bluuebunny/crossref_metadata_2025_split huggingface dataset:
Substrate reduction therapy in aDrosophila melanogastermodel of Sanfilippo syndrome
Authors: Tan Sher Li , Hewson Laura J. , Mustaffar Nooramirah Farhana , He Qi Qi , Wimmer Norbert , Trim Paul J. , King Barbara , Snel Marten F. , Hemsley Kim M. , Ferro Vito , O’Keefe Louise V. , Lau Adeline A. 
Published: March 2023

AbstractSanfilippo syndrome, or mucopolysaccharidosis (MPS) types A, B, C or D, are neurodegenerative lysosomal storage disorders resulting from the lack of a specific enzyme involved in heparan sulfate (HS) catabolism. Several treatments are under evaluation for these conditions including substrate reduction therapy, with the most studied compound of this class being the isoflavone genistein. However, recent outcomes from a Phase III clinical trial have shown that high dose oral genistein does not significantly improve neurodevelopmental outcomes in MPS III patients. Here, we have tested anN-acetylglucosamine (GlcNAc) analogue inhibitor, 4-deoxy-GlcNAc peracetate, at reducing HS accumulation in cells from patients with Sanfilippo syndrome as a novel substrate reduction therapy. We then confirmed the capacity of this compound to modulate substrate accumulationin vivoin a SanfilippoDrosophilamodel. Treatment with this compound significantly reduced HS in cultured MPS IIIA patient fibroblasts in a time-dependent manner. Neuronal and ubiquitous knockdownDrosophilamodels of MPS IIIC displaying elevated heparan sulfate and behavioural defects exhibited reduced HS burden relative to vehicle-treated controls following oral feeding with the GlcNAc analogue inhibitor. These findings indicate that this compound may be beneficial in slowing the accumulation of HS and may represent a novel therapeutic for Sanfilippo syndrome.
Understanding dynamics of respiration amongst sexes: who breathe more efficiently?
Authors: Himanshubhai Desai Dev , Dave Prahasth , Verma Anita , Mahajan Neeraj 
Published: March 2023

AbstractIntroductionRespiration is a complex phenomenon requiring diaphragm, inter-costal muscles and other supporting structures. Contemplating the anatomical & physiological differences between males and females, it is essential to know how the respiratory system works in both of them. No such other study has been conducted in an Indian setup, which guided us to take up this topic.MethodologyTotal of N= 216 (Males 63, Females- 153) student were enrolled. All the participants were between the age of 17-19. Their data of Tidal volume, inspiratory reserve volume, expiratory reserve volume, maximum expiratory pressure and their vital capacity both sitting and standing were gathered and analysee.ResultsTidal volume was in males 553±56 ml and 666±60 in females(p-value = 0.031). IRV was in males 2103±139 ml and 1717±99 in females(p-value>0.0001). ERV was in males 1638±113 ml and 1323±65 in females (p-value>0.0001). VC Standing was in males 3947±155 ml and 3278±105 in females(p-value>0.0001). VC sitting was in males 3492±151 ml and 2743±107 in females(pvalue>0.0001). MEP was in males 90±8 mmHg and 64±6 mmHg in females(p-value>0.0001).Range of Pearson correlation coefficient for all=(+0.2)-(−0.2).ConclusionTidal volume was found to be higher in females than in males. Vital capacity was higher in males than in females by 700 ml in both position and vital capacity was higher by 500ml in standing than in sitting in both males and females. Body mass index weakly correlatable positively or negatively with all parameters. MEP was found to be higher in males but was weakly correlated negatively with BMI.
Confidence in visual detection, familiarity and recollection judgements is preserved in schizophrenia spectrum disorder
Authors: Rouy Martin , Pereira Michael , Saliou Pauline , Sanchez Rémi , el Mardi Wassila , Sebban Hanna , Baqué Eugénie , Dezier Childéric , Porte Perrine , Micaux Julia , de Gardelle Vincent , Mamassian Pascal , Moulin Chris J.A. , Dondé Clément , Roux Paul , Faivre Nathan 
Published: March 2023

AbstractAn effective way to quantify metacognitive abilities is to ask participants to estimate their confidence in the accuracy of their response during a cognitive task. A recent meta-analysis1raised the issue that most assessments of metacognitive abilities in schizophrenia spectrum disorders may be confounded with cognitive deficits, which are known to be present in this population. Therefore, it remains unclear whether the reported metacognitive deficits are metacognitive in nature, or rather inherited from cognitive deficits. Arbitrating between these two possibilities requires equating task performance between experimental groups. Here, we aimed to characterize metacognitive performance among individuals with schizophrenia across three tasks (visual detection, familiarity, recollection) using a within-subject design, while controlling experimentally for intra-individual task performance and statistically for between-subject task performance. In line with our hypotheses, we found no metacognitive deficit for visual detection and familiarity judgements. While we expected metacognition for recollection to be specifically impaired among individuals with schizophrenia, we found evidence in favor of an absence of a deficit in that domain also. The clinical relevance of our findings is discussed in light of a hierarchical framework of metacognition.
Modeling Supraventricular Tachycardia Using Dynamic Computer-Generated Left Atrium
Authors: Wyatt Bryant , Campbell Avery , McIntosh Gavin , Little Melanie , Wyatt Brandon 
Published: March 2023

ABSTRACTSupraventricular Tachycardia (SVT) is a type of irregular heartbeat seen when the heart’s upper chambers beat either too quickly or out of rhythm with the heart’s lower chambers. The loss of synchronization between the upper and lower chambers will result in perturbations of, blood flow. This is why SVT, which includes atrial fibrillation and atrial flutter, is a leading cause of strokes, heart attacks, and heart failure in the world today. The most successful treatment for SVT is catheter ablation, a procedure in which an electrophysiologist (EP) maps the heart to find areas with abnormal electrical activity. The EP then runs a catheter into the heart to ablate the abnormal areas, blocking the electrical signals or destroying the myocytes causing them. Not much is known about what triggers SVT and much research is still being done to find effective ablation strategies for various forms of SVT. We have produced a dynamic model of the left atrium accelerated on NVIDIA GPUs. An interface allows researchers to insert ectopic signals into the simulated atrium and ablate sections of the atrium allowing them to rapidly gain insight into what causes SVT and how to terminate them.
Medication administration errors during general anesthesia – a systematic review of prospective studies
Authors: Murphy Bradley P , Sivaratnam Gayatri , Wong Jean , Chung Frances , Abrishami Amir 
Published: March 2023

AbstractIntroductionThe incidence of medication error in anesthesia can be variable among different studies likely due to recall bias in retrospective studies. In prospective survey studies, questionnaires are sent to anesthesia care providers to facilitate self-reports of medication errors during a pre-planned follow-up period. This systematic review investigates all prospective survey studies of medication errors in adult patients undergoing general anesthesia. Our objective is to identify the incidence and characteristics of the common medication errors during general anesthesia. We also want to determine the contributing factors and outcomes of these errors.MethodsWe conducted database searches of Embase and Medline for medication errors in anesthesia between 1980 to 2019 and 2020 to 2021. Ten prospective survey studies detailing medication errors involving adult patients under general anesthesia were included. Data on response rate, incidence of errors, types of error and medications, patient outcomes, and contributing factors were collected.ResultsTen studies were included of which six studies provided a response rate ranging from 53% to 97.5%. The incidence of medication errors ranged from 0.02% to 1.12% or 1 in every 90 to 5000 anesthetics. A total of 1,676 medication errors during general anesthesia were analyzed. The most reported error was the substitution error (31.6% [530/1676]), followed by incorrect dose (28.4% [476/1676]). The class of medication most associated with administration errors were muscle relaxants, opioids, and antibiotics. Most patient outcomes were of no harm. Inexperience of the anesthesiologist, nurse or student was the most reported contributing factor, followed by haste or pressure to proceed, and communication problems.ConclusionThe incidence of medication errors during general anesthesia were as high as 1.12% and the most common errors were substitution error and incorrect dose. Inexperience, time pressure, and communication problems were contributing factors. This information can be used to inform safety practices in anesthesia.
Clinically meaningful metrics of speech in neurodegenerative disease: Quantification of speech intelligibility and naturalness in ataxia
Authors: Vogel Adam P. , Maruff Paul , Reece Hannah , Carter Hannah , Tai Geneieve , Schultz Benjamin G. , Corben Louise , Delatycki Martin B , Tsanas Athanasios 
Published: March 2023

AbstractBackgroundDigital biomarkers continue to make headway in the clinic and clinical trials for neurological conditions. Speech is a domain with great promise.ObjectivesUsing Friedreich ataxia (FRDA) as an exemplar population, we aimed to align objective measures of speech with markers of disease severity, speech related quality of life and subjective judgements of speech using supervised machine learning techniques.Methods132 participants with genetically confirmed diagnosis of FRDA were assessed using digital speech tests, disease severity scores (Friedreich Ataxia Rating Scale, FARS) and speech related quality of life ratings over a 10-year period. Speech was analyzed perceptually by expert listeners for intelligibility (ability to be understood) and naturalness (deviance from healthy norm) and acoustically across 344 features. Features were selected and presented into a random forest and a support vector machine classifier in a standard supervised learning setup designed to replicate expert-produced scores.ResultsWe demonstrate a subset of measures are strongly associated with all four clinical scales. Objective speech data replicated experts’ assessments of naturalness and intelligibility. These scores represent a lower level of variability than observed between subjective listener ratings. Findings provide evidence there are specific objective markers of speech that change over time and reflect clinical aspects of the disease.DiscussionThe use of a large dataset yielded a speech assay capable of accurately approximating expert listener ratings of key clinical aspects of dysarthria severity. Distinct but complementary subsets align with disease severity and speech related quality of life.
Informal payments in health facilities in Peru in 2018: analysis of a cross-sectional survey
Authors: Espinoza Laura A. , Mallma Patricia , Leslie Hannah H. , García Patricia J. 
Published: March 2023

AbstractBackgroundThe Latin American region demonstrates the lowest levels of trust in health systems globally. Institutional corruption is a major factor in eroding trust. Corruption in health services, including extracting bribes and informal payments from patients, directly harms health outcomes and weakens services intended as public goods. In this study, we aim to characterize the frequency and distribution of informal payments within public health services in Peru.MethodsWe conducted a secondary analysis of a nationally representative cross-sectional survey, the 2018 National Household Survey of Living Conditions and Poverty and identified all individuals reporting health insurance from the Ministry of Health (SIS-MINSA) or Social Security (ESSALUD). To assessed self-reported informal payments in the population, we defined it in 2 ways: 1) being asked to pay a bribe at a health establishment in the past year (direct method), and 2) non-zero cost of care for services that should be free (indirect method) in the last month. We used descriptive statistics to quantify informal payments and bivariate analysis to identify sociodemographic characteristics of those most frequently reporting such payments.Findings132,355 people were surveyed, including 69,839 (52.8%) with coverage from SIS-MINSA and 30,461 (23.03%) from ESSALUD. 39% of respondents reported informal payments in public health services. Less than 1% of participants directly reported them, either at SIS-MINSA services (0.22%); or at ESSALUD (0.42%). Indirect reporting was more prevalent, including up to 10% of surgery patients and 17% of those hospitalized in SIS-MINSA facilities. Wealthier patients (19%) were more likely to report such payments. Interpretation: While direct reporting of bribery was uncommon, we found moderate prevalence of informal payments in public health services in Peru using an indirect assessment method. Indirect reporting may exceed direct reporting due to difficulty in distinguishing appropriate and inappropriate payments, fear of reporting health care workers’ behavior, or social tolerance of informal payments. Informal payments were more common among those with greater social and financial capital, indicating they may obtain enhanced services. Further research on patients’ perception and reporting of informal payments is a key step towards accurate measurement and evidence-based intervention.
Built Environment Features Obtained from Google Street View Are Associated with Coronary Artery Disease Prevalence: A Deep-Learning Framework
Authors: Chen Zhuo , Khalifa Yassin , Dazard Jean-Eudes , Motairek Issam , Rajagopalan Sanjay , Al-Kindi Sadeer 
Published: March 2023

AbstractBackgroundBuilt environment plays an important role in development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches has been limited. We sought to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in urban cities.MethodsThis cross-sectional study used features extracted from Google Street view (GSV) images to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient boosting machines and activation maps were utilized to predict health outcomes and identify feature associations with coronary heart disease (CHD). The study obtained 0.53 million GSV images covering 789 census tracts in 7 cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO). Analyses were conducted from February 2022 to December 2022. We used census tract-level data from the Centers for Disease Control and Prevention’s PLACES dataset. Main outcomes included census tract-level estimated prevalence of CHD based on GSV built environment features.ResultsBuilt environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The ExtraTrees Regressor achieved the best result among all models with the lowest average mean absolute error of 1.11% and Root mean square of error of 1.58. The addition of GSV features outperformed and improved a model that only included census-tract level age, sex, race, income and education. Activation maps from the features revealed a set of neighborhood features represented by buildings and roads associated with CHD prevalence.ConclusionsIn this cross-sectional study, a significant portion of CHD prevalence were explained by GSV-based built environment factors analyzed using deep learning, independent of census tract demographics. Machine vision enabled assessment of the built environment could help play a significant role in designing and improving heart-heathy cities.
Using photographs and deep neural networks to understand flowering phenology and diversity in mountain meadows
Authors: John Aji , Theobald Elli J. , Cristea Nicoleta , Tan Amanda , Hille Ris Lambers Janneke 
Published: March 2023

ABSTRACTMountain meadows are an essential part of the alpine-subalpine ecosystem; they provide ecosystem services like pollination and are home to diverse plant communities. Changes in climate affect meadow ecology on multiple levels, for example by altering growing season dynamics. Tracking the effects of climate change on meadow diversity through the impacts on individual species and overall growing season dynamics is critical to conservation efforts. Here, we explore how to combine crowd sourced camera images with machine learning to quantify flowering species richness across a range of elevations in alpine meadows located in Mt Rainier National Park, Washington, USA. We employed three machine learning techniques (Mask R-CNN, RetinaNet and YOLOv5) to detect wildflower species in images taken during two flowering seasons. We demonstrate that deep learning techniques can detect multiple species, providing information on flowering richness in photographed meadows. The results indicate higher richness just above the tree line for most of the species, which is comparable with patterns found using field studies. We found that the two-stage detector Mask R-CNN was more accurate than single-stage detectors like RetinaNet and YOLO, with the Mask R-CNN network performing best overall with mean average precision (mAP) of 0.67 followed by RetinaNet (0.5) and YOLO (0.4). We found that across the methods using anchor box variations in multiples of 16 led to enhanced accuracy. We also show that detection is possible even when pictures are interspersed with complex backgrounds and are not in focus. We found differential detection rates depending on species abundance, with additional challenges related to similarity in flower characteristics, labeling errors, and occlusion issues. Despite these potential biases and limitations in capturing flowering abundance and location-specific quantification, accuracy was notable considering the complexity of flower types and picture angles in this data set. We therefore expect that this approach can be used to address many ecological questions that benefit from automated flower detection, including studies of flowering phenology and floral resources, and that this approach can therefore complement a wide range of ecological approaches (e.g., field observations, experiments, community science, etc.). In all, our study suggests that ecological metrics like floral richness can be efficiently monitored by combining machine learning with easily accessible publicly curated datasets (e.g., Flickr, iNaturalist).
Effect of caldesmon mutations in the development of zebrafish embryos
Authors: Virtanen Verneri , Paunu Kreetta , Niva Saana , Sundvall Maria , Paatero Ilkka 
Published: March 2023

AbstractCancer is a profound medical concern and better treatments are needed for cancer patients. Therefore, new cancer targets are constantly being studied. These targets need not only be relevant for cancer progression, but their modulation needs to be tolerated reasonably well by the host. Caldesmon is one of these proposed novel targets for cancer therapy. Therefore, we analysed effects of caldesmon mutations in normal development using genetically modified zebrafish embryos. We analysed mutations in both zebrafish caldesmon genes,cald1aandcald1band analysed effects of either mutation alone or as in combination in double homozygous embryos using molecular, morphological and functional analyses. The effects of caldesmon mutations were mild and the gross development of zebrafish embryos was normal. The caldesmon mutant embryos had, however, alterations in response to light-stimulus in behavioral assays. Taken together, the effects of caldesmon mutations in the development of zebrafish embryos were reasonably well tolerated and did not indicate significant concerns for caldesmon being a potential target for cancer therapy.
CREaTor: Zero-shotcis-regulatory pattern modeling with attention mechanisms
Authors: Li Yongge , Ju Fusong , Chen Zhiyuan , Qu Yiming , Xia Huanhuan , He Liang , Wu Lijun , Zhu Jianwei , Shao Bin , Deng Pan 
Published: March 2023

AbstractLinkingcis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to modelcis-regulatory patterns for genomic elements up to 2Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidatecis-regulatory elements (cCREs), CREaTor can model cell type-specificcis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of RNA-seq and ChIP-seq data only, allows for the readily generalization of CREaTor to a broad range of cell types. Evaluation reveals that CREaTor outperforms existing methods in capturing cCRE-gene interactions across various distance ranges in held-out cell types. Further analysis indicates that the superior performance of CREaTor can be attributed to its capacity to model regulatory interactions at multiple levels, including the higher-order genome organizations that govern cCRE activities as well as cCRE-gene interactions. Collectively, our findings highlight CREaTor as a powerful tool for systematically investigatingcis-regulatory programs across various cell types, both in normal developmental processes and disease-associated contexts.
High-level production of nervonic acid in the oleaginous yeastYarrowia lipolyticaby systematic metabolic engineering
Authors: Su Hang , Shi Penghui , Shen Zhaoshuang , Meng Huimin , Men Ziyue , Han Xingfeng , Chen Yanna , Fan Weiming , Fa Yun , Yang Chunyu , Li Fuli , Wang Shi’an 
Published: March 2023

AbstractBrain and neurological diseases are influencing more than one billion world’s people. Nervonic acid (cis-15-tetracosenoic acid, C24:1 Δ15) benefits the treatment of neurological diseases and the health of brain. Currently, the sources of nervonic acid are limited to the seeds of a couple of plants. In this study, we employed the oleaginous yeastYarrowia lipolyticato overproduce nervonic acid oil by systematic metabolic engineering. First, engineering the fatty acid elongation (FAE) pathway by expressing a heterologous β-ketoacyl-CoA synthase geneCgKCSenabled the production of nervonic acid inY. lipolytica.Second, modulation of endogenous pathways by expressing a C16:0-acyl-CoA preferred fatty acid elongase gELOVL6 together with a C18:0-acyl-CoA preferred fatty acid desaturase MaOLE2 increased the content of nervonic acid in total fatty acids (TFA). Third, iterative expression ofCgKCS,gELOVL6andMaOLE2at the genomic loci ofrDNA,FAD2,TGL4,GSY1andSNF1dramatically improved the production of nervonic acid. Fourth, the biosynthesis of both nervonic acid and lipids were further enhanced by expression of the MaOLE2-CgKCS fusion protein and glycerol-3-phosphate acyltransferases (GPAT) and diacylglycerol acyltransferases (DGAT) fromMalania oleiferain the endoplasmic reticulum (ER) membrane. Fifth, an ER structure regulator YlINO2 was identified inY. lipolyticaand the overexpression of YlINO2 led to a 39.3% increase in lipid production. Next, pilot-scale fermentation in 50-L reactor using the strain YLNA9 exhibited a lipid titer of 96.7 g/L and a nervonic acid titer of 17.3 g/L, the highest reported titer to date forde novonervonic acid production. We also found that disruption of the AMP-activated S/T protein kinaseSNF1increased the ratio of nervonic acid (C24:1) to lignoceric acid (C24:0) by 61.6% and a ratio of 3.5:1 (nervonic acid to lignoceric acid) was achieved in the strain YLNA10. Finally, a proof-of-concept purification and separation of nervonic acid were performed and the purity of it reached 98.7%. This study suggested that oleaginous yeasts are attractive hosts for the cost-efficient production of nervonic acid and possibly other very long-chain fatty acids (VLCFAs).