bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influencia de las enfermedades sistémicas en la reparación postendodóntica
Authors: Astudillo Kenia Karen Kun , Azanza Santacruz Carolina Michelle , Freire Narvaez Johanna Estefanía 
Published: April 2024

Las enfermedades sistémicas son afecciones que impactan en varios sistemas de órganos, lo que afecta el sistema inmunológico de quienes las padecen. A nivel oral, estas enfermedades manifiestan signos y síntomas los cuales, combinados con la deficiencia en los mecanismos de defensa de los pacientes y las alteraciones en los procesos de curación, influyen directamente en la eficacia de los tratamientos dirigidos a abordar patologías endodónticas. El propósito de esta investigación consiste en examinar la literatura seleccionada que aborda la influencia de las enfermedades sistémicas (diabetes, VIH, enfermedades cardiovasculares, autoinmunitarias, enfermedades inflamatorias intestinales y el tabaquismo) en la reparación posendodóntica. Materiales y métodos: La obtención de información se llevó a cabo a través de búsquedas en bases de datos como Pubmed, Google académico, Scopus, EBSCO host, Science Direct, Elsevier y ResearchGate, seleccionando investigaciones de acuerdo a criterios de inclusión y exclusión. Resultados: Las enfermedades sistémicas como la diabetes, enfermedades cardiovasculares, infección por VIH, enfermedad inflamatoria intestinal y el hábito de fumar pueden interferir en el proceso de reparación de los tejidos periapicales después de un tratamiento endodóntico. Conclusiones: Se sugiere el impacto de las enfermedades sistémicas en el proceso de reparación posterior al tratamiento endodóntico. Sin embargo, se requiere más estudios longitudinales para evaluar el efecto de las enfermedades sistémicas en la curación periapical. Además, se resalta la importancia del conocimiento sobre la salud sistémica de los pacientes ya que puede alterar el resultado del tratamiento de infecciones endodónticas.
Anemia de Fanconi: Métodos diagnósticos e a aplicabilidade da genética laboratorial
Authors: Dantas Dary Medeiros , Pereira Hannaly Wana Bezerra 
Published: April 2024

A Anemia de Fanconi (AF) é uma doença hereditária autossômica recessiva ou ligada ao cromossomo X que se manifesta nos primeiros anos de vida, sendo responsável por anomalias congênitas e falência medular progressiva tendo como principal característica a anemia aplástica (AA). Este artigo de revisão teve como objetivo identificar as metodologias diagnósticas utilizadas na detecção da AF envolvendo a aplicabilidade genética, clínica e laboratoriais. Para esse estudo foram consultadas as bases de dados científicas eletrônicas como, o Portal States National Library of Medicine National Institutes of Health (Medline/PubMed), Web Of Science, e Scientific Electronic Library Online (SciELO). Foram encontrados um total de 1563 artigos científicos nos idiomas inglês, espanhol e português, em que 6 deles foram incluídos para análise. Evidenciou-se a existência de dois testes de fragilidade cromossômica, um método em biologia molecular e um método de sequenciamento de DNA, e que o grau de especificidade encontrado nos métodos Western blot e mitomicina C (MMC) complementam o diagnóstico da AF. O diepoxibutano (DEB) se diferencia em relação ao Western blot e o MMC por ser mais específico em quebras cromossômicas, sendo o sequenciamento de nova geração (NGS) o mais específico e mais informativo por conduzir a uma abordagem personalizada devido a facilidade de acesso as variações somáticas em tumores e alterações na expressão gênica. Diante disso, conclui-se que esses métodos proporcionam maior especificidade diagnóstica ao identificarem a associação gênica da AF e o mosaicismo somático, entendendo os níveis de gravidade trazendo maior agilidade na tomada de medidas terapêuticas.
Score based on contrast-enhanced ultrasound predict central lymph node metastasis in papillary thyroid cancer
Authors: He Lin , Chen Xiao , Hu Jiayin , Meng Yun , Zhang Yan , Chen Wei , Fan Yuhong , Li Tao , Fang Jingqin 
Published: April 2024

ObjectivesTo investigate the association between contrast-enhanced ultrasound (CEUS) features of PTC and central lymph node metastasis (CLNM) and to develop a predictive model for the preoperative identification of CLNM.MethodsThis retrospective study evaluated 750 consecutive patients with PTC from August 2020 to April 2023. Conventional ultrasound and qualitative CEUS features were analyzed for the PTC with or without CLNM using univariate and multivariate logistic regression analysis. A nomogram integrating the predictors was constructed to identify CLNM in PTC. The predictive nomogram was validated using a validation cohort.ResultsA total of 684 patients were enrolled. The 495 patients in training cohort were divided into two groups according to whether they had CLNM (pCLNM, n= 191) or not (nCLNM, n= 304). There were significant differences in terms of tumor size, shape, echogenic foci, enhancement direction, peak intensity, and score based on CEUS TI-RADS between the two groups. Independent predictive US features included irregular shape, larger tumor size (≥ 1.0cm), and score. Nomogram integrating these predictive features showed good discrimination and calibration in both training and validation cohort with an AUC of 0.72 (95% CI: 0.68, 0.77) and 0.79 (95% CI: 0.72, 0.85), respectively. In the subgroup with larger tumor size, age ≤ 35 years, irregular shape, and score &gt; 6 were independent risk factors for CLNM.ConclusionThe score based on preoperative CEUS features of PTC may help to identify CLNM. The nomogram developed in this study provides a convenient and effective tool for clinicians to determine an optimal treatment regimen for patients with PTC.
Hydrodynamic analysis of a floating platform integrated with buoys and spring components for energy conversion
Authors: Sun Shi Yan , Gao Ruili , Li Yueyang , Ren Kang 
Published: April 2024

Introduction: The study presents an integrated system comprising a central platform and four wave-energy converters, with a focus on investigating their coupled motions induced by ocean waves. The interaction between the buoys and the central platform is achieved through the implementation of spring components. The power take-off system is simulated by incorporating damping coefficients and stiffness into these spring components, enabling a detailed analysis of the energy conversion of such system.Methods: Numerical simulations based on the continuity equation and the Reynolds-Averaged Navier-Stokes (RANS) equations, coupled with the realizable k−ε turbulence model, are conducted. The two-phase flow model employs the Volume of Fluid (VOF) method to accurately capture free surface elevations. Additionally, frequency-domain predictions, based on the linearized velocity potential flow theory, are provided for a single central platform and buoy for comparative purposes.Results: Detailed results regarding the effects of wave frequency and the damping coefficient of the power take-off system are presented.Discussion: The results reveal that while both the platform’s motion and the relative motions between buoys and the platform are suppressed, the absolute motion of buoys varies depending on their respective locations within the system and ocean waves. This variation is deeply influenced by the interaction between incident, reflected and diffracted waves within the system.
EMPT: a sparsity Transformer for EEG-based motor imagery recognition
Authors: Liu Ming , Liu Yanbing , Shi Weiyou , Lou Yitai , Sun Yuan , Meng Qi , Wang Dezheng , Xu Fangzhou , Zhang Yang , Zhang Lei , Leng Jiancai 
Published: April 2024

IntroductionTransformer network is widely emphasized and studied relying on its excellent performance. The self-attention mechanism finds a good solution for feature coding among multiple channels of electroencephalography (EEG) signals. However, using the self-attention mechanism to construct models on EEG data suffers from the problem of the large amount of data required and the complexity of the algorithm.MethodsWe propose a Transformer neural network combined with the addition of Mixture of Experts (MoE) layer and ProbSparse Self-attention mechanism for decoding the time-frequency-spatial domain features from motor imagery (MI) EEG of spinal cord injury patients. The model is named as EEG MoE-Prob-Transformer (EMPT). The common spatial pattern and the modified s-transform method are employed for achieving the time-frequency-spatial features, which are used as feature embeddings to input the improved transformer neural network for feature reconstruction, and then rely on the expert model in the MoE layer for sparsity mapping, and finally output the results through the fully connected layer.ResultsEMPT achieves an accuracy of 95.24% on the MI EEG dataset for patients with spinal cord injury. EMPT has also achieved excellent results in comparative experiments with other state-of-the-art methods.DiscussionThe MoE layer and ProbSparse Self-attention inside the EMPT are subjected to visualisation experiments. The experiments prove that sparsity can be introduced to the Transformer neural network by introducing MoE and kullback-leibler divergence attention pooling mechanism, thereby enhancing its applicability on EEG datasets. A novel deep learning approach is presented for decoding EEG data based on MI.
Predicting the occurrence of mild cognitive impairment in Parkinson’s disease using structural MRI data
Authors: Beheshti Iman , Ko Ji Hyun 
Published: April 2024

IntroductionMild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson’s disease (PD) and a main risk factor for progressing to dementia. Our objective was to identify early anatomical brain changes that precede the transition from healthy cognition to MCI in PD.MethodsStructural T1-weighted magnetic resonance imaging data of PD patients with healthy cognition at baseline were downloaded from the Parkinson’s Progression Markers Initiative database. Patients were divided into two groups based on the annual cognitive assessments over a 5-year time span: (i) PD patients with unstable healthy cognition who developed MCI over a 5-year follow-up (PD-UHC, n = 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC, n = 52). These 52 PD-SHC were selected among 192 PD-SHC patients using propensity score matching method to have similar demographic and clinical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier.ResultsThe average accuracy of classifying PD-UHC vs. PD-SHC was 80.76%, with 82.05% sensitivity and 79.48% specificity using 10-fold cross-validation. The performance was similar in the hold-out test sets with all accuracy, sensitivity, and specificity at 76.92%. SHAP analysis showed that the most influential brain regions in the prediction model were located in the frontal, occipital, and cerebellar regions as well as midbrain.DiscussionOur machine learning-based analysis yielded promising results in identifying PD individuals who are at risk of cognitive decline from the earliest disease stage and revealed the brain regions which may be linked to the prospective cognitive decline in PD before clinical symptoms emerge.
Learning by observing: a systematic exploration of modulatory factors and the impact of observationally induced placebo and nocebo effects on treatment outcomes
Authors: Klauß Helena , Kunkel Angelika , Müßgens Diana , Haaker Jan , Bingel Ulrike 
Published: April 2024

IntroductionObservational learning (OL) refers to learning through observing other people’s behavior. OL has been suggested as an effective and simple tool to evoke treatment expectations and corresponding placebo and nocebo effects. However, the exact mechanisms by which OL shapes treatment outcomes, its moderating factors and possible areas of application remain unclear. We thus reviewed the existing literature with two different literature searches to answer the following questions: Which influencing factors contribute to OL-induced placebo and nocebo effects (in healthy volunteers and patients) and how large are these effects (search 1)? In which medical fields has OL been used so far to modulate treatment expectancy and treatment outcomes in patients, their caregivers, and at-risk groups (search 2)? We also aimed to explore whether and how the assessment of treatment expectations has been incorporated.MethodsWe conducted two independent and comprehensive systematic literature searches, both carried out on September 20, 2022.ResultsWe identified 21 studies that investigated OL-mediated placebo and nocebo effects for pain and itch, the (placebo) efficacy of sham treatment on anxiety, and the (nocebo) induction of medication side effects (search 1). Studies showed that OL can efficiently induce placebo and nocebo effects across different presentation modes, with medium effect sizes on average: placebo effects, d = 0.79 (range: d = −0.36–1.58), nocebo effects, d = 0.61 (range: d = 0.04–1.5). Although several moderating factors have been investigated, their contribution to OL-induced effects remains unclear because of inconsistent results. Treatment expectation was assessed in only four studies. Regarding medical applications of OL (search 2), we found 12 studies. They showed that OL was effectively applied in preventive, therapeutic and rehabilitative interventions and that it was mainly used in the field of psychosomatics.DiscussionOL effects on treatment outcomes can be both positive and negative. Future research should investigate which individuals would benefit most from OL and how OL can be implemented most effectively to induce placebo and avoid nocebo effects in clinical settings.Systematic review registrationThis work was preregistered at the Center for Open Science as open-ended registration (doi: 10.17605/OSF.IO/FVHKE). The protocol can be found here: https://archive.org/details/osf-registrations-fvhke-v1.
Perceived social support, perceived stress, and quality of sleep among COVID-19 patients in Iran: assessing measurement invariance of the multidimensional scale of perceived social support across gender and age
Authors: Habibi Asgarabad Mojtaba , Vahabi Zahra , Cheung Ho Nam , Ahmadi Reza , Akbarpour Samaneh , Sadeghian Mohammad Hossein , Etesam Farnaz 
Published: April 2024

BackgroundPerceived social support (PSS) plays a considerable role in mental health. The Multidimensional Scale of Perceived Social Support (MSPSS) is one of the most widely used scales, leading to much research evidence. The present study investigated its measurement model, equivalence across gender (male and female) and age groups (older patients= above 60 and non-older patients= below 60), and concurrent validity.MethodsA cross-sectional survey was conducted between March and October 2020, on patients hospitalized due to COVID-19 in Tehran, Iran. The scales were administered to 328 COVID-19 patients (54.6% male, aged 21 to 92) from two general hospitals; participants completed MSPSS (including friends, family, and significant others subscales), Pittsburgh Sleep Quality Index (PSQI, include sleep latency, subjective sleep quality, habitual sleep efficiency, sleep duration, use of sleep medication, daytime dysfunction, and sleep disturbances subscales), and the Perceived Stress Scale-10 (PSS-10, to assess patients’ appraisal of stressful conditions).ResultsThe MSPSS three-factor structure was confirmed among COVID-19 patients by Confirmatory Factor Analysis (CFA). The results support the MSPSS internal consistency and configural, metric, and scalar invariance across gender and age groups. Nevertheless, small but significant differences were found across ages based on the latent factor mean of the MSPSS from friends, with a lower mean level in older patients. The coefficients of Cronbach’s alpha (ranging from.92 to.96), the ordinal theta (ranging from.95 to.98), and Omega (ranging from.93 to.97) suggested high internal consistency of MSPSS. The concurrent validity of MSPSS was evidenced by its significant negative correlation with PSS-10 (τb = -.13, p &lt;.01) and also subjective sleep quality (τb = -.22, p &lt;.01), sleep disturbances (τb = -.26, p &lt;.001), and daytime dysfunction (τb = -.26, p &lt;.001).ConclusionsThe MSPSS was valid and reliable for measuring individuals’ perception of social support between males and females and older and non-older COVID-19 patients.
A bidirectional causal relationship study between mental disorders and male and female infertility
Authors: Chen Xiangyu , Hao Xuexue , Xie Lijun , Liu Xiaoqiang 
Published: April 2024

BackgroundThe relation between mental disorders (MDs) and infertility can be reciprocal. But exactly which MD affects infertility remains controversial. Our aim was to use Mendelian randomization (MR) to explore bidirectional causality between 15 MDs and male infertility and female infertility.MethodsThe data of MDs, male infertility, and female infertility were derived from published genome-wide association studies (GWAS). The inverse variance weighted method was considered to be the main analytical approach. Sensitivity analysis was performed using MR-Egger, Cochran’s Q, radial MR, and MR-PRESSO tests.ResultsOur results found that mood disorders (OR, 1.4497; 95% CI, 1.0093 – 2.0823; P = 0.0444) and attention deficit hyperactivity disorder (OR, 1.3921; 95% CI, 1.0943 – 1.7709; P = 0.0071) were positively correlated with male infertility, but obsessive-compulsive disorder (OR, 0.8208; 95% CI, 0.7146 – 0.9429; P = 0.0052) was negatively associated with male infertility. For females, anorexia nervosa (OR, 1.0898; 95% CI, 1.0070 – 1.1794; P = 0.0329), attention deficit hyperactivity disorder (OR, 1.1013; 95% CI, 1.0041 – 1.2079; P = 0.0406), and major depressive disorder (OR, 1.1423; 95% CI, 1.0213 – 1.2778; P = 0.0199) increased risk of infertility. In reverse relationship, female infertility increased the incidence of bipolar disorder (OR, 1.0009; 95% CI, 1.0001 – 1.0017; P = 0.0281).ConclusionWe demonstrated the association between five MDs and male or female infertility. Female infertility was also found to be associated with an increased risk of one MD. We look forward to better designed epidemiological studies to support our results.
Sexual and Reproductive Health and Rights for Young Migrants in Sweden: An Ideal-Type Analysis Exploring Regional Variations of Accessible Documents
Authors: Brunet Johansson Albert , Hurtig Anna-Karin , Nkulu Kalengayi Faustine Kyungu 
Published: April 2024

Objectives: This study aims to map sexual and reproductive health and rights (SRHR) policies, strategies, and interventions targeting young migrants and describe the patterns of organisation, resources, and services across Sweden’s 21 regions.Methods: We conducted a document analysis of accessible online documents on SRHR policies, strategies, and interventions targeting young migrants in Sweden’s 21 regions. We used ideal-type analysis of the documents to create a typology, which formed the basis of a ratings system illustrating variations in organisation, resources, and services across regions.Results: Findings suggest that efforts aimed at addressing young migrants’ SRHR are fragmented and unequal across regions. While SRHR policies and strategies are commonplace, they routinely lack specificity. Available resources vary depending on region and resource type. Additionally, information and interventions, although common, do not consistently meet the specific needs of migrant youths.Conclusion: This study suggests that fragmented efforts are fuelling geographic inequalities in fulfilling SRHR among young migrants. There is an urgent need to improve national coordination and collaboration between national and local actors in SRHR efforts targeting young migrants to ensure equity.
PURIFICATION AND CHARACTERIZATION OF AMYLASE PRODUCED FROM PROBIOTIC LACTOBACILLUS PLANTARUM CS FOR INDUSTRIAL APPLICATIONS
Authors: Nwachukwu Ujunwa Felicia , George-Okafor Uzoamaka Ogechi , Mba-Omeje Kelechi Nkechinyere , Ezeme-Nwafor Amara Chioma , Onah Ifeoma Agatha , Egbuji Ifeanyi Jude Victor 
Published: April 2024

Previous studies have demonstrated that probiotic Lactobacillus plantarum CS was able to generate an appreciable amount of extracellular amylase, hence the need to purify and characterize it. The aim of the study was to purify and characterize crude amylase from probiotic Lactobacillus plantarum CS for its industrial applications Three purification steps including ammonium sulphate precipitation, ion exchange chromatography on carboxymethyl sephadex and gel filtration on Sephadex G-75 were utilized. The homogeneity of the purified enzyme was confirmed using sodium deodocyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). The purified amylase was characterized on different parameters including substrates hydrolyses, pH and temperature activity and stability profiles. The general purification elution profile revealed two different peaks of amylase activities with outstanding one having a molecular weight of 59.7kDa. Its purification fold was 4.0 with specific activity of 16.44U/mg protein and enzyme yield of 3%. Temperature optimal activity and stability was at 400C and 7.5 for pH activity and stability. Mangenese (Mn2+) (135.17%), tween 80 (128.30%) and some food condiments garlic, thyme, ginger, and tumeric) significantly (p> 0.05) enhanced amylase activity (≥262.40%). However, selenium (Se4+) and hydrogen peroxide (H2O2) were observed to have greatest inhibiting effect (≥30.9%) on the enzyme. Substrate hydrolysis profiles showed that the amylase hydrolyzed all the test starchy substrates with the highest hydrolytic potential on indigenous sweet potato starch (Km value/ Vmax of 1.33mg/ml/ 7.89ml). The rate of hydrolysis of other test substrates had yam> rice>cassava>corn with km values ≤ 4.0mg/ml and Vmax ≤ 25ml. The obtained results gave an insight that amylase produced from Lactobacillus plantarum CS met with the possessed properties suitable for any industrial application especially in food
Digitalization Impact on Sustainable Firm Performance of Small, Medium, and Large Businesses
Authors: Hermanto Indra Revata , Widyarini Lydia Ari , Darma Dio Caisar 
Published: April 2024

Currently, the world is presented with sophistication in the field of technology, including company management. A superior company is an organization that can optimize all aspects. Speaking of existence, one of the essential pillars that support company sustainability is technology adoption. Referring to this premise, this study is structured to elaborate on the elements that determine firm performance (FP). These elements are designed into three factors: (1) digital technology usage/DTU, (2) digital transformation strategy/DTS, and (3) organizational agility/OA. This study concentrated on 159 samples compiled from small, medium, and large-scale businesses. Nonprobability sampling and purposive sampling data were extracted via PLS-SEM. Quantitative findings revealed that DTU has positive implications for DTS, OA, and FP. Empirical studies prove that DTS and OA also have a positive impact on FP. The current empirical research concludes that the increase in digital technology usage further develops digital transformation strategy, organizational agility, and firm performance. Improvements in digital transformation strategy or organizational agility can improve firm performance. Policy implications open up space for managerial actors to prioritize more complex ideas, solutions, and alternatives in strengthening mastery of the technology.