Long-term prediction of Iranian blood product supply using LSTM: a 5-year forecast.

BACKGROUND: This study aims to predict the trend of procurement and storage of various blood products, as well as planning and monitoring the consumption of blood products in different centers across Iran based on artificial intelligence until the year 2027. METHODS: This research constitutes a time-series investigation within the realm of longitudinal studies. In this study, information on the number of packed red blood cells (RBC), leukoreduced red blood cells (LR-RBC), and platelets (PLT), PLT-Apheresis, and fresh frozen plasma (FFP) was requested from all blood transfusion centers in the country and extracted using a unified protocol. After the initial examination of the information and addressing data issues and inconsistencies, the corrected data were analyzed. Both conventional and artificial intelligence approaches were used to predict each product in this study. The best model was selected based on goodness-of-fit indicators RMSE and MAPE. RESULTS: Based on the obtained results, the FFP product will follow a relatively consistent process similar to previous years in the next five years. The PLT product is predicted to have a growing trend over the next 5 years, which applies to both the demand and supply of the product. The PLT-Apheresis product also shows a similar upward trend, albeit with a lower growth rate. The RBC product will have a constant trend over a 5-year period (long-term) according to both models, taking into account short-term changes. Similarly, there is a similar trend in LR-RBC, with the expectation that short-term pattern repetition will continue over a 5-year period (long-term). Comparing the goodness-of-fit results, the LSTM model proved to be better for predicting the dominant blood products. CONCLUSIONS: The growth of the elderly population and diseases related to old age, and on the other hand, the trend of increasing the consumption of the product with a short lifespan (PLT) requires the activation of the management of the patient's blood, especially in relation to this product in medical centers. The trend for other products in the next five years is similar to previous years, and no growth in demand is observed. The LSTM method, considering periodic and cyclical events, has performed the prediction.

The association of the apolipoprotein B/A1 ratio and the metabolic syndrome in children and adolescents: a systematic review and meta-analysis.

Objectives: Metabolic syndrome (MetS) is a constellation of coexisting cardiovascular risk factors. This study aimed to assess the evidence for the association between the apolipoprotein B/A1 ratio, apolipoprotein B, and apolipoprotein A1, and the MetS in children and adolescents. Methods: The English electronic databases including PubMed, Embase, Web of Science, and Scopus were searched up to February 28, 2022. To ascertain the validity of eligible studies, modified JBI scale was used. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were pooled using the random-effects model to evaluate the association between the apolipoprotein B/A1 ratio, apolipoprotein B, and apolipoprotein A1 and the MetS. Heterogeneity amongst the studies was determined by the use of the Galbraith diagram, Cochran's Q-test, and I2 test. Publication bias was assessed using Egger's and Begg's tests. Results: From 7356 records, 5 studies were included in the meta-analysis, representing a total number of 232 participants with MetS and 1320 participants as control group. The results indicated that increased levels of apolipoprotein B/A1 ratio (SMD 1.26; 95% CI: 1.04, 1.47) and apolipoprotein B (SMD 0.75; 95% CI: 0.36, 1.14) and decreased levels of apolipoprotein A1 (SMD -0.53; 95% CI: -0.69, -0.37) are linked to the presence of MetS. The notable findings were, children and adolescents with MetS had elevated levels of the apolipoprotein B/A1 ratio, apolipoprotein B, and decreased levels of apolipoprotein A1. Conclusions: Our results suggest the need to evaluate the levels of apolipoproteins for detecting the risk of MetS in children and adolescents. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-023-01235-z.

Which risk factor best predicts coronary artery disease using artificial neural network method?

BACKGROUND: Coronary artery disease (CAD) is recognized as the leading cause of death worldwide. This study analyses CAD risk factors using an artificial neural network (ANN) to predict CAD. METHODS: The research data were obtained from a multi-center study, namely the Iran-premature coronary artery disease (I-PAD). The current study used the medical records of 415 patients with CAD hospitalized in Razi Hospital, Birjand, Iran, between May 2016 and June 2019. A total of 43 variables that affect CAD were selected, and the relevant data was extracted. Once the data were cleaned and normalized, they were imported into SPSS (V26) for analysis. The present study used the ANN technique. RESULTS: The study revealed that 48% of the study population had a history of CAD, including 9.4% with premature CAD and 38.8% with CAD. The variables of age, sex, occupation, smoking, opium use, pesticide exposure, anxiety, sexual activity, and high fasting blood sugar were found to be significantly different among the three groups of CAD, premature CAD, and non-CAD individuals. The neural network achieved success with five hidden fitted layers and an accuracy of 81% in non-CAD diagnosis, 79% in premature diagnosis, and 78% in CAD diagnosis. Anxiety, acceptance, eduction and gender were the four most important factors in the ANN model. CONCLUSIONS: The current study shows that anxiety is a high-prevalence risk factor for CAD in the hospitalized population. There is a need to implement measures to increase awareness about the psychological factors that can be managed in individuals at high risk for future CAD.