Search Results (34)

The seasonal patterns of viral diseases in farm animals present significant challenges to global livestock productivity, with cold stress emerging as a potential modulator of host–pathogen interactions. This review synthesizes current knowledge on the expression dynamics of heat shock protein 70 (HSP70) in farm animals under cold-stress conditions and its potential roles as (1) a viral replication facilitator and (2) an immune response regulator. This review highlights cold-induced HSP70 overexpression in essential organs, as well as its effects on significant virus life cycles, such as porcine epidemic diarrhea virus (PEDV), porcine reproductive and respiratory syndrome virus (PRRSV), and bovine viral diarrhea virus (BVDV), through processes like viral protein chaperoning, replication complex stabilization, and host defense modulation. By integrating insights from thermophysiology, virology, and immunology, we suggest that HSP70 serves as a crucial link between environmental stress and viral disease seasonality. We also discuss translational opportunities targeting HSP70 pathways to break the cycle of seasonal outbreaks, while addressing key knowledge gaps requiring further investigation. This article provides a framework for understanding climate-driven disease patterns and developing seasonally adjusted intervention strategies. Full article

Carbonate-hosted clay-type lithium deposits have emerged as strategic resources critical to the global energy transition, yet their exploration faces the dual challenges of technical complexity and environmental sustainability. Traditional methods often entail extensive land disruption, particularly in ecologically sensitive ecosystems where vegetation coverage and weathered layers hinder mineral detection. This study presents a case study of the San Dan lithium deposit in central Yunnan, where we propose a hierarchical anomaly extraction and multidimensional weighted comprehensive analysis. This comprehensive method integrates multi-source data from GF-3 QPSI SAR, GF-5B hyperspectral, and Landsat-8 OLI datasets and is structured around two core parts, as follows: (1) Hierarchical Anomaly Extraction: Utilizing principal component analysis, this part extracts hydroxyl and iron-stained alteration anomalies. It further employs the spectral hourglass technique for the precise identification of lithium-rich minerals, such as montmorillonite and illite. Additionally, concealed structures are extracted using azimuth filtering and structural detection in radar remote sensing. (2) Multidimensional Weighted Comprehensive Analysis: This module applies reclassification, kernel density analysis, and normalization preprocessing to five informational layers—hydroxyl, iron staining, minerals, lithology, and structure. Dynamic weighting, informed by expert experience and experimental adjustments using the weighted weight-of-evidence method, delineates graded target areas. Three priority target areas were identified, with field validation conducted in the most promising area revealing Li₂O contents ranging from 0.10% to 0.22%. This technical system, through the collaborative interpretation of multi-source data and quantitative decision-making processes, provides robust support for exploring carbonate-clay-type lithium deposits in central Yunnan. By promoting efficient, data-driven exploration and minimizing environmental disruption, it ensures that lithium extraction meets the growing demand while preserving ecological integrity, setting a benchmark for the sustainable exploration of clay-type lithium deposits worldwide. Full article

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by chronic inflammation that affects multiple organs, with its prevalence varying by ethnicity. Intestinal dysbiosis has been observed in both SLE patients and murine models. Additionally, intestinal barrier impairment is thought to contribute to the ability of pathobionts to evade and breach immune defenses, resulting in antigen cross-reactivity, microbial translocation, subsequent immune activation, and, ultimately, multiple organ failure. Since the detailed mechanisms underlying these processes are difficult to examine using human samples, murine models are crucial. Various SLE murine models, including genetically modified spontaneous and inducible murine models, offer insights into pathobionts and how they dysregulate systemic immune systems. Furthermore, since microbial metabolites modulate systemic immune responses, bacteria and their metabolites can be targeted for treatment. Based on human and mouse research insights, this review examines how lupus pathobionts trigger intestinal and systemic immune dysregulation. Therapeutic approaches, such as fecal microbiota transplantation and dietary adjustments, show potential as cost-effective and safe methods for preventing and treating SLE. Understanding the complex interactions between the microbiota, host factors, and immune dysregulation is essential for developing novel, personalized therapies to tackle this multifaceted disease. Full article

Innate immunity is critical for insects to adjust to complicated environments. Studying the insect immune system can aid in identifying novel insecticide targets and provide insights for developing novel pest control strategies. Insects recognize environmental pathogens through pattern recognition receptors, thus activating the innate immune system to eliminate pathogens. The innate immune system of insects primarily comprises cellular immunity and humoral immunity. Toll, immune deficiency, and Janus kinase/signal transducers and activators of transcription are the main signaling pathways regulating insect humoral immunity. Nevertheless, increasing research has revealed that immune signaling activated by microbes also performs non-immune roles while exerting immune roles, and insulin signaling performs a key role in mediating the connection between the immune system and non-immune physiological activities. Therefore, this paper first briefly reviews the main innate immune signaling and insulin signaling of insects, then summarizes the relationship between immune signaling activated by microbes and insect growth and development, reproduction, pesticide resistance, chemical communication, cell turnover, lifespan, sleep, energy generation pathways and their possible underlying mechanisms. Future research directions and methodologies are also proposed, aiming to provide insights into further study on the physiological mechanism linking microbes and insect hosts. Full article

Background: Richter syndrome (RS) represents a major unmet need in the lymphoma field, being refractory to chemoimmunotherapy and targeted agents. The BCL-2 inhibitor venetoclax in combination with dose-adjusted EPOCH-R chemoimmunotherapy showed promising efficacy in patients affected by RS. However, responses were not durable, suggesting the need for further treatment optimization. Methods: Here, we report two cases of RS achieving long-term complete remission with intensified chemoimmunotherapy (Rituximab-G-MALL B-ALL/NHL2002 regimen) plus venetoclax induction, followed by haploidentical hematopoietic stem cell transplant (allo-HSCT). Venetoclax was given continuously for 14 consecutive days after every Rituximab-G-MALL cycle in off-label use. An accelerated venetoclax rump-up schedule was used in both patients to reach the maximal dose. Maximal venetoclax dose was 300 mg and 400 mg in patient 1 and patient 2, respectively. Results: The combined treatment was well tolerated, with no major infective complications or non-hematological toxicities. In both patients, immunosuppression was discontinued within day 180 after transplant with no graft-versus-host-disease flares. Both patients are alive and in continuous complete remission after 60 and 72 months following allo-HSCT. Conclusions: This report supports the feasibility of a combination treatment with BCL-2 inhibitors and intensive chemoimmunotherapy as a bridge to allo-HSCT in RS. Full article

Co-resident attacks are serious security threats in multi-tenant public cloud platforms. They are often implemented by building side channels between virtual machines (VMs) hosted on the same cloud server. Traditional defense methods are troubled by the deployment cost. The existing tenant classification methods can hardly cope with the real dataset that is quite large and extremely unevenly distributed, and may have problems in the processing speed considering the computation complexity of the DBSCAN algorithm. In this paper, we propose a novel co-resident attack defense strategy which solve these problems through an improved and efficient multi-level clustering algorithm and semi-supervised classification method. We propose a novel multi-level clustering algorithm which can efficiently reduce the complexity, since only a few parameter adjustments are required. Built on the proposed clustering algorithm, a semi-supervised classification model is designed. The experimental results of the classification effect and training speed show that our model achieves F-scores of over 85% and is significantly faster than traditional SVM classification methods. Based on the classification of unlabeled tenants into different security groups, the cloud service provider may modify the VM placement policy to achieve physical isolation among different groups, reducing the co-residency probability between attackers and target tenants. Experiments are conducted on a large-scale dataset collected from Azure Cloud Platform. The results show that the proposed model achieves 97.86% accuracy and an average 96.06% F-score, proving the effectiveness and feasibility of the proposed defense strategy. Full article

Wireless Local-Area Networks (WLANs), as a popular internet access solution, are widely used in numerous places, including enterprises, campuses, and public venues. As the number of devices increases, large-scale deployments will cause the problem of dense wireless networks, including a lot of energy consumption. Thus, the optimization of energy-efficient wireless AP devices has become a focal point of attention. To reduce energy consumption, we have proposed the active access-point (AP) configuration algorithm for WLANs using APs with a dual interface. This uses the greedy algorithm combined with the local search optimization method to find the minimum number of activated APs while satisfying the minimum throughput constraint. However, the previous algorithm basically satisfies only the average throughput among the multiple hosts associated with one AP, wherein some hosts may not reach the required one. In this paper, to overcome this limitation, we propose an enhanced active AP configuration algorithm by incorporating the throughput request satisfaction method that controls the actual throughput at the target value (target throughput) for every host by applying traffic shaping. The target throughput is calculated from the single and concurrent communicating throughput of each host based on channel occupancy time. The minimum throughput constraint will be iteratively adjusted to obtain the required target throughput and achieve the fair throughput allocation. For evaluations, we conducted simulations using the WIMNET simulator and experiments using the testbed system with a Raspberry Pi 4B for APs in four topology cases with five APs and ten hosts. The results show that the proposed method always achieved the required minimum throughput in simulations as well as in experiments, while minimizing the number of active APs. Thus, the validity and effectiveness of our proposal were confirmed. Full article

Background and Objectives: Chlamydia trachomatis (C. trachomatis) represents one of the most prevalent bacterial sexually transmitted diseases. This study aims to explore the relationship between HLA alleles/genotypes/haplotypes and C. trachomatis infection to better understand high-risk individuals and potential complications. Materials and Methods: This prospective study recruited participants from Transylvania, Romania. Patients with positive NAAT tests for C. trachomatis from cervical/urethral secretion or urine were compared with controls regarding HLA-DR and -DQ alleles. DNA extraction for HLA typing was performed using venous blood samples. Results: Our analysis revealed that the presence of the DRB1*13 allele significantly heightened the likelihood of C. trachomatis infection (p = 0.017). Additionally, we observed that individuals carrying the DRB1*01/DRB1*13 and DQB1*03/DQB1*06 genotype had increased odds of C. trachomatis infection. Upon adjustment, the association between the DRB1*01/DRB1*13 genotype and C. trachomatis remained statistically significant. Conclusions: Our findings underscore the importance of specific HLA alleles and genotypes in influencing susceptibility to C. trachomatis infection. These results highlight the intricate relationship between host genetics and disease susceptibility, offering valuable insights for targeted prevention efforts and personalized healthcare strategies. Full article

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains. Full article

The oncogenicity of the human cytomegalovirus (CMV) is currently being widely debated. Most recently, mounting clinical evidence suggests an anti-cancer effect via CMV-induced T cell-mediated tumor destruction. However, the data were mostly obtained from single-center studies and in vitro experiments. Broad geographic coverage is required to offer a global perspective. Our study examined the correlation between country-specific CMV seroprevalence (across 73 countries) and the age-standardized incidence rate (of 34 invasive tumors). The populations studied were stratified according to decadal age periods as the immunologic effects of CMV seropositivity may depend upon age at initial infection. The International Agency for Research on Cancer of the World Health Organization (IARC WHO) database was used. The multivariate linear regression analysis revealed a worldwide inverse correlation between CMV seroprevalence and the incidences of 62.8% tumors. Notably, this inverse link persists for all cancers combined (Spearman’s ρ = −0.732, p < 0.001; β = −0.482, p < 0.001, adjusted R² = 0.737). An antithetical and significant correlation was also observed in particular age groups for the vast majority of tumors. Our results corroborate the conclusions of previous studies and indicate that this oncopreventive phenomenon holds true on a global scale. It applies to a wide spectrum of cancer histologies, additionally supporting the idea of a common underlying mechanism—CMV-stimulated T cell tumor targeting. Although these results further advance the notion of CMV-based therapies, in-depth investigation of host–virus interactions is still warranted. Full article

The mammalian or mechanistic target of rapamycin (mTOR) integrates multiple intracellular and extracellular upstream signals involved in the regulation of anabolic and catabolic processes in cells and plays a key regulatory role in cell growth and metabolism. The activation of the mTOR signaling pathway has been reported to be associated with a wide range of human diseases. A growing number of in vivo and in vitro studies have demonstrated that gut microbes and their complex metabolites can regulate host metabolic and immune responses through the mTOR pathway and result in disorders of host physiological functions. In this review, we summarize the regulatory mechanisms of gut microbes and mTOR in different diseases and discuss the crosstalk between gut microbes and their metabolites and mTOR in disorders in the gastrointestinal tract, liver, heart, and other organs. We also discuss the promising application of multiple potential drugs that can adjust the gut microbiota and mTOR signaling pathways. Despite the limited findings between gut microbes and mTOR, elucidating their relationship may provide new clues for the prevention and treatment of various diseases. Full article

A promising new approach to broad spectrum antiviral drugs is the inhibition of the eukaryotic translation initiation factor 4A (elF4A), a DEAD-box RNA helicase that effectively reduces the replication of several pathogenic virus types. Beside the antipathogenic effect, modulation of a host enzyme activity could also have an impact on the immune system. Therefore, we performed a comprehensive study on the influence of elF4A inhibition with natural and synthetic rocaglates on various immune cells. The effect of the rocaglates zotatifin, silvestrol and CR-31-B (−), as well as the nonactive enantiomer CR-31-B (+), on the expression of surface markers, release of cytokines, proliferation, inflammatory mediators and metabolic activity in primary human monocyte-derived macrophages (MdMs), monocyte-derived dendritic cells (MdDCs), T cells and B cells was assessed. The inhibition of elF4A reduced the inflammatory potential and energy metabolism of M1 MdMs, whereas in M2 MdMs, drug-specific and less target-specific effects were observed. Rocaglate treatment also reduced the inflammatory potential of activated MdDCs by altering cytokine release. In T cells, the inhibition of elF4A impaired their activation by reducing the proliferation rate, expression of CD25 and cytokine release. The inhibition of elF4A further reduced B-cell proliferation, plasma cell formation and the release of immune globulins. In conclusion, the inhibition of the elF4A RNA helicase with rocaglates suppressed the function of M1 MdMs, MdDCs, T cells and B cells. This suggests that rocaglates, while inhibiting viral replication, may also suppress bystander tissue injury by the host immune system. Thus, dosing of rocaglates would need to be adjusted to prevent excessive immune suppression without reducing their antiviral activity. Full article

Diet is currently considered one of the most important adjustable determinants of human health. The gut microbiota, the collection of microorganisms that inhabit (mainly) the distal bowel, has recently been shown to ensure critical physiological functions, such as immune, metabolic and neuropsychiatric. Many of these biological effects result from the production of bacterial metabolites that may target host cells, tissues and organs. In line with this rationale, epigenetics has brought new insights to our understanding of how environmental factors influence gene expression and, interestingly, gut microbiota metabolites have recently been proposed as novel and significant inducers of epigenetic modifications. Efforts have been dedicated to unveil how the production of specific metabolites influences the activity of epigenetic writers and erasers in order to establish a mechanistic link between gut microbiota, epigenetic modifications and health. Recent data is now evidencing how specific microbial metabolites shape the epigenetic landscape of eukaryotic cells, paving new avenues for innovative therapeutic strategies relying on diet-driven microbiota: epigenetic interactions. Herein is discussed the impact of diet on gut microbiota and the molecular mechanisms underlying microbiota–host interactions, highlighting the influence of diet on microbiota metabolome and how this may induce epigenetic modifications in host cells. Furthermore, it is hypothesized that epigenetics may be a key process transducing the effects of diet on gut microbiota with consequences for health and disease. Accordingly, innovating strategies of disease prevention based on a “precision diet”, a personalized dietary planning according to specific epigenetic targets, are discussed. Full article

China is one of the major cotton producers globally with small farmers. Lepidopteran pests have always been the main factor affecting cotton production. To reduce the occurrence of and damage caused by lepidopteran pests, China has employed a pest control method focused on planting Bt (Cry1Ac) cotton since 1997. Chinese resistance management tactics for the main target pests, the cotton bollworm and pink bollworm, were also implemented. For polyphagous (multiple hosts) and migratory pests such as the cotton bollworm (Helicoverpa armigera), the “natural refuge” strategy, consisting of non-Bt crops such as corn, soybean, vegetables, peanuts, and other host crops, was adopted in the Yellow River Region (YRR) and Northwest Region (NR). For a single host and weak migration ability pest, such as the pink bollworm (Pectinophora gossypiella), the seed mix refuge strategy yields a random mixture within fields of 25% non-Bt cotton by sowing second-generation (F2) seeds. According to field monitoring results for more than 20 years in China, practical resistance (Bt cotton failure) of target pests was avoided, and there were no cases of Bt (Cry1Ac) failure of pest control in cotton production. This indicated that this Chinese resistance management strategy was very successful. The Chinese government has decided to commercialize Bt corn, which will inevitably reduce the role of natural refuges; therefore, this paper also discusses adjustments and future directions of cotton pest resistance management strategies. Full article

COVID-19 infection triggered a global public health crisis during the 2020–2022 period, and it is still evolving. This highly transmissible respiratory disease can cause mild symptoms up to severe pneumonia with potentially fatal respiratory failure. In this cross-sectional study, 41 PCR-positive patients for SARS-CoV-2 and 42 healthy controls were recruited during the first wave of the pandemic in Mexico. The plasmatic expression of five circulating miRNAs involved in inflammatory and pathological host immune responses was assessed using RT-qPCR (Reverse Transcription quantitative Polymerase Chain Reaction). Compared with controls, a significant upregulation of miR-146a, miR-155, and miR-221 was observed; miR-146a had a positive correlation with absolute neutrophil count and levels of brain natriuretic propeptide (proBNP), and miR-221 had a positive correlation with ferritin and a negative correlation with total cholesterol. We found here that CDKN1B gen is a shared target of miR-146a, miR-221-3p, and miR-155-5p, paving the way for therapeutic interventions in severe COVID-19 patients. The ROC curve built with adjusted variables (miR-146a, miR-221-3p, miR-155-5p, age, and male sex) to differentiate individuals with severe COVID-19 showed an AUC of 0.95. The dysregulation of circulating miRNAs provides new insights into the underlying immunological mechanisms, and their possible use as biomarkers to discriminate against patients with severe COVID-19. Functional analysis showed that most enriched pathways were significantly associated with processes related to cell proliferation and immune responses (innate and adaptive). Twelve of the predicted gene targets have been validated in plasma/serum, reflecting their potential use as predictive prognosis biomarkers. Full article