Different Stages Of Infection Research Articles

Multiple Forms of Resistance to the Phomopsis Stem Canker Pathogens Diaporthe helianthi and D. gulyae in Sunflower.

Phomopsis stem canker of cultivated sunflower (Helianthus annuus L.) can be caused by multiple necrotrophic fungi in the genus Diaporthe, with Diaporthe helianthi and D. gulyae being the most common causal agents in the United States. Infection begins at the leaf margins and proceeds primarily through the vasculature, progressing from the leaf through the petiole to the stem, resulting in formation of brown stem lesions centered around the petiole. Sunflower resistance to Phomopsis stem canker is quantitative and genetically complex. Due to the intricate disease process, resistance is possible at different stages of infection, and multiple forms of defense may contribute to the overall level of quantitative resistance. In this study, sunflower lines exhibiting field resistance to Phomopsis stem canker were evaluated for stem and leaf resistance to multiple isolates of D. helianthi and D. gulyae in greenhouse experiments, and responses to the two species were compared. Additionally, selected resistant and susceptible lines were evaluated for petiole transmission resistance to D. helianthi. Lines with distinct forms of resistance were identified, and results indicated that responses to stem inoculation were strongly correlated (Spearman's coefficient 0.598, P < 0.001) for the two fungal species, while leaf responses were not (Spearman's coefficient 0.396, P = 0.076). These results provide a basis for genetic dissection of distinct forms of sunflower resistance to Phomopsis stem canker and will facilitate combining different forms of resistance to potentially achieve durable control of this disease in sunflower hybrids.

Genetic variability of hepatitis B virus in acute and in different phases of chronic infection in Brazil

The selection pressure imposed by the host immune system impacts on hepatitis B virus (HBV) variability. This study evaluates HBV genetic diversity, nucleos(t)ide analogs resistance and HBsAg escape mutations in HBV patients under distinct selective pressures. One hundred and thirteen individuals in different phases of HBV infection were included: 13 HBeAg-positive chronic infection, 9 HBeAg-positive chronic hepatitis, 47 HBeAg-negative chronic infection (ENI), 29 HBeAg-negative chronic hepatitis (ENH) and 15 acute infected individuals. Samples were PCR amplified, sequenced and genetically analyzed for the overlapping POL/S genes. Most HBV carriers presented genotype A (84/113; 74.3%), subgenotype A1 (67/84; 79.7%), irrespective of group, followed by genotypes D (20/113; 17.7%), F (8/113; 7.1%) and E (1/113; 0.9%). Clinically relevant mutations in polymerase (tL180M/M204V) and in the Major Hydrophilic Region of HBsAg (sY100C, T118A/M, sM133T, sD144A and sG145R) were observed. Our findings, however, indicated that most polymorphic sites were located in the cytosolic loops (CYL1-2) and transmembrane domain 4 (TMD4) of HBsAg. Lower viral loads and higher HBV genetic diversity were observed in ENI and ENH groups (p < 0.001), suggesting that these groups are subjected to a higher selective pressure. Our results provide information on the molecular characteristics of HBV in a diverse clinical setting, and may guide future studies on the balance of HBV quasispecies at different stages of infection.

The ever-changing microenvironment of Staphylococcus aureus in cutaneous infections.

Staphylococcus aureus is responsible for the majority of skin and soft tissue infections, which are often diagnosed at a late stage, thereby impacting treatment efficacy. Our study was designed to reveal the physiological changes at different stages of infection by S. aureus through the combined analysis of variations in the skin microenvironment, providing insights for the diagnosis and treatment of S. aureus infections. We established a murine model of skin and soft tissue infection with S. aureus as the infectious agent to investigate the differences in the microenvironment at different stages of infection. By combining analysis of the host immune status and histological observations, we elucidate the progression of S. aureus infection in mice. The results indicate that the infection process in mice can be divided into at least two stages: early infection (1-3 days post-infection) and late infection (5-7 days post-infection). During the early stage of infection, notable symptoms such as erythema and abundant exudate at the infection site were observed. Histological examination revealed infiltration of numerous neutrophils and bacterial clusters, accompanied by elevated levels of cytokines (IL-6, IL-10). There was a decrease in microbial alpha diversity within the microenvironment (Shannon, Faith's PD, Chao1, Observed species, Simpson, Pielou's E). In contrast, during the late stage of infection, a reduction or even absence of exudate was observed at the infected site, accompanied by the formation of scabs. Additionally, there was evidence of fibroblast proliferation and neovascularization. The levels of cytokines and microbial composition gradually returned to a healthy state. This study reveals synchrony between microbial composition and histological/immunological changes during S. aureus-induced SSTIs.

Identification of Phytophthora cinnamomi CRN effectors and their roles in manipulating cell death during Persea americana infection

The oomycete Phytophthora cinnamomi is a devastating plant pathogen with a notably broad host range. It is the causal agent of Phytophthora root rot (PRR), arguably the most economically important yield-limiting disease in Persea americana (avocado). Despite this, our understanding of the mechanisms P. cinnamomi employs to infect and successfully colonize avocado remains limited, particularly regarding the pathogen’s ability to maintain its biotrophic and necrotrophic lifestyles during infection. The pathogen utilises a large repertoire of effector proteins which function in facilitating and establishing disease in susceptible host plants. Crinkling and necrosis effectors (CRN/Crinklers) are suspected to manipulate cell death to aid in maintenance of the pathogens biotrophic and necrotrophic lifestyles during different stages of infection. The current study identified 25 P. cinnamomi CRN effectors from the GKB4 genome using an HMM profile and assigned putative function to them as either cell death inducers or suppressors. Function was assigned to 10 PcinCRNs by analysing their RNA-seq expression profiles, relatedness to other functionally characterised Phytophthora CRNs and tertiary protein predictions. The full-length coding sequences for these PcinCRNs were confirmed by Sanger sequencing, six of which were found to have two divergent alleles. The presence of alleles indicates that the proteins encoded may perform contradicting functions in cell death manipulation, or function in different host plant species. Overall, this study provides a foundation for future research on P. cinnamomi infection and cell death manipulation mechanisms.

Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant

Rationale and objectivesThe computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors. Materials and methodsThis retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0–3 days), intermediate phase (4–7 days), and late phase (8–14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination. ResultsA total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88–0.94), 0.82 (95% CI 0.76–0.87), and 0.83 (95% CI 0.77–0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94–0.98), 0.90 (95% CI 0.87–0.94), and 0.89 (95% CI 0.84–0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed. ConclusionThe time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.

Mitochondrial and apicoplast genome copy abundances of haemosporidian parasites are explained by host species and parasitic lineage

Endosymbiotic organelles, such as mitochondria and plastids, contain own remnant genomes (nucleoids), whose variable abundance in cells may be adaptive to the physiological necessities and functions of the cells. Unicellular apicomplexan parasites contain one mitochondrium and one apicoplast with variable genome copy numbers. We measured the abundance of mitochondrial, apicoplast and nuclear genome copies in a set of avian blood samples infected with haemosporidian blood parasites, belonging to the three main genera Plasmodium, Haemoproteus and Leucocytozoon. We designed general primers suitable for qPCR, amplifying fragments of the mitogenome, plastome and nuclear genomes of avian malaria and related haemosporidian parasites. We measured the amplification of these fragments in 153 samples of 23 avian host species and infected with 33 parasitic lineages. We estimate on average several hundred mitochondrial genome copies and several tens of apicoplast copies per haploid gametocyte cell with substantial variation among samples. Host species appeared to differ in their mitogenome abundance while parasitic lineages differed in plastome abundance per cell (per nuclear copy signal). We did not find consistent differences between parasite genera or higher avian taxa. Parasite lineages and host bird species did not differ consistently in infection intensity, estimated from parasite to host nuclear signals, which may indicate that samples were taken at different stages of infection. However, this and similar results remain to be cross-validated with in-situ imaging techniques. The novel molecular tools introduced here offer avenues for the characterization of nucleoid abundance of haemosporidian parasites over environmental conditions and parasitic developmental stages. Such measures will improve our understanding of parasite physiology, ecology, the coadaptation and coevolution with hosts and suggest possible augmentations to standard methods in the research field.

Identification of differentially recognized T cell epitopes in the spectrum of tuberculosis infection

There is still incomplete knowledge of which Mycobacterium tuberculosis (Mtb) antigens can trigger distinct T cell responses at different stages of infection. Here, a proteome-wide screen of 20,610 Mtb-derived peptides in 21 patients mid-treatment for active tuberculosis (ATB) reveals IFNγ-specific T cell responses against 137 unique epitopes. Of these, 16% are recognized by two or more participants and predominantly derived from cell wall and cell processes antigens. There is differential recognition of antigens, including TB vaccine candidate antigens, between ATB participants and interferon-gamma release assay (IGRA + /−) individuals. We developed an ATB-specific peptide pool (ATB116) consisting of epitopes exclusively recognized by ATB participants. This pool can distinguish patients with pulmonary ATB from IGRA + /− individuals from various geographical locations, with a sensitivity of over 60% and a specificity exceeding 80%. This proteome-wide screen of T cell reactivity identified infection stage-specific epitopes and antigens for potential use in diagnostics and measuring Mtb-specific immune responses.

Temporal genome-wide fitness analysis of Mycobacterium marinum during infection reveals the genetic requirement for virulence and survival in amoebae and microglial cells.

Tuberculosis remains the most pervasive infectious disease and the recent emergence of drug-resistant strains emphasizes the need for more efficient drug treatments. A key feature of pathogenesis, conserved between the human pathogen Mycobacterium tuberculosis and the model pathogen Mycobacterium marinum, is the metabolic switch to lipid catabolism and altered expression of virulence genes at different stages of infection. This study aims to identify genes involved in sustaining viable intracellular infection. We applied transposon sequencing (Tn-Seq) to M. marinum, an unbiased genome-wide strategy combining saturation insertional mutagenesis and high-throughput sequencing. This approach allowed us to identify the localization and relative abundance of insertions in pools of transposon mutants. Gene essentiality and fitness cost of mutations were quantitatively compared between in vitro growth and different stages of infection in two evolutionary distinct phagocytes, the amoeba Dictyostelium discoideum and the murine BV2 microglial cells. In the M. marinum genome, 57% of TA sites were disrupted and 568 genes (10.2%) were essential, which is comparable to previous Tn-Seq studies on M. tuberculosis and M. bovis. Major pathways involved in the survival of M. marinum during infection of D. discoideum are related to DNA damage repair, lipid and vitamin metabolism, the type VII secretion system (T7SS) ESX-1, and the Mce1 lipid transport system. These pathways, except Mce1 and some glycolytic enzymes, were similarly affected in BV2 cells. These differences suggest subtly distinct nutrient availability or requirement in different host cells despite the known predominant use of lipids in both amoeba and microglial cells.IMPORTANCEThe emergence of biochemically and genetically tractable host model organisms for infection studies holds the promise to accelerate the pace of discoveries related to the evolution of innate immunity and the dissection of conserved mechanisms of cell-autonomous defenses. Here, we have used the genetically and biochemically tractable infection model system Dictyostelium discoideum/Mycobacterium marinum to apply a genome-wide transposon-sequencing experimental strategy to reveal comprehensively which mutations confer a fitness advantage or disadvantage during infection and compare these to a similar experiment performed using the murine microglial BV2 cells as host for M. marinum to identify conservation of virulence pathways between hosts.

Genetic Association Studies in Host-Pathogen Interaction Analysis.

Studying host-pathogen interactions at a molecular level has always been technically challenging. Identifying the different biochemical and genetic pathways involved in the different stages of infection traditionally require complex molecular biology tools and often the use of costly animal models. In this chapter, we illustrate a complementary approach to address host-pathogen interactions, taking advantage of the natural interindividual genetic diversity. The application of genetic association studies allows us to identify alleles involved in infection progression or resistance. Thus, this strategy may be useful to unravel new molecular pathways underlying host-pathogen interactions. Here we present the general steps that might be followed to plan, execute, and analyze a population-based study in order to identify genetic variants affecting human exposition to pathogens.

Biomimetic Targeting Nanoadjuvants for Sonodynamic and Chronological Multi-Immunotherapy against Holistic Biofilm-Related Infections.

Biofilm-related infections (BRIs) present significant challenges owing to drug resistance, adverse immune responses, and implant failure; however, current approaches inadequately cater to the diverse therapeutic requirements at different stages of infection. To address this issue, a multi-immunotherapy strategy in combination with sonodynamic therapy is proposed for the chronological treatment of BRIs. Macrophage membrane-decorated targeting sonosensitive nanoadjuvants are fabricated to load cytosine-phosphate-guanine oligodeoxynucleotide (CPG-ODN) or microRNA (miR)-21-5p. In the early stages of BRI (Stage I), CPG-ODN-loaded nanoadjuvants (CPG@HMPN@M) promote the formation of neutrophil extracellular traps to capture and neutralize detached microbes. During the late stage of infection (Stage II), CPG-ODNs redirect macrophage polarization into the M1 phase to combat infections via TLR9/Myd88/TRAF6 pathway. During these stages, CPG@HMPN@M generates singlet oxygen through sonodynamic processes, eradicating the biofilms under US irradiation. Once the BRIs are eliminated, miR-21-5p-loaded nanoadjuvants (miR@HMPN@M) are delivered to the lesions to suppress excessive inflammation and promote tissue integration by evoking macrophage M2 polarization during the repair phase (Stage III) through PTEN/PI3K/Akt pathway. This innovative approach aims to provide comprehensive treatment strategies for the chronological treatment of BRI by effectively eliminating infections, promoting tissue restoration, and implementing different immune regulations at different stages, thus demonstrating promising clinical value.

Comparative transcriptomics of stem rust resistance in wheat NILs mediated by Sr24 rust resistance gene

Stem rust of wheat is a deleterious fungal disease across the globe causing severe yield losses. Although, many stem rust resistance genes (Sr) are being used in wheat breeding programs, new emerging stem rust pathotypes are a challenge to important Sr genes. In recent years, multiple studies on leaf and yellow rust molecular mechanism have been done, however, for stem rust such studies are lacking. Current study investigated stem rust induced response in the susceptible wheat genotype C306 and its Near Isogenic Line (NIL) for Sr24 gene, HW2004, using microarray analysis to understand the transcriptomic differences at different stages of infection. Results showed that HW2004 has higher basal levels of several important genes involved in pathogen detection, defence, and display early activation of multiple defence mechanisms. Further Gene Ontology (GO) and pathway analysis identified important genes responsible for pathogen detection, downstream signalling cascades and transcription factors (TFs) involved in activation and mediation of defence responses. Results suggest that generation of Reactive Oxygen Species (ROS), cytoskeletal rearrangement, activation of multiple hydrolases, and lipid metabolism mediated biosynthesis of certain secondary metabolites are collectively involved in Sr24-mediated defence in HW2004, in response to stem rust infection. Novel and unannotated, but highly responsive genes were also identified, which may also contribute towards resistance phenotype. Furthermore, certain DEGs also mapped close to the Sr24-linked marker on Thinopyrum elongatum translocated fragment on wheat 3E chromosome, which advocate further investigations for better insights of the Sr24-mediated stem rust resistance.

Mathematical modeling to investigate the influence of vaccination and booster doses on the spread of Omicron

The emergence of new variants, such as Omicron, has raised concerns regarding the transmission dynamics of COVID-19 and the effectiveness of vaccination strategies. This paper proposes a mathematical model to investigate the impact of vaccination and booster doses on Omicron transmission dynamics, considering various infection compartments. The model incorporates multiple compartments representing different stages of infection, including susceptible individuals, vaccinated individuals, boosted individuals, and those infected with Omicron. The infection dynamics are captured by parameters such as vaccine efficacy, vaccination with booster received efficacy, and infection rate. Using mathematical analysis and numerical simulations, we explore how different vaccination and booster strategies affect the spread of Omicron. The normalized sensitivity analysis method of R0 is investigated to understand the importance of parameters in disease transmission. Furthermore, we assess the influence of infection compartments, such as asymptomatic and symptomatic cases, on overall transmission dynamics.

Pixel-level regression for UAV hyperspectral images: Deep learning-based quantitative inverse of wheat stripe rust disease index

Previous research on utilizing unmanned aerial vehicle (UAV) remote sensing imagery for plant disease detection has predominantly focused on the qualitative identification of healthy and infected plants. Notably, pixel-level regression analysis for the quantification of the wheat stripe rust disease index (DI) using hyperspectral imaging data and deep learning methods is still lacking. Traditionally, quantitative inversion has been achieved by employing radiative transfer model inversion techniques or a combination of vegetation indices and machine learning methodologies. This investigation presents an end-to-end, pixel-level quantitative regression methodology, underpinned by deep learning techniques. This methodology carries substantial importance not only for the accurate assessment of disease distribution maps, but also for an array of common quantitative regression challenges within agricultural systems. For example, the approach can be utilized for the regression inversion of continuous phenotypes, including crop yield and plant height. In this study, 1,560 local wheat varieties (lines) from Henan Province were selected as experimental subjects, resulting in a wide-ranging gradient of DI. Hyperspectral images at a height of 100 m were obtained based on UAVs at different stages of infection. This work utilized a deep learning semantic segmentation method with continuous loss functions such as Laplacian loss to achieve pixel-level regression and end-to-end quantitative inversion of the DI. The performance of models with different loss functions, model architectures and datasets was compared. The optimal results were achieved using the Laplacian + MSE loss function combined with the HRNet_W18 algorithm model, yielding a test set R2 value of 0.875 and an MSE of 0.0129. Incorporating a PSA module further improved the outcomes, resulting in an R2 value of 0.880 and an MSE of 0.0123. Modeling with a limited number of feature indices (e.g., six feature indices) reduced the model recognition performance to 0.829 compared to full-band modeling. These findings suggest that full-band, end-to-end modeling based on deep learning algorithms can lead to superior inversion outcomes and streamline data analysis steps. The insights from this research hold relevance for high-throughput crop phenotyping, plant disease and pest monitoring, and quantitative yield assessment.

Gossypol Affects Viral Replication by Inhibiting Pseudorabies Virus Adsorption

Pseudorabies virus (PRV) has spread widely in swine herds since it was introduced into China, and PRV variants have appeared in many regions in China since 2011. Owing to the ongoing emergence of viral mutations, the protective efficacy of PRV vaccines faces great challenges. Gossypol is a polyphenolic natural compound purified from cotton roots, stems, or seeds. This study found that gossypol has a notable inhibitory effect on PRV. Based on the biological effects of gossypol, the anti-PRV effect of gossypol was studied from the aspects of mitochondrial damage, reactive oxygen species, energy metabolism, and autophagy. The results showed that gossypol had no direct inactivation effect on PRV in solution within concentrations of ≤3 µM; gossypol did not stimulate interferon-β to exert an anti-PRV effect by triggering mitochondrial DNA leakage; the anti-PRV effect of gossypol was reactive oxygen species and ATP-independent in cells; and the PRV-inhibitory effect of gossypol was independent of its induced autophagy. By adding gossypol to PRV-infected cells at different stages of infection and performing virus titer detection, atomic force microscopy, qPCR, fluorescence microscopy, and firefly luciferase activity assays, we found that gossypol exerted an anti-PRV effect by inhibiting the adsorption of PRV on the cell surface.

Comparative proteomics analysis of kidney in chicken infected by infectious bronchitis virus

The gamma coronavirus infectious bronchitis virus (IBV) is known to cause an acute and highly contagious infectious disease in poultry. Here, this study aimed to investigate the impact of virulent or avirulent IBV infection on the avian host by conducting proteomics with data-independent acquisition mass spectrometry (DIA-MS) in the kidneys of IBV-infected chickens. The results revealed 267, 489, and 510 differentially expressed proteins (DEPs) in the chicken kidneys at 3, 5, and 7 days postinfection (dpi), respectively, when infected with the GD17/04 strain, which is a highly nephrogenic strain and belongs to the 4/91 genotype. In contrast, the attenuated 4/91 vaccine resulted in the identification of 144, 175, and 258 DEPs at 3, 5, and 7 dpi, respectively. Functional enrichment analyses indicated distinct expression profiles between the 2 IBV strains. Upon GD17/04 infection, metabolic pathways respond initially in the early stage (3 dpi) and immune-related signaling pathways respond in the middle and late stages (5 and 7 dpi). The 4/91 vaccine elicited a completely opposite response compared to the GD17/04 infection. Among all DEPs, 62 immune-related DEPs were focused on and found to be mainly enriched in the type I interferon (IFN-I) signaling pathway and involved in humoral and cellular immunity. Notably, key molecules in the IFN-I signaling pathway including MDA5, LGP2, and TBK1 may serve as regulatory targets of IBV. Overall, this study highlights similarities and discrepancies in the patterns of protein expression at different stages of infection with virulent and avirulent IBV strains, with the IFN-I signaling pathway emerging as a critical response to IBV infection.

Thymic atrophy induced by Plasmodium berghei ANKA and Plasmodium yoelii 17XL infection

The thymus is the anatomical site where T cells undergo a complex process of differentiation, proliferation, selection, and elimination of autorreactive cells which involves molecular signals in different intrathymic environment. However, the immunological functions of the thymus can be compromised upon exposure to different infections, affecting thymocyte populations. In this work, we investigated the impact of malaria parasites on the thymus by using C57BL/6 mice infected with Plasmodium berghei ANKA and Plasmodium yoelii 17XL; these lethal infection models represent the most severe complications, cerebral malaria, and anemia respectively. Data showed a reduction in the thymic weight and cellularity involving different T cell maturation stages, mainly CD4−CD8− and CD4+CD8+ thymocytes, as well as an increased presence of apoptotic cells, leading to significant thymic cortex reduction. Thymus atrophy showed no association with elevated serum cytokines levels, although increased glucocorticoid levels did. The severity of thymic damage in both models reached the same extend although it occurs at different stages of infection, showing that thymic atrophy does not depend on parasitemia level but on the specific host-parasite interaction.

EBV Impact in Peripheral Macrophages' Polarization Cytokines in Pediatric Patients.

Macrophages are exceptionally flexible cells. The presence of inflammatory cytokines such as IFN-γ and TNF-α results in an M1 (CD68) activation, while cytokines such as IL-10 or TGF-β induce the M2 (CD163) activation. Our aim was to study the behavior of peripheral cytokines involved in macrophage polarization and relate them with tissue findings to further comprehend the role of macrophages in EBV pediatric infection. We studied cytokine expression in tonsils and peripheral blood samples of children in different stages of infection. Peripheral cytokines were compared with macrophage polarization markers and viral protein expression in tonsils. Only IL-10 showed a negative correlation between compartments, exclusively in patients undergoing viral reactivation (R). Higher expressions of peripheral IL-1β, IL-23, and IL-12p40 in R children were observed. Lower expressions of local and peripheral TNF-α in patients with broader expressions of latent and lytic viral proteins were demonstrated. In healthy carrier (HC) patients, IL-23 positively correlated with CD163, and IP-10 positively correlated with CD68. Our results indicated that EBV might modulate antigen expression in the presence of TNF-α and influence peripheral cytokine expression differently in each stage of infection. Moreover, peripheral cytokines might have a particular role in macrophage polarization in HC.

Cooperation of ILC2s and TH2 cells in the expulsion of intestinal helminth parasites.

Type 2 immune responses form a critical defence against enteric worm infections. In recent years, mouse models have revealed shared and unique functions for group 2 innate lymphoid cells and T helper 2 cells in type 2 immune response to intestinal helminths. Both cell types use similar innate effector functions at the site of infection, whereas each population has distinct roles during different stages of infection. In this Perspective, we review the underlying mechanisms used by group 2 innate lymphoid cells and T helper 2 cells to cooperate with each other and suggest an overarching model of the interplay between these cell types over the course of a helminth infection.

Homotopy decomposition method to analysis fractional hepatitis B virus infection model

Mathematical models have been employed to investigate the factors that govern the progression of infectious diseases in viral infections. This article investigates a fractionalised model for hepatitis B virus infection, curing infected cells. We analysed the fractional hepatitis B virus infection model using the homotopy decomposition method (HDM). Additionally, the existence and uniqueness of the fractional model are considered. The graphical solutions depicted that when introducing a fractional-order derivative to the model, the peak of viral propagation is reduced, and cell damage is minimised by initiating treatment at an early stage; however, the disease takes a longer time to be eradicated. The outcomes may also be valuable in the fields of clinical science and medicine. This model provided insights into the natural history of HBV infection, including the different stages of infection (acute, chronic, and resolved) and the likelihood of progression from one stage to another. This information is essential for predicting disease outcomes and planning appropriate clinical management for patients.

Label-free Raman microspectroscopic imaging with chemometrics for cellular investigation of apple ring rot and nondestructive early recognition using near-infrared reflection spectroscopy with machine learning

Apple ring rot caused by Botryosphaeria dothidea can cause fruit decay during the growth and storage stages of apple fruit. Understanding the infection process and cellular defense response at the cellular micro-level holds immense importance in the field of prevention and control. Consequently, there is a pressing need to develop suitable chemical imaging analysis methods. Here we proposed a label-free, high-throughput imaging method for cellular investigation of apple fruit ring rot infected by Botryosphaeria dothidea, based on confocal Raman microspectroscopic imaging technology combined with multivariate curve resolution-alternating least squares algorithm (MCR-ALS). We conducted Raman measurements on every apple fruit and obtain an image cube. This cube was then unfolded into an augmented matrix in a column-wise manner. We proceeded with simultaneous MCR-ALS analysis, resolving the single-substance spectrum and concentration profile from the mixed signals. Lastly, the accurate and pure molecular imaging of low methoxyl pectin, high methoxyl pectin, cellulose, lignin, and phenols were realized by refolding the resolved concentration data to construct the composition image. Thereafter, we realized the study of the spatial-temporal changes distribution of the above substances in the cuticle and cell wall of green and red apples at different stages of infection. The imaging method proposed in this paper is expected to provide a chemical imaging strategy for studying pathogen infection process and fruit defense response at the cellular level. In addition, by utilizing a fiber-optic probe near-infrared reflection spectrometer in conjunction with machine learning, we developed a rapid and non-destructive classification method. This method allows for the timely identification of apples exhibiting early infection by Botryosphaeria dothidea. Notably, both principal component analysis-quadratic discriminant analysis and support vector machine achieved a classification accuracy of 100%.