Browsing by Subject "Diagnostic markers"
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Item Alkaline Phosphatase Treatment of Acute Kidney Injury in an Infant Piglet Model of Cardiopulmonary Bypass with Deep Hypothermic Circulatory Arrest(Springer Nature, 2019-10-02) Davidson, Jesse A.; Khailova, Ludmila; Treece, Amy; Robison, Justin; Soranno, Danielle E.; Jaggers, James; Ing, Richard J.; Lawson, Scott; Osorio Lujan, Suzanne; Pediatrics, School of MedicineAcute kidney injury (AKI) is associated with prolonged hospitalization and mortality following infant cardiac surgery, but therapeutic options are limited. Alkaline phosphatase (AP) infusion reduced AKI in phase 2 sepsis trials but has not been evaluated for cardiac surgery-induced AKI. We developed a porcine model of infant cardiopulmonary bypass (CPB) with deep hypothermic circulatory arrest (DHCA) to investigate post-CPB/DHCA AKI, measure serum/renal tissue AP activity with escalating doses of AP infusion, and provide preliminary assessment of AP infusion for prevention of AKI. Infant pigs underwent CPB with DHCA followed by survival for 4 h. Groups were treated with escalating doses of bovine intestinal AP (1, 5, or 25U/kg/hr). Anesthesia controls were mechanically ventilated for 7 h without CPB. CPB/DHCA animals demonstrated histologic and biomarker evidence of AKI as well as decreased serum and renal tissue AP compared to anesthesia controls. Only high dose AP infusion significantly increased serum or renal tissue AP activity. Preliminary efficacy evaluation demonstrated a trend towards decreased AKI in the high dose AP group. The results of this dose-finding study indicate that AP infusion at the dose of 25U/kg/hr corrects serum and tissue AP deficiency and may prevent AKI in this piglet model of infant CPB/DHCA.Item Astroglial tracer BU99008 detects multiple binding sites in Alzheimer's disease brain(Springer Nature, 2021) Kumar, Amit; Koistinen, Niina A.; Malarte, Mona-Lisa; Nennesmo, Inger; Ingelsson, Martin; Ghetti, Bernardino; Lemoine, Laetitia; Nordberg, Agneta; Pathology and Laboratory Medicine, School of MedicineWith reactive astrogliosis being established as one of the hallmarks of Alzheimer's disease (AD), there is high interest in developing novel positron emission tomography (PET) tracers to detect early astrocyte reactivity. BU99008, a novel astrocytic PET ligand targeting imidazoline-2 binding sites (I2BS) on astrocytes, might be a suitable candidate. Here we demonstrate for the first time that BU99008 could visualise reactive astrogliosis in postmortem AD brains and propose a multiple binding site [Super-high-affinity (SH), High-affinity (HA) and Low-affinity (LA)] model for BU99008, I2BS specific ligands (2-BFI and BU224) and deprenyl in AD and control (CN) brains. The proportion (%) and affinities of these sites varied significantly between the BU99008, 2-BFI, BU224 and deprenyl in AD and CN brains. Regional binding studies demonstrated significantly higher 3H-BU99008 binding in AD brain regions compared to CN. Comparative autoradiography studies reinforced these findings, showing higher specific binding for 3H-BU99008 than 3H-Deprenyl in sporadic AD brain compared to CN, implying that they might have different targets. The data clearly shows that BU99008 could detect I2BS expressing reactive astrocytes with good selectivity and specificity and hence be a potential attractive clinical astrocytic PET tracer for gaining further insight into the role of reactive astrogliosis in AD.Item Characteristics of discordance between amyloid positron emission tomography and plasma amyloid-β 42/40 positivity(Springer Nature, 2024-02-10) Pyun, Jung-Min; Park, Young Ho; Youn, Young Chul; Kang, Min Ju; Shim, Kyu Hwan; Jang, Jae-Won; You, Jihwan; Nho, Kwangsik; Kim, SangYun; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineVarious plasma biomarkers for amyloid-β (Aβ) have shown high predictability of amyloid PET positivity. However, the characteristics of discordance between amyloid PET and plasma Aβ42/40 positivity are poorly understood. Thorough interpretation of discordant cases is vital as Aβ plasma biomarker is imminent to integrate into clinical guidelines. We aimed to determine the characteristics of discordant groups between amyloid PET and plasma Aβ42/40 positivity, and inter-assays variability depending on plasma assays. We compared tau burden measured by PET, brain volume assessed by MRI, cross-sectional cognitive function, longitudinal cognitive decline and polygenic risk score (PRS) between PET/plasma groups (PET-/plasma-, PET-/plasma+, PET+/plasma-, PET+/plasma+) using Alzheimer's Disease Neuroimaging Initiative database. Additionally, we investigated inter-assays variability between immunoprecipitation followed by mass spectrometry method developed at Washington University (IP-MS-WashU) and Elecsys immunoassay from Roche (IA-Elc). PET+/plasma+ was significantly associated with higher tau burden assessed by PET in entorhinal, Braak III/IV, and Braak V/VI regions, and with decreased volume of hippocampal and precuneus regions compared to PET-/plasma-. PET+/plasma+ showed poor performances in global cognition, memory, executive and daily-life function, and rapid cognitive decline. PET+/plasma+ was related to high PRS. The PET-/plasma+ showed intermediate changes between PET-/plasma- and PET+/plasma+ in terms of tau burden, hippocampal and precuneus volume, cross-sectional and longitudinal cognition, and PRS. PET+/plasma- represented heterogeneous characteristics with most prominent variability depending on plasma assays. Moreover, IP-MS-WashU showed more linear association between amyloid PET standardized uptake value ratio and plasma Aβ42/40 than IA-Elc. IA-Elc showed more plasma Aβ42/40 positivity in the amyloid PET-negative stage than IP-MS-WashU. Characteristics of PET-/plasma+ support plasma biomarkers as early biomarker of amyloidopathy prior to amyloid PET. Various plasma biomarker assays might be applied distinctively to detect different target subjects or disease stages.Item Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data(Springer Nature, 2022-04-21) Thistlethwaite, Lillian R.; Li, Xiqi; Burrage, Lindsay C.; Riehle, Kevin; Hacia, Joseph G.; Braverman, Nancy; Wangler, Michael F.; Miller, Marcus J.; Elsea, Sarah H.; Milosavljevic, Aleksandar; Medical and Molecular Genetics, School of MedicineUntargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that "connects the dots" between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease-specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic L-amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.Item Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma(Nature Research, 2020) Cheng, Jun; Han, Zhi; Mehra, Rohit; Shao, Wei; Cheng, Michael; Feng, Qianjin; Ni, Dong; Huang, Kun; Cheng, Liang; Zhang, Jie; Medicine, School of MedicineTFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin- stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis.Item Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data(Nature Publishing Group, 2020-10-20) Ho, Chang Y.; Kindler, John M.; Persohn, Scott; Kralik, Stephen F.; Robertson, Kent A.; Territo, Paul R.; Radiology and Imaging Sciences, School of MedicineWe assessed the accuracy of semi-automated tumor volume maps of plexiform neurofibroma (PN) generated by a deep neural network, compared to manual segmentation using diffusion weighted imaging (DWI) data. NF1 Patients were recruited from a phase II clinical trial for the treatment of PN. Multiple b-value DWI was imaged over the largest PN. All DWI datasets were registered and intensity normalized prior to segmentation with a multi-spectral neural network classifier (MSNN). Manual volumes of PN were performed on 3D-T2 images registered to diffusion images and compared to MSNN volumes with the Sørensen-Dice coefficient. Intravoxel incoherent motion (IVIM) parameters were calculated from resulting volumes. 35 MRI scans were included from 14 subjects. Sørensen-Dice coefficient between the semi-automated and manual segmentation was 0.77 ± 0.016. Perfusion fraction (f) was significantly higher for tumor versus normal tissue (0.47 ± 0.42 vs. 0.30 ± 0.22, p = 0.02), similarly, true diffusion (D) was significantly higher for PN tumor versus normal (0.0018 ± 0.0003 vs. 0.0012 ± 0.0002, p < 0.0001). By contrast, the pseudodiffusion coefficient (D*) was significantly lower for PN tumor versus normal (0.024 ± 0.01 vs. 0.031 ± 0.005, p < 0.0001). Volumes generated by a neural network from multiple diffusion data on PNs demonstrated good correlation with manual volumes. IVIM analysis of multiple b-value diffusion data demonstrates significant differences between PN and normal tissue.Item Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review(Springer Nature, 2024-04-06) Felton, Jamie L.; Redondo, Maria J.; Oram, Richard A.; Speake, Cate; Long, S. Alice; Onengut-Gumuscu, Suna; Rich, Stephen S.; Monaco, Gabriela S. F.; Harris-Kawano, Arianna; Perez, Dianna; Saeed, Zeb; Hoag, Benjamin; Jain, Rashmi; Evans-Molina, Carmella; DiMeglio, Linda A.; Ismail, Heba M.; Dabelea, Dana; Johnson, Randi K.; Urazbayeva, Marzhan; Wentworth, John M.; Griffin, Kurt J.; Sims, Emily K.; ADA/EASD PMDI; Pediatrics, School of MedicineBackground: Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies. Methods: We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment. Results: Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation. Conclusions: Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.Item Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism(Springer Nature, 2021-06-09) Matthews, Harold S.; Palmer, Richard L.; Baynam, Gareth S.; Quarrell, Oliver W.; Klein, Ophir D.; Spritz, Richard A.; Hennekam, Raoul C.; Walsh, Susan; Shriver, Mark; Weinberg, Seth M.; Hallgrimsson, Benedikt; Hammond, Peter; Penington, Anthony J.; Peeters, Hilde; Claes, Peter D.; Biology, School of ScienceCraniofacial dysmorphism is associated with thousands of genetic and environmental disorders. Delineation of salient facial characteristics can guide clinicians towards a correct clinical diagnosis and understanding the pathogenesis of the disorder. Abnormal facial shape might require craniofacial surgical intervention, with the restoration of normal shape an important surgical outcome. Facial anthropometric growth curves or standards of single inter-landmark measurements have traditionally supported assessments of normal and abnormal facial shape, for both clinical and research applications. However, these fail to capture the full complexity of facial shape. With the increasing availability of 3D photographs, methods of assessment that take advantage of the rich information contained in such images are needed. In this article we derive and present open-source three-dimensional (3D) growth curves of the human face. These are sequences of age and sex-specific expected 3D facial shapes and statistical models of the variation around the expected shape, derived from 5443 3D images. We demonstrate the use of these growth curves for assessing patients and show that they identify normal and abnormal facial morphology independent from age-specific facial features. 3D growth curves can facilitate use of state-of-the-art 3D facial shape assessment by the broader clinical and biomedical research community. This advance in phenotype description will support clinical diagnosis and the understanding of disease pathogenesis including genotype–phenotype relations.Item Plasma phosphorylated-tau181 as a predictive biomarker for Alzheimer's amyloid, tau and FDG PET status(Springer Nature, 2021-11-13) Shen, Xue-Ning; Huang, Yu-Yuan; Chen, Shi-Dong; Guo, Yu; Tan, Lan; Dong, Qiang; Yu, Jin-Tai; Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of MedicinePlasma phosphorylated-tau181 (p-tau181) showed the potential for Alzheimer's diagnosis and prognosis, but its role in detecting cerebral pathologies is unclear. We aimed to evaluate whether it could serve as a marker for Alzheimer's pathology in the brain. A total of 1189 participants with plasma p-tau181 and PET data of amyloid, tau or FDG PET were included from ADNI. Cross-sectional relationships of plasma p-tau181 with PET biomarkers were tested. Longitudinally, we further investigated whether different p-tau181 levels at baseline predicted different progression of Alzheimer's pathological changes in the brain. We found plasma p-tau181 significantly correlated with brain amyloid (Spearman ρ = 0.45, P < 0.0001), tau (0.25, P = 0.0003), and FDG PET uptakes (-0.37, P < 0.0001), and increased along the Alzheimer's continuum. Individually, plasma p-tau181 could detect abnormal amyloid, tau pathologies and hypometabolism in the brain, similar with or even better than clinical indicators. The diagnostic accuracy of plasma p-tau181 elevated significantly when combined with clinical information (AUC = 0.814 for amyloid PET, 0.773 for tau PET, and 0.708 for FDG PET). Relationships of plasma p-tau181 with brain pathologies were partly or entirely mediated by the corresponding CSF biomarkers. Besides, individuals with abnormal plasma p-tau181 level (>18.85 pg/ml) at baseline had a higher risk of pathological progression in brain amyloid (HR: 2.32, 95%CI 1.32-4.08) and FDG PET (3.21, 95%CI 2.06-5.01) status. Plasma p-tau181 may be a sensitive screening test for detecting brain pathologies, and serve as a predictive biomarker for Alzheimer's pathophysiology.Item Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis(Springer Nature, 2024-04-11) Kolbinger, Fiona R.; Veldhuizen, Gregory P.; Zhu, Jiefu; Truhn, Daniel; Kather, Jakob Nikolas; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground: The field of Artificial Intelligence (AI) holds transformative potential in medicine. However, the lack of universal reporting guidelines poses challenges in ensuring the validity and reproducibility of published research studies in this field. Methods: Based on a systematic review of academic publications and reporting standards demanded by both international consortia and regulatory stakeholders as well as leading journals in the fields of medicine and medical informatics, 26 reporting guidelines published between 2009 and 2023 were included in this analysis. Guidelines were stratified by breadth (general or specific to medical fields), underlying consensus quality, and target research phase (preclinical, translational, clinical) and subsequently analyzed regarding the overlap and variations in guideline items. Results: AI reporting guidelines for medical research vary with respect to the quality of the underlying consensus process, breadth, and target research phase. Some guideline items such as reporting of study design and model performance recur across guidelines, whereas other items are specific to particular fields and research stages. Conclusions: Our analysis highlights the importance of reporting guidelines in clinical AI research and underscores the need for common standards that address the identified variations and gaps in current guidelines. Overall, this comprehensive overview could help researchers and public stakeholders reinforce quality standards for increased reliability, reproducibility, clinical validity, and public trust in AI research in healthcare. This could facilitate the safe, effective, and ethical translation of AI methods into clinical applications that will ultimately improve patient outcomes.