DTTZ suppresses ferroptosis and reverses mitochondrial dysfunction in normal tissues affected by chemotherapy.

Conventional antineoplastic therapies cause severe normal tissue damage and existing cytoprotectants with acute toxicities or potential tumor protection limit their clinical application. We evaluated the selective cytoprotection of 2,2-dimethylthiazolidine hydrochloride in this study, which could protect normal tissue toxicity without interfering antineoplastic therapies. By using diverse cell lines and A549 xenograft model, we discovered a synthetic aminothiol 2,2-dimethylthiazolidine hydrochloride selectively diminished normal cellular ferroptosis via SystemXc-/Glutathione Peroxidase 4 pathway upon antineoplastic therapies without interfering the anticancer efficacy. We revealed the malignant and non-malignant tissues presenting different energy metabolism patterns. And cisplatin induces disparate replicative stress, contributing to the distinguishable cytoprotection of 2,2-dimethylthiazolidine in normal and tumor cells. The compound pre-application could mitigate cisplatin-induced normal cellular mitochondrial oxidative phosphorylation (OXPHOS) dysfunction. Pharmacologic ablation of mitochondria reversed 2,2-dimethylthiazolidine chemoprotection against cisplatin in the normal cell line. Combined, these results provide a potential therapeutic adjuvant to selectively diminish normal tissue damages retaining antineoplastic efficacy.

CRE-TSCAE: A Novel Classification Model Based on Stacked Convolutional Autoencoder for Dual-target RSVP-BCI Tasks.

OBJECTIVE: The RSVP (Rapid Serial Visual Presentation) paradigm facilitates target identification in a rapid picture stream, which is applied extensively in military target surveillance and police monitoring. Most researchers concentrate on the single target RSVP-BCI whereas the study of dual-target is scarcely conducted, limiting RSVP application considerably. METHODS: This paper proposed a novel classification model named Common Representation Extraction-Targeted Stacked Convolutional Autoencoder (CRE-TSCAE) to detect two targets with one nontarget in RSVP tasks. CRE generated a common representation for each target class to reduce variability from different trials of the same class and distinguish the difference between two targets better. TSCAE aimed to control uncertainty in the training process while requiring less target training data. The model learned a compact and discriminative feature through the training from several learning tasks so as to distinguish each class effectively. RESULTS: It was validated on the World Robot Contest 2021 and 2022 ERP datasets. Experimental results showed that CRE-TSCAE outperformed the state-of-the-art RSVP decoding algorithms and the Average ACC was 71.25%, improving 6.5% at least over the rest. CONCLUSION: It demonstrated that CRE-TSCAE showed a strong ability to extract discriminative latent features in detecting the differences among two targets with nontarget, which guaranteed increased classification accuracy. SIGNIFICANCE: CRE-TSCAE provided an innovative and effective classification model for dual-target RSVP-BCI tasks and some insights into the neurophysiological distinction between different targets.

Identification of cell death-related biomarkers and immune infiltration in ischemic stroke between male and female patients.

Background: Stroke is the second leading cause of death and the third leading cause of disability worldwide, with ischemic stroke (IS) being the most prevalent. A substantial number of irreversible brain cell death occur in the short term, leading to impairment or death in IS. Limiting the loss of brain cells is the primary therapy target and a significant clinical issue for IS therapy. Our study aims to establish the gender specificity pattern from immune cell infiltration and four kinds of cell-death perspectives to improve IS diagnosis and therapy. Methods: Combining and standardizing two IS datasets (GSE16561 and GSE22255) from the GEO database, we used the CIBERSORT algorithm to investigate and compare the immune cell infiltration in different groups and genders. Then, ferroptosis-related differently expressed genes (FRDEGs), pyroptosis-related DEGs (PRDEGs), anoikis-related DEGs (ARDEGs), and cuproptosis-related DEGs (CRDEGs) between the IS patient group and the healthy control group were identified in men and women, respectively. Machine learning (ML) was finally used to generate the disease prediction model for cell death-related DEGs (CDRDEGs) and to screen biomarkers related to cell death involved in IS. Results: Significant changes were observed in 4 types of immune cells in male IS patients and 10 types in female IS patients compared with healthy controls. In total, 10 FRDEGs, 11 PRDEGs, 3 ARDEGs, and 1 CRDEG were present in male IS patients, while 6 FRDEGs, 16 PRDEGs, 4 ARDEGs, and 1 CRDEG existed in female IS patients. ML techniques indicated that the best diagnostic model for both male and female patients was the support vector machine (SVM) for CDRDEG genes. SVM's feature importance analysis demonstrated that SLC2A3, MMP9, C5AR1, ACSL1, and NLRP3 were the top five feature-important CDRDEGs in male IS patients. Meanwhile, the PDK4, SCL40A1, FAR1, CD163, and CD96 displayed their overwhelming influence on female IS patients. Conclusion: These findings contribute to a better knowledge of immune cell infiltration and their corresponding molecular mechanisms of cell death and offer distinct clinically relevant biological targets for IS patients of different genders.

A Double-Layer Blockchain Based Trust Management Model for Secure Internet of Vehicles.

The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. To eliminate the spread of false information and detect malicious nodes, we propose a double-layer blockchain trust management (DLBTM) mechanism to objectively and accurately evaluate the trustworthiness of vehicle messages. The double-layer blockchain consists of the vehicle blockchain and the RSU blockchain. We also quantify the evaluation behavior of vehicles to show the trust value of the vehicle's historical behavior. Our DLBTM uses logistic regression to accurately compute the trust value of vehicles, and then predict the probability of vehicles providing satisfactory service to other nodes in the next stage. The simulation results show that our DLBTM can effectively identify malicious nodes, and over time, the system can recognize at least 90% of malicious nodes.

[Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials].

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.

A Binocular Vision SSVEP Brain-Computer Interface Paradigm for Dual-Frequency Modulation.

OBJECTIVE: This study presents a novel brain-computer interface paradigm of dual-frequency modulated steady-state visual evoked potential (SSVEP), aiming to suppress the unpredictable intermodulation components in current applications. This paradigm is especially suitable for training-free scenarios. APPROACH: This study built a dual-frequency binocular vision SSVEP brain-computer interface system using circularly polarized light technology. Two experiments, including a 6-target offline experiment and a 40-target online experiment, were taken with this system. Meanwhile, an improved algorithm filter bank dual-frequency canonical correlation analysis (FBDCCA) was presented for the dual-frequency SSVEP paradigm. MAIN RESULTS: Energy analysis was conducted for 9 subjects in the 6-target dual-frequency offline experiment, among which the signal-to-noise ratio of target frequency components have increased by 2 dB compared to the one of unpredictable intermodulation components. Subsequently, the online experiment with 40 targets was conducted with 12 subjects. With this new dual-frequency paradigm, the online training-free experiment's average information transmission rate (ITR) reached 104.56 ± 15.74 bits/min, which was almost twice as fast as the current best dual-frequency paradigm. And the average information transfer rate for offline training analysis of this new paradigm was 180.87 ± 17.88 bits/min. SIGNIFICANCE: These results demonstrate that this new dual-frequency SSVEP brain-computer interface paradigm can suppress the unpredictable intermodulation harmonics and generate higher quality responses while completing dual-frequency encoding. Moreover, its performance shows high ITR in applications both with and without training. It is thus believed that this paradigm is competent for achieving large target numbers in brain-computer interface systems and has more possible practices.

ROS-Responsive and pH-Sensitive Aminothiols Dual-Prodrug for Radiation Enteritis.

Radiation exposure can immediately trigger a burst of reactive oxygen species (ROS), which can induce severe cell death and long-term tissue damage. Therefore, instantaneous release of sufficient radioprotective drugs is vital to neutralize those accumulated ROS in IR-exposed areas. To achieve this goal, we designed, synthesized, and evaluated a novel oral ROS-responsive radioprotective compound (M1) with high biocompatibility and efficient ROS-scavenging ability to act as a promising oral drug for radiation protection. The compound is stably present in acidic environments and is hydrolyzed in the intestine to form active molecules rich in thiols. M1 can significantly remove cellular ROS and reduce DNA damage induced by γ-ray radiation. An in vivo experiment showed that oral administration of M1 effectively alleviates acute radiation-induced intestinal injury. Immunohistochemical staining showed that M1 improved cell proliferation, reduced cell apoptosis, and enhanced the epithelial integrity of intestinal crypts. This study provides a promising oral ROS-sensitive agent for acute intestinal radiation syndrome.

[Research advances in non-invasive brain-computer interface control strategies].

Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.

Studies on irritable bowel syndrome associated with anxiety or depression in the last 20 years: A bibliometric analysis.

Irritable bowel syndrome (IBS) associated with anxiety or depression is ubiquitous in clinical practice, and multiple related articles have been published. However, studies that utilize bibliometric analyses to address this topic are rare. In our study, we aimed to reveal research trends in IBS with anxiety or depression. Publications on IBS in relation to anxiety or depression in the last 20 years were obtained from the Web of Science Core Collection (WoSCC). CiteSpace software (5.8.R3) and GraphPad Prism 8 were used to perform bibliometric analysis of authors, countries, institutions, journals, keywords, and references involved in this topic. A total of 2,562 publications from 716 academic journals were included in this study. The majority of publications (n = 833, 32.51%) were from the USA, and the University of California, Los Angeles, contributed the most publications (n = 97, 3.79%). Active cooperations among countries and institutions were observed. Neurogastroenterology and Motility [impact factor (IF) 2020 = 3.598] published the most papers (170 publications, 6.64%), followed by Alimentary Pharmacology Therapeutics (IF 2020 = 8.171; 88 publications; 3.44%). The literatures related to IBS and anxiety or depression were primarily published in journals related to medicine/medical/clinical, neurology/sports/ophthalmology, and molecular/biology/immunology. Cryan JF and Drossman DA, with the largest number of articles (84 publications) and citations (917 citations), respectively, were considered as the most influential authors in this field. A total of 336 co-cited references were divided into 17 clusters, and #1 fecal microbiota transplantation contained most of the documents published in recent years. Moreover, the keyword "psychosocial factor" had the largest burst strength of 13.52, followed by the keyword "gut microbiota" with a burst strength of 11.71. This study shows the research performance of IBS related to anxiety or depression from 2002 to 2021 and helps researchers master the trend in this field, which should receive more attention.

Knowledge Mapping of Acupuncture for Fibromyalgia from 1990 to 2022: A Bibliometric Analysis.

Background: Fibromyalgia is a rheumatic disease with no specific laboratory markers and is insensitive to hormonal drugs and nonsteroidal anti-inflammatory drugs commonly used to treat rheumatism. Guidelines recommend that non-pharmacological therapy should be the first-line treatment for fibromyalgia. Since the publication of the first diagnostic criteria for fibromyalgia in 1990, studies on acupuncture for fibromyalgia have been reported periodically. This study aims to explore the intellectual landscape of acupuncture for fibromyalgia since 1990, and to identify research trends and fronts in this field. Methods: The Web of Science Core Collection Database was searched for publications on acupuncture for fibromyalgia from 1990 to 2022. VOSviewer and CiteSpace were used to analyze the annual publication, countries, institutions, authors and cited authors, journals and cited journals, references and keywords. Results: A total of 280 publications were retrieved, and the number of publications showed an overall upward trend. The United States was the most productive country. China Medical University was the institution with the most publications. Lin Yi-wen was the most prolific author, while Wolfe was the most cited author. Evidence-Based Complementary and Alternative Medicine was the journal in which most of the research was published, while Pain was the most cited journal. An article by Wolfe (1990) had the most citations, but an article by Crofford (2001) had the highest centrality. The four most frequently used keywords in the included articles were mechanism, spinal cord, activation and sensitivity. Conclusion: Acupuncture can effectively relieve pain in patients with fibromyalgia and improve accompanying symptoms such as anxiety and depression. However, the design of clinical trials still needs to be optimized to better verify the efficacy of acupuncture on various clinical symptoms of fibromyalgia. Exploring the central analgesic mechanism of acupuncture on fibromyalgia is also the focus research direction now and future.

Spatial-temporal network for fine-grained-level emotion EEG recognition.

Electroencephalogram (EEG)-based affective computing brain-computer interfaces provide the capability for machines to understand human intentions. In practice, people are more concerned with the strength of a certain emotional state over a short period of time, which was called as fine-grained-level emotion in this paper. In this study, we built a fine-grained-level emotion EEG dataset that contains two coarse-grained emotions and four corresponding fine-grained-level emotions. To fully extract the features of the EEG signals, we proposed a corresponding fine-grained emotion EEG network (FG-emotionNet) for spatial-temporal feature extraction. Each feature extraction layer is linked to raw EEG signals to alleviate overfitting and ensure that the spatial features of each scale can be extracted from the raw signals. Moreover, all previous scale features are fused before the current spatial-feature layer to enhance the scale features in the spatial block. Additionally, long short-term memory is adopted as the temporal block to extract the temporal features based on spatial features and classify the category of fine-grained emotions. Subject-dependent and cross-session experiments demonstrated that the performance of the proposed method is superior to that of the representative methods in emotion recognition and similar structure methods with proposed method.

BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks.

In the rapid serial visual presentation (RSVP) classification task, the data from the target and non-target classes are incredibly imbalanced. These class imbalance problems (CIPs) can hinder the classifier from achieving better performance, especially in deep learning. This paper proposed a novel data augmentation method called balanced Wasserstein generative adversarial network with gradient penalty (BWGAN-GP) to generate RSVP minority class data. The model learned useful features from majority classes and used them to generate minority-class artificial EEG data. It combines generative adversarial network (GAN) with autoencoder initialization strategy enables this method to learn an accurate class-conditioning in the latent space to drive the generation process towards the minority class. We used RSVP datasets from nine subjects to evaluate the classification performance of our proposed generated model and compare them with those of other methods. The average AUC obtained with BWGAN-GP on EEGNet was 94.43%, an increase of 3.7% over the original data. We also used different amounts of original data to investigate the effect of the generated EEG data on the calibration phase. Only 60% of original data were needed to achieve acceptable classification performance. These results show that the BWGAN-GP could effectively alleviate CIPs in the RSVP task and obtain the best performance when the two classes of data are balanced. The findings suggest that data augmentation techniques could generate artificial EEG to reduce calibration time in other brain-computer interfaces (BCI) paradigms similar to RSVP.

Indole-3-propionic Acid-aggravated CCl4-induced Liver Fibrosis via the TGF-ß1/Smads Signaling Pathway.

BACKGROUND AND AIMS: The pathogenesis of liver fibrosis involves liver damage, inflammation, oxidative stress, and intestinal dysfunction. Indole-3-propionic acid (IPA) has been demonstrated to have antioxidant, anti-inflammatory and anticancer activities, and a role in maintaining gut homeostasis. The current study aimed to investigate the role of IPA in carbon tetrachloride (CCl4)-induced liver fibrosis and explore the underlying mechanisms. METHODS: The liver fibrosis model was established in male C57BL/6 mice by intraperitoneal injection of CCl4 twice weekly. IPA intervention was made orally (20 mg/kg daily). The degree of liver injury and fibrosis were assessed by serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and histopathology. Enzyme-linked immunosorbent assay and quantitative real-time polymerase chain reaction (qPCR) were used to detect the inflammatory cytokines. The malondialdehyde (MDA), glutathione, glutathione peroxidase, superoxide dismutase, and catalase were determined via commercial kits. Hepatocyte apoptosis was detected by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling assay. The expression of mRNA and protein was assayed by qPCR, Western blotting, or immunohistochemical staining. RESULTS: After IPA treatment, the ALT and AST, apoptotic cells, and pro-inflammatory factor levels were enhanced significantly. Moreover, IPA intervention up-regulated the expression of collagen I, α-smooth muscle actin, tissue inhibitor of matrix metalloproteinase-1, matrix metalloproteinase-2, transforming growth factor-ß1 (TGF-ß1), Smad3, and phosphorylated-Smad2/3. Additionally, IPA intervention did not affect the MDA level. Attractively, the administration of IPA remodeled the gut flora structure. CONCLUSIONS: IPA aggravated CCl4-induced liver damage and fibrosis by activating HSCs via the TGF-ß1/Smads signaling pathway.

Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs.

Objective.Achieving high precision rapid serial visual presentation (RSVP) task often requires many electrode channels to obtain more information. However, the more channels may contain more redundant information and also lead to its limited practical applications. Therefore, it is necessary to reduce the number of channels to enhance the classification performance and users experience. Furthermore, cross-subject generalization has always been one of major challenges in electroencephalography channel reduction, especially in the RSVP paradigm. Most search-based channel selection method presented in the literature are single-objective methods, the classification accuracy (ACC) is usually chosen as the only criterion.Approach.In this article, the idea of multi-objective optimization was introduced into the RSVP channel selection to minimize two objectives: classification error and the number of channels. By combining a multi-objective evolutionary algorithm for solving large-scale sparse problems and hierarchical discriminant component analysis (HDCA), a novel channel selection method for RSVP was proposed. After that, the cross-subject generalization validation through the proposed channel selection method.Main results.The proposed method achieved an average ACC of 95.41% in a public dataset, which is 3.49% higher than HDCA. The ACC was increased by 2.73% and 2.52%, respectively. Besides, the cross-subject generalization models in channel selection, namely special-16 and special-32, on untrained subjects show that the classification performance is better than the Hoffmann empirical channels.Significance.The proposed channel selection method could reduce the calibration time in the experimental preparation phase and obtain a better accuracy, which is promising application in the RSVP scenario that requires low-density electrodes.

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Sodium Tanshinone IIA sulfonate for acute myocardial infarction: a systematic review and Meta-analysis.

OBJECTIVE: To investigate the efficacy and safety of Sodium tanshinone ⅡA sulfonate (STS) plus the conventional treatment on acute myocardial infarction (AMI) patients. METHODS: We searched several electrical databases and hand searched several Chinese medical journals up to January 2019. Randomized controlled trials (RCTs) comparing STS plus conventional treatment with conventional treatment were retrieved. Study screening, data extraction, quality assessment, and data analysis were conducted in accordance with the Cochrane standards. RESULTS: Sixteen trials involving 1383 people were included. The Meta-analysis showed STS combined with conventional treatment was a better treatment option than conventional treatment alone in reducing the risk of mortality, heart failure, arrhythmia and shock. In addition, STS was associated with improvement in left ventricular ejection fraction (LVEF) and left ventricular end diastolic dimension (LVEDD). No significant difference of STS was found on recurrent angina and recurrent AMI. However, the safety of STS remained uncertain for limite data. CONCLUSION: Compared with conventional treatment alone, STS combined with conventional treatment may provide more benefits for patients with AMI. Due to the fact that the overall quality of all included trials is generally low, further large-scale high quality trials are warranted.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

Changes in NK Cell Subsets and Receptor Expressions in HIV-1 Infected Chronic Patients and HIV Controllers.

Natural killer (NK) cells are major effectors of the innate immune response and purported to play an influential role in the spontaneous control of HIV infection. In the present study, we compared the phenotypes of NK cells in the peripheral blood of three groups of subjects with chronic HIV-1 infection, HIV controllers, and healthy donors. The results showed that CD56+/CD16- NK cell subsets decreased in chronic patients and remained unchanged in controllers. Notably, we found that people living with chronic HIV-1 infection had suppressed NKp80, NKp46, and NKG2D expressions on NK cells compared to healthy donors, while HIV controllers remained unchanged. In contrast, NKG2D expression was substantially higher in controllers than in chronic patients (M=97.67, p<0.001). There were no significant differences in inhibitory receptors KIR3DL1 and KIR2DL1 expressions. In addition, plasma cytokine IFN-γ, TNF-α and IL-12showed higher levels in HIV controllers compared to chronic patients. Overall, our study revealed that, as compared to chronic patients, HIV controllers show an increased activating receptors expression and higher number ofCD56+/CD16-NK cell subset, with increased expression levels of plasma cytokines, suggesting that higher immune activation in controllers may have a key role in killing and suppressing HIV.

A cross-sectional study of mental health, suicidal ideation and suicide attempt among transgender women in Jiangsu province, China.

OBJECTIVES: To measure prevalence and risk factors of poor mental health, suicidal ideation, and suicide attempt among transgender women in Jiangsu province, China. METHODS: We surveyed 250 transgender women with mean age of 27.9 years from July 2018 to May 2019 in Jiangsu province, China. Logistic regression analysis characterized risk factors for mental health, suicidal ideation, and suicide attempt. RESULTS: Many transgender women experienced discrimination, including verbal abuse (56.0%), physical abuse (32.0%), and sexual violence (32.0%) during their lifetime. Classification by the Kessler Psychological Distress Scale found 32.4%% to be in good mental health, while 24.0%, 16.8%, and 26.8% had mild, moderate, and severe psychological distress respectively. In multivariable logistic regression, severe psychological distress was correlated with bisexual orientation (vs. gay/lesbian, aOR = 2.9, 95%CI = 1.5-5.8), being unsure of sexual orientation (vs. gay/lesbian, aOR = 4.7, 95%CI = 1.8-12.8), experience of verbal abuse (aOR = 1.8, 95%CI = 1.1-3.1), and less severe with alcohol use (aOR = 0.6, 95%CI = 0.3-0.9) (P< 0.05). Many transgender women also reported suicidal ideation (22.0%) and prior suicide attempt (25.6%). Compared to no suicidal ideation, moderate (aOR = 4.9, 95%CI = 1.6-15.2) or severe psychological distress (aOR = 38.6) were associated with prior suicide attempt (P< 0.05). Residing in Jiangsu province (vs. not residing in Jiangsu, aOR = 0.3, 95%CI = 0.1-0.6) and higher income (vs. lower income, aOR = 0.6, 95%CI = 0.4-0.9) were protective factors for suicide attempt (P< 0.05). CONCLUSION: Chinese transgender women experience high levels of discrimination, abuse, poor mental health, suicidal ideation, and suicide attempts in Jiangsu province. Society-wide interventions need to be developed to mitigate transphobic discrimination among transgender women in China.

Variational Channel Estimation with Tempering: An Artificial Intelligence Algorithm for Wireless Intelligent Networks.

With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs.