Autism in gifted youth is associated with low processing speed and high verbal ability BackgroundHigh cognitive ability is an almost universally positive prognostic indicator in the context of neurodevelopmental, neuropsychiatric, and neurodegenerative conditions. However, "twice-exceptional" individuals, those who demonstrate exceptionally high cognitive ability (gifted) and exhibit profound behavioral and mental health challenges, are a striking exception to this rule.

MethodsWe digitized the clinical records of N=1,074 clients from a US-based specialty clinic serving gifted students. This included a broad array of diagnostic, cognitive, achievement, and behavioral data, including self, teacher, and parent reported items. We conducted both hypothesis-driven and unsupervised learning analyses to 1) identify characteristics whose association with full-scale IQ (FSIQ) was dependent on autism diagnosis and 2) identify cognitive archetypes associated with autism diagnosis and related behaviors. We tested the generalization of our findings using data from the SPARK (N=17,634) and ABCD studies (N=10,602).

ResultsAutistic individuals with IQ >= 120 were nearly 15 times more likely to enter adulthood undiagnosed compared to lower-IQ (IQ < 70) counterparts. Self-reported sense of inadequacy was most strongly associated with increasing FSIQ specifically among autistic clients (beta=0.3, 95% CI:[0.15,0.45], p=7.1x10-5). Similarly, self, parent, and teacher reports of anxiety increased with FSIQ (all p<0.05) in autistic individuals, in striking opposition to the ameliorating effect of FSIQ seen in non-autistic individuals. We uncovered a pattern of decreased processing speed (PS) coupled with very high verbal comprehension (VC), a PS/VC discrepancy, that was associated with autism, inattention, and internalizing problems. Similar cognitive-behavioral links were also observed in the ABCD study. Finally, we found a significant association between the PS/VC discrepancy and polygenic risk for autism in the ABCD sample (t=2.9, p=0.004).

ConclusionsOur results suggest that autistic individuals with exceptional ability are underserved and suffer disproportionately from high anxiety and low self-worth. In addition, elevated IQ with a significant PS/VC discrepancy appears to be a clinically and genetically meaningful biotype linked to autism.
One Year of Evidence on Mental Health Disorders in China during the COVID-19 Crisis - A Systematic Review and Meta-Analysis This paper provides a systematic review and meta-analysis on the prevalence rate of mental health issues of general population, general and frontline healthcare workers (HCWs) in China over one year of the COVID-19 crisis. We systematically searched PubMed, Embase, Web of Science, and Medrxiv at November 16th, 2020, pooled data using random-effects meta-analyses to estimate the prevalence rates, and ran meta-regression to tease out the heterogeneity. The meta-regression results uncovered several predictors of the prevalence rates, including severity, type of mental issues, population, sampling location, and study quality. Pooled prevalence rates are significantly different from, yet largely between, the findings of previous meta-analyses, suggesting the results of our larger study are consistent with yet more accurate than the findings of the smaller, previous meta-analyses. The prevalence rates of distress and insomnia and those of frontline HCWs are higher suggest future research and interventions should pay more attention to those mental outcomes and populations. Our findings suggest a need to examine the prevalence rates at varying levels of severity. The one-year cumulative evidence on sampling locations (Wuhan vs. non-Wuhan) corroborates the typhoon eye effect theory.

Trial registrationCRD4202022059
Decreased Diastolic Hydraulic Forces Incrementally Associate With Survival Beyond Conventional Measures of Diastolic Dysfunction AIMSDecreased hydraulic forces during diastole contribute to reduced left ventricular (LV) filling and heart failure with preserved ejection fraction. However, their association with diastolic function and patient outcomes are unknown. The aim of this study was to determine the association between diastolic hydraulic forces, estimated by echocardiography as the atrioventricular area difference (AVAD), and both diastolic function and survival.

METHODS AND RESULTSPatients (n=11,734, median [interquartile range] 3.9 [2.4-5.0] years follow-up, 1,213 events) were selected from the National Echo Database Australia based on the presence of relevant transthoracic echocardiographic measures, LV ejection fraction (LVEF) [&ge;]50%, heart rate 50-100 beats/minute, the absence of moderate or severe valvular disease, and no prior cardiac surgery. AVAD was calculated as the cross-sectional area difference between the LV and left atrium. LV diastolic dysfunction was graded according to 2016 guidelines. AVAD was weakly associated with E/e, left atrial volume index, and LVEF (multivariable global R2=0.15, p<0.001), and not associated with e and peak tricuspid regurgitation velocity. Decreased AVAD was independently associated with poorer survival, and demonstrated improved model discrimination after adjustment for diastolic function grading (C-statistic [95% confidence interval] 0.645 [0.629-0.662] vs 0.607 [0.592-0.623], p<0.001) and E/e (0.639 [0.623-0.655] vs 0.621 [0.604-0.637], p<0.001), respectively.

CONCLUSIONDecreased hydraulic forces, estimated by AVAD, are weakly associated with diastolic dysfunction and demonstrate an incremental prognostic association with survival beyond conventional measures used to grade diastolic dysfunction. Molecular analysis suggests oligoclonality and metastasis of endometriosis lesions across anatomically defined subtypes. Endometriosis symptoms are heterogeneous with controversy on whether it constitutes a single disease or multiple distinct types. Our previous work found recurrent somatic cancer-driver alterations in endometriosis; however, these have not been found ubiquitously. A handful of cases spread across studies also suggest mutations might be shared (clonal) between lesions of the same type. As current classification systems correlate poorly with symptoms or outcomes, somatic genomics may improve the current system. Here, we investigate heterogeneity of somatic cancer-driver mutations within patients and across endometriosis types. We examined anatomically distinct types of endometriosis (ovarian, deep infiltrating, and superficial endometriosis) in 27 individual patients all of whom had at least two types of endometriosis. Specimens were analyzed using high-sensitivity targeted sequencing with orthogonal validation from droplet digital PCR and mutation-surrogate immunohistochemistry. Results found 13/27 patients had informative somatic driver mutation in endometriosis, 9/13 had identical mutations across distinct lesions. Endometriomas tended to have a higher mutational complexity, with functionally redundant driver mutations in same gene and within the same lesions.

Our data are consistent with clonality across endometriosis lesions regardless of subtype. Further the finding of redundancy in mutations with the same gene and lesions is also consistent with endometriosis representing an oligoclonal disease with dissemination likely to consist of multiple epithelial clones travelling together. This suggests the current anatomically defined classification of endometriosis does not fully recognize the etiology of the disease. A novel classification should take into account genomic and other molecular features. These findings could further contribute to development of a more personalized endometriosis diagnosis and care. Benchmarking saliency methods for chest X-ray interpretation Saliency methods, which "explain" deep neural networks by producing heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. Although many saliency methods have been proposed for medical imaging interpretation, rigorous investigation of the accuracy and reliability of these strategies is necessary before they are integrated into the clinical setting. In this work, we quantitatively evaluate three saliency methods (Grad-CAM, Grad-CAM++, and Integrated Gradients) across multiple neural network architectures using two evaluation metrics. We establish the first human benchmark for chest X-ray interpretation in a multilabel classification set up, and examine under what clinical conditions saliency maps might be more prone to failure in localizing important pathologies compared to a human expert benchmark. We find that (i) while Grad-CAM generally localized pathologies better than the two other saliency methods, all three performed significantly worse compared with the human benchmark; (ii) the gap in localization performance between Grad-CAM and the human benchmark was largest for pathologies that had multiple instances, were smaller in size, and had shapes that were more complex; (iii) model confidence was positively correlated with Grad-CAM localization performance. Our work demonstrates that several important limitations of saliency methods must be addressed before we can rely on them for deep learning explainability in medical imaging. COVID-19 Acceleration and Vaccine Status in France - Summer 2021 ObjectivesThis note provides an assessment of COVID-19 acceleration among groups with different vaccine status in France.

MethodsWe assess viral acceleration using a novel indicator introduced in Baunez et al. (2021). The acceleration index relates the percentage change of tests that have been performed on a given day to the percentage change in the associated positive cases that same day. We compare viral acceleration among vaccinated and unvaccinated individuals in France over the period May 31st - August 29, 2021.

ResultsOnce the state of the epidemic within each groups is accounted for, it turns out that viral acceleration has since mid-July converged to similar levels among vaccinated and unvaccinated individuals in France, even though viral speed is larger for the latter group compared to the former.

ConclusionOur results call for an increasing testing effort for both vaccinated and unvaccinated individuals, in view of the fact that viral circulation is currently accelerating at similar levels for both groups in France. 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. 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. Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis: a cohort study in UK Biobank ObjectivesTo evaluate the benefit of combining polygenic risk scores (PRS) with the QCancer-10 (colorectal cancer) non-genetic risk prediction model to identify those at highest risk of colorectal cancer (CRC).

DesignPopulation based cohort study. Six different PRS for CRC were developed (using LDpred2 PRS software, clumping and thresholding approaches, and genome-wide significant models). The top-performing genome-wide and GWAS-significant PRS were then combined with QCancer-10 and performance compared to QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women.

Setting and participantsUK Biobank Study. A total of 434587 individuals with complete genetic and QCancer-10 predictor data were included in the QCancer-10+PRS modelling cohorts.

Main outcome measuresPrediction of colorectal cancer diagnosis by genetic, non-genetic and combined risk models.

FindingsPRS derived using the LDpred2 program performed best, with an odds-ratio per standard deviation of 1.58, and top age- and sex-adjusted C-statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10+PRS models out-performed QCancer-10 alone. In men, the integrated LDpred2 (QCancer-10+LDP) model produced a C-statistic of 0.730 (0.720 to 0.741) and explained variation of 28.1% (26.3% to 30.0%), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9% to 23.1%) for QCancer-10 alone. Performance improvements in women were similar. In the top 20% of individuals at highest absolute risk, the sensitivity of QCancer-10+LDP models for predicting CRC diagnosis within 5 years was 47.6% in men and 42.5% in women, with respective 3.49-fold and 2.75-fold absolute increases in the top 5% of risk compared to average. Decision curve analysis showed that adding PRS to QCancer-10 improved net-benefit and interventions avoided, across most probability thresholds.

ConclusionsIntegrating PRS with QCancer-10 significantly improves risk prediction over QCancer-10 alone. Evaluation of risk stratified population screening using this approach is warranted.

Summary BoxO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIRisk stratification based on genetic or environmental risk factors could improve cancer screening outcomes
C_LIO_LINo previously published study has examined integrated models combining genome-wide PRS and non-genetic risk factors beyond age
C_LIO_LIQCancer-10 (colorectal cancer) is the top-performing non-genetic risk prediction model for CRC
C_LI

What this study addsO_LIAdding PRS to the QCancer-10 (colorectal cancer) risk prediction model improves performance and clinical benefit, with greatest gain from the LDpred2 genome-wide PRS, to a level that suggests utility in stratifying CRC screening and prevention
C_LI The Addiction Risk Factor: A Unitary Genetic Vulnerability Characterizes Substance Use Disorders and Their Associations with Common Correlates Substance use disorders commonly co-occur with one another and with other psychiatric disorders. They share common features including high impulsivity, negative affect, and lower executive function. We tested whether a common genetic factor undergirds liability to problematic alcohol use (PAU), problematic tobacco use (PTU), cannabis use disorder (CUD), and opioid use disorder (OUD) by applying genomic structural equation modelling to genome-wide association study summary statistics for individuals of European ancestry (Total N = 1,019,521; substance specific Ns range: 82,707-435,563), while adjusting for the genetics of substance use (Ns = 184,765-632,802). We also tested whether shared liability across SUDs is associated with behavioral constructs (risk taking, executive function, neuroticism; Ns = 328,339-427,037) and non-substance use psychopathology (psychotic, compulsive, and early neurodevelopmental disorders). Shared genetic liability to PAU, PTU, CUD, and OUD was characterized by a unidimensional addiction risk factor (termed The Addiction-Risk-Factor, independent of substance use. OUD and CUD demonstrated the largest loadings, while problematic tobacco use showed the lowest loading. The Addiction-Risk-Factor was associated with risk taking, neuroticism, executive function, and non-substance psychopathology, but retained specific variance before and after accounting for genetics of substance use. Thus, a common genetic factor partly explains susceptibility for alcohol, tobacco, cannabis, and opioid use disorder. The Addiction-Risk-Factor has a unique genetic architecture that is not shared with normative substance use or non-substance psychopathology, suggesting that addiction is not the linear combination of substance use and psychopathology. 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. 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. Patient attrition in Molecular Tumour Boards: A Systematic Review Molecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision making within precision medicine. Though these meetings are in use globally reporting often focuses on the small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment. A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 54 papers were reviewed spanning a 6 year period from 11 different countries. 20% of patients received treatment through the MTB process. Of those that did not receive treatment the main reasons were no mutations identified (26%), no actionable mutations (22%) and clinical deterioration (15%). However, the data was often incomplete due to inconsistent reporting of MTBs with only 53% reporting on patients having no mutations, 48% reporting on presence of actionable mutations with no treatment options and 57% reporting on clinical deterioration. As patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data. The energy balance theory: an unsatisfactory model of body composition fluctuations Differential weight and fat losses under isocaloric diets of distinct macronutrient composition are well-documented findings in obesity research.1-6 Such data are considered a result of inadequate methodology as it disagree with the energy balance theory.7 A recent mathematical analysis of this paradigm has found, however, serious analytical contradictions in its foundations.8 As an alternative, a mass balance model was proposed to explain the aforesaid body composition alterations. Here, we expand on this observation by contrasting both models. We show that mass balance explains a wide range of fending experiments including those concurring with the energy balance principle. The latter, however, is less flexible and results in poor forecasts in settings consistent with mass balance. The energy balance theory is thus an unsatisfactory model of body composition changes. Consequently, by shifting to a mass balance paradigm of obesity a much deeper understanding of this disease may follow in the near future. How Informative Were Early SARS-CoV-2 Treatment and Prevention Trials? A longitudinal cohort analysis of trials registered on clinicaltrials.gov BackgroundEarly in the SARS-CoV-2 pandemic, commentators warned that some COVID trials were inadequately conceived, designed and reported. Here, we retrospectively assess the prevalence of informative COVID trials launched in the first 6 months of the pandemic.

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

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

ConclusionsLess than one third of COVID-19 trials registered on ClinicalTrials.gov during the first six months met all three criteria for informativeness. Shortcomings in trial design, recruitment feasibility and redundancy reflect longstanding weaknesses in the clinical research enterprise that were likely amplified by the exceptional circumstances of a pandemic. "What do you mean I can't have a doctor? This is Canada!" - A Qualitative Study of the Myriad Consequences for Unattached Patients Awaiting Primary Care Attachment BackgroundPatient access to primary healthcare (PHC) is the foundation of a strong healthcare system and healthy populations. Attachment to a regular PHC provider, a key to healthcare access, has seen a decline in some jurisdictions. This study explored the consequences of unattachment from a patient perspective, an under-studied phenomenon to date.

MethodsA realist-informed qualitative study was conducted with unattached patients in Nova Scotia, Canada. Semi-structured interviews with nine participants were conducted and transcribed for analysis. The framework method was used to carry out analysis, which was guided by Donabedians model of assessing healthcare access and quality.

ResultsFive key findings were noted in this study: 1) Participants experienced a range of consequences from not having a regular PHC provider. Participants used creative strategies to 2) attempt to gain attachment to a regular PHC provider, and, to 3) address their health needs in the absence of a regular PHC provider. 4) Participants experienced negative feelings about themselves and the healthcare system, and 5) stress related to the consequences and added work of being unattached and lost care.

ConclusionsUnattached patients experienced a burden of care related to lost care and managing their own health and related information, due to the download of medical record management and system navigation to them. These findings may underestimate the consequences for further at-risk populations who would not have been included in our recruitment. This may result in poorer health outcomes, which could be mitigated by interventions at the structural level, such as enhanced centralized waitlists to promote attachment. Such waitlists may benefit from a triage approach to appropriately attach patients based on need. 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. Short Practical Regimen of Acupuncture for Melasma: A Prospective Cohort Study in a Tertiary Hospital in Thailand BackgroundMelasma is a common skin pigmentation disorder. Acupuncture shows benefits for patients with melasma, although no standardized method or optimal number of sessions have been determined.

ObjectiveTo comparatively assess clinical outcomes of melasma between five vs ten standard acupuncture sessions.

MethodsWe performed a prospective observational cohort study in all melasma patients at the Acupuncture Clinic, King Chulalongkorn Memorial Hospital, Bangkok, Thailand. All patients were treated with standard acupuncture procedure twice a week and were evaluated after the fifth and the tenth sessions of acupuncture, with a follow-up one week after the last session. Participants were divided into two treatment groups: Groups A and B received five and ten acupuncture sessions respectively. Melasma outcomes were assessed by using the Melanin Index (MI), Melasma Area and Severity Index (MASI), patient-reported improvement scores and acupuncture-related adverse events.

ResultsOf 113 participants, 67 received five sessions of acupuncture treatment while 39 received ten sessions. 7 participants did not finish treatment. After five sessions of acupuncture in Group A, the mean MI decreased by 28.7 (95%CI -38.5 to -18.8), whereas the median MASI decreased by 3.4 (-6.9, -1.2) points. After ten sessions of acupuncture in Group B, the mean MI decreased by 31.3 (95%CI -45.0 to -17.6), whereas the median MASI decreased by 5.4 (-9.9, - 3.0) points. The first five sessions of acupuncture had a higher effect on the change in magnitude than the last five sessions, although there was no statistically significant difference. However, ten-session of acupuncture, had not reached the peak benefit on melasma yet. Further studies should be done with a higher number of sessions to determine the optimal rounds of treatment. Twenty-nine participants reported minor side effects, mostly consisting of persistent pain and small hematoma. Group B had RR of having adverse events 1.8 times (95% CI 1.0-3.4) compared to group A.

ConclusionAcupuncture has a potential dose-response effect on clinical and patient-reported melasma outcomes with minor side effects. Comparative profiles of SARS-CoV-2 Spike-specific human milk antibodies elicited by mRNA- and adenovirus-based COVID-19 vaccines Numerous COVID-19 vaccines are authorized globally. To date, [~]71% of doses are comprised of the Pfizer/BioNTech vaccine, and [~]17% the Moderna/NIH vaccine, both of which are mRNA-based. The chimpanzee Ad-based Oxford/AstraZeneca (AZ) vaccine comprises [~]9%, while the Johnson & Johnson/Janssen (J&J) human adenovirus (Ad26) vaccine ranks 4th at [~]2% [1]. No COVID-19 vaccines are yet available for children 0-4. One method to protect this population may be passive immunization via antibodies (Abs) provided in the milk of a lactating vaccinated person. Our early work [2] and other reports [3-5] have demonstrated that unlike the post-SARS-CoV-2 infection milk Ab profile, which is rich in specific secretory (s)IgA, the vaccine response is highly IgG-dominant. In this report, we present a comparative assessment of the milk Ab response elicited by Pfizer, Moderna, J&J, and AZ vaccines. This analysis revealed 86% -100% of mRNA vaccine recipient milk exhibited Spike-specific IgG endpoint titers, which were 12 - 28-fold higher than those measured for Ad vaccine recipient milk. Ad-based vaccines elicited Spike-specific milk IgG in only 33%-38% of recipients. Specific IgA was measured in 52%-71% of mRNA vaccine recipient milk and 17%-23% of Ad vaccine recipient milk. J&J recipient milk exhibited significantly lower IgA than Moderna recipients, and AZ recipients exhibited significantly lower IgA titers than Moderna and Pfizer. <50% of milk of any group exhibited specific secretory Ab, with Moderna recipient IgA titers measuring significantly higher than AZ. Moderna appeared to most frequently elicit >2-fold increases in specific secretory Ab titer relative to pre-vaccine sample. These data indicate that current Ad-based COVID-19 vaccines poorly elicit Spike-specific Ab in milk compared to mRNA-based vaccines and that mRNA vaccines are preferred for immunizing the lactating population. This study highlights the need to design vaccines better aimed at eliciting an optimal milk Ab response. What delirium detection tools are used in routine clinical practice in the United Kingdom? Survey results from 91% of acute healthcare organisations. PurposeOur aim was to collect information on delirium assessment processes and pathways in non-intensive care settings in the United Kingdom (UK).

MethodsWe sent a Freedom of Information request to 169 UK National Health Service (NHS) hospitals, trusts and health boards (units) in July 2020 to obtain data on usage of delirium assessment tools in clinical practice and delirium pathways or guidelines.

ResultsWe received responses from 154/169 units (91% response rate). Of these, 146/154 (95%) units reported use of formal delirium assessment processes and 131/154 (85%) units had guidelines or pathways in place. The 4  As Test (4AT) was the most widely used tool, with 117/146 (80%) units reporting use. The Confusion Assessment Method was used in 65/146 (45%) units, and the Single Question to identify Delirium (SQiD) in 52/146 (36%) units.

ConclusionsOur findings show that the 4AT is the most commonly used tool in the UK, with 80% of units reporting use. This study adds to our knowledge of real-world uptake of delirium detection methods at scale. Future studies should evaluate real-world implementation of delirium assessment tools further via (i) tool completion rates and (ii) rates of positive scores against the expected prevalence delirium in the clinical population concerned.

KEY SUMMARY POINTSO_ST_ABSAimC_ST_ABSTo seek information on delirium assessment processes and pathways in non-intensive clinical care settings in the United Kingdom (UK), and to assess usage of specific delirium assessment tools: the 4  As Test (4AT), Confusion Assessment Method and Single Question to identify Delirium (SQiD).

FindingsIn total, 95% of National Health Service (NHS) units (hospitals, trusts and health boards) reported use of formal delirium assessment processes and 85% of units had guidelines or pathways in place. The 4AT was the most widely used tool, with 80% of units reporting use; the Confusion Assessment Method was reportedly used in 45% of units and the SQiD in 36% of units.

MessageThis study shows real-world, large-scale uptake of delirium detection methods and delirium guidelines in UK hospitals, which contributes to ongoing efforts to improve delirium care. 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. Elevated Levels of IL-6 in IgA Nephropathy patients are induced by an Epigenetically Driven Mechanism Triggered by Viral and Bacterial RNA Recently, a several models has been proposed to describe the pathogenesis of Immunoglobulin A nephropathy (IgAN), and among them the multihit and the gut-microbiota. These models explain the pathogenesis of IgAN caused by the production of aberrant IgA, but it is believed further predisposing factors are present, including immunological, genetic, environmental, or nutritional factors that can influence the pathogenesis and that could be useful for development of precision nephrology and personalized therapy.

Newly, the role of IL-6 in pathogenesis is becoming increasingly important. It is essential for glomerular immunoglobulin A deposition and the development of renal pathology in Cd37-deficient mice, even if the reason why levels of IL-6 are elevated in IgAN patients is not well understood. One attainable hypothesis on high levels of IL-6 in IgAN comes out from our recent whole genome DNA methylation screening in IgAN patients, that identified, among others, a hypermethylated region comprising Vault RNA 2-1 (VTRNA2-1), a non-coding RNA also known as precursor of miR-886 (pre-mi-RNA). Consistently, the VTRNA2-1 expression was found down-regulated in IgAN patients.

Here we confirm that VTRNA2-1 is low expressed in IgAN subjects compared to HS and we found that also in transplanted IgAN patients (TP-IgAN), compared to non IgAN transplanted patients (TP), the VTRNA2-1 transcript was expressed at level very low. We found that in IgAN patients with downregulated VTRNA2-1, PKR is overactivated, coherently with the role of the VTRNA2-1 that binds to PKR and inhibits its phosphorylation. The loss of the VTRNA2-1 natural restrain causes, in turn, the activation of CREB by PKR. We found CREB, a classical cAMP-inducible CRE-binding factor interacting with a region of the IL-6 promoter and leading to IL-6 production, overactivated both in IgAN and in TP-IgAN patients. Effectively, in the same patients, we found elevated levels of IL-6 correlating with CREB and PKR phosphorylation.

Since PKR is normally activated by bacterial and viral RNA we hypothesized that these microrganisms can further activate the PKR/CREB/IL-6 pathway leading to an excess of IL-6 production, explaining both the high levels of IL-6, both infection involvement in the disease, both cases of IgAN associated with COVID-19 infection and with COVID-19 RNA-vaccination, and recent data showing microbiota involvment in IgAN.

Effectively, we found that Effectively, we sfound that both the RNA poly(I:C) and the COVID-19 RNA-vaccine stimulation significantly increase the IL-6 levels in IgAN patient PBMCs.

The PKR/CREB/IL-6 pathway may be very important also in the setting of renal transplantation. We found that this pathway is upregulated also in IgAN transplanted patients. Recent studies showed that the cumulative risk of IgA nephropathy recurrence increases after transplant and is associated with a 3.7-fold greater risk of graft loss. Finally, we showed that the IL-6 secretion can be reduced by the PKR inhibitor imoxin.

In conclusion, the discovery of the upregulated VTRNA2-1/PKR/CREB/IL-6 pathway in IgAN patients may provide novel approach to treat the disease and may be useful for development of precision nephrology and personalized therapy, possibly by checking the VTRNA2-1 methylation level in IgAN patients. 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.