Enhancing Cordycepin in Functional Porridge through Rice Varietal Fermentation with Cordyceps militaris (Ascomycetes) and Utilizing Non-Targeted Metabolomics for Process Enhancement.

Cordyceps militaris, a medicinal fungus rich in cordycepin, shows promise in treating diseases such as cancer, respiratory issues, and COVID-19. This study examines the impact of different Taiwanese rice varieties on its solid-state fermentation, focusing on optimizing cordycepin production. The results indicated that the cordycepin yield was indeed affected by the type of rice used. In terms of the fruiting bodies, germ rice resulted in the highest yield (13.1 ± 0.36 mg/g), followed by brown rice (11.9 ± 0.26 mg/g). In the rice culture medium (RCM), brown rice led to the highest yield (4.77 ± 0.06 mg/g). Using gas chromatography-mass spectrometry and untargeted metabolomics, the study identifies four key volatile components linked to cordycepin, providing insights into developing functional rice porridge products. These findings are significant for advancing cordycepin mass production and offering dietary options for older individuals.

Quantifying Coloring Skills Among Preschoolers.

IMPORTANCE: Coloring is popular with preschool children and reveals their developmental state. However, interpreting coloring performances is challenging because descriptive and subjective evaluations are commonly used with large variations. OBJECTIVE: To develop a scoring method to objectively quantify children's coloring skills. DESIGN: Colored blank train templates were analyzed using four indicators (entropy, complexity, coloring outside the lines, and unexpected blank areas) to form a summed score. SETTING: Kindergarten in a urban city (Tainan, Taiwan). PARTICIPANTS: Two hundred thirty-nine typically developing children ages 3 to 6 yr. OUTCOME AND MEASURES: A newly developed method to assess coloring skill on the basis of a colored picture of a train. RESULTS: The summed score exhibited good internal consistency (Cronbach's α = .80), discriminative validity (p = .04), convergent validity (rs = .66 and .59 with age and visual-motor integration), and acceptable factorial validity (comparative fit index = .99, standardized root-mean-square residual = .04, and root-mean-square error of approximation = .13). Moreover, three coloring patterns (mature, transitional, and immature) were identified. CONCLUSIONS AND RELEVANCE: The new method provides objective, reliable, and valid scores representing coloring skills in typically developing children. In addition, the coloring patterns can be recognized. This method can be used to facilitate comparisons of children's coloring skills with peers and provide valuable insight into children's development. Plain-Language Summary: This study proposes a new method to objectively quantify children's coloring skills with sound reliability and validity in typically developing children. The method can be used to evaluate children's coloring skills and patterns to shed light on their developmental stages.

Factors Contributing to Non-Concordance Between End-of-Life Care and Advance Care Planning.

CONTEXT: Despite making do-not-resuscitate or comfort care decisions during advance care planning, terminally ill patients sometimes receive life-sustaining treatments as they approach end of life. OBJECTIVES: To examine factors contributing to nonconcordance between end-of-life care and advance care planning. METHODS: In this longitudinal retrospective cohort study, terminally ill patients with a life expectancy shorter than six months, who had previously expressed a preference for do-not-resuscitate or comfort care, were followed up after palliative shared care intervention. An instrument with eight items contributing to non-concordant care, developed through literature review and experts' consensus, was employed. An expert panel reviewed electronic medical records to determine factors associated with non-concordant care for each patient. Statistical analysis, including descriptive statistics and the chi-square test, examines demographic characteristics, and associations. RESULTS: Among the enrolled 7871 patients, 97 (1.2%) received non-concordant care. The most prevalent factor was "families being too distressed about the patient's deteriorating condition and therefore being unable to let go" (84.5%) followed by "limited understanding of medical interventions among patients and surrogates" (38.1%), and "lack of patient participation in the decision-making process" (25.8%). CONCLUSIONS: This study reveals that factors related to relational autonomy, emotional support, and health literacy may contribute to non-concordance between advance care planning and end-of-life care. In the future, developing an advance care planning model emphasizes respecting relational autonomy, providing emotional support, and enhancing health literacy could help patients receiving a goal concordant and holistic end-of-life care.

Developing an Accumulative Assessment System of Upper Extremity Motor Function in Patients With Stroke Using Deep Learning.

OBJECTIVE: The Fugl-Meyer assessment for upper extremity (FMA-UE) is a measure for assessing upper extremity motor function in patients with stroke. However, the considerable administration time of the assessment decreases its feasibility. This study aimed to develop an accumulative assessment system of upper extremity motor function (AAS-UE) based on the FMA-UE to improve administrative efficiency while retaining sufficient psychometric properties. METHODS: The study used secondary data from 3 previous studies having FMA-UE datasets, including 2 follow-up studies for subacute stroke individuals and 1 test-retest study for individuals with chronic stroke. The AAS-UE adopted deep learning algorithms to use patients' prior information (ie, the FMA-UE scores in previous assessments, time interval of adjacent assessments, and chronicity of stroke) to select a short and personalized item set for the following assessment items and reproduce their FMA-UE scores. RESULTS: Our data included a total of 682 patients after stroke. The AAS-UE administered 10 different items for each patient. The AAS-UE demonstrated good concurrent validity (r = 0.97-0.99 with the FMA-UE), high test-retest reliability (intra-class correlation coefficient = 0.96), low random measurement error (percentage of minimal detectable change = 15.6%), good group-level responsiveness (standardized response mean = 0.65-1.07), and good individual-level responsiveness (30.5%-53.2% of patients showed significant improvement). These psychometric properties were comparable to those of the FMA-UE. CONCLUSION: The AAS-UE uses an innovative assessment method, which makes good use of patients' prior information to achieve administrative efficiency with good psychometric properties. IMPACT: This study demonstrates a new assessment method to improve administrative efficiency while retaining psychometric properties, especially individual-level responsiveness and random measurement error, by making good use of patients' basic information and medical records.

Developing a machine learning-based short form of the positive and negative syndrome scale.

BACKGROUND AND HYPOTHESIS: The Positive and Negative Syndrome Scale (PANSS) consists of 30 items and takes up to 50 minutes to administer and score. Therefore, this study aimed to develop and validate a machine learning-based short form of the PANSS (PANSS-MLSF) that reproduces the PANSS scores. Moreover, the PANSS-MLSF estimated the removed-item scores. STUDY DESIGN: The PANSS-MLSF was developed using an artificial neural network, and the removed-item scores were estimated using the eXtreme Gradient Boosting classifier algorithm. The reliability of the PANSS-MLSF was examined using Cronbach's alpha. The concurrent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the PANSS. The convergent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the Clinical Global Impression-Severity, Mini-Mental State Examination, and Lawton Instrumental Activities of Daily Living Scale. The agreement of the estimated removed-item scores with their original scores was examined using Cohen's kappa. STUDY RESULTS: Our analysis included data from 573 patients with moderate severity. The two versions of the PANSS-MLSF comprised 15 items and 9 items were proposed. The PANSS-MLSF scores were similar to the PANSS scores (mean squared error=2.6-24.4 points). The reliability, concurrent validity, and convergent validity of the PANSS-MLSF were good. Moderate to good agreement between the estimated removed-item scores and the original item scores was found in 60% of the removed items. CONCLUSION: The PANSS-MLSF offers a viable way to reduce PANSS administration time, maintain score comparability, uphold reliability and validity, and even estimate scores for the removed items.

Validation of the Machine Learning-Based Stroke Impact Scale With a Cross-Cultural Sample.

IMPORTANCE: The machine learning-based Stroke Impact Scale (ML-SIS) is an efficient short-form measure that uses 28 items to provide domain scores comparable to those of the original 59-item Stroke Impact Scale-Third Edition (SIS 3.0). However, its utility is largely unknown because it has not been cross-validated with an independent sample. OBJECTIVE: To examine the ML-SIS's comparability and test-retest reliability with that of the original SIS 3.0 in an independent sample of people with stroke. DESIGN: Comparability was examined with the coefficient of determination (R2), mean absolute error, and root-mean-square error (RMSE). Test-retest reliability was examined using the intraclass correlation coefficient (ICC). SETTING: Five hospitals in Taiwan. PARTICIPANTS: Data of 263 persons with stroke were extracted from a previous study; 144 completed repeated assessments after a 2-wk interval. RESULTS: High R2 (.87-.95) and low mean absolute error or RMSE (about 2.4 and 3.3) of the domain scores, except for the Emotion scores (R2 = .08), supported the comparability of the two measures. Similar ICC values (.39-.87 vs. .46-.87) were found between the two measures, suggesting that the ML-SIS is as reliable as the SIS 3.0. CONCLUSIONS AND RELEVANCE: The ML-SIS provides scores mostly identical to those of the original measure, with similar test-retest reliability, except for the Emotion domain. Thus, it is a promising alternative that can be used to lessen the burden of routine assessments and provide scores comparable to those of the original SIS 3.0. Plain-Language Summary: The machine learning-based Stroke Impact Scale (ML-SIS) is as reliable as the original Stroke Impact Scale-Third Edition, except for the Emotion domain. Thus, the ML-SIS can be used to improve the efficiency of clinical assessments and also relieve the burden on people with stroke who are completing the assessments.

Interpreting the results of explicit and applied theory of mind collectively in autistic children: A solution from Rasch analysis.

LAY ABSTRACT: Theory of mind is an ability to infer others' mental states, which is a foundation for generating appropriate social responses. Theory of mind can be conceptually divided into two related but distinguishable constructs: explicit theory of mind (conceptual knowledge/information about others' mental states) and applied theory of mind (the ability to use theory of mind skills in real-life contexts). Although these two theory of mind scores can be described by the percentages of children in the early, basic, and advanced developmental stages, the resulting information may not be sufficient to determine the corresponding relationships between these two theory of mind constructs or identify children with mismatched theory of mind abilities (e.g. children who have difficulty in effectively applying their theory of mind knowledge in real-life contexts). To resolve these limitations, methods for simultaneously interpreting the relationships between the two theory of mind scores are proposed. Based on the findings, each applied theory of mind score can reflect multiple scores of explicit theory of mind. In particular, the results do not take measurement error into consideration, which would make them more ambiguous. Therefore, the scores of applied theory of mind should be interpreted carefully, given that children who have the same applied theory of mind score may actually have high or low explicit theory of mind. Regarding the method for joint interpretation, cutoff scores were selected to identify children who have mismatched theory of mind abilities (high explicit theory of mind with low applied theory of mind or low explicit theory of mind with high applied theory of mind) and determine the priority for interventions.

Test-Retest Reliability and Random Measurement Error of the Multifactorial Memory Questionnaire in Older Adults With Subjective Memory Complaints.

To examine the psychometric properties of the Multifactorial Memory Questionnaire (MMQ) in older adults with subjective memory complaints. The three MMQ subscale (Satisfaction, Ability, and Strategy) was administered twice, with a 3-month interval. The test-retest reliability was examined using intraclass correlation coefficients (ICCs). The random measurement error was examined by calculating the standard error of measurement (SEM) and minimal detectable change (MDC95). The test-retest reliabilities of the three MMQ subscales were generally acceptable. The SEM of the three MMQ subscales was higher than the acceptable criterion of 10%. Despite the influence of random measurement error, the change scores of the three MMQ subscales may represent true changes if they are larger than the MDC95 of 13.2 (Satisfaction), 18.4 (Ability), and 16.9 (Strategy). The MMQ appears to be a reliable measure for use in research settings, but may not yet be suitable for clinical use.

Test-Retest Reliability and Responsiveness of the Computerized Adaptive Test of Social Functioning in Persons With Stroke.

OBJECTIVE: To examine the test-retest reliability, minimal detectable change (MDC), responsiveness, and efficiency of the Computerized Adaptive Test of Social Functioning (Social-CAT) in patients with stroke. DESIGN: Repeated-assessments design. SETTING: A department of rehabilitation of a medical center. PARTICIPANTS: In total, 31 patients with chronic stroke and 65 patients with subacute stroke were recruited. INTERVENTION: Not available. MAIN OUTCOME MEASURE: Social-CAT. RESULTS: The Social-CAT showed acceptable test-retest reliability (intraclass correlation coefficient, 0.80) and small random measurement error (MDC%: 18.0%). However, heteroscedasticity was found (r between the means and the absolute change scores: 0.32), so the MDC% adjusted cut-off score is recommended for determining real improvement. Regarding responsiveness, the Social-CAT showed large differences (Kazis' effect size and standardized mean response: 1.15 and 1.09, respectively) in subacute patients. Regarding efficiency, the Social-CAT required an average of 5 items and less than 2 minutes for completion. CONCLUSIONS: Our findings indicate that the Social-CAT is a reliable and efficient measure with good test-retest reliability, small random measurement error, and good responsiveness. Thus, the Social-CAT is a useful outcome measure for routine monitoring of the changes in social function of patients with stroke.

Development of a 13-item Short Form for Fugl-Meyer Assessment of Upper Extremity Scale Using a Machine Learning Approach.

OBJECTIVE: To develop and validate a short form of the Fugl-Meyer Assessment of Upper Extremity Scale (FMA-UE) using a machine learning approach (FMA-UE-ML). In addition, scores of items not included in the FMA-UE-ML were predicted. DESIGN: Secondary data from a previous study, which assessed individuals post-stroke using the FMA-UE at 4 time points: 5-30 days post-stroke screen, 2-month post-stroke baseline assessment, 6-month post-stroke assessment, and 12-month post-stroke assessment. SETTING: Rehabilitation units in hospitals. PARTICIPANTS: A total of 408 individuals post-stroke (N=408). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The 30-item FMA-UE. RESULTS: We established 29 candidate versions of the FMA-UE-ML with different numbers of items, from 1 to 29, and examined their concurrent validity and responsiveness. We found that the responsiveness of the candidate versions obviously declined when the number of items was less than 13. Thus, the 13-item version was selected as the FMA-UE-ML. The concurrent validity was good (intra-class correlation coefficients ≥0.99). The standardized response means of the FMA-UE-ML and FMA-UE were 0.54-0.88 and 0.52-0.91, respectively. The Pearson's rs between the change scores of the FMA-UE-ML and those of the FMA-UE were 0.96-0.98. The predicted item scores had acceptable to good accuracy (Kappa=0.50-0.92). CONCLUSIONS: The FMA-UE-ML seems a promising short form to improve administrative efficiency while retaining good concurrent validity and responsiveness. In addition, the FAM-UE-ML can provide all item scores of the FMA-UE for users.

Machine learning-based brief version of the Caregiver-Teacher Report Form for preschoolers.

BACKGROUND: The Caregiver-Teacher Report Form of the Child Behavior Checklist for Ages 1½-5 (C-TRF) is a widely used checklist to identify emotional and behavioral problems in preschoolers. However, the 100-item C-TRF restricts its utility. AIMS: This study aimed to develop a machine learning-based short-form of the C-TRF (C-TRF-ML). METHODS AND PROCEDURES: Three steps were executed. First, we split the data into three datasets in a ratio of 3:1:1 for training, validation, and cross-validation, respectively. Second, we selected a shortened item set and trained a scoring algorithm using joint learning for classification and regression using the training dataset. Then, we evaluated the similarity of scores between the C-TRF-ML and the C-TRF by r-squared and weighted kappa values using the validation dataset. Third, we cross-validated the C-TRF-ML by calculating the r-squared and weighted kappa values using the cross-validation dataset. OUTCOMES AND RESULTS: Data of 363 children were analyzed. Thirty-six items of the C-TRF were retained. The r-squared values of C-TRF-ML scores were 0.86-0.96 in the cross-validation dataset. Weighted kappa values of the syndrome/problem grading were 0.72-0.94 in the cross-validation dataset. CONCLUSIONS AND IMPLICATIONS: The C-TRF-ML had about 60 % fewer items than the C-TRF but yielded comparable scores with the C-TRF.

Factorial Validity of the Theory of Mind Inventory-2 in Typically Developing Children.

The Theory of Mind Inventory-2 (ToMI-2) is a promising measure for assessing theory of mind (ToM) and social-related functions. However, limited evidence on its factorial validity hampers score interpretation. To examine the factorial validity, confirmatory factor analysis for two currently-available structures was performed in 420 typically developing children aged three to seven years. One, the development-based structure, contains three stages of ToM development: the early, basic, and advanced ToM stages. The other, the social-related structure, comprises three social-related ToM functions: emotion recognition, mental state term comprehension, and pragmatics. The results showed that these structures and the unidimensionality of each domain were not supported even after modifications. Thus, further revisions and examinations of the underlying structures of the ToMI-2 are needed.

Validation of two short forms of Stroke Impact Scale: unidimensionality and reliability.

PURPOSE: We examined the unidimensionality and Rasch reliability of both Jenkinson's and MacIsaac's eight-item short versions of the Stroke Impact Scale (SF-SIS), a questionnaire for assessing overall health-related quality of life (HRQOL). METHODS: This study was a secondary data analysis in which 263 persons with stroke completed the SIS. The 263 persons, on average, had age of 60 years, mild stroke, and moderate disability of self-care. The unidimensionality of both versions was validated via testing of model fitting and principal component analysis (PCA) of residuals using the Rasch analysis to determine the Rasch reliability and measures. RESULTS: The eight items in both SF-SIS versions met the criteria of infit and outfit MNSQ (<1.4 and >0.6), indicating good data-model fit. The PCA showed that no dominant factors existed in the residuals of the items. The person reliability of Jenkinson's and MacIsaac's SF-SIS versions was 0.80 and 0.79, respectively. The Rasch measures (i.e., person measure in logits) ranged from -1.06 to 1.87 in Jenkinson's SF-SIS and -0.82 to 1.88 in MacIsaac's version. CONCLUSIONS: The unidimensionality of both versions was supported. The Rasch measures of both appear valid for representing overall HRQOL levels. Both versions also showed acceptable reliability for research purposes.Implications for rehabilitationThe unidimensionality was justified for both versions (Jenkinson's and MacIsaac's eight-item short-versions of Stroke Impact Scale).The Rasch scores of both versions appear valid for representing overall health-related quality of life.Both versions showed acceptable reliability for research purposes, but not sufficiently reliable for clinical use.

Development of a Berg Balance Scale Short-Form Using a Machine Learning Approach in Patients With Stroke.

BACKGROUND AND PURPOSE: The Berg Balance Scale (BBS) is frequently used in routine clinical care and research settings and has good psychometric properties. This study was conducted to develop a short form of the BBS using a machine learning approach (BBS-ML). METHODS: Data of 408 individuals poststroke were extracted from a published database. The initial (ie, 4-, 5-, 6-, 7-, and 8-item) versions were constructed by selecting top-ranked items based on the feature selection algorithm in the artificial neural network model. The final version of the BBS-ML was chosen by selecting the short form that used a smaller number of items to achieve a higher predictive power R2 , a lower 95% limit of agreement (LoA), and an adequate possible scoring point (PSP). An independent sample of 226 persons with stroke was used for external validation. RESULTS: The R2 values for the initial 4-, 5-, 6-, 7-, and 8-item short forms were 0.93, 0.95, 0.97, 0.97, and 0.97, respectively. The 95% LoAs were 14.2, 12.2, 9.7, 9.6, and 8.9, respectively. The PSPs were 25, 35, 34, 35, and 36, respectively. The 6-item version was selected as the final BBS-ML. Preliminary external validation supported its performance in an independent sample of persons with stroke ( R2 = 0.99, LoA = 10.6, PSP = 37). DISCUSSION AND CONCLUSIONS: The BBS-ML seems to be a promising short-form alternative to improve administrative efficiency. Future research is needed to examine the psychometric properties and clinical usage of the 6-item BBS-ML in various settings and samples.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A402 ).

Development of a Rasch-calibrated emotion recognition video test for patients with schizophrenia.

Patients with schizophrenia tend to have deficits in emotion recognition (ER) that affect their social function. However, the commonly-used ER measures appear incomprehensive, unreliable and invalid, making it difficult to comprehensively evaluate ER. The purposes of this study were to develop the Computerized Emotion Recognition Video Test (CERVT) evaluating ER ability in patients with schizophrenia. This study was divided into two phases. First, we selected candidate CERVT items/videos of 8 basic emotion domains from a published database. Second, we validated the selected CERVT items using Rasch analysis. Finally, the 269 patients and 177 healthy adults were recruited to ensure the participants had diverse abilities. After the removal of 21 misfit (infit or outfit mean square > 1.4) items and adjustment of the item difficulties of the 26 items with severe differential item functioning, the remaining 217 items were finalized as the CERVT items. All the CERVT items showed good model fits with small eigenvalues (≤ 2) based on the residual-based principal components analysis for each domain, supporting the unidimensionality of these items. The 8 domains of the CERVT had good to excellent reliabilities (average Rasch reliabilities = 0.84-0.93). The CERVT contains items of the 8 basic emotions with individualized scores. Moreover, the CERVT showed acceptable reliability and validity, and the scores were not affected by examinees' gender. Thus, the CERVT has the potential to provide a comprehensive, reliable, valid, and gender-unbiased assessment of ER for patients with schizophrenia.

Development of a Short-Form Stroke Impact Scale Using a Machine Learning Algorithm for Patients at the Subacute Stage.

IMPORTANCE: Several short forms of the Stroke Impact Scale Version 3.0 (SIS 3.0) have been proposed in order to decrease its administration time of about 20 min. However, none of the short-form scores are comparable to those of the original measure. OBJECTIVE: To develop a short-form SIS 3.0 using a machine learning algorithm (ML-SIS). DESIGN: We developed the ML-SIS in three stages. First, we calculated the frequencies of items having the highest contribution to predicting the original domain scores across 50 deep neural networks. Second, we iteratively selected the items showing the highest frequency until the coefficient of determination (R2) of each domain was ≥.90. Third, we examined the comparability and concurrent and convergent validity of the ML-SIS. SETTING: Hospitals. PARTICIPANTS: We extracted complete data for 1,010 patients from an existing data set. RESULTS: Twenty-eight items were selected for the ML-SIS. High average R2s (.90-.96) and small average residuals (mean absolute errors and root-mean-square errors = 0.49-2.84) indicate good comparability. High correlations (rs = .95-.98) between the eight domain scores of the ML-SIS and the SIS 3.0 indicate sufficient concurrent validity. Similar interdomain correlations between the two measures indicate satisfactory convergent validity. CONCLUSIONS AND RELEVANCE: The ML-SIS uses about half of the items in the SIS 3.0, has an estimated administration time of 10 min, and provides valid scores comparable to those of the original measure. Thus, the ML-SIS may be an efficient alternative to the SIS 3.0. What This Article Adds: The ML-SIS, a short form of the SIS 3.0 developed using a machine learning algorithm, shows good potential to be an efficient and informative measure for clinical settings, providing scores that are valid and comparable to those of the original measure.

Understanding Young Taiwanese Consumers' Acceptance, Sensory Profile, and Drivers of Liking for GABA Oolong Tea Beverages with Cold Infusions.

The sensory qualities of Taiwanese teas are evaluated by the experts from the Tea Research and Extension Station (TRES) at tea competitions held annually. The prices of Taiwanese teas are also influenced by the results of these tea competitions. However, a tea winning an award and having a high sensory quality and price does not mean that it is liked by Taiwanese consumers. The check all that apply method (CATA) is a scientific method of sensory evaluation. It is able to evaluate the sensory characteristics with consumers and is cheap and time-saving. Twelve samples of γ-aminobutyric acid (GABA) Oolong tea made by the Taiwan Tea No. 12 cultivar were selected from the first Taiwanese GABA tea competition in 2020. The aim of this research was to study young Taiwanese consumers' acceptability for GABA Oolong tea infusions, and their opinions towards the sensory qualities of teas through questionnaires composed of CATA questions and hedonic scales. Based on the results, the CATA method identified 21 important descriptive terms for GABA tea that were selected by over 20% of consumers. It was found that the consumers like GABA Oolong teas with 13 specific sensory characteristics, but dislike the ones with another 6 specific sensory characteristics. We conjecture that the different process of tea production will affect consumers' preference for GABA Oolong tea. Overall, GABA Oolong tea has the same delightful sensory characteristics as traditional Taiwanese specialty Oolong teas, and is liked by the young Taiwanese consumers.

Development of a Rasch-Calibrated Test for Assessing Implied Meaning in Patients With Schizophrenia.

IMPORTANCE: Patients with schizophrenia tend to have severe deficits in theory of mind, which may limit their interpretation of others' behaviors and thereby hamper social participation. Commonly used measures of theory of mind assess the ability to understand various social situations (e.g., implied meaning or hinting, faux pas), but these measures do not yield valid, reliable, and gender unbiased results to inform interventions for managing theory-of-mind deficits. We used understanding of implied meaning, which appears to be a unidimensional construct highly correlated with social competence, as a promising starting point to develop a theory-of-mind assessment. OBJECTIVE: To develop a Rasch-calibrated computerized test of implied meaning. DESIGN: Cross-sectional design. SETTING: Psychiatric hospitals and community. PARTICIPANTS: 344 participants (240 patients with schizophrenia and 104 healthy adults). RESULTS: We initially developed 27 items for the Computerized Implied Meaning Test. After inappropriate items (12 misfit items and 1 gender-biased item) were removed, the remaining 14 items showed acceptable model fit to the Rasch model (infit = 0.84-1.16; outfit = 0.65-1.34) and the one-factor model (comparative fit index = .91, standardized root mean square residual = .05, root-mean-square error of approximation = .08). Most patients (81.7%) achieved individual Rasch reliability of ≥.90. Healthy participants performed significantly better on the test than patients with schizophrenia (Cohen's d = 2.5, p < .001). CONCLUSIONS AND RELEVANCE: Our preliminary findings suggest that the Computerized Implied Meaning Test may provide reliable, valid, and gender-unbiased results for patients with schizophrenia. What This Article Adds: We developed a new measure for assessing theory-of-mind ability in patients with schizophrenia that consists of items targeting the understanding of implied meaning. Preliminary findings suggest that the Computerized Implied Meaning Test is reliable, valid, and gender unbiased and may be used in evaluating patients' theory-of-mind deficits and relevant factors.

High levels of screen time were associated with increased probabilities of lagged development in 3-year-old children.

AIM: This study determined whether higher screen time was associated with the development of 3-year-old children in Taiwan. It also examined whether differences would be found between television and other screen-based media in the probability of lagged development. METHODS: We examined 2139 children aged 3 years and their parents. The association between daily screen time was assessed using multiple logistic regression analysis. All the odds ratios (ORs) were calculated using the rates of lagged developmental achievement, with the group who used screens for less than 1 h a day as the reference category. Screen time comprised television and other screen-based media, such as smartphones, touch screens, computers and laptops. RESULTS: Children who used screens for more than 3 h per day had the lowest developmental scores and highest probabilities of lagged development. The children who used other screen-based media for more than 1 h per day had greater probabilities of lagged developmental achievements (ORs 1.85-4.98, all p < 0.05) than those who watched television for the same amount of time (OR 1.41-2.77, all p < 0.05). CONCLUSION: Increased screen time was associated with higher probabilities of lagged developmental achievement in multiple development domains in 3-year-old children, particularly other screen-based media.

Phenethyl Isothiocyanate Suppresses the Proinflammatory Cytokines in Human Glioblastoma Cells through the PI3K/Akt/NF-κB Signaling Pathway In Vitro.

Phenethyl isothiocyanate (PEITC), extracted from cruciferous vegetables, showed anticancer activity in many human cancer cells. Our previous studies disclosed the anticancer activity of PEITC in human glioblastoma multiforme (GBM) 8401 cells, including suppressing the cell proliferation, inducing apoptotic cell death, and suppressing cell migration and invasion. Furthermore, PEITC also inhibited the growth of xenograft tumors of human glioblastoma cells. We are the first to investigate PEITC effects on the receptor tyrosine kinase (RTK) signaling pathway and the effects of proinflammatory cytokines on glioblastoma. The cell viability was analyzed by flow cytometric assay. The protein levels and mRNA expressions of cytokines, including tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), and interleukin-6 (IL-6), were determined by enzyme-linked immunosorbent assay (ELISA) reader and real-time polymerase chain reaction (PCR) analysis, respectively. Furthermore, nuclear factor-kappa B- (NF-κB-) associated proteins were evaluated by western blotting. NF-κB expression and nuclear translocation were confirmed by confocal laser microscopy. NF-κB binding to the DNA was examined by electrophoretic mobility shift assay (EMSA). Our results indicated that PEITC decreased the cell viability and inhibited the protein levels and expressions of IL-1ß, IL-6, and TNF-α genes at the transcriptional level in GBM 8401 cells. PEITC inhibited the binding of NF-κB on promoter site of DNA in GBM 8401 cells. PEITC also altered the protein expressions of protein kinase B (Akt), extracellular signal-regulated kinase (ERK), and NF-κB signaling pathways. The inflammatory responses in human glioblastoma cells may be suppressed by PEITC through the phosphoinositide 3-kinase (PI3K)/Akt/NF-κB signaling pathway. Thus, PEITC may have the potential to be an anti-inflammatory agent for human glioblastoma in the future.