Quality improvement initiative approach to increase the duration of Kangaroo Mother Care in a neonatal intensive care unit of a tertiary care institute in South India during the COVID-19 pandemic.

INTRODUCTION: Kangaroo mother care (KMC) provided to stable babies in hospitals is associated with 40% relative risk reduction in death, 65% risk reduction in nosocomial infections. Despite clear existing evidence of advantages of KMC, its implementation remains limited.This study aimed to improve the median KMC practice hours in eligible preterm and low birth weight (LBW) neonates by 50% from the baseline practice. METHODS: This was a Quality Improvement study conducted at Neonatal unit of a tertiary care institute in South India. All stable preterm and LBW neonates were included after obtaining written informed consent from mother. Those who needed interruption in KMC due to medical reason were excluded. A team comprising of 2 principal investigators (UG students), 2 consultants and 2 in-charge nurses was formed. Baseline data were collected between January and February 2021 to find out the median duration of KMC practice and to identify limiting factors (barriers) and the facilitating ones through in-depth interviews and team meetings. The study was conducted over a 10 month period. Steps were taken to tackle these in two PDSA cycles, each lasting for 3 weeks (1st PDSA: Education of Mothers and Nurses; 2nd PDSA: KMC technique, orders by residents). The PDSA was followed by monitoring for 10 weeks for sustenance. RESULTS: The baseline data showed that the median duration (in hours) of KMC practice was 2.6 which increased to 5.0 and 5.5 h by the end of first and second PDSA cycle, respectively and showed a lasting change, peaking at a median value of 6.1 h during the sustenance phase over the next 10 weeks. CONCLUSION: Through simple measures and closing the communication gap between health care workers and mothers, we were able to increase the duration of KMC, which remained high during the 10 week follow up period.

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.

BACKGROUND: COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization. METHOD: ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted. RESULTS: Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions. CONCLUSIONS: Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

Recharging and rejuvenation of decontaminated N95 masks.

N95 respirators comprise a critical part of the personal protective equipment used by frontline health-care workers and are typically meant for one-time usage. However, the recent COVID-19 pandemic has resulted in a serious shortage of these masks leading to a worldwide effort to develop decontamination and re-use procedures. A major factor contributing to the filtration efficiency of N95 masks is the presence of an intermediate layer of charged polypropylene electret fibers that trap particles through electrostatic or electrophoretic effects. This charge can degrade when the mask is used. Moreover, simple decontamination procedures (e.g., use of alcohol) can degrade any remaining charge from the polypropylene, thus severely impacting the filtration efficiency post-decontamination. In this report, we summarize our results on the development of a simple laboratory setup allowing measurement of charge and filtration efficiency in N95 masks. In particular, we propose and show that it is possible to recharge the masks post-decontamination and recover filtration efficiency.