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.

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.

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.

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.

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.

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.

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.

Copper exerts cytotoxicity through inhibition of iron-sulfur cluster biogenesis on ISCA1/ISCA2/ISCU assembly proteins.

Copper is an essential mineral nutrient that provides the cofactors for some key enzymes. However, excess copper is paradoxically cytotoxic. Wilson's disease is an autosomal recessive hereditary disease characterized by pathological copper accumulation in many organs, with high mortality and disability. Nevertheless, many questions about the molecular mechanism in Wilson's disease remain unknown and there is an imperative need to address these questions to better exploit therapeutic strategy. In this study, we constructed the mouse model of Wilson's disease, ATP7A-/- immortalized lymphocyte cell line and ATP7B knockdown cells to explore whether copper could impair iron-sulfur cluster biogenesis in eukaryotic mitochondria. Through a series of cellular, molecular, and pharmacological analyses, we demonstrated that copper could suppress the assembly of Fe-S cluster, decrease the activity of the Fe-S enzyme and disorder the mitochondrial function both in vivo and in vitro. Mechanistically, we found that human ISCA1, ISCA2 and ISCU proteins have a strong copper-binding activity, which would hinder the process of iron-sulfur assembly. Of note, we proposed a novel mechanism of action to explain the toxicity of copper by providing evidence that iron-sulfur cluster biogenesis may be a primary target of copper toxicity both in cells and mouse models. In summary, the current work provides an in-depth study on the mechanism of copper intoxication and describes a framework for the further understanding of impaired Fe-S assembly in the pathological processes of Wilson's diseases, which helps to develop latent therapeutic strategies for the management of copper toxicity.

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.

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.

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.

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.

Central sleep apnea and daytime sleepiness in Niemann-Pick type C disease: a report of 2 cases.

Niemann-Pick disease type C (NPC) is an autosomal recessive hereditary disease in which sphingomyelin and cholesterol are deposited in various organs of the body. The clinical manifestations of NPC include neurologic symptoms and cataplexy; other symptoms related to sleep have seldom been reported. One previous study described various sleep disorders including chronic insomnia, obstructive sleep apnea, restless legs syndrome, and rapid eye movement sleep behavior disorder, thus suggesting that sleep disorders in patients with NPC are more prevalent than previously thought and warrant close attention. Here, we describe sleep disorders in 2 patients with NPC and discuss the clinical characteristics and, for the first time, discuss potential pathogenic mechanisms underlying sleep disorders in such patients. CITATION: Zhang Y, Cheng Y, Li N, et al. Central sleep apnea and daytime sleepiness in Niemann-Pick type C disease: a report of 2 cases. J Clin Sleep Med. 2023;19(2):409-414.

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.

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.

Efficacy and safety of simultaneous rTMS-tDCS over bilateral angular gyrus on neuropsychiatric symptoms in patients with moderate Alzheimer's disease: A prospective, randomized, sham-controlled pilot study.

BACKGROUND: Treating neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) remains highly challenging. Noninvasive brain stimulation using repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) is of considerable interest in this context. OBJECTIVE: To investigate the efficacy and safety of a novel technique involving simultaneous application of rTMS and tDCS (rTMS-tDCS) over bilateral angular gyrus (AG, P5/P6 electrode site) for AD-related NPS. METHODS: Eighty-four AD patients were randomized to receive rTMS-tDCS, single-rTMS, single-tDCS, or sham stimulation for 4 weeks, with evaluation at week-4 (W4, immediately after treatment) and week-12 (W12, follow-up period) after initial examination. Primary outcome comprising Neuropsychiatric Inventory (NPI) score and secondary outcomes comprising mini-mental state examination (MMSE), AD assessment scale-cognitive subscale (ADAS-cog), and Pittsburgh sleep quality index (PSQI) scores were collected and analyzed by a two-factor (time and treatment), mixed-design ANOVA. RESULTS: rTMS-tDCS produced greater improvement in NPI scores than single-tDCS and sham at W4 and W12 (both P < 0.017) and trended better than single-rTMS (W4: P = 0.058, W12: P = 0.034). rTMS-tDCS improved MMSE scores compared with single-tDCS at W4 (P = 0.011) and sham at W4 and W12 (both P < 0.017). rTMS-tDCS also significantly improved PSQI compared with single-rTMS and sham (both P < 0.017). Interestingly, rTMS-tDCS-induced NPI/PSQI improvement was significantly associated with MMSE/ADAS-cog improvement. tDCS- and/or rTMS-related adverse events appeared slightly and briefly. CONCLUSIONS: rTMS-tDCS application to bilateral AG can effectively improve AD-related NPS, cognitive function, and sleep quality with considerable safety.

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.

An improved multi-input deep convolutional neural network for automatic emotion recognition.

Current decoding algorithms based on a one-dimensional (1D) convolutional neural network (CNN) have shown effectiveness in the automatic recognition of emotional tasks using physiological signals. However, these recognition models usually take a single modal of physiological signal as input, and the inter-correlates between different modalities of physiological signals are completely ignored, which could be an important source of information for emotion recognition. Therefore, a complete end-to-end multi-input deep convolutional neural network (MI-DCNN) structure was designed in this study. The newly designed 1D-CNN structure can take full advantage of multi-modal physiological signals and automatically complete the process from feature extraction to emotion classification simultaneously. To evaluate the effectiveness of the proposed model, we designed an emotion elicitation experiment and collected a total of 52 participants' physiological signals including electrocardiography (ECG), electrodermal activity (EDA), and respiratory activity (RSP) while watching emotion elicitation videos. Subsequently, traditional machine learning methods were applied as baseline comparisons; for arousal, the baseline accuracy and f1-score of our dataset were 62.9 ± 0.9% and 0.628 ± 0.01, respectively; for valence, the baseline accuracy and f1-score of our dataset were 60.3 ± 0.8% and 0.600 ± 0.01, respectively. Differences between the MI-DCNN and single-input DCNN were also compared, and the proposed method was verified on two public datasets (DEAP and DREAMER) as well as our dataset. The computing results in our dataset showed a significant improvement in both tasks compared to traditional machine learning methods (t-test, arousal: p = 9.7E-03 < 0.01, valence: 6.5E-03 < 0.01), which demonstrated the strength of introducing a multi-input convolutional neural network for emotion recognition based on multi-modal physiological signals.

A Prognostic Analysis of the Outcomes in Patients With Anti-γ-Aminobutyric Acid B Receptor Encephalitis.

Objective: To evaluate neurological function and its influencing factors in patients with anti-γ -aminobutyric acid B receptor (GABABR) encephalitis. Methods: This was a clinical cohort study of patients diagnosed with anti-GABABR encephalitis; long-term follow-up was performed by telephone. Clinical factors associated with prognosis were analyzed, including clinical manifestations, laboratory examinations, imaging features, tumor comorbidities and therapeutic responses. Results: Twenty-two patients with anti-GABABR encephalitis were evaluated (median age: 55 years). Lung cancer was detected in eight patients. All were with serum tumor markers (mainly NSE), and three of them had additional onconeuronal antibodies. The patients with tumors were older than the patients without tumors and more likely to develop status epilepticus (62.5% vs. 14.3%; p = 0.052), central hypoventilation (50% vs. 7.1%; p = 0.039), and hyponatremia (87.5% vs. 14.3%; p = 0.001). The patients with tumors had higher mortality (87.5% vs. 0%; p < 0.05). Although 92.9% of the patients without tumors became functionally independent (mRS ≤2), sequelae of symptomatic seizures, neuropsychiatric symptoms, and cognitive impairment were still observed in 14.3%, 21.4%, and 21.4% of patients, respectively. Conclusions: (1) Elderly patients with anti-GABABR antibodies, especially those with severe symptoms, serum tumor markers, and additional onconeuronal antibodies, should be screened for lung cancer. (2) Anti-GABABR encephalitis with tumors has a poor prognosis. (3) Most patients without tumors achieve self-care, but some still experience remaining neurological deficits.

CPT1A-Mediated Fatty Acid Oxidation Promotes Precursor Osteoclast Fusion in Rheumatoid Arthritis.

The overproduction of osteoclasts, leading to bone destruction in patients with rheumatoid arthritis (RA), is well established. However, little is known about the metabolic dysfunction of osteoclast precursors (OCPs) in RA. Herein, we show that increasing fatty acid oxidation (FAO) induces OCP fusion. Carnitine palmitoyltransferase IA (CPT1A), which is important for carnitine transportation and is involved in FAO in the mitochondria, is upregulated in RA patients. This metabolic change further increases the expression of clathrin heavy chain (CLTC) and clathrin light chain A (CLTA) by enhancing the binding of the transcription factor CCAAT/enhancer binding protein ß (C/EBPß) to the promoters of CLTA and CLTC. This drives clathrin-dependent endocytosis pathway, which attenuates fusion receptors in the cellular membrane and contributes to increased podosome structure formation. This study reveals a new mechanism through which FAO metabolism participates in joint destruction in RA and provides a novel therapeutic direction for the development of drugs against bone destruction in patients with RA.