bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deriving Insights From Open-Ended Learner Feedback: An Exploration of Natural Language Processing Approaches
Authors: Maslej Marta M. , Donner Kayle , Thakur Anupam , Islam Faisal , Costa-Dookhan Kenya A. , Sockalingam Sanjeev 
Published: February 2025

Introduction:
Open-ended feedback from learners offers valuable insights for adapting continuing health education to their needs; however, this feedback is burdensome to analyze with qualitative methods. Natural language processing offers a potential solution, but it is unclear which methods provide useful insights. We evaluated natural language processing methods for analyzing open-ended feedback from continuing professional development training at a psychiatric hospital.

Methods:
The data set consisted of survey responses from staff participants, which included two text responses on how participants intended to use the training (“intent to use”; n = 480) and other information they wished to share (“open-ended feedback”; n = 291). We analyzed “intent-to-use” responses with topic modeling, “open-ended feedback” with sentiment analysis, and both responses with large language model (LLM)–based clustering. We examined outputs of each approach to determine their value for deriving insights about the training.

Results:
Our results indicated that because the “intent-to-use” responses were short and lacked diversity, topic modeling was not useful in differentiating content between the topics. For “open-ended feedback,” sentiment scores did not accurately reflect the valence of responses. The LLM-based clustering approach generated meaningful clusters characterized by semantically similar words for both responses.

Discussion:
LLMs may be a useful approach for deriving insights from learner feedback because they capture context, making them capable of distinguishing between responses that use similar words to convey different topics. Future directions involve exploring other methods involving LLMs, or examining how these methods fare on other data sets or types of learner feedback.
Border Digit Transposition and Intramedullary Nail Fixation for Third or Fourth Ray Amputation
Authors: Candela Xavier J. , Kotamarti Vasanth S. , Teunis Teun 
Published: February 2025

Summary:
Central ray amputation of the third or fourth finger leaves a gap between the remaining digits. This gap can allow small objects to fall out of the hand. The gap may be narrowed by suturing the remaining intermetacarpal ligaments or transposing the remaining border digit to the amputated ray. Both methods are often followed by prolonged immobilization to prevent gapping after suture repair and nonunion after transposition, respectively. We propose the use of an intramedullary nail for metacarpal fixation after ray transposition. This provides the advantage of early mobilization, potentially reducing stiffness and hardware irritation, thus enabling an early return to work.
The Effect of Touch Therapy on Symptoms and Psychosocial Variables in Individuals Diagnosed With Cancer
Authors: Muz Gamze , Bilgin Aylin , Yüce Gülyeter Erdoğan , Döner Ayser 
Published: February 2025

Background
Touch therapy is used in the control of physical and psychological symptoms in cancer patients. However, its effectiveness has not been well studied.

Objectives
The aim of this study was to examine the effectiveness of touch therapy intervention on symptoms and psychosocial factors for patients with cancer.

Method
Four electronic databases were used to identify all experimental studies that examine the effects of touch therapy on symptoms and psychosocial factors for patients with cancer. The standardized mean differences between groups in levels of symptoms and psychosocial factors for postintervention were computed for each study.

Result
The pooled results suggest that touch therapy intervention effectively reduces pain, fatigue, anxiety, and negative mood among patients with cancer postintervention. However, touch therapy intervention did not affect the quality of life and stress.

Conclusion
A significant effect of touch therapy was the reduction of physical and psychological symptoms in cancer patients.

Implications for Practice
Touch therapy could be safely integrated into the clinical practice of patients with cancer.
The Routine Collection of “Just-in-Case” Thyroid Aspirates for Molecular Testing at the Time of Initial Fine Needle Aspiration. Our Experience
Authors: Dawkins Adrian , Richards Dana , Sabujan Asmi , Nair Rashmi 
Published: February 2025

Abstract
Patients with Bethesda III/IV thyroid nodules may benefit from molecular testing in order to determine the likelihood of malignancy. The goal is to avoid unnecessary surgery for these often-benign nodules. The decision to acquire samples for molecular testing is typically made after formal cytologic interpretation. This creates anxiety and inconvenience for patients who must return for a repeat biopsy. We explored the impact of routinely collecting “just-in-case” samples from each thyroid nodule. Thus, a minimum of 4 samples were acquired from each nodule: 2 for routine cytologic analysis and 2 for possible genomic sequencing. Nodules subsequently determined to be Bethesda III/IV, were further evaluated by sending the prospectively acquired aspirates for genomic sequencing. Over a 7-month period, there were 69 consecutive patients with ages ranging from 24 to 88 years. There were 16 males and 63 females. Six patients had 2 nodules sampled, yielding total of 75 nodules. Of the 75 nodules, 4 (5.3%) were Bethesda I, 37 (49.3%) Bethesda II, 30 (40%) Bethesda III, 0 (0%) Bethesda IV, 1 (1.3%) Bethesda V, and 3 (4%) Bethesda VI. Of the 30 Bethesda III, 18 (60%) were deemed benign (4% risk of malignancy) by genomic testing. Nine (30%) were deemed 50% or 75% suspicious for malignancy. Two (6.7%) yielded insufficient material for analysis and 1 (3.3%) analysis was canceled because of nonpayment. There were no patient complications. Just-in-case samples prevented call-back of 40% of sampled nodules and thus deemed worthwhile and efficient despite adding an estimated 10 minutes to overall procedure time.
The growth benefits and toxicity of quinone synthesis are balanced by a dual regulatory mechanism and substrate limitations
Authors: Li Siliang , Zhang Jiangguo , Ajo-Franklin Caroline M. , Igoshin Oleg A. 
Published: February 2025

AbstractQuinones play a central role in maintaining redox balance and conserving energy but can trigger oxidative stress at high levels. However, the mechanisms by which microbes regulate quinone levels remain poorly0020understood, hindering effective metabolic engineering to modulate microbes for quinone production. Here, we show that the synthesis of the menaquinone precursor DHNA in the lactic acid bacteriumLactococcus lactisis regulated by a combined genetic, enzymatic, and metabolic mechanism. Using synthetic biology approaches, we found that enzymes MenF and MenD both contribute to DHNA regulation, with MenD playing a more prominent role in controlling DHNA concentrations. A mathematical model elucidates a two-phase regulatory pattern resulting from the interplay of reversible flux and allosteric feedback inhibition, where either MenF or MenD can serve as the regulatory enzyme, depending on their relative expression ratio. Additionally, the overproduction of DHNA is constrained by substrate availability, ensuring a sufficient but not excessive DHNA level to benefit cell growth while mitigating cytotoxicity. Collectively, these mechanisms maintain a fine-tuned physiological quinone level and suggest that modulating substrate supplement and MenF-to-MenD ratio could be keys for engineering DHNA production
Genome-Wide Analysis Reveals a Role of Non-coding 3’-untranslated Region Variants Affecting Cleavage and Polyadenylation in Undiagnosed Rare Disorders
Authors: Kainov Y. , Dias C. , Hubbard T. 
Published: February 2025

AbstractCleavage and polyadenylation of mRNAs is essential for transcription termination and gene expression, yet variants disrupting conserved sequences essential to this process are often overlooked as putative causal variants in individuals with rare Mendelian disorders. Using deep learning models, we identified enrichment of ultrarare alleles predicted to disrupt polyadenylation in a large cohort of undiagnosed probands with rare disease disorders from the Genomics England 100kGP dataset. These alleles are predominantly located in the 3’ UTRs of genes where protein-coding variants have previously been identified as causal in rare diseases. Experimental validation of a putative causal variant predicted to disrupt polyadenylation of SLC16A2 in a proband with intellectual disability, confirmed the allele’s deleterious impact on gene expression.
Evaluating cotinine cutoffs and sociodemographic factors associated with exclusive heavy e-cigarette vaping behavior
Authors: Arun Adith S. , Shah Rishi , Lu Yuan , Baldassarri Stephen R. , Krumholz Harlan M. 
Published: February 2025

AbstractIntroductionElectronic cigarette (e-cig) use in previously tobacco-naïve individuals may increase the risk of nicotine addiction and adverse health outcomes, including lung injury and cardiovascular disease. This study identifies a subgroup of heavy exclusive e-cig vapers, characterizes their demographics, and establishes an optimal serum cotinine threshold to differentiate them from tobacco-naïve individuals.MethodsWe analyzed data from the National Health and Nutrition Examination Survey (2013– 2020) to characterize serum cotinine levels by e-cig use frequency. The optimal cotinine cutoff for identifying heavy exclusive vapers was 2.2 ng/mL (sensitivity 98%, specificity 96%).We used receiver operating characteristic curve analysis to identify the cotinine threshold for discriminating heavy exclusive e-cig vapers from tobacco-naïve individuals and logistic regression to explore sociodemographic factors.ResultsExclusive heavy e-cig vapers—defined as those who vaped on four or more of the past five days—had mean cotinine levels (220 ± 38 ng/mL) comparable to cigarette smokers (212 ± 6 ng/mL). These vapers were more likely to be 12-18 (AOR 7.96; 95% CI: [3.38 – 18.7]) or 19-25 [AOR 7.36 [2.84 - 19]) years old and less likely to be female (AOR 0.3 [0.16-0.57]).ConclusionHeavy exclusive e-cig vapers had serum cotinine levels similar to cigarette smokers and were predominantly young males. Public health interventions should target this high-risk subgroup to reduce potential long-term health consequences.
Targeting the ClpP-αSynuclein Interaction with a Decoy Peptide to Mitigate Neuropathology in Parkinson’s Disease Models
Authors: Hu Di , Sun Xiaoyan , Qi Xin 
Published: February 2025

ABSTRACTParkinson’s disease (PD), the most prevalent neurodegenerative movement disorder, is characterized by the progressive loss of dopaminergic (DA) neurons and the accumulation of α-synuclein (αSyn)-rich inclusions. Despite advances in understanding PD pathophysiology, disease-modifying therapies remain elusive, underscoring gaps in our knowledge of its underlying mechanisms. Mitochondria are key targets of αSyn toxicity, and growing evidence suggests that αSyn-mitochondrial interactions contribute to PD progression. Our recent findings identify mitochondrial protease ClpP as a crucial regulator of αSyn pathology, with pathological αSyn binding to and impairing ClpP function, thereby exacerbating mitochondrial impairment and neurodegeneration. To disrupt this deleterious interaction, we developed a decoy peptide, CS2, which directly binds to the non-amyloid-β component (NAC) domain of αSyn, preventing its association with ClpP. CS2 treatment effectively mitigated αSyn toxicity in an αSyn-stable neuronal cell line, primary cortical neurons inoculated with αSyn pre-formed fibrils (PFFs), and DA neurons derived from PD patient-induced pluripotent stem cells (iPSCs). Notably, subcutaneous administration of CS2 in transgenic mThy1-hSNCA PD mice rescued cognitive and motor deficits while reducing αSyn aggregation and neuropathology. These findings establish the ClpP-αSyn interaction as a druggable target in PD and position CS2 as a promising therapeutic candidate for PD and other αSyn-associated neurodegenerative disorders.
Wnt-Activated Immunoregulatory Myeloid Cells Prevent Relapse in Experimental Autoimmune Encephalomyelitis and Offer a Potential Therapeutic Strategy for Multiple Sclerosis
Authors: Alkhimovitch Anastasia , Miller Stephen D. , Ifergan Igal 
Published: February 2025

ABSTRACTMultiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) characterized by recurrent inflammatory relapses and neurodegeneration. Myeloid cells play a critical role in shaping the inflammatory environment and influencing disease progression. Here, we demonstrate that activation of the Wnt signaling pathway reprograms myeloid cells into an immunoregulatory phenotype, leading to reduced neuroinflammation and disease severity. Using both experimental autoimmune encephalomyelitis (EAE) and human-derived myeloid cells, we show that Wnt agonist treatment promotes the expression of inhibitory molecules such as PD-L1 and PD-L2, suppressing pro-inflammatory responses. In the chronic and relapsing-remitting EAE models, Wnt activation significantly reduced disease severity, immune cell infiltration into the CNS, and pathogenic T cell responses. Notably, in relapsing-remitting EAE, Wnt treatment prevented new relapses in a PD-L1–dependent manner, highlighting the crucial role of myeloid cell–mediated immune regulation. These findings reveal a previously unrecognized role for Wnt signaling in myeloid cell immunoregulation and suggest that targeting this pathway could provide a novel therapeutic strategy for MS and other autoimmune diseases.
A Non-Invasive Ultrasound-Based Model for Assessing Endometrial Cancer Risk in Postmenopausal Bleeding
Authors: Khatoon Nagma , Mohsin Zehra , Wahab Shagufta , Akhtar Kafil 
Published: February 2025

AbstractAimsThis study evaluates the risk of endometrial carcinoma in postmenopausal women presenting with bleeding using an ultrasound-based risk scoring system. It aims to integrate imaging parameters and clinical variables for improved differentiation between benign and malignant endometrial conditions.MethodsA prospective analysis was conducted on postmenopausal women presenting with bleeding per vagina. Clinical variables including age, parity, BMI, hypertension, and diabetes were combined with ultrasound findings such as endometrial thickness, echogenicity, endo-myometrial interface, vascular patterns and Doppler scores. A comprehensive scoring system, based on International Endometrial Tumor Analysis (IETA) terminology, was employed to stratify patients’ risk levels. Diagnostic accuracy was determined by comparing the scoring system outcomes with histopathological findings.ResultsThe ultrasound-based scoring system demonstrated significant diagnostic accuracy in differentiating between benign and malignant endometrial pathologies. Endometrial thickness greater than 4 mm, heterogeneous echogenicity, irregular endo-myometrial interface and increase vascular flow were identified as strong predictors of malignancy. The model achieved a high sensitivity and specificity, providing a non-invasive yet reliable tool for risk stratification in clinical settings. The findings emphasize the potential of an integrated ultrasound-based scoring system in guiding clinical decisions for postmenopausal women with bleeding.ConclusionWe concluded that by incorporating both clinical and imaging parameters improves diagnostic accuracy, minimizes unnecessary invasive procedures, and aids in timely intervention.
A foundation model for protein-ligand affinity prediction through jointly optimizing virtual screening and hit-to-lead optimization
Authors: Feng Bin , Liu Zijing , Yang Mingjun , Zou Junjie , Cao He , Li Yu , Zhang Lei , Wang Sheng 
Published: February 2025

AbstractProtein-ligand binding affinity plays an important role in drug discovery, especially during virtual screening and hit-to-lead optimization. Computational chemistry and machine learning methods have been developed to investigate these tasks. Despite the encouraging performance, virtual screening and hit-to-lead optimization are often studied separately by existing methods, partially because they are performed sequentially in the existing drug discovery pipeline, thereby overlooking their interdependency and complementarity. To address this problem, we propose LigUnity, a foundation model for protein-ligand binding prediction by jointly optimizing virtual screening and hit-to-lead optimization. In particular, LigUnity learns coarse-grained active/inactive distinction for virtual screening, and fine-grained pocket-specific ligand preference for hit-to-lead optimization. We demonstrate the effectiveness and versatility of LigUnity on eight benchmarks across both tasks. In virtual screening, LigUnity outperforms 24 competing methods with more than 50% improvement on the DUD-E and Dekois 2.0 benchmarks, and shows robust generalization to novel proteins. In hit-to-lead optimization, LigUnity achieves the best performance on split-by-time, split-by-scaffold, and split-by-unit settings, further demonstrating its potential as a cost-effective alternative to free energy perturbation (FEP) calculations. We further showcase how LigUnity can be employed in an active learning framework to efficiently identify active ligands for TYK2, a therapeutic target for autoimmune diseases, yielding over 40% improved prediction performance. Collectively, these comprehensive results establish LigUnity as a versatile foundation model for both virtual screening and hit-to-lead optimization, offering broad applicability across the drug discovery pipeline through accurate protein-ligand affinity predictions.
Systematic engineering of synthetic serine cycles in Pseudomonas putida uncovers emergent topologies for methanol assimilation
Authors: Puiggené Òscar , Muñoz-Triviño Jaime , Civil-Ferrer Laura , Gille Line , Schulz-Mirbach Helena , Bergen Daniel , Erb Tobias J. , Ebert Birgitta E. , Nikel Pablo I. 
Published: February 2025

ABSTRACTThe urgent need for a circular carbon economy has driven research into sustainable substrates, including one-carbon (C1) compounds. The non-pathogenic soil bacteriumPseudomonas putidais a promising host for exploring synthetic methylotrophy due to its versatile metabolism. In this work, we implemented synthetic serine cycle variants inP. putidafor methanol assimilation combining modular engineering and growth-coupled selection, whereby methanol assimilation supported biosynthesis of the essential amino acid serine. The serine cycle forms acetyl-coenzyme A from C1molecules without carbon loss but has bottlenecks that hinder engineering efforts. We adopted three synthetic variants (serine-threonine cycle, homoserine cycle, and modified serine cycle) that yield serine in a methanol-dependent fashion to overcome these challenges. By dividing these metabolic designs into functional modules, we systematically compared their performance for implementationin vivo. Additionally, we harnessed native pyrroloquinoline quinone-dependent dehydrogenases for engineering methylotrophy. Recursive rewiring of synthetic and native activities revealed novel metabolic topologies for methanol utilization, termed enhanced serine-threonine cycle, providing a blueprint for engineering C1assimilation in non-model heterotrophic bacteria.GRAPHICAL ABSTRACT
A Case for Automated Segmentation of MRI Data in Milder Neurodegenerative Diseases
Authors: Lewis Connor J. , Johnston Jean M. , D’Souza Precilla , Kolstad Josephine , Zoppo Christopher , Vardar Zeynep , Kühn Anna Luisa , Peker Ahmet , Rentiya Zubir S. , Gahl William A. , Shazeeb Mohammed Salman , Tifft Cynthia J. , Acosta Maria T. 
Published: February 2025

AbstractBackgroundVolumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared 5 fully automated segmentation pipelines including FSL, Freesurfer, volBrain, SPM12, and SimNIBS with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls.MethodsWe analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared results for 7 brain structures including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum.ResultsWe found volBrain’svol2Brainpipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’srecon-allpipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’svol2Brainand SimNIBS’headrecoto have the strongest correlations depending on the cohort. For the lentiform nucleus, we found a combination ofrecon-alland FSL’sFIRSTto give the strongest correlations depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable.ConclusionPrevious studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process including the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.