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The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method's robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.

Background: Dexmedetomidine, an alpha 2 agonist, has emerged as a desirable sedative agent in the pediatric intensive care unit due to its minimal effect on respiratory status and reduction in delirium. Bradycardia and hypotension are common side effects, however there are emerging reports of more serious cardiovascular events, including sinus arrest and asystole. These case reports have been attributed to high vagal tone or underlying cardiac conduction dysfunction. Objectives: To describe the development of sinus arrest during sedation with dexmedetomidine in a patient without clinical features of high vagal tone, underlying cardiac conduction dysfunction, or intervening episodes of bradycardia. Case Presentation: An 11 month-old patient requiring sedation during mechanical ventilation for acute respiratory failure secondary to Adenovirus. To facilitate sedation, a dexmedetomidine infusion was initiated at.5 mcg/kg/hr and increased to maximum 1 mcg/kg/hr. Within 8 hours of initiating therapy, the patient had three episodes of sinus arrest. There was no intervening bradycardia between episodes and no further episodes occurred following discontinuation of dexmedetomidine. The patient did not have any clinical features associated with high vagal tone or underlying cardiac conduction dysfunction. Conclusions: As result of these findings, understanding risk factors for bradycardia, or more serious hemodynamic instability with dexmedetomidine infusions, is important to help identify high risk patients and weigh the associated risks and benefits of its administration. [ABSTRACT FROM AUTHOR]

BackgroundThe INSPIRE framework was developed by 10 global agencies as the first global package for preventing and responding to violence against children. The framework includes seven complementary strategies. Delivering all seven strategies is a challenge in resource-limited contexts. Consequently, governments are requesting additional evidence to inform which 'accelerator' provisions can simultaneously reduce multiple types of violence against children.Methods and findingsWe pooled data from two prospective South African adolescent cohorts including Young Carers (2010-2012) and Mzantsi Wakho (2014-2017). The combined sample size was 5,034 adolescents. Each cohort measured six self-reported violence outcomes (sexual abuse, transactional sexual exploitation, physical abuse, emotional abuse, community violence victimisation, and youth lawbreaking) and seven self-reported INSPIRE-aligned protective factors (positive parenting, parental monitoring and supervision, food security at home, basic economic security at home, free schooling, free school meals, and abuse response services). Associations between hypothesised protective factors and violence outcomes were estimated jointly in a sex-stratified multivariate path model, controlling for baseline outcomes and socio-demographics and correcting for multiple-hypothesis testing using the Benjamini-Hochberg procedure. We calculated adjusted probability estimates conditional on the presence of no, one, or all protective factors significantly associated with reduced odds of at least three forms of violence in the path model. Adjusted risk differences (ARDs) and adjusted risk ratios (ARRs) with 95% confidence intervals (CIs) were also calculated. The sample mean age was 13.54 years, and 56.62% were female. There was 4% loss to follow-up. Positive parenting, parental monitoring and supervision, and food security at home were each associated with lower odds of three or more violence outcomes (p < 0.05). For girls, the adjusted probability of violence outcomes was estimated to be lower if all three of these factors were present, as compared to none of them: sexual abuse, 5.38% and 1.64% (ARD: -3.74% points, 95% CI -5.31 to -2.16, p < 0.001); transactional sexual exploitation, 10.07% and 4.84% (ARD: -5.23% points, 95% CI -7.26 to -3.20, p < 0.001); physical abuse, 38.58% and 23.85% (ARD: -14.72% points, 95% CI -19.11 to -10.33, p < 0.001); emotional abuse, 25.39% and 12.98% (ARD: -12.41% points, 95% CI -16.00 to -8.83, p < 0.001); community violence victimisation, 36.25% and 28.37% (ARD: -7.87% points, 95% CI -11.98 to -3.76, p < 0.001); and youth lawbreaking, 18.90% and 11.61% (ARD: -7.30% points, 95% CI -10.50 to -4.09, p < 0.001). For boys, the adjusted probability of violence outcomes was also estimated to be lower if all three factors were present, as compared to none of them: sexual abuse, 2.39% to 1.80% (ARD: -0.59% points, 95% CI -2.24 to 1.05, p = 0.482); transactional sexual exploitation, 6.97% to 4.55% (ARD: -2.42% points, 95% CI -4.77 to -0.08, p = 0.043); physical abuse from 37.19% to 25.44% (ARD: -11.74% points, 95% CI -16.91 to -6.58, p < 0.001); emotional abuse from 23.72% to 10.72% (ARD: -13.00% points, 95% CI -17.04 to -8.95, p < 0.001); community violence victimisation from 41.28% to 35.41% (ARD: -5.87% points, 95% CI -10.98 to -0.75, p = 0.025); and youth lawbreaking from 22.44% to 14.98% (ARD -7.46% points, 95% CI -11.57 to -3.35, p < 0.001). Key limitations were risk of residual confounding and not having information on protective factors related to all seven INSPIRE strategies.ConclusionIn this cohort study, we found that positive and supervisory caregiving and food security at home are associated with reduced risk of multiple forms of violence against children. The presence of all three of these factors may be linked to greater risk reduction as compared to the presence of one or none of these factors. Policies promoting action on positive and supervisory caregiving and food security at home are likely to support further efficiencies in the delivery of INSPIRE.

Heart failure (HF) remains a major source of morbidity and mortality in the US. The multifunctional [Ca.sup.2+]/calmodulin-dependent kinase II (CaMKII) has emerged as a critical regulator of cardiac hypertrophy and failure, although the mechanisms remain unclear. Previous studies have established that the cytoskeletal protein [[beta].sub.IV]-spectrin coordinates local CaMKII signaling. Here, we sought to determine the role of a spectrin-CaMKII complex in maladaptive remodeling in HF. Chronic pressure overload (6 weeks of transaortic constriction [TAC]) induced a decrease in cardiac function in WT mice but not in animals expressing truncated [[beta].sub.IV]-spectrin lacking spectrin-CaMKII interaction ([qv.sub.3J] mice). Underlying the observed differences in function was an unexpected differential regulation of STAT3-related genes in [qv.sub.3J] TAC hearts. In vitro experiments demonstrated that [[beta].sub.IV]-spectrin serves as a target for CaMKII phosphorylation, which regulates its stability. Cardiac-specific [[beta].sub.IV]-spectrin-KO ([[beta].sub.IV]-cKO) mice showed STAT3 dysregulation, fibrosis, and decreased cardiac function at baseline, similar to what was observed with TAC in WT mice. STAT3 inhibition restored normal cardiac structure and function in [[beta].sub.IV]-cKO and WT TAC hearts. Our studies identify a spectrin-based complex essential for regulation of the cardiac response to chronic pressure overload. We anticipate that strategies targeting the new spectrin-based 'statosome' will be effective at suppressing maladaptive remodeling in response to chronic stress., IntroductionHeart failure (HF) represents a major burden on the US health care system, with 870,000 new cases annually and a total cost of $30.7 billion. By 2030, the incidence of [...]

The use of single-case effect sizes (SCESs) has increased in the intervention literature. Meta-analyses based on single-case data have also increased in popularity. However, few researchers who have adopted these metrics have provided an adequate rationale for their selection. We review several important statistical assumptions that should be considered prior to calculating and interpreting SCESs. We then more closely investigate a sampling of these newer procedures and conclude with critical analysis of the potential utility of these metrics.