations in conventional and liquid-based cytology, human papillomavirus testing, and organized screening programs established the current prevention framework. Building on this foundation, recent studies demonstrate promising performance of deep learning algorithms applied to conventionally prepared cervical cytology slides, with systems capable of binary normal versus abnormal classification as well as more granular diagnostic categorization. Artificial intelligence-assisted colposcopy and computer-vision approaches have also shown improved diagnostic concordance, workflow efficiency, and potential to expand screening capacity in resource-limited environments.

There has been much work done in the past several years surrounding the implementation of deep learning algorithms in regard to cervical cancer screening. The work in this field shows promise in enhancing diagnostic accuracy, streamlining diagnostic workflow, and decreasing turnaround times from specimen collection to rendering a diagnosis. However, there are still many technical, legal, and ethical questions that must be answered prior to widespread adoption of these algorithms for patient care.