Explore the vast range of titles available.

Understanding the mechanism for antibody neutralization of SARS-CoV-2 is critical for the development of effective therapeutics and vaccines. We recently isolated a large number of monoclonal antibodies from SARS-CoV-2 infected individuals. Here we select the top three most potent yet variable neutralizing antibodies for in-depth structural and functional analyses. Crystal structural comparisons reveal differences in the angles of approach to the receptor binding domain (RBD), the size of the buried surface areas, and the key binding residues on the RBD of the viral spike glycoprotein. One antibody, P2C-1F11, most closely mimics binding of receptor ACE2, displays the most potent neutralizing activity in vitro and conferred strong protection against SARS-CoV-2 infection in Ad5-hACE2-sensitized mice. It also occupies the largest binding surface and demonstrates the highest binding affinity to RBD. More interestingly, P2C-1F11 triggers rapid and extensive shedding of S1 from the cell-surface expressed spike glycoprotein, with only minimal such effect by the remaining two antibodies. These results offer a structural and functional basis for potent neutralization via disruption of the very first and critical steps for SARS-CoV-2 cell entry. Here, the authors compare the crystal structures and investigate the neutralization mechanisms of three neutralizing antibodies against SARS-CoV-2 and find that one antibody, P2C-1F11, closely mimics binding of receptor ACE2 and displays the most potent neutralizing activity in vitro, as well as conferring protection against SARS-CoV-2 infection in Ad5-hACE2-sensitized mice.

Background The central nervous system (CNS) is vulnerable to viral infection, yet few host factors in the CNS are known to defend against invasion by neurotropic viruses. Long noncoding RNAs (lncRNAs) have been revealed to play critical roles in a wide variety of biological processes and are highly abundant in the mammalian brain, but their roles in defending against invasion of pathogens into the CNS remain unclear. Results We report here that multiple neurotropic viruses, including rabies virus, vesicular stomatitis virus, Semliki Forest virus, and herpes simplex virus 1, elicit the neuronal expression of a host-encoded lncRNA EDAL. EDAL inhibits the replication of these neurotropic viruses in neuronal cells and rabies virus infection in mouse brains. EDAL binds to the conserved histone methyltransferase enhancer of zest homolog 2 (EZH2) and specifically causes EZH2 degradation via lysosomes, reducing the cellular H3K27me3 level. The antiviral function of EDAL resides in a 56-nt antiviral substructure through which its 18-nt helix-loop intimately contacts multiple EZH2 sites surrounding T309, a known O -GlcNAcylation site. EDAL positively regulates the transcription of Pcp4l1 encoding a 10-kDa peptide, which inhibits the replication of multiple neurotropic viruses. Conclusions Our findings show that a neuronal lncRNA can exert an effective antiviral function via blocking a specific O -GlcNAcylation that determines EZH2 lysosomal degradation, rather than the traditional interferon-dependent pathway.

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

Background Research on quality of life (QOL) with Parkinson’s disease (PD) has examined direct influencing factors, not mediators. The study aim was to explore whether PD severity and poor cognitive function may decrease physical and mental QOL by reducing activities of daily living (ADL) and increasing depression in sequence. Methods We conducted a cross-sectional questionnaire study of 150 PD hospital patients in China. PD severity, cognitive function, ADL, depression, and QOL were evaluated. We used structural equation modeling to analyze the mediating effects of ADL and depression on the association between PD severity/cognition and the physical health and mental health component summary scores measured by the SF36 quality of life instrument. Results There was a significant mediating effect of PD severity on physical health via ADL and depression (95% CI: − 0.669, − 0.026), and a significant direct effect ( p  < 0.001). The mediating effect of PD severity on mental health via ADL and depression was significant (95% CI: − 2.135, − 0.726), but there was no direct effect ( p  = 0.548). There was a significant mediating effect of cognitive function on physical health via ADL and depression (95% CI: 0.025, 0.219) and a significant direct effect ( p  < 0.001). The mediating effect of cognitive function on mental health via ADL and depression was significant (95% CI: 0.256, 0.645), but there was no direct effect ( p  = 0.313). The physical health models showed a partial mediation, and the mental health models showed a complete mediation, of ADL and depression. Conclusions PD severity and cognitive function increase depression by reducing ADL, leading to lower QOL, and directly or indirectly affect physical health and mental health through different pathways.

Background & Aims: This study aimed to evaluate the feasibility of vonoprazan–minocycline (VM) dual therapy for Helicobacter pylori infection. Methods: In this open-label RCT, 120 H. pylori-infected patients (60 treatment-naïve and 60 treatment-failure cases) were randomized to receive vonoprazan–amoxicillin dual therapy (VA: vonoprazan 20 mg b.i.d. + amoxicillin 1.0 g t.i.d., control group) or VM dual therapy (vonoprazan 20 mg b.i.d. + minocycline 100 mg b.i.d., test group) for 14 days. The primary outcome was eradication rates. Secondary outcomes included adverse effects (AEs). Results: As first-line treatment, eradication rates were 96.7% (VA) vs. 90.0% (VM) in intention-to-treat (ITT) analysis (p = 0.30) and 96.7% (VA) vs. 96.4% (VM) in per-protocol (PP) analysis (p = 0.96). For rescue treatment, eradication rates were 86.7% (VA) vs. 76.7% (VM) in ITT (p = 0.32) and 89.7% (VA) vs. 79.3% (VM) in PP (p = 0.28). Overall eradication rates were 91.7% (VA) vs. 83.3% (VM) in ITT (p = 0.17) and 93.2% (VA) vs. 87.7% (VM) in PP (p = 0.31). VM had a higher incidence of AEs than VA (30.0% vs. 10.0%, p = 0.006), with dizziness being the most common (18.3%). Conclusions: VM dual therapy was shown to be an effective and safe treatment option, demonstrating comparable eradication rates to VA dual therapy. While VM had a slightly higher incidence of AEs, they were generally mild and manageable. VM remained a valuable alternative for patients with penicillin allergies or amoxicillin resistance.

Human adenovirus (HAdV-7) is a highly contagious pathogen that causes severe respiratory illnesses. However, the epidemic patterns and genetic variability of HAdV-7 circulating in mainland China have not been well elucidated. In this study, we used Chinese HAdV sentinel surveillance data obtained from 2012-2015 to investigate the clinical features of 122 HAdV-7-positive cases and performed amplification and sequence determination of three capsid genes (penton base, hexon, and fiber) from 69 isolated viruses covering from seven provinces of China. Additionally, we compared with data from representative sequences of 21 strains covering seven more provinces in China and 32 international HAdV-7 strains obtained from GenBank database to determine the phylogenetic, sequence variations, and molecular evolution of HAdV-7. The results indicated that HAdV-7 infection occurred throughout the year, and a high proportion of severe cases (27 cases, 22.1%) exhibited infantile pneumonia. Moreover, phylogenetic analysis showed that all HAdV-7 strains could be divided into two major evolutionary branches, including subtype 1 and subtype 2, and subtype 3 was also formed according to analysis of the penton base gene. Subtypes 1 and 2 co-circulated in China before 2008, and HAdV-7 strains currently circulating in China belonged to subtype 2, which was also the predominant strain circulating worldwide in recent years. Further sequence variation analysis indicated that three genes of HAdV-7 were relatively stable across time and geographic space, particularly for viruses within subtypes, which shared almost the same variation sites. Owing to continuous outbreaks caused by HAdV-7, resulting in increased illness severity and fatality rates in China, the establishment of a national HAdV surveillance system is urgently needed for the development of effective preventive and infection-control interventions for adenovirus respiratory infections in China.

Due to the clinical continuum of Alzheimer’s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: 1) to develop an effective hierarchical multi-class framework for clinical populations, including normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD, and 2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled from the Alzheimer’s Disease Neuroimaging Initiative database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, including cortical thickness (CTh), fractal dimension, gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination scores at four time points after the initial visit, we identified an optimal subset of forty imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus, parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus. Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice.

Background There is no clear time point for the onset of depression and anxiety in Parkinson's disease (PD), and their atypical physical symptoms often overlap with other nonmotor symptoms. Autonomic dysfunction usually appears earlier than motor symptoms, seriously impairing activities of daily living (ADL), even quality of life. Whether autonomic dysfunction can affect depression and anxiety in PD patients through ADL is still unclear. Methods We conducted three progressive autoregressive mediation models to evaluate whether ADL may mediate the association between autonomic symptom burden, where the mediation chain with autonomic function as an independent variable, ADL as a mediating variable, and anxiety and depression as dependent variables. The ADL of PD patients were measured by the Scales for Outcomes in Parkinson's disease‐Autonomic (SCOPA‐AUT) and Modified Schwab and England ADL scale, respectively, and the status of depression and anxiety were measured by the Geriatric Depression Scale (GDS) and State‐Trait Anxiety Inventory (STAI). Results There were 338 PD patients, including 220 males and 118 females. Demographic information, including age, gender, and education level, were not correlated with the depression and anxiety. Model III had the smallest AIC (AIC = 12,669.89), and the cross‐lagged relations were not statistically significant, so we selected Model II as the optimal model. In Model II, longitudinal autoregressive mediated effect and longitudinal mediated effect of autonomic dysfunction affecting anxiety and depression through ADL were not statistically significant, suggesting longitudinal changes of autonomic dysfunction were independent of anxiety and depression through ADL. Contemporaneous mediated effects of autonomic dysfunction affecting anxiety and depression through ADL were statistically significant, suggesting contemporaneous autonomic dysfunction may contribute to anxiety and depression through ADL. Conclusions Targeted prevention and intervention measures for autonomic dysfunction and ADL should be taken to preserve and improve self‐perceived life satisfaction in the clinical practice and preventive health care of PD. Path graphs of autoregressive model I, II and III

Background Pioglitazone (PIO), a thiazolidinediones drug, is a well-known anti-diabetic medicine, but its anti-atherosclerotic effects remain controversial. Thus it is important to investigate the effects of PIO on atherogenesis and the relevant mechanisms. Methods For in vitro studies, primary cultured or AMP-activated protein kinase (AMPK) inhibited splenocytes were treated with oxidized low density lipoprotein (ox-LDL) or ox-LDL plus PIO. Percentage of T helper 17 (Th17) and regulatory T (Treg) cells were determined by flow cytometry. Expression of AMPK, interleukin-17 (IL-17) and forkhead box P3 (FoxP3) were detected by Western blots. For in vivo studies, apolipoprotein E–deficient (apoE−/−) mice fed with western diet were treated with PIO or vehicle for 8 weeks respectively. Percentage of Th17 and Treg cells in spleen were measured by immunohistochemical analysis. The atherosclerotic lesions were analyzed using oil red O staining, and collagen types I and III in atherosclerotic lesions were stained by Sirius red. Expression of IL-17 and FoxP3 were determined by quantitative polymerase chain reaction. Results In cultured primary splenocytes, PIO dramatically inhibited Th17 and raised Treg. Intriguingly, pharmacological and genetic AMPK inhibitions abolished PIO-induced Treg elevation and Th17 inhibition. Moreover, PIO significantly induced AMPK phosphorylation, decreased IL-17 + and increased FoxP3 + cells in spleen of apoE−/− mice. Finally, PIO did not alter plaque area, but intriguingly, stabilized atherosclerotic plaque through collagen induction in apoE−/− mice. PIO treatment also improved Th17/Treg balance in atherosclerotic lesions. Conclusions PIO exhibits anti-atherosclerotic effects for stabilization of atherosclerotic plaque through regulating the Th17/Treg balance in an AMPK-dependent manner.

The automatic video recognition of depression is becoming increasingly important in clinical applications. However, traditional depression recognition models still face challenges in practical applications, such as high computational costs, the poor application effectiveness of facial movement features, and spatial feature degradation due to model stitching. To overcome these challenges, this work proposes a lightweight Time-Context Enhanced Depression Detection Network (TCEDN). We first use attention-weighted blocks to aggregate and enhance video frame-level features, easing the model’s computational workload. Next, by integrating the temporal and spatial changes of video raw features and facial movement features in a self-learning weight manner, we enhance the precision of depression detection. Finally, a fusion network of 3-Dimensional Convolutional Neural Network (3D-CNN) and Convolutional Long Short-Term Memory Network (ConvLSTM) is constructed to minimize spatial feature loss by avoiding feature flattening and to achieve depression score prediction. Tests on the AVEC2013 and AVEC2014 datasets reveal that our approach yields results on par with state-of-the-art techniques for detecting depression using video analysis. Additionally, our method has significantly lower computational complexity than mainstream methods.