bluuebunny/crossref_metadata_2025_split huggingface dataset:
XGBoost-SHAP-based interpretable diagnostic framework for early cognitive impairment in type 2 diabetes mellitus
Authors: Shao Yuan , Gu Chaofei , Xu Hanwen , Shu Zhenyu , Hu Ye , Song Yingxiang 
Published: April 2024

Abstract

Objective To develop and validate a radiomic-clinical model to assess early cognitive impairment in type 2 diabetes mellitus (T2DM) using the XGBoost algorithm.
Methods We retrospectively enrolled 193 patients with T2DM from two medical centers. According the Montreal Cognitive Assessment (MoCA), patients were categorized into normal control (NC) and mild cognitive impairment (MCI) groups. We used ComBat to normalize and gather the data distributions of two centers. The Elastic Net Regression were used to filter redundant and irrelevant features. Based on the eXtreme Gradient Boosting Machine algorithm (XGBoost), clinical factors and radiomic features was used to construct the combined model. The SHAP method explained the model by prioritizing the importance of features, in terms of assessment contribution.
Results The radscore, along with two clinical factors (education level and drinking), were used to build the combined model. The AUCs for predicting MCI in the training set, testing set, and validation set were 0.802, 0.817, and 0.852, respectively. The radscore was the most important feature for discriminating MCI/NC classification, and higher SHAP values of radscore were associated with a higher risks of MCI onset. Conversely, higher SHAP values of education level and drinking were associated with a lower risks of MCI onset. However, the contribution of drinking to the model was minimal.
Conclusion The radiomic-clinical model, utilizing the XGBoost algorithm, can be an auxiliary tool for predicting early cognitive impairment in T2DM.
ClC-1 Inhibition as a Novel Mechanism for Accelerating Recovery of Skeletal Muscle Function After Neuromuscular Block in an Experimental Rat Model
Authors: Pedersen Thomas , Skals Marianne , Broch-Lips Martin , Skov Martin , Riisager Anders , Ceelen Judith , Nielsen Ole , Brull Sorin , de Boer Hans 
Published: April 2024

Abstract

Neuromuscular blocking agents (NMBAs) are commonly used to induce skeletal muscle relaxation during surgery. While muscle relaxation facilitates surgical procedures and tracheal intubation, adequate recovery of muscle function after surgery is required to support pulmonary function, and even mild residual neuromuscular block increases the risk of severe postoperative pulmonary complications. While recovery of muscle function after surgery involving NMBAs can be monitored and, in addition, be accelerated by use of current antagonists (reversal agents), there is a clear clinical need for a safe drug to antagonize all types of NMBAs. Here we show that inhibition of the skeletal muscle-specific chloride ion (Cl-) channel, the ClC-1 channel, markedly accelerates recovery of both single contraction (twitch) and physiologically important sustained contractions (tetanic) in a novel rat model mimicking NMBA-induced muscle block used during surgery. ClC-1 inhibition was found to reverse any NMBA with superior properties to existing reversal agents. This suggests ClC-1 inhibition as a novel mechanism for fast and efficacious reversal from neuromuscular block.
Digitally supported physical activity counselling for people with chronic back pain: A randomized controlled parallel feasibility study
Authors: Lindner Nicole , Kornder Nele , Heisig Julia , Becker Annette , Wardt Veronika van der , Viniol Annika 
Published: April 2024

Abstract
Background Guiding individuals with chronic back pain (CBP) to initiate and adhere to physical activity (PA) remains challenging. The aim of this study was to evaluate the feasibility and acceptability of using the digital consultation app ExPa (Exercise against Pain) to support PA consultations for CBP and its potential for a future effectiveness trial. The ExPa app shows the effect of PA on pain and provides individually tailored support to increase PA.Methods In a 2-arm randomised controlled feasibility study, we recruited 9 physicians and 37 CBP patients in Hesse (Germany). Using computer assisted cluster randomisation, 14 patients received ExPa counselling from their physician, while 17 patients received standard treatment. Main outcomes focused on study procedures and software use, with secondary outcome including pre- and post-intervention measurements of PA (International Physical Activity Questionnaire (IPAQ), pain and mood (Short Form-12 (SF-12), Von Korff pain intensity and disability score and Hospital Anxiety and Depression Scale (HADS)). Additionally, project-tailored questionnaires and qualitative interviews assessed study procedures and software performance.Results Study procedures were generally feasible. However, they took more time and dropouts as well as missing data presented challenges. This provided valuable insights for planning an effectiveness trial. Quantitative and qualitative data indicated that ExPa could have benefits for increasing PA and reducing pain.Conclusions Results from the feasibility study suggested that ExPa could have a positive influence on PA and pain. A larger randomised controlled study with improved procedures needs to confirm these findings.
SmartPlus: A Computer-based Image Analysis Method to Predict Continuous-valued Vascular Abnormality Index in Retinopathy of Prematurity
Authors: Sharafi Sayed Mehran , Ebrahimiadib Nazanin , Roohipourmoallai Ramak , Farahani Afsar Dastjani , Fooladi Marjan Imani , Gharehbaghi Golnaz , Pour Elias Khalili 
Published: April 2024

Abstract

Plus disease is characterized by abnormal changes in retinal vasculature of premature infants. Presence of Plus disease is an important criterion for identifying treatment-requiring in Retinopathy of Prematurity (ROP). However, diagnosis of Plus disease has been shown to be subjective and there is wide variability in the classification of Plus disease by ROP experts, which is mainly because experts have different cut points for distinguishing the levels of vascular abnormality. This suggests that a continuous ROP Plus disease severity score may reflect more accurately the behavior of expert clinicians and may better standardize the classification. The effect of using quantitative methods and computer-based image analysis to improve the objectivity of Plus disease diagnosis have been well established. Nevertheless, the current methods are based on discrete classifications of the disease severity and lack the compatibility with the continuous nature of abnormal changes in retinal vasculatures. In this study, we developed a computer-based method that performs a quantitative analysis of vascular characteristics associated with Plus disease and utilizes them to build a regression model that outputs a continuous spectrum of Plus severity. We evaluated the proposed method against the consensus diagnosis made by four ROP experts on 76 posterior ROP images. The findings of our study indicate that our approach demonstrated a relatively acceptable level of accuracy in evaluating the severity of Plus disease, which is comparable to the diagnostic abilities of experts.
A Comprehensive Flood Risk Assessment for Railroad Network: Case Study for Iowa
Authors: Cikmaz Atiye Beyza , Alabbad Yazeed , Yildirim Enes , Demir Ibrahim 
Published: April 2024

Abstract

Climate-induced disasters, particularly floods, pose a substantial risk to human well-being. These risks encompass economic losses, infrastructural damage, disruption of daily life, and potential loss of life. This study focuses on understanding flood risks to critical infrastructure, emphasizing the resilience and reliability of essential services during such disasters. In the United States, the railway network is vital for the distribution of goods and services. This research specifically targets the railway network in Iowa, a state where the impact of flooding on railways has not been extensively studied. We employ comprehensive GIS analysis to assess the vulnerability of the railway network, bridges, rail crossings, and facilities under 100- and 500-year flood scenarios at the state level. Additionally, we conduct a detailed investigation into the most flood-affected counties, focusing on the susceptibility of railway bridges. Our state-wide analysis reveals that in a 100-year flood scenario, up to 9% of railroads, 8% of rail crossings, 58% of bridges, and 6% of facilities are impacted. In a 500-year flood scenario, these figures increase to 16%, 14%, 61%, and 13%, respectively. Further, our secondary analysis using flood depth maps indicates that approximately half of the railway bridges in the flood zones of the studied counties could become non-functional in both flood scenarios. These findings are crucial for developing effective disaster risk management plans and strategies, ensuring adequate preparedness for climate change impacts on railway infrastructure.
Lifetime Cost Analysis of Concrete Barriers and Steel Guardrails
Authors: Nemati Alireza , Haas Meridian , Torick David , Nazari Shima 
Published: April 2024

Abstract
This study investigates the lifetime costs associated with concrete barriers and steel guardrails. We introduce a cost analysis methodology that incorporates critical factors such as construction costs, maintenance costs, exposure risks during maintenance activities, and the costs imposed to traveling public through the increased traffic and the crash outcomes. We integrate various parameters including economic factors, road geometry, general weather condition, and traffic mix to estimate a location-dependent cost for each type of barrier accurately. A software tool, named CalBarrier, was developed during this study to carry out the calculations and the comparison of lifetime cost of aforementioned barriers. An inherent strength of this research is its reliance on recent real data extracted from various databases of California Department of Transportation (Caltrans), ensuring precision and relevance in accounting for various influential factors. Drawing insights from Caltrans practices and interviews with their personnel, this study emphasizes the intricate decision-making process involved in mitigating safety risks and reducing operational expenses. Although our data originates from California, the methodology for life cycle cost analysis, and our software are applicable for regions with different socio-economic conditions by adjusting the baseline costs, making our findings a valuable resource for other areas facing comparable challenges.
Economic evaluation of a community dental care model for people experiencing homelessness.
Authors: Nguyen Tan , Withers Lyndsey , Witton Robert , Paisi Martha 
Published: April 2024

Abstract
Aim
The study aims to conduct economic evaluation of the Peninsula Dental Social Enterprise (PDSE) programme for people experiencing homelessness over an 18-month period, when compared to a hypothetical base-case scenario (‘status quo’).
Methods
A decision tree model was generated in Treeage Pro Healthcare 2024. Benefit-cost analysis and cost-effectiveness analysis were performed using data informed by the literature and probabilistic sensitivity analysis (Monte-Carlo simulation with 1,000 cycles). The predetermined willingness to pay threshold was estimated to be £59,502 per disability-adjusted life year (DALY) averted. Costs (£) and benefits were valued in 2020 prices. Health benefits in DALYs included dental treatment for dental caries, periodontitis and severe tooth loss.
Results
The hypothetical cohort of 89 patients costs £11,502 (SD 488) and £57,118 (SD 2,784) for the base-scenario and the PDSE programme, respectively. The health outcomes generated 0.9 (SD 0.2) DALYs averted for the base-case scenario, and 5.4 (SD 0.9) DALYs averted for the PDSE programme. The DALYs averted generated £26,648 (SD 4,805) and £163,910 (SD 28,542) in benefits for the base-scenario and the PDSE programme, respectively. The calculated incremental benefit-cost ratio was 3.02 (SD 0.5) and incremental cost-effectiveness ratio was £10,472 (SD 2,073) per DALY averted. Uncertainty analysis demonstrated that the PDSE programme was 100% cost-effective.
Conclusions
Funding a targeted dental programme from the UK healthcare perspective that provides timely and affordable access to dental services for people experiencing homelessness is cost-effective.
Muon Spin Relaxation Study of Spin Dynamics on a Kitaev honeycomb material H3LiIr2O6
Authors: Shu Lei , Shu Lei , Yang Yanxing , Yang Yanxing , Jiang Chengyu , Jiang Chengyu , Huang Liang-Long , Huang Liang-Long , zhu Zihao , zhu Zihao , Chen Changshen , Chen Changshen , Wu Qiong , Wu Qiong , Ding Zhao-Feng , Ding Zhao-Feng , Tan Cheng , Tan Cheng , Biswas Pabitra , Biswas Pabitra , Hillier Adrian , Hillier Adrian , Shi Youguo , Shi Youguo , Yu Dapeng , Yu Dapeng , Liu Cai , Liu Cai , Wang Le , Wang Le , Ye Fei , Ye Fei , Mei Jiawei , Mei Jiawei 
Published: April 2024

Abstract

The vacancy effect in quantum spin liquid (QSL) has been extensively studied. A finite density of random vacancies in the Kitaev model can lead to a pileup of low-energy density of states (DOS), which is generally experimentally determined by a scaling behavior of thermodynamic or magnetization quantities. Here, we report detailed muon spin relaxation (μSR) results of H3LiIr2O6, a Kitaev QSL candidate with vacancies. The absence of magnetic order is confirmed down to 80 mK, and the dynamical spin fluctuations are found to be persistent at low temperatures. Intriguingly, the time-field scaling law of longitudinal-field μSR polarization is observed with the critical exponent of 0.46, exhibiting excellent consistency with scaling behavior of specific heat and magnetization data. This points to the finite DOS with the form of N(E) ~ Eν, wihch is expected for the Kitaev QSL in the presence of vacanncies.
Enhancing Mixed Methods Research with your generative AI assistant – A Tutorial and Evaluation for Scholars
Authors: Combrinck Celeste 
Published: April 2024

Abstract
Scholars from all disciplines can benefit from learning how to use generative Artificial Intelligence (GAI or AI) for data analysis. The current article used real data to demonstrate the analysis and synthesis of mixed methods research (MMR) data with generative AI. I also explore how reliable and valid data outputs are and how to improve the use of generative AI for research. The demonstration data used emanated from a study done in South Africa, with a quantitative sample size of 969 first-year engineering students and, for the qualitative part, 14 second-year students. In the current article, I compare my original analysis to ChatGPT results. Generative AI is a mind tool that is ideal when utilised with human insight to check the cohesion, consistency, and accuracy of the data analysis. The current content is geared towards enhancing methodological application regardless of field or discipline and includes access to a prompt library and examples of using outputs. For the qualitative analysis, I found that ChatGPT could detect similar themes but missed some, and its write-up was shallower than our human version. The quantitative analysis was accurate for the descriptive statistics, but the researcher had to use best judgment to select the correct inferential analysis. A quantitative and qualitative analysis should be conducted separately in generative AI before asking the bot for help with mixed methods research. I give guidelines and a tutorial on how to use chatbots in an ethically responsible and scientifically sound manner for research in social and human sciences.
Sustainable Wardrobe: Recycled Clothing Towards Sustainability and Eco-Friendliness
Authors: K.P Jaheer Mukthar , Nagadeepa C , Selvaratnam Doris Padmini , Pushpa A 
Published: April 2024

Abstract
Apparel is an essential aspect of our daily lives, but it comes with a high environmental cost. The production and disposal of clothing contribute to pollution, greenhouse gas emissions, and the depletion of natural resources. However, recycled clothing presents an opportunity to reduce the impact of the fashion industry on the environment. The fashion industry is a major contributor to both social and environmental issues, starting from the manufacturing process and ending with consumer disposal habits. Despite up to 95% of discarded clothing being reusable or recyclable, every year, about 150 million tons of apparel and footwear are sold worldwide, with 85% of them ending up in landfills or being incinerated. The lack of consumer awareness and inadequate infrastructures in many countries are key reasons for this. To tackle this issue, the industry needs to move away from the current linear "take-make-dispose" system. Many clothing brands have already adopted recycling and sustainability as their slogans, and new clothing technologies such as producing fibers from discarded materials, dyeing fabrics without water, and creating zero-waste patterns are providing innovative ways to ensure a clean and green environment. This study investigates the impact of environmental concerns, perceived value, personal norms, and willingness to pay on consumers' intentions to buy recycled clothing, providing valuable insights for recycled clothing marketers and filling a research gap in consumer behavior literature. By choosing recycled clothing, consumers can support sustainable production practices and contribute to a cleaner and healthier planet. Individuals can contribute positively to the environment by making conscientious choices when they make purchases.
Comprehensive genomic analysis of sporadic multiple meningiomas reveals clonal origin and histotype-specific evolution: a case report
Authors: Sakaguchi Maki , Horie Masafumi , Ito Yukinobu , Tanaka Shingo , Mizuguchi Keishi , Ikeda Hiroko , Kiyokawa Etsuko , Nakada Mitsutoshi , Maeda Daichi 
Published: April 2024

Abstract

Meningioma is the most common primary intracranial tumor in adults, with up to 10% manifesting as multiple tumors. Data on the genomic and molecular changes in sporadic multiple meningiomas are scarce, leading to ongoing debates regarding their evolutionary processes. A comprehensive genetic analysis of a large number of lesions, including minute occult meningiomas (MOMs), is necessary to explore these two possible origins: clonal and independent. In the present study, we performed whole-exome sequencing and analyzed somatic single-nucleotide variants (SNVs), insertions/deletions (INDELs), and copy number alterations (CNAs) in a patient with sporadic multiple meningiomas. These meningiomas included two mass-forming lesions of different histological subtypes (transitional and chordoid) and three MOMs. Genetic analysis revealed CNAs on chromosomes 22q and Y as common abnormalities in the two largest tumors. Furthermore, we identified SNV/INDELs unique to each focus, with NF2 mutation prevalent in the transitional meningioma and CREBBP mutation in the chordoid meningioma. Loss of chromosome 22 was detected in all three MOMs, whereas an NF2 somatic mutation was found only in the largest MOM. Overall, we elucidated the clonal origin and histotype-specific evolution of multiple meningiomas in this case. CNAs may serve as the initial driving event in meningioma development.
Pretreatment with BTK inhibitors could improve the sensitivity of DLBCL cells to CAR-T cells in co-culture system by down-regulating the polarization of M2 macrophages
Authors: Deng Qi , Qi Yao , Wang Jia , Li Xin , Mu Juan , Cui Rui 
Published: April 2024

Abstract

The Tumor microenvironment (TME) of relapsed/refractory (R/R) diffuse large B-Cell lymphoma (DLBCL) patients is associated with resistance of DLBCL cells to CD19 CAR-T cells. How to improve TME in DLBCL and improve the efficacy of CAR-T cell therapy remains to be further explored. We observed the sensitivity of HBL-1/U2932 cells pretreated with BTK inhibitors to CAR-T cells with flow cytometry (FCM), then observed the sensitivity of HBL-1 cells extracted from the co-culture system to CAR-T cells. Effect of pretreatment of BTK inhibitors on the substitute activated M2 macrophages was observed with FCM, Real-time PCR and Western blot method. Then the expression consistency of Notch-1 and RBP-J in activated M2 macrophages was observed by siRNA transfection of Notch-1. After substitute activated M2 macrophages and HBL-1 cells were pretreated with ibrutinib/orelabrutinib respectively, the cytotoxicity of CAR-T cells to HBL-1 cells was higher than that of in substitute activated M2 macrophages pretreated with ibrutinib/orelabrutinib group, and higher than that of in HBL-1 cells pretreated with ibrutinib/orelabrutinib group. Pretreatment with BTK inhibitors could down-regulate the expression of CD206 and IL-10 in activated M2 macrophages. Pretreatment with BTK inhibitors down-regulated the expression of Arg-1 and up-regulated the expression of iNOS in activated M2 macrophages. The up-regulation polarization of M2 macrophages by Notch1 agonists could be reversed by BTK inhibitors. Expression of RBP-J protein decreased in substitute activated M2 macrophages by siRNA silencing Notch 1. Pretreatment with BTK inhibitors could down-regulate the polarization of M2 macrophages and reverse the resistance of DLBCL cells which were co-cultured with substitute activated M2 macrophages to CAR-T cells. This effect might be achieved by down-regulating the Notch-RBP-J pathway.
Trustworthy and Self-explanatory Artificial Intelligence for the Classification of Non-Hodgkin Lymphoma by Immunophenotype
Authors: Thrun Michael , Hoffmann Jörg , Krause Stefan , Weit Nicole , Krawitz Peter , Stier Quirin , NEUBAUER Andreas , Brendel Cornelia , Ultsch Alfred 
Published: April 2024

Abstract

Diagnostic immunophenotyping of malignant non-Hodgkin-lymphoma (NHL) by multiparameter flow cytometry (MFC) relies on highly trained physicians. Artificial intelligence (AI) systems have been proposed for this diagnostic task, often requiring more learning examples than are usually available. In contrast, Flow XAI has reduced the number of needed learning data by a factor of 100. It selects and reports diagnostically relevant cell populations and expression patterns in a discernable and clear manner so that immunophenotyping experts can understand the rationale behind the AI’s decisions.
A self-organized and unsupervised view of the complex multidimensional MFC data provides information about the immunophenotypic structures in the data. Flow XAIintegrates human expert knowledge into its decision process. It reports a self-competence estimation for each case and delivers human-understandable explanations for its decisions. Flow XAI outperformed comparable AI systems in qualitative and quantitative assessments. This self-explanatory AI system can be used for real-world AI lymphoma immunophenotyping.