Unified platform for genetic and serological detection of COVID-19 with single-molecule technology The COVID-19 pandemic raises the need for diverse diagnostic approaches to rapidly detect different stages of viral infection. The flexible and quantitative nature of single-molecule imaging technology renders it optimal for development of new diagnostic tools. Here we present a proof-of-concept for a single-molecule based, enzyme-free assay for detection of SARS-CoV-2. The unified platform we developed allows direct detection of the viral genetic material from patients samples, as well as their immune response consisting of IgG and IgM antibodies. Thus, it establishes a platform for diagnostics of COVID-19, which could also be adjusted to diagnose additional pathogens.
Mobility network reveals the impact of spatial vaccination heterogeneity on COVID-19 Mass vaccination is one of the most effective epidemic control measures. Because ones vaccination decision is shaped by social processes, the pattern of vaccine uptake tends to show strong social and spatial heterogeneity, such as urban-rural divide and clustering. Examining through network perspectives, we develop a framework for quantifying the impact of spatial vaccination heterogeneity on epidemic outbreaks. Leveraging fine-grained mobility data and computational models, we investigate two network effects--the "hub effect" (vaccinating mobility hubs reduces transmission) and the "homophily effect" (stronger homophily in vaccination rates increases transmission). Applying Bayesian deep learning and fine-grained epidemic simulations, we show a negative effect of homophily and a positive effect of highly vaccinated hubs on reducing case counts for both the synthetic network and the U.S. mobility network. Our framework enables us to evaluate the outcome of various hypothetical spatial vaccine distributions and propose a vaccination campaign strategy that targets a small number of regions with the largest gain in protective power, given the state of the COVID-19 as of January 2022. Our simulation shows that our strategy can prevent about 2.5 times more cases than a uniform strategy with an additional 1% of the population vaccinated. Our study suggests that we must examine the interplay between vaccination patterns and mobility networks beyond the overall vaccination rate, and that accurate location-based targeting can be just as important as improving the overall vaccination rate.

One sentence summaryWe examine network effects of heterogeneous vaccine uptake on epidemic outcomes and show a location-based vaccination may be a potent strategy.
Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs BackgroundPalliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patients frailty are usual dimensions to decide PC inclusion.

ObjectivesThe main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting PC decision making: one-year mortality, survival regression and one-year frailty classification.

MethodsThe dataset used in this study is composed of 39,310 hospital admissions for 19,753 older patients (age >= 65) from January 1st, 2011 to December 30th, 2018. All prediction models were based on Gradient Boosting Machines. From the initial pool of variables at hospital admission, 20 were selected by a recursive feature elimination algorithm based on the random forests GINI importance criterion. Besides, we run an independent grid search to find the best hyperparameters in each model. The evaluation was performed by 10-fold cross-validation and area under the receiver operating characteristic curve and mean absolute error were reported. The Cox proportional-hazards model was used to compare our proposed approach with classical survival methods.

ResultsThe one-year mortality model achieved an AUC ROC of 0.87 {+/-} 0.01; the mortality regression model achieved an MAE of 329.97 {+/-} 5.24 days. The one-year frailty classification reported an AUC ROC of 0.9 {+/-} 0.01. The Spearmans correlation between the admission frailty index and the survival time was -0.1, while the point-biserial correlation between one-year frailty index and survival time was -0.16.

ConclusionsOne-year mortality model performance is at a state-of-the-art level. Frailty Index used in this study behaves coherently with other works in the literature. One-year frailty classifier demonstrated that frailty status within the year could be predicted accurately. To our knowledge, this is the first study predicting one-year frailty status based on a frailty index. We found mortality and frailty as two weakly correlated and complementary PC needs assessment criteria. Predictive models are available online at http://demoiapc.upv.es. Chemosensory dysfunctions induced by COVID-19 can persist up to 7 months: A study of over 700 healthcare workers Several studies have revealed either self-reported chemosensory alterations in large groups or objective quantified chemosensory impairments in smaller populations of patients diagnosed with COVID-19. However, due to the great variability in published results regarding COVID-19-induced chemosensory impairments and their follow-up, prognosis for chemosensory functions in patients with such complaints remains unclear. Our objective is to describe the various chemosensory alterations associated with COVID-19 and their prevalence and evolution after infection. A cross-sectional study of 704 healthcare workers with a RT-PCR confirmed SARS-CoV-2 infection between 28/2/2020 and 14/6/2020 was conducted 3 to 7 months after onset of symptoms. Data were collected with an online questionnaire. Outcomes included differences in reported chemosensory self-assessment of olfactory, gustatory, and trigeminal functions across time points and Chemosensory Perception Test scores from an easy-to-use at-home self-administered chemosensory test. Among the 704 participants, 593 (84.2%) were women, the mean (SD) age was 42 (12) years, and the questionnaire was answered on average 4.8 (0.8) months after COVID-19. During COVID-19, a decrease in olfactory, gustatory, and trigeminal sensitivities were reported by 81.3%, 81.5% and 48.0% respectively. Three to seven months later, reduced sensitivity was still reported by 52.0%, 41.9% and 23.3% respectively. Chemosensory Perception Test scores indicate that 19.5% of participants had objective olfactory impairment. These data suggest a significant proportion of COVID-19 cases have persistent chemosensory impairments at 3 to 7 months after their infection but the majority of those who had completely lost their olfactory, gustatory, and trigeminal sensitivity have improved. Simple risk scores to predict hospitalization or death in outpatients with COVID-19 including the Omicron variant ImportanceOutpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, in particular whether to hospitalize a patient and if managed as an outpatient, how closely to follow them.

ObjectiveTo develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging.

DesignDerivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules.

SettingPrimary and Express care clinics in a Pennsylvania health system.

ParticipantsPatients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19.

Main outcomes and measuresClassification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC).

ResultsOverall, 7.4% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21) and 5.5% in the late validation cohort (3806 patients presenting 3/1/21 or later) were ultimately hospitalized. We developed and temporally validated three risk scores that all included age, dyspnea, and the presence of comorbidities, adding respiratory rate for the second score and oxygen saturation for the third. All had very good overall accuracy (AUC 0.77 to 0.78) and classified over half of patients in the validation cohort as very low risk with a 1.7% or lower likelihood of hospitalization. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization (2.8%).

Conclusions and relevanceSimple risk scores applicable to outpatient and telehealth settings can identify patients with very low (1.6% to 1.7%), low (5.2% to 5.9%), moderate (14.7% to 15.6%), and high risk (32.0% to 34.2%) of hospitalization. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/. A Hybrid Machine Learning Framework for Enhancing the Prediction Power in Large Scale Population Studies: The ATHLOS Project The ATHLOS cohort is composed of several harmonized datasets of international cohorts related to health and aging. The healthy aging scale has been constructed based on a selection of particular variables from 16 individual studies. In this paper, we consider a selection of additional variables found in ATHLOS and investigate their utilization for predicting the healthy aging. For this purpose motivated by the datasets volume and diversity we focus our attention upon the clustering for prediction scheme, where unsupervised learning is utilized to enhance prediction power, showing the predictive utility of exploiting structure in the data by clustering. We show that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering within a clustering for ensemble classification scheme while retaining prediction benefits. We propose a complete methodology which is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A strait-forward open source implementation is provided for the R project. Evaluating a first fully automated interview grounded in Multiple Mini Interview (MMI) methodology: results from a feasibility study ObjectivesGlobal, Covid-driven restrictions around face-to-face interviews for healthcare student selection have forced admissions staff to rapidly adopt adapted online systems before supporting evidence is available. We have developed, what we believe is, the first fully automated interview grounded in Multiple Mini-Interview (MMI) methodology. This study aimed to explore test re-test reliability, acceptability and usability of the system.

Design, setting and participantsmixed-methods feasibility study in Physician Associate (PA) programmes from two UK and one US university during 2019 - 2020.

Primary, secondary outcomesFeasibility measures (test retest reliability acceptability and usability) were assessed using intra-class correlation (ICC), descriptive statistics, thematic and content analysis.

MethodsVolunteers took (T1), then repeated (T2), the automated MMI, with a seven-day interval (+/- 2) then completed an evaluation questionnaire. Admissions staff participated in focus group discussions.

ResultsSixty-two students and seven admission staff participated; 34 students and four staff from UK and 28 students and three staff from US universities.

Good-excellent test-retest reliability was observed with T1 and T2 ICC between 0.62-0.81 (p<0.001) when assessed by individual total scores (range 80.6-119), station total scores 0.6-0.91, p<0.005, individual site (all ICC[&ge;] 0.76 p<0.001) and mean test retest across sites 0.82 p<0.001 (95% CI 0.7-0.9).

Admissions staff reported potential to reduce resource costs and bias through a more objective screening tool for pre-selection or to replace some MMI stations in a  hybrid model. Maintaining human interaction through  touch points was considered essential.

Users positively evaluated the system, stating it was intuitive with an accessible interface. Concepts chosen for dynamic probing needed to be appropriately tailored.

ConclusionThese preliminary findings suggest that the system is reliable, generating consistent scores for candidates and is acceptable to end-users provided human touchpoints are maintained. Thus, there is evidence for the potential of such an automated system to augment healthcare student selection processes. Serum methylation of GALNT9, UPF3A, WARS, and LDB2 as non-invasive biomarkers for the early detection of colorectal cancer and premalignant adenomas BackgroundEarly detection through screening programs has proven to be the most effective strategy to reduce the incidence and mortality of colorectal cancer. The most widely implemented non-invasive screening test is the fecal immunochemical test, which presents an inadequate sensitivity for the detection of precancerous advanced adenomas. This fact, together with the modest participation rates in screening programs, highlights the need for a blood test that could improve both the adherence to screening and the selection to colonoscopy.

MethodsIn this study, we conducted a serum-based discovery and validation of circulating cell-free DNA (cfDNA) methylation biomarkers for colorectal cancer screening in a multicentre cohort of 433 serum samples including healthy controls, benign pathologies, advanced adenomas, and colorectal cancer. First, we performed an epigenome-wide methylation analysis with the MethylationEPIC array in 280 cfDNA samples using a pooling approach, followed by a robust prioritization of candidate biomarkers for the joint detection of advanced adenomas and colorectal cancer (advanced neoplasia). Then, candidate biomarkers were validated by pyrosequencing in independent individual 153 cfDNA samples.

ResultsWe report GALNT9, UPF3A, WARS, and LDB2 as new non-invasive methylation biomarkers for the early detection of colorectal advanced neoplasia. A model composed of GALNT9, UPF3A, WARS, and LDB2 reported a sensitivity of 62.1% and a specificity of 97.4% for the detection of advanced neoplasia. On the other hand, the combination of GALNT9 and UPF3A by logistic regression discriminated advanced neoplasia with 78.8% sensitivity and 100% specificity, outperforming the commonly used fecal immunochemical test and the methylated SEPT9 blood test.

ConclusionsSerum methylation levels of GALNT9, UPF3A, WARS, and LDB2 represent highly specific and sensitive novel blood-based biomarkers for the detection of colorectal cancer and premalignant advanced adenomas of both distal and proximal locations. The reported results show the feasibility of DNA sample pooling strategies for biomarker discovery. Overall, this study highlights the utility of cfDNA methylation for the early detection of colorectal neoplasia, with the potential to be implemented as a non-invasive test for colorectal cancer screening. SARS-CoV-2 nucleocapsid antigen in urine of hospitalized patients with Covid-19 IntroductionSARS-CoV-2 nucleocapsid antigen (N-Ag) can be detected in the blood of patients with Covid-19. In this study, we used a highly sensitive and specific nucleocapsid-Ag assay to explore the presence of N-Ag in urine during the course of Covid-19, and explore its relationship with the severity of the disease.

Material and MethodsUrine and blood samples were collected from 82 patients with a SARS-CoV-2 infection proven by PCR and included in the COVIDotheque. We explored the presence of N-Ag in urine and blood using the AAZ N-Ag test, studied the kinetics of the marker according to the time since the onset of symptoms and evaluated the association between N-Ag levels, clinical severity and inflammation.

ResultsIn the first and second weeks of Covid-19, hospitalized patients tested positive for urinary N-Ag (81.25% and 71.79%, respectively) and blood N-Ag (93.75% and 94.87%, respectively). N-Ag levels in urine and blood were moderately correlated with the number of days after the onset of symptoms (r=-0.43, p<0.0001; r=-0.55 p<0.0001, respectively). The follow up of seven SARS-CoV-2 infected patients confirmed the waning of N-Ag in urine and blood over the course of the disease. High urinary N-Ag levels were associated with the absence of SARS-CoV-2 nucleocapsid-IgG (N-IgG), admission in intensive care units, high C-reactive protein levels, lymphopenia, eosinopenia, and high lactate dehydrogenase (LDH).

ConclusionOur study demonstrate that N-Ag is present in the urine of patients hospitalized in the early phase of Covid-19. As a direct marker of SARS-CoV-2, urinary N-Ag reflects the dissemination of viral compounds in the body. Urine N-Ag is a promising marker to predict adverse evolution of SARS-CoV-2 infections. Exploring the experiences of loneliness in adults with mental health problems: a participatory qualitative interview study BackgroundMany mental health conditions are associated with loneliness, which is both a potential trigger and an exacerbating factor in mental health conditions. Richer evidence about how people with mental health problems experience loneliness, and about what exacerbates or alleviates it, is needed to underpin research on strategies to help with loneliness in this context.

MethodsOur aim was to explore experiences of loneliness, as well as what contributes to or helps address it, among a diverse sample of adults living with mental health problems in the UK. We recruited purposively via online networks and community organisations. Qualitative semi-structured interviews were conducted with 59 consenting participants by video call or telephone. Researchers with relevant lived experience were involved at all stages, including design, data collection, analysis and writing up of results.

FindingsAnalysis led to identification of four overarching themes: 1. What the word "lonely" meant to participants, 2. Contributory factors to ongoing loneliness, 3. Connections between loneliness & mental health, 4. Ways of reducing loneliness. Central aspects of loneliness were lack of meaningful connections with others and lack of a sense of belonging to valued groups and communities. Some drivers of loneliness, such as losses and transitions, were universal, but specific links were made between living with mental health problems and being lonely. These included direct effects of psychiatric symptoms, the need to withdraw to cope with mental health problems, and impacts of stigma and poverty.

ConclusionsThe multiplicity of contributors to loneliness that we identified, and of potential strategies for reducing it, suggest that a variety of approaches are relevant to reducing loneliness among people with mental health problems, including peer support and supported self-help, psychological and social interventions, and strategies to facilitate change at community and societal levels. The views and experiences of adults living with mental health problems are a rich source for understanding why loneliness is a frequent experience in this context and what may address it. Co-produced approaches to developing and testing interventions have potential to draw on this experiential knowledge in formulating effective approaches to loneliness. Impacts of Teaching Modality on U.S. COVID-19 Spread in Fall 2020 Semester We study the impact of college reopening in Fall 2020 on county-level COVID-19 cases and deaths using the information of 1,076 U.S. colleges. We match college and county characteristics using several methods and calculate the average treatment effects of three teaching modalities: in-person, online, and hybrid on COVID-19 outcomes up to two months after college reopening. In pairwise comparison, colleges reopened with in-person teaching mode were found to have about 36% point more cases within 15 days of reopening, compared to those reopened online, and the gap widens over time at a decreasing rate. Death rates follow the pattern with a time lag. However, colleges with hybrid mode catch the pattern of in-person mode after some time. We also find that greater endowment and student population, and fewer republican votes in the county are major predictors of choosing remote teaching modes over in-person.

JEL codesA23, I18, I23. RELATIONSHIP BETWEEN ADVERSE EVENTS PREVALENCE, PATIENT SAFETY CULTURE AND PATIENT SAFETY PERCEPTION IN A SINGLE SAMPLE OF PATIENTS: A CROSS-SECTIONAL AND CORRELATIONAL STUDY ObjectiveTo assess the relationship between Adverse Events Prevalence, Patient Safety Culture and Patient Safety Perception.

DesignCross-sectional, ex post facto comparative study on a single sample of patients.

SettingFour medium-high-level hospitals were included in the study -- two public and two private from Zulia State in Venezuela.

Participants556 medical records and patients were studied for the prevalence and patient safety perception study, and 397 of the healthcare providers involved in the care of these patients were surveyed for the patient safety culture study, at two public and two private hospitals.

Outcome measurementThe primary outcome of this study was the Association between Adverse Events Prevalence, Patient Safety Culture and Patient Safety Perception, and according to hospital funding type, private and public.

ResultsAn inverse association was observed between Adverse Events Prevalence and its severity and Patient Safety Culture Index (PSCI: rho=-0.8 p=0.5) (95% CI=0.26-0.10) and Patient Safety Perception Index (PSPI: rho=-0.6 p=0.18) (95% CI=0.10-0.28), which were protective factors for patient safety. No association was identified between Patient Safety Culture and Patient Safety Perception (rho=0.0001). No statistical differences were identified by hospital type (p=0.93) (95% CI=0.70-1.2).

ConclusionsThe analysis of the variable correlations studied (AEP, PSC and PSP) within the same sample offers an interesting and useful perspective. In this sample, although no correlation was observed between the three variables as an interacting set, some correlation patterns were observed between pairs of variables that could guide further studies.

Strengths and limitations of this studyO_LIThe association analysis of the three main variables (adverse events prevalence, patient safety culture and patient safety perception) in the same sample of patients and health professionals and over the same time period during which the care process and, consequently, the adverse events took place is an unprecedented and innovative approach, which contributes to the body of knowledge on this field of study.
C_LIO_LIThe relational theoretical framework produced provides a "map" that can facilitate decision-taking and which establishes the variables that should be considered a priority. As a result, it is highly likely to have an impact on the clinical efficiency and safety of healthcare institutions and services.
C_LIO_LIThe study was carried out at both private and public hospitals, enabling the behaviour of the aforementioned variables to be compared and analysed at each type of institution. The literature to date on this approach is scarce and offers little information.
C_LIO_LIAlthough the sample of hospitals selected is not representative of Venezuela, it is representative of the hospitals of one of the most productive and populated states in the country.
C_LIO_LIDuring stages 1 and 2, some resistance to collaborating with and completing the evaluation instruments was identified among the health professionals.
C_LI Stability of the stroke code during the COVID-19 pandemic in the region of Madrid: a retrospective cohort study Acute Stroke (AS) is the most common time-dependent disease attended in the Emergency Medicine Service (EMS) of Madrid (SUMMA 112). Community of Madrid has been one of the most affected regions in Spain by the coronavirus disease 2019 (COVID19) pandemic. A significant reduction in AS hospital admissions has been reported during the COVID-19 pandemic compared to the same period one year before. As international clinical practice guidelines support those patients with suspected acute stroke should be accessed via EMS, it is important to know whether the pandemic has jeopardized urgent pre-hospital stroke care, the first medical contact for most patients. We aimed to examine the impact of the COVID-19 in stroke codes (SC) in our EMS among three periods of time: the COVID-19 period, the same period the year before, and the 2019-2020 seasonal influenza period. Methods: We compared the SC frequency among the periods with high cumulative infection rate (above the median of the series) of the first wave of COVID-19, seasonal influenza, and also with the same period of the year before. Results: 1,130 SC were attended during the three periods. No significant reduction in SC was found during the COVID-19 pandemic. The reduction of hospital admissions might be attributable to patients attending the hospital by their means. The maximum SC workload seen during seasonal influenza has not been reached during the pandemic. We detected a non-significant deviation from the SC protocol, with a slight increase in hospitals transfers to hospitals without stroke units. Pausing methotrexate improves immunogenicity of COVID-19 vaccination in elderly patients with rheumatic diseases ObjectiveTo study the effect of methotrexate (MTX) and its discontinuation on the humoral immune response after COVID-19 vaccination in patients with autoimmune rheumatic diseases (AIRD).

MethodsNeutralising SARS-CoV-2 antibodies were measured after second vaccination in 64 rheumatic patients on methotrexate therapy, 31 of whom temporarily paused medication without a fixed regimen. The control group consisted of 21 AIRD patients without immunosuppressive medication.

ResultsMTX patients showed a significantly lower median antibody response compared to AIRD patients without immunosuppressive therapy (p< 0.001). Young age (<60 years) and MTX-hold after vaccination were found to be the main factors influencing antibody response after vaccination, while BMI or MTX dose demonstrated no effect. For patients taking MTX, age correlated negatively with immune response (r=-0.49; p<0.001) and all patients with antibody levels (14 %) below the cut-off were older than 60 years. Patients who held MTX during at least one vaccination showed significantly higher median neutralising antibody levels after second vaccination, compared to patients who continued MTX therapy during both vaccinations (68.82 %, 92.73 %, p=<0.001). This effect was particularly pronounced in patients older than 60 years (p=0.0016). The impact of the time period after vaccination was greater than of the time before vaccination with the critical cut-off being 10 days.

ConclusionMTX reduces the immunogenicity of SARS-CoV-2 vaccination in an age-dependent manner. Our data further suggest that holding MTX for at least 10 days after vaccination significantly improves the antibody response in patients over 60 years of age. Signals of significantly increased vaccine breakthrough, decreased hospitalization rates, and less severe disease in patients with COVID-19 caused by the Omicron variant of SARS-CoV-2 in Houston, Texas Genetic variants of SARS-CoV-2 continue to dramatically alter the landscape of the COVID-19 pandemic. The recently described variant of concern designated Omicron (B.1.1.529) has rapidly spread worldwide and is now responsible for the majority of COVID-19 cases in many countries. Because Omicron was recognized very recently, many knowledge gaps exist about its epidemiology and clinical severity and disease course. A comprehensive genome sequencing study of SARS-CoV-2 in the Houston Methodist healthcare system identified 862 symptomatic patients with infections caused by Omicron from late November 2021 through December 18, 2021. Omicron very rapidly increased in only three weeks to cause 90% of all new COVID-19 cases. Compared to patients infected with either Alpha or Delta variants in our healthcare system, Omicron patients were significantly younger, had significantly increased vaccine breakthrough rates, and were significantly less likely to be hospitalized. Omicron patients required less intense respiratory support and had a shorter length of hospital stay, consistent with decreased disease severity. Although the number of Omicron patients we studied is relatively small, in the aggregate the data document the unusually rapid spread and increased occurrence of COVID-19 caused by the Omicron variant in metropolitan Houston, and provide information about disease character. Potential impacts of Washington State's wildfire worker protection rule on construction workers Driven by climate change, wildfires are increasing in frequency, duration, and intensity across the Western United States. Outdoor workers are being exposed to increasing wildfire-related particulate matter and smoke. Recognizing this emerging risk, Washington adopted an emergency rule and is presently engaged in creating a permanent rule to protect outdoor workers from wildfire smoke exposure. While there are growing bodies of literature on the exposure to and health effects of wildfire smoke in the general public and wildland firefighters, there is a gap in knowledge about wildfire smoke exposure among outdoor workers generally, and construction workers specifically, a large category of outdoor workers in Washington totaling 200,000 people. In this study, several data sources were linked including state-collected employment data and national ambient air quality data to gain insight into the risk of PM2.5 exposure among construction workers and evaluate the impacts of different air quality thresholds that would have triggered a new Washington emergency wildfire smoke rule aimed at protecting workers from high PM2.5 exposure. Results indicate the number of poor air quality days has increased in August and September in recent years. Over the last decade these months with the greatest potential for particulate matter exposure coincided with an annual peak in construction employment that was typically 9.4 to 42.7% larger across Washington counties (one county was 75.8%). Lastly, the "encouraged" threshold of the Washington emergency rule (20.5 g/m3) would have resulted in 5.5 times more days subject to the wildfire rule on average across all Washington counties compared to its "required" threshold (55.5 g/m3), and in 2020 the rule could have created demand for 1.35 million N-95 filtering facepiece respirators among construction workers. These results have important implications for both employers and policy makers as rules are developed. The potential policy implications of wildfire smoke exposure, exposure control strategies, and data gaps that would improve understanding of construction worker exposure to wildfire smoke are also discussed. Adjunctive Ketamine for Sedation in Critically Ill Mechanically Ventilated Patients: An Active-Controlled, Pilot, Feasibility Clinical Trial ObjectiveKetamine has been shown to decrease sedative requirements in intensive care unit (ICU). Randomized trials are limited on patient-centered outcomes. We designed this pilot clinical trial to evaluate the feasibility of using ketamine as an adjunct analgosedative compared with standard of care (SOC) alone and determine preliminary effect size on 28-day mechanical ventilation (MV) duration and ventilator-free days (VFDs).

DesignPilot, single-center, active-controlled, open-label, randomized clinical trial.

SettingMedical, surgical, and transplant ICUs at King Faisal Specialist Hospital and Research Center, Saudi Arabia.

Patients and MethodsAdult patients who were intubated within 24 hours, expected to require MV for the next calendar day, and had institutional pain and sedation protocol initiated.

InterventionAdjunct ketamine infusion 1-2 g/kg/min for 48 hours versus SOC.

Measurements and Main ResultsTotal of 83 patients (43 in SOC and 40 in ketamine) were included. Demographics were balanced between groups. Median MV duration was 7 (interquartile range [IQR] 3-9.25 days) in ketamine and 5 (IQR 2-8 days) in SOC, p= 0.15. Median VFDs was 19 (IQR 0-24.75 days) in ketamine and 19 (IQR 0-24 days) in the SOC (p=0.70). More patients attained goal RASS score at 24 and 48 hours in ketamine (67.5% and 73.5%, respectively) compared with SOC (52.4% and 66.7%, respectively). Sedatives and vasopressors cumulative doses, and hemodynamic changes were similar. ICU length-of-stay was 12.5 (IQR 6-21.2 days) in ketamine, compared with 12 (IQR 5.5-23 days) in SOC, p=0.89. Consent and protocol adherence rate were adequate. No serious adverse events were observed in either group.

ConclusionsUse of ketamine as an adjunct analgosedative agent appeared to be feasible and safe with no negative impact on outcomes, including hemodynamics. The protocol of this pilot trial could be improved by modifying ketamine dosing regimen. These findings provide a basis for future, adequately powered, multicenter trial to investigate its association with patient-centered outcomes further. Macronutrient mass intake explains deferential weight and fat loss in isocaloric diets Differential weight and fat losses under isocaloric diets of distinct macronutrient composition are well-documented findings in obesity research.1-6 Such data are considered a result of inadequate methodology as it disagree with the energy balance theory.7 A recent mathematical analysis of this paradigm has found, however, serious analytical contradictions in its foundations.8 As an alternative, a mass balance model was proposed to explain the aforesaid body composition alterations. Here, we expand on this observation by contrasting both models. We show that mass balance explains a wide range of fending experiments including those concurring with the energy balance principle. The latter, however, is less flexible and results in poor forecasts in settings consistent with mass balance. The energy balance theory is thus an unsatisfactory model of body composition changes. Consequently, by shifting to a mass balance paradigm of obesity a much deeper understanding of this disease may follow in the near future. Dental biofilm microbiota dysbiosis is associated with the risk of acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation Acute graft-versus-host disease (aGVHD) is one of the major causes of death after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Recently, aGVHD onset was linked to intestinal microbiota (IM) dysbiosis. However, other bacterial-rich gastrointestinal sites, such as the mouth, which hosts several distinctive microbiotas, may also impact the risk of GVHD. The dental biofilm microbiota (DBM) is highly diverse and, like the IM, interacts with host cells and modulates immune homeostasis. We characterized changes in the DBM of patients during allo-HSCT and evaluated whether the DBM could be associated with the risk of aGVHD. DBM dysbiosis during allo-HSCT was marked by a gradual loss of bacterial diversity and changes in DBM genera composition, with commensal genera reductions and potentially pathogenic bacteria overgrowths. High Streptococcus and high Corynebacterium relative abundance at preconditioning were associated with a higher risk of aGVHD (67% vs. 33%; HR = 2.89, P = 0.04 and 73% vs. 37%; HR = 2.74, P = 0.04, respectively), while high Veillonella relative abundance was associated with a lower risk of aGVHD (27% vs. 73%; HR = 0.24, P < 0.01). Enterococcus faecalis bloom during allo-HSCT was observed in 20% of allo-HSCT recipients and was associated with a higher risk of aGVHD (100% vs. 40%; HR = 4.07, P < 0.001) and severe aGVHD (60% vs. 12%; HR = 6.82, P = 0.01). To the best of our knowledge, this is the first study demonstrating that DBM dysbiosis is associated with the aGVHD risk after allo-HSCT. How Informative Were Early SARS-CoV-2 Treatment and Prevention Trials? A longitudinal cohort analysis of trials registered on clinicaltrials.gov BackgroundEarly in the SARS-CoV-2 pandemic, commentators warned that some COVID trials were inadequately conceived, designed and reported. Here, we retrospectively assess the prevalence of informative COVID trials launched in the first 6 months of the pandemic.

MethodsWe created a cohort of SARS-CoV-2 treatment and prevention efficacy trials that were initiated from 2020-01-01 to 2020-06-30 using ClinicalTrials.gov registration records. We evaluated trials on 3 criteria of informativeness: potential redundancy, design quality and feasibility of patient-participant recruitment. The study protocol was prospectively registered with the Open Science Framework (https://osf.io/fp726/).

ResultsWe included 500 trials in our cohort, 58% of which were Phase 2 and 84.8% were directed towards the treatment of SARS-CoV-2. Close to one third of trials met all three criteria and were deemed informative (29.0% (95% Confidence Interval 23.7 - 36.9)). The proportion of potentially redundant trials in our cohort was 4.1%. Over half of the trials in our cohort (56.2%) did not meet our criteria for high quality trial design. The proportion of trials with infeasible patient-participant recruitment was 22.6%.

ConclusionsLess than one third of COVID-19 trials registered on ClinicalTrials.gov during the first six months met all three criteria for informativeness. Shortcomings in trial design, recruitment feasibility and redundancy reflect longstanding weaknesses in the clinical research enterprise that were likely amplified by the exceptional circumstances of a pandemic. Spatial Allocation of Scarce COVID-19 Vaccines The COVID-19 Vaccines Global Access (COVAX) is an initiative led by the World Health Organization (WHO) and other partners that aims for an equitable access of COVID-19 vaccines. Despite a potential heterogeneous disease burden across space, countries receiving allotments of vaccines via COVAX may want to follow WHOs allocation rule and distribute vaccines to their jurisdictions based on the jurisdictions relative population size. Utilizing economic-epidemiological modeling, we benchmark the performance of this ad hoc allocation rule by comparing it to the rule that minimizes the economic damages and expenditures over time, including a penalty cost representing the social costs of deviating from the ad hoc allocation. Under different levels of vaccine scarcity and different demographic characteristics, we consider scenarios where length of immunity and compliance to travel restrictions vary, and consider the robustness of the rules when assumptions regarding these factors are incorrect. The benefits from deviating are especially high when immunity is permanent, when there is compliance to travel restrictions, when the supply of vaccine is low, and when there is heterogeneity in demographic characteristics. Interestingly, a lack of compliance to travel restrictions pushes the optimal allocations of vaccine towards the ad hoc and improves the relative robustness of the ad hoc rule, as the mixing of the populations reduces the spatial heterogeneity in disease burden.

JEL ClassificationC61, H12, H84, I18, Q54 Substance Use Onset in High-Risk 9-13 Year-Olds in the ABCD Study AimA key aim of the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study is to document substance use onset, patterns, and sequelae across adolescent development. However, substance use misreporting obscures accurate drug use characterization. Hair toxicology tests provide objective historical substance use data, but are rarely investigated in youth. Here, we compare objective hair toxicology results with self-reported substance use in youth.

MethodsA literature-based substance use risk algorithm identified 696 ABCD Study(R) participants for hair sample collections between baseline and 2-year follow-up (spanning ages 9-13) for laboratory analysis. Chi-square and t-tests assessed differences between participants demographics, positive and negative hair tests, risk algorithm scores, and self-reported substance use.

ResultsHair testing confirmed that 17% of at-risk 9-13 year-olds had evidence of past 3-month use of one (n=99), two (n=17), three (n=3), or four (n=2) drug classes. After considering prescribed medication use, 11% had a positive test incongruent with self- or parent-report. No participant with a positive result self-reported substance use consistent with their toxicology results. Participants with positive tests under-reported use (p<.001), reported less sipping of alcohol (p<.001), and scored higher on the risk algorithm (p<.001) than those with negative hair toxicology.

ConclusionsAn alarming 11% of tested samples in at-risk 9-13 year-olds were positive for at least one unreported substance, suggesting underreporting in this population when participating in a national healthy development study. The degree of underreporting cannot yet be calculated, as at-risk samples were prioritized for assays. Expanded toxicology testing is key to characterize substance use in youth. Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis BackgroundElectroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, mono-center studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers.

ObjectiveTo develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting.

MethodsMultimodal data (i.e., clinical, sMRI and resting-state fMRI) was collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluated which data modalities or combinations thereof could provide the best predictions for treatment response ([&ge;]50% symptom reduction) or remission (HAM-D score [&le;]7) using a support vector machine classifier.

ResultsRemission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers, and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC).

ConclusionsThese results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible.