Mediating effects of depression on sleep disturbance and frailty in older adult type 2 diabetes patients in the community.

Introduction: With the progressive aging of the population, frailty is now a significant challenge in geriatrics research. A growing amount of evidence suggests that sleep disturbance and depression have independent effects on frailty, although the underlying mechanisms are not yet clear. This study aimed to investigate the mediating role of depression in the relationship between sleep disturbance and frailty in older adult patients with type 2 diabetes (T2DM) in the community. Method: Purposive sampling was used to collect face-to-face data from 342 community-dwelling T2DM patients in Chengdu, Sichuan Province, China, between February and May 2023. The Pittsburgh Sleep Quality Index (PSQI) scale was used to evaluate sleep quality, the Simple Geriatric Depression Scale (GDS-15) was used to evaluate depressive symptoms, and the FRAIL Scale (FRAIL) was used to evaluate frailty. Linear regression equation and bootstrap self-sampling were used to verify the mediating role of depressive symptoms in sleep disturbance and frailty. Result: The study found that sleep disturbance had a direct positive effect with frailty [ß = 0.040, 95% CI: (0.013, 0.069)]. Additionally, depression had a direct positive effect on frailty [ß = 0.130, 95% CI: (0.087, 0.173)], and depression was found to partially mediate the relationship between sleep disturbance and frailty. Conclusion: Poor sleep quality and frailty are common in patients with T2DM. To reduce the frailty of older adult T2DM patients, all levels of society (government, medical institutions, and communities) must pay more attention to mental health. A variety of interventions should be considered to improve sleep quality and depression, which in turn may prevent or control frailty.

Mediating effect of self-concealment between non-suicidal self-injury and internet addiction in college students: a cross-sectional study.

BACKGROUND: Internet addiction, defined as uncontrolled behaviour resulting from the use of the Internet without the influence of addictive substances, which can seriously impair academic, occupational and social functioning. Non-suicidal self-injury, defined as self-injurious behaviour without the intent to die, and its addictive characteristics are similar to those of Internet addiction. Currently, there is a lack of research on the relationship between non-suicidal self-injury and Internet addiction. The purpose of this study was to examine the relationship between non-suicidal self-injury and internet addiction among college students and the role of self-concealment in this relationship. METHODS: In this study, data were collected online between December 2022 and January 2023 from 600 university students in Chengdu, Sichuan Province, China, using purposive sampling. The questionnaires included the Non-Suicidal Self-Injury Inventory (NSSI), the Self-Concealment Scale (SCS) and the Internet Addiction Test (IAT). RESULTS: A total of 573 valid questionnaires were recovered, with a valid recovery rate of 95.50%. CONCLUTION: The results suggest that self-concealment plays a partial mediating role between non-suicidal self-injury and internet addiction among college students. The authors emphasized the importance of internet addiction. In order to reduce the occurrence of internet addiction, schools should provide targeted interventions to promote the psychological health of college students' internet addictive behaviours.

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction.

In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients' time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient's clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient's time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.

P-ResUnet: Segmentation of brain tissue with Purified Residual Unet.

Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medical image segmentation, which achieved promising performance by using the skip connection to fuse the low-level and high-level information. However, In the process of integrating the low-level and high-level information, the non-object information (noise) will be added, which reduces the accuracy of medical image segmentation. Likewise, the same problem also exists in the residual unit. Since the output and input of the residual unit are fused, the non-object information (noise) of the input of the residual unit will be in the integration. To address this challenging problem, in this paper we propose a Purified Residual U-net for the segmentation of brain tissue. This model encodes the image to obtain deep semantic information and purifies the information of low-level features and the residual unit from the image, and acquires the result through a decoder at last. We use the Dilated Pyramid Separate Block (DPSB) as the first block to purify the features for each layer in the encoder without the first layer, which expands the receptive field of the convolution kernel with only a few parameters added. In the first layer, we have explored the best performance achieved with DPB. We find the most non-object information (noise) in the initial image, so it is good for the accuracy to exchange the information to the max degree. We have conducted experiments with the widely used IBSR-18 dataset composed of T-1 weighted MRI volumes from 18 subjects. The results show that compared with some of the cutting-edge methods, our method enhances segmentation performance with the mean dice score reaching 91.093% and the mean Hausdorff distance decreasing to 3.2606.

The protective effect of grit on clinical nurses' occupational psychological distress: Mediating and suppressing effects of Hope.

As at a high-risk group of psychological distress, nurses generally experience varying degrees of stress, anxiety, and depression. This paper identifies the positive factors that may negatively regulate the psychological pain of clinical nurses and their mechanisms of action, providing reliable references for clinical nurse support management. The effects and mechanisms of hope and the two components of grit consistency of interest and perseverance of effort) on clinical nurses' psychological distress (depression, anxiety, and stress) were observed in this study. A total of 635 Chinese clinical nurses (90.4% female) completed an anonymous questionnaire for the survey. As expected, hope, consistency of interest, and perseverance of effort were negatively correlated with the three indicators of psychological distress (r = -0.21 ~ -0.38, p < 0.01). Path analysis results showed that hope significantly mediated the negative effect of consistency of interest on psychological distress, with an effect of 12.96%. Hope also covered up the perseverance of effort on psychological distress, the effect of 110.63%. In the influence of consistency of interest and perseverance of effort on psychological distress, hope contributed a vital mediating. Based on these results, it can be concluded that grit and hope have protective effects on psychological distress in clinical nurses. Significantly increasing the level of hope or grit may effectively prevent and reduce psychological distress in clinical nurses.

A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy.

Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.

Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network.

In recent years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate, and feasible electroencephalogram (EEG) grading scale for infantile spasms. However, manual EEG annotation is, in general, very time-consuming, and BASED scoring is no exception. Convolutional neural networks (CNNs) have proven their great potential in many EEG classification problems. However, very few research studies have focused on the use of CNNs for BASED scoring, a challenging but vital task in the diagnosis and treatment of infantile spasms. This study proposes an automatic BASED scoring framework using EEG and a deep CNN. The feasibility of using CNN for automatic BASED scoring was investigated in 36 patients with infantile spasms by annotating their long-term EEG data with four levels of the BASED score (scores 5, 4, 3, and ≤2). In the validation set, the accuracy was 96.9% by applying a multi-layer CNN to classify the EEG data as a 4-label problem. The extensive experiments have demonstrated that our proposed approach offers high accuracy and, hence, is an important step toward an automatic BASED scoring algorithm. To the best of our knowledge, this is the first attempt to use a CNN to construct a BASED-based scoring model.

Long non-coding RNA-X-inactive specific transcript inhibits cell viability, and induces apoptosis through the microRNA-30c-5p/Bcl2-like protein 11 signaling axis in human granulosa-like tumor cells.

The role of long noncoding RNAs (lncRNAs) is being actively explored in polycystic ovary syndrome (PCOS). Recent research has shown that long non-coding RNA (lncRNA) X-inactive Specific Transcript (XIST) is overexpressed in patients with PCOS and is associated with poor pregnancy outcomes. However, the precise function and mechanism of action of lncRNA XIST in PCOS are unknown. We aimed to determine whether lncRNA XIST contributes to PCOS by modulating ovarian granulosa cell physiology. We also investigated any potential molecular regulatory mechanisms. In this study, we discovered that the lncRNA XIST was significantly downregulated in human ovarian granulosa-like tumor (KGN) cells. Notably, overexpression of lncRNA XIST decreased miR-30c-5p expression in KGN cells, inhibited proliferation, and induced apoptosis in KGN cells. However, cotransfection with amiR-30c-5p mimic significantly reduced these effects. Additionally, we discovered that the miR-30c-5p mimic effectively inhibited Bcl2-like protein 11 (BCL2L11) expression, a critical apoptotic promoter, whereas silencing of miR-30c-5p increased BCL2L11 expression, inhibited KGN cell proliferation, and induced apoptosis. In contrast, cotransfection of BCL2L11 with siRNA significantly reversed these effects. In conclusion, this study established that lncRNA XIST plays a critical role in PCOS by modulating the miR-30c-5p/BCL2L11 signaling axis and regulating ovarian granulosa cell physiology.

Transformer-Based High-Frequency Oscillation Signal Detection on Magnetoencephalography From Epileptic Patients.

High-frequency oscillations (HFOs), observed within 80-500 Hz of magnetoencephalography (MEG) data, are putative biomarkers to localize epileptogenic zones that are critical for the success of surgical epilepsy treatment. It is crucial to accurately detect HFOs for improving the surgical outcome of patients with epilepsy. However, in clinical practices, detecting HFOs in MEG signals mainly depends on visual inspection by clinicians, which is very time-consuming, labor-intensive, subjective, and error-prone. To accurately and automatically detect HFOs, machine learning approaches have been developed and have demonstrated the promising results of automated HFO detection. More recently, the transformer-based model has attracted wide attention and achieved state-of-the-art performance on many machine learning tasks. In this paper, we are investigating the suitability of transformer-based models on the detection of HFOs. Specifically, we propose a transformer-based HFO detection framework for biomedical MEG one-dimensional signal data. For signal classification, we develop a transformer-based HFO (TransHFO) classification model. Then, we investigate the relationship between depth of deep learning models and classification performance. The experimental results show that the proposed framework outperforms the state-of-the-art HFO classifiers, increasing classification accuracy by 7%. Furthermore, we find that shallow TransHFO ( < 10 layers) outperforms deep TransHFO models (≥10 layers) on most data augmented factors.

Fusion of sequential visits and medical ontology for mortality prediction.

The goal of mortality prediction task is to predict the future death risk of patients according to their previous Electronic Healthcare Records (EHR). The main challenge of mortality prediction is how to design an accurate and robust predictive model with sequential, multivariate, sparse and irregular EHR data. In addition, the performance of model may be affected by lack of sufficient information of some patients with rare diseases in EHRs. To address these challenges, we propose a model to fuse Sequential visits and Medical Ontology to predict patients' death risk. SeMO not only learns reasonable embeddings for medical concepts from sequential and irregular visits, but also exploits medical ontology to improve the prediction performance. With integration of multivariate features, SeMO learns robust representations of medical codes, mitigating data insufficiency and insightful sequential dependencies among patient's visits. Experimental results on real world datasets prove that the proposed SeMO improves the prediction performance compared with the baseline approaches. Our model achieves an precision of up to 0.975. Compared with RNN, the precision has been improved up to 2.204%.

Multi-Head Self-Attention Model for Classification of Temporal Lobe Epilepsy Subtypes.

As a long-standing chronic disease, Temporal Lobe Epilepsy (TLE), resulting from abnormal discharges of neurons and characterized by recurrent episodic central nervous system dysfunctions, has affected more than 70% of drug-resistant epilepsy patients across the world. As the etiology and clinical symptoms are complicated, differential diagnosis of TLE mainly relies on experienced clinicians, and specific diagnostic biomarkers remain unclear. Though great effort has been made regarding the genetics, pathology, and neuroimaging of TLE, an accurate and effective diagnosis of TLE, especially the TLE subtypes, remains an open problem. It is of a great importance to explore the brain network of TLE, since it can provide the basis for diagnoses and treatments of TLE. To this end, in this paper, we proposed a multi-head self-attention model (MSAM). By integrating the self-attention mechanism and multilayer perceptron method, the MSAM offers a promising tool to enhance the classification of TLE subtypes. In comparison with other approaches, including convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), experimental results on our collected MEG dataset show that the MSAM achieves a supreme performance of 83.6% on accuracy, 90.9% on recall, 90.7% on precision, and 83.4% on F1-score, which outperforms its counterparts. Furthermore, effectiveness of varying head numbers of multi-head self-attention is assessed, which helps select the optimal number of multi-head. The self-attention aspect learns the weights of different signal locations which can effectively improve classification accuracy. In addition, the robustness of MSAM is extensively assessed with various ablation tests, which demonstrates the effectiveness and generalizability of the proposed approach.