Development of Biomarkers and Prognosis Model of Mortality Risk in Patients with COVID-19

Background

As of 30 April 2023, the COVID-19 pandemic has resulted in over 6.9 million deaths worldwide. The virus continues to spread and mutate, leading to continuously evolving pathological and physiological processes. It is imperative to reevaluate predictive factors for identifying the risk of early disease progression.

Methods

A retrospective study was conducted on a cohort of 1379 COVID-19 patients who were discharged from Xin Hua Hospital affiliated with Shanghai Jiao Tong University School of Medicine between 15 December 2022 and 15 February 2023. Patient symptoms, comorbidities, demographics, vital signs, and laboratory test results were systematically documented. The dataset was split into testing and training sets, and 15 different machine learning algorithms were employed to construct prediction models. These models were assessed for accuracy and area under the receiver operating characteristic curve (AUROC), and the best-performing model was selected for further analysis.

Results

AUROC for models generated by 15 machine learning algorithms all exceeded 90%, and the accuracy of 10 of them also surpassed 90%. Light Gradient Boosting model emerged as the optimal choice, with accuracy of 0.928 ± 0.0006 and an AUROC of 0.976 ± 0.0028. Notably, the factors with the greatest impact on in-hospital mortality were growth stimulation expressed gene 2 (ST2,19.3%), interleukin-8 (IL-8,17.2%), interleukin-6 (IL-6,6.4%), age (6.1%), NT-proBNP (5.1%), interleukin-2 receptor (IL-2R, 5%), troponin I (TNI,4.6%), congestive heart failure (3.3%) in Light Gradient Boosting model.

Conclusion

ST-2, IL-8, IL-6, NT-proBNP, IL-2R, TNI, age and congestive heart failure were significant predictors of in-hospital mortality among COVID-19 patients.

An innovative approach of urban noise monitoring using cycle in Nagpur, India

Noise is one of the most significant sources of environmental pollution in metro cities. Traffic noise plays an important role to contribute to ambient noise pollution. Noise pollution monitoring was carried out using sound level meter mounted on the cycle with the help of mould fixed on it. Cycle is a noiseless mode of transport and therefore, it was used for noise monitoring over 700 locations in Nagpur City. Noise levels were recorded at each location for 5 min during peak rush hours of traffic in the morning and in the evening at an interval of 1 s leading to around 300 readings each. Noise indices like L_eq, L₉₀, L₁₀, L_max and L_min were calculated for noise level data analysis. TNI was also determined for analysis of traffic noise on intersections of roads. Equivalent noise levels were transferred into the Geographic Information System (GIS) and spatial analysis tool was used to create spatial and strategic noise maps. Based on the maps, it is revealed that noise levels are exceeded all over the city with respect to ambient noise standards. It was experienced that intersections of roads during peak rush time are very noisy due to honking. The study recommends the noise monitoring through cycle-mounted sound level meter since it covers unapproachable sites, more coverage in the city that helps in identifying hotspot area of concern for noise abatement strategies and planning.

Challenging the catechism of therapeutics for chronic neuropathic pain: Targeting CaV2.2 interactions with CRMP2 peptides

Chronic neuropathic pain management is a worldwide concern. Pharmaceutical companies globally have historically targeted ion channels as the therapeutic catechism with many blockbuster successes. Remarkably, no new pain therapeutic has been approved by European or American regulatory agencies over the last decade. This article will provide an overview of an alternative approach to ion channel drug discovery: targeting regulators of ion channels, specifically focusing on voltage-gated calcium channels. We will highlight the discovery of an anti-nociceptive peptide derived from a novel calcium channel interacting partner - the collapsin response mediator protein 2 (CRMP2). In vivo administration of this peptide reduces pain behavior in a number of models of neuropathic pain without affecting sympathetic-associated cardiovascular activity, memory retrieval, sensorimotor function, or depression. A CRMP2-derived peptide analgesic, with restricted access to the CNS, represents a completely novel approach to the treatment of severe pain with an improved safety profile. As peptides now represent one of the fastest growing classes of new drugs, it is expected that peptide targeting of protein interactions within the calcium channel complex may be a paradigm shift in ion channel drug discovery.