Genetic regulatory and biological implications of the 10q24.32 schizophrenia risk locus.

Genome-wide association studies have identified 10q24.32 as a robust schizophrenia risk locus. Here we identify a regulatory variant (rs10786700) that disrupts binding of transcription factors at 10q24.32. We independently confirmed the association between rs10786700 and schizophrenia in a large Chinese cohort (n = 11 547) and uncovered the biological mechanism underlying this association. We found that rs10786700 resides in a super-enhancer element that exhibits dynamic activity change during the development process and that the risk allele (C) of rs10786700 conferred significant lower enhancer activity through enhancing binding affinity to repressor element-1 silencing transcription factor (REST). CRISPR-Cas9-mediated genome editing identified SUFU as a potential target gene by which rs10786700 might exert its risk effect on schizophrenia, as deletion of rs10786700 downregulated SUFU expression. We further investigated the role of Sufu in neurodevelopment and found that Sufu knockdown inhibited proliferation of neural stem cells and neurogenesis, affected molecular pathways (including neurodevelopment-related pathways, PI3K-Akt and ECM-receptor interaction signalling pathways) associated with schizophrenia and altered the density of dendritic spines. These results reveal that the functional risk single nucleotide polymorphism rs10786700 at 10q24.32 interacts with REST synergistically to regulate expression of SUFU, a novel schizophrenia risk gene which is involved in schizophrenia pathogenesis by affecting neurodevelopment and spine morphogenesis.

Structure, network analysis, psychometric properties and clinical utility of the self-absorption scale in China.

BACKGROUND: Self-absorption refers an excessive, persistent, and rigid preoccupation with information regarding the self. This study aims to introduce the Self-Absorption Scale (SAS) into China with an assessment of its latent variable structure, network structure, psychometric properties, and clinical utility in a nonclinical Chinese sample. METHODS: 209 participants completed the translated SAS as well as the Short General Health Questionnaire (GHQ-12), rumination subscale of the Rumination-Reflection Questionnaire (RRQ), the Mindful Attention Awareness Scale (MAAS), the Private Self-Consciousness Scale (PrSCS), the Obsessive-Compulsive Inventory-Revised (OCI-R) and the Dissociative Experiences Scale (DES-II). In addition, 30 respondents completed the Chinese version of the SAS and retested it 2 weeks later. RESULTS: The Chinese version of the SAS (CH-SAS) had a desirable two-correlated-factor structure with the reverse scored item removed, which was invariant across different genders. The core items in the network structure of the CH-SAS were related to excessive self-immersion, uncontrollability and anxiety aspects of self-absorption. The Cronbach's alpha coefficient for the CH-SAS was 0.903 while the McDonald's omega coefficient was 0.916 and the test-retest reliability was 0.908. The CH-SAS and its two subscales had moderate positive correlations with the rumination subscale of the RRQ (ranging from 0.474 to 0.616; p < .001) and the GHQ-12 (ranging from 0.479 to 0.538; p < .001), and moderate negative correlations with the MAAS (ranging from - 0.413 to - 0.360; p < .001). The PrSCS has almost no correlation with the CH-SAS and PrSAS (p > .05), and its correlation with the PubSAS was significant at the 0.05 level, with a remarkably low correlation coefficient (r = .157). The hierarchical regression analysis suggested that the CH-SAS can significantly predict the severity of OCD beyond factors such as depression, anxiety, rumination, dissociation, and mindful attention awareness. CONCLUSIONS: The CH-SAS demonstrates excellent reliability, including internal consistency and test-retest reliability. Additionally, it exhibits favorable structural validity, as well as strong evidence of convergent and divergent validity. Furthermore, the self-absorption measured using the CH-SAS contributed significantly to the prediction of OCD beyond other relevant psychological factors, suggesting its clinical utility.

Regulatory variants at 2q33.1 confer schizophrenia risk by modulating distal gene TYW5 expression.

Genome-wide association studies have shown that genetic variants at 2q33.1 are strongly associated with schizophrenia. However, potential causal variants in this locus and their roles in schizophrenia remain unknown. Here, we identified two functional variants (rs796364 and rs281759) that disrupt CTCF, RAD21 and FOXP2 binding at 2q33.1. We systematically investigated the regulatory mechanisms of these two variants with serial experiments, including reporter gene assays and electrophoretic mobility shift assay. Intriguingly, these two single nucleotide polymorphisms physically interacted with TYW5 and showed the most significant associations with TYW5 expression in human brain. Consistently, CRISPR-Cas9-mediated genome editing confirmed the regulatory effect of the two single nucleotide polymorphisms on TYW5 expression. Additionally, expression analysis indicated that TYW5 was significantly upregulated in brains of schizophrenia cases compared with controls, suggesting that rs796364 and rs281759 might confer schizophrenia risk by modulating TYW5 expression. We over-expressed TYW5 in mouse neural stem cells and rat primary neurons to mimic its upregulation in schizophrenia and found significant alterations in the proliferation and differentiation of neural stem cells, as well as dendritic spine density following TYW5 overexpression, indicating its important roles in neurodevelopment and spine morphogenesis. Furthermore, we independently confirmed the association between rs796364 and schizophrenia in a Chinese cohort of 8202 subjects. Finally, transcriptome analysis revealed that TYW5 affected schizophrenia-associated pathways. These lines of evidence consistently revealed that rs796364 and rs281759 might contribute to schizophrenia risk by regulating the expression of TYW5, a gene whose expression dysregulation affects two important schizophrenia pathophysiological processes (i.e. neurodevelopment and dendritic spine formation).

Functional variant rs2270363 on 16p13.3 confers schizophrenia risk by regulating NMRAL1.

Recent genome-wide association studies have reported multiple schizophrenia risk loci, yet the functional variants and their roles in schizophrenia remain to be characterized. Here we identify a functional single nucleotide polymorphism (rs2270363: G>A) at the schizophrenia risk locus 16p13.3. rs2270363 lies in the E-box element of the promoter of NMRAL1 and disrupts binding of the basic helix-loop-helix leucine zipper family proteins, including USF1, MAX and MXI1. We validated the regulatory effects of rs2270363 using reporter gene assays and electrophoretic mobility shift assay. Besides, expression quantitative trait loci analysis showed that the risk allele (A) of rs2270363 was significantly associated with elevated NMRAL1 expression in the human brain. Transcription factors knockdown and CRISPR-Cas9-mediated editing further confirmed the regulatory effects of the genomic region containing rs2270363 on NMRAL1. Intriguingly, NMRAL1 was significantly downregulated in the brain of schizophrenia patients compared with healthy subjects, and knockdown of Nmral1 expression affected proliferation and differentiation of mouse neural stem cells, as well as genes and pathways associated with brain development and synaptic transmission. Of note, Nmral1 knockdown resulted in significant decrease of dendritic spine density, revealing the potential pathophysiological mechanisms of NMRAL1 in schizophrenia. Finally, we independently confirmed the association between rs2270363 and schizophrenia in the Chinese population and found that the risk allele of rs2270363 was the same in European and Chinese populations. These lines of evidence suggest that rs2270363 may confer schizophrenia risk by regulating NMRAL1, a gene whose expression dysregulation might be involved in the pathogenesis of schizophrenia by affecting neurodevelopment and synaptic plasticity.

The causal relationship of depression, anxiety, and neuroticism with main indicators of sarcopenia: A Mendelian randomization study.

BACKGROUND: Observational studies have shown the relationship between sarcopenia and psychiatric disorders. However, due to the limitations of traditional research methods, the causal relationship between them has not been accurately concluded. At the same time, considering that sarcopenia is mainly manifested by low muscle strength and low muscle mass, we used Mendelian randomization (MR) analysis in this study to explore the causal relationship of anxiety, depression, and neuroticism with muscle strength and muscle mass, respectively. METHODS: Genetic variants associated with depression were obtained from FinnGen Biobank (Ncase = 33,812, Ncontrol = 271,380), those associated with anxiety were from FinnGen Biobank (Ncase = 21,519, Ncontrol = 307,558), and those associated with neuroticism, including 12 items, were from a large-scale genome-wide association study (N range: 366,301-375,913). Muscle strength was represented by the hand grip strength (HGS), and muscle mass was represented by the appendicular lean mass (ALM) and the body fat percentage. The inverse-variance weighted (IVW) method was used as the primary analysis method, and the Mendelian Randomization Egger (MR-Egger) and the weighted median were used as supplementary methods to test whether the three psychological factors were causally related to these two main indicators of sarcopenia severity. RESULTS: Depression and neuroticism had different degrees of causal influence on muscle mass and strength, which was statistically significant. Specifically, the depression predicted by genes was significantly associated with ALM (beta = -0.043, p = 0.027), low hand grip strength (LHGS, measured for people of 60 years and older) (odds ratio (OR) = 1.129 (1.019-1.251), p = 0.019), right HGS (beta = -0.050, p = 0.001), left HGS (beta = -0.06, p = 0.001), and body fat percentage (beta = 0.035, p = 0.0138). The neuroticism predicted by genes was significantly associated with ALM (beta = -0.073, p = 0.034), LHGS (OR = 1.222 (1.085-1.377), p = 0.001), right HGS (beta = -0.058, p = 0.000), left HGS (beta = -0.080, p < 0.000), and body fat percentage (beta = 0.063, p = 0.008). However, anxiety was only significantly associated with LHGS (OR = 1.215 (1.008-1.465), p = 0.041) but not significantly associated with ALM (beta = 0.033, p = 0.313), right HGS (beta = -0.008, p = 0.678), left HGS (beta = 0.007, p = 0.712), or body fat percentage (beta = 0.022, p = 0.559). CONCLUSION: This study supported the causal association of depression and neuroticism with muscle strength and mass, which are the two main indicators of sarcopenia. At the same time, there was no sufficient evidence for the causal relationship between anxiety and muscle strength or mass. The results of this study pointed to the need to intervene in the mental health of the elderly to prevent sarcopenia or reduce its severity.

A missense variant in NDUFA6 confers schizophrenia risk by affecting YY1 binding and NAGA expression.

Genome-wide association studies (GWASs) have revealed that genetic variants at the 22q13.2 risk locus were robustly associated with schizophrenia. However, the causal variants at this risk locus and their roles in schizophrenia remain elusive. Here we identify the risk missense variant rs1801311 (located in the 1st exon of NDUFA6 gene) as likely causal for schizophrenia at 22q13.2 by disrupting binding of YY1, TAF1, and POLR2A. We systematically elucidated the regulatory mechanisms of rs1801311 and validated the regulatory effect of this missense variant. Intriguingly, rs1801311 physically interacted with NAGA (encodes the alpha-N-acetylgalactosaminidase, which is mainly involved in regulating metabolisms of glycoproteins and glycolipids in lysosome) and showed the most significant association with NAGA expression in the human brain, with the risk allele (G) associated with higher NAGA expression. Consistent with eQTL analysis, expression analysis showed that NAGA was significantly upregulated in brains of schizophrenia cases compared with controls, further supporting that rs1801311 may confer schizophrenia risk by regulating NAGA expression. Of note, we found that NAGA regulates important neurodevelopmental processes, including proliferation and differentiation of neural stem cells. Transcriptome analysis corroborated that NAGA regulates pathways associated with neuronal differentiation. Finally, we independently confirmed the association between rs1801311 and schizophrenia in a large Chinese cohort. Our study elucidates the regulatory mechanisms of the missense schizophrenia risk variant rs1801311 and provides mechanistic links between risk variant and schizophrenia etiology. In addition, this study also revealed the novel role of coding variants in gene regulation and schizophrenia risk, i.e., genetic variant in coding region of a specific gene may confer disease risk through regulating distal genes (act as regulatory variant for distal genes).

Dexmedetomidine protects cardiomyocytes against hypoxia/reoxygenation injury via multiple mechanisms.

BACKGROUND: Myocardial infarction (MI) is a serious cardiovascular disease associated with myocardial ischemia/reperfusion (I/R) injury. Dexmedetomidine (Dex), an α2-adrenoceptor agonist, has been reported to protect against I/R injury. We examined the cardioprotective effects of Dex on cardiomyocytes under hypoxia/reoxygenation (H/R) conditions and explored the underlying mechanisms. MATERIALS AND METHODS: A H/R model was established to mimic the MI injury. The CCK-8 assay was performed to measure cell viability. Cellular apoptosis was measured using the Annexin V fluorescein isothiocyanate (FITC)-propidium iodide (PI) staining. The levels of interleukin (IL)-1α and tumor necrosis factor (TNF)-α, and the activity of lactate dehydrogenase (LDH) were measured using a commercial enzyme-linked immunosorbent assay (ELISA) kit. Reactive oxygen species (ROS) were measured using the 2'-7' dichlorofluorescein diacetate (DCFH-DA) staining assay. In addition, the levels of malondialdehyde (MDA) and the activity of superoxide dismutase (SOD), catalase (CAT), and caspase-3 were measured using a commercial kit. siRNA was used to silence Bcl-2, catalase, or STAT3. Western blotting was used to measure the change in the levels of proteins. RESULTS: Dex improved the cell viability and inhibited the inflammatory response in H9c2 cells exposed to H/R treatment. In addition, Dex inhibited apoptosis and alleviated the endoplasmic reticulum (ER) stress and oxidative stress in H9c2 cells under the H/R treatment. Mechanism investigation showed that Dex inhibited the intrinsic pathway of apoptosis. Moreover, Dex enhanced the activation of the JAK2/STAT3 signaling pathway in H/R-treated H9c2 cells. CONCLUSION: Altogether, our findings suggested Dex as a promising therapeutic agent for myocardial I/R.

Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons.

People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors-specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and -5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side's mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders.

A hybrid butterfly algorithm in the optimal economic operation of microgrids.

With the increasing capacity of renewable energy generators, microgrid (MG) systems have experienced rapid development, and the optimal economic operation is one of the most important and challenging issues in the MG field. To reduce the overall generation cost of microgrids, a hybrid butterfly algorithm (HBOA) is proposed to address the optimal economic operation problem in MG systems. This algorithm uses adaptive switching thresholds to balance the global exploration capability and local exploitation capability of the algorithm. It introduces a diversity learning mechanism to enhance information exchange among populations to improve the algorithm's accuracy and proposes an elite-guided guidance strategy to accelerate the convergence speed of the algorithm. Numerical simulation experiments on 10 standard test functions validate that the HBOA algorithm has higher optimization accuracy and faster convergence speed. Simulation experiments are conducted on two operation modes of microgrids: Islanded and grid-connected, and compared with other algorithms. In islanded and grid-connected modes, HBOA can reduce operating costs by up to 11.7% and 17.7%, respectively. The experimental results confirm the applicability and superiority of the proposed algorithm for solving the optimal economic operation problem in microgrids.

Automated information extraction model enhancing traditional Chinese medicine RCT evidence extraction (Evi-BERT): algorithm development and validation.

Background: In the field of evidence-based medicine, randomized controlled trials (RCTs) are of critical importance for writing clinical guidelines and providing guidance to practicing physicians. Currently, RCTs rely heavily on manual extraction, but this method has data breadth limitations and is less efficient. Objectives: To expand the breadth of data and improve the efficiency of obtaining clinical evidence, here, we introduce an automated information extraction model for traditional Chinese medicine (TCM) RCT evidence extraction. Methods: We adopt the Evidence-Bidirectional Encoder Representation from Transformers (Evi-BERT) for automated information extraction, which is combined with rule extraction. Eleven disease types and 48,523 research articles from the China National Knowledge Infrastructure (CNKI), WanFang Data, and VIP databases were selected as the data source for extraction. We then constructed a manually annotated dataset of TCM clinical literature to train the model, including ten evidence elements and 24,244 datapoints. We chose two models, BERT-CRF and BiLSTM-CRF, as the baseline, and compared the training effects with Evi-BERT and Evi-BERT combined with rule expression (RE). Results: We found that Evi-BERT combined with RE achieved the best performance (precision score = 0.926, Recall = 0.952, F1 score = 0.938) and had the best robustness. We totally summarized 113 pieces of rule datasets in the regulation extraction procedure. Our model dramatically expands the amount of data that can be searched and greatly improves efficiency without losing accuracy. Conclusion: Our work provided an intelligent approach to extracting clinical evidence for TCM RCT data. Our model can help physicians reduce the time spent reading journals and rapidly speed up the screening of clinical trial evidence to help generate accurate clinical reference guidelines. Additionally, we hope the structured clinical evidence and structured knowledge extracted from this study will help other researchers build large language models in TCM.

Soft ankle exoskeleton to counteract dropfoot and excessive inversion.

Introduction: Wearable exoskeletons are emerging technologies for providing movement assistance and rehabilitation for people with motor disorders. In this study, we focus on the specific gait pathology dropfoot, which is common after a stroke. Dropfoot makes it difficult to achieve foot clearance during swing and heel contact at early stance and often necessitates compensatory movements. Methods: We developed a soft ankle exoskeleton consisting of actuation and transmission systems to assist two degrees of freedom simultaneously: dorsiflexion and eversion, then performed several proof-of-concept experiments on non-disabled persons. The actuation system consists of two motors worn on a waist belt. The transmission system provides assistive force to the medial and lateral sides of the forefoot via Bowden cables. The coupling design enables variable assistance of dorsiflexion and inversion at the same time, and a force-free controller is proposed to compensate for device resistance. We first evaluated the performance of the exoskeleton in three seated movement tests: assisting dorsiflexion and eversion, controlling plantarflexion, and compensating for device resistance, then during walking tests. In all proof-of-concept experiments, dropfoot tendency was simulated by fastening a weight to the shoe over the lateral forefoot. Results: In the first two seated tests, errors between the target and the achieved ankle joint angles in two planes were low; errors of <1.5° were achieved in assisting dorsiflexion and/or controlling plantarflexion and of <1.4° in assisting ankle eversion. The force-free controller in test three significantly compensated for the device resistance during ankle joint plantarflexion. In the gait tests, the exoskeleton was able to normalize ankle joint and foot segment kinematics, specifically foot inclination angle and ankle inversion angle at initial contact and ankle angle and clearance height during swing. Discussion: Our findings support the feasibility of the new ankle exoskeleton design in assisting two degrees of freedom at the ankle simultaneously and show its potential to assist people with dropfoot and excessive inversion.

Machine learning aided non-invasive diagnosis of coronary heart disease based on tongue features fusion.

BACKGROUND: Coronary heart disease (CHD) is the first cause of death globally. Hypertension is considered to be the most important independent risk factor for CHD. Early and accurate diagnosis of CHD in patients with hypertension can plays a significant role in reducing the risk and harm of hypertension combined with CHD. OBJECTIVE: To propose a non-invasive method for early diagnosis of coronary heart disease according to tongue image features with the help of machine learning techniques. METHODS: We collected standard tongue images and extract features by Diagnosis Analysis System (TDAS) and ResNet-50. On the basis of these tongue features, a common machine learning method is used to customize the non-invasive CHD diagnosis algorithm based on tongue image. RESULTS: Based on feature fusion, our algorithm has good performance. The results showed that the XGBoost model with fused features had the best performance with accuracy of 0.869, the AUC of 0.957, the AUPR of 0.961, the precision of 0.926, the recall of 0.806, and the F1-score of 0.862. CONCLUSION: We provide a feasible, convenient, and non-invasive method for the diagnosis and large-scale screening of CHD. Tongue image information is a possible effective marker for the diagnosis of CHD.

Negative Emotions Are Associated With Older Self-perceived Age: A Cross-section Study From the UK Biobank.

BACKGROUND: Prior research has indicated a potential connection between psychological stress and how individuals perceive their own age. Building on this foundation, the current study explores the relationship between negative emotions and self-perceived age. METHODS: We conducted a cross-sectional analysis using data from the UK Biobank, a comprehensive cohort study representing the UK population. The analysis included 347 892 participants, aged between 39 and 73 years, of which 184 765 were women, accounting for 53.1% of the sample. Participants were categorized into three groups based on their self-perceived age: feeling younger than their chronological age (group Younger), feeling older than their chronological age (group Older), and feeling as old as their actual age (group Same). To investigate the relationship between negative emotions and self-perceived age, we utilized a multinomial logistic regression model with the Younger group serving as the reference category. RESULTS: Of 347 892 participants, after adjusted for covariates, the results showed that participants with irritability, nervous feelings, worrier/anxious feelings or fed-up feelings, worry too long and loneliness/isolation are more likely to be rated as "about your age" or "older than you are," with "younger than you are" as the reference group, indicating that negative emotions may influence one's self-perceived age. Among those negative emotions, irritability has the most significant impact self-perceived age, with the odds ratios (ORs) being 1.44 (95% CI: 1.35-1.54) and 1.11 (95% CI: 1.09-1.14). CONCLUSION: Negative emotions are associated with older self-perceived age, and irritability has the greatest impact. Further studies analyzing self-perceived age are needed to take psychological factors into consideration.

A high-quality dataset featuring classified and annotated cervical spine X-ray atlas.

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm's development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.

Joint Kinematics, Kinetics and Muscle Synergy Patterns During Transitions Between Locomotion Modes.

There is an increasing demand for accurately predicting human movement intentions. To be effective, predictions must be performed as early as possible in the preceding step, though precisely how early has been studied relatively little; how and when a person's movement patterns in a transition step deviate from those in the preceding step must be clearly defined. In this study, we collected motion kinematics, kinetics and electromyography data from 9 able-bodied participants during 7 locomotion modes. Twelve types of steps between the 7 locomotion modes were studied, including 5 continuous steps (taking another step in the same locomotion mode) and 7 transitions steps (taking a step from one locomotion mode into another). For each joint degree of freedom, joint angles, angular velocities, moments, and moment rates were compared between continuous steps and transition steps, and the relative timing during the transition step at which these parameters diverged from those of a continuous step, which we refer to as transition starting times, were identified using multiple analyses of variance. Muscle synergies were also extracted for each step, and we studied in which locomotion modes these synergies were common (task-shared) and in which modes they were specific (task-specific). The transition starting times varied among different transitions and joint degrees of freedom. Most transitions started in the swing phase of the transition step. These findings can be applied to determine the critical timing at which a powered assistive device must adapt its control to enable safe and comfortable support to a user.

Retrospective reports of perceived parental invalidation and borderline personality traits: The indirect effect of personality functioning.

This study examined the indirect effect via Criterion A (personality functioning) of the Alternative Model for Personality Disorders (AMPD) in the relationship between retrospective reports of perceived parental invalidation and borderline personality traits measured by Criterion B (pathological personality traits). A total of 3,019 college students completed self-report scales of the Chinese Invalidating Family Scale, Level of Personality Functioning Scale-Brief Form 2.0, and Personality Inventory for DSM-5. It was found that the indirect effect of personality functioning was significant for the association between levels of perceived overall-B = 0.52, 95% CI [0.47, 0.57], maternal-B = 0.83, [0.75, 0.91], and paternal-B = 0.97, [0.87, 1.08] invalidation, and BPD traits. The outcomes of the research suggested the critical role of personality functioning as a potential mediator in the pathological effect of perceived parental invalidation on the development of borderline personality disorder features. While the study is limited by the use of self-report measurement, retrospective responding, and cross-sectional design, significant implications on the biosocial model and AMPD were discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Method of Detecting Human Movement Intentions in Real Environments.

Accurate and timely movement intention detection can facilitate exoskeleton control during transitions between different locomotion modes. Detecting movement intentions in real environments remains a challenge due to unavoidable environmental uncertainties. False movement intention detection may also induce risks of falling and general danger for exoskeleton users. To this end, in this study, we developed a method for detecting human movement intentions in real environments. The proposed method is capable of online self-correcting by implementing a decision fusion layer. Gaze data from an eye tracker and inertial measurement unit (IMU) signals were fused at the feature extraction level and used to predict movement intentions using 2 different methods. Images from the scene camera embedded on the eye tracker were used to identify terrains using a convolutional neural network. The decision fusion was made based on the predicted movement intentions and identified terrains. Four able-bodied participants wearing the eye tracker and 7 IMU sensors took part in the experiments to complete the tasks of level ground walking, ramp ascending, ramp descending, stairs ascending, and stair descending. The recorded experimental data were used to test the feasibility of the proposed method. An overall accuracy of 93.4% was achieved when both feature fusion and decision fusion were used. Fusing gaze data with IMU signals improved the prediction accuracy.

An Integrated Eye-Tracking and Motion Capture System in Synchronized Gaze and Movement Analysis.

Integrating mobile eye-tracking and motion capture emerges as a promising approach in studying visual-motor coordination, due to its capability of expressing gaze data within the same laboratory-centered coordinate system as body movement data. In this paper, we proposed an integrated eye-tracking and motion capture system, which can record and analyze temporally and spatially synchronized gaze and motion data during dynamic movement. The accuracy of gaze measurement were evaluated on five participants while they were instructed to view fixed vision targets at different distances while standing still or walking towards the targets. Similar accuracy could be achieved in both static and dynamic conditions. To demonstrate the usability of the integrated system, several walking tasks were performed in three different pathways. Results revealed that participants tended to focus their gaze on the upcoming path, especially on the downward path, possibly for better navigation and planning. In a more complex pathway, coupled with more gaze time on the pathway, participants were also found having the longest step time and shortest step length, which led to the lowest walking speed. It was believed that the integration of eye-tracking and motion capture is a feasible and promising methodology quantifying visual-motor coordination in locomotion.

Efficacy of acupuncture for hypertension in the elderly: a systematic review and meta-analysis.

Background: Hypertension has now developed into a major public health problem worldwide. Under the existing antihypertensive drug treatment paradigm, problems such as decreasing drug resistance and increasing drug side effects can occur for elderly patients. Acupuncture, a core technique in the non-pharmacological treatment of Chinese medicine, plays an important role in the treatment of elevated blood pressure. Objective: This study aimed to systematically evaluate the effect of acupuncture alone or in combination with antihypertensive drugs on the efficiency of reducing blood pressure and controlling blood pressure in elderly patients with hypertension. Methods: Articles of randomized controlled trials of acupuncture for hypertension in the elderly published before November 2022 were searched in 7 databases. The methodological quality of the literature was evaluated using the Cochrane Risk of Bias Assessment Tool. The primary outcome was the efficiency rate of blood pressure reduction, and the secondary outcome was the change in blood pressure after treatment. Results: This study conducted a systematic review and meta-analysis of 12 randomized controlled trials with a total of 1,466 subjects. Among the primary outcome-efficiency rate, acupuncture-only treatment (RR = 1.11, 95% CI: 1.03-1.20, P < 0.01) and acupuncture combined with antihypertensive drug treatment (RR = 1.18, 95% CI: 1.06-1.31, P < 0.01) were significantly different compared with drugs-only treatment. Among the secondary outcomes, SBP (MD: -4.85, 95% CI: -10.39 to -0.69, P = 0.09) and DBP (MD: -1.45, 95% CI: -5.35 to 2.45, P = 0.47) show no significant difference between acupuncture-only treatment and drug-only treatment. Compared to drugs-only treatment, acupuncture plus drugs has more significant efficiency in lowering SBP (MD: -9.81, 95% CI: -13.56 to -6.06, P < 0.01) and DBP (MD: -7.04, 95% CI: -10.83 to -3.24, P < 0.01). Conclusion: For elderly patients with hypertension, acupuncture-only treatment has the same efficiency and antihypertensive effect compared to drug therapy and acupuncture plus drugs outperforms drugs-only treatment. If the patients receive therapy with less frequency per week and longer duration, there will be a more obvious antihypertensive effect. Due to the methodological defects in the included study and the limited sample size of this paper, more well-designed randomized controlled trials are needed for verification. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022376407, PROSPERO (CRD42022376407).

Effect of mindfulness on anxiety and depression in insomnia patients: A systematic review and meta-analysis.

Background: As a common clinical symptom, insomnia has a high incidence of combined mental illness and it is also a risk factor for the development of depression, anxiety and suicide. As a new concept in the field of health in recent years, mindfulness therapy can improve insomnia, anxiety and depression, which is a new way to solve such diseases. Objective: This study aims to systematically evaluate the effects of mindfulness compared with conventional treatment on scores of the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) in people with insomnia and anxiety-depressive symptoms. Methods: Articles published before October 2022 were searched from seven databases and included in randomized controlled trials (RCTs) to evaluate mindfulness therapy. The assessment tool of Cochrane bias risk was used to evaluate the methodological quality of the literature. The main outcome indicators were HAMD and HAMA scores, and the secondary outcome indicators were SDS and SAS scores. Results: Ten randomized controlled trials including 1,058 subjects were systematically evaluated and meta-analyzed in this study. In the main outcome indicators, there was a significant difference between mindfulness therapy and conventional treatment in reducing HAMD score (MD: -3.67, 95% CI: -5.22-2.11, p < 0.01) and HAMA score (MD: -3.23, 95% CI: -3.90-2.57, p < 0.01). In the secondary outcome indicators, mindfulness therapy also showed a significant difference in reducing SDS scores (MD: -6.49, 95% CI: -6.86-6.11, p < 0.01) and SAS scores (MD: -7.97, 95% CI: -9.68-6.27, p < 0.01) compared with conventional treatment. Conclusion: For the people with insomnia, anxiety and depression, the use of conventional treatment with the addition of 4-12 weeks of mindfulness treatment can significantly improve anxiety and depression symptoms of patients. This is a new diagnosis and treatment idea recommended for insomniacs with or without anxiety and depression symptoms. Due to the methodological defects in the included study and the limited sample size of this paper, more well-designed randomized controlled trials are needed for verification.