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Porcine circovirus type 2 (PCV2) is the major causative agent of postweaning multisystemic wasting syndrome which leads to significant economic losses in the global swine industry. In China, there is a widespread dissemination of PCV2 infection in the pig population. Serologi­cal diagnosis of the disease is considered as an effective control measure. Here, we developed a capsid protein (Cap)-based enzyme-linked immunosorbent assay (Cap-ELISA) for the detection of PCV2 antibodies in swine serum using a nuclear localization signal-truncated capsid protein produced in Escherichia coli. The Cap protein was expressed as water-soluble and purified using nickel-nitrilotriacetic acid (Ni-NTA) chromatography. After the optimization of the working conditions of the Cap-ELISA using chessboard titrations, a total of 649 serum samples were tested using the Cap-ELISA and a commercial ELISA kit. The diagnostic sensitivity (DSN), diagnostic specificity (DSP) and accuracy of the Cap-ELISA were determined to be 96.7%, 94.1% and 99.5%, respectively. Cross-reactivity analysis indicated that the Cap-ELISA was PCV2-specific and possessed no cross-reactions with antibodies against other common swine pathogens including porcine circovirus type 1 (PCV1), porcine reproductive and respiratory syndrome virus (PRRSV), classical swine fever virus (CSFV), porcine parvovirus (PPV), foot and mouth disease virus (FMDV), porcine epidemic diarrhea virus (PEDV) and pseudorabies virus (PRV). Repeatability of the experiment showed that Cap-ELISA was highly repeatable with the intra- and inter-plate coefficients of variation less than 10%. Hence, the Cap-ELISA has the potential for the swine industry to monitor PCV2 epidemiology and to evaluate PCV2 vaccine efficacy.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands—or even millions—of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Quantitative systems pharmacology (QSP) has been proposed as a scientific domain that can enable efficient and informative drug development. During the past several years, there has been a notable increase in the number of regulatory submissions that contain QSP, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), and Biologics License Applications (BLAs) to the US Food and Drug Administration. However, there has been no comprehensive characterization of the nature of these regulatory submissions regarding model details and intended applications. To address this gap, a landscape analysis of all the QSP submissions as of December 2020 was conducted. This report summarizes the (1) yearly trend of submissions, (2) proportion of submissions between INDs and NDAs/BLAs, (3) percentage distribution along the stages of drug development, (4) percentage distribution across various therapeutic areas, and (5) nature of QSP applications. In brief, QSP is increasingly applied to model and simulate both drug effectiveness and safety throughout the drug development process across disease areas.

BackgroundUlcerative colitis (UC) is an inflammatory bowel disease characterized by idiopathic and recurrent mucosal inflammation [1]. The pathogenesis is multifactorial, involving genetic susceptibility, epithelial barrier defects, dysregulation of the immune response, and environmental factors. UC is a highly heterogeneous disease [2]. Despite efforts to characterize disease subgroups and predict differential outcomes in UC patients, disease heterogeneity is not adequately translated into current clinical subclassifications.ObjectivesThis study seeks to unravel this complexity by using an integrated systems approach that classifies UC patients into different phenotypes based on the immune microenvironment, capable of providing new insights into the development of stratified therapies.MethodsRNA sequencing datasets of 602 colon tissue samples from 455 patients with UC and 147 healthy controls (HC) was imported from GEO database. The microarray datasets were transformed into immune cell components by CIBERSORT, and patients were stratified by a consensus clustering algorithm. Then, the cellular characteristics of the subtypes and the overall role of specific pathways were determined by immune cell infiltration and pathway enrichment. The clinical characteristics of the different subtypes were also analyzed to clarify the characteristics of these classifications.ResultsPatients can be classified into two distinct immune phenotypes (Subtype A, B) based on their specific RIME, which have different cellular and pathway characteristics. (Figure A, B) The results show that Subtype A is mainly enriched in immune cells such as B cells, CD8+ T cells, CD4+ memory T cells, DC cells, macrophages, mast cells and plasma cells. Acidic granulocytes and neutrophils were more active in Subtype B. It was also significantly enriched in the peroxisome proliferator-activated receptor (PPAR) signaling pathway in Subtype B. In contrast, Subtype A showed a strong enrichment for most inflammatory pathways B cell and T cell receptor signaling, IL-17 signaling pathway, TNF, and interleukin 4 and interleukin 13 signaling pathway. (Figure 1 D, E) In addition, there was a significant difference between molecular subclassification and disease status, with Subtype A being more active in patients (69.3% of Subtype A vs.30.6% of Subtype B subtypes in 137 cases), while inactive patients, Subtype B were more prevalent (44.8% of Subtype A vs. 55.2% of Subtype B in 105 cases). (Figure 1C)ConclusionAnalysis of the immune microenvironment of colonic mucosal tissue provides good insight into the pathophysiological characteristics of UC. These results can be used as a model for future studies. the response and clinical outcomes of patients with different classification need to be further investigated.References[1]Ghosh S, Shand A, Ferguson A. Ulcerative colitis. BMJ (Clinical research ed). 2000;320(7242):1119-23.[2]Ordás I, Eckmann L, Talamini M, Baumgart DC, Sandborn WJ. Ulcerative colitis. Lancet (London, England). 2012;380(9853):1606-19.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

The number of quantitative systems pharmacology (QSP) submissions to the U.S. Food and Drug Administration has continued to increase over the past decade. This report summarizes the landscape of QSP submissions as of December 2023. QSP was used to inform drug development across various therapeutic areas and throughout the drug development process of small molecular drugs and biologics and has facilitated dose finding, dose ranging, and dose optimization studies. Though the majority of QSP submissions (>66%) focused on drug effectiveness, QSP was also utilized to simulate drug safety including liver toxicity, risk of cytokine release syndrome (CRS), bone density, and others. This report also includes individual contexts of use from a handful of new drug applications (NDAs) and biologics license applications where QSP modeling was used to demonstrate the utility of QSP modeling in regulatory drug development. According to the models submitted in QSP submissions, an anonymous case was utilized to illustrate how QSP informed development of a bispecific monoclonal antibody with respect to CRS risk. QSP submissions for informing pediatric drug development were summarized along with highlights of a case in inborn errors of metabolism. Furthermore, simulations of response variability with QSP were described. In summary, QSP continues to play a role in informing drug development.

BackgroundPsoriasis is a common chronic inflammatory disease of the skin[1]. The IL-23/IL-17 immune pathway plays a vital role in promoting Psoriasis pathogenesis[2]. Tildrakizumab, a humanized IgG1 monoclonal antibody against interleukin 23 p19, is currently used in the treatment of patients with psoriasis.ObjectivesThis study aimed to evaluate the efficacy of tildrakizumab in the treatment of psoriasis.MethodsFive databases, including PubMed, Embase, Medline, Web of Science, and Cochrane Library, were retrieved from their establishment to January 2, 2023. We used the EndNote X9 software to filter the retrieved articles according to the inclusion and exclusion criteria. Heterogeneity was tested using I-squared (I2). When I2>50%, we choosed the random effects model for data analysis, conversely, a fixed effects model. Publication bias was assessed using the Egger test. All analyzes were performed in STATA 12.0.ResultsWe included 6 studies, 2,395 patients in the Tildrakizumab group and 552 patients in the placebo group (Table 1). Results of the meta-analysis showed that 68% of patients with psoriasis met the PAIS75 remission criteria after taking Tildrakizumab [Rate =0.68 95%CI(0.66, 0.70), P<0.001], and they had significantly higher PASI75 response rates than the placebo group [RR=11.390, 95%CI (8.08, 16.06), P<0.001]. Compared to the placebo group, patients in the Tildrakizumab group had a significantly higher remission rate of PASI90 [RR=26.751, 95%CI (15.282,46.827), P<0.001]. In addition, patients taking Tildrakizumab had an average 15-point reduction in PASI scores [Rate=-14.854 95%CI(-19.146, -10.561), P<0.001], and 45% of patients achieved PASI100 remission criteria [Rate= 0.450, 95%CI (0.131, 0.769), P=0.006] (Figure 1).ConclusionThis study showed that Tildrakizumab improved disease activity and increased clinical remission rates in patients with psoriasis, demonstrating a better therapeutic effect.References[1]Griffiths CEM, Armstrong AW, Gudjonsson JE, et al. Psoriasis. Lancet 2021; 397 (10281): 1301-1315. doi: 10.1016/s0140-6736(20)32549-6.[2]Blauvelt A, Chiricozzi A. The Immunologic Role of IL-17 in Psoriasis and Psoriatic Arthritis Pathogenesis. Clin Rev Allergy Immunol 2018; 55 (3): 379-390. doi: 10.1007/s12016-018-8702-3.Table 1.Population characteristics included in the studyAuthor(Year)Population (include in analysis)Age (Mean±SD)Gender (male %)TILPBOTILPBOTILPBOKristian Reich(2017)617(617)155(154)46.65±13.1447.9±13.570.264.5K. Papp(2015)309(102)46(41)44.79±13.0445.9±11.775.182.5Alessandra Narcisi.(2022)237(121)NA48.6±14.6NA59.9NAY Poulin(2020)1413(1413)357(357)NA72.170.0Cantrell, W.(2021)248(135)NANANAT Graier(2022)15(7)NA45.9±14.1NA73.3NAAcknowledgements:NIL.Disclosure of InterestsNone Declared.

Exposure to psychological trauma is a strong risk factor for several debilitating disorders including post-traumatic stress disorder (PTSD) and depression. Besides the impact on mental well-being and behavior in the exposed individuals, it has been suggested that psychological trauma can affect the biology of the individuals, and even have biological and behavioral consequences on the offspring of exposed individuals. While knowledge of possible epigenetic underpinnings of the association between exposure to trauma and risk of PTSD has been discussed in several reviews, it remains to be established whether trauma-induced epigenetic modifications can be passed from traumatized individuals to subsequent generations of offspring. The aim of this paper is to review the emerging literature on evidence of transgenerational inheritance due to trauma exposure on the epigenetic mechanism of DNA methylation in humans. Our review found an accumulating amount of evidence of an enduring effect of trauma exposure to be passed to offspring transgenerationally via the epigenetic inheritance mechanism of DNA methylation alterations and has the capacity to change the expression of genes and the metabolome. This manuscript summarizes and critically reviews the relevant original human studies in this area. Thus, it provides an overview of where we stand, and a clearer vision of where we should go in terms of future research directions.

Plasmodium knowlesi , a simian malaria parasite responsible for all recent indigenous cases of malaria in Malaysia, infects humans throughout Southeast Asia. There are two genetically distinct subpopulations of Plasmodium knowlesi in Malaysian Borneo, one associated with long-tailed macaques (termed cluster 1) and the other with pig-tailed macaques (cluster 2). A prospective study was conducted to determine whether there were any between-subpopulation differences in clinical and laboratory features, as well as in epidemiological characteristics. Over 2 years, 420 adults admitted to Kapit Hospital, Malaysian Borneo with knowlesi malaria were studied. Infections with each subpopulation resulted in mostly uncomplicated malaria. Severe disease was observed in 35/298 (11.7%) of single cluster 1 and 8/115 (7.0%) of single cluster 2 infections ( p  = 0.208). There was no clinically significant difference in outcome between the two subpopulations. Cluster 1 infections were more likely to be associated with peri-domestic activities while cluster 2 were associated with interior forest activities consistent with the preferred habitats of the respective macaque hosts. Infections with both P. knowlesi subpopulations cause a wide spectrum of disease including potentially life-threatening complications, with no implications for differential patient management.

During the clinical developments of alirocumab and evolocumab, two anti‐proprotein convertase subtilisin/kexin type 9 monoclonal antibodies, 4 several plasma lipids were measured, even though reduction of low‐density lipoprotein cholesterol was primarily used for their approval for marketing. Simple but pharmacologically and clinically meaningful Whether a clinically oriented QSP model can be adequately qualified and validated is contingent on the balance between the quantity of patient response data and the number of model variables and parameters. [...]ideally, a clinically oriented model is parsimonious, with a model structure as simple as possible in the context of balancing the amount of clinical data for model qualification/validation while maintaining the connectivity between the pharmacological target and pathways of a medical entity and its PD responses. The impact of genetic mutation on PD responses has been leveraged to calibrate QSP models; however, applications seen in literature seem to be limited to predicting phase II results. 7 Inclusion of genetic mutations in QSP modeling, which are associated with differential patient responses, can conceivably be expanded to facilitate patient stratification for phase III trials. Can the criteria for designing a phase III trial be the same as those for a phase II clinical waiver? SUMMARY The workflow of QSP modeling to guide the development of a medical entity would consist of (1) modeling with preclinical granular data to fully explore the profile of a candidate and guide the early stage of its development program; (2) maximizing use of efficacy and safety PD biomarkers in a clinically oriented model; (3) conducting pharmacologically and clinically meaningful risk‐based QSP modeling and simulation with a parsimonious model; and (4) fully utilizing the unique extrapolation capability of QSP by validating a QSP model with specific predetermined quantitative and/or statistical criteria

Modern day Latin America resulted from the encounter of Europeans with the indigenous peoples of the Americas in 1492, followed by waves of migration from Europe and Africa. As a result, the genomic structure of present day Latin Americans was determined both by the genetic structure of the founding populations and the numbers of migrants from these different populations. Here, we analyzed DNA collected from two well-established communities in Colorado (33 unrelated individuals) and Ecuador (20 unrelated individuals) with a measurable prevalence of the BRCA1 c.185delAG and the GHR c.E180 mutations, respectively, using Affymetrix Genome-wide Human SNP 6.0 arrays to identify their ancestry. These mutations are thought to have been brought to these communities by Sephardic Jewish progenitors. Principal component analysis and clustering methods were employed to determine the genome-wide patterns of continental ancestry within both populations using single nucleotide polymorphisms, complemented by determination of Y-chromosomal and mitochondrial DNA haplotypes. When examining the presumed European component of these two communities, we demonstrate enrichment for Sephardic Jewish ancestry not only for these mutations, but also for other segments as well. Although comparison of both groups to a reference Hispanic/Latino population of Mexicans demonstrated proximity and similarity to other modern day communities derived from a European and Native American two-way admixture, identity-by-descent and Y-chromosome mapping demonstrated signatures of Sephardim in both communities. These findings are consistent with historical accounts of Jewish migration from the realms that comprise modern Spain and Portugal during the Age of Discovery. More importantly, they provide a rationale for the occurrence of mutations typically associated with the Jewish Diaspora in Latin American communities.