Objectives: Determining low-density lipoprotein (LDL) is a costly and time-consuming operation, but triglyceride value above 400 (TG>400) always requires LDL measurement. Obtaining a fast LDL forecast by accurate prediction can be valuable to experts. However, if a high error margin exists, LDL prediction can be critical and unusable. Our objective is LDL value and level prediction with an error less than low total acceptable error rate (% TEa). Methods: Our present work used 6392 lab records to predict the patient LDL value using state-of-the-art Artificial Intelligence methods. The designed model, p-LDL-M, predicts LDL value and class with an overall average test score of 98.70 %, using custom, hyper-parameter-tuned Ensemble Machine Learning algorithm. Results: The results show that using our innovative p-LDL-M is advisable for subjects with critical TG>400. Analysis proved that our model is positively affected by the Hopkins and Friedewald equations normally used for (TG≤400). The conclusion follows that the test score performance of p-LDL-M using only (TG>400) is 7.72 % inferior to the same p-LDL-M, using Hopkins and Friedewald supported data. In addition, the test score performance of the NIH-Equ-2 for (TG>400) is much inferior to p-LDL-M prediction results. Conclusions: In conclusion, obtaining an accurate and fast LDL value and level forecast for people with (TG>400) using our innovative p-LDL-M is highly recommendable.

Human SH-SY5Y cell line has been used as an in vitro model in neuroscience research. However, many researchers emphasized that there are many differences between differentiated and undifferentiated characteristics of SH-SY5Y. Although Retinoic Acid (RA) generally have been used for differentiation of SH-SY5Y cells, which protocol provides better differentiation have not been cleared yet. Therefore, we compared RA and the other mediums in different treatment periods for obtaining better differentiated, carrying a neuron-like phenotype (N-type) characteristics, of SH-SY5Y human cells. The cells were pre-treated with different mediums for different treatment periods and compared with both for each other and the control group. When we pre-treated the cells with RA for 5-day and followed by the Mix medium (Neurobasal, B27, db-cAMP, KCI) and BDNF, neurite length and MAP2 expression of the cells were found significantly higher than the control group. In conclusion, we showed that more than one agent (RA) is clearly necessary to reach better differentiation of SH-SY5Y cells.

Background/Objectives: Vitamin B12 deficiency is a prevalent yet frequently underdiagnosed condition, largely due to the limited diagnostic accuracy of serum total B12 and the restricted availability of confirmatory biomarkers such as holotranscobalamin and methylmalonic acid. This study aimed to develop and validate explainable machine learning (ML) models capable of predicting vitamin B12 deficiency using only routinely available laboratory examinations, thereby supporting early detection within standard diagnostic workflows. Methods: This retrospective study included 51,630 adult patients who underwent concurrent vitamin B12 testing and routine laboratory evaluation between 2015 and 2025. An independent temporal validation cohort of 34,744 patients was used to assess generalizability. Eight supervised ML algorithms were developed within a four-stage experimental framework incorporating default modeling, probability-threshold optimization, hyperparameter tuning, and feature engineering. Model performance was evaluated using AUC-ROC, AUC-PR, sensitivity, specificity, F1 score, accuracy, Matthews correlation coefficient, and likelihood ratios. Model explainability and clinical utility were assessed using SHAP, LIME, and decision curve analysis. Results: Among all algorithms, CatBoost demonstrated the most balanced and clinically relevant performance. In the threshold-optimized configuration, the model achieved a sensitivity of 0.92, specificity of 0.67, F1 score of 0.82, AUC-ROC of 0.88, and AUC-PR of 0.86 in the test set. Temporal validation confirmed robust generalizability, with improved discrimination (AUC-ROC 0.90; AUC-PR 0.91) and stable calibration. Explainability analyses identified hematologic indices (MCV, HGB, HCT, RDW), iron-related markers, inflammatory measurands, and age as the most influential contributors, consistent with known pathophysiology. Conclusions: This study presents a large-scale, explainable, and temporally validated ML framework for predicting vitamin B12 deficiency using routine laboratory data alone. The model demonstrates strong diagnostic performance, biological plausibility, and potential for seamless integration into laboratory and clinical decision-support systems, enabling cost-effective and early identification of patients at risk.