Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.
Lassa hemorrhagic fever, caused by Lassa mammarenavirus (LASV) infection, accumulates up to 5000 deaths every year. Currently, there is no vaccine available to combat this disease. In this study, a library of 200 bioactive compounds was virtually screened to study their drug-likeness with the capacity to block the α-dystroglycan (α-DG) receptor and prevent LASV influx. Following rigorous absorption, distribution, metabolism, and excretion (ADME) and quantitative structure-activity relationship (QSAR) profiling, molecular docking was conducted with the top ligands against the α-DG receptor. The compounds chrysin, reticuline, and 3-caffeoylshikimic acid emerged as the top three ligands in terms of binding affinity. Post-docking analysis revealed that interactions with Arg76, Asn224, Ser259, and Lys302 amino acid residues of the receptor protein were important for the optimum binding affinity of ligands. Molecular dynamics simulation was performed comprehensively to study the stability of the protein-ligand complexes. In-depth assessment of root-mean-square deviation (RMSD), root mean square fluctuation (RMSF), polar surface area (PSA), B-Factor, radius of gyration (Rg), solvent accessible surface area (SASA), and molecular surface area (MolSA) values of the protein-ligand complexes affirmed that the candidates with the best binding affinity formed the most stable protein-ligand complexes. To authenticate the potentialities of the ligands as target-specific drugs, an in vivo study is underway in real time as the continuation of the research.