bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rubble Stone Masonry Buildings with Cement Mortar: Base Shear Seismic Demand Comparison for Selected Countries Worldwide
Authors: Schildkamp Martijn , Silvestri Stefano , Araki Yoshikazu 
Published: March 2021

Full base shear seismic demand analyses with calculated examples for heavy stone masonry buildings are not present in the literature. To address this shortcoming, analyses and calculations are performed on nominally reinforced rubble stone masonry house and school designs, as typically built in Nepal. The seismic codes are literally applied for countries where the technique is still allowed (Nepal, India, China, Tajikistan, Iran, Croatia), or should be reintroduced based on current practices (Pakistan, Afghanistan, Turkey). First, this paper compares the base shear formulas and the inertia forces distributions of these codes, as well as material densities, seismic weights, seismic zoning, natural periods of vibration, response spectra, importance factors and seismic load combinations. Large differences between approaches and coefficients are observed. Then, by following Equivalent Lateral Force-principles for Ultimate Limit State verifications (10%PE50y), the base shear and story shears are calculated for a design peak ground acceleration of 0.20 g, as well as the effects of critical load combinations on the forces and moments acting on the lateral-resisting elements. It is concluded that Pakistan has the most tolerant code, Nepal represents an average value, whereas India and China are most conservative toward the case study buildings. Overall, it is observed that heavy-masonry-light-floor systems with negligible diaphragm action behave different under seismic motion than most other building typologies. Given the observations in this paper, the applicability of conventional ELF, S-ELF and S-Modal methods for heavy masonry buildings is questionable. The codes however do not introduce modified approaches that address these differences. Possible implications of the exclusion of plinth masonry and large portions of seismic weight need further assessment and validation, for which different (possibly more sophisticated) concepts must be considered, such as the equivalent frame method or distributed mass system. Since Nepal allows stone masonry in areas with higher seismic hazard levels &gt;0.40 g (opposed to India &lt;0.12 and China &lt;0.15 g), their code is taken as the reference and starting point for follow-up research, which aims to verify the seismic demand by performing seismic capacity checks of the masonry piers and spandrels. The paper ends with an appeal for global collaboration under the research project SMARTnet.
Nomogram for Preoperative Estimation of Cervical Lymph Node Metastasis Risk in Papillary Thyroid Microcarcinoma
Authors: Sun Jinxiao , Jiang Qi , Wang Xian , Liu Wenhua , Wang Xin 
Published: March 2021

ObjectiveAccurate preoperative identification of cervical lymph node metastasis (CLNM) is essential for clinical management and established of different surgical protocol for patients with papillary thyroid microcarcinoma (PTMC). Herein, we aimed to develop an ultrasound (US) features and clinical characteristics-based nomogram for preoperative diagnosis of CLNM for PTMC.MethodOur study included 552 patients who were pathologically diagnosed with PTMC between January 2015 and June 2019. All patients underwent total thyroidectomy or lobectomy and divided into two groups: CLNM and non-CLNM. Univariate and multivariate analysis were performed to examine risk factors associated with CLNM. A nomogram comprising the prognostic model to predict the CLNM was established, and internal validation in the cohort was performed.ResultsCLNM and non-CLNM were observed in 216(39.1%) and 336(60.9%) cases, respectively. Seven variables of clinical and US features as potential predictors including male sex (odd ratio [OR] = 1.974, 95% confidence interval [CI], 1.243-2.774; P =0.004), age &lt; 45 years (OR = 4.621, 95% CI, 2.160-9.347; P &lt; 0.001), US-reported CLN status (OR = 1.894, 95% CI, 0.754-3.347; P =0.005), multifocality (OR = 1.793, 95% CI, 0.774-2.649; P =0.007), tumor size ≥ 0.6cm (OR = 1.731, 95% CI,0.793-3.852; P =0.018), ETE (OR = 3.772, 95% CI, 1.752-8.441;P&lt; 0.001) and microcalcification (OR = 2.316, 95% CI, 1.099-4.964; P &lt; 0.001) were taken into account. The predictive nomogram was established by involving all the factors above used for preoperative prediction of CLNM in patients with PTCM. The nomogram model showed an AUC of 0.839 and an accuracy of 77.9% in predicting CLNM. Furthermore, the calibration curve demonstrated a strong consistency between nomogram and clinical findings in prediction CLNM for PTMC.ConclusionsThe nomogram achieved promising results for predicting preoperative CLNM in PTMC by combining clinical and US risk factor. Our proposed prediction model is able to help determine an individual’s risk of CLNM in PTMC, thus facilitate reasonable therapy decision making.
Identification of Major Loci and Candidate Genes for Meat Production-Related Traits in Broilers
Authors: Yang Xinting , Sun Jiahong , Zhao Guiping , Li Wei , Tan Xiaodong , Zheng Maiqing , Feng Furong , Liu Dawei , Wen Jie , Liu Ranran 
Published: March 2021

BackgroundCarcass traits are crucial characteristics of broilers. However, the underlying genetic mechanisms are not well understood. In the current study, significant loci and major-effect candidate genes affecting nine carcass traits related to meat production were analyzed in 873 purebred broilers using an imputation-based genome-wide association study.ResultsThe heritability estimates of nine carcass traits, including carcass weight, thigh muscle weight, and thigh muscle percentage, were moderate to high and ranged from 0.21 to 0.39. Twelve genome-wide significant SNPs and 118 suggestively significant SNPs of 546,656 autosomal variants were associated with carcass traits. All SNPs for six weight traits (body weight at 42 days of age, carcass weight, eviscerated weight, whole thigh weight, thigh weight, and thigh muscle weight) were clustered around the 24.08 Kb region (GGA24: 5.73–5.75 Mb) and contained only one candidate gene (DRD2). The most significant SNP, rs15226023, accounted for 4.85–7.71% of the estimated genetic variance of the six weight traits. The remaining SNPs for carcass composition traits (whole thigh percentage and thigh percentage) were clustered around the 42.52 Kb region (GGA3: 53.03–53.08 Mb) and contained only one candidate gene (ADGRG6). The most significant SNP in this region, rs13571431, accounted for 11.89–13.56% of the estimated genetic variance of two carcass composition traits. Some degree of genetic differentiation inADGRG6between large and small breeds was observed.ConclusionWe identified one 24.08 Kb region for weight traits and one 42.52 Kb region for thigh-related carcass traits.DRD2was the major-effect candidate gene for weight traits, andADGRG6was the major-effect candidate gene for carcass composition traits. Our results supply essential information for causative mutation identification of carcass traits in broilers.
Prediction of Mortality in Surgical Intensive Care Unit Patients Using Machine Learning Algorithms
Authors: Yun Kyongsik , Oh Jihoon , Hong Tae Ho , Kim Eun Young 
Published: March 2021

Objective: Predicting prognosis of in-hospital patients is critical. However, it is challenging to accurately predict the life and death of certain patients at certain period. To determine whether machine learning algorithms could predict in-hospital death of critically ill patients with considerable accuracy and identify factors contributing to the prediction power.Materials and Methods: Using medical data of 1,384 patients admitted to the Surgical Intensive Care Unit (SICU) of our institution, we investigated whether machine learning algorithms could predict in-hospital death using demographic, laboratory, and other disease-related variables, and compared predictions using three different algorithmic methods. The outcome measurement was the incidence of unexpected postoperative mortality which was defined as mortality without pre-existing not-for-resuscitation order that occurred within 30 days of the surgery or within the same hospital stay as the surgery.Results: Machine learning algorithms trained with 43 variables successfully classified dead and live patients with very high accuracy. Most notably, the decision tree showed the higher classification results (Area Under the Receiver Operating Curve, AUC = 0.96) than the neural network classifier (AUC = 0.80). Further analysis provided the insight that serum albumin concentration, total prenatal nutritional intake, and peak dose of dopamine drug played an important role in predicting the mortality of SICU patients.Conclusion: Our results suggest that machine learning algorithms, especially the decision tree method, can provide information on structured and explainable decision flow and accurately predict hospital mortality in SICU hospitalized patients.
Endometriosis-Related Pleural Effusion: A Case Report and a PRISMA-Compliant Systematic Review
Authors: Wang Ping , Meng Zhilan , Li Yakun , Xu Zuojun 
Published: March 2021

Background: Endometriosis-related pleural effusion (PE) is a relatively rare but treatable cause of bloody PE. The clinical characteristics and outcome of patients with endometriosis-related PE remain unknown.Objectives: We present a case of endometriosis-related PE diagnosed on pleural fluid cytology. A systematic review of all endometriosis-related PE cases in literature was conducted to elucidate the clinical characteristics, explore the diagnostic yield of pathological examinations, and to summarize the outcomes of therapeutic approaches in this disease.Methods: A case of endometriosis-related PE diagnosed in study hospital was reported. PubMed, Web of Science, and EMBASE were searched for publications pertaining to cases of endometriosis-related PE using predefined search terms. This case and those identified from PubMed, Web of Science, and EMBASE were analyzed.Results: A total of 67 patients were included. Catamential symptoms were presented in 30 (44.8%) patients. Dyspnea was presented in 50 patients (74.6%), followed by right chest/shoulder pain in 34 (50.7%) and cough in 18 (26.9%). 82.8% of the patients had concomitant pelvic endometriosis and 76.7% was infertile or nulliparous. The diagnostic yield of pleural fluid cytological examination, percutaneous pleural biopsy, and surgical biopsy was 9.0, 45.5, and 78.7%, respectively. The patients who received surgery-based therapy had a significantly longer time to relapse than those who received progestational agents or GnRH analogs alone (P = 0.025) or hysterectomy and bilateral salpingoophorectomy (HBSO) (P = 0.040).Conclusions: High clinical awareness of pleural endometriosis is essential in all female with hemorrhagic PE, especially in young females who have infertility and/or pelvic endometriosis. Plerual fluid cytology might be a simple minimally invasive and cost-effective modality in the diagnosis of endometriosis-related PE. Treatment is challenging due to high recurrence and the optimal management of endometriosis-related PE needs further evaluation. The combined approach by surgery and hormonal therapy may achieve the best relapse-free survival.
Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
Authors: Darabi Negar , Hosseinichimeh Niyousha , Noto Anthony , Zand Ramin , Abedi Vida 
Published: March 2021

Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions.Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables.Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69).Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Global Variability in Deep Brain Stimulation Practices for Parkinson’s Disease
Authors: Mahajan Abhimanyu , Butala Ankur , Okun Michael S. , Mari Zoltan , Mills Kelly A. 
Published: March 2021

IntroductionDeep brain stimulation (DBS) has become a standard treatment option for select patients with Parkinson’s disease (PD). The selection process and surgical procedures employed have, to date, not been standardized.MethodsA comprehensive 58-question web-based survey was developed with a focus on DBS referral practices and peri-operative management. The survey was distributed to the Parkinson’s Foundation Centers of Excellence, members of the International Parkinson’s Disease and Movement Disorders Society, and the Parkinson Study Group (Functional Neurosurgery Working Group) between December 2015 and May 2016.ResultsThere were 207 individual respondents (20% response rate) drawn from 59 countries and 6 continents, of whom 64% received formal training in DBS. Thirteen percent of centers reported that DBS could proceed despite a confidence level of &lt; 50% for PD diagnosis. A case-based approach to DBS candidacy was applied in 51.3% of centers without a cut-off for levodopa-responsiveness. Surprisingly, 33% of centers regularly used imaging for diagnostic confirmation of idiopathic PD. Thirty-one percent of centers reported that neuropsychological evaluation did not affect DBS target selection. Approximately half of the respondents reported determination of DBS candidacy based on a multidisciplinary committee evaluation and 1/3rd reported that a committee was used for target selection. Eight percent of respondents felt that psychosocial factors should not impact DBS candidacy nor site selection. Involvement of allied health professionals in the preoperative process was sparse. There was high variability in preoperative education about DBS outcome expectations. Approximately half of the respondents did not utilize a “default brain target,” though STN was used more commonly than GPi. Specific DBS procedure techniques applied, as well as follow-up timelines, were highly variable.ConclusionResults revealed high variability on the best approaches for DBS candidate selection, brain target selection, procedure type, and postoperative practices. Cognitive and mood assessments were underutilized. There was low reliance on multidisciplinary teams or psychosocial factors to impact the decision-making process. There were small but significant differences in practice across global regions, especially regarding multidisciplinary teams. The wide variability of responses across multiple facets of DBS care highlights the need for prospective studies to inform evidence-based guidelines.
Cooking at Home and Adherence to the Mediterranean Diet During the COVID-19 Confinement: The Experience From the Croatian COVIDiet Study
Authors: Pfeifer Danijela , Rešetar Josip , Gajdoš Kljusurić Jasenka , Panjkota Krbavčić Ines , Vranešić Bender Darija , Rodríguez-Pérez Celia , Ruíz-López María Dolores , Šatalić Zvonimir 
Published: March 2021

Introduction: The primary aims of this study were to evaluate the changes in dietary behavior among the Croatian adult population during the COVID-19 outbreak and to explore the impact of confinement on cooking habits.Methods: The study was based on results from COVIDiet_Int cross-sectional study—a part of COVIDiet project (NCT04449731). A self-administered online questionnaire was used to assess the frequency of food consumption, eating habits, and sociodemographic information. A total number of 4,281 participants (80.5% females and 19.4% males) completed the questionnaire.Results: The Mediterranean Diet Adherence Screener (MEDAS) score before the confinement was 5.02 ± 1.97, while during the confinement, the MEDAS score increased to 5.85 ± 2.04. Participants who had higher adherence to the Mediterranean diet (MedDiet) during the confinement were mostly females (88.8%), aged between 20 and 50 years, with the highest level of education (66.3%) and normal BMI (70.6%). The majority of participants maintained their dietary behavior as it was before COVID-19 confinement, while 36.9% decreased their physical activity. Participants with higher MEDAS score were more eager to increase their physical activity. Additionally, higher median values of MEDAS score were noted for participants with body mass index values below 24.9 kg/m2 (6.0 vs. 5.0 for participants with BMI above 25 kg/m2). Participants in all residence places increased their cooking frequency during the confinement (53.8%), which was associated with an increase in vegetables, legumes, as well as fish and seafood consumption.Conclusions: According to our findings, Croatian adults exhibited medium adherence to the MedDiet during the COVID-19 confinement. The results suggest that cooking frequency could be positively associated with overall dietary quality, which is of utmost importance in these demanding times.
Development and Validation of a Simple-to-Use Nomogram for Predicting the Upgrade of Atypical Ductal Hyperplasia on Core Needle Biopsy in Ultrasound-Detected Breast Lesions
Authors: Huang Yun-Xia , Chen Ya-Ling , Li Shi-Ping , Shen Ju-Ping , Zuo Ke , Zhou Shi-Chong , Chang Cai 
Published: March 2021

BackgroundThe rate of carcinoma upgrade for atypical ductal hyperplasia (ADH) diagnosed on core needle biopsy (CNB) is variable on open excision. The purpose of the present study was to develop and validate a simple-to-use nomogram for predicting the upgrade of ADH diagnosed with ultrasound (US)-guided core needle biopsy in patients with US-detected breast lesions.MethodsTwo retrospective sets, the training set (n = 401) and the validation set (n = 186), from Fudan University Shanghai Cancer Center between January 2014 and December 2019 were retrospectively analyzed. Clinicopathological and US features were selected using univariate and multivariable logistic regression, and the significant features were incorporated to build a nomogram model. Model discrimination and calibration were assessed in the training set and validation set.ResultsOf the 587 ADH biopsies, 67.7% (training set: 267/401, 66.6%; validation set: 128/186, 68.8%) were upgraded to cancers. In the multivariable analysis, the risk factors were age [odds ratio (OR) 2.739, 95% confidence interval (CI): 1.525–5.672], mass palpation (OR 3.008, 95% CI: 1.624–5.672), calcifications on US (OR 4.752, 95% CI: 2.569–9.276), ADH extent (OR 3.150, 95% CI: 1.951–5.155), and suspected malignancy (OR 4.162, CI: 2.289–7.980). The model showed good discrimination, with an area under curve (AUC) of 0.783 (95% CI: 0.736–0.831), and good calibration (p = 0.543). The application of the nomogram in the validation set still had good discrimination (AUC = 0.753, 95% CI: 0.666–0.841) and calibration (p = 0.565). Instead of surgical excision of all ADHs, if those categorized with the model to be at low risk for upgrade were surveillanced and the remainder were excised, then 63.7% (37/58) of surgeries of benign lesions could have been avoided and 78.1% (100/128) malignant lesions could be treated in time.ConclusionsThis study developed a simple-to-use nomogram by incorporating clinicopathological and US features with the overarching goal of predicting the probability of upgrade in women with ADH. The nomogram could be expected to decrease unnecessary surgery by nearly two-third and to identify most of the malignant lesions, helping guide clinical decision making with regard to surveillance versus surgical excision of ADH lesions.
Apolipoprotein B Is Associated With the Microenvironment of Cholangiocarcinoma
Authors: Xu Xiaofeng , Chen Diyu , Feng Xiaode , Hu Jiating , Ge Jiangzhen , Yan Chaobiao , Zhang Deguo , Ling Zhenan , Chen Jianzhong , Wu Jian 
Published: March 2021

BackgroundCholangiocarcinoma (CCA) is a kind of devastating malignancy, which is correlated with the extremely high mortality. Due to the occult pathogenesis of CCA, most patients are diagnosed in the advanced stage. However, the efficacy of chemotherapy and immunotherapy is limited for these patients. The cause for this phenomenon is unclear, the recent researches indicate that it could be related to predisposing genetic factors and tumor microenvironment (TME) changes. The TME is created by the tumor and dominated by tumor-induced interactions. And the tumor prognosis could be influenced by the extent of infiltrating immune cells and stromal cells in TME.Materials and methodsThe abundance ratio of immune cells for each sample was obtained via the CIBERSORT algorithm, and we used ESTIMATE score system to calculate the immune and stromal scores in CCA. The CCA cases in TCGA database were categorized into high and low score groups according to their immune/stromal scores. And then, we identified the differential expressed genes (DEGs) in two groups. Functional enrichment analysis and protein‐protein interaction networks were carried out for DEGs. Interestingly, we found out that apolipoprotein B (APOB) is the most down-regulated among these genes. Then we performed the immunohistochemistry staining of APOB in a CCA tumor microarray which contained 100 CCA cases, APOB was down-regulated in CCA samples. Thus, we evaluated the APOB function in the TME of CCA through TIMER.Results and ConclusionThe results demonstrate that the infiltration degree of immune cells in CCA could be influenced by the expression of APOB, and the APOB expression could be mediated by DNA methylation. Our study not only indicates APOB is a potential target for CCA immunotherapy but also provides new ideas for researchers to explore the immunotherapy of various tumors.