bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Laparoscopic Entry Point in Patients With Previous Laparotomy: A Prospective Comparative Study
Authors: Elbanna Mohey R. , Helmy Ramy F. , Sabry Ahmed M. , El Zemeity Ahmed O. 
Published: July 2022

Introduction:
Intraperitoneal access and establishing pneumoperitoneum for laparoscopy is a critical step especially in patients who underwent previous laparotomy due to the higher risk of visceral or vascular injuries. In this study, we propose a new entry point for safe laparoscopic access in cases having previous laparotomy.

Materials and Methods:
This is a prospective controlled randomized trial conducted between January 2016 and January 2022 in Ain Shams University Hospitals. It included 232 patients who underwent laparoscopic procedures after previous laparotomy. They were randomly divided into 2 equal groups. In group 1, laparoscopic access was carried out by an optical trocar through the new point situated in the subxiphoid region 1 cm below the costal margin and centered 2.5 cm from the midline on either side. In group 2, laparoscopic access was performed by an optical subumbilical trocar after Verres needle insufflation in Palmer point. The primary end points were success and safety of entry, measured by the number of entry attempts and the incidence of bowel and vascular injuries. The secondary end point was the entry time.

Results:
In group 1, safe entry into the abdomen was achieved without visceral or vascular injury. In the 3 cases, minor liver injuries occurred. In group 2, 2 major vascular injuries and 5 bowel injuries occurred. There was a significant difference in procedure time (55±7.2 s in group 1 vs. 192±11.6 s in group 2).

Conclusion:
The suggested entry point is fast, safe, and reliable in patients having previous laparotomy.
SARS-CoV-2 omicron variants succumbin vitrotoArtemisia annuahot water extracts
Authors: Nair M.S. , Huang Y. , Weathers P.J. 
Published: July 2022

AbstractThe SARS-CoV-2 (COVID-19) global pandemic continuous to infect and kill millions while rapidly evolving new variants that are more transmissible and evading vaccine-elicited antibodies.Artemisia annuaL. extracts have shown potency against all previously tested variants. Here we further queried extract efficacy against omicron and its recent subvariants. Using Vero E6 cells, we measured thein vitroefficacy (IC50) of stored (frozen) dried-leaf hot-waterA. annuaL. extracts of four cultivars (A3, BUR, MED, and SAM) against SARS-CoV-2 variants: original WA1 (WT), BA.1.1.529+R346K (omicron), BA.2, BA.2.12.1, and BA.4. IC50values normalized to the extract artemisinin (ART) content ranged from 0.5-16.5 µM ART. When normalized to dry mass of the extractedA. annualeaves, values ranged from 20-106 µg. Although IC50values for these new variants are slightly higher than those reported for previously tested variants, they were within limits of assay variation. There was no measurable loss of cell viability at leaf dry weights ≤50 µg of any cultivar extract. Results continue to indicate that oral consumption ofA. annuahot-water extracts (tea infusions) could potentially provide a cost-effective approach to help stave off this pandemic virus and its rapidly evolving variants.
Convolutional neural networks for quantitative smartphone video nystagmography: ConVNG
Authors: Friedrich M. , Schneider E. , Buerklein M. , Taeger J. , Hartig J. , Volkmann J. , Peach R. , Zeller D. 
Published: July 2022

AbstractBackgroundEye movement abnormalities are paramount in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography (VOG) improves diagnostic accuracy, resource intensiveness preclude its broad use. To bridge this care gap, we here validate a framework for smartphone video-based nystagmography capitalizing on recent computer vision advances.MethodsA recurrent convolutional network was fine-tuned for pupil tracking using >550 annotated frames: ConVNG. Slow phase velocity of optokinetic nystagmus was calculated in 10 subjects using both ConVNG and VOG. Equivalence of accuracy and precision was assessed using the “two one-sample t-test” (TOST) and Bayesian interval-null approaches.ResultsConVNG tracking accuracy reached 9-15% of an average pupil diameter. SPV measurement accuracy was equivalent to VOG (p< .017; Bayes factors (BF) > 24). Average precision was 0.30° for ConVNG and 0.12° for VOG.ConclusionsConVNG enables smartphone video nystagmography with an accuracy comparable to VOG and precision approximately one order of magnitude higher than comparable ARKit applications. This serves as a blueprint for highly accessible tools with potential to accelerate progress toward precise and personalized Medicine.
Highlighting the importance of semantics in COVID-19 fake news detection using a CNN hashmap color-based method
Authors: El Azhari Maryam 
Published: July 2022

AbstractWith the exponential development and exploitation of social media sites and platforms such as facebook, twitter and instagram, a diversity type of news are reached to the users,resulting in a major influence on human health and safety.Spreading misinformation and disinformation during the Covid-19 pandemic has become increasingly significant. Although it is usually not a criminal act, it can cause serious endangerment to public health. Such infodemic movement is often lead to advance geopolitical interests by the states, to achieve some sort of profit by some opportunists and individuals or discredit official sources. Hence,it has become crucial to automate the detection of fake news in order to shield people from any harmful repercussions. In this paper, the importance of semantics in Covid-19 fake news detection is highlighted based on a convolutional neural network classifier and a hashmap color-based technique. The experiments are performed with CoAID(Covid-19 heAlthcare mIsinformation Dataset),and the results prove that the loss of semantics yields to a poor performance of the classifier. This implicates additional constraints to the training images,with focus on creating a CNN-based color hashmap classifier that includes anterior and posterior neighbors.
Can MRI-based multivariate gray matter volumetric distance predict motor progression and classify slow versus fast progressors in Parkinson’s disease?
Authors: Vijayakumari Anupa A , Fernandez Hubert H , Walter Benjamin L 
Published: July 2022

AbstractIntroductionWhile Parkinson’s disease (PD) related neurodegeneration is associated with structural changes in the brain, magnetic resonance imaging (MRI) has not been helpful in diagnosing PD or predicting the progression of motor symptoms. In this study, we aimed to develop a structural MRI-based biomarker to predict the rate of progression of motor symptoms and to classify patients based on the symptom severity (i.e. slow vs. fast progressors) in the early stages of PD.MethodsThe study included 59 patients with PD (n=40 for the primary analysis, 19 for the validation analysis), and 55 healthy controls with structural MRI from the Parkinson’s Progression Markers Initiative (PPMI) database. We developed a patient-specific multivariate gray matter volumetric distance using Mahalanobis distance (MGMV) to investigate the changes in MGMVover time using longitudinal linear mixed-effect model, its potential as a biomarker to predict the rate of progression of motor function (MDS-UPDRS-part III) using multiple linear regression model, and classification of patients based on symptom severity using machine learning (ML).ResultsMGMVat BL significantly predicted changes in motor severity (p<0.05) and a trend level increase in MGMVover time (p = 0.09) were noted. We obtained 85% accuracy in discriminating patients according to their symptom severity, and on an independent test cohort, an accuracy of 90% was achieved.ConclusionsWe identified a promising structural MRI-based biomarker for predicting the rate of progression of motor symptoms and classification of patients based on motor symptom severity.
Cost-effectiveness analysis of a maternal pneumococcal vaccine in low-income, high-burden settings such as Sierra Leone
Authors: Bilgin Gizem M. , Munira Syarifah Liza , Lokuge Kamalini , Glass Kathryn 
Published: July 2022

AbstractMaternal pneumococcal vaccines have been proposed as a method of protecting infants in the first few months of life. In this paper, we assess the cost-effectiveness of a maternal pneumococcal polysaccharide vaccine using a health sector perspective. We estimate the costs of delivering a maternal pneumococcal polysaccharide vaccine, and the healthcare costs averted through the prevention of severe pneumococcal outcomes such as pneumonia and meningitis, with estimates of vaccine effectiveness based on previous research. Our model estimates that a maternal pneumococcal program would cost $570 per DALY averted (2020 USD, range $558-582), and hence not be cost-effective in our study setting of Sierra Leone using the nation’s GDP per capita of $527 as a benchmark. However, the choice of discounting rates for health outcomes determines whether the maternal pneumococcal vaccine was deemed cost-effective. Without discounting, the cost per DALY averted would be $277 (53% of Sierra Leone’s GDP per capita). Further, the cost per DALY averted would be $128 (24% GDP per capita) if PPV could be procured at the same cost relative to PCV in Sierra Leone as on the PAHO reference price list. Overall, our paper demonstrates that maternal pneumococcal vaccines have the potential to be cost-effective in low-income settings; however, their cost-effectiveness depends on the choice of discounting rates determined by social values, and negotiations with vaccine providers on vaccine price. Vaccine price is the largest cost driving the cost-effectiveness of a future maternal pneumococcal vaccine.
Transfer learning for Covid-19 detection in medical images
Authors: Azhari Maryam El 
Published: July 2022

AbstractAs of late, the Covid infection 2019 (COVID-19) has caused a pandemic sickness in more than 200 nations, therefore impacting billions of people. To control the spread of the coronavirus, it is crucial to detect infected individuals and ensure their complete isolation to prevent further infection. Chest X-rays and CT-scans have been proven to be very promising as signals of the infection can be clearly shown in lung areas. Transfer learning from ImageNet dataset has become the latent trend in medical imaging applications. However, there are major differences between ImageNet and medical imaging datasets. Therefore, the feasibility of transfer learning in medical applications remains questionable. This paper investigates the performance of five fine-tuned pre-trained models for chest x-rays and CT-scans classification in contrast with a deep CNN model built from scratch. DenseNet121, Resnet-50, Inception v2, Resnet101-V2 and VGG16 are selected and initialized with either random or pre-trained weights to classify augmented images into two classes: Covid and non-Covid. The performance evaluation proves the minuscule impact of training transfer learning models for good quality results, as all CNN models contribute almost equally to the classification and achieve considerable results in terms of precision, accuracy, recall and F1 score.
Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer and coronary disease pathways
Authors: Wang Lihua , Western Dan , Timsina Jigyasha , Repaci Charlie , Song Won-Min , Norton Joanne , Kohlfeld Pat , Budde John , Climer Sharlee , Butt Omar H. , Jacobson Daniel , Garvin Michael , Templeton Alan R , Campagna Shawn , O’Halloran Jane , Presti Rachel , Goss Charles W. , Mudd Philip A. , Ances Beau M. , Zhang Bin , Sung Yun Ju , Cruchaga Carlos 
Published: July 2022

AbstractIdentification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal (https://covid.proteomics.wustl.edu/). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR ≤ 3.72×10-14), Alzheimer’s disease (FDR ≤ 5.46×10-10) and coronary artery disease (FDR ≤ 4.64×10-2). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer’s disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.
Female investment in terminal reproduction or somatic maintenance depends on infection dose
Authors: Schulz Nora K. E. , Stewart Carly M. , Tate Ann T. 
Published: July 2022

AbstractImmune responses are energetically costly to produce and often trade off with investment in reproduction and offspring provisioning. We predicted that hosts can interpret the magnitude of the infection threat from cues like initial bacterial load to adjust reproductive investment into offspring quantity and quality. To test this prediction, we exposed female flour beetles (Tribolium castaneum) to naïve or sterile media controls or one of three increasing doses of heat-killed Bacillus thuringiensis (Bt). To estimate offspring quantity, we measured the number of eggs and hatched larvae from each maternal treatment group. To estimate offspring quality, we measured egg protein content, development to pupation, pupal weight, and offspring survival against Bt infection. Compared to naïve controls, low and intermediate bacterial doses resulted in lower female fecundity, suggesting a shift to a somatic maintenance strategy. Meanwhile, in the highest-dose group, fecundity was negatively correlated with egg protein content, suggesting a trade-off between offspring quality and quantity that could mask terminal investment based on metrics of quantity alone. Our results underscore the need to account for the magnitude of environmental cue when quantifying plasticity and trade-offs among life history traits, and provide new insight into the trans-generational effects of immune responses.
Zinc transporter somatic gene mutations cause primary aldosteronism
Authors: Rege Juilee , Nanba Kazutaka , Bandulik Sascha , Kosmann Carla , Blinder Amy R. , Vats Pankaj , Kumar-Sinha Chandan , Lerario Antonio M. , Else Tobias , Yamazaki Yuto , Satoh Fumitoshi , Sasano Hironobu , Giordano Thomas J. , Williams Tracy Ann , Reincke Martin , Turcu Adina F. , Udager Aaron M. , Warth Richard , Rainey William E. 
Published: July 2022

AbstractPrimary aldosteronism (PA) is the most common form of endocrine hypertension and effects one in 50 adults. PA is characterized by inappropriately elevated aldosterone production via renin-independent mechanisms. Driver somatic mutations for aldosterone excess have been found in approximately 90% of aldosterone-producing adenomas (APAs). Using next-generation sequencing, we identified recurrent in-frame deletions inSLC30A1in five APAs (p.L51_A57del, n=3; p.L49_L55del, n=2).SLC30A1encodes the ubiquitous zinc efflux transporter ZnT1 (zinc transporter 1). The identifiedSLC30A1variants are situated in close proximity of the zincbinding site (H43 and D47) in transmembrane domain II and likely cause abnormal ion transport. PA cases with the uniqueSLC30A1mutations showed male dominance and demonstrated increased aldosterone and 18-oxo-cortisol concentrations. Functional studies of the mutant SLC30A151_57delvariant in a doxycycline-inducible adrenal cell system revealed abnormal Na+conductivity caused by the mutant, which in turn led to the depolarization of the resting membrane potential, and thus to the opening of voltage-gated calcium channels. This resulted in an increase in cytosolic Ca2+activity, which stimulatedCYP11B2mRNA expression and aldosterone production. Collectively, these data implicate the first-in-field zinc transporter mutations as a dominant driver of aldosterone excess in PA.
Integration ofCandida albicans-induced single-cell gene expression data and circulatory protein concentrations reveal genetic regulators of inflammation
Authors: Boahen Collins K. , Oelen Roy , Le Kieu T.T , Netea Mihai G. , Franke Lude , van der Wijst Monique G.P. , Kumar Vinod 
Published: July 2022

AbstractBoth gene expression and protein concentrations are regulated by genetic variants. Exploring the regulation of both eQTLs and pQTLs simultaneously in a context- and cell-type dependent manner may help to unravel mechanistic basis for genetic regulation of pQTLs. Here, we performed meta-analysis ofCandida albicans-induced pQTLs from two population-based cohorts and intersected the results withCandida-induced cell-type specific expression association data (eQTL). This revealed systematic differences between the pQTLs and eQTL, with only 35% of common genetic variants modulating both intermediate molecular phenotypes. By taking advantage of the tightly co-regulated pattern of the proteins, we also identified SNPs affecting protein network uponCandidastimulations. Colocalization of pQTLs and eQTLs signals implicated several genomic loci including MMP-1 and AMZ1. Analysis ofCandida-induced single cell gene expression data implicated specific cell types that exhibit significant expression QTLs upon stimulation. By highlighting the role of trans-regulatory networks in determining the abundance of proteins in blood, our study serve as a framework to gain insights into the mechanisms of genetic regulation of protein levels in a context-dependent manner.
Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT
Authors: Du Jin-Hong , Cai Zhanrui , Roeder Kathryn 
Published: July 2022

AbstractRecent advances in single-cell technologies enable joint profiling of multiple omics. These profiles can reveal the complex interplay of different regulatory layers in single cells; still, new challenges arise when integrating datasets with some features shared across experiments and others exclusive to a single source; combining information across these sources is called mosaic integration. The difficulties lie in imputing missing molecular layers to build a self-consistent atlas, finding a common latent space, and transferring learning to new data sources robustly. Existing mosaic integration approaches based on matrix factorization cannot efficiently adapt to nonlinear embeddings for the latent cell space and are not designed for accurate imputation of missing molecular layers. By contrast, we propose a probabilistic variational autoencoder model, scVAEIT, to integrate and impute multimodal datasets with mosaic measurements. A key advance is the use of a missing mask for learning the conditional distribution of unobserved modalities and features, which makes scVAEIT flexible to combine different panels of measurements from multimodal datasets accurately and in an end-to-end manner. Imputing the masked features serves as a supervised learning procedure while preventing overfitting by regularization. Focusing on gene expression, protein abundance, and chromatin accessibility, we validate that scVAEIT robustly imputes the missing modalities and features of cells biologically different from the training data. scVAEIT also adjusts for batch effects while maintaining the biological variation, which provides better latent representations for the integrated datasets. We demonstrate that scVAEIT significantly improves integration and imputation across unseen cell types, different technologies, and different tissues.Significance StatementSingle-cell multimodal assays provide an unprecedented opportunity for investigating heterogeneity of cell types and novel associations with disease and development. Although analyses of such multimodal datasets have the potential to provide new insights that cannot be inferred with a single modality, access typically requires the integration of multiple data sources. We propose a probabilistic variational autoencoder model for mosaic integration, which involves merging data sources that include features shared across datasets and features exclusive to a single data source. Our model is designed to provide a lower dimensional representation of the cells for visualization, clustering, and other downstream tasks; accurate imputation of missing features and observations; and transfer learning for robustly imputing new datasets when only partial measurements are available.
Faster responses of photosynthesis to light transitions increase biomass and grain yield in transgenicSorghum bicoloroverexpressing Rieske FeS
Authors: Ermakova Maria , Woodford Russell , Taylor Zachary , Furbank Robert T. , Belide Srinivas , von Caemmerer Susanne 
Published: July 2022

AbstractSorghum is one of the most important crops providing food and feed in many of the world’s harsher environments. Sorghum utilises the C4pathway of photosynthesis in which a biochemical carbon concentrating mechanism results in high CO2assimilation rates. Overexpressing the Rieske subunit of the Cytochromeb6fcomplex was previously shown to increase the rate of photosynthetic electron transport and stimulate CO2assimilation in the model C4plantSetaria viridis. To test whether productivity of C4crops could be improved by Rieske overexpression, we created transgenicSorghum bicolorplants with increased Rieske content. The transgenic plants showed no marked changes in abundance of other photosynthetic proteins or chlorophyll content. Increases in yield of Photosystem II and CO2assimilation rate as well as faster responses of non-photochemical quenching during transient photosynthetic responses were observed as a result of an elevatedin vivoCytochromeb6factivity in plants overexpressing Rieske. The steady-state rates of electron transport and CO2assimilation did not differ between transgenic and control plants, suggesting that Cytochromeb6fis not the only factor limiting electron transport in sorghum at high light and high CO2. Nevertheless, more agile responses of photosynthesis to light transitions led to increases in biomass and grain yield in plants overexpressing Rieske. Our results indicate that increasing Rieske content could boost productivity of C4crops by improving the efficiency of light utilisation and conversion to biomass.