Influence of vitamin D-calcium on metabolic profile for gestational diabetes: a meta-analysis of randomized controlled trials.

INTRODUCTION: The use of vitamin D-calcium supplementation for treating gestational diabetes remains unclear. This meta-analysis aims to evaluate the efficacy of vitamin D-calcium supplementation in the treatment of gestational diabetes. METHODS: Several databases including PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases were systemically searched from inception to August 2023, and we included the randomized controlled trials (RCTs) assessing the effect of vitamin D-calcium supplementation on the metabolic profile of gestational diabetes. RESULTS: We included five eligible RCTs and 306 pregnant women in this meta-analysis. Compared with control group in pregnant women with gestational diabetes, vitamin D-calcium supplementation was associated with remarkably decreased fasting plasma glucose (SMD=-0.67; 95% CI=-0.93 to -0.41; p <0.00001), serum insulin (SMD=-1.09; 95% CI=-1.89 to -0.29; p = .007) and LDL (SMD=-0.35; 95% CI=-0.63 to -0.06; p = .02), but demonstrated no impact on total cholesterol (SMD=-0.05; 95% CI=-0.81 to 0.71; p = .90) or triglycerides (SMD=-0.14; 95% CI=-0.86 to 0.58; p = .70). CONCLUSIONS: Vitamin D-calcium supplementation is effective to improve metabolic profile for the treatment of gestational diabetes.

Clinical Comparison between HD-tDCS and tDCS for Improving Upper Limb Motor Function: A Randomized, Double-Blinded, Sham-Controlled Trial.

Background: Stroke is a common and frequently occurring disease among middle-aged and elderly people, with approximately 55%-75% of patients remaining with upper limb dysfunction. How to promote the recovery of motor function at an early stage is crucial to the life of the patient. Objectives: This study aimed to investigate whether high-definition transcranial direct current stimulation (HD-tDCS) of the primary motor cortex (M1) functional area in poststroke patients in the subacute phase is more effective in improving upper limb function than conventional tDCS. Methods: This randomized, sham-controlled clinical trial included 69 patients with subcortical stroke. They were randomly divided into the HD-tDCS, anodal tDCS (a-tDCS), and sham groups. Each group received 20 sessions of stimulation. The patients were assessed using the Action Research Arm Test, Fugl-Meyer score for upper extremities, Motor Function Assessment Scale, and modified Barthel index (MBI) pretreatment and posttreatment. Results: The intragroup comparison scores improved after 4 weeks of treatment. The HD-tDCS group showed a slightly greater, but nonsignificant improvement as compared to a-tDCS group in terms of mean change observed in function of trained items. The MBI score of the HD-tDCS group was maintained up to 8 weeks of follow-up and was higher than that in the a-tDCS group. Conclusion: Both HD-tDCS and a-tDCS can improve upper limb motor function and daily activities of poststroke patients in the subacute stage. This trial is registered with ChiCTR2000031314.

Multiple semantic X-ray medical image retrieval using efficient feature vector extracted by FPN.

OBJECTIVE: Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy of image retrieval. Highly expressive feature vectors play a crucial role in the search process. In this paper, we propose an effective deep convolutional neural network (CNN) model to extract concise feature vectors for multiple semantic X-ray medical image retrieval. METHODS: We build a feature pyramid based CNN model with ResNet50V2 backbone to extract multi-level semantic information. And we use the well-known public multiple semantic annotated X-ray medical image data set IRMA to train and test the proposed model. RESULTS: Our method achieves an IRMA error of 32.2, which is the best score compared to the existing literature on this dataset. CONCLUSIONS: The proposed CNN model can effectively extract multi-level semantic information from X-ray medical images. The concise feature vectors can improve the retrieval accuracy of multi-semantic and unevenly distributed X-ray medical images.

[An efficient and practical electrode optimization method for transcranial electrical stimulation].

Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.

[Simulation model of tumor-treating fields].

Tumor-treating fields (TTFields) is a novel treatment modality for malignant solid tumors, often employing electric field simulations to analyze the distribution of electric fields on the tumor under different parameters of TTFields. Due to the present difficulties and high costs associated with reproducing or implementing the simulation model construction techniques, this study used readily available open-source software tools to construct a highly accurate, easily implementable finite element simulation model for TTFields. The accuracy of the model is at a level of 1 mm 3. Using this simulation model, the study carried out analyses of different factors, such as tissue electrical parameters and electrode configurations. The results show that factors influncing the distribution of the internal electric field of the tumor include changes in scalp and skull conductivity (with a maximum variation of 21.0% in the treatment field of the tumor), changes in tumor conductivity (with a maximum variation of 157.8% in the treatment field of the tumor), and different electrode positions and combinations (with a maximum variation of 74.2% in the treatment field of the tumor). In summary, the results of this study validate the feasibility and effectiveness of the proposed modeling method, which can provide an important reference for future simulation analyses of TTFields and clinical applications.

Clinical application of enhanced recovery after surgery concept in laparoscopic treatment of pediatric acute appendicitis.

BACKGROUND: This study assesses whether enhanced recovery after surgery (ERAS) is beneficial in treating acute appendicitis in pediatrics by laparoscopic techniques. METHOD: The children with acute appendicitis (n = 116) were divided into the ERAS group (n = 54) and the control group (n = 62). Then the preoperative data, intraoperative observation indexes, and postoperative data were analyzed. RESULTS: There was no significant difference in preoperative data and intraoperative observation indexes between the two groups. C-reactive protein (CRP) and white blood cell (WBC) in the ERAS group were significantly lower than those in the control group 3 days after the operation. Moreover, no significant difference in the visual analog score (VAS) between the two groups 3 days after the operation, but the other postoperative observation indexes in the ERAS group were significantly better than those in the control group. Nausea and vomiting in the ERAS group were significantly lower than those in the control group, with no significant difference in other complications between the two groups. CONCLUSION: ERAS could improve children's comfort, reduce some postoperative complications, reduce hospitalization expenses, and speed up recovery from acute appendicitis treated by laparoscopy. Therefore, it has clinical application value.

[Electrocardiogram signal classification algorithm of nested long short-term memory network based on focal loss function].

Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.

Is frailty a prognostic factor for adverse outcomes in older patients with acute coronary syndrome?

BACKGROUND: There is very limited guidance in regard to how biological age should be estimated and how different comorbidity conditions influence the benefit-risk ration of interventions. Frailty is an important health-related problem in patients, especially in older adults. It is a reflection of biologic rather than chronologic age; frailty may explain why there remains substantial heterogeneity in clinical outcomes within the older patients' population. AIMS: We aimed to review the prognostic value of frailty for adverse outcomes in older patients with acute coronary syndrome (ACS). METHODS: Studies published until December 31, 2018, identified by systematic Medline, Embase, and Cochrane Controlled Register of Trials (CENTRAL) searches were reviewed for the association between frailty and mortality in older patients with ACS. We used the Newcastle-Ottawa Quality Assessment Scale to assess the quality of the included studies. We extracted the information of hazard ratios (HR) and odds ratios (OR) with accompanying 95% confidence intervals (CI), and P values of multivariable analysis. Heterogeneity across studies was determined using the Cochran Q value by Review Manager 5.3. RESULTS: A total of 11 articles involving 7212 patients were included in this meta-analysis. Two studies (Sujino, Y 2015 and Alonso, S.GL 2016; n = 264) reported that frailty was significantly associated with in-hospital mortality in patients with ACS (range of reported OR between 6.38 and 12.0). We performed a subgroup analysis of the other nine studies based on differences in the follow-up time. Pooled meta-analysis demonstrates that frailty was associated with short-term, medium-term, and long-term mortality (HR = 3.67, 4.09, 1.66). There was no association between frailty and bleeding in older patients with ACS. CONCLUSIONS: Frailty measured by Canadian Study of Health and Aging Clinical Frailty Scale (CSHA-CFS), the Edmonton Frail Scale (EFS), Fried score, Green scores, frailty instrument from the Survey of Health, Ageing and Retirement in Europe (SHARE-FI) index, and FRAIL (Fatigue, Resistance, Ambulation, Illnesses, Loss of weight) scale, leads to significantly higher mortality rates in older patients with ACS.

A Highly Sensitive Amperometric Glutamate Oxidase Microbiosensor Based on a Reduced Graphene Oxide/Prussian Blue Nanocube/Gold Nanoparticle Composite Film-Modified Pt Electrode.

A simple method that relies only on an electrochemical workstation has been investigated to fabricate a highly sensitive glutamate microbiosensor for potential neuroscience applications. In this study, in order to develop the highly sensitive glutamate electrode, a 100 µm platinum wire was modified by the electrochemical deposition of gold nanoparticles, Prussian blue nanocubes, and reduced graphene oxide sheets, which increased the electroactive surface area; and the chitosan layer, which provided a suitable environment to bond the glutamate oxidase. The optimization of the fabrication procedure and analytical conditions is described. The modified electrode was characterized using field emission scanning electron microscopy, impedance spectroscopy, and cyclic voltammetry. The results exhibited its excellent sensitivity for glutamate detection (LOD = 41.33 nM), adequate linearity (50 nM-40 µM), ascendant reproducibility (RSD = 4.44%), and prolonged stability (more than 30 repetitive potential sweeps, two-week lifespan). Because of the important role of glutamate in neurotransmission and brain function, this small-dimension, high-sensitivity glutamate electrode is a promising tool in neuroscience research.

Invasive Brain Machine Interface System.

Because of high spatial-temporal resolution of neural signals obtained by invasive recording, the invasive brain-machine interfaces (BMI) have achieved great progress in the past two decades. With success in animal research, BMI technology is transferring to clinical trials for helping paralyzed people to restore their lost motor functions. This chapter gives a brief review of BMI development from animal experiments to human clinical studies in the following aspects: (1) BMIs based on rodent animals; (2) BMI based on non-human primates; and (3) pilot BMIs studies in clinical trials. In the end, the chapter concludes with a summary of potential opportunities and future challenges in BMI technology.

Brain-Machine Interface-Based Rat-Robot Behavior Control.

Brain-machine interface (BMI) provides a bidirectional pathway between the brain and external facilities. The machine-to-brain pathway makes it possible to send artificial information back into the biological brain, interfering neural activities and generating sensations. The idea of the BMI-assisted bio-robotic animal system is accomplished by stimulations on specific sites of the nervous system. With the technology of BMI, animals' locomotion behavior can be precisely controlled as robots, which made the animal turning into bio-robot. In this chapter, we reviewed our lab works focused on rat-robot navigation. The principles of rat-robot system have been briefly described first, including the target brain sites chosen for locomotion control and the design of remote control system. Some methodological advances made by optogenetic technologies for better modulation control have then been introduced. Besides, we also introduced our implementation of "mind-controlled" rat navigation system. Moreover, we have presented our efforts made on combining biological intelligence with artificial intelligence, with developments of automatic control and training system assisted with images or voices inputs. We concluded this chapter by discussing further developments to acquire environmental information as well as promising applications with write-in BMIs.

Nonlinear Modeling of Neural Interaction for Spike Prediction Using the Staged Point-Process Model.

Neurons communicate nonlinearly through spike activities. Generalized linear models (GLMs) describe spike activities with a cascade of a linear combination across inputs, a static nonlinear function, and an inhomogeneous Bernoulli or Poisson process, or Cox process if a self-history term is considered. This structure considers the output nonlinearity in spike generation but excludes the nonlinear interaction among input neurons. Recent studies extend GLMs by modeling the interaction among input neurons with a quadratic function, which considers the interaction between every pair of input spikes. However, quadratic effects may not fully capture the nonlinear nature of input interaction. We therefore propose a staged point-process model to describe the nonlinear interaction among inputs using a few hidden units, which follows the idea of artificial neural networks. The output firing probability conditioned on inputs is formed as a cascade of two linear-nonlinear (a linear combination plus a static nonlinear function) stages and an inhomogeneous Bernoulli process. Parameters of this model are estimated by maximizing the log likelihood on output spike trains. Unlike the iterative reweighted least squares algorithm used in GLMs, where the performance is guaranteed by the concave condition, we propose a modified Levenberg-Marquardt (L-M) algorithm, which directly calculates the Hessian matrix of the log likelihood, for the nonlinear optimization in our model. The proposed model is tested on both synthetic data and real spike train data recorded from the dorsal premotor cortex and primary motor cortex of a monkey performing a center-out task. Performances are evaluated by discrete-time rescaled Kolmogorov-Smirnov tests, where our model statistically outperforms a GLM and its quadratic extension, with a higher goodness-of-fit in the prediction results. In addition, the staged point-process model describes nonlinear interaction among input neurons with fewer parameters than quadratic models, and the modified L-M algorithm also demonstrates fast convergence.

[Effects of Exogenous Nerve Growth Factor on Late Reperfusion after Myocardial Infarction].

This study demonstrates that nerve growth factor (NGF) plays a protective role in myocardial infarction and early reperfusion by reducing the myocardial cell apoptosis and by improving ventricular remodeling and seeks to assess the effects and mechanisms of NGF on late reperfusion after myocardial infarction. The models of late reperfusion were established by ligating the left main coronary artery and then cutting the suture 2 hours after coronary artery ligation. The rats in NGF treatment group were injected 10 µL Ad-NGF (by constructing the adenovirus vector Ad-NGF containing NGF gene) at four locations around infarction. The rats in adenoviral vector (Adv) group were injected 10 µL adenoviral cector as the NGF group. The late reperfusion group and the sham group were given normal saline as above, and the sham group underwent thracotomy without coronary ligation. On the 3rd, 7th, 14th and 28th day after operation, we investigated the role of NGF on late reperfusion by recording cardiac structure and function with echocardiography, by examining the expression of NGF and VIII factor with immunohistochemical method, and by evaluating the myocardial cell apoptosis with terminal dUTP nick end-labeling method (TUNEL). We found that the NGF group had higher expression of NGF protein (P < 0.01) and lower apoptosis index (AI) (P < 0.01 or P < 0.05) compared to the late reperfusion group and Adv group on all time points. The NGF group had remarkably higher level of neovascularization compared to the late reperfusion group on the 14th day (P < 0.01) and the 28th day (P < 0.05). The NGF group also had higher LVEF and FS levels compared to the late reperfusion group on the 14th day (P = 0.006, P = 0.006) and on the 28th day (P = 0.000, P = 0.000). Whereas the NGF group had lower LVEDD, LVESD (P = 0.038, P = 0.000) and lower LVEDV, LVESV (P = 0.001, P = 0.000) on the 28th day compared to late reperfusion group. In this experiment, the NGF gene carried by adenovirus vector had been transfected and obviously increased the expression of NGF protein in NGF group. NGF may help postpone the myocardial remodeling and improve the heart function by promoting the myocardial neovascularization and inhibiting myocardial apoptosis.

Anatomical and functional volume concordance between FDG PET, and T2 and diffusion-weighted MRI for cervical cancer: a hybrid PET/MR study.

PURPOSE: To evaluate the concordance among (18)F-FDG PET imaging, MR T2-weighted (T2-W) imaging and apparent diffusion coefficient (ADC) maps with diffusion-weighted (DW) imaging in cervical cancer using hybrid whole-body PET/MR. METHODS: This study prospectively included 35 patients with cervical cancer who underwent pretreatment (18)F-FDG PET/MR imaging. (18)F-FDG PET and MR images were fused using standard software. The percent of the maximum standardized uptake values (SUV max) was used to contour tumours on PET images, and volumes were calculated automatically. Tumour volumes measured on T2-W and DW images were calculated with standard techniques of tumour area multiplied by the slice profile. Parametric statistics were used for data analysis. RESULTS: FDG PET tumour volumes calculated using SUV max (14.30 ± 4.70) and T2-W imaging volume (33.81 ± 27.32 cm(3)) were similar (P > 0.05) at 35 % and 40 % of SUV max (32.91 ± 18.90 cm(3) and 27.56 ± 17.19 cm(3) respectively) and significantly correlated (P < 0.001; r = 0.735 and 0.766). The mean DW volume was 30.48 ± 22.41 cm(3). DW volumes were not significantly different from FDG PET volumes at either 35 % SUV max or 40 % SUV max or from T2-W imaging volumes (P > 0.05). PET subvolumes with increasing SUV max cut-off percentage showed an inverse change in mean ADC values on DW imaging (P < 0.001, ANOVA). CONCLUSION: Hybrid PET/MR showed strong volume concordance between FDG PET, and T2-W and DW imaging in cervical cancer. Cut-off at 35 % or 40 % of SUV max is recommended for (18)F-FDG PET/MR SUV-based tumour volume estimation. The linear tumour subvolume concordance between FDG PET and DW imaging demonstrates individual regional concordance of metabolic activity and cell density.

Comparison of tumor volume between PET and MRI in cervical cancer with hybrid PET/MR.

OBJECTIVE: This study aimed to compare the tumor volume between magnetic resonance imaging-defined gross tumor volume (MR-GTV) and positron emission tomography-defined GTV (PET-GTV) in cervical cancer with hybrid PET/MR. MATERIALS AND METHODS: Twenty-seven patients with cervical cancer underwent PET/MR pelvic examination before radiotherapy. The MR-GTV was manually outlined on T2-weighted MR images. The PET-GTV was autocontoured on PET images using a 40% maximum standardized uptake value threshold. Results were analyzed by Pearson analysis, Bland-Altman plot, and 1-way analysis of variance. RESULTS: Magnetic resonance imaging-GTV significantly correlated with PET-GTV (r(2) = 0.797, P < 0.001). The Bland-Altman plot showed a bad agreement between MR-GTV and PET-GTV. The PET-GTV underestimated the MR-GTV in 23 of 27 tumors. Patients were divided into the following 3 groups according to MR-GTV: less than 14 mL (n = 6), 14 to 62 mL (n = 12), and 62 mL or more (n = 9). The mean (SD) MR-GTV, PET-GTV, ratio, and overlap between MR-GTV and PET-GTV for the less than 14 mL cohort were 9.6 (2.6) mL, 16.7 (10.1) mL, 0.77 (0.40), and 0.47 (0.20), respectively. The PET-GTV overestimated MR-GTV in 4 of the 6 lesions by a mean (SD) of 11.1 (9.4) mL. Among the 14 to 62 mL cohort, the mean (SD) MR-GTV, PET-GTV, ratio, and overlap were 38.6 (14.5) mL, 24.9 (8.6 mL), 1.54 (0.25), and 0.87 (0.08), respectively. The PET-GTV underestimated MR-GTV for 12 tumors by a mean (SD) of 13.7 (8.4) mL. In the 62 mL or more cohort, the mean (SD) MR-GTV, PET-GTV, ratio, and overlap were 85.9 (25.8) mL, 54.3 (14.1) mL, 1.61 (0.35), and 0.87 (0.09), respectively. The PET-GTV underestimated MR-GTV 9 tumors by a mean (SD) of 31.6 (19.5) mL. The ratio and overlap differences were statistically significant among groups (F = 14.619, P < 0.001; F = 25.134, P < 0.001). CONCLUSIONS: Tumor volume discrepancies were observed between MR-GTV and PET-GTV for cervical cancer. With an increasing tumor volume, there was an increase in the difference between MR-GTV and PET-GTV. In addition, larger tumors had a higher degree of overlap compared with small tumors.

A Dual-Branch Framework With Prior Knowledge for Precise Segmentation of Lung Nodules in Challenging CT Scans.

Lung cancer is one of the deadliest cancers globally, and early diagnosis is crucial for patient survival. Pulmonary nodules are the main manifestation of early lung cancer, usually assessed using CT scans. Nowadays, computer-aided diagnostic systems are widely used to assist physicians in disease diagnosis. The accurate segmentation of pulmonary nodules is affected by internal heterogeneity and external data factors. In order to overcome the segmentation challenges of subtle, mixed, adhesion-type, benign, and uncertain categories of nodules, a new mixed manual feature network that enhances sensitivity and accuracy is proposed. This method integrates feature information through a dual-branch network framework and multi-dimensional fusion module. By training and validating with multiple data sources and different data qualities, our method demonstrates leading performance on the LUNA16, Multi-thickness Slice Image dataset, LIDC, and UniToChest, with Dice similarity coefficients reaching 86.89%, 75.72%, 84.12%, and 80.74% respectively, surpassing most current methods for pulmonary nodule segmentation. Our method further improved the accuracy, reliability, and stability of lung nodule segmentation tasks even on challenging CT scans.

Magnetically Compatible Brain Electrode Arrays Based on Single-Walled Carbon Nanotubes for Long-Term Implantation.

Advancements in brain-machine interfaces and neurological treatments urgently require the development of improved brain electrodes applied for long-term implantation, where traditional and polymer options face challenges like size, tissue damage, and signal quality. Carbon nanotubes are emerging as a promising alternative, combining excellent electronic properties and biocompatibility, which ensure better neuron coupling and stable signal acquisition. In this study, a new flexible brain electrode array based on 99.99% purity of single-walled carbon nanotubes (SWCNTs) was developed, which has 30 um × 40 um size, about 5.1 kΩ impedance, and 14.01 dB signal-to-noise ratio (SNR). The long-term implantation experiment in vivo in mice shows the proposed brain electrode can maintain stable LFP signal acquisition over 12 weeks while still achieving an SNR of 3.52 dB. The histological analysis results show that SWCNT-based brain electrodes induced minimal tissue damage and showed significantly reduced glial cell responses compared to platinum wire electrodes. Long-term stability comes from SWCNT's biocompatibility and chemical inertness, the electrode's flexible and fine structure. Furthermore, the new brain electrode array can function effectively during 7-Tesla magnetic resonance imaging, enabling the collection of local field potential and even epileptic discharges during the magnetic scan. This study provides a comprehensive study of carbon nanotubes as invasive brain electrodes, providing a new path to address the challenge of long-term brain electrode implantation.

40 Hz light flickering promotes sleep through cortical adenosine signaling.

Flickering light stimulation has emerged as a promising non-invasive neuromodulation strategy to alleviate neuropsychiatric disorders. However, the lack of a neurochemical underpinning has hampered its therapeutic development. Here, we demonstrate that light flickering triggered an immediate and sustained increase (up to 3 h after flickering) in extracellular adenosine levels in the primary visual cortex (V1) and other brain regions, as a function of light frequency and intensity, with maximal effects observed at 40 Hz frequency and 4000 lux. We uncovered cortical (glutamatergic and GABAergic) neurons, rather than astrocytes, as the cellular source, the intracellular adenosine generation from AMPK-associated energy metabolism pathways (but not SAM-transmethylation or salvage purine pathways), and adenosine efflux mediated by equilibrative nucleoside transporter-2 (ENT2) as the molecular pathway responsible for extracellular adenosine generation. Importantly, 40 Hz (but not 20 and 80 Hz) light flickering for 30 min enhanced non-rapid eye movement (non-REM) and REM sleep for 2-3 h in mice. This somnogenic effect was abolished by ablation of V1 (but not superior colliculus) neurons and by genetic deletion of the gene encoding ENT2 (but not ENT1), but recaptured by chemogenetic inhibition of V1 neurons and by focal infusion of adenosine into V1 in a dose-dependent manner. Lastly, 40 Hz light flickering for 30 min also promoted sleep in children with insomnia by decreasing sleep onset latency, increasing total sleep time, and reducing waking after sleep onset. Collectively, our findings establish the ENT2-mediated adenosine signaling in V1 as the neurochemical basis for 40 Hz flickering-induced sleep and unravel a novel and non-invasive treatment for insomnia, a condition that affects 20% of the world population.

The ecology, genetic diversity, and phylogeny of Huaiyangshan virus in China.

Surveys were carried out to better understand the tick vector ecology and genetic diversity of Huaiyangshan virus (HYSV) in both regions of endemicity and regions of nonendemicity. Haemaphysalis longicornis ticks were dominant in regions of endemicity, while Rhipicephalus microplus is more abundant in regions of nonendemicity. HYSV RNA was found in human and both tick species, with greater prevalence in H. longicornis and lesser prevalence in R. microplus. Phylogenetic analyses indicate that HYSV is a novel species of the genus Phlebovirus.