bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and external validation of a predictive model for type 2 diabetic retinopathy
Authors: Li Yongsheng , Hu Bin , Lu Lian , Li Yongnan , Caika Siqingaowa , Song Zhixin , Sen Gan 
Published: July 2024

AbstractDiabetes retinopathy (DR) is a critical clinical disease with that causes irreversible visual damage in adults, and may even lead to permanent blindness in serious cases. Early identification and treatment of DR is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DR. 2381 patients with type 2 diabetes mellitus (T2DM) were retrospective study from the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China, hospitalised between Jan 1, 2019 and Jun 30, 2022. 962 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China hospitalised between Jul 1, 2020 to Jun 30, 2022 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of DR. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and decision curve analysis (DCA). Neutrophil, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, hemoglobin A1c (HbA1c), and Apolipoprotein A1 (ApoA1) were used to establish a nomogram model for predicting the risk of DR. In the development and external validation groups, the areas under the curve of the nomogram constructed from the above five factors were 0.834 (95%CI 0.820–0.849) and 0.851 (95%CI 0.829–0.874), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. This research has developed and externally verified that the nomograph model shows a good predictive ability in assessing DR risk in people with type 2 diabetes. The application of this model will help clinicians to intervene early, thus effectively reducing the incidence rate and mortality of DR in the future, and has far-reaching significance in improving the long-term health prognosis of diabetes patients.
Interaction between ambient CO and temperature or relative humidity on the risk of stroke hospitalization
Authors: Liu Zhuo , Meng Hua , Wang Xingtian , Lu Wenwen , Ma Xiaojuan , Geng Yuhui , Su Xinya , Pan Dongfeng , Liang Peifeng 
Published: July 2024

AbstractAlthough the independent effects of ambient CO, temperature or humidity on stroke have been confirmed, it is still unclear where there is an interaction between these factors and who is sensitive populations for these. The stroke hospitalization and ambient CO, temperature, humidity data were collected in 22 Counties and districts of Ningxia, China in 2014–2019. The lagged effect of ambient CO, temperature or humidity were analyze by the generalized additive model; the interaction were evaluated by the bivariate response surface model and stratified analysis with relative excessive risk (RERI). High temperature and CO levels had synergistic effects on hemorrhagic stroke (RERI = 0.05, 95% CI 0.033–0.086) and ischemic stroke (RERI = 0.035, 95% CI 0.006–0.08). Low relative humidity and CO were synergistic in hemorrhagic stroke (RERI = 0.192, 95% CI 0.184–0.205) and only in ischemic stroke in the elderly group (RERI = 0.056, 95% CI 0.025–0.085). High relative humidity and CO exhibited antagonistic effects on the risk of ischemic stroke hospitalization in both male and female groups (RERI = − 0.088, 95% CI − 0.151to − 0.031; RERI = − 0.144, 95% CI − 0.216 to − 0.197). Exposure to CO increases the risk of hospitalization related to hemorrhagic and ischemic strokes. CO and temperature or humidity interact with risk of stroke hospitalization with sex and age differences.
Machine learning analysis of PM1 impact on visibility with comprehensive sensitivity evaluation of concentration, composition, and meteorological factors
Authors: Majewski Grzegorz , Szeląg Bartosz , Rogula-Kozłowska Wioletta , Rogula-Kopiec Patrycja , Brandyk Andrzej , Rybak Justyna , Radziemska Maja , Liniauskiene Ernesta , Klik Barbara 
Published: July 2024

AbstractThis study introduces a novel approach to visibility modelling, focusing on PM1 concentration, its chemical composition, and meteorological conditions in two distinct Polish cities, Zabrze and Warsaw. The analysis incorporates PM1 concentration measurements as well as its chemical composition and meteorological parameters, including visibility data collected during summer and winter measurement campaigns (120 samples in each city). The developed calculation procedure encompasses several key steps: formulating a visibility prediction model through machine learning, identifying data in clusters using unsupervised learning methods, and conducting global sensitivity analysis for each cluster. The multi-layer perceptron methods developed demonstrate high accuracy in predicting visibility, with R values of 0.90 for Warsaw and an RMSE of 1.52 km for Zabrze. Key findings reveal that air temperature and relative humidity significantly impact visibility, alongside PM1 concentration and specific heavy metals such as Rb, Vi, and Cd in Warsaw and Cr, Vi, and Mo in Zabrze. Cluster analysis underscores the localized and complex nature of visibility determinants, highlighting the substantial but previously underappreciated role of heavy metals. Integrating the k-means clustering and GSA methods emerges as a powerful tool for unravelling complex mechanisms of chemical compound changes in particulate matter and air, significantly influencing visibility development.
Cost-utility and budget impact analysis of neoadjuvant dual HER2 targeted therapy for HER2-positive breast cancer in Sri Lanka
Authors: Gunasekara Agampodi Danushi Mendis , Youngkong Sitaporn , Anothaisintawee Thunyarat , Dejthevaporn Thitiya , Fernandopulle Rohini , Chaikledkaew Usa 
Published: July 2024

AbstractThis study aimed to assess the cost-utility and budget impact of dual to single HER2 targeted neoadjuvant therapy for HER2-positive breast cancer in Sri Lanka. A five-health state Markov model with lifetime horizon was used to assess the cost-utility of neoadjuvant trastuzumab (T) plus pertuzumab (P) or lapatinib (L) compared to single therapy of T with chemotherapy (C), in public healthcare system and societal perspectives. Input parameters were estimated using local data, network meta-analysis, published reports and literature. Costs were adjusted to year 2021 (1USD = LKR194.78). Five-year budget impact for public healthcare system was assessed. Incremental cost-effectiveness ratios in societal perspective for neoadjuvantLTC plus adjuvantT (strategy 3), neoadjuvantPTC plus adjuvantT (strategy 2), neoadjuvantLTC plus adjuvantLT (strategy 5), and neoadjuvantPTC plus adjuvantPT (strategy 4) compared to neoadjuvantTC plus adjuvantT (strategy 1) were USD2716, USD5600, USD6878, and USD12127 per QALY gained, respectively. One GDP per-capita (USD3815) was considered as the cost-effectiveness threshold for the analysis. Even though only the ICER for strategy 3 was cost-effective, uncertainty of efficacy parameter was revealed. For strategy 2 neoadjuvant PTC plus adjuvant T, a 25% reduction of neoadjuvant regimen cost was required to be cost effective for use in early HER2 positive breast cancer.
Risk of chronic kidney disease in patients with a hyperglycemic crisis as the initial presentation of type 2 diabetes
Authors: Huang Chun-Ta , Muo Chih-Hsin , Sung Fung-Chang , Chen Pei-Chun 
Published: July 2024

AbstractLimited data exist on long-term renal outcomes in patients with hyperglycemic crisis (HC) as initial type 2 diabetes presentation. We evaluated the risk of chronic kidney disease (CKD) development in those with concurrent HC at diagnosis. Utilizing Taiwan’s insurance claims from adults newly diagnosed with type 2 diabetes during 2006–2015, we created HC and matched non-HC cohorts. We assessed incident CKD/diabetic kidney disease (DKD) by 2018’s end, calculating the hazard ratio (HR) with the Cox model. Each cohort comprised 13,242 patients. The combined CKD and DKD incidence was two-fold higher in the HC cohort than in the non-HC cohort (56.47 versus 28.49 per 1000 person-years) with an adjusted HR (aHR) of 2.00 (95% confidence interval [CI] 1.91–2.10]). Risk increased from diabetic ketoacidosis (DKA) (aHR:1.69 [95% CI 1.59–1.79]) to hyperglycemic hyperosmolar state (HHS) (aHR:2.47 [95% CI 2.33–2.63]) and further to combined DKA-HHS (aHR:2.60 [95% CI 2.29–2.95]). Subgroup analysis in individuals aged ≥ 40 years revealed a similar trend with slightly reduced incidences and HRs. Patients with HC as their initial type 2 diabetes presentation face a higher CKD risk than do those without HC. Enhanced medical attention and customized interventions are crucial to reduce this risk.
The N400 effect captures nuances in implicit political preferences
Authors: Mahieux Emmanuel , de-Wit Lee , Otten Leun J. , Devlin Joseph T. , Wicha Nicole Y. Y. 
Published: July 2024

AbstractWe conducted a study in San Antonio, Texas, in the weeks preceding the 2022 state Governor election to determine if implicit or explicit measures of political preference could predict voter behavior. We adapted an established event-related potential (ERP) paradigm showing political statements to participants one word at the time where the last word made the statement pro-Republican or pro-Democratic. Our sample of college students included decided and undecided voters, and was reflective of the demographic make-up of south-central Texas. Our implicit measures were an established authoritarianism scale and the N400 effect to the sentence-final word. The N400 is an ERP to any stimulus that engages semantic memory and has been shown to measure implicit disagreement with political statements. Explicit measures of political preference and authoritarianism were predictive of vote choice. The expected N400 effect was found for Democratic voters, with larger amplitude to pro-Republican than pro-Democratic statements. Surprisingly, decided Republican voters showed no difference in N400 responses to pro-Republican and pro-Democratic statements and there was no group difference in the N400 effect. In turn, the N400 was not predictive of voter behavior. We argue that the N400 effect reflected individual political preferences, but that ultimately voter behavior aligned with partisan identity.
Retrospective analysis of interpretable machine learning in predicting ICU thrombocytopenia in geriatric ICU patients
Authors: Xu Yingting , Zhang Weimin , Ma Xuchao , Wu Muying , Jiang Xuandong 
Published: July 2024

AbstractWe developed an interpretable machine learning algorithm that prospectively predicts the risk of thrombocytopenia in older critically ill patients during their stay in the intensive care unit (ICU), ultimately aiding clinical decision-making and improving patient care. Data from 2286 geriatric patients who underwent surgery and were admitted to the ICU of Dongyang People’s Hospital between 2012 and 2021 were retrospectively analyzed. Integrated algorithms were developed, and four machine-learning algorithms were used. Selected characteristics included common demographic data, biochemical indicators, and vital signs. Eight key variables were selected using the Least Absolute Shrinkage and Selection Operator and Random Forest Algorithm. Thrombocytopenia occurred in 18.2% of postoperative geriatric patients, with a higher mortality rate. The C5.0 model showed the best performance, with an area under the receiver operating characteristic curve close to 0.85, along with unparalleled accuracy, precision, specificity, recall, and balanced accuracy scores of 0.88, 0.98, 0.89, 0.98, and 0.85, respectively. The support vector machine model excelled at predictively assessing thrombocytopenia severity, demonstrating an accuracy rate of 0.80 in the MIMIC database. Thus, our machine learning-based models have considerable potential in effectively predicting the risk and severity of postoperative thrombocytopenia in geriatric ICU patients for better clinical decision-making and patient care.
Dynamics of ionized poly(4-hydroxystyrene)-type resist polymers with tert-butoxycarbonyl-protecting group
Authors: Okamoto Kazumasa , Muroya Yusa , Kozawa Takahiro 
Published: July 2024

AbstractThe imaging reactions of resist materials used for nano-patterning have become radiation-chemical reactions, with the shortening of wavelengths of the exposure light sources in lithography systems. The most widely used patterning materials in industrial lithography are chemically amplified resists (CAR). Understanding the deprotonation mechanism of ionized polymers (radical cations) is important for acid generation in CARs. In this study, the dynamics of radical cations in poly(4-hydroxystyrene) (PHS)–type resist polymers, partially and totally protected by tert-butoxycarbonyl (t-BOC) groups, are investigated using a combination of electron pulse radiolysis experiments, acid yield measurements, and quantum chemical calculations. The t-BOC(oxy) group exhibits π-electron-donating behavior in the monomer cation but changes to electron-accepting behavior in the polymer cation, owing to the interaction between substituents. The destabilization of radical cations due to decreased intramolecular charge resonance may contribute to the high deprotonation efficiency of t-BOC-capped PHS polymers.
Estimating the household secondary attack rate and serial interval of COVID-19 using social media
Authors: Dhiman Aarzoo , Yom-Tov Elad , Pellis Lorenzo , Edelstein Michael , Pebody Richard , Hayward Andrew , House Thomas , Finnie Thomas , Guzman David , Lampos Vasileios ,   Virus Watch Consortium, Aldridge Rob , Beale Sarah , Byrne Thomas , Kovar Jana , Braithwaite Isobel , Fragaszy Ellen , Fong Wing Lam Erica , Geismar Cyril , Hoskins Susan , Navaratnam Annalan , Nguyen Vincent , Patel Parth , Shrotri Maddie , Yavlinsky Alexei , Hardelid Pia , Wijlaars Linda , Nastouli Eleni , Spyer Moira , Aryee Anna , McKendry Rachel , Cheng Tao , Johnson Anne , Michie Susan , Gibbs Jo , Gilson Richard , Rodger Alison , Cox Ingemar J. 
Published: July 2024

AbstractWe propose a method to estimate the household secondary attack rate (hSAR) of COVID-19 in the United Kingdom based on activity on the social media platform X, formerly known as Twitter. Conventional methods of hSAR estimation are resource intensive, requiring regular contact tracing of COVID-19 cases. Our proposed framework provides a complementary method that does not rely on conventional contact tracing or laboratory involvement, including the collection, processing, and analysis of biological samples. We use a text classifier to identify reports of people tweeting about themselves and/or members of their household having COVID-19 infections. A probabilistic analysis is then performed to estimate the hSAR based on the number of self or household, and self and household tweets of COVID-19 infection. The analysis includes adjustments for a reluctance of Twitter users to tweet about household members, and the possibility that the secondary infection was not acquired within the household. Experimental results for the UK, both monthly and weekly, are reported for the period from January 2020 to February 2022. Our results agree with previously reported hSAR estimates, varying with the primary variants of concern, e.g. delta and omicron. The serial interval (SI) is based on the time between the two tweets that indicate a primary and secondary infection. Experimental results, though larger than the consensus, are qualitatively similar. The estimation of hSAR and SI using social media data constitutes a new tool that may help in characterizing, forecasting and managing outbreaks and pandemics in a faster, affordable, and more efficient manner.
Dissecting task-based fMRI activity using normative modelling: an application to the Emotional Face Matching Task
Authors: Savage Hannah S. , Mulders Peter C. R. , van Eijndhoven Philip F. P. , van Oort Jasper , Tendolkar Indira , Vrijsen Janna N. , Beckmann Christian F. , Marquand Andre F. 
Published: July 2024

AbstractFunctional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N = 7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals’ transdiagnostic functioning. Taken together, we demonstrate that normative modelling of fMRI task-activation can be used to illustrate the influence of different task choices and map replicable individual differences, and we encourage its application to other neuroimaging tasks in future studies.
Enhancing shift current response via virtual multiband transitions
Authors: Chen Sihan , Chaudhary Swati , Refael Gil , Lewandowski Cyprian 
Published: July 2024

AbstractMaterials exhibiting a significant shift current response could potentially outperform conventional solar cell materials. The myriad of factors governing shift-current response, however, poses significant challenges in finding such strong shift-current materials. Here we propose a general design principle that exploits inter-orbital mixing to excite virtual multiband transitions in materials with multiple flat bands to achieve an enhanced shift current response. We further relate this design principle to maximizing Wannier function spread as expressed through the formalism of quantum geometry. We demonstrate the viability of our design using a 1D stacked Rice-Mele model. Furthermore, we consider a concrete material realization - alternating angle twisted multilayer graphene (TMG) - a natural platform to experimentally realize such an effect. We identify a set of twist angles at which the shift current response is maximized via virtual transitions for each multilayer graphene and highlight the importance of TMG as a promising material to achieve an enhanced shift current response at terahertz frequencies. Our proposed mechanism also applies to other 2D systems and can serve as a guiding principle for designing multiband systems that exhibit an enhanced shift current response.
Copper oxide coupled with photon upconversion for solar water splitting
Authors: Magazov Yerbolat , Kudryashov Vladislav , Moldabekov Kuanysh , Amze Magzhan , Nurmanova Aiisha , Aliyev Asset , Nuraje Nurxat 
Published: July 2024

AbstractPhotoelectrochemical water splitting is a promising solution for harnessing solar radiation for hydrogen production. Copper oxide semiconductors, particularly materials based on cuprous oxide, have attracted attention due to their abundant elemental availability and scalable synthesis methods. To improve the generated photocurrent of the photoelectrode system, photon upconversion materials can be implemented into water-splitting devices. Here, we demonstrate the potential application of triplet-triplet annihilation-based upconversion in solar-assisted water splitting and highlight the significance of photonic designs to improve the light-harnessing properties of photoactive materials. The triplet-triplet annihilation mechanism is particularly suitable due to its efficient conversion at low photon intensity, namely under 1-sun illumination. Our results show that Cu2O coupled with an upconverter outperforms bare Cu2O by 56% in terms of produced photocurrent density. We construct a hybrid water-splitting device with an extended absorption range by utilizing a semi-transparent 600 nm Cu2O film with a 5 nm Au underlayer.
Tamoxifen metabolites treatment promotes ERα+ transition to triple negative phenotype in vitro, effects of LDL in chemoresistance
Authors: Muñoz-Ayala Andrea , Chimal-Vega Brenda , Serafín-Higuera Nicolás , Galindo-Hernández Octavio , Ramírez-Rosales Gladys , Córdova-Guerrero Iván , Gómez-Lucas Luis Fernando , García-González Victor 
Published: July 2024

Abstract
Objective: Estrogen receptor-positive (ER+) breast cancer represents about 80% of cases, tamoxifen is the election neoadjuvant chemotherapy. However, a large percentage of patients develop chemoresistance, compromising recovery. Clinical evidence suggests that high plasmatic levels of low-density lipoproteins (LDL) could promote cancer progression. The present study analyzed the effect of LDL on the primary plasmatic active Tamoxifen’s metabolites resistance acquisition, 4-hydroxytamoxifen (4OH-Tam) and 4-hydroxy-N-desmethyl-tamoxifen (endoxifen), in breast cancer ERα + cells (MCF-7). Methods: Two resistant cellular variants, MCF-7Var-H and MCF-7Var-I, were generated by a novel strategy and their phenotype features were evaluated. Phenotypic assessment was performed by MTT assays, cytometry, immunofluorescence microscopy, zymography and protein expression analysis. Results: MCF-7Var-H, generated only with tamoxifen metabolites, showed a critical down-regulation in hormone receptors, augmented migration capacity, metalloprotease 9 extracellular medium excretion, and a mesenchymal morphology in contrast with native MCF-7, suggesting the transition towards Triple-negative breast cancer (TNBC) phenotype. In contrast, MCF-7Var-I which was generated in a high LDL media, showed only a slight upregulation in ER and other less noticeable metabolic adaptations. Results suggest a potential role of transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) in phenotypic differences observed among variants. Conclusion: LDL high or low concentrations during Tamoxifen´s metabolites chemoresistance acquisition leads to different cellular mechanisms related to chemoresistance. A novel adaptative cellular response associated with Nrf2 activity could be implicated.
Innovate and empower: the malate dehydrogenase course-based undergraduate research experiences and community of practice
Authors: DeChenne-Peters Sue Ellen , Scheuermann Nicole L. , Parente Amy D. , Zhang Jing 
Published: July 2024

Abstract
College science programs exhibit high rates of student attrition, especially among Students of Color, women, members of the LGBTQ+ community, and those with disabilities. Many of the reasons students choose to leave or feel pushed out of science can be mitigated through participation in faculty-mentored research. However, faculty resources are limited, and not every student has access to faculty mentoring due to systemic or structural barriers. By bringing authentic scientific research into the classroom context, course-based undergraduate research experiences (CUREs) expand the number of students who participate in research and provide benefits similar to faculty-mentored research. Instructors also benefit from teaching CUREs. Using a systematic review of 14 manuscripts concerning the Malate Dehydrogenase CUREs Community (MCC) and malate dehydrogenase (MDH) CUREs, we demonstrate that CUREs can be implemented flexibly, are authentic research experiences, generate new scientific discoveries, and improve student outcomes. Additionally, CURE communities offer substantial advantages to faculty wishing to implement CUREs.