A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study.

BACKGROUND: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. METHODS: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. RESULTS: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. CONCLUSION: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles.

Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint-motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint-motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, a precision of 0.983, a recall of 0.982, and an F1 score of 0.982. These results underscore this time-warping technique's contribution to enhancing feature representation, as well as the proposed method's scalability and effectiveness in recognizing Taekwondo unit actions.

Two-Stream Network One-Class Classification Model for Defect Inspections.

Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect inspection method using a one-class classification (OCC) model to deal with imbalanced datasets. A two-stream network architecture consisting of global and local feature extractor networks is presented, which can alleviate the representation collapse problem of OCC. By combining an object-oriented invariant feature vector with a training-data-oriented local feature vector, the proposed two-stream network model prevents the decision boundary from collapsing to the training dataset and obtains an appropriate decision boundary. The performance of the proposed model is demonstrated in the practical application of automotive-airbag bracket-welding defect inspection. The effects of the classification layer and two-stream network architecture on the overall inspection accuracy were clarified by using image samples collected in a controlled laboratory environment and from a production site. The results are compared with those of a previous classification model, demonstrating that the proposed model can improve the accuracy, precision, and F1 score by up to 8.19%, 10.74%, and 4.02%, respectively.

Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets.

Issues of fairness and consistency in Taekwondo poomsae evaluation have often occurred due to the lack of an objective evaluation method. This study proposes a three-dimensional (3D) convolutional neural network-based action recognition model for an objective evaluation of Taekwondo poomsae. The model exhibits robust recognition performance regardless of variations in the viewpoints by reducing the discrepancy between the training and test images. It uses 3D skeletons of poomsae unit actions collected using a full-body motion-capture suit to generate synthesized two-dimensional (2D) skeletons from desired viewpoints. The 2D skeletons obtained from diverse viewpoints form the training dataset, on which the model is trained to ensure consistent recognition performance regardless of the viewpoint. The performance of the model was evaluated against various test datasets, including projected 2D skeletons and RGB images captured from diverse viewpoints. Comparison of the performance of the proposed model with those of previously reported action recognition models demonstrated the superiority of the proposed model, underscoring its effectiveness in recognizing and classifying Taekwondo poomsae actions.

[Analysis of traditional Chinese medicine diagnosis and treatment for erectile dysfunction related to COVID-19 infection].

The incidence of erectile dysfunction (ED) has been reported to increase after COVID-19 infection, and it is common in COVID-19 patients during recovery. This paper presents a summary of the latest research progress on COVID-19-induced ED and explores the traditional Chinese medicine (TCM) diagnosis and treatment approach based on TCM theory and clinical experience. COVID-19 infection may lead to ED through endothelial dysfunction, testicular injury, hormonal imbalance, and psychological factors. The pathogenesis of COVID-19-related ED is mainly characterized by deficiency of the body's essence and excess of pathogenic factors. In the early stage, it is dominated by deficiency of qi and yin, while in the middle stage, it is mainly due to deficiency of qi and blood stasis. In the long-term, ED is based on the imbalance of yin and yang, with liver stagnation and qi stagnation often co-existing. Clinical manifestations of ED vary, and treatment should focus on tonifying qi and nourishing yin, promoting blood circulation, regulating yin and yang, and soothing liver depression according to TCM diagnosis and treatment principles.

Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities.

The dominance effect is considered to be a key factor affecting complex traits. However, previous studies have shown that the improvement of the model, including the dominance effect, is usually less than 1%. This study proposes a novel genomic prediction method called CADM, which combines additive and dominance genetic effects through locus-specific weights on heterozygous genotypes. To the best of our knowledge, this is the first study of weighting dominance effects for genomic prediction. This method was applied to the analysis of chicken (511 birds) and pig (3534 animals) datasets. A 5-fold cross-validation method was used to evaluate the genomic predictive ability. The CADM model was compared with typical models considering additive and dominance genetic effects (ADM) and the model considering only additive genetic effects (AM). Based on the chicken data, using the CADM model, the genomic predictive abilities were improved for all three traits (body weight at 12th week, eviscerating percentage, and breast muscle percentage), and the average improvement in prediction accuracy was 27.1% compared with the AM model, while the ADM model was not better than the AM model. Based on the pig data, the CADM model increased the genomic predictive ability for all the three pig traits (trait names are masked, here designated as T1, T2, and T3), with an average increase of 26.3%, and the ADM model did not improve, or even slightly decreased, compared with the AM model. The results indicate that dominant genetic variation is one of the important sources of phenotypic variation, and the novel prediction model significantly improves the accuracy of genomic prediction.

Analysis of the value of enhanced CT combined with texture analysis in the differential diagnosis of pulmonary sclerosing pneumocytoma and atypical peripheral lung cancer: a feasibility study.

BACKGROUND: As a rare benign lung tumour, pulmonary sclerosing pneumocytoma (PSP) is often misdiagnosed as atypical peripheral lung cancer (APLC) on routine imaging examinations. This study explored the value of enhanced CT combined with texture analysis to differentiate between PSP and APLC. METHODS: Forty-eight patients with PSP and fifty patients with APLC were retrospectively enrolled. The CT image features of the two groups of lesions were analysed, and MaZda software was used to evaluate the texture of CT venous phase thin-layer images. Independent sample t-test, Mann-Whitney U tests or χ2 tests were used to compare between groups. The intra-class correlation coefficient (ICC) was used to analyse the consistency of the selected texture parameters. Spearman correlation analysis was used to evaluate the differences in texture parameters between the two groups. Based on the statistically significant CT image features and CT texture parameters, the independent influencing factors between PSP and APLC were analysed by multivariate logistic regression. Extremely randomized trees (ERT) was used as the classifier to build models, and the models were evaluated by the five-fold cross-validation method. RESULTS: Logistic regression analysis based on CT image features showed that calcification and arterial phase CT values were independent factors for distinguishing PSP from APLC. The results of logistic regression analysis based on CT texture parameters showed that WavEnHL_s-1 and Perc.01% were independent influencing factors to distinguish the two. Compared with the single-factor model (models A and B), the classification accuracy of the model based on image features combined with texture parameters was 0.84 ± 0.04, the AUC was 0.84 ± 0.03, and the sensitivity and specificity were 0.82 ± 0.13 and 0.87 ± 0.12, respectively. CONCLUSION: Enhanced CT combined with texture analysis showed good diagnostic value for distinguishing PSP and APLC, which may contribute to clinical decision-making and prognosis evaluation.

Notch signaling leads to a slower progression of embryonic myogenic differentiation in Landrace than in Langtang pigs.

Delving into porcine embryonic myogenesis is the key to elucidate the complex regulation of breed-specific differences in growth performance and meat production. Increasing evidence proves that pigs with less meat production show earlier embryonic myogenesis, but little is known about the underlying mechanisms. In this study, we examine the longissimus dorsi muscle (LDM) by immunohistochemistry and confirm that the differentiation of myogenic progenitors is increased ( P<0.05) in Lantang (LT, fatty) pigs compared with that in Landrace (LR, lean) pigs, which results in more ( P<0.001) differentiated myoblasts (Pax7 -/MyoD +) and less ( P<0.001) myogenic progenitors (Pax7 +/MyoD -) in LT pigs at 35 days post-conception (35dpc). Additionally, embryonic myogenic progenitors isolated from LT pigs show greater ( P<0.001) differentiation capacity with earlier expression of MyoD compared with those from LR pigs. Moreover, Notch signaling is more active ( P<0.05) in LR pig myogenic progenitors than in LT pig myogenic progenitors. Inhibition of Notch signaling in LR myogenic progenitors suppresses Pax7 expression and increases MyoD expression, thus promoting myogenic differentiation. Consistently, the process of myogenic progenitors differentiating into myoblasts in ex vivo embryo limbs is accelerated when Notch signaling is inhibited. These results indicate that Notch signaling facilitates the maintenance of myogenic progenitors and antagonizes myogenic differentiation by promoting Pax7 expression and preventing MyoD expression in LR pigs.

Integrative analysis of histomorphology, transcriptome and whole genome resequencing identified DIO2 gene as a crucial gene for the protuberant knob located on forehead in geese.

BACKGROUND: During domestication, remarkable changes in behavior, morphology, physiology and production performance have taken place in farm animals. As one of the most economically important poultry, goose owns a unique appearance characteristic called knob, which is located at the base of the upper bill. However, neither the histomorphology nor the genetic mechanism of the knob phenotype has been revealed in geese. RESULTS: In the present study, integrated radiographic, histological, transcriptomic and genomic analyses revealed the histomorphological characteristics and genetic mechanism of goose knob. The knob skin was developed, and radiographic results demonstrated that the knob bone was obviously protuberant and pneumatized. Histologically, there were major differences in structures in both the knob skin and bone between geese owing knob (namely knob-geese) and those devoid of knob (namely non-knob geese). Through transcriptome analysis, 592 and 952 genes differentially expressed in knob skin and bone, and significantly enriched in PPAR and Calcium pathways in knob skin and bone, respectively, which revealed the molecular mechanisms of histomorphological differences of the knob between knob- and non-knob geese. Furthermore, integrated transcriptomic and genomic analysis contributed to the identification of 17 and 21 candidate genes associated with the knob formation in the skin and bone, respectively. Of them, DIO2 gene could play a pivotal role in determining the knob phenotype in geese. Because a non-synonymous mutation (c.642,923 G > A, P265L) changed DIO2 protein secondary structure in knob geese, and Sanger sequencing further showed that the AA genotype was identified in the population of knob geese, and was prevalent in a crossing population which was artificially selected for 10 generations. CONCLUSIONS: This study was the first to uncover the knob histomorphological characteristics and genetic mechanism in geese, and DIO2 was identified as the crucial gene associated with the knob phenotype. These data not only expand and enrich our knowledge on the molecular mechanisms underlying the formation of head appendages in both mammalian and avian species, but also have important theoretical and practical significance for goose breeding.

Food intake-related genes in chicken determined through combinatorial genome-wide association study and transcriptome analysis.

The chicken gizzard is the primary digestive and absorptive organ regulating food intake and metabolism. Body weight is a typical complex trait regulated by an interactive polygene network which is under the control of an interacting network of polygenes. To simplify these genotype-phenotype associations, the gizzard is a suitable target organ to preliminarily explore the mechanism underlying the regulation of chicken growth through controlled food intake. This study aimed to identify key food intake-related genes through combinatorial GWAS and transcriptome analysis. We performed GWAS of body weight in an F2 intercrossed population and transcriptional profiling analysis of gizzards from chickens with different body weight. We identified a major 10 Mb quantitative trait locus (QTL) on chromosome 1 and numerous minor QTL distributed among 24 chromosomes. Combining data regarding QTL and gizzard gene expression, two hub genes, MLNR and HTR2A, and a list of core genes with small effect were found to be associated with food intake. Furthermore, the neuroactive ligand-receptor interaction pathway was found to play a key role in regulating the appetite of chickens. The present results show the major-minor gene interactions in metabolic pathways and provide insights into the genetic architecture and gene regulation during food intake in chickens.

A Complex Structural Variation on Chromosome 27 Leads to the Ectopic Expression of HOXB8 and the Muffs and Beard Phenotype in Chickens.

Muffs and beard (Mb) is a phenotype in chickens where groups of elongated feathers gather from both sides of the face (muffs) and below the beak (beard). It is an autosomal, incomplete dominant phenotype encoded by the Muffs and beard (Mb) locus. Here we use genome-wide association (GWA) analysis, linkage analysis, Identity-by-Descent (IBD) mapping, array-CGH, genome re-sequencing and expression analysis to show that the Mb allele causing the Mb phenotype is a derived allele where a complex structural variation (SV) on GGA27 leads to an altered expression of the gene HOXB8. This Mb allele was shown to be completely associated with the Mb phenotype in nine other independent Mb chicken breeds. The Mb allele differs from the wild-type mb allele by three duplications, one in tandem and two that are translocated to that of the tandem repeat around 1.70 Mb on GGA27. The duplications contain total seven annotated genes and their expression was tested during distinct stages of Mb morphogenesis. A continuous high ectopic expression of HOXB8 was found in the facial skin of Mb chickens, strongly suggesting that HOXB8 directs this regional feather-development. In conclusion, our results provide an interesting example of how genomic structural rearrangements alter the regulation of genes leading to novel phenotypes. Further, it again illustrates the value of utilizing derived phenotypes in domestic animals to dissect the genetic basis of developmental traits, herein providing novel insights into the likely role of HOXB8 in feather development and differentiation.

Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens.

BACKGROUND: Growth and carcass traits are very important traits for broiler chickens. However, carcass traits can only be measured postmortem. Genomic selection may be a powerful tool for such traits because of its accurate prediction of breeding values of animals without own phenotypic information. This study investigated the efficiency of genomic prediction in Chinese triple-yellow chickens. As a new line, Chinese triple-yellow chicken was developed by cross-breeding and had a small effective population. Two growth traits and three carcass traits were analyzed: body weight at 6 weeks, body weight at 12 weeks, eviscerating percentage, breast muscle percentage and leg muscle percentage. RESULTS: Genomic prediction was assessed using a 4-fold cross-validation procedure for two validation scenarios. In the first scenario, each test data set comprised two half-sib families (family sample) and the rest represented the reference data. In the second scenario, the whole data were randomly divided into four subsets (random sample). In each fold of validation, one subset was used as the test data and the others as the reference data in each single validation. Genomic breeding values were predicted using a genomic best linear unbiased prediction model, a Bayesian least absolute shrinkage and selection operator model, and a Bayesian mixture model with four distributions. The accuracy of genomic estimated breeding value (GEBV) was measured as the correlation between GEBV and the corrected phenotypic value. Using the three models, the correlations ranged from 0.448 to 0.468 for the two growth traits and from 0.176 to 0.255 for the three carcass traits in the family sample scenario, and were between 0.487 and 0.536 for growth traits and between 0.312 and 0.430 for carcass traits in the random sample scenario. The differences in the prediction accuracies between the three models were very small; the Bayesian mixture model was slightly more accurate. According to the results from the random sample scenario, the accuracy of GEBV was 0.197 higher than the conventional pedigree index, averaged over the five traits. CONCLUSIONS: The results indicated that genomic selection could greatly improve the accuracy of selection in chickens, compared with conventional selection. Genomic selection for growth and carcass traits in broiler chickens is promising.

Polymorphism of avian leukosis virus subgroup E loci showing selective footprints in chicken.

Avian leukosis virus subgroup E (ALVE) is a family of endogenous retroviruses in the chicken genome. To investigate the genetic consequences of chicken domestication, we analyzed 18 ALVE loci in red jungle fowls, layers, broilers, and Chinese indigenous chickens. None of the ALVE loci tested were found in red jungle fowls, but 12 were present in domestic chickens. ALVE1 and ALVE16 are found in regions of the genome that harbor quantitative trait loci (QTL) affecting egg production traits. ALVE1 was fixed and ALVE16 was detected only in layers. By contrast, ALVE-b1, ALVE-b5, ALVE-b6, and ALVE-b8 integrated into regions of the genome that harbor QTL affecting meat production traits. Carrier frequencies of these four ALVE loci were high in broilers and low in Chinese local chickens; the loci were not found in the layers. This study demonstrated that insertionally polymorphic ALVE loci can illustrate the selective footprints in the chicken genome.

Genetic Diversity, Population Structure and Selection Signature in Begait Goats Revealed by Whole-Genome Sequencing.

Goats belong to a group of animals called small ruminants and are critical sources of livelihood for rural people. Genomic sequencing can provide information ranging from basic knowledge about goat diversity and evolutionary processes that shape genomes to functional information about genes/genomic regions. In this study, we exploited a whole-genome sequencing data set to analyze the genetic diversity, population structure and selection signatures of 44 individuals belonging to 5 Ethiopian goat populations: 12 Aberegalle (AB), 5 Afar (AF), 11 Begait (BG), 12 Central highlands (CH) and 5 Meafure (MR) goats. Our results revealed the highest genetic diversity in the BG goat population compared to the other goat populations. The pairwise genetic differentiation (FST) among the populations varied and ranged from 0.011 to 0.182, with the closest pairwise value (0.003) observed between the AB and CH goats and a distant correlation (FST = 0.182) between the BG and AB goats, indicating low to moderate genetic differentiation. Phylogenetic tree, ADMIXTURE and principal component analyses revealed a classification of the five Ethiopian goat breeds in accordance with their geographic distribution. We also found three top genomic regions that were detected under selection on chromosomes 2, 5 and 13. Moreover, this study identified different candidate genes related to milk characteristics (GLYCAM1 and SRC), carcass (ZNF385B, BMP-7, PDE1B, PPP1R1A, FTO and MYOT) and adaptive and immune response genes (MAPK13, MAPK14, SCN7A, IL12A, EST1 DEFB116 and DEFB119). In conclusion, this information could be helpful for understanding the genetic diversity and population structure and selection scanning of these important indigenous goats for future genetic improvement and/or as an intervention mechanism.

Case report: Retroperitoneal solid pseudopapillary neoplasm associated with multiple hepatic metastases.

Solid pseudopapillary neoplasm (SPN) is a rare tumor mostly occurring in the pancreas. They are low-grade malignant tumors of the exocrine pancreas that occasionally metastasize, usually to the liver or peritoneum. Additionally, multiple metastases of extrapancreatic SPN to the liver are extremely rare and have been reported before. This study presents a case of a 13-year-old male patient with retroperitoneal SPN and multiple hepatic metastases. The patient presented with abdominal trauma and underwent enhanced CT, which revealed upper pancreatic occupancy and three hypodense foci in the right lobe of the liver. Moreover, increased spleen size was noted. The patient's serum tumor marker CA125 was increased to 39.00 U/mL (N < 35.0 U/mL), and circulating tumor cells were elevated to 10.2 FU/3 mL (N < 8.7 FU/3 mL). The patient underwent retroperitoneal occupancy resection and splenectomy, followed by resection of liver metastases 7 months after the surgery. Furthermore, multiple liver metastases from retroperitoneal SPN were confirmed postoperatively. The patient recovered for 1 year without tumor recurrence. This case emphasizes the importance of evaluating serum tumor markers and medical imaging in young patients as well as the fact that surgery appears to be the preferred treatment option for multiple metastases in SPN.

Genetic dissection of growth traits in a Chinese indigenous × commercial broiler chicken cross.

BACKGROUND: In China, consumers often prefer indigenous broiler chickens over commercial breeds, as they have characteristic meat qualities requested within traditional culinary customs. However, the growth-rate of these indigenous breeds is slower than that of the commercial broilers, which means they have not yet reached their full economic value. Therefore, combining the valuable meat quality of the native chickens with the efficiency of the commercial broilers is of interest. In this study, we generated an F2 intercross between the slow growing native broiler breed, Huiyang Beard chicken, and the fast growing commercial broiler breed, High Quality chicken Line A, and used it to map loci explaining the difference in growth rate between these breeds. RESULTS: A genome scan to identify main-effect loci affecting 24 growth-related traits revealed nine distinct QTL on six chromosomes. Many QTL were pleiotropic and conformed to the correlation patterns observed between phenotypes. Most of the mapped QTL were found in locations where growth QTL have been reported in other populations, although the effects were greater in this population. A genome scan for pairs of interacting loci identified a number of additional QTL in 10 other genomic regions. The epistatic pairs explained 6-8% of the residual phenotypic variance. Seven of the 10 epistatic QTL mapped in regions containing candidate genes in the ubiquitin mediated proteolysis pathway, suggesting the importance of this pathway in the regulation of growth in this chicken population. CONCLUSIONS: The main-effect QTL detected using a standard one-dimensional genome scan accounted for a significant fraction of the observed phenotypic variance in this population. Furthermore, genes in known pathways present interesting candidates for further exploration. This study has thus located several QTL regions as promising candidates for further study, which will increase our understanding of the genetic mechanisms underlying growth-related traits in chickens.

Genetic parameters and genome-wide association study of hyperpigmentation of the visceral peritoneum in chickens.

BACKGROUND: Hyperpigmentation of the visceral peritoneum (HVP) has recently garnered much attention in the poultry industry because of the possible risk to the health of affected animals and the damage it causes to the appearance of commercial chicken carcasses. However, the heritable characters of HVP remain unclear. The objective of this study was to investigate the genetic parameters of HVP by genome-wide association study (GWAS) in chickens. RESULTS: HVP was found to be influenced by genetic factors, with a heritability score of 0.33. HVP had positive genetic correlations with growth and carcass traits, such as leg muscle weight (rg = 0.34), but had negative genetic correlations with immune traits, such as the antibody response to Newcastle disease virus (rg = -0.42). The GWAS for HVP using 39,833 single nucleotide polymorphisms indicated the genetic factors associated with HVP displayed an additive effect rather than a dominance effect. In addition, we determined that three genomic regions, involving the 50.5-54.0 Mb region of chicken (Gallus gallus) chromosome 1 (GGA1), the 58.5-60.5 Mb region of GGA1, and the 10.5-12.0 Mb region of GGA20, were strongly associated (P < 6.28 × 10-7) with HVP in chickens. Variants in these regions explained >50% of additive genetic variance for HVP. This study also confirmed that expression of BMP7, which codes for a bone morphogenetic protein and is located in one of the candidate regions, was significantly higher in the visceral peritoneum of Huiyang Beard chickens with HVP than in that of chickens without pigmentation (P < 0.05). CONCLUSIONS: HVP is a quantitative trait with moderate heritability. Genomic variants resulting in HVP were identified on GGA1 and GGA20, and expression of the BMP7 gene appears to be upregulated in HVP-affected chickens. Findings from this study should be used as a basis for further functional validation of candidate genes involved in HVP.

Genome-wide association study of antibody response to Newcastle disease virus in chicken.

BACKGROUND: Since the first outbreak in Indonesia in 1926, Newcastle disease has become one of the most common and contagious bird diseases throughout the world. To date, enhancing host antibody response by vaccination remains the most efficient strategy to control outbreaks of Newcastle disease. Antibody response plays an important role in host resistance to Newcastle disease, and selection for antibody response can effectively improve disease resistance in chickens. However, the molecular basis of the variation in antibody response to Newcastle disease virus (NDV) is not clear. The aim of this study was to detect genes modulating antibody response to NDV by a genome-wide association study (GWAS) in chickens. RESULTS: To identify genes or chromosomal regions associated with antibody response to NDV after immunization, a GWAS was performed using 39,833 SNP markers in a chicken F(2) resource population derived from a cross between two broiler lines that differed in their resistance. Two SNP effects reached 5% Bonferroni genome-wide significance (P<1.26×10(-6)). These two SNPs, rs15354805 and rs15355555, were both on chicken (Gallus gallus) chromosome 1 and spanned approximately 600 Kb, from 100.4 Mb to 101.0 Mb. Rs15354805 is in intron 7 of the chicken Roundabout, axon guidance receptor, homolog 2 (ROBO2) gene, and rs15355555 is located about 243 Kb upstream of ROBO2. Rs15354805 explained 5% of the phenotypic variation in antibody response to NDV, post immunization, in chickens. Rs15355555 had a similar effect as rs15354805 because of its linkage disequilibrium with rs15354805 (r(2)=0.98). CONCLUSION: The region at about 100 Mb from the proximal end of chicken chromosome 1, including the ROBO1 and ROBO2 genes, has a strong effect on the antibody response to the NDV in chickens. This study paves the way for further research on the host immune response to NDV.

Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study.

PURPOSE: To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC). MATERIALS AND METHODS: This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined. RESULTS: RS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test, p = 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram. CONCLUSION: CT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization. CRITICAL RELEVANCE STATEMENT: Radiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen.

Changes in antibiotic residues and the gut microbiota during ciprofloxacin administration throughout Silkie chicken development.

The use of antibiotics leads to antibiotic residues in livestock and poultry products, adversely affecting human health. Ciprofloxacin (CFX) is a broad-spectrum antibiotic shared between animals and humans that is useful in treatments besides infections. However, changes in the gut microbiota caused by CFX and the possible link with the elimination of CFX residues have not been investigated. Herein, we used the Silkie chicken model to study the changes in the gut microbiota during the entire CFX-metabolic repertoire. We detected CFX residues in different tissues and showed that the elimination time of CFX from different tissues was dissimilar (liver > kidney > chest muscle > skin). Analysis of liver and kidney injury biomarkers and plasma antioxidant indices indicated slight hepatotoxicity and nephrotoxicity in the Silkie chickens. Importantly, the changes in the gut microbial community predominantly occurred early in the metabolic process. Correlation analysis revealed that the particular bacterial microbiota were associated with the pharmacokinetics of CFX in different Silkie chicken tissues (e.g., aerobic bacteria, including Escherichia and Coprococcus, and anaerobic bacteria, including Fusobacterium, Ruminococcus, Bifidobacterium, and Eubacterium). Collectively, certain microbiota may boost antibiotic metabolism and participate in restoring the microbial consortia after CFX is metabolized. Therefore, regulating the core intestinal microbiota may reduce foodborne antibiotics and accelerate the development of drug resistance.