Citations

ABSTRACT Background Gynecological cancers are among the most common cancers in women that affect female reproductive organs. The most common gynecological cancers are ovarian, cervical, uterine/endometrial, vaginal, and vulvar cancer. Women's reproductive organs have a dynamic and relative microbial balance. The disruption in the balance of the microbiome could result in numerous gynecological diseases, as well as, gynecological cancers. In this study, we aimed to review new findings on the role of different bacterial infections in various types of gynecological cancers. Recent Findings The role of bacterial infection, as an external factor, has been established in several cancers. However, the ways in which bacteria can promote the development of cancer are not fully understood. It seems that inflammation induced by bacterial infections could promote carcinogenesis. In addition, bacterial toxins and effector proteins play important roles in the progression of cancer. In this review, we attempt to present the different bacterial infections, which have been linked to gynecological cancers development. According to different researches, Chlamydia , Mycoplasma , and Bacteroides spp. are the most common bacterial infections associated with gynecological cancers. Conclusion Evaluation of microbiome in reproductive organs of the patients with gynecological cancer and studies on prevention and control of the infections in the patients could be useful in verification of pathogenesis of the diseases and also founding suitable therapeutic interventions.

ABSTRACT Objectives Investigate synchronous lower airway findings during bronchoscopy in idiopathic subglottic stenosis (iSGS). Methods Retrospective analysis of bronchoscopic images from adult iSGS patients (January 2018–June 2021). Images were reviewed for pits, depressions, nodules, and striations distal to the cricotracheal junction. Results Forty‐eight female patients; mean age 56 years. Comorbidities: gastroesophageal reflux 34%, hypertension 34%, diabetes mellitus 25%. All patients had pits and depressions; striations in 48/48, with both longitudinal and transverse in 32/48 (66.7%), transverse only in 13/48 (27.1%), and longitudinal only in 3/48 (6.3%). Spirometry available in 32; 60% showed normal or mild–moderate fixed obstruction. Four patients had FEV 1 /FVC < 0.7; two had asthma, one COPD, and one no obstructive diagnosis; none were smokers. Conclusion Bronchial mucosal abnormalities are prevalent in iSGS and may indicate distal airway involvement. Routine flexible bronchoscopy should be considered during evaluation and surveillance. Level of Evidence 4.

Abstract Background and Aims Acute myocarditis (AM) is a T cell-mediated myocardial disease with clinical manifestations ranging from mild chest pain to cardiogenic shock. Reliable biomarkers to stratify patients and guide therapy are currently lacking. In particular, the extent of the dysregulation of inflammatory pathways, and the impact on myocardial dysfunction, remain elusive. Methods Serum analyses were performed in prospectively recruited AM patients (n = 103) from two independent cohorts. Multimodal data integration combining profiling of cytokine and chemokine dysregulation with clinical biomarkers was used to define clinical phenotypes with distinct inflammatory signatures. Machine-learning and regression models were applied to determine biomarkers that indicate clinical severity. Results Immuno-proteomic profiling revealed conserved inflammatory patterns across AM cohorts, dominated by T cell-related cytokines and chemokines. In addition, AM patients showed dysregulation of fibroblast-derived cytokines, including hepatocyte growth factor (HGF), bone morphogenic protein 4 (BMP4) and the BMP4 inhibitors Gremlin-1 (GREM1) and Gremlin-2 (GREM2). Data integration and unsupervised clustering revealed two immuno-clinical phenotypes, linking T cell activation and fibroblast dysregulation to disease severity. Machine learning-based analysis identified CXCL10, GREM2 and LVEF as critical parameters for stratifying disease severity. Conclusions These findings highlight a systemic T cell activation signature as diagnostic hallmark of AM. In addition, dysregulation of fibroblast-derived tissue cytokines serves as an indicator for distinct immuno-clinical phenotypes in myocardial inflammatory disease. Thus, the clinically relevant link between T cell-driven immune activation, myocardial inflammation and fibroblast-driven remodelling provides a versatile set of parameters to identify severe manifestations of AM. Graphical Abstract Key Question: Are serological immune signatures linked to clinical severity in acute myocarditis and do they enable patient stratification? Key Findings: T cell- and fibroblast associated proteomic signatures indicate disease severity in acute myocarditis. Novel immuno-clinical phenotypes stratify patients according to distinct immune responses and clinical manifestations. CXCL10, GREM2 and LVEF are the most important parameters to identify immuno-clinical phenotypes. Take Home Message: CXCL10, GREM2 and LVEF emerge as key determinants for a severe immuno-clinical phenotype in acute myocarditis, highlighting the role of T cell-fibroblast interaction in the disease process and linking T cell activation, fibroblastic tissue remodelling and impaired cardiac function.

Abstract Scientific foundation models learn high-dimensional representations from diverse data modalities, yet what they encode and how to extract that knowledge remain open questions. Here we show that probing the internal representations of Evo 2, a 7-billion-parameter genomic foundation model, enables accurate and interpretable genetic variant effect prediction. We introduce a covariance-based probe that captures second-order structure from Evo 2 sequence embeddings to predict variant pathogenicity across variant types and functional consequences, matching or exceeding specialized predictors within their domains. To ground these predictions in known biological mechanisms, we train a complementary panel of probes on existing annotations to detect which genomic properties are disrupted by a variant. This categorized evidence is then integrated with each variant’s genomic context through a language model to generate variant-specific mechanistic hypotheses. Our pathogenicity predictions correlate with experimental measures of variant function, clinical penetrance, and biobank disease associations while the mechanistic hypotheses are consistent with expert reviews, known mechanism classes, and downstream molecular readouts. We release pathogenicity scores, disruption profiles, and contextualized interpretations for 4.2 million variants from the ClinVar database as an open resource through the Evo Variant Effect Explorer (EVEE). More broadly, this structured probing approach offers a general framework for interrogating foundation models across scientific disciplines and grounding their outputs in existing domain concepts.

Abstract Background Approximately 12 000 adolescents and young adults (ages 15-39, AYAs) are diagnosed with a primary central nervous system (CNS) tumor each year. DNA methylation profiling has transformed CNS tumor classification by refining diagnostic accuracy and identifying biologically distinct subtypes, but its application has not been systematically evaluated in the AYA population. In this study, we examined the spectrum of CNS tumor types across age groups, with a focus on the AYA population. Methods We assembled a large dataset of CNS tumor samples with age annotations and methylation profiles matching with high confidence to a CNS tumor type using the NCI/Bethesda classifier. Prevalence of tumor type, methylation class and DNA copy number aberrations were compared across age strata (pediatric, AYA, and adult). Results The cohort of 21 712 CNS tumors included 5351 tumors from AYAs (25%), which showed a mixed pattern, with tumors typical of childhood (eg, medulloblastoma) as well as those common in older adults (eg, glioblastoma). Several tumor types were specifically enriched in the AYA, including IDH-mutant astrocytoma, pleomorphic xanthoastrocytoma, posterior fossa group B ependymoma, and diffuse hemispheric glioma, H3 G34-mutant, among others. Distinct methylation subclasses of multiple tumor types were observed in the AYA, and patterns of genomic aberrations showed age-specific distributions. Conclusion Large-scale methylation profiling revealed unique classification patterns of CNS tumors in AYAs, with specific tumor types, subclasses, and genomic alterations enriched in this population. These data may serve as a valuable reference resource for better understanding the spectrum of CNS tumors affecting AYA patients.