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Donor Age Matters for Patients Undergoing Haploidentical Donor HCT Even after Adjusting for HLA Factors

Transplantation and Cellular Therapy 2025年第2期31卷 S260-S261页

作者： Mehta, Rohtesh S., MD, MPH, MS Sparapani, Rodney, PhD Wang, Tao, PhD Spellman, Stephen R., MBS Lee, Stephanie J., MD, MPH Petersdorf, Effie W., MD Clinical Research Division Fred Hutchinson Cancer Center Seattle WA Data Science Institute MCW Milwaukee WI CIBMTR® (Center for International Blood and Marrow Transplant Research) Medical College of Wisconsin Milwaukee WI CIBMTR® (Center for International Blood and Marrow Transplant Research) NMDP Minneapolis MN Translational Science and Therapeutics Division Fred Hutchinson Cancer Center Seattle WA

BackgroundA previous study showed the significance of several HLA factors on haploidentical (haplo) donor hematopoietic cell transplantation (HCT) outcomes [ Fuchs, Blood 2022]. The most favorable combination of HLA factors linked to superior progression-free survival (PFS) was B leader match, DRB1 mismatch, DQB1 match, and non-permissive DPB1 mismatch based on the T-cell epitope model. Donor age was not identified as a risk factor for PFS. A calculator based on these results is available [ https://***/v1.0/] to aid in donor selection and may suggest choosing a much older donor over a younger one (e.g., a 65-year old B leader matched/DRB1 mismatched donor would be favored over a 30-year old B leader mismatched/DRB1 mismatched donor). Since donor age is a known predictor of survival, we investigated donor age in the original study cohort using machine learning regression methodology. MethodsWe reanalyzed the publicly available dataset from the study using a Gradient Boosting Machine (GBM) and a Cox proportional hazard model, each with donor age as a continuous variable. GBM models can capture complex, non-linear relationships and interactions between covariates and survival. Given the complexity of haplo HCT, where multiple predictors (donor age and relationship) are inter-related as well as to recipient factors (age), this approach is particularly attractive. The GBM C index (65.6%) was higher than Cox's (60.4%), suggesting that it was more effective at predicting which individuals are more likely to experience an event. Prediction calculators based on both models are being tested. Herein, we report the results of the GBM model [ https://***/HaploGBM/]. ResultsBoth GBM and Cox [ Fig 1] models showed an independent effect of donor age. For illustration, PFS of a 65-year-old patient with AML-CR1 with 4 different donors was predicted using GBM model. Fig 2 shows the effect of donor age, keeping other factors constant. Some

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GRADIENTS OF BRAIN ORGANIZATION: SMOOTH SAILING FROM METHODS DEVELOPMENT TO USER COMMUNITY

arXiv

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arXiv 2024年

作者： Royer, Jessica Paquola, Casey Valk, Sofie L. Kirschner, Matthias Hong, Seok-Jun Park, Bo-Yong Bethlehem, Richard A.I. Leech, Robert Yeo, B.T. Thomas Jefferies, Elizabeth Smallwood, Jonathan Margulies, Daniel Bernhardt, Boris C. Montreal Neurological Institute and Hospital McGill University MontrealQC Canada Forschungszentrum Julich Julich Germany Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Division of Adult Psychiatry Department of Psychiatry University Hospitals of Geneva Thonex Switzerland Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of Center for the Developing Brain Child Mind Institute New York United States Department of Biomedical Engineering Sungkyunkwan University Suwon Korea Republic of Department of Data Science Inha University Incheon Korea Republic of Department of Statistics and Data Science Inha University Incheon Korea Republic of Department of Psychology University of Cambridge Cambridge United Kingdom Centre for Neuroimaging Science King's College London London United Kingdom Centre for Sleep áCognition Centre for Translational Magnetic Resonance Research Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute for Health Institute for Digital Medicine National University of Singapore Singapore Singapore National University of Singapore Singapore Singapore Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Psychology University of York York United Kingdom Department of Psychology Queens University KingstonON Canada Universite de Paris Paris France

Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends - or gradients - in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization. © 2024, CC BY.

关键词： Gradient methods

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Classifying handedness with MRI

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Neuroimage: Reports 2021年第4期1卷 100057-100057页

作者： Panta, Sandeep R. Anderson, Nathaniel E. Maurer, J. Michael Harenski, Keith A. Nyalakanti, Prashanth K. Calhoun, Vince D. Kiehl, Kent A. The Mind Research Network Albuquerque NM United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States Departments of Psychology Neuroscience and Law University of New Mexico Albuquerque NM United States

When aggregating neuroimaging data across many subjects, an important consideration is establishing some group-level uniformity prior to further statistical analysis. Spatial normalization and motion correction are two important preprocessing steps that help achieve this goal. researchers have also often excluded left-handed subjects due to presumptions about variable asymmetries relating to both brain structure and function, which may interfere with achieving a desired level of group homogeneity. It is well-known, however, that hand-preference is not a binary attribute and is not a perfect representation of structural asymmetry or hemispheric specialization. In an effort to demonstrate a more objective, data-driven approach for quantifying asymmetries across handedness, we tested the reliability of single-subject classification of handedness using data obtained from structural MRI in extant samples. We utilized data from deformation fields created during the spatial normalization process within a priori regions of interest (ROIs), including the motor and somatosensory cortex, and Broca's and Wernicke's areas. Using these deformation fields as features in machine learning classifiers, we achieved classification accuracies greater than 75% across two independent datasets (i.e., a sample of incarcerated adult offenders and a sample of community adults from the Netherlands). These results demonstrate reliability of morphological features attributable to handedness as represented in neuroimaging data and further suggest that application of data-driven techniques may be a principled approach for addressing asymmetries in group analysis. © 2021

关键词： Asymmetries Classification Handedness Machine learning

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Polydopamine-Assisted Dual Metal Ion Modification of Titanium: Enhancing Osseointegration and Antibacterial Performance

SSRN

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SSRN 2025年

作者： Kang, Jingyang Meng, Sikun Liu, Chenhui Wang, Huachun Zhang, Tianzhen Qi, Cheng Li, Mei Cheeloo College of Medicine Shandong University 758 Hefei Road. Shandong Qingdao266035 China Department of Medical Data Science Center of Medical Innovation and Translational Research Osaka University Graduate School of Medicine Suita Yamadaoka 2-2 Osaka565-0871 Japan Key Laboratory for Liquid Solid Structural Evolution and Processing of Materials Ministry of Education School of Materials Science and Engineering Shandong University 17923 Jingshi Road Shandong Jinan250061 China Department of Stomatology Shandong Provincial Third Hospital Shandong University 11 Wuyingshan Middle Road Shandong Jinan250031 China

Titanium (Ti) implants are widely used for tooth replacement due to their exceptional mechanical properties and high biocompatibility. However, their inherently inert surface limits osteogenic potential and makes them prone to bacterial colonization, increasing the risk of biofilm formation and implant-related infections. To address these limitations, surface modification of Ti is essential. This study aimed to enhance the surface properties of Ti by coating it with polydopamine (PDA) and further doping it with copper and calcium ions. TPDA was prepared and subsequently used to fabricate TPDA@Cu and TPDA@CuCa samples. Material characterization confirmed that TPDA@CuCa exhibited excellent surface wettability and biocompatibility, with Cu2+ and Ca2+ being continuously and stably released in liquid environments. Additionally, TPDA@CuCa significantly improved protein adsorption, facilitating favorable cellular interactions. In vitro experiments demonstrated that TPDA@CuCa exhibited strong antimicrobial activity against Escherichia coli and Staphylococcus aureus, enhanced osteoblast adhesion, mineralization, and upregulated osteogenic gene expression. This bifunctional surface modification strategy offers a promising approach to enhancing both the osteogenic and antibacterial properties of Ti implants. © 2025, The Authors. All rights reserved.

关键词： Escherichia coli

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B cell infiltration is highly associated with prognosis and an immune-infiltrated tumor microenvironment in neuroblastoma

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Journal of Cancer Metastasis and Treatment 2021年第1期7卷 466-477页

作者： Evelien Schaafsma Chongming Jiang Chao Cheng Department of Molecular and Systems Biology Dartmouth CollegeHanoverNH 03755USA Department of Biomedical Data Science Geisel School of Medicine at DartmouthLebanonNH 03756USA Department of Medicine Baylor College of MedicineHoustonTX 77030USA Dan L Duncan Comprehensive Cancer Center Baylor College of MedicineHoustonTX 77030USA The Institute for Clinical and Translational Research Baylor College of MedicineHouston TX77030USA.

Aim:Neuroblastoma is the most common extracranial solid tumor in *** advances in immunotherapy Approaches,including in neuroblastoma,have shown the important role of the immune system in mounting an effective anti-tumor *** this study,we aimed to provide a comprehensive investigation of immune cell infiltration in neuroblastoma utilizing a large number of gene expression ***:We inferred immune cell infiltration using an established immune inference method and evaluated the association between immune cell abundance and patient prognosis as well as common chromosomal abnormalities found in *** addition,we evaluated co-infiltration patterns among distinct immune cell ***:The infiltration of naïve B cells,NK cells,and CD8+T cells was associated with improved patient ***ïve B cells were the most consistent indicator of prognosis and associated with an active immune tumor *** with high B cell infiltration showed high co-infiltration of other immune cell types and the enrichment of immune-related *** presence of high B cell infiltration was associated with both recurrence-free and overall survival,even after adjusting for clinical ***:In this study,we have provided a comprehensive evaluation of immune cell infiltration in neuroblastoma using gene expression *** propose an important role for B cells in the neuroblastoma tumor microenvironment and suggest that B cells can be used as a prognostic biomarker to predict recurrence-free and overall survival independently of currently utilized prognostic variables.

关键词： Neuroblastoma immune cell infiltration prognosis MYCN amplification B cells

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Association of Mid-life Discrimination Stress and Coping on Brain Volumes

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Ambar Kulshreshtha Maria Misiura William T. Hu Danielle D Verble Kelly D. S. Likos Whitney Wharton Emory University Atlanta GA USA Georgia State University Atlanta GA USA Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Rutgers University New Brunswick NJ USA

Background Chronic stress is related with poor health outcomes including changes in brain structure and function. Stress-related glucocorticoid and adrenergic receptors are richly expressed in hippocampus, prefrontal cortex, and amygdala, but it is unclear if all forms of stress impact these brain regions. Perceived discrimination is a form of stress, and there is limited research in how perceived discrimination can impact brain volumetrics. Our goal is to assess the association of perceived discrimination in brain regions prone to stress-related affect. Methods We examined data from 35 cognitively normal, racially diverse, middle-aged adults with a parental history of Alzheimer’s disease (AD). Participants provided medical history, demographic and discrimination survey data. Discrimination scales included the Expanded Everyday Discrimination Scale (EED), Major Experiences of Discrimination Scale (MED), Heightened Vigilance Scale (HVIGIL), and Coping with Discriminating scale (COPE). Participants underwent structural MRI with high resolution anatomical images of regions including the hippocampal, prefrontal cortex and amygdala. Multivariate linear regression was used to model the association between stress scales and brain volumes controlling for age and race. Volumes are adjusted for intracranial volume. Results Participants were average 61.5 years, 60% women, 8.6% African American and 40% had a postgraduate education. 54.3% participants had hypertension, 74.3% had dyslipidemia. EED score was associated with an increase in left (p = 0.49) and right (p = 0.03) hippocampal volume. MED score was associated with an increase in left (p< 0.01) and right (p = 0.01) hippocampal volume. HVIGIL sum score was associated with an increase in left (p = 0.02) and right (p = 0.03) hippocampal volume. The COPE sum score was associated with increased in left p = 0.01) and right (p< 0.01) hippocampal volume. There was a positive association in the above stress scores with left and ri

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Appendicitis is associated with an increased risk of systemic lupus erythematosus: a nationwide, population-based, case–control study

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Clinical Rheumatology 2023年第7期42卷 1783-1791页

作者： Chen, Yun-Wen Hsu, Po-Ke Lin, Shih-Ping Chen, Hsin-Hua Division of General Medicine Department of Internal Medicine Taichung Veterans General Hospital 1650 Boulevard Sect. 4 Taichung 40705 Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine Taichung Veterans General Hospital 1650 Boulevard Sect. 4 Taichung 40705 Taiwan Department of Post-Baccalaureate Medicine National Chung Hsing University Taichung Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Department of Gastroenterology Changhua Christian Hospital Changhua Taiwan Division of Infection Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan School of Medicine National Yang-Ming Chiao Tung University Taipei Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University 1650 Boulevard Sect. 4 Taichung 40705 Taiwan Institute of Biomedical Science and Rong Hsing Research Center for Translational Medicine & Big Data Center National Chung Hsin University Taichung Taiwan

Objective: To examine the association between appendicitis and the risk of systemic lupus erythematosus (SLE). Methods: Using claims data from the 2003–2013 Taiwanese National Health Insurance research database, we selected 6054 patients with newly diagnosed SLE from 2007 to 2012 and 36,324 age-, sex- and year of SLE diagnosis date-matched (1:6) non-SLE controls. After controlling for potential confounders, a multivariable conditional logistic regression model was used to calculate the adjusted odds ratio (aOR) with 95% confidence interval (CI) for the association of appendicitis history with SLE. Sensitivity analyses were conducted using various definitions of appendicitis. Subgroup analyses were conducted to examine possible modification effects by age, gender, level of urbanization, income and Charlson Comorbidity Index (CCI). Results: The average age of patients was 38 years old in both groups. The proportion of females was 86.5%. 75 (1.2%) of SLE cases and 205 (0.6%) of non-SLE controls had appendicitis history before the index date. After adjusting for potential confounding factors, appendicitis was associated with a higher risk of SLE (aOR, 1.84;95% CI, 1.34–2.52), and such association remained robust after variation of appendicitis definition. No significant modification effects were found for the association between appendicitis and SLE by age, gender, urbanization level, income and CCI. Conclusion: This nationwide, population-based case–control study demonstrates an association between appendicitis and incident SLE. Lack of individual smoking status is a major *** Points• Appendicitis was significantly associated with an increased risk of SLE.• Such association remained robust using various definitions of appendicitis. © 2023, The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR).

关键词： Appendicitis Case–control study Claims data Systemic lupus erythematosus

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ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder

ICA-based Individualized Differential Structure Similarity N...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xiang Li Ming Xu Rongtao Jiang Xuemei Li Vince D. Calhoun Xinyu Zhou Jing Sui National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Radiology and Biomedical Imaging Yale School of Medicine CT United States Department of Psychiatry The First Affiliated Hospital of Chongqing Medical University Chongqing China Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emorv University Atlanta GA United States IDG/McGovern Institute for Brain Research State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified large scale structural brain alterations in MDD, yet most are group analyses with atlas-parcellated anatomical regions. Here we proposed a method to measure individual difference by independent component analysis (ICA)-based individual difference structural similarity network (IDSSN). This approach provided a data-adaptive, atlas-free solution that can be applied to new individual subjects. Specifically, we constructed individualized whole-brain structural covariance networks based on network perturbation approach using spatially constrained ICA. First, a set of benchmark independent components (ICs) were generated using gray matter volume (GMV) from all healthy controls. Then individual heterogeneity was obtained by calculating differences and other similarity metrics between ICs derived from "each one patient + all controls" and the benchmark ICs, resulting in 32 imaging features and structural similarity networks for each patient, which can be used for predicting multiple clinical symptoms. We estimated IDSSN for 189 adolescent MDD patients aged 10-20 years and compared them to 112 healthy adolescents. We tested their predictability of the Hamilton Anxiety Scale , the 17-item Hamilton Depression Scale and six clinical syndromes using connectome-based predictive modeling. The prediction results showed that ICA-based IDSSN features reveal more disease-specific information than those using other brain templates. We also found that depression-associated networks mainly involved the default-mode network and visual network. In conclusion, our study proposed an adaptive method that improves the ability to detect GMV deviations and specificity between one individual patient and healthy groups, providing a new perspectives and insights for evaluating individual-level clinical heterogeneity based on brain structures.

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Multimodal Deep Learning for Pediatric Mild Traumatic Brain Injury Detection

Multimodal Deep Learning for Pediatric Mild Traumatic Brain ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Badhan Mazumder Deepan Krishna Tripathy Keith Owen Yeates Miriam H. Beauchamp William Craig Quynh Doan Stephen B. Freedman Catherine Lebel Roger Zemek Ashley L. Ware Dong Hye Ye Department of Computer Science Georgia State University Atlanta GA USA Department of Psychology University of Calgary Calgary Canada Department of Psychology University of Montreal and CHU Sainte-Justine Hospital Research Center Montreal Quebec Canada University of Alberta and Stollery Children’s Hospital Edmonton Canada Pediatric Emergency Medicine University of British Columbia Vancouver Canada Departments of Pediatrics and Emergency Medicine Cumming School of Medicine University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Department of Pediatrics and Emergency Medicine University of Ottawa Children’s Hospital of Eastern Ontario Research Institute Ottawa Canada Department of Psychology Georgia State University Atlanta GA USA Department of Neurology University of Utah Salt Lake City Utah USA Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech and Emory University

Despite its prevalence, little is known about the pathophysiology of mild traumatic brain injury (mTBI). This makes it difficult for clinicians to accurately diagnose mTBI and to predict outcomes in affected children, thereby highlighting the urgent need to identify novel and efficacious biomarkers of pediatric mTBI. To address this important knowledge gap, this study introduced a multimodal magnetic resonance imaging (MRI) based deep learning approach toward the classification of mTBI as compared with mild orthopedic injury (OI) by considering both structural MRI (sMRI) and diffusion tensor imaging (DTI). Firstly, convolutional features were extracted by employing a pre-trained DenseNet to capture the morphological features of both modalities. Next, by employing Deep Canonical Correlation Analysis (DCCA), distinct features obtained from the sMRI and DTI data were integrated into a multi-modal embedding. The obtained DCCA fused compact multimodal features were then fed to a random forest (RF) classifier that was used to classify mTBI versus mild OI. Additionally, to visualize the intra-individually heterogeneous brain regions that DenseNet most heavily relied upon for making classification, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to the DenseNet outcomes for both modalities. According to the experimental outcomes on the clinical dataset, the introduced multimodal deep learning strategy improved the classification accuracy by 8.6% (from 75.8% to 84.4%) and 7.8% (from 76.6% to 84.4%) when compared to the unimodal morphological features, as generated from sMRI and DTI.

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

arXiv

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arXiv 2024年

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Heidelberg Division of Medical Image Computing Germany Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany West German Cancer Center Essen University Hospital Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/ Longitudinal-Difference-Weighting. © 2024, CC BY.

关键词： Image segmentation

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