Neutrophil to lymphocyte ratio and in-hospital mortality among patients with SARS-CoV-2: A retrospective study.

The goal of this study was to investigate in-hospital mortality in patients suffering from acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relative to the neutrophil to lymphocyte ratio (NLR) and to determine if there are gender disparities in outcome. Between February 26 and September 8, 2020, patients having SARS-CoV-2 infection were enrolled in this retrospective cohort research, which was categorized by NLR levels ≥9 and < 9. In total, 6893 patients were involved included of whom6591 had NLR <9, and 302 had NLR ≥9. The age of most of the patients in the NLR<9 group was 50 years, on the other hand, the age of most of the NLR ≥9 group patients was between 50 and 70 years. The majority of patients in both groups were male 2211 (66.1%). The ICU admission time and mortality rate for the patients with NLR ≥9 was significantly higher compared to patients with NLR <9. Logistic regression's outcome indicated that NLR ≥9 (odds ratio (OR), 24.9; 95% confidence interval (CI): 15.5-40.0; p < 0.001), male sex (OR, 3.5; 95% CI: 2.0-5.9; p < 0.001) and haemoglobin (HB) (OR, 0.95; 95% CI; 0.94-0.96; p < 0.001) predicted in-hospital mortality significantly. Additionally, Cox proportional hazards analysis (B = 4.04, SE = 0.18, HR = 56.89, p < 0.001) and Kaplan-Meier survival probability plots also indicated that NLR>9 had a significant effect on mortality. NLR ≥9 is an independent predictor of mortality(in-hospital) among SARS-CoV-2 patients.

Fatality assessment and variant risk monitoring for COVID-19 using three new hospital occupancy related metrics.

BACKGROUND: Though case fatality rate (CFR) is widely used to reflect COVID-19 fatality risk, its use is limited by large temporal and spatial variation. Hospital mortality rate (HMR) is also used to assess the severity of COVID-19, but HMR data is not directly available globally. Alternative metrics are needed for COVID-19 severity and fatality assessment. METHODS: We introduce new metrics for COVID-19 fatality risk measurements/monitoring and a new mathematical model to estimate average hospital length of stay for deaths (Ldead) and discharges (Ldis). Multiple data sources were used for our analyses. FINDINGS: We propose three, new metrics: hospital occupancy mortality rate (HOMR), ratio of total deaths to hospital occupancy (TDHOR), and ratio of hospital occupancy to cases (HOCR), for dynamic assessment of COVID-19 fatality risk. Estimated Ldead and Ldis for 501,079 COVID-19 hospitalizations in 34 US states between 7 August 2020 and 1 March 2021 were 18·2(95%CI:17·9-18·5) and 14·0(95%CI:13·9-14·0) days, respectively. We found the dramatic changes in COVID-19 CFR observed in 27 countries during early stages of the pandemic were mostly caused by undiagnosed cases. Compared to the first week of November 2021, the week mean HOCRs (mimics hospitalization-to-case ratio) for Omicron variant (58·6% of US new cases as of 25 December 2021) decreased 65·16% in the US as of 16 January 2022. INTERPRETATION: The new and reliable measurements described here could be useful for COVID-19 fatality risk and variant-associated risk monitoring. FUNDING: No specific funding was associated with the present study.

Leveraging AI for advanced analytics to forecast altered tourism industry parameters: A COVID-19 motivated study.

COVID-19 pandemic has given a sudden shock to economy indices worldwide and especially to the tourism sector, which is already very sensitive to such crises as natural calamities, terrorist activities, virus outbreaks and unwanted conditions. The economic implications for a reduction in tourism demand, and the need to analyse post-COVID-19 tourism motivates our research. This study aims to forecast the future trends for foreign tourist arrivals and foreign exchange earnings for India and to formulate a model to predict the future trends based on the COVID-19 parameters, vaccinations and stringency index (Government travelling guidelines). In the study, we have developed artificial intelligence models (random forest, linear regression) using the stacked based ensemble learning method for the development of base models and meta models for the study of COVID-19 and its effect on the tourism industry. The architecture of a stacking model consists of two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, and is referred to as a level-1 model (Smyth & Wolpert, 1999). The results show that the projected losses require quick action on developing new practices to sustain and complement the resilience of tourism per se.

The Influence of Maximum Squatting Strength on Jump and Sprint Performance: A Cross-Sectional Analysis of 492 Youth Soccer Players.

This study aims to analyze the influence of relative strength performance, determined by parallel back squats (REL SQ), on 30 m sprinting (LS) and on jumping performance (squat [SJ], countermovement [CMJ]) in a large sample (n = 492) of elite youth soccer players. The soccer players were divided into subgroups based on their strength performance: strength level 1 (0.0-0.5 REL SQ), strength level 2 (>0.5-1.0 REL SQ), strength level 3 (>1.0 to 1.5 REL SQ), strength level 4 (>1.5 to 2.0 REL SQ), and strength level 5 (>2.0 REL SQ). The results of this study show that REL SQ explains 45-53% (r = |0.67-0.73|) of the variance of SJ, CMJ, and LS for the total sample. Strength levels 2-4 showed similar coefficients of correlation in jumping performance (r = |0.42-0.55|) and strength levels 2 and 3 in sprint performance (r = |0.41|). The respective extreme strength levels showed lower coefficients of correlation with the sprinting and jumping performance variables (r = |0.11-0.29|). No coefficients could be calculated for strength level 5 because no athlete achieved an appropriate strength level (>2.0 REL SQ). The data from this study show a clear influence of REL SQ on sprint and jump performance, even in a large sample.

Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models.

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking.

Development of SARS-CoV-2 packaged RNA reference material for nucleic acid testing.

Nucleic acid tests to detect the SARS-CoV-2 virus have been performed worldwide since the beginning of the COVID-19 pandemic. For the quality assessment of testing laboratories and the performance evaluation of molecular diagnosis products, reference materials (RMs) are required. In this work, we report the production of a lentiviral SARS-CoV-2 RM containing approximately 12 kilobases of its genome including common diagnostics targets such as RdRp, N, E, and S genes. The RM was measured with multiple assays using two different digital PCR platforms. To measure the homogeneity and stability of the lentiviral SARS-CoV-2 RM, reverse transcription droplet digital PCR (RT-ddPCR) was used with in-house duplex assays. The copy number concentration of each target gene in the extracted RNA solution was then converted to that of the RM solution. Their copy number values are measured to be from 1.5 × 105 to 2.0 × 105 copies/mL. The RM has a between-bottle homogeneity of 4.80-8.23% and is stable at 4 °C for 1 week and at -70 °C for 6 months. The lentiviral SARS-CoV-2 RM closely mimics real samples that undergo identical pre-analytical processes for SARS-CoV-2 molecular testing. By offering accurate reference values for the absolute copy number of viral target genes, the developed RM can be used to improve the reliability of SARS-CoV-2 molecular testing.

"MuCovid-21" study: Mucormycosis at an Indian tertiary care centre during the COVID-19 pandemic.

BACKGROUND: Mucormycosis has been reported in several Indian states with the second wave of COVID-19 from January 2021 onwards. This prospective study was conducted with the objective of defining the contribution or association of various known and many suspected factors in the incidence of this disease during the ongoing pandemic. METHOD: The study included 464 adults with mucormycosis. Basic demographic data were collected. Patient history of COVID infection, its severity, duration of treatment, and oxygen use was taken to assess the association. History of use of antibiotics, steroids, antivirals, biologicals, and other complementary treatments was sought. History of diabetes and other comorbidities was noted. Patients were investigated for mucor confirmation using a nasal swab KOH mount, nasal endoscopy with biopsy, and radiological investigations were done to assess the extent. RESULTS: Out of 464 patients, 175 were known diabetics, and 157 were treated with steroids during COVID infection. Out of 287 post-COVID patients, 125 (44%) had rhino-mucormycosis (RM), 102 (35%) had rhino-oculo-mucormycosis (ROM) and 60 (21%) had rhino-oculo-cerebral-mucormycosis (ROCM). 162 did not have a history of COVID, of which 93 (57%) had RM, 48 (30%) had ROM and 21 (13%) had ROCM. CONCLUSION: This study has showed that COVID was not the only factor contributing to mucor, rather other factors such as diabetes, steroid use etc. were also contributory. Many patients who were suffering from mucormycosis did not have a history of COVID. Advanced age, ROCM, and ICU admission were associated with increased mortality.

The physical and mental health of the medical staff in Wuhan Huoshenshan Hospital during COVID-19 epidemic: A Structural Equation Modeling approach.

INTRODUCTION: Early in the epidemic of coronavirus disease 2019, the Chinese government recruited a proportion of healthcare workers to support the designated hospital (Huoshenshan Hospital) in Wuhan, China. The majority of front-line medical staff suffered from adverse effects, but their real health status during COVID-19 epidemic was still unknown. The aim of the study was to explore the latent relationship of the physical and mental health of front-line medical staff during this special period. METHODS: A total of 115 military medical staff were recruited between February 17th and February 29th, 2020 and asked to complete questionnaires assessing socio-demographic and clinical characteristics, self-reported sleep status, fatigue, resilience and anxiety. RESULTS: 55 medical staff worked within Intensive Care and 60 worked in Non-intensive Care, the two groups were significantly different in reported general fatigue, physical fatigue and tenacity (P<0.05). Gender, duration working in Wuhan, current perceived stress level and health status were associated with significant differences in fatigue scores (P<0.05), the current perceived health status (P<0.05) and impacted on the resilience and anxiety of participants. The structural equation modeling analysis revealed resilience was negatively associated with fatigue (ß=-0.52, P<0.01) and anxiety (ß=-0.24, P<0.01), and fatigue had a direct association with the physical burden (ß=0.65, P<0.01); Fatigue mediated the relationship between resilience and anxiety (ß=-0.305, P=0.039) as well as resilience and physical burden (ß=-0.276, P=0.02). CONCLUSION: During an explosive pandemic situation, motivating the effect of protective resilience and taking tailored interventions against fatigue are promising ways to protect the physical and mental health of the front-line medical staff.

Remote Learning for Children with Auditory Access Needs: What We Have Learned during COVID-19.

COVID-19 has challenged most everyone in every facet of life. In the beginning of the pandemic shutdown, schools had to make decisions quickly often with limited planning. For students who were deaf or hard of hearing, communication access during instruction was the urgent focus of teachers of the deaf/hard of hearing and educational audiologists. The move from the classroom to home resulted in both predictable and unpredictable challenges as well as some unexpected benefits. Based on numerous conversations with these professionals as well as parents, the challenges encountered with online learning and solutions that were implemented to support students are reported.

Impact of SARS-CoV-2 on Provided Healthcare. Evidence From the Emergency Phase in Italy.

The SARS-CoV-2 (COVID-19) pandemic led to an emergency scenario within all aspects of health care, determining reduction in resources for the treatment of other diseases. A literature review was conducted to identify published evidence, from 1 March to 1 June 2020, regarding the impact of COVID-19 on the care provided to patients affected by other diseases. The research is limited to the Italian NHS. The aim is to provide a snapshot of the COVID-19 impact on the NHS and collect useful elements to improve Italian response models. Data available for oncology and cardiology are reported. National surveys, retrospective analyses, and single-hospital evidence are available. We summarized evidence, keeping in mind the entire clinical pathway, from clinical need to access to care to outcomes. Since the beginning, the COVID-19 pandemic was associated with a reduced access to inpatient (-48% for IMA) and outpatient services, with a lower volume of elective surgical procedures (in oncology, from 3.8 to 2.6 median number of procedures/week). Telehealth may plays a key role in this, particularly in oncology. While, for cardiology, evidence on health outcome is already available, in terms of increased fatality rates (for STEMI: 13.7 vs. 4.1%). To better understand the impact of COVID-19 on the health of the population, a broader perspective should be taken. Reasons for reduced access to care must be investigated. Patients fears, misleading communication campaigns, re-arranged clinical pathways could had played a role. In addition, impact on other the status of other patients should be mitigated.