Citations

Abstract Repetitive head impacts are common in contact and collision sports and are linked to structural brain changes and an elevated risk of neurodegenerative diseases such as Chronic Traumatic Encephalopathy. Identifying early in vivo structural markers remains challenging. Although diagnosis currently requires post-mortem confirmation, clinical symptoms, including cognitive impairment and behavioural changes, are reflected in the diagnosis of Traumatic Encephalopathy Syndrome. These symptoms align with dysfunction in key brain regions—amygdala, hippocampus and thalamus—which support memory, emotion and behaviour and commonly show tau pathology in Chronic Traumatic Encephalopathy. This study uses shape analysis to examine structural differences in these regions between former American football players and unexposed asymptomatic controls and evaluates the influence of age, head impact exposure and clinical diagnosis on brain structure. We analyzed brain morphology in former American football players (n = 163) and unexposed, asymptomatic controls (n = 53). Structural segmentation was performed with FreeSurfer 7.1, and the shape analysis pipeline was used to generate subregional reconstructions. Vertex-level morphometry, based on the logarithm of the Jacobian determinant and radial distance, quantified local surface area dilation and thickness. Group differences were examined with covariate-adjusted linear regression models contrasting football players and controls, as well as participants with and without a Traumatic Encephalopathy Syndrome diagnosis. Partial correlations examined the influence of age, age of first football exposure and cumulative head impact index metrics, including frequency, linear acceleration and rotational force. Models were adjusted accordingly for age, body mass index, education, race, imaging site, apolipoprotein ϵ4 status and total intracranial volume. Former football players exhibited bilateral surface area contractions in the hippocampus and amygdala, along with reduced amygdala thickness, compared to controls. Older age was associated with widespread surface contractions and thinning across all regions, except for preserved thickness in the left hippocampus. An earlier age of first exposure to football correlated with surface contractions in the thalamus and left hippocampus. Greater cumulative linear acceleration was linked to bilateral hippocampal surface contractions and reduced thickness in the left thalamus, while greater rotational force exposure was associated with hippocampal thinning. No significant structural differences were found between players with and without a diagnosis of Traumatic Encephalopathy Syndrome. These findings extend volume-based research by revealing localized alterations in surface area dilation and thickness and emphasize the roles of age and repetitive head impact exposure in long-term brain changes.

Abstract The addition of pembrolizumab to preoperative radiotherapy (RT) improved disease-free survival (DFS) for patients with stage III undifferentiated pleomorphic sarcoma (UPS) and dedifferentiated/pleomorphic liposarcoma (LPS) in the randomized SU2C-SARC032 trial. To precisely identify patients who benefit from pembrolizumab and RT, we performed comprehensive multi-omics profiling of pre- and post-treatment tumor and blood samples, including bulk RNA-seq, flow cytometry, and cytometry by time of flight. Additionally, we built a single-cell RNA-seq atlas spanning 65,786 cells from UPS and LPS to recover single-cell states in bulk tumor samples using digital cytometry. Two opposing tumor microenvironments (TMEs), immune-cold sarcoma ecotype 1 (SE1) and immune-hot sarcoma immune class E (SIC E), benefited from pembrolizumab. Pembrolizumab combined with RT caused an overall increase in activated CD8 + T cells, CD56 low NK cells, and T cell receptor diversity, while diminishing matrix-remodeling stromal cells and sarcoma cells. Our findings identify different mechanisms of response to pembrolizumab in localized, high-risk UPS/LPS and suggest that sarcoma TME signatures may identify patients most likely to benefit from adding pembrolizumab to preoperative RT.

Background: Cardiomyopathy occurring during pregnancy or in the postpartum period is a leading case of maternal death in the US. The most commonly described form of pregnancy related cardiomyopathy (PRCM) associated with left ventricular systolic dysfunction is peripartum cardiomyopathy, defined as left ventricular ejection fraction (LVEF) <45% on echocardiography. PRCM symptoms often overlap with those of normal pregnancy and the lack of validated screening tools limits effective recognition, contributing to diagnostic delays. Recently, several artificial intelligence enabled electrocardiogram (AI-ECG) models have been developed and evaluated for PRCM detection. Aim&Hypothesis: We sought to investigate the pooled diagnostic performance of AI-ECG models for detecting PRCM. We hypothesized that AI-ECG models would be effective for detecting PRCM. Methods: A systematic search of PubMed, Scopus, and Cochrane was performed by combining search terms “artificial intelligence”, “machine learning”, “deep learning”, “algorithm”, “ECG”, “EKG”, “electrocardiogram”, “peripartum cardiomyopathy”, “pregnancy”, “pregnancy related cardiomyopathy” , to identify studies investigating the use of AI-ECG models for PRCM detection. Studies included those utilizing standard 12-lead ECGs as well as single lead ECGs from various devices. For inclusion in the meta-analysis, studies had to provide complete confusion matrix data for the AI-ECG model. Inverse variance random effects model meta-analysis was performed. Pooled performance estimates with corresponding 95% CIs are presented in forest plots and a summary receiver operating characteristics (sROC) curve. Results: Our search identified a total of 201 studies (PubMed: 64, Embase: 117, Cochrane: 20) from which 6 studies involving 11 AI-ECG models and 3,006 patients were included in the meta-analysis (Fig. 1) . Following random effect bivariate meta-analysis, the AI-ECG models yielded a pooled sensitivity of 0.721 (95% CI 0.676–0.762); specificity of 0.931 (95% CI 0.932–0.938); diagnostic odds ratio (DOR) of 44.658 (95% CI 23.843–83.644); and AUC of 0.896 (95% CI 0.87–0.91), for PRCM detection (Fig. 2A-C, 3) . Conclusion: AI-ECG models appear to be effective tools for detecting PRCM and may represent a viable solution to reduce diagnostic delays consequently improving maternal outcomes. Additional studies are needed to evaluate its impact on clinical outcomes in pragmatic settings.

Abstract Women make up 51% of the world's population, and the global population of postmenopausal women is growing. About 25% of these women experience debilitating menopausal symptoms. Since it is important that all health care professionals have a fundamental knowledge of managing women presenting with symptoms related to the menopause, this European Society of Endocrinology Clinical Practice Guideline was developed. It provides guidance on evaluation and optimal clinical management of women who go through the menopause in middle age, those with Premature Ovarian Insufficiency (POI), Early Menopause and those for whom hormones are not appropriate, including women with, or at high risk of, breast cancer. This guideline discusses the benefits and risks of hormone therapy administration as well as summarizing other treatments for menopausal symptoms. Further, the contentious issue of the impact of menopausal hormone therapy in the prevention of chronic disease is considered.

Abstract Repetitive head impacts are associated with structural brain changes and an increased risk for chronic traumatic encephalopathy, a progressive neurodegenerative disease that can only be diagnosed after death. Chronic traumatic encephalopathy is defined by the abnormal accumulation of phosphorylated tau protein, particularly at the depths of the superior frontal sulci, suggesting that sulcal morphology may serve as a relevant structural biomarker. Contact sport athletes, such as former football players, are at elevated risk due to their prolonged exposure to repetitive head impacts. Cortical atrophy linked to underlying tau accumulation may result in shallower and wider sulci, potentially making sulcal morphology an imaging marker for identifying individuals at risk for this disease. This study investigated sulcal morphological differences in former football players and examined associations with age, football-related exposure, clinical diagnosis of traumatic encephalopathy syndrome, levels of certainty for chronic traumatic encephalopathy pathology, neuropsychological performance, and positron emission tomography imaging using flortaucipir. We analysed structural magnetic resonance imaging data from 169 male former football players (mean age 57.2 (8.2) years, range 45–74) and 54 age-matched, unexposed asymptomatic male controls (mean age 59.4 (8.5) years, range 45–74). Sulcal depth and width were quantified using the CalcSulc, focusing on two regions in each hemisphere commonly affected by chronic traumatic encephalopathy pathology: the superior frontal and occipitotemporal sulci. Generalized least squares models were used to assess group differences and interactions with age and football exposure variables, including age of first exposure, total years played, and cumulative head impact exposure. An analysis of covariance evaluated relationships between sulcal morphology, clinical measures, and flortaucipir uptake, adjusting for age, race, body mass index, education, imaging site, apolipoprotein E4 status, and total intracranial volume. Former football players demonstrated significantly shallower sulcal depth in the left superior frontal sulcus compared to unexposed controls. Earlier age of first exposure and longer football careers were associated with greater widening of the left occipitotemporal sulcus. Higher cumulative head impact exposure was linked to reduced sulcal depth in the left superior frontal region. However, sulcal morphology was not associated with clinical diagnosis, levels of certainty, neuropsychological test performance, or flortaucipir imaging. These findings suggest that sulcal morphology may reflect cumulative exposure to repetitive head impacts, particularly in brain regions vulnerable to chronic traumatic encephalopathy pathology. Future ante- and post-mortem validation studies are needed to determine whether sulcal morphology can serve as a reliable in vivo biomarker of risk.

Abstract Background: Black or African Americans (AA) with Parkinson’s disease (PD) are underrepresented in both care and research and experience significant health disparities. The existing literature provides limited guidance on how to enhance the engagement of AA individuals in PD care and research, particularly from the perspectives of AA patients, care partners, and healthcare providers. This project aimed to (1) describe the use of Community Engagement (CE) Studios as a community-engaged research approach to inform culturally appropriate and inclusive research and (2) examine factors influencing AA engagement in PD-related activities. Methods: We conducted three CE Studios: one with AA with PD and care partners (N = 6), one with healthcare providers of AA with PD (N = 8), and one with AA with PD, care partners, and healthcare providers (N = 4). Results: The CE Studios informed the design (e.g., cultural appropriateness) and conduct (e.g., accessibility) of the planned PD project, as well as identifying stakeholders to engage with, improving alignment between research and the AA community. We highlighted the importance of multifaceted factors, including environmental (e.g., segregation), biological (e.g., symptoms), sociocultural (e.g., not being invited), and behavioral (e.g., empowerment) domains, which influence AA engagement. Conclusions: The CE Studios method is a feasible and useful approach for understanding the perspectives of AA in PD. It is possible to conduct an in-depth exploration of community perspectives by synthesizing comprehensive analyses and leveraging additional frameworks. These efforts include identifying barriers to engagement, recognizing locally relevant individuals, and refining PD-related care to enhance cultural appropriateness.

Abstract Rationale Inflammation is central to chronic obstructive pulmonary disease (COPD) pathogenesis but incompletely represented in COPD prognostic models. The neutrophil-to-lymphocyte ratio (NLR) is a readily available inflammatory biomarker. Objectives To explore the associations of NLR with smoking status, clinical features of COPD, and future adverse outcomes. Methods We analyzed NLR calculated from the complete blood count of participants who currently or formerly smoked (n = 2,624) and tobacco-naive control subjects (n = 187) in the SPIROMICS multicenter observational cohort study. We assessed the stability of NLR at 6 weeks and 1 year, the association with select blood biomarkers, and the impact of smoking on NLR and cell counts. We stratified participants by NLR quartiles to compare cross-sectional clinical features at enrollment, prospectively observed exacerbations at 1 year, and mortality during longitudinal follow up. Results Higher NLR quartiles were broadly associated with more severe clinical features of COPD. NLR values were repeatable at 6 weeks (intraclass correlation coefficient, 0.74) and 1 year (intraclass correlation coefficient, 0.62). The impact of smoking on NLR varied with the severity of airflow limitation, mediated by an interaction between smoking, forced expiratory volume in 1 second percent predicted, and neutrophil counts but not lymphocyte counts. The highest NLR quartile (&gt;3.11) was associated with an increased risk of exacerbation over 1 year (adjusted odds ratio, 1.51; 95% confidence interval, 1.18, 1.92) and increased risk of mortality (adjusted hazard ratio, 1.41; 95% confidence interval, 1.20, 1.66) compared with quartiles 1–3. Conclusions Elevated NLR in stable COPD is a widely available biomarker associated with increased risk for exacerbation and death. The impact of cigarette smoking on NLR varies with disease severity.

Background: Studies have suggested a synergism between lenalidomide (LEN) and ibrutinib (IBR) in multiple myeloma (MM). Both downregulate IRF4, a key target and master transcriptional factor regulating myeloma cell survival. Method: A 3 + 3 phase I trial was conducted to determine the maximum tolerated dose (MTD) of IBR in combination with LEN + dexamethasone (DEX) in patients with relapsed/refractory (RR) MM who had at least one prior line of therapy. Three dose levels (DLs) were planned. The cycle length was 28 days. IBR was administered orally daily in doses of 560 mg on DL1-2 and 840 mg on DL3, LEN was administered orally on days 1–21 in doses of 15 mg on DL1 and 25 mg on DL2-3, and DEX was administered orally on days 1, 8, 15, and 22 in a dose of 40 mg if age < 75 years or in a dose of 20 mg if it was ≥75 years for DL1-3. Patients with a glomerular filtration rate (GFR) <60 but ≥30 mL/min were treated in accordance with the manufacturer’s instructions with LEN 10 mg. Dose-limiting toxicities (DLTs) included the following: grade 4 neutropenia lasting more than 5 days, thrombocytopenia, febrile neutropenia, nausea, vomiting or diarrhea; grade 3 thrombocytopenia with bleeding or platelet transfusion; and grade 3–4 hyperglycemia or a thrombotic/embolic event, and other nonhematologic toxicities. The overall response rate (ORR) was defined as the percentage of patients with a partial response (PR), very good partial response (VGPR), or complete response (CR) according to IMWG criteria on two consecutive evaluations at least 4 weeks apart. The clinical benefit rate (CBR) was defined as the percentage of patients with stable disease (SD) or a better outcome on two consecutive evaluations at weeks apart. Results: Fourteen patients (DL1: six patients; DL2: three patients; DL3: five patients) were registered for the study from March 2019 to May 2023, prior to its closure due to limited accrual. Thirteen patients are included in the summary of toxicities and response as one patient on DL3 halted participation prior to the start of the treatment. Two patients on DL3 were excluded from the determination of MTD: one having discontinued cycle 1 treatment due to COVID-19 infection and the another having mistakenly taken 280 mg/day of IBR instead of the assigned 840 mg/day dose during cycle 1. Only one patient developed a DLT, on DL1 with grade 3 non-viral hepatitis. The median number of cycles administered was 4 (range: 1–56). Severe toxicities reported included grade 4 lymphocytopenia (1), grade 4 thrombocytopenia (1), and grade 5 sepsis in the setting of PD (1). Disease responses included a VGPR on DL1 and CR on DL3. Thus, the ORR was 15.4% (90% CI: 2.8–41.0%). One patient on DL1 maintained SD for 4.6 years before discontinuing the treatment to undergo an alternative therapy. Another five patients maintained SD for ≥ 2 consecutive cycles. Thus, the CBR was 61.5% (90% CI: 35.5–83.4%). Conclusions: The combination of LEN with IBR in RR MM proved feasible, with manageable toxicities and the majority of discontinuations being due to disease progression.

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.

Abstract Severe aplastic anemia (SAA) is a rare and life-threatening bone marrow failure disorder. Immunosuppressive therapy (IST) with antithymocyte globulin and cyclosporine has long been a frontline treatment option in SAA; however, its limited durability and risk of long-term complications such as secondary malignancies remain a drawback in this treatment modality. Allogeneic hematopoietic cell transplantation (HCT) is a potentially curative option with significantly improved outcomes over the long term, particularly with HLA-matched related donors. However, the use of alternative donors, such as haploidentical, mismatched, or matched unrelated donors, has previously been limited due to increased transplant-related morbidity, particularly graft-versus-host disease (GVHD). HCTs have therefore been limited to young recipients and those with HLA-matched related donors, creating significant disparity for older adults and those who lack matched donor options. Nevertheless, more recent advances in HCT, such as posttransplant cyclophosphamide for GVHD prophylaxis, have led to improved outcomes of HCT with alternative donors; however, alternative donor HCT remains underused as up-front therapy, in part because of limited multicenter trial data. This review discusses current SAA treatment approaches, including both IST and HCT, and highlights remaining gaps. It also discusses how ongoing clinical trials such as CureAA and TransIT could help address these gaps. Furthermore, we discuss the importance of stakeholder engagement and implementation science in the integration of research-based evidence into clinical practice. Bridging these gaps is necessary for achieving equitable access for patients historically excluded from frontline HCT, including older adults and racially or ethnically diverse populations.