Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

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INTRODUCTION:As a result of this global health crisis caused by the COVID-19 pandemic, the medical industry is searching for innovations that have the potential to automate the diagnostic process of COVID-19 and serve as an assistive tool for clinicians. OBJECTIVES: X-ray images have shown to be useful in the diagnosis of COVID-19. The goal of this research is to demonstrate an approach for automatic segmentation of lungs in chest X-ray images. METHODS: In this research DeepLabv3+ with Xception_65, MobileNetV2, and ResNet101 as backbones are used in order to perform lung segmentation. RESULTS: The proposed approach was experimented on X-ray images and has achieved an average mIOU of 0.910, F1 of 0.925, accuracy of 0.968, precision of 0.916, sensitivity of 0.935, and specificity of 0.977. CONCLUSION: Based on the obtained results, the proposed approach proved to be successful in terms of lung segmentation in chest X-ray images and has a great potential for clinical use.

Cilj: Veno-arterijska izvantjelesna membranska oksigenacija (engl. veno-arterial extracorporeal membrane oxygenation; VA-ECMO) metoda je pružanja potpore funkciji srca u pacijenata s kardiogenim šokom kod kojih medikamentozna terapija nije dovoljna. Cilj rada je ukazati, kroz prikaz liječenja pacijenta koji je u ranom poslijeoperacijskom tijeku po transplantaciji bubrega razvio kardiogeni šok, na veliki potencijal primjene izvantjelesne mehaničke potpore radu srca kao metode liječenja kardiogenog šoka u ovih pacijenata, koja je, u opisanom tijeku liječenja, omogućila ne samo preživljavanje pacijenta, već i očuvanje funkcije transplantata. Prikaz slučaja: Na Odjel intenzivnog liječenja (OIL) primljen je šezdesetjednogodišnji pacijent sa završnim stadijem kronične bubrežne bolesti, kojem je učinjena transplantacija bubrega. Tijek operacijskog zahvata komplicirao se razvojem značajne hemodinamske nestabilnosti. Pacijent je, usprkos svim poduzetim medikamentoznim mjerama liječenja, razvio infarkt miokarda s kardiogenim šokom. S obzirom na to da je stanje bilo refraktorno na primijenjenu medikamentoznu terapiju, donesena je odluka o primjeni izvantjelesne mehaničke potpore cirkulaciji. Mehanička potpora dovela je do hemodinamske stabilizacije pacijenta i poslužila je kao most do oporavka srčane funkcije s uspješnim očuvanjem funkcije transplantata. Zaključci: U rastućem broju radova mehanička cirkulatorna potpora je označena kao budućnost liječenja kardiogenog šoka, neovisno o njegovoj etiologiji. Kao i sve druge metode liječenja, nosi rizik od razvoja komplikacija, koji se razvojem tehnologije polako smanjuje. Ona postaje sve dostupnija te se njezina primjena širi na različite skupine pacijenata. Važno je naglasiti da je za uspjeh terapije nužna suradnja specijalista različitih specijalnosti u liječenju ovako kompleksnih pacijenata.