bluuebunny/crossref_metadata_2025 huggingface dataset:
Crack propagation modeling of strengthening reinforced concrete deep beams with CFRP plates
Authors: Shahbazpanahi Shahriar , Karim Hogr , Abdullah Wrya , Reuter Uwe , Mosavi Amir H
Published: August 2021

Fracture analysis of reinforced concrete deep beam strengthened with carbon fiber-reinforced polymer (CFRP) plates was carried out. The present research aimed to discover whether crack propagation in a strengthened deep beam follows linear elastic fracture mechanics (LEFM) theory or nonlinear fracture mechanics theory. To do so, a new energy release rate based on nonlinear fracture mechanics theory was formulated on the finite element method and the discrete cohesive zone model (DCZM) was developed in deep beams. To validate and compare with numerical models, three deep beams with rectangular cross-sections were tested. The code results based on nonlinear fracture mechanics models were compared with the experimental results and the ABAQUS results carried out based on LEFM. The predicted values of initial stiffness, yielding point and failure load, energy absorption, and compressive strain in the concrete obtained by the proposed model were very close to the experimental results. However, the ABAQUS software results displayed greater differences from the experimental results. For instance, the predicted failure load for the shear-strengthened deep beam using the proposed model only had a 6.3% difference from the experimental result. However, the predicted failure load using ABAQUS software based on LEFM indicated greater differences (25.1%) compared to the experimental result.
Brain criticality beyond avalanches: open problems and how to approach them
Authors: Girardi-Schappo Mauricio
Published: August 2021

A homeostatic mechanism that keeps the brain highly susceptible to stimuli and optimizes many of its functions—although this is a compelling theoretical argument in favor of the brain criticality hypothesis, the experimental evidence accumulated during the last two decades is still not entirely convincing, causing the idea to be seemingly unknown in the more clinically-oriented neuroscience community. In this perspective review, we will briefly review the theoretical framework underlying such bold hypothesis, and point to where theory and experiments agree and disagree, highlighting potential ways to try and bridge the gap between them. Finally, we will discuss how the stand point of statistical physics could yield practical applications in neuroscience and help with the interpretation of what is a healthy or unhealthy brain, regardless of being able to validate the critical brain hypothesis.
Critically Ill Coronavirus Disease 2019 Patients Exhibit Hyperactive Cytokine Responses Associated With Effector Exhausted Senescent T Cells in Acute Infection
Authors: Arcanjo Angélica , Guimarães Pinto Kamila , Logullo Jorgete , Leite Paulo Emílio Corrêa , Menezes Camilla Cristie Barreto , Freire-de-Lima Leonardo , Diniz-Lima Israel , Decoté-Ricardo Debora , Nunes Rodrigues-da-Silva Rodrigo , Geraldo Freire-de-Lima Celio , Almeida Filardy Alessandra , Lima-Junior Josué da Costa , Luiz Bertho Alvaro , De Luca Paula Mello , Mauro Granjeiro José , Coutinho Barroso Shana Priscila , Conceição-Silva Fátima , Savino Wilson , Morrot Alexandre
Published: August 2021

Background

Coronavirus disease 2019 (COVID-19) can progress to severe pneumonia with respiratory failure and is aggravated by the deregulation of the immune system causing an excessive inflammation including the cytokine storm.

Methods

In this study, we report that severe acutely infected patients have high levels of both type-1 and type-2 cytokines.

Results

Our results show abnormal cytokine levels upon T-cell stimulation, in a nonpolarized profile. Furthermore, our findings indicate that this hyperactive cytokine response is associated with a significantly increased frequency of late-differentiated T cells with particular phenotype of effector exhausted/senescent CD28−CD57+ cells. Of note, we demonstrated for the first time an increased frequency of CD3+CD4+CD28−CD57+ T cells with expression of programmed death 1, one of the hallmarks of T-cell exhaustion.

Conclusions

These findings reveal that COVID-19 is associated with acute immunodeficiency, especially within the CD4+ T-cell compartment, and points to possible mechanisms of loss of clonal repertoire and susceptibility to viral relapse and reinfection events.
Safety of intraarticular corticosteroid injection preceding hip and knee arthroplasty: a systematic review and meta-analysis amid resolving COVID-19 arthroplasty restrictions
Authors: Cheok Tim , Jennings Matthew , Aprato Alessandro , Jayasekera Narlaka , Jaarsma Ruurd L
Published: August 2021

Intraarticular corticosteroid injection (ICSI) is a widely practiced management for hip and knee osteoarthritis. Imposed delays to arthroplasty during coronavirus disease 2019 pandemic have led us to postulate that many patients have opted for recent ICSI. We compared the odds of prosthetic joint infection (PJI) in patients who were or were not administered ICSI within 12 months prior to hip or knee arthroplasty. A systematic search of PubMed, Embase, The Cochrane Library and Web of Science was performed in February 2021, with studies assessing the effect of ICS on PJI rates identified. All studies, which included patients that received ICSI in the 12 months prior to primary hip and knee arthroplasty, were included. In total 12 studies were included: four studies with 209 353 hips and eight studies with 438 440 knees. ICSI administered in the 12 months prior to hip arthroplasty increased the odds of PJI [odds ratio (OR) = 1.17, P = 0.04]. This was not the case for knees. Subgroup analysis showed significantly higher odds of PJI in both hip [OR = 1.45, P = 0.002] and knee arthroplasty [OR = 2.04; P = 0.04] when ICSI was within the preceding 3 months of surgery. A significantly higher odds of PJI were seen in patients receiving ICSI within the 12 months prior to hip arthroplasty. Subgroup analysis showed increased odds of PJI in both hip and knee arthroplasty, in patients receiving ICSI within 3 months prior to their arthroplasty. We recommend delaying knee arthroplasty for at least 3 months after ICSI and possibly longer for hip arthroplasty.

Level of Evidence

Level III - Systematic Review of Level II and III Studies.
Systemic control of nodule formation by plant nitrogen demand requires autoregulation-dependent and independent mechanisms
Authors: Pervent Marjorie , Lambert Ilana , Tauzin Marc , Karouani Alicia , Nigg Martha , Jardinaud Marie-Françoise , Severac Dany , Colella Stefano , Martin-Magniette Marie-Laure , Lepetit Marc
Published: August 2021

In legumes interacting with rhizobia, the formation of symbiotic organs involved in the acquisition of atmospheric nitrogen gas (N2) is dependent on the plant nitrogen (N) demand. We used Medicago truncatula plants cultivated in split-root systems to discriminate between responses to local and systemic N signaling. We evidenced a strong control of nodule formation by systemic N signaling but obtained no clear evidence of a local control by mineral nitrogen. Systemic signaling of the plant N demand controls numerous transcripts involved in root transcriptome reprogramming associated with early rhizobia interaction and nodule formation. SUPER NUMERIC NODULES (SUNN) has an important role in this control, but we found that major systemic N signaling responses remained active in the sunn mutant. Genes involved in the activation of nitrogen fixation are regulated by systemic N signaling in the mutant, explaining why its hypernodulation phenotype is not associated with higher nitrogen fixation of the whole plant. We show that the control of transcriptome reprogramming of nodule formation by systemic N signaling requires other pathway(s) that parallel the SUNN/CLE (CLAVATA3/EMBRYO SURROUNDING REGION-LIKE PEPTIDES) pathway.
Jane Addams
Authors: Hamington Maurice
Published: August 2021

Jane Addams (b. 1860–d. 1935) is considered one of the founding figures in American pragmatist philosophy, social work, and sociology. The daughter of a prominent American politician from Illinois, Addams grew up in a privileged environment that included graduating from college, an opportunity not afforded most women at the time. Addams co-founded one of the earliest social settlements of the Progressive Era, Hull House in Chicago. The settlement began with an unfocused commitment to social amelioration and would evolve into a dynamo for progressive social projects. Living there for the remaining nearly fifty years of her life amid one of the most significant migrant influxes that the United States has known, Addams led a community of mostly women who pioneered improving community welfare in education, recreation, labor, sanitation, health, criminal justice, and the arts. An accomplished writer and speaker, Addams engaged the public through academic articles, popular articles, books, and speeches amid her community activism. She would become a friend and colleague of John Dewey, William James, and George Herbert Mead, influencing them as much as they did her. A dominant theme of her work is that democracy is more than a system of government and entails a moral way of being with one another. Addams’s first book was Democracy and Social Ethics (1902), and the title reveals her abiding belief that democracy is a social morality that requires ongoing enrichment. A relational social democracy underpins Addams’s social analysis. Her writings address various social and political subjects, including education, peace, labor organizing, child labor laws, race, women’s rights, philanthropy, sex trafficking, and familial relations. Internationally, Addams is best known for her work on peace. She was the first American woman to receive a Nobel Peace Prize in 1931. Able to adapt her message to audience and context, Addams also wrote about events such as the Pullman Strike of 1894 and the murder trial of Nicola Sacco and Bartolomeo Vanzetti during the 1920s, as well as about people such as Abraham Lincoln, Leo Tolstoy, and Julia Lathrop. In the spirit of American pragmatism, Addams would draw more prominent social themes from individual experiences she confronted, as she did in finding psychosocial forces at work in the proliferation of “Devil Baby” stories among immigrant populations in the summer of 1916. Consistently recognized for her outstanding activism, prominent male figures overshadowed her intellectual legacy during most of the twentieth century. However, a reclamation of Addams’s unique contribution began in the 1990s, and in the 2020s, Addams studies are growing as more scholars find inspiration in her methods and writings.
Pivoting during a pandemic: lessons learned from transitioning a multisite randomized controlled trial to a remote protocol in response to COVID-19
Authors: Inverso Hailey , Abadula Fayo , Morrow Troy , LeStourgeon Lauren , Parmar Angelee , Streisand Randi , Jaser Sarah S
Published: August 2021

THR1VE! is an ongoing multisite randomized clinical trial of a positive psychology intervention designed to treat diabetes distress and improve glycemic outcomes in teens with type 1 diabetes. Due to the COVID-19 pandemic restrictions on clinical research and changes in diabetes clinical care, THR1VE! was adapted from an in-person enrollment protocol to a remote protocol through a series of development and testing strategies. We discuss the process of transitioning the protocol and the demonstrated feasibility of ongoing recruitment, enrollment, and retention outcomes. These findings offer support for a remotely transitioned protocol that has larger applications for ongoing and future clinical research.
Nanopore Metagenomic Sequencing for Detection and Characterization of SARS-CoV-2 in Clinical Samples
Authors: Gauthier Nick P.G. , Nelson Cassidy , Bonsall Michael B. , Locher Kerstin , Charles Marthe , MacDonald Clayton , Krajden Mel , Chorlton Samuel D. , Manges Amee R.
Published: August 2021

The COVID-19 pandemic has underscored the need for rapid novel diagnostic strategies to detect and characterize pathogens from clinical specimens. The MinION sequencing device allows for rapid, cost-effective, high-throughput sequencing; useful features for translation to clinical laboratory settings. Metagenomic Next-Generation Sequencing (mNGS) approaches provide the opportunity to examine the entire genomic material of a sample; allowing for detection of emerging and clinically relevant pathogens that may be missed in targeted assays. Here we present a pilot study on the performance of Sequence-Independent Single Primer Amplification (SISPA) to amplify RNA randomly for the detection and characterization of SARS-CoV-2. We designed a classifier that corrects for barcode crosstalk between specimens. Our assay yielded 100% specificity overall and 95.2% sensitivity for specimens with a RT-qPCR cycle threshold value less than 30. We assembled 10 complete (>95% coverage at 20x depth), and one near-complete (>80% coverage at 20x depth) genomes from 20 specimens that were classified as positive by mNGS. We characterized these genomes through phylogenetic analysis and found that 10/11 specimens from British Columbia had a closest relative to another British Columbian specimen. Of five samples that we had both assembled genomes, as well as Variant of Concern (VOC) PCR results, we found 100% concordance between these results. Additionally, our assay was able to distinguish between the Alpha and Gamma variants, which was not possible with our VOC PCR technique. This study supports future work examining the broader feasibility of SISPA as a diagnostic strategy for the detection and characterization of viral pathogens.
DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans
Authors: Pham Hieu H. , Do Dung V. , Nguyen Ha Q.
Published: August 2021

X-ray imaging in Digital Imaging and Communications in Medicine (DICOM) format is the most commonly used imaging modality in clinical practice, resulting in vast, non-normalized databases. This leads to an obstacle in deploying artificial intelligence (AI) solutions for analyzing medical images, which often requires identifying the right body part before feeding the image into a specified AI model. This challenge raises the need for an automated and efficient approach to classifying body parts from X-ray scans. Unfortunately, to the best of our knowledge, there is no open tool or framework for this task to date. To fill this lack, we introduce a DICOM Imaging Router that deploys deep convolutional neural networks (CNNs) for categorizing unknown DICOM X-ray images into five anatomical groups: abdominal, adult chest, pediatric chest, spine, and others. To this end, a large-scale X-ray dataset consisting of 16,093 images has been collected and manually classified. We then trained a set of state-of-the-art deep CNNs using a training set of 11,263 images. These networks were then evaluated on an independent test set of 2,419 images and showed superior performance in classifying the body parts. Specifically, our best performing model (i.e., MobileNet-V1) achieved a recall of 0.982 (95% CI, 0.977– 0.988), a precision of 0.985 (95% CI, 0.975–0.989) and a F1-score of 0.981 (95% CI, 0.976–0.987), whilst requiring less computation for inference (0.0295 second per image). Our external validity on 1,000 X-ray images shows the robustness of the proposed approach across hospitals. These remarkable performances indicate that deep CNNs can accurately and effectively differentiate human body parts from X-ray scans, thereby providing potential benefits for a wide range of applications in clinical settings. The dataset, codes, and trained deep learning models from this study will be made publicly available on our project website at https://vindr.ai/datasets/bodypartxr.
Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic
Authors: Queralt-Rosinach Núria , Kaliyaperumal Rajaram , Bernabé César H. , Long Qinqin , Joosten Simone A. , van der Wijk Henk Jan , Flikkenschild Erik L.A. , Burger Kees , Jacobsen Annika , Mons Barend , Roos Marco ,   BEAT-COVID Group,   COVID-19 LUMC Group
Published: August 2021

Background

The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data ‘silos’ that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR.

Results

In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors’ research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital.

Conclusions

Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR digital objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.
Preimmunization correlates of protection shared across malaria vaccine trials in adults
Authors: Neal Maxwell L. , Duffy Fergal J. , Du Ying , Aitchison John D. , Stuart Kenneth D.
Published: August 2021

Identifying preimmunization biological characteristics that promote an effective vaccine response offers opportunities for illuminating the critical immunological mechanisms that confer vaccine-induced protection, for developing adjuvant strategies, and for tailoring vaccination regimens to individuals or groups. In the context of malaria vaccine research, studying preimmunization correlates of protection can help address the need for a widely-effective malaria vaccine, which remains elusive. In this study, common preimmunization correlates of protection were identified using transcriptomic data from four independent, heterogeneous malaria vaccine trials in adults. Systems-based analyses showed that a moderately elevated inflammatory state prior to immunization was associated with protection against malaria challenge. Functional profiling of protection-associated genes revealed the importance of several inflammatory pathways, including TLR signaling. These findings, which echo previous studies that associated enhanced preimmunization inflammation with protection, illuminate common baseline characteristics that set the stage for an effective vaccine response across diverse malaria vaccine strategies in adults.
Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina
Authors: Tsori Yoav , Granek Rony
Published: August 2021

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-”sub-populations” (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASA, and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson’s correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.
Diagnostic evaluation of the presence of residues of glyphosate-AMPA and 2.4D pesticides in urine samples from people living in a rural Brazilian community
Authors: Candiotto Luciano Z. P. , Ferreira Mariane Okamoto , Ferreira Isadora Nunes , Teixeira Géssica Tuani , da Silva Janaína Carla , Tedesco Emanueli Hammes , Gaboardi Shaiane Carla , Panis Carolina
Published: August 2021

Pesticide contamination in rural communities is a known problem worldwide. In this study, we investigated the levels of pesticide residues in urine samples from residents of a rural community located in the municipality of Francisco Beltrão-Paraná, Brazil. According to the residents’ report obtained through a structured interview, the main form of exposure has been due to the drift of pesticides resulting from the spraying carried out on crops neighboring the community, with one crop existing on the left edge and another on the right edge of the site. The investigation was based on a demand from residents concerned about the frequent occurrence of symptoms after spraying on neighboring crops, such as nausea, headaches, and dizziness. Thus, samples were collected immediately after the occurrence of spraying in the crop surrounding the site. In the collected samples (n=35, 1 sample per family) laboratory analyzes were performed to identify possible pesticide residues. To identify possible contaminating pesticides in the samples, multi-residue analysis was performed by gas chromatography coupled with mass spectrometry (GC-MS/MS). To identify 2.4D residues, an active widely used in the region and not detectable by the technique used for other pesticides, the analyzes were performed using the enzyme immunoassay technique. The results indicate that all urine samples collected showed the presence of 2.4D residues and that 90% of them presented the presence of glyphosate-AMPA residues, without the detection of the other investigated residues. The analysis of data obtained from interviews with residents shows an association between living in the place for longer and having cases of abortion (p<0.05, R=0.43) or feeling unwell after the application of the poison by the neighboring crop (p<0.05, R=0.47). In addition, residents who reported being poisoned with poison also reported having cancer (p<0.05, R=0.42). Assuming that it is unacceptable that urine samples have pesticide residues, it is concluded that the residents of this community are widely contaminated by pesticides potentially coming from the spraying of the neighboring crop, especially by pesticides that have the active ingredients in their formulas. 2,4D and glyphosate-AMPA and that may be impacting the health of these people in the long term.
Federal Vaccine Policy and Interstate Variation in COVID-19 Vaccine Coverage in India
Authors: Mukherjee Kanchan
Published: August 2021

Introduction

On August 13, 2021, India completed 30 weeks of vaccination against COVID-19 for its eligible citizens. While the vaccination has made progress, there has been no study analyzing the federal/union vaccine policy and its effect on vaccination coverage across Indian states. In this context, this study analyses the federal vaccination policy and its effect on interstate variation in vaccine coverage and the correlation of state economy with vaccination coverage.

Methods

The study analyses vaccine policy documents, secondary data on vaccination coverage and state gross domestic product (GDP) available in public domain. ANOVA test has been used to assess the effect of vaccine policy on interstate vaccine coverage and correlation-regression analysis has been conducted to assess the type and strength of association between gross state domestic product and vaccination coverage.

Results

Interstate variation in vaccination coverage in the first 15 weeks was the least (F=3.5), when vaccine procurement and supply was entirely provided by the union/federal government and vaccination was limited to priority groups. However, with the extension of vaccine policy to other groups and reduction in federal government involvement in vaccine procurement, the interstate variation in vaccination coverage increased significantly (F=10.74) by the end of 30 weeks. The highest interstate variation was observed in the period between 23-30 weeks (F=25.31). State GDP was positively and strongly correlated with state vaccination coverage with a high coefficient of correlation (R=0.94) and high coefficient of determination (R2= 0.88).

Conclusions

The study finds that federal procurement and supply of vaccination among prioritized groups has been the best strategy till date to address the inequity in vaccination coverage across the states of India.
The IHME vs Me: Modeling USA CoVID-19 Spread, Early Data to the Fifth Wave
Authors: Eng Genghmun
Published: August 2021

Epidemiologists have never had such high-quality real-time pandemic data. Modeling CoVID-19 pandemic data became a predictive tool in-stead of an afterwards analysis. How early CoVID-19 model predictions impacted US Government policies and practices is first reviewed here as an important part of the pandemic history. It spurred independent modeling efforts, such as this, to help develop a better understanding of CoVID-19 spread, and to provide a substitute for the IHME (Institute for Health Metrics & Evaluation, U. Washington) 4-month predictions for the expected pandemic evolution, which they had to revise every couple of weeks. Our alternative model, which was developed over the course of several earlier medrxiv.org preprints, is shown here to provide a good description for the entire USA CoVID-19 pandemic to date, covering: (1) the original CoVID-19 wave [3/21/20-6/07/20], (2) the Summer 2020 Resurgence [6/07/20-9/25/20], (3) the large Winter 2020 Resurgence [9/25/20-3/19/21], (4) a small Spring 2021 “Fourth Wave”, [3/19/21-6/07/21], and (5) the present-day Summer 2021 “Fifth Wave” [6/07/21-present], which the USA is now in the midst of. Our analysis of the initial “Fifth Wave” data shows that this wave presently has the capacity to infect virtually all susceptible non-vaccinated persons who practice NO Mask-Wearing and minimal Social Distancing.
Medical laboratory waste generation rate, management practices and associated factors in Addis Ababa, Ethiopia
Authors: Endris Salem , Tamir Zemenu , Sisay Abay
Published: August 2021

Background

Biomedical wastes (BMW) generated from medical laboratories are hazardous and can be deleterious to humans and the environment. Highly infectious types of biomedical wastes are commonly generated at an unacceptably high rate from health laboratories of developing countries with a poor management system like Ethiopia. This study was aimed to evaluate the generation rate of biomedical wastes, management practices and associated factors among public healthcare medical laboratories in Addis Ababa, Ethiopia.

Materials and methods

Health institution based cross-sectional study was conducted from July 13 to September 25, 2020 in 6 hospital laboratories and 20 health center laboratories in Addis Ababa, Ethiopia.Data on socio-demographic characteristics, knowledge and practice of biomedical wast managment of the health facilities, Biomedical waste generation rate were collected using data collection tools. Data were managed using SPSS version 20 software. Descriptive statistcs,Pearson correlation, linear and logistic regression analysis were computed to identify indepedent predictors of the dependent variable. Odds ratio with 95% confidence interval was used to determine the strength of association.

Results

The finding revealed that the mean ± SD of the daily generation rate of biomedical wastes was 4.9 ± 3.13 kg/day per medical laboratory. Nineteen (74.3%) medical laboratories had a proper practice of biomedical waste management which is significantly associated with knowledge of professsionals on biomedical waste management policies and guidelines, availability of separate financial sources for biomedical waste management and training level of professionals.

Conclusion

The study showed high generation of biomedical waste from medical laboratories in public healthcare of Addis Ababa,Ethiopia. Near two-thirds of health facilities had a proper practice of waste segregation, collection, storage, and treatment of biomedical wastes generated from their laboratory. However, there was a poor practice of transportation and disposal. Hence, due attention and practicing as per the current national guidelines of biomedical waste management is recoomended.
Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score
Authors: Hua Rong , Xiong Jianhao , Li Gail , Zhu Yidan , Ge Zongyuan , Ma Yanjun , Fu Meng , Li Chenglong , Wang Bin , Dong Li , Zhao Xin , Ma Zhiqiang , Chen Jili , He Chao , Wang Zhaohui , Wei Wenbin , Wang Fei , Gao Xiangyang , Chen Yuzhong , Zeng Qiang , Xie Wuxiang
Published: August 2021

The Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognized tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, effective, convenient and noninvasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed. A deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score was developed and internally validated by a medical check-up dataset included 271,864 participants in 19 province-level administrative regions of China, and externally validated based on an independent dataset included 20,690 check-up participants in Beijing. The performance for identifying individuals with high dementia risk (CAIDE dementia risk score ≥10 points) was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI). We found that the algorithm achieved an AUC of 0.944 (95% CI 0.939–0.950) in the internal validation group and 0.926 (95% CI, 0.913–0.939) in the external group, respectively. Besides, the estimated CAIDE dementia risk score derived from the algorithm was significantly associated with both comprehensive cognitive function and specific cognitive domains. In conclusion, this algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population setting. Therefore, it has potential to be utilized as a noninvasive and more expedient method for dementia risk stratification. It might also be adopted in dementia clinical trials, incorporated as inclusion criteria to efficiently select eligible participants.
Avoiding bias in Mendelian randomization when stratifying on a collider
Authors: Coscia Claudia , Gill Dipender , Benítez Raquel , Pérez Teresa , Malats Núria , Burgess Stephen
Published: August 2021

Background

Mendelian randomization (MR) uses genetic variants as instrumental variables to investigate the causal effect of a risk factor on an outcome. A collider is a variable influenced by two or more other variables. Naive calculation of MR estimates in strata of the population defined by a variable affected by the risk factor can result in collider bias.

Methods

We propose an approach that allows MR estimation in strata of the population while avoiding collider bias. This approach constructs a new variable, the residual collider, as the residual from regression of the collider on the genetic instrument, and then calculates causal estimates in strata defined by quantiles of the residual collider. Estimates stratified on the residual collider will typically have an equivalent interpretation to estimates stratified on the collider, but they are not subject to collider bias. We apply the approach in several simulation scenarios considering different characteristics of the collider variable and strengths of the instrument. We then apply the proposed approach to investigate the causal effect of smoking on bladder cancer in strata of the population defined by bodyweight.

Results

The new approach generated unbiased estimates in all the simulation settings. In the applied example, we observed a trend in the stratum-specific MR estimates at different bodyweight levels that suggested stronger effects of smoking on bladder cancer among individuals with lower bodyweight.

Conclusions

The proposed approach can be used to perform MR studying heterogeneity among subgroups of the population while avoiding collider bias.
Dynamics of the Third wave, modelling COVID-19 pandemic with an outlook towards India
Authors: Basak Ayanava , Rahaman Sayanur , Guha Abhishek , Sanyal Tanmay
Published: August 2021

Since 2020, the COVID-19 pandemic has devastated human civilization throughout the earth. The pandemic is returning in different waves because of constant changes in the genetic components of the virus. Had we been able to predict the nature and timing of these waves earlier, numerous lives could, in essence, have been saved. It is evident that the situation has spiraled out of control in several countries for want of proper preventive measures. In this article, we described a comprehensive mathematical approach to understand the nature of the pandemic waves. Also, we determined the probable timing of the third wave that will help the concerned government(s) to take the necessary steps to better prepare for the unforeseen situation.
Inexpensive workflow for simultaneous monitoring of HIV viral load and detection of SARS-CoV-2 infection
Authors: Gulati Gaurav K , Panpradist Nuttada , Stewart Samuel W A , Beck Ingrid A , Boyce Ceejay , Oreskovic Amy K , García-Morales Claudia , Avila-Ríos Santiago , Han Peter D. , Reyes-Terán Gustavo , Starita Lea M. , Frenkel Lisa M , Lutz Barry R , Lai James J
Published: August 2021

Background

COVID-19 pandemic interrupted routine care for individuals living with HIV, putting them at risk of becoming virologically unsuppressed and ill. Often they are at high risk for exposure to SARS-CoV-2 infection and severe disease once infected. For this population, it is urgent to closely monitor HIV plasma viral load (VL) and screen for SARS-COV-2 infection.

Method

We have developed a non-proprietary method to isolate RNA from plasma, nasal secretions (NS), or both. HIV, SARS-CoV-2, and human RP targets in extracted RNA are then RT-qPCR to estimate the VL and classify HIV/SARS-CoV-2 status (i.e., HIV as VL failure or suppressed; SARS-CoV-2 as positive, presumptive positive, negative, or indeterminate). We evaluated this workflow on 133 clinical specimens: 40 plasma specimens (30 HIV-seropositive), 67 NS specimens (31 SARS-CoV-2-positive), and 26 pooled plasma/NS specimens (26 HIV-positive with 10 SARS-CoV-2-positive), and compared the results obtained using the in-house extraction to those using a commercial extraction kit.

Results

In-house extraction had a detection limit of 200-copies/mL for HIV and 100-copies/mL for SARS-CoV-2. In-house and commercial methods yielded positively correlated HIV VL (R2: 0.98 for contrived samples; 0.81 for seropositive plasma). SARS-CoV-2 detection had 100% concordant classifications in contrived samples, and in clinical NS extracted by in-house method, excluding indeterminate results, was 95% concordant (25 positives, 6 presumptive positives, and 31 negatives) to those using the commercial method. Analysis of pooled plasma/NS showed R2of 0.91 (contrived samples) and 0.71 (clinical specimens) for HIV VL correlations obtained by both extraction methods, while SARS-CoV-2 detection showed 100% concordance in contrived and clinical specimens.

Interpretation

Our low-cost workflow for molecular testing of HIV and SARS-CoV-2 could serve as an alternative to current standard assays for laboratories in low-resource settings.
Vaccine Effectiveness against Referral to Hospital and Severe Lung Injury Associated with COVID-19: A Case-Control Study in St. Petersburg, Russia
Authors: Barchuk Anton , Cherkashin Mikhail , Bulina Anna , Berezina Natalia , Rakova Tatyana , Kuplevatskaya Darya , Stanevich Oksana , Skougarevskiy Dmitriy , Okhotin Artemiy
Published: August 2021

Background

Results of a randomised trial showed the safety and efficacy of Gam-COVID-Vac against COVID-19. However, compared to other vaccines used across the globe, the real-world data on the effectiveness of Gam-COVID-Vac, especially against the disease caused by Delta variant of concern, was not available. We aimed to assess the effectiveness of vaccination mainly conducted with Gam-COVID-Vac in St. Petersburg, Russia.

Methods

We designed a case-control study to assess the vaccine effectiveness (VE) against lung injury and referral to hospital. Self-reported vaccination status was collected for individuals with confirmed SARS-CoV-2 infection who were referred for initial low-dose computed tomography triage in two outpatient centres in July 3 – August 9, 2021 in St. Petersburg, Russia. We used logistic regression models to estimate the adjusted (for age, sex, and triage centre) VE for complete (>14 days after the second dose) vaccination. We estimated the VE against referral for hospital admission, COVID-19-related lung injury assessed with LDCT, and decline in oxygen saturation.

Results

In the final analysis, 13,893 patients were included, 1,291 (9.3%) of patients met our criteria for complete vaccination status, and 495 (3.6%) were referred to hospital. In the primary analysis, the adjusted VE against referral to hospital was 81% (95% CI: 68–88) for complete vaccination. The VE against referral to hospital was more pronounced in women (84%, 95% CI: 66–92) compared to men (76%, 95% CI: 51–88). Vaccine protective effect increased with increasing lung injury categories, from 54% (95% CI: 48–60) against any sign of lung injury to 76% (95% CI: 59–86) against more than 50% lung involvement. A sharp increase was observed in the probability of hospital admission with age for non-vaccinated patients in relation to an almost flat relationship for the completely vaccinated group.

Conclusions

COVID-19 vaccination was effective against referral to hospital in patients with symptomatic SARS-CoV-2 infection in St. Petersburg, Russia. This protection is probably mediated through VE against lung injury associated with COVID-19.
Measuring the digital divide among people with Severe Mental Ill Health using the Essential Digital Skills framework
Authors: Panagiotis Spanakis , Ruth Wadman , Lauren Walker , Paul Heron , Alice Mathers , John Baker , Gordon Johnston , Simon Gilbody , Emily Peckham
Published: August 2021

Aims

Amidst the vast digitalisation of health and other services during the pandemic, people with no digital skills are at risk for digital exclusion. This risk might not abide by the end of the pandemic. This paper seeks to understand whether people with severe mental ill health (SMI) have the necessary digital skills to adapt to these changes and avoid digital exclusion.

Methods

249 adults with SMI across England completed a survey online or offline. They provided information on their digital skills based on the Essential Digital Skills (EDS) framework, sociodemographic information, and digital access. This is the first time the EDS is benchmarked in people with SMI.

Results

42.2% had no Foundation Skills and 46.2% lacked skills for daily life (lacking Foundation or Life Skills). 23.0% of those working lacked skills for professional life (lacking Foundation or Work Skills). The most commonly missing skills were handling passwords and using the device settings (Foundation Skills) and online problem solving (Skills for Life). People were interested in learning more about approximately half of the skills they did not have. People were more likely to lack Foundation Skills if they were older, not in employment, had a psychosis-spectrum disorder, or had no Internet access at home.

Conclusion

A significant portion of people with SMI lacked Foundation Skills in this objective and benchmarked survey. This points to a high risk for digital exclusion and the need for focused policy and tailored health sector support, to ensure people retain access to key services and develop digital skills and confidence. To our knowledge this is the first time this has been described using the Essential Digital Skills (EDS) framework. Services, including the NHS, need to be aware and mitigate the risks.
Communicating risk to patients: developing an “Efficiency Index” for diagnostic and screening tests
Authors: Larner Andrew J
Published: August 2021

Diagnostic and screening tests may have risks such as misdiagnosis, as well as the potential benefits of correct diagnosis. Effective communication of this risk to both clinicians and patients can be problematic. The purpose of this study was to develop a metric called the “efficiency index” (EI), defined as the ratio of test accuracy and inaccuracy, or the number needed to misdiagnose divided by number needed to diagnose. This was compared with a previously described “likelihood to be diagnosed or misdiagnosed” (LDM), also based on “numbers needed” metrics. Datasets from prospective pragmatic test accuracy studies examining four brief cognitive screening instruments (Mini-Mental State Examination; Montreal Cognitive Assessment; Mini-Addenbrooke’s Cognitive Examination, MACE; and Free-Cog) were analysed to calculate values for EI and LDM, and to examine their variation with test cut-off for MACE. EI values were also calculated using a modification of McGee’s heuristic for the simplification of likelihood ratios to estimate percentage change in diagnostic probability. The findings indicate that EI is easier to calculate than LDM and, unlike LDM, may be classified either qualitatively or quantitatively in a manner similar to likelihood ratios. EI shows the utility or inutility of diagnostic and screening tests, illustrating the inevitable trade-off between diagnosis and misdiagnosis. It may be a useful metric to communicate risk in a way that is easily intelligible for both clinicians and patients.
Association of COVID-19 vaccines ChAdOx1 and BNT162b2 with major venous, arterial, or thrombocytopenic events: whole population cohort study in 46 million adults in England
Authors:   CVD-COVID-UK consortium,   Writing committee, Whiteley William N , Ip Samantha , Cooper Jennifer A , Bolton Thomas , Keene Spencer , Walker Venexia , Denholm Rachel , Akbari Ashley , Omigie Efosa , Hollings Sam , Di Angelantonio Emanuele , Denaxas Spiros , Wood Angela , Sterne Jonathan A C , Sudlow Cathie
Published: August 2021

Background

Thromboses in unusual locations after the COVID-19 vaccine ChAdOx1-S have been reported. Better understanding of population-level thrombotic risks after COVID-19 vaccination is needed.

Methods

We analysed linked electronic health records from adults living in England, from 8th December 2020 to 18th March 2021. We estimated incidence rates and hazard ratios (HRs) for major arterial, venous and thrombocytopenic outcomes 1-28 and >28 days after first vaccination dose for ChAdOx1-S and BNT162b2 vaccines. Analyses were performed separately for ages <70 and ≥70 years, and adjusted for age, sex, comorbidities, and social and demographic factors.

Results

Of 46,162,942 adults, 21,193,814 (46%) had their first vaccination during follow-up. Adjusted HRs 1-28 days after ChAdOx1-S, compared with unvaccinated rates, at ages <70 and ≥70 respectively, were 0.97 (95% CI: 0.90-1.05) and 0.58 (0.53–0.63) for venous thromboses, and 0.90 (0.86-0.95) and 0.76 (0.73-0.79) for arterial thromboses. Corresponding HRs for BNT162b2 were 0.81 (0.74–0.88) and 0.57 (0.53–0.62) for venous thromboses, and 0.94 (0.90-0.99) and 0.72 (0.70-0.75) for arterial thromboses. HRs for thrombotic events were higher at younger ages for venous thromboses after ChAdOx1-S, and for arterial thromboses after both vaccines.

Rates of intracranial venous thrombosis (ICVT) and thrombocytopenia in adults aged <70 years were higher 1-28 days after ChAdOx1-S (adjusted HRs 2.27, 95% CI:1.33– 3.88 and 1.71, 1.35–2.16 respectively), but not after BNT162b2 (0.59, 0.24–1.45 and 1.00, 0.75–1.34) compared with unvaccinated. The corresponding absolute excess risks of ICVT 1-28 days after ChAdOx1-S were 0.9–3 per million, varying by age and sex.

Conclusions

Increases in ICVT and thrombocytopenia after ChAdOx1-S vaccination in adults aged <70 years were small compared with its effect in reducing COVID-19 morbidity and mortality, although more precise estimates for adults <40 years are needed. For people aged ≥70 years, rates of arterial or venous thrombotic, events were generally lower after either vaccine.
Distributed genetic architecture across the hippocampal formation implies common neuropathology across major brain disorders
Authors: Bahrami Shahram , Nordengen Kaja , Shadrin Alexey A. , Frei Oleksandr , van der Meer Dennis , Dale Anders M. , Westlye Lars T. , Andreassen Ole A. , Kaufmann Tobias
Published: August 2021

Despite its major role in complex human behaviours across the lifespan, including memory formation and decline, navigation and emotions, much of the genetic architecture of the hippocampal formation is currently unexplored. Here, through multivariate genome-wide association analysis in volumetric data from 35,411 individuals, we revealed 173 unique genetic loci with distributed associations across the hippocampal formation. We identified profound genetic overlap with eight major developmental and degenerative brain disorders, where common genes suggest partly age- and disorder-independent mechanisms underlying hippocampal pathology.