Search

Antibodies targeting a tumor antigen, GD2, show some efficacy for neuroblastoma but induce severe neuropathic pain and peripheral neuropathy. Here the authors design a gated chimeric antigen receptor (CAR), using GD2 as the gate and another tumor antigen, B7H3, as the target, to find this GD2-B7H3 CAR capable of suppressing neuroblastoma in mouse models with little adverse effects.

Collaborative Problem Solving (CPS) has received increasing attention for its role in promoting learners' cognitive and social development in STEM education. However, little is known about how learners interact dynamically within a group at different time granularities. This gap mainly resulted from overlooking the time dimension of interactions, leading to a lack of nuanced understanding of moment-to-moment interaction in CPS. In this study, we demonstrated the potential of "temporal group interaction density" in modeling online CPS interactions and investigated the impact of temporal interaction density on CPS processes and outcomes. Specifically, we proposed using cumulative weighted density to measure the holistic state of group interactions and explained the differences in group interactions with different collaborative performance and interaction densities by modeling the transition and evolution of interaction sequences through Apriori and cumulative relative centrality. Results indicated that group interaction density cannot directly predict their collaborative performance, but notable differences in interaction patterns existed in the high-performance groups with different interaction densities, while low-performance groups showed interactive commonalities towards the completion of CPS. The findings of this study guided the design of CPS interventions and supported the process mining of CPS interactions, with vital practical implications for CPS assessment and skills development.

Population-specific profiling of mutations in cancer genes is of critical importance for the understanding of cancer biology in general as well as the establishment of optimal diagnostics and treatment guidelines for that particular population. Although genetic analysis of tumor tissue is often used to detect mutations in cancer genes, the invasiveness and limited accessibility hinders its application in large-scale population studies. Here, we used ultra-deep massive parallel sequencing of plasma cell free DNA (cfDNA) to identify the mutation profiles of 265 Vietnamese patients with advanced non-small cell lung cancer (NSCLC). Compared to a cohort of advanced NSCLC patients characterized by sequencing of tissue samples, cfDNA genomic testing, despite lower mutation detection rates, was able to detect major mutations in tested driver genes that reflected similar mutation composition and distribution pattern, as well as major associations between mutation prevalence and clinical features. In conclusion, ultra-deep sequencing of plasma cfDNA represents an alternative approach for population-wide genetic profiling of cancer genes where recruitment of patients is limited to the accessibility of tumor tissue site.

Abstract Background Recent advances in sequencing technologies and bioinformatics tools have allowed for large-scale microbiome studies that are rapidly advancing medical research. However, small changes in technique or analysis can significantly alter the results and lead to conflicting findings. Quantifying the technical versus biological variation expected in targeted 16S rRNA gene sequencing studies and how this variation changes with input biomass is critical to guide meaningful interpretation of the current literature and plan future research. Results Data were compiled from 469 sequencing libraries across 19 separate targeted 16S rRNA gene sequencing runs over a 2.5-year time period. Following removal of contaminant sequences identified from negative controls, 244 samples retained sufficient reads for further analysis. Coefficients of variation for intra- and inter-assay variation from repeated measurements of a bacterial mock community ranged from 8.7 to 37.6% (intra) and 15.6 to 80.5% (inter) for all but one genus of bacteria whose relative abundance was greater than 1%. Intra- versus inter-assay Bray-Curtis pairwise distances for a single stool sample were 0.11 versus 0.31, whereas intra-assay variation from repeat stool samples from the same donor was greater at 0.38 (Wilcoxon p = 0.001). A dilution series of the bacterial mock community was used to assess the effect of input biomass on variability. Pairwise distances increased with more dilute samples, and estimates of relative abundance became unreliable below approximately 100 copies of the 16S rRNA gene per microliter. Using this data, we created a prediction model to estimate the expected variation in microbiome measurements for given input biomass and relative abundance values. Conclusions Well-controlled microbiome studies are sufficiently robust to capture small biological effects and can achieve levels of variability consistent with clinical assays. Relative abundance is negatively associated with measures of variability and has a stronger effect on variability than does absolute biomass, suggesting that it is feasible to detect differences in bacterial populations in very low-biomass samples. Further, by quantifying the effect of biomass and relative abundance on compositional variability, we developed a tool for defining the expected variance in a given microbiome study.

This study aims to track college students' on-task rate during the teaching process and to analyze the influence of instructional strategies on on-task rate through the aspects of observable and internal engagement indicators. Thirty-six undergraduate students at a higher education institution in China participated in the study. Students' behaviors and their EEG signals were recorded from fifty-one learning activities. Analyses have been focused on identifying the determinants of student's engagement levels and revealing the impacts of behavioral sequences and cognitive sequences on student's engagement levels. The results show that: (1) instructional strategies, classroom behaviors, and cognitive states were significant predictors of students' on-task rate; (2) the continuity of classroom behaviors improved the on-task rate; and (3) the standard deviations of attention and cognitive load were positively correlated with the on-task rate. This study describes a case of integrating multimodal data analysis in classroom teaching and discusses practical implications for improving classroom teaching.

Blended learning, integrating online and in-person components, has been increasingly adopted in higher education to enhance students' learning experience and outcomes. While the advantages of blended learning are well-evidenced, research has primarily focused on the online pre-learning component, neglecting the significance of in-class activities. In-class activities play a crucial role in affording active learning opportunities (e.g., discussion, elaboration), necessitating a systemic understanding of their dynamics. The purpose of this study was thus to systemically investigate college students' learning behaviors during in-class activities in a blended course. In-class activities were video-recorded and labelled manually following a coding scheme. By establishing a linear regression model, the study identified listening to the instructor's lecture and taking notes as two predictors of students' learning gains. Additionally, sequential patterns of learning behaviors during in-class activities were examined. The reciprocal interactions between students' behavior of listening to the lecture and their note-taking actions were noted. The findings of this study contributed to a systemic view of blended learning by shedding light on students' learning behaviors and their implications for instructional practice.

Blended learning has been widely integrated in college-level computer science education. Despite evidence about benefits of blended learning, students' in-class activities remain underexplored. To afford effective blended learning experience, supporting students in both modalities is essential. This study thus took an initial step to fill the gap by investigating college students' in-class activities in a blended course from the perspective of attention. Using non-intrusive electroencephalography (EEG) instruments to collect attentional data, this study found students' attention in in-class activities positively correlated with their learning gains. Students' attention also varied across in-class activities, reaching a higher level in group discussions than in pre-tests and lectures. Linear regression analysis indicated students' length of time spent viewing online resources and their pre-test scores significantly predicted their in-class attention. The findings of the study provide insight into course design and facilitation for effective blended computer science courses.

Molecular and functional characterization of alveolar epithelial type I (AT1) cells has been challenging due to difficulty in isolating sufficient numbers of viable cells. Here we performed single-cell RNA-sequencing (scRNA-seq) of tdTomato+ cells from lungs of AT1 cell-specific Aqp5-Cre-IRES-DsRed (ACID);R26tdTomato reporter mice. Following enzymatic digestion, CD31-CD45-E-cadherin+tdTomato+ cells were subjected to fluorescence-activated cell sorting (FACS) followed by scRNA-seq. Cell identity was confirmed by immunofluorescence using cell type-specific antibodies. After quality control, 92 cells were analyzed. Most cells expressed ‘conventional’ AT1 cell markers (Aqp5, Pdpn, Hopx, Ager), with heterogeneous expression within this population. The remaining cells expressed AT2, club, basal or ciliated cell markers. Integration with public datasets identified three robust AT1 cell- and lung-enriched genes, Ager, Rtkn2 and Gprc5a, that were conserved across species. GPRC5A co-localized with HOPX and was not expressed in AT2 or airway cells in mouse, rat and human lung. GPRC5A co-localized with AQP5 but not pro-SPC or CC10 in mouse lung epithelial cell cytospins. We enriched mouse AT1 cells to perform molecular phenotyping using scRNA-seq. Further characterization of putative AT1 cell-enriched genes revealed GPRC5A as a conserved AT1 cell surface marker that may be useful for AT1 cell isolation.

Background: The SARS-CoV-2 virus has been a global public health threat since December 2019. This study aims to investigate the neurological characteristics and risk factors of coronavirus disease 2019 (COVID-19) in Taiwanese children, using data from a collaborative registry. Methods: A retrospective, cross-sectional, multi-center study was done using an online network of pediatric neurological COVID-19 cohort collaborative registry. Results: A total of 11160 COVID-19-associated emergency department (ED) visits and 1079 hospitalizations were analyzed. Seizures were the most common specific neurological symptom, while encephalitis and acute disseminated encephalomyelitis (ADEM) was the most prevalent severe involvement.In ED patients with neurological manifestations, severe neurological diagnosis was associated with visual hallucination, seizure with/without fever, behavior change, decreased GCS, myoclonic jerk, decreased activity/fatigue, and lethargy. In hospitalized patients with neurological manifestations, severe neurological diagnosis was associated with behavior change, visual hallucination, decreased GCS, seizure with/without fever, myoclonic jerk, fatigue, and hypoglycemia at admission. Encephalitis/ADEM was the only risk factor for poor neurological outcomes at discharge in hospitalized patients. Conclusion: Neurological complications are common in pediatric COVID-19. Visual hallucination, seizure, behavior change, myoclonic jerk, decreased GCS, and hypoglycemia at admission are the most important warning signs of severe neurological involvement such as encephalitis/ADEM.

Abstract Diabetes mellitus remains a major global health issue, and great attention is directed at natural therapeutics. This systematic review aimed to assess the potential of flavonoids as antidiabetic agents by investigating their inhibitory effects on α-glucosidase and α-amylase, two key enzymes involved in starch digestion. Six scientific databases (PubMed, Virtual Health Library, EMBASE, SCOPUS, Web of Science, and WHO Global Index Medicus) were searched until August 21, 2022, for in vitro studies reporting IC50 values of purified flavonoids on α-amylase and α-glucosidase, along with corresponding data for acarbose as a positive control. A total of 339 eligible articles were analyzed, resulting in the retrieval of 1643 flavonoid structures. These structures were rigorously standardized and curated, yielding 974 unique compounds, among which 177 flavonoids exhibited inhibition of both α-glucosidase and α-amylase are presented. Quality assessment utilizing a modified CONSORT checklist and structure–activity relationship (SAR) analysis were performed, revealing crucial features for the simultaneous inhibition of flavonoids against both enzymes. Moreover, the review also addressed several limitations in the current research landscape and proposed potential solutions. The curated datasets are available online at https://github.com/MedChemUMP/FDIGA . Graphical Abstract

Ewing sarcoma family tumors (ESFT) are aggressive bone and soft tissue tumors that express EWS-ETS fusion genes as driver mutations. Although the histogenesis of ESFT is controversial, mesenchymal (MSC) and/or neural crest (NCSC) stem cells have been implicated as cells of origin. For the current study we evaluated the consequences of EWS-FLI1 expression in human embryonic stem cell-derived NCSC (hNCSC). Ectopic expression of EWS-FLI1 in undifferentiated hNCSC and their neuro-mesenchymal stem cell (hNC-MSC) progeny was readily tolerated and led to altered expression of both well established as well as novel EWS-FLI1 target genes. Importantly, whole genome expression profiling studies revealed that the molecular signature of established ESFT is more similar to hNCSC than any other normal tissue, including MSC, indicating that maintenance or reactivation of the NCSC program is a feature of ESFT pathogenesis. Consistent with this hypothesis, EWS-FLI1 induced hNCSC genes as well as the polycomb proteins BMI-1 and EZH2 in hNC-MSC. In addition, up-regulation of BMI-1 was associated with avoidance of cellular senescence and reversible silencing of p16. Together these studies confirm that, unlike terminally differentiated cells but consistent with bone marrow-derived MSC, NCSC tolerate expression of EWS-FLI1 and ectopic expression of the oncogene initiates transition to an ESFT-like state. In addition, to our knowledge this is the first demonstration that EWS-FLI1-mediated induction of BMI-1 and epigenetic silencing of p16 might be critical early initiating events in ESFT tumorigenesis.

Developmental delay (DD) has a great impact on children at the developmental stage, and is often manifested by varying degrees of motor delays, intellectual disabilities, and other defects [...]

Abstract The purpose of this study aimed to analyze the process of online collaborative problem solving (CPS) via brain-to-brain synchrony (BS) at the problem-understanding and problem-solving stages. Aiming to obtain additional insights than traditional approaches (survey and observation), BS refers to the synchronization of brain activity between two or more people, as an indicator of interpersonal interaction or common attention. Thirty-six undergraduate students participated. Results indicate the problem-understanding stage showed a higher level of BS than the problem-solving stage. Moreover, the level of BS at the problem-solving stage was significantly correlated with task performance. Groups with all high CPS skill students had the highest level of BS, while some of the mixed groups could achieve the same level of BS. BS is an effective indicator of CPS to group performance and individual interaction. Implications for the online CPS design and possible supports for the process of online CPS activity are also discussed.

Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features.

Abstract Dravet syndrome (DS) is an uncommon epilepsy syndrome that may negatively affect the patients and their caregivers. However, reliable and valid measures of its impact on caregivers and the characteristics of patients with DS in Taiwan are lacking. This study aimed to describe the characteristics of patients with DS and concerns of their caregivers and establish a baseline frequency of disease characteristics using a cross-sectional survey in Taiwan. We assessed the caregivers of patients with DS using an online anonymous questionnaire. The seizure frequency decreased with age, although lacking statistical significance. Vaccines show no influence on the condition of patients with DS. Our findings revealed the highest impact on the domains affecting the caregivers’ daily life, including additional household tasks, symptom observation, further medical plan, and financial issues. Caregivers also expressed concerns regarding the lack of independence/constant care, seizure control, speech/communication, and impacts on siblings because of long-term care of the patients in parents’ absence. Our findings highlight the significant effects of caring for a child with DS on the lives of their caregivers in Taiwan; these findings will help raise awareness regarding the needs of these families. Furthermore, we discussed the possible pathophysiological mechanisms of associated comorbidities.

Background: Vagus nerve stimulation (VNS) is used as an add-on treatment for epilepsy. This study aimed to use Taiwanese nationwide registry data to analyze the therapeutic effects of VNS in children with refractory epilepsy (RE) and try to explore predictive factors of VNS treatment effectiveness. Methods: This retrospective study collected data from December 2007 to December 2014. Patient variables included gender, age, VNS implantation date, epilepsy duration, seizure frequency, seizure type, etiology, and antiepileptic drug (AED) history. We divided patients into three groups: Group I as seizure frequency >80 times per month, Group II as seizure frequency 24–80 times per month, and Group III as seizure frequency

The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward.

Abstract Background Babesia gibsoni (B. gibsoni) is an intraerythrocytic protozoan parasite of dogs that causes fever and hemolytic illness. A timely diagnosis is essential for the disease management. Results Here, we report a QubeMDx PCR system which enables a rapid, sensitive and reliable diagnosis of B. gibsoni near the dog patient. Within 30 min, this diagnostic assay was able to detect as low as 0.002% parasitemia of the dog blood. Using clinical samples, this new assay was validated to demonstrate 100% agreement with real-time PCR. Conclusions This novel diagnostic method provides a reliable point-of-care test to assist in the identification of B. gibsoni.

Cancer is a major health burden and a leading cause of death worldwide, with numerous new molecules being studied and developed as therapeutic agents. In this study, the cytotoxicity of compounds derived from Goniothalamus elegans was evaluated for possible anticancer activity. It was observed that the crude methanol extract of G. elegans exerted the strongest cytotoxic activity against SW-480, AGS, and SK-LU-1 cell lines. In addition, two isolated alkaloids—namely, lysicamine and liriodenine—also showed strong inhibitory ability against similar cancer cell lines. To further investigate the compounds' mechanism of action, a molecular docking approach was utilized to evaluate the potential of the two candidates to interact with the epidermal growth factor receptor. This assay estimated that lysicamine and liriodenine acquired protein binding affinities of −8.8 and −9.7 kcal/mol, respectively. Finally, the stabilities of the ligand–protein complexes were evaluated using molecular dynamics simulations of 100 ns each. [ABSTRACT FROM AUTHOR]

The genus Isotrema (Aristolochiaceae) in East Asia had been intensely revised in the recent decade. However, the classification of Taiwanese Isotrema has not been reviewed since 1996. In current study, we examine the habit, leaf shape, and flower morphology and conducted morphometrical analyses in order to revise the classification of Taiwanese Isotrema. As a result, we recognized five species in Taiwan, including one new species, I. pahsienshanianum. Additionally, I. hohuanense, an overlooked species, which was omitted from the Flora of Taiwan is recovered. [ABSTRACT FROM AUTHOR]

As a student modeling technique, knowledge tracing is widely used by various intelligent tutoring systems to infer and trace the individual's knowledge state during the learning process. In recent years, various models were proposed to get accurate and easy-to-interpret results. To make sense of the wide Knowledge tracing (KT) modeling landscape, this paper conducts a systematic review to provide a detailed and nuanced discussion of relevant KT techniques from the perspective of assumptions, data, and algorithms. The results show that most existing KT models consider only a fragment of the assumptions that relate to the knowledge components within items and student's cognitive process. Almost all types of KT models take "quize data" as input, although it is insufficient to reflect a clear picture of students' learning process. Dynamic Bayesian network, logistic regression and deep learning are the main algorithms used by various knowledge tracing models. Some open issues are identified based on the analytics of the reviewed works and discussed potential future research directions. [ABSTRACT FROM AUTHOR]

Population-specific profiling of mutations in cancer genes is of critical importance for the understanding of cancer biology in general as well as the establishment of optimal diagnostics and treatment guidelines for that particular population. Although genetic analysis of tumor tissue is often used to detect mutations in cancer genes, the invasiveness and limited accessibility hinders its application in large-scale population studies. Here, we used ultra-deep massive parallel sequencing of plasma cell free DNA (cfDNA) to identify the mutation profiles of 265 Vietnamese patients with advanced non-small cell lung cancer (NSCLC). Compared to a cohort of advanced NSCLC patients characterized by sequencing of tissue samples, cfDNA genomic testing, despite lower mutation detection rates, was able to detect major mutations in tested driver genes that reflected similar mutation composition and distribution pattern, as well as major associations between mutation prevalence and clinical features. In conclusion, ultra-deep sequencing of plasma cfDNA represents an alternative approach for population-wide genetic profiling of cancer genes where recruitment of patients is limited to the accessibility of tumor tissue site. [ABSTRACT FROM AUTHOR]

Background: Stunted growth is a notable public health crisis in several countries that have low income, especially Vietnam. Objective: This study aimed to evaluate improved growth among stunted children from 24 to 48 months of age through the use of PediaSure fortified milk in Tien Hai District, Thai Binh Province, Vietnam. Methods: A clinical trial study was conducted with 140 children who were 24 to 48 months old and had height-for-age Z-scores (LAZ) lower than -2 SD and weight-for-height Z- scores (WHZ) lower than -1 SD. Each child was given 2 glasses of PediaSure per day continuously for 6 months. Blood samples were taken from all children at the start of the intervention and during 6 months of intervention; this was done to access haemoglobin, albumin, zinc concentrations, C-reactive protein (CRP), and Alpha-1 glycoprotein (AGP). Results: Of the 140 children who participated in the initial study, only 106 children were eligible for analysis and evaluation of intervention effectiveness. After 6 months, the anaemia rate decreased from 29.2% to 10.4%. The prevalence of albumin deficiency also improved significantly from 82.1% to 20.8%. Zinc deficiency decreased from 66.0% to 29.2%. Assessing the improvement of biochemical markers by gender and age, our results showed that women had better improvement than men in Hb, albumin, and zinc. A comparing the two age groups, the results also showed that the 24-month to 36-month age groups had a better level of improvement in biochemical indices. Conclusion: Milk is an effective and widely accepted vehicle for the delivery of zinc, iron and other micronutrients in young children. Also, the multiple-micronutrient supplementation of milk shows strong potential to increase serum levels and decrease deficiencies of micronutrients known to impair growth, cognition, and immune responses. [ABSTRACT FROM AUTHOR]