bluuebunny/crossref_metadata_2025_split huggingface dataset:
Klebsiella pneumoniaeexhibiting a phenotypic hyper-splitting phenomenon including the formation of small colony variants
Authors: Doğan Eyüp , Sydow Katharina , Heiden Stefan E. , Eger Elias , Wassilew Georgi , Proctor Richard A. , Bohnert Jürgen A. , Idelevich Evgeny A. , Schaufler Katharina , Becker Karsten 
Published: January 2024

AbstractIn this study, we characterized aKlebsiella pneumoniaestrain in a patient with shrapnel hip injury, which resulted in multiple phenotypic changes, including the formation of a small colony variant (SCV) phenotype. Although already described since the 1960s, there is little knowledge about SCV phenotypes inEnterobacteriaceae. The formation of SCVs has been recognized as a bacterial strategy to evade host immune responses and compromise the efficacy of antimicrobial therapies, leading to persistent and recurrent courses of infections. In this case, 14 different, clonally identical resisto- and morpho-types were distinguished from the patient’s urine and tissue samples. Whole genome sequencing revealed theK. pneumoniaehigh-risk clonal lineage belonging to sequence type 147. Subculturing the SCV colonies consistently resulted in the reappearance of the initial SCV phenotype and three stable normal-sized phenotypes with distinct morphological characteristics. Additionally, an increase in resistance was observed over time in isolates that shared the same colony appearance. Our findings highlight the complexity of bacterial behavior by revealing a case of phenotypic “hyper-splitting” in aK. pneumoniaeSCV and its potential clinical significance.
Neuronal subtypes and connectivity of the adult mouse paralaminar amygdala
Authors: Saxon David , Alderman Pia J , Sorrells Shawn F , Vicini Stefano , Corbin Joshua G 
Published: January 2024

AbstractThe paralaminar nucleus of the amygdala (PL) is comprised of neurons which exhibit delayed maturation. PL neurons are born during gestation but mature during adolescent ages, differentiating into excitatory neurons. The PL is prominent in the adult amygdala, contributing to its increased neuron number and relative size compared to childhood. However, the function of the PL is unknown, as the region has only recently begun to be characterized in detail. In this study, we investigated key defining features of the adult PL; the intrinsic morpho-electric properties of its neurons, and its input and output connectivity. We identify two subtypes of excitatory neurons in the PL based on unsupervised clustering of electrophysiological properties. These subtypes are defined by differential action potential firing properties and dendritic architecture, suggesting divergent functional roles. We further uncover major axonal inputs to the adult PL from the main olfactory network and basolateral amygdala. We also find that axonal outputs from the PL project reciprocally to major inputs, and to diverse targets including the amygdala, frontal cortex, hippocampus, hypothalamus, and brainstem. Thus, the adult PL is centrally placed to play a major role in the integration of olfactory sensory information, likely coordinating affective and autonomic behavioral responses to salient odor stimuli.Significance StatementMammalian amygdala development includes a growth period from childhood to adulthood, believed to support emotional and social learning. This amygdala growth is partly due to the maturation of neurons during adolescence in the paralaminar amygdala. However, the functional properties of these neurons are unknown. In our recent studies, we characterized the paralaminar amygdala in the mouse. Here, we investigate the properties of the adult PL in the mouse, revealing the existence of two neuronal subtypes that may play distinct functional roles in the adult brain. We further reveal the brain-wide input and output connectivity of the PL, indicating that the PL combines olfactory cues for emotional processing and delivers information to regions associated with reward and autonomic states.
The novel linkage betweenFuzandGpr161genes regulates sonic hedgehog signaling during mouse embryonic development
Authors: Kim Sung-Eun , Kim Hyun Yi , Wlodarczyk Bogdan J. , Finnell Richard H. 
Published: January 2024

AbstractSonic hedgehog (Shh) signaling regulates embryonic morphogenesis utilizing primary cilia, the cell antenna acting as a signaling hub. Fuz, an effector of planar cell polarity (PCP) signaling, involves Shh signaling via cilia formation, while the G protein-coupled receptor 161 (Gpr161) is a negative regulator of Shh signaling. The range of phenotypic malformations observed in mice bearing mutations in either of these two genes is similar; however, their functional relations have not been previously explored. This study identified the genetic and biochemical link between Fuz and Gpr161 in mouse embryonic development.Fuzwas genetically epistatic toGpr161via Shh signaling during mouse embryonic development. The FUZ biochemically interacted with GPR161, and Fuz regulated Gpr161 ciliary trafficking via β-arrestin2. Our study suggested the novel Gpr161-Fuz axis that regulates Shh signaling during mouse embryonic development.Summary statementThis study illuminates the novel genetic and biochemical linkages between Fuz and Gpr161 to regulate sonic hedgehog signaling during mouse embryonic development.
Cell-type-specific origins of locomotor rhythmicity at different speeds in larval zebrafish
Authors: Agha Moneeza A. , Kishore Sandeep , McLean David L. 
Published: January 2024

AbstractDifferent speeds of locomotion require heterogeneous spinal populations, but a common mode of rhythm generation is presumed to exist. Here, we explore the cellular versus synaptic origins of spinal rhythmicity at different speeds by performing electrophysiological recordings from premotor excitatory interneurons in larval zebrafish. Chx10-labeled V2a neurons are divided into at least two morphological subtypes proposed to play distinct roles in timing and intensity control. Consistent with distinct rhythm generating and output patterning functions within the spinal V2a population, we find that descending subtypes are recruited exclusively at slow or fast speeds and exhibit intrinsic cellular properties suitable for rhythmogenesis at those speeds, while bifurcating subtypes are recruited more reliably at all speeds and lack appropriate rhythmogenic cellular properties. Unexpectedly, however, phasic firing patterns during locomotion in rhythmogenic and non-rhythmogenic V2a neurons alike are best explained by distinct modes of synaptic inhibition linked to cell-type and speed. At fast speeds reciprocal inhibition in descending V2a neurons supports phasic firing, while recurrent inhibition in bifurcating V2a neurons helps pattern motor output. In contrast, at slow speeds recurrent inhibition in descending V2a neurons supports phasic firing, while bifurcating V2a neurons rely on reciprocal inhibition alone to pattern output. Our findings suggest cell-type-specific, not common, modes of rhythmogenesis generate and coordinate different speeds of locomotion.
Mathematical Modeling of Drug Delivery from Bi-Layered Core-Shell Polymeric Microspheres
Authors: Ruiz Eduardo A. Chacin , Carpenter Samantha L. , Swindle-Reilly Katelyn E. , Versypt Ashlee N. Ford 
Published: January 2024

AbstractChronic diseases usually require repetitive dosing. Depending on factors such as dosing frequency, mode of administration, and associated costs this can result in poor patient compliance. A better alternative involves using drug delivery systems to reduce the frequency of dosing and extend drug release. However, reaching the market stage is a time-consuming process. In this study, we used two numerical approaches for estimating the values of the critical parameters that govern the diffusion-controlled drug release within bilayered core-shell microspheres. Specifically, the estimated parameters include burst release, drug diffusion coefficient in two polymers, and the drug partition coefficient. Estimating these parameters provides insight for optimizing device design, guiding experimental efforts, and improving the device’s effectiveness. We obtained good agreement between the models and the experimental data. The methods explored in this work apply not only to bi-layered spherical systems but can also be extended to multi-layered spherical systems.
Sonic restoration: Acoustic stimulation enhances soil fungal biomass and activity of plant growth-promoting fungi
Authors: Robinson Jake M. , Cando-Dumancela Christian , Breed Martin F. 
Published: January 2024

AbstractEcosystem restoration interventions often utilise visible elements to restore an ecosystem (e.g., replanting native plant communities and reintroducing lost species). However, using acoustic stimulation to restore ecosystems has received little attention. Our study aimed to (a) investigate the potential effects of acoustic stimulation on fungal biomass and organic matter decomposition, which are both crucial components of ecosystem functioning and (b) assess the effect of acoustic stimulation on the growth rate and sporulation of the plant growth-promoting fungusTrichoderma harzianum. We played 70 dB and 90 dB soundscape treatments (@ 8 kHz) to green and rooibos teabags in compost in experimental mesocosms for 8 hours per day for 14 days to test whether acoustic stimulation affected fungal biomass and organic matter decomposition (a control mesocosm received only ambient sound stimulation <30 dB). We played a monotone soundscape (80 dB @ 8 kHz) over five days toTrichoderma harzianumto assess whether this stimulation affected the growth rate and sporulation of this fungus (control samples received only ambient sound stimulation <30 dB). We show that the acoustic stimulation treatments resulted in increased fungal biomass, greater decomposition, and enhancedT. harzianumconidia (spore) activity compared to controls. These results indicate that acoustic stimulation influences soil fungal growth and potentially facilitates their functioning. A piezoelectric effect and/or fungal mechanoreceptor stimulation are possible mechanisms. Our study highlights the potential of acoustic stimulation to alter important functional soil components, which could, with further development, be harnessed to aid ecosystem restoration.
Do People Reduce Attempts At Lifestyle Change After Initiating Weight Loss Medication? A Review Of Trial Evidence
Authors: Macfarlane Megan , Cooper David , Avenell Alison 
Published: January 2024

ABSTRACTAnti-obesity medications (AOMs) are used with lifestyle change support for weight management for adults with obesity. We investigated whether initiation of AOMs appeared to reduce attempted lifestyle changes. We searched for orlistat, liraglutide, semaglutide, and low-fat reducing diet (LFRD) trials in a trial register, Medline, Embase and systematic reviews from 1990 to June 2023. Weight changes in placebo groups of AOM trials with lifestyle interventions were compared to weight changes in LFRD trials. 16 orlistat, 7 liraglutide, 5 semaglutide, and 7 LFRD trials were included. Weight loss in orlistat placebo groups was 1.1kg less than LFRDs, a statistically non-significant difference, where last observation carried forward values were used for missing data in trials. Adjustment with baseline observation carried forward found 2.6kg less weight lost, a significant difference. Last observation carried forward data for orlistat trials providing completer data found a 0.2kg difference.Liraglutide placebo groups had 2.1kg less weight lost than predicted from LFRD trials. Semaglutide placebo groups had 2.0kg less weight loss.Compensatory reductions in changes to lifestyle behaviours may result following AOM initiation, but this needs to be explored further. Variability in trial design and study populations may contribute to the differences in weight change found.
Lethal phenotypes in Mendelian disorders
Authors: Cacheiro Pilar , Lawson Samantha , Van den Veyver Ignatia B. , Marengo Gabriel , Zocche David , Murray Stephen A. , Duyzend Michael , Robinson Peter N. , Smedley Damian 
Published: January 2024

AbstractEssential genes are those whose function is required for cell proliferation and/or organism survival. A gene’s intolerance to loss-of-function can be allocated within a spectrum, as opposed to being considered a binary feature, since this function might be essential at different stages of development, genetic backgrounds or other contexts. Existing resources that collect and characterise the essentiality status of genes are based on either proliferation assessment in human cell lines, embryonic and postnatal viability evaluation in different model organisms, and gene metrics such as intolerance to variation scores derived from human population sequencing studies. There are also several repositories available that document phenotypic annotations for rare disorders in humans such as the Online Mendelian Inheritance in Man (OMIM) and the Human Phenotype Ontology (HPO) knowledgebases. This raises the prospect of being able to use clinical data, including lethality as the most severe phenotypic manifestation, to further our characterisation of gene essentiality. Here we queried OMIM for terms related to lethality and classified all Mendelian genes into categories, according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. To showcase this curated catalogue of human essential genes, we developed the Lethal Phenotypes Portal (https://lethalphenotypes.research.its.qmul.ac.uk), where we also explore the relationships between these lethality categories, constraint metrics and viability in cell lines and mouse. Further analysis of the genes in these categories reveals differences in the mode of inheritance of the associated disorders, physiological systems affected and disease class. We highlight how the phenotypic similarity between genes in the same lethality category combined with gene family/group information can be used for novel disease gene discovery. Finally, we explore the overlaps and discrepancies between the lethal phenotypes observed in mouse and human and discuss potential explanations that include differences in transcriptional regulation, functional compensation and molecular disease mechanisms. We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.
Artificial intelligence-based automated left ventricular mass quantification from non-contrast cardiac CT scans: correlation with contrast CT and cardiac MRI
Authors: Han Donghee , Shanbhag Aakash , Miller Robert JH , Kwok Nicholas , Waechter Parker , Builoff Valerie , Newby David E , Dey Damini , Berman Daniel S , Slomka Piotr 
Published: January 2024

AbstractBackgroundNon-contrast CT scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (MRI). We assessed the feasibility of LV mass estimation from standard, ECG-gated, non-contrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and cardiac MRI.MethodsWe enrolled consecutive patients who underwent coronary CTA, which included non-contrast CT calcium scanning and contrast CTA, and cardiac MRI. The median interval between coronary CTA and MRI was 22 days (IQR: 3-76). We utilized an nn-Unet AI model that automatically segmented non-contrast CT structures. AI measurement of LV mass was compared to contrast CTA and MRI.ResultsA total of 316 patients (Age: 57.1±16.7, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r=0.84, p<0.001), with no significant differences (136.5±55.3 vs. 139.6±56.9 g, p=0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to MRI, measured LV mass was higher with AI (136.5±55.3 vs. 127.1±53.1 g, p<0.001). There was an excellent correlation between AI and MRI (r=0.85, p<0.001), with a small bias (−9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and MRI, or AI and MRI.ConclusionsThe AI-based automated estimation of LV mass from non-contrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and MRI.
Methylation profiles at birth linked to early childhood obesity
Authors: Lariviere Delphine , Craig Sarah J.C. , Paul Ian M. , Hohman Emily E. , Savage Jennifer S. , Wright Robert O. , Chiaromonte Francesca , Makova Kateryna D. , Reimherr Matthew L. 
Published: January 2024

AbstractChildhood obesity represents a significant global health concern and identifying risk factors is crucial for developing intervention programs. Many ‘omics’ factors associated with the risk of developing obesity have been identified, including genomic, microbiomic, and epigenomic factors. Here, using a sample of 48 infants, we investigated how the methylation profiles in cord blood and placenta at birth were associated with weight outcomes (specifically, conditional weight gain, body mass index, and weight-for-length ratio) at age six months. We characterized genome-wide DNA methylation profiles using the Illumina Infinium MethylationEpic chip, and incorporated information on child and maternal health, and various environmental factors into the analysis. We used regression analysis to identify genes with methylation profiles most predictive of infant weight outcomes, finding a total of 23 relevant genes in cord blood and 10 in placenta. Notably, in cord blood, the methylation profiles of three genes (PLIN4, UBE2F,andPPP1R16B) were associated with all three weight outcomes, which are also associated with weight outcomes in an independent cohort suggesting a strong relationship with weight trajectories in the first six months after birth. Additionally, we developed a Methylation Risk Score (MRS) that could be used to identify children most at risk for developing childhood obesity. While many of the genes identified by our analysis have been associated with weight-related traits (e.g., glucose metabolism, BMI, or hip-to-waist ratio) in previous genome-wide association and variant studies, our analysis implicated several others, whose involvement in the obesity phenotype should be evaluated in future functional investigations.
PrimateAI-3D outperforms AlphaMissense in real-world cohorts
Authors: Parry David A. , Bosc Tom , Hamp Tobias , Fiziev Petko P. , Sharma Abhishek , Kassam Irfahan , McRae Jeremy , Farh Kyle Kai-How 
Published: January 2024

AbstractAccurately predicting the impact of genetic variants is essential for interpreting genomic data, yet no consensus exists on how to measure classifier performance. We prepared the most comprehensive set of benchmarks to date and applied them to the recently published models PrimateAI-3D and AlphaMissense. PrimateAI-3D outperforms AlphaMissense on rare-disease cohort and biobank benchmarks, indicating that performance on clinical databases orin vitroassays does not reliably generalize to real-world cohorts.
Brain morphology mediating the effect of genetic risk variants on Alzheimer’s disease
Authors: Breddels Esmee M. , Snihirova Yelyzaveta , Pishva Ehsan , Gülöksüz Sinan , Blokland Gabriëlla A.M. , Luykx Jurjen , Andreassen Ole A. , Linden David E.J. , van der Meer Dennis ,   Alzheimer’s Disease Neuroimaging Initiative
Published: January 2024

AbstractINTRODUCTIONLate-onset Alzheimer’s disease (LOAD) has been associated with alterations in the morphology of multiple brain structures and it is likely that disease mechanisms differ between brain regions. Coupling genetic determinants of LOAD with measures of brain morphology could localize and identify primary causal neurobiological pathways.METHODSMediation and Mendelian randomization (MR) analysis were performed using common genetic variation, T1 MRI and clinical data collected by UK Biobank and Alzheimer’s Disease Neuroimaging Initiative.RESULTSThickness of the entorhinal cortex and the volumes of the hippocampus, amygdala, choroid plexus and inferior lateral ventricle mediated the effect ofAPOEε4 on LOAD. MR showed that a thinner entorhinal cortex, a smaller hippocampus and amygdala, and a larger volume of the choroid plexus and inferior lateral ventricles, increased the risk of LOAD as well as vice versa.DISCUSSIONCombining neuroimaging and genetic data can give insight into the causal neuropathological pathways of LOAD.
Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker Identification
Authors: Climente-González Héctor , Oh Min , Chajewska Urszula , Hosseini Roya , Mukherjee Sudipto , Gan Wei , Traylor Matthew , Hu Sile , Fatemifar Ghazaleh , Del Villar Paul Pangilinan , Vernet Erik , Koelling Nils , Du Liang , Abraham Robin , Li Chuan , Howson Joanna M. M. 
Published: January 2024

AbstractCardiovascular diseases (CVD), primarily coronary heart disease and stroke, rank amongst the leading causes of long-term disability and mortality. Providing accurate disease risk predictions and identifying genes associated with CVD are crucial for prevention, early intervention, and the development of novel medications.The recent availability of UK Biobank Proteomics data enables the investigation of the blood proteome and its association with a wide variety of diseases. We employed the Explainable Boosting Machine (EBM), an interpretable machine learning model, for CVD risk prediction. The EBM model using proteomics outperforms traditional clinical models with an AUROC of 0.767 and an AUPRC of 0.2405. Adding clinical features further improves the AUROC to 0.785 and the AUPRC to 0.2835. Our models demonstrate consistent performance across sexes and ethnicities.While most prior studies using proteomics data for disease prediction have primarily focused on maximizing the accuracy at the population level, our model provides additional enriched insights into individualized disease risk predictions and in-depth biological insights into biomarkers. Our analysis also uncovers nonlinear risks linked to varying feature values. We further corroborate our findings using statistical approaches and evidence from the literature.In conclusion, we present a highly accurate and explanatory framework for proteomics data analysis, offering comprehensive and in-depth molecular and clinical insights. Our findings support future approaches that prioritize individualized disease risk prediction and the identification of target genes for drug development.