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"Zhao, Haitong"
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An improved exhausted-food-sources-identification mechanism for the artificial bee colony algorithm
Artificial bee colony (ABC) algorithm has been widely used to solve the optimization problems. In the existing ABC algorithms, choosing which employed bee giving up its food source only based on its current trial number. It may cause some promising areas are exploited insufficiently and some non-significant areas are searched excessively, which leads to a waste of much more searching resources. To cope with this problem, an improved exhausted food source identification mechanism based on space partitioning is designed, which considers the food source states both in the objective space and searching space simultaneously. Then, the proposed mechanism is applied to the basic ABC algorithm and a recently improved ABC algorithm. The experimental results have demonstrated that the ABC algorithms with the designed exhausted food source identification mechanism perform better than the original ABC algorithms in almost all the functions on the CEC2015 test suit.
Journal Article
Metabolic syndrome increases osteoarthritis risk: findings from the UK Biobank prospective cohort study
Objective
The association between Metabolic Syndrome (MetS), its components, and the risk of osteoarthritis (OA) has been a topic of conflicting evidence in different studies. The aim of this present study is to investigate the association between MetS, its components, and the risk of OA using data from the UK Biobank.
Methods
A prospective cohort study was conducted in the UK Biobank to assess the risk of osteoarthritis (OA) related to MetS. MetS was defined according to the criteria set by the International Diabetes Federation (IDF). Additionally, lifestyle factors, medications, and the inflammatory marker C-reactive protein (CRP) were included in the model. Cox proportional hazards regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CI). The cumulative risk of OA was analyzed using Kaplan–Meier curves and log-rank tests. To explore potential nonlinear associations between MetS components and OA risk, a restricted cubic splines (RCS) model was employed. In addition, the polygenic risk score (PRS) of OA was calculated to characterize individual genetic risk.
Results
A total of 45,581 cases of OA were identified among 370,311 participants, with a median follow-up time of 12.48 years. The study found that individuals with MetS had a 15% higher risk of developing OA (HR = 1.15, 95%CI:1.12–1.19). Additionally, central obesity was associated with a 58% increased risk of OA (HR = 1.58, 95%CI:1.5–1.66), while hyperglycemia was linked to a 13% higher risk (HR = 1.13, 95%CI:1.1–1.15). Dyslipidemia, specifically in triglycerides (HR = 1.07, 95%CI:1.05–1.09) and high-density lipoprotein (HR = 1.05, 95%CI:1.02–1.07), was also found to be slightly associated with OA risk. When stratified by PRS, those in the high PRS group had a significantly higher risk of OA compared to those with a low PRS, whereas no interaction was found between MetS and PRS on OA risks. Furthermore, the presence of MetS significantly increased the risk of OA by up to 35% in individuals with elevated CRP levels (HR = 1.35, 95% CI:1.3–1.4).
Conclusion
MetS and its components have been found to be associated with an increased risk of OA, particularly in individuals with elevated levels of CRP. These findings highlight the significance of managing MetS as a preventive and intervention measure for OA.
Journal Article
Learning Guided Binary PSO Algorithm for Feature Selection and Reconstruction of Ultrasound Contrast Images in Endometrial Region Detection
Accurate identification of the endometrial region is critical for the early detection of endometrial lesions. However, current detection models still face two major challenges when processing endometrial imaging data: (1) In complex and noisy environments, recognition accuracy remains limited, partly due to the insufficient exploitation of color information within the images; (2) Traditional Two-dimensional PCA-based (2DPCA-based) feature selection methods have limited capacity to capture and represent key characteristics of the endometrial region. To address these challenges, this paper proposes a novel algorithm named Feature-Level Image Fusion and Improved Swarm Intelligence Optimization Algorithm (FLFSI), which integrates a learning guided binary particle swarm optimization (BPSO) strategy with an image feature selection and reconstruction framework to enhance the detection of endometrial regions in clinical ultrasound images. Specifically, FLFSI contributes to improving feature selection accuracy and image reconstruction quality, thereby enhancing the overall performance of region recognition tasks. First, we enhance endometrial image representation by incorporating feature engineering techniques that combine structural and color information, thereby improving reconstruction quality and emphasizing critical regional features. Second, the BPSO algorithm is introduced into the feature selection stage, improving the accuracy of feature selection and its global search ability while effectively reducing the impact of redundant features. Furthermore, we refined the BPSO design to accelerate convergence and enhance optimization efficiency during the selection process. The proposed FLFSI algorithm can be integrated into mainstream detection models such as YOLO11 and YOLOv12. When applied to YOLO11, FLFSI achieves 96.6% Box mAP and 87.8% Mask mAP. With YOLOv12, it further improves the Mask mAP to 88.8%, demonstrating excellent cross-model adaptability and robust detection performance. Extensive experimental results validate the effectiveness and broad applicability of FLFSI in enhancing endometrial region detection for clinical ultrasound image analysis.
Journal Article
A Novel Fireworks Algorithm for the Protein-Ligand Docking on the AutoDock
The AutoDock is a widely used protein-ligand docking simulation platform which is a simulator to bring the field of computer-aided drug design (CADD) conveniences. The protein-ligand docking problem is of great significance to design more effective and ideal drugs. In order to solve the protein-ligand docking problem more effectively, we propose an improved fireworks algorithm called FWADOCK. FWADOCK utilizes the position updating information of fireworks with the best fitness value to determine the shape of the fireworks and the distribution of sparks. During the process of searching for the optimal solution, it is also considered that the diversity of understanding and how to avoid premature convergence of the algorithm. The proposed algorithm is tested on some benchmark test cases of protein-ligand docking problems and compared with some other related algorithms from different perspectives on the AutoDock platform. The results show that FWADOCK has a competitive performance in solving the protein-ligand docking problem.
Journal Article
HFSOF: A Hierarchical Feature Selection and Optimization Framework for Ultrasound-Based Diagnosis of Endometrial Lesions
Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets.
Journal Article
Effects of dietary chlorogenic acid supplementation on laying performance, egg quality, antioxidant capacity, and liver lipid metabolism in late-peak laying hens
This investigation focused on the effects of chlorogenic acid (CGA) on laying performance, egg quality, antioxidant capacity, and liver lipid metabolism in late-peak laying hens. Two hundred forty 43–week-old Hy-Line Brown laying hens were randomly distributed into 4 treatments receiving 0, 400, 600, and 800 mg/kg CGA for twelve weeks. Egg production rate and average egg weight were significantly elevated with 600 and 800 mg/kg CGA relative to the control group. Significant improvements were also noted in the Haugh unit, albumen height, and yolk colour in both CGA groups, along with elevated levels of ovomucin in the albumen and lutein in the yolk. CGA at 600 and 800 mg/kg doses significantly reduced serum hydrogen peroxide and malondialdehyde levels, as well as hepatic malondialdehyde levels. Meanwhile, CGA treatment significantly upregulated hepatic Nrf2 and SOD2 gene expression while downregulating Keap1 gene expression. Additionally, 600 and 800 mg/kg CGA treatment significantly decreased serum TG, T-CHO, and LDL-C levels, as well as liver TG and T-CHO levels, while increasing serum lipoprotein lipase activity. Moreover, CGA treatment significantly decreased hepatocyte lipid droplet levels, along with a notable reduction in SREBP1, FASN, ACC, and DGAT2 gene expressions, and a significant upregulation in PPARα, ACOX1, and CPT1 gene expressions. Our findings demonstrate that CGA can improve laying performance, egg quality, and antioxidant capacity, and reduce liver lipid accumulation by inhibiting fatty acid synthesis and stimulating fatty acid oxidation in late-peak laying hens. A dietary supplement of 600–800 mg/kg CGA is recommended under the current experimental conditions.HIGHLIGHTS Dietary supplementation CGA improved the laying performance, egg quality, and antioxidant capacity in the late-peak laying hens. CGA reduced hepatic lipid accumulation by inhibiting fatty acid synthesis and stimulating fatty acid oxidation. Under the current experimental conditions, a dietary supplemental level of 600–800 mg/kg CGA is recommended for laying hens.
Journal Article
Association between the number of retrieved lymph nodes and demographic/tumour-related characteristics in colorectal cancer: a systematic review and meta-analysis
ObjectiveClinical practice guidelines recommend retrieving at least 12 lymph nodes for correct staging in colorectal cancer. However, it is difficult to retrieve adequate lymph nodes because of various factors. We aimed to evaluate the association between the number of retrieved lymph nodes and demographic/tumour-related characteristics in colorectal cancer.DesignSystematic review and meta-analysis of primary studies.Data sourcesPubMed, Embase, Cochrane and Web of Science were searched from January 2016 to June 2023.Eligibility criteria for selecting studiesStudies that evaluated the association between retrieved lymph nodes and demographic/tumour-related characteristics in colorectal cancer were included.Data extraction and synthesisOR with 95% conference intervals was extracted and pooled.ResultsA total of 54 studies containing 2 05 821 patients were included in this meta-analysis. The results showed that fewer nodes were retrieved from elderly patients (OR=0.70, 95% CI (0.54 to 0.90), p=0.005), and from tumours located in the left colon than in the right colon (OR=0.43, 95% CI (0.33 to 0.56), p<0.001). More lymph nodes were obtained from females than males (OR=1.15, 95% CI (1.04 to 1.28), p=0.006), from the advanced T stage (T3+T4) than T1+T2 stage (OR=1.57, 95% CI (1.25 to 1.97), p<0.001) and from the N2 stage than N0 stage (OR=1.32, 95% CI (1.15 to 1.51), p<0.001). Body mass index, ethnicity, N1 stage, M stage, tumour differentiation and lymph–vascular invasion were not significantly associated with the lymph node yield.ConclusionsThe study results suggest that clinicians have an increased opportunity to retrieve sufficient lymph nodes for accurate pathological staging to guide treatment decisions in patients with colorectal cancer who are young, female, with tumours located in the right colon, advanced T stage and N2 stage.
Journal Article
Responses of Intestinal Antioxidant Capacity, Morphology, Barrier Function, Immunity, and Microbial Diversity to Chlorogenic Acid in Late-Peak Laying Hens
This study examined the influence of chlorogenic acid (CGA) on gut antioxidant status, morphology, barrier function, immunity, and cecal microbiota in late-peak laying hens. A total of 240 Hy-Line Brown hens, aged 43 weeks, were randomly assigned to four groups, the basal diet +0, 400, 600, and 800 mg/kg CGA, for 12 weeks. The results revealed that CGA significantly reduced ileal H2O2 and malondialdehyde levels; increased duodenal height, ileal villus height, and villus height-to-crypt depth ratio; while decreasing jejunal crypt depth. The 600 and 800 mg/kg CGA significantly upregulated the duodenal, jejunal, and ileal ZO-1 and occludin gene expression; increased IgG levels in serum and ileum; and upregulated ileal IgA gene expression. The 600 mg/kg CGA significantly upregulated CD3D and CD4 gene expression, while downregulating IL-1β gene expression in duodenum, jejunum, and ileum. Moreover, CGA changed the gut microbiota structure. The SCFA-producing bacteria unclassified_f__Peptostreptococcaceae, unclassified_f_Oscillospiraceae, Pseudoflavonifractor, Lachnospiraceae_FCS020_group, Oscillospira, Elusimicrobium, Eubacterium_ventriosum_group, Intestinimonas, and norank_f_Coriobacteriales_Incertae_Sedis were significantly enriched in the 400, 600, and/or 800 mg/kg CGA groups. The bacteria Lactobacillus, Bacillus, and Akkermansia were significantly enriched in the 600 mg/kg CGA group. Conclusively, dietary CGA (600–800 mg/kg) improved intestinal antioxidant status, morphology, barrier and immune function, and beneficial microbiota growth in late-peak laying hens.
Journal Article
Supplementation of Chlorogenic Acid Alleviates the Effects of H2O2-Induced Oxidative Stress on Laying Performance, Egg Quality, Antioxidant Capacity, Hepatic Inflammation, Mitochondrial Dysfunction, and Lipid Accumulation in Laying Hens
This research examined the impact of chlorogenic acid (CGA) on laying performance, antioxidant capacity, egg quality, hepatic inflammation, mitochondrial function, and lipid metabolism in hens subjected to hydrogen peroxide (H2O2)-induced oxidative stress (OS). Three hundred sixty healthy 43-wk-old Hy-Line brown hens were randomly assigned to six treatments: a basal diet + 0 (control and H2O2), 600 (600 mg/kg CGA and 600 mg/kg CGA + H2O2), and 800 (800 mg/kg CGA and 800 mg/kg CGA + H2O2) mg/kg CGA for 84 d. On the 64th and 78th days of the trial, hens in groups H2O2, 600 mg/kg CGA + H2O2, and 800 mg/kg CGA + H2O2 were injected intraperitoneally with 10% H2O2. The results demonstrated that 600 and 800 mg/kg CGA significantly improved the egg production rate (EPR) and egg quality and reduced lipid peroxidation compared to the control group. The 800 mg/kg CGA showed greater improvements in the EPR and average egg weight (AEW) compared to the 600 mg/kg dose. Conversely, H2O2 exposure significantly decreased the EPR, AEW, and egg quality and increased feed conversion rate and average daily feed intake. H2O2 exposure significantly decreased serum T-AOC and increased serum MDA levels while reducing hepatic T-SOD, GSH-Px, and CAT activities. Meanwhile, H2O2 exposure significantly elevated liver reactive oxygen species levels, pathological damage, and NF-κB, TNFα, and IL-1β gene expression. Additionally, H2O2 treatment disrupted hepatocyte mitochondrial structure and significantly increased the expression of VDAC1 protein, and IP3R, GRP75, MCU, Fis1, and MFF genes, while downregulating the expression of MFN2 protein and PGC1α gene. Oil Red O staining demonstrated that H2O2 induced significant lipid accumulation in hepatocytes. Concurrently, H2O2 significantly increased serum triglycerides, total cholesterol, and liver triglycerides levels while decreasing serum hepatic lipase activity. This was primarily attributed to the significant upregulation of liver SREBP1, FASN, and ACC genes and the downregulation of the liver CPT1 gene induced by H2O2. Furthermore, CGA pretreatment effectively prevented the degeneration in laying performance and egg quality, as well as OS, liver inflammation, pathological damage, and mitochondrial dysfunction induced by H2O2. CGA inhibited H2O2-induced hepatic lipid accumulation by upregulating fatty acid oxidation-related gene expression and downregulating fatty acid synthesis-related gene expression. These findings indicate that the dietary addition of 800 mg/kg of CGA is the optimum supplementation dose. CGA can enhance laying performance and egg quality while alleviating OS, hepatic inflammation, mitochondrial dysfunction, and lipid accumulation in H2O2-challenged laying hens.
Journal Article