Search Results (1,070)

Background: Varicella zoster virus (VZV) is the causative agent for chickenpox and herpes zoster (HZ, shingles). HZ is a debilitating disease affecting elderly and immunocompromised populations. Glycoprotein E (gE) is indispensable for viral replication and cell-to-cell spread and is the primary target for anti-VZV antibodies. Importantly, gE is the sole antigen in Shingrix, a highly efficacious, AS01_B-adjuvanted vaccine approved in multiple countries for the prevention of HZ, yet the three-dimensional (3D) structure of gE remains elusive. Objectives: We sought to determine the structure of VZV gE and to understand in detail its interactions with neutralizing antibodies. Methods: We used X-ray crystallography and cryo-electron microscopy to elucidate structures of gE bound by recombinant Fabs of antibodies previously elicited through vaccination with Zostavax, a live, attenuated vaccine. Results: The 3D structures resolve distinct central and C-terminal antigenic domains, presenting an array of diverse conformational epitopes. The central domain has two beta-sheets and two alpha helices, including an IgG-like fold. The C-terminal domain exhibits 3 beta-sheets and an Ig-like fold and high structural similarity to HSV1 gE. Conclusions: gE from VZV-infected cells elicits a human antibody response with a preference for the gI binding domain of gE. These results yield insights to VZV gE structure and immunogenicity, provide a framework for future studies, and may guide the design of additional herpesvirus vaccine antigens. Teaser: Structures of varicella zoster virus glycoprotein E reveal distinct antigenic domains and define epitopes for vaccine-elicited human antibodies. Full article

Pine wilt disease (PWD) is a severe forest disease caused by the invasion of pine wood nematode (Bursaphelenchus xylophilus), which has caused significant damage to China’s forestry resources due to its short disease cycle and strong infectious ability. Benefiting from the development of unmanned aerial vehicle (UAV)-based remote sensing technology, the use of UAV images for the detection of PWD-infected trees has become one of the mainstream methods. However, current UAV-based detection studies mostly focus on multispectral and hyperspectral images, and few studies have focused on using red–green–blue (RGB) images for detection. This study used UAV-based RGB images to extract feature information using different color space models and then utilized semantic segmentation techniques in deep learning to detect individual PWD-infected trees. The results showed that: (1) The U-Net model realized the optimal image segmentation and achieved the highest classification accuracy with F1-score, recall, and Intersection over Union (IoU) of 0.9586, 0.9553, and 0.9221, followed by the DeepLabv3+ model and the feature pyramid networks (FPN) model. (2) The RGBHSV feature scheme outperformed both the RGB feature scheme and the hue saturation value (HSV) feature scheme, which were unrelated to the choice of the semantic segmentation techniques. (3) The semantic segmentation techniques in deep-learning models achieved superior model performance compared with traditional machine-learning methods, with the U-Net model obtaining 4.81% higher classification accuracy compared with the random forest model. (4) Compared to traditional semantic segmentation models, the newly proposed segment anything model (SAM) performed poorly in identifying pine wood nematode disease. Its success rate is 0.1533 lower than that of the U-Net model when using the RGB feature scheme and 0.2373 lower when using the HSV feature scheme. The results showed that the U-Net model using the RGBHSV feature scheme performed best in detecting individual PWD-infected trees, indicating that the proposed method using semantic segmentation technique and UAV-based RGB images to detect individual PWD-infected trees is feasible. The proposed method not only provides a cost-effective solution for timely monitoring forest health but also provides a precise means to conduct remote sensing image classification tasks. Full article

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson’s correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R² is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding. Full article

The emotional health benefits of urban green space have been widely recognized. Flower borders, as a perennial plant landscape, have gradually become a current form of plant application in urban green spaces due to their rich color configurations. However, the related research primarily focuses on the impact of urban green spaces on public health, with relatively little attention given to how the colors of flower borders affect public emotional health. This study explored the relationship between the flower borders color characteristics and the public emotional health. In this study, 24 sample images were used as experimental materials, which selected based on their color richness and harmony. Additionally, face recognition technology and online random questionnaires were utilized to measure the public basic emotions and pleasure, respectively. The result shows that, based on the HSV color model and expert recommendations, 19 color characteristics were identified. The correlation analysis of the results from the public emotion with these color characteristics revealed that 13 color characteristics correlated with public emotional pleasure. Among them, blue, neutral purple, and low saturation were positively correlated. Through factor analysis, these thirteen color characteristics were summarized and categorized into four common factors (F1–F4), three of which are related to color. They are “low saturation of blue-violet percentage” (F1), “color configuration diversity” (F2), “bright red percentage” (F3), and “base green percentage” (F4), with F1 having the largest variance explained (27.88%). Finally, an evaluation model of color characteristics was constructed based on the variance explained by these four factors, which was demonstrated to effectively predict the level of public emotional pleasure when viewing flower borders. The results shed light on the effects of color characteristics on public emotions and provide new perspectives for subsequent flower border evaluations. Our results provide a valuable reference for future flower border color design, aiming to better improve public emotional health. Full article

Background: Aseptic meningitis comprises meningeal inflammation and cerebrospinal fluid (CSF) pleocytosis without positive Gram stain and culture. Regional differences exist in the prevalence of viral etiologies of aseptic meningitis. We aimed to assess the etiologies of aseptic meningitis in immunocompetent adults, focusing on herpes simplex virus type 2 (HSV-2). Methods: This study retrospectively analyzed immunocompetent adults diagnosed with meningitis at a Korean tertiary care hospital from 2016 to 2018. Aseptic meningitis was defined through clinical and CSF analysis. We compared clinical and laboratory characteristics across viral etiologies and investigated predictors of HSV-2 meningitis. Results: A total of 98 patients (46.9% female) with aseptic meningitis were finally enrolled. The etiologies of aseptic meningitis were identified in 62 patients (63.3%), including enterovirus (28.5%), HSV-2 (16.3%), and varicella zoster virus (VZV, 15.3%). HSV-2 showed female predominance, with shorter admission times with longer hospital stays and a recurrent meningitis history. Compared to other viral etiologies, HSV-2 showed higher CSF white blood cell (WBC) counts and protein levels but lower C-reactive protein (CRP) levels. A random forest model identified previous meningitis history and serum CRP level as key predictors of HSV-2 meningitis. Conclusions: This study provides insights into the etiologies of aseptic meningitis in a specific Korean region, identifying HSV-2 as a notable cause. The prediction model suggested that the clinical history of previous meningitis and serum CRP level may guide clinical assessment of meningitis. Full article

Deep learning technology can automatically learn features from large amounts of data, with powerful feature extraction and pattern recognition capabilities, thereby improving the accuracy and efficiency of object detection. [The objective of this study]: In order to improve the accuracy and speed of mask wearing deep learning detection models in the post pandemic era, the [Problem this study aimed to resolve] was based on the fact that no research work has been reported on standardized detection models for mask wearing with detecting nose targets specially. [The topic and method of this study]: A mask wearing normalization detection model (towards the wearing style exposing the nose to outside, which is the most obvious characteristic of non-normalized style) based on improved YOLOv5s (You Only Look Once v5s is an object detection network model) was proposed. [The improved method of the proposed model]: The improvement design work of the detection model mainly includes (1) the BottleneckCSP (abbreviation of Bottleneck Cross Stage Partial) module was improved to a BottleneckCSP-MASK (abbreviation of Bottleneck Cross Stage Partial-MASK) module, which was utilized to replace the BottleneckCSP module in the backbone architecture of the original YOLOv5s model, which reduced the weight parameters’ number of the YOLOv5s model while ensuring the feature extraction effect of the bonding fusion module. (2) An SE module was inserted into the proposed improved model, and the bonding fusion layer in the original YOLOv5s model was improved for better extraction of the features of mask and nose targets. [Results and validation]: The experimental results indicated that, towards different people and complex backgrounds, the proposed mask wearing normalization detection model can effectively detect whether people are wearing masks and whether they are wearing masks in a normalized manner. The overall detection accuracy was 99.3% and the average detection speed was 0.014 s/pic. Contrasted with original YOLOv5s, v5m, and v5l models, the detection results for two types of target objects on the test set indicated that the mAP of the improved model increased by 0.5%, 0.49%, and 0.52%, respectively, and the size of the proposed model compressed by 10% compared to original v5s model. The designed model can achieve precise identification for mask wearing behaviors of people, including not wearing a mask, normalized wearing, and wearing a mask non-normalized. Full article

Background/Objectives: Previous evidence has shown that American football headgear (e.g., facemasks, visors/eye shields) differentially impairs reaction time (RT) to visual stimuli, most notably in peripheral fields of view. However, this has only been established with stationary RT testing, which may not translate to gameplay situations that require gross motor skills. Therefore, the purpose of this study was to build upon previous findings to elucidate the effects of various American football headgear on gross motor visuomotor drill performance. Methods: Division 1 NCAA football players (n = 16) with normal/corrected-to-normal vision participated and completed two experiments (EXP), each with differing conditions: EXP1- Varying facemask reinforcement and EXP2- Varying visor/eye shield light transmittance. In EXP1, participants completed an agility test for the following conditions: baseline/no helmet (BL), helmet + light (HL), helmet + medium (HM), and helmet + heavy (HH) face mask reinforcement. In EXP2, participants completed an agility test for the following conditions: baseline/no helmet (BL), helmet + clear visor (HCV), helmet + smoke-tinted visor (HSV), and helmet + mirrored visor (HMV). For each condition in EXP1 and EXP2, participants completed a reactive agility task using a FITLIGHT trainer system where five poles were equipped with a total of ten LED sensors and were placed in a semi-circle 1 m around a center point. Participants were asked to step and reach with their hands to hit each ten lights individually as fast as possible upon illumination. Each reactive agility test was repeated for a total of three attempts. Results: Average reaction time was analyzed and compared between conditions and according to visual fields of interest (e.g., central vs. peripheral). Results from EXP1 showed that compared to BL, reactive agility was worsened by HL (p = 0.030), HM (p = 0.034), and HH (p = 0.003) conditions. No differences between facemask conditions existed for overall performance (p > 0.05). For EXP2, HCV (p < 0.001), HSV (p < 0.001), and HMV (p < 0.001) conditions resulted in worsened reactive agility performance compared to BL. No differences between visor conditions existed for overall performance (p > 0.05). Conclusions: Overall, these findings suggest that American football headgear impairs reactive agility, which could result in worsened game performance and safety. Future studies investigating training strategies to overcome impairments are warranted. Full article

Background: Functionalized nanoparticles (NPs) represent a cutting edge in innovative clinical approaches, allowing for the delivery of selected compounds with higher specificity in a wider time frame. They also hold promise for novel theranostic applications that integrate both diagnostic and therapeutic functions. Pathogens are continuously evolving to try to escape the strategies designed to treat them. Objectives: In this work, we describe the development of a biotechnological device, Nano-Immuno-Probes (NIPs), for early detection and infections treatment. Human Herpes Simplex Virus 2 was chosen as model pathogen. Methods: NIPs consist of PLGA-PEG-Sulfone polymeric NPs conjugated to recombinant Fab antibody fragments targeting the viral glycoprotein G2. NIPs synthesis involved multiple steps and was validated through several techniques. Results: DLS analysis indicated an expected size increase with a good polydispersity index. Z-average and z-potential values were measured for PLGA-PEG-Bis-Sulfone NPs (86.6 ± 10.9 nm; –0.7 ± 0.3 mV) and NIPs (151 ± 10.4 nm; −5.1 ± 1.9 mV). SPR assays confirmed NIPs’ specificity for the glycoprotein G2, with an apparent K_D of 1.03 ± 0.61 µM. NIPs exhibited no cytotoxic effects on VERO cells at 24 and 48 h. Conclusions: This in vitro study showed that NIPs effectively target HSV-2, suggesting the potential use of these nanodevices to deliver both contrast agents as well as therapeutic compounds. Full article

Herpes simplex virus (HSV) has coevolved with Homo sapiens for over 100,000 years, maintaining a tenacious presence by establishing lifelong, incurable infections in over half the human population. As of 2024, an effective prophylactic or therapeutic vaccine for HSV remains elusive. In this review, we independently screened PubMed, EMBASE, Medline, and Google Scholar for clinically relevant articles on HSV vaccines. We identified 12 vaccines from our literature review and found promising candidates across various classes, including subunit vaccines, live vaccines, DNA vaccines, and mRNA vaccines. Notably, several vaccines—SL-V20, HF10, VC2, and mRNA-1608—have shown promising preclinical results, suggesting that an effective HSV vaccine may be within reach. Additionally, several other vaccines such as GEN-003 (a subunit vaccine from Genocea), HerpV (a subunit vaccine from Agenus), 0ΔNLS/RVx201 (a live-attenuated replication-competent vaccine from Rational Vaccines), HSV 529 (a replication-defective vaccine from Sanofi Pasteur), and COR-1 (a DNA-based vaccine from Anteris Technologies) have demonstrated potential in clinical trials. However, GEN-003 and HerpV have not advanced further despite promising results. Continued progress with these candidates brings us closer to a significant breakthrough in preventing and treating HSV infections. Full article

Pancreatic cancer presents formidable challenges due to rapid progression and resistance to conventional treatments. Oncolytic viruses (OVs) selectively infect cancer cells and cause cancer cells to lyse, releasing molecules that can be identified by the host’s immune system. Moreover, OV can carry immune-stimulatory payloads such as interleukin-12, which when delivered locally can enhance immune system-mediated tumor killing. OVs are very well tolerated by cancer patients due to their ability to selectively target tumors without affecting surrounding normal tissues. OVs have recently been combined with other therapies, including chemotherapy and immunotherapy, to improve clinical outcomes. Several OVs including adenovirus, herpes simplex viruses (HSVs), vaccinia virus, parvovirus, reovirus, and measles virus have been evaluated in preclinical and clinical settings for the treatment of pancreatic cancer. We evaluated the safety and tolerability of a replication-competent oncolytic adenoviral vector carrying two suicide genes (thymidine kinase, TK; and cytosine deaminase, CD) and human interleukin-12 (hIL12) in metastatic pancreatic cancer patients in a phase 1 trial. This vector was found to be safe and well-tolerated at the highest doses tested without causing any significant adverse events (SAEs). Moreover, long-term follow-up studies indicated an increase in the overall survival (OS) in subjects receiving the highest dose of the OV. Our encouraging long-term survival data provide hope for patients with advanced pancreatic cancer, a disease that has not seen a meaningful increase in OS in the last five decades. In this review article, we highlight several preclinical and clinical studies and discuss future directions for optimizing OV therapy in pancreatic cancer. We envision OV-based gene therapy to be a game changer in the near future with the advent of newer generation OVs that have higher specificity and selectivity combined with personalized treatment plans developed under AI guidance. Full article

Alphaherpesviruses, including herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), and varicella-zoster virus (VZV), infect a diverse array of hosts, spanning both humans and animals. Alphaherpesviruses have developed a well-adapted relationship with their hosts through long-term evolution. Some alphaherpesviruses exhibit a typical neurotropic characteristic, which has garnered widespread attention and in-depth research. Virus latency involves the retention of viral genomes without producing infectious viruses. However, under stress, this can be reversed, resulting in lytic infection. Such reactivation events can lead to recurrent infections, manifesting as diseases like herpes labialis, genital herpes, and herpes zoster. Reactivation is a complex process influenced by both viral and host factors, and identifying how latency and reactivation work is vital to developing new antiviral therapies. Recent research highlights a complex interaction among the virus, neurons, and the immune system in regulating alphaherpesvirus latency and reactivation. Neurotropic alphaherpesviruses can breach host barriers to infect neurons, proliferate extensively within their cell bodies, and establish latent infections or spread further. Whether infecting neurons or spreading further, the virus undergoes transmission along axons or dendrites, making this process an indispensable part of the viral life cycle and a critical factor influencing the virus’s invasion of the nervous system. Research on the transmission process of neurotropic alphaherpesviruses within neurons can not only deepen our understanding of the virus but can also facilitate the targeted development of corresponding vaccines. This review concentrates on the relationship between the transmission, latency, and activation of alphaherpesviruses within neurons, summarizes recent advancements in the field, and discusses how these findings can inform the design of live virus vaccines for alphaherpesviruses. Full article

Research is underway to develop a vaccine to prevent and cure infection from herpes simplex virus (HSV). It emphasizes the critical need for immunization to address public health issues and the shortcomings of existing treatment options. Furthermore, studies on the HSV vaccine advance the field of immunology and vaccine creation, which may help in the battle against other viral illnesses. The current lack of such a vaccine is, in part, due to herpes viral latency in sensory ganglions. Current vaccines rely on tissue-resident memory CD8⁺ T cells, which are known to provide protection against subsequent HSV reinfection and reactivation without correlating with other immune subsets. For that reason, there is no effective vaccine that can provide protection against latent or recurrent herpes infection. This review focuses on conventional methods for evaluating the efficacy of a herpes vaccine using differential CD8⁺ T cells and important unaccounted immune aspects for designing an effective vaccine against herpes. Full article

To achieve the color matching rules for the textiles discovered during Silk Road excavations between the 4th and 8th centuries, this research proposed an image-based matching network modeling method. The Silk Road facilitated trade and cultural exchange between the East and West, and the textiles found along the way depict the development of fabrics in a color scheme with great cultural significance. A total of 165 images with brocade patterns were collected from a book with a detailed description of the Western influences on textiles along the Silk Road. Two different clustering methods, including the K-means clustering method and octree quantization approach, were used to extract the primary and secondary colors. By combining the HSV color space with the PCCS color system, the color distribution was analyzed to discover the features of representative color patterns. The co-occurrence relationship of the auxiliary colors was explored using the Apriori algorithm, and a total of eight association rules were established. The results showed that the K-means clustering algorithm can show a better effect of color classification to obtain three primary colors and nine secondary colors. The matching mechanism with a visualized network model was also proposed, which showed that reddish-yellow tones are the main colors in the brocade patterns, and the light and soft tones separately account for 27% and 20%. Beige and brown are the most common colorways, with a confidence level of 47%. One style of brocade pattern was used to demonstrate different appearances within various color networks, which could be applied to 3D virtual fitting. This image-based matching network modeling approach makes the color matching schemes visible, and can assist fashion design with fabric features influenced by historical and cultural development. Full article

Herpes simplex virus (HSV) is one of the most common etiologic agents of corneal disease and a significant cause of corneal blindness worldwide. Although most cases can be successfully managed with medical therapy, HSV keratitis associated with visually significant stromal scarring often requires corneal transplantation for visual rehabilitation. While penetrating keratoplasty (PK) represented the traditional keratoplasty technique, the past few decades have seen a shift towards lamellar keratoplasty procedures, including deep anterior lamellar keratoplasty and mushroom keratoplasty. This paper describes the current surgical techniques and perioperative antiviral prophylaxis regimen for herpetic keratitis and reviews their postoperative clinical outcomes. Full article

Autophagy, an evolutionarily conserved cellular process, influences the regulation of viral infections. While the existing understanding indicates that Herpes Simplex Virus type 2 (HSV-2) maintains a basal level of autophagy to support its viral yield, the precise pathways governing the induction of autophagy during HSV-2 infection remain unknown. Therefore, this study aims to explore the role of type I interferons (IFN-I) in modulating autophagy during HSV-2 infection and to decode the associated signaling pathways. Our findings revealed an interplay wherein IFN-I regulates the autophagic response during HSV-2 infection. Additionally, we investigated the cellular pathways modulated during this complex process. Exploring the intricate network of signaling events involved in autophagy induction during HSV-2 infection holds promising therapeutic implications. Identifying these pathways advances our understanding of host–virus interactions and holds the foundation for developing targeted therapeutic strategies against HSV-2. The insight gained from this study provides a platform for exploring potential therapeutic targets to restrict HSV-2 infections, addressing a crucial need in antiviral research. Full article