bluuebunny/crossref_metadata_2025_split huggingface dataset:
Is central line type an independent risk factor of central line-associated bloodstream infection in a neonatal intensive care unit population? Experiences at a pediatric hospital in South Texas
Authors: Durant Danielle J. , Fallwell Nancy , Martinez Lesley , Guerrazzi-Young Claudia 
Published: January 2024

AbstractBackground:Central line-associated bloodstream infection (CLABSI) causes significant harm in neonatal intensive care unit (NICU) patients. However, data regarding risk factors and prevention strategies for CLABSI in NICU patients is limited.Objective:To examine risk factors for CLABSI in a NICU population, with particular interest in central line type and site placement.Design:Retrospective case–control study.Setting:NICU (Level IV, 67 bed) at a pediatric hospital in South Texas.Participants:All central line insertions and subsequent CLABSI cases were extracted from the EHR for NICU admissions occurring from January 1, 2018, to November 3, 2022 (N= 1,356), along with potential CLABSI risk factors.Methods:Central line insertions resulting in CLABSI (N= 35) were compared to instances without CLABSI (N= 1,321) using bivariate and multivariate analysis, with propensity score matching.Results:Multivariate risk factors include implantable device (odds ratio [OR] = 14.5,P< .001), neck site placement (OR = 7.2,P< .001), and device dwell time (OR = 5.6,P= .001), as well as years 2021 (OR = 5.1,P= .017) and 2022 (OR = 5.9,P= .011). This indicates the odds of contracting CLABSI are 14.5 times higher when an implantable central line is used compared to the reference category (PICC devices). When cases are paired with matched controls, likelihood of CLABSI is 7.1% higher in patients with an implantable device than in similar patients with other central lines (p= 0.034).Conclusions:Implantable central lines are an independent risk factor for CLABSI in NICU patients at this facility.
A qualitative study evaluating barriers and enablers to improving antimicrobial use for the management of bacteriuria in hospitalized adults
Authors: Black Emily K. , MacLean Dianne , Bell Madison , Neville Heather L. , Kits Olga , Ramsey Tasha D. , Sketris Ingrid , Johnston Lynn 
Published: January 2024

Abstract

Objective:
The objective of this study was to explore barriers and enablers to improving the management of bacteriuria in hospitalized adults.

Design:
Qualitative study.

Setting:
Nova Scotia, Canada.

Participants:
Nurses, physicians, and pharmacists involved in the assessment, diagnosis, and treatment of bacteriuria in hospitalized patients.

Methods:
Focus groups (FGs) were completed between May and July 2019. FG discussions were facilitated using an interview guide that consisted of open-ended questions coded to the theoretical domains framework (TDF) v2. Discussions were transcribed verbatim then independently coded to the TDFv2 by two members of the research team and compared. Thematic analysis was used to identify themes.

Results:
Thirty-three healthcare providers from five hospitals participated (15 pharmacists, 11 nurses, and 7 physicians). The use of antibiotics for the treatment of asymptomatic bacteriuria (ASB) was the main issue identified. Subthemes that related to management of ASB included: “diagnostic uncertainty,” difficulty “ignoring positive urine cultures,” “organizational challenges,” and “how people learn.” Barriers and/or enablers to improving the management of bacteriuria were mapped to 12 theoretical domains within these subthemes. Barriers and enablers identified by participants that were most extensively discussed related to the domains of environmental context and resources, belief about capabilities, social/professional role and identity, and social influences.

Conclusions:
Healthcare providers highlighted barriers and recognized enablers that may improve delivery of care to patients with bacteriuria. A wide range of barriers at the individual and organization level to address diagnostic challenges and improve workload should be considered to improve management of bacteriuria.
The rationale and recommendations for inclusion of screening for benign paroxysmal positional vertigo in falls clinics
Authors: Metz Dean , Bryce Kate 
Published: January 2024

AbstractBackgroundThere have been many studies linking falls and benign paroxysmal positional vertigo. This article collates those studies, and demonstrates how a community falls service fast-tracked patients with benign paroxysmal positional vertigo by implementing validated screening tools and recognised guidance.ObjectiveThis study aimed to explore whether routine screening of referrals to a community falls service can identify those with benign paroxysmal positional vertigo, for fast-tracked management.MethodsPatients referred to a community falls service were screened for possible benign paroxysmal positional vertigo using the Dizziness Handicap Inventory, and triaged to a physiotherapy-led falls and benign paroxysmal positional vertigo assessment service.ResultsTwenty-five per cent of patients were fast-tracked to a falls and benign paroxysmal positional vertigo assessment service for management. The community falls service waiting list reduced by 25 per cent.ConclusionThe data support incorporating assessment and treatment of benign paroxysmal positional vertigo into routine practice within all falls services.
Changes in United States Summer Temperatures Revealed by Explainable Neural Networks
Authors: Labe Zachary M. , Johnson Nathaniel C. , Delworth Thomas L. 
Published: January 2024

AbstractTo better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science.
Ocean Mixing in a Shelf Sea Driven by Energetic Internal Waves
Authors: Whitwell C. A. , Jones N. L. , Ivey G. N. , Rosevear M. G. , Rayson M. D. 
Published: January 2024

AbstractInternal waves drive ocean mixing and enhance the transport of heat, momentum and other tracers in shelf seas. We collected observations of mixing over a 30‐day period from three cross‐shore moorings placed on the 330, 200 and 150 m isobaths on the offshore side of a pelagic ridge on the Australian North West Shelf. The region is forced by energetic surface and internal tides, exhibits non‐linear internal waves, experiences flow‐topography interactions, and is subject to episodic intense wind events. This complex forcing drove energetic diapycnal mixing at all sites. We identified five dominant internal wave forcing categories: mode‐1 waves at low‐frequency (time scales from double the buoyancy period to 4 hr), mode‐1 waves at high‐frequency (HF) (time scales between the buoyancy period and double the buoyancy period), mode‐2 waves, internal bores, and internal hydraulic jumps. Overall, just 15% of mixing events accounted for 90% of the total observed heat flux over the record. Mixing during internal wave events accounted for as much as 50% of the total heat flux in some locations. Of the internal wave categories, HF mode‐1 waves were the most significant contributors to the total heat flux at all sites (∼20%). On the other hand, internal bores made significant contributions to mixing only at the 200 and 150 m moorings; they made no contribution to mixing at the 330 m mooring. At the shallowest mooring, the different internal wave categories all made similar contributions to the total flux, indicating an increasingly complicated relationship between the evolving internal wavefield and the mixing.
Forecasting of Global Ionosphere Maps With Multi‐Day Lead Time Using Transformer‐Based Neural Networks
Authors: Shih Chung‐Yu , Lin Cissi Ying‐tsen , Lin Shu‐Yu , Yeh Cheng‐Hung , Huang Yu‐Ming , Hwang Feng‐Nan , Chang Chia‐Hui 
Published: January 2024

AbstractIonospheric total electron content (TEC) is a key indicator of the space environment. Geophysical forcing from above and below drives its spatial and temporal variations. A full understanding of physical and chemical principles, available and well‐representable driving inputs, and capable computational power are required for physical models to reproduce simulations that agree with observations, which may be challenging at times. Recently, data‐driven approaches, such as deep learning, have therefore surged as means for TEC prediction. Owing to the fact that the geophysical world possesses a sequential nature in time and space, Transformer architectures are proposed and evaluated for sequence‐to‐sequence TEC predictions in this study. We discuss the impacts of time lengths of choice during the training process and analyze what the neural network has learned regarding the data sets. Our results suggest that 12‐layer, 128‐hidden‐unit Transformer architectures sufficiently provide multi‐step global TEC predictions for 48 hr with an overall root‐mean‐square error (RMSE) of ∼1.8 TECU. The hourly variation of RMSE increases from 0.6 TECU to about 2.0 TECU during the prediction time frame.
Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning
Authors: Yu Jingzhi , Yang Xiaoyun , Deng Yu , Krefman Amy E. , Pool Lindsay R. , Zhao Lihui , Mi Xinlei , Ning Hongyan , Wilkins John , Lloyd-Jones Donald M. , Petito Lucia C. , Allen Norrina B. 
Published: January 2024

AbstractIt is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Of the 15,565 participants in our dataset, 2170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI 0.782–0.844) vs 0.792 (CI 0.760–0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.
Method for multi-task learning fusion network traffic classification to address small sample labels
Authors: Liu Lan , Yu Yongjie , Wu Yafeng , Hui Zhanfa , Lin Jun , Hu Junhan 
Published: January 2024

AbstractIn the context of the proliferated evolution of network service types and the expeditious augmentation of network resource deployment, the requisition for copious labeled datasets to facilitate superior performance in traffic classification methods, particularly those hinging on deep learning, is imperative. Nonetheless, the procurement and annotation of such extensive datasets necessitate considerable temporal and human resource investments. In response to this predicament, this work introduces a methodology, termed MTEFU, leveraging a deep learning model-based multi-task learning algorithm, strategically designed to mitigate the reliance on substantial labeled training samples. Multiple classification tasks, encompassing duration, bandwidth size, and business traffic category, are incorporated, with a shared parameter strategy implemented amongst tasks to assure the transference of information across disparate tasks. Employing CNN, SAE, GRU, and LSTM as multi-task learning classification models, training validation and experimental testing were conducted on the QUIC dataset. A comparative analysis with single-task and ensemble learning methods reveals that, in the context of predicting network traffic types, the accuracy derived from the multi-task learning strategy, even with a mere 150 labeled samples, can emulate the 94.67% accuracy achieved through single-task learning with a fully labeled dataset of 6139 samples.
Prevalence and determinants of hand hygiene behavior among Indian population: a systematic review and meta-analysis
Authors: Hareesh P. V. , Rajkumar Eslavath , Gopi Aswathy , Sri Lakshmi K N. V. , Romate John 
Published: January 2024

AbstractDespite a global call to action, many deaths occur yearly in developing nations from contagious diseases due to poor sanitation and hygiene. Although hand hygiene (HH) behavior was critical in preventing the COVID-19 pandemic, the sustainability of such practices is still questionable. Therefore, the current systematic review and meta-analysis investigated the prevalence and determinants of HH behavior among the Indian population (PROSPERO registration ID: CRD42022344961). Systematic searches on electronic databases, including ScienceDirect, Scopus, Web of Science, JSTOR, PubMed, and Google Scholar, targeted qualitative and quantitative studies that report HH behaviors in India. Pooled effect sizes were calculated with the inverse-variance method using random-effects models, acknowledging the study heterogeneity. Out of 1053 studies, 15 studies that met eligibility criteria were included in the qualitative synthesis. Among them, five studies were included in the meta-analyses. The overall prevalence of HH before food was 55% (95% CI = 31–78), and after the toilet was 84% (95% CI = 65–96). Subgroup analysis showed that before-food HH prevalence pre- and post-COVID-19 was 61% and 36%, respectively, whereas after-toilet HH prevalence was 91% and 74%, respectively. Meta-regression revealed statistically non-significant results for COVID-19 status. While it could not adequately explain the heterogeneity of the ‘before-food prevalence’ studies (Adj. R2 = − 34.80%), it did account for more than 19% in ‘after-toilet prevalence’ (Adj. R2 = 19.72%). This systematic review highlights various demographic, psychosocial, and environmental determinants of HH behavior. The results offer the potential for a deeper comprehension of the key factors influencing HH in India and could find implications for developing viable interventions. This aids in planning efficient promotional campaigns to enhance personal hygiene and control infectious diseases in the nation.
Comparing automated and manual assessments of tear break-up time using different non-invasive devices and a fluorescein procedure
Authors: Zeri Fabrizio , Rizzo Giulia Carlotta , Ponzini Erika , Tavazzi Silvia 
Published: January 2024

AbstractTo evaluate the agreement and repeatability of an automated topography-based method for non-invasive break-up time (NIBUT) analyses in comparison with two other NIBUT procedures, the fluorescein procedure (fBUT), and with the manual assessment with the same device. In the first experiment, a semi-randomised crossover study was performed on forty-three participants (23.1 ± 2.1 years). NIBUT measurements were collected in a randomised order, in both eyes of participants with EasyTear View + (Easytear, Rovereto), Polaris, and Sirius + (CSO, Firenze). Then a fBUT was collected. The overall measurement procedure was repeated in a further session (retest) on the same day. In a second experiment, a retrospective randomised crossover study was performed on eighty-five NIBUT videos previously recorded by the Sirius+. Two observers assessed manually the videos and the NIBUTs were compared with the automatic ones. In the first experiment, ANOVA showed a significant difference between the four measures in both eyes (p < 0.001). Significant differences were found in the paired comparisons between each NIBUT procedure and fBUT (Wicoxon; p < 0.05). Sirius+ resulted in agreement only with Polaris in the left eye. Correlations between all NIBUT procedures resulted in statistical significance in both eyes. All procedures showed very good test-rest reliability. In the second experiment, a significant correlation between automated and manual NIBUT was found, but also a significant statistical difference between the two measurements, although clinically negligible (0.3 s). The investigated NIBUT devices perform differently from each other (and from fBUT), so they cannot be considered interchangeable. The automated measure of NIBUT with Sirius+ has a negligible clinical difference compared to manual assessment on the same device.
Oral health related quality of life in head and neck cancer survivors within the first year following treatment: a cross-sectional study in Karachi, Pakistan
Authors: Qamar Sana , Rozi Shafquat , Sawani Sobia , Awan Muhammad Sohail , Akhtar Shabbir , Siddiqui Moghira Iqbaluddin , Abbas Syed Akbar , Taimoor Shazia , Raza Khan Farhan 
Published: January 2024

AbstractAfter completing treatment for head and neck cancer (HNC), patients often face oral complications like oral pain, limited mouth opening and dry mouth which significantly reduce their oral health related quality of life (OHRQoL). These issues impact their overall well-being, social activities and long-term survival. The primary objective of this study was to evaluate OHRQoL and its association with sociodemographic characteristics, oral hygiene practices and oral clinical parameters such as oral hygiene status and oral mucositis grade in patients who have completed treatment for head and neck cancer. This cross-sectional study involved 79 HNC-treated patients within first year after completion of cancer treatment attending ENT and dental clinics at outpatient department (OPD) setting in Karachi. Data was collected electronically using structured questionnaire comprising of EORTC QLQ H&N – 35 to measure OHRQoL, patients were also examined for oral hygiene status using oral hygiene index- simplified (OHI-s) and oral mucositis grade using WHO oral mucositis scale. Multiple linear regression was used to test OHRQoL associations with the sociodemographic and different clinical factors. The result showed an overall mean score for oral health related quality of life (OHRQoL) of 25.02 ± 15.86 (95% CI 21.46–28.57), with difficulty in mouth opening 53.16 ± 18.88 and dry mouth 45.14 ± 24.48 being predominant concerns for decline in the OHRQoL in the population. Male predilection was observed among participants n = 60 (75.9%), majority of the participants n = 41 (51.9%) were below 52 years of age. n = 63 (80%) participants received radiotherapy alongside surgery and chemotherapy. Most of participants n = 66 (83.5%) experienced moderate to severe oral mucositis with poor oral hygiene status n = 56 (71%). Significant associations were found between OHRQoL and BMI, OH status, marital status, monthly income, gender and fluoride toothpaste (p < 0.05). These findings suggest that Quality of Life (QoL) among HNC treated patients is negatively impacted by their poor oral health, post cancer treatment. Therefore, it is important to evaluate and modify the current treatment modalities and involve multidisciplinary teams, to improve their OHRQoL thereby enhancing overall QoL.
HBV promotes its replication by up-regulating RAD51C gene expression
Authors: Peng Ting-wei , Ma Qing-feng , Li Jie , Wang Xue , Zhang Cong-hui , Ma Junwen , Li Jun-yi , Wang Wei , Zhu Cheng-liang , Liu Xing-hui 
Published: January 2024

AbstractChronic hepatitis B virus (HBV) infection is a major cause of hepatocellular carcinoma (HCC), pegylated-interferon-α(PEG-IFNα) and long-term nucleos(t)ide analogs (NUCs) are mainly drugs used to treat HBV infection, but the effectiveness is unsatisfactory in different populations, the exploration of novel therapeutic approaches is necessary. RAD51C is associated with DNA damage repair and plays an important role in the development and progression of tumors. Early cDNA microarray results showed that RAD51C expression was significantly increased in HBV-infected HCC cells, however, the relationship between HBV infection and abnormal expression of RAD51C has not been reported. Therefore, we conducted RT-PCR, western blot, Co-immunoprecipitation(Co-IP), and immunofluorescence(IF) to detect HBV-RAD51C interaction in RAD51C overexpression or interfering HCC cells. Our results showed that RAD51C and HBV X protein(HBX) produced a direct interaction in the nucleus, the HBV infection of HCC cells promoted RAD51C expression, and the increased expression of RAD51C promoted HBV replication. This indicated that RAD51C is closely related to the occurrence and development of HCC caused by HBV infection, and may bring a breakthrough in the the prevention and treatment study of HCC.