Consistent high prevalence of undiagnosed blood-borne virus infection in patients attending large urban emergency departments in England.

Innovative care pathways including case finding and linkage to care are crucial to achieve the World Health Organization targets for the elimination of viral hepatitis.(1) In England there were over 23.4 million attendances at Emergency departments (EDs) in 2016-17 representing a significant opportunity to engage for case finding.(2) EDs may be the only healthcare access point for some marginalised groups including recent migrants, homeless, or people who inject drugs. Seroprevalence studies have been used in the USA to guide public health interventions where large scale, integrated ED testing and linkage programs are increasingly common.(3) Since 2008 routine opt-out testing for HIV in UK ED settings has been recommended for those in high prevalence areas (>0.2%). This article is protected by copyright. All rights reserved

Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood counts results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94–95%) and those not admitted to hospital or in the community (AUC = 80–86%). Here, AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood counts can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited

One-pot Selective Formylation and Claisen Rearrangement on Calix[4]arenes

A versatile synthon with formyl and allyl groups at the upper rim of calix[4]arene has been synthesized in two steps. Selective formylation of 25,27-diallyloxy-26,28-dihydroxycalix[4]arene, along with the Claisen rearrangement of the allyl groups, was achieved by reaction with hexamethylenetetraamine (hexamine) in glacial acetic acid. A control reaction of the dipropyl analogue shows that the selective formylation takes place independently of the Claisen rearrangement. The crystal structure of the dimethylacetal derivative of 5,17-diformyl-11,23-diallylcalix[4]arene is reported

Programmatic Evaluation of a Combined Antigen and Antibody Test for Rapid HIV Diagnosis in a Community and Sexual Health Clinic Screening Programme

Background A substantial proportion of HIV-infected individuals in the UK are unaware of their status and late presentations continue, especially in low prevalence areas. Fourth generation antigen/antibody rapid test kits could facilitate earlier diagnosis of HIV in non-clinical settings but lack data on performance under programmatic conditions. Methods and Findings We evaluated the performance of Determine HIV-1/2 Ag/Ab Combo Test (Determine Combo), a rapid test with indicators for both HIV antibodies and p24 antigen, in participants recruited from community outreach and hospital-based sexual health clinics. HIV infection was confirmed using laboratory enzyme-linked immunosorbent assay (EIA), Line Immuno Assay (LIA) and quantitative polymerase chain reaction (PCR). In total, 953 people underwent HIV testing. HIV antibody (Ab) prevalence was 1.8% (17/953). Four false positive rapid tests were identified: two antibody and two p24 antigen (Ag) reactions. Of participants diagnosed as HIV Ab positive, 2/17 (12%) were recent seroconverters based on clinical history and HIV antibody avidity test results. However, none of these were detected by the p24 antigen component of the rapid test kit. There were no other true positive p24 Ag tests. Conclusion These data lend support to an increasing body of evidence suggesting that 4th generation rapid HIV tests have little additional benefit over 3rd generation HIV kits for routine screening in low prevalence settings and have high rates of false positives. In order to optimally combine community-based case-finding among hard-to-reach groups with reliable and early diagnosis 3rd generation kits should be primarily used with laboratory testing of individuals thought to be at risk of acute HIV infection. A more reliable point of care diagnostic is required for the accurate detection of acute HIV infection under programmatic conditions

Improved Classification of Alzheimer's Disease Data via Removal of Nuisance Variability

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making

Selective Processing of Multiple Features in the Human Brain: Effects of Feature Type and Salience

Identifying targets in a stream of items at a given constant spatial location relies on selection of aspects such as color, shape, or texture. Such attended (target) features of a stimulus elicit a negative-going event-related brain potential (ERP), termed Selection Negativity (SN), which has been used as an index of selective feature processing. In two experiments, participants viewed a series of Gabor patches in which targets were defined as a specific combination of color, orientation, and shape. Distracters were composed of different combinations of color, orientation, and shape of the target stimulus. This design allows comparisons of items with and without specific target features. Consistent with previous ERP research, SN deflections extended between 160–300 ms. Data from the subsequent P3 component (300–450 ms post-stimulus) were also examined, and were regarded as an index of target processing. In Experiment A, predominant effects of target color on SN and P3 amplitudes were found, along with smaller ERP differences in response to variations of orientation and shape. Manipulating color to be less salient while enhancing the saliency of the orientation of the Gabor patch (Experiment B) led to delayed color selection and enhanced orientation selection. Topographical analyses suggested that the location of SN on the scalp reliably varies with the nature of the to-be-attended feature. No interference of non-target features on the SN was observed. These results suggest that target feature selection operates by means of electrocortical facilitation of feature-specific sensory processes, and that selective electrocortical facilitation is more effective when stimulus saliency is heightened

What’s retinoic acid got to do with it? Retinoic acid regulation of the neural crest in craniofacial and ocular development

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151310/1/dvg23308.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151310/2/dvg23308_am.pd