bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fibroblast activation in response to TGFβ1 is modulated by co-culture with endothelial cells in a vascular organ-on-chip platform
Authors: Luu Rebeccah J. , Hoefler B. Christopher , Gard Ashley L. , Ritenour Casey R. , Rogers Miles T. , Kim Ernest S. , Coppeta Jonathan R. , Cain Brian P. , Isenberg Brett C. , Azizgolshani Hesham , Fajardo-Ramirez Oscar R. , García-Cardeña Guillermo , Lech Matthew P. , Tomlinson Lindsay , Charest Joseph L. , Williams Corin 
Published: July 2023

Background: Tissue fibrosis is a major healthcare burden that affects various organs in the body for which no effective treatments exist. An underlying, emerging theme across organs and tissue types at early stages of fibrosis is the activation of pericytes and/or fibroblasts in the perivascular space. In hepatic tissue, it is well known that liver sinusoidal endothelial cells (EC) help maintain the quiescence of stellate cells, but whether this phenomenon holds true for other endothelial and perivascular cell types is not well studied.Methods: The goal of this work was to develop an organ-on-chip microvascular model to study the effect of EC co-culture on the activation of perivascular cells perturbed by the pro-fibrotic factor TGFβ1. A high-throughput microfluidic platform, PREDICT96, that was capable of imparting physiologically relevant fluid shear stress on the cultured endothelium was utilized.Results: We first studied the activation response of several perivascular cell types and selected a cell source, human dermal fibroblasts, that exhibited medium-level activation in response to TGFβ1. We also demonstrated that the PREDICT96 high flow pump triggered changes in select shear-responsive factors in human EC. We then found that the activation response of fibroblasts was significantly blunted in co-culture with EC compared to fibroblast mono-cultures. Subsequent studies with conditioned media demonstrated that EC-secreted factors play at least a partial role in suppressing the activation response. A Luminex panel and single cell RNA-sequencing study provided additional insight into potential EC-derived factors that could influence fibroblast activation.Conclusion: Overall, our findings showed that EC can reduce myofibroblast activation of perivascular cells in response to TGFβ1. Further exploration of EC-derived factors as potential therapeutic targets in fibrosis is warranted.
Percutaneous lordoplasty for the treatment of severe osteoporotic vertebral compression fractures with kyphosis
Authors: Song Tengfei , Sun Fan , Liu Shu , Ye Tianwen 
Published: July 2023

ObjectiveThe study aimed to explore the safety and effectiveness of percutaneous lordoplasty (PLP) in the treatment of severe osteoporotic vertebral compression fracture (OVCF).MethodsIncluded in this prospective study were patients with single-segment acute severe OVCF who were treated with PLP in our institution from July 2016 to October 2019. Patients' back pain and quality of life were assessed using the visual analog scale (VAS) and SF-36 scores. Lateral X-ray radiography of the spine was performed to measure the vertebral height, vertebral kyphotic angle, and segmental kyphotic angle, and to evaluate the outcome of fracture reduction and kyphotic correction. Intra-and postoperative complications were recorded.ResultsOf the 51 included patients, 47 patients were followed up for 12 months. The VAS score decreased from preoperative 7.33 ± 1.92 to postoperative 1.76 ± 0.85 at the 12th month (p &lt; 0.05), and the SF-36 score increased from preoperative 79.50 ± 9.22 to postoperative 136.94 ± 6.39 at the 12th month (p &lt; 0.05). During the 1-year follow-up period, the anterior height of the vertebral body increased significantly from preoperative 10.49 ± 1.93 mm to 19.33 ± 1.86 mm (p &lt; 0.05); the posterior height of the vertebral body increased insignificantly from preoperative 22.23 ± 2.36 mm to 23.05 ± 1.86 mm (p &gt; 0.05); the vertebral kyphotic angle decreased significantly from preoperative 18.33° ± 11.49° to 8.73° ± 1.21° (p &lt; 0.05); and the segmental kyphotic angle decreased significantly from preoperative 24.48° ± 4.64° to 11.70° ± 1.34° (p &lt; 0.05). During the 1-year follow-up period, there was no significant difference in the radiologic parameters, VAS scores, and SF-36 scores, between the 1st day and the 12th month of post-operation (P &gt; 0.05). No nerve damage occurred in any of the cases. Intraoperative cement leakage occurred in six cases, and the fracture of the adjacent vertebral body occurred in one case.ConclusionPLP can well reduce the risk of fracture and achieve good kyphotic correction and may prove to be a safe, cost-effective and minimally invasive alternative option for the treatment of severe OVCF with kyphotic deformity.
Automated acute ischemic stroke lesion delineation based on apparent diffusion coefficient thresholds
Authors: Gosch Vitus , Villringer Kersten , Galinovic Ivana , Ganeshan Ramanan , Piper Sophie K. , Fiebach Jochen B. , Khalil Ahmed 
Published: July 2023

PurposeAutomated lesion segmentation is increasingly used in acute ischemic stroke magnetic resonance imaging (MRI). We explored in detail the performance of apparent diffusion coefficient (ADC) thresholding for delineating baseline diffusion-weighted imaging (DWI) lesions.MethodsRetrospective, exploratory analysis of the prospective observational single-center 1000Plus study from September 2008 to June 2013 (clinicaltrials.org; NCT00715533). We built a fully automated lesion segmentation algorithm using a fixed ADC threshold (≤620 × 10–6 mm2/s) to delineate the baseline DWI lesion and analyzed its performance compared to manual assessments. Diagnostic capabilities of best possible ADC thresholds were investigated using receiver operating characteristic curves. Influential patient factors on ADC thresholding techniques’ performance were studied by conducting multiple linear regression.Results108 acute ischemic stroke patients were selected for analysis. The median Dice coefficient for the algorithm was 0.43 (IQR 0.20–0.64). Mean ADC values in the DWI lesion (β = −0.68, p &lt; 0.001) and DWI lesion volumes (β = 0.29, p &lt; 0.001) predicted performance. Optimal individual ADC thresholds differed between subjects with a median of ≤691 × 10−6 mm2/s (IQR ≤660–750 × 10−6 mm2/s). Mean ADC values in the DWI lesion (β = −0.96, p &lt; 0.001) and mean ADC values in the brain parenchyma (β = 0.24, p &lt; 0.001) were associated with the performance of individual thresholds.ConclusionThe performance of ADC thresholds for delineating acute stroke lesions varies substantially between patients. It is influenced by factors such as lesion size as well as lesion and parenchymal ADC values. Considering the inherent noisiness of ADC maps, ADC threshold-based automated delineation of very small lesions is not reliable.
The incidence of medium vessel occlusions: a population-based study
Authors: Liu Michael , Nasr Deena , Brinjikji Waleed 
Published: July 2023

IntroductionThe incidence of medium vessel occlusion (MeVO) is not well known. The objective of our study is to perform a population-based assessment to estimate the incidence of MeVOs.MethodsConsecutive patients from Olmsted County, Minnesota who presented for acute ischemic stroke seen at Mayo Clinic Hospital from 1/1/2018 to 12/31/2020 who were found to have a MeVO were included in this study. MeVO was defined as occlusion at or beyond the level of the middle cerebral artery M2 segment, anterior cerebral artery A2 segment, posterior cerebral artery P1 segment, and cerebellar arteries. Census data for Olmsted County was obtained from the United States Census Bureau from the year 2020.ResultsA total of 1,718 patients were screened for the study, 77 patients fulfilled inclusion criteria to be included in the study. Presenting NIHSS was 9 (± 7). The population of Olmsted County was estimated to be 162,847. The incidence rate for MeVO was 16 cases (95% CI 12–19) per 100,000 people per year. Based on estimates of the US population in 2020 of 331,449,281 people, we estimate there are 52,236 (95% CI 40,635-64,002) new cases of MeVOs per year.ConclusionAs the only stroke center in Olmsted County, we have been able to estimate the incidence of ischemic stroke due to MeVO. While the incidence of MeVOs is less than both large and small vessel occlusions, they still represent a significant proportion of strokes with significant morbidity and mortality that would benefit from further studies in both acute intervention and prevention.
Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study
Authors: Zhuan Bing , Ma Hong-Hong , Zhang Bo-Chao , Li Ping , Wang Xi , Yuan Qun , Yang Zhao , Xie Jun 
Published: July 2023

BackgroundPatients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient’s disease and routine clinical data.MethodsData were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People’s Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models.Results135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987.ConclusionThe use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients.
A nomogram for individually predicting overall survival for elderly patients with early breast cancer: a consecutive cohort study
Authors: Zhong Ying , Zhou Yidong , Xu Yali , Wang Zhe , Mao Feng , Shen Songjie , Lin Yan , Sun Qiang , Sun Kai 
Published: July 2023

BackgroundElderly patients with breast cancer are highly heterogeneous, and tumor load and comorbidities affect patient prognosis. Prediction models can help clinicians to implement tailored treatment plans for elderly patients with breast cancer. This study aimed to establish a prediction model for breast cancer, including comorbidities and tumor characteristics, in elderly patients with breast cancer.MethodsAll patients were ≥65 years old and admitted to the Peking Union Medical College Hospital. The clinical and pathological characteristics, recurrence, and death were observed. Overall survival (OS) was analyzed using the Kaplan–Meier curve and a prediction model was constructed using Cox proportional hazards model regression. The discriminative ability and calibration of the nomograms for predicting OS were tested using concordance (C)-statistics and calibration plots. Clinical utility was demonstrated using decision curve analysis (DCA).ResultsBased on 2,231 patients, the 5- and 10-year OS was 91.3% and 78.4%, respectively. We constructed an OS prediction nomogram for elderly patients with early breast cancer (PEEBC). The C-index for OS in PEEBC in the training and validation cohorts was 0.798 and 0.793, respectively. Calibration of the nomogram revealed a good predictive capability, as indicated by the calibration plot. DCA demonstrated that our model is clinically useful.ConclusionThe nomogram accurately predicted the 3-year, 5-year, and 10-year OS in elderly patients with early breast cancer.
Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer
Authors: Jia Tongtong , Lv Qingfu , Cai Xiaowei , Ge Shushan , Sang Shibiao , Zhang Bin , Yu Chunjing , Deng Shengming 
Published: July 2023

PurposeThe aim of this study was to investigate the predictive role of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in the prognostic risk stratification of patients with invasive breast cancer (IBC). To achieve this, we developed a clinicopathologic-radiomic-based model (C-R model) and established a nomogram that could be utilized in clinical practice.MethodsWe retrospectively enrolled a total of 91 patients who underwent preoperative 18F-FDG PET/CT and randomly divided them into training (n=63) and testing cohorts (n=28). Radiomic signatures (RSs) were identified using the least absolute shrinkage and selection operator (LASSO) regression algorithm and used to compute the radiomic score (Rad-score). Patients were assigned to high- and low-risk groups based on the optimal cut-off value of the receiver operating characteristic (ROC) curve analysis for both Rad-score and clinicopathological risk factors. Univariate and multivariate Cox regression analyses were performed to determine the association between these variables and progression-free survival (PFS) or overall survival (OS). We then plotted a nomogram integrating all these factors to validate the predictive performance of survival status.ResultsThe Rad-score, age, clinical M stage, and minimum standardized uptake value (SUVmin) were identified as independent prognostic factors for predicting PFS, while only Rad-score, age, and clinical M stage were found to be prognostic factors for OS in the training cohort. In the testing cohort, the C-R model showed superior performance compared to single clinical or radiomic models. The concordance index (C-index) values for the C-R model, clinical model, and radiomic model were 0.816, 0.772, and 0.647 for predicting PFS, and 0.882, 0.824, and 0.754 for OS, respectively. Furthermore, decision curve analysis (DCA) and calibration curves demonstrated that the C-R model had a good ability for both clinical net benefit and application.ConclusionThe combination of clinicopathological risks and baseline PET/CT-derived Rad-score could be used to evaluate the prognosis in patients with IBC. The predictive nomogram based on the C-R model further enhanced individualized estimation and allowed for more accurate prediction of patient outcomes.
Common nutritional/inflammatory indicators are not effective tools in predicting the overall survival of patients with small cell lung cancer undergoing first-line chemotherapy
Authors: Tian Huohuan , Li Guo , Hou Wang , Jin Jing , Wang Chengdi , Ren Pengwei , Wang Haoyu , Wang Jie , Li Weimin , Liu Dan 
Published: July 2023

ObjectiveVarious studies have investigated the predictive significance of numerous peripheral blood biomarkers in patients with small cell lung cancer (SCLC). However, their predictive values have not been validated. This study assessed and evaluated the ability of common nutritional or inflammatory indicators to predict overall survival (OS) in patients with SCLC who received first-line chemotherapy.MethodsBetween January 2008 and July 2019, 560 patients with SCLC were enrolled at the Sichuan University West China Hospital. Eleven nutritional or inflammatory indices obtained before chemotherapy were evaluated. The cutoff values of continuous peripheral blood indices were confirmed through maximally selected rank statistics. The relationship of peripheral blood indices with OS was investigated through univariate and multivariate Cox regression analyses. Harrell’s concordance (C-index) and time-dependent receiver operating characteristic curve were used to evaluate the performance of these indices.ResultsA total of 560 patients with SCLC were enrolled in the study. All the patients received first-line chemotherapy. In the univariate Cox analysis, all indices, except the Naples score, were related to OS. In the multivariate analysis, albumin–globulin ratio was an independent factor linked with prognosis. All indices exhibited poor performance in OS prediction, with the area under the curve ranging from 0.500 to 0.700. The lactic dehydrogenase (LDH) and prognostic nutritional index (PNI) were comparatively superior predictors with C-index of 0.568 and 0.550, respectively. The LDH showed incremental predictive values, whereas the PNI showed diminishing values as survival time prolonged, especially for men or smokers. The LDH with highest sensitivity (0.646) and advanced lung cancer inflammation index (ALI) with highest specificity (0.952) were conducive to identifying death and survival at different time points.ConclusionCommon inflammatory or nutritional biomarkers are only marginally useful in predicting outcomes in patients with SCLC receiving first-line chemotherapy. Among them, LDH, PNI, and ALI are relatively promising biomarkers for prognosis evaluation.
Identifying predictors of clinical outcomes using the projection-predictive feature selection—a proof of concept on the example of Crohn’s disease
Authors: Wirthgen Elisa , Weber Frank , Kubickova-Weber Laura , Schiller Benjamin , Schiller Sarah , Radke Michael , Däbritz Jan 
Published: July 2023

ObjectivesSeveral clinical disease activity indices (DAIs) have been developed to noninvasively assess mucosal healing in pediatric Crohn’s disease (CD). However, their clinical application can be complex. Therefore, we present a new way to identify the most informative biomarkers for mucosal inflammation from current markers in use and, based on this, how to obtain an easy-to-use DAI for clinical practice. A further aim of our proof-of-concept study is to demonstrate how the performance of such a new DAI can be compared to that of existing DAIs.MethodsThe data of two independent study cohorts, with 167 visits from 109 children and adolescents with CD, were evaluated retrospectively. A variable selection based on a Bayesian ordinal regression model was applied to select clinical or standard laboratory parameters as predictors, using an endoscopic outcome. The predictive performance of the resulting model was compared to that of existing pediatric DAIs.ResultsWith our proof-of-concept dataset, the resulting model included C-reactive protein (CRP) and fecal calprotectin (FC) as predictors. In general, our model performed better than the existing DAIs. To show how our Bayesian approach can be applied in practice, we developed a web application for predicting disease activity for a new CD patient or visit.ConclusionsOur work serves as a proof-of-concept, showing that the statistical methods used here can identify biomarkers relevant for the prediction of a clinical outcome. In our case, a small number of biomarkers is sufficient, which, together with the web interface, facilitates the clinical application. However, the retrospective nature of our study, the rather small amount of data, and the lack of an external validation cohort do not allow us to consider our results as the establishment of a novel DAI for pediatric CD. This needs to be done with the help of a prospective study with more data and an external validation cohort in the future.
Abundance considerations for modeling yield of rapeseed at the flowering stage
Authors: Li Yuanjin , Yuan Ningge , Luo Shanjun , Yang Kaili , Fang Shenghui , Peng Yi , Gong Yan 
Published: July 2023

IntroductionTo stabilize the edible oil market, it is necessary to determine the oil yield in advance, so the accurate and fast technology of estimating rapeseed yield is of great significance in agricultural production activities. Due to the long flowering time of rapeseed and the characteristics of petal color that are obviously different from other crops, the flowering period can be carefully considered in crop classification and yield estimation.MethodsA field experiment was conducted to obtain the unmanned aerial vehicle (UAV) multispectral images. Field measurements consisted of the reflectance of flowers, leaves, and soils at the flowering stage and rapeseed yield at physiological maturity. Moreover, GF-1 and Sentinel-2 satellite images were collected to compare the applicability of yield estimation methods. The abundance of different organs of rapeseed was extracted by the spectral mixture analysis (SMA) technology, which was multiplied by vegetation indices (VIs) respectively to estimate the yield.ResultsFor the UAV-scale, the product of VIs and leaf abundance (AbdLF) was closely related to rapeseed yield, which was better than the VIs models for yield estimation, with the coefficient of determination (R2) above 0.78. The yield estimation models of the product of normalized difference yellowness index (NDYI), enhanced vegetation index (EVI) and AbdLF had the highest accuracy, with the coefficients of variation (CVs) below 10%. For the satellite scale, most of the estimation models of the product of VIs and rapeseed AbdLF were also improved compared with the VIs models. The yield estimation models of the product of AbdLF and renormalized difference VI (RDVI) and EVI (RDVI×AbdLF and EVI×AbdLF) had the steady improvement, with CVs below 13.1%. Furthermore, the yield estimation models of the product of AbdLF and normalized difference VI (NDVI), visible atmospherically resistant index (VARI), RDVI, and EVI had consistent performance at both UAV and satellite scales.DiscussionThe results showed that considering SMA could improve the limitation of using only VIs to retrieve rapeseed yield at the flowering stage. Our results indicate that the abundance of rapeseed leaves can be a potential indicator of yield prediction during the flowering stage.
Towards deep learning based smart farming for intelligent weeds management in crops
Authors: Saqib Muhammad Ali , Aqib Muhammad , Tahir Muhammad Naveed , Hafeez Yaser 
Published: July 2023

IntroductionDeep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve production. In this work, we have proposed a DL-based weed detection model that can efficiently be used for effective weed management in crops.MethodsOur proposed model uses Convolutional Neural Network based object detection system You Only Look Once (YOLO) for training and prediction. The collected dataset contains RGB images of four different weed species named Grass, Creeping Thistle, Bindweed, and California poppy. This dataset is manipulated by applying LAB (Lightness A and B) and HSV (Hue, Saturation, Value) image transformation techniques and then trained on four YOLO models (v3, v3-tiny, v4, v4-tiny).Results and discussionThe effects of image transformation are analyzed, and it is deduced that the model performance is not much affected by this transformation. Inferencing results obtained by making a comparison of correctly predicted weeds are quite promising, among all models implemented in this work, the YOLOv4 model has achieved the highest accuracy. It has correctly predicted 98.88% weeds with an average loss of 1.8 and 73.1% mean average precision value.Future workIn the future, we plan to integrate this model in a variable rate sprayer for precise weed management in real time.