Nicotinamide n-Oxide Attenuates HSV-1-Induced Microglial Inflammation through Sirtuin-1/NF-κB Signaling.

HSV-1 is a typical neurotropic virus that infects the brain and causes keratitis, cold sores, and occasionally, acute herpes simplex encephalitis (HSE). The large amount of proinflammatory cytokines induced by HSV-1 infection is an important cause of neurotoxicity in the central nervous system (CNS). Microglia, as resident macrophages in CNS, are the first line of defense against neurotropic virus infection. Inhibiting the excessive production of inflammatory cytokines in overactivated microglia is a crucial strategy for the treatment of HSE. In the present study, we investigated the effect of nicotinamide n-oxide (NAMO), a metabolite mainly produced by gut microbe, on HSV-1-induced microglial inflammation and HSE. We found that NAMO significantly inhibits the production of cytokines induced by HSV-1 infection of microglia, such as IL-1ß, IL-6, and TNF-α. In addition, NAMO promotes the transition of microglia from the pro-inflammatory M1 type to the anti-inflammatory M2 type. More detailed studies revealed that NAMO enhances the expression of Sirtuin-1 and its deacetylase enzymatic activity, which in turn deacetylates the p65 subunit to inhibit NF-κB signaling, resulting in reduced inflammatory response and ameliorated HSE pathology. Therefore, Sirtuin-1/NF-κB axis may be promising therapeutic targets against HSV-1 infection-related diseases including HSE.

Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion.

Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases.

Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion

Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases.

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm.

Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors.

Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO.

The quality of asymptomatic corona virus disease 2019 (COVID-19) computed tomography (CT) image is reduced due to interference from Gaussian noise, which affects the subsequent image processing. Aiming at the problem that asymptomatic COVID-19 CT image often have small flake ground-glass shadow in the early lesions, and the density is low, which is easily confused with noise. A denoising method of wavelet transform with shrinkage factor is proposed. The threshold decreases with the increase of decomposition scale, and it reduces the misjudgment of signal points. In the advanced stage, the range of lesions increases, with consolidation and fibrosis in different sizes, which have similar gray value to the CT images of suspected cases. Aiming at the problems of low contrast and fuzzy boundary in the traditional wavelet transform, the threshold function based on the optimization of parameters combined with the improved particle swam optimization (PSO) is proposed, so that the parameters of wavelet threshold function can change adaptively according to the lung lobe and ground-glass lesions with fewer iterations. The simulation results show that the paper method is significantly better than other algorithms in peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and mean absolute error (MSE). For example, aiming at the early asymptomatic COVID-19, compared with the comparison methods, the PSNR under the proposed method has increased by about 5 dB, the MSE has been greatly reduced, and the SNR has increased by about 6.1 dB. It can be seen that the denoising effect under the proposed method is the best.

Promoting early management of frailty in the new normal: An updated software tool in addressing the need of virtual assessment of frailty at points of care.

Introduction: Frailty is a state of diminished physiological reserve and can be assessed using the frailty index. Early management of frailty is crucial for preventing adverse outcomes. Intended for assessing home-living older adults, the initial release of the eFI-CGA software was prior to the coronavirus disease 2019 (COVID-19) pandemic. Methods: In addressing the increased need of virtual assessment, the eFI-CGA was upgraded to version 3.0. In this paper, we introduce the updated electronic frailty assessment tool, reporting the newly developed features and validating its use. Results: End-user experiences with the previous versions are discussed. The updated features include a search function to resume disrupted assessments. The improved user interface enabled clinicians to record care management details. Conclusion: This study represents an example of software solutions in moving from disruption to transformation, benefiting healthcare for older adults during this challenging time.

COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter.

CT image of COVID-19 is disturbed by impulse noise during transmission and acquisition. Aiming at the problem that the early lesions of COVID-19 are not obvious and the density is low, which is easy to confuse with noise. A median filtering algorithm based on adaptive two-stage threshold is proposed to improve the accuracy for noise detection. In the advanced stage of ground-glass lesion, the density is uneven and the boundary is unclear. It has similar gray value to the CT images of suspected COVID-19 cases such as adenovirus pneumonia and mycoplasma pneumonia (reticular shadow and strip shadow). Aiming at the problem that the traditional weighted median filter has low contrast and fuzzy boundary, an adaptive weighted median filter image denoising method based on hybrid genetic algorithm is proposed. The weighted denoising parameters can adaptively change according to the detailed information of lung lobes and ground-glass lesions, and it can adaptively match the cross and mutation probability of genetic combined with the steady-state regional population density, so as to obtain a more accurate COVID-19 denoised image with relatively few iterations. The simulation results show that the improved algorithm under different density of impulse noise is significantly better than other algorithms in peak signal-to-noise ratio (PSNR), image enhancement factor (IEF) and mean absolute error (MSE). While protecting the details of lesions, it enhances the ability of image denoising.

Promoting Early Assessment of Frailty in the New Normal: An Updated eFI-CGA Software Tool

Frailty is a state of diminished physiological reserves. Being able to detect and manage frailty early is crucial for effective controlling of frailty-related adverse outcomes. Frailty can be assessed using the frailty index that counts the number of health deficits accumulated over time. Our previous research has enabled an electronic Comprehensive Geriatric Assessment (eCGA) and the calculation of the frailty index based on the eCGA (eFI-CGA). While the standalone eFI-CGA has been used by primary care providers in assessing home-living patients, its initial release was prior to the covid-19 pandemic;the associated new challenges were not targeted by the early version. In facilitating effective virtual assessment and care planning during the current “lockdown” and in the upcoming “new normal”, most recently the eFI-CGA version 3.0 was released. In this paper, we 1) introduce the updated electronic frailty assessment tool and its usage, 2) describe the major updates of the software in dealing with challenges due to social isolation and remote assessment, and 3) evaluate the end-user experience with the upgraded methods in frailty assessment. These new developments and implementations allowed a search function to resume disrupted assessment sessions and quickly retrieve previously saved assessment records. The improved user interface promoted the clinicians to conveniently record detailed care plans and management details. The study provided a successful example of moving from disruption to transformation, benefiting the highly demanded healthcare of older adults in this challenging time.

Brainstem Abnormalities in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Scoping Review and Evaluation of Magnetic Resonance Imaging Findings.

Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multisystem medical condition with heterogeneous symptom expression. Currently, there is no effective cure or treatment for the standard care of patients. A variety of ME/CFS symptoms can be linked to the vital life functions of the brainstem, the lower extension of the brain best known as the hub relaying information back and forth between the cerebral cortex and various parts of the body. Objective/Methods: Over the past decade, Magnetic Resonance Imaging (MRI) studies have emerged to understand ME/CFS with interesting findings, but there has lacked a synthesized evaluation of what has been found thus far regarding the involvement of the brainstem. We conducted this study to review and evaluate the recent MRI findings via a literature search of the MEDLINE database, from which 11 studies met the eligibility criteria. Findings: Data showed that MRI studies frequently reported structural changes in the white and gray matter. Abnormalities of the functional connectivity within the brainstem and with other brain regions have also been found. The studies have suggested possible mechanisms including astrocyte dysfunction, cerebral perfusion impairment, impaired nerve conduction, and neuroinflammation involving the brainstem, which may at least partially explain a substantial portion of the ME/CFS symptoms and their heterogeneous presentations in individual patients. Conclusions: This review draws research attention to the role of the brainstem in ME/CFS, helping enlighten future work to uncover the pathologies and mechanisms of this complex medical condition, for improved management and patient care.

SARS-CoV-2 infection activates a subset of intrinsic pathways to inhibit type I interferons in vitro and in vivo.

SARS-CoV-2 infection poses a global challenge to human health. Upon viral infection, host cells initiate the innate antiviral response, which primarily involves type I interferons (I-IFNs), to enable rapid elimination of the invading virus. Previous studies revealed that SARS-CoV-2 infection limits the expression of I-IFNs in vitro and in vivo, but the underlying mechanism remains incompletely elucidated. In the present study, we performed data mining and longitudinal data analysis using SARS-CoV-2-infected normal human bronchial epithelial (NHBE) cells and ferrets, and the results confirmed the strong inhibitory effect of SARS-CoV-2 on the induction of I-IFNs. Moreover, we identified genes that are negatively correlated with IFNB1 expression in vitro and in vivo based on Pearson correlation analysis. We found that SARS-CoV-2 activates numerous intrinsic pathways, such as the circadian rhythm, phosphatidylinositol signaling system, peroxisome, and TNF signaling pathways, to inhibit I-IFNs. These intrinsic inhibitory pathways jointly facilitate the successful immune evasion of SARS-CoV-2. Our study elucidates the underlying mechanism by which SARS-CoV-2 evades the host innate antiviral response in vitro and in vivo, providing theoretical evidence for targeting these immune evasion-associated pathways to combat SARS-CoV-2 infection.

Toward improved homecare of frail older adults: A focus group study synthesizing patient and caregiver perspectives.

BACKGROUND: Adopting a better understanding of how both older adults and health care providers view the community management of frailty is necessary for improving home health, especially facing the coronavirus disease 2019 (COVID-19) pandemic. We conducted a qualitative focus group study to assess how both older adults and health care providers view frailty and virtual health care in home health. METHODS: Two focus groups enrolled home-living older adults and health care professionals, respectively (n = 15). Questions targeting the use of virtual / telehealth technologies in-home care for frail older adults were administered at audio-recorded group interviews. Transcribed discussions were coded and analyzed using NVivo software. RESULTS: The older adult group emphasized the autonomy related to increasing frailty and social isolation and the need for transparent dissemination of health care planning. They were optimistic about remote technology-based supports and suggested that telehealth / health-monitoring/tracking were in high demand. Health care professionals emphasized the importance of a holistic biopsychosocial approach to frailty management. They highlighted the need for standardized early assessment and management of frailty. CONCLUSIONS: The integrated perspectives provided an updated understanding of what older adults and practitioners value in home-living supports. This knowledge is helpful to advancing virtual home care, providing better care for frail individuals with complex health care needs.

A comprehensive investigation of the mRNA and protein level of ACE2, the putative receptor of SARS-CoV-2, in human tissues and blood cells.

The outbreak of pneumonia caused by SARS-CoV-2 posed a great threat to global human health, which urgently requires us to understand comprehensively the mechanism of SARS-CoV-2 infection. Angiotensin-converting enzyme 2 (ACE2) was identified as a functional receptor for SARS-CoV-2, distribution of which may indicate the risk of different human organs vulnerable to SARS-CoV-2 infection. Previous studies investigating the distribution of ACE2 mRNA in human tissues only involved a limited size of the samples and a lack of determination for ACE2 protein. Given the heterogeneity among humans, the datasets covering more tissues with a larger size of samples should be analyzed. Indeed, ACE2 is a membrane and secreted protein, while the expression of ACE2 in blood and common blood cells remains unknown. Herein, the proteomic data in HIPED and the antibody-based immunochemistry result in HPA were collected to analyze the distribution of ACE2 protein in human tissues. The bulk RNA-seq profiles from three separate public datasets including HPA tissue Atlas, GTEx, and FANTOM5 CAGE were also obtained to determine the expression of ACE2 in human tissues. Moreover, the abundance of ACE2 in human blood and blood cells was determined by analyzing the data in the PeptideAtlas and the HPA Blood Atlas. We found that the mRNA expression cannot reflect the abundance of ACE2 factor due to the strong differences between mRNA and protein quantities of ACE2 within and across tissues. Our results suggested that ACE2 protein is mainly expressed in the small intestine, kidney, gallbladder, and testis, while the abundance of which in brain-associated tissues and blood common cells is low. HIPED revealed enrichment of ACE2 protein in the placenta and ovary despite a low mRNA level. Further, human secretome shows that the average concentration of ACE2 protein in the plasma of males is higher than those in females. Our research will be beneficial for understanding the transmission routes and sex-based differences in susceptibility of SARS-CoV-2 infection.