Rapid Clinical Screening and Staging for COVID-19 Severe Outcome BackgroundWe aimed to evaluate the risk factors for Coronavirus disease 2019 (COVID-19) related severe outcome in New York State (NYS) and proposed a method that could be used to inform future work to develop clinical algorithms and predict resource needs for COVID-19 patients.

MethodsWe analyzed COVID-19 related hospital encounter and hospitalization in NYS from April 1st to November 17th, 2020, using Statewide Planning and Research Cooperative System (SPARCS) hospital discharge dataset. Logistic regression was performed to evaluate the risk factors for COVID-19 related in-hospital death using demographic variables, symptom, rapid clinical examination, and medical history of chronic co-morbid conditions. Receiver operating characteristic (ROC) curve was calculated, and cut-off points for predictors were selected to stage the risk of COVID-19 related fatal outcome.

FindingsLogistic regression analysis showed age was the greatest risk factor for COVID-19 related fatal outcome, which by itself achieved the diagnostic accuracy of 0.78 represented by the area under the ROC curve. By adding other demographic variables, dyspnea or hypoxemia and multiple chronic co-morbid conditions, the diagnostic accuracy was improved to 0.85. We selected cut-off points for predictors and provided a general recommendation to categorize the levels of risk for COVID-19 related fatal outcome.

InterpretationWe assessed risk factors associated with in-hospital COVID-19 mortality and identified cut-off points that might be used to categorize the level of risk. Further studies are warranted to evaluate laboratory tests and develop laboratory biomarkers to improve the diagnostic accuracy for early intervention.
Direct Comparison of SARS-CoV-2 Nasal RT- PCR and Rapid Antigen Test (BinaxNOW(TM)) at a Community Testing Site During an Omicron Surge In 731 persons seeking COVID-19 testing at a walk-up San Francisco community site in January 2022, simultaneous nasal rapid antigen testing (BinaxNOW) and RT-PCR testing was performed. There were 296 (40.5%) positive tests by RT-PCR; 98.5% of a random sample (N=67) were the omicron variant. Sensitivity of a single antigen test was 95.2% (95% CI 92-98%); 82.1% (95% CI 77-87%) and 65.2% (95% CI 60-71%) for Ct threshold of < 30, < 35 and no threshold, respectively. We also compared BinaxNOW to RT-PCR from oral cheek swabs to nasal swabs (N=75); oral cheek specimen was significantly less sensitive than nasal swab. A single BinaxNOW oral cheek rapid antigen test failed to detect 91% (20 of 22) of specimens that were BinaxNOW positive from the standard nasal sampling. In a separate direct comparison of BinaxNOW between specimens collected from nasal or throat (tonsillar) swab (N=115), sensitivity was 97.7% for nasal and 48.8% for throat swabs that were PCR-positive on nasal swab with a Ct threshold < 30. Among persons with either a nasal or throat RT-PCR positive swab with Ct<30, BinaxNOW sensitivity was 85.7% for nasal and 89.8% for nasal plus throat swabs. BinaxNOW continues to be a very useful diagnostic during the omicron surge; oral (throat or cheek swab) should not replace nasal swabs due to significantly reduced sensitivity compared to nasal. As currently recommended, repeat testing should be done for high-risk persons with an initial negative antigen test result.
Macular vascular complexity analysis of diabetes mellitus by swept-source optical coherence tomographic angiography PurposeTo evaluate the associations of retinal vascular complexity features, including fractal dimension (FD) and blood vessel tortuosity (BVT), with the severity of diabetic retinopathy (DR) by using noninvasive optical coherence tomographic angiography (OCTA).

MethodsThis prospective cross-sectional study recruited ocular-treatment-naive patients with type 2 diabetes mellitus (DM) registered in the community of Guangzhou, China. OCTA was used to obtain the measurements of FD and BVT in the superficial capillary plexus (SCP) and the deep capillary plexus (DCP). Univariate and multivariate linear regression was used to analyse the correlation of FD and BVT in different layers with DR severity.

ResultsA total of 1282 patients with DM (1282 eyes), with a mean age of 64.2{+/-}7.8 years, were included. FD in the DCP decreased and BVT in the DCP increased in DR patients compared to non-DR patients, even after adjusting for confounding factors (P< 0.05). Trend analysis showed a significant decrease in the FD values as the DR progressed, while the BVT progressively increased with worsening DR severity (P< 0.01). The FD in DCP had a statistically significant positive correlation with FD in SCP and a negative correlation with BVT in SCP and BVT in DCP in all of the participants, including the non-DR group, moderate DR group and severe DR group (P< 0.01).

ConclusionFD and BVT determined using OCTA might be useful parameters for objectively distinguishing DR from non-DR and indicating DR progression. Correction of measurement error in a commercial multiple-breath washout device RationaleNitrogen multiple-breath washout (N2MBW) is an established technique to assess functional residual capacity (FRC) and ventilation inhomogeneity in the lung. Accurate measurement of gas concentrations is essential for the appropriate calculation of clinical outcomes.

ObjectivesWe investigated the accuracy of oxygen and carbon dioxide measurements used for the indirect calculation of nitrogen concentration in a commercial MBW device (Exhalyzer D, Eco Medics AG, Duernten, Switzerland) and its impact on FRC and lung clearance index (LCI).

MethodsHigh precision calibration gas mixtures and mass spectrometry were used to evaluate sensor output. We assessed the impact of corrected signal processing on FRC and LCI in a dataset of healthy children and children with cystic fibrosis using custom analysis software.

ResultsWe found inadequate correction for the cross sensitivity of the oxygen and carbon dioxide sensors in the Exhalyzer D device. This results in an overestimation of expired nitrogen concentration, and consequently FRC and LCI outcomes. Breath-by-breath correction of this error reduced mean (SD) FRC by 8.9 (2.2)% and LCI by 11.9 (4.0)%. It also resulted in almost complete disappearance of the tissue nitrogen signal at the end of measurements.

ConclusionsInadequate correction for cross sensitivity between the oxygen and carbon dioxide gas sensors of the Exhalyzer D device leads to an overestimation of FRC and LCI. Correction of this error is possible and could be applied by re-analysing the measurements breath-by-breath in an updated software version.

Grants, Gifts, Equipment, DrugsEco Medics AG (Duernten, Switzerland) provided a research version of their commercial software Spiroware 3.2.1 including insight on signal processing algorithms and helped with the acquisition of mass spectrometry measurements. This project was funded by the Swiss National Science Foundation, Grant Nr. 182719 (P. Latzin) and 168173 (K. Ramsey) Acute-stress induced change in salience network coupling prospectively predicts post-trauma symptom-development. Substantial individual differences exist in how acute stress affects large-scale neurocognitive networks, including Salience (SN), Default Mode (DMN) and Central Executive Networks (CEN). Changes in the connectivity strength of these network upon acute stress may predict vulnerability to long-term stress effects, which can only be tested in prospective longitudinal studies. Using such longitudinal design, we investigated whether the magnitude of acute-stress induced functional connectivity changes (delta-FC) predicts the development of posttraumatic stress disorder (PTSD) symptoms in a relatively resilient group of young police students that are known to be at high risk for trauma-exposure.

Using resting-state fMRI, we measured acute-stress induced delta-FC in 190 police recruits before (baseline) and after trauma exposure during repeated emergency aid services (16-month follow-up). Delta-FC was then linked to the changes in perceived stress levels (PSS) and post-traumatic stress symptoms (PCL and CAPS).

Weakened connectivity between the SN and DMN core regions upon acute stress induction at baseline predicted longitudinal increases in perceived stress level but not of post-traumatic stress symptoms, whereas increased coupling between the overall SN and anterior cerebellum was observed in participants with higher clinician-rated PTSD symptoms, particularly intrusion levels. All effects remained significant when controlling for trauma exposure-levels and cortisol stress-reactivity. Neither hormonal nor subjective measures exerted similar predictive or acquired effects.

The reconfiguration of large-scale neural networks upon acute stress induction is relevant for assessing and detecting risk and resilience factors for PTSD. This study highlights the SN connectivity-changes as a potential marker for trauma-related symptom-development, which is sensitive even in a relatively resilient sample. A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response in only [~]30% of cases. There is pressing need for more accurate biomarkers for immunotherapy response prediction.

We sought to identify peripheral blood biomarkers, predictive of response to immunotherapies against lung cancer, based on whole blood microRNA profiling. Using three well characterized cohorts consisting of a total of 334 stage IV NSCLC patients, we have defined a 5 microRNA risk score (miRisk) that is predictive of overall survival following immunotherapy in training and independent validation (HR 2.40, 95% CI 1.37-4.19; P < 0.01) cohorts. We have traced the signature to a myeloid origin and performed miRNA target prediction to make a direct mechanistic link to the PD-L1 signalling pathway and PD-L1 itself. The miRisk score offers a potential blood-based companion diagnostic for immunotherapy that outperforms tissue-based PD-L1 staining. 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. Classifying Radiology Abstracts with Deep Learning BackgroundThe Radiological Society of North America (RSNA) receives more than 8000 abstracts yearly for scientific presentations, scientific posters, and scientific papers. Each abstract is assigned manually one of 16 top-level categories (e.g. "Breast Imaging") for workflow purposes. Additionally, each abstract receives a grade from 1-10 based on a variety of subjective factors such as style and perceived writing quality. Using machine learning to automate, at least partially, the categorization of abstract submissions can result in saving many hours of manual labor.

MethodsA total of 45527 RSNA abstract submissions from 2014 through 2019 were ingested, tokenized, and pre-processed with a standard natural language programming protocol. A bag-of-words (BOW) model was used as a baseline to evaluate two more sophisticated models, convolutional neural networks and recurrent neural networks, and also evaluate an ensemble model featuring all three neural networks.

Resultsensemble model was able to achieve 73% testing accuracy for classifying the 16 top-level categories, outperforming all other models. The top model for classifying abstract grade was also an ensemble model, achieving a mean average error (MAE) of 1.01.

ConclusionWhile the baseline BOW model was the highest performing individual classifier, ensemble models that included state-of-the-art neural networks were able to outperform it. Our research shows that machine learning techniques can, to a reasonable degree of accuracy, predict both objective factors such as abstract category as well as subjective factors such as abstract grade. This work builds upon previous research involving using natural language processing on scientific abstracts to make useful inferences that address a meaningful problem. How has the prevalence of smoking cigarettes among HIV-infected patients changed over the last decade? Single-center analysis. IntroductionThe WHO report from 2016 estimates the prevalence of smoking as 22% worldwide and 28% in Poland. The prevalence of smoking in HIV-infected persons is 2-3 times higher than in the general population. The study conducted in 2009 in our department among n=116 HIV-infected patients showed 87.1% smoking cigarettes at least 2.5 pack-years (85.0% of males and 93.5% of females). The aim of the present study was to evaluate the change in the prevalence of cigarette smoking among HIV-infected patients over the last decade.

MethodsWe analyzed data from the survey conducted in n=204 HIV-infected patients hospitalized from November 2018 to November 2019 in our department. The survey included: age; sex, number of cigarettes smoked daily presently and in the past, the number of years as a smoker; the influence of HIV diagnosis on the number of cigarettes smoked.

ResultsAmong all n=204 patients current smokers n=78/204 (38.2%) with n=65/176 (36.9%) males and n=13/28 (46.4%) females. Among all n=115 current smokers and ex-smokers n=29 (25.2%) diagnosing the HIV infection had an impact on their smoking habits: n=26 (89.7%) increased and n=3 - all males (10.3%) decreased the number of smoked cigarettes.

ConclusionThe prevalence of smoking cigarettes among HIV-infected individuals in our department has decreased since 2009 but is still much higher than in the general population. The outcome indicates the need to improve smoking education provided by HIV care professionals. Smoking cessation guidance or treatment should become an integral part of HIV care for patients to fully benefit from antiretroviral therapy. The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients The factors involved in the persistence of antibodies to SARS-CoV-2 are unknown. We evaluated the antibody response to SARS-CoV-2 in personnel from 10 healthcare facilities and its association with individuals characteristics and COVID-19 symptoms in an observational study. We enrolled 4735 subjects (corresponding to 80% of all personnel) for three time points over a period of 8-10 months. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach. In individuals positive for IgG ([&ge;] 12 AU/mL) at the beginning of the study, we found an increase [p= 0.0002] in antibody response in symptomatic subjects, particularly with anosmia/dysgeusia (OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis. This may be linked to the lingering of SARS-CoV-2 in the olfactory bulb. Inflammasome-related markers at the ICU admission are not associated with outcome in the critically ill COVID-19 patients. PurposeDevelopment of targeted biological therapies for COVID-19 requires reliable biomarkers that could help indicate the responding patients. Hyperactivation of the inflammasome by SARS-CoV2 virus is hypothesized to contribute to severe course of the COVID-19 disease. Therefore, we aimed to evaluate the prognostic value of several inflammasome-related cytokines and proteins at the admission to the intensive care unit (ICU).

Patients and methodsPlasma samples were obtained from 45 critically ill COVID-19 patients and from 10 patients any without any signs of infection (TBI, traumatic brain injury) on admission to the ICU. The concentration of IL-1, IL-1{beta}, IL-18, IL-1RA, galectin-1, ASC, LDH, ferritin, and gasdermin D were analyzed. A novel cell-free caspase-1 plasma assay was developed by inhibitor-based immunoprecipitation followed by Western Blot. Demographic and clinical characteristics were recorded.

ResultsIn-hospital mortality in COVID-19 patients reached 62%. Galectin-1 was 1.8-fold lower in COVID-19 than in TBI patients (17101.84 vs. 30764.20 pg/ml, p=0.007), but other inflammasome-related biomarkers were at similar concentrations. Patients with SOFA score of >9 on admission who were at high risk of death had significantly higher galectin-1 but lower IL-1RA in comparison to low-risk patients (25551.3 pg/ml vs 16302.7 pg/ml, p=0.014; 14.5 pg/ml vs 39.4 pg/ml, p=0.04, respectively). Statistically significant correlations were observed between: IL-1 and platelets (r=-0.37), IL-1{beta} and platelets (r=-0.36), ferritin and INR (r=0.39). Activated caspase-1 p35 was detectable in 12/22 COVID-19 patients but in none of the TBI patients. Its presence was related with higher fibrinogen and lower D-dimers. Moreover, the densitometric analysis showed a significantly higher amount of p35 in patients with SOFA> 9.

ConclusionOur results indicate that the systemic markers of activation of the inflammasome in critically ill COVID-19 patients is not directly related with outcome. Therefore, potential interventions aimed at the inflammasome pathway in this group of patients may be of limited effectiveness and should be biomarker-guided. 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. iBKH: The integrative Biomedical Knowledge Hub The rapidly increasing biomedical knowledge, derived from biological experiments or gained from clinical practice, has become the important treasure in the biomedical research. The emerging knowledge graphs (KGs) provide an efficient and effective way to organize and retrieval the huge and increasing volume of biomedical knowledge. A biomedical KG (BKG) typically stores and represents knowledge by constructing a semantic network describing entities and the relationships between them. Previous efforts have been conducted to construct and curate BKGs by comprehensively integrating various biomedical data resources. Though the resulting BKGs have made a significant progress in this filed in advancing biological and medical research, there remain a big gap to a perfect one that is comprehensive and fine-grained enough. To this end, in the present study, we collected and integrated data from diverse well-curated biomedical knowledge bases and BKGs to curate a more comprehensive one, named the integrative Biomedical Knowledge Hub (iBKH). To enhance the usage in accelerating biomedical research, we deployed iBKH using the famous graph database, Neo4j. This is a continuing effort and we are adding in more and more contents in iBKH to support the various complex needs in biomedical data analysis. Please contact us if you have better ideas and suggestions. 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. Feasibility of an online Langerian mindfulness program for stroke survivors and caregivers BackgroundMindfulness is promising for individuals with neurological disorders and their caregivers to improve psychological well-being. The potential application of a Langerian mindfulness intervention, focused on attention to variability, however, is still unknown.

ObjectiveTo determine the feasibility (usability, satisfaction, and potential effectiveness on psychological well-being) of an online mindfulness intervention for stroke survivors and caregivers.

MethodologyUsing mixed-methods, 11 stroke survivors and 3 caregivers participated in a 3-week, online, Langerian mindfulness intervention. A semi-structured interview assessed the interventions usability and gathered feedback. Self-reported measures about psychological well-being were documented remotely three times (pre, post, one-month follow-up).

ResultsQualitatively, participants were highly satisfied with the intervention, reported subjective benefits, but the usability of the online platform was poor. None of the self-reported measures changed over time.

ConclusionThis study provided evidence of feasibility of an online Langerian mindfulness intervention in a new population: stroke survivors and caregivers. Thromboembolism risk among patients with diabetes/stress hyperglycemia and COVID-19 AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSPurposeC_ST_ABSIndividuals with diabetes/stress hyperglycemia carry an increased risk for adverse clinical outcome in case of SARS-CoV-2 infection. The purpose of this study was to evaluate whether this risk is, at least in part, modulated by an increase of thromboembolic complications.

MethodsWe prospectively followed 180 hospitalized patients with confirmed COVID-19 pneumonia admitted to the Internal Medicine Units of San Raffaele Hospital. Data from 11 out of 180 patients were considered incomplete and excluded from the analysis. We analysed inflammation, tissue damage biomarkers, hemostatic parameters, thrombotic events (TEs) and clinical outcome according to the presence of diabetes/stress hyperglycemia.

ResultsAmong 169 patients, 51 (30.2%) had diabetes/stress hyperglycemia. Diabetes/stress hyperglycemia and fasting blood glucose (FBG) were associated with increased inflammation and tissue damage circulating markers, higher D-dimer levels, increased prothrombin time and lower antithrombin III activity. Forty-eight venous and 10 arterial TEs were identified in 49 (29%) patients. Diabetes/stress hyperglycemia (HR 2.71, p=0.001), fasting blood glucose (HR 4.32, p<0.001) and glucose variability (HR 1.6, p < 0.009) were all associated with an increased risk of thromboembolic complication. TEs significantly increased the risk for an adverse clinical outcome only in the presence of diabetes/stress hyperglycemia (HR 3.05, p=0.01) or fasting blood glucose [&ge;] 7 mmol/l (HR 3.07, p=0.015).

ConclusionsThromboembolism risk is higher among patients with diabetes/stress hyperglycemia and COVID-19 pneumonia and is associated to poor clinical outcome. In case of SARS-Cov-2 infection patients with diabetes/stress hyperglycemia could be considered for a more intensive prophylactic anticoagulation regimen. Socioeconomic status effects on health vary between rural and urban Turkana Background and objectivesUnderstanding the social determinants of health is a major goal in evolutionary biology and human health research. Low socioeconomic status (often operationalized as absolute material wealth) is consistently associated with chronic stress, poor health, and premature death in high income countries. However, the degree to which wealth gradients in health are universal--or are instead made even steeper under contemporary, post-industrial conditions--remains poorly understood.

MethodologyWe quantified absolute material wealth and several health outcomes among a population of traditional pastoralists, the Turkana of northwest Kenya, who are currently transitioning toward a more urban, market-integrated lifestyle. We assessed whether wealth associations with health differed in subsistence-level versus urban contexts. We also explored the causes and consequences of wealth-health associations by measuring serum cortisol, potential sociobehavioral mediators in early life and adulthood, and adult reproductive success (number of surviving offspring).

ResultsHigher socioeconomic status and greater material wealth predicts better self-reported health and more offspring in traditional pastoralist Turkana, but worse cardiometabolic health and fewer offspring in urban Turkana. We do not find robust evidence for either direct biological mediators (i.e., cortisol) or indirect sociobehavioral mediators (e.g., adult diet or health behaviors, early life experiences) of wealth-health relationships in either context.

Conclusions and implicationsWhile social gradients in health are well-established in humans and animals across a variety of socioecological contexts, we show that the relationship between wealth and health can vary within a single population. Our findings emphasize that changes in economic and societal circumstances may directly alter how, why, and under what conditions socioeconomic status predicts health.

Lay summaryHigh socioeconomic status predicts better health and more offspring in traditional Turkana pastoralists, but worse health and fewer offspring in individuals of the same group living in urban centers. Together, our work shows that under different economic and societal circumstances, wealth effects on health may manifest in very different ways. Development of machine learning models for the prediction of complications after colorectal and small intestine surgery in psychiatric and non-psychiatric patient collectives (P-Study) Psychiatric and psychosomatic diseases are an increasingly cumbersome burden for the medical system. Indeed, hospital costs associated with mental health conditions have been constantly on the rise in recent years. Moreover, psychiatric conditions are likely to have a negative effect on the treatment of other medical conditions and surgical outcomes, in addition to their direct effects on the overall quality of life. Our study aims to investigate the impact of preoperative Risk factor, psychiatric and psychosomatic diseases on the outcomes of colorectal surgery and length of hospital stay via predictive modeling techniques.

MethodPatient data will be collected from several participating national and international surgical centers The machine learning models will be calculated and coded, but also published according to the TRIPOD guidelines (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis).

ResultIt is conceivable to arrive at generalizable models predicting the abovementioned endpoints through large amounts of data from several centers. The models will be subsequently deployed as a free-to-use web-based prediction tool using the Shiny environment [47]. The cost for the hosting server and digital infrastructure will be covered by Dr. Anas Taha. The sponsor will store the data for ten years. Deceased donor kidney degradomics indicates cytoskeletal proteolytic alterations impacting post-transplant function. BackgroundIn brain death, cerebral injury contributes to systemic biological dysregulation, causing significant cellular stress in donor kidneys that adversely impacts the quality of grafts. Here, we hypothesized that DBD kidneys may undergo proteolytic processes that renders grafts susceptible to post-transplant dysfunction.

Material & MethodsUsing mass spectrometry and immunoblotting analyses, we profiled degradation patterns of cytoskeletal proteins in deceased (n=55) and living (n=10) donor kidneys.

ResultsWe found that in DBD kidneys, key podocyte cytoskeletal proteins had been proteolytically cleaved. Generated degradation profiles were not associated to donor related demographic and clinical factors but were associated to suboptimal post-transplant function. Strikingly, -actinin -4 and Talin-1 degradation profiles were not observed in circulatory-death or living-donor kidneys. As Talin-1 is a specific proteolytic target of Calpain-1, we investigated a potential trigger of Calpain activation and Talin-1 degradation using ex-vivo precision-cut human kidney slices and in-vitro immortalised human podocytes. Notably, we found that Transforming-Growth Factor-{beta} (TGF-{beta}) activated Calpain-1 and proteolytically cleaved Talin-1 to generate distinct peptide fragments. These peptide fragments were of similar size and matched the degradation patterns observed in DBD kidneys. Talin-1 degradation was prevented in vitro by Calpain-1 inhibition.

ConclusionsHere, we provide initial evidence that DBD kidneys are susceptible to cytoskeletal protein degradation that impacts posttransplant kidney function. Subsequent studies should aim to further investigate the link between brain death and activation of proteolytic pathways exploring new therapeutic opportunities. Neutrophils and triglycerides predict critical illness in COVID-19 and reveal CDK6 inhibitors as potential preventive treatment Despite the recent development of vaccines and monoclonal antibodies preventing SARS-CoV-2 infection, treating critically ill COVID-19 patients still remains a top goal. In principle, drug repurposing - the use of an already existing drug for a new indication - could provide a shortcut to a treatment. However, drug repurposing is often very speculative due to lack of clinical evidence. We report here on a methodology to find and test drug target candidates for drug repurposing. Using UK Biobank data, we screened for significant differences in 33 blood cell types, 30 blood biochemistries, and body mass index between an infectious disease phenotype and healthy controls. We then matched critically ill COVID-19 cases with controls that exhibited mild or no symptoms after SARS-CoV-2 infection. Using data from the UK Biobank, we describe a workflow to find evidence for high neutrophil cell count and high concentrations of blood triglycerides as predictors of the immune overreaction in critical illness due to COVID-19. Based on these findings, we identified the enzyme CDK6 as a potential drug target to prevent in high risk individuals with high neutrophil cell count the immune overreaction in critical illness due to COVID-19. Three existing CDK4/6 inhibitors -- abemaciclib, ribociclib, and palbociclib -- have been approved for the treatment of breast cancer. Clinical evidence for CDK4/6 inhibitors in treating critically ill COVID-19 patients has been reported. Further clinical investigations are ongoing. Covid-19 Associated Hepatitis in children (CAH-C) during the second wave of SARS-CoV-2 infections in Central India: Is it a complication or transient phenomenon. Objective: While pediatric population has largely remained free of severe COVID-19, in some cases SARS-CoV-2 infection has been associated with complications like Multiple Inflammatory Syndrome in children (MIS-C). We mention another unique presentation subsequent to asymptomatic infection of SARS-CoV-2, a unique form of hepatitis designated by us as COVID-19 Associated Hepatitis in Children (CAH-C). The contrasting clinical presentations, temporal association and viral parameters of CAH-C cases, to the MIS-C cases are presented here. Methods: As a retrospective and follow-up observational study we reviewed all children testing positive for SARS-CoV-2 during study period. Children presenting with sudden onset of hepatitis, elevated transaminases, non-obstructive jaundice, lacking marked inflammatory responses and without evidence of (a) other known causes of acute hepatitis or previous underlying liver disease (b) multi-system involvement were classified as CAH-C, are described here. Results: Among 475 children who tested positive, 47 patients presented with hepatitis, 37 patients who had features of CAH-C, having symptoms of hepatitis only, with un-elevated inflammatory markers and uneventful recovery following supportive treatment. Whereas remaining 10 MIS-C hepatitis had protracted illness, multiple system involvement, required admission to critical care, and had mortality of 30%. Conclusion: With the emergence of newer variants of concern (VOC) including the Delta variant which predominated the second wave of infections in India and has now spread to more than 142 countries with changing presentations, CAH-C might be one of them. Cases of such new entities need to be identified early and differentiated from other emerging syndromes in children during the ongoing pandemic for preventing adversities by timely intervention. Immediate recruitment of dormant coronary collaterals can provide more than half of normal resting perfusion during coronary occlusion in patients with coronary artery disease AimsDormant coronary collaterals are highly prevalent and clinically beneficial in cases of coronary occlusion. However, the magnitude of myocardial perfusion provided by immediate coronary collateral recruitment during acute occlusion is unknown. The aim of this study was to quantify collateral myocardial perfusion during balloon occlusion in patients with coronary artery disease (CAD).

Methods and resultsPatients without angiographically visible collaterals undergoing elective percutaneous transluminal coronary angioplasty (PTCA) to a single epicardial vessel underwent two scans with 99mTc-sestamibi myocardial perfusion single-photon emission computed tomography (SPECT). All subjects underwent at least three minutes of angiographically verified complete balloon occlusion, at which time an intravenous injection of the radiotracer was administered, followed by SPECT imaging. A second radiotracer injection followed by SPECT imaging was performed 24 hours after PTCA.

The study included 22 patients (median [interquartile range] age 68 [54-72] years, 10 (45%) female). The perfusion defect extent was 19 [11-38] % of the LV, and the collateral perfusion at rest was 64 [58-67]% of normal.

ConclusionThis is the first study to describe the magnitude of short-term changes in coronary microvascular collateral perfusion in patients with CAD. On average, despite coronary occlusion and an absence of angiographically visible collateral vessels, collaterals provided more than half of the normal perfusion.

Graphical abstract

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/21264445v3_ufig1.gif" ALT="Figure 1">
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org.highwire.dtl.DTLVardef@bc0429org.highwire.dtl.DTLVardef@1b5567dorg.highwire.dtl.DTLVardef@41589borg.highwire.dtl.DTLVardef@203ecf_HPS_FORMAT_FIGEXP  M_FIG C_FIG Dormant collaterals are highly prevalent but the magnitude of myocardial perfusion provided by immediate coronary collateral recruitment during acute occlusion in humans is unknown. Patients without angiographically visible collaterals underwent intravenous injection with 99mTc-sestamibi during coronary balloon inflation followed by SPECT imaging. A control scan was performed 24 hours later..Median collateral perfusion during coronary occlusion was 64% of normal. An observational study of the association between COVID-19 vaccination rates and entry into the "Million Dollar Vax" competition ObjectivesAre financial incentives from entry in a vaccine lottery associated with a higher probability of vaccination for COVID-19?

DesignA cross-sectional study with adjustment for covariates using logistic regression

SettingOctober and November 2021, Australia.

Participants2,375 respondents of the Taking the Pulse of the Nation Survey

InterventionsParticipation in the Million Dollar Vaccination Lottery

Primary and secondary outcome measuresThe proportion of respondents who had any vaccination, a first dose only, or second dose compared to all other respondents

ResultsThose who participated in the lottery were 2.27 times more likely to be vaccinated after the lottery opened on October 1st than those who did not. This was driven by those receiving second doses. Lottery participants were 1.38 times more likely to receive their first dose after October 1st and 2.31 times more likely to receive their second dose after October 1st.

ConclusionsLottery participation is associated with a higher vaccination rate, with this effect dominated by a higher rate of second doses. There is a smaller insignificant difference for those receiving a first dose, suggesting lotteries may not be as effective at reducing vaccine hesitancy, compared to  nudging people to get their second dose more quickly.

Strengths and limitations of this studyO_LIWe use a nationally representative sample of individuals.
C_LIO_LIWe distinguish between the association between lottery participation and first and second doses.
C_LIO_LIWe adjust for a rich set of individual characteristics associated with vaccination status
C_LIO_LIThe strong association for second dose vaccinations may reflect some individuals who had already had scheduled their second dose after the lottery opened, potentially leading to an overestimate of the association.
C_LI

Ethics statementThis study was approved by the University of Melbourne Faculty of Business and Economics & Melbourne Business School Human Ethics Advisory Group (Ref: 2056754.1). Characterising SARS-CoV-2 viral clearance kinetics to improve the design of antiviral pharmacometric studies A consensus methodology for pharmacometric assessment of candidate SARS-CoV-2 antiviral drugs would be useful for comparing trial results and improving trial design. The time to viral clearance, assessed by serial qPCR of nasopharyngeal swab samples, has been the most widely reported measure of virological response in clinical trials, but it has not been compared formally with other metrics, notably model-based estimates of the rate of viral clearance. We analysed prospectively gathered viral clearance profiles from 280 infection episodes in vaccinated and unvaccinated individuals. We fitted different phenomenological pharmacodynamic models (single exponential decay, bi-exponential, penalised splines) and found that the clearance rate, estimated from a mixed effects single exponential decay model, is a robust pharmacodynamic summary of viral clearance. The rate of viral clearance, estimated from viral densities during the first week following peak viral load, provides increased statistical power (reduced type 2 error) compared with time to clearance. We recommend that pharmacometric antiviral assessments should be conducted in early illness with serial qPCR samples taken over one week. Statistical modeling for quality risk assessment of clinical trials: follow-up at the era of remote auditing BackgroundAs investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote Quality Assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology.

MethodsWe modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 - 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints.

ResultsWe could identify 15 interpretable factors influencing audit finding risk of 4 out of 5 CIF categories. They can be used to realistically predict differences in risk between 25 and 43% for different sites which suffice to rank sites by audit and inspection finding risk.

ConclusionContinuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times.