bluuebunny/crossref_metadata_2025_split huggingface dataset:
Subgroups of young type 2 diabetes in India reveal insulin deficiency as a major driver
Authors: Prasad Rashmi B , Asplund Olof , Shukla Sharvari R , Wagh Rucha , Kunte Pooja , Bhat Dattatrey , Parikh Malay , Shah Meet , Phatak Sanat , Käräjämäki Annemari , Datta Anupam , Kakati Sanjeeb , Tuomi Tiinamaija , Saboo Banshi , Ahlqvist Emma , Groop Leif , Yajnik Chittaranjan S 
Published: May 2021

AbstractAim/HypothesisFive subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian young-onset type 2 diabetes patients.MethodsWe applied the European derived centroids to the Indian type 2 diabetes patients diagnosed before 45 years of age from the WellGen (n = 1612) cohort. We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts ANDIS (n= 962) and DIREVA (n=420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205).ResultsBoth Indian and European young type 2 diabetes patients were predominantly classified into severely insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severely insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to figures in Europeans (Swedish: 26% vs 68%, Finnish: 24% vs 71% respectively). A higher proportion of SIDD compared to MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterized by insulin deficiency and hyperglycemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared to MOD while the latter had higher prevalence of neuropathy.Conclusions /InterpretationOur data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European patients in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in young Indian type 2 diabetes, and suggest a need to review management strategies.
Can we use machine learning to discover risk factors? Testing the proof of
principle using data on >11,000 predictors and mortality in the UK
Biobank
Authors: Madakkatel Iqbal , Zhou Ang , McDonnell Mark , Hyppönen Elina 
Published: May 2021

Abstract

Background
We present a simple and fast hypothesis-free machine learning pipeline
for risk factor discovery that accounts for non-linearity and interaction in
large biomedical databases with minimal variable pre-processing.

Methods
Mortality models were built using gradient boosting decision trees
(GBDT) and important predictors were identified using SHAP values. Cox
models controlled for false discovery rate were used for interpretability
and further validation. The pipeline was tested using information from
502,506 UK Biobank participants, aged 37–73 years at recruitment and
followed over seven years for mortality registrations.

Results
From the 11,639 predictors included in GBDT, 193 potential risk factors
had SHAP values 0.05 or greater and were selected for further modelling. Of
the total variable importance summed up, 60% was directly health related,
and baseline characteristics, sociodemographics, and lifestyle factors each
contributed about 10%.
Cox models adjusted for baseline characteristics, showed evidence for an
association with mortality for 166 out of the 193 predictors. These included
mostly well-known risk factors (e,g, age, sex, ethnicity, education,
material deprivation, smoking, physical activity, self-rated health, BMI,
and many disease outcomes), and the pipeline was able to detect associations
in the presence of interactions and non-linear relationships. For 19
predictors we saw evidence for an association in the unadjusted but not
adjusted analyses, suggesting bias by confounding..

Conclusion
Our GBDT-SHAP pipeline was able to identify relevant predictors ‘hidden’
within thousands of variables, providing a fast and pragmatic solution for
the first stage of hypothesis free risk factor identification.

What was known before this study?
Existing studies have used ML for variable selection in general
settings, however, we are not aware of studies integrating
epidemiological approaches with ML for risk factor discovery in
deeply phenotyped large scale population cohorts. We present a
simple and fast method for risk factor discovery using GBDT-SHAP
pipeline, followed by subsequent standard epidemiological analyses.
We test this pipeline using mortality prediction in the UK Biobank
as an example.

What this study adds?
Machine learning pipeline integrated with traditional
epidemiological practices as described in our study can be used a
simple and fast method for discovering potential risk factors in
extensively phenotyped high dimensional biomedical databases.
GBDT-SHAP pipeline was able to detect associations with factors
presenting with interactions with other explanatory variables and
non-linear relationships with the outcome with minimal variable
pre-processing. However, ML does not replace the requirement for
content knowledge and more refined modelling to ensure lack of
confounding and accurate interpretation.
Evaluation the angle of posterosuperior horn of normal fetal Sylvian fissure and its morphological changes by trans-cerebellar section
Authors: Pingping Xu , Dirong Zhang , Yu Shi , Fengbei Kong , Chunxiao Yao , Ying She , Guoru Wu 
Published: May 2021

AbstractObjectiveOn the basis of retrospectively analysis the trans-cerebellar section showing the Sylvian fissure of normal fetus is better than trans-thalamic section in middle and late trimester, we prospectively studied the morphological changes of the Sylvian fissure of normal fetus on the trans-cerebellar section in order to provide valuable information for the diagnosis of fetal cerebral cortical dysplasia.MethodsA prospective cross-sectional study was conducted on 845 normal fetuses at 21 to 32 weeks of gestation from January 2019 to September 2020. The angle of the posterosuperior horn of the Sylvian fissure was measured. Based on the angle, the morphology of the Sylvian fissure was divided into four shapes included shape (Angle >90°), П shape (Angle ≈90°), π shape (Angle <90°) and 工 shape (Angle ≈0°).ResultsThe angle of the posterosuperior horn of the Sylvian fissure was negatively correlated with gestational age which correlation coefficient was 0.966 (P < 0.001). Taking gestational age as the independent variable and the angle of posterosuperior horn of the Sylvian fissure as the dependent variable, it showed that there was a linear relationship between the gestational age and the angle of posterosuperior horn of the Sylvian fissure. We got a simple correlation formula that the angle of posterosuperior horn of the Sylvian fissure (°) =140-13×(gestational weeks-21). It was found that the morphological changes of the Sylvian fissure were related to the gestational age. The morphology of the Sylvian fissure was -shaped at 21 to 24 weeks of gestation, П-shaped at 25 to 26 weeks of gestation, π-shaped at 26 to 30 weeks of gestation, and the Sylvian fissure was almost closed and appeared 工 shape after 31 to 32 weeks of gestation.ConclusionThis study preliminarily elucidates that the morphological changes of the posterosuperior horn of the Sylvian fissure of normal fetuses at 21 to 32 weeks of gestation through the trans-cerebellar section, which could provide valuable information for the evaluation of the normal development of the Sylvian fissure and the prenatal diagnosis of cerebral cortical hypoplasia of fetuses.
Results of an Academic Dialysis Program-Wide SARS-CoV-2 Vaccination Effort
Authors: Bowman Brendan T. , Lobo Benjamin J. , Sharma Binu , Ma Jennie Z. 
Published: May 2021

AbstractPatients with End Stage Kidney Disease requiring dialysis are exceedingly vulnerable to SARS-CoV-2 infection with high hospitalization rates and mortality. Despite this risk, little is known about real world dialysis patient SARS-CoV-2 vaccination acceptance. Surveys of the general population suggest significant vaccine hesitancy and high potential for refusal. From January 27th to March 12th 2021, the University of Virginia (UVA) Health System, in partnership with the Virginia Department of Health / Blue Ridge Health District (BRHD) provided on-site mobile vaccination clinics at 12 UVA dialysis sites. We conducted a cross-sectional study to evaluate vaccine acceptance and evaluate factors associated with refusal. Of 859 dialysis patients with complete vaccination data, 80% received at least one dose of vaccine and 87% of these vaccinations were provided by the UVA/BRHD partnership. The overall patient refusal rate was low at 14%. Patients refusing SARS-CoV-2 vaccine were more likely to be female, younger and missing a documented flu vaccination during the 2020-2021 season. Attributes such as race or prior infection with SARS-CoV-2 were not significantly associated with vaccine refusal. In conclusion, dialysis patients in our program were surprisingly likely to accept vaccination for SARS-CoV-2. Identifying attributes associated with refusal may help target populations at high risk of vaccine refusal.
Association of machine learning-derived measures of body fat distribution with cardiometabolic diseases in >40,000 individuals
Authors: Agrawal Saaket , Klarqvist Marcus D. R. , Diamant Nathaniel , Stanley Takara L. , Ellinor Patrick T. , Mehta Nehal N. , Philippakis Anthony , Ng Kenney , Claussnitzer Melina , Grinspoon Steven K. , Batra Puneet , Khera Amit V. 
Published: May 2021

ABSTRACTBackgroundThe clinical implications of BMI-independent variation in fat distribution are not fully understood.MethodsWe studied MRI imaging data of 40,032 UK Biobank participants. Using previously quantified visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volume in up to 9,041 to train convolutional neural networks (CNNs), we quantified these depots in the remainder of the participants. We derived new metrics for each adipose depot – fully independent of BMI – by quantifying deviation from values predicted by BMI (e.g. VAT adjusted for BMI, VATadjBMI) and determined associations with cardiometabolic diseases.ResultsCNNs based on two-dimensional projection images enabled near-perfect estimation of VAT, ASAT, and GFAT, with r2 in a holdout testing dataset (r2 = 0.978-0.991). Using the newly derived measures of local adiposity – residualized based on BMI – we note marked heterogeneity in associations with cardiometabolic diseases. Taking presence of type 2 diabetes as an example, VATadjBMI was associated with significantly increased risk (odds ratio per standard deviation increase (OR/SD) 1.49; 95%CI: 1.43-1.55), while ASATadjBMI was largely neutral (OR/SD 1.08; 95%CI: 1.03-1.14) and GFATadjBMI conferred protection (OR/SD 0.75; 95%CI: 0.71-0.79). Similar patterns were observed for coronary artery disease.ConclusionsDeep learning models trained on a simplified MRI input enable near perfect quantification of VAT, ASAT, and GFAT. For any given BMI, measures of local adiposity have variable and divergent associations with cardiometabolic diseases.
Key kinematic features in early training predict performance of adult female mice in a single pellet reaching and grasping task
Authors: Mykins Michael , Espinoza-Wade Eric , An Xu , Lau Billy You Bun , Krishnan Keerthi 
Published: May 2021

AbstractDetailed analyses of overly trained animal models have been long employed to decipher foundational features of skilled motor tasks and their underlying neurobiology. However, initial trial-and-error features that ultimately give rise to skilled, stereotypic movements, and the underlying neurobiological basis of flexibility in learning, to stereotypic movement in adult animals are still unclear. Knowledge obtained from addressing these questions is crucial to improve quality of life in patients affected by movement disorders.We sought to determine if known kinematic parameters of skilled movement in humans could predict learning of motor efficiency in mice during the single pellet reaching and grasping assay. Mice were food restricted to increase motivation to reach for a high reward food pellet. Their attempts to retrieve the pellet were recorded for 10 minutes a day for continuous 4 days. Individual successful and failed reaches for each mouse were manually tracked using Tracker Motion Analysis Software to extract time series data and kinematic features. We found the number of peaks and time to maximum velocity were strong predictors of individual variation in failure and success, respectively. Overall, our approach validates the use of select kinematic features to describe fine motor skill acquisition in mice and establishes peaks and time to maximum velocity as predictive measure of natural variation in motion efficiency in mice. This manually curated dataset, and kinematic parameters would be useful in comparing with pose estimation generated from deep learning approaches.
Inhibition-Dominated Rich-Club Shapes Dynamics in Cortical Microcircuits
Authors: Hafizi Hadi , Nigam Sunny , Barnathan Josh , Ren Naixin , Stevenson Ian H , Masmanidis Sotiris C , Newman Ehren L , Sporns Olaf , Beggs John M 
Published: May 2021

ABSTRACTFunctional networks of cortical neurons contain highly interconnected hubs, forming a rich-club structure. However, the cell type composition within this distinct subnetwork and how it influences large-scale network dynamics is unclear. Using spontaneous activity recorded from hundreds of cortical neurons in orbitofrontal cortex of awake behaving mice and from organotypic cultures, we show that the rich-club is disproportionately composed of inhibitory neurons, and that inhibitory neurons within the rich-club are significantly more synchronous than other neurons. At the population level, neurons in the rich-club exert higher than expected Granger causal influence on overall population activity at a broad range of frequencies compared to other neurons. Finally, neuronal avalanche duration is significantly correlated with the fraction of rich neurons that participate in the avalanche. Together, these results suggest an unexpected role of a highly connected, inhibition-rich subnetwork in driving and sustaining activity in local cortical networks.SIGNIFICANCE STATEMENTIt is widely believed that the relative abundance of excitatory and inhibitory neurons in cortical circuits is roughly 4:1. This relative abundance has been widely used to construct numerous cortical network models. Here we show that contrary to this notion, a sub-network of highly connected hub neurons (rich-club) consists of a higher abundance of inhibitory neurons compared to that found in the entire network or the non-rich subnetwork. Inhibitory hub neurons contribute to higher synchrony within the rich club compared to the rest of the network. Strikingly, higher activation of the inhibition-dominated rich club strongly correlates with longer avalanches in cortical circuits. Our findings reveal how network topology combined with cell-type specificity orchestrates population wide activity in cortical microcircuits.
Accuracy and Power Analysis of Social Networks Built From Count Data
Authors: Hart Jordan D. A. , Franks Daniel W. , Brent Lauren J. N. , Weiss Michael N. 
Published: May 2021

AbstractPower analysis is used to estimate the probability of correctly rejecting a null hypothesis for a given statistical model and dataset. Conventional power analyses assume complete information, but the stochastic nature of behavioural sampling can mean that true and estimated networks are poorly correlated. Power analyses do not currently take the effect of sampling into account. This could lead to inaccurate estimates of statistical power, potentially yielding misleading results.Here we develop a method for computingnetwork correlation: the correlation between an estimated social network and its true network, using a Gamma-Poisson model of social event rates for networks constructed from count data. We use simulations to assess how the level of network correlation affects the power of nodal regression analyses. We also develop a generic method of power analysis applicable to any statistical test, based on the concept of diminishing returns.We demonstrate that our network correlation estimator is both accurate and moderately robust to its assumptions being broken. We show that social differentiation, mean social event rate, and the harmonic mean of sampling times positively impacts the strength of network correlation. We also show that the required level of network correlation to achieve a given power level depends on many factors, but that 0.80 network correlation usually corresponds to around 0.80 power for nodal regression in ideal circumstances.We provide guidelines for using our network correlation estimator to verify the accuracy of networks built from count data, and to conduct power analysis. This can be used prior to data collection, in post hoc analyses, or even for subsetting networks in dynamic network analysis. The network correlation estimator and custom power analysis methods have been made available as an R package.
HIPPOCAMPAL DNA METHYLATION, DNAm AGE AND SPATIAL MEMORY PERFORMANCE IN YOUNG AND OLD RATS
Authors: Chiavellini Priscila , Lehmann Marianne , Canatelli Mallat Martina , Zoller Joseph A. , Herenu Claudia B. , Morel Gustavo R. , Horvath Steve , Goya Rodolfo G. 
Published: May 2021

ABSTRACTIn humans and rats, aging is associated with a progressive deterioration of spatial learning and memory. These functional alterations are correlated with morphological and molecular changes in the brain, particularly in the hippocampus. Here, we assessed the age-related changes in the DNA methylation (DNAm) landscape in the rat hippocampus and assessed the correlation of spatial memory performance with hippocampal DNAm age in young (2.6 mo.) and old (26.6 mo.) rats.Spatial memory performance was assessed with a modified version of the Barnes maze test. In order to evaluate learning ability as well as spatial memory retention, we assessed the time spent (permanence) by animals in goal sector 1 (GS1) and 3 (GS3) when the escape box was removed. The rat pan-tissue clock was applied to DNA methylation profiles of hippocampal tissue. The bisulfite converted genomic DNA was analyzed by Illumina Infinium HorvathMammalMethylChip40. The Horvath Mammal Methyl Chip40 assay provides quantitative measurements of DNA methylation for 22528 CpG dinucleotides that map to the Rattus norvegicus UCSC 6.0 genome. An enrichment pathway analysis revealed that neuron fate commitment, brain development, and central nervous system development were processes whose underlying genes were enriched in positively methylated CpGs. In the old rat hippocampi, the methylation levels of CpGs proximal to transcription factors associated with genes Pax5, Lbx1, Nr2f2, Hnf1b, Zic1, Zic4, Hoxd9; Hoxd10, Gli3, Gsx1 and Lmx1b, and Nipbl showed a significant regression with spatial memory performance. Regression analysis of different memory performance indices with hippocampal DNAm age was significant when data from young and old rats were taken together. The above results suggest that age-related hypermethylation of certain gene families, like Zic and Gli, may play a causal role in the decline in spatial memory in old rats. Hippocampal DNAm age seems to be a reliable index of spatial memory performance in young and old rats.
Top-down generation of low-resolution representations improves visual perception and imagination
Authors: Bi Zedong , Tian Liang 
Published: May 2021

AbstractPerception or imagination requires top-down signals from high-level cortex to primary visual cortex (V1) to reconstruct or simulate the representations bottom-up stimulated by the seen images. Interestingly, top-down signals in V1 have lower spatial resolution than bottom-up representations. It is unclear why the brain uses low-resolution signals to reconstruct or simulate high-resolution representations. By modeling the top-down pathway of the visual system using the decoder of variational auto-encoder (VAE), we reveal that low-resolution top-down signals can better reconstruct or simulate the information contained in the sparse activities of V1 simple cells, which facilitates perception and imagination. This advantage of low-resolution generation is related to facilitating high-level cortex to form geometry-respecting representations observed in experiments. Moreover, our finding inspires a simple artificial- intelligence (AI) technique to significantly improve the generation quality and diversity of sketches, a style of drawings made of thin lines. Specifically, instead of directly using original sketches, we use blurred sketches to train VAE or GAN (generative adversarial network), and then infer the thin-line sketches from the VAE- or GAN- generated blurred sketches. Collectively, our work suggests that low-resolution top-down generation is a strategy the brain uses to improve visual perception and imagination, and advances sketch-generation AI techniques.
Ankle and Toe Brachial Index Extraction from Clinical Reports For Peripheral Artery Disease Identification: Unlocking Clinical Data through Novel Methods
Authors: Friberg Julia E. , Qazi Abdul H. , Boyle Brenden , Franciscus Carrie , Vaughan-Sarrazin Mary , Westerman Dax , Patterson Olga V. , Parr Sharidan K. , Matheny Michael E. , Arya Shipra , Smolderen Kim G. , Lund Brian C. , Gobbel Glenn T. , Girotra Saket 
Published: May 2021

ABSTRACTImportanceDespite its high prevalence and poor outcomes, research on peripheral artery disease (PAD) remains limited due to the poor accuracy of billing codes for identifying PAD in health systems.ObjectiveDesign a natural language processing (NLP) system that can extract ankle brachial index (ABI) and toe brachial index (TBI) values and evaluate the performance of extracted ABI/TBI values to identify patients with PAD in the Veterans Health Administration (VHA).Design, Setting, ParticipantsFrom a corpus of 392,244 ABI test reports at 94 VHA facilities during 2015-2017, we selected a random sample of 800 documents for NLP development. Using machine learning, we designed the NLP system to extract ABI and TBI values and laterality (right or left).Performance was optimized through sequential iterations of 10-fold cross validation and error analysis on 3 sets of 200 documents each, and tested on a final, independent set of 200 documents.Performance of NLP-extracted ABI and TBI values to identify PAD in a random sample of Veterans undergoing ABI testing was compared to structured chart review.ExposureABI ≤0.9, or TBI ≤0.7 in either right or left limb was used to define PAD at the patient-levelMain OutcomePrecision (or positive predictive value), recall (or sensitivity), F-1 measure (overall measure of accuracy, defined as harmonic mean of precision and recall)ResultsThe NLP system had an overall precision of 0.85, recall of 0.93 and F1-measure of 0.89 to correctly identify ABI/TBI values and laterality. The F-1 measure was similar for both ABI and TBI (0.88 to 0.91). Recall was higher for ABI (0.95 to 0.97) while precision was higher for TBI (0.94 to 0.95). Among 261 patients with ABI testing (49% with PAD), the NLP system achieved a positive predictive value of 92.3%, sensitivity of 83.1% and specificity of 93.1% to identify PAD when compared to a structured chart review.ConclusionWe have successfully developed and validated an NLP system to extract ABI and TBI values which can be used to accurately identify PAD within the VHA. Our findings have broad implications for PAD research and quality improvement efforts in large health systems.