bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predisposing Factors, Treatment, and Pathophysiology of Retronychia in China: A Retrospective Study of 107 Cases
Authors: Chen Liang , Wang Wei , Wang Heng , Dong Dong , Liu Tianyi 
Published: October 2024

BACKGROUND
Retronychia, a condition marked by abnormal nail growth, is frequently underdiagnosed and misinterpreted, with a few reported cases in the literature.

OBJECTIVE
To investigate the pathophysiology, identify contributing factors, and assess the outcomes of surgical and nonsurgical treatments for retronychia.

METHODS AND MATERIALS
A retrospective review of 107 retronychia cases diagnosed at the center between January 2021 and July 2022 was conducted.

RESULTS
Predisposing factors were identified in 93.46% of cases, with 88.79% of patients reporting a habit of wearing socks during sleep before symptom onset. Noninvasive treatments targeting these factors led to full recovery in 79.55% of cases, while 13.64% experienced partial improvement, inducing reduced proximal nail fold inflammation. Nail avulsion, which involves removing the entire nail, achieved a relapse-free cure rate of 90.91%. When the innermost nail plate was preserved, the cure rate rose to 96.67%.

CONCLUSION
Chronic mechanical forces, frequently overlooked, play a pivotal role in retronychia onset and progression. Conservative management targeting these mechanical factors is recommended for patients with mild cases.
A Pivotal Study on the Safety and Effectiveness of a Targeted Alkali Thermolysis Patch for Treatment of Primary Axillary Hyperhidrosis or Excessive Axillary Sweating
Authors: Smith Stacy , Glaser Dee Anna , Green Lawerence J. , Kaminer Micheal S. , Tims Erin , Pariser David M. 
Published: October 2024

BACKGROUND
One-third of US adults are bothered by excessive sweating, approximately 5% are diagnosed with hyperhidrosis. A topical patch using targeted alkali thermolysis (TAT) was developed for treatment of this condition.

OBJECTIVE
This study was intended to assess the efficacy and safety of the TAT-Patch for axillary sweat reduction.

MATERIALS AND METHODS
A randomized, multicenter, double-blind, sham-controlled, pivotal trial enrolled 120 subjects to a bilateral axillary treatment with a TAT patch (63 subjects) or sham patch (57 subjects).

RESULTS
The primary end point was achieved; 64% of TAT-treated versus 44% of sham-treated subjects (p = .0332) improved from Hyperhidrosis Disease Severity Scale (HDSS) 3/4 to HDDS 1/2 at 4 weeks. Targeted alkali thermolysis treatment also showed a statistically significant improvement over sham treatment for all secondary end points, including gravimetric sweat production and subject-reported quality-of-life (QoL) assessments. The duration of effect is approximately 3 months, determined by the time to return to baseline HDSS. Mild-to-moderate treatment-site adverse events (AEs) were reported in 22% of TAT patch subjects. No serious or severe AEs were reported.

CONCLUSION
HDSS, GSP, and QoL findings confirm clinically meaningful sweat reduction and a significant improvement in quality of life following a single TAT patch treatment. This device has potential to offer a new, noninvasive treatment option that is well tolerated with minimal downtime.
Development of Undergraduate Competencies in Caring for People Experiencing Homelessness
Authors: Richmond Rachel , Noone Joanne , Voss Heather , Hale Aina , Gran-Moravec Marilyn 
Published: October 2024

Background:
Nurses play a critical role in reducing health inequities in people experiencing homelessness (PEH), a key social determinant of health. Health disparities specific to PEH are exacerbated by numerous factors, including bias, stigma, and a lack of nursing competency in caring for PEH.

Purpose:
The purpose of this study was to validate registered nurse key competencies; associated knowledge, skills, and attitudes; and concepts, content, and learning activities to serve as an educational framework in providing care for PEH.

Methods:
Twenty-four nursing experts on the care of PEH rated draft competency and subcompetency statements for relevance and clarity, proposed revisions and missing competencies, and identified essential and useful content, concepts, and learning activities.

Results:
Four key competencies, 38 subcompetencies, and associated concepts, content, and learning activities were validated by the experts.

Conclusions:
This framework can be used to guide curricular development across nursing programs to advance health equity for PEH.
Qualitatively Assessing ChatGPT Responses to Frequently Asked Questions Regarding Sexually Transmitted Diseases
Authors: Moothedan Elijah , Jhumkhawala Vama , Burgoa Sara , Martinez Lisa , Sacca Lea 
Published: October 2024

Background
ChatGPT, a large language model artificial intelligence platform that uses natural language processing, has seen its implementation across a number of sectors, notably in health care. However, there remains limited understanding regarding the efficacy of ChatGPT in addressing commonly asked questions on public health subjects. This study aimed to investigate whether ChatGPT could appropriately answer frequently asked questions related to sexually transmitted diseases (STDs).

Methods
Ten frequently asked questions on STDs were gathered from 25 different government agency websites. The questions were inputted into ChatGPT, and subsequent responses were analyzed for accuracy, clarity, and appropriateness using an evidence-based approach on a 4-point grading scale.

Results
Of the responses provided by ChatGPT, 4 were determined to be excellent requiring no clarification and 6 requiring minimal clarification. No responses were graded as unsatisfactory. Additionally, the responses appropriately emphasized consulting a health care specialist.

Conclusion
Although the majority of responses required minimal clarification, ChatGPT has the potential to be an effective supplementary tool for patient education. Additional research is necessary to explore possible public health strategies that incorporate artificial intelligence to address concerns related to STDs.
Analysis of 2019 Ohio Disease Intervention Specialist Records for Syphilis Cases Using Clustering Algorithms
Authors: Chakraborty Payal , Ning Xia , McNeill Mary , Kline David M. , Shoben Abigail B. , Miller William C. , Norris Turner Abigail 
Published: October 2024

Background
Developments in natural language processing and unsupervised machine learning methodologies (e.g., clustering) have given researchers new tools to analyze both structured and unstructured health data. We applied these methods to 2019 Ohio disease intervention specialist (DIS) syphilis records, to determine whether these methods can uncover novel patterns of co-occurrence of individual characteristics, risk factors, and clinical characteristics of syphilis that are not yet reported in the literature.

Methods
The 2019 disease intervention specialist syphilis records (n = 1996) contain both structured data (categorical and numerical variables) and unstructured notes. In the structured data, we examined case demographics, syphilis risk factors, and clinical characteristics of syphilis. For the unstructured text, we applied TF-IDF (term frequency multiplied by inverse document frequency) weights, a common way to convert text into numerical representations. We performed agglomerative clustering with cosine similarity using the CLUTO software.

Results
The cluster analysis yielded 6 clusters of syphilis cases based on patterns in the structured and unstructured data. The average internal similarities were much higher than the average external similarities, indicating that the clusters were well formed. The factors underlying 3 of the clusters related to patterns of missing data. The factors underlying the other 3 clusters were sexual behaviors and partnerships. Notably, 1 of the 3 consisted of individuals who reported oral sex with male or anonymous partners while intoxicated, and one comprised mainly of males who have sex with females.

Conclusions
Our analysis resulted in clusters that were well formed mathematically, but did not reveal novel epidemiological information about syphilis risk factors or transmission that were not already known.
Extranodal Mantle Cell Lymphoma Presented as Bilateral Adrenal Masses on 18F-FDG PET/CT and 68Ga-DOTATATE PET/CT
Authors: Filizoglu Nuh , Ozguven Salih , Ones Tunc , Turoglu Halil Turgut , Erdil Tanju Yusuf 
Published: October 2024

Abstract
Mantle cell lymphoma (MCL) is a rare type of B-cell non-Hodgkin lymphoma that commonly presents with constitutional symptoms, lymphadenopathy, and laboratory abnormalities. Although less common, extranodal involvement of MCL may be seen at initial presentation and most commonly occurs in the spleen, liver, bone marrow, and gastrointestinal tract. Adrenal involvement in MCL is extremely rare, and only a few cases have been reported in the literature. Herein, we present 18F-FDG PET/CT and 68Ga-DOTATATE PET/CT findings of a unique case of primary extranodal MCL with gastric and adrenal involvement.
After 30 Years of Remission, Recurrence With Renal Cell Carcinoma Metastasis Mimicking Pancreatic Neuroendocrine Tumor on 18F-FDG and 68Ga-DOTATATE PET/CT
Authors: Aydinbelge Dizdar Nur , Erdem Karaoglu Aysenur , Ozmen Ozlem , Kaya Ozge 
Published: October 2024

Abstract
A 75-year-old man with a previous history of left nephrectomy for clear cell renal cell carcinoma in remission was underwent 68Ga-DOTATATE PET/CT imaging for evaluation of pancreatic body lesion. It showed a mass with intense 68Ga-DOTATATE uptake in the pancreatic corpus. During the follow-up of the patient who did not want to undergo surgery, 18F-FDG PET/CT scan was performed due to the increase in the size of the mass, and mild FDG uptake was detected in the pancreatic corpus lesion, and histopathological examination was confirmed as renal cell carcinoma metastasis.
UnCOT-AD: Unpaired Cross-Omics Translation Enables Multi-Omics Integration for Alzheimer’s Disease Prediction
Authors: Abir Abrar Rahman , Dip Sajib Acharjee , Zhang Liqing 
Published: October 2024

AbstractAlzheimer’s Disease (AD) is a progressive neurodegenerative disorder, posing a growing public health challenge. Traditional machine learning models for AD prediction have relied on single omics data or phenotypic assessments, limiting their ability to capture the disease’s molecular complexity and resulting in poor performance. Recent advances in high-throughput multi-omics have provided deeper biological insights. However, due to the scarcity of paired omics datasets, existing multi-omics AD prediction models rely on unpaired omics data, where different omics profiles are combined without being derived from the same biological sample, leading to biologically less meaningful pairings and causing less accurate predictions. To address these issues, we proposeUnCOT-AD, a novel deep learning framework forUnpairedCross-OmicsTranslation enabling effective multi-omics integration forADprediction. Our method introduces the first-ever cross-omics translation model trained on unpaired omics datasets, using two coupled Variational Autoencoders and a novel cycle consistency mechanism to ensure accurate bidirectional translation between omics types. We integrate adversarial training to ensure that the generated omics profiles are biologically realistic. Moreover, we employ contrastive learning to capture the disease specific patterns in latent space to make the cross-omics translation more accurate and biologically relevant. We rigorously validate UnCOT-AD on both cross-omics translation and AD prediction tasks. Results show thatUnCOT-ADempowers multi-omics based AD prediction by combining real omics profiles with corresponding omics profiles generated by our cross-omics translation module and achieves state-of-the-art performance in accuracy and robustness. Source code is available athttps://github.com/abrarrahmanabir/UnCOT-AD
LCL: Contrastive Learning for Lineage Barcoded scRNA-seq Data
Authors: Yang Shizhao Joshua , Wang Yixin , Lin Kevin Z 
Published: October 2024

AbstractSingle-cell lineage tracing technology has advanced the investigation of progenitor cells’ development using static, inheritable barcodes. It can determine temporal dynamics in progenitor-progeny relationships through single-cell RNA-sequencing (scRNA-seq) data. However, studying fate commitment from scRNA-seq can be difficult since the gene expression profiles are confounded with information about many cell processes beyond fate commitment. This paper demonstrates a novel framework to specifically isolate lineage signals driving cell fate, allowing us to learn the gene pathways that differentiate different lineages based on their eventual fates.Our novel approach, LCL (Lineage-aware Contrastive Learning), is a contrastive-learning deep learning model for analyzing lineage-tracing scRNA-seq data. Using two lineage-tracing datasets, one about reprogramming embryonic fibroblasts and the other about hematopoietic progenitor cells, we demonstrate that LCL can produce low-dimensional representations that effectively isolate fate-determining signals from other key biological signals. We evaluate the quality of LCL embeddings and demonstrate that they perform well in out-of-sample evaluation, both in terms of predicting the lineage and cell type compositions at a future time point. LCL also enables us to identify differential genes stably expressed within a lineage and visualize the fate-determining landscape using self-organizing maps based on the results from LCL. Lastly, we demonstrate the consistency of our approach across datasets of varying complexity using a series of pseudo-real datasets. In conclusion, our results demonstrate that LCL allows researchers to explore fate commitment in single-cell lineage-tracing data and uncover lineage-specific gene pathways.
Linguistic and Affective Prosody – A Unifying Perspective: A Systematic Review and ALE Meta-Analysis of the Cortical Organization of Prosody
Authors: Themistocleous Charalambos 
Published: October 2024

AbstractProsody is a cover term referring to the melodic aspects of speech, with linguistic and affective (a.k.a. emotional) meanings. This review provides an overview of linguistic and affective prosody, testing two hypotheses on healthy individuals’ linguistic and affective prosody. The first hypothesizes that the biological nature of affective prosody triggers activations unrelated to language (biological hypothesis), and the second that the aspects of affective prosody have been grammaticalized, i.e., incorporated into the language (linguistic hypothesis). We employed a systematic ALE metanalytic approach to identify neural correlates of prosody from the literature. Specifically, we assessed papers that report brain coordinates from healthy individuals selected using systematic research from academic databases, such as PubMed (NLM), Scopus, and Web of Science. We found that affective and linguistic prosody activate bilateral frontotemporal regions, like the Superior Temporal Gyrus (STG). A key difference is that affective prosody involves subcortical structures like the amygdala, and linguistic prosody activates linguistic areas and brain areas of social cognition and engagement. The shared activations, therefore, suggest that linguistic and affective meanings are combined, involving shared underlying brain connectivity mechanisms and acoustic manifestations. We propose the “prosodic blending hypothesis” to account for the findings. The hypothesis unifies the linguistic and affective prosody distinction, suggesting that prosody incorporates affective, social, and linguistic meanings in the acoustic signal and involves selective bilateral neural contributions. Although brain regions responsible for core emotions can be activated, those meanings are produced through a coordinated and structured linguistic manner, accounting for the gradient expression of non-linguistic and categorical linguistic aspects. We argue that the process is similar to speech, lexicon, and grammar domains, as they, too, convey affective, social, and linguistic meanings without their explicit separation into linguistic and affective. Therefore, our blending hypothesis views prosody as a unified system that combines affective and linguistic functions.
Neural mechanisms underlying robust target selection in response to microstimulation of the oculomotor system
Authors: Rakhshan Mohsen , Schafer Robert J , Moore Tirin , Soltani Alireza 
Published: October 2024

AbstractDespite its prevalence in studying the causal roles of different brain circuits in cognitive processes, electrical microstimulation often results in inconsistent behavioral effects. These inconsistencies are assumed to be due to multiple mechanisms, including habituation, compensation by other brain circuits, and contralateral suppression. Considering the presence of reinforcement in most experimental paradigms, we hypothesized that interactions between reward feedback and microstimulation could contribute to inconsistencies in behavioral effects of microstimulation. To test this, we analyzed data from electrical microstimulation of the frontal eye field of male macaques during a value-based decision-making task and constructed network models to capture choice behavior. We found evidence for microstimulation-dependent adaptation in saccadic choice, such that in stimulated trials, monkeys’ choices were biased toward the target in the response field of the microstimulated site (Tin). In contrast, monkeys showed a bias away fromTinin non-stimulated trials following microstimulation. Critically, this bias slowly decreased as a function of the time since the last stimulation. Moreover, microstimulation-dependent adaptation was influenced by reward outcomes in preceding trials. Despite these local effects, we found no evidence for the global effects of microstimulation on learning and sensitivity to the reward schedule. By simulating choice behavior across various network models, we found a model in which microstimulation and reward-value signals interact competitively through reward-dependent plasticity can best account for our observations. Our findings indicate a reward-dependent compensatory mechanism that enhances robustness to perturbations within the oculomotor system and could explain the inconsistent outcomes observed in previous microstimulation studies.
Using a Deep Learning Approach for Model-based Control of Deep Brain Stimulation
Authors: Saradhi Srikar , Tian Yupeng , Rezaei Mohammed R. , Lankarany Milad 
Published: October 2024

AbstractDeep brain stimulation (DBS) has been developed as a treatment method for various neurological disorders, including Parkinson’s disease, essential tremor and depression. Although DBS is effective, it often loses efficacy over sustained periods because a constant stimulation is applied without adapting to the patient’s current clinical state. In contrast, an adaptive closed-loop DBS system can offer more tailored stimulation in real-time based on a feedback biomarker. In early 2024, we developed a model-based DBS control framework that consists of three main functions: (1) a biophysically reasonable encoding model, (2) a simple decoding model, and (3) a controller. We used a polynomial fit function in the decoding model to approximate the neural-motor relationship, from DBS-induced Vim neural activity to muscle fiber electromyography (EMG). Despite promising results, the polynomial method is inaccurate in capturing the full representation of the neural-motor (EMG) function across different DBS frequencies. In this work, to capture the nonlinear intricate relationship between the neural and EMG patterns, we developed a one-dimensional convolutional neural network (1-D CNN) as a decoding model to predict the EMG signal directly from the DBS-induced Vim neural activity. The 1-D CNN network outputted a high R2value of 0.997 which significantly outperformed the polynomial method (R2= 0.277) and a deep learning approach based on long short-term memory (R2= 0.296). We anticipate that our work highlights the need for a data-driven approach that can reliably map neural activities to symptomatic signals like EMG for better adjusting DBS parameters.
Promiscuous and unbiased recombination underlies the sequence-discrete species of the SAR11 lineage in the deep ocean
Authors: Zhao Jianshu , Pachiadaki Maria , Conrad Roth E. , Hatt Janet K. , Bristow Laura A. , Rodriguez-R Luis M. , Rossello-Mora Ramon , Stewart Frank J. , Konstantinidis Konstantinos T. 
Published: October 2024

AbstractSurveys of microbial communities (metagenomics) or isolate genomes have revealed sequence-discrete species. That is, members of the same species usually show >95% Average Nucleotide Identity (ANI) of shared genes among themselves vs. <83% ANI to members of other species while genome pairs showing between 83-95% ANI are comparatively rare. In these surveys, aquatic bacteria of the ubiquitous SAR11 clade (ClassAlphaproteobacteria), which play a major role in carbon cycling, are an outlier and often do not exhibit discrete species boundaries, suggesting the potential for alternate modes of genetic differentiation. To explore evolution in SAR11, we analyzed high-quality, single-cell amplified genomes (SAGs) and companion metagenomes from an oxygen minimum zone (OMZ) in the Eastern Tropical Pacific Ocean, where the SAR11 make up ∼20% of the total microbial community. Our results show that SAR11 do form several sequence-discrete species, but their ANI range of discreteness is shifted to lower identities between 86-91%, with intra-species ANI ranging between 91-100%. Measuring recent gene exchange among these genomes based on a newly developed methodology revealed higher frequency of homologous recombination within compared to between species that affects sequence evolution at least twice as much as diversifying point mutation across the genome. Recombination in SAR-11 appears to be more promiscuous compared to other prokaryotic species and has facilitated the spreading of adaptive mutations within the species, further promoting the high intra-species diversity observed. These results implicate rampant, genome-wide homologous recombination as the mechanism that underlies the evolution of SAR11 into discrete species.SignificanceDistinguishing “species” is a pressing issue in microbiology, partly because the mechanisms that create and maintain clusters of genetically similar microbes are sparsely documented for natural populations. By leveraging high quality single-cell genomic data and a novel method for assessing homologous recombination, we show that rampant homologous recombination maintains species-level clusters of genomes for the most abundant order of marine bacteria, suggesting that these genomes may be evolving sexually to a much greater extent than previously thought. Therefore, our results identify a mechanism explaining the evolution of species in a major microbial group and have implications for understanding microbial diversity and the species concept more broadly.