State of Emergency and Human Mobility during the COVID-19 Pandemic in Japan BackgroundTo help control the spread of the coronavirus disease 2019 (COVID-19), the Japanese government declared a state of emergency (SoE) four times. However, these were less stringent than other nations. It has not been assessed whether soft containment policies were sufficiently effective in promoting social distancing or reducing human contact.

MethodsUtilising the Google mobility index to assess social distancing behaviour in all Japanese prefectures between 15 February 2020 and 21 September 2021, mobility changes were assessed by an interrupted time-series analysis after adjusting for seasonality and various prefecture-specific fixed-effects and distinguishing potential heterogeneity across multiple SoEs and time passed after the declaration.

ResultsThe mobility index for retail and recreation showed an immediate decline after the declaration of the SoE by 7.94 percent-points (95%CI: -8.77 to -7.12) and a further decline after the initial period (beta: -1.27 95%CI: -1.43 to -1.11), but gradually increased by 0.03 percent-points (95%CI: 0.02 - 0.03). This trend was similar for mobilities in other places. Among the four SoEs, the overall declines in human mobility outside the home in the third and fourth SoE were the least significant, suggesting that people were less compliant with social distancing measures during these periods.

ConclusionAlthough less stringent government responses to the pandemic may help promote social distancing by controlling human mobilities outside the home, their effectiveness may decrease if these interventions are repeated and enforced for extended periods, distorting ones health belief by heuristics biases. By combining these with other measures (i.e. risk-communication strategies), even mild containment and closure policies can be effective in curbing the spread of the virus.

What is already known?O_LIHuman mobility, in terms of tracing social distancing and human contact in places such as shops, restaurants, and workplaces, was reported to be a useful indicator for predicting COVID-19 outbreaks.
C_LIO_LIContainment and closure policies, such as country lockdowns and a State of Emergency (SoE) declarations, effectively reduce human mobility.
C_LI

What are the new findings?O_LIThis study first evaluated if longer and repeated SoEs were effective to reduce human mobility.
C_LIO_LIThe findings from this study suggests that although less stringent government responses to the pandemic may help promote social distancing by controlling human mobilities outside the home, their effectiveness decreases if these interventions are repeated and enforced for extended periods.
C_LI

What do the new findings imply?O_LIWhile less stringent government responses to the pandemic are effective in promoting social distancing by controlling human mobilities outside the home, their effectiveness may decrease if similar interventions are repeated for extended periods of time.
C_LIO_LIHowever, by combining these with other measures such as risk-communication strategies, even less costly interventions such as mild containment and closure policies can be effective in curbing the spread of the COVID-19 virus.
C_LI

Research in contextO_ST_ABSEvidence before this studyC_ST_ABSIt has been shown that human mobility, in terms of tracing social distancing and human contact in places such as shops, restaurants, and workplaces, was reported to be a useful indicator for predicting COVID-19 outbreaks. Also, previous studies have shown that containment and closure policies, such as country lockdowns and a State of Emergency (SoE) declarations, effectively reduce human mobility. However, it is not explicitly known whether longer and repeated  alerts requesting citizens to avoid nonessential activities with risk communication strategies are equally effective.

Added-value of this studyThis study first evaluated if longer and repeated SoEs were effective to reduce human mobility, suggesting three main findings. First, individuals engage in social distancing behaviours during the initial periods of the SoE but become less compliant as time passes. Second, when mobility changes during each SoE were distinguished, overall declines in mobilities outside the home and increases in stay-at-home time were less obvious during the succeeding SoEs. Third, under the stringent government responses to the pandemic and decline in mobilities, the consumption level--especially for activities outside the home-- sharply declined, suggesting that strong public interventions may worsen the economy.

Implications of all the available evidenceWhile less stringent government responses to the pandemic are effective in promoting social distancing by controlling human mobilities outside the home, their effectiveness may decrease if similar interventions are repeated for extended periods of time. However, by combining these with other measures such as risk-communication strategies, even less costly interventions such as mild containment and closure policies can be effective in curbing the spread of the COVID-19 virus.
Crowdfunding for health research: A global systematic review, qualitative evidence synthesis and TDR pilot for LMIC researchers BackgroundMany low-and middle-income country (LMIC) researchers have disadvantages when applying for research grants. Crowdfunding may help LMIC researchers to fund their research. Crowdfunding organizes large groups of people to make small contributions to support a research study. This manuscript synthesizes global qualitative evidence and describes a TDR crowdfunding pilot for LMIC-based researchers.

MethodsOur global systematic review and qualitative evidence synthesis searched six databases for qualitative data. We used a thematic synthesis approach and assessed our findings using the GRADE-CERQual approach. Building on the review findings, we organized a crowdfunding pilot to support LMIC researchers and use crowdfunding. The pilot provided an opportunity to assess the feasibility of crowdfunding for infectious diseases of poverty research in resource-constrained settings.

ResultsNine studies were included in the qualitative evidence synthesis and we identified seven themes. Seven studies demonstrated that strong public engagement facilitated crowdfunding for research. Other themes included the correlates of crowdfunding success, risks of crowdfunding, and risk mitigation strategies. Our pilot data suggest that crowdfunding is feasible in diverse LMIC settings. Three researchers launched crowdfunding campaigns, met their goals and received substantial monetary (raising a total of $26,546 across all five campaigns) and non-monetary contributions. Two researchers are still preparing for campaign launch due to COVID-19 related difficulties.

ConclusionPublic engagement provides a foundation for effective crowdfunding for health research. Our evidence synthesis and pilot data provide practical strategies for LMIC researchers to engage the public and use crowdfunding. A practical guide was created alongside to facilitate these activities across multiple settings.

What is already known?Crowdfunding has been used to fund health causes, technology start-ups, creative projects, and more recently, scientific research. Although crowdfunding has been used for research funding in high-income settings, there is less evidence from LMIC settings. In addition, previous reviews of crowdfunding have not focused on public engagement strategies that may be important for developing effective crowdfunding campaigns.

What are the new findings?- Our qualitative evidence synthesis finds that crowdfunding research focuses on creators and backers in high-income settings, neglecting LMIC researchers.
- The TDR pilot programme suggests that crowdfunding is feasible for LMIC researchers. Three of the five pilot finalists exceeded their crowdfunding goals and received substantial non-monetary contributions.
Alternative Covid-19 mitigation measures in school classrooms: analysis using an agent-based model of SARS-CoV-2 transmission 1.The SARS-CoV-2 epidemic continues to have major impacts on childrens education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgement. The effectiveness of mitigation strategies are considered in terms of effectiveness at supressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission thought likely to play a major role in an incipient new wave of the epidemic. Our results were in good agreement with available data. These findings indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine, both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only modest impact on classroom infection levels. However, maintaining reduced contact rates within the classroom can have a major beneficial impact for managing Covid-19 in school settings. CD19+IgD+CD27- naive B Cells as predictors of humoral response to COVID 19 mRNA vaccination in immunocompromised patients Immunocompromised patients are considered high-risk and prioritized for vaccination against COVID-19. We aimed to analyze B-cell subsets in these patients to identify potential predictors of humoral vaccination response. Patients (n=120) suffering from hematologic malignancies or other causes of immunodeficiency and healthy controls (n=79) received a full vaccination series with an mRNA vaccine. B-cell subsets were analyzed prior to vaccination. Two independent anti-SARS-CoV-2 immunoassays targeting the receptor-binding domain (RBD) or trimeric S protein (TSP) were performed three to four weeks after the second vaccination. Seroconversion occurred in 100% of healthy controls, in contrast to 67% (RBD) and 82% (TSP) of immunocompromised patients, while only 32% (RBD) and 22% (TSP) achieved antibody levels comparable to those of healthy controls. The number of circulating CD19+IgD+CD27- naive B cells was strongly associated with antibody levels ({rho}=0.761, P<0.001) and the only independent predictor for achieving antibody levels comparable to healthy controls (OR 1.07 per 10-{micro}l increase, 95%CI 1.02-1.12, P=0.009). Receiver operating characteristic analysis identified a cut-off at [&ge;]61 naive B cells per {micro}l to discriminate between patients with and without an optimal antibody response. Consequently, measuring naive B cells in immunocompromised hematologic patients could be useful in predicting their humoral vaccination response. The Preventive Treatment of Migraine with Low-Dose naltrexone and acetaminophen Combination: Findings of a Small, Randomized, Double-Blind, and Placebo-Controlled Clinical Trial with an Open-Label Extension for None-Responders We tested a low-dose naltrexone and acetaminophen combination for episodic migraine prevention. We randomly assigned patients to naltrexone and acetaminophen combination (n=6) or placebo (n=6) for a 12-week double-blind treatment. Non-responders continued into open-label treatment with naltrexone and acetaminophen combination (n=5) for additional 12 weeks. Patients were adults who experienced 5 to 17 (average 9.7) migraine days at baseline. The primary endpoint was the mean change in the monthly migraine days during the last 4 weeks of the double-blind treatment period. The key secondary endpoint was the mean change in the monthly migraine days from the 4-week double-blind follow-up (2nd baseline) to the last 4 weeks of the open-label treatment period.

The magnitude of the treatment effect for the naltrexone and acetaminophen combination observed in the double-blind period was 2.2 fewer monthly migraine days than placebo (p=0.43). Four out of 6 (66.7%) naltrexone and acetaminophen-treated patients experienced 75% reduction in migraine days compared to 1 out of 6 (16.7%) placebo-treated patients (p=0.09).

In the open-label phase, treatment with the naltrexone and acetaminophen combination (n=5) led to 8.2 fewer mean monthly migraine days (from 11.8 to 3.6), representing 69.5% improvement (p=0.03), and 100% of the patients experienced a 50% reduction in monthly migraine days. Adverse events were mild to moderate and transient, included dry mouth, fatigue, sedation, nausea, and feeling jittery.

We postulate that naltrexones toll-like receptor (TLR4) antagonism properties prevent pro-inflammatory cytokines production in the trigeminal ganglion averting "overactive nerves" (laymans term) and migraine. Although this trial used low-dose naltrexone (defined as 1 - 5 mg/day), in future phase 3 studies we will test a range of naltrexone and acetaminophen combination doses. 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. 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. 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. Post-viral parenchymal lung disease following COVID-19 and viral pneumonitis hospitalisation: A systematic review and meta-analysis BackgroundApproximately half of COVID-19 survivors present persisting breathlessness, which may include development of pulmonary fibrosis.

Research QuestionWhat is the prevalence of long-term radiological and functional pulmonary sequelae of parenchymal lung disease following hospitalisation with COVID-19 and other viral pneumonia?

Study design and methodsWe performed systematic review and random effects meta-analysis of studies in adults hospitalised with SARS-CoV-2, SARS-CoV, MERS-CoV, or Influenza pneumonia and followed within 12 months from discharge. Searches were run on MEDLINE and Embase, updated 29 July 2021. Primary outcomes were proportion of 1) radiologic sequelae at CT scans; 2) restrictive impairment; 3) impaired gas transfer. Heterogeneity was explored in meta-regression.

ResultsNinety-five studies were included for qualitative synthesis, of which 70 were suitable for meta-analysis, including 60 studies of SARS-CoV-2 with a median follow up of 3 months. In SARS-CoV-2 the overall estimated proportion of inflammatory changes during follow up was 0.50 (95%CI 0.41 to 0.58, I2=94.6%), whilst fibrotic changes were estimated at 0.29 (95%CI 0.22 to 0.37, I2=94.1%). Inflammatory changes reduced compared with CTs performed during hospitalisation (-0.47; 95%CI -0.56 to -0.37), whereas no significant resolution was observed in fibrotic changes (-0.09; 95%CI -0.25 to 0.07). Impaired gas transfer was estimated at 0.38 (95%CI 0.32 to 0.44, I2=92.1%), which was greater than estimated restrictive impairment (0.17; 95%CI 0.13 to 0.23, I2=92.5%). High heterogeneity means that estimates should be interpreted with caution. Confidence in the estimates was deemed low due to the heterogeneity and because studies were largely observational without controls.

InterpretationA substantial proportion of radiological and functional sequelae consistent with parenchymal lung disease are observed following COVID-19 and other viral pneumonitis. Estimates of prevalence are limited by differences in case mix and initial severity. This highlights the importance of extended radiological and functional follow-up post hospitalisation.

PROSPERO registrationCRD42020183139 (April 2020) 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. Early management of severe COVID-19 coagulopathy should be guided by TEG, microclot and platelet mapping An important component of severe COVID-19 disease is virus-induced endothelilitis. This leads to disruption of normal endothelial function, initiating a state of failing normal clotting physiology. Massively increased levels of von Willebrand Factor (VWF) lead to overwhelming platelet activation, as well as activation of the enzymatic (intrinsic) clotting pathway. In addition, there is an impaired fibrinolysis, caused by, amongst others, increased levels of alpha-(2) antiplasmin. The end result is hypercoagulation [proven by thromboelastography(R) (TEG(R))] and reduced fibrinolysis, inevitably leading to a difficult-to-overcome hypercoagulated physiological state. Platelets in circulation also plays a significant role in clot formation, but themselves may also drive hypercoagulation when they are overactivated due to the interactions of their receptors with the endothelium, immune cells or circulating inflammatory molecules. From the literature it is clear that the role of platelets in severely ill COVID-19 patients has been markedly underestimated or even ignored. We here highlight the value of early management of severe COVID-19 coagulopathy as guided by TEG(R), microclot and platelet mapping. We also argue that the failure of clinical trials, where the efficacy of prophylactic versus therapeutic clexane (low molecular weight heparin (LMWH)) were not always successful, might be because the significant role of platelet activation was not taken into account during the planning of the trial. We conclude that, because of the overwhelming alteration of clotting, the outcome of any trial evaluating an any single anticoagulant, including thrombolytic, would be negative. Here we suggest the use of the degree of platelet dysfunction and presence of microclots in circulation, together with TEG(R), should be used as a guideline for disease severity. A multi-pronged approach, guided by TEG(R) and platelet mapping, would be required to maintain normal clotting physiology in severe COVID-19 disease. The effect of stimulus duration on preferences for gain adjustments when listening to speech ObjectivesIn the personalisation of hearing aid fittings, gain is often adjusted to suit patient preferences using live speech. When using brief sentences as stimuli, the minimum gain adjustments necessary to elicit consistent preferences ( preference thresholds) were previously found to be much greater than typical adjustments in current practice. The current study examined the role of duration on preference thresholds.

DesignParticipants heard 2, 4 and 6-s segments of a continuous monologue presented successively in pairs. The first segment of each pair was presented at each individuals real-ear or prescribed gain. The second segment was presented with a {+/-}0-12 dB gain adjustment in one of three frequency bands. Participants judged whether the second was "better", "worse" or "no different" from the first.

Study SampleTwenty-nine adults, all with hearing-aid experience.

ResultsThe minimum gain adjustments needed to elicit "better" or "worse" judgments decreased with increasing duration for most adjustments. Inter-participant agreement and intra-participant reliability increased with increasing duration up to 4 s, then remained stable.

ConclusionsProviding longer stimuli improves the likelihood of patients providing reliable judgments of hearing-aid gain adjustments, but the effect is limited, and alternative fitting methods may be more viable for effective hearing-aid personalisation. Prevalence of vitamin D is not associated with the COVID-19 epidemic in Europe. A critical update of the existing evidence. BackgroundCOVID-19 has emerged as a global pandemic, affecting nearly 104 million people worldwide as of February 4th 2021. In previous published studies, the association between the mean Vit D status of each country and COVID-19 infection rate, and mortality among the adult population in European countries was examined. The aim of this study was to re-examine the relationship between the Vit D status of each country and COVID-19 infection, recovery, and mortality using updated data and a different methodological approach.

MethodsInformation only form the last decade on Vit D concentration/deficiency for each country was retrieved through literature search on PubMed(R) database. As of February, 4th 2021, COVID-19 infections and mortalities per one million population as well as total recoveries were extracted from the Worldometer website. The association between vitamin D deficiency and COVID-19 infection, recovery, and mortality were explored using correlation coefficients and scatterplots.

FindingsThe prevalence of vitamin D deficiency among European countries ranged from 6.0 (Finland) to 75.5% (Turkey), with several countries facing more than 50% of vitamin D deficiency among their population. Non-significant correlations were observed between the number of COVID-19 infections (r=0.190; p=0.374), recoveries (rs=0.317, p=0.131), and mortalities (r=0.129; p=0.549) per one million population, with the prevalence of vitamin D deficiency.

InterpretationPrevalence of vitamin D deficiency was not significantly associated with either number of infections, recoveries or mortality rate of COVID-19 among European countries. Thus, it is an important parameter to be considered when implementing preventive measures to face COVID-19.

FundingNone 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. Mutation burden-orthogonal tumor genomic subtypes delineate responses to immune checkpoint therapy Higher-resolution tumor-agnostic biomarkers that predict response to immune checkpoint inhibitor (ICI) therapy are needed. Mutation signatures reflect underlying oncogenic processes that can affect tumor immunogenicity, and thus potentially delineate ICI treatment response among tumor types. Systematic mutational signature analysis on all solid tumors in The Cancer Genome Atlas yielded eight distinct tumor genomic subtypes, which were characterized by smoking exposure, ultraviolet light exposure, APOBEC enzyme activity, POLE mutation, mismatch repair deficiency, homologous recombination deficiency, genomic stability, and aging. The former five subtypes were construed to form an immune-responsive group acting as candidates for ICI therapy because of their high expression of immune-related genes and enrichment in cancer types with FDA approval for ICI monotherapy. Under this hypothesis, we developed a software to classify new tumors submitted to whole-exome sequencing. A pan-cancer dataset containing 973 cases treated with ICIs and clinical response was used for validation, where the classified tumor subtypes were significantly associated with ICI response, independent of cancer type and tumor mutational burden high or low status. The new tumor subtyping method can serve as a tumor-agnostic biomarker for ICI response prediction and will improve decision making in cancer treatment.

Structured abstractO_ST_ABSBackgroundC_ST_ABSIn cancer therapy, higher-resolution tumor-agnostic biomarkers that predict response to immune checkpoint inhibitor (ICI) therapy are needed. Mutation signatures reflect underlying oncogenic processes that can affect tumor immunogenicity, and thus potentially delineate ICI treatment response among tumor types.

MethodsBased on mutational signature analysis, we developed a stratification for all solid tumors in The Cancer Genome Atlas (TCGA). Subsequently, we developed a new software (GS-PRACTICE) to classify new tumors submitted to whole-exome sequencing. Using existing data from 973 pan-cancer ICI-treated cases with outcomes, we evaluated the subtype-response predictive performance.

ResultsSystematic analysis on TCGA samples identified eight tumor genomic subtypes, which were characterized by features represented by smoking exposure, ultraviolet light exposure, APOBEC enzyme activity, POLE mutation, mismatch repair deficiency, homologous recombination deficiency, genomic stability, and aging. The former five subtypes were presumed to form an immune-responsive group acting as candidates for ICI therapy because of their high expression of immune-related genes and enrichment in cancer types with FDA approval for ICI monotherapy. In the validation cohort, the samples assigned by GS-PRACTICE to the immune-reactive subtypes were significantly associated with ICI response independent of cancer type and TMB high or low status.

ConclusionsThe new tumor subtyping method can serve as a tumor-agnostic biomarker for ICI response prediction and will improve decision making in cancer treatment.

One Sentence SummaryA novel tumor genome subtyping method based on mutation signature analysis can predict response to immune checkpoint therapy orthogonally to tumor mutational burden. Machine Learning with Objective Serum Markers and Algorithmic Deep Learning CT Scan Analysis Predicts Brain Death Due to Trauma and Hypoxia BackgroundPrediction of which traumatic and hypoxic brain injuries will progress to brain death is currently based predominantly on history, physical examination and radiographic findings. We investigated how accurately purely objective measures including three neurologic serum markers and algorithmic CT (computed tomograph) scan analysis can predict brain death and differentiate its etiology.

MethodsThis prospective observational study enrolled 51 isolated trauma subjects and 19 clinically brain dead subjects that were further divided by mechanism of injury into high-velocity trauma with presumed diffuse axonal injury, cardiopulmonary/respiratory arrest, and found down groups. Levels of glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and S100 Calcium-Binding Protein B (S100B) were compared, and algorithmic analysis of CT scan imaging was performed using an open-source deep learning software (BLAST-CT).

FindingsThe prognostic value of various biomarker combinations in identifying subjects progressing to brain death was assessed using machine learning. Prediction accuracy for GFAP, UCH-L1, and S100B and algorithmic CT analysis in combination to predict brain death from among all other cohorts yielded an area under the receiver operator curve of 0.98 versus all other brain injury subjects. This model was also able to distinguish brain death attributed to cardiopulmonary/respiratory arrest from combined non-trauma controls and diffuse axonal injury with an area under the curve of 0.99.

InterpretationSerum concentrations of GFAP, UCH-L1, and S100B measured within 32 hours of traumatic and hypoxic brain injury (cardiopulmonary/respiratory arrest) and algorithmic CT analysis have utility in prognosticating brain death and predicting its mechanism of injury as either hypoxic or traumatic/unknown (diffuse axonal injury/found down).

FundingAbbott Labs and the Minnesota State Office of Higher Education. Precision Diagnostic Approach to Predict 5-Year Risk for Microvascular Complications in Type 1 Diabetes BackgroundIndividuals with long standing diabetes duration can experience damage to small microvascular blood vessels leading to diabetes complications (DCs) and increased mortality. Precision diagnostic tailors a diagnosis to an individual by using biomedical information. Blood small molecule profiling coupled with machine learning (ML) can facilitate the goals of precision diagnostics, including earlier diagnosis and individualized risk scoring.

MethodsUsing data in a cohort of 537 adults with type 1 diabetes (T1D) we predicted five-year progression to DCs. Prediction models were computed first with clinical risk factors at baseline and then with clinical risk factors and blood-derived molecular data at baseline. Progression of diabetic kidney disease and diabetic retinopathy were predicted in two complication-specific models.

FindingsThe model predicts the progression to diabetic kidney disease with accuracy: 0.96{+/-}0.25 and 0.96{+/-}0.06 area under curve, AUC, with clinical measurements and with small molecule predictors respectively and highlighted main predictors to be albuminuria, glomerular filtration rate, retinopathy status at baseline, sugar derivatives and ketones. For diabetic retinopathy, AUC 0.75{+/-}0.14 and 0.79{+/-}0.16 with clinical measurements and with small molecule predictors respectively and highlighted key predictors, albuminuria, glomerular filtration rate and retinopathy status at baseline. Individual risk scores were built to visualize results.

InterpretationWith further validation ML tools could facilitate the implementation of precision diagnosis in the clinic. It is envisaged that patients could be screened for complications, before these occur, thus preserving healthy life-years for persons with diabetes.

FundingThis study has been financially supported by Novo Nordisk Foundation grant NNF14OC0013659.

Research in context

Evidence before this studyMicrovascular diabetes complications (DCs), such as diabetic kidney disease (DKD) and diabetic retinopathy (DR), lead to increased mortality, blindness, kidney failure and overall decreased quality of life in individuals with diabetes.

Added value of this studyWe have developed four algorithms based on traditional risk factors alone and risk factors with metabolomic and lipidomic data. Two accurately predicted the future progression in individuals with type 1 diabetes. The top predictors chosen by the algorithm were hemoglobin A1c, albuminuria and retinopathy status at baseline for diabetic retinopathy. The most promising models predicted future diabetic kidney disease in which albuminuria, eGFR, retinopathy status at baseline were complemented with a number of ketones and sugar derivatives.

Implications of all the available evidenceWith further validation in several cohorts, the prediction models presented here have the potential for early diagnosis of CKD in persons with diabetes, thus enabling appropriate decisions on the available therapies. It is envisaged that these algorithms will be trialed and support clinicians with precision diagnosis and treatments. Redeployment and training of healthcare professionals to Intensive Care during COVID-19: a systematic review BackgroundA rapid influx of patients to intensive care and infection control measures during the COVID-19 pandemic required the rapid development of innovative redeployment and training strategies.

MethodsWe conducted a systematic search of 9 databases including key terms related to intensive care AND training AND redeployment AND healthcare workers. Analysis consisted of a narrative synthesis of quantitative study outputs, and a framework-based thematic analysis of qualitative study outputs and grey literature. These results were then combined applying an interpretative synthesis.

ResultsTwenty papers were analysed. These took place primarily in the UK (N=8, 40%) and USA (N=5, 25%). Themes included in the results are Redeployment: Implementation strategies and learnings; Redeployed staff experience and strategies to address their needs; Redeployed staff learning needs; Training formats offered and training evaluations; and Future redeployment and training concerns. Some of the redeployment implementation and training strategies documented in this review are: Skills-based redeployment, buddy support systems, and agreeing on locally-specific principles, rather than strict procedures.

ConclusionThe COVID-19 pandemic presented unique challenges to deliver training promptly while following infection control recommendations and develop flexible redeployment strategies. This study synthesises original approaches to tackle these challenges which are relevant to inform the development of targeted and adaptative training and redeployment plans. Hyperreflective foci, OCT progression indicators in age-related macular degeneration, include transdifferentiated retinal pigment epithelium PurposeBy optical coherence tomography (OCT) imaging, hyperreflective foci (HRF) indicate progression risk for advanced age-related macular degeneration (AMD) and are in part attributable to ectopic retinal pigment epithelium (RPE). We hypothesized that ectopic RPE are molecularly distinct from in-layer cells and that their cross-retinal course follows Muller glia.

MethodIn clinical OCT (61 eyes, 44 AMD patients, 79.4 {+/-} 7.7 years; 29 female; follow-up = 4.7 {+/-} 0.9 years) one HRF type, RPE plume, was reviewed. Twenty eyes of 20 donors characterized by ex vivo OCT were analyzed by histology (normal, 4; early/intermediate AMD, 7; geographic atrophy (GA), 6; 3 neovascular AMD (nvAMD). Cryosections were stained with antibodies to retinoid (RPE65, CRALPB) and immune (CD68, CD163) markers. In published RPE cellular phenotypes, red immunoreactivity was assessed semi-quantitatively by one observer (none, some cells, all cells).

ResultsTrajectories of RPE plume and cellular debris paralleled Muller glia, whether ordered or subsident, near atrophy borders. RPE corresponding to HRF lost immunoreactivity for retinoid markers and gained immunoreactivity for immune markers. Aberrant immunoreactivity appeared in individual in-layer RPE cells and extended to all abnormal phenotypes. Muller glia remained CRALBP-positive. Plume cells approached and contacted retinal capillaries.

ConclusionGain- and loss-of-function starts with individual in-layer RPE cells and extends to all abnormal phenotypes. Down-regulated RPE retinoid handling may impair rod vision while Muller glia sustain cone vision. Evidence for RPE transdifferentiation, possibly due to ischemia, supports a proposed process of epithelial-mesenchyme transition. Data can propel new biomarkers and therapeutic strategies for AMD.

PrecisHyperreflective foci (HRF) are OCT progression risk indicators in age-related macular degeneration. Abnormal RPE cells including some that correspond to HRF lose immunoreactivity for retinoid markers and gain immunoreactivity for immune markers, indicating molecular transdifferentiation. Factors affecting first return to work following arthroscopic rotator cuff repair: A retrospective observational study in an occupational medicine clinic and single-surgeon practice BackgroundReturn to work (RTW) following arthroscopic rotator cuff repair (aRCR) within New South Wales (NSW), Australia, under compensable schemes has not been reported.

AimsDetermine factors affecting RTW status and time in patients treated with aRCR under state-based compensation schemes, compared to those outside such schemes.

MethodsPatients undergoing aRCR by one surgeon with minimum 1-year follow-up were grouped into those under (CP) or outside (non-CP) workers or vehicle accident compensation schemes, matched by age and gender. RTW status and time were assessed using chi-square analysis and multivariable linear regression.

ResultsOf 1054 available patients, 90 CP patients were identified with 29 consented and matched to non-CP (N=29). A higher proportion of CP patients (17.2 vs 0%, P<0.001) never returned to work and a lower proportion resumed pre-injury duties at first RTW (3 vs 52%, P<0.01). Median time to first RTW did not differ between CP and non-CP groups (5.1 vs 4.4, P=0.86). Smoking (P=0.007) and post-injury activity level (P=0.004) were significantly associated with longer time to first RTW, whereas compensation status was not.

ConclusionsCP patients undergoing aRCR in NSW are at risk of not returning to work. For those that return, there is no significant difference compared to non-CP in time to first RTW. Particularly, patient and management factors associated with extended time to first RTW have been identified. Interventions aimed at modifiable factors such as smoking cessation and increasing preoperative activity may improve future outcomes.

What is already known about this subjectO_LIWork-related injury and disease present a significant financial burden to the Australian economy ($AUD61.8billion per annum), with shoulder conditions due to body stressing accounting for 7% of this total cost (>$AUD4.2billion)
C_LIO_LIPatients receiving compensation appear to have poorer outcomes and take longer to return to work than their non-compensated counterparts, but the degree to which this occurs is variably reported in the literature
C_LIO_LIThe effect of modifiable factors on return to work status and time remain unclear, and have not previously been reported for the Australian population under the New South Wales workers compensation scheme
C_LI

What this study addsO_LIPatients receiving arthroscopic rotator cuff repair under a compensable scheme in New South Wales are at increased risk of not returning to work compared to patients presenting outside of a compensable scheme and patients in equivalent schemes internationally
C_LIO_LIPatients reporting a longer duration of symptoms and smokers are at increased risk of extended time to first return to work
C_LI

What impact this may have on practice or policyO_LIThe study has identified smoking and pre-surgery activity levels as key targets for intervention in patients receiving arthroscopic rotator cuff repair under a workers compensation scheme in New South Wales
C_LIO_LIBy further investigating whether interventions designed to modify these factors are able to favourably affect patient outcomes, there is potential to return patients to their preoperative level of work sooner
C_LI Prospectively validated disease-agnostic predictive medicine with augmented intelligence Despite numerous superhuman achievements in complex challenges1,2, standalone AI does not free life science from the long-term bottleneck of linearly extracting new knowledge from exponentially growing new data3, severely limiting the success rate of drug discovery4. Inspired by the state-of-the-art AI training methods, we trained a human-centric hybrid augmented intelligence5 (HAI) to learn a foundation model6 that extracts all-encompassing knowledge of human physiology and diseases. To evaluate the quality of HAIs extracted knowledge, we designed the public, prospective prediction of pivotal ongoing clinical trial outcomes at large scale (PROTOCOLS) challenge to benchmark HAIs real-world performance of predicting drug clinical efficacy without access to human data. HAI achieved a 10.5-fold improvement from the baseline with 99% confidence7 in the PROTOCOLS validation, readily increasing the average clinical success rate of investigational new drugs from 7.9%8 to 90% for almost any human diseases9,10. The validated HAI confirms that exponentially extracted knowledge alone is sufficient for accurately predicting drug clinical efficacy, effecting a total reversal of Erooms law4. HAI is also the worlds first clinically validated model of human aging that could substantially speed up the discovery of preventive medicine for all age-related diseases11. Our results demonstrate that disruptive breakthroughs necessitate the smallest team size to attain the largest HAI for optimal knowledge extraction from high-dimensional low-quality data space, thus establishing the first prospective proof of the previous discovery that small teams disrupt12. The global adoption of training HAI provides a well-beaten economic path to mass-produce scientific and technological breakthroughs via exponential knowledge extraction and better designs of data labels for training better-performing AI13. Airway management, procedural data, and mortality records of patients undergoing surgery for mucormycosis associated with coronavirus disease (COVID-19) In India, the second wave of coronavirus disease (COVID -19) was associated with a distinct surge in cases of invasive fungal infection with mucormycosis. This disease was seen typically in the sinonasal form in COVID-19 patients. Uncontrolled diabetes, steroid use in COVID-19 treatment, etc. were some of the postulated risk factors for the association of COVID 19 and Mucormycosis. The management plan of these cases included surgical debridement, systemic antifungal therapy, sugar control, and management of antifungal related systemic adverse effects. In this retrospective case record review, we aimed to evaluate the airway management plan, demographics, and overall outcomes in patients undergoing surgical resection for COVID-19 associated mucormycosis. Forty-one (71.9 %) patients had a diagnosis of sino-nasal mucormycosis, fourteen (24.6%) had a diagnosis of rhino-orbital mucormycosis, and 2 patients (3.5%) were diagnosed with palatal mucormycosis. Total 44 (77.19 %) patients had co-morbidities. The most common co-morbidity was Diabetes Mellitus 42 (73.6%), followed by hypertension 21 (36.84%) and Acute kidney injury 14 (28.07%). We used the intubation difficulty scale score to assess intubating conditions. Intubation was easy to slightly difficult in 53 out of 57 patients. In our study, mortality occurred in 7 (12.28 %) patients. The median mortality time was 60 (range, 27-74) days. The median time to hospital discharge was 53.5 (range,10-85) days. Managing COVID-19 on its own is challenging and additional mucormycosis can lead to increased morbidity and mortality. Despite challenges and risks, timely and meticulous interventions can reduce complications. Lower Risk of Multisystem Inflammatory Syndrome in Children (MIS-C) with the Delta and Omicron variants of SARS-CoV-2 Paediatric Inflammatory Multisystem Syndrome (PIMS-TS, also known as MIS-C) typically occurs 2-6 weeks after exposure to SARS-CoV-2. Early estimates suggested a risk of PIMS-TS of 1 in 3-4000 infected children. Whether this risk is sustained with new SARS-CoV-2 variants remains unknown.

We utilised prospective data from the NHS South Thames Paediatric Network (STPN), which manages all cases of PIMS-TS amongst 1.5 million children in South-East England, to assess trends over time. We compared PIMS-TS cases with two independent SARS-CoV-2 infection datasets. We used publicly available UK Health Security Agency case numbers weighted to child population distributions according to area population estimates from the Office for National Statistics (ONS). To avoid bias due to evolving testing behaviour, we also compared PIMS-TS cases to community infection rates, obtained from the ONS COVID-19 Infection Survey, which randomly selects individuals for fortnightly PCR tests. All three datasets were normalised to the peak of the Alpha wave, and plotted against time. PIMS-TS cases were plotted 40 days prior to hospitalisation, corresponding to the best fit of rising SARS-CoV-2 infection and PIMS-TS cases during the Alpha wave.

Compared with the Alpha wave, we found fewer cases of PIMS-TS relative to SARS-CoV-2 infections during both initial and subsequent Delta waves. This relative reduction continued into the Omicron wave.

Re-infection rates with the Alpha or Delta variants and vaccination rates were very low during the Delta wave. As a result, lower PIMS-TS rate relative to SARS-CoV-2 infections during the Delta wave is unlikely to be explained by population level immunity from prior infection or vaccination. It is most likely due to viral mutations in key antigenic epitopes responsible for triggering the hyperinflammatory response observed with PIMS-TS. The incidence of delirium in an acute geriatric community hospital: an observational cohort feasibility study. ObjectivesDelirium in hospitalised older adults is associated with negative health outcomes. Admission to an alternative care setting may lower the incidence of delirium. The Acute Geriatric Community Hospital (AGCH) was recently opened in the Netherlands and uses a multi-component non-pharmacological intervention strategy to prevent delirium. The objective of this study was to describe the incidence of delirium at the AGCH and compare this incidence to existing rates from literature. If a possible effect on delirium is seen in this comparison this would support conducting a larger prospectively controlled study on delirium in this new care setting.

DesignProspective cohort feasibility study; exploratory meta-analysis of proportions.

Setting and ParticipantsThe AGCH is an acute geriatric unit in a skilled nursing facility for patients aged >65 years with acute medical conditions.

MethodsDelirium assessment using the Confusion Assessment Method (CAM) upon admission and on day one, two and three or until delirium had resolved. Patients charts were reviewed if CAM was missing. In an linear mixed-effects model, the delirium incidence rate in AGCH was compared to pooled delirium incidence rates from six studies found in a high-quality review.

Results214 patients from the AGCH (mean age 81.9 years, 47% male, 12% with a history of dementia) were included in the analysis. Delirium developed in 8% (18/214) (95% confidence interval [CI] 5-13%) of patients during AGCH admission compared to 16% (95% CI 12-21%) in hospitals. Admission to the AGCH was associated with a decreased delirium incidence rate compared to the hospital control group (OR[odds ratio]= 0.49, 95% CI 0.24-0.98, p-value=0.044).

Conclusions and implicationsThe delirium incidence in the AGCH was low compared to those incidences found in general hospitals in literature. Based on these findings a controlled observational or randomized study measuring delirium in this care setting is recommended. 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. The role of classroom volume, occupancy, voice reduction and ffp2 masks in transmission risk of SARS-CoV2 in schools Issues linked to aerosol physics within school buildings and related infection risk still lack a proper recognition in school safety regulations. In this theoretical work we try to shed more light on the critical role of air ventilation, classroom volume, occupancy, and face mask types (surgical vs ffp2) in controlling airborne transmission risk in educational settings. Limited spaces available in many schools require to precisely assess the occupancy/volume ratio in each classroom and to investigate the specific risk levels from aerosolization of viral loads from infective sources. Moreover, most schools are not provided with mechanical HVAC systems. Fundamental questions are therefore: how the specific classroom volume affects the long-range contagion risk in a given classroom? is linear social distancing the right way to assess a volumetric risk problem? How effective are other countermeasures like reduced speaking volume or equipping teachers with microphones? We present here the results of a numerical analysis based on the Gammaitoni-Nucci infection risk model and the consolidated Wells-Riley like approach, with SARS-CoV2 input data and geometric data from a typical high-school classroom in Italy. We investigated separately the case of infective asymptomatic student and infective asymptomatic teacher as source of viral quanta, taking into account thermal gradient effects on the air ventilation rates. First recommendations based on the volumetric nature of aerosol risk are suggested to extend the linear social distancing approach applied so far. Finally we discuss the concept of "cumulative infection risk" over multiple lesson+break cycles in Wells-Riley-like infection models. We believe that any attempt to proper model infection risk in closed environments with cycled changes of the source and the susceptible individuals, should carefully consider this point, particularly when modelling air ventilation breaks in classrooms.