Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing fetal alcohol spectrum disorders diagnosis with a classifier based on the intracerebellar gradient of volumetric undersizing

In fetal alcohol spectrum disorders (FASD), brain growth deficiency is a hallmark of subjects both with fetal alcohol syndrome (FAS) and with non‐syndromic FASD (NS‐FASD, i.e., those without specific diagnostic features). However, although the cerebellum was suggested to be more severely undersized than the rest of the brain, it has not yet been given a specific place in the FASD diagnostic criteria where neuroanatomical features still count for little if anything in diagnostic specificity. We applied a combination of cerebellar segmentation tools on a 1.5 T 3DT1 brain MRI dataset from a monocentric population of 89 FASD (52 FAS, 37 NS‐FASD) and 126 typically developing controls (6–20 years old), providing 8 volumes: cerebellum, vermis and 3 lobes (anterior, posterior, inferior), plus total brain volume. After adjustment of confounders, the allometric scaling relationship between these cerebellar volumes (Vi) and the total brain or cerebellum volume (Vt) was fitted (Vi = bVta), and the effect of group (FAS, control) on allometric scaling was evaluated. We then estimated for each cerebellar volume in the FAS population the deviation from the typical scaling (vDTS) learned in the controls. Lastly, we trained and tested two classifiers to discriminate FAS from controls, one based on the total cerebellum vDTS only, the other based on all the cerebellar vDTS, comparing their performance both in the FAS and the NS‐FASD group. Allometric scaling was significantly different between FAS and control group for all the cerebellar volumes (p < .001). We confirmed the excess of total cerebellum volume deficit (vDTS = −10.6%) and revealed an antero‐inferior‐posterior gradient of volumetric undersizing in the hemispheres (−12.4%, 1.1%, 2.0%, respectively) and the vermis (−16.7%, −9.2%, −8.6%, repectively). The classifier based on the intracerebellar gradient of vDTS performed more efficiently than the one based on total cerebellum vDTS only (AUC = 92% vs. 82%, p = .001). Setting a high probability threshold for >95% specificity of the classifiers, the gradient‐based classifier identified 35% of the NS‐FASD to have a FAS cerebellar phenotype, compared to 11% with the cerebellum‐only classifier (pFISHER = 0.027). In a large series of FASD, this study details the volumetric undersizing within the cerebellum at the lobar and vermian level using allometric scaling, revealing an anterior‐inferior‐posterior gradient of vulnerability to prenatal alcohol exposure. It also strongly suggests that this intracerebellar gradient of volumetric undersizing may be a reliable neuroanatomical signature of FAS that could be used to improve the specificity of the diagnosis of NS‐FASD.
Reduced loss aversion in value-based decision-making and edge-centric functional connectivity in patients with internet gaming disorder

Abstract Background and aims Impaired value-based decision-making is a feature of substance and behavioral addictions. Loss aversion is a core of value-based decision-making and its alteration plays an important role in addiction. However, few studies explored it in internet gaming disorder patients (IGD). Methods In this study, IGD patients (PIGD) and healthy controls (Con-PIGD) performed the Iowa gambling task (IGT), under functional magnetic resonance imaging (fMRI). We investigated group differences in loss aversion, brain functional networks of node-centric functional connectivity (nFC) and the overlapping community features of edge-centric functional connectivity (eFC) in IGT. Results PIGD performed worse with lower average net score in IGT. The computational model results showed that PIGD significantly reduced loss aversion. There was no group difference in nFC. However, there were significant group differences in the overlapping community features of eFC1. Furthermore, in Con-PIGD, loss aversion was positively correlated with the edge community proﬁle similarity of the edge2 between left IFG and right hippocampus at right caudate. This relationship was suppressed by response consistency3 in PIGD. In addition, reduced loss aversion was negatively correlated with the promoted bottom-to-up neuromodulation from the right hippocampus to the left IFG in PIGD. Discussion and conclusions The reduced loss aversion in value-based decision making and their related edge-centric functional connectivity support that the IGD showed the same value-based decision-making deficit as the substance use and other behavioral addictive disorders. These findings may have important significance for understanding the definition and mechanism of IGD in the future.
A Caenorhabditis elegans Male Pheromone Feminizes Germline Gene Expression in Hermaphrodites and Imposes Life-History Costs

Abstract Sex pheromones not only improve the reproductive success of the recipients, but also impose costs, such as a reduced life span. The underlying mechanisms largely remain to be elucidated. Here, we show that even a brief exposure to physiological amounts of the dominant Caenorhabditis elegans male pheromone, ascr#10, alters the expression of thousands of genes in hermaphrodites. The most dramatic effect on the transcriptome is the upregulation of genes expressed during oogenesis and the downregulation of genes associated with male gametogenesis. This result reveals a way in which social signals help to resolve the inherent conflict between spermatogenesis and oogenesis in a simultaneous hermaphrodite, presumably to optimally align reproductive function with the presence of potential mating partners. We also found that exposure to ascr#10 increased the risk of persistent intestinal infections in hermaphrodites due to pathological pharyngeal hypertrophy. Thus, our study reveals ways in which the male pheromone can not only have beneficial effects on the recipients’ reproduction, but also cause harmful consequences that reduce life span.
The impact of flavored e-cigarette bans on e-cigarette use in three US states

Introduction Beginning in 2019, several U.S. states implemented temporary or permanent bans on the sale of flavored e-cigarettes. This study examined the impact of flavor bans on adult e-cigarette use in Washington, New Jersey, and New York. Methods Adults who used e-cigarettes at least once a week before the flavor bans were recruited online. Respondents reported their e-cigarette use, primarily used flavor, and ways of obtaining e-cigarettes before and after the bans. Descriptive statistics and multinomial logistic regression models were applied. Results After the ban, 8.1% of respondents (N=1624) quit using e-cigarettes, those primarily used banned menthol or other flavors declined from 74.4% to 50.8, those using tobacco-flavored declined from 20.1% to 15.6%, and those using non-flavored increased from 5.4% to 25.4%. More frequent e-cigarette use and smoking cigarettes were associated with being less likely to quit e-cigarettes and more likely to use banned flavors. Of those primarily using banned flavors, 45.1% obtained e-cigarettes from in-state stores, 31.2% from out-of-state stores, 32% from friends, family, or others, 25.5% from Internet/mail sellers, 5.2% from illegal sellers, 4.2% mixed flavored e-liquids themselves, and 6.9% stocked up on e-cigarettes before the ban. Conclusions Most respondents continued to use e-cigarettes with banned flavors post-ban. Compliance of local retailers with the ban was not high, and many respondents obtained banned-flavor e-cigarettes through legal channels. However, the significant increase in the use of non-flavored e-cigarettes post-ban suggests that these may serve as a viable alternative among those who used previously used banned or tobacco flavors.
Utility of multimodal longitudinal imaging data for dynamic prediction of cardiovascular and renal disease: the CARDIA study

Background: Medical examinations contain repeatedly measured data from multiple visits, including imaging variables collected from different modalities. However, the utility of such data for the prediction of time-to-event is unknown, and only a fraction of the data is typically used for risk prediction. We hypothesized that multimodal longitudinal imaging data could improve dynamic disease prognosis of cardiovascular and renal disease (CVRD). Methods: In a multi-centered cohort of 5114 CARDIA participants, we included 166 longitudinal imaging variables from five imaging modalities: Echocardiography (Echo), Cardiac and Abdominal Computed Tomography (CT), Dual-Energy X-ray Absorptiometry (DEXA), Brain Magnetic Resonance Imaging (MRI) collected from young adulthood to mid-life over 30 years (1985-2016) to perform dynamic survival analysis of CVRD events using machine learning dynamic survival analysis (Dynamic-DeepHit, LTRCforest, and Extended Cox for Time-varying Covariates). Risk probabilities were continuously updated as new data were collected. Model performance was assessed using integrated AUC and C-index and compared to traditional risk factors. Results: Longitudinal imaging data, even when being irregularly collected with high missing rates, improved CVRD dynamic prediction (+0.03 integrated AUC gain, up to 0.05 C-index gain compared to traditional risk factors) from young adulthood followed up to midlife. Among imaging variables, Echo and CT variables contributed significantly to improved risk estimation. Echo measured in early adulthood predicted midlife CVRD risks almost as well as Echo measured 10-15 years later (0.01 C-index difference). The most recent CT exam provided the most accurate prediction for short-term risk estimation. Brain MRI markers provided additional information from cardiac Echo and CT variables that led to a slightly improved prediction. Conclusions: Longitudinal multimodal imaging data readily collected from follow-up exams can improve CVRD dynamic prediction. Echocardiography measured early can provide a good long-term risk estimation, while CT/calcium scoring variables carry atherosclerotic signatures that benefit more immediate risk assessment starting in middle-age.
Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure

BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied. METHODS: This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex. RESULTS: There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores. CONCLUSIONS: The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.
Sensitivity schemes for dynamic glucose‐enhanced magnetic resonance imaging to detect glucose uptake and clearance in mouse brain at 3 T

The article from this special issue was previously published in NMR In Biomedicine , Volume 35, Issue 3, 2022. For completeness we are including the title page of the article below. The full text of the article can be read in Issue 35:3 on Wiley Online Library: https://doi.org/10.1002/nbm.4640
Linear projection‐based chemical exchange saturation transfer parameter estimation

Isolated evaluation of multiparametric in vivo chemical exchange saturation transfer (CEST) MRI often requires complex computational processing for both correction of B0 and B1 inhomogeneity and contrast generation. For that, sufficiently densely sampled Z‐spectra need to be acquired. The list of acquired frequency offsets largely determines the total CEST acquisition time, while potentially representing redundant information. In this work, a linear projection‐based multiparametric CEST evaluation method is introduced that offers fast B0 and B1 inhomogeneity correction, contrast generation and feature selection for CEST data, enabling reduction of the overall measurement time. To that end, CEST data acquired at 7 T in six healthy subjects and in one brain tumor patient were conventionally evaluated by interpolation‐based inhomogeneity correction and Lorentzian curve fitting. Linear regression was used to obtain coefficient vectors that directly map uncorrected data to corrected Lorentzian target parameters. L1‐regularization was applied to find subsets of the originally acquired CEST measurements that still allow for such a linear projection mapping. The linear projection method allows fast and interpretable mapping from acquired raw data to contrast parameters of interest, generalizing from healthy subject training data to unseen healthy test data and to the tumor patient dataset. The L1‐regularization method shows that a fraction of the acquired CEST measurements is sufficient to preserve tissue contrasts, offering up to a 2.8‐fold reduction of scan time. Similar observations as for the 7‐T data can be made for data from a clinical 3‐T scanner. Being a fast and interpretable computation step, the proposed method is complementary to neural networks that have recently been employed for similar purposes. The scan time acceleration offered by the L1‐regularization (“CEST‐LASSO”) constitutes a step towards better applicability of multiparametric CEST protocols in a clinical context.
Intelligent framework for brain tumor grading using advanced feature analysis

ABSTRACT The analysis of digital pathology images, catalyzes research and automate diagnosis for improving clinical care. Regardless of the advances in high-resolution and speedy scanning devices, the clinical analysis of Whole Slide Image (WSI) remains mainly as the work of an image analyst. The complications when working on these images are primarily, its enormous image size, as well as the heterogeneous nature of tissue regions. These reasons may lead to inefficient computer analysis. We propose a comprehensive algorithm to analyse, these large images by using small tile image analysis. The small tile images can be learnt individually in detail, rather than processing the whole high resolution WSI. The analysis of these small tile images helps us configure, the localised pathology image characteristics. The work flow involves extracting Perceptual Features (PF), Fourier Local Binary Patterns (FLBP), Histogram, Gabor and Gray Level Co-occurrence Matrix (GLCM) features from the tiled WSI. Then, the multidimensional feature set is provided to the classifiers like K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR) for performance analysis. We evaluated our predictive models by automatically classifying image datasets (186 and 124) of brain tumour cases, using 10 fold cross validation. The model classified the images into Low Grade Glioma (LGG) and Glioblastoma Multiforme (GBM). It gives the highest accuracy of 93.5% for a feature combination of PF+FLBP+GLCM+GABOR with linear Support Vector Machine (linSVM) and Area under the Curve (AUC) of 0.9741 for the 186 image dataset. Also linSVM gives an accuracy of 96.8% with various feature combinations for the 124 image dataset. We did an exhaustive study on the performance of various sets of texture descriptors and classification models. This resulted in finding an optimised classification approach with a combined feature set that outperformed the original feature set classification. The proposed work is unique because it combines a wide range of feature extracting methods and an exhaustive list of classifiers showcasing the best method. The idea that, rather than selecting a region of interest from WSI for grade designation, all the regions in a slide are considered. This enables the involvement of each tile in making the final diagnostic decision, considering the heterogeneous nature of WSI. Our work can be extended for different tumour subtypes. Also, the experiment can be scaled with different sample sizes and finally the method works without a pathologist input.
Association of Baseline Cerebrovascular Reactivity and Development of Enlarged Perivascular Spaces in the Basal Ganglia

Background: Increasing evidence suggests that enlarged perivascular spaces (ePVS) are associated with cognitive dysfunction in aging. However, the etiology of ePVS remains unknown. Here we tested the possibility that baseline cerebrovascular dysfunction, as measured by an MRI measure of cerebrovascular reactivity (CVR), contributes to the later development of ePVS.Methods: A total of 79 cognitively normal, older adults (46 women, age range 60-84) were recruited to undergo MRI scanning at baseline and 50 participants returned for a follow-up scan approximately 2.5 years later. ePVS were counted in the basal ganglia, centrum semiovale, midbrain, and hippocampus. CVR, an index of the vasodilatory capacity of cerebral small vessels, was assessed using carbon-dioxide inhalation while acquiring blood oxygen-level dependent (BOLD) MR images.Results: Low baseline CVR values in the basal ganglia were associated with increased follow-up ePVS counts in the basal ganglia after controlling for age, sex, and baseline ePVS values (coefficient estimate (SE) = -15.87 (3.92), p < 0.001, 95% confidence interval [CI] -23.68 to -8.05). This effect remained significant after accounting for self-reported risk factors of cerebral small vessel disease (cSVD) (coefficient estimate (SE) = -15.03 (4.00), p < 0.001, CI -23.02 to -7.05) and neuroimaging markers of cSVD (coefficient estimate (SE) = -13.99 (4.02), p < 0.001, CI -22.03 to -5.95). Conclusions: Our results demonstrate that low baseline CVR is a risk factor for later development of ePVS. MRI-based CVR may represent a promising biomarker of cSVD.
Design of multi-epitope vaccine candidate against Brucella type IV secretion system (T4SS)

Objective the dominant epitopes of T and B cells of VirB8 and VirB10 of Brucella type IV. Secretory systems were predicted and analyzed by bioinformatics, and then multi-epitope vaccines were constructed by reverse vaccination. Methods The amino acid sequences of VirB8 and VirB10 were obtained from the UniProt database, T and B cell dominant epitopes were selected and supplemented with adjuvants to construct a multi-epitope vaccine, SOPMA and RoseTTAFold were applied to predict secondary and tertiary structures respectively for immune simulations. Finally, molecular dynamics simulations of iMEV-TLR4 were then performed. Results one cytotoxic T lymphocyte (CTL) epitope, five helper T lymphocyte (HTL) epitopes, two linear B cell epitopes and three conformational B cell epitopes were obtained in VirB8. 1 cytotoxic T lymphocyte (CTL) epitope, 4 helper T lymphocyte (HTL) epitopes, 4 linear B cell epitopes, and 3 conformational B cell epitopes were obtained in VirB10. The resulting multiepitope vaccine is a protein with good antigenicity, hydrophilicity, and stability. Conclusion a novel brucella multiepitope vaccine was designed by reverse vaccination, and this study provides a theoretical basis for further research.
Morphological and Molecular Characterization of Diplogasteroides sp., a Cryptic Population of the Haslacheri Group (Diplogastridae), and Parasitorhabditis terebranus (Rhabditidae) from Korea

Abstract Diplogasteroides sp. , a cryptic population of D. haslacheri, and Parasitorhabditis terebranus were reported from the frass of Monochamus alternatus galleries in dead Pinus thunbergii for the first time in Korea. Females and males are morphologically characterized and their linked DNA barcodes (18S-rRNA, 28S-rRNA, ITS-rRNA and COI) supplied. Females and males of the two species from Korea conform to the original species descriptions from Europe and the USA, with variations in a few details in morphometrics. Specifically, Diplogasteroides sp. is morphologically very similar to D. haslacheri. However, it cannot be designated as D. haslacheri due to the existence of cryptic species complex within the haslacheri group (D. haslacheri, D. asiaticus, D. nix, D. andrassyi, and D. carinthiacus), a condition requiring hybridization studies to test species identity within the group. Based on analysis of COI sequences, differences among these cryptic species are evident. Thus, in addition to hybridization tests, the COI might be a powerful DNA barcoding marker for the precise identification of these cryptic species within the genus. Additionally, this is the first molecular characterization of P. terebranus, and the species is herein recorded for the first time outside its type locality.
Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children

We tested the performance of a machine learning (ML) algorithm based on signs and symptoms for the diagnosis of RSV infection or pertussis in the first year of age to support clinical decisions and provide timely data for public health surveillance. We used data from a retrospective case series of children in the first year of life investigated for acute respiratory infections in the emergency room from 2015 to 2020. We collected data from PCR laboratory tests for confirming pertussis or RSV infection, clinical symptoms, and routine blood testing results, which were used for the algorithm development. We used a LightGBM model to develop 2 sets of models for predicting pertussis and RSV infection: for each type of infection, we developed one model trained with the combination of clinical symptoms and results from routine blood test (white blood cell count, lymphocyte fraction and C-reactive protein), and one with symptoms only. All analyses were performed using Python 3.7. 4 with Shapley values (Shap values) visualization package for predictor visualization. The performance of the models was assessed through confusion matrices. The models were developed on a dataset of 599 children. The recall for the pertussis model combining symptoms and routine laboratory tests was 0.72, and 0.74 with clinical symptoms only. For RSV infection, recall was 0.68 with clinical symptoms and laboratory tests and 0.71 with clinical symptoms only. The F1 score for the pertussis model was 0.72 in both models, and, for RSV infection, it was 0.69 and 0.75. ML models can support the diagnosis and surveillance of infectious diseases such as pertussis or RSV infection in children based on common symptoms and laboratory tests. ML-based clinical decision support systems may be developed in the future in large networks to create accurate tools for clinical support and public health surveillance.