Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes

Accurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, diseasemodifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of commonly available multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (study 1: Ilsan Dementia Cohort; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 subjective memory complaints [SMC]) to test and validate the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 (study 2) to test the generalizability of the approach and the prognostic models with longitudinal follow up data. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) with iterative nested cross-validation in a single-site study, outperforming the benchmark model (FLAIR-based white matter hyperintensity volumes). In a generalizability study using ADNI-2, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) as CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). We also predicted MCI to AD progression with 69% accuracy, compared with the 70% accuracy using CSF biomarker model. The optimal classification accuracy in a single-site dataset and the reproduced results in multisite dataset show the feasibility of the high-throughput imaging analysis of multimodal MRI and data-driven machine learning for predictive modeling in AD

Tricho-dento-osseous Syndrome Mutant Dlx3 Shows Lower Transactivation Potential but Has Longer Half-life than Wild-type Dlx3

Dlx3 is a homeodomain protein and is known to play a role in development and differentiation of many tissues. Deletion of four base pairs in DLX3 (NT3198) is causally related to tricho-dento-osseous (TDO) syndrome (OMIM #190320), a genetic disorder manifested by taurodontism, hair abnormalities, and increased bone density in the cranium. The molecular mechanisms that explain the phenotypic characteristics of TDO syndrome have not been clearly determined. In this study, we examined phenotypic characteristics of wild type DLX3 (wtDlx3) and 4-BP DEL DLX3 (TDO mtDlx3) in C2C12 cells. To investigate how wtDlx3 and TDO mtDlx3 differentially regulate osteoblastic differentiation, reporter assays were performed by using luciferase reporters containing the promoters of alkaline phosphatase, bone sialoprotein or osteocalcin. Both wtDlx3 and TDO mtDlx3 enhanced significantly all the reporter activities but the effect of mtDlx3 was much weaker than that of wtDlx3. In spite of these differences in reporter activity, electrophoretic mobility shift assay showed that both wtDlx3 and TDO mtDlx3 formed similar amounts of DNA binding complexes with Dlx3 binding consensus sequence or with ALP promoter oligonucleotide bearing the Dlx3 binding core sequence. TDO mtDlx3 exhibits a longer half-life than wtDlx3 and it corresponds to PESTfind analysis result showing that potential PEST sequence was missed in carboxy terminal of TDO mtDlx3. In addition, co-immunoprecipitation demonstrated that TDO mtDlx3 binds to Msx2 more strongly than wtDlx3. Taken together, though TDO mtDlx3 acted as a weaker transcriptional activator than wtDlx3 in osteoblastic cells, there is possibility that during in vivo osteoblast differentiation TDO mtDlx3 may antagonize transcriptional repressor activity of Msx2 more effectively and for longer period than wtDlx3, resulting in enhancement of osteoblast differentiation

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings

Extrasinonasal infiltrative process associated with a sinonasal fungus ball: does it mean invasive fungal sinusitis?

PURPOSE:Invasive fungal sinusitis (IFS) has rarely been reported to develop from non-IFS. The purpose of this study was to disclose the nature of the extrasinonasal infiltrative process in the presence of a sinonasal fungus ball (FB).METHODS:We retrospectively reviewed the medical records, computed tomography, magnetic resonance images of 13 patients with sinonasal FB and the extrasinonasal infiltrative process. Based on histology and clinical course, we divided the extrasinonasal infiltrative process into IFS and the nonfungal inflammatory/infectious process (NFIP). The images were analyzed with particular attention to the presence of cervicofacial tissue infarction (CFTI).RESULTS:Of the 13 patients, IFS was confirmed in only one, while the remaining 12 were diagnosed to have presumed NFIP. One patient with IFS died shortly after diagnosis. In contrast, all 12 patients with presumed NFIP, except one, survived during a mean follow-up of 17 months. FB was located in the maxillary sinus (n=4), sphenoid sinus (n=8), and both sinuses (n=1). Bone defect was found in five patients, of whom four had a defect in the sphenoid sinus. Various sites were involved in the extrasinonasal infiltrative process, including the orbit (n=10), intracranial cavity (n=9), and soft tissues of the face and neck (n=7). CFTI was recognized only in one patient with IFS.CONCLUSION:In most cases, the extrasinonasal infiltrative process in the presence of sinonasal FB did not seem to be caused by IFS but probably by NFIP. In our study, there were more cases of invasive changes with the sphenoid than with the maxillary FB

Prognostic influence of body mass index and body weight gain during adjuvant FOLFOX chemotherapy in Korean colorectal cancer patients

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Background: Asian population has different body mass index (BMI) profile compared to Caucasian population. However, the effect of obesity and body weight gain in Asian colorectal cancer patients treated with adjuvant chemotherapy has not been studied thus far. Methods: We have analyzed the association between disease-free survival (DFS) and obesity/body weight change during treatment in Korean stage III or high-risk stage II colorectal cancer patients treated with adjuvant 5-fluorouracil/leucovorin/oxaliplatin. BMI was classified according to WHO Asia-Pacific classification. Weight change was calculated by comparing body weights measured at the last chemotherapy cycle and before surgery. Results: Among a total of 522 patients, 35.7 % of patients were obese (BMI >= 25 kg/m(2)) and 29.1 % were overweight (BMI, 23-24.9 kg/m(2)) before surgery. 18.0 % of patients gained = 5 kg and 26.1 % gained 2-4.9 kg during the adjuvant chemotherapy period. Baseline BMI or body weight change was not associated with DFS in the overall study population. However, body weight gain (>= 5 kg) was associated with inferior DFS (adjusted hazard ratio 2.04, 95 % confidence interval 1.02-4.08, p = 0.043) in overweight and obese patients (BMI >= 23.0 kg/m(2)). Conclusion: In Korean colorectal cancer patients treated with adjuvant FOLFOX chemotherapy, body weight gain during the treatment period has a negative prognostic influence in overweight and obese patients

Ultra-high-molecular-weight polyethylene as a hypervelocity impact shielding material for space structures

Space debris impacts at hypervelocity of several tens of km/s threaten the survival of space structures. In the case of International Space Station, the concept of Whipple shield is applied to protect the astronauts and the electronic devices from impact of space debris. In this study, a Whipple shield design comprising of ultra-high-molecular-weight polyethylene were proposed to improve the space debris impact shielding efficiency over conventional Whipple shields. Ballistic performance was evaluated by a two-stage lightweight gas gun capable of accelerating 5.56 mm diameter aluminum projectiles to 4 km/s. High-temperature impact tests and outgassing tests were performed for space environment application. Through the test, ultra-high-molecular-weight polyethylene was better ballistic performance and outgassing properties than Kevlar used in conventional Whipple shield. Ultra-high-molecular-weight polyethylene can be an effective way to provide cosmic radiation shielding and ballistic capability for future spacecraft designs.N

Dlx3 Plays a Role as a Positive Regulator of Osteoclast Differentiation

Dlx3 is a homeodomain protein and is known to playa role in development and differentiation of many tissues. Deletion of four base pairs in DLX3 (NT3198) is causally related to tricho-dento-osseous (TDO) syndrome (OMIM # 190320), a genetic disorder manifested by taurodontism, hair abnormalities, and increased bone density in the cranium. Although the observed defects of TDO syndrome involves bone, little is known about the role of Dlx3 in bone remodeling process. In this study, we examined the effect of wild type DLX3 (wtDlx3) expression on osteoclast differentiation and compared it with that of 4-BP DEL DLX3 (TDO mtDlx3). To examine whether Dlx3 is expressed during RANKL-induced osteoclast differentiation, RAW264.7 cells were cultured in the presence of receptor activator of nuclear factor-B ligand (RANKL). Dlx3 protein level increased slightly after RANKL treatment for 1 day and peaked when the fusion of prefusion osteoclasts actively progressed. When wtDlx3 and TDO mtDlx3 were overexpressed in RAW264.7 cells, they enhanced RANKL-induced osteoclastogenesis and the expression of osteoclast differentiation marker genes such as calcitonin receptor, vitronectin receptor and cathepsin K. Since osteoclast differentiation is critically regulated by the balance between RANKL and osteoprotegerin (OPG), we examined the effect of Dlx3 overexpression on expression of RANKL and OPG in C2C12 cells in the presence of bone morphogenetic protein 2. Overexpression of wtDlx3 enhanced RANKL mRNA expression while slightly suppressed OPG expression. However, TDO mtDlx3 did not exert significant effects. This result suggests that inability of TDO mtDlx3 to regulate expression of RANKL and OPG may contribute to increased bone density in TDO syndrome patients. Taken together, it is suggested that Dlx3 playa role as a positive regulator of osteoclast differentiation via up-regulation of osteoclast differentiation-associated genes in osteoclasts, as well as via increasing the ratio of RANKL to OPG in osteoblastic cells.This study was supported by a grant from the Basic Research Program of the Korea Science & Engineering Foundation (R01-2005-000-106650) and by the Korea Science & Engineering Foundation(KOSEF) grant funded by the government(MOST)(No. M10646010002-06N4601-00210)

Electronic Health Records Based Prediction of Future Incidence of Alzheimer’s Disease Using Machine Learning

Background: Prediction of future incidence of Alzheimer’s disease may facilitate intervention strategy to delay disease onset. Existing AD risk prediction models require collection of biospecimen (genetic, CSF, or blood samples), cognitive testing, or brain imaging. Conversely, EHR provides an opportunity to build a completely automated risk prediction model based on individuals’ history of health and healthcare. We tested machine learning models to predict future incidence of AD using administrative EHR in individuals aged 65 or older. Methods: We obtained de-identified EHR from Korean elders age above 65 years old (N=40,736) collected between 2002 and 2012 in the Korean National Health Insurance Service database system. Consisting of Participant Insurance Eligibility database, Healthcare Utilization database, and Health Screening database, this EHR contain 4,894 unique clinical features including ICD-9/10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. Our event of interest was new incidence of AD defined from the EHR based on both AD codes and prescription of anti-dementia medication. Two definitions were considered: a more stringent one requiring a diagnosis and dementia medication resulting in n=614 cases (“definite AD”) and a more liberal one requiring only diagnostic codes (n=2,026; “probable AD”). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years using the EHR available since 2002. The length of the EHR used in the models ranged from 1,571 to 2,239 days. Data was randomly split into training (60%), validation (20%), and test sets (20%) so that AUC values represent true out of sample prediction are based on the test set. Results: Average duration of EHR was 1,936 days in AD and 2,694 days in controls. For predicting future incidence of AD using the “definite AD” outcome, the machine learning models showed the best performance in 1 year prediction with AUC of 0.781; in 2 year, 0.739; in 3 year, 0.686; in 4 year, 0.662. Using “probable AD” outcome, the machine learning models showed the best performance in 1 year prediction with AUC of 0.730; in 2 year, 0.645; in 3 year, 0.575; in 4 year, 0.602. Important clinical features selected in logistic regression included hemoglobin level (b=-0.902), age (b=0.689), urine protein level (b=0.303), prescription of Lodopin (antipsychotic drug) (b=0.303), and prescription of Nicametate Citrate (vasodilator) (b=-0.297). Conclusion: This study demonstrates that EHR can i detect risk for incident AD. This approach could enable risk-specific stratification of elders for better targeted clinical trials