Dynamic evolution of frontal-temporal network connectivity in temporal lobe epilepsy: A magnetoencephalography study.

Temporal lobe epilepsy (TLE) frequently involves an intricate, extensive epileptic frontal-temporal network. This study aimed to investigate the interactions between temporal and frontal regions and the dynamic patterns of the frontal-temporal network in TLE patients with different disease durations. The magnetoencephalography data of 36 postoperative seizure-free patients with long-term follow-up of at least 1 year, and 21 age- and sex-matched healthy subjects were included in this study. Patients were initially divided into LONG-TERM (n = 18, DURATION >10 years) and SHORT-TERM (n = 18, DURATION ≤10 years) groups based on 10-year disease duration. For reliability, supplementary analyses were conducted with alternative cutoffs, creating three groups: 0 < DURATION ≤7 years (n = 11), 7 < DURATION ≤14 years (n = 11), and DURATION >14 years (n = 14). This study examined the intraregional phase-amplitude coupling (PAC) between theta phase and alpha amplitude across the whole brain. The interregional directed phase transfer entropy (dPTE) between frontal and temporal regions in the alpha and theta bands, and the interregional cross-frequency directionality (CFD) between temporal and frontal regions from the theta phase to the alpha amplitude were further computed and compared among groups. Partial correlation analysis was conducted to investigate correlations between intraregional PAC, interregional dPTE connectivity, interregional CFD, and disease duration. Whole-brain intraregional PAC analyses revealed enhanced theta phase-alpha amplitude coupling within the ipsilateral temporal and frontal regions in TLE patients, and the ipsilateral temporal PAC was positively correlated with disease duration (r = 0.38, p <.05). Interregional dPTE analyses demonstrated a gradual increase in frontal-to-temporal connectivity within the alpha band, while the direction of theta-band connectivity reversed from frontal-to-temporal to temporal-to-frontal as the disease duration increased. Interregional CFD analyses revealed that the inhibitory effect of frontal regions on temporal regions gradually increased with prolonged disease duration (r = -0.36, p <.05). This study clarified the intrinsic reciprocal connectivity between temporal and frontal regions with TLE duration. We propose a dynamically reorganized triple-stage network that transitions from balanced networks to constrained networks and further develops into imbalanced networks as the disease duration increases.

The transcription factor OsWRKY10 inhibits phosphate uptake via suppressing OsPHT1;2 expression under phosphate-replete conditions in rice.

Plants have evolved delicate systems for stimulating or inhibiting inorganic phosphate (Pi) uptake in response to the fluctuating Pi availability in soil. However, the negative regulators inhibiting Pi uptake at the transcriptional level are largely unexplored. Here, we functionally characterized a transcription factor in rice (Oryza sativa), OsWRKY10. OsWRKY10 encodes a nucleus-localized protein and showed preferential tissue localization. Knockout of OsWRKY10 led to increased Pi uptake and accumulation under Pi-replete conditions. In accordance with this phenotype, OsWRKY10 was transcriptionally induced by Pi, and a subset of PHOSPHATE TRANSPORTER 1 (PHT1) genes were up-regulated upon its mutation, suggesting that OsWRKY10 is a transcriptional repressor of Pi uptake. Moreover, rice plants expressing the OsWRKY10-VP16 fusion protein (a dominant transcriptional activator) accumulated even more Pi than oswrky10. Several lines of biochemical evidence demonstrated that OsWRKY10 directly suppressed OsPHT1;2 expression. Genetic analysis showed that OsPHT1;2 was responsible for the increased Pi accumulation in oswrky10. Furthermore, during Pi starvation, OsWRKY10 protein was degraded through the 26S proteasome. Altogether, the OsWRKY10-OsPHT1;2 module represents a crucial loop in the Pi signaling network in rice, inhibiting Pi uptake when there is ample Pi in the environment.

Psychiatric features in NMDAR and LGI1 antibody-associated autoimmune encephalitis.

Patients with autoimmune encephalitis (AE) often developed psychiatric features during the disease course. Many studies focused on the psychiatric characteristic in anti-NMDAR encephalitis (NMDAR-E), but anti-LGI1 encephalitis (LGI1-E) had received less attention regarding the analysis of psychiatric features, and no study compared psychiatric characteristic between these two groups. The clinical data of AE patients (62 NMDAR-E and 20 LGI1-E) who developed psychiatric symptoms were analyzed in this study. In NMDAR-E, the most common higher-level feature was "behavior changes" (60/62, 96.8%) and the lower-level feature "incoherent speech" was observed in 33 patients (33/62, 53.2%), followed by "agitation" (29/62, 46.8%) and "incongruent laughter/crying" (20/62, 32.3%). Similar to NMDAR-E, "behavior changes" was most common in LGI1-E (17/20, 85.0%), but the features of suicidality, eating, and obsessive-compulsive were not reported. The top three lower-level features were visual hallucinations (9/20, 45.0%), incoherent speech (8/20, 40.0%), and mood instability (7/20, 35.0%). The comparative study found that "incongruent laughter/crying", in lower-level features, was more frequently observed in NMDAR-E (32.3% vs. 0%, p = 0.002). Moreover, the Bush Francis Catatonia Rating Scale (BFCRS) assessing the catatonic symptoms in NMDAR-E were higher than LGI1-E, but the 18 item-Brief Psychiatric Rating Scale (BPRS-18) showed no difference in the two groups. In summary, both NMDAR-E and LGI1-E often developed psychiatric symptoms. In contrast with LGI1-E, the psychiatric feature "incongruent laughter/crying" was more frequently associated with NMDAR-E, and catatonic symptoms were more severe in NMDAR-E.

LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors.

Light Detection and Ranging (LiDAR) systems are novel sensors that provide robust distance and reflection strength by active pulsed laser beams. They have significant advantages over visual cameras by providing active depth and intensity measurements that are robust to ambient illumination. However, the systemsstill pay limited attention to intensity measurements since the output intensity maps of LiDAR sensors are different from conventional cameras and are too sparse. In this work, we propose exploiting the information from both intensity and depth measurements simultaneously to complete the LiDAR intensity maps. With the completed intensity maps, mature computer vision techniques can work well on the LiDAR data without any specific adjustment. We propose an end-to-end convolutional neural network named LiDAR-Net to jointly complete the sparse intensity and depth measurements by exploiting their correlations. For network training, an intensity fusion method is proposed to generate the ground truth. Experiment results indicate that intensity-depth fusion can benefit the task and improve performance. We further apply an off-the-shelf object (lane) segmentation algorithm to the completed intensity maps, which delivers consistent robust to ambient illumination performance. We believe that the intensity completion method allows LiDAR sensors to cope with a broader range of practice applications.

Neurofunctional outcomes in patients with anti-leucine-rich glioma inactivated 1 encephalitis.

OBJECTIVE: To evaluate the cognitive and neurofunctional outcomes in patients with anti-leucine-rich glioma inactivated 1 (LGI1) encephalitis. METHODS: A cohort follow-up study was performed after a median of 33 months (range 6-78) from disease onset to the last follow-up in patients diagnosed with anti-LGI1 encephalitis, to assess the neurofunctional outcomes using modified Rankin Scale (mRS), activities of daily living (ADL), neuropsychiatric inventory (NPI) and modified telephone interview for cognitive status (TICS-M). Remote symptomatic seizure and clinical relapses were also recorded. The clinical, laboratory features, and treatment responses that characterize the disability were analyzed. RESULTS: The results showed that 81 of 86 (94.2%) patients with anti-LGI1 encephalitis were successfully followed up, while eight (9.9%) died after discharge. Among the 73 survivors, clinical relapses occurred in 18 (24.7%) patients, and those with relapses were at a higher risk of developing remote symptomatic seizure (p = .019). Although 85.2% of the patients became functionally independent (mRS ≤2), the sequelae of symptomatic seizure, neuropsychiatric symptoms, and cognitive deficits were found in 11.0%, 21.9%, and 39.7% of the patients, respectively. Residual cognitive deficits primarily occurred in the elderly subjects as well as those with symptoms of memory deficit, psychiatric disorders, sleep disturbance, disturbance of consciousness at diagnosis, and higher CSF protein levels. CONCLUSIONS: Although most patients survived and became functionally independent, a subset of patients could not return to all premorbid activities. They may have clinical relapses or suffer from remote symptomatic seizure, neuropsychiatric symptoms, and cognitive impairment.

Amygdalar and hippocampal beta rhythm synchrony during human fear memory retrieval.

BACKGROUND: Fear, as one of the basic emotions, is crucial in helping humans to perceive hazards and adapt to social activities. Clinically, fear memory is also involved in a wide spectrum of psychiatric disorders. A better understanding of the neural mechanisms of fear thereby has both neuroscientific and clinical significance. In recent years, data from animal models have demonstrated the key role of the amygdala-hippocampal circuit in the development of fear. However, the neural processing of fear memory remains unclear in humans, which is mainly due to the limitation of indirect measure of neural activity. METHODS: Herein, we investigated fear memory by direct intracranial recordings from 8 intractable epilepsy patients with depth electrodes in both the hippocampus and ipsilateral amygdala. All the patients were subjected to a well-established Pavlovian fear memory paradigm consisted of the familiarization task, conditioning task, and retrieval task, respectively. Simultaneous local field potentials from the hippocampus and amygdala were recorded during different stages. The oscillatory activities from the amygdala and hippocampus were analyzed during fear memory retrieval compared with neutral stages. RESULTS: Consistent with previous rodent studies, our results showed that the amygdala was involved in fear memory retrieval rather than neutral memory retrieval, while the hippocampus was involved both in fear memory retrieval and neutral memory retrieval. In particular, we found that there was an enhanced synchronized activity between the amygdala and hippocampus at beta frequencies (14-30 Hz), which suggested that enhanced synchronized activity at beta frequencies between the amygdala and hippocampus play a pivotal role during retrieval of fear memory in human. CONCLUSIONS: Thus, our observation that the amygdala-hippocampal system contributing to fear memory retrieval in human with frequency-depended specificity has provided new insights into the mechanism of fear and have potential clinical relevance.

Molecular mechanism of tumour necrosis factor alpha regulates hypocretin (orexin) expression, sleep and behaviour.

Hypocretin 1 and hypocretin 2 (orexin A and B) regulate sleep, wakefulness and emotion. Tumour necrosis factor alpha (TNF-α) is an important neuroinflammation mediator. Here, we examined the effects of TNF-α treatment on hypocretin expression in vivo and behaviour in mice. TNF-α decreased hypocretin 1 and hypocretin 2 expression in a dose-dependent manner in cultured hypothalamic neurons. TNF-α decreased mRNA stability of prepro-hypocretin, the single precursor of hypocretin 1 and hypocretin 2. Mice challenged with TNF-α demonstrated decreased expression of prepro-hypocretin, hypocretin 1 and hypocretin 2 in hypothalamus. In response to TNF-α, prepro-hypocretin mRNA decay was increased in hypothalamus. TNF-α neutralizing antibody restored the expression of prepro-hypocretin, hypocretin 1 and hypocretin 2 in vivo in TNF-α challenged mice, supporting hypocretin system can be impaired by increased TNF-α through decreasing hypocretin expression. Repeated TNF-α challenge induced muscle activity during rapid eye movement sleep and sleep fragmentation, but decreased learning, cognition and memory in mice. TNF-α neutralizing antibody blocked the effects of TNF-α; in contrast, hypocretin receptor antagonist enhanced the effects of TNF-α. The data support that TNF-α is involved in the regulation of hypocretin expression, sleep and cognition. The findings shed some lights on the role of neuroinflammation in neurodegenerative diseases including Alzheimer's disease and Parkinson's disease.

Zinc Toxicity and Iron-Sulfur Cluster Biogenesis in Escherichia coli.

While zinc is an essential trace metal in biology, excess zinc is toxic to organisms. Previous studies have shown that zinc toxicity is associated with disruption of the [4Fe-4S] clusters in various dehydratases in Escherichia coli Here, we report that the intracellular zinc overload in E. coli cells inhibits iron-sulfur cluster biogenesis without affecting the preassembled iron-sulfur clusters in proteins. Among the housekeeping iron-sulfur cluster assembly proteins encoded by the gene cluster iscSUA-hscBA-fdx-iscX in E. coli cells, the scaffold IscU, the iron chaperone IscA, and ferredoxin have strong zinc binding activity in cells, suggesting that intracellular zinc overload inhibits iron-sulfur cluster biogenesis by binding to the iron-sulfur cluster assembly proteins. Mutations of the conserved cysteine residues to serine in IscA, IscU, or ferredoxin completely abolish the zinc binding activity of the proteins, indicating that zinc can compete with iron or iron-sulfur cluster binding in IscA, IscU, and ferredoxin and block iron-sulfur cluster biogenesis. Furthermore, intracellular zinc overload appears to emulate the slow-growth phenotype of the E. coli mutant cells with deletion of the iron-sulfur cluster assembly proteins IscU, IscA, and ferredoxin. Our results suggest that intracellular zinc overload inhibits iron-sulfur cluster biogenesis by targeting the iron-sulfur cluster assembly proteins IscU, IscA, and ferredoxin in E. coli cells.IMPORTANCE Zinc toxicity has been implicated in causing various human diseases. High concentrations of zinc can also inhibit bacterial cell growth. However, the underlying mechanism has not been fully understood. Here, we report that zinc overload in Escherichia coli cells inhibits iron-sulfur cluster biogenesis by targeting specific iron-sulfur cluster assembly proteins. Because iron-sulfur proteins are involved in diverse physiological processes, the zinc-mediated inhibition of iron-sulfur cluster biogenesis could be largely responsible for the zinc-mediated cytotoxicity. Our finding provides new insights on how intracellular zinc overload may inhibit cellular functions in bacteria.

The localizing and lateralizing value of palpitation aura in patients with focal epilepsy.

The semiology of auras is essential to presurgical evaluation of patients with focal epilepsy. To assess the localizing and lateralizing value of palpitation aura in focal epilepsy, we retrospectively analyzed the demography, electroclinical, neuroimaging, surgical, pathology data, and outcomes of 114 patients with focal epilepsy and the palpitation aura occurrence in relation to epileptogenic (temporal vs extratemporal, left vs right) origin. Out of 114 patients (mean age, 23.44 ±â€¯9.69 years), 17 (14.9%) patients experienced palpitation as the first aura. Twelve had temporal, one had parietal, one had occipital lobe, and three had multiple lobes junction onset seizures. Palpitation aura was observed more frequently in temporal epilepsy: 22.2% of temporal lobe epilepsy (TLE) and 8.3% of extratemporal lobe epilepsy (EX-TLE) exhibited palpitation aura (p = 0.038). However, palpitation aura had no difference between the left or right side: 16.4% with right-sided epilepsy and 13.2% with left-sided epilepsy exhibited palpitation aura (p = 0.634). Thus, our study suggested that palpitation was a frequent aura in patients with focal epilepsy. It is more commonly seen with temporal lobe origin, but it has no lateralizing value.

Dual-Teacher Feature Distillation: A Transfer Learning Method for Insomniac PSG Staging.

Insomnia is the most common sleep disorder linked with adverse long-term medical and psychiatric outcomes. Automatic sleep staging plays a crucial role in aiding doctors to diagnose insomnia disorder. Only a few studies have been conducted to develop automatic sleep staging methods for insomniacs, and most of them have utilized transfer learning methods, which involve pre-training models on healthy individuals and then fine-tuning them on insomniacs. Unfortunately, significant differences in feature distribution between the two subject groups impede the transfer performance, highlighting the need to effectively integrate the features of healthy subjects and insomniacs. In this paper, we propose a dual-teacher cross-domain knowledge transfer method based on the feature-based knowledge distillation to improve the performance of sleep staging for insomniacs. Specifically, the insomnia teacher directly learns from insomniacs and feeds the corresponding domain-specific features into the student network, while the health domain teacher guide the student network to learn domain-generic features. During the training process, we adopt the OFD (Overhaul of Feature Distillation) method to build the health domain teacher. We conducted the experiments to validate the proposed method, using the Sleep-EDF database as the source domain and the CAP-Database as the target domain. The results demonstrate that our method surpasses advanced techniques, achieving an average sleep staging accuracy of 80.56% on the CAP-Database. Furthermore, our method exhibits promising performance on the private dataset.

Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching.

Accurately identifies the cellular composition of complex tissues, which is critical for understanding disease pathogenesis, early diagnosis, and prevention. However, current methods for deconvoluting bulk RNA sequencing (RNA-seq) typically rely on matched single-cell RNA sequencing (scRNA-seq) as a reference, which can be limiting due to differences in sequencing distribution and the potential for invalid information from single-cell references. Hence, a novel computational method named SCROAM is introduced to address these challenges. SCROAM transforms scRNA-seq and bulk RNA-seq into a shared feature space, effectively eliminating distributional differences in the latent space. Subsequently, cell-type-specific expression matrices are generated from the scRNA-seq data, facilitating the precise identification of cell types within bulk tissues. The performance of SCROAM is assessed through benchmarking against simulated and real datasets, demonstrating its accuracy and robustness. To further validate SCROAM's performance, single-cell and bulk RNA-seq experiments are conducted on mouse spinal cord tissue, with SCROAM applied to identify cell types in bulk tissue. Results indicate that SCROAM is a highly effective tool for identifying similar cell types. An integrated analysis of liver cancer and primary glioblastoma is then performed. Overall, this research offers a novel perspective for delivering precise insights into disease pathogenesis and potential therapeutic strategies.

Impaired vestibular function associated with orthostatic hypotension in patients with multiple system atrophy.

BACKGROUND: Orthostatic hypotension (OH) is one of the most common symptoms in patients with multiple system atrophy (MSA). Vestibular system plays an important role in blood pressure regulation during orthostatic challenges through vestibular-sympathetic reflex. The current study aimed to investigate the relationship between vestibular function and OH in patients with MSA. METHODS: Participants with MSA, including 20 with OH (mean age, 57.55 ± 8.44 years; 7 females) and 15 without OH (mean age, 59.00 ± 8.12 years; 2 females) and 18 healthy controls (mean age, 59.03 ± 6.44 years; 8 females) were enrolled. Cervical and ocular vestibular evoked myogenic potentials (cVEMPs and oVEMPs) tests were conducted to evaluate vestibular function. RESULTS: Patients with MSA presented with significantly higher rate of absent cVEMPs (57.1% vs 11.1%, p = 0.001) and oVEMPs (25.7% vs 0, p = 0.021) than controls. MSA patients with OH showed more absent cVEMPs (75.0% vs 11.1%, Bonferroni corrected p < 0.001) and oVEMPs (40.0% vs 0, Bonferroni corrected p = 0.003) than controls. Patients with OH also showed higher rate of absent cVEMPs than those without OH (33.3%, Bonferroni corrected p = 0.014). CONCLUSIONS: Our results demonstrated that impairment of vestibular function was associated with MSA, particularly in those with OH. Absent VEMPs may be a potential marker for MSA severity. Our findings suggest that impaired vestibular function is involved in OH development and may serve as an intervention target.

Serum ApoB/ApoA1 ratio in patients with CHB and the occurrence of HBV related cirrhosis and HBV related hepatocellular carcinoma.

Clinical research has suggested that chronic HBV infection exerts a certain effect on the occurrence of cardiovascular disease by regulating cholesterol metabolism in liver cells. High serum apolipoprotein B/apolipoprotein A1 (ApoB/ApoA1) ratio plays a certain role in the above regulation, and it serves as a risk factor for cardiovascular disease. However, whether the ApoB/ApoA1 ratio is correlated with chronic HBV infection and its disease progression remains unclear. In accordance with the inclusion and exclusion criteria, all 378 participants administrated at Renmin Hospital of Wuhan University from March 2021 to March 2022, fell into Healthy Control (HC) group (50 participants), Hepatocellular carcinoma (HCC) group (107 patients), liver cirrhosis (LC) group (64 patients), chronic hepatitis B (CHB) group (62 patients), chronic hepatitis C (CHC) group (46 patients) and Hepatitis E Virus (HEV) group (49 patients). Serum ApoA1 and ApoB concentrations were measured at admission, and the ApoB/ApoA1 ratio was determined. The levels of laboratory parameters in the respective group were compared and ApoB/ApoA1 ratios in HCC patients and LC patients with different severity were further analyzed. ROC curves were plotted to analyze the early diagnostic ability of ApoB/ApoA1 ratio for HBV-associated HCC. Logistic regression and restricted cubic spline analysis were used to explore the correlation between ApoB/ApoA1 ratio and LC and HCC risk. A comparison was drawn in terms of ApoB/ApoA1 ratio between the groups, and the result was expressed in descending sequence: HEV group > CHB group > LC group > HCC group > CHC group > HC group, early-stage HCC < middle-stage HCC < advanced-stage HCC, Class A LC < Class B LC < Class C LC. Serum ApoB/ApoA1 ratio combined diagnosis with AFP exhibited the capability of increasing the detection efficacy and specificity of AFP for HCC and AFP-negative HCC. The incidence of LC and HCC in the respective logistic regression model showed a negative correlation with the serum ApoB/ApoA1 ratio in CHB patients (P < 0.05). After all confounding factors covered in this study were regulated, the result of the restricted cubic spline analysis suggested that in a certain range, serum ApoB/ApoA1 ratio showed an inverse correlation with the prevalence of LC or HCC in CHB patients. Serum ApoB/ApoA1 ratio in CHB patients may be conducive to identifying high-risk patients for HCC or LC, such that LC and HCC can be early diagnosed and treated.

CGOF++: Controllable 3D Face Synthesis With Conditional Generative Occupancy Fields.

Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, previous methods focus on controllable 2D image generative models, which are prone to producing inconsistent face images under large expression and pose changes. In this paper, we propose a new NeRF-based conditional 3D face synthesis framework, which enables 3D controllability over the generated face images by imposing explicit 3D conditions from 3D face priors. At its core is a conditional Generative Occupancy Field (cGOF++) that effectively enforces the shape of the generated face to conform to a given 3D Morphable Model (3DMM) mesh, built on top of EG3D (Chan et al. 2022), a recent tri-plane-based generative model. To achieve accurate control over fine-grained 3D face shapes of the synthesized images, we additionally incorporate a 3D landmark loss as well as a volume warping loss into our synthesis framework. Experiments validate the effectiveness of the proposed method, which is able to generate high-fidelity face images and shows more precise 3D controllability than state-of-the-art 2D-based controllable face synthesis methods.

Efficacy and safety of Dimdazenil in adults with insomnia disorder: results from a multicenter, randomized, double-blind, placebo-controlled phase III trials.

STUDY OBJECTIVES: To evaluate the efficacy and safety of Dimdazenil, a novel partial positive allosteric modulator for GABAA receptor in adults with insomnia disorder. METHODS: This was a 2-week, multicenter, randomized, double-blind, placebo-controlled, parallel-group phase III study of Dimdazenil. The primary efficacy outcome was total sleep time (TST) analyzed by polysomnography (PSG) on day 13/14. Latency to persistent sleep (LPS), sleep efficiency (SE), and wake after sleep onset (WASO) were analyzed in the same way by polysomnography (PSG). The other secondary outcomes including the average subjective sleep latency (sSL), subjective TST (sTST), subjective SE (sSE), subjective WASO (sWASO), and subjective number of awakenings (sNAW) were analyzed from sleep diary data, and the insomnia severity index (ISI) was also assessed. Treatment-emergent adverse events (TEAEs) were monitored throughout the study. RESULTS: A total of 546 participants with insomnia (age ≥18 years) were randomized (2:1), received treatment with an oral dose of Dimdazenil (2.5 mg) or placebo, and analyzed. Compared to baseline and placebo, Dimdazenil demonstrated significant improvements in PSG measures, increased TST (71.09, 31.68 minutes, respectively; both p < 0.001), increased SE (13.26%, 5.55%, respectively; both < 0.001), reduced WASO (49.67, 20.16 minutes, respectively; both p < 0.001), and reduced LPS (21.65 minutes, p < 0.001; 6.46 minutes, p = 0.023). Compared to placebo, Dimdazenil also improved key self-reported measures of sTST (18.33 minutes, p < 0.001), sWASO (14.60 minutes, p < 0.001), sSL (4.23 minutes, p < 0.001), sSE (2.97%, p < 0.001), and sNAW (0.29, p < 0.001). Participants treated with Dimdazenil reported a significant improvement in ISI. Dimdazenil was well tolerated. The majority of TEAEs were mild or moderate. There were no clinically relevant treatment-related serious AEs and no deaths. CONCLUSIONS: Dimdazenil of 2.5 mg provided significant benefit on sleep maintenance and sleep onset in individuals with insomnia disorder versus placebo, with a favorable safety profile and was well tolerated. CLINICAL TRIAL INFORMATION: A multicenter, randomized, double-blind phase III clinical study evaluating the efficacy and safety of EVT201 capsules compared to placebo in patients with insomnia disorders (http://www.chinadrugtrials.org), with the number of CTR20201068.

Predicting new drug indications based on double variational autoencoders.

Experimental drug development is costly, complex, and time-consuming, and the number of drugs that have been put into application treatment is small. The identification of drug-disease correlations can provide important information for drug discovery and drug repurposing. Computational drug repurposing is an important and effective method that can be used to determine novel treatments for diseases. In recent years, an increasing number of large databases have been utilized for biological data research, particularly in the fields of drugs and diseases. Consequently, researchers have begun to explore the application of deep neural networks in biological data development. One particularly promising method for unsupervised learning is the deep generative model, with the variational autoencoder (VAE) being among the mainstream models. Here, we propose a drug indication prediction algorithm called DIDVAE (predicting new drug indications based on double variational autoencoders), which generates new data by learning the latent variable distribution of known data to achieve the goal of predicting drug-disease associations. In the experiment, we compared the DIDVAE algorithm with the BBNR, DrugNet, MBiRW and DRRS algorithms on a unified dataset. The comprehensive experimental results show that, compared with these prediction algorithms, the DIDVAE algorithm provides an overall improved prediction. In addition, further analysis and verification of the predicted unknown drug-disease association also proved the practicality of the method.

LGI1 antibody-associated encephalitis without evidence of inflammation in CSF and brain MRI.

OBJECTIVE: This study aimed to explore the frequency and distinct characteristics of adult patients with LGI1 antibody-associated encephalitis in the absence of inflammatory abnormalities in both routine CSF analysis and brain MRI. METHODS: We conducted a retrospective study of adult patients with antibodies targeting LGI1 and then screened patients with no evidence of inflammation in brain MRI and normal results in routine CSF analysis, including white blood cell count, protein concentration, IgG, and oligoclonal bands. RESULTS: Among 80 patients with LGI1 antibody-associated encephalitis in our center, 31 (38.8%) fulfilled the screening criteria. For these patients, the onset age was 57.0 ± 14.7 years, and 19 (61.3%) were female. Viral prodrome occurred in 5 patients (16.1%). Faciobrachial dystonic seizures (FBDS) were the most predominant symptom (38.7%), followed by seizure onset (22.6%) and memory deficits (19.4%). The sensitivity of antibody detection in serum was higher than CSF (96.8% vs. 48.4%, p < 0.001). Most patients (30/31, 96.8%) benefited from the first-line immunotherapy, and 23 patients (74.2%) achieved complete recovery, yet 3 patients (9.7%) had clinical relapses in 2-year follow-up after discharge. The patients had a higher prevalence of females (61.9% vs. 26.7%, p = 0.003) and were more frequently associated with FBDS during the disease course (38.7% vs. 10.2%, p = 0.004). However, there was no difference in treatment outcomes and recurrence ratio between the two groups (p = 0.144 and p = 0.515). Moreover, we divided all 80 patients into four groups according to antibody titer levels in serum and CSF at the time of diagnosis, respectively. WBC and protein concentrations in CSF showed no difference among the four groups. CONCLUSIONS: The absence of evidence of inflammation in routine CSF analysis and brain MRI did not rule out anti-LGI1 associated encephalitis. FBDS and the subacute onset of cognitive dysfunction should push forward with autoantibody testing for patients even without inflammatory abnormalities. The routine inflammatory indicators in CSF seemed to be unrelated to antibody titer levels.

Dysfunction of the cardiac parasympathetic system in fatal familial insomnia: a heart rate variability study.

STUDY OBJECTIVES: Although sympathetic hyperactivity with preserved parasympathetic activity has been extensively recognized in fatal familial insomnia (FFI), the symptoms of parasympathetic nervous system failure observed in some patients are difficult to explain. Using heart rate variability (HRV), this study aimed to discover evidence of parasympathetic dysfunction in patients with FFI and the difference of parasympathetic activity between patients with FFI and Creutzfeldt-Jakob disease (CJD). METHODS: This study enrolled nine patients with FFI, eight patients with CJD and 18 healthy controls (HCs) from May 2013 to August 2020. All participants underwent a nocturnal video-polysomnography with lead II electrocardiography, and the data were analyzed using linear and nonlinear indices of HRV during both wake and sleep states. RESULTS: Compared to the HC and CJD groups, the FFI group had a continuously higher heart rate with a lower amplitude of oscillations. The low frequency (LF)/high frequency (HF) ratio and ratio of SD1 to SD2 and correlation dimension D2 (CD2) were significantly different in the FFI group compared to the HC group. The root mean square of successive differences (RMSSD), HF and SD1 in the FFI group were significantly lower than in the HC group. RMSSD, SD1, and CD2 in the FFI group were all significantly lower than in the CJD group. CONCLUSIONS: Cardiovascular dysautonomia in FFI may be partly attributable to parasympathetic abnormalities, not just sympathetic activation. HRV may be helpful as a noninvasive, quantitative, and effective autonomic function test for FFI diagnosis.

Hierarchical fusion detection algorithm for sleep spindle detection.

Background: Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person's learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The "gold standard" of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity. Methods: To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness. Results: The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score. Conclusion: A spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.

A comparative analysis of sleep spindle characteristics of sleep-disordered patients and normal subjects.

Spindles differ in density, amplitude, and frequency, and these variations reflect different physiological processes. Sleep disorders are characterized by difficulty in falling asleep and maintaining sleep. In this study, we proposed a new spindle wave detection algorithm, which was more effective compared with traditional detection algorithms such as wavelet algorithm. Besides, we recorded EEG data from 20 subjects with sleep disorders and 10 normal subjects, and then we compared the spindle characteristics of sleep-disordered subjects and normal subjects (those without any sleep disorder) to assess the spindle activity during human sleep. Specifically, we scored 30 subjects on the Pittsburgh Sleep Quality Index and then analyzed the association between their sleep quality scores and spindle characteristics, reflecting the effect of sleep disorders on spindle characteristics. We found a significant correlation between the sleep quality score and spindle density (p = 1.84 × 10-8, p-value <0.05 was considered statistically significant.). We, therefore, concluded that the higher the spindle density, the better the sleep quality. The correlation analysis between the sleep quality score and mean frequency of spindles yielded a p-value of 0.667, suggesting that the spindle frequency and sleep quality score were not significantly correlated. The p-value between the sleep quality score and spindle amplitude was 1.33 × 10-4, indicating that the mean amplitude of the spindle decreases as the score increases, and the mean spindle amplitude is generally slightly higher in the normal population than in the sleep-disordered population. The normal and sleep-disordered groups did not show obvious differences in the number of spindles between symmetric channels C3/C4 and F3/F4. The difference in the density and amplitude of the spindles proposed in this paper can be a reference characteristic for the diagnosis of sleep disorders and provide valuable objective evidence for clinical diagnosis. In summary, our proposed detection method can effectively improve the accuracy of sleep spindle wave detection with stable performance. Meanwhile, our study shows that the spindle density, frequency and amplitude are different between the sleep-disordered and normal populations.