Citations

Abstract Nociplastic pain is defined by altered nociceptive processing in the absence of clear peripheral damage or somatosensory lesions. The Fibromyalgia Index (FMI), derived from the 2016 diagnostic criteria, is increasingly used as a marker of nociplastic pain severity in clinical studies, yet its neurobiological validity remains untested at scale. Using multimodal neuroimaging data from over 40 000 participants in UK Biobank, we examined whether FMI scores were associated with altered functional and structural connectivity within the descending pain modulatory system (DPMS), a brain network involved in endogenous pain control and implicated in nociplastic pain conditions. Functional connectivity was assessed using resting-state functional MRI (rfMRI), and structural connectivity using diffusion-weighted MRI (dMRI) tractography. Connectivity was quantified between seven DPMS regions: periaqueductal grey (PAG), rostral ventromedial medulla (RVM), hypothalamus, amygdala, rostral and subgenual anterior cingulate cortex (rACC, sgACC), and dorsolateral prefrontal cortex (dlPFC). Multi-group structural equation models tested associations between FMI scores and connectivity, stratified by chronic pain status. Mediation models evaluated which aspects of nociplastic pain accounted for the observed associations: widespread pain and SPACE symptoms (sleep disturbance, pain, affect, cognitive problems, and low energy). To assess specificity, we repeated analyses using the Douleur Neuropathique 4 (DN4), a measure of neuropathic pain, and average pain intensity as comparison outcomes. In 22 139 individuals with chronic pain (58% female; mean age 64.8, standard deviation 7.59), FMI scores were associated with altered structural connectivity between the PAG and amygdala [β = 0.023, 95% confidence interval (CI): 0.0087 to 0.039; Pcorr = 0.0125] and between the PAG and hypothalamus (β = −0.029, 95% CI: −0.043 to −0.015; Pcorr = 0.0013). Functional connectivity in the same circuits showed smaller effects. These associations were not observed in individuals without chronic pain. Mediation analyses revealed that PAG-amygdala and PAG-hypothalamus connectivity were partially explained by fatigue, sleep duration, and widespread pain. DPMS connectivity was not significantly associated with neuropathic pain or average pain intensity. These findings suggest that FMI scores reflect biologically meaningful changes in brain connectivity, particularly in subcortical DPMS circuits implicated in affective and homeostatic dimensions of pain. Structural connectivity was more strongly associated with FMI than functional measures, possibly reflecting cumulative effects of chronic pain on white matter architecture. The absence of similar associations for other pain outcomes supports the specificity of FMI as a marker of nociplastic pain severity. These results provide a neurobiological basis for the FMI and support its use in population research and biomarker development for nociplastic pain.

Abstract The link between regional tau load and clinical manifestation of Alzheimer’s disease (AD) highlights the importance of characterizing spatial tau distribution across disease variants. In typical (memory-predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicentres. However, given the limited spatial heterogeneity of tau in typical AD, atypical (non-amnestic-predominant) AD variants with distinct tau patterns provide a key opportunity to investigate the universality of connectivity as a scaffold for tau progression. In this large-scale, multicentre study across 14 international sites, we included cross-sectional tau-PET data from 320 individuals with atypical AD (n = 139 posterior cortical atrophy/PCA-AD; n = 103 logopenic variant primary progressive aphasia/lvPPA-AD; n = 35 behavioural variant AD/bvAD; n = 43 corticobasal syndrome/CBS-AD), with a subset of individuals (n = 78) having longitudinal tau-PET data. Additionally, as an independent sample, we included regional post-mortem tau stainings from 93 atypical AD patients from two sites (n = 19 PCA-AD, n = 32 lvPPA-AD, n = 23 bvAD, n = 19 CBS-AD). Gaussian mixture modelling was used to harmonize different tau-PET tracers by transforming tau-PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether brain regions with stronger resting-state functional MRI-based functional connectivity, derived from healthy elderly controls in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), showed greater covariance in cross-sectional and longitudinal tau-PET and post-mortem tau pathology. Furthermore, we examined whether functional connectivity of tau-PET epicentres (i.e. the top 5% of regions with the highest baseline tau load) and tau-PET accumulation epicentres (i.e. the top 5% of regions with the highest tau accumulation rates) was associated with cross-sectional and longitudinal tau patterns. Our findings show that tau-PET epicentres aligned with clinical variants, e.g. a visual network predominant pattern in PCA-AD (‘visual AD’) and left-hemispheric temporal predominance, particularly within the language network, in lvPPA-AD (‘language AD’). Moreover, more strongly functionally connected regions showed correlated concurrent tau-PET levels (confirmed with post-mortem data) and tau-PET accumulation rates. The functional connectivity profile of tau-PET epicentres and accumulation epicentres corresponded to tau-PET progression patterns, with higher tau-PET levels and accumulation rates in functionally close regions, and lower tau-PET levels and accumulation rates in functionally distant regions. Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicentres, across all AD clinical variants. Since tau proteinopathy is a major driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant-specific end points in clinical trials.

Abstract Dementia with Lewy bodies (DLB), a progressive neurodegenerative disease with heterogeneous clinical presentations, greatly impacts patients, caregivers, and society. Despite its frequency, diagnosing and treating DLB remains challenging. Advances in in vivo biomarker assays reflecting underlying pathology are improving disease identification, diagnostic accuracy, and therapeutic development for biologically targeted, disease‐modifying agents. Consequently, definitions of Alzheimer's disease and Parkinson's disease (PD) have shifted to focus on pathological changes occurring before clinical features, with proposed frameworks for detecting pathological amyloid and tau, neurodegeneration, and other markers (National Institute on Aging‐Alzheimer's Association) and alpha‐synucleinopathy and dopaminergic degeneration (Neuronal α‐synuclein Disease Integrated Staging System, SynNeurGe). The biological frameworks, particularly those related to alpha‐synuclein (α‐synuclein), have sparked debate about unifying DLB and PD under a single pathobiologic disease. This paper discusses the implications of these biological frameworks for the DLB community, addressing topics regarding multiple pathologies and neurochemical systems, clinical heterogeneity, and functional impairment, and exploring the potential impact on clinical trials and care. Highlights DLB is a progressive neurodegenerative disease with varied clinical presentations. Diagnosing and treating DLB remains challenging despite its frequency. Biological frameworks are reshaping Alzheimer's and Parkinson's definitions. In vivo biomarkers are improving DLB identification and diagnostic accuracy. Debate exists regarding unifying DLB and Parkinson's under one pathobiology.

Purpose To develop an expert consensus statement on the diagnosis, management, and rehabilitation of medial meniscal root tears (MMRTs) using a modified Delphi technique. Methods A working group developed statements on MMRT diagnosis, nonoperative management, surgical indications, surgical management, alignment, and rehabilitation using modified Delphi techniques. Fifty‐six experts were surveyed over 3 rounds to reach consensus, with agreement measured on a 5‐point Likert scale. Statements were included, revised, or excluded on the basis of predefined thresholds (≥75% agreement, <20% disagreement). Experts suggested revisions or new statements in the first 2 rounds, and final consensus statements were included. Results All 56 experts completed 3 survey rounds. Experts agreed that root tears may occur with no known history of trauma, typically in older patients, and that it should be diagnosed with an magnetic resonance imaging. In symptomatic patients with MMRTs without advanced osteoarthritis should be repaired using an anatomic transtibial pull‐out technique (performing a pie‐crusting technique can be helpful for visualization). Nonoperative management is advised for patients with advanced osteoarthritis. High tibial osteotomy may be considered for significant varus malalignment during MMRT repair. The only statement without consensus was the management of asymptomatic MMRTs with mild medial compartment cartilage wear, indicating ongoing debate. Conclusions Overall, 98% of statements reached consensus. There is agreement that magnetic resonance imaging is the gold standard for diagnosis. Symptomatic MMRTs without advanced osteoarthritis should be repaired early using an anatomic transtibial pull‐out technique. End‐stage knee osteoarthritis warrants nonoperative management of MMRTs, and a structured postoperative protocol with limited weightbearing and range of motion is essential after repair. No agreement was reached on managing asymptomatic MMRTs in patients without significant medial compartment degeneration. Meniscal centralization sutures may help in cases of substantial extrusion, but their routine use is debated. Level of Evidence Level V, consensus of expert opinion.

Importance Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologist-read TIL assessment on hematoxylin and eosin–stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approaches that produce more reliable and consistent readouts is important. Objective To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods. Design, Setting, and Participants This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditional pathologist-read methods and an artificial intelligence (AI)-driven approach. The study was conducted using retrospective cohorts of patients with melanoma between January 2022 and June 2023 across 45 institutions, with tissue evaluated by participants from academic, clinical, and research institutions. Participants were selected to ensure diverse expertise and professional backgrounds. Main Outcomes and Measures Intraclass correlation coefficient (ICC) values were calculated for the manual and AI-assisted arms using log-transformed data. Kendall W values were calculated for Clark scores (brisk = 3, nonbrisk = 2, and sparse = 1). Reliabilities of ICC and W values were classified as moderate (0.40-0.60), good (0.61-0.80), or excellent (&amp;gt;0.80). AI TIL measurements were dichotomized using the 16.6 and median cutoffs. Univariable and multivariable Cox regression analyses assessed the prognostic value of TIL scores adjusted for clinicopathologic variables. Results There were 111 patients with melanoma in the independent testing cohort (median [range] age at diagnosis, 61.0 [25.0-87.0] years; 56 [50.5%] male) who contributed melanoma whole tissue sections. A total of 98 participants evaluated TILs on 60 hematoxylin and eosin–stained melanoma tissue sections. All 40 participants in the manual arm were pathologists, while the AI-assisted arm included 11 pathologists and 47 nonpathologists (scientists). The AI algorithm demonstrated superior reproducibility, with ICCs higher than 0.90 for all machine learning TIL variables, significantly outperforming manual assessments (ICC, 0.61 for AI-derived stromal TILs vs Kendall W , 0.44 for manual Clark TIL scoring). AI-based TIL scores showed prognostic associations with patient outcomes (n = 111) using the median cutoff approach with a hazard ratio (HR) of 0.45 (95% CI, 0.26-0.80; P = .005), and using the cutoff of 16.6, with an HR of 0.56 (95% CI, 0.32-0.98; P = .04). Conclusions and Relevance In this prognostic study of TIL quantification in melanoma, the AI algorithm demonstrated superior reproducibility and prognostic associations compared with traditional methods. Although the retrospective nature of the cohorts limits demonstration of clinical utility, the publicly available dataset and open-source AI tool offer a foundation for future validation and integration into melanoma management.

Rochester Healthy Community Partnership, a 20-year community based participatory research partnership, used photovoice to learn about Hispanic/Latino and Somali immigrants’ health concerns. Participants photographed their health-related priorities, reflected, developed policy recommendations, and shared photographs at a public event and rotating exhibit in 2024. More than 100 people attended the event (19 decision makers, 64 members of the public, 11 immigrant leader participants, 11 research staff). All decision-maker and public survey respondents reported learning something new about the priorities of Hispanic/Latino and Somali immigrants. ( Am J Public Health. 2025;115(S2):S120–S125. https://doi.org/10.2105/AJPH.2025.308098 )

ABSTRACT Background Over the years, prognostic indexes have been developed to help clinicians stratify patients with biliary tract cancers (BTC) into risk groups. This study aims to identify a new prognostic index for patients with BTC treated with cisplatin, gemcitabine and durvalumab (CGD) in the first‐line setting. Patients and Methods The study population consisted of patients with BTC from 11 Eastern and Western Countries. Using multivariate analysis for overall survival (OS), we identified 5 baseline statistically significant variables: stage, carcinoembryonic antigen (CEA) levels, albumin levels, gamma glutamyl transferase (GGT) levels, neutrophil‐to‐lymphocyte ratio (NLR). Metastatic disease is a prognostic factor with a superior weight considering the HR of 3.62, while all the others can be considered prognostic factors with equivalent weight as the HRs are quite similar (HRs between 1.55 and 1.92). Based on these reasons, we developed a prognostic model called the MAGIC‐D index by assigning a score of 2 for metastatic disease, and a score of 1 for CEA increased levels, albumin decreased levels, GGT increased levels, NLR ≥ 3. Patients were stratified into three risk groups as follows: low‐risk group (Score from 0 to 2), intermediate‐risk group (Score from 3 to 4) and high‐risk group (Score from 5 to 6). At the first data cutoff (April 2024), these data were available for 319 patients that composed the training cohort used for the analysis. At the second data cutoff (May 2025), 79 patients were further enrolled and composed the validation cohort. Results Median progression‐free survival was 11.7 months in low‐risk group (20.7%), 8.7 months in intermediate‐risk group (46.4%) and 5.4 months in high‐risk group (32.9%) [low‐risk hazard ratio (HR): 0.27, intermediate‐risk HR: 0.55, high‐risk HR: 1, p < 0.0001]. Median OS was 18.4 months in low‐risk group,15.9 months in intermediate‐risk group and 7.8 months in high‐risk group (low‐risk HR: 0.17, intermediate‐risk HR: 0.43, high‐risk HR: 1, p < 0.0001). There was no difference in overall response rate (low‐risk: 31.8%, intermediate‐risk: 36.5% and high‐risk: 25.7%; p = 0.0718), while disease control rate was significantly different across the three risk groups (low‐risk: 83.3%, intermediate‐risk: 70.9% and high‐risk: 60.9%; p < 0.0001) as well as the rate of patients receiving a second‐line therapy (low‐risk: 54.5%, intermediate‐risk: 48.6% and high‐risk: 25.7%; p = 0.0061). Finally, the prognostic role in terms of OS and PFS of the MAGIC‐D index was confirmed in a validation cohort of 79 patients. Conclusion The MAGIC‐D index is an easy‐to‐use tool able to stratify patients with BTC with different prognoses undergoing first‐line therapy with CGD.

Abstract Objectives In 2023, AMIA’s Inclusive Language and Context Style Guidelines (the “Guidelines”) were approved by the Board of Directors and made a publicly available resource. This work began in 2021 through AMIA’s DEI Task Force and subsequent DEI Committee; many members provided input, feedback, and time to create the Guidelines. In this paper, the authors provide a transparent account of the origin, development, contents, and dissemination of the Guidelines and share plans for their future development and use. Materials and Methods Our approach to drafting, refining, and distributing the Guidelines included consulting existing language guides, AMIA member reviews, external expert reviews, webinars, and workshops. Through an iterative approach to drafting and refining the Guidelines, the authors consulted relevant language guidelines and many experts throughout and beyond the AMIA community. Results The Inclusive Language Context Guidelines were formally approved by the AMIA Board of Directors on February 15, 2023. The Guidelines included four principles to be considered in scientific communications: Plurality, Precision, Transparency, and Destigmatization. Discussion A moment of vulnerability where an AMIA member raised concerns about the use of harmful language during a presentation resulted in the creation of a principled approach to support inclusive language within biomedical and health informatics communications. We envision that the Guidelines will support health equity by challenging dominant public narratives around health, fostering stronger interdisciplinary collaboration and critical thinking about the impact of language, and creating a more welcoming environment for the broader AMIA community. This work could not have been completed without the support of many AMIA members and other researchers in biomedical and health informatics. The Guidelines are a living document that will continue to be updated with input and feedback from the AMIA community into the future.

Despite significant improvements in survival of patients with multiple myeloma (MM), outcomes remain heterogeneous, and a significant proportion of patients experience suboptimal outcomes. Importantly, traditional prognostic factors based on data from patients treated with older therapies no longer capture prognosis accurately in the contemporary era of novel triplet or quadruplet therapies. Therefore, risk stratification requires refinement in the context of available and investigational treatment options in routine practice and clinical trials, respectively. The current identification of high-risk MM (HRMM) in routine practice is based on the Revised International Staging System, which stratifies patients using a combination of widely available serum biomarkers and chromosomal abnormalities assessed via fluorescence in situ hybridization. In recent years, a substantial body of evidence concerning additional clinical, biological, and molecular/genomic prognostic factors has accumulated, along with new MM risk stratification tools and consensus reports. The International Myeloma Society, along with the International Myeloma Working Group, convened an Expert Panel with the primary aim of revisiting the definition of HRMM and formulating a practical and data-driven consensus definition, based on new evidence from molecular/genomic assays, updated clinical data, and contemporary risk stratification concepts. The Panel proposes the following Consensus Genomic Staging (CGS) of HRMM which relies upon the presence of at least one of these abnormalities: (1) del(17p), with a cutoff of >20% clonal fraction, and/or TP53 mutation; (2) an IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q+ and/or del(1p32); (3) monoallelic del(1p32) along with 1q+ or biallelic del(1p32); or (4) β2 microglobulin ≥5.5 mg/L with normal creatinine (<1.2 mg/dL).