Citations

Abstract Purpose: To explore the potential of the metaverse, a three-dimensional platform merging physical and virtual realities (VRs), in advancing the field of ophthalmology. This study examines its applications in areas such as VR-based surgical training, augmented reality (AR)-assisted surgeries, artificial intelligence (AI)-driven diagnostics, and teleophthalmology. This review builds upon previous literature by integrating recent evidence and technological advancements, offering a more comprehensive analysis of particular applications and barriers in ophthalmology. Methods: Academic literature published between 2019 and 2024 was identified through searches in the PubMed database and Google Scholar search engine using relevant keywords. This review focused on studies discussing the application of the metaverse in health care and ophthalmology, with particular attention to key trends, emerging uses, technological features, and associated challenges. Results: A total of 60 studies were deemed relevant for inclusion in this review. The infrastructure supporting the Metaverse comprises technologies such as the Internet of Things, digital twins, mirror worlds, nonfungible tokens, blockchain technology, and lifelogging. Together, these technologies create immersive environments that users can access through VR headsets, AR glasses, and handheld controllers. The metaverse presents promising opportunities in ophthalmology, particularly in enhancing clinical training through simulators such as the EyeSi™ surgical simulator, VRmagic EyeSi Slit Lamp, and the HelpMeSee™ VR simulator. In addition, it facilitates remote consultations and improves diagnostic and treatment options, exemplified by tools such as the Vivid Vision™ system for amblyopia, the Microsoft HoloLens™ for navigating complex anatomical structures, and AI-based platforms for the early detection of diseases such as glaucoma and diabetic retinopathy. Furthermore, the metaverse may enhance patient education by simulating the visual effects of ophthalmic conditions and visualizing postoperative outcomes. However, challenges, including financial limitations, data security concerns, and cultural acceptance, must be addressed. Conclusions: A gradual and context-specific integration of the metaverse is essential, particularly in underdeveloped regions. As the metaverse evolves, it holds significant potential to transform the field of ophthalmology by promoting global collaboration and innovation, complementing traditional practices, and ensuring equitable access to advanced healthcare solutions.

Abstract Background Adipose tissue influences blood pressure regulation through depot-specific endocrine, inflammatory, and exosome-dependent mechanisms. While obesity is a well-established risk factor for hypertension, emerging evidence suggests that distinct fat depots may contribute to vascular dysfunction independent of total adiposity. Methods: This review synthesizes post-2020 literature on 6 anatomically defined depots: visceral, perivascular, epicardial, bone marrow, brown, and beige adipose tissue, with an emphasis on their mechanistic roles, imaging biomarkers, and translational relevance. Results Alterations in adipose depot function can impair nitric oxide signaling, promote oxidative stress, and modulate sympathetic tone, even in the absence of obesity. Exosomal microRNAs from dysfunctional adipose tissue further disrupt vascular homeostasis. Although clinical tools for assessing depot dysfunction are limited, advances in imaging and molecular profiling may enable targeted therapeutic strategies. Conclusions Reframing adipose tissue as a modular regulator of blood pressure, rather than a passive consequence of obesity, may unlock new venues for precision management of hypertension.

Abstract Purpose Elective genomic sequencing (EGS) returns monogenic disease findings in multiple genes, including potentially novel variants, and may also provide participants with carrier status, pharmacogenomic and other health-related information. The PeopleSeq Study assessed participants’ motivations for and concerns about EGS and the associated clinical and psychosocial outcomes across diverse EGS providers. Methods We administered a shared questionnaire to participants who chose to undergo EGS via 18 academic, clinical, or commercial EGS platforms. Results We enrolled 1575 participants, of whom 1147 (72.8%) completed a questionnaire after receiving their EGS results. A majority (60.3%) of the participants who completed a post-result questionnaire self-reported receiving results they assessed as important, including negative findings, and 75.9% reported a form of health-related utility. Among a subset (19.4%) who shared their EGS reports, 16.6% (37 of n =223) received a monogenic finding and self-reported results deemed “important” were consistent with EGS reports. Most participants (74.1%) discussed their results with their family, but fewer discussed their results with a healthcare provider other than the site team (41.7%) or had one or more medical visits as a direct result of their EGS testing (23.1%). Participants expressed diverse motivations for EGS, with 91.4% expressing interest in their personal disease risk and 54% who expressed quasi-indication-based motivations related to family medical history. Individuals motivated by family history reported important results at a significantly higher rate. Conclusions Early adopters of EGS are motivated by general interest in their health as well as quasi-indication-based considerations such as family history. A majority of participants learned results they considered medically important, but a much smaller segment engaged healthcare providers with their results.

Abstract In 2024, the National Academies of Sciences, Engineering, and Medicine (NASEM) published a report entitled Advancing Research on Chronic Conditions in Women. A major purpose of this report was to summarize the current state of research into a series of 21 chronic conditions that affect women differently or that are more common in women. The American Society for Bone and Mineral Research (ASBMR) established a working group to evaluate the NASEM report’s implications for women’s musculoskeletal (MSK) health. This perspective summarizes key research gaps identified in the NASEM report, including the lack of sex-specific data, underrepresentation of women in clinical studies, and minimal integration of MSK health with other chronic conditions. The NASEM report underscores the need for more comprehensive study designs that consider hormonal, genetic, and social determinants of health across the lifespan. In addition to osteoporosis, in this perspective, the ASBMR working group highlights emerging but poorly understood evidence of detrimental effects on musculoskeletal biology in non-MSK, chronic diseases that were identified in the NASEM report to disproportionately affect women. To advance the field, we advocate for better diagnostics, increased investment, and stronger representation of women in research studies. Addressing these gaps is critical for improving prevention, care, and outcomes for chronic conditions that uniquely or disproportionately affect women.

Artificial intelligence (AI) is now applied across oncology, including imaging, pathology, therapy selection, toxicity assessment, and survival modeling, and large language models (LLMs) have been adopted particularly quickly. Reported physician use of AI roughly doubled between 2023 and 2026. The frameworks used to evaluate these tools have not advanced at the same rate. Many are available, but each was designed for a single stage of the AI lifecycle, and none directly answers the question most relevant to a treating clinician: whether a given tool is appropriate for the individual patient under care. We reviewed the frameworks in current use, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, and CREMLS, together with the oncology-specific ESMO EBAI and the newer LLM-specific guidance (TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP). Each addresses part of the evaluation problem, but none is sufficient on its own for oncology, where the standard of care changes rapidly, assays drift, biomarker-defined subgroups are small, and a model validated in one period may perform poorly in the next. LLMs introduce additional challenges, including sensitivity to prompting, undisclosed vendor updates, behavioral drift, and hallucination rates approaching 50% on clinical quality-assurance tasks. The predictive-model frameworks were not designed to capture these failure modes. The central argument of this review is that the frameworks the field requires already exist; what is missing is their mandatory adoption. We propose that journals and regulators move from recommending these frameworks to requiring their use, and we outline a cancer-aware evaluation pathway together with the specific responsibilities of authors, reviewers, journals, and regulators in implementing it.

Background: Colorectal neuroendocrine carcinomas (NECs), including small cell (SCNEC) and large cell (LCNEC) subtypes, are rare but aggressive malignancies with limited population-level data. Moreover, prior studies have limited stratification by histologic subtype and anatomic location, despite evidence of disease heterogeneity in colorectal malignancies. This study aimed to characterize the clinical characteristics, treatment patterns, and outcomes of colorectal NEC by histologic subtype and tumor location, and to compare them with neuroendocrine tumors (NETs) and adenocarcinomas (ACs). Methods: A retrospective cohort review was conducted utilizing the SEER database from 2000 to 2022 to identify patients with colorectal SCNEC, LCNEC, NET, and AC. Demographic, clinical, therapeutic and survival data were analyzed and compared using Kaplan–Meier methods, log-rank tests, and Cox proportional hazards regression. Results: A total of 790 SCNEC, 498 LCNEC, 31,200 NET, and 638,898 AC cases were identified. SCNEC and LCNEC were associated with advanced stage at diagnosis (66% and 55% stage IV, respectively) and inferior survival outcomes compared to NET and AC (median OS (mOS) of 7 and 8 months for SCNEC and LCNEC, respectively, p = 0.0002). Stratified by location, colonic SCNEC had the poorest survival (mOS 5 months), worse than rectal SCNEC (mOS 9 months); this pattern was not observed in LCNEC. Surgical resection was associated with improved OS in both NEC subtypes (HR range 0.25–0.56, all p < 0.012). Radiation therapy was associated with improved survival in NEC overall (SCNEC HR 0.58; LCNEC HR 0.71, both p < 0.05), with the observed benefit appearing greatest in rectal NEC. Demographic factors were associated with survival in NET and AC but had no significant impact in NEC. Conclusions: Colorectal SCNEC and LCNEC are highly aggressive malignancies with poor outcomes, with SCNEC demonstrating modestly inferior outcomes compared to LCNEC. This study demonstrates that both histologic subtype and anatomic site of disease are important determinants of prognosis and treatment response. This supports the concept that colorectal NEC represents a biologically heterogeneous disease, with colonic SCNEC showing the worst outcomes and rectal NEC associated with a potential enhanced response to radiation. These findings support the need for further prospective and molecular studies to better define treatment approaches and targeted therapies for these rare malignancies.

ABSTRACT Overview Systemic mastocytosis (SM) results from clonal proliferation of mast cells (MC) in extra‐cutaneous organs. Diagnosis The major criterion is the presence of multifocal MC clusters in the bone marrow and/or extracutaneous organs. Minor diagnostic criteria include elevated serum tryptase level, MC CD25/CD2/CD30 expression, and the presence of activating KIT mutations. Risk Stratification Establishing SM subtype as per the International Consensus Classification/World Health Organization classification systems is an important first step. Patients either have indolent/smoldering SM (ISM/SSM) or advanced SM, including aggressive SM (ASM), SM with associated myeloid neoplasm (SM‐AMN), or mast cell leukemia. Identification of poor‐risk mutations (i.e., ASXL1, RUNX1, SRSF2, NRAS ) further refines the risk stratification. Several risk models are available to help assign prognosis in SM patients. Management Treatment goals for ISM patients are primarily directed towards anaphylaxis prevention/symptom control/osteoporosis treatment. Patients with advanced SM frequently need MC cytoreductive therapy to reverse disease‐related organ dysfunction. Tyrosine kinase inhibitors (TKI) (midostaurin, avapritinib) have changed the treatment landscape in SM. While deep biochemical, histological, and molecular responses have been documented with avapritinib treatment, its efficacy as monotherapy against a multimutated AMN disease component in SM‐AMN patients remains unclear. Cladribine continues to have a role for MC debulking, whereas interferon‐α has a diminishing role in the TKI era. Treatment of SM‐AMN primarily targets the AMN component, particularly if an aggressive disease such as acute leukemia is present. Allogeneic stem cell transplant has a role in such patients. Imatinib has a therapeutic role only in the rare patient with an imatinib‐sensitive KIT mutation.

Abstract Spatial transcriptomics has been widely applied to study the spatial distribution of cell types, cell states, and specific gene expression in tissue samples. However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.