Distinctive modularity and resilience of structural covariance network in first-episode antipsychotic-naive psychoses ObjectiveDivergent findings in structural brain alterations in psychoses suggest that often-observed regions may exist within a network of susceptible regions. We built structural covariance networks (SCN) of volumes, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation of 358 regions on structural MRI data from 79 first-episode antipsychotic-naive psychosis patients (FEAP) and 68 controls.

MethodsUsing graph theoretic methods, we obtained representative graph metrics of integration, segregation, resilience, centrality, importance, community structure, and hub distribution for all 3-morphometric features. We compared networks for resilience by simulated removal ("attacks") of nodes and hubs, and simulated replacement of correlations among nodes in FEAP with that of controls using DeltaCon metric.

ResultsVolume and thickness SCNs of FEAP showed differences in many graph metrics in opposite directions compared to controls but not surface area SCN. Network resilience did not show differences in the replacement simulation and hub "attacks, but betweenness centrality-based node "attacks" showed FEAP network disintegrating with relatively fewer node removals with preserved global efficiency compared to controls. In FEAP, communities consisted of regions from different lobes and hubs were more distributed than in controls.

ConclusionsOur findings suggest decreased heterogeneity and less differentiated community structure of FEAP network that may not be attributed to illness chronicity and medications. Contribution of cortical thickness but not surface area suggests that disease/developmental processes may preferentially affect cortical thickness. Near-similar resilience of FEAP and control networks may shield controls from developing illness but may prevent fuller long-term recovery in FEAP.
Uncovering Survivorship Bias in Longitudinal Mental Health Surveys During the COVID-19 Pandemic AimsMarkedly elevated adverse mental health symptoms were widely observed early in the coronavirus disease 2019 (COVID-19) pandemic. Unlike the U.S., where cross-sectional data indicate anxiety and depression symptoms have remained elevated, such symptoms reportedly declined in the U.K., according to analysis of repeated measures from a largescale longitudinal study. However, nearly 40% of U.K. respondents (those who did not complete multiple follow-up surveys) were excluded from analysis, suggesting that survivorship bias might partially explain this discrepancy. We therefore sought to assess survivorship bias among participants in our longitudinal survey study as part of The COVID-19 Outbreak Public Evaluation (COPE) Initiative.

MethodsSurvivorship bias was assessed 4,039 U.S. respondents who completed surveys including the assessment of mental health as part of The COPE Initiative in April 2020 and were invited to complete follow-up surveys. Participants completed validated screening instruments for symptoms of anxiety, depression, and insomnia. Survivorship bias was assessed for (1) demographic differences in follow-up survey participation, (2) differences in initial adverse mental health symptom prevalences adjusted for demographic factors, and (3) differences in follow-up survey participation based on mental health experiences adjusted for demographic factors.

ResultsAdjusting for demographics, individuals who completed only one or two out of four surveys had higher prevalences of anxiety and depression symptoms in April 2020 (e.g., one-survey versus four-survey, anxiety symptoms, adjusted prevalence ratio [aPR]: 1.30, 95% confidence interval [CI]: 1.08-1.55, P=0.0045; depression symptoms, aPR: 1.43, 95% CI: 1.17-1.75, P=0.00052). Moreover, individuals who experienced incident anxiety or depression symptoms had higher odds of not completing follow-up surveys (adjusted odds ratio [aOR]: 1.68, 95% CI: 1.22-2.31, P=0.0015, aOR: 1.56, 95% CI: 1.15-2.12, P=0.0046, respectively).

ConclusionsOur findings revealed significant survivorship bias among longitudinal survey respondents, indicating that restricting analytic samples to only respondents who provide repeated assessments in longitudinal survey studies could lead to overly optimistic interpretations of mental health trends over time. Cross-sectional or planned missing data designs may provide more accurate estimates of population-level adverse mental health symptom prevalences than longitudinal surveys.
Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation (PEACHPIE): A systematic review of evidence strength IntroductionAssessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature.

MethodsWe included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on November 26, 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation, assessing what impact evaluation method was used, graphical display of outcomes data, functional form for the outcomes, timing between policy and impact, concurrent changes to the outcomes, and an overall rating.

ResultsAfter 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. The majority (n=23/36) of studies in our sample examined the impact of stay-at-home requirements. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-section), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 1/27 studies passed all of the above checks, and 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes.

DiscussionThe reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigor to be actionable by policy-makers. This was largely driven by the circumstances under which policies were passed making it difficult to attribute changes in COVID-19 outcomes to particular policies. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible. DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine Recent global developments underscore the prominent role big data have in modern medical science. Privacy issues are a prevalent problem for collecting and sharing data between researchers. Synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue.

In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs.

In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by addressing the relevant privacy issues in medical datasets. Comparison of respiratory protection during exercising tasks between different wearing methods of replaceable particulate respirators and powered air-purifying respirators BackgroundThis study evaluated the differences in respiratory protection between replaceable particulate respirators (RPRs) and powered air-purifying respirators (PAPRs), with different wearing methods, during exercising tasks.

MethodsTen participants wore either RPR or PAPR according to the recommended method, with a knit cover placed between the facepiece cushion and the face, or with the headband on a helmet. We measured the number of particles inside and outside the respiratory protective equipment (RPE) during exercising tasks for each wearing variation. The exercise state was set to exercise with an ergometer set at 80W load. While exercising tasks, the participants performed five actions adopted from JIS T8150 in 2018 (1. Normal breathing, 2. Deep breathing, 3. Turning head side to side, 4. Moving head up and down, 5. Talking). Each action was performed for 1 min. For measurements of exercise state, after 10 minutes of exercise tasks, we measured while the exercise was continued. The fit factor was calculated by dividing the concentration within the RPE by the concentration outside of it. Data were analyzed after they were log-transformed with a linear mixed model, with fit factor as the dependent variable.

ResultsWe compared the results with experimental data of resting state reported in our previous studies. Fit factor of RPRs in the exercise state was significantly lower (p<0.001) than that in the resting state, indicating inadequate respiratory protection. In contrast, the fit factor of PAPRs during exercising tasks was significantly lower (p<0.001) than that at rest; however, respiratory protection was maintained. PAPR did not show a significant decrease (p=1.000) in fit factor owing to the wearing variations during exercising tasks.

ConclusionsPAPRs were found to be superior to RPRs in terms of respiratory protection. PAPRs are better than RPRs for workers who have to wear RPE inappropriately due to health problems. A genome-wide association study of chronic ALT-based non-alcoholic fatty liver disease in the Million Veteran Program with histological and radiological validation. Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic alanine aminotransferase elevation (cALT) without other liver diseases, we performed a trans-ancestry genome-wide association study in the Million Veteran Program including 90,408 cALT cases and 128,187 controls. In the Discovery stage, seventy-seven loci exceeded genome-wide significance - including 25 without prior NAFLD or ALT associations - with one additional locus identified in European-American-only and two in African-American-only analyses (P<5x10-8). External replication in cohorts with NAFLD defined by histology (7,397 cases, 56,785 controls) or liver fat extracted from radiologic imaging (n=44,289) validated 17 SNPs (P<6.5x10-4) of which 9 were novel (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH, and IFI30). Pleiotropy analysis showed that 61 of 77 trans-ancestry and all 17 validated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD. Statistical modeling for quality risk assessment of clinical trials: follow-up at the era of remote auditing BackgroundAs investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote Quality Assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology.

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

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

ConclusionContinuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times. 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. Characterization of small fiber neuropathy in hypermobile Ehlers Danlos Syndrome Ehlers Danlos syndromes (EDS) are heritable connective tissue disorders. The hypermobile form (hEDS) is the most frequent, yet the only one without an identified genetic mutation. Patients suffering from symptomatic joint hypermobility, yet not fulfilling the hEDS criteria, enter the Hypermobility Spectrum Disorder (HSD) category. In addition to musculoskeletal pain affecting joints, hEDS/HSD patients often describe painful sensations, hypoesthesia, and autonomic symptoms suggesting a neuropathic component. Small fiber neuropathy (SFN) refers to the dysfunction or damage of A-{delta} and C-fibers, which relay thermal and nociceptive information as well as mediating autonomic function. SFN has been suggested by prior studies in hEDS but these early findings (case series N[&le;]20) with sole reliance on intraepidermal nerve fiber density (IENFD) called for a larger sample combined with functional testing.

In this retrospective chart extraction from 79 hEDS/HSD patients referred to a pain center due to neuropathic pain or dysautonomia, both functional (Quantitative Sensory Testing (QST), N=79) and structural (IENFD, N=69) evaluations of small nerve fibers were analyzed in combination with clinical data and standardized questionnaires.

All the patients reported moderate to severe pain interfering with daily life. A decreased thermal detection (QST) was shown in 55/79 patients (70%) and a decreased IENFD in 54/69 patients (78%). Hence a small fiber neuropathy (both abnormal IENFD and QST) was definite in 40/69 patients (58%), possible in 23/69 patients (33%) and excluded in only 6/69 patients (9%).

These results add strong evidence for a peripheral neuropathic contribution to pain symptoms in hEDS/HSD, in addition to the known nociceptive and central sensitization components. Such neuropathic contribution could raise the hypothesis of a neurological cause of hEDS, the only EDS syndrome still without a known genetic cause. Hence, our data is leading the way to a better stratification of this very heterogeneous population, which could improve symptommanagement and expand pathophysiological research. Hairpin structure facilitates multiplex high-fidelity DNA amplification in real-time PCR Clinically and biologically, it is essential to detect rare DNA-sequence variants for early cancer diagnosis or drug-resistance mutations identification. Some of the common quantitative PCR (qPCR)-based variant detection methods are restricted in the limit of detection (LoD) because the DNA polymerases used have a high polymerase misincorporation rate, thus the detection sensitivity is sometimes unsatisfactory. With the proofreading activity, high-fidelity (HiFi) DNA polymerases have a 50- to 250-fold higher fidelity. However, there are currently no proper probe-based designs as the fluorescence indicator allowing multiplexed HiFi qPCR reactions, thus restricting the application of HiFi DNA polymerases like the variant detection. We presented the Occlusion System, composed of a 5-overhanged primer with fluorophore modification and a probe with a short-stem hairpin and a 3 quencher modification. We demonstrated that the Occlusion System allowed multiplexing HiFi qPCR reaction, and it was compatible with the current variant-enrichment method to improve the LoD up to 10-fold. Thus, the Occlusion System satisfactorily functioned as efficient fluorescence indicator in HiFi qPCR reactions and allowed application of HiFi DNA polymerases in variant detection methods to improve detection sensitivity. COMPARISON OF sPLA2-IIA PERFORMANCE WITH HIGH-SENSITIVE CRP, NEUTROPHIL PERCENTAGE, PCT AND LACTATE TO IDENTIFY BACTERIAL INFECTION: A PROSPECTIVE STUDY Early bacterial infection (BI) identification in resource-limiting Emergency Departments (ED) is challenging especially in low- and middle-income counties (LMIC). Misdiagnosis predisposes to antibiotic overuse and propagates antimicrobial resistance. This study evaluates new emerging biomarkers, secretory phospholipase A2 group IIA (sPLA2-IIA), and compares with other biomarkers on their performance characteristic of BI detection in Malaysia, an LMIC. A prospective cohort study was conducted involving 151 consecutive patients admitted to the ED. A single measurement was taken upon patient arrival in ED and was analysed for serum levels of sPLA2-IIA, high-sensitive C-reactive protein (CRP), procalcitonin (PCT), neutrophil percentage (N%), and lactate. All biomarkers performance was compared for the outcomes using area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The performance of sPLA2-IIA (AUROC 0.93 [95% CI: 0.89-0.97]; Sn 80% [95% CI: 72-87] Sp 94% [95% CI: 81-89]) was the highest among all. It was comparable with high-sensitive CRP (AUROC 0.93 [95% CI: 0.88-0.97]; Sn 75% [95% CI: 66-83]; Sp 91 [95% CI: 77-98]) but had a higher Sn and Sp. The sPLA2-IIA was also found superior to N%, PCT, and lactate. This finding suggested sPLA2-IIA was recommended biomarkers for BI detection in LMIC. 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. 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. A Randomised Trial of a Wearable UV dosimeter for Skin Cancer Prevention BackgroundNon-melanoma skin cancer (NMSC) is the most prevalent cancer in the United States, affecting 5 million people and costing $8.1 billion per year. Despite well-defined guidelines on ultraviolet radiation (UVR) avoidance, it remains difficult for people to assess their individual exposure, as UVR is invisible and the onset of symptoms due to UVR damage are delayed.

MethodsIn a prospective, randomized-controlled trial, 97 patients with a history of actinic keratoses were enrolled from March 2018 to July 2018 and followed over 6 months. 50 patients were given a wearable device that measured UV exposure and a smartphone application that provided both real-time and cumulative UV exposure information, and 47 patients were provided with UV protection counseling by a dermatologist.

ResultsWe observed a significant decrease in the incidence rate of NMSC in the intervention group compared to the control group over 3 months (p = 0.02). We did not observe a significant decrease in the incidence rate of actinic keratoses (AK) in the intervention group compared to the control group. The observed clinical benefit was not accompanied by psychological side effects such as anxiety and depression.

ConclusionsThis study suggests that providing real-time UV exposure data using a wearable UV dosimeter is a safe and effective behavioral change strategy to prevent NMSC. (Funded by the National Cancer Institute, contract HHSN261201700005C; ClinicalTrials.gov number NCT03315286.) Preferences and patterns of response to public health advice during the COVID-19 pandemic With recurring waves of the Covid-19 pandemic, a dilemma facing public health leadership is whether to provide public advice that is medically optimal (e.g., most protective against infection if followed), but unlikely to be adhered to, or advice that is less protective but is more likely to be followed. To provide insight about this dilemma, we examined and quantified public perceptions about the tradeoff between (a) the stand-alone value of health behavior advice, and (b) the advices adherence likelihood. In a series of studies about preference for public health leadership advice, we asked 1,061 participants to choose between (1) strict advice that is medically optimal if adhered to but which is less likely to be broadly followed, and (2) relaxed advice, which is less medically effective but more likely to gain adherence - given varying infection expectancies. Participants preference was consistent with risk aversion. Offering an informed choice alternative that shifts volition to advice recipients only strengthened risk aversion, but also demonstrated that informed choice was preferred as much or more than the risk-averse strict advice. 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 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. Impact of tozinameran (BNT162b2) mRNA vaccine on kidney transplant and chronic dialysis patients: 3-5 months followup BackgroundDetermining the humoral immunogenicity of tozinameran (BNT162b2) vaccine in patients requiring chronic renal replacement therapy, and its impact on COVID-19 morbidity several months after vaccination, will guide risk assessment and subsequent changes in vaccination policy.

MethodsIn a prospective post-vaccination cohort study with up to 5 months follow-up we studied outpatient dialysis and kidney transplant patients and respective healthcare teams. Outcomes were anti S1/S2 antibody response to vaccine or infection and infection rate during followup.

Results175 dialysis patients (40% women, 65{+/-}15 years), 252 kidney transplant patients (33% women, 54{+/-}14 years) and 71 controls (65% women, 44{+/-}14 years) were followed. Three months or longer after vaccination we detected anti S1/S2 IgG antibodies in 80% of dialysis patients, 44% of transplant recipients and 100% of controls, whereas respective rates after infection were 94%, 75% and 100%. Predictors of non-response were older age, diabetes, history of cancer, lower lymphocyte count and lower vitamin-D levels. Factors associated with lower titers in dialysis patients were modality (hemodialysis vs peritoneal) and high serum ferritin levels. In transplant patients, hypertension and higher calcineurin or mTOR inhibitor drug levels were linked with diminished antibody response. Vaccination associated with fewer subsequent infections (HR=0.23, p<0.05). Moreover, higher antibody titers associated with fewer events, HR 0.41 for each unit increased in log10titer (p<0.05).

ConclusionsDialysis patients, and more so kidney transplant recipients, mounted reduced antibody response to COVID-19 mRNA vaccination, and lesser humoral response associated with more infections. Measures to identify and protect non-responsive patients are urgently required.

SignificanceReports on the humoral immunogenicity of SARS-CoV-2 mRNA vaccines in patients with end stage renal disease are scarce, and association with subsequent COVID-19 morbidity is unknown. In this cohort study that included 175 patients treated with dialysis, 252 kidney transplant recipients and 71 control volunteers, the proportion achieving an antibody response was time- and group-dependent, reaching 80%, 44% and 100% at 3 months post prime inoculation. Personal history of vaccination, positive antibody responses and antibody titers associated with significantly lower risk of COVID-19 infection. Thus, in patients with end stage renal disease, SARS-CoV-2 antibody testing may be warranted after vaccination, to identify non-responders at higher risk for disease. Decision curve analysis to identify optimal candidates of liver resection for intermediate-stage hepatocellular carcinoma with hepatitis B cirrhosis：A cohort study BackgroundThe selection criterion for liver resection (LR) in intermediate-stage (IM) hepatocellular carcinoma (HCC) is still controversial. This study aims to compare LR and transarterial chemoembolization in the range of predicted death risk

MethodsThe multivariable Cox regression model (MVR) was estimated to predict mortality at 5yr. The cut-off values were determined by a two-piece-wise linear regression model, decision curve analysis with MVR model, and hazard ratio curve for treatment plotted against the predicted mortality.

Results825 IM-HCC with hepatitis B cirrhosis were included for analysis (TACE, n=622; LR, n=203). The 5-yr overall survival rate of LR patients was higher than the TACE group (52.8% vs. 20.8%; P<0.0001). The line of LR and TACE were crossing with predicted death risk at 100% (P for interaction =0.008). The benefit of LR versus TACE decreased progressively as predicted death risk>0.55 (95%CI: 0.45,0.62). When predicted death risk over 0.7, decision curve analysis suggested that LR and TACE did not increase net benefit. Patients were then divided into four subgroups by the cut-off values (<0.45, 0.45[&ge;] /<0.62, 0.62[&ge;] /<0.7, [&ge;] 0.7). The stratified analysis of treatment in different subgroups, hazard ratios were 0.39 (95%CI: 0.27, 0.56), 0.36 (95%CI: 0.23, 0.56), 0.51 (95%CI: 0.27, 0.98), and 0.46 (95%CI: 0.27, 0.80), respectively.

ConclusionsLR reached the maximal relative utility in the interval of 0.45 to 0.62, and both LR and TACE did not increase net benefit at the 5-yr death risk over 0.7. 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. 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. 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. Psychological factors associated with instrumental activities of daily living disability in older adults with knee osteoarthritis PurposeThe population of older adults with knee osteoarthritis (OA)-related disabilities is increasing globally. This study aimed to investigate the psychological factors associated with instrumental activities of daily living (IADL) disability in older adults with knee OA.

Materials and MethodsA cross-sectional study was conducted on 179 patients with knee OA aged [&ge;] 65 years. The six-item short form of the Pain Catastrophizing Scale (PCS-6), the four-item short form of the Pain Self-Efficacy Questionnaire (PSEQ-4), and the fifteen-item Geriatric Depression Scale (GDS-15) were used to assess psychological factors. IADL status was assessed using eight activity items. Participants were divided into IADL-disabled and non-disabled groups. Binary logistic regression analyses were performed with the IADL disability status as the dependent variable. The PCS-6, PSEQ-4, and GDS-15 tools were included as independent variables in the logistic regression model. Demographics, motor functions, and pain intensity were also included as confounders.

ResultsOf the total participants, 88 (49.1%) showed disability in conducting IADL. PSEQ-4 (odds ratio =0.90, 95%; confidence interval=0.82-0.99, p=0.02) was a significant independent variable among all psychological factors.

ConclusionOur study showed the importance of assessing self-efficacy using the PSEQ-4 in relation to IADL disability in older adults with knee OA. 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. Modeling Covid-19 incidence by the renewal equation after removal of administrative bias and noise The sanitary crisis of the past two years has focused the publics attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number Rt, defined by the average number of new infections caused by a single infected individual at time t, is one of the best metrics for estimating the epidemic trend. In this paper, we give a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements. The model is governed by the classic renewal equation using an empirical reproduction kernel, and subject to two perturbations: a time-varying gain with a weekly period and a white observation noise. We estimate this noise model and its parameters by extending a variational inversion of the model recovering its main driving variable Rt. Using Rt, a restored incidence curve, corrected of the weekly and festive day bias, can be deduced through the renewal equation. We verify experimentally on many countries that, once the weekly and festive days bias have been corrected, the difference between the incidence curve and its expected value is well approximated by an exponential distributed white noise multiplied by a power of the magnitude of the restored incidence curve.

Simple SummaryIn the past two years, the COVID-19 incidence curves and reproduction number Rt have been the main metrics used by policy makers and journalists to monitor the spread of this global pandemic. However, these metrics are not always reliable in the short term, because of a combination of delay in detection, administrative delays and random noise. In this article, we present a complete model of COVID-19 incidence, faithfully reconstructing the incidence curve and reproduction number from the renewal equation of the disease and precisely estimating the biases associated with periodic weekly bias, festive day bias and residual noise.