Effects of positive psychological interventions on positive and negative emotions in depressed individuals: a systematic review and meta-analysis.

BACKGROUND: Positive psychological interventions (PPIs) are known to be effective in alleviating depression. However, the effect of PPIs on positive and negative emotions in depressed participants is not unclear. AIMS: To systematically investigate the effects of PPIs on positive and negative emotions in depressed individuals. METHODS: 6 databases were searched for randomized controlled trials of PPIs in individuals with depressive disorders or depressive symptoms. Hedges' g value was computed using a random-effects model to determine effect sizes. RESULTS: 14 trials from 13 studies were included. Our meta-analysis showed that PPIs had significant but small effects on improving positive affect (g = 0.33, p = .02), life satisfaction (g = 0.26, p = .03), happiness (g = 0.62, p = .03) and depression (g = -0.32, p = .001), and negligible effects on improving well-being (g = 0.13, p = .24) and negative affect (g = -0.15, p = .31). Subgroup analyses of depression showed that PPIs have experienced benefits in improving depression in most subgroups. In addition, none of the subgroup analyses performed for outcomes other than depression found PPIs to be more effective than controls. CONCLUSION: PPIs can improve positive affect, life satisfaction, happiness and depression in depressed individuals, but further studies are needed to validate their effects on well-being, and negative affect.

Characterization of the novel HLA-DQB1*03:516 allele by next-generation sequencing.

HLA-DQB1*03:516 differs from DQB1*03:03:02:03 by one nucleotide substitution at position 197G>A in exon 2.

Determination of the 3'UTR region of HLA-B*53:01:03 sequence by next generation sequencing.

The 3'UTR of the HLA-B*53:01:03 allele has been determined by next generation sequencing.

Measurement properties of patient-reported outcome measures of mental help-seeking attitude: a systematic review of psychometric properties.

Background: At present, the phenomenon of patients with mental disorders not seeking mental help is very serious, and the mental help-seeking attitude is the central structure of the help process. However, there is no consensus on which patient-reported outcome measure (PROM) should be used to assess mental help-seeking attitudes. Objective: The systematic review aims to critically appraise, compare, and summarize the measurement quality of the all-available PROMs about mental help-seeking attitudes to provide evidence-based guidance and reference for clinical researchers. Methods: A systematic search was conducted in 9 databases (PubMed, Embase, Web of Science, Medline, APA, CINAHL, Sinomed, CNKI, and WanFang) since the establishment of the database until November 30, 2022 to identify articles on the PROMs of mental help-seeking attitudes. We used the COSMIN guidelines to evaluate the methodological quality and measurement properties of all-available PROMs, and a modified Grading, Recommendation, Assessment, Development, and Evaluation (GRADE) system to evaluate the level of evidence supporting each rating. Finally, the recommendation level is given according to the overall quality of each PROM. Results: We identified 29 studies representing 13 PROMs out of 2,828 screening studies. The overall quality of the included PROMs varied, with 6 rated as class B, 6 as class C, and only the Mental Help Seeking Attitudes Scale (MHSAS) as class A. Conclusion: The measurement characteristics of MHSAS have been the most comprehensive evaluation, and it has good reliability and validity, and high feasibility for clinical application, so it can be temporarily recommended for use, but the above conclusions still need to be supported by more high-quality evidence.

A randomized controlled trial of a nurse-led psychological pain solution-focused intervention for depressed inpatients: study protocol.

BACKGROUND: Depressed patients commonly experience psychological pain. Research pointed to positive psychological interventions as an effective means of ameliorating psychological pain, although the exact effect is unclear. Based on the hope theory and solution-focused brief therapy (SFBT), this study combines hope theory with solution-focused brief therapy to develop a nurse-led psychological pain solution-focused (PPSF) intervention in depressed patients. METHODS: This is an assessor-blinded randomized controlled trial following the SPIRIT guidance. A total of 84 depressed patients will be recruited from the inpatient wards of a psychiatric hospital and randomly assigned to the control and experimental groups. Patients in the control group will be treated as usual. In contrast, patients in the experimental group will receive 6 sessions of the PPSF intervention for two weeks on top of the routine care. Primary outcomes are psychological pain, hope, cognitive distortions. Secondary outcomes are depression and suicidal ideation. Data will be collected at 5-time points: baseline, 1 and 2 weeks (post-intervention), 1 month (follow up), and 6 months after baseline. Generalized equation evaluation will be used to assess the effectiveness of the PPSF intervention. DISCUSSION: From a positive psychology perspective, there remains much room for developing psychological pain interventions in depressed patients. SFBT and hope theory are both based on positive psychology. With hope theory as the general framework and SFBT questions as the practical guide, the PPSF intervention program is designed that nursing staff can implement. If the intervention is effective, it will advance the development of psychological pain interventions for patients with depression. TRIAL REGISTRATION: ChiCTR2100048223.

Effect of interpersonal psychotherapy on social functioning, overall functioning and negative emotions for depression: A meta-analysis.

BACKGROUND: Interpersonal psychotherapy (IPT) has been widely used for depression. However, current studies of IPT have been restricted to depressive symptoms, and the results for improving social functioning were controversial. METHODS: A comprehensive literature search of randomized controlled trials (RCTs) was conducted through eleven databases. Data analysis was performed by RevMan5.3, and effects were summarized by using a random effects model of mean differences with 95 % confidence intervals. RESULTS: From 2443 records, eleven studies met inclusion and exclusion criteria were used for meta-analysis. The results showed that IPT had significant effects on improving social functioning (SMD: -0.53, 95 % CI: -0.80 to -0.26), reducing depression (SMD: -0.49, 95 % CI: -0.80 to -0.19) and anxiety (SMD: -0.90, 95 % CI: -1.28 to -0.52), but the effect on the overall functioning (SMD: -0.37, 95 % CI: -0.73 to -0.01) is not obvious. Moreover, subgroup analysis showed that IPT was effective in improving social functioning in adolescent depression (SMD: -0.35, 95 % CI: -0.58 to -0.13) and perinatal depression (SMD: -1.01, 95 % CI: -1.35 to -0.67), while there was no significant difference in the adult depression group (SMD: -0.39, 95 % CI: -1.05 to 0.27). LIMITATION: The blind method cannot be carried out in most studies due to the particularity of psychotherapy, heterogeneity in some results. CONCLUSION: IPT has a significant effect on improving social functioning and reducing depression and anxiety, while the effect on overall functioning requires further research. Overall, IPT is one of the effective nonpharmacological treatments for depression.

GraphSKT: Graph-Guided Structured Knowledge Transfer for Domain Adaptive Lesion Detection.

Adversarial-based adaptation has dominated the area of domain adaptive detection over the past few years. Despite their general efficacy for various tasks, the learned representations may not capture the intrinsic topological structures of the whole images and thus are vulnerable to distributional shifts especially in real-world applications, such as geometric distortions across imaging devices in medical images. In this case, forcefully matching data distributions across domains cannot ensure precise knowledge transfer and are prone to result in the negative transfer. In this paper, we explore the problem of domain adaptive lesion detection from the perspective of relational reasoning, and propose a Graph-Structured Knowledge Transfer (GraphSKT) framework to perform hierarchical reasoning by modeling both the intra- and inter-domain topological structures. To be specific, we utilize cross-domain correspondence to mine meaningful foreground regions for representing graph nodes and explicitly endow each node with contextual information. Then, the intra- and inter-domain graphs are built on the top of instance-level features to achieve a high-level understanding of the lesion and whole medical image, and transfer the structured knowledge from source to target domains. The contextual and semantic information is propagated through graph nodes methodically, enhancing the expressive power of learned features for the lesion detection tasks. Extensive experiments on two types of challenging datasets demonstrate that the proposed GraphSKT significantly outperforms the state-of-the-art approaches for detection of polyps in colonoscopy images and of mass in mammographic images.

The mediating and moderating role of psychological resilience between occupational stress and mental health of psychiatric nurses: a multicenter cross-sectional study.

BACKGROUND: The particular occupational stress of psychiatric nurses has a negative impact on their mental health. There is evidence that psychological resilience can promote mental health; however, the relationship between resilience in occupational stress and mental health of psychiatric nurses is unclear, and mental health was assessed from a psychopathological perspective, neglecting the role of positive psychology. Therefore, this study was based on a dual-factor model of mental health, describing mental health in terms of both positive well-being and psychiatric symptoms. We aimed to investigate the level of psychiatric nurses' mental health and whether resilience plays a mediating or moderating role between occupational stress and mental health in psychiatry nurses. METHODS: A cross sectional survey of 450 psychiatric nurses in five hospitals in Jiangsu Province was conducted using a convenience sampling method, of which 413 were valid questionnaires with an effective rate of 91.8%. The evaluation included the Chinese Nurses' Stress Scale, the Connor-Davidson Resilience Scale, the Warwick-Edinburgh Mental Well-being Scale, and the General Health Questionnaire. Descriptive and Spearman correlation analyses were performed using SPSS25.0 while mediating and moderating effects were performed using SmartPLS3.0. RESULTS: Based on a dual-factor model of mental health, this study found that psychiatric nurses had a low general state of mental health, with 54.5% positive mental health, 7% vulnerable, 21.8% symptomatic but content, and 16.7% completely troubled. In addition, we found that resilience plays a mediating role in stress and mental health [ß = -0.230, 95% CI of (-0.310, -0.150)] and does not play a moderating role [ß = -0.018, 95%CI (-0.091, 0.055)]. CONCLUSIONS: Psychiatric nurses are in a poor state of mental health, and psychological resilience partly mediates occupational stress and mental health. This study suggests that attention should be paid to both positive and negative aspects of psychiatric nurses' mental health, and strategies should be developed to reduce occupational stress and develop psychological resilience.

Automatic quantification of retinal photoreceptor integrity to predict persistent disease activity in neovascular age-related macular degeneration using deep learning.

Purpose: Using deep learning (DL)-based technique, we identify risk factors and create a prediction model for refractory neovascular age-related macular degeneration (nAMD) characterized by persistent disease activity (PDA) in spectral domain optical coherence tomography (SD-OCT) images. Materials and methods: A total of 671 typical B-scans were collected from 186 eyes of 186 patients with nAMD. Spectral domain optical coherence tomography images were analyzed using a classification convolutional neural network (CNN) and a fully convolutional network (FCN) algorithm to extract six features involved in nAMD, including ellipsoid zone (EZ), external limiting membrane (ELM), intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelium detachment (PED), and subretinal hyperreflective material (SHRM). Random forest models were probed to predict 1-year disease activity (stable, PDA, and cured) based on the quantitative features computed from automated segmentation and evaluated with cross-validation. Results: The algorithm to segment six SD-OCT features achieved the mean accuracy of 0.930 (95% CI: 0.916-0.943), dice coefficients of 0.873 (95% CI: 0.847-0.899), a sensitivity of 0.873 (95% CI: 0.844-0.910), and a specificity of 0.922 (95% CI: 0.905-0.940). The six-metric model including EZ and ELM achieved the optimal performance to predict 1-year disease activity, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.980, the accuracy of 0.930, the sensitivity of 0.920, and the specificity of 0.962. The integrity of EZ and ELM significantly improved the performance of the six-metric model than that of the four-metric model. Conclusion: The prediction model reveals the potential to predict PDA in nAMD eyes. The integrity of EZ and ELM constituted the strongest predictive factor for PDA in nAMD eyes in real-world clinical practice. The results of this study are a significant step toward image-guided prediction of long-term disease activity in the management of nAMD and highlight the importance of the automatic identification of photoreceptor layers.

[Construction and application of lentivirus overexpression vector with two labeling genes fused with CopGFP and PuroR].

Objective: To construct the lentivirus overexpression vector with two label genes fused with CopGFP and PuroR and to detect the emission of green fluorescence as well as resistance to puromycin in liver cancer cells infected with lentivirus packaged with the above vector. Methods: Firstly, two fragments containing copGFP and PuroR coding sequences were amplified from pCDH-CMV-MCS-copGFP and pLKO.1 respectively; secondly, the two amplified regions were fused with each other by recombinant PCR; thirdly, the fusion DNA fragment was cut and inserted into pCDH-CMV-MCS-copGFP vector, which was linearized with the same restriction endonuclease as used to digest fusion DNA fragment: BamH Ⅰ and Sal Ⅰ. The fusion region in the constructed vector was confirmed by DNA sequencing. The checked vector was co-transfected with package assistant plasmids, namely PLP1, PLP2 and VSVG into in 293T cells and the culture supernatant was subjected to centrifuge and infect liver cancer MHCC97H cells, which were then used to detect their resistance to puromycin (infected cells were treated with 1 mg/ml puromycin for 7 days after infection) and to observe green fluorescence emission in microscope. To determine its efficiency in expressing foreign target protein, the Sp1 coding region was inserted into the MCS sites of the vector, and Sp1 mRNA and protein expression levels were compared with the vehicle vector by RT-qPCR and Western blot. Results: The lentivirus overexpression vector with two label genes fused with CopGFP and PuroR was successfully constructed, and the liver cancer cells infected with lentivirus packaged with the vector expressing two labeling genes fused with CopGFP and PuroRshowed both emission of green fluorescence and resistance to puromycin simultaneously, while cells containing with the vector inserted with Sp1 coding region improved Sp1 mRNA level with 3.3 fold and protein level with 2.2 fold higher in comparison with cells containing the vehicle vector (P＜0.01). Conclusion: The fused label genes consisting of copGFP and PuroR are correctly cloned into the lentivirus vector and confer cells with the ability to emission of green fluorescence and resistance to puromycin, besides, the vector may promote the expression of the target gene with long coding sequence.

Enhancing Thermal Insulation of EPDM Ablators via Constructing Alternating Planar Architectures.

Ethylene-propylene-diene monomer (EPDM) composites were usually enhanced with ablative additives to protect solid rocket motor (SRMs) casings. However, the poor thermal insulation caused by the high thermal conductive ablative fillers can lead to rocket motor failure. Herein, the novel EPDM composites containing alternating layers of ablative EPDM (AM) and heat-insulated EPDM (HM) were prepared through layer-multiplying extrusion. Compared with conventional EPDM ablative material, the multilayer composites showed enhanced thermal insulation and mechanical properties that could be further improved by tuning the number of layers. The ablation and thermal insulation properties possessing in AM and HM layers could be combined by forced assembly during co-extrusion, and the alternating multilayer composite was capable of showing the effect of each component. In particular, compared with AM, the maximum back-face temperature with 40 alternating layers of AM/HM decreased from 96.2 °C to 75.6 °C during oxyacetylene test, while the good ablation properties were preserved in the AM component. This significant improvement was attributed to the planar orientation and densification of ablative additives, and the interruption of conductive pathways in the through-plane direction of AM/HM alternating laminate. The anisotropic EPDM composites featuring mechanical robustness, good ablative resistance and thermal insulation suggest considerable potential application in the aerospace industry.

TW-GAN: Topology and width aware GAN for retinal artery/vein classification.

Automatic artery/vein (A/V) classification, as the basic prerequisite for the quantitative analysis of retinal vascular network, has been actively investigated in recent years using both conventional and deep learning based methods. The topological connection relationship and vessel width information, which have been proved effective in improving the A/V classification performance for the conventional methods, however, have not yet been exploited by the deep learning based methods. In this paper, we propose a novel Topology and Width Aware Generative Adversarial Network (named as TW-GAN), which, for the first time, integrates the topology connectivity and vessel width information into the deep learning framework for A/V classification. To improve the topology connectivity, a topology-aware module is proposed, which contains a topology ranking discriminator based on ordinal classification to rank the topological connectivity level of the ground-truth mask, the generated A/V mask and the intentionally shuffled mask. In addition, a topology preserving triplet loss is also proposed to extract the high-level topological features and further to narrow the feature distance between the predicted A/V mask and the ground-truth mask. Moreover, to enhance the model's perception of vessel width, a width-aware module is proposed to predict the width maps for the dilated/non-dilated ground-truth masks. Extensive empirical experiments demonstrate that the proposed framework effectively increases the topological connectivity of the segmented A/V masks and achieves state-of-the-art A/V classification performance on the publicly available AV-DRIVE and HRF datasets. Source code and data annotations are available at https://github.com/o0t1ng0o/TW-GAN.

The Correlation Between MDR1 Gene Polymorphism and Clopidogrel Resistance in People of the Hui and Han Nationalities.

To investigate the differences in the correlation between multidrug resistance protein 1 (MDR1) (ABCB1) gene polymorphism and clopidogrel resistance in patients of the Hui and Han nationalities with percutaneous coronary intervention (PCI). A total of 377 subjects (154 people of Hui nationality, 223 people of Han nationality) with PCI were enrolled in the study. Each patient's platelet aggregation rate was induced by adenosine diphosphate and measured using light turbidimetry. Based on the results, the patients were divided into two groups: a clopidogrel resistance (CR) group and a non-clopidogrel resistance (NCR) group. Restrictive fragment-length polymorphism polymerase chain reaction technology was then used to determine the genotype and alleles at two loci (C3435 T[rs1045642] and C1236 T[rs1128503]), calculate the frequencies of the genotype and alleles at these two loci, and conduct correlation analysis. The incidence rate of clopidogrel resistance was 23.4%, and the frequencies of the TT genotype and T allele at C3435 T for patients of both nationalities were significantly higher in the CR group than in the NCR group (P < 0.05). There were no significant differences between the two groups in genotype or allele frequency at C1236 T. There was a significant difference in the distribution of C1236 T polymorphism between the two nationalities (P < 0.05), but there was no significant difference between the two nationalities in C3435 T polymorphism. Patients with a T allele at MDR1 C3435 T are more likely to show clopidogrel resistance, and no significant differences were identified in C3435 T gene polymorphism between the two nationalities.

Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation.

Due to the lack of properly annotated medical data, exploring the generalization capability of the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in recent years to equip the deep model with the ability to recognize unseen classes. However, existing studies mainly focus on natural images, which utilize linguistic models to extract auxiliary information for ZSL. It is impractical to apply the natural image ZSL solutions directly to medical images, since the medical terminology is very domain-specific, and it is not easy to acquire linguistic models for the medical terminology. In this work, we propose a new paradigm of ZSL specifically for medical images utilizing cross-modality information. We make three main contributions with the proposed paradigm. First, we extract the prior knowledge about the segmentation targets, called relation prototypes, from the prior model and then propose a cross-modality adaptation module to inherit the prototypes to the zero-shot model. Second, we propose a relation prototype awareness module to make the zero-shot model aware of information contained in the prototypes. Last but not least, we develop an inheritance attention module to recalibrate the relation prototypes to enhance the inheritance process. The proposed framework is evaluated on two public cross-modality datasets including a cardiac dataset and an abdominal dataset. Extensive experiments show that the proposed framework significantly outperforms the state of the arts.

Rapid neural reorganization during retrieval practice predicts subsequent long-term retention and false memory.

Active retrieval can alter the strength and content of a memory, yielding either enhanced or distorted subsequent recall. However, how consolidation influences these retrieval-induced seemingly contradictory outcomes remains unknown. Here we show that rapid neural reorganization over an eight-run retrieval practice predicted subsequent recall. Retrieval practice boosted memory retention following a 24-hour (long-term) but not 30-minute delay, and increased false memory at both delays. Long-term retention gains were predicted by multi-voxel representation distinctiveness in the posterior parietal cortex (PPC) that increased progressively over retrieval practice. False memory was predicted by unstable representation distinctiveness in the medial temporal lobe (MTL). Retrieval practice enhanced the efficiency of memory-related brain networks, through building up PPC and MTL connections with the ventrolateral and dorsolateral prefrontal cortex that predicted long-term retention gains and false memory, respectively. Our findings indicate that retrieval-induced rapid neural reorganization together with consecutive consolidation fosters long-term retention and false memories via distinct pathways.

Effect of LncPVT1/miR-20a-5p on Lipid Metabolism and Insulin Resistance in NAFLD.

PURPOSE: Nonalcoholic fatty liver disease (NAFLD) is closely related to lipid metabolism and insulin resistance. The current research mainly attempted to verify the clinical value of LncRNA plasmacytoma variant translocation 1 (PVT1), and whether microRNA regulates lipid metabolism and insulin resistance to participate in NAFLD. PATIENTS AND METHODS: 81 patients with NAFLD and 78 healthy individuals were enrolled in this study. In addition, C57BL/6 mice were fed a high-fat diet to establish NAFLD model in vivo. Serum PVT1 and miR-20a-5p expression in NAFLD patients and mice were assessed by RT-qPCR. ROC curves determine the diagnostic value of PVT1 and miR-20a-5p. NAFLD mice were subjected to IPGTT to detect changes in insulin sensitivity, and the common indicators of lipid metabolism and insulin resistance were also evaluated. Dual-luciferase reporter assay verified the regulation mechanism of PVT1 and miR-20a-5p. RESULTS: PVT1 was upregulated in NAFLD patients and mice, while miR-20a-5p was decreased. Their expression trends were similar in patients with HOMA-IR ≥2.5. What's more, miR-20a-5p, FBG, ALT, and HOMA-IR were independently correlated with PVT1. And PVT1 and miR-20a-5p show high clinical diagnostic value. Bodyweight, insulin sensitivity, lipid metabolism inductors were increased in NAFLD mice, but these increases were attenuated by PVT1 elimination. Finally, miR-20a-5p might function as the possible miRNA target of PVT1 via the binding sites at 3'-UTR and negatively regulated by it. CONCLUSION: PVT1 and miR-20a-5p are potential clinical biomarkers of NAFLD, and PVT1 promotes the occurrence of NAFLD by regulating insulin sensitivity and lipid metabolism, which may be achieved by targeting miR-20a-5p.

Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs.

BACKGROUND: Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. METHODS: We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. RESULTS: Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. CONCLUSIONS: Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.

Genome­wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing.

BACKGROUND: Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features. RESULTS: We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model. CONCLUSIONS: A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features.

Clinical Value and Role of miR-129-5p in Non-Alcoholic Fatty Liver Disease.

This study was to evaluate the expression of miR-129-5p in non-alcoholic fatty liver (NAFLD) patients and its clinical value and explore its regulatory effect on insulin resistance (IR). A total of 117 NAFLD patients and 110 healthy controls were included. The levels of miR-129-5p were detected by qRT-PCR. To assess the diagnostic value of miR-129-5p for NAFLD, the receiver operating characteristic curve (ROC) was established. C57Bl/6 mice were supplied with high-fat diet to establish NAFLD model. Intraperitoneal insulin tolerance test (IPITT) was carried out to evaluate the effect of miR-129-5p on IR in NAFLD animal model. miR-129-5p was highly expressed in the serum of NAFLD patients, and patients with HOMA-IR ≥2.5 had higher level of miR-129-5p than those with HOMA-IR <2.5. miR-129-5p had the ability to differentiate NAFLD patients from healthy individuals and might be associated with the development of IR. Serum miR-129-5p was positively correlated with the levels of HOMA-IR, BMI, total cholesterol (TC), and triglyceride (TG) in NAFLD patients. Downregulation of miR-129-5p regulates lipid metabolism and insulin sensitivity in NAFLD mice model. MiR-129-5p was upregulated in NAFLD patients and might be a potential diagnostic biomarker. The regulatory effect of miR-129-5p on NAFLD may function by regulating lipid accumulation and insulin sensitivity.