Wilms tumor is the most common kidney tumor in children aged 0-14 years. MicroRNAs are small, noncoding RNAs linked to the development of malignant tumors. Several studies have shown the association between single nucleotide polymorphism in miR-27a and cancer risk. This study aimed to explore the potential impact of the miR-27a rs895819 T>C polymorphism on Wilms tumor susceptibility.

The rs895819 T>C polymorphism was genotyped using the TaqMan method in 145 patients with Wilms tumors and 531 controls. Logistic regression models were used to assess the association between this polymorphism and Wilms tumor risk. A stratified analysis was also performed based on age, sex, and clinical stage.

The rs895819 T>C polymorphism showed genotypic distribution consistent with Hardy-Weinberg equilibrium (P = 0.749). The differences were not statistically significant. The miR-27a rs895819 T>C polymorphism was not significantly associated with Wilms tumor susceptibility, and the stratified analysis did not yield any significant differences.

Our study provides evidence of a lack of association between the miR-27a rs895819 T>C polymorphism and Wilms tumor susceptibility. Further validation through larger sample sizes and additional genetic polymorphisms is warranted.

Incomplete immune reconstitution is characterized by chronic immune activation and systemic inflammation, which are not fully reversed by antiretroviral therapy. Dihydroartemisinin (DHA) has demonstrated anti-inflammatory and immunosuppressive properties, which may benefit individuals with incomplete immune reconstitution. This study aimed to investigate the biological mechanisms underlying incomplete immune reconstitution and evaluate the therapeutic potential of DHA in modulating immune activation in immunological non-responders (INRs). This study aimed to investigate the biological mechanisms underlying incomplete immune reconstitution and evaluate the therapeutic potential of DHA in modulating immune activation in immunological non-responders (INRs).

RNA sequencing data (GSE106792) was retrieved from the Gene Expression Omnibus database. R software and Bioconductor packages were used to identify differentially expressed genes (DEGs) among INRs, immune responders (IRs), and healthy controls (HCs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, along with protein-protein interaction (PPI) network construction, were performed. Potential DHA-binding proteins were predicted using the STITCH server and molecular docking studies. Validation experiments were conducted on peripheral blood mononuclear cells from 18 INRs. Cells were treated with varying concentrations of DHA, and CD4+ and CD8+ T cell activation markers (CD38 and HLA-DR) were measured via flow cytometry.

Enrichment and PPI network analysis identified 119, 56, and 189 DEGs in the INR vs. HC, INR vs. IR, and IR vs. HC comparisons, respectively. Enrichment and PPI analyses showed that DEGs were mainly involved in immune response pathways. DHA was predicted to interact with multiple target proteins, indicating anti-inflammatory effects. In vitro, DHA significantly reduced the frequency of CD38− HLA-DR+ CD4+ T cells and CD38+ HLA-DR+ CD8+ T cells at 1,000 µM and 500 µM compared to the control.

This study provides insights into the biological mechanisms underlying incomplete immune reconstitution and supports DHA’s potential as a therapeutic agent. DHA effectively inhibits T cell activation in INRs, presenting a novel and promising treatment strategy.

The number of molecular abnormalities identified in hematopoietic and lymphocytic neoplasms has grown exponentially over the past decades. Patients with genetic biomarker-matched targeted therapies have experienced significantly improved survival rates. Modern molecular laboratories, equipped with advanced technologies such as next-generation sequencing, can simultaneously test hundreds of genes and thousands of hotspots in a single run with multiple samples analyzed side by side. Bioinformatics tools provide seamless, evidence-based information to determine whether the detected mutations are benign or pathogenic, somatic or germline, druggable or diagnostic. This review is divided into five sections, each aiming to provide a comprehensive overview of the genetic landscape of myeloid and lymphocytic neoplasms. It highlights the challenges and proposes potential solutions to facilitate interpretation and maximize the clinical utility of molecular profiling results.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by distinct histological subtypes and a poor prognosis. Among these, the micropapillary pattern, typically observed focally, has been associated with worse outcomes in various cancers. This study aimed to evaluate the prognostic significance of the micropapillary pattern in PDAC, focusing on its percentage within the tumor and its impact on overall survival.

A retrospective analysis was conducted on 71 patients with surgically resected PDAC. Micropapillary patterns were categorized based on their percentage within the tumor (≥20%) and compared to non-micropapillary cases. Demographic, clinical, and histological data, including tumor nodule metastasis stage, tumor grade, peripancreatic fat tissue invasion, and resection margin status, were analyzed. Survival data were assessed using Kaplan-Meier and Cox proportional hazards models. A p-value < 0.05 was considered statistically significant.

The cohort included 28 female and 43 male patients, with a mean age of 63.25 years. Of the 71 cases, 23.9% (n = 17) exhibited a micropapillary pattern. The median overall survival for the micropapillary group was eight months, compared to 18 months for the non-micropapillary group (p = 0.017). Multivariate analysis revealed that the micropapillary group had an increased risk of mortality (hazard ratio = 1.892, p = 0.042), independent of tumor nodule metastasis stage.

Our findings indicate that the micropapillary pattern, even when present in as little as 20% of the tumor, serves as an independent prognostic factor for decreased survival in PDAC. Incorporating the percentage of the micropapillary pattern into pathology reports could provide valuable insights into the tumor’s biological behavior, potentially enhancing patient management strategies.

Oral potentially malignant disorders (OPMDs), characterized by a wide variety of types and diverse clinical manifestations, have always been difficult to diagnose and differentiate. All of them carry a risk of malignant transformation. In addition to pathological examination, which remains the gold standard, various auxiliary diagnostic tests are used in clinical practice. Deep learning, a branch of artificial intelligence, has been applied to medical image analysis. Among deep learning techniques, convolutional neural networks are commonly used for image segmentation, detection, classification, and computer-aided diagnosis. We reviewed several image analysis methods based on deep learning neural networks for the diagnosis and prognosis of OPMDs, including photographic images, autofluorescence images, exfoliative cytology images, histopathological images, and optical coherence tomography images. Additionally, we assessed the current limitations and challenges in applying deep learning to the diagnosis of OPMDs.

Human papillomavirus (HPV) infection is the primary cause of cervical, anogenital, and oropharyngeal cancers in the United States. These cancers are preventable through HPV vaccination. Research is critically needed to identify effective strategies for promoting HPV vaccination among high-risk groups. This study develops a risk prediction model to identify patients who are unlikely to complete HPV vaccination, with the goal of using the model to direct resources and increase vaccination rates.

We assessed vaccination status along with patient, provider, and clinic characteristics that predict vaccination completion. We then developed a predictive model to assess the likelihood of completing HPV vaccination, which can be used to target interventions based on patient needs. We used a retrospective cohort from a large integrated delivery system in Oregon. Using logistic regression with data available in the electronic health record, we created a risk model to determine the likelihood of vaccination completion among patients aged 11–17 years.

In a cohort of 61,788 patients, 40,570 (65.7%) had received at least one dose of the HPV vaccine. The full model included 17 demographic, clinical, provider, and community characteristics, achieving a bootstrap-corrected C-statistic of 0.67 with adequate calibration. The reduced model, which retained five demographic and clinical characteristics (age, language, race, ethnicity, and prior vaccinations), had a bootstrap-corrected C-statistic of 0.65 and adequate calibration.

Our findings suggest that a risk prediction model can guide the implementation of targeted interventions and the intensity of those interventions based on the likelihood of vaccination completion.

Diagnosing and treating cytopenic myelofibrosis in children is challenging due to the wide spectrum of clinical and pathological features, underlying etiologies, and variable therapeutic responses. In this review, we summarize the related literature and present our diagnostic algorithm to differentiate pediatric myelofibrosis and guide therapy. In brief, primary myelofibrosis is extremely rare in children, while myelofibrosis secondary to non-neoplastic or neoplastic disorders should be thoroughly ruled out in ambiguous cases. Moreover, it is reasonable to closely follow up patients and repeat bone marrow biopsy before reaching a definitive diagnosis.