BACKGROUND: Inpatient glucose management can be challenging due to evolving factors that influence a patient\'s blood glucose (BG) throughout hospital admission. The purpose of our study was to predict the category of a patient\'s next BG measurement based on electronic medical record (EMR) data.
METHODS: EMR data from 184,361 admissions containing 4,538,418 BG measurements from five hospitals in the Johns Hopkins Health System were collected from patients who were discharged between January 1, 2015 and May 31, 2019. Index BGs used for prediction included the 5th to penultimate BG measurements (N = 2,740,539). The outcome was category of next BG measurement: hypoglycemic (BG  ≤  70 mg/dl), controlled (BG 71-180 mg/dl), or hyperglycemic (BG > 180 mg/dl). A random forest algorithm that included a broad range of clinical covariates predicted the outcome and was validated internally and externally.
RESULTS: In our internal validation test set, 72·8%, 25·7%, and 1·5% of BG measurements occurring after the index BG were controlled, hyperglycemic, and hypoglycemic respectively. The sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·77/0·81, 0·77/0·89, and 0·73/0·91, respectively. On external validation in four hospitals, the ranges of sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·64-0·70/0·80-0·87, 0·75-0·80/0·82-0·84, and 0·76-0·78/0·87-0·90, respectively.
CONCLUSIONS: A machine learning algorithm using EMR data can accurately predict the category of a hospitalized patient\'s next BG measurement. Further studies should determine the effectiveness of integration of this model into the EMR in reducing rates of hypoglycemia and hyperglycemia.

BACKGROUND: Multiple myeloma (MM) is the second incurable hematological malignancy. In recent years, due to the rise of microRNA (miRNA), many scholars have participated in the study of its value in the diagnosis of MM, and have obtained good but inconsistent results. Therefore, in order to determine the role of miRNA in the early diagnosis of MM, we performed this meta-analysis.
METHODS: We searched for related studies including PubMed, Web of Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI) and Wanfang Database as of July 20, 2020 to conduct this meta-analysis. To improve the accuracy, the quality assessment of Diagnostic Accuracy Study 2 (QUADAS-2) was used. We also applied random effects models to summarize sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) to measure diagnostic values, and subgroup analysis used to discover potential sources of heterogeneity.
RESULTS: We finally collected 32 studies from 15 articles that included a total of 2053 MM patients and 1118 healthy controls in this meta-analysis. The overall sensitivity, specificity, PLR, NLR, DOR and AUC were 0.81, 0.85, 5.5, 0.22, 25 and 0.90, respectively. Subgroup analysis shows that the down-regulation of microRNA clusters with larger samples size of plasma type could carry out a better diagnostic accuracy of MM patients. In addition, publication bias was not found.
CONCLUSIONS: Circulating miRNA could be a potential non-invasive biomarker for early diagnosis of MM. However, multi-center, more rigorous, and larger-scale studies are needed to verify our conclusions.

Animal testing is used in pharmaceutical and industrial research to predict human toxicity, and yet analysis suggests that animal models are poor predictors of drug safety in humans. The cost of animal research is high-in dollars, delays in drug approval, and in the loss of potentially beneficial drugs for human use. Human subjects have been harmed in the clinical testing of drugs that were deemed safe by animal studies. Increasingly, investigators are questioning the scientific merit of animal research. This review discusses issues in using animals to predict human toxicity in pharmaceutical development. Part 1 focuses on scientific concerns over the validity of animal research. Part 2 will discuss alternatives to animal research and their validation and use in production of human pharmaceuticals.

UNASSIGNED: Machine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization, or prediction. ML techniques have been traditionally applied to large, highly dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathologic features. Recently, the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly dimensional database of patients with glioma.
UNASSIGNED: We applied 3 ML techniques (artificial neural networks [ANNs], decision trees [DTs], and support vector machines [SVMs]) and classical logistic regression (LR) to a dataset consisting of 76 patients with glioma of all grades. We compared the effect of applying the algorithms to the raw database versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).
UNASSIGNED: Raw input consisted of 21 variables and achieved performance of accuracy/area (C.I.) under the curve of 70.7%/0.70 (49.9-88.5) for ANN, 68%/0.72 (53.4-90.4) for SVM, 66.7%/0.64 (43.6-85.0) for LR, and 65%/0.70 (51.6-89.5) for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 (62.9-87.9) for ANN, 73.3%/0.74 (62.1-87.4) for SVM, 69.3%/0.73 (60.0-85.8) for LR, and 65.2%/0.63 (49.1-76.9) for DT.
UNASSIGNED: We demonstrate that these techniques can also be applied to small, highly dimensional datasets. Our ML techniques achieved reasonable performance compared with similar studies in the literature. Although local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis; however, traditional statistical methods are of similar benefit.