bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brain inflammation and cognitive decline induced by spinal cord injury can be reversed by spinal cord cell transplants
Authors: Delarue Quentin , Robac Amandine , Duclos Célia , Pileyre Baptiste , Raimond Clémence , Neveu Pauline , Riou Gaëtan , Guérout Nicolas 
Published: September 2024

AbstractSpinal cord injuries (SCIs) affect between 250,000 and 500,000 people worldwide each year, most commonly due to road accidents or falls. These injuries result in permanent disabilities, the severity and impact of which are directly related to the extent and location of the injury. Recent studies have also shown that SCIs can lead to cognitive disorders due to inflammation in the brain.From a therapeutic perspective, numerous treatments have been explored, including cell therapy. It has been established that a common mechanism across various cellular transplant models is the modulation of inflammation at the injury site. However, it remains unclear whether the immunomodulatory effects observed in the spinal cord also extend to the brain.To test this hypothesis, we induced SCI in wild-type mice and treated them with transplants of differentiated cells, specifically olfactory ensheathing cells, or stem cells, such as mesenchymal stem cells. Our results demonstrate that both types of transplants can reverse cognitive disorders induced by SCI. Additionally, we found that these cellular transplants modulate brain inflammation and increase neuronal density in the hippocampus.To our knowledge, this is the first study to show that cells transplanted into the spinal cord can modulate the inflammatory response in the brain, thereby reversing the negative effects of injury on brain function following SCI. These findings underscore the complex interactions between the brain and spinal cord under both physiological and pathological conditions.
Heart rate variability impairment during sleep in Veterans with REM sleep behavior disorder, traumatic brain injury, and posttraumatic stress disorder: An early potential window into autonomic dysfunction?
Authors: Cunningham Hannah A. , Dovek Laura , Recoder Natasha , Bryant-Ekstrand Mohini D. , Ligman Brittany R. , Piantino Juan , Lim Miranda M. , Elliott Jonathan E. 
Published: September 2024

ABSTRACTIndividuals with comorbid REM sleep behavior disorder (RBD) and neurotrauma (defined by traumatic brain injury and post-traumatic stress disorder) have an earlier age of RBD symptom onset, increased RBD-related symptom severity and more neurological features indicative of prodromal synucleinopathy compared to RBD only. An early sign of neurodegenerative condition is autonomic dysfunction, which we sought to evaluate by examining heart rate variability during sleep. Participants with overnight polysomnography were recruited from the VA Portland Health Care System. Veterans without neurotrauma or RBD (controls; n=19), with RBD only (RBD, n=14), and with RBD and neurotrauma (RBD+NT, n=19) were evaluated. Eligible 5-minute NREM and REM epochs without apneas/hypopneas, microarousals, and ectopic beats were analyzed for frequency and time domain (e.g. low frequency power, LF; high frequency power, HF; root mean square of successive RR intervals, RMSSD; % of RR intervals that vary ≥50 ms, pNN50) heart rate variability outcomes. Heart rate did not significantly differ between groups in any sleep stage. Time domain and frequency domain variables (e.g., LF power, HF power, RMSSD, and pNN50) were significantly reduced in the RBD and RBD+NT groups compared to controls and RBD only during NREM sleep. There were no group differences detected during REM sleep. These data suggest significant reductions in heart rate variability during NREM sleep in RBD+NT participants, suggesting greater autonomic dysfunction compared to controls or RBD alone. Heart rate variability during sleep may be an early, promising biomarker, yielding mechanistic insight for diagnosis and prognosis of early neurodegeneration in this vulnerable population.STATEMENT OF SIGNIFICANCEComorbid REM sleep behavior disorder (RBD) and neurotrauma (NT, traumatic brain injury + post-traumatic stress disorder; RBD+NT) is associated with increased neurodegenerative symptom burden and worsened health. Sleep and autonomic function are integrally and bidirectionally related to neurodegenerative processes. In the current study, we sought to determine if early signs of autonomic dysfunction, measured via heart rate variability (HRV), were present during sleep in comorbid RBD+NT compared to RBD only and controls. Our data show reduced time and frequency domain HRV during NREM sleep in RBD+NT Veterans compared to RBD only and controls. These data contribute evidence that participants with RBD and comorbid NT demonstrate significantly worse autonomic dysfunction compared to age/sex matched participants with RBD alone.
DrugTar Improves Druggability Prediction by Integrating Large Language Models and Gene Ontologies
Authors: Borhani Niloofar , Izadi Iman , Motahharynia Ali , Sheikholeslami Mahsa , Gheisari Yousof 
Published: September 2024

AbstractTarget discovery is crucial in drug development, especially for complex chronic diseases. Recent advances in high-throughput technologies and the explosion of biomedical data have highlighted the potential of computational druggability prediction methods. However, most current methods rely on sequence-based features with machine learning, which often face challenges related to hand-crafted features, reproducibility, and accessibility. Moreover, the potential of raw sequence and protein structure has not been fully investigated. Here, we leveraged both protein sequence and structure using deep learning techniques, revealing that protein sequence, especially pre- trained embeddings, is more informative than protein structure. Next, we developedDrugTar, a highl7lperformance deep learning algorithm integrating sequence embeddings from the ESM-2 pre-trained protein language model with protein ontologies to predict druggability. DrugTar achieved areas under the curve and precision-recall curve values above 0.90, outperforming state-of-the-art methods. In conclusion, DrugTar streamlines target discovery as a bottleneck in developing novel therapeutics.
Designing of thermostable proteins with a desired melting temperature
Authors: Tijare Purva , Kumar Nishant , Raghava Gajendra P. S. 
Published: September 2024

1.AbstractThe stability of proteins at higher temperatures is crucial for its functionality that is measured by their melting temperature (Tm). The Tm is the temperature at which 50% of the protein loses its native structure and activity. Existing methods for predicting Tm have two major limitations: first, they are often trained on redundant proteins, and second, they do not allow users to design proteins with the desired Tm. To address these limitations, we developed a regression method for predicting the Tm value of proteins using 17,312 non-redundant proteins, where no two proteins are more than 40% similar. We used 80% of the data for training and testing; remaining 20% of the data for validation. Initially, we developed a machine learning model using standard features from protein sequences. Our best model, developed using Shannon entropy for all residues, achieved the highest Pearson correlation of 0.80 with an R² of 0.63 between the predicted and actual Tm of proteins on the validation dataset. Next, we fine-tuned large language models (e.g., ProtBert, ProtGPT2, ProtT5) on our training dataset and generated embeddings. These embeddings have been used for developing machine learning models. Our best model, developed using ProtBert embeddings, achieved a maximum correlation of 0.89 with an R² of 0.80 on the validation dataset. Finally, we developed an ensemble method that combines standard protein features and embeddings. One of the aims of the study is to assist the scientific community in the design of targeted melting temperatures. We created a user-friendly web server and a python package for predicting and designing thermostable proteins. Our standalone software can be used to screen thermostable proteins in genomes and metagenomes. We demonstrated the application of PPTstab in identifying thermostable proteins in different organisms from their genomes, the model and data is available at:https://webs.iiitd.edu.in/raghava/pptstab.HighlightsPrediction of melting temperature (Tm) on non-redundant proteinsMachine learning models based on sequence composition and ProtBert embeddingsA Webserver for predicting Tm and designing thermostable proteins
Using disease symptomatology to guide treatment in patients with central centrifugal cicatricial alopecia: introduction of C-CAT scoring tool
Authors: Qadri Aasheen , Will Elizabeth , Aguh Crystal 
Published: September 2024

ABSTRACTBackgroundCentral centrifugal cicatricial alopecia (CCCA) is a progressive scarring alopecia. No disease activity scale exists, making assessment of therapeutic intervention difficult.ObjectiveThis study introduces the CCCA Clinical Assessment Tool (C-CAT), a novel scale that quantifies symptom severity to facilitate tailored treatment and track disease progression. Methods: A retrospective review of patients with CCCA were assessed on degree of pruritus, erythema, pain, disease progression, and scalp resistance, each scored from 0-2, over the course of a minimum of six months of therapy.ResultsEighty-two patients were included. The average initial C-CAT score was 3.4, consistent with mild to moderate disease activity. After six months of treatment, 88% of patients had improvement in score, with 48% of all patients achieving remission. Intralesional kenalog injections and topical clobetasol were the most commonly used therapies. Patients required an average of 3.6 different treatments to achieve therapeutic response.LimitationsThe subjective nature of some C-CAT components may lead to inter-rater variability.ConclusionThe C-CAT provides a structured, quantitative method to assess CCCA severity, which facilitates tracking of disease improvement for the majority of patients with targeted therapy. This tool promotes personalized, symptom-based treatment and supports clinicians and patients in understanding this complex condition.CAPSULE SUMMARYA novel central centrifugal cicatricial alopecia scoring tool demonstrates that established treatments quantitatively improve symptoms in most patients with central centrifugal cicatricial alopecia over 6 months.The novel disease activity scale guides clinicians in structuring treatment based on specific symptomatology and aids patients in understanding progress.
Amygdala subregional functional connectivity in Treatment-Resistant Depression
Authors: Foster Sheryl L. , Landin-Romero Ramon , Lewis Sarah , Barreiros Ana Rita , Matis Sophie , Harris Anthony , Korgaonkar Mayuresh S. 
Published: September 2024

AbstractBackgroundTreatment resistance impacts almost 50% of depression patients, with amygdala dysfunction being widely implicated. The amygdala is a complex amalgamation of subnuclei with diverse functions but fMRI studies have typically focussed on identifying whole rather than subregional amygdala functional connectivity. This study used high resolution 3T fMRI data to investigate subregional alterations that may differentiate treatment-resistant cohorts from healthy individuals and depressed patients who respond to treatment.MethodsResting-state fMRI data were obtained in 35 participants diagnosed with Treatment-Resistant Depression (TRD), 38 healthy control participants (HC), and 35 treatment-sensitive participants (TSD). Seed-based functional connectivity analyses of the three main subregions bilaterally (laterobasal, centromedial and superficial), as well as the whole amygdala, were performed and comparisons made between groups.ResultsWe foundconnectivity differences in the right laterobasal amygdala subregion in TRD compared to both groups. TRD patients displayed hypoconnectivity to the right fusiform gyrus relative to HC whereas hyperconnectivity to the left inferior frontal gyrus relative to TSD was identified. No connectivity differences were found for the whole amygdala or any of the other subregions bilaterally.LimitationsModest sample size and cross-sectional study design are limitations. A causal relationship between functional connectivity alterations and treatment resistance cannot be established.ConclusionAltered connectivity of the right laterobasal subregion is a distinguishing feature of TRD. These alterations may underlie severe impairments in emotion processing and social functioning that are characteristic of TRD. These results emphasise the need for further investigation of the functional role of the amygdala subregions in depression.
Navigating Memorability Landscapes: Hyperbolic Geometry Reveals Hierarchical Structures in Object Concept Memory
Authors: Lee Fiona M. , Berman Marc G. , Stier Andrew J. , Bainbridge Wilma A. 
Published: September 2024

AbstractWhy are some object concepts (e.g., birds, cars, vegetables, etc.) more memorable than others? Prior studies have suggested that features (e.g., color, animacy, etc.) and typicality (e.g., robin vs. penguin) of object images influences the likelihood of being remembered. However, a complete understanding of object memorability remains elusive. In this study, we examine whether the geometric relationship between object concepts explains differences in their memorability. Specifically, we hypothesize that image concepts will be geometrically arranged in hierarchical structures and that memorability will be explained by a concept’s depth in these hierarchical trees. To test this hypothesis, we construct a Hyperbolic representation space of object concepts (N=1,854) from the THINGS database (Hebart et al., 2019), which consists of naturalistic images of concrete objects, and a space of 49 feature dimensions derived from data-driven models. Using ALBATROSS (Stier, A. J., Giusti, C., & Berman, M. G., In prep), a stochastic topological data analysis technique that detects underlying structures of data, we demonstrate that Hyperbolic geometry efficiently captures the hierarchical organization of object concepts above and beyond a traditional Euclidean geometry and that hierarchical organization is related to memorability. We find that concepts closer to the center of the representational space are more prototypical and also more memorable. Importantly, Hyperbolic distances are more predictive of memorability and prototypicality than Euclidean distances, suggesting that concept memorability and typicality are organized hierarchically. Taken together, our work presents a novel hierarchical representational structure of object concepts that explains memorability and typicality.
Steerable Tape-Spring Needle for Autonomous Sharp Turns Through Tissue
Authors: Abdoun Omar , Tjandra Davin , Yin Katie , Kurzan Pablo , Yin Jessica , Yim Mark 
Published: September 2024

AbstractSteerable needles offer a minimally invasive method to deliver treatment to hard-to-reach tissue regions. We introduce a new class oftape-springsteerable needles capable of sharp turns ranging from 15 to 150 degrees with a turn radius as low as 3mm, which minimizes surrounding tissue damage. In this work, we derive and experimentally validate a geometric model for our steerable needle design. We evaluate both manual and robotic steering of the needle along a Dubins path in 7 kPa and 13 kPa tissue phantoms, simulating our target clinical application in healthy and unhealthy liver tissue. We conduct experiments to measure needle robustness to stiffness transitions between non-homogeneous tissues. We demonstrate progress towards clinical use with needle tip tracking via ultrasound imaging, navigation around anatomical obstacles, and integration with a robotic autonomous steering system.
Framework for Martini-based Coarse-grained Model of Enzymes: Model Development and Experimental Validation
Authors: Hooten Mason , Murthy Sanjeeva , Pal Nityananda , Khare Sagar D. , Gormley Adam J. , Dutt Meenakshi 
Published: September 2024

ABSTRACTRecent experiments have shown that enzyme activity can preserved in harsh environments by complexing enzyme with polymer into a Protein-Polymer Hybrid (PPH). In a successful PPH, heteropolymer strands bind to the enzyme surface and restrain the folded protein without adversely affecting the binding and active sites. It is believed that hybridization is driven by non-covalent interactions at the enzyme surface including hydrophobicity and electrostatics. Molecular modeling of these interactions is not practical at the all-atom scale due to the long timescales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse-grained model in which one particle represents several atoms, significantly reducing the cost of simulation. In this study we present two coarse-grained enzyme models – lipase and dehalogenase – prepared using a top-down modeling strategy. We simulate each enzyme in aqueous solution and calculate statistics of protein surface features and shape descriptors. The values from the coarse-grained data are compared with the same calculations performed on all-atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small-angle X-ray scattering (SAXS) experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment.
Identification and study of Prolyl Oligopeptidases and related sequences in bacterial lineages
Authors: Nayak Soumya , Sowdhamini Ramanathan 
Published: September 2024

AbstractProteases are enzymes that break down proteins, and serine proteases are an important subset of these enzymes. Prolyl oligopeptidase (POP) is a family of serine proteases (S9 family) that has the ability to cleave peptide bonds involving proline residues and it is unique for its ability to cleave various small oligopeptides shorter than 30 amino acids. The S9 family from the MEROPS database, is classified into four subfamilies based on active site motifs. These S9 subfamilies assume a crucial position owing to their diverse biological roles and potential therapeutic applications in various diseases. In this study, we have examined ∼32000 completely annotated bacterial genomes from the NCBI RefSeq Assembly database to identify annotated S9 family proteins. This results in the discovery of ∼53,000 bacterial S9 family proteins (referred to as POP homologues). These sequences are classified into distinct subfamilies through various machine-learning approaches and comprehensive analysis of their distribution across various phyla and species and domain architecture analysis are also conducted. Distinct subclusters and class-specific motifs of POPs were identified, suggesting differences in substrate specificity in POP homologues. This study can enable future research of these gene families that are involved in many important biological processes.
Distinct Longitudinal Clinical-Neuroanatomical Trajectories in Parkinson’s Disease Clinical Subtypes: Insight Towards Precision Medicine
Authors: Fereshtehnejad Seyed-Mohammad , Moqadam Roqaie , Azizi Houman , Postuma Ronald B. , Dadar Mahsa , Lang Anthony E. , Marras Connie , Zeighami Yashar 
Published: September 2024

ABSTRACTBackgroundParkinson’s disease (PD) varies widely across individuals in clinical manifestations and course of progression. Identification and characterization of distinct biological subtypes could help explain this heterogeneity, identify the underlying pathophysiology, and predict disease progression across the subgroups of PD.ObjectiveWe aimed to compare long-term trajectories of various motor and non-motor clinical features, as well as patterns of brain atrophy between PD subtypes, using longitudinally acquired brain MRIs.MethodsData on 421 individuals with early-stage PD was retrieved from the Parkinson’s Progression Markers Initiative (PPMI), with an average follow-up time of 8.2 years until February 2024. Participants were classified into three clinical subtypes at thede novostage using a previously validated subtyping criteria based on major motor and non-motor classifiers (early cognitive impairment, REM sleep behavior disorder (RBD), dysautonomia): ‘mild-motor predominant’ (n=223), ‘intermediate’ (n=146), and diffuse-malignant (n=52). To investigate the pattern of brain atrophy, we used T1-weighted MRIs from a subset of the PPMI population with at least two MRIs obtained, which consisted of 134 PD individuals and 60 healthy controls. Deformation-based morphometry (DBM) maps were calculated and mixed effect models were used to examine the interaction between PD subtypes and rate of atrophy across brain regions, controlling for sex and age at baseline.ResultsCompared to the ‘mild motor-predominant’ subtype, participants who were categorized as diffuse-malignant PD at baseline experienced greater worsening in motor severity (p=0.007), cognition (p<0.0001) and activities of daily living (ADL) (p<0.0001) after 8 years. Individuals with diffuse-malignant PD showed a significantly higher rate of atrophy across multiple brain regions, including precuneus, paracentral lobule, inferior temporal gyrus, fusiform gyrus, and lateral hemisphere of the cerebellum (correctedp<0.05).ConclusionOur study revealed a distinct pattern of long-term progression in various motor and non-motor clinical outcomes between different subtypes of idiopathic PD. Furthermore, we demonstrated an accelerated atrophy pattern within several brain regions in the diffuse-malignant PD subtype. These findings suggest a more widespread and aggressive neurodegenerative process in a subgroup of people with PD, favoring the existence of diverse underlying pathophysiology with clinical relevance for future precision medicine in PD.
Personal protective beliefs and behaviors during the COVID-19 pandemic at a large, multi-campus, public university in Pennsylvania: a cross-sectional survey
Authors: Stachowski Jill , Murphy Ryan , Pomerantz Lauren , Kuruppumullage Don Prabhani , Long Bradley 
Published: September 2024

AbstractIntroductionThe COVID-19 pandemic impacted many higher education institutions. Understanding the personal protective beliefs and behaviors surrounding public health measures such as face masking, social distancing, and handwashing helps to inform public health outreach measures.ObjectiveThis study evaluates the beliefs and occurrences of face masking, social distancing, and handwashing behaviors among people at a multi-campus, public university in Pennsylvania.MethodsFrom October 5-November 30, 2021, a 10-minute REDCap survey was available to students, faculty, and staff 18 years of age and older at the Pennsylvania State University (PSU). Recruitment included targeted email, social media, digital advertisements, and university newspapers. 4,231 responses were received. Simple percentage associations between the selected factors and face masking behaviors as well as beliefs on the origin of the COVID-19 virus were made with IBM SPSS Statistics, version 29.0.2.0 (20).ResultsOf 3,662 completed surveys, 65.2% of respondents were positive that facemasks prevented the transmission of airborne disease, 49.9% of the respondents stated that they wore facemasks always (27.9%), almost always (26.9%), or often (11.1%). 65.3% believed social distancing prevented transmission of COVID-19, 57.5% reported social distancing while on-campus or out in public (always: 18.8%, almost always: 20.3%, often: 18.4%). The response rate for some level of conscientiously washing hands for the 20 second duration was 87.5% (always: 26.0%, almost always: 31.9%, sometimes: 19.9%, only after being out in public: 4.9%, and only when convenient: 4.8%).ConclusionsBeliefs in public health and safety measures during the COVID-19 pandemic did not always match the behaviors of individuals surveyed. Understanding barriers to performing behaviors or public health safety may help inform future areas for targeted intervention.