Trehalose attenuates testicular aging by activating autophagy and improving mitochondrial quality.

BACKGROUND: Reproductive aging can adversely affect male fertility and the health of offspring. The aging process is accompanied by impaired autophagy. Recent studies have shown that Trehalose plays an important role in the prevention of various diseases by regulating autophagy. However, the roles of Trehalose in testicular aging and reproductive decline remain to be clarified. OBJECTIVE: The present study aimed to evaluate the protective effects of Trehalose on testes in an aging mouse model. MATERIALS AND METHODS: In this study, an in vivo aging model in mice by administering D-galactose was established to explore the protective effect of Trehalose on testicular aging. We examined histological changes and related indicators of apoptosis, autophagy, mitochondrial biogenesis, and sperm quality. RESULTS: D-galactose treatment induced oxidative stress, apoptosis, and impairment of autophagy of testicular cells in mouse testes. Trehalose administration significantly reduced germ cell apoptosis and DNA damage caused by D-galactose-induced oxidative stress. Notably, Trehalose activated autophagy activity and improved mitochondrial function in testicular cells. Furthermore, Trehalose treatment increased the expression level of the tight junction protein ZO-1, and accelerated clearance of damaged mitochondria in Sertoli cells, indicating that Trehalose ameliorated Sertoli cell function in D-galactose-induced aging testes. DISCUSSION AND CONCLUSION: These findings suggest that Trehalose administration activated the autophagy activity in testicular cells and improved mitochondrial function, thereby effectively preventing testicular aging. Trehalose and its activated autophagy are crucial for preventing testicular aging, thus restoring autophagy activity by administering Trehalose could be a promising means to delay aging.

A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG.

Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based on Siamese networks, based on single-channel EEG. Our proposed method consists of a Siamese network architecture and a redesigned loss with distance metrics. Two encoders are used in the Siamese network to generate latent features of the EEG epochs, and the contrastive loss, which is also a distance metric, is used to compare the similarity or differences between EEG epochs from the same or different sleep stages. We evaluated our method on single-channel EEGs from different channels (Fpz-Cz and F4-EOG (left)) from two public datasets SleepEDF and MASS-SS3 and achieved the overall accuracies MF1 and Cohen's kappa coefficient of 85.2%, 78.3% and 0.79 on SleepEDF and 87.2%, 82.1% and 0.81 on MASS-SS3. The results show that our method can significantly improve the performance of sleep staging models and outperform the state-of-the-art sleep staging methods. The performance of our method also confirms that the features captured by Siamese networks and distance metrics are useful for sleep staging.

Evaluating the completeness of the reporting of abstracts since the publication of the CONSORT extension for abstracts: an evaluation of randomized controlled trial in ten nursing journals.

BACKGROUND: As a practice-oriented discipline, strict adherence to reporting guidelines is particularly important in randomized controlled trial (RCT) abstracts of the nursing area. However, whether abstract reports after 2010 have complied with the Consolidated Standards of Reporting Trials for Abstracts (CONSORT-A) guideline is unclear. This study aimed to evaluate whether the publication of CONSORT-A has improved abstract reporting in nursing and explores the factors associated with better adherence to the guidelines. METHODS: We searched the Web of Science for 200 RCTs randomly selected from ten nursing journals. We used a data extraction form based on CONSORT-A, including 16 items, to analyze the reporting adherence to the guidelines, and the reporting rate of each item and the total score for each abstract were used to indicate adherence and overall quality score (OQS, range 0-16). A comparison of the total mean score between the two periods was made, and affecting factors were analyzed. RESULTS: In the studies we included, 48 abstracts were published pre-CONSORT-A whereas 152 post-CONSORT-A. The overall mean score for reporting adherence to 16 items was 7.41 ± 2.78 and 9.16 ± 2.76 for pre- and post-CONSORT-A, respectively (total score: 16). The most poorly reported items are "harms (0%)," "outcomes in method (8.5%)," "randomization (25%)," and "blinding (6.5%)." Items including the year of publication, impact factor, multiple center trial, word count, and structured abstract are significantly associated with higher adherence. CONCLUSIONS: The adherence to abstract reporting in nursing literature has improved since the CONSORT-A era, but the overall completeness of RCT abstracts remained low. A joint effort by authors, editors, and journals is necessary to improve reporting quality of RCT abstracts.

An attention-based temporal convolutional network for rodent sleep stage classification across species, mutants and experimental environments with single-channel electroencephalogram.

Objective.Sleep perturbation by environment, medical procedure and genetic background is under continuous study in biomedical research. Analyzing brain states in animal models such as rodents relies on categorizing electroencephalogram (EEG) recordings. Traditionally, sleep experts have classified these states by visual inspection of EEG signatures, which is laborious. The heterogeneity of sleep patterns complicates the development of a generalizable solution across different species, genotypes and experimental environments.Approach.To realize a generalizable solution, we proposed a cross-species rodent sleep scoring network called CSSleep, a robust deep-learning model based on single-channel EEG. CSSleep starts with a local time-invariant information learning convolutional neural network. The second module is the global transition rules learning temporal convolutional network (TRTCN), stacked with bidirectional attention-based temporal convolutional network modules. The TRTCN simultaneously captures positive and negative time direction information and highlights relevant in-sequence features. The dataset for model evaluation comprises the single-EEG signatures of four cohorts of 16 mice and 8 rats from three laboratories.Main results.In leave-one-cohort-out cross-validation, our model achieved an accuracy of 91.33%. CSSleep performed well on generalization across experimental environments, mutants and rodent species by using single-channel EEG.Significance.This study aims to promote well-standardized cross-laboratory sleep studies to improve our understanding of sleep. Our source codes and supplementary materials will be disclosed later.

FASSNet: fast apnea syndrome screening neural network based on single-lead electrocardiogram for wearable devices.

Objective. Sleep apnea (SA) is a chronic condition that fragments sleep and results in intermittent hypoxemia, which in long run leads to cardiovascular diseases like stroke. Diagnosis of SA through polysomnography is costly, inconvenient, and has long waiting list. Wearable devices provide a low-cost solution to the ambulatory detection of SA syndrome for undiagnosed patients. One of the wearables are the ones based on minute-by-minute analysis of single-lead electrocardiogram (ECG) signal. Processing ECG segments online at wearables contributes to memory conservation and privacy protection in long-term SA monitoring, and light-weight models are required due to stringent computation resource.Approach.We propose fast apnea syndrome screening neural network (FASSNet), an effective end-to-end neural network to perform minute-apnea event detection. Low-frequency components of filtered ECG spectrogram are selected as input. The model initially processes the spectrogram via convolution blocks. Bidirectional long-short-term memory blocks are used along the frequency axis to complement position information of frequency bands. Layer normalisation is implemented to retain in-epoch information since apnea periods have variable lengths. Experiments were carried out on 70 recordings of Apnea-ECG database, where each 60 s ECG segment is manually labelled as an apnea or normal minute by technician. Both ten-fold and patient-agnostic validation protocols are adopted.Main results.FASSNet is light-weighted, since its value of model parameters and multiply accumulates are 0.06% and 28.33% of those of an AlexNet benchmark, respectively. Meanwhile, FASSNet achieves an accuracy of 87.09%, a sensitivity of 77.96%, a specificity of 91.74%, and an F1 score of 81.61% in apnea event detection. Its accuracy of diagnosing SA syndrome severity exceeds 90% under the patient-agnostic protocol.Significance:FASSNet is a computationally efficient and accurate neural network for wearables to detect SA events and estimate SA severity based on minute-level diagnosis.

Inpatient satisfaction with nursing care in a backward region: a cross-sectional study from northwestern China.

OBJECTIVES: The aim of the study was to examine the level of patient satisfaction with nursing care and identify the factors affecting satisfaction from the inpatient's perspective in a backward region of China. DESIGN: This was a cross-sectional study. SETTING: The study was conducted at a tertiary hospital located in northwest China. PARTICIPANTS: Patients admitted to the ward for at least 48 hours were chosen to participate in the survey. PRIMARY OUTCOME MEASURE: The Newcastle Satisfaction with Nursing Care Scale was used. Data were collected from 219 patients. RESULTS: The overall inpatient satisfaction with nursing care was 78.15±4.74. Patients were more satisfied with nurses who respected their privacy and treated them as individuals (67.7%). Patients were least satisfied with the type of information nurses gave them (11.7%) and with the sufficient awareness of their needs. Patients who were married, had a history of hospitalisation, surgery and were taken charge of by junior nurses had higher satisfaction. CONCLUSIONS: The overall level of patient satisfaction was moderate. Patient-centred individualised care and providing sufficient information model of care are needed. There was a need for nurses to be aware of patients' individualised care needs and to provide them with more information. This study may suggest/urge hospital administrators, policymakers and nurses to be more sensitive with patients' married status, history of hospitalisation and surgery, the professional title of in charged nurses when care is provided. Ultimately to achieve better outcome of patients' hospitalisation.

Self-assembly of electroactive layer-by-layer films of heme proteins with anionic surfactant dihexadecyl phosphate.

Anionic surfactant dihexadecyl phosphate (DHP) with two hydrocarbon chains can be self-assembled into a double-layer structure with tail-to-tail configuration and negatively charged head groups toward outside in its aqueous dispersion. Due to this unique biomembrane-like structure, the "charge reversal" in DHP adsorption on solid surface was realized, and the DHP was successfully assembled with positively charged myoglobin (Mb) or hemoglobin (Hb) into {DHP/protein}(n) layer-by-layer films. Quartz crystal microbalance (QCM), UV-vis spectroscopy, and cyclic voltammetry (CV) were used to monitor or confirm the film assembly process. The {DHP/protein}(n) films grown on pyrolytic graphite (PG) electrodes showed a pair of well-defined and nearly reversible CV peaks at about -0.35 V vs SCE in pH 7.0 buffers, characteristic of the protein heme Fe(III)/Fe(II) redox couples. Based on the direct electrochemistry of heme proteins, the {DHP/protein}(n) films could also be used to electrochemically catalyze reduction of oxygen, hydrogen peroxide and nitrite with significant lowering of reduction overpotentials. Scanning electron microscopy (SEM), UV-vis spectroscopy, and reflectance absorption infrared (RAIR) spectroscopy were employed to characterize the {DHP/protein}(n) films, suggesting that the proteins in the films retain their near-native structure.