bluuebunny/crossref_metadata_2025 huggingface dataset:
Public Opinion Evolution Law and Sentiment Analysis of Campus Online Public Opinion Events
Authors: Xu Zhengzhi , Ye Zi , Ye Haiyang , Zhu Lijia , Lu Ke , Quan Hong , Wang Jun , Gu Shanchuan , Zhang Shangfeng , Zhang Guodao
Published: July 2024

In the context of the new era, teachers and students in colleges and universities, as well as the general public, rely more on the Internet and social media to obtain news, express their opinions, and share information, and the dissemination of public opinion events in colleges and universities is not only related to the physical and mental health of teachers and students but also to the reform and development of colleges and universities. In this study, we took campus public opinion events as the main research object in which we selected three recent campus public opinion events to be analyzed. The public opinion data used in the research was collected from Weibo social media platforms. Firstly, we analyzed the dissemination cycle and regional dissemination patterns of a college food safety public opinion hot event through the popularity and regional distribution of public opinion data, thus revealing its formation and evolution patterns. Secondly, the LDA topic mining method is used to mine the themes of the three hot public opinion events, and then analyze the hot factors of the dissemination of each public opinion event from the massive public opinion data. This is crucial for the management department to grasp the dynamics of public opinion. Then, we used the SKEP sentiment classification method to analyze the emotional factors of the public opinion data of the three events to obtain the overall public opinion sentiment situation of the events. Finally, based on the characteristics of time, region, and gender, the evolution and diffusion rules of public topics and emotional distribution under different types of events are analyzed. The precision of the analyses associated with this paper may be limited to the effects of current mainstream as well as state-of-the-art analytical models. The analysis methods and conclusions in this paper provide a scientific theoretical basis and improvement measures for campus public opinion management, which helps to enhance the level of campus public opinion management and safeguard campus stability and public order.
A study of phased-array ultrasonic testing (PAUT) for detecting, sizing, and characterizing flaws in the welds of existing hydraulic steel structures (HSS)
Authors: Schultz Martin , Campbell Leslie , Bell Ramsay , Sauser Phillip
Published: July 2024

Hydraulic steel structures (HSS) are components of navigation, flood control, and hydropower projects that control or regulate the flow of water. Damage accumulates in HSS as they are operated over time, and they must be inspected periodically. This is often accomplished using nondestructive testing (NDT) techniques. If damage is detected, the structure’s fitness for continued service must be evaluated, which requires information on the location and size of discontinuities. This information can be obtained using ultrasonic testing (UT) techniques. However, there is limited information on the reliability of UT techniques with respect to detecting, sizing, and characterizing flaws in HSS. This study addresses this gap. Round-robin experiments were carried out using phased-array ultrasonic testing (PAUT) to scan weld specimens representing a variety of HSS geometries. The results of the round-robin experiments were analyzed to estimate the probability of detection (POD) and to assess the influence of factors potentially affecting POD. Uncertainty in estimates of flaw length and height were described, and partial safety factors were derived for use in fitness-for-service analyses. These results demonstrate the importance of the technician as a factor influencing the reliability of NDT techniques applied to HSS.
Analysing the Performance of Queuing Model with the Busy Period Breakdown
Authors: P Lidiya , Mary K Julia Rose
Published: July 2024

في هذه الورقة ، نقوم بتحليل M / M (أ ، ب) / 1 / MWV مع الانهيار. بدلاً من أن يكون الخادم خاملاً تمامًا خلال فترة الإجازة ، يخدم الخادم بمعدل مختلف أثناء إجازات العمل المتعددة. يختلف سعر الخدمة حسب حالة الوصول. يصل العميل إلى النظام مع المعلمة λ_v يتبع خادم توزيع Poisson الذي يوفر الخدمة مع المعلمة μ وتحت خادم الإجازات المتعددة العامل يوفر الخدمة مع المعلمة μ_v مع التوزيع الأسي. في هذا النموذج ، يتم تقديم دفعات العملاء بموجب القاعدة العامة للخدمة المجمعة. وبالتالي تحتوي كل دفعات من الخدمة على وحدات "أ" كحد أدنى وحد أقصى "ب". لنفترض أن العملاء المنتظرين في قائمة الانتظار أقل من خادم يبدأ متغيرًا عشوائيًا V للعطلة مع المعلمة η. هنا يحدث الانهيار β_v أثناء حالة الانشغال. قمنا بتحليل معادلة الحالة المستقرة ومقاييس الأداء للنظام. تحليل أداء مختلف ، أي متوسط ​​الطول وخصائص أخرى مثل احتمال تحليل الخادم في فترة الخمول ، والانشغال المنتظم ، والإجازة المزدحمة. نقوم أيضًا بحساب النتائج باستخدام نماذج الإجازات المتعددة الكلاسيكية وعطلات العمل المتعددة.
Pengembangan Instrumen Tes Kemampuan Matematika untuk Mahasiswa Calon Guru Matematika
Authors: Kurniawati Annisa Dwi , Dhanurendra Arkana Rezky , Rahmita Fiki
Published: July 2024

Research in mathematics education related to mathematical abilities, especially in prospective mathematics teacher students, is increasing. However, instruments related to mathematics ability tests for prospective mathematics teachers are still limited and rarely found. The three stages of the instrument development model, namely design, define, and develop, were carried out by researchers to produce mathematics ability test instruments aimed at prospective mathematics teacher students. Based on the development process and expert validation, the results showed that the mathematics ability test instruments developed by researchers met the valid criteria. So, the mathematics ability test instruments developed by researchers can be used and utilized for researchers in mathematics education who are interested in examining the mathematical abilities of prospective mathematics teacher students.
Analisis Kemampuan Representasi Matematis dalam Menyelesaikan Masalah Kontekstual materi PLSV Melalui Pendekatan PMRI
Authors: Nisa Ikhfina Khoirun , Putri Agnia Rahmata , Rohmah Siti Nina Ainun Bidara , Hamidah Dewi
Published: July 2024

This study examines the mathematical representation ability of seventh grade junior high school students in solving contextual problems of Linear Equation One Variable (PLSV) through Indonesian Realistic Mathematics Education (PMRI). Using qualitative methods and randomly selected samples, this study evaluates how students apply mathematical representations in the context of PLSV through essay assessments, documentation, and interviews. Data analysis was conducted according to the Miles and Huberman method, which includes data reduction, data presentation, and conclusion drawing. Results showed that PMRI was effective in improving students' mathematical representation ability, enabling them to effectively translate and solve everyday problems into mathematical models. The findings confirm the importance of PMRI as a learning approach that facilitates conceptual understanding and practical application of mathematics, suggesting wider application of PMRI in mathematics learning to develop strong mathematical representation skills among students.
Science in the Community (SciCom) Daerah Indonesia sebagai konteks Pembelajaran IPA: a Systematic Literatur Review
Authors: Latip Abdul , Asep Kadarohman  , Hernani  , Norazilawati Abdullah
Published: July 2024

Penelitian ini bertujuan menjelaskan hasil literatur review mengenai konsep dan implementasi Science in the Community (SciCom) di daerah Indonesia dalam konteks pembelajaran IPA. Penelitian ini menggunakan Systematic Literatur Review (SLR) dengan lima tahapan, yaitu penentuan tema, pencarian data, seleksi, analisis, dan penarikan simpulan. Pencarian data menggunakan Publish or Perish (PoP) dengan sumber utama dari google scholar. Setelah dilakukan seleksi sesuai kriteria yang ditentukan diperoleh artikel sebanyak 38 yang dianalisis berdasarkan kriteria dan fokus penelitian. Hasil penelitian menunjukkan bahwa 1) Science in the Community di Indonesia disajikan sebagai bahan kajian dan konteks pada beberapa konsep dalam pembelajaran IPA di Sekolah, 2) Bentuk SciCom di Indonesia yang banyak diintegrasikan dalam pembelajaran berupa sumber daya alam (55,26%), kebudayaan dan tradisi (28,94%), dan aktivitas masyarakat (15,78%). 3) SciCom sebagai konteks pembelajaran IPA disajikan dalam bentuk bahan ajar (57,89%), perangkat pembelajaran (34,21%), dan media pembelajaran (7,89%). Hasil tersebut menunjukkan bahwa SciCom di daerah Indonesia sudah dikembangkan dan diimplemetasikan dalam pembelajaran IPA dengan berbagai jenis dan bentuk integrasi sesuai kebutuhan proses pembelajaran
Warrick Score in Rheumatoid Arthritis-Associated Interstitial Lung Disease: Predicting Progression and Revealing Relationships
Authors: Karadag Duygu Temiz , Dogan Sevtap , Gokcen Neslihan , Cagdas Oznur Sadioglu , Yazici Ayten , Cefle Ayse
Published: July 2024

Background
The clinical manifestations and course of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) exhibits considerable heterogeneity. In this study, we aimed to explore radiographic progression over a defined period, employing the Warrick score as a semi-quantitative measure in early RA-ILD, and to assess the associated risk factors for progression.
Methods
RA-ILD patients underwent consecutive Warrick scoring based on initial high-resolution computed tomography (HRCT) at diagnosis and the first follow-up. Associations between Warrick scores, pulmonary function tests, and patient characteristics were analyzed. The ROC curve assessed the predictive performance of the Warrick score change rate for ILD progression, while multivariable logistic regression analysis identified risk factors for progression.
Results
Significant correlations were observed between Warrick scores and age at RA-ILD diagnosis, as well as baseline DAS28-ESR scores. The correlations for severity, extent, and total scores were as follows: r = 0.359, p = 0.001; r = 0.372, p = < 0.001; r = 0.298, p = 0.014; r = 0.364, p = 0.001; r = 0.318, p = 0.005; r = 0.255, p = 0.038; r = 0.376, p = < 0.001; r = 0.367, p = 0.001; r = 0.280, p = 0.022, respectively. The annual changes in severity, extent, and total Warrick scores demonstrated sensitivities ranging from 91–97% and specificities of 98% across all parameters for progression over an average follow-up period of 5 years. Corresponding cut-off values were 0.0278, 0.0227, and 0.0694, with corresponding AUCs of 0.954, 0.976, and 0.946, respectively. Warrick severity, extent, and total scores exhibited significant increases during follow-up. Age > 50 years (OR 7.7; p = 0.028) and baseline usual interstitial pneumonia (UIP) pattern (OR 3.1, p = 0.041) were identified as associated factors with progression.
Conclusions
Our findings demonstrate the utility of Warrick's scoring as a valuable tool for systematically evaluating the severity and extent of fibrosis on chest CT scans in RA-ILD patients. While baseline scores did not independently predict the risk of progression, they exhibited correlations with specific patient characteristics. Furthermore, our study introduces the concept of the change rate, quantified as the alteration per unit of time, which could provide new perspectives in the ongoing discussions surrounding the definition of progression in connective tissue-related ILD.
Favorable outcome of Lasiodiplodia theobromae keratomycosis : a clinical case and systematic review.
Authors: Lazrak Adam , Desbois Nicole , Bonnafous Martin , Ouamrane Katia , Merle Harold
Published: July 2024

Backroud:
Keratitis caused by Lasiodiplodia theobromae is rare and typically associated with a poor prognosis. Current literature lacks sufficient evidence on effective management of patients with this condition.
Case presentation:
A 74-year-old former agricultural worker presented with a red right eye, discomfort, and decreased visual acuity, progressing over three days without treatment. Examination revealed type 2 diabetes and a non-perforating, spiculated corneal abscess with a hypopyon in the right eye. Initial treatment included a triple antibiotic therapy and supportive care. Direct mycological examination identified numerous septate mycelial filaments. Antifungal treatment with natamycin and voriconazole, both topically and orally, was initiated. Cultures confirmed Lasiodiplodia theobromae. The patient showed significant improvement. Treatment continued for eight weeks, with a final visual acuity of 20/50 due to a stromal scar.
Conclusion
An extensive literature review conducted in November 2023, using databases such as PubMed and Google Scholar with the keywords "lasiodiplodia” and “keratitis" yielded no previous cases of this specific condition being managed solely with the combined use of natamycin and voriconazole. This antifungal combination is commonly included in most management protocols for fungal keratitis. Factors such as the use of corticosteroids and delayed diagnosis were noted to adversely affect the prognosis. This case and this systematic review underscores the potential for non-surgical management options in severe fungal keratitis.
Mechanisms of rehmannioside A against systemic lupus erythematosus based on network pharmacology, molecular docking and molecular dynamics simulation
Authors: Yang Guofei , Zhang Ying , Li Xiaohui , Xin Tiantian , Li Mingfang , Hao Jin
Published: July 2024

Background
The effect of rehmannioside A (RA) on systemic lupus erythematosus (SLE) is not clear and needs further study.
Methods
SLE-related targets were obtained from the DisGeNet and GeneCards databases, while RA-related targets were obtained from the SwissTarget and SuperPred databases. A protein-protein interaction network of potential targets was constructed using the String platform. After selecting the potential targets, GO and KEGG enrichment analyses were performed via the R packages. The relationships between RA and various core targets were assessed via molecular docking. Molecular dynamics simulation was conducted for optimal core protein–compound complexes obtained by molecular docking.
Results
The top 5 targets in the ranking of degree value were HSP90AA1, HIF1A, PIK3CA, MTOR, and TLR4. GO functional enrichment analysis revealed that 715 biological processes, 18 cellular components and 72 molecular functions were enriched while KEGG pathway enrichment analysis revealed 128 enriched signaling pathways. Molecular docking showed that RA had the highest binding affinity for MTOR, suggesting that MTOR is a key target of RA against SLE. Molecular dynamics simulations revealed good binding abilities between RA and MTOR.
Conclusions
RA exerts its effects on SLE through multiple targets and pathways. MTOR may be a key target of RA against SLE.
Variational Quantum Kolmogorov-Arnold Network
Authors: Wakaura Hikaru , Suksmono Andriyan B. , Mulyawan Rahmat
Published: July 2024

We implemented a quantum version of the Kolmogorov-Arnold Network (KAN) on a quantum circuit. It optimizes the weight of each synaptic connection to enhance the overall performance of the neurons. This approach offers a significant boost in efficiency over traditional neural networks, as it can be optimized more accurately and requires only a few neurons to solve complex problems. The KAN is a multi-layer network, similar to a traditional neural network, with each layer represented as a matrix containing parameters and feedback. This structure is linear and mirrors the form of Variational Quantum Algorithms. Thus, we propose the Variational Quantum Kolmogorov-Arnold Network (VQKAN) as a quantum version of the Kolmogorov-Arnold Network. We demonstrated the optimization of VQKAN for fitting the given function in various ways, such as by using ansatzes and comparisons.
Screening of municipal effluents with the peroxidase toxicity assay
Authors: Gagné Francois , André Chantale , Smyth Shirley -Ann
Published: July 2024

The peroxidase (Per) reaction is a quick and inexpensive biosensor for the screening of environmental contaminants. The purpose of this study was to screen various municipal wastewaters before and after 7 different types of treatment processes. Wastewater samples before (influents) and after the following treatments (effluent) were tested using the Per activity test: advanced biofiltration, biofiltration, aerated lagoons, secondary aeration sludge, trickling filter, secondary membrane filtration, and primary. The influents and effluents were collected for 3 days composites and concentrated to 500 X on a reverse-phase (C18) extraction cartridge. The ethanol extracts were examined for dissolved organic carbon, plastic-like materials, polyaromatic hydrocarbons and polystyrene nanoplastics. The samples were then tested using the Per reaction alone and in the presence of DNA to detect DNA binding agents. The result show that population size tended to increase Per activity and 60% of the effluents decreased Per activity leading to H2O2 persistence. More advanced treatments (biofiltration, membrane biofiltration, secondary aeration) produced stronger changes from the corresponding untreated influents. The addition of DNA during the Per reaction revealed that population size had no influence and that significant changes occurred in 60% of treated effluents suggesting release of genotoxic compounds in the aquatic environment by most treated wastewaters. The toxic implications of these results to aquatic organisms are discussed.
Exploring the Landscape: Perspective on Medical Research among Resident Doctors in a Public and Private Tertiary Hospital in Nigeria
Authors: Ani Chidiebere Kingsley , Bassey Aniekeme Sunday , Osaghae Osahon Joseph , Eroh Miriam Uzezi
Published: July 2024

Background: Medical research is an essential aspect of the training of resident doctors and is required by postgraduate medical colleges in Nigeria as part of the fulfilment offellowship awards. However, research participation among resident doctors in Nigeria is low, despite seemingly high interest in research among resident doctors. The objective of our study is to examine the knowledge, attitudes, practices, and barriers related to medical research among resident doctors in a public and private tertiary care in Nigeria.
Methodology: The study is a descriptive cross-sectional study carried out between November 2023 and December 2023 in a public tertiary hospital and a private tertiary hospital in Nigeria. A total of 352 participants were recruited for the study—303from the public tertiary hospital and 49 from the private tertiary hospital. The obtained data were analysedusing SPSS Statistics version 26 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.). The significance levelwas set at P<0.005.
Results: The majority(63.92%) of the participants had spent between 2 and 5 years in residency training. A total of 57.67% had participated in research other than dissertations,while 35.23% had participated in research publications. The majority (48.58%) of participants assessed their research knowledge to be fair, whereas 2.56% assessed their knowledge to be excellent. Multivariate analysis revealed asignificant positive correlation between self-reported knowledge of research and the number of research publications (adjusted ρ = 0.332; P<0.001). The leading (60.8%) barrier to research participation among resident doctors in Nigeria was lack of time due to stress/pressure of the residency program. An overwhelming majority (95.17%) of resident doctors have shown interest in research participation, and up to 75.87% even want it to be mandatory for all resident doctors in Nigeria.
Conclusion: This study showed that despite limited participation, resident doctors have a positive attitude towards research. However, there is a struggle to translate this enthusiasm into practical application; hence, there is aneed to addressthe barriers to effective participation of resident doctors in medical research.
Development and Validation of a Nomogram model for Predicting One-Year Unplanned Readmission in Patients with Chronic Obstructive Pulmonary Disease
Authors: Zhu Jieyun , Lu Zhao , Gao Min , Huang Chunli , Pan Dongzan , Zhou Juan , Meng Xiaoning , Cai Zhaoqiang , He Lei , Ye Changguang , Shen Yin
Published: July 2024

Background
Unplanned readmission among patients with Chronic Obstructive Pulmonary Disease (COPD) is increasingly prevalent and imposes significant clinical and economic burdens. The aim of this study was to investigate the influencing factors of unplanned readmission in patients with COPD within 1 year after discharge, construct a risk prediction model and evaluate its effect.
Methods
We conducted a prospective observational study on 719 individuals diagnosed with COPD at HePu People's Hospital from January 2023 to May 2024. Participants were randomly divided into a model group (n = 427) and a validation group (n = 180), with a ratio of 7:3. We employed LASSO regression to identify optimal predictors and developed a nomogram prediction model using multivariable logistic regression. The model's performance was assessed through ROC curves, calibration plots, and decision curve analysis.
Results
Of 607 patients included in the final analysis, the incidence of readmission within one year was 40.0%. Multivariate logistic regression analysis identified several independent risk factors for readmission: white blood cell count (WBC; OR = 1.07, 95% CI = 1.03–1.12, P = 0.002), disease duration over ten years (OR = 1.36, 95% CI = 0.75–2.462, P = 0.043), more than one acute exacerbation in the past year (OR = 1.12, 95% CI = 1.05–1.20, P = 0.001), and concurrent respiratory failure (OR = 1.50, 95% CI = 0.97–2.33, P = 0.047). A predictive nomogram model was developed based on these factors. The nomogram exhibited an AUC of 0.719 in the model group and 0.676 in the validation group, demonstrating good predictive performance. The calibration curve showed a good degree of fit, and the Hosmer-Lemeshow test confirmed no significant deviations in model fit (P > 0.05).The clinical decision curve demonstrated that both the model and the validation groups provided better net benefits than the treat-all tactics or the treat-none tactics with threshold probability values of 0.25–0.95 and 0.25–0.85.
Conclusion
The developed nomogram model, integrating WBC count, disease duration, number of acute exacerbations within the past year and concurrent respiratory failure, effectively predicts the risk of one-year unplanned readmission in patients with COPD, offering a valuable tool for clinical decision-making.
The Effects of Concrete Temperature on Air Void Parameters in Pumped Concrete
Authors: ABDELRAHMAN BAHAA , Ley M Tyler
Published: July 2024

This study investigates the effects of concrete pumping on air content, SAM Number, spacing factor, and freeze-thaw performance. This work focuses on how the air dissolves under pressure and then returns to the concrete at room (30°C/86°F), cold (8°C/46°F), and hot (40°C/104°F) temperatures. The research reveals that concrete pumping leads to a significant reduction in air content, with cold mixtures experiencing higher air loss compared to room temperature and hot mixtures. Despite these changes, freeze-thaw performance remains satisfactory for mixtures with initial air content above 4% and SAM Number below 0.32. The study also observes that the dissolved air bubbles return to the concrete with a similar bubble distribution as was in the original mixture.
Technical Feasibility of the implementation of an intensive upper-limb rehabilitation system (NeuroVirt) intervention for stroke survivors
Authors: Mares Kathryn , Mas Maria del Rocio Hidalgo , Watt Alison , Gregoriou Evridiki , Clark Allan
Published: July 2024

Background
80% of stroke survivors have upper limb (UL) disability. NeuroVirt is a portable immersive virtual reality (VR) platform that is designed to encourage high-repetition and high-quality UL movement training. The aim of the study is to investigate the technical feasibility, and the useability and acceptability of NeuroVirt.
Methods
8 adults with a stroke (≥ 3-months) completed the study. Participants used the device at home for up to two 1-hour sessions each day, 6 days a week, for 6 weeks. Participants also received a 15-minute weekly telephone call. Technical feasibility was measured by the percentage of Wi-Fi disconnections, data push failures, and mean scene frames per second (fps). Usability and acceptability were explored through interview feedback and analysed with a thematic inductive analysis approach. We also recorded the number of movement repetitions per session as an indication of compliance.
Results
From 12 participants enrolled in the study, 8 (67%) participants started the NeuroVirt exercise program and were included in the study analysis. Results indicated good Wi-Fi stability with 1(1.51%) disconnection out of 198 sessions, 1 (0.09%) push attempt failed out of 1052 data pushes and no data loss. An overall mean of 67.5 (2.27) fps during a session. Data from the interviews suggested that participants found NeuroVirt acceptable and indicated improvements in function. Participants completed on average 3.5 (1.3) sessions per week and performed on average 338.2 (172.7) movement repetitions per session.
Conclusion
NeuroVirt had no data loss and consistent Wi-Fi stability. The frame rate was above the minimum industry standards of 60-fps required to prevent motion sickness. Preliminary useability and acceptability results showed that a Home-based NeuroVirt program for stroke survivors with UL impairments was both, feasible and well accepted.
Trial registration:
registration number ISRCTN46051085; prospectively registered the 24/02/2023.
Enriching building function classification using Large Language Model embeddings of OpenStreetMap Tags
Authors: Memduhoğlu Abdulkadir , Fulman Nir , Zipf Alexander
Published: July 2024

Automated methods for building function classification are becoming necessary due to restricted access to accurate building use data. Traditional on-site surveys conducted by government agencies are costly and can be influenced by subjective judgment, highlighting the need for more objective and cost-effective approaches. Existing approaches utilize Natural Language Processing (NLP) techniques such as text similarity and topic modeling, which typically struggle with the ambiguity of semantic contexts in textual data representing human activities. This study introduces a method for classifying urban building functions by integrating physical and spatial metrics with contextual embeddings from OpenStreetMap (OSM) tags, employing Large Language Models (LLMs) to improve the precision and relevance of function classifications in urban settings. We employed an XGBoost model trained on 32 features from six city datasets to classify urban building functions, demonstrating varying F1 scores from 67.80% in Madrid to 91.59% in Liberec. Integrating LLM embeddings enhanced the model's performance by an average of 12.5% across all cities compared to models using only physical and spatial metrics, and by 6.2% over models that incorporate direct tags from OSM. This suggests that deep contextual understanding is beneficial for classification. Moving forward, we suggest investigating the discrepancies in classification accuracy across different urban contexts, which is a common occurrence in existing research.
The Effect of Continuum Beliefs on Public Stigma of Depression: Evidence from Explicit and Implicit Sources
Authors: Wang Xuanyi , Lv Qiuyu , Lu Minghui , Qu Weiguo
Published: July 2024

Objective
The Continuum Belief (CB) intervention approach has the potential to reduce the stigma of depression by suggesting that people with and without depression exist on one mental health-illness continuum. To date, few studies have examined the impact of implicit aspects.
Methods
Participants were randomly assigned to one of three experimental conditions. Continuum beliefs were induced by texts from a continuum view of depressive disorders, a categorized view, or a neutral text. Study 1 (N = 349) investigated explicit attitudes toward public stigma. Study 2 (N = 269) investigated implicit attitudes. Study 3 (N = 28) re-administered the continuum beliefs at a four-month interval.
Results
Inducing continuum beliefs significantly reduced the explicit stigma of depression among the public. At the implicit level, inducing continuum beliefs failed to decrease the public’s underlying stigma of depression. However, public stigma was slightly reduced through longitudinal studies.
Conclusions
This study found that continuum beliefs have a certain potential in reducing the stigma of depression.
Corrosion resistance and biocompatibility of carbon-ion-implanted AZ31B magnesium alloy
Authors: Zheng Chengdong , lin bingpeng , Li Tong , Wang Shuang , Hou Yuxia , Zhang Zhiyong , Yang Fan , Cao Baocheng
Published: July 2024

The poor corrosion resistance of magnesium limits its clinical applications. Accordingly, in the present study, carbon ions were incorporated into a AZ31b magnesium alloy surface via carbon plasma immersion ion-implantation to improve its corrosion resistance and biocompatibility. The surface morphology and properties of the modified alloy were evaluated by field-emission scanning electron microscopy, water contact angle measurement, Raman scattering, X-ray photoelectron spectroscopy, and energy dispersive X-ray spectroscopy. Furthermore, compositional depth profiles were obtained by glow discharge optical emission spectroscopy, revealing a Gaussian-like distribution of carbon concentration. Electrochemical and hydrogen-evolution analysis demonstrated the successfully improved corrosion resistance of the AZ31b Mg alloy, while its biocompatibility was demonstrated by MTT and cell-adherence assays.
The Diagnostic and Prognostic Value of 18F-FDG PET/MR in Hypopharyngeal Cancer
Authors: Fan Cui , Shen Yilin , Wang Hao , Wu Jichang , Hu Haixia , Shen Chenling , Huang Xinyun , Ye Bin , Xiang Mingliang
Published: July 2024

Objective
To evaluate the diagnostic performance of fluorine 18 fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MR) in the preoperative staging of hypopharyngeal cancer (HPC), compare it with conventional enhanced computed tomography (CT) and MR, and further explore the prognostic value of its metabolic and diffusion metrics for HPC.
Methods
This retrospective study included 33 patients with pathologically confirmed HPC. All patients underwent preoperative 18F-FDG PET/MR, CT, and MR examination. The staging performance of the three modalities was evaluated using pathological staging as a reference. Additionally, metabolic indicators and diffusion-related parameters from PET/MR were collected to investigate their impact on larynx preservation and survival.
Results
PET/MR demonstrated accuracies of 90.9% and 71.4% in the preoperative T and N staging, respectively, significantly higher than those of CT (54.5%, P = 0.001; 42.9%, P = 0.021) and MR (66.7%, P = 0.016; 42.9%, P = 0.021). Significant differences were observed in the maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), minimum apparent diffusion coefficient (ADCmin), and mean ADC (ADCmean) across different T stages, and in SUVmax, mean SUV (SUVmean), total lesion glycolysis (TLG), and MTV across different N stages. ADCmin and ADCmean showed good predictive capability for larynx preservation, with AUCs of 0.857 and 0.920 (P < 0.05), respectively. In survival analysis, high ADCmin (P < 0.0001) and high ADCmean (P = 0.0011) were significantly associated with better survival, with AUCs of 0.815 and 0.866 (P < 0.05), respectively.
Conclusion
In HPC, 18F-FDG PET/MR imaging significantly surpasses CT and MR in preoperative diagnostic staging. Its diffusion-related parameters have substantial prognostic value, with high ADC values associated with larynx preservation and favorable survival, serving as prognostic indicators for HPC.
Post-operative Nausea and Vomiting Following Regional Anaesthesia at a Low-income Country Tertiary Hospital
Authors: Turitwenka Dorothy , Nabukenya Mary T. , Charles Kojjo , Luggya Tonny Stone
Published: July 2024

INTRODUCTION: Post-Operative Nausea and Vomiting (PONV) is one of the complications associated with surgery and anesthesia especially with current advancements in both specialties increasing operations done under regional anesthesia rather than general anesthesia. PONV incidence and associated factors in our setting after regional anesthesia remain unknown yet suboptimal management causes distress postoperatively.
OBJECTIVE: We studied the incidence and the factors associated with Post-Operative Nausea and Vomiting among surgical patients undergoing regional anesthesia in Mulago National Referral and Teaching Hospital.
METHODOLOGY: This was a prospective cohort study, registered at ClinicalTrials.gov as study number NCT03835234. Patients aged 18 years and above that were planned for surgery under regional anesthesia at Mulago National Referral and Teaching Hospital were consented and enrolled into the study over a six month period. Data was collected using pre-tested, semi structured questionnaires. Patients were post operatively observed for 24 hours. We determined the incidence of and factors associated with PONV which were submitted to multiple logistic regression analysis to determine a PONV causal relationship.
RESULTS: Out of 364 participants, 140(38%) experienced PONV. Factors associated with PONV in our setting were history of nausea or vomiting(AOR=3.49, 95% CI 1.53 - 7.96 ), motion sickness(AOR= 2.36, 95% CI 1.20 - 4.65), history of PONV(AOR= 2.61, 95% CI 1.25 - 5.46 ), age(AOR= 1.03,95% CI 1.01 - 1.06),ASA 2 classification(AOR=0.36,p=0.016), less than two hours of preoperative fasting(AOR=4.09, 95% CI 0.16 - 0.83) and low blood pressure(AOR=4.99, 95% CI 1.72 - 14.48 ). Ninety two participants (25.3%) received anti-emetics perioperatively.
CONCLUSION: The incidence of PONV following surgery under regional anesthesia at MNRTH is higher than elsewhere reported. Some factors associated with PONV were similar to those in other studies, while others were unique to this study. 25% of the patients received PONV prophylaxis.
Polypharmacy and Anticholinergic Burden Scales in Older Adults: A Longitudinal Cross-Sectional Study Among Psychiatric Outpatients in a Tertiary Care Hospital
Authors: Bidarolli Manjunath , Das Biswadeep , Rawat Vikram Singh , Sony Hannah Theresa , Agnihotri Akash
Published: July 2024

Introduction
Assessment of pharmacotherapy quality in older adults relies on indicators such as polypharmacy and anticholinergic burden. This study aimed to investigate the correlation between polypharmacy and various anticholinergic burden scales in an elderly patient population.
Methods
This study was conducted at a psychiatry outpatient clinic at AIIMS, Rishikesh, India, from December 2021 to March 2023. Elderly patients (aged ≥ 60 years) who were on at least one psychotropic drug and had a primary working diagnosis of psychiatric illness were included. All psychotropic drugs, including antidepressants, antipsychotics, mood stabilizers, and hypnotics, were evaluated. Anticholinergic burden scales were calculated by the respective tools. Univariate analysis was adopted to determine the factors that may affect polypharmacy.
Results
The study included 1165 elderly patients aged ≥ 60 years. The prevalence of polypharmacy was 20.43%. The most common medications prescribed were clonazepam (61.8%), an anxiolytic; escitalopram (25.52%), an antidepressant; and risperidone (26.14%), an antipsychotic. In terms of hypnotic and sedative medications, 51.92% of the patients were prescribed zolpidem. The strongest association with polypharmacy was observed for the Anticholinergic Load Scale (ALS), with an odds ratio of 4.3 (p < 0.001).
Conclusion
The anticholinergic burden in this cohort of elderly psychiatry patients was high, with 95.1% experiencing a significant burden. Adverse drug events and anticholinergic burden scales were positively associated with polypharmacy, with a stronger correlation between polypharmacy and ALS scores than with other anticholinergic burden scales in older adults.
A predictive model for depression in Chinese middle-aged and elderly people with arthritis
Authors: Yin Li , Pu Kehong , Ke Wei
Published: July 2024

BACKGROUND This study examines the prevalence of depression and its determinants among Chinese middle-aged and elderly arthritis patients, aiming to establish a theoretical foundation for enhancing their mental well-being and to inform the development of targeted prevention and intervention strategies.
METHODS Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) were selected for this study. We used whether middle-aged and elderly arthritis patients were depressed as the dependent variable and included 16 predictor variables. The data were randomly divided into training and validation sets according to 7:3. LASSO and binary logistic regression analyses were performed in the training set to screen the predictor variables of the model and construct the model, and the model was internally validated in the validation set.
RESULTS This study included 1302 middle-aged and elderly arthritis patients, and LASSO and binary logistic regression analysis were used to construct a prediction model for depression applicable to middle-aged and elderly arthritis patients in China. The column-line graph analysis revealed that gender, age, self-rated health, trouble with body pain, life satisfaction, marital satisfaction, child satisfaction, and instrumental activities of daily living were risk factors for depression (P<0.05). The area under the receiver operating characteristic curve(ROC) exceeded 0.70 in both the model training and internal validation phases, demonstrating the high accuracy of the model in predicting depression risk. In addition, decision curve analysis (DCA) and model mean calibration curve analysis further confirmed the practical value and validity of the model in depression prediction.
CONCLUSION In this study, we demonstrated that female, middle-aged, self-rated poor health, trouble with body pain, life dissatisfaction, marital dissatisfaction, children dissatisfaction, and instrumental activities of daily living difficulties are risk factors for depression among arthritis patients in the middle-aged and elderly population. We developed a predictive model for depression based on the above risk factors to provide early identification, intervention, and treatment for a high-risk group of middle-aged and elderly arthritis patients.
Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio(NHHR) with depressive symptoms: recent findings from NHANES 2005– 2018
Authors: Yan chunyu , Zhou Yabin , wang he , Liu Changxing
Published: July 2024

Objective
This study's main goal was to look into the connection between the prevalence of depression in the US population and the non-HDL cholesterol/HDL cholesterol ratio (NHHR).
Methods
The National Health and Nutrition Examination Survey (NHANES) provided the data for our investigation from 2005 to 2018. and primarily included participants who contained complete data on NHHR and depression in U.S. adults (age ≥ 20 years). Associations between NHHR and depression were assessed using multifactorial logistic regression analysis,subgroup analysis, and smoothed curve fitting.
Results
In our study, 29,561 subjects in total showed a mean NHHR index of 3.12 ± 1.58,A noteworthy positive correlation was observed between NHHR and depression in multifactorial logistic regression analysis.Subgroup analyses and tests of interaction showed that gender, age, ethnicity, PIR, smoking, alcohol consumption, coronary heart disease, diabetes mellitus, hypertension, and stroke did not influence the NHHR and the association between depression (P for interaction > 0.05), whereas two stratification factors, BMI and sleep disturbance, may be potential factors in the association between NHHR and depression (P for interaction < 0.05).
Conclusion
According to our present study, if the level of NHHR rises in American adults, their likelihood of developing depression also increases.
Cell-type-specific response to silicon treatment in soybean leaves revealed by single nucleus RNA-sequencing and targeted gene-editing
Authors: Patil Gunvant , Devkar Vikas , D’Agostino Leonidas , Kshetry Arjun , Yong-Villalobos Lenin , Nadaf Altafhusain , Thirumalaikumar Venkatesh , Skirycz Aleksandra , Ma Jianxin , Stupar Robert , Herrera-Estrella Luis , Deshmukh Rupesh
Published: July 2024

In agriculture, mineral nutrient uptake and deposition profoundly influences plant development, stress resilience, and productivity. Despite its classification as a non-essential element, silicon (Si) uptake and deposition alters plant physiology and particularly improves defense response and stress mitigation. While genetic and molecular mechanisms of Si uptake and transport are well-studied in monocots, particularly rice, its role in dicot species, such as soybean, remains unclear at the cellular and molecular levels. Traditional bulk transcriptomics methods lack the resolution to uncover cellular heterogeneity. Here, we present a study utilizing single-nucleus RNA sequencing (snRNA-seq) to dissect cellular responses to Si accumulation in soybean leaves. Our analysis revealed distinct cellular populations, including a novel Si-induced cell cluster within vascular cells, suggesting a specific mechanism of Si distribution. Si treatment induced the expression of defense-related genes, particularly enriched in vascular cells, highlighting their specialized role in activating plant defense mechanisms. Moreover, Si modulated the expression of genes involved in RNA silencing, phytoalexin biosynthesis, and immune receptor signaling, suggesting transcriptional priming of genes involved in defense responses. We also investigated putative Si transporters, revealing differential expression patterns in response to Si treatment, suggesting presence of active and gradient-based transport mechanisms. Furthermore, by employing CRISPR/Cas9 genome editing we functionally validated the role of efflux Si transporters in composite soybean plants. Our findings shed light on the vital biotic stress regulatory networks governed by Si treatment in soybean leaves, paving potential strategies for enhancing stress tolerance and agronomic performance in crops.
Machine learning-based prediction of return to work within 12 months in acute myocardial infarction patients
Authors: Wu Xiaojun , Wang Shiyu , Cui Haoning , Zheng Xianghui , Hou Xinyu , Wang Zhuozhong , Li Qifeng , Liu Qi , Cao Tianhui , Zheng Yang , Wu Jian , Yu Bo
Published: July 2024

Return to work is a critical indicator of recovery after acute myocardial infarction (AMI), and accurate identification of patients with low return-to-work rates is critical for timely intervention. The aim of this study was to develop a machine learning (ML) model for predicting the return to work in AMI patients. A retrospective study of data from 1473 patients was conducted using the Incidence Rate of Heart Failure After Acute Myocardial Infarction With Optimal Treatment database. Patients were randomly divided into a training cohort and a validation cohort (7:3). A total of five ML models were developed based on the training cohort to predict return to work. Model performance was assessed according to numerous learning metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, F1-score, and Brier score. The ML models were constructed using 12 features, including age, occupation, income, anterior wall AMI, hypertension, fasting plasma glucose (FPG), beta-blockers, marriage, aspartate transaminase (AST), body mass index (BMI), TG (triglyceride) and phase II cardiac rehabilitation (CR). Among the five ML models, the LR model achieved the best performance, with an AUC of 0.793 (95% CI, 0.712-0.874), an accuracy of 0.719 (95% CI, 0.642-0.787), an F1 score of 0.800, and a Brier score of 0.135, and was subsequently transformed into a nomogram. A new return-to-work prediction model was developed based on a machine learning algorithm, which may help identify patients with low return-to-work rates and may become an effective management tool for AMI patients.
Clinical trial registration: Clinical Trials.gov ID: NCT03297164.
Incidence of Diabetic Retinopathy and Predictors among Adult Diabetic Patients in Central and Southern Ethiopia: A Multicentre Retrospective Cohort Study.
Authors: Barata Tagese Yakob , Abiraham Awoke , Yakob Begidu , Menza Mesfin
Published: July 2024

Background
Diabetic retinopathy (DR) is emerging as a global public health issue that may result in visual impairment. It has become the leading cause of blindness among working-age adults globally, despite established treatments that can reduce the risk by 60%. Disease progression, as indicated by longitudinal HbA1c measurements, may affect the time of interest (retinopathy). Hence, poor glycemic control increases and hastens the risk of retinopathy.
Objective
This study aimed to determine the incidence of diabetic retinopathy and its predictors among adult diabetic patients in public hospitals in Central and Southern Ethiopia.
Methods
A retrospective follow-up study was conducted at selected hospitals in Central and Southern Ethiopia among newly diagnosed patients with adult diabetes between January 1, 2015, and June 30, 2022. A systematic random sampling technique was applied. The data were collected and entered into Epi-data version 4.6.0.2 and exported to STATA version 14 for analysis. Descriptive statistics of the variables were obtained. The Cox proportional hazard assumption was checked. The Cox regression model was used to quantify the effects of covariates on the time to diabetic retinopathy. A p value less than 0.25 was the cut-off point for selecting variables for the bivariable analysis and candidates for the final analysis. In the multivariable analysis, variables with a p value less than 0.05 and a corresponding 95% confidence interval in the final model were used. Model adequacy was checked.
Results
A total of 376 adult diabetic patients were followed for 45752 person-months. Overall, 96 (25.5%) patients developed diabetic retinopathy, with an incidence rate of 11.7 per 1000 person-months of observation. Positive proteinuria (AHR = 2.19: 95% CI: 1.18, 4.08), hypertension (Yes) (AHR = 2.23: 95% CI: 1.39, 3.55) and type II DM (AHR = 2.89: 95% CI: 1.19, 7.05) were identified as significant predictors of diabetic retinopathy.
Conclusion
The incidence rate of diabetic retinopathy was high. Hypertension, proteinuria and type of diabetes were identified as predictors of diabetic retinopathy. Aggressive management should be implemented, and DM patients with hypertension and positive proteinuria should be followed to optimize positive outcomes.