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.

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.

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.

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.

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.

Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study.

Background: Assessing patients' suicide risk is challenging, especially among those who deny suicidal ideation. Primary care providers have poor agreement in screening suicide risk. Patients' speech may provide more objective, language-based clues about their underlying suicidal ideation. Text analysis to detect suicide risk in depression is lacking in the literature. Objective: This study aimed to determine whether suicidal ideation can be detected via language features in clinical interviews for depression using natural language processing (NLP) and machine learning (ML). Methods: This cross-sectional study recruited 305 participants between October 2020 and May 2022 (mean age 53.0, SD 11.77 years; female: n=176, 57%), of which 197 had lifetime depression and 108 were healthy. This study was part of ongoing research on characterizing depression with a case-control design. In this study, 236 participants were nonsuicidal, while 56 and 13 had low and high suicide risks, respectively. The structured interview guide for the Hamilton Depression Rating Scale (HAMD) was adopted to assess suicide risk and depression severity. Suicide risk was clinician rated based on a suicide-related question (H11). The interviews were transcribed and the words in participants' verbal responses were translated into psychologically meaningful categories using Linguistic Inquiry and Word Count (LIWC). Results: Ordinal logistic regression revealed significant suicide-related language features in participants' responses to the HAMD questions. Increased use of anger words when talking about work and activities posed the highest suicide risk (odds ratio [OR] 2.91, 95% CI 1.22-8.55; P=.02). Random forest models demonstrated that text analysis of the direct responses to H11 was effective in identifying individuals with high suicide risk (AUC 0.76-0.89; P<.001) and detecting suicide risk in general, including both low and high suicide risk (AUC 0.83-0.92; P<.001). More importantly, suicide risk can be detected with satisfactory performance even without patients' disclosure of suicidal ideation. Based on the response to the question on hypochondriasis, ML models were trained to identify individuals with high suicide risk (AUC 0.76; P<.001). Conclusions: This study examined the perspective of using NLP and ML to analyze the texts from clinical interviews for suicidality detection, which has the potential to provide more accurate and specific markers for suicidal ideation detection. The findings may pave the way for developing high-performance assessment of suicide risk for automated detection, including online chatbot-based interviews for universal screening.

Objective and subjective sleep in patients with non-seasonal major depressive disorder and eveningness - Results from a randomized controlled trial of bright light therapy.

STUDY OBJECTIVES: This study examines the (dis)agreement between objective and subjective sleep and their prospective changes in a randomized controlled trial of bright light therapy (BLT) in patients with major depressive disorder (MDD) and eveningness. METHODS: A total of 93 adults were randomized to receive either 30-min daily of 10,000 lux BLT or 50lux placebo dim red light therapy (DRL group) for a total of 5 weeks. Actigraphic data were collected at the baseline and during the last week of treatment. (Dis)Concordance of diary and actigraphic sleep parameters were assessed by partial correlations and Bland-Altman plots, and the associations between these discrepancies to depression severity was assessed by linear regression models. Changes of sleep parameters were assessed by linear mixed models. RESULTS: Significant correlations were found between subjective sleep timings and chronotype to actigraphic parameters. Discrepancies between diary- and actigraphic-measures were observed, and patients with more severe depressive symptoms were associated with a greater under-estimation of total sleep time (TST). A greater advance in the diary-based time to fall sleep and rise time were achieved in the BLT group as compared to the DRL group, while diary-based wake after sleep onset (WASO), TST and sleep efficiency (SE) comparably improved with time in both groups. There was no significant difference between the two groups in the actigraphic parameters after treatment. CONCLUSIONS: In this study, we found that depression severity influenced subjective report of sleep. BLT led to a greater advance in subjective sleep timings when compared to the placebo group.

Effects of transcranial pulse stimulation on autism spectrum disorder: a double-blind, randomized, sham-controlled trial.

Transcranial pulse stimulation has been proven effective to improve cognition, memory and depressive symptoms of Alzheimer's disease, but supporting evidence on other neurological diseases or neuropsychiatric disorders remains limited. This study aimed to investigate the effects of transcranial pulse stimulation on the right temporoparietal junction, which is a key node for social cognition for autism spectrum disorder, and to examine the association between transcranial pulse stimulation and executive and social functions. This double-blinded, randomized, sham-controlled trial included 32 participants (27 males), aged 12-17 years with autism spectrum disorder. All eligible participants were randomized into either the verum or sham transcranial pulse stimulation group, on a 1:1 ratio, based on the Childhood Autism Rating Scale screening score. Sixteen participants received six verum transcranial pulse stimulation sessions (energy level: 0.2-0.25 mJ/mm2; pulse frequency: 2.5-4.0 Hz, 800 pulse/session) in 2 weeks on alternate days. The remaining 16 participants received sham transcranial pulse stimulation. The primary outcome measure included Childhood Autism Rating Scale score changes, evaluated by parents, from baseline to 3-month follow-ups. Secondary outcomes included a self-reported questionnaire responded to by parents and cognitive tests responded to by participants. A licensed mental health professional evaluated clinical global impression severity, improvement, efficacy and total score. Results revealed significant interactions in Childhood Autism Rating Scale and other secondary outcomes. Significant group and time effects were found in most secondary outcomes. Additionally, significant differences were found between the transcranial pulse stimulation and sham transcranial pulse stimulation groups in Childhood Autism Rating Scale and clinical global impression improvement and total score immediately after 2 weeks of transcranial pulse stimulation intervention (all P < 0.05), and effects were sustainable at 1- and 3-month follow-up, compared with baseline. The effect size of Childhood Autism Rating Scale (d = 0.83-0.95) and clinical global impression improvement (d = 4.12-4.37) were large to medium immediately after intervention and sustained at 1-month post-stimulation; however, the effects were reduced to small at 3-month post-stimulation (d = 2.31). These findings indicated that transcranial pulse stimulation over right temporoparietal junction was effective to reduce the core symptoms of autism spectrum disorder, as evidenced by a 24% reduction in the total Childhood Autism Rating Scale score in the verum transcranial pulse stimulation group. Additionally, the clinical global impression total score was reduced by 53.7% in the verum transcranial pulse stimulation group at a 3-month follow-up, compared with the baseline. Participants in the verum transcranial pulse stimulation group had shown substantial improvement at 1- and 3-month follow-ups, compared with baseline, although some of the neuropsychological test results were deemed statistically insignificant. Future replication of this study should include a larger sample derived from multi-nations to determine transcranial pulse stimulation as an alternative top-on treatment option in neuropsychiatry.

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.

Utility of Ultrasound-Guided Erector Spinae Plane Blocks for Postoperative Pain Management Following Thoracolumbar Spinal Fusion Surgery.

Purpose: The primary objective of this study is to determine if ultrasound-guided erector spinae plane blocks (ESPB) prior to thoracolumbar spinal fusion reduces opioid consumption in the first 24 hours postoperatively. Secondary objectives include ESPB effects on administration of opioids, utilization of intravenous patient-controlled analgesia (IV-PCA), pain scores, length of stay, and opioid related side effects. Methods: A retrospective cohort analysis was performed on consecutive, adult patients undergoing primary thoracolumbar fusion procedures. Demographic and baseline characteristics including diagnoses of chronic pain, anxiety, depression, and preoperative use of opioids were collected. Surgical data included surgical levels, opioid administration, and duration. Postoperative data included pain scores, opioid consumption, IV-PCA duration, opioid-related side effects, ESPB-related complications, and length of stay (LOS). Statistical analysis was performed using chi-squared and t-test analyses, multivariable analysis, and covariate adjustment with propensity score. Results: A total of 118 consecutive primary thoracolumbar fusions were identified between October 2019 and December 2021 (70 ESPB, 48 no-block [NB]). There were no significant demographic or surgical differences between groups. Median surgical time (262.50 mins vs 332.50 mins, p = 0.04), median intraoperative opioid consumption (8.11 OME vs 1.73 OME, p = 0.01), and median LOS (152.00 hrs vs 128.50 hrs, p = 0.01) were significantly reduced in the ESPB group. Using multivariable covariate adjustment with propensity score analysis only intraoperative opioid administration was found to be significantly less in the ESPB cohort. Conclusion: ESPB for thoracolumbar fusion can be performed safely in index cases. There was a reduction of intraoperative opioid administration in the ESPB group, however the care team was not blinded to the intervention. Extensive thoracolumbar spinal fusion surgery may require a different approach to regional anesthesia to be similarly effective as ESPB in isolated lumbar surgeries.

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.

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.

The Effectiveness of Electrical Vestibular Stimulation (VeNS) on Symptoms of Anxiety: Study Protocol of a Randomized, Double-Blinded, Sham-Controlled Trial.

The prevalence of symptoms of anxiety is increasing, especially during the COVID-19 pandemic. A home use transdermal neurostimulation device might help to minimize the severity of anxiety disorder. To the best of our knowledge, there is no clinical trial using transdermal neurostimulation to treat individuals with symptoms of anxiety in Asia. This gives us the impetus to execute the first study which aims at evaluating the efficacy of Electrical Vestibular Stimulation (VeNS) on anxiety in Hong Kong. This study proposes a two-armed, double-blinded, randomized, sham-controlled trial including the active VeNS and sham VeNS group. Both groups will be measured at baseline (T1), immediately after the intervention (T2), and at the 1-month (T3) and 3-month follow-up (T4). A total of 66 community-dwelling adults aged 18 to 60 with anxiety symptoms will be recruited in this study. All subjects will be computer randomised into either the active VeNS group or the sham VeNS group in a 1:1 ratio. All subjects in each group will receive twenty 30 min VeNS sessions during weekdays, which will be completed in a 4-week period. Baseline measurements and post-VeNS evaluation of the psychological outcomes (i.e., anxiety, insomnia, and quality of life) will also be conducted on all participants. The 1-month and 3-month follow-up period will be used to assess the long-term sustainability of the VeNS intervention. For statistical analysis, ANOVA with repeated measures will be used to analyze data. Missing data were managed with multiple mutations. The level of significance will be set to p < 0.05. Results of this study will be used to determine whether this VeNS device can be considered as a self-help technological device to reduce perceived anxiety in the general population in the community setting. This clinical Trial was registered with the Clinical Trial government, identifier: NCT04999709.

Effects of Transcranial Pulse Stimulation (TPS) on Adults with Symptoms of Depression-A Pilot Randomized Controlled Trial.

Transcranial pulse stimulation (TPS) is a recent development in non-invasive brain stimulations (NIBS) that has been proven to be effective in terms of significantly improving Alzheimer patients' cognition, memory, and execution functions. Nonetheless, there is, currently, no trial evaluating the efficacy of TPS on adults with major depression disorder (MDD) nationwide. In this single-blinded, randomized controlled trial, a 2-week TPS treatment comprising six 30 min TPS sessions were administered to participants. Participants were randomized into either the TPS group or the Waitlist Control (WC) group, stratified by gender and age according to a 1:1 ratio. Our primary outcome was evaluated by the Hamilton depression rating scale-17 (HDRS-17). We recruited 30 participants that were aged between 18 and 54 years, predominantly female (73%), and ethnic Chinese from 1 August to 31 October 2021. Moreover, there was a significant group x time interaction (F(1, 28) = 18.8, p < 0.001). Further, when compared with the WC group, there was a significant reduction in the depressive symptom severity in the TPS group (mean difference = -6.60, p = 0.02, and Cohen's d = -0.93). The results showed a significant intervention effect; in addition, the effect was large and sustainable at the 3-month follow-up. In this trial, it was found that TPS is effective in reducing depressive symptoms among adults with MDD.

North Atlantic oscillation controls multidecadal changes in the North Tropical Atlantic-Pacific connection.

By exciting subtropical teleconnections, sea surface temperature (SST) anomalies in the North Tropical Atlantic (NTA) during boreal spring can trigger El Niño-Southern Oscillation (ENSO) events in the following boreal winter, thereby providing a precursor for ENSO predictability. However, this NTA-ENSO connection is not stationary, and it varies considerably over multidecadal timescales, which cannot be directly explained by the Atlantic multidecadal oscillation or the global warming trend. Here we show that multidecadal changes in the NTA-ENSO connection are principally controlled by multidecadal variability associated with the North Atlantic Oscillation (NAO). During the positive phase of the NAO, the amplification of the NTA impact on ENSO mainly arises from strengthening of the boreal spring mean precipitation over the equatorial Atlantic and enhancement of the persistence of NTA SST anomalies, which enhance the NTA influence by exciting stronger and more persistent subtropical teleconnections. Our findings show that multidecadal variability of the NAO is key to understanding the impacts of the NTA SST on the tropical Pacific Ocean.

Evaluation of a group-based sensorimotor intervention programme to improve Chinese handwriting of primary school students.

Objective: Sensorimotor performance is influential in Chinese handwriting, but few studies have examined the efficacy of sensorimotor-based interventions on Chinese handwriting among primary school students with poor handwriting performance. The study aims to evaluate a sensorimotor-based intervention to improve handwriting in the mainstream primary schools. Methods: This study adopted a two-group pretest-posttest design. An 8-session group-based sensorimotor intervention was delivered to school-aged children (mean age = 8.1, 68% male). Group A had 2 sessions every week, while Group B had 4 sessions every week. Analysis of variance with repeated measures was used to test the effects. Results: The intervention had a significant time effect (p < .05) in terms of improving handwriting process (d = 0.33-1.10), manual dexterity (d = 0.57), visual memory (d = 0.70), visual-spatial perception (d = 0.37), and motor and postural skills (d = 0.73). The effect sizes ranged from medium to large. For the handwriting process, time per character had a significant group × time interaction, with post hoc analysis showing that Group A had a significantly large effect (d = 1.89, p < .001) while Group B did not. Conclusions: The group-based sensorimotor intervention programme appeared to show improvements in students with fair skills in writing Chinese characters. It appears that the effect is better if the training sessions are spaced out in one month rather than intensively conducted within two weeks. It might be related to more involvement from parents, and students need more time for practice after the training sessions.

Evaluating the Efficacy of Electrical Vestibular Stimulation (VeNS) on Insomnia Adults: Study Protocol of a Double-Blinded, Randomized, Sham-Controlled Trial.

Insomnia is a common health problem in the general population. There are different ways to improve sleeping habits and quality of sleep; however, there is no clinical trial using transdermal neurostimulation to treat individuals with symptoms of insomnia in Asia. This gives us the impetus to execute the first study in Asia which aims to evaluate the efficacy of Electrical Vestibular Stimulation (VeNS) on individuals with insomnia in Hong Kong. This study proposes a two-armed, double-blinded, randomized, sham-controlled trial including the active VeNS and sham VeNS group. Both groups will be measured at baseline (T1), immediately after the intervention (T2), and at the 1-month (T3) and 3-month follow-up (T4). A total of 60 community-dwelling adults aged 18 to 60 years, with insomnia symptoms will be recruited in this study. All subjects will be computer randomized into either the active VeNS group or the sham VeNS group on a 1:1 ratio. All subjects in each group will receive twenty 30-min VeNS sessions during weekdays, which will be completed in a 4-week period. Baseline measurements and post-VeNS evaluation of the psychological outcomes (i.e., insomnia severity, sleep quality and quality of life) will also be conducted on all participants. The 1-month and 3-month follow-up period will be used to assess the short-and long-term sustainability of the VeNS intervention. For statistical analysis, a mixed model will be used to analyze the repeated measures data. Missing data will be managed by multiple imputations. The level of significance will be set to p < 0.05. Significance of the study: The results of this study will be used to determine whether this VeNS device can be considered as a self-help technological device to reduce the severity of insomnia in the community setting. We registered this clinical trial with the Clinical trial government, identifier: NCT04452981.

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.

Distinctive South and East Asian monsoon circulation responses to global warming.

The Asian summer monsoon (ASM) is the most energetic circulation system. Projecting its future change is critical for the mitigation and adaptation of billions of people living in the region. There are two important components within the ASM: South Asian summer monsoon (SASM) and East Asian summer monsoon (EASM). Although current state-of-the-art climate models projected increased precipitation in both SASM and EASM due to the increase of atmospheric moisture, their circulation changes differ markedly-A robust strengthening (weakening) of EASM (SASM) circulation was projected. By separating fast and slow processes in response to increased CO2 radiative forcing, we demonstrate that EASM circulation strengthening is attributed to the fast land warming and associated Tibetan Plateau thermal forcing. In contrast, SASM circulation weakening is primarily attributed to an El Niño-like oceanic warming pattern in the tropical Pacific and associated suppressed precipitation over the Maritime Continent.