Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of a Simple, Rapid, and Cost-Effective Method for Acute Rejection Monitoring in Lung Transplant Recipients

Despite advances in immunosuppression therapy, acute rejection remains the leading cause of graft dysfunction in lung transplant recipients. Donor-derived cell-free DNA is increasingly being considered as a valuable biomarker of acute rejection in several solid organ transplants. We present a technically improved molecular method based on digital PCR that targets the mismatch between the recipient and donor at the HLA-DRB1 locus. Blood samples collected sequentially post-transplantation from a cohort of lung recipients were used to obtain proof-of-principle for the validity of the assay, correlating results with transbronchial biopsies and lung capacity tests. The results revealed an increase in dd-cfDNA during the first 2 weeks after transplantation related to ischemia-reperfusion injury (6.36 ± 5.36%, p < 0.0001). In the absence of complications, donor DNA levels stabilized, while increasing again during acute rejection episodes (7.81 ± 12.7%, p < 0.0001). Respiratory tract infections were also involved in the release of dd-cfDNA (9.14 ± 15.59%, p = 0.0004), with a positive correlation with C-reactive protein levels. Overall, the dd-cfDNA percentages were inversely correlated with the lung function values measured by spirometry. These results confirm the value of dd-cfDNA determination during post-transplant follow-up to monitor acute rejection in lung recipients, achieved using a rapid and inexpensive approach based on the HLA mismatch between donor and recipient. GRAPHICAL ABSTRACT
A Radiomics Nomogram for Classifying Hematoma Entities in Acute Spontaneous Intracerebral Hemorrhage on Non-contrast-Enhanced Computed Tomography

Aim To develop and validate a radiomics nomogram on non-contrast-enhanced computed tomography (NECT) for classifying hematoma entities in patients with acute spontaneous intracerebral hemorrhage (ICH). Materials and Methods One hundred and thirty-five patients with acute intraparenchymal hematomas and baseline NECT scans were retrospectively analyzed, i.e., 52 patients with vascular malformation-related hemorrhage (VMH) and 83 patients with primary intracerebral hemorrhage (PICH). The patients were divided into training and validation cohorts in a 7:3 ratio with a random seed. After extracting the radiomics features of hematomas from baseline NECT, the least absolute shrinkage and selection operator (LASSO) regression was applied to select features and construct the radiomics signature. Multivariate logistic regression analysis was used to determine the independent clinical-radiological risk factors, and a clinical model was constructed. A predictive radiomics nomogram was generated by incorporating radiomics signature and clinical-radiological risk factors. Nomogram performance was assessed in the training cohort and tested in the validation cohort. The capability of models was compared by calibration, discrimination, and clinical benefit. Results Six features were selected to establish radiomics signature via LASSO regression. The clinical model was constructed with the combination of age [odds ratio (OR): 6.731; 95% confidence interval (CI): 2.209–20.508] and hemorrhage location (OR: 0.089; 95% CI: 0.028–0.281). Radiomics nomogram [area under the curve (AUC), 0.912 and 0.919] that incorporated age, location, and radiomics signature outperformed the clinical model (AUC, 0.816 and 0.779) and signature (AUC, 0.857 and 0.810) in the training cohort and validation cohorts, respectively. Good calibration and clinical benefit of nomogram were achieved in the training and validation cohorts. Conclusion Non-contrast-enhanced computed tomography-based radiomics nomogram can predict the individualized risk of VMH in patients with acute ICH.
Long-Term Azithromycin Treatment in Pediatric Primary Ciliary Dyskinesia: A Retrospective Study

Objectives Primary ciliary dyskinesia (PCD) is a rare genetic disease mainly involved in lung dysfunction. PCD patient outcomes after azithromycin (AZM) treatment have rarely been reported. This study was aimed to assess AZM treatment effects on disease progression of pediatric PCD patients. Study Design This retrospective follow-up study involved PCD patients diagnosed from 2009 to 2021. Changes of clinical outcomes, pulmonary function, and chest computed tomography findings were compared between untreated and AZM-treated patients. Results Of 71 enrolled patients (median follow-up duration of 3.1 years), 34 received AZM (AZM-treated group) and 37 received no AZM (AZM-untreated group). At diagnosis, no significant intergroup differences in age, sex, height, weight, number of respiratory exacerbations, and FEV1% and FVC% predicted values were found, although FEF25–75% predicted was lower in AZM-treated group. Between treatment initiation and follow-up, patients in AZM-treated group had less respiratory exacerbations than that of AZM-untreated group (mean ± SD, 1.4 ± 0.8 vs. 3.0 ± 2.1, times/year P = 0.001) and fewer AZM-treated group patients exhibited exercise intolerance. Increases above baseline of AZM-treated FEV1% and FVC% predicted values exceeded that of AZM-untreated group, but intergroup differences were insignificant (FEV1% predicted: (median, IQR) 5.3 [−13.4, 9.4] vs. 1.8 [−12.1, 9.5], P = 0.477; FVC% predicted: (median, IQR) 6.7 [−7.6, 18.8] vs. 1.6 [−5.6, 7.6], P = 0.328). Conclusion Long-term AZM treatment can reduce respiratory infection frequency and may maintain pulmonary diseases stable in pediatric PCD patients with worse lung function.
Differences in Prediction May Underlie Language Disorder in Autism

Language delay is often one of the first concerns of parents of toddlers with autism spectrum disorder (ASD), and early language abilities predict broader outcomes for children on the autism spectrum. Yet, mechanisms underlying language deficits in autistic children remain underspecified. One prominent component of linguistic behavior is the use of predictions or expectations during learning and processing. Several researcher teams have posited prediction deficit accounts of ASD. The basic assumption of the prediction accounts is that information is processed by making predictions and testing violations against expectations (prediction errors). Flexible (neurotypical) brains attribute differential weights to prediction errors to determine when new learning is appropriate, while autistic individuals are thought to assign disproportionate weight to prediction errors. According to some views, these prediction deficits are hypothesized to lead to higher levels of perceived novelty, resulting in “hyperplasticity” of learning based on the most recent input. In this article, we adopt the perspective that it would be useful to investigate whether language deficits in children with ASD can be attributed to atypical domain-general prediction processes.
The Effect of Big Data Analytics Capability on Competitive Performance: The Mediating Role of Resource Optimization and Resource Bricolage

Although big data analytics capability (BDAC) leads to competitive performance, the mechanism of the relationship is still unclear. To narrow the research gap, this paper investigates the mediating roles of two forms of resource integration (resource optimization and resource bricolage) in the relationship between two forms of BDAC [big data analytics (BDA) management capability and BDA technology capability] and competitive performance. Supported by Partial Least Squares-Structural Equation Modeling (PLS-SEM) and the cross-sectional survey data from 219 Chinese enterprises, the results show that the resource bricolage plays a significantly mediating role in the relationships between BDA management capability and competitive performance as well as in the relationship between BDA technology capability and competitive performance. Furthermore, the mediating effect in the former relationship is stronger than that in the latter relationship. Additionally, BDA technology capability only has a direct effect on resource bricolage, while BDA management capability has a stronger effect on resource optimization than that on resource bricolage. Finally, resource bricolage has a stronger impact on competitive performance than resource optimization. These findings contribute to understanding how enterprises could apply different forms of BDAC to other kinds of resource integration to achieve outstanding competitive performance.
Thymus Functionality Needs More Than a Few TECs

The thymus, a primary lymphoid organ, produces the T cells of the immune system. Originating from the 3rd pharyngeal pouch during embryogenesis, this organ functions throughout life. Yet, thymopoiesis can be transiently or permanently damaged contingent on the types of systemic stresses encountered. The thymus also undergoes a functional decline during aging, resulting in a progressive reduction in naïve T cell output. This atrophy is evidenced by a deteriorating thymic microenvironment, including, but not limited, epithelial-to-mesenchymal transitions, fibrosis and adipogenesis. An exploration of cellular changes in the thymus at various stages of life, including mouse models of in-born errors of immunity and with single cell RNA sequencing, is revealing an expanding number of distinct cell types influencing thymus functions. The thymus microenvironment, established through interactions between immature and mature thymocytes with thymus epithelial cells (TEC), is well known. Less well appreciated are the contributions of neural crest cell-derived mesenchymal cells, endothelial cells, diverse hematopoietic cell populations, adipocytes, and fibroblasts in the thymic microenvironment. In the current review, we will explore the contributions of the many stromal cell types participating in the formation, expansion, and contraction of the thymus under normal and pathophysiological processes. Such information will better inform approaches for restoring thymus functionality, including thymus organoid technologies, beneficial when an individuals’ own tissue is congenitally, clinically, or accidentally rendered non-functional.
Cerebral Cortical Activity During Academic Stress Amongst Undergraduate Medical Students at Kampala International University (Uganda)

Background Stress among medical students is related to their academic lifespan; however, information on brain health among medical students from developing countries continues to be scarce. The objective of this study was to establish perceived academic stress levels, assess the ability to cope with stress, and investigate its effects on the visual reaction time (VRT), audio reaction time (ART), and tactile reaction time (TRT) in the somatosensory cortex among medical students of Uganda. Methods This was a cross-sectional study conducted among preclinical (n = 88) and clinical (n = 96) undergraduate medical students at Kampala International University Western Campus. A standard Perceived Stress Scale (PSS) was used to categorize stress into low, moderate, and severe while the ability to cope with stress was categorized into below average, average, above average, and superior stresscoper (SS). Data on reaction time were acquired through VRT, ART, and TRT using the catch-a-ruler experiment, and this was analyzed using SPSS version 20. Results This study shows that preclinical students are more stressed than clinical students (PSS prevalence for low stress = preclinical; clinical: 40, 60%). Moderate stress was 48.4 and 51.6% while high perceived stress was 75 and 25% among preclinical and clinical students. Among male and female students in preclinical years, higher TRT and VRT were found in clinical students showing that stress affects the tactile and visual cortical areas in the brain, although the VRT scores were only significantly (P = 0.0123) poor in male students than female students in biomedical sciences. Also, highly stressed individuals had higher TRT and ART and low VRT. SS had high VRT and ART and low TRT in preclinical students, demonstrating the importance of the visual cortex in stress plasticity. Multiple regression showed a close relationship between PSS, ability to cope with stress, age, and educational level (P < 0.05), demonstrating the importance of social and psychological support, especially in the biomedical sciences. Conclusion Preclinical students suffer more from stress and are poorer SS than clinical students. This strongly impairs their cortical regions in the brain, thus affecting their academic productivity.
Traveler Pro-social Behaviors at Heritage Tourism Sites

This study aimed to explain the development of tourists’ pro-social intentions during heritage tourism within the pandemic context by combining the norm activation model (NAM) and two significant variables in the theory of planned behavior (TPB). The quantitative data analysis results indicated that the proposed hypotheses have been partially supported, which resonated and enriched the existing studies on COVID-19-related pro-social tourism and tourist behaviors from a theoretical angle. Based on the research outcomes, the corresponding managerial implications for heritage tourism practitioners and meaningful references for future researchers to promote sustainable and pro-social heritage tourism products have been discussed.
The Prognostic and Immunotherapeutic Significance of AHSA1 in Pan-Cancer, and Its Relationship With the Proliferation and Metastasis of Hepatocellular Carcinoma

The AHSA1 is a main activator of ATPase of Hsp90. Hsp90 is involved in various metabolic and developmental processes of tumor cells. Although, the role of AHSA1 in tumor cells is still unrecognized. In the current research, the RNA-seq of 33 tumors were downloaded using The Cancer Genome Atlas (TCGA) database for the analysis of AHSA1 expression in tumors. The Kaplan-Meier method was used for the evaluation of the prognostic significance of AHSA1 in patients with pan-cancer. Additionally, the correlation between AHSA1 and immune cell infiltration, immune checkpoint, pyroptosis-related molecules, epithelial cell transformation-related molecules, and autophagy-related molecules were analyzed by co-expression. Furthermore, we examined the effect of AHSA1 knockdown on cell function in Huh7 and HCCLM3 cells of hepatocellular carcinoma (HCC) cell lines. According to the finding of this study, up-regulation of AHSA1 expression was observed in numerous tumor tissues, and its over-expression in liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), and esophageal carcinoma (ESCA) could affect the overall survival and disease-specific survival of the patients. Meanwhile, as per the correlation analysis the expression of AHSA1 was greatly correlated with the expression of various immune cell infiltrates, immune checkpoint inhibitors, tumor mutation load, and microsatellite instability. Moreover, this study focused on analyzing the association of AHSA1 expression with multiple pathological stages in HCC, and confirmed that AHSA1 was an independent prognostic factor of HCC by univariate and multivariate COX regression in TCGA and The International Cancer Genome Consortium (ICGC) cohorts. At the same time, cellular experiments proved that the AHSA1 knockdown could decrease the proliferation activity, cell migration and invasion ability of HCC cells. Therefore, the results of this study indicated that AHSA1 can be used as a potential prognostic biomarker of tumors and it may have a significant role in the proliferation as well as migration of HCC cells.
Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals

Background Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis. Methods This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers. Data from the First Affiliated Hospital and Changshu No. 1 Hospital of Soochow University were adopted for training and internal validation, and data from the Second Affiliated Hospital of Soochow University were adopted for external validation from January 2017 to December 2021. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of acute pancreatitis. Models were built using traditional logistic regression (LR) and automated machine learning (AutoML) analysis with five types of algorithms. The performance of models was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) based on LR and feature importance, SHapley Additive exPlanation (SHAP) Plot, and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML. Results A total of 1,012 patients were included in this study to develop the AutoML models in the training/validation dataset. An independent dataset of 212 patients was used to test the models. The model developed by the gradient boost machine (GBM) outperformed other models with an area under the ROC curve (AUC) of 0.937 in the validation set and an AUC of 0.945 in the test set. Furthermore, the GBM model achieved the highest sensitivity value of 0.583 among these AutoML models. The model developed by eXtreme Gradient Boosting (XGBoost) achieved the highest specificity value of 0.980 and the highest accuracy of 0.958 in the test set. Conclusions The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability.
Emotion Recognition With Knowledge Graph Based on Electrodermal Activity

Electrodermal activity (EDA) sensor is emerging non-invasive equipment in affect detection research, which is used to measure electrical activities of the skin. Knowledge graphs are an effective way to learn representation from data. However, few studies analyzed the effect of knowledge-related graph features with physiological signals when subjects are in non-similar mental states. In this paper, we propose a model using deep learning techniques to classify the emotional responses of individuals acquired from physiological datasets. We aim to improve the execution of emotion recognition based on EDA signals. The proposed framework is based on observed gender and age information as embedding feature vectors. We also extract time and frequency EDA features in line with cognitive studies. We then introduce a sophisticated weighted feature fusion method that combines knowledge embedding feature vectors and statistical feature (SF) vectors for emotional state classification. We finally utilize deep neural networks to optimize our approach. Results obtained indicated that the correct combination of Gender-Age Relation Graph (GARG) and SF vectors improve the performance of the valence-arousal emotion recognition system by 4 and 5% on PAFEW and 3 and 2% on DEAP datasets.
Systemic Blood Predictors of Elevated Pulmonary Artery Pressure Assessed by Non-invasive Echocardiography After Acute Exposure to High Altitude: A Prospective Cohort Study

Aim Elevated pulmonary artery pressure (ePAP) in response to high-altitude hypoxia is a critical physiopathological factor in the hypoxic adaptation that may lead to high-altitude pulmonary edema in the acute phase or high-altitude pulmonary hypertension in the long term. However, the sea-level predictors of risk factors for altitude-induced ePAP have not been examined. Thus, we aimed to identify the baseline systemic blood predictors of ePAP after acute high-altitude exposure. Materials and Methods A total of 154 participants were transported to a high altitude 3,700 m from sea level within 2 h. Echocardiography examinations were performed to assess the mean pulmonary artery pressure (mPAP) and hemodynamics at both altitudes. All the individuals underwent blood tests to determine the concentrations of vascular regulatory factors. Univariate and adjusted logistic regression analyses were performed to identify the independent predictors of ePAP and factors related to ePAP. Results The mPAP increased significantly from sea level to high altitude (19.79 ± 6.53–27.16 ± 7.16 mmHg, p < 0.05). Increased levels of endothelin (ET-1), Ang (1–7), Ang II, and bradykinin were found after high-altitude exposure, while the levels of nitric oxide (NO), prostaglandin E2 (PEG2), and serotonin decreased sharply (all p-values < 0.05). At high altitude, 52.6% of the subjects exhibited ePAP, and the mPAP was closely correlated with the baseline Ang II level (r = 0.170, p = 0.036) and follow-up levels of NO (r = −0.209, p = 0.009), Ang II (r = 0.246, p = 0.002), and Ang (1–7) (r = −0.222, p = 0.006) and the left atrial inner diameter (LAD, r = 0.270, p < 0.001). Both the baseline and follow-up NO and Ang II levels were significantly different between the ePAP and non-ePAP groups. Finally, we identified the baseline Ang II and NO concentrations as two independent predictors of ePAP (p < 0.05). We also found that two vascular regulatory factors with inverse roles, namely, Ang (1–7) and Ang II, at high altitudes were independently associated with ePAP. Additionally, ET-1, NO, PEG2, and LAD were associated with ePAP. Conclusion The baseline concentrations of Ang II and NO at sea level are two independent predictors of ePAP after acute high-altitude exposure. Furthermore, Ang (1-7) and Ang II combined with ET-1, NO, PEG2, and LAD at high altitudes may contribute to the development of ePAP.
Metagenomic and Microscopic Analysis of Magnetotactic Bacteria in Tangyin Hydrothermal Field of Okinawa Trough

Magnetotactic bacteria (MTB) have been found in a wide variety of marine habitats, ranging from intertidal sediments to deep-sea seamounts. Deep-sea hydrothermal fields are rich in metal sulfides, which are suitable areas for the growth of MTB. However, MTB in hydrothermal fields have never been reported. Here, the presence of MTB in sediments from the Tangyin hydrothermal field was analyzed by 16S rRNA gene amplicon analysis, metagenomics, and transmission electron microscopy. Sequencing 16S rRNA gene yielded a total of 709 MTB sequences belonging to 20 OTUs, affiliated with Desulfobacterota, Alphaproteobacteria, and Nitrospirae. Three shapes of magnetofossil were identified by transmission electron microscopy: elongated-prismatic, bullet-shaped, and cuboctahedron. All of these structures were composed of Fe3O4. A total of 121 sequences were found to be homologous to the published MTB magnetosome-function-related genes, and relevant domains were identified. Further analysis revealed that diverse MTB are present in the Tangyin hydrothermal field, and that multicellular magnetotactic prokaryote (MMPs) might be the dominant MTB.
Enhancing Chinese EFL Students’ Academic Engagement: The Impact of L2 Enjoyment and Academic Motivation

Students’ personal, emotional, and psychological traits are perceived to be highly influential in their academic engagement; therefore, many investigations have been conducted into the role of students’ characteristics in their level of engagement. Yet, the role of L2 enjoyment and academic motivation as two instances of students’ emotional traits was disregarded. To narrow this gap, this article aimed to assess the effects of these two constructs on Chinese EFL students’ academic engagement. To accomplish this, three pre-designed scales were virtually administered to 490 Chinese students. Using the Spearman Rho test, significant correlations were discovered among the variables. Further, through regression analysis, the predictive power of dependent variables was also assessed. Chinese students’ academic engagement was proved to be favorably predicted by L2 enjoyment and academic motivation. The implications and limitations are finally discussed.