bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feasibility of the enhanced neuromuscular blockade recovery protocol with selective use of sugammadex in thyroid surgery with intraoperative neuromonitoring
Authors: Yang Shuwen , Sun Zhirong , Zhou Changming , Wei Wenjun , Qu Ning , Shi Rongliang , Ma Ben , Xu Weibo , Ji Qinhai , Zhang Jun , Wang Yu 
Published: June 2024

AbstractBackgroundTo investigate feasibility of utilizing enhanced neuromuscular blocking agents with selective recovery protocol during thyroid surgery with intraoperative neuromonitoring (IONM).MethodsTwo‐hundred and ninety patients were randomized into two groups: group A 0.3 mg/kg rocuronium and group B 0.6 mg/kg. Sugammadex 2 mg/kg was injected if needed followed initial vagal stimulation (V0). Electromyography signals from vagus and recurrent laryngeal nerves before and after resection were recorded as V1, V2, R1, and R2.ResultsIn group B, 30 patients (20.7%) had V0 signals <100 μV, compared to 9 (6.2%) in group A. After sugammadex administration, 144 patients (99.3%) in both groups achieved positive V1 signals. Group B demonstrated a shorter surgical time from rocuronium injection to V2 stimulation compared to group A, accompanied by a significantly lower incidence of intraoperative body movement (0 vs. 16 patients).Conclusions0.6 mg/kg rocuronium with selective use 2 mg/kg sugammadex for IONM in thyroid surgery can meet both anesthesia and surgery demands.
A pretreatment multiparametric MRI‐based radiomics‐clinical machine learning model for predicting radiation‐induced temporal lobe injury in patients with nasopharyngeal carcinoma
Authors: Wang Li , Qiu Ting , Zhou Jiawei , Zhu Yinsu , Sun Baozhou , Yang Guanyu , Huang Shengfu , Wu Lirong , He Xia 
Published: June 2024

AbstractBackgroundTo establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging‐based radiomics data with clinical data to predict radiation‐induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity‐modulated radiotherapy (IMRT).MethodsData from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2‐weighted imaging (T2WI), and CE‐T1‐weighted imaging (CE‐T1WI). T‐test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical‐radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical‐radiomics nomogram.ResultsA total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE‐T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility.ConclusionsThe clinical‐radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.
Test‐set results can predict participants' development in breast‐screen cancer detection: An observational cohort study
Authors: Qenam Basel A. , Li Tong , Alshabibi Abdulaziz , Frazer Helen , Ekpo Ernest , Brennan Patrick 
Published: June 2024

AbstractBackground and AimTest‐sets are standardized assessments used to evaluate reader performance in breast screening. Understanding how test‐set results affect real‐world performance can help refine their use as a quality improvement tool. The aim of this study is to explore if mammographic test‐set results could identify breast‐screening readers who improved their cancer detection in association with test‐set training.MethodsTest‐set results of 41 participants were linked to their annual cancer detection rate change in two periods oriented around their first test‐set participation year. Correlation tests and a multiple linear regression model investigated the relationship between each metric in the test‐set results and the change in detection rates. Additionally, participants were divided based on their improvement status between the two periods, and Mann–Whitney U test was used to determine if the subgroups differed in their test‐set metrics.ResultsTest‐set records indicated multiple significant correlations with the change in breast cancer detection rate: a moderate positive correlation with sensitivity (0.688, p < 0.001), a moderate negative correlation with specificity (−0.528, p < 0.001), and a low to moderate positive correlation with lesion sensitivity (0.469, p = 0.002), and the number of years screen‐reading mammograms (0.365, p = 0.02). In addition, the overall regression was statistically significant (F (2,38) = 18.456 p < 0.001), with an R² of 0.493 (adjusted R² = 0.466) based on sensitivity (F = 27.132, p < 0.001) and specificity (F = 9.78, p = 0.003). Subgrouping the cohort based on the change in cancer detection indicated that the improved group is significantly higher in sensitivity (p < 0.001) and lesion sensitivity (p = 0.02) but lower in specificity (p = 0.003).ConclusionSensitivity and specificity are the strongest test‐set performance measures to predict the change in breast cancer detection in real‐world breast screening settings following test‐set participation.
Tuberculosis and HIV/AIDS coinfection in patients attending Directly Observed Treatment Short‐course (DOTS) centers in Anambra State, Nigeria: A retrospective study
Authors: Okeke Monique , Eze Peter M. , Chukwudebelu Adaeze E. , Nwankwo Chidiebere J. , Eze Nchekwube K. , Okafor Uchenna U. , Abonyi Isaiah C. , Okereke Eric E. , Obasi Kalu O. , Ede Okorie A. , Ejikeugwu Chika P. , Ilo Cajetan I. , Okafor Jerome O. 
Published: June 2024

AbstractBackground and Aim(S)This study retrospectively assessed the prevalence of TB and human immunodeficiency virus (HIV)/AIDS coinfection among patients that attended the Directly Observed Treatment Short‐course (DOTS) centers in Anambra State, Southeast, Nigeria, between 2013 and 2017.MethodsThe study adopted a descriptive and retrospective epidemiological survey design. A total of 1443 case files of patients aged 15−60 who were treated in DOTS centers selected from Anambra State's 21 Local Government Areas between 2013 and 2017 were investigated. The uniform data form, a standardized instrument used in Anambra State's health facilities for data collection, was used to collect data from case files of all those identified as coinfected with TB and HIV/AIDS.ResultsThe mean prevalence rate of TB and HIV/AIDS coinfection in the state during the 5‐year period (2013–2017) was 20.00%. The highest annual prevalence of TB and HIV/AIDS coinfection was recorded in 2014 (23.84%). The state's prevalence of TB and HIV/AIDS coinfection increased dramatically from 13.17% in 2013 to 23.84% in 2014, followed by a slight downward trend to 22.80% in 2015, 20.17% in 2016, and 20.03% in 2017. In terms of gender, age, marital status, and occupation, females (59.5%), those aged 15 to 25 years (30.7%), married people (43.90%), and traders/business owners (50.7%), respectively, had the highest rates of tuberculosis and HIV/AIDS coinfection during the study period.ConclusionThe findings of this study show that young people, females, married people, and traders/business owners appear to be the most vulnerable groups affected by TB and HIV/AIDS coinfection, accounting for the majority of the disease burden in the state. To address the high prevalence of TB and HIV/AIDS coinfection in the Anambra State, novel intervention and control programs should be developed and implemented, and existing intervention frameworks should be strengthened.
Effectiveness of stormwater control measures in protecting stream channel stability
Authors: Khan Sami Towsif , Wynn‐Thompson Theresa , Sample David , Al‐Smadi Mohammad , Behrouz Mina Shahed , Miller Andrew J. 
Published: June 2024

AbstractWhile research on the hydrologic impact of different types of stormwater control measures (SCMs) is extensive, little research exists linking urbanization, widespread implementation of SCMs and channel stability in headwater streams. This study evaluated whether the unified stormwater sizing criteria (USSC) regulations in the state of Maryland, USA, which require the use of both end‐of‐pipe and distributed, small‐scale SCMs, protect channel stability. To achieve this goal, a coupled hierarchical modelling approach utilizing the Storm Water Management Model (SWMM) and the Hydrologic Engineering Center River Analysis System 6.3 (HEC‐RAS) was developed to predict changes in streamflow and sediment transport dynamics in a first‐order gravel‐bed, riffle‐pool channel. Storm event discretization revealed that 88% of observed storm events during the 16 years (2004–2020) had durations less than 18 h and that the greatest peak flows resulted from storm events with durations less than 24 h. HEC‐RAS simulation results also showed that both channel degradation and aggradation, as high as 1.2 m, will likely occur due to regulations which require the use of 24 h duration design storms with a target stormwater detention time rather than bed material sediment transport limits. Overall, this study provides valuable insights into the complex interactions between SCM practises, flow regimes and sediment transport dynamics in heavily urbanized watersheds. It is recommended that SCMs be designed using a continuous simulation model with at least 10 years of continuous rainfall data. Furthermore, to protect channel stability, the SCM design goal should focus on maintaining pre‐development sediment transport regimes across a range of flows.
Ohio River basin snow ablation and the role of rain‐on‐snow
Authors: Suriano Zachary J. , Davidson Samuel , Dixon Ross D. , Roy Tirthankar 
Published: June 2024

AbstractSnow cover ablation within the Ohio River Basin (ORB) plays an important role in regional hydroclimatology, while also representing a potential hazard during large and rapid events. Rain‐on‐snow ablation is a particular challenge, where runoff rates are typically enhanced due to the dual inputs of snowmelt and liquid precipitation. Here, we present a 40‐year climatology of snow ablation events frequency, intensity, and timing for the ORB using a 4‐km gridded snow‐water‐equivalent dataset, focusing on the relative proportion of events caused by rain‐on‐snow and changes over time. Spatial patterns of snow ablation frequency and intensity mirror that of seasonal snowfall totals, with higher (lower) values in the northern and eastern (southern) portions of the basin. Rain‐on‐snow events represent approximately 40% of all ablation events within the basin and result in approximately 24%–25% more snow‐water‐equivalent loss than non‐rain‐on‐snow events, plus an additional 3–12 mm of liquid precipitation per event on average. Peak frequency of ablation and rain‐on‐snow events occurs in late winter and early spring, similar to that of the surrounding region. Over time, the frequency of ablation and rain‐on‐snow events has decreased in the northern and eastern portions of the basin, in some cases by as much as 30%. Trends in event magnitudes were more isolated but decreased across portions of central IN, northern KY, eastern OH and northern WV. Additionally, the magnitude of precipitation during rain‐on‐snow events has increased across the region, extending from northern KY into western PA by over 100% in many cases. Broadly, we find tendencies towards fewer events with less snow loss but more liquid precipitation that suggest complicated impacts to the hydroclimatology warranting further investigation.
Intra‐annual radial growth of Quercus acutissma and its response to hydrometeorological factors in the Nandadish experimental catchment, eastern China
Authors: Liu Hui , Liao Aimin , Li Yike , Hu Pengcheng , Pang Hongxi 
Published: June 2024

AbstractInvestigating the relationship between tree growth within a year and environmental factors is crucial for understanding how climate change affects seasonal tree growth patterns. In this study, high‐resolution point dendrometers were used to monitor the stem radial changes of Oak trees (Quercus acutissma) over two years (2020 and 2021) in the eastern subtropical monsoon region of China. We find that the main growth period of Oak trees spans from March to September, and air temperature significantly affects the growth onset of Oak trees but with no clear impact on their growth cessation. The observations show that precipitation substantially affects daily stem radial increment (SRI), but the frequency of precipitation days plays a more crucial role in enhancing seasonal growth than the total precipitation amount. In the growing season, the stem radius of the Oak trees shows obvious diurnal cycles with shrinkage during the day and expansion at night, reflecting a delicate balance between canopy water loss and soil water absorption. The diurnal variations of the stem radius during the cold period (January and December) show an opposite pattern to that of the growing season, due to the sap's freezing under the condition of low air temperature at night as well as no or weak transpiration in the daytime. Because the temporal dynamics and intensity of tree activities significantly affect the timing and mechanisms of carbon assimilation in terrestrial ecosystems, our results are helpful to evaluate the carbon sequestration capacity of subtropical forests under the global climate change.
Combined application of mesenchymal stem cells and different glucocorticoid dosing alleviates osteoporosis in SLE murine models
Authors: Ding Sisi , Ren Tian , Song Saizhe , Peng Cheng , Liu Cuiping , Wu Jian , Chang Xin 
Published: June 2024

AbstractObjectiveBone mesenchymal stem cells (BMSCs) have been tentatively applied in the treatment of glucocorticoid‐induced osteoporosis (GIOP) and systemic lupus erythematosus (SLE). However, the effects of BMSCs on osteoporosis within the context of glucocorticoid (GC) application in SLE remain unclear. Our aim was to explore the roles of BMSCs and different doses of GC interventions on osteoporosis in SLE murine models.MethodsMRL/MpJ‐Faslpr mice were divided into eight groups with BMSC treatment and different dose of GC intervention. Three‐dimensional imaging analysis and hematoxylin and eosin (H&E) staining were performed to observe morphological changes. The concentrations of osteoprotegerin (OPG) and receptor activator of nuclear factor κB ligand (RANKL) in serum were measured by enzyme‐linked immunosorbent assay (ELISA). The subpopulation of B cells and T cells in bone marrows and spleens were analyzed by flow cytometry. Serum cytokines and chemokines were assessed using Luminex magnetic bead technology.ResultsBMSCs ameliorated osteoporosis in murine SLE models by enhancing bone mass, improving bone structure, and promoting bone formation through increased bone mineral content and optimization of trabecular morphology. BMSC and GC treatments reduced the number of B cells in bone marrows, but the effect was not significant in spleens. BMSCs significantly promoted the expression of IL‐10 while reducing IL‐18. Moreover, BMSCs exert immunomodulatory effects by reducing Th17 expression and rectifying the Th17/Treg imbalance.ConclusionBMSCs effectively alleviate osteoporosis induced by SLE itself, as well as osteoporosis resulting from SLE combined with various doses of GC therapy. The therapeutic effects of BMSCs appear to be mediated by their influence on bone marrow B cells, T cell subsets, and associated cytokines. High‐dose GC treatment exerts a potent anti‐inflammatory effect but may hinder the immunotherapeutic potential of BMSCs. Our research may offer valuable guidance to clinicians regarding the use of BMSC treatment in SLE and provide insights into the judicious use of GCs in clinical practice.
Classification of the Stages of Nonalcoholic Steatohepatitis via Federated General Visual Representation Learning
Authors: Nergiz Mehmet 
Published: June 2024

ABSTRACTThe excess buildup of fat within a patient's liver, even in the absence of any previous alcohol consumption, defines nonalcoholic fatty liver disease (NAFLD). Accurate classification of NAFLD subtypes, especially nonalcoholic steatohepatitis (NASH), through histology biopsies is vital for risk stratification and patient management, including liver transplantation decisions. Therapeutic options targeting liver molecular pathways are being explored, as approved therapies for NASH/NAFLD are currently lacking. Deep learning‐driven pathological diagnosis holds promise for precise assessments, and the application of the federated learning version of the Big Transfer (BiT) model in this study represents a significant advancement in NAFLD/NASH classification and staging using histopathological imaging data. The AUC performance results of federated learning settings of “mainclass” and “multiclass” scenarios are obtained respectively as 0.9299 and 0.9582. The AUC results of each “subclass” are achieved as 0.9376, 0.9435, 0.9867, and 0.9618 respectively, for ballooning, inflammation, steatosis, and fibrosis. The effectiveness of the federated learning configurations in the scenarios categorized as “main‐class” and “multi‐class” is reflected in their respective accuracy (ACC) performance outcomes, which are recorded as 0.7650 and 0.6653. Furthermore, the ACC achievements for each specific “sub‐class” namely, ballooning, inflammation, steatosis, and fibrosis are 0.9016, 0.8568, 0.9203, and 0.8815, respectively.
Deep learning enhancing guide RNA design for CRISPR/Cas12a‐based diagnostics
Authors: Huang Baicheng , Guo Ling , Yin Hang , Wu Yue , Zeng Zihan , Xu Sujie , Lou Yufeng , Ai Zhimin , Zhang Weiqiang , Kan Xingchi , Yu Qian , Du Shimin , Li Chao , Wu Lina , Huang Xingxu , Wang Shengqi , Wang Xinjie 
Published: June 2024

AbstractRapid and accurate diagnostic tests are fundamental for improving patient outcomes and combating infectious diseases. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Cas12a‐based detection system has emerged as a promising solution for on‐site nucleic acid testing. Nonetheless, the effective design of CRISPR RNA (crRNA) for Cas12a‐based detection remains challenging and time‐consuming. In this study, we propose an enhanced crRNA design system with deep learning for Cas12a‐mediated diagnostics, referred to as EasyDesign. This system employs an optimized convolutional neural network (CNN) prediction model, trained on a comprehensive data set comprising 11,496 experimentally validated Cas12a‐based detection cases, encompassing a wide spectrum of prevalent pathogens, achieving Spearman's ρ = 0.812. We further assessed the model performance in crRNA design for four pathogens not included in the training data: Monkeypox Virus, Enterovirus 71, Coxsackievirus A16, and Listeria monocytogenes. The results demonstrated superior prediction performance compared to the traditional experiment screening. Furthermore, we have developed an interactive web server (https://crispr.zhejianglab.com/) that integrates EasyDesign with recombinase polymerase amplification (RPA) primer design, enhancing user accessibility. Through this web‐based platform, we successfully designed optimal Cas12a crRNAs for six human papillomavirus (HPV) subtypes. Remarkably, all the top five predicted crRNAs for each HPV subtype exhibited robust fluorescent signals in CRISPR assays, thereby suggesting that the platform could effectively facilitate clinical sample testing. In conclusion, EasyDesign offers a rapid and reliable solution for crRNA design in Cas12a‐based detection, which could serve as a valuable tool for clinical diagnostics and research applications.
Revealing processing stability landscape of organic solar cells with automated research platforms and machine learning
Authors: Du Xiaoyan , Lüer Larry , Heumueller Thomas , Classen Andrej , Liu Chao , Berger Christian , Wagner Jerrit , Le Corre Vincent M. , Cao Jiamin , Xiao Zuo , Ding Liming , Forberich Karen , Li Ning , Hauch Jens , Brabec Christoph J. 
Published: June 2024

AbstractWe use an automated research platform combined with machine learning to assess and understand the resilience against air and light during production of organic photovoltaic (OPV) devices from over 40 donor and acceptor combinations. The standardized protocol and high reproducibility of the platform results in a dataset of high variety and veracity to deploy machine learning models to encounter links between stability and chemical, energetic, and morphological structure. We find that the strongest predictor for air/light resilience during production is the effective gap Eg,eff which points to singlet oxygen rather than the superoxide anion being the dominant agent in degradation under processing conditions. A similarly good prediction of air/light resilience can also be achieved by considering only features from chemical structure, that is, information which is available prior to any experimentation.
Simultaneous resistance switching and rectifying effects in a single hybrid perovskite
Authors: Song Xuefen , Zhang Junran , Qian Yuchi , Xia Zhongjing , Chen Jinlian , Yin Hao , Liu Jing , Feng Linbo , Liu Tianyu , Zhu Zihong , Hua Yuyang , Liu You , Yuan Jiaxiao , Ge Feixiang , Zhou Dawei , Li Mubai , Hang Yang , Wang Fangfang , Qin Tianshi , Wang Lin 
Published: June 2024

AbstractHalide perovskites with naturally coupled electron‐ion dynamics hold great potential for nonvolatile memory applications. Self‐rectifying memristors are promising as they can avoid sneak currents and simplify device configuration. Here we report a self‐rectifying memristor firstly achieved in a single perovskite (NHCINH3)3PbI5 (abbreviated as (IFA)3PbI5), which is sandwiched by Ag and ITO electrodes as the simplest cell in a crossbar array device configuration. The iodide ions of (IFA)3PbI5 can be easily activated, of which the migration in the bulk contributes to the resistance hysteresis and the reaction with Ag at the interface contributes to the spontaneous formation of AgI. The perfect combination of n‐type AgI and p‐type (IFA)3PbI5 gives rise to the rectification function like a p–n diode. Such a self‐rectifying memristor exhibits the record‐low set power consumption and voltage. This work emphasizes that the multifunction of ions in perovskites can simplify the fabrication procedure, decrease the programming power, and increase the integration density of future memory devices.image