bluuebunny/crossref_metadata_2025_split huggingface dataset:
Algorithmic identification of treatment-emergent adverse events from clinical notes using large language models: a pilot study in inflammatory bowel disease
Authors: Silverman Anna L , Sushil Madhumita , Bhasuran Balu , Ludwig Dana , Buchanan James , Racz Rebecca , Parakala Mahalakshmi , El-Kamary Samer , Ahima Ohenewaa , Belov Artur , Choi Lauren , Billings Monisha , Li Yan , Habal Nadia , Liu Qi , Tiwari Jawahar , Butte Atul J , Rudrapatna Vivek A 
Published: September 2023

AbstractBackground and AimsOutpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLM) like BERT have shown progress in a range of natural language processing tasks but have not yet been evaluated on adverse event detection.MethodsWe adapted a new clinical LLM, UCSF BERT, to identify serious adverse events (SAEs) occurring after treatment with a non-steroid immunosuppressant for inflammatory bowel disease (IBD). We compared this model to other language models that have previously been applied to AE detection.ResultsWe annotated 928 outpatient IBD notes corresponding to 928 individual IBD patients for all SAE-associated hospitalizations occurring after treatment with a non-steroid immunosuppressant. These notes contained 703 SAEs in total, the most common of which was failure of intended efficacy. Out of 8 candidate models, UCSF BERT achieved the highest numerical performance on identifying drug-SAE pairs from this corpus (accuracy 88-92%, macro F1 61-68%), with 5-10% greater accuracy than previously published models. UCSF BERT was significantly superior at identifying hospitalization events emergent to medication use (p < 0.01).ConclusionsLLMs like UCSF BERT achieve numerically superior accuracy on the challenging task of SAE detection from clinical notes compared to prior methods. Future work is needed to adapt this methodology to improve model performance and evaluation using multi-center data and newer architectures like GPT. Our findings support the potential value of using large language models to enhance pharmacovigilance.
Cryoballoon Ablation of RooF line combined with pulmonary vein Isolation for persistent atrial fibrillation (The CARFI-PerAF Randomized Clinical Trial)
Authors: Huang Songqun , Zhao Yao , Ju Ruihan , Liu Chao , Dong Shaohua , Qin Aihong , Cao Jiang , Yu Manli , Guo Zhifu , Huang Xinmiao 
Published: September 2023

AbstractBackgroundThe limited effectiveness of pulmonary vein isolation (PVI) alone using cryoballoon ablation (CBA) led to addictive ablation in procedures of persistent atrial fibrillation (AF) ablation. Roof line (RL) ablation in addition to PVI hold great promise for reduction of AF recurrence after CBA. The randomized controlled CARFI-PerAF trial aimed to prospectively investigate the efficacy of a novel CBA strategy for block of RL and reduction of AF recurrence.MethodsOne hundred and ten patients who were diagnosed with persistent AF were randomized into PVI group and PVI+RL group. ‘Quarter balloon ablation technique’ and ‘roof distortion technique’ were used to improve quality of RL ablation. Conduction block of RL was confirmed by both voltage mapping and upper right atrial septum pacing. Primary effectiveness was freedom from AF or atrial tachycardia absent class I/III antiarrhythmic drugs through 12-month follow-up according to ECGs collected by portable device and 24-hour Holter.ResultsThere was no significant difference in AF recurrence between PVI group and PVI+RL group (63.5% vs 76.2%, P = 0.296) after 532.7 ± 171.0 days of follow-up. However, blocked RL was associated with a significant reduction in risk of AF recurrence in the PVI+RL group (84.0% vs 45.5%, P = 0.025). The shape of RL was the only factor affecting the success rate of RL block. Patients with ‘Regular’ shape of RL predicted a higher rate of RL block than other types (89.7% vs 56.3%, P = 0.014).ConclusionsBlocked roof line ablation was associated with a significant reduction in risk of atrial fibrillation recurrence after cryoballoon ablation. Patients with ‘Regular’ shape of roof line may benefit more from roof line ablation.
The Value of the Gensini Score For Prognostic Assessment in Patients with Acute Coronary Syndrome--A Retrospective Cohort Study Based on Machine Learning Methods
Authors: Chen Lixia , Jia Sixiang , Mou Xuanting , Tu Yiting , Lin Wenting , Chao Feng , Xia Shudong 
Published: September 2023

AbstractBackgroundThe Gensini score (GS) provides a good assessment of the degree of coronary plate loading. However, its clinical significance has been little explored.MethodsIn this retrospective cohort study, we implemented model development and performance comparison on database of The Fourth Affiliated Hospital of Zhejiang University School of Medicine (2019.1-2020.12). The patients were followed up for 2 years. Follow-up endpoint was the occurrence of MACCEs. We extracted clinical baseline data from each ACS patient within 24 hours of hospital admission and randomly divided the datasets into 70% for model training and 30% for model validation. Area under the curve (AUC) was used to compare the prediction performance of XGBoost, SGD and KNN. A decision tree model was constructed to predict the probability of MACCEs using a combination of weight features picked by XGBoost and clinical significance.ResultsA total of 361 ACS patients who met the study criteria were included in this study. It could be observed that the probability of a recurrent MACCEs within 2 years was 25.2%. XGboost had the best predictive efficacy (AUC:0.97). GS has high clinical significance. Then we used GS, Age and CK-MB to construct a decision tree model to predict the probability model of MACCEs reoccurring, and the final AUC value reached 0.771.ConclusionsGS is a powerful indicator for assessing the prognosis of patients with ACS. The cut-off value of GS in the decision tree model provides a reference standard for grading the risk level of patients with ACS.
Genome-Wide Association Study and Meta-Analysis of Phytosterols Identifies a Novel Locus for Serum Levels of Campesterol
Authors: Alenbawi Jamil , Al-Sarraj Yasser A. , Umlai Umm-Kulthum I. , Kadhi Ayat , Hendi Nagham N. , Nemer Georges , Albagha Omar M. E. 
Published: September 2023

AbstractBackgroundSitosterolemia is a rare inherited disorder caused by mutations in theABCG5/ABCG8genes. These genes encode proteins that are involved in the transport of plant sterols (phytosterols) out of the body. Mutations in these genes lead to decreased excretion of phytosterols, which can accumulate in the body and lead to a variety of health problems, including xanthoma formation, atherosclerosis, and premature coronary artery disease.MethodsWe conducted the first genome-wide association study (GWAS) in the Middle East/North Africa (MENA) population to identify genetic determinants of plant sterol levels in Qatari people. GWAS was performed on serum levels of β-sitosterol and campesterol measured using the Metabolon platform from the Qatar Biobank Cohort and genome sequence data provided by Qatar Genome Program. Furthermore, a trans-ancestry meta-analysis of data from our Qatari cohort with summary statistics from a previously published large cohort (9,758 subjects) of European ancestry was conducted.ResultsUsing conditional analysis, we identified two independent single nucleotide polymorphisms (SNPs) associated with β-sitosterol (rs145164937 and rs4299376), and two independent SNPs associated with campesterol (rs7598542 and rs75901165) in the Qatari population in addition to two previously reported variants (rs199689137 and rs4245791). All of them map to theABCG5/8locus except rs75901165 which is located within the Intraflagellar Transport 43 (IFT43) gene. The meta-analysis replicated most of the reported variants, and our study provided significant support for the association of variants inSCARB1andABOwith sitosterolemia.ConclusionsThis is the first study to shed light on genetic determinants of phytosterols in the MENA region using a genome-wide association approach. We also established the first polygenic risk score for this trait using the European large cohort study. These findings may have future implications on the personalized treatment of hyperlipidemia in general while showing the importance of building population-specific multi-omics databases.Clinical PerspectiveThe formulation of the first polygenic risk score for sitosterolemia provides an exceptionally reliable tool for polygenic evaluation.Plant sterol measurement should be included in lipid panel checkups and genetic screening for patients with tendon xanthomas to ensure an accurate diagnosis.The establishment of regional and national registries for sitosterolemia in the Qatari community is essential for effective monitoring of the incidence of the disease and reducing the risk of early coronary artery disease.
Poor Accuracy of Triage Blood Pressure Measurements in a Pediatric Obesity Hypertension Clinic
Authors: Wen William , Psoter Kevin J. , Solomon Barry S. , Urbina Elaine M. , Brady Tammy M. 
Published: September 2023

AbstractAccurate triage blood pressure (BP) measurements are essential for clinical decision making. We investigated the agreement and diagnostic accuracy of usual triage vs. standardized BP measurements in an obesity-related hypertension clinic.This was a retrospective study of paired triage and standardized BP measurements from patients 4-21 years old. Triage BPs are measured by a medical assistant or nurse, often by automated device. Triplicate manual BP measurements are obtained by the clinic physician. Bland-Altman analyses determined mean differences between paired triage and mean standardized BPs. GEE- based multivariable relative risk (RR) regression determined the RR of triage BP overestimation by ≥5mmHg. Overall agreement, sensitivity, specificity, positive predictive value, and negative predictive value of usual triage BP measurements identifying hypertensive BP were determined.130 participants with 347 clinic encounters were included. Mean age was 13.3 years (standard deviation 3.94), 76% were Black race, 58% were male. Overall mean systolic and diastolic BP difference was 8.7 mmHg (95% limits on agreement: −16.66, 34.07) and 4.1 mmHg (95% limits on agreement: −18.56, 26.68), respectively. Triage systolic BP was more likely overestimated by ≥5mmHg when a large adult (RR=1.49; 95% confidence interval: 1.00, 2.21) or thigh cuff (RR=1.94; 95% confidence interval: 1.08, 3.51) was required vs. when a child/adult cuff was required. Overall agreement in identifying hypertensive BP was 57.6%. Sensitivity (52.6%), specificity (63.4%), positive predictive value (60.8%), and negative predictive value (55.3%) were low across all cuff sizes.There was poor agreement between usual triage and standardized BP measurements, with potential for significant clinical implications.Clinical Trial RegistrationReNEW Clinic Cohort Study (ReNEW),NCT03816462,https://clinicaltrials.gov/ct2/show/NCT03816462
Knot or Not? Sequence-Based Identification of Knotted Proteins With Machine Learning
Authors: Šrámková Denisa , Sikora Maciej , Uchal Dawid , Klimentová Eva , Perlinska Agata P. , Nguyen Mai Lan , Korpacz Marta , Malinowska Roksana , Rubach Pawel , Šimeček Petr , Sulkowska Joanna I. 
Published: September 2023

AbstractKnotted proteins, although scarce, are crucial structural components of certain protein families, and their roles remain a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold, this study computationally examines the entire UniProt database to create a robust dataset of knotted and unknotted proteins. Utilizing this dataset, we develop a machine learning model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences, with our best-performing model demonstrating a 98.5% overall accuracy. Unveiling the sequence factors that contribute to knot formation, we discover that proteins predicted to be unknotted from known knotted families are typically non-functional fragments missing a significant portion of the knot core. The study further explores the significance of the substrate binding site in knot formation, particularly within the SPOUT protein family. Our findings spotlight the potential of machine learning in enhancing our understanding of protein topology and propose further investigation into the role of knotted structures across other protein families.TOC Graphic
Dual Roles of the Conditional Exosomes Derived fromPseudomonas aeruginosaBiofilms: Promoting and Inhibiting Bacterial Biofilm Growth
Authors: Saad Marwa Gamal , Beyenal Haluk , Dong Wen-Ji 
Published: September 2023

AbstractAntibiotic-resistant biofilm infections have emerged as public health concerns because of their enhanced tolerance of high-dose antibiotic treatments. The biofilm life cycle involves multiple developmental stages, which are tightly regulated by active cell-cell communication via specific extracellular signal messengers such as exosomes. This study was aimed at exploring the roles ofPseudomonas aeruginosaexosomes secreted at different developmental stages in controlling biofilm growth. Our results show that exosomes secreted byP. aeruginosabiofilms during their exponential growth phase (G-Exo) enhance biofilm growth. In contrast, exosomes secreted byP. aeruginosabiofilms during their death/survival phase (D-Exo) can effectively inhibit/eliminateP. aeruginosaPAO1 biofilms up to 4.8-log10CFU/cm2. The inhibition effectiveness of D-Exo againstP. aeruginosabiofilms grown for 96 hours improved further in the presence of 10-50 μM Fe3+ions. Proteomic analysis suggests the inhibition involves an iron-dependent ferroptosis mechanism. This study is the first to report the functional role of bacterial exosomes in bacterial growth, which depends on the developmental stage of the parent bacteria. The finding of D-Exo-activated ferroptosis-based bacterial death may have significant implications for preventing antibiotic resistance in biofilms.Significance statementAntibiotic-resistant bacterial infections caused 1.27 million deaths in 2019 [1], and this number is projected to increase to 10 million deaths annually worldwide by 2050 [2]. Of these infections, up to 80% are caused by biofilm-associated infections [3, 4], which pose a significant challenge to human health. The treatment of biofilm infections remains a formidable problem because of the limited effectiveness of the currently available antibiotics against drug-resistant biofilms [5, 6]. The development of new therapeutic approaches that can effectively combat biofilm infections is required. This study represents a promising solution to antibiotic-resistant biofilm infections. The successful use of exosomes against biofilms opens new possibilities for combating challenging antibiotic-resistant biofilm infections.
Systematic review and meta-analysis of intraventricular antibiotics for neonatal meningitis and ventriculitis
Authors: Valdivia Doriam Alejandrino Perera , Pérez Edgar Abraham Herrera , Vega Luis Roberto Zapata , García José Miguel Hurtado , Herrera Karen Vanessa 
Published: September 2023

AbstractBackgroundMany pediatric and neurosurgical studies have been published regarding intraventricular antibiotics in neonatal meningitis and ventriculitis. We aimed to determine the safety and effectiveness of intraventricular antibiotics in neonates with meningitis and/or ventriculitis and analyze the quality of available evidence.MethodsWe systematically reviewed scientific literature from the PubMed, EMBASE, LILACS, and SCOPUS databases. Randomized experimental and observational studies were included. The Cochrane methodology was used for systematic reviews.ResultsTwenty six observational studies and one randomized clinical trial involving 272 patients were included. The risk of bias in both pediatric and neurosurgical studies was high, and the quality of evidence was low (evidence level C). In the pediatric studies, no significant differences in mortality were found between intraventricular antibiotics and only systemic antibiotic [25.4% vs 16.1%, OR=0.96 (0.42– 2.24),P=0.93]. However, when analyzing the minimum administered doses, we found a lower mortality when a minimum duration of 3 days for intraventricular antibiotics was used compared to only systemic antibiotic [4.3% vs 17%, OR=0.22 (0.07–0.72),P=0.01]. In the neurosurgical studies, the use of intraventricular antibiotics in ventriculitis generally results in a mortality of 5% and a morbidity of 25%, which is lower than that in cases where intraventricular antibiotics were not used, with an average mortality of 37.3% and a morbidity of 50%.ConclusionConsidering the low quality of evidence in pediatric and neurosurgical studies, we can conclude with a low level of certainty that intraventricular antibiotics may not significantly impact mortality in neonatal meningitis and ventriculitis. However, reduced mortality was observed in cases treated with a minimum duration of 3 days of intraventricular antibiotic, particularly the multidrug-resistant or treatment-refractory infections. Higher-quality studies are needed to improve the quality of evidence and certainty regarding the use of intraventricular antibiotics for treating neonatal meningitis and ventriculitis.
Smoking Habits and the Onset of Stroke: Insights from the Kyoto Stroke Registry
Authors: Shigematsu Kazuo , Nakano Hiromi 
Published: September 2023

AbstractCigarette smoking is a well-established risk factor for stroke due to its contribution to atherosclerosis. This study utilized data from the Kyoto Stroke Registry (KSR) spanning April 1997 to March 2020 to investigate the relationship between smoking history and the age at which stroke occurs.The study focused on stroke patients registered in the KSR, categorizing them into four groups based on their smoking history: those with no smoking history, individuals who quit smoking more than one year ago, those who smoked fewer than 20 cigarettes per day, and those who smoked more than 20 cigarettes per day. The primary objective was to compare the age at which stroke onset occurred among these groups, employing statistical analysis through a Student t-test to identify significant differences.After excluding patients with missing smoking status information, the analysis involved 30,491 stroke patients. The results revealed a noteworthy association: individuals with a smoking history, particularly heavy smokers, experienced a significantly younger age at stroke onset compared to those who had never smoked. Moreover, the study found that quitting smoking for more than one year was linked to a later onset of stroke compared to continuing to smoke. This observation held true even among those who smoked less than 20 cigarettes per day. Furthermore, the study’s findings remained consistent across all stroke types, including cerebral infarction, cerebral hemorrhage, and subarachnoid hemorrhage, regardless of gender.In conclusion, this comprehensive analysis of the KSR data reinforces the well-established notion that smoking history plays a critical role in the timing of stroke onset. Importantly, it underscores the potential benefits of smoking cessation, as well as the avoidance of smoking altogether, in reducing the risk of stroke. These findings carry significant implications for public health initiatives aimed at stroke prevention and emphasize the pivotal role of smoking cessation programs in mitigating the risk of stroke-associated morbidity and mortality.
A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks
Authors: Alghamdi Sumaya , Turki Turki 
Published: September 2023

AbstractAccurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learning useful feature representation. However, existing DL tools are far from perfect and do not provide appropriate interpretation as a guideline to explain and promote superior performance in the target task. Therefore, we provide an interpretable approach for our presented deep transfer learning (DTL) models to overcome such drawbacks, working as follows. We utilize several pre-trained models including SEResNet152, and SEResNeXt101. Then, we transfer knowledge from pre-trained models via keeping the weights in the convolutional base (i.e., feature extraction part) while modifying the classification part with the use of Adam optimizer to deal with classifying healthy controls and T2D based on single-cell gene regulatory network (SCGRN) images. Another DTL models work in a similar manner but just with keeping weights of the bottom layers in the feature extraction unaltered while updating weights of consecutive layers through training from scratch. Experimental results on the whole 224 SCGRN images using 5-fold cross-validation show that our model (TFeSEResNeXT101) achieving the highest average balanced accuracy (BAC) of 0.97 and thereby significantly outperforming the baseline that resulted in an average BAC of 0.86. Moreover, the simulation study demonstrated that the superiority is attributed to the distributional conformance of model weight parameters obtained with Adam optimizer when coupled with weights from a pre-trained model.
EpiSegMix: A Flexible Distribution Hidden Markov Model with Duration Modeling for Chromatin State Discovery
Authors: Schmitz Johanna Elena , Aggarwal Nihit , Laufer Lukas , Walter Jörn , Salhab Abdulrahman , Rahmann Sven 
Published: September 2023

AbstractMotivationAutomated chromatin segmentation based on ChIP-seq data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality.ResultsWe introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with two existing tools, ChromHMM, Segway and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states.Availability and implementationSource code:https://gitlab.com/rahmannlab/episegmix.