bluuebunny/crossref_metadata_2025_split huggingface dataset:
Shifting research priorities in maternal and child health in the COVID-19 pandemic era in India: a renewed focus on systems strengthening
Authors: Mehta Kayur , Zodpey Sanjay , Banerjee Preetika , Pocius Stephanie L. , Dhaliwal Baldeep K. , DeLuca Andrea , Bhattacharya Sangeeta Das , Hedge Shailendra , Sengupta Paramita , Gupta Madhu , Shet Anita 
Published: March 2021

AbstractBackgroundThe remarkable progress seen in maternal and child health (MCH) in India over the past two decades has been impacted by setbacks from the COVID-19 pandemic. We aimed to undertake a rapid assessment to identify key priorities for public health research in MCH in India within the context and aftermath of the COVID-19 pandemic.MethodsA web-based survey was developed to identify top research priorities in MCH. It consisted of 26 questions on six broad domains: vaccine preventable diseases, outbreak preparedness, primary healthcare integration, maternal health, neonatal health, and infectious diseases. Key stakeholders were invited to participate between September and November 2020. Participants assigned importance on a 5-point Likert scale, and assigned overall ranks to each sub-domain research priority. Descriptive statistics were used to examine Likert scale responses, and a ranking analysis was done to obtain an “average ranking score” and identify the top research priority under each domain.ResultsAmongst the 84 respondents, 37% were public-health researchers, 25% healthcare providers, 20% academic faculty and 13% were policy makers. Across the six domains, most respondents considered conducting research on systems strengthening as extremely important. The highest ranked research priorities were strengthening the public sector workforce (vaccine preventable diseases), enhancing public-health surveillance networks (outbreak preparedness), nutrition support through community workers (primary care integration), encouraging at least 4-8 antenatal visits (maternal health), neonatal resuscitation to reduce birth asphyxia (neonatal health) and pediatric and maternal screening and treatment of tuberculosis (infectious diseases). Common themes identified through open-ended questions were also systems strengthening priorities across domains.ConclusionsThe overall focus for research priorities in MCH in India during the COVID-19 pandemic is on strengthening existing services and service delivery, rather than novel research. Our results highlight pivotal steps within the roadmap for advancing and sustaining maternal and child health gains during the ongoing COVID-19 pandemic and beyond.
Phosphate transporter genes: genome-wide identification and characterization in Camelina sativa
Authors: Hasanzadeh Soosan , Faraji Sahar ,   Abdullah, Heidari Parviz 
Published: March 2021

AbstractPhosphorus is known as a key element associated with growth, energy, and cell signaling. In plants, phosphate transporters (PHTs) are responsible for moving and distributing phosphorus in cells and organs. PHT genes have been genome-wide identified and characterized in various plant species, however, these genes have not been widely identified based on available genomic data in Camellia sativa, which is an important oil seed plant. In the present study, we found 66 PHT genes involved in phosphate transporter/translocate in C. sativa. The recognized genes belonged to PHTs1, PHTs2, PHTs4, PHOs1, PHO1 homologs, glycerol-3-PHTs, sodium dependent PHTs, inorganic PHTs, xylulose 5-PHTs, glucose-6-phosphate translocators, and phosphoenolpyruvate translocators. Our finding revealed that PHT proteins are divers based on their physicochemical properties such as Isoelectric point (pI), molecular weight, GRAVY value, and exon-intron number(s). Besides, the expression profile of PHT genes in C. sativa based on RNA-seq data indicate that PHTs are involved in response to abiotic stresses such as cold, drought, salt, and cadmium. The tissue specific expression high expression of PHO1 genes in root tissues of C. sativa. In additions, four PHTs, including a PHT4;5 gene, a sodium dependent PHT gene, and two PHO1 homolog 3 genes were found with an upregulation in response to aforementioned studied stresses. In the current study, we found that PHO1 proteins and their homologs have high potential to post-translation modifications such as N-glycosylation and phosphorylation. Besides, different cis-acting elements associated with response to stress and phytohormone were found in the promoter region of PHT genes. Overall, our results show that PHT genes play various functions in C. Sativa and regulate Camellia responses to external and intracellular stimuli. The results can be used in future studies related to the functional genomics of C. sativa.
A breast cancer patient-derived xenograft and organoid platform for drug discovery and precision oncology
Authors: Guillen Katrin P. , Fujita Maihi , Butterfield Andrew J. , Scherer Sandra D. , Bailey Matthew H. , Chu Zhengtao , DeRose Yoko S. , Zhao Ling , Cortes-Sanchez Emilio , Yang Chieh-Hsiang , Toner Jennifer , Wang Guoying , Qiao Yi , Huang Xiaomeng , Greenland Jeffery A. , Vahrenkamp Jeffery M. , Lum David H. , Factor Rachel E. , Nelson Edward W. , Matsen Cindy B. , Poretta Jane M. , Rosenthal Regina , Beck Anna C. , Buys Saundra S. , Vaklavas Christos , Ward John H. , Jensen Randy L. , Jones Kevin B. , Li Zheqi , Oesterreich Steffi , Dobrolecki Lacey E. , Pathi Satya S. , Woo Xing Yi , Berrett Kristofer C. , Wadsworth Mark E. , Chuang Jeffrey H. , Lewis Michael T. , Marth Gabor T. , Gertz Jason , Varley Katherine E. , Welm Bryan E. , Welm Alana L. 
Published: March 2021

AbstractModel systems that recapitulate the complexity of human tumors and the reality of variable treatment responses are urgently needed to better understand cancer biology and to develop more effective cancer therapies. Here we report development and characterization of a large bank of patient-derived xenografts (PDX) and matched organoid cultures from tumors that represent some of the greatest unmet needs in breast cancer research and treatment. These include endocrine-resistant, treatment-refractory, and metastatic breast cancers and, in some cases, multiple tumor collections from the same patients. The models can be grown long-term with high fidelity to the original tumors. We show that development of matched PDX and PDX-derived organoid (PDxO) models facilitates high-throughput drug screening that is feasible and cost-effective, while also allowingin vivovalidation of results. Our data reveal consistency between drug screening results in organoids and drug responses in breast cancer PDX. Moreover, we demonstrate the feasibility of using these patient-derived models for precision oncology in real time with patient care, using a case of a triple negative breast cancer with early metastatic recurrence as an example. Our results uncovered an FDA-approved drug with high efficacy against the models. Treatment with the PDxO-directed therapy resulted in a complete response for the patient and a progression-free survival period more than three times longer than her previous therapies. This work provides valuable new methods and resources for functional precision medicine and drug development for human breast cancer.Graphical Abstract
CADA: Phenotype-driven gene prioritization based on a case-enriched knowledge graph
Authors: Peng Chengyao , Dieck Simon , Schmid Alexander , Ahmad Ashar , Knaus Alexej , Wenzel Maren , Mehnert Laura , Zirn Birgit , Haack Tobias , Ossowski Stephan , Wagner Matias , Brunet Teresa , Ehmke Nadja , Danyel Magdalena , Rosnev Stanislav , Kamphans Tom , Nadav Guy , Fleischer Nicole , Fröhlich Holger , Krawitz Peter 
Published: March 2021

AbstractMany rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the human phenotype ontology (HPO), which is increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed, however, the performance of many such tools suffers from an overrepresentation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With CADA we built a knowledge-graph that is based on case annotations and disorder annotations and show that CADA exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Crucial in the design of our approach is the use of the growing amount of phenotypic information that diagnostic labs deposit in databases such as ClinVar. By this means CADA is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.
The impact of COVID-19 on primary health care and antibiotic prescribing in rural China: qualitative study
Authors: Zhang Tingting , Shen Xingrong , Liu Rong , Zhao Linhai , Wang Debin , Lambert Helen , Cabral Christie 
Published: March 2021

ABSTRACTIntroductionPrimary health care (PHC) system is designated to be responsible for epidemic control and prevention during the outbreak of COVID-19 in China, while COVID-19 suspected cases in PHC are required to be transferred to specialist fever clinics at higher level hospitals. This study aims to understand to impact of COVID-19 on PHC delivery and antibiotic prescribing at community level in the rural areas of central China.MethodsQualitative semi-structured interviews were conducted with 18 PHC practitioners and seven patients recruited from two township health centres (THCs) and nine village clinics (VCs) in two rural residential areas of Anhui province. Interviews were transcribed verbatim and thematically analysed.ResultsPractitioners’ and patients’ views and perspectives on COVID-19 impacts on PHC services and antibiotic prescribing are organised into four broad themes: switch from PHC to epidemic prevention and control, concerns and challenges faced by those delivering PHC, diminished PHC, and COVID-19 as a different class of illness.ConclusionThe COVID-19 epidemic has had a considerable impact on the roles of rural PHC clinics in China that shifted to public health from principal medical, and highlighted the difficulties in rural PHC including inadequately trained practitioners, additional work and financial pressure, particularly in VCs. Antibiotic prescribing practices for non-COVID-19 respiratory tract infections remained unchanged since the knowledge of COVID-19 was not seen as relevant to practitioners’ antibiotic treatment practices, although overall rates were reduced because fewer patients were attending rural PHC clinics. Since COVID-19 epidemic control work has been designated as a long-term task in China, rural PHC clinics now face the challenge of how to balance their principal clinical and public health roles and, in the case of the VCs, remain financially viable.
Determinants of adolescent lung function in Indians: race, nutrition and systemic inflammation
Authors: Aggarwal Mohit , Bansal Anubhuti , Desiraju Bapu Koundinya , Singh Shailendra , Agrawal Anurag 
Published: March 2021

AbstractRationaleWhile determining normal variation of spirometric volumes, the geo-political construct of ‘Indian’ inadequately captures the diverse racial structure and varied lifestyles that exist for 1 in 5 people globally. It is necessary to determine the degree of racial heterogeneity and other underlying factors to know whether lower spirometric volumes of ‘Indians’ is normal or abnormal.ObjectivesTo investigate the differences in spirometric volumes and their associations with overall health parameters, for adolescent children and young adults, across geo-ethnic regions within India.MethodsData was analyzed for 2338 healthy subjects aged 9-19 years with acceptable spirometry from SOLID cohort. Associations between lung function and potential determinants (Race, anthropometry, nutrition, family/life history, airway oscillometric parameters and systemic inflammation) were examined by using multiple regression. Latent profile analysis (LPA) was performed for 903 subjects to uncover hidden sub-phenotypes.ResultsFEV1 and FVC varied significantly between Indian genetic ancestries, being highest in Tibeto-Burmans and lowest in Dravidians. After statistical adjustments, FEV1 and FVC were positively correlated with waist-height-ratio, shoulder-height-ratio, normalized BMI, and blood hemoglobin. Lower FVC was associated with lower expiratory flows (PEF and FEF25-75), higher lung reactance (X5), higher airway resistance (R10 and R15), gastro-intestinal symptoms, and higher inflammation (IL-8 and IL-17). A sub-phenotype of thinness, higher inflammation (IFN-gamma, IL-17, TNF-alpha, IL-8) and lower FVC was identified on LPA for 35% of the sample.ConclusionsSpirometric volumes in Indians are different between major racial subgroups but may also be abnormally low due to prevalent nutritional or environmental adversity. Care must be taken while establishing normative standards.
Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
Authors: Greenwald Noah F. , Miller Geneva , Moen Erick , Kong Alex , Kagel Adam , Fullaway Christine Camacho , McIntosh Brianna J. , Leow Ke , Schwartz Morgan Sarah , Dougherty Thomas , Pavelchek Cole , Cui Sunny , Camplisson Isabella , Bar-Tal Omer , Singh Jaiveer , Fong Mara , Chaudhry Gautam , Abraham Zion , Moseley Jackson , Warshawsky Shiri , Soon Erin , Greenbaum Shirley , Risom Tyler , Hollmann Travis , Keren Leeat , Graf Will , Angelo Michael , Van Valen David 
Published: March 2021

AbstractUnderstanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.
A drastic shift in the energetic landscape of toothed whale sperm cells
Authors: Alves L. Q. , Ruivo R. , Valente R. , Fonseca M. M. , Machado A. M. , Plön S. , Monteiro N. , García-Parraga D. , Ruiz-Díaz S. , Sánchez-Calabuig M.L. , Gutiérrez-Adán A. , Castro L. Filipe C. 
Published: March 2021

AbstractMammalia spermatozoa are a notable example of energetic compartmentalization. While mitochondrial oxidative phosphorylation is restricted to the midpiece, sperm-specific glycolysis operates in the flagellum. Consequently, these highly specialized cells exhibit a clear adaptability to fuel substrates. This plasticity is essential to ensure sperm motility, and is known to vary among species. Here we describe an extreme example of spermatozoa-energetics adaptation. We show that toothed whales exhibit impaired sperm glycolysis, due to gene and exon erosion, and demonstrate that dolphin spermatozoa motility depends uniquely on endogenous fatty acidβ-oxidation, but not carbohydrates. Our findings substantiate the observation of large mitochondria in spermatozoa, possibly boosting ATP production from endogenous fatty acids. This unique energetic rewiring emphasizes the physiological body reorganisation imposed by the carbohydrate-depleted marine environment.
Employing connectome-based models to predict working memory in multiple sclerosis
Authors: Manglani Heena R. , Fountain-Zaragoza Stephanie , Shankar Anita , Nicholas Jacqueline A. , Prakash Ruchika Shaurya 
Published: March 2021

AbstractBackgroundIndividuals with multiple sclerosis (MS) are vulnerable to deficits in working memory, and the search for neural correlates of working memory in circumscribed areas has yielded inconclusive findings. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual-level working memory in this population.MethodsHere, we applied connectome-based predictive modeling to functional MRI data from working memory tasks in two independent samples with relapsing-remitting MS. In the internal validation sample (ninternal= 36), functional connectivity data were used to train a model through cross-validation to predict accuracy on the Paced Visual Serial Addition Test, a gold-standard measure of working memory in MS. We then tested its ability to predict performance on the N-back working memory task in the external validation sample (nexternal= 36).ResultsThe resulting model successfully predicted working memory in the internal validation sample but did not extend to the external sample. We also tested the generalizability of an existing model of working memory derived in healthy young adults to people with MS. It showed successful prediction in both MS samples, demonstrating its translational potential. We qualitatively explored differences between the healthy and MS models in intra- and inter-network connectivity amongst canonical networks.DiscussionThese findings suggest that connectome-based predictive models derived in people with MS may have limited generalizability. Instead, models identified in healthy individuals may offer superior generalizability to clinical samples, such as MS, and may serve as more useful targets for intervention.Impact StatementWorking memory deficits in people with multiple sclerosis have important consequence for employment, leisure, and daily living activities. Identifying a functional connectivity-based marker that accurately captures individual differences in working memory may offer a useful target for cognitive rehabilitation. Manglani et al. demonstrate machine learning can be applied to whole-brain functional connectivity data to identify networks that predict individual-level working memory in people with multiple sclerosis. However, existing network-based models of working memory derived in healthy adults outperform those identified in multiple sclerosis, suggesting translational potential of brain networks derived in large, healthy samples for predicting cognition in multiple sclerosis.
GMSECT: Genome-Wide Massive Sequence Exhaustive Comparison Tool for structural and copy number variations
Authors: Singh Abhishek Narain 
Published: March 2021

AbstractGMSECT is a parallel robust ‘Application Interface’ that efficiently handles the large genomic sequences for rapid and efficient processing. It is a ‘message passing interface’ based parallel computing ‘Tool’ that can be operated on a cluster for ‘Massive Sequences Exhaustive Comparison’, to identify matches such as the structural variants. The GMSECT algorithm can be implemented using other parallel application programming interfaces as well such as Posix-Threads or can even be implemented in a serial submission fashion. There is complete flexibility to the choice of comparison tool that can be deployed and with the optional parameters as of the choice of comparison tools to suit the speed, sensitivity and specificity of pair wise alignment. The algorithm is simple and robust, and can be applied to compare multiple genomes, chromosomes or large sequences, of different individuals for personalized genome comparison and works good for homologous as well as distant species. The tool can even be applied to smaller genomes like the microbial genome such as theEscherichia colior algae such asChlamydomonas reihardttior yeastSaccharomyces cerevisiaeto quickly conduct comparisons, and thus finds its application to the pharmaceuticals and microbial product based firms for research and development. The application Interface can efficiently and rapidly compare massive sequences to detect for the presence of numerous types of DNA variation existing in the genome ranging from single nucleotide polymorphism (SNPs) to larger structural alterations, such as copy-number variants (CNVs) and inversions. The new algorithm has been tested for comparing the chromosome 21 of Celera’s R27c compilation with all the 48 chromosomes of Celera’s R27c compilation and with all the 48 chromosomes of the human Build 35 reference sequence, which took just 2 Hours and 10 minutes using the pair wise blast algorithm choice and with 110 processors each with 2.2 GHz capacity and 2 GB memory. GMSECT facilitates rapid scanning and interpretation in personalized sequencing project. The application interface with the above resources and alignment choice is estimated to do exhaustive comparison of the human genome with itself in just 2.35 days. An exhaustive comparison of an individual’s genome with a reference genome would comprise of a two ‘self genome’ comparison and a ‘non-self genome’ comparison which is estimated to take about 9.4 days with the above resources. With the advent of personalized genome sequencing project, it would be desirable to compare 100s of individual’s genome with a reference genome. This would involve a ‘non-self genome’ and a ‘self genome’ comparison for each genome, and would take around 7 days for each individual’s genome using GMSECT and the above mentioned resources.
DNMT family induced down-regulation of NDRG1 via DNA methylation and clinicopathological significance in gastric cancer
Authors: Chang Xiaojing , MA Jinguo , Xue Xiaoying , Wang Guohui , Su Linlin , Han Xuetao , Zhou Huandi , Hou Liubing 
Published: March 2021

AbstractBackgroundAberrant DNA methylation of tumor suppressor genes is a common event in the development and progression of gastric cancer(GC). Our previous study showed NDRG1, which could suppress cell invasion and migration, was frequently down-regulated by DNA methylation of its promoter in GC.Purpose and MethodsTo analyze the relationship between the expression and DNA methylation of NDRG1 and DNA methyltransferase (DNMT) family. We performed a comprehensive comparison analysis using 407 patients including sequencing analysis data of GC from TCGA.ResultsNDRG1 was negatively correlative to DNMT1 (p=0.03), DNMT3A(p=0.01), DNMT3B(p=0.88), respectively. Whereas, the DNA methylation of NDRG1 was positively correlative to DNMT family(DNMT1 p<0.01, DNMT3A p<0.001, DNMT3B p=0.57, respectively). NDRG1 expression was significantly inverse correlated with invasion depth (p=0.023), and DNMT1 was significantly positive correlated with the degree of tumor cell differentiation (p=0.049). DNMT3B was significantly correlated with tumor cell differentiation (p=0.030). However, there was no association between the expression of DNMT3A and clinicopathological features. The univariate analysis showed that NDRG1and DNMTs had no association with prognosis of GC patients. But, multivariate analysis showed DNMT1 was significantly correlated with prognosis of GC patients.ConclusionThese data suggest that down-regulation of NDRG1 in gastric cancer is due to DNA methylation of NDRG1 gene promoter via DNMT family. The demethylating agent maybe a potential target drug for GC patients.