bluuebunny/crossref_metadata_2025_split huggingface dataset:
A decade of firearm injuries: Have we improved?
Authors: Hatfield Sarah A. , Medina Samuel , Gorman Elizabeth , Barie Philip S. , Winchell Robert J. , Villegas Cassandra V. 
Published: January 2024

BACKGROUNDFirearm injuries are a growing public health issue, with marked increases coinciding with the coronavirus disease 2019 (COVID-19) pandemic. This study evaluates temporal trends over the past decade, hypothesizing that despite a growing number of injuries, mortality would be unaffected. In addition, the study characterizes the types of centers affected disproportionately by the reported firearm injury surge in 2020.METHODSPatients 18 years and older with firearm injuries from 2011 to 2020 were identified retrospectively using the National Trauma Data Bank (NTDB®). Trauma centers not operating for the entirety of the study period were excluded to allow for temporal comparisons. Joinpoint regression and risk-standardized mortality ratios (SMR) were used to evaluate injury counts and adjusted mortality over time. Subgroup analysis was performed to describe centers with the largest increases in firearm injuries in 2020.RESULTSA total of 238,674 patients, treated at 420 unique trauma centers, met inclusion criteria. Firearm injuries increased by 31.1% in 2020, compared to an annual percent change of 2.4% from 2011 to 2019 (p= 0.01). Subset analysis of centers with the largest changes in firearm injuries in 2020 found that they were more often Level I centers, with higher historic trauma volumes and percentages of firearm injuries (p< 0.001). Unadjusted mortality decreased by 0.9% from 2011 to 2020, but after controlling for demographics, injury characteristics and physiology, there was no difference in adjusted mortality over the same time period. However, among patients with injury severity scores ≥25, adjusted mortality improved compared with 2011 (SMR of 0.950 in 2020; 95% confidence interval, 0.916–0.986).CONCLUSIONFirearm injuries pose an increasing burden to trauma systems, with Level I and high-volume centers seeing the largest growth in 2020. Despite increasing numbers of firearm injuries, mortality has remained unchanged over the past decade.LEVEL OF EVIDENCEPrognostic and Epidemiological; Level III.
Recent advances in clinical electroencephalography
Authors: Frauscher Birgit , Rossetti Andrea O. , Beniczky Sándor 
Published: January 2024

Purpose of review
Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients.

Recent findings
Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field.

Summary
This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.
Longitudinal trajectories of cognitive aging
Authors: Lobo Elena , Gracia-García Patricia , Lobo Antonio 
Published: January 2024

Purpose of review
This review summarizes recent evidence related to the cognitive trajectories of aging, the factors associated with the different trajectories, and the effect of sex on cognitive decline.

Recent findings
Trajectories of cognitive aging identified in different studies vary in number, in the proportion of individuals falling into each of the classes and in the predictors of class membership. Trajectories observed include types with ‘rapid decline’, those with ‘gradual decline’ and those with ‘maintenance of high level’ of cognitive performance. Predictors of decline and predictors of maintenance of cognitive performance may be different. While factors such as education were in general associated with high performance, and reversely with low performance, other factors, such as depression were predictors only for some groups, particularly the declining ones. Sex differences in cognitive trajectories and the associated predictive factors have also been identified.

Summary
The findings on education may be particularly important in populations with low educational level, especially among women and the findings on depression have special interest in preventing cognitive decline in women. Further work is required to explain intriguing inconsistencies observed in the literature.
Deep learning applications in vascular dementia using neuroimaging
Authors: Dong Chao , Hayashi Shizuka 
Published: January 2024

Purpose of review
Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning applications in VaD-related imaging biomarkers and diagnosis.

Recent findings
The main deep learning technology applied in VaD using neuroimaging data is convolutional neural networks (CNN). CNN models have been widely used for lesion detection and segmentation, such as white matter hyperintensities (WMH), cerebral microbleeds (CMBs), perivascular spaces (PVS), lacunes, cortical superficial siderosis, and brain atrophy. Applications in VaD subtypes classification also showed excellent results. CNN-based deep learning models have potential for further diagnosis and prognosis of VaD.

Summary
Deep learning neural networks with neuroimaging data in VaD research represent significant promise for advancing early diagnosis and treatment strategies. Ongoing research and collaboration between clinicians, data scientists, and neuroimaging experts are essential to address challenges and unlock the full potential of deep learning in VaD diagnosis and management.
Long-Term Effect of Maintenance Electroconvulsive Therapy in Patients With Depression—Data From a Small Randomized Controlled Trial
Authors: Brus Ole , Cao Yang , Carlborg Andreas , Engström Ingemar , von Knorring Lars , Nordenskjöld Axel 
Published: January 2024

Objectives
This study aimed to compare the long-term effects of maintenance electroconvulsive therapy (M-ECT) with medication and medication only in patients with depression.

Methods
A randomized controlled trial of 1 year of M-ECT with medication or medication only investigated relapse/recurrence among 56 patients in remission after electroconvulsive therapy (ECT) for depression was conducted. The results of the first year are published already and showed a significant advantage of M-ECT with medication.
The current study was a long-term follow-up. When the randomized treatment allocation ended, medication was continued in both groups but M-ECT was terminated. Patients were followed for up to 10 years via Swedish national registers until the study endpoint of a new psychiatric diagnosis as an inpatient, suicide, suspected suicide, or death of another cause. Time to relapse was compared between the M-ECT with medication group and the medication-only group using Kaplan-Meier estimates.

Results
The median follow-up time was 6.5 years for the M-ECT and medication group and 3.1 years for the medication-only group. One year after randomization 22 patients remained in the M-ECT and medication group, and 14 patients remained in the medication-only group. Relapse patterns between the treatment groups after the completion of M-ECT seemed to be similar according to visual inspection.

Conclusions
This long-term follow-up study suggests that most of the benefit achieved during the treatment period with M-ECT is maintained over several years, but the small sample size, with accompanying large statistical imprecision, makes the results uncertain. More long-term studies of M-ECT are required.
Trial registration: ClinicalTrials.gov identifier: NCT00627887
Lack of detection of SARS-CoV-2 in Wildlife from Kerala, India in 2020-21
Authors: Zachariah Arun , Krishnankutty Sajesh P , Manazhi Jishnu , Omanakuttan Vishnu , Santosh Sam , Blanchard Adam , Tarlinton Rachael 
Published: January 2024

Spillover of SARS-CoV-2 into a variety of wild and domestic animals has been an ongoing feature of the human pandemic. The establishment of a new reservoir in white tailed deer in North America and increasing divergence of the viruses circulating in them from those circulating in the human population has highlighted the ongoing risk this poses for global health. Some parts of the world have seen more intensive monitoring of wildlife species for SARS-CoV-2 and related coronaviruses but there are still very large gaps in geographical and species-specific information. This paper reports negative results for SARS-CoV-2 PCR based testing using a pan coronavirus end point RDRP PCR and a Sarbecovirus specific E gene qPCR on lung and or gut tissue from wildlife from the Indian State of Kerala. These animals included: 121 Rhinolophus rouxii (Rufous Horsehoe Bat), 6 Rhinolophus bedommei (Lesser Woolly Horseshoe Bat), 15 Rossettus leschenaultii (Fulvous Fruit Bat), 47 Macaca radiata (Bonnet macaques), 35 Paradoxurus hermaphroditus (Common Palm Civet), 5 Viverricula indica (Small Indian Civet) , 4 Herpestes edwardsii (Common Mongoose), 10 Panthera tigris (Bengal Tiger), 8 Panthera pardus fusca ( Indian Leopard), 4 Prionailurus bengalensis (Leopard cats), 2 Felis chaus (Jungle cats), 2 Cuon alpinus (Wild dogs) and 1 Melursus ursinus (sloth bear).
Studies on Human-Coronavirus protein-protein interaction network from the perspective of viral adaptation in a novel host
Authors: Acharya Debarun , Dutta Tapan K 
Published: January 2024

AbstractHost-pathogen interaction is the best example of an evolutionary arms race where pathogen and host continuously coevolve to survive and exert negative effects on each other. The adaptability of both host and pathogen is critical for this association. In this study, we explored the adaptation of the severe acute respiratory syndrome (SARS) coronavirus (CoV) in humans from the genomic and evolutionary perspectives based on a comparative analysis of SARS-CoV2-human and Other-CoV-human interactions. We observed that human proteins that are part of the SARS-CoV2-human association are less enriched in hubs and bottlenecks. Again, they also take part in fewer protein complexes and show faster evolutionary rates compared to the Other-CoV-associated human proteins. The human proteins involved in the interaction with SARS-CoV2 are mostly longer proteins harboring long disordered stretches and a higher level of disordered protein binding sites. Codon usage analysis also revealed that the novel coronavirus is least adapted to codons, used in housekeeping genes and genes that get expressed in lung tissues, compared to other two deadly coronaviruses, SARS CoV1 and MERS CoV. We conclude that the signatures showed by SARS-CoV2-human protein interaction network represent a model for understanding the evolutionary feature of an early stage of host-virus association in comparison to that obtained from relatively long-term-associated host-virus interactions, achieving higher levels of adaptation.ImportanceThe current study focuses on the evolution of viruses, using novel coronavirus as a model. The host-pathogen interaction is better viewed from molecular perspective, where host and pathogen proteins interact and co-evolve with each other. This is even more important for viruses, which use the host’s cellular machinery for protein synthesis. As for a deadly virus, killing the host is inadvertent as it is also suicidal for the virus itself. A more stable association that is beneficial for both host and virus will require host adaptation, achievable through evolutionary time. In this study, the deadly novel human coronavirus has been viewed as the early stage of host viral interaction, which has been achieved through a more random way, with lower adaptation to host codons compared to the other human coronaviruses, having more stable host-association for being evolutionarily older.
Targeted Perturb-seq Reveals EGR1 and FOS as Key Regulators of the Transcriptional RAF-MAPK Response
Authors: Kassem Ghanem El , Sieber Anja , Klinger Bertram , Uhlitz Florian , Steinbrecht David , van Bentum Mirjam , Hillmer Jasmine , von Schlichting Jennifer , Schäfer Reinhold , Blüthgen Nils , Boettcher Michael 
Published: January 2024

AbstractThe MAPK pathway is an important cellular signaling cascade whose dysregulation causes a variety of diseases. While the upstream regulators of this cascade have been extensively characterized, the understanding of how its activation translates into different transcriptional responses remains poorly understood. This study attempts to fill this knowledge gap by using targeted Perturb-seq against 22 transcription factors in an inducible model system for RAF-MAPK signaling. A topology-based modeling approach is applied to the obtained data to construct a directional interaction network. By removing coherent feed-forward loops and integrating the expression kinetics of transcription factors, a parsimonious network structure is derived that distinguishes direct from indirect interactions between the investigated transcription factors and their targets. In particular, EGR1 and FOS are found to act as orthogonal upstream regulators of the RAF-MAPK response. The results presented here provide valuable insights into the organization of the transcriptional network downstream of RAF-MAPK signaling and thus provide a basis for a better understanding of this complex process.
Urban sensory conditions alter rival interactions and mate choice in urban and forest túngara frogs
Authors: Smit Judith A.H. , Thijssen Vera , Cronin Andrew D. , Ellers Jacintha , Halfwerk Wouter 
Published: January 2024

AbstractSexual communication often takes place in networks with multiple competing signalers being simultaneously assessed by mate choosers. Altered sensory conditions, such as noise and light pollution, can affect communication by altering signal production and perception. While evidence of sensory pollution affecting sexual signaling is widespread, few studies assess impacts on sexual signaling during rival interactions as well as mate choice, let alone whether urban and forest populations have diverged in their response. Here, we investigate the effects of urban sensory conditions on sexual communication in urban and forest túngara frogs (Engystomops pustulosus). We recorded dyadic vocal rival interactions and assessed mate choice with and without noise and light pollution in the lab. We show that urban sensory conditions can directly impact the intensity of rival interactions, differences between rivals and mate choice, though changes were often in opposite directions for frogs of urban and forest origins. Moreover, we demonstrate that changes in rival interactions in response to light and noise pollution can also indirectly affect how females choose between potential mates. Our study reveals origin-dependent direct and indirect effects of noise and light pollution, which ultimately translate into fitness consequences for sexual communication in urban populations.
Resource-explicit interactions in spatial population models
Authors: Champer Samuel E. , Chae Bryan , Haller Benjamin C. , Champer Jackson , Messer Philipp W. 
Published: January 2024

ABSTRACTContinuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.
Genetic Associations of Lipid-Lowering Drugs and Parkinson’s Disease: A Mendelian Randomization Study
Authors: Shen Lin , Yang Yifan , Li Yi , Chin Hok Leong , Huang Helen , Cheung Bernard Man Yung , Tse Gary , Chou Oscar Hou In , Zhou Jiandong 
Published: January 2024

AbstractBackgroundThere is a need to establish the role of lipid-lowering agents as a therapeutic option for Parkinson’s Disease (PD), but its associations remain elusive. This study investigated genetic variants proxying lipid-lowering agents through HMGCR, NPC1L1, and PCSK9 inhibitors to determine casual associations with PD risk.MethodsWe utilized a two-sample Mendelian randomization (MR) framework, where low-density lipoprotein (LDL) was the outcome of interest. Genetic associations with LDL were extracted from the Global Lipids Genetics Consortium. Summary statistics for PD were extracted from two GWAS datasets, consistent of 1,843 PD cases and 216,630 control in the first dataset and 1,570 PD cases and 1,259 controls in the second dataset. Instrumental variables (IV) were optimized with positive control analyses on cardiovascular and metabolic outcomes. IV-exposure associations from LDL GWAS data were integrated with IV-outcome associations from the PD GWAS data. The inverse variance weighted method was applied. Bayesian colocalization analysis identified target gene regions for LDL and PD.ResultsGenetic variations inHMGCRwere significantly associated with a reduced risk of PD (odds ratio [OR] = 0.54, 95% CI 0.34-0.86). However, variation inHMGCRwas associated with an increased risk of the tremor-dominant (TD) subtype compared to the postural instability/gait difficulty (PIGD) subtype (OR = 8.43, 95% CI 2.12-33.52). There were trends with increased risk for the TD subtype inNPC1L1and a decreased risk inPCSK9but these findings did not meet the Bonferroni threshold. We identified two single nucleotide polymorphisms (SNPs) inHMGCRwithin the same genomic region of close proximity, with rs12916 as the leading SNP associated with LDL and rs10942735 as the leading SNP associated with PD.ConclusionA casual association betweenHMGCRinhibition and reduced overall PD risk was identified, but there were increased the risks of tremor-dominant subtypes.
The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer’s Disease Using GPT Embeddings
Authors: Runde Benjamin S. , Alapati Ajit , Bazan Nicolas G. 
Published: January 2024

AbstractAs the impact of Alzheimer’s disease (AD) is projected to grow in the coming decades as the world’s population ages, the development of noninvasive and cost-effective methods of detecting AD is essential for the early prevention and mitigation of the progressive disease, alleviating its expected global impact. This study analyzes audio processing techniques and transcription methodologies to optimize the detection of AD through the natural language processing (NLP) of spontaneous speech. We enhanced audio fidelity using Boll Spectral Subtraction and evaluated the transcription accuracy of state-of-the-art AI services—locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI—against traditional manual transcription methods. The choice between local and cloud-based solutions hinges on a trade-off between privacy, ongoing costs, and computational requirements. Leveraging OpenAI’s GPT for word embeddings, we enhanced the training of Support Vector Machine (SVM) classifiers, which were crucial in analyzing transcripts and refining detection accuracy. Our findings reveal that AI-driven transcriptions significantly outperform manual counterparts when classifying AD and Control samples, with Wav2Vec using enhanced audio exhibiting the highest accuracy and F-1 scores (0.99 for both metrics) for locally based systems and Rev AI using unenhanced audio leading cloud-based methods with comparable precision (0.96 for both metrics). The study also uncovers the detrimental effect of including interviewer speech in recordings on model performance, advocating for the exclusion of such interactions to improve data quality for AD classification algorithms. Our comprehensive evaluation demonstrates that AI transcription (both Cloud and Local) and NLP technologies in their current forms can classify AD, as well as probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, accurately but suffer from a lack of available training data. The insights garnered from this research lay the groundwork for future advancements in the noninvasive monitoring and early detection of cognitive impairments through linguistic analysis.
Metagenomics insights into microbial diversity shifts among seagrass sediments
Authors: Detcharoen Matsapume , Rattanachot Ekkalak , Prathep Anchana 
Published: January 2024

AbstractSeagrasses are important in marine ecosystems by providing food and shelter for animals, storing carbon, and reducing sulfide and methane emissions. Previous studies found variations in the microbiomes of seagrass both above and below sediment surface, with certain microbes serving as indicators of seagrass health. Despite numerous studies on bacterial communities in specific seagrass species, little is known about bacterial communities in mixed seagrass beds. This study used metagenomics to investigate microbial diversity in bare and seagrass-associated sediments, across seven seagrass species combinations. The findings revealed higher alpha diversity of bacteria and archaea in the upper layers, whereas microbial eukaryotes were more diverse in the lower layers. Significant taxonomic differences were found between bare and seagrass sediments, and among seagrass species. Several metagenome-assembled genomes were reconstructed, primarily from Pseudomonadota and Thermodesulfobacteriota. This study improves our understanding of complex interactions between seagrasses and their associated microbial communities and laying a foundation for efficient strategies for the management and restoration of seagrass ecosystems.