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Diabetes mellitus remains a major global health burden and great attention is directed at natural therapeutics. This systematic review aimed to evaluate the potential of flavonoids as antidiabetic agents through their ability to inhibit α-amylase and α-glucosidase, two key starch digestive enzymes. Six scientific databases were queried up until August 21, 2022, for in vitro studies reporting the IC50 results of purified flavonoids on α-amylase or α-glucosidase, along with the respective data of acarbose control. A total of 339 articles were assessed as eligible and subjected to the data extraction process, resulting in 1643 retrieved flavonoid structures. Chemical structures were then rigorously standardized and curated to 974 unique compounds, in which 177 flavonoids showed both inhibitions against α-amylase and α-glucosidase. Quality assessment was conducted following a modified CONSORT checklist. The structure-activity relationships revealed that a double bond C2=C3 and a keto group C4=O is essential for simultaneous inhibition. The hydroxyl group at C3 is favourable for α-glucosidase inhibition but detrimental to the effect against α-amylase. Further notable features which affect α-glucosidase and α-amylase inhibition were also discussed. Several limitations were considered, including the inconsistency among included studies, language restriction, and the contemporaneity of the review. In conclusion, the systematic review has summarized some crucial findings in the investigation of flavonoids as dual-target inhibitors against α-glucosidase and α-amylase and proposed several orientations for future research.

Supplementary Table 1 from BMI-1 Promotes Ewing Sarcoma Tumorigenicity Independent of CDKN2A Repression

Deregulation of the polycomb group gene BMI-1 is implicated in the pathogenesis of many human cancers. In this study, we have investigated if the Ewing sarcoma family of tumors (ESFT) expresses BMI-1 and whether it functions as an oncogene in this highly aggressive group of bone and soft tissue tumors. Our data show that BMI-1 is highly expressed by ESFT cells and that, although it does not significantly affect proliferation or survival, BMI-1 actively promotes anchorage-independent growth in vitro and tumorigenicity in vivo. Moreover, we find that BMI-1 promotes the tumorigenicity of both p16 wild-type and p16-null cell lines, demonstrating that the mechanism of BMI-1 oncogenic function in ESFT is, at least in part, independent of CDKN2A repression. Expression profiling studies of ESFT cells following BMI-1 knockdown reveal that BMI-1 regulates the expression of hundreds of downstream target genes including, in particular, genes involved in both differentiation and development as well as cell-cell and cell-matrix adhesion. Gain and loss of function assays confirm that BMI-1 represses the expression of the adhesion-associated basement membrane protein nidogen 1. In addition, although BMI-1 promotes ESFT adhesion, nidogen 1 inhibits cellular adhesion in vitro. Together, these data support a pivotal role for BMI-1 ESFT pathogenesis and suggest that its oncogenic function in these tumors is in part mediated through modulation of adhesion pathways. [Cancer Res 2008;68(16):6507–15]

Supplementary Figures 1-4 from BMI-1 Promotes Ewing Sarcoma Tumorigenicity Independent of CDKN2A Repression

PurposeCritical thinking is considered important in psychological science because it enables students to make effective decisions and optimizes their performance. Aiming at the challenges and issues of understanding the student's critical thinking, the objective of this study is to analyze online discussion data through an advanced multi-feature fusion modeling (MFFM) approach for automatically and accurately understanding the student's critical thinking levels.Design/methodology/approachAn advanced MFFM approach is proposed in this study. Specifically, with considering the time-series characteristic and the high correlations between adjacent words in discussion contents, the long short-term memory–convolutional neural network (LSTM-CNN) architecture is proposed to extract deep semantic features, and then these semantic features are combined with linguistic and psychological knowledge generated by the LIWC2015 tool as the inputs of full-connected layers to automatically and accurately predict students' critical thinking levels that are hidden in online discussion data.FindingsA series of experiments with 94 students' 7,691 posts were conducted to verify the effectiveness of the proposed approach. The experimental results show that the proposed MFFM approach that combines two types of textual features outperforms baseline methods, and the semantic-based padding can further improve the prediction performance of MFFM. It can achieve 0.8205 overall accuracy and 0.6172 F1 score for the “high” category on the validation dataset. Furthermore, it is found that the semantic features extracted by LSTM-CNN are more powerful for identifying self-introduction or off-topic discussions, while the linguistic, as well as psychological features, can better distinguish the discussion posts with the highest critical thinking level.Originality/valueWith the support of the proposed MFFM approach, online teachers can conveniently and effectively understand the interaction quality of online discussions, which can support instructional decision-making to better promote the student's knowledge construction process and improve learning performance.

PurposeThe paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.Design/methodology/approachGuided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.FindingsThe study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.Originality/valueFor theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

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.

Purpose This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed. Design/methodology/approach The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method. Findings Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students. Research limitations/implications The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation. Originality/value This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.

Purpose The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods. Design/methodology/approach This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research. Findings After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments. Research limitations/implications This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account. Originality/value The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

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.

The ability to utilize preclinical models to predict the clinical toxicity of chimeric antigen receptor (CAR) T cells in solid tumors is tenuous, thereby necessitating the development and evaluation of gated systems. Here we found that murine GD2 CAR-T cells, specific for the tumor-associated antigen GD2, induce fatal neurotoxicity in a costimulatory domain-dependent manner. Meanwhile, human B7H3 CAR-T cells exhibit efficacy in preclinical models of neuroblastoma. Seeking a better CAR, we generated a SynNotch gated CAR-T, GD2-B7H3, recognizing GD2 as the gate and B7H3 as the target. GD2-B7H3 CAR-T cells control the growth of neuroblastoma in vitro and in metastatic xenograft mouse models, with high specificity and efficacy. These improvements come partly from the better metabolic fitness of GD2-B7H3 CAR-T cells, as evidenced by their naïve T-like post-cytotoxicity oxidative metabolism and lower exhaustion profile., 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.

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 main objectives of this research are to provide a new approach for flash flood prediction in Lao Cai, where frequent typhoons happen. This method is based on the Random Forest classification algorithm. The researcher applied GIS database in combination with construction machine learning model and verified the forecasting model, extracted the data based on field survey of the flash flood area of Lao Cai and GIS (Geographic Information System). The results have proved that the model can be a useful tool for flash flood forecasting model, providing more data for land planning and management for preventing and predicting flash flood for Lao Cai area.

Distance education programs have become the preferred option for most higher education institutions to continue teaching during the COVID-19 pandemic, but the effectiveness of some online courses, especially those engineering courses with experimentation activities, remains disputed. The main challenge is fostering collaborative problem solving skills for novice students as online collaboration increases their cognitive load. This research thus tapped into novice engineering students' cognitive load to develop a more granular, multimodal view of how cognitive load influences student performance in collaborative problem solving during virtual experimentation activities. The findings of this research provided significant implications for the future design and implementation of virtual laboratories in computer science engineering education.

Supplemental material, sj-docx-1-npx-10.1177_1934578X221138435 for Phytochemicals Derived from Goniothalamus elegans Ast Exhibit Anticancer Activity by Inhibiting Epidermal Growth Factor Receptor by Linh Thuy Thi Tran, Long-Hung Dinh Pham, Nhi Yen Thi Dang, Nguyen Thao Nguyen Le, Huu Bao Nguyen and Tan Khanh Nguyen in Natural Product Communications

Supplemental material, sj-docx-1-npx-10.1177_1934578X221138435 for Phytochemicals Derived from Goniothalamus elegans Ast Exhibit Anticancer Activity by Inhibiting Epidermal Growth Factor Receptor by Linh Thuy Thi Tran, Long-Hung Dinh Pham, Nhi Yen Thi Dang, Nguyen Thao Nguyen Le, Huu Bao Nguyen and Tan Khanh Nguyen in Natural Product Communications

This article proposes two innovative approaches, the one-channel learning image recognition and the three-channel learning image recognition, to convert student's course involvements into images for early warning predictive analysis. Multiple experiments with 5235 students and 576 absolute/1728 relative input variables were conducted to verify their effectiveness. The results indicate that both methods can significantly capture more at-risk students (the highest average recall rate is equal to 77.26%) than the following machine learning algorithms—support vector machine, random forest, and deep neural network—in the middle of the semester. In addition, the innovative approaches allow minor subtypes of at-risk student identification and provide visual insights for personalized interventions. Implications and future directions are also discussed in this article.

Background/purpose The prevalence of developmental disabilities in Taiwan remains unclear, especially in young children under the age 3. We aimed to study the prevalence of developmental disabilities and verify a useful developmental screening tool in a community setting in Taiwan. Methods We conducted a prospective cross-sectional study in northeastern Taiwan from July 2008 to December 2009 in children aged 4 months to 6 years old from well-child visits. We devised a screening program using Taipei City Developmental Screening Checklist for Preschoolers, 2nd Version (Taipei-II), a validated parent-report milestone checklist tailored to the Taiwanese culture and language to assess the prevalence of developmental disabilities in Taiwan. Information about the children's medical conditions and their family were recorded. Results A total of 3214 children were recruited, of whom 365 had developmental disabilities, with an overall prevalence of 11.36%. Speech and language delay/disorders were the most common developmental problems followed by motor delays, with prevalence rates of 4.79% and 2.33%, respectively. Low economic status, prematurity and/or small for gestational age and a history of perinatal hypoxia or underlying medical disorders were the main risk factors correlated with developmental delays. However, foreign-born mother and aboriginal families were not important factors for poor developmental outcomes. Conclusion The prevalence rate of developmental disabilities in northeastern Taiwan was 11.36%. Low economic status, prematurity and/or small for gestational age and a history of underlying medical disorders were the main risk factors correlated with developmental disabilities. Taipei II is an easy-to-use and effective developmental surveillance tool for Taiwanese children.

This study identifies the factors that attract U.S. residents to eco-friendly events. To achieve our research objective, we obtained data (N = 1,115) from a cross-national, web-based survey distrib...

Purpose For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis. Design/methodology/approach This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach. Findings The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students. Originality/value The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

Purpose Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education. Design/methodology/approach This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research. Findings Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward. Research limitations/implications Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals. Originality/value This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

This paper reports how a commercial bank in Asia uses big data analytic as a tool to explore the internal B2B data to improve supply chain finance and the efficiency of marketing tactics and campaigns. A case study was conducted by analyzing two types of supply chain relationships: (1) supply chain relationships in the credit reports; (2) e-wiring transactions among supply chain companies. The results show that big data analytics is very useful in terms of improving the commercial banks' marketing and risk management performances. The case study also set a good example for B2B firms seeking to understand how they could leverage big data analytics to differentiate customer solutions, sustain profitability and generate new business values. Theorical and practical implications are also discussed.

Adequate control of pharmacoresistant epilepsy continues to be a challenge. Multiple studies have reported the benefits of epilepsy surgery and vagus nerve stimulation for children with pharmacoresistant epilepsy. Little is known about the role of vagus nerve stimulation for children with failed epilepsy surgeries. The aim of this study was to examine the effects of vagus nerve stimulation on seizure frequency reduction for children with failed epilepsy surgeries. We retrospectively reviewed 85 children with pharmacoresistant epilepsy who underwent vagus nerve stimulation. Six of these patients underwent epilepsy surgery before vagus nerve stimulation (group I) and 79 patients received only vagus nerve stimulation (group II). We recorded seizure frequency at 3, 12, 24 and 36 months after vagus nerve stimulator implantation. Both groups had reduced seizure frequencies at the 3-, 12-, 24- and 36-month follow-up (p = 0.044 for group I trends and 0.008 for group II trends). Vagus nerve stimulator implantations significantly improve seizure frequency for children with or without previous epilepsy surgery at 3, 12, 24 and 36 months. These findings suggest that vagus nerve stimulation should be considered an alternative therapy for pediatric patients with previous failed surgeries.

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.

Purpose Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown. Design/methodology/approach This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance. Findings Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences. Research limitations/implications A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns. Practical implications The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs. Originality/value This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

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.

Background: The pathogenesis and clinical significance of lenticulostriate vasculopathy (LSV) are unclear. Our study aimed to determine the prevalence, presentation, and evolution of LSV, and the perinatal risk factors associated with LSV among very-low-birth-weight (VLBW) preterm infants. Methods: One-hundred-and-thirty VLBW preterm infants were retrospectively enrolled in this study. Serial cranial ultrasound examinations were performed regularly from birth until a corrected age of 1 year. Infants with LSV were assigned to early-onset (≤10 postnatal days) and late-onset (>10 postnatal days) groups. Data describing the infants’ perinatal characteristics, placental histopathology, and neonatal morbidities were collected, and the groups were compared. Results: Of the VLBW infants, 39.2% had LSV before they were 1 year old. Linear-type LSV was the most common presentation, and >50% of the infants had bilateral involvement. LSV was first detected at 112 ± 83 postnatal days, and its detection timing correlated negatively with gestational age (GA) (R2 = 0.153, p = 0.005) and persisted for 6 months on average. The infants with and without LSV had similar perinatal characteristics, placental pathologies, cytomegalovirus infection rates, and clinical morbidities. The late-onset LSV group comprised 45 (88.2%) infants who had a significantly higher rate of being small for gestational age (SGA) and used oxygen for longer than the infants without LSV. After adjusting a multivariable regression model for GA and SGA, analysis showed that the duration of oxygen usage was an independent risk factor for late-onset LSV development in VLBW infants (odds ratio: 1.030, p = 0.032). Conclusion: LSV may be a nonspecific marker of perinatal insult to the developing brains of preterm infants. Prolonged postnatal oxygen usage may predispose VLBW preterm infants to late-onset LSV development. The long-term clinical impacts of LSV should be clarified.

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.

Tubulin proteins play a role in the cortical development. Mutations in the tubulin genes affect patients with brain malformations. The present report describes two cases of developmental and epileptic encephalopathy (DEE) due to tubulinopathy. Case 1, a 23-year-old boy, was found to have a brain malformation with moderate ventriculomegaly prenatally. Hypotonia was noted at birth. Seizures were noted on the 1st day with multifocal discharges on the EEGs, which became intractable to many anticonvulsants. Brain MRI showed marked dilated ventricles and pachy/polymicrogyri. He became a victim of DEE. A de novo mutation in TUBB2B was proven through next-generation sequencing (NGS). Case 2, a mature male baby, began to have myoclonic jerks of his limbs 4 h after birth. EEG showed focal sharp waves from central and temporal regions. Brain MRI showed lissencephaly, type I. The seizures were refractory initially. A de novo mutation in TUBA1A was proven at the 6th week through NGS. He showed the picture of DEE at 1 year and 2 months of age. The clinical features of the tubulinopathies include motor delay, intellectual disabilities, epilepsy, and other deficits. Our cases demonstrated the severe form of tubulinopathy due to major tubulin gene mutations. NGS makes the early identification of genetic etiology possible for clinical evaluation.

As an emerging field of research, learning analytics (LA) offers practitioners and researchers information about educational data that is helpful for supporting decisions in management of teaching ...

The study proposes two new measures, time and location entropy, to depict students’ physical spatio-temporal contexts when engaged in an online course. As anytime, anywhere access has been ...

Background: Biomechanical measurement tools have been developed and widely used to precisely quantify knee anterior-posterior laxity after anterior cruciate ligament (ACL) injury. However, validated objective device to document knee rotational laxity, though being developed by different researchers, are not yet widely used in the daily clinical practice. A new biomechanical device was developed to quantify knee internal and external rotations. Methods: The reliability of the new biomechanical device which measures knee rotations were tested. Different torques (1-10Nm) were applied by the device to internally and externally rotate human cadaveric knees, which were held in a flexion angle of 30°. The rotations were measured by the device in degrees. There were two independent testers, and each tester carried out three trials. Intra-rater and inter-rater reliability were quantified in terms of intraclass correlation (ICC) coefficient among trials and between testers. The device was verified by the comparison with a computer assisted navigation system. ICC was measured. Mean, standard deviation and 95% confident interval of the difference as well as the root mean square difference were calculated. The correlations were deemed to be reliable if the ICC was above 0.75. Results: The intra-rater and inter-rater reliability achieved high correlation for both internal and external rotation, ranged from 0.959 to 0.992. ICC between the proposed meter and the navigation system for both internal and external rotation was 0.78. The mean differences were 2.3° and 2.5° for internal and external rotation respectively. Conclusions: A new knee rotational laxity meter was proposed in this study. Its reliability was verified by showing high correlation among trials. It also showed good correlation to a gold standard of measurement. It might be used to document knee rotational laxity for various purposes, especially after ACL injury, after further validation of the device in human subjects. Keywords: Tibial rotation, Knee stability, Measurement, Laxity

Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction rather than identifying key performance factors; the lack of common predictors identified for both K-12 and higher education environments; and the misplaced focus on absolute engagement levels rather than relative engagement levels. Two datasets, one from higher education and the other from a K-12 online school with 13 368 students in more than 300 courses, were applied using the predictive modeling technique. The results showed the newly suggested approach had higher overall accuracy and sensitivity rates than the traditional approach. In addition, two generalizable predictors were identified from instruction-intensive and discussion-intensive courses.

Dravet syndrome (DS) is a rare and devastating epilepsy syndrome, and it can affect the patients and their caregivers. However, the lack of a reliable and valid measures of caregiver impact and the characteristic pattern in Taiwan. The purpose of this study was to describe the characteristics of patients with DS and caregivers’ concerns, and to establish a baseline frequency of characteristics of the disease using a cross-sectional survey in Taiwan. We recruit the caregivers of patients with DS and assessed their condition via online anonymous questionnaire. Seizure frequency decreased with age though not statistically significant. Vaccine may not influence the condition of DS. We highlighted the greatest impact on the domains that affect caregivers’ daily life, including additional household tasks, symptoms observation, further medical plan and financial problem. Caregivers may also concern about lack of independence/constant care, seizure control, speech/communication and sibling impacts/long-term care when patents are gone. The current findings highlight the significant life effects of caring for a child with DS in Taiwan, and can be used to raise the attention about the need for these families. The possible pathogenic mechanisms of these comorbidities were also discussed.

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 &lsquo, conventional&rsquo, 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.

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.

Abusive head trauma (AHT), used to be named shaken baby syndrome, is an injury to the skull and intracranial components of a baby or child younger than 5 years due to violent shaking and/or abrupt impact. It is a worldwide leading cause of fatal head injuries in children under 2 years. The mechanism of AHT includes shaking as well as impact, crushing or their various combinations through acceleration, deceleration and rotational force. The diagnosis of AHT should be based on the existence of multiple components including subdural hematoma, intracranial pathology, retinal hemorrhages as well as rib and other fractures consistent with the mechanism of trauma. The differential diagnosis must exclude those medical or surgical diseases that can mimic AHT such as traumatic brain injury, cerebral sinovenous thrombosis, and hypoxic-ischemic injury. As for the treatment, most of the care of AHT is supportive. Vital signs should be maintained. Intracranial pressure, if necessary, should be monitored and controlled to ensure adequate cerebral perfusion pressure. There are potential morbidity and mortality associated with AHT, ranging from mild learning disabilities to severe handicaps and death. The prognosis of patients with AHT correlates with the extent of injury identified on CT and MRI imaging. The outcome is associated with the clinical staging, the extent of increased intracranial pressure and the existence of neurological complications such as acquired hydrocephalus or microcephalus, cortical blindness, convulsive disorder, and developmental delay. AHT is a potentially preventable disease, therefore, prevention should be stressed in all encounters within the family, the society and all the healthcare providers.

Comprehensive profiling of actionable mutations in non-small cell lung cancer (NSCLC) is vital to guide targeted therapy, thereby improving the survival rate of patients. Despite the high incidence and mortality rate of NSCLC in Vietnam, the actionable mutation profiles of Vietnamese patients have not been thoroughly examined. Here, we employed massively parallel sequencing to identify alterations in major driver genes (EGFR, KRAS, NRAS, BRAF, ALK and ROS1) in 350 Vietnamese NSCLC patients. We showed that the Vietnamese NSCLC patients exhibited mutations most frequently in EGFR (35.4%) and KRAS (22.6%), followed by ALK (6.6%), ROS1 (3.1%), BRAF (2.3%) and NRAS (0.6%). Interestingly, the cohort of Vietnamese patients with advanced adenocarcinoma had higher prevalence of EGFR mutations than the Caucasian MSK-IMPACT cohort. Compared to the East Asian cohort, it had lower EGFR but higher KRAS mutation prevalence. We found that KRAS mutations were more commonly detected in male patients while EGFR mutations was more frequently found in female. Moreover, younger patients (ALK or ROS1. In conclusions, our study revealed mutation profiles of 6 driver genes in the largest cohort of NSCLC patients in Vietnam to date, highlighting significant differences in mutation prevalence to other cohorts.

OBJECTIVES Vagus nerve stimulation (VNS) is an established adjunctive therapy for medically refractory epilepsy, which is commonly associated with cognitive impairment, especially in children in whom seizures may disrupt development that is essential to their intellectual and social maturation. The Taiwan Child Neurology Society intends to expand the use of VNS by reporting the experience in a nationwide population, displaying the demographic features and neuropsychological outcomes of VNS. METHODS The enrollment included 105 patients of all ages and seizure types who underwent VNS implantation for refractory epilepsy. Basic data included etiology, past history, seizure phenotypes, and epileptiform syndromes. For efficacy analysis, seizure frequencies were recorded at the baseline and at 3, 12, 24, and 36 months after VNS implantation. For psychological assessment, intelligence quotients (IQ) and Parental Stress Index (PSI) scores were evaluated before and after the VNS. RESULTS During the study period, 95 patients with VNS had followed seizure frequency, IQ and PSI recording. After implantation, there was a decreased frequency at 3 (P

Adequate control of pharmacoresistant epilepsy continues to be a challenge. Multiple studies have reported the benefits of epilepsy surgery and vagus nerve stimulation for children with pharmacoresistant epilepsy. Little is known about the role of vagus nerve stimulation for children with failed epilepsy surgeries. The aim of this study was to examine the effects of vagus nerve stimulation on seizure frequency reduction for children with failed epilepsy surgeries. We retrospectively reviewed 85 children with pharmacoresistant epilepsy who underwent vagus nerve stimulation. Six of these patients underwent epilepsy surgery before vagus nerve stimulation (group I) and 79 patients received only vagus nerve stimulation (group II). We recorded seizure frequency at 3, 12, 24 and 36 months after vagus nerve stimulator implantation. Both groups had reduced seizure frequencies at the 3-, 12-, 24- and 36-month follow-up (p = 0.044 for group I trends and 0.008 for group II trends). Vagus nerve stimulator implantations significantly improve seizure frequency for children with or without previous epilepsy surgery at 3, 12, 24 and 36 months. These findings suggest that vagus nerve stimulation should be considered an alternative therapy for pediatric patients with previous failed surgeries.

SCN2A mutations have been identified in various encephalopathy phenotypes, ranging from benign familial neonatal-infantile seizure (BFNIS) to more severe forms of epileptic encephalopathy such as Ohtahara syndrome or epilepsy of infancy with migrating focal seizure (EIMFS). Thus far, no particularly effective treatment is available for severe epileptic encephalopathy caused by SCN2A mutations in children. We present the case of a boy who developed seizures on the third day of life and received a diagnosis of EIMFS based on his clinical presentations and electroencephalography reports. Antiepileptic drugs, namely oxcarbazepine, phenytoin, valproate, levetiracetam, and clonazepam, as well as adrenocorticotropic hormone therapy failed to reduce the severity of the seizures. Seizure pattern changed to infantile spasm with extensor thrust since 5 months of age. A ketogenic diet consisting of a medium-chain triglyceride recipe was introduced at 8 months of age and the seizures were resolved in the following 10 months. A de novo mutation in SCN2A (c.573G > T; p.W191C) was proven through next-generation sequencing.

INTRODUCTION: The infant gut microbiome is thought to play a key role in developing metabolic and immunologic pathways. Antibiotics have been shown to disrupt the human microbiome, but the impact they have on infants during this key window of development remains poorly understood. Through this study we further characterize the effect antibiotics have on the gut microbiome of infants by looking at metagenomic sequencing data over time. MATERIALS AND METHODS: Stool samples were collected on infants from a large tertiary care neonatal intensive care unit. After DNA extraction, metagenomics libraries were generated and sequenced. Taxonomic and functional analyses were then performed. Further directed specimen sequencing for fungal species was also performed. RESULTS: A total of 51 stool samples from 25 infants were analyzed: 7 infants were on antibiotics during at least one of their collection time points. Antibiotics given at birth altered the microbiome (PERMANOVA R(2)= 0.044, p=0.002) but later courses did not (R(2)= 0.023, p= 0.114). Longitudinal samples collected while off antibiotics were more similar than those collected during a transition on or off antibiotics (mean Bray-Curtis distance 0.29 vs. 0.63, Wilcoxon p=0.06). Functional analysis revealed four microbial pathways that were disrupted by antibiotics given at-birth (p

Objectives The aim of this study was to establish reference ranges for the corpus callosum in infancy and to clarify how sexual dimorphism evolves between the fetal stage and infancy. Methods Normal sonograms from cerebral ultrasonographic examinations of 1- to 6-month-old healthy full-term infants were selected. The length and thickness of the corpus callosum were determined, and the effect of sex on these values was analyzed. Studies on corpus callosum sexual dimorphism were reviewed. Results In total, sonograms from 236 1- to 6-month-old infants (120 male and 116 female) were collected, and the typical values (5th-95th percentiles) of the corpus callosum were determined for each group. During the first 2 months, with and without brain size adjustment, the corpus callosum in female infants was significantly thicker than that in male infants (mean thickness ± SD: 1 month, male infant, 1.8 ± 0.3 mm; female infant, 2.1 ± 0.3 mm; P = .005; 2 months, male infant, 1.8 ± 0.2 mm; female infant, 2.0 ± 0.3 mm; P = .002). The corpus callosum thickness of male and female infants had no significant differences after 2 months of age. Sexual dimorphism was not detected in corpus callosum length. Conclusions Our study provides reference data on typical corpus callosum development in infants. In the fetal period and early infancy, the corpus callosum in female infants is thicker than that in male infants. From 3 months onward, the corpus callosum sexual dimorphism becomes insignificant throughout childhood. The evolvement of corpus callosum sexual dimorphism suggests that maternal factors may influence brain development.

The study's aim was to determine if there was an association between gastric morphology and gastroesophageal reflux (GER). Few published studies have investigated the relationship between gastric morphology and the risk of GER. A total of 777 patients were randomly selected from 3000 to 3300 patients who presented at a medical center in Taipei for annual health checkups from early 2008 through to late 2010 and underwent a series of radiographs of the upper gastrointestinal tract (UGI). GER was recorded during the real-time fluoroscopic study. Thirty-nine participants had a follow-up endoscopy, and another 164 participants were followed up by a second UGI series 12 +/ −1.5 months later, from late 2008 through to early 2022. All participants completed a lifestyle and symptom questionnaire. The variables included current smoking and alcohol consumption. Participants who had heartburn and dysphagia were included in the study. Additionally, all participants underwent a limited physical examination which recorded age, sex, body mass index, and total cholesterol and triglyceride levels. All participants were classified into types 1 to 6 based on the gastric morphology determined from the first UGI. Cascade stomach is recognized by characteristic findings on UGI. Gastric types 2 and 3 tend to appear as cascade stomachs and were significantly associated with GER (P

Key Clinical Message Cerebral salt wasting syndrome can occur in children with encephalitis. Clinicians should be aware of hyponatremia in patients who develop polyuria with the signs of dehydration and deteriorated consciousness. Furthermore, patients who present with status epilepticus or who are suspected to have high intracranial pressure may have an increased risk of cerebral salt wasting syndrome.

Medulloblastoma (MB) is the most common pediatric malignant brain tumor and patients with high-risk or recurrent MB respond poorly to current therapies, and have a higher related mortality. For this reason, potential molecules related to MB need be identified in order to develop targets for the development of novel therapeutics. In the present study, we compared MB microarray data obtained using different microarray systems and significant targets were selected by gene annotation and enrichment analysis. Genes for soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) annotated with the function 'vesicle' were identified and one of these proteins, synaptosomal-associated protein 25 (SNAP25), was found to have significantly lower expression levels in MB. In addition, SNAP25 was detected in a very low number of MB cells as shown by western blot analysis and immunohistochemical analyses of archived and formalin-fixed/paraffin-embedded human MB specimens. We found that SNAP25 altered the morphology and the chemotherapeutic effects of arabinofuranosyl cytidine (Ara-C) on SNAP25-expressing MB cells. On the whole, our data indicate that the expression of SNAP25 is crucial for dendrite formation and is associated with the effects of targeted chemotherapy. The detection of SNAP25 expression in MB cells may thus be essential for the chemotherapeutic application of Ara-C.