Citations

Background/Objectives: Characterizing spinal cord multiple sclerosis (MS) lesions in MRI is critical for diagnosis, monitoring, and treatment evaluation. However, current automated approaches for lesion detection and segmentation are typically designed for specific MRI contrasts or acquisition sites, limiting their generalizability in real-world clinical settings where imaging protocols vary widely. This work proposes a robust multi-site, multi-contrast segmentation framework for spinal cord lesions. Methods: The segmentation model was trained and evaluated on a large-scale dataset comprising 4428 annotated images from 1849 persons with MS across 23 imaging centers, encompassing six MRI contrasts (T1w, T2w, T2*w, PSIR, STIR, and UNIT1) acquired at 1.5 tesla (T), 3 T, and 7 T. Results: Likert-type assessment performed by neuroradiologist ratings demonstrated superior generalization of the model compared to existing contrast-specific pipelines ( p < 0.01). Additional experiments evaluated robustness across spinal levels, acquisition resolutions, binarization thresholds, and quantitative evaluation on external labeled datasets. Conclusions: The proposed model can achieve accurate and reliable spinal cord MS lesion segmentation across heterogeneous MRI data, addressing a key barrier to clinical translation. The model is available in the Spinal Cord Toolbox v7.2 and higher. Code repository: https://github.com/ivadomed/seg-sc-ms-lesion-multicontrast

A new systems approach to diseased states and wellness result in a new branch in the healthcare services, namely, Personalized and Precision Medicine (PPM). To achieve the implementation of PPM concept, it is necessary to create a fundamentally new strategy based upon the recogni-tion of biomarkers and thus the targets to secure the grand future of drug design and drug dis-covery.Each decision-maker values the impact of their decision to use PPM on their own budget and well-being, which may not necessarily be optimal for society as a whole. It would be extremely useful to integrate data harvesting from different databanks for applications to provide more tai-lored measures for the patients resulting in improved patient outcomes, reduced adverse events, and more cost-effective use of the latest health care resources including diagnostic (companion ones or theranostics), preventive and therapeutic (targeted molecular and cellular) etc.Meanwhile, along with the impact of genomics and bioinformatics, pathology is the central specialty of PPM-related resources. Coupled with IT, the upgraded tools are ever more efficient and robust within clinical cancer settings. The latest algorithms may be able to identify and vali-date patterns which humans are not capable of quantifying easily, such as slight differences in textures or morphologies. In this sense, the impact of pathology allows a modular approach, as its various aspects are under development in several areas of PPM.Among the above-mentioned medical specialists involved in PPM, the pathologists play an im-portant and key role in the implementation and development of PPM-driven resources that are in the center of decision of many therapeutic choices. Recent advances in systems biology have tremendously affected the practice of precision pathology, gradually transforming it from a morphology-based into a precise molecular-based discipline. The enormous development of precision pathology research (integrating molecular OMICS and digital approaches) has raised great expectations concerning its impact on PPM aiming to custom-ize medical practice with a focus on the individual, based on the use of molecular tests, identifi-cation of genomic biomarkers, and development of targeted drugs.The precision pathology process introduces a new paradigm in pathology by leveraging digital imaging and IT technologies, and provides significant advancements in accuracy, efficiency, and collaboration. Among the latest innovations the precision pathology supported by digital tools, would dictate the implementation of high-resolution imaging, image analysis tools and integra-tion with molecular OMICS data. Integration of the approaches and resources will provide a true challenge for the future, requiring collaboration between oncologists, pathologists, biodesigners and bioengineers and remaining a challenge to precision cancer practice and profiled bioindustry.The latter requires precision pathologists highly trained in pre-analytic processes, tumor area mi-crodissection for tumor cell enrichment, methodology analysis and results. The in-depth study of molecular alterations in patients allows optimizing molecular diagnosis and selecting candidates for receive novel treatments against specific molecular targets. These patients would benefit from multidisciplinary approach and learning. In this sense, molecular diagnostics has a long tradition in pathology, especially in clinical one, where various OMICS-analyses of cancers are incorpo-rated into diagnostic and decision-making algorithms to secure a way where the pathologists con-tinue to play an essential role in developing and implementing molecular profiling tests in practice and communicate the results and their relevance with cancer practitioners.Cancer pathology is the central specialty of personalized and precision oncology (PPO). Meanwhile, the combination of comprehensive biobanking and the next wave of theranostic pathology technologies provides a natural, externally visible infrastructure that now allows pa-thology as a discipline – to engage directly with the biotechnology and pharma sector. We’re at an exciting junction in precision cancer pathology’s growth as a medical specialty, and pathology-driven biobanking is becoming both central to our core expertise and, even more importantly, a powerful enabler for many of the most promising growth areas of our discipline: PPM healthcare, clinical trials and drug development, theranostics, and functional assessment and monitoring of disease. In the context of these changes and challenges, the precision cancer pathology can play a fundamental role in both PPO-guided clinical practice and biodesign-driven cancer research.

ABSTRACT Background Chronic obstructive pulmonary disease (COPD) is associated with musculoskeletal comorbidities, including cachexia. Weight loss (WL) is the major criterion for cachexia and increases risk for mortality in COPD. Risk factors for WL in COPD are incompletely understood. We performed this whole genome sequencing (WGS) analysis to identify genetic risk variants for WL in COPD. Methods We studied 16 972 participants from the Trans‐Omics for Precision Medicine (TOPMed) Initiative and All of Us Research Program. COPD was diagnosed using spirometry in TOPMed, while diagnosis codes were used in All of Us. WL was defined as WL ≥ 5% or a final body mass index (BMI) < 20 kg/m 2 . WGS data came from white blood cells in all cohorts. Single‐variant testing was conducted on both race‐ and study‐stratified cohorts and in a cosmopolitan, ancestry‐independent manner using GENESIS in TOPMed and SAIGE in All of Us. SAIGE‐GENE+ gene‐based analyses were performed on race‐stratified and cosmopolitan cohorts. Single variant meta‐analyses were conducted using METAL within (B/AA and NHW analyses of Black/African–American and non‐Hispanic white participants, respectively) and across racial groups (COSMO). Rare variant gene‐based results were combined using Fisher's method. Transcriptomic effects were predicted using MetaXcan. We used the GWAS Catalogue to analyse for colocalization with other related traits. Results Two single variants were associated with WL in COPD among All of Us participants: one intronic variant in HCN1 in Black/African–American participants (chr5:45271359:TACACAC:T, odds ratio with 95% confidence interval (OR (CI 95 )) = 2.43(1.78–3.31), p = 1.95 × 10 −8 ) and one intergenic variant between PPP4R2 and PDZRN3 in the cosmopolitan and NHW cohorts (chr3:73345901:A:G, OR (CI 95 ) = 0.21(0.12–0.35), p = 8.84 × 10 −9 in cosmopolitan and OR (CI 95 ) = 0.18(0.10–0.33), p = 9.44 × 10 −9 in NHW). Single‐variant meta‐analysis identified two loci associated with WL in COPD: five variants within DRAIC in the B/AA meta‐analysis (lead variant chr15:69571341:A:G, OR (CI 95 ) = 1.37(1.23–1.51), p = 1.29 × 10 −9 ) and two intronic variants within RFX3 in the cosmopolitan meta‐analysis (lead variant chr9:3390983:T:C, OR (CI 95 ) = 1.50(1.31–1.73), p = 1.06 × 10 −8 ). Rare variants within RNU6‐565P (NHW analysis in All of Us; p = 2.83 × 10 −7 ) and LOC339298 (B/AA analysis in All of Us, p = 1.85 × 10 −6 ) were associated with WL in COPD. The RNU‐565P signal remained significant after combination with TOPMed results (p combined = 1.23 × 10 −6 ). MetaXcan predicted differential expression of LNC00959 in visceral adipose tissue ( p = 1.16 × 10 −6 in COSMO analysis). Colocalization analyses identified genomic associations between BMI and variants in or near DRAIC , RFX3 , PDZRN3 , and LINC00959 . Conclusions In the first WGS analysis of WL in COPD, we have identified seven novel loci. Further characterization of these loci will validate our findings and improve our understanding of the molecular pathophysiology of this condition.

Purpose To project the future utilization of open and arthroscopic rotator cuff repair (RCR) in the Medicare population through 2050, incorporating post‐COVID recovery data to provide insight into evolving surgical preferences, economic pressures, and implications for health care delivery. Methods We analyzed 2003‐2024 data from the Centers for Medicare and Medicaid Services Medicare Part‐B National Summary, which captures all Medicare beneficiaries undergoing open or arthroscopic RCR as identified by Current Procedural Terminology codes. Projections were based on 2003‐2019 and 2023‐2024 data, reflecting post‐COVID‐19 recovery and avoiding distortions from 2020 to 2022 pandemic‐related disruptions. Statistical models evaluated included log‐linear, Poisson, negative binomial, and autoregressive integrated moving average. Autoregressive integrated moving average was selected based on the lowest normalized root mean square error, reflecting superior predictive accuracy and its ability to capture time‐dependent trends, and was used to forecast RCR volumes through 2050. Results In 2024, there were 23,154 open and 188,700 arthroscopic RCR procedures, which served as the baseline for modeling. Open RCR is projected to decline on average by 5.91% annually, while arthroscopic RCR is projected to grow by 2.88% annually. By 2050, arthroscopic RCR volume is expected to reach 394,434 procedures (95% forecast interval: 363,424‐425,321), whereas open RCR is expected to decline to 4748 procedures (95% forecast interval: 1243‐43,747). Conclusions Autoregressive integrated moving average modeling projects that by 2050 arthroscopic RCR utilization will increase by 2‐fold and open RCR will decline to one‐fifth compared with 2024 baseline volumes. Level of Evidence Level III, retrospective cohort study.

ABSTRACT Immediate early genes (IEGs) encode master transcription factors, including EGR1 , FOS , JUN , and MYC genes that drive robust transcription in the G1 phase of cell cycle. Our previous studies indicate that topoisomerase IIβ (TOP2B) critically regulates IEG transcription and that the activities of TOP2B are dynamically controlled through post-translational modifications by BRCA1-BARD1 and ERKs. Here, we show that two E2 enzymes, UBCH5b and UBC13/MMS2, differentiate the effects and functions of TOP2B ubiquitination by BRCA1-BARD1. Comprehensive transcriptomics, proteomics, and biochemical and molecular cellular analyses revealed a close relationship between BARD1 and key mitogen-activated protein kinase pathway genes and identified activated ERK2 as a novel kinase that phosphorylates BARD1 at S391, a previously reported mitotic phosphorylation site, whose genetic mutation has been linked to tumorigenesis. Mechanistically, the catalytic activity of ERK2 stimulates TOP2B ubiquitination mediated by BRCA1-BARD1 in complex with UBCH5b and UBC13/MMS2, which controls the binding and function of TOP2B and BARD1 for transcriptional activation at representative IEGs. Taken together, our data propose that there is a functional regulatory circuit involving TOP2B, BARD1, and ERK2, three key transcriptional activators for IEG transcription, in which the gene association and catalytic activity of TOP2B are regulated through E2-differentiated ubiquitination by BRCA1-BARD1 and the phosphorylation of BARD1 by ERK2 for productive transcription.

ABSTRACT Background Metabolic dysfunction‐associated steatotic liver disease (MASLD) exhibits marked heterogeneity in fibrosis progression and liver‐related outcomes. Liver biopsy is not feasible for longitudinal risk stratification at scale, creating a need for validated non‐invasive biomarkers, particularly imaging biomarkers, that can predict clinically meaningful disease progression and liver‐related outcomes. Aims To describe the design and rationale of the GOLDMINE study, established to determine whether non‐invasive imaging biomarkers predict MASLD progression and liver‐related clinical outcomes. Methods GOLDMINE is an investigator‐initiated, multi‐centre, international longitudinal cohort enrolling up to 1000 adults with either biopsy‐proven MASLD or MASLD cirrhosis across the full fibrosis spectrum. Participants are recruited from 15 sites in the US and 4 international sites (Japan, Singapore and France). At baseline, participants undergo clinical phenotyping, vibration‐controlled transient elastography, and advanced magnetic resonance imaging (MRI), including proton‐density‐fat‐fraction and magnetic resonance elastography (MRE). MRI (and biospecimen banking) is repeated at 2‐year intervals (years 2 and 4), with annual follow‐up visits for up to 10 years. Baseline liver histology is centrally processed, digitized and reviewed by a single expert hepatopathologist; all MRI/MREs are centrally interpreted. Results The prespecified clinical outcomes include progression to cirrhosis, clinically significant portal hypertension, major adverse liver‐related outcomes (ascites, hepatic encephalopathy, portal hypertensive bleeding, liver transplantation/qualification), hepatocellular carcinoma, major adverse cardiovascular events, and all‐cause mortality, with independent central adjudication of all events. Conclusions GOLDMINE establishes a rigorously phenotyped MASLD cohort integrating centralized histology, advanced MRI‐based biomarkers, longitudinal biobanking, and adjudicated outcomes, providing a platform to validate imaging and blood‐based prognostic biomarkers in MASLD.