bluuebunny/crossref_metadata_2025_split huggingface dataset:
Life-threatening bilateral internal carotid artery and unilateral vertebral artery total occlusion presenting with dizziness: a case report
Authors: Jobran Afnan W.M. , Warasna Haya Jebreen Mohammad , Awad Mohammad Yaser Hasan , Awad Bashar Yaser Hasan , Shahin Farah Bilal Yousef , Alhadad Baha , Amr Bajis , Abuturki Abdelwadod A , Smerat Mohammad I. 
Published: July 2024

Introduction:
It is well known that whereas bilateral occlusion is less common, unilateral internal carotid artery blockage happens regularly. Asymptomatic to fatal ischemic stroke can be the clinical presentation, depending on whether there is adequate collateral blood flow. Internal carotid artery occlusion is often associated with significant neurologic events, both at the time of initial occlusion and during follow-up.

Case presentation:
The authors describe a patient’s experience of dizziness followed by a fall. The hyperdense position of the basilar artery near its bifurcation was identified following a computed tomography (CT) scan. Furthermore, the pons, midbrain, and medial parts of the right temporal lobe showed numerous small hypodensities suggestive of an ischemic injury. The patient had medical treatment without surgery after it was determined that they had bilateral internal carotid arteries and a blocked right vertebral artery. After taking dual anti-platelet therapy for 5 days, the patient recovered without incident and was discharged from the hospital.

Discussion:
In a young patient with bilateral internal carotid arteries occlusion (BICAO), the authors highlighted the significance of prompt diagnosis of stroke-like symptoms, diagnostic possibilities, and treatment options. Options for diagnosis include brain MRI and CT head to check for ischemia and CT angiography (CTA) head and neck to assess for artery obstruction. Options for treatment include severe medical and surgical treatments, such as carotid endarterectomy, stent implantation, or balloon angioplasty, or medical management alone, such as dual anti-platelet medication and thrombolysis.

Conclusion:
BICAO is associated with a grave prognosis and significant cerebrovascular complications. High-quality studies are needed to establish the best treatment strategy, considering the complex and individualized nature of the condition.
Effects of Footbath on Pain, Anxiety, Sleep, and Comfort Levels in Patients With Postlaparoscopic Cholecystectomy: A Randomized Controlled Study
Authors: Gürçayir Dilek , Karabulut Neziha 
Published: July 2024

Purpose:
The aim of the study is to determine the effect of of hot footbaths on the pain, anxiety, sleep, and comfort levels of patients who underwent laparoscopic cholecystectomy.

Design:
The study is a randomized controlled designed.

Methods:
This study was conducted in surgery clinic of a university hospital between January 2022 and November 2022. The research was completed with 54 patients in the experimental group and 54 patients in the control group.

Findings:
The mean state anxiety score and VAS-Sleep score of the patients in the experimental group 120 minutes after the application was 31.07±4.70 and 612.62±82.37, respectively, which was statistically significantly lower than that of the patients in the control group (P<0.05). On the other hand, at the 120th minutes after the application, the mean VAS-Comfort scores of the patients in the experimental group were statistically significantly higher than those of the patients in the control group (P<0.05). A positive relationship was found between the mean VAS-Pain and VAS-Sleep scores of the patients in the experimental group, and a significant negative relationship was found between the mean VAS-Comfort and state anxiety scores.

Conclusions:
Foot bath is effective in reducing the pain and anxiety levels of patients undergoing laparoscopic cholecystectomy surgery.
Patterns of Injury in a Gaza War Hospital
Authors: Alsalqawi Abdalkarim , Villar Richard 
Published: July 2024

AbstractThis study describes the patterns of injury observed in a Gaza war hospital, focusing on 110 consecutive patients. Of these, two had conditions unrelated to trauma, so the analysis was of the remaining 108 casualties. No military personnel were seen. The data reveal clear trends, including a high prevalence of explosive injuries (86.36%), a low prevalence of gunshot wounds (8.18%), a notable proportion of female (34.55%) and child (23.64%) casualties, and the occurrence of multiple injuries (1.73 injuries/patient) because of the use of high explosive. There were 187 injuries identified including 128 fractures. Of these fractures, 64.84% were of the lower limb and 28.91% of the upper limb. Of the 128 fractures, 79 (61.72%) were clinically and/or radiologically infected. The most frequently infected fracture was the compound tibial and fibular fracture, which showed an infection rate of 92.86%. These findings highlight the unique and tragic nature of the Gaza conflict, the increasing injuries to civilians, including women and children, and the long-term healthcare that will be needed.
Association of serum adiponectin and leptin levels with inner retinal thickness among individuals with or without elevated HbA1c
Authors: Koseoglu Neslihan D , Wang Jiangxia , Anokye-Danso Frederick , Moreno Jose Amezcua , Cha Eumee , Fuchs Franklin , Teed Jacqueline , Yao Jianing , Zhang Yan , Ahima Rexford S , Sachdeva Mira M 
Published: July 2024

AbstractInner retinal thinning precedes clinical evidence of retinopathy in prediabetes and diabetes mellitus (DM), and may contribute to retinopathy development and progression. Serum levels of the adipokines leptin and adiponectin are inversely related in the setting of impaired glucose homeostasis, but their potential association with inner retinal thickness is unknown. In this prospective study, both eyes from 24 individuals with prediabetes or type 2 DM (glycated hemoglobin [HbA1c] ≥5.7) and 16 controls (HbA1c<5.7) underwent spectral-domain optical coherence tomography imaging of the macula, and thickness of the nerve fiber layer (NFL) and ganglion cell layer-inner plexiform layer (GCL-IPL) was analyzed in each subfield of the Early Treatment Diabetic Retinopathy Study grid. Serum samples were collected and metabolic factors, including adiponectin and leptin, were measured. Adjusted regression analyses revealed inverse associations of these adipokines with NFL thickness that did not differ between prediabetes/DM and controls, but differential positive associations of adiponectin with GCL-IPL thickness only in the prediabetes/DM group. The results of our pilot study suggest opposing roles for adiponectin and leptin in the retina, similar to their relationship in systemic disease, and suggest that serum adiponectin may represent a potential clinical biomarker for inner retinal thickness in patients with elevated HbA1c.
Precision Medicine in Cardiology: An Evolving Understanding of Biomarkers in Coronary Artery Disease Prevention a 10-year Thematic Review
Authors: Borges Julian Yin Vieira 
Published: July 2024

AbstractBackgroundCoronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Precision medicine, utilizing biomarkers for early detection and prevention of CAD, has emerged as a promising approach to improve patient outcomes. This systematic review aims to summarize the current state of knowledge regarding biomarkers in CAD prevention, focusing on the most promising and well-studied biomarkers over the past decade.MethodsFollowing PRISMA guidelines, a systematic review was conducted. PubMed, Embase, and Cochrane Library databases were searched for relevant studies published between 2013 and 2023. Inclusion criteria were original research articles, systematic reviews, or meta-analyses focusing on biomarkers in CAD prevention, with sufficient data on biomarker performance. Exclusion criteria were case reports, editorials, conference abstracts, and studies on biomarkers in established CAD. The STARD “Standards for Reporting Diagnostic accuracy studies” 2015 guideline criteria for assessing diagnostic tools was also utilized to ensure the precision of the methodology and help readers to appraise the applicability of the study findings and the validity of conclusions and recommendations. The main outcome assessed was the association between biomarkers and CAD risk, using various statistical methods.FindingsThe search identified 2,345 articles, of which 40 met the inclusion criteria, including 32 original research articles, 6 systematic reviews, and 2 meta-analyses. The biomarkers studied included traditional risk factors (lipid profiles and blood pressure), novel biomarkers (high-sensitivity C-reactive protein, homocysteine, and adipokines), and imaging biomarkers (coronary artery calcium scores and carotid intima-media thickness). Several studies demonstrated the association between these biomarkers and increased CAD risk, independent of traditional risk factors. Multi-marker approaches combining various biomarkers showed improved accuracy in CAD risk assessment compared to traditional risk factors alone. The risk of bias and variability between studies were assessed and reported.InterpretationThis systematic review provides a comprehensive overview of the current landscape of biomarkers in CAD prevention. While traditional risk factors remain important, novel and imaging biomarkers have shown promise in improving risk stratification and guiding personalized prevention strategies.However, challenges remain in translating biomarker research into clinical practice, including the need for standardized guidelines, cost-effectiveness analyses, and further research on multi-marker approaches and personalized prevention strategies. Addressing these challenges and developing evidence-based guidelines for biomarker use in CAD prevention can improve risk assessment accuracy, tailor prevention strategies, and ultimately reduce the global burden of CAD.
Effectiveness of XBB.1.5 Vaccines Against Omicron Subvariants
Authors: Lin Dan-Yu , Du Yi , Xu Yangjianchen , Paritala Sai , Donahue Matthew , Maloney Patrick 
Published: July 2024

AbstractBackgroundThe updated Moderna, Pfizer-BioNTech, and Novavax COVID-19 vaccines containing the SARS-CoV-2 omicron XBB.1.5 strain have replaced their predecessors in the United States and in other countries since the fall of 2023. The clinical impact of these vaccines on currently circulating variants was unknown.AimsWe aimed to assess the effectiveness of the updated XBB.1.5 vaccines against currently circulating omicron subvariants.MethodsWe examined data on the administration of XBB.1.5 vaccines and the incidence of COVID-19 between September 11 and November 27, 2023 for approximately 2 million persons by linking records from the Nebraska Electronic Disease Surveillance System and the Nebraska State Immunization Information System. We used Cox regression to estimate the effects of XBB.1.5 vaccines on the risk of COVID-19, as a function of time elapsed since vaccination, while adjusting for demographic factors, previous infection history, and previous vaccination history.ResultsThe effectiveness (i.e., proportionate reduction of risk) for XBB.1.5 vaccines against SARS-CoV-2 infection was 63.0% (95% confidence interval [CI], 48.6 to 73.4) 4 weeks after vaccination and 67.1% (95% CI, 49.9 to 78.4) 6 weeks after vaccination; vaccine effectiveness started to decline after 6 weeks. Vaccine effectiveness was broadly similar across subgroups defined by age, sex, race and ethnicity, socioeconomic status, and previous immunity status.ConclusionXBB.1.5 vaccines were effective against currently circulating variants, regardless of age, sex, race and ethnicity, socioeconomic status, or previous immunity status. These findings can be used to develop effective prevention strategies against COVID-19.
Leveraging machine learning to streamline the development of liposomal drug delivery systems
Authors: Eugster Remo , Orsi Markus , Buttitta Giorgio , Serafini Nicola , Tiboni Mattia , Casettari Luca , Reymond Jean-Louis , Aleandri Simone , Luciani Paola 
Published: July 2024

AbstractDrug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible.
Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins
Authors: de Crécy-Lagard Valérie , Dias Raquel , Friedberg Iddo , Yuan Yifeng , Swairjo Manal A. 
Published: July 2024

AbstractThirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein “unknome”. This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for over 450 enzymes of unknown function from the model bacteriaEscherichia coliuxgsing the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.Article SummaryMany proteins in any genome, ranging from 30 to 70%, lack an assigned function. This knowledge gap limits the full use of the vast available genomic data. Machine learning has shown promise in transferring functional knowledge from proteins of known functions to similar ones, but largely fails to predict novel functions not seen in its training data. Understanding these failures can guide the development of better machine-learning methods to help experts make accurate functional predictions for uncharacterized proteins.
Genetic architecture of the thermal tolerance landscape ofDrosophila melanogaster
Authors: Soto Juan , Pinilla Francisco , Olguín Patricio , Castañeda Luis E. 
Published: July 2024

ABSTRACTIncreased environmental temperatures associated with global warming strongly impact the natural populations of ectothermic species. Therefore, it is crucial to understand the genetic foundations and evolutionary potential of heat tolerance. However, heat tolerance and its genetic components depend on the methodology, making it difficult to predict the adaptive responses to global warming. Here, we measured the knockdown time for 100 lines from theDrosophilaGenetic Reference Panel (DGRP) at four different static temperatures, and we estimated their thermal death time (TDT) curves, which incorporate the magnitude and the time of exposure to thermal stress. Through quantitative genetic analyses, it was observed that the knockdown time showed a significant heritability at different temperatures and that its genetic correlations decreased as temperatures were dissimilar. Significant genotype-by-sex and genotype-by-environment interactions were noted for knockdown time. We also discovered heritable variation for the two parameters of TDT:CTmaxand thermal sensitivity. Taking advantage of the DGRP, we performed a GWAS and identified multiple variants positively associated with the TDT parameters, which map to genes related to signaling and developmental functions. We performed functional validations for some candidate genes using RNAi, which revealed that genes such asmam,KNCQ,orrobo3affect the knockdown time at a specific temperature but are not associated with the TDT parameters. Ultimately, the thermal tolerance landscape must possess the ability to adapt to the selective pressures caused by global warming. Genetic variation of phenotypic plasticity and sexually antagonistic pleiotropy should also facilitate the adaptive process and maintain the genetic diversity inDrosophilapopulations.
A diverse high-fibre plant-based dietary intervention improves gut microbiome composition, gut symptoms, energy and hunger in healthy adults: a randomised controlled trial
Authors: Creedon Alice C. , Bernard Hannah , Amati Federica , Segata Nicola , Wallace Meg , Arrè Alberto , Smith Harry A. , Platts Alex , Bulsiewicz William J. , Bermingham Kate M. , Capdevila Pujol Joan , Piperni Elisa , Roomans Ledo Ana , Johnson Claire , Caro Catherine , Karimjee Nafisa , Linenberg Inbar , Giordano Francesca , Davies Richard , Wolf Jonathan , Asnicar Francesco , Spector Tim D. , Berry Sarah E. 
Published: July 2024

AbstractDiets low in diverse fibre-rich plant foods are a major factor in the rise of chronic diseases globally. The BIOME study (NCT06231706) was a 6-week, parallel design randomised controlled trial in 399 healthy adults in the UK, investigating a simple dietary intervention containing 30+ whole-food ingredients high in plant polyphenolic compounds, fibre and micronutrients. Participants were randomised to the primary intervention (prebiotic blend; 30g/d) or control (bread croutons; 28g/d; isocaloric functional equivalent) or a daily probiotic (L. rhamnosus). The primary outcome was change in ‘favourable’ and ‘unfavourable’ microbiome species compared to control, secondary outcomes included changes in blood metabolites, gut symptoms, stool output, anthropometric measures, subjective hunger, sleep, energy and mood. A crossover test meal challenge sub-study was conducted in 34 participants, investigating postprandial glucose responses, subjective hunger, satiety and mood.In the 349 male and female participants (mean age 50yrs) included in the analysis (intention-to-treat), self-reported adherence was high (> 98% for all treatments). Following the prebiotic blend, significant improvements were seen in the change and ranking of ‘favourable’ and ‘unfavourable’ species as well as beta diversity (weighted-UniFrac measure), but not in the control or probiotic group. There were significantly greater improvements in self reported indigestion, constipation, heartburn, flatulence and energy, following the prebiotic vs control, and hunger following the prebiotic vs probiotic. Addition of the prebiotic to a high carbohydrate test meal challenge resulted in significant improvements in subjective hunger, fullness, and energy (3h incremental area under the curve). No other significant differences between groups were observed.This prebiotic blend is a simple dietary strategy that benefits gut microbiome composition, gut symptoms and self-reported energy and hunger.Graphical abstract
Predicting diagnostic progression to schizophrenia or bipolar disorder via machine learning applied to electronic health record data
Authors: Hansen Lasse , Bernstorff Martin , Enevoldsen Kenneth , Kolding Sara , Damgaard Jakob Grøhn , Perfalk Erik , Nielbo Kristoffer L. , Danielsen Andreas A. , Østergaard Søren D. 
Published: July 2024

AbstractImportanceThe diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment.ObjectiveTo investigate whether machine learning models trained on routine clinical data from electronic health records (EHRs) can predict diagnostic progression to schizophrenia or bipolar disorder among patients undergoing treatment in psychiatric services for other mental illness.DesignCohort study based on data from EHRs.SettingThe psychiatric services of the Central Denmark Region.ParticipantsAll patients between ≥15 and <60 years with at least one contact with the psychiatric services of the Central Denmark Region between 2011 and 2021. Patients with only a single contact were removed, leaving a total of 24,449 eligible patients with 398,922 outpatient contacts with the psychiatric services.ExposuresPredictors based on EHR data, including medications, diagnoses, and clinical notes.Main Outcomes and MeasuresDiagnostic transition to schizophrenia or bipolar disorder within 5 years, predicted one day before outpatient contacts by means of regularized logistic regression and Extreme Gradient Boosting (XGBoost) models.ResultsTransition to the first occurrence of either schizophrenia or bipolar disorder was predicted by the XGBoost model with an area under the receiver operating characteristics curve (AUROC) of 0.70 on the training set, and 0.64 on the test set which consisted of two held-out hospital sites. At a predicted positive rate of 4%, the XGBoost model had a sensitivity of 9.3%, a specificity of 96.3%, and a positive predictive value of 13.0%. Predicting schizophrenia and bipolar disorder separately yielded AUROCs of 0.80 and 0.62, respectively, on the test set.The clinical notes proved particularly informative for prediction.Conclusions and relevanceIt is possible to predict diagnostic transition to schizophrenia and bipolar disorder from routine clinical data extracted from EHRs, with schizophrenia being notably easier to predict than bipolar disorder.Key PointsQuestionCan diagnostic progression to schizophrenia or bipolar disorder be accurately predicted from routine clinical data extracted from electronic health records?FindingsIn this study, which included all patients aged between ≥15 and <60 years with contacts to the psychiatric services of the Central Denmark Region between 2011 and 2021, progression to schizophrenia was predicted with high accuracy, with bipolar disorder proving a more difficult target.MeaningDetecting progression to schizophrenia through machine learning based on routine clinical data is feasible. This may reduce diagnostic delay and duration of untreated illness.
Disability Inclusion in National Surveys
Authors: Cerilli Caroline , Varadaraj Varshini , Choi Jennifer , Sweeney Fiona , Castro Franz , Landes Scott D. , Swenor Bonnielin K. 
Published: July 2024

AbstractNational surveys are important for understanding the disparities that disabled people experience across social determinants of health; however, limited research has examined the methods used to include disabled people in these surveys. This study reviewed nationally representative surveys administered by the Centers for Disease Control and Prevention (CDC) and the U.S. Census Bureau that collected data in the past five years and sampled adults ≥18 years. Data from both publicly available online survey documents and a questionnaire emailed to survey administrators were used to determine whether surveys: 1) oversampled disabled people; 2) had a data accessibility protocol to support data collection; and 3) provided multiple data collection modalities (e.g., phone, paper). Of the 201 surveys identified, 30 met the inclusion criteria for the study. Of these 30 surveys, one oversampled disabled people, none had a data collection accessibility protocol, and 21 provided multiple data collection modalities. This study highlights barriers and opportunities to including disabled people in national surveys, which is essential for ensuring survey data are generalizable to the U.S. population.