SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO₂ Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO₂) waveforms 5 and 30 minutes in the future using only prior SpO₂ values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO₂ waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.     

LIBS Based Screening of Glycemic Elements of Ficus religiosa

The effectiveness of various trace element concentrations in medicinal plants in the cure of various diseases can be determined by their quantitative estimation. Elemental concentrations of aqueous extract of F. religiosa leaves were measured by Laser-induced breakdown spectroscopy (LIBS). LIBS is a very powerful and efficient analytical tool for determining elemental constitution. The present study deals with the LIBS-based validation of elements responsible for the glycemic potential of aqueous extract of F. religiosa leaves in streptozotocin-induced diabetic models. The significant decrease in blood glucose level and marked improvement in glucose tolerance test of diabetic models is correlated to the concentration of elements present in the extract as revealed by LIBS spectra. Elements such as Mg and Ca have been observed in the LIBS spectra of F. religiosa.     

Radiation resistance of Deinococcus radiodurans R1 with respect to growth phase

Deinococcus species exhibit an extraordinary ability to withstand ionizing radiation (IR). Most of the studies on radiation resistance have been carried out with exponential phase cells. The studies on radiation resistance of Deinococcus radiodurans R1 with respect to different phases of growth showed that late stationary phase cells of D. radiodurans R1 were fourfold more sensitive to IR and heat as compared with exponential or early stationary phase cells. The increased sensitivity of D. radiodurans R1 to IR in the late stationary phase was not due to a decrease in the intracellular Mn/Fe ratio or an increase in the level of oxidative protein damage. The resistance to IR was restored when late stationary phase cells were incubated for 15 min in fresh medium before irradiation, indicating that replenishment of exhausted nutrients restored the metabolic capability of the cells to repair DNA damage. These observations suggest that stress tolerance mechanisms in D. radiodurans R1 differ from established paradigms.