bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differential Outcomes of Placebo Treatment Across 9 Psychiatric Disorders
Authors: Bschor Tom , Nagel Lea , Unger Josephine , Schwarzer Guido , Baethge Christopher 
Published: May 2024

ImportancePlacebo is the only substance systematically evaluated across common psychiatric diagnoses, but comprehensive cross-diagnostic comparisons are lacking.ObjectiveTo compare changes in placebo groups in recent high-quality randomized clinical trials (RCTs) across a broad spectrum of psychiatric disorders in adult patients.Data SourcesMEDLINE and the Cochrane Database of Systematic Reviews were systematically searched in March 2022 for the latest systematic reviews meeting predetermined high-quality criteria for 9 major psychiatric diagnoses.Study SelectionUsing these reviews, the top 10 highest-quality (ie, lowest risk of bias, according to the Cochrane Risk of Bias tool) and most recent placebo-controlled RCTs per diagnosis (totaling 90 RCTs) were selected, adhering to predetermined inclusion and exclusion criteria.Data Extraction and SynthesisFollowing the Cochrane Handbook, 2 authors independently carried out the study search, selection, and data extraction. Cross-diagnosis comparisons were based on standardized pre-post effect sizes (mean change divided by its SD) for each placebo group. This study is reported following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline.Main Outcome and MeasureThe primary outcome, pooled pre-post placebo effect sizes (dav) with 95% CIs per diagnosis, was determined using random-effects meta-analyses. A Q test assessed statistical significance of differences across diagnoses. Heterogeneity and small-study effects were evaluated as appropriate.ResultsA total of 90 RCTs with 9985 placebo-treated participants were included. Symptom severity improved with placebo in all diagnoses. Pooled pre-post placebo effect sizes differed across diagnoses (Q = 88.5; df = 8; P &amp;lt; .001), with major depressive disorder (dav = 1.40; 95% CI, 1.24-1.56) and generalized anxiety disorder (dav = 1.23; 95% CI, 1.06-1.41) exhibiting the largest dav. Panic disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, social phobia, and mania showed dav between 0.68 and 0.92, followed by OCD (dav = 0.65; 95% CI, 0.51-0.78) and schizophrenia (dav = 0.59; 95% CI, 0.41-0.76).Conclusion and RelevanceThis systematic review and meta-analysis found that symptom improvement with placebo treatment was substantial in all conditions but varied across the 9 included diagnoses. These findings may help in assessing the necessity and ethical justification of placebo controls, in evaluating treatment effects in uncontrolled studies, and in guiding patients in treatment decisions. These findings likely encompass the true placebo effect, natural disease course, and nonspecific effects.
Concussion burden and later‐life cardiovascular risk factors in former professional American‐style football players
Authors: Tan Can Ozan , Grashow Rachel , Thorpe Roland , Miller Karen K. , Nathan David M. , Izzy Saef , Radmanesh Farid , Kim Jonathan H. , Weisskopf Marc G. , Taylor Herman A. , Zafonte Ross D. , Baggish Aaron L. 
Published: May 2024

AbstractObjectiveMid‐life cardiovascular risk factors are associated with later cognitive decline. Whether repetitive head injury among professional athletes impacts cardiovascular risk is unknown. We investigated associations between concussion burden and postcareer hypertension, high cholesterol, and diabetes among former professional American‐style football (ASF) players.MethodsIn a cross‐sectional study of 4080 professional ASF players conducted between January 2015 and March 2022, we used an mulitsymptom concussion symptom score (CSS) and the number of loss‐of‐consciousness (LOC) episodes as a single severe symptom to quantify football‐related concussion exposure. Primary outcomes were hypertension, dyslipidemia, and diabetes, defined by current or recommended prescription medication use.ResultsThe prevalence of hypertension, high cholesterol, and diabetes among former players (52 ± 14 years of age) was 37%, 34%, and 9%. Concussion burden was significantly associated with hypertension (lowest vs. highest CSS quartile, odds ratio (OR) = 1.99; 95%CI: 1.33–2.98; p < 0.01) and high cholesterol (lowest vs. moderate CSS, OR = 1.46, 95%CI, 1.11–1.91; p < 0.01), but not diabetes. In fully adjusted models, the prevalence of multiple CVD was associated with CSS. These results were driven by younger former players (≤ 40 year of age) in which the odds of hypertension were over three times higher in those in the highest CSS quartile (OR = 3.29, 95%CI: 1.39–7.61; p = 0.01). Results were similar for LOC analyses.InterpretationPrior concussion burden is associated with postcareer atherogenic cardiovascular risk profiles among former professional American football players.
Bioinspired Superhydrophobic All‐In‐One Coating for Adaptive Thermoregulation
Authors: Liu Bing‐Ying , Wu Jiawei , Xue Chao‐Hua , Zeng Yijun , Liang Jun , Zhang Shiliang , Liu Mingxiang , Ma Chao‐Qun , Wang Zuankai , Tao Guangming 
Published: May 2024

AbstractThe development of scalable and passive coatings that can adapt to seasonal temperature changes while maintaining superhydrophobic self‐cleaning functions is crucial for their practical applications. However, the incorporation of passive cooling and heating functions with conflicting optical properties in a superhydrophobic coating is still challenging. Herein, an all‐in‐one coating inspired by the hierarchical structure of a lotus leaf that combines surface wettability, optical structure, and temperature self‐adaptation is obtained through a simple one‐step phase separation process. This coating exhibits an asymmetrical gradient structure with surface‐embedded hydrophobic SiO2 particles and subsurface thermochromic microcapsules within vertically distributed hierarchical porous structures. Moreover, the coating imparts superhydrophobicity, high infrared emission, and thermo‐switchable sunlight reflectivity, enabling autonomous transitions between radiative cooling and solar warming. The all‐in‐one coating prevents contamination and over‐cooling caused by traditional radiative cooling materials, opening up new prospects for the large‐scale manufacturing of intelligent thermoregulatory coatings.
Capturing of neurotoxins causing diabetes stress and nervous breakdown in the aqueous medium by naphthazarin esters
Authors: Sharma Nidhi , Gulati Ashu 
Published: May 2024

AbstractThe fear of an increase in blood sugar can be very traumatic. Being diabetic either type I or type II leads to a disorder called diabetes distress having traits of stress, depression, and anxiety. Among risk factors of diabetes mellitus heavy and trace metal toxicity emerges as new risk factors reported in many studies. In this study we target toxic metals, viz., Ni2+, Zn2+, and Cu2+, involved in the pathogenesis of diabetes and diabetic stress with naphthazarin esters. The compounds C1–C3 isolated from the leaves and roots of Arnebia guttata were tested for their metal‐binding ability in an aqueous medium in UV–Visible and nuclear magnetic resonance (NMR) studies. These probes are well‐known naphthoquinones present in the Arnebia species. In the UV–Visible titrations of compounds C1–C3 with Na2+, K2+, Zn2+, Ca2+, Cu2+, Mg2+, Co2+, and Ni2+ ions, significant binding was observed with Ni2+, Cu2+, and Zn2+ ions in MeOH/H2O. There occurs a beautiful formation of red‐shifted bands between the 520 to 620 nm range with a synergistic increase in absorbance. Also, the disappearance of proton peaks in the 1H NMR spectrum on addition of metal ions confirmed binding. Compounds C1–C3 isolated from A. guttata came out as potent Ni2+, Zn2+, and Cu2+ sensors that are reportedly involved in islet function and induction of diabetes.
An integrated multi‐system to screen quality markers of blossom of Citrus aurantium L. var. amara Engl. via combining lipid‐lowering and expectorant assays
Authors: Zeng Wenhui , Wu Dong , Li Mengchu , Huang Wenping , Zhang Jie , Jiang Ying , Li Jing 
Published: May 2024

AbstractThe present research demonstrated that an integrated multi‐system based on the assays of lipid‐lowering and expectorant effects was used to screen quality markers of an edible and medical material—the blossom of Citrus aurantium L. var. amara Engl. (BCAVA)—and a portion of active constituents were quantified in multiple batches to provide scientific data to establish a quality standard for BCAVA. Mouse models were developed to evaluate the lipid‐lowering and expectorant effects, facilitating the investigation of medicinal parts through different polar extractions of BCAVA. Subsequently, ultra‐high‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry was utilized for the in vivo and in vitro identification of chemical profiles within the medicinal parts of BCAVA. This methodological approach led to the selection and quantification of several active compounds from 21 batches of BCAVA sourced from different geographical regions samples. Notably, the ethanol extract of BCAVA exhibited significant lipid‐lowering and expectorant effects while 183 compounds were identified in vitro and 109 in vivo, respectively. Then, five key ingredients were quantified, and the quantitative data were subjected to statistical analysis to discriminate between samples from various geographical regions. Overall, the findings underscore the significance of an integrated, assay‐based approach for the characterization and quality assessment of BCAVA.
A dynamic multigraph and multidimensional attention neural network model for metro passenger flow prediction
Authors: Xi Yuliang , Yan Xin , Jia Zhao‐hong , Yang Bo , Su Rui , Liu Xin 
Published: May 2024

SummaryPassenger flow prediction is an important part of daily metro operation, and its accuracy affects the deployment of train resources management. Due to the complex spatiotemporal correlation characteristics of metro passenger flow, it is necessary to describe it to improve the accuracy of passenger flow prediction. However, the existing models mainly construct the weight matrix based on the static graph and the similarity between stations when describing the spatial correlation of station passenger flow but ignore the time‐varying characteristics of the spatial correlation of station passenger flow. To address this problem, this study introduces a dynamic multi‐graph and multidimensional attention spatiotemporal model. Specifically, the Graph Convolutional Neural Network combined with dynamic multigraph extracts spatial features and the Gated Recurrent Unit extracts temporal features of passenger flow. The multidimensional attention can obtain the spatiotemporal correlation of passenger flow data by assigning weights to them. Finally, this model has been used to conduct experiments on Beijing metro passenger flow datasets with time granularity of 10 and 15 min. The result indicates that the DGMANN model outperforms state‐of‐the‐art other deep learning methods in passenger flow prediction. In addition, the effectiveness of its key submodules has been verified through ablation experiments.
Deriving and interpreting robust features for survival prediction of brain tumor patients
Authors: Rajput Snehal , Kapdi Rupal A. , Raval Mehul S. , Roy Mohendra , Bhalodiya Jayendra M. 
Published: May 2024

AbstractAccurate prediction of survival days (SD) is vital for planning treatments in glioma patients, as type‐IV tumors typically have a poor prognosis and meager survival rates. SD prediction is challenging and heavily dependent on the extracted feature sets. Additionally, comprehending the behavior of complex machine learning models is a vital yet challenging aspect, particularly to integrate such models into the medical domain responsibly. Therefore, this study develops a robust feature set and an ensemble‐based regressor model to predict patients' SD accurately. We aim to understand how these features behave and contribute to predicting SD. To accomplish this, we employed post‐hoc interpretable techniques, precisely Shapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), and Accumulated Local Effects (ALE) plots. Furthermore, we introduced an investigation to establish a direct connection between radiomic features and their biological significance to enhance the interpretability of radiomic features. The best SD scores on the BraTS2020 training set are 0.504 for accuracy, 59927.38 mean squared error (MSE), 20101.86 median squared error (medianSE), and 0.725 Spearman ranking coefficient (SRC). The validation set's accuracy is 0.586, MSE is 76529.43, medianSE is 41402.78, and SRC is 0.52. The proposed predictor model exhibited superior performance compared with leading contemporary approaches across multiple performance metrics.
SwinRes: A hybrid model that effectively diagnoses COVID‐19 through x‐ray lung images
Authors: He Xuanlong , Yang Hong , Xu Jipan , Mu Hongbo 
Published: May 2024

AbstractCOVID‐19 has been ravaging the world for a long time, and although its effects are currently the same as those of a cold or a fever, timely diagnosis of COVID‐19 in the elderly and in patients with related illnesses is still a matter of great urgency. To address this challenge, we propose a model that combines the strengths of the Swin Transformer and ResNet34 architectures to efficiently diagnose COVID‐19 in elderly and vulnerable patients. In this paper, we design a model that integrates Swin transformer and resnet34, which not only integrates the advantages of transformer and CNN but also achieves excellent performance in this image classification problem. Moreover, a pre‐processing method is also proposed to increase the accuracy of the model to 99.08%. In this paper, experiments were conducted on Kaggle's publicly available three‐classification and four‐classification datasets, respectively, and on the three main evaluation metrics of Accuracy, Precision, and Recall, the first dataset obtained 98.81%, 99.49%, and 97.99%, while the second dataset obtained 88.82%, 88.92%, and 86.38%. These findings highlight the validity and potential of our proposed model for diagnosing the presence or absence of COVID‐19 in elderly and vulnerable patients.
Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis
Authors: Wang Si‐Rui , Cao Chun‐Li , Du Ting‐Ting , Wang Jin‐Li , Li Jun , Li Wen‐Xiao , Chen Ming 
Published: May 2024

ObjectiveThis study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis—encompassing both the intratumoral and peritumoral—to predict the status of axillary lymph nodes in patients with early‐stage breast cancer.MethodsThe study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1–5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K‐Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process.ResultsAmong the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer.ConclusionThe predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long‐term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.
Feasibility study on hybrid manufacturing – combining laser‐based powder‐bed fusion and chill casting on the example of EN AC‐42000 alloy
Authors: Brozio M. , Apfelbacher L.‐S. , Hitzler L. , Krempaszky C. 
Published: May 2024

AbstractLaser‐based powder‐bed fusion can be combined with casting into a hybrid manufacturing process. This is done to simultaneously utilize advantages of both processes. To examine related challenges stemming from this combination when using aluminium alloys, first experiments were conducted with EN AC‐42000 alloy, an alloy common in both standalone processes. EN AC‐42000 alloy samples with different surface treatments were placed into a sand mould and cast on to with an aluminium melt. The contact region between the partially molten insert and the cast material was examined with light microscopy. The main challenges were shown to be comprised of bonding issues due to present oxides and a high porosity. The high porosity was traced back to a porosity increase in the laser‐based powder‐bed fusion insert.
Effect of Ni and Sr on the microstructure and tensile properties of the squeeze cast Al‐Si‐Cu alloy at elevated temperatures
Authors: Fang L. , Hu A. , Shen W. , Hu H. 
Published: May 2024

AbstractThe influence of the transition alloying element nickel and the alkaline earth element strontium on the microstructure and tensile properties of squeeze cast Al−Si‐Cu alloy under as‐cast condition at elevated temperature of 100 °C, 200 °C and 300 °C is investigated in comparison with the conventional Al−Si‐Cu alloy (A380). Aluminum alloy A380 is alloyed and modified with 2 wt% additional nickel (Ni) and 0.02 wt% strontium (Sr). Squeeze casting is employed to cast the modified A380 under an applied pressure of 90 MPa. The results of tensile testing at the selected elevated temperatures indicate that the Ni and Sr containing A380 alloy exhibits a significantly improvement on tensile properties, specifically ultimate tensile strength and yield strength with 10 %‐30 % increases. The as‐cast microstructures of both the conventional and Ni and Sr‐containing alloys are observed by an optical microscope and further analyzed with scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The presence of Ni‐containing intermetallic phases with Ni addition, and the modified eutectic Si phase with refined morphologies due to the Sr addition should be responsible for the considerable enhancement on the as‐cast strengths of the squeeze cast Ni‐ and Sr‐containing alloy over those of the conventional A380 alloy.
Properties of silicon dioxide coatings obtained by nano physical vapor deposition (PVD) method on the titanium 13‐niobium 13‐zirconium alloy
Authors: Basiaga M. , Walke W. , Paszenda Z. , Taratuta A. , Rynkus B. , Kolasa J. , Cichoń T. , Kompert‐Konieczna E. 
Published: May 2024

AbstractSurface modification techniques play an important role in adjusting the physicochemical properties of titanium and its alloys. To reduce the penetration of alloying element ions into the body, various types of oxide coatings are used to protect it from the corrosive environment. Another important issue related to the surface layer requirement is ensuring an appropriate set of mechanical properties. Accordingly, in this study, the mechanical and electrochemical properties of the silicon dioxide layers formed by the deposition of nano physical vapor deposition on the surface of titanium and titanium 13‐niobium 13‐zirconium alloy samples were investigated. To evaluate the mechanical properties of the layers produced by this method, hardness tests were carried out, as well as tests on the adhesion of these layers to the metal substrate. On the other hand, electrochemical properties were studied using potentiodynamic measurements to assess the resistance to pitting corrosion, followed by impedance measurements to interpret the processes and phenomena occurring at the silicon dioxide layer/electrolyte interface. The data obtained showed different mechanical and electrochemical properties of the silicon dioxide layers generated with varying process parameters.
Correlation of structure development and electric characteristics within high pressure torsion processed copper
Authors: Kocich R. , Kopeček J. , Marek M. 
Published: May 2024

AbstractCopper of a high purity features excellent electric conductivity, but generally very low mechanical properties. Nevertheless, optimized deformation/thermomechanical treatment can introduce favorable combinations of both. The presented study characterizes the correlation of microstructure development and electric properties within copper processed by the severe plastic deformation method of high pressure torsion, the primary advantage of which is that it enables to achieve grains with the sizes in the ultra‐fine, or even nano scales. The study investigates structure development during progressive deformation. In other words, samples processed by single and double high pressure torsion revolutions were evaluated from the viewpoints of grain sizes and grain boundaries, and the results were correlated with the experimentally measured electric conductivity. The single high pressure torsion revolution contributed to grain size decrease, while the structure after double revolution exhibited very fine grains, especially at the sample periphery featuring the highest imposed strain. Both the samples also exhibited increases in microhardness (especially after double revolution), and electric conductivity higher than 100 % IACS. The results confirmed that copper conductors featuring enhanced mechanical properties and favorable electric conductivity can be manufactured by severe plastic deformation.