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Background and Aims: C-MAC ® video laryngoscope (VL) with Macintosh blade has been found to improve Cormack-Lehane (C-L) laryngoscopic view as well as intubating conditions for orotracheal intubation. However, studies done on the performance of C-MAC ® VL for nasotracheal intubation (NTI) are very few in number. Hence, we compared laryngoscopy and intubating conditions between Macintosh direct laryngoscope and C-MAC ® VL for NTI. Methods: Sixty American Society of Anesthesiologists Physical Status I, II patients, aged 8-18 years, posted for tonsillectomy surgeries under general anaesthesia with NTI were randomised, into two groups. Patients in group 1 were intubated using Macintosh direct laryngoscope and group 2 with C-MAC ® VL. C-L grading, time required for intubation, need for additional manoeuvres and haemodynamic changes during and after intubation were compared between the groups. Results: C-L grade 1 views were obtained in 26 and 29 patients in group 1 and group 2, respectively (86.7% vs. 96.7%). Remaining patients were having C-L grade 2 (13.3% vs. 3.3%). Duration of intubation was less than a minute in group 2 (93.3%). Need for additional manoeuvres (M1-M5) were more in group 1 (97% vs. 77%). M1 (external manipulation) was needed more in group 2 compared to group 1 (53.3% vs. 30%). Magill′s forceps alone (M4) and M4 with additional external manipulation (M5) were needed more in group 1 compared to group 2 (60% vs. 16%). Conclusion: The overall performance of C-MAC ® VL was better when compared to conventional direct Macintosh laryngoscope during NTI in terms of glottis visualisation, intubation time and need for additional manoeuvres.

BackgroundDisease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards. ObjectiveWe sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources. MethodsFollowing a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems—a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds. ResultsData from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability. ConclusionsWe described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance.

Manihot esculenta, commonly called cassava, is an economically valuable crop and important staple food, grown in tropical and subtropical regions of the world. Demand for cassava in the food and fuel industry is growing worldwide. However, anthracnose disease caused by Colletotrichum gloeosporioides severely affects cassava yield and production. The bioactive molecules from Bacillus are widely used to control fungal diseases in several plants. Therefore, in this study, bioactive compounds (erucamide, behenic acid, palmitic acid, phenylacetic acid, and β-sitosterol) from Bacillus megaterium were assessed against CDC42, a key protein for virulence, from C. gloeosporioides. Structure of the CDC42 protein was generated through the comparative homology modeling method. The binding site of the ligands and the stability of the complex were analyzed through docking and molecular dynamics simulation studies, respectively. Furthermore, a protein interaction network was envisaged through the STRING database, followed by enrichment analysis in the WebGestalt tool. From the enrichment analysis, it is apparent that bioactive from B. megaterium chiefly targets the MAP kinase pathway that is essential for filamentous growth and virulence. Further exploration through experimental studies could be advantageous for cassava improvement as well as to combat against C. gloeosporioides pathogen.

Background Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. Methods INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. Results Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. Conclusions This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection.

Objectives Severe Acute Malnutrition is a significant cause of mortality in children under the age of 5 years in low-resource settings, including Northern Nigeria. The study aimed to determine the associations between selected risk factors and mortality outcomes in children admitted with SAM in a facility in Katsina State, Northern Nigeria. Methods A prospective observational cohort of 201 children aged 6 to 59 months who were admitted with severe acute malnutrition (SAM) in stabilization centers in Katsina State, Northern Nigeria between May 18, 2021, and July 20, 2021, (63 days) were assessed followed up. Outcomes were analyzed using Kaplan-Meir analysis to estimate time to death, and Cox proportional-hazard regression model was used to determine predictors of mortality. Results The log-rank test showed significant differences in the probability of death between categories of diarrheal status (log-rank statistic = 9.760, P = .021) and presence of existing disease (comorbidity) (log-rank statistic = 5.338, P = .021). The study identified that severely malnourished children admitted with comorbidities showed significant association with time to event (death) (AHR: 4.109, 95% CI: 1.51, 32.60). The estimated mean time until death was 57.9 days (±3.0) for children without comorbidities and 20.1 (±3.0) days for children with comorbidities. The median survival time was 18 days for children with comorbidities. Conclusion The presence of comorbidities was significantly associated with mortality. Severely malnourished children with comorbidities had 4 times higher mortality risk than severely malnourished children admitted without comorbidities. Clinicians and health workers should give due emphasis to the early detection and effective management of comorbidities in children with severe acute malnutrition.

ABSTRACT Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of health care-associated (HA) and community-associated (CA) infections. USA300 strains are historically CA-MRSA, while USA100 strains are HA-MRSA. Here, we update an antibiotic prediction rule to distinguish these two genotypes based on antibiotic resistance phenotype using whole-genome sequencing (WGS), a more discriminatory methodology than pulsed-field gel electrophoresis (PFGE). MRSA clinical isolates collected from 2007 to 2017 underwent WGS; associated epidemiologic data were ascertained. In developing the rule, we examined MRSA isolates that included a population with a history of incarceration. Performance characteristics of antibiotic susceptibility for predicting USA300 compared to USA100, as defined by WGS, were examined. Phylogenetic analysis was performed to examine resistant USA300 clades. We identified 275 isolates (221 USA300, 54 USA100). Combination susceptibility to clindamycin or levofloxacin performed the best overall (sensitivity 80.7%, specificity 75.9%) to identify USA300. The average number of antibiotic classes with resistance was higher for USA100 (3 versus 2, P < 0.001). Resistance to ≤2 classes was predictive for USA300 (area under the curve (AUC) 0.84, 95% confidence interval 0.78 to 0.90). Phylogenetic analysis identified a cluster of USA300 strains characterized by increased resistance among incarcerated individuals. Using a combination of clindamycin or levofloxacin susceptibility, or resistance to ≤2 antibiotic classes, was predictive of USA300 as defined by WGS. Increased resistance was observed among individuals with incarceration exposure, suggesting circulation of a more resistant USA300 clade among at-risk community networks. Our phenotypic prediction rule could be used as an epidemiologic tool to describe community and nosocomial shifts in USA300 MRSA and quickly identify emergence of lineages with increased resistance. IMPORTANCE Methicillin-resistant Staphylococcus aureus (MRSA) is an important cause of health care-associated (HA) and community-associated (CA) infections, but the epidemiology of these strains (USA100 and USA300, respectively) now overlaps in health care settings. Although sequencing technology has become more available, many health care facilities still lack the capabilities to perform these analyses. In this study, we update a simple prediction rule based on antibiotic resistance phenotype with integration of whole-genome sequencing (WGS) to predict strain type based on antibiotic resistance profiles that can be used in settings without access to molecular strain typing methods. This prediction rule has many potential epidemiologic applications, such as analysis of retrospective data sets, regional monitoring, and ongoing surveillance of CA-MRSA infection trends. We demonstrate application of this rule to identify an emerging USA300 strain with increased antibiotic resistance among incarcerated individuals that deviates from the rule.