Hospital Memories and Six-Month Psychological Outcome: A Prospective Study in Critical Ill Patients with COVID-19 Respiratory Failure.

ICU survivors suffer from various long-term physical and psychological impairments. Memories from the critical illness may influence long-term psychological outcome. In particular, the role of ICU memories in COVID-19 critically ill patients is unknown. In a prospective observational study, we aimed to investigate patients' memories from the experience of critical illness and their association with a six-month psychological outcome involving quality of life evaluation. Patients' memories were investigated with ICU Memory tool, while psychological outcome and quality of life were evaluated by means of a battery of validated questionnaires during an in-person interview at the follow-up clinic. 149 adult patients were enrolled. 60% retained memories from pre-ICU days spent on a general ward, while 70% reported memories from the in-ICU period. Delusional memories (i.e., memories of facts that never happened) were reported by 69% of patients. According to a multivariable analysis, the lack of pre-ICU memories was an independent predictor of worse psychological outcomes in terms of anxiety, depression and Post-traumatic Stress Disorder (PTDS). Factors associated with long-term outcome in ICU survivors are not still fully understood and patients' experience during the day spent before ICU admission may be associated with psychological sequelae.

On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks.

In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of pIC50 factor, which is a negative logarithmic expression of the half maximal inhibitory concentration (IC50). Determining this value can be a lengthy and complicated process. A tool allowing for a quick approximation of pIC50 based on the molecular makeup of medicine could be valuable. In this paper, the creation of the artificial intelligence (AI)-based model is performed using a publicly available dataset of molecules and their pIC50 values. The modeling algorithms used are artificial and convolutional neural networks (ANN and CNN). Three approaches are tested-modeling using just molecular properties (MP), encoded SMILES representation of the molecule, and the combination of both input types. Models are evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE) in a five-fold cross-validation scheme to assure the validity of the results. The obtained models show that the highest quality regression (R2¯=0.99, σR2¯=0.001; MAPE¯=0.009%, σMAPE¯=0.009), by a large margin, is obtained when using a hybrid neural network trained with both MP and SMILES.

Surface-enhanced Raman scattering as a potential strategy for wearable flexible sensing and point-of-care testing non-invasive medical diagnosis.

As a powerful and effective analytical tool, surface-enhanced Raman scattering (SERS) has attracted considerable research interest in the fields of wearable flexible sensing and non-invasive point-of-care testing (POCT) medical diagnosis. In this mini-review, we briefly summarize the design strategy, the development progress of wearable SERS sensors and its applications in this field. We present SERS substrate analysis of material design requirements for wearable sensors and highlight the benefits of novel plasmonic particle-in-cavity (PIC)-based nanostructures for flexible SERS sensors, as well as the unique interfacial adhesion effect and excellent mechanical properties of natural silk fibroin (SF) derived from natural cocoons, indicating promising futures for applications in the field of flexible electronic, optical, and electrical sensors. Additionally, SERS wearable sensors have shown great potential in the fields of different disease markers as well as in the diagnosis testing for COVID-19. Finally, the current challenges in this field are pointed out, as well as the promising prospects of combining SERS wearable sensors with other portable health monitoring systems for POCT medical diagnosis in the future.

COVID-19 Prevention Systems within Hardware and Software Design Computer Science Courses

This paper proposes pandemic support system design exercises from both hardware and software perspective as constituent part of higher education computer science courses. Two case studies in context of computer science and automation study programmes at University of Niš, Faculty of Electronic Engineering in Serbia ae covered: Intelligent Information Systems and Microcontroller Programming. In case of the first one, the topics cover information system implementation relying on Java Enterprise Edition (JEE) technology with presence of machine learning elements provided by Weka API, so smart vaccination process support information system is presented as example. On the other side, the focus on the second course is on PIC16 family microcontrollers and RTOS-based system implementation using CCS C compiler and presented example represents control unit for indoor coronavirus safety control. © 2022 IEEE.

Antiviral phytochemicals as potent inhibitors against NS3 protease of dengue virus.

Dengue, a mosquito-borne disease, has appeared as a major infectious disease globally. The virus requires its proteins to replicate and reproduce in the host cell. The NS3 protease converts the polyprotein to functional proteins with the help of the NS2B cofactor. Thus, NS3 protease is a promising target to develop antiviral inhibitors against the dengue virus. A systematic screening including ADMET properties, molecular docking, molecular dynamics (MD) simulation, binding free energy calculation, and QSAR studies is carried out to predict potent inhibitors against the NS3 protease. From the screening of 40 antiviral phytochemicals, ADMET properties analysis was used to screen out ligands that violate ADME rules and have probable toxicity. Cyanidin 3-Glucoside, Dithymoquinone, and Glabridin were predicted to be potent inhibitors against the NS3 protease according to their binding affinity. These ligands showed several noncovalent interactions, including hydrogen bond, hydrophobic interaction, electrostatic interaction, pi-sulfur interactions. The ligand-protein complexes were further scrutinized using 250 ns molecular dynamics simulation. The MM-PBSA binding free energy calculation was conducted to investigate their binding stability in dynamic conditions. The calculated pIC50(mM) value was predicted using the QSAR model with 89.91% goodness of fit. The predicted biologocal activity value for the ligands indicates they might have good potency.

Coagulopathy: Another side effect of coronavirus infection.

Recently, coronavirus disease 2019 (COVID-19) has been considered as a major health problem around the globe. This severe acute respiratory syndrome has a bunch of features, such as high transmission rate, which are adding to its importance. Overcoming this disease relies on a complete understanding of the viral structure, receptors, at-risk cells or tissues, and pathogenesis. Currently, researches have shown that besides the lack of a proper anti-viral therapeutic method, complications provided by this virus are also standing in the way of decreasing its mortality rate. One of these complications is believed to be a hematologic manifestation. Commonly, three kinds of coagulopathies are detected in COVID-19 patients: disseminated intravascular coagulation (DIC), pulmonary embolism (PE), and deep vein thrombosis (DVT). In this paper, we have reviewed the relation between these conditions and coronavirus-related diseases pathogenesis, severity, and mortality rate.