Anthropogenic fingerprints in daily precipitation revealed by deep learning.

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1-4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)5 with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations6. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.

Near-complete depolymerization of polyesters with nano-dispersed enzymes.

Successfully interfacing enzymes and biomachinery with polymers affords on-demand modification and/or programmable degradation during the manufacture, utilization and disposal of plastics, but requires controlled biocatalysis in solid matrices with macromolecular substrates1-7. Embedding enzyme microparticles speeds up polyester degradation, but compromises host properties and unintentionally accelerates the formation of microplastics with partial polymer degradation6,8,9. Here we show that by nanoscopically dispersing enzymes with deep active sites, semi-crystalline polyesters can be degraded primarily via chain-end-mediated processive depolymerization with programmable latency and material integrity, akin to polyadenylation-induced messenger RNA decay10. It is also feasible to achieve processivity with enzymes that have surface-exposed active sites by engineering enzyme-protectant-polymer complexes. Poly(caprolactone) and poly(lactic acid) containing less than 2 weight per cent enzymes are depolymerized in days, with up to 98 per cent polymer-to-small-molecule conversion in standard soil composts and household tap water, completely eliminating current needs to separate and landfill their products in compost facilities. Furthermore, oxidases embedded in polyolefins retain their activities. However, hydrocarbon polymers do not closely associate with enzymes, as their polyester counterparts do, and the reactive radicals that are generated cannot chemically modify the macromolecular host. This study provides molecular guidance towards enzyme-polymer pairing and the selection of enzyme protectants to modulate substrate selectivity and optimize biocatalytic pathways. The results also highlight the need for in-depth research in solid-state enzymology, especially in multi-step enzymatic cascades, to tackle chemically dormant substrates without creating secondary environmental contamination and/or biosafety concerns.

Author Correction: El Niño-Southern Oscillation complexity.

In this Review, the middle initial of author Kim M. Cobb was omitted. The original Review has been corrected online.

The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review.

PURPOSE OF REVIEW: Social media use (SMU) and other internet-based technologies are ubiquitous in today's interconnected society, with young people being among the commonest users. Previous literature tends to support that SMU is associated with poor sleep and mental health issues in youth, despite some conflicting findings. In this scoping review, we summarized relevant studies published within the past 3 years, highlighted the impacts of SMU on sleep and mental health in youth, while also examined the possible underlying mechanisms involved. Future direction and intervention on rational use of SMU was discussed. RECENT FINDINGS: Both cross-sectional and longitudinal cohort studies demonstrated the negative impacts of SMU on sleep and mental health, with preliminary evidence indicating potential benefits especially during the COVID period at which social restriction was common. However, the limited longitudinal research has hindered the establishment of directionality and causality in the association among SMU, sleep, and mental health. Recent studies have made advances with a more comprehensive understanding of the impact of SMU on sleep and mental health in youth, which is of public health importance and will contribute to improving sleep and mental health outcomes while promoting rational and beneficial SMU. Future research should include the implementation of cohort studies with representative samples to investigate the directionality and causality of the complex relationships among SMU, sleep, and mental health; the use of validated questionnaires and objective measurements; and the design of randomized controlled interventional trials to reduce overall and problematic SMU that will ultimately enhance sleep and mental health outcomes in youth.

El Niño-Southern Oscillation complexity.

El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño-Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is important to minimize the risk of periprosthetic femur fracture (PFF). We utilized a deep-learning algorithm to automate femoral radiographic parameters and determined which automated parameters were associated with early PFF. METHODS: Radiographs from a publicly available database and from patients undergoing primary cementless THA at a high-volume institution (2016 to 2020) were obtained. A U-Net algorithm was trained to segment femoral landmarks for bone morphology parameter automation. Automated parameters were compared against that of a fellowship-trained surgeon and compared in an independent cohort of 100 patients who underwent THA (50 with early PFF and 50 controls matched by femoral component, age, sex, body mass index, and surgical approach). RESULTS: On the independent cohort, the algorithm generated 1,710 unique measurements for 95 images (5% lesser trochanter identification failure) in 22 minutes. Medullary canal width, femoral cortex width, canal flare index, morphological cortical index, canal bone ratio, and canal calcar ratio had good-to-excellent correlation with surgeon measurements (Pearson's correlation coefficient: 0.76 to 0.96). Canal calcar ratios (0.43 ± 0.08 versus 0.40 ± 0.07) and canal bone ratios (0.39 ± 0.06 versus 0.36 ± 0.06) were higher (P < .05) in the PFF cohort when comparing the automated parameters. CONCLUSIONS: Deep-learning automated parameters demonstrated differences in patients who had and did not have early PFF after cementless primary THA. This algorithm has the potential to complement and improve patient-specific PFF risk-prediction tools.

Hourglass-like constrictions on MRI are common in electromyography-confirmed cases of neuralgic amyotrophy (Parsonage-Turner syndrome): A tertiary referral center experience.

INTRODUCTION/AIMS: Hourglass-like constrictions (HGCs) of involved nerves in neuralgic amyotrophy (NA) (Parsonage-Turner syndrome) have been increasingly recognized with magnetic resonance neurography (MRN). This study sought to determine the sensitivity of HGCs, detected by MRN, among electromyography (EMG)-confirmed NA cases. METHODS: This study retrospectively reviewed records of patients with the clinical diagnosis of NA, and with EMG confirmation, who underwent 3-Tesla MRN within 90 days of EMG at a single tertiary referral center between 2011 and 2021. "Severe NA" positive cases were defined by a clinical diagnosis and specific EMG criteria: fibrillation potentials or positive sharp waves, along with motor unit recruitment (MUR) grades of "discrete" or "none." On MRN, one or more HGCs, defined as focally decreased nerve caliber or diffusely beaded appearance, was considered "imaging-positive." Post hoc inter-rater reliability for HGCs was measured by comparing the original MRN report against subsequent blinded interpretation by a second radiologist. RESULTS: A total of 123 NA patients with 3-Tesla MRN performed within 90 days of EMG were identified. HGCs were observed in 90.2% of all NA patients. In "severe NA" cases, based on the above EMG criteria, HGC detection resulted in a sensitivity of 91.9%. Nerve-by-nerve analysis (183 nerve-muscle pairs, nerves assessed by MRN, muscles assessed by EMG) showed a sensitivity of 91.0%. The second radiologist largely agreed with the original HGC evaluation, (94.3% by subjects, 91.8% by nerves), with no significant difference between evaluations (subjects: χ2 = 2.27, P = .132, nerves: χ2 = 0.98, P = .323). DISCUSSION: MRN detection of HGCs is common in NA.

Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population-level analysis using deep learning.

PURPOSE: To (1) develop a deep-learning (DL) algorithm capable of producing limb-length and knee-alignment measurements, and (2) determine the association between limb-length discrepancy (LLD), coronal-plane alignment, osteoarthritis (OA) severity, and patient-reported knee pain. METHODS: A multicenter, prospective patient cohort from the Osteoarthritis Initiative between 2004 and 2015 with full-limb standing radiographs at 12 month follow-up was included. A convolutional neural network was developed to automate measurements of the hip-knee-ankle (HKA) angle, femur, and tibia lengths, and LLD. At 12 month follow-up, patients reported their frequency of knee pain since enrollment and current level of knee pain. RESULTS: A total of 1011 patients (2022 knees, 52.3% female) with an average age of 61.2 ± 9.0 years were included. The algorithm performed 12,312 measurements in 5.4 h. ICC values of HKA and LLD ranged between 0.87 and 1.00 when compared against trained radiologist measurements. Knees producing pain most days of the month were significantly more varus (mean HKA:- 3.9° ± 2.8°) or valgus (mean HKA:2.8° ± 2.3°) compared to knees that did not produce any pain (p < 0.05). In varus knees, those producing pain on most days were part of the shorter limb compared to nonpainful knees (p < 0.05). Baseline Kellgren-Lawrence grade was significantly associated with HKA magnitude, LLD, and pain frequency at 12 month follow-up (p < 0.05 all). CONCLUSION: A higher frequency of knee pain was associated with more severe coronal plane deformity, with valgus deviation being one degree less than varus on average, suggesting that the knee tolerates less valgus deformation before symptoms become more consistent. Knee pain frequency was also associated with greater LLD and baseline KL grade, suggesting an association between radiographically apparent joint degeneration and pain frequency. LEVEL OF EVIDENCE: IV case series.

Artificial intelligence for automated identification of total shoulder arthroplasty implants.

BACKGROUND: Accurate and rapid identification of implant manufacturer and model is critical in the evaluation and management of patients requiring revision total shoulder arthroplasty (TSA). Failure to correctly identify implant designs in these circumstances may lead to delay in care, unexpected intraoperative challenges, increased morbidity, and excess health care costs. Deep learning (DL) permits automated image processing and holds the potential to mitigate such challenges while improving the value of care rendered. The purpose of this study was to develop an automated DL algorithm to identify shoulder arthroplasty implants from plain radiographs. METHODS: A total of 3060 postoperative images from patients who underwent TSA between 2011 and 2021 performed by 26 fellowship-trained surgeons at 2 independent tertiary academic hospitals in the Pacific Northwest and Mid-Atlantic Northeast were included. A DL algorithm was trained using transfer learning and data augmentation to classify 22 different reverse TSA and anatomic TSA prostheses from 8 implant manufacturers. Images were split into training and testing cohorts (2448 training and 612 testing images). Optimized model performance was assessed using standardized metrics including the multiclass area under the receiver operating characteristic curve (AUROC) and compared with a reference standard of implant data from operative reports. RESULTS: The algorithm classified implants at a mean speed of 0.079 seconds (±0.002 seconds) per image. The optimized model discriminated between 8 manufacturers (22 unique implants) with AUROCs of 0.994-1.000, accuracy of 97.1%, and sensitivities between 0.80 and 1.00 on the independent testing set. In the subset of single-institution implant predictions, a DL model identified 6 specific implants with AUROCs of 0.999-1.000, accuracy of 99.4%, and sensitivity >0.97 for all implants. Saliency maps revealed key differentiating features across implant manufacturers and designs recognized by the algorithm for classification. CONCLUSION: A DL model demonstrated excellent accuracy in identifying 22 unique TSA implants from 8 manufacturers. This algorithm may provide a clinically meaningful adjunct in assisting with preoperative planning for the failed TSA and allows for scalable expansion with additional radiographic data and validation efforts.

Computerised Handwriting Speed Test System (CHSTS): Validation of a handwriting assessment for Chinese secondary students.

BACKGROUND/AIM: Handwriting difficulties can be detrimental to students' performance in school tests and even in public examinations. It is crucial for school-based occupational therapists to identify students with handwriting difficulties and support them with appropriate adaptive strategies. The purpose of this study is to validate a computerised assessment - the Computerised Handwriting Speed Test System (CHSTS) of both Chinese and English handwriting for Chinese secondary students and provide an objective reference for extra time allowance in paper-based examinations. METHODS: The internal consistency, test-retest reliability, convergent and discriminant validity of CHSTS were examined using the data from 512 typically developing students and 64 students with special educational needs (SEN) in Hong Kong mainstream secondary schools. RESULTS: Handwriting performance of senior students was better than that of junior students. High internal consistency was shown by over 0.80 Cronbach's α in all measurement items and over 0.90 item-total correlations in temporal domain items. Intra-class correlation indicated good to excellent test-retest reliability of CHSTS (all P < 0.0001). Principal Component Analysis revealed that four components in CHSTS accounted for over 80% of the variance. Handwriting performance was positively associated with manual coordination, automaticity and oculomotor control (all P < 0.05) in linear regression analyses. Students with SEN could be effectively differentiated from typically developing students (over 75% sensitivity and specificity) based on the CHSTS items. CONCLUSION: Validation of CHSTS is the groundwork for identifying students with handwriting difficulties and providing adaptive strategies including fair special examination arrangements for these students.

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study.

BACKGROUND: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention. OBJECTIVE: The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one's suicide risk and emotional distress in Chinese social media. METHODS: A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants' Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors. RESULTS: A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health- (OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC. CONCLUSIONS: SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life.

Effectiveness of a culturally attuned Internet-based depression prevention program for Chinese adolescents: A randomized controlled trial.

BACKGROUND: Depression prevention among adolescents is crucial for reducing the global disease burden. Internet-based depression prevention approaches are found to be effective but they were mostly evaluated in a Western context. Grasping the Opportunity is a Chinese Internet intervention, which was translated and modified from CATCH-IT developed in the West. We aimed to evaluate the effectiveness of Grasp the Opportunity in reducing depressive symptoms in Chinese adolescents. METHODS: In this randomized controlled trial, Chinese adolescents aged 13 to 17 years with mild-to-moderate depressive symptoms were recruited from three secondary schools in Hong Kong. The participants (n = 257) were randomly assigned to receive either intervention or attention control. The primary outcome was the improvement in depressive symptoms according to the revised Center for Epidemiologic Studies Depression Scale (CESD-R) at the 12-month follow-up. Analyses were performed using intention to treat (ITT). RESULTS: The participants were randomly assigned to receive the intervention (n = 130) or attention control (n = 127). Follow-up data were obtained from 250 (97%) participants. Only 26 (10%) participants completed the intervention. Compared to the attention control, Grasp the Opportunity led to reductions in depressive symptoms at the 12-month follow-up with a medium effect size using ITT analysis (mean difference 2.6, 95% CI 0.59-5.55, effect size d = 0.36). CONCLUSIONS: Grasp the Opportunity is effective in reducing depressive symptoms in Chinese adolescents over a long follow-up period. Poor completion rate is the major challenge in the study.

Editorial Perspective: Pathological social withdrawal during in adolescence: a culture-specific or a global phenomenon?

Impairing patterns of long-term adolescent social withdrawal and self-exclusion, including nonattendance at school or work, and minimal social contact, have been identified as a significant clinical and social problem in Japan since the late 1990s, where it is termed hikikomori. As well clinical impairment for the withdrawn youths and burden for the families, hikikomori has brought societal and health service costs in Japan. Since its first identification, similar cases have been reported in other countries. Socially withdrawn youths, unfortunately, are difficult to identify and their risks can be 'invisible' because of their withdrawn nature and the traditional perspective of what is perceived as at-risk youth. Understanding of the issue including its causes, risks, and outcomes is very limited. In this editorial perspective, we highlight how youth social withdrawal is becoming a clinical and social concern in some parts of the world and respond to the lack of research on this issue by synthesizing some of the basic research findings, and suggesting future directions for research and practice relating to this emerging youth phenomenon in middle-and-high-income countries in the hope of bringing more attention to this issue.

Youth social withdrawal behavior (hikikomori): A systematic review of qualitative and quantitative studies.

OBJECTIVE: Acute and/or severe social withdrawal behavior among youth was seen as a culture-bound psychiatric syndrome in Japan, but more youth social withdrawal cases in different countries have been discovered recently. However, due to the lack of a formal definition and diagnostic tool for youth social withdrawal, cross-cultural observational and intervention studies are limited. We aimed to consolidate existing knowledge in order to understand youth social withdrawal from diverse perspectives and suggest different interventions for different trajectories of youth social withdrawal. METHOD: This review examined the current available scientific information on youth social withdrawal in the academic databases: ProQuest, ScienceDirect, Web of Science and PubMed. We included quantitative and qualitative studies of socially withdrawn youths published in English and academic peer-reviewed journals. RESULTS: We synthesized the information into the following categories: (1) definitions of youth social withdrawal, (2) developmental theories, (3) factors associated with youth social withdrawal and (4) interventions for socially withdrawn youths. Accordingly, there are diverse and controversial definitions for youth social withdrawal. Studies of youth social withdrawal are based on models that lead to quite different conclusions. Researchers with an attachment perspective view youth social withdrawal as a negative phenomenon, whereas those who adopt Erikson's developmental theory view it more positively as a process of seeking self-knowledge. Different interventions for socially withdrawn youths have been developed, mainly in Japan, but evidence-based practice is almost non-existent. CONCLUSION: We propose a theoretical framework that views youth social withdrawal as resulting from the interplay between psychological, social and behavioral factors. Future validation of the framework will help drive forward advances in theory and interventions for youth social withdrawal as an emerging issue in developed countries.

Hypersomnolence is associated with non-remission of major depressive disorder.

OBJECTIVE: This study aimed to investigate the prevalence, clinical correlates and the relationship between hypersomnolence and clinical outcomes in a cohort of MDD patients. METHODS: This is a cross-sectional study of a MDD cohort in an university-affiliated adult psychiatric outpatient clinic. The diagnosis of MDD and severity of depression were ascertained by the clinician with structured clinical interviews. Each participant completed the Epworth Sleepiness Scale (ESS), 1-week sleep diary, and a battery of questionnaires that assessed usual sleep pattern, insomnia, anxiety, depression, fatigue and circadian preference. Hypersomnolence was defined as ESS score ≥14 among those reported ≥7 h of nighttime sleep. Univariate analysis and multiple logistic regression were used to analyze the relationships between the variables. RESULTS: Among 252 recruited subjects, 11 % met the criteria of hypersomnolence as defined by a ESS score ≥14 despite ≥7 h of nighttime sleep. Patients with hypersomnolence had greater depression ratings, higher rates of suicidal ideations over the past week, and more likely to meet a diagnosis of atypical depression (p < 0.05) than those without hypersomnolence. Step-wise logistic regression demonstrated that hypersomnolence was an independent risk factor associated with a 3-fold increase in the risk of depression non-remission (adjusted OR 3.13; 95 % CI 1.10-8.95; p = 0.034). CONCLUSION: Patients with hypersomnolence despite seemingly adequate sleep represent a subgroup of MDD patients who have a more severe illness profile with higher non-remission rate and suicidality. The findings highlight the importance of addressing both sleep and mood symptoms in the management of MDD.

Multimodal digital assessment of depression with actigraphy and app in Hong Kong Chinese.

There is an emerging potential for digital assessment of depression. In this study, Chinese patients with major depressive disorder (MDD) and controls underwent a week of multimodal measurement including actigraphy and app-based measures (D-MOMO) to record rest-activity, facial expression, voice, and mood states. Seven machine-learning models (Random Forest [RF], Logistic regression [LR], Support vector machine [SVM], K-Nearest Neighbors [KNN], Decision tree [DT], Naive Bayes [NB], and Artificial Neural Networks [ANN]) with leave-one-out cross-validation were applied to detect lifetime diagnosis of MDD and non-remission status. Eighty MDD subjects and 76 age- and sex-matched controls completed the actigraphy, while 61 MDD subjects and 47 controls completed the app-based assessment. MDD subjects had lower mobile time (P = 0.006), later sleep midpoint (P = 0.047) and Acrophase (P = 0.024) than controls. For app measurement, MDD subjects had more frequent brow lowering (P = 0.023), less lip corner pulling (P = 0.007), higher pause variability (P = 0.046), more frequent self-reference (P = 0.024) and negative emotion words (P = 0.002), lower articulation rate (P < 0.001) and happiness level (P < 0.001) than controls. With the fusion of all digital modalities, the predictive performance (F1-score) of ANN for a lifetime diagnosis of MDD was 0.81 and 0.70 for non-remission status when combined with the HADS-D item score, respectively. Multimodal digital measurement is a feasible diagnostic tool for depression in Chinese. A combination of multimodal measurement and machine-learning approach has enhanced the performance of digital markers in phenotyping and diagnosis of MDD.

Exploring the Role of First-Person Singular Pronouns in Detecting Suicidal Ideation: A Machine Learning Analysis of Clinical Transcripts.

Linguistic features, particularly the use of first-person singular pronouns (FPSPs), have been identified as potential indicators of suicidal ideation. Machine learning (ML) and natural language processing (NLP) have shown potential in suicide detection, but their clinical applicability remains underexplored. This study aimed to identify linguistic features associated with suicidal ideation and develop ML models for detection. NLP techniques were applied to clinical interview transcripts (n = 319) to extract relevant features, including four cases of FPSP (subjective, objective, dative, and possessive cases) and first-person plural pronouns (FPPPs). Logistic regression analyses were conducted for each linguistic feature, controlling for age, gender, and depression. Gradient boosting, support vector machine, random forest, decision tree, and logistic regression were trained and evaluated. Results indicated that all four cases of FPSPs were associated with depression (p < 0.05) but only the use of objective FPSPs was significantly associated with suicidal ideation (p = 0.02). Logistic regression and support vector machine models successfully detected suicidal ideation, achieving an area under the curve (AUC) of 0.57 (p < 0.05). In conclusion, FPSPs identified during clinical interviews might be a promising indicator of suicidal ideation in Chinese patients. ML algorithms might have the potential to aid clinicians in improving the detection of suicidal ideation in clinical settings.

Efficacy and safety of transcranial pulse stimulation in young adolescents with attention-deficit/hyperactivity disorder: a pilot, randomized, double-blind, sham-controlled trial.

Background: This is the first study to evaluate the efficacy and safety of transcranial pulse stimulation (TPS) for the treatment of attention-deficit/hyperactivity disorder (ADHD) among young adolescents in Hong Kong. Methods: This double-blind, randomized, sham-controlled trial included a TPS group and a sham TPS group, encompassing a total of 30 subjects aged 12-17 years who were diagnosed with ADHD. Baseline measurements SNAP-IV, ADHD RS-IV, CGI and executive functions (Stroop tests, Digit Span) and post-TPS evaluation were collected. Both groups were assessed at baseline, immediately after intervention, and at 1-month and 3-month follow-ups. Repeated-measures ANOVAs were used to analyze data. Results: The TPS group exhibited a 30% reduction in the mean SNAP-IV score at postintervention that was maintained at 1- and 3-month follow-ups. Conclusion: TPS is an effective and safe adjunct treatment for the clinical management of ADHD. Clinical trial registration: ClinicalTrials.Gov, identifier NCT05422274.

Evaluation of a Web-based social network electronic game in enhancing mental health literacy for young people.

BACKGROUND: Internet-based learning programs provide people with massive health care information and self-help guidelines on improving their health. The advent of Web 2.0 and social networks renders significant flexibility to embedding highly interactive components, such as games, to foster learning processes. The effectiveness of game-based learning on social networks has not yet been fully evaluated. OBJECTIVES: The aim of this study was to assess the effectiveness of a fully automated, Web-based, social network electronic game on enhancing mental health knowledge and problem-solving skills of young people. We investigated potential motivational constructs directly affecting the learning outcome. Gender differences in learning outcome and motivation were also examined. METHODS: A pre/posttest design was used to evaluate the fully automated Web-based intervention. Participants, recruited from a closed online user group, self-assessed their mental health literacy and motivational constructs before and after completing the game within a 3-week period. The electronic game was designed according to cognitive-behavioral approaches. Completers and intent-to-treat analyses, using multiple imputation for missing data, were performed. Regression analysis with backward selection was employed when examining the relationship between knowledge enhancement and motivational constructs. RESULTS: The sample included 73 undergraduates (42 females) for completers analysis. The gaming approach was effective in enhancing young people's mental health literacy (d=0.65). The finding was also consistent with the intent-to-treat analysis, which included 127 undergraduates (75 females). No gender differences were found in learning outcome (P=.97). Intrinsic goal orientation was the primary factor in learning motivation, whereas test anxiety was successfully alleviated in the game setting. No gender differences were found on any learning motivation subscales (P>.10). We also found that participants' self-efficacy for learning and performance, as well as test anxiety, significantly affected their learning outcomes, whereas other motivational subscales were statistically nonsignificant. CONCLUSIONS: Electronic games implemented through social networking sites appear to effectively enhance users' mental health literacy.