This study evaluates the effectiveness of various non-echocardiographic medical biomarkers, integrated with machine learning (ML) models, for classification of different types of Heart Failure (HF), specifically heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF). This differentiation is critical due to the distinct pathophysiology and management strategies required for these heart failure subtypes. A retrospective clinical dataset was collected from three medical centers in Serbia, comprising anonymized records of 481 heart failure patients ($221 \text{HFrEF}, 260 \text{HFpEF}$). The dataset included three primary types of variables: health records, laboratory test results, and electrocardiogram (ECG) data. Data preprocessing involved standardization of laboratory values and Multivariate Imputation by Chained Equations (MICE) to address missing parameters. Five different machine learning algorithms were employed, with an$80 / 20$train-test split and exhaustive Grid Search for hyperparameter optimization: Decision Tree, Random Forest, eXtreme Gradient Boosting Tree (XGBoost), Multilayer Perceptron (MLP), and Gradient Boosting Tree. Model performance was assessed using accuracy, precision, recall, and$\mathbf{F 1}$-score. The results indicate that ensemble methods, specifically Gradient Boosting Tree and Random Forest, consistently achieved superior performance across various data subsets. Notably, the combination of Health Records and ECG data yielded the highest predictive performance, with the Gradient Boosting Tree model achieving an F1-score of 0.7736 and a recall of 0.8039. Conversely, models trained predominantly on, or solely with, laboratory testing features consistently exhibited lower performance, suggesting that a broad inclusion of these biomarkers may introduce noise or redundancy for this specific classification task. These findings demonstrate that non-echocardiographic biomarkers can be effectively leveraged by machine learning models to differentiate between HFrEF and HFpEF, offering a promising diagnostic tool.

Heart failure (HF) is one of the most common medical conditions around the world in the modern age. Patients with HF struggle with reduced quality of life and have a greater risk of death. For these reasons it is important for HF to be diagnosed early before it starts to majorly impact patients and cause major concern for death. Left ventricular ejection fraction (LVEF) is one of the most important features which can be used to further classify HF patients into patients with reduced ejection fraction (HFrEF), mid-range ejection fraction (HFmrEF) and preserved ejection fraction (HFpEF). In this paper we describe a machine learning based pipeline for multistage classification of patients according to presence of HF and degree of reduced ejection fraction. Two separate pipelines are created in the scope of our research. Both pipelines classify patients based on whether HF is present or not. The first pipeline classifies HF patients into 3 classes based on their ejection fraction into reduced, mid-range and preserved classes. The second pipeline conducts classification of patients into LVEF$<50 {\%}$and LVEF$>50 {\%}$based on available features, followed by a second classification of HFrEF and HFpEF classes. HF classification model achieved 97% accuracy and$\mathbf{9 8 \%} \mathbf{F 1}$score for the confirmed HF class. The 3-class classification model achieved an overall prediction accuracy of 82% with F1 scores of 90%, 51% and 86% for HFpEF, HFmrEF and HFrEF respectively. The final two stage classification model achieved an overall accuracy of 96% and 87% for LVEF$>50$% versus LVEF$<50$% and mid-range versus reduced ejection fraction respectively. In the case of the twostage classification model F1 scores of$\mathbf{9 2 \%, 9 5 \%}$and 97% were achieved for HFpEF, HFmrEF and HFrEF respectively.

Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. Traditionally image segmentation was used on 2D imaging data, but due to the increased usage of 3D volumetric data there is a need for 3D segmentation techniques that could utilize spatial information contained in these volumes. One of the fields where there is a great amount of 3D data is microscopy. This paper introduces convolutional neural network based on 3D U-net architecture for segmentation of confocal microscopy images of cells in an in vitro sprouting angiogenesis model. Developed model contains 4 layers where each encoder block contains two 3D convolutional layers, Batch Normalization, ReLU activation function and 3D max pooling layer, while each decoder block contains upconvolution, skip connections and two 3D convolutional layers. Preprocessing of this data resulted in the volumes of shape 256 × 256 × 256 voxels which were used for training of the developed model. The model achieves great segmentation results as showed by Jaccard index value of 94.52% and Dice coefficient value of 99.31% compared to the preprocessed dataset. Even when segmentation results are compared to the original dataset, model still achieves respectable results of 84.22% Jaccard index and 88.18% Dice coefficient. This introduction of automatic 3D image segmentation could greatly reduce the time required for data preparation, while achieving high degree of segmentation accuracy.

The use of image segmentation is widespread in many different fields of research including medicine, biology, computer vision and others. In most cases, image segmentation is done through the use of supervised learning approaches, which utilize ground truth segmentation masks. However, in biomedical research, segmentation masks are often not available, which leads to the use of unsupervised segmentation approaches. In these situations, filtering paired with region expansion and reduction and edge detection techniques are used, which in some cases require time-consuming manual parameter tuning, in order to achieve satisfactory results. This paper implements fully unsupervised image segmentation based on a deep learning convolutional neural network to segment cell geometries from confocal microscopy images of an in vitro model of angiogenesis. The proposed network architecture is a W-Net which contains a single U-Encoder and U-Decoder, both of which contain 3 convolution blocks and 3 deconvolution blocks, with 2 convolution layers and one max-pooling or up-convolution layer respectively. The results were evaluated using available ground truth images and achieved a pixel wise classification accuracy score of 98.73% and 73.71% intersection over union. The achieved high degree of accuracy shows great promise in cell segmentation without a need for ground truth masks over time and increases the accuracy of calculations of exerted forces.

Colorectal cancer is a leading cause of cancerrelated deaths, and understanding its molecular mechanisms is key to improving treatments. This study applies machine learning algorithms to analyze the expression of genes related to redox balance, apoptosis, and cell migration in HCT-116 colorectal cancer cells treated with chemotherapeutics (5fluorouracil, oxaliplatin, irinotecan, and leucovorin). The analyzed genes include those for redox homeostasis (GPX1, GPX2, GPX3, GPX4, TXNRD1, GSTP1, NFE2L2, NFKB1, HIF1A), apoptosis (CASP3, CASP8, CASP9, FAS, BCL-2, BAX), and genes coding cytoskeleton proteins (CDH1, CTNNB1, CDH2). Machine learning models, such as decision trees and random forests were used to analyze gene expression changes based on qPCR data. Notably, GPX4 and BAX were linked to chemoresistance, while CDH1 upregulation suggested an effect on cancer cell migration. This study demonstrates the potential of machine learning in identifying gene expression patterns and aiding in the development of personalized CRC treatments.

Medical imaging plays an important role in medicine today, assisting in illness diagnosis and therapy. For limited medical image datasets, training from scratch is not an option, hence transfer learning emerges as a solution, with ImageNet weights being utilized as initial weights, followed by fine-tuning. This paper takes a different approach by introducing transfer learning approach with pretrained architecture DenseNet121 with CheXNeXt weights. Collected dataset consisted of 227269 X-ray images from public databases and 684 chest X-ray images from a retrospective study conducted in the University Clinical Center of Kragujevac and includes information on atelectasis, cardiomegaly, parenchymal consolidation, edema, effusion, emphysema, fibrosis, hiatus hernia, infiltration, pleural thickening, non-viral pneumonia, pneumothorax, viral pneumonia in the form of Covid-19, tuberculosis as well as tumors in the form of mass and nodules. The results show that the model is able to distinguish between the healthy and diseased lungs with average AUC of 0.91 (the lowest AUC of 0.8 for emphysema and the highest AUC for of 0.99 for pneumonia and 0.98 for COVID-19). Although the results seem promising, additional fine tuning may be necessary to improve other metrics. Future research will focus on this aspect, as well as on creating a glass box system for classification.