Antimicrobial polysaccharide hydrogels embedded with methyl-ß-cyclodextrin/thyme oil inclusion complexes for exceptional mechanical performance and chilled chicken breast preservation.

While hydrogels have potential for food packaging, limited research on hydrogels with excellent mechanical performance and antibacterial activity for preserving chicken breasts. Herein, we created antibacterial hydrogels by embedding methyl-ß-cyclodextrin/thyme oil inclusion complexes (MCD/TO-ICs) into a polyvinyl alcohol matrix containing dendrobium polysaccharides and guar gum in varying ratios using freeze-thaw cycling method. The resulting hydrogels exhibited a more compact structure than those without MCD/TO-ICs, enhancing thermal stability and increasing glass transition temperature due to additional intermolecular interactions between polymer chains that inhibited chain movement. XRD analysis showed no significant changes in crystalline phase, enabling formation of a 3D network through abundant hydrogen bonding. Moreover, the hydrogel demonstrated exceptional durability, with a toughness of 350 ± 25 kJ/m3 and adequate tearing resistance of 340 ± 30 J/m2, capable of lifting 3 kg weight, 1200 times greater than the hydrogel itself. Additionally, the hydrogels displayed excellent antimicrobial activity and antioxidant properties. Importantly, the hydrogels effectively maintained TVB-N levels and microbial counts within acceptable ranges, preserving sensory properties and extending the shelf life of chilled chicken breasts by four days. This study highlights the potential of MCD/TO-IC-incorporated polysaccharide hydrogels as safe and effective active packaging solutions for preserving chilled chicken in food industry.

Identification of DNA N4-methylcytosine sites based on multi-source features and gradient boosting decision tree.

N4-methylcytosine (4 mC) is an important and common methylation which widely exists in prokaryotes. It plays a crucial role in correcting DNA replication errors and protecting host DNA against degradation by restrictive enzymes. Hence, the accurate identification for 4 mC sites is greatly significant for understanding biological functions and treating gene diseases. In this paper, a novel model is designed for identifying 4 mC sites. Firstly, we extract features from original sequences by multi-source feature representation methods, which are mono-nucleotide binary and k-mer frequency, dinucleotide binary and position-specific frequency, ring-function-hydrogen-chemical properties, dinucleotide-based DNA properties and trinucleotide-based DNA properties. Subsequently, gradient boosting decision tree is applied to select the optimal feature set and remove redundant information. Finally, support vector machine is employed to predict 4 mC or non-4mC sites. The accuracies of six datasets reach 0.851, 0.859, 0.801, 0.87, 0.859 and 0.901, respectively, which are superior to previous prediction methods. Therefore, the results show that our predictor is a feasible and effective tool for identifying 4 mC sites. Furthermore, an online web server is established at http://dnan4c.zhanglab.site.

Berbamine Suppresses the Growth of Gastric Cancer Cells by Inactivating the BRD4/c-MYC Signaling Pathway.

PURPOSE: Berbamine (Ber), a bioactive constituent extracted from a traditional Chinese medicinal herb, has been shown to exhibit broad inhibitory activity on a panel of cancer cell types. However, its effects and the underlying molecular mechanisms on gastric cancer (GC) remain poorly understood. METHODS: The anti-growth activity of Ber on two GC cell lines and normal gastric epithelial cell line were evaluated using MTS and clone formation assay. Flow cytometry analysis was employed to evaluate the cell cycle distribution and apoptosis of GC cells. Western blot and quantitative PCR (qPCR) analysis were employed to investigate the anti-GC mechanism of Ber. The inhibitory activity and binding affinity of Ber against BRD4 were evaluated by homogeneous time-resolved fluorescence (HTRF) and surface plasmon resonance (SPR) assay, respectively. Molecular docking and molecular simulations were conducted to predict the interaction mode between BRD4 and Ber. RESULTS: The results demonstrated that Ber reduced the proliferation of GC cell lines SGC-7901 and BGC-823 and induced cell cycle arrest and apoptosis. Mechanistically, Ber was identified as a novel natural-derived BRD4 inhibitor through multiple experimental assay, and its anti-GC activity was probably mediated by BRD4 inhibition. Molecular modeling studies suggested that Ber might bind to BRD4 primarily through hydrophobic interactions. CONCLUSION: Our study uncovered the underlying anti-GC activity of Ber in vitro and suggested that Ber holds promise as a potential lead compound in the discovery of novel BRD4 inhibitors.

UNet retinal blood vessel segmentation algorithm based on improved pyramid pooling method and attention mechanism.

The segmentation results of retinal vessels have a significant impact on the automatic diagnosis of retinal diabetes, hypertension, cardiovascular and cerebrovascular diseases and other ophthalmic diseases. In order to improve the performance of blood vessels segmentation, a pyramid scene parseing U-Net segmentation algorithm based on attention mechanism was proposed. The modified PSP-Net pyramid pooling module is introduced on the basis of U-Net network, which aggregates the context information of different regions so as to improve the ability of obtaining global information. At the same time, attention mechanism was introduced in the skip connection part of U-Net network, which makes the integration of low-level features and high-level semantic features more efficient and reduces the loss of feature information through nonlinear connection mode. The sensitivity, specificity, accuracy and AUC of DRIVE and CHASE_DB1 data sets are 0.7814, 0.9810, 0.9556, 0.9780; 0.8195, 0.9727, 0.9590, 0.9784. Experimental results show that the PSP-UNet segmentation algorithm based on the attention mechanism enhances the detection ability of blood vessel pixels, suppresses the interference of irrelevant information and improves the network segmentation performance, which is superior to U-Net algorithm and some mainstream retinal vascular segmentation algorithms at present.

MicroRNA-126 Level Increases During Exercise Rehabilitation of Heart Failure with a Preserved Ejection Fraction.

OBJECTIVE: To evaluate the changes of plasma levels of miR-126 in heart failure with a preserved ejection fraction (HFpEF) patients undergoing an exercise rehabilitation intervention. METHODS: miR-126 levels in plasma were compared between 60HFpEF patients and 30 healthy volunteers. HFpEF patients underwent exercise rehabilitation for 12 weeks. Before and after rehabilitation, indicators of cardiac function, exercise tolerance, quality of life scores and miR-126 levels were measured and compared. Correlations between plasma levels of miR-126 and HFpEF were evaluated. RESULTS: The plasma levels of miR-126 in HFpEF patients were lower than those in healthy volunteers and increased significantly after exercise rehabilitation. HFpEF patients also showed significantly better cardiac function, exercise tolerance, and quality of life after rehabilitation. The results of Pearson correlation analysis and multiple linear regression showed that miR-126 levels were positively correlated with peak oxygen consumption (peak VO2) and metabolic equivalents (METs), and inversely associated with score on the Minnesota Living with Heart Failure Questionnaire (MLHF) as well as plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels. CONCLUSION: miR-126 levels are low expressed in plasma among HFpEF patients. Effective exercise rehabilitation in HFpEF patients may positively impact the plasma level of miR-126, which is probably associated with the restoration of cardiac function, exercise tolerance and quality of life. miR-126 may be a potential biomarker for evaluating the efficacy of exercise rehabilitation for HFpEF patients.

UMAP-DBP: An Improved DNA-Binding Proteins Prediction Method Based on Uniform Manifold Approximation and Projection.

DNA-binding proteins play a vital role in cellular processes. It is an extremely urgent to develop a high-throughput method for efficiently identifying DNA-binding proteins. According to the current research situation, some methods in machine learning and deep learning show excellent computational speed and accuracy, which are worthy of application. In this work, a novel predictor was proposed to predict DNA binding proteins called UMAP-DBP. Firstly, the feature extraction of primary protein sequence was realized based on physicochemical distance transformation, Profile-based auto-cross covariance and General series correlation pseudo amino acid composition. Secondly, uniform manifold approximation and projection (UMAP) and feature importance score methods were used for feature selection; there is a progressive relationship between them. Finally, the Adaboost operation engine with jackknife test were adopted for predicting DNA-binding proteins. For the jackknife test on the BP1075 and BP594, we obtained an overall accuracy of 82.97% and 82.14%, Cohen's kappa (CK) of 0.66 and 0.64, respectively. The results illustrate that a feasible method has been developed for predicting DNA-binding proteins by UMAP and Adaboost. This is the first study in which UMAP has been successfully applied to identify DNA-binding proteins. All the datasets and codes are accessible at https://github.com/Wang-Jinyue/UMAP-DBP .

Densely connected U-Net retinal vessel segmentation algorithm based on multi-scale feature convolution extraction.

PURPOSE: The segmentation results of retinal blood vessels have a significant impact on the automatic diagnosis of various ophthalmic diseases. In order to further improve the segmentation accuracy of retinal vessels, we propose an improved algorithm based on multiscale vessel detection, which extracts features through densely connected networks and reuses features. METHODS: A parallel fusion and serial embedding multiscale feature dense connection U-Net structure are designed. In the parallel fusion method, features of the input images are extracted for Inception multiscale convolution and dense block convolution, respectively, and then the features are fused and input into the subsequent network. In serial embedding mode, the Inception multiscale convolution structure is embedded in the dense connection network module, and then the dense connection structure is used to replace the classical convolution block in the U-Net network encoder part, so as to achieve multiscale feature extraction and efficient utilization of complex structure vessels and thereby improve the network segmentation performance. RESULTS: The experimental analysis on the standard DRIVE and CHASE_DB1 databases shows that the sensitivity, specificity, accuracy, and AUC of the parallel fusion and serial embedding methods reach 0.7854, 0.9813, 0.9563, 0.9794; 0.7876, 0.9811, 0.9565, 0.9793 and 0.8110, 0.9737, 0.9547, 0.9667; 0.8113, 0.9717, 0.9574, 0.9750, respectively. CONCLUSIONS: The experimental results show that multiscale feature detection and feature dense connection can effectively enhance the network model's ability to detect blood vessels and improve the network segmentation performance, which is superior to U-Net algorithm and some mainstream retinal blood vessel segmentation algorithms at present.

Application of Machine Learning Techniques in Drug-target Interactions Prediction.

BACKGROUND: Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field. RESULTS: The machine learning methods can be divided into three categories basically: Supervised methods, Semi-Supervised methods and Unsupervised methods. We reviewed recent representative methods applying machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used in drug discovery. In addition, we compared the advantages and limitations of these methods in each category. CONCLUSION: Every prediction model has both strengths and weaknesses and should be adopted in proper ways. Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, over optimistic results due to the biases and the exploiting of regression models on DTIs prediction should be seriously considered.

Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm.

Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.

Phillygenin, a MELK Inhibitor, Inhibits Cell Survival and Epithelial-Mesenchymal Transition in Pancreatic Cancer Cells.

INTRODUCTION: Pancreatic cancer (PC) is one of the leading causes of cancer, with the lowest 5-year survival rate of all cancer types. Given the fast metastasis of PC and its resistance to surgery, radiotherapy, chemotherapy, and combinations thereof, it is imperative to develop more effective anti-PC drugs. Phillygenin (PHI) has been reported to exert anti-cancer, anti-bacterial, and anti-inflammatory properties. However, the mechanism of PHI in the development of PC is still unclear. METHODS: The cytotoxicity of PHI in pancreatic cancer cells was evaluated by MTT assay, and clonogenic assay was used to test the anti-proliferation of PHI. The pro-apoptotic effect of PHI was detected by flow cytometry analysis. The changes of epithelial-mesenchymal transition (EMT) in pancreatic cancer cells treated with PHI were determined by Western blot. Transwell assay was used to test the migration and invasion of PC cells after treatment with PHI. Molecular docking was used to predict the potential binding site of candidate target with PHI. RESULTS: PHI could inhibit the proliferation, migration, and EMT of PC cells (PANC-1 and SW1990) and induce its apoptosis. Analysis of the Cancer Genome Atlas database indicated that elevated MELK levels correlated with poor overall survival (OS) and disease-free survival (DFS) of PC patients. In addition, molecular modeling showed that PHI may potentially target the catalytic domain of maternal embryonic leucine zipper kinase (MELK). Overexpression of MELK muted the anti-PC effects of PHI. CONCLUSION: PHI holds promise as a potent candidate drug for the treatment of PC via targeted MELK.

Extrapyramidal side effect of donepezil hydrochloride in an elderly patient: A case report.

INTRODUCTION: Alzheimer disease (AD) is a neurodegenerative disease characterized by progressive cognitive dysfunction, which is mainly manifested as memory impairment and a reduced ability to self-care, often accompanied by neuropsychiatric and behavioral disorders. Donepezil is the second drug to be approved by the US FDA for the treatment of AD. Of the five FDA-approved drugs for AD treatment, donepezil is currently the most widely used. Here, we report an extrapyramidal adverse reaction to donepezil in an elderly patient with AD. PATIENT CONCERNS: An 87-year-old woman presented with a 1-year history of forgetfulness that was aggravated since the past 2 months. She had a long-term history of multiple major conditions, including hypertension, diabetes, osteoporosis, and arterial plaques. Brain imaging showed age-related changes, and her Mini Mental State Examination score was 20. Other tests revealed no abnormalities apart from multiple thyroid nodules on ultrasonography. DIAGNOSIS: She was diagnosed with AD, hypertension, type 2 diabetes mellitus, diabetic neuropathy, osteoporosis, carotid and lower-extremity arterial plaques, thyroid nodules. INTERVENTIONS: She was treated with donepezil (5 mg/day), amlodipine besylate (5 mg/day), glimepiride (4 mg/day), methylcobalamin (1.5 mg/day), calcium carbonate D3 (600 mg/day), simvastatin (20 mg/day) and enteric-coated aspirin (100 mg/day). OUTCOMES: Four days later, she experienced fatigue, panic, sweating, and one episode of vomiting. On the 5th day, she developed increased muscle tension, speech difficulty, and involuntary tremors. Imaging and blood tests revealed no obvious abnormality, and the patient was not receiving psychotropic drugs. An extrapyramidal adverse reaction to donepezil was considered, and the drug was discontinued, after which the symptoms gradually disappeared. CONCLUSION: Serious adverse reactions to donepezil can occur in elderly patients, who typically require multiple medications for a variety of comorbidities. In particular, extrapyramidal reactions have occurred when donepezil is administered in combination with psychotropic drugs. However, in our patient, an extrapyramidal adverse reaction occurred in the absence of psychotropic drugs. Thus, clinicians must be aware of inter-individual differences in drug actions and possible serious adverse reactions, and carefully monitor these patients to ensure the timely detection of adverse events and their safe treatment.

Improved Bat Algorithm Based on Multipopulation Strategy of Island Model for Solving Global Function Optimization Problem.

The bat algorithm (BA) is a heuristic algorithm that globally optimizes by simulating the bat echolocation behavior. In order to improve the search performance and further improve the convergence speed and optimization precision of the bat algorithm, an improved algorithm based on chaotic map is introduced, and the improved bat algorithm of Levy flight search strategy and contraction factor is proposed. The optimal chaotic map operator is selected based on the simulation experiments results. Then, a multipopulation parallel bat algorithm based on the island model is proposed. Finally, the typical test functions are used to carry out the simulation experiments. The simulation results show that the proposed improved algorithm can effectively improve the convergence speed and optimization accuracy.

Association of obesity with chronic kidney disease in elderly patients with nonalcoholic fatty liver disease.

BACKGROUND/AIMS: This study investigated an association between obesity and impaired renal functions in elderly patients with nonalcoholic fatty liver disease (NAFLD) and evaluated the risk factors for chronic kidney disease (CKD) in these patients. MATERIALS AND METHODS: A cross-sectional study was performed involving 515 elderly patients (≥ 60 years old) with NAFLD. Demographics, body mass index (BMI), medical history, and laboratory parameters were compared for groups stratified by obesity (≥ 28 kg/m2) or CKD. An association between obesity and CKD was analyzed, and a multivariate logistic regression analysis was conducted for risk factors associated with CKD. RESULTS: In the overall population, 28.7% were obese and 54.8% had CKD; there were more women (58.8%) than men. The prevalence of hypertension and diabetes was similar between the obese and nonobese groups and between the CKD and non-CKD groups. Obese patients had significantly higher levels of serum uric acid and estimated glomerular filtration rates when compared with the nonobese group. When compared with those without CKD, patients with CKD were significantly older in addition to having higher BMI and serum uric acid levels. The multivariate logistic regression analysis indicated that CKD was positively associated with age, BMI, and serum uric acid levels. CONCLUSION: Elderly obese patients with NAFLD are at a higher risk of CKD. NAFLD patients with advanced age, greater BMI, or higher serum uric acid levels are more prone to developing CKD. The renal function of NAFLD patients should be closely monitored.

An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism.

The grey wolf optimizer (GWO) is a novel type of swarm intelligence optimization algorithm. An improved grey wolf optimizer (IGWO) with evolution and elimination mechanism was proposed so as to achieve the proper compromise between exploration and exploitation, further accelerate the convergence and increase the optimization accuracy of GWO. The biological evolution and the "survival of the fittest" (SOF) principle of biological updating of nature are added to the basic wolf algorithm. The differential evolution (DE) is adopted as the evolutionary pattern of wolves. The wolf pack is updated according to the SOF principle so as to make the algorithm not fall into the local optimum. That is, after each iteration of the algorithm sort the fitness value that corresponds to each wolf by ascending order, and then eliminate R wolves with worst fitness value, meanwhile randomly generate wolves equal to the number of eliminated wolves. Finally, 12 typical benchmark functions are used to carry out simulation experiments with GWO with differential evolution (DGWO), GWO algorithm with SOF mechanism (SGWO), IGWO, DE algorithm, particle swarm algorithm (PSO), artificial bee colony (ABC) algorithm and cuckoo search (CS) algorithm. Experimental results show that IGWO obtains the better convergence velocity and optimization accuracy.

[Berberine regulates glycemia via local inhibition of intestinal dipeptidyl peptidase-â £].

Objective: To investigate the effect of berberine on glycemia regulation in rats with diabetes and the related mechanisms. Methods: Diabetic-like rat model was successfully induced by intraperitoneal injection of streptozotocin in 50 out of 60 male SD rats, which were then randomly divided into 5 groups with 10 rats in each:control group (received vehicle only), positive drug control group (sitagliptin 10 mg·kg-1·d-1), low-dose berberine group (30 mg·kg-1·d-1), moderate-dose berberine group (60 mg·kg-1·d-1), and high-dose berberine group (120 mg·kg-1·d-1). All animals were fed for 3 d, and fasting blood sampling was performed on day 3 of administration. Rats were given glucose (2 g/kg) by gavage 30 min after the last dose. Blood and intestinal samples were obtained 2 h after glucose loading. Fasting blood glucose (FBG) and 2-h postprandial plasma glucose (2h-PPG) were detected by using biochemical analyzer, and insulin, glucagon-like peptide-1 (GLP-1) and dipeptidyl peptidase-Ⅳ(DPP-Ⅳ) were measured by using ELISA kit. Results: No significant difference in FBG and serum DPP-Ⅳ level were found between berberine groups and control group (all P>0.05). Compared with control group, serum levels of GLP-1 and insulin were increased in high-and moderate-dose berberine groups, while 2h-PPG was decreased (all P<0.05); GLP-1 levels in the intestinal samples were increased, while DPP-Ⅳ levels were decreased in all berberine groups (all P<0.05). Conclusions: Short-term berberine administration can decrease 2h-PPG level in streptozotocin-induced diabetic rat model through local inhibition of intestinal DPP-Ⅳ. The efficacy of DPP-Ⅳ inhibitor may be associated with its intestinal pharmacokinetics.

Effect of the Infrared Absorption Properties of Sodium Nitrate with Photonic Crystal.

The two-dimensional photonic crystal (Ni mesh with square lattice of 10 µm) was prepared and Ni film was thickened to 3 µm by electroplating. Its optical properties and its influence on the infrared absorption of sodium nitrate were investigated. The results show that there is a transmission peak centered at 1 450 cm-1 under normal incidence, and the peak covered the frequencies ranging from 1 300 to 1 500 cm-1, the antisymmetric stretching vibration of nitrate. Moreover, the change of absorption intensities of nitrate's antisymmetric stretching vibration is essentially consisting with the transmittance of photonic crystal. It indicates that the absorption intensity of sodium nitrate is improved by the modulated infrared light.

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm.

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization.

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.

For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.

Features extraction of flotation froth images and BP neural network soft-sensor model of concentrate grade optimized by shuffled cuckoo searching algorithm.

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.