Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Previously, we constructedWe applied the LDL-DNN model to an electronic health record (EHR) system in real time (deep LDL-EHR). a deep neural network (DNN) model for estimating low-density lipoprotein (LDL) cholesterol (LDL-DNN). Objective: We applied the LDL-DNN model to an electronic health record (EHR) system in real time (deep LDL-EHR). Methods: The Korea National Health and Nutrition Examination Survey and the Wonju Severance Christian Hospital (WSCH) datasets were used as training and testing datasets, respectively. We measured the model’s performance by using four indices, including bias, root mean square error, P10 to P30, and concordance. For transfer learning (TL), we pre-trained the DNN model using a training dataset, and fine-tuned it using 30% of the testing datasets. Results: Based on the four accuracy criteria, the DNN-EHR model generated inaccurate results compared to other methods for LDL-C estimation. By comparing the training and testing datasets, we found there to be an overfitting problem. We revised the LDL-DNN model using the TL algorithms and randomly selected sub-data from the WSCH dataset. As a result, the LDL-DNN-TL model exhibited the best performance among the other methods. Conclusions: The LDL-DNN-TL model is expected to be suitable for routine real-time clinical application for LDL-C estimation in a clinical laboratory.

Cho J, et al. Ann Lab Med. 2020 May;40(3):201-208. https://doi.org/10.3343/alm.2020.40.3.201

Diabetic foot ulcers (DFUs) and their life-threatening complications, such as necrotizing fasciitis (NF) and osteomyelitis (OM), increase the healthcare cost, morbidity and mortality in patients with diabetes mellitus. While the early recognition of these complications could improve the clinical outcome of diabetic patients, it is not straightforward to achieve in the usual clinical settings. In this study, we proposed a classification model for diabetic foot, NF and OM. To select features for the classification model, multidisciplinary teams were organized and data were collected based on a literature search and automatic platform. A dataset of 1581 patients (728 diabetic foot, 76 NF, and 777 OM) was divided into training and validation datasets at a ratio of 7:3 to be analyzed. The final prediction models based on training dataset exhibited areas under the receiver operating curve (AUC) of the 0.80 and 0.73 for NF model and OM model, respectively, in validation sets. In conclusion, our classification models for NF and OM showed remarkable discriminatory power and easy applicability in patients with DFU.

Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.

Mesenchymal stem cells (MSCs) can differentiate into multiple connective tissue lineages, including osteoblasts, chondrocytes, and adipocytes. MSCs secrete paracrine molecules that are associated with immunomodulation, anti-fibrotic effects, and angiogenesis. Due to their orchestrative potential, MSCs have been therapeutically applied for several diseases. An important aspect of this process is the delivery of high-quality MSCs to patients at the right time, and cryo-biology and cryo-preservation facilitate the advancement of the logistics thereof. This study aimed to compare the biological signatures between freshly preserved and cryo-preserved MSCs by using big data sourced from the Pharmicell database. From 2011 to 2022, data on approximately 2300 stem cell manufacturing cases were collected. The dataset included approximately 60 variables, including viability, population doubling time (PDT), immunophenotype, and soluble paracrine molecules. In the dataset, 671 cases with no missing data were able to receive approval from an Institutional Review Board and were analyzed. Among the 60 features included in the final dataset, 20 were selected by experts and abstracted into two features by using a principal component analysis. Circular clustering did not introduce any differences between the two MSC preservation methods. This pattern was also observed when using viability, cluster of differentiation (CD) markers, and paracrine molecular indices as inputs for unsupervised analysis. The individual average PDT and cell viability at most passages did not differ according to the preservation method. Most immunophenotypes (except for the CD14 marker) and paracrine molecules did not exhibit different mean levels or concentrations between the frozen and unfrozen MSC groups. Collectively, the biochemical signatures of the cryo-preserved and unfrozen bone marrow MSCs were comparable.

Distinguishing syncope from epileptic seizures in patients with sudden loss of consciousness is important. Various blood tests have been used to indicate epileptic seizures in patients with impaired consciousness. This retrospective study aimed to predict the diagnosis of epilepsy in patients with transient loss of consciousness using the initial blood test results. A seizure classification model was constructed using logistic regression, and predictors were selected from a cohort of 260 patients using domain knowledge and statistical methods. The study defined the diagnosis of seizures and syncope based on the consistency of the diagnosis made by an emergency medicine specialist at the first visit to the emergency room and the diagnosis made by an epileptologist or cardiologist at the first outpatient visit using the International Classification of Diseases 10th revision (ICD-10) code. Univariate analysis showed higher levels of white blood cells, red blood cells, hemoglobin, hematocrit, delta neutrophil index, creatinine kinase, and ammonia levels in the seizure group. The ammonia level had the highest correlation with the diagnosis of epileptic seizures in the prediction model. Therefore, it is recommended to be included in the first examination at the emergency room.