Everest University

Over the past decade, NGS technology has transformed the way to study and explore genomics, epigenomics, and transcriptomics. Improvements in throughput, and accuracy now allow researchers to move from bulk analyses to single-cell and high-resolution approaches, uncovering cellular heterogeneity and dynamic gene regulation that were previously unattainable. These advances, however, have amplified challenges associated with the broad range of applications feeding into sequencing technologies, including sample preparation, data management, integrative analysis, and interpretation. The articles in this special issue collectively address these challenges, offering solutions that advance both fundamental research and translational applications. Reliable sample preparation remains the foundation of any sequencing study. Bentz et al. present optimized high-throughput PacBio workflows for genomic DNA and Threose Nucleic Acid (TNA), demonstrating improvements in throughput, cost-efficiency, and analysis resolution. These methodological innovations illustrate how early-stage experimental improvements directly influence what can be measured and understood downstream. As data volume and complexity grow, effective management and accessibility become increasingly critical for the researchers. Large publicly available repositories, such as The Cancer Genome Atlas (TCGA), have become essential for integrative multi-omic studies, yet accurate data extraction and preprocessing remain critical to unlock their full potential. Baumann et al. address this challenge by introducing a tool that simplifies access to TCGA datasets, thus improving data retrieval and harmonization, and enabling reproducible analyses across genomics, epigenomics, and transcriptomics studies. The work highlights the importance of precise data handling to gain robust insights from multi-omic repositories. Computational strategies are central to interpreting high-dimensional datasets, particularly in single-cell studies where technical noise, sparsity, and batch effects complicate analysis. In this context, Elshiekh et al. review how deep learning and advanced computational models help in extracting meaningful biological signals from high-dimensional single-cell datasets, underlining the importance of integrating experimental and computational expertise to interpret complex omics data effectively. Complementing these strategies, Zhang et al. focus on transcriptional bursting models, demonstrating how modeling assumptions influence our understanding of gene expression variability and its relationship to phenotypic diversity and disease. Quantitative frameworks like these allow researchers to move from descriptive observations to mechanistic insights. Translating sequencing insights into clinical contexts remains a translational gap. Lin et al. investigate RNA N6-methyladenosine methylation and gene expression in colorectal cancer, using an integrative transcriptome-wide approach to uncover regulatory mechanisms directly relevant to precision oncology. Together with genomic and epigenomic analyses, RNA-focused studies provide a more comprehensive view of molecular regulation and help bridge molecular insight with the clinical application of high-resolution sequencing approaches. Similarly, single-cell technologies are also approaching clinical maturity, enabling detailed characterization of patient-specific cellular heterogeneity. The translation into clinical practice requires robust, adaptable pipelines that integrate high-resolution data with computational models and curated databases, providing a pathway toward biomarker discovery and personalized therapies. Taken together, the contributions to this Research Topic illustrate how sequencing has become the common integrative backbone of diverse high-throughput approaches, evolving from a data-generation tool into a framework for biological discovery and translational research. By advancing experimental protocols, improving data accessibility, and developing computational frameworks, these studies represent a step toward a more integrated and clinically relevant understanding of molecular biology.

For individuals with hepatitis B virus (HBV), hepatitis B surface antigen (HBsAg) loss is associated with substantially decreased risk of liver-related morbidity and mortality. In recent years, many determinants of HBsAg loss have been investigated in several studies involving persons with chronic HBV infection living with and without HIV. The purpose of this review is to summarize factors that could help predict HBsAg loss in persons with HIV (PWH). Rates of HBsAg loss can be higher in PWH with HBV compared to those without HIV, which has been partially attributed to immune reconstitution after starting antiretroviral therapy. In recent years, quantitative HBsAg (qHBsAg) levels were identified as the most important single serum marker predicting HBsAg loss. Other viral markers, such as hepatitis B core-related antigen, circulating HBV RNA, and immunological markers (i.e., quantitative hepatitis B core antibody, assessment of HBV-specific immune responses, peripheral blood mononuclear cells phenotypes), might also help predict HBsAg loss in PWH with HBV, particularly for certain sub-populations. Low qHBsAg before or fast qHBsAg declines after initiating potent anti-HBV therapy has been identified as the most reliable predicting serum marker. Other markers might be useful in certain sub-populations and clinical situations.

Abstract Developing new therapeutic strategies against hepatitis B virus (HBV) and hepatitis delta virus (HDV) is essential to cure these infections. Yet evaluation of host-targeting agents (HTAs) with in vitro and in vivo models remains challenging. Therefore, we assessed precision-cut liver slices (PCLS) as an HBV/HDV co-infection model to study virus-host interactions and HTAs. Our ex vivo PCLS infection protocol allows to establish HBV and HDV infection within the liver’s three-dimensional architecture. Treatment with two well-characterized HTAs produced distinct antiviral effects consistent with each mechanism: Bulevirtide blocked entry; Lonafarnib induced intracellular hepatitis delta antigen accumulation. The TLR8 agonist Selgantolimod showed anti-HBV activity not previously seen in cultured hepatocytes, indicating crosstalk between immune and infected cells within PCLS. These results provide the first characterization of PCLS as an ex vivo model of HBV/HDV co-infection and antiviral testing. Thus, PCLS could expedite pre-clinical HTA characterization against viral liver diseases while reducing animal experimentation.

PPD is a state of mood, behavior, or emotional disturbances caused by the interaction of biological, psychological, social, and behavioral factors that a woman experiences after childbirth. It is still a global public health issue. The Context-Aware Adaptive Meta-Learner (CAML) framework is a cutting-edge prediction model that uses multi-modal data and context detection to properly predict PPD risk. We collected 1,503 data on maternal health, network connections, and activity rhythms from a medical institution using e-survey questionnaires and wearable sensors. LSTM networks for temporal analysis, XGBoost for ranking accuracy, CNN for visual pattern identification, and Gradient Boosting Machines make up the CAML framework. Contextual modules teach social and family activity identification, and an Explainable Meta-Learner based on SHAP and LIME offers PPD risk variables. After k-fold cross-validation, transfer learning, and domain adaptation, this research had 98.7% predicted accuracy. It outperformed state-of-the-art approaches in CAML model predictability and interpretability. According to the study, multi-modal data integration, explainable AI, and context-wise analysis may change maternity care. The CAML framework gives healthcare practitioners clinical and assignable information to customize PPD prevention, mental health therapies, and ALM living experiences to new mothers' fundamentally vulnerable and heterogeneous social and clinical backgrounds.

Este trabalho relata a realização do V Fórum do Programa de Pós-Graduação em Desenvolvimento Territorial e Políticas Públicas, e do I Seminário Oeste Metropolitano do Rio de Janeiro, que ocorreram conjuntamente e em colaboração do Programa de Pós-Graduação em Geografia, ambos da Universidade Federal Rural do Rio de Janeiro. Foram abordadas temáticas sobre questões metropolitanas e seus desdobramentos no desenvolvimento dos territórios, principalmente os do Oeste Metropolitano. Realizado remotamente por videoconferências, o evento conjunto fomentou a integração entre discentes e docentes de ambos os programas e a comunidade acadêmica, e promoveu diálogos e intercâmbios acerca dos temas das pesquisas aderentes aos dois Programas, principalmente concernentes aos processos de produção e construção do espaço urbano e periurbano, incluindo questões relacionadas ao planejamento e às políticas públicas. De abrangência internacional, durante os cinco dias do evento foram realizadas conferências e mesas redondas com palestrantes nacionais e internacionais e apresentações temáticas, onde mestrandos e recém-mestres apresentaram suas pesquisas. Em todos os momentos, foi estimulada a interlocução com o público, enriquecendo o debate. O evento correspondeu aos objetivos propostos, aprofundando as reflexões sobre a conceituação de Oeste Metropolitano do Rio de Janeiro, pensado como categoria analítica, bem como sobre o desenvolvimento territorial e local e suas interfaces e rebatimentos regionais.

This study investigated how widows' fears of death are affected by their age at widowhood and suddenness of death. Researchers administered the Collett-Lester Fear of Death Scale to 423 participants, categorized according to age (50 and 51) and suddenness of death (expected, unexpected, and non-widowed). No significant differences in fears of the dying process were found among groups. Fears of death of others and death of self were influenced by a significant effect of age: younger women expressed greater fears than older women. A significant interaction between age and suddenness of death was found regarding fear of death of others. Results are discussed and implications for research and practice are suggested.