Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models.

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm's efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation.

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data.

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

A MnO2 nanosheet-mediated CRISPR/Cas12a system for the detection of organophosphorus pesticides in environmental water.

Nowadays, easy, convenient, and sensitive sensing strategies are still critical for organophosphorus pesticides in environmental water samples. Herein, a novel organophosphorus pesticide (OP) assay based on acetylcholinesterase (AChE) and a MnO2 nanosheet-mediated CRISPR/Cas12a reaction is reported. The single-strand DNA (ssDNA) activator of CRISPR/Cas12a was simply adsorbed on the MnO2 nanosheets as the nanoswitches of the assay. In the absence of target OPs, AChE hydrolyzed acetylcholine (ATCh) to thiocholine (TCh), which reduced the MnO2 nanosheets to Mn2+, resulting in the release of the activator followed by activation of the CRISPR/Cas12a system. The activated Cas12a thereafter nonspecifically cleaved the FAM/BHQ1-labeled ssDNA (FQ-reporter), producing a fluorescence signal. Upon the addition of target OPs, the hydrolysis of ATCh by AChE was inhibited owing to OPs combining with AChE, and thus effective quantification of OPs could be achieved by measuring the fluorescence changes of the system. As a proof of concept, dichlorvos (DDVP) was chosen as a model OP analyte to address the feasibility of the proposed method. Attributed to the excellent trans-cleavage activity of Cas12a, the fluorescent biosensor exhibits a satisfactory limit of detection (LOD) for DDVP at 0.135 ng mL-1. In addition, the excellent recoveries for the detection of DDVP in environmental water samples demonstrate the applicability of the proposed assay in real sample research.

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design.

The slime mould algorithm (SMA) is a population-based swarm intelligence optimization algorithm that simulates the oscillatory foraging behavior of slime moulds. To overcome its drawbacks of slow convergence speed and premature convergence, this paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm (DE) and the Powell mechanism. PSMADE utilizes crossover and mutation operations of DE to enhance individual diversity and improve global search capability. Additionally, it incorporates the Powell mechanism with a taboo table to strengthen local search and facilitate convergence toward better solutions. The performance of PSMADE is evaluated by comparing it with 14 metaheuristic algorithms (MA) and 15 improved MAs on the CEC 2014 benchmarks, as well as solving four constrained real-world engineering problems. Experimental results demonstrate that PSMADE effectively compensates for the limitations of SMA and exhibits outstanding performance in solving various complex problems, showing potential as an effective problem-solving tool.

Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine.

The evaluation of coronary morphology provides important guidance for the treatment of coronary heart disease (CHD). A chaotic Gaussian mutation antlion optimizer algorithm (CGALO) is proposed in the paper, and it is combined with SVM to construct a classification prediction model for Fractional flow reserve (FFR). To overcome the limitations of the original antlion optimizer (ALO) algorithm, the chaotic Gaussian mutation strategy is introduced, which leads to an improvement in its convergence speed and accuracy. To evaluate the proposed algorithm's performance, comparative experiments were conducted on 23 benchmark functions alongside 12 other cutting-edge optimization algorithms. The experimental outcomes demonstrate that the proposed algorithm achieves superior convergence accuracy and speed compared to the alternative comparison algorithms. Additionally, it is combined with SVM and FS to construct a hierarchical FFR classification model, which is utilized to make effective predictions for 84 patients at the affiliated hospital of medical school, Ningbo university. The experimental results demonstrate that the proposed model achieves an average accuracy of 92%. Moreover, it concludes that smoking history, number of lesion vessels, lesion location, diffuse lesions and ST segment changes, and other factors are the most critical indicators for FFR. Therefore, the model that has been established is a new FFR intelligent classification prediction technology that can effectively assist doctors in making corresponding decisions and evaluation plans.

A reinforcement federated learning based strategy for urinary disease dataset processing.

Urinary disease is a complex healthcare issue that continues to grow in prevalence. Urine tests have proven valuable in identifying conditions such as kidney disease, urinary tract infections, and lower abdominal pain. While machine learning has made significant strides in automating urinary tract infection detection, the accuracy of existing methods is hindered by concerns surrounding data privacy and the time-intensive nature of training and testing with large datasets. Our proposed method aims to address these limitations and achieve highly accurate urinary tract infection detection across various healthcare laboratories, while simultaneously minimizing data security risks and processing delays. To tackle this challenge, we approach the problem as a combinatorial optimization task. We optimize the accuracy objective as a concave function and minimize computation delay as a convex function. Our work introduces a framework enabled by federated learning and reinforcement learning strategy (FLRLS), leveraging lab urine data. FLRLS employs deterministic agents to optimize the exploration and exploitation of urinary data, while the actual determination of urinary tract infections is performed at a centralized, aggregated node. Experimental results demonstrate that our proposed method improves accuracy by 5% and reduces total delay. By combining federated learning, reinforcement learning, and a combinatorial optimization approach, we achieve both high accuracy and minimal delay in urinary tract infection detection.

An intent classification method for questions in "Treatise on Febrile diseases" based on TinyBERT-CNN fusion model.

"Treatise on Febrile Diseases" is an important classic book in the academic history of Chinese material medica. Based on the knowledge map of traditional Chinese medicine established by the study of "Treatise on Febrile Diseases", a question-answering system of traditional Chinese medicine was established to help people better understand and use traditional Chinese medicine. Intention classification is the basis of the question-answering system of traditional Chinese medicine, but as far as we know, there is no research on question intention classification based on "Treatise on Febrile Diseases". In this paper, the intent classification research is carried out based on the Chinese material medica-related content materials in "Treatise on Febrile Diseases" as data. Most of the existing models perform well on long text classification tasks, with high costs and a lot of memory requirements. However, the intent classification data of this paper has the characteristics of short text, a small amount of data, and unbalanced categories. In response to these problems, this paper proposes a knowledge distillation-based bidirectional Transformer encoder combined with a convolutional neural network model (TinyBERT-CNN), which is used for the task of question intent classification in "Treatise on Febrile Diseases". The model used TinyBERT as an embedding and encoding layer to obtain the global vector information of the text and then completed the intent classification by feeding the encoded feature information into the CNN. The experimental results indicated that the model outperformed other models in terms of accuracy, recall, and F1 values of 96.4%, 95.9%, and 96.2%, respectively. The experimental results prove that the model proposed in this paper can effectively classify the intent of the question sentences in "Treatise on Febrile Diseases", and provide technical support for the question-answering system of "Treatise on Febrile Diseases" later.

Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems.

There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.

Boosting Slime Mould Algorithm for High-Dimensional Gene Data Mining: Diversity Analysis and Feature Selection.

Slime mould algorithm (SMA) is a new metaheuristic algorithm, which simulates the behavior and morphology changes of slime mould during foraging. The slime mould algorithm has good performance; however, the basic version of SMA still has some problems. When faced with some complex problems, it may fall into local optimum and cannot find the optimal solution. Aiming at this problem, an improved SMA is proposed to alleviate the disadvantages of SMA. Based on the original SMA, Gaussian mutation and Levy flight are introduced to improve the global search performance of the SMA. Adding Gaussian mutation to SMA can improve the diversity of the population, and Levy flight can alleviate the local optimum of SMA, so that the algorithm can find the optimal solution as soon as possible. In order to verify the effectiveness of the proposed algorithm, a continuous version of the proposed algorithm, GLSMA, is tested on 33 classical continuous optimization problems. Then, on 14 high-dimensional gene datasets, the effectiveness of the proposed discrete version, namely, BGLSMA, is verified by comparing with other feature selection algorithm. Experimental results reveal that the performance of the continuous version of the algorithm is better than the original algorithm, and the defects of the original algorithm are alleviated. Besides, the discrete version of the algorithm has a higher classification accuracy when fewer features are selected. This proves that the improved algorithm has practical value in high-dimensional gene feature selection.

Mutational Slime Mould Algorithm for Gene Selection.

A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data's dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.

Multi-level thresholding segmentation for pathological images: Optimal performance design of a new modified differential evolution.

The effective analytical processing of pathological images is crucial in promoting the development of medical diagnostics. Based on this matter, in this research, a multi-level thresholding segmentation (MLTS) method based on modified different evolution (MDE) is proposed. The MDE is the primary benefit offered by the suggested MLTS technique, which is a novel proposed evolutionary algorithm in this article with significant convergence accuracy and the capability to leap out of the local optimum (LO). This optimizer came into being mostly as a result of the incorporation of the movement mechanisms of white holes, black holes, and wormholes into various evolutions. Thus, the developed MLTS approach may provide high-quality segmentation results and is less susceptible to segmentation process stagnation. To validate the efficacy of the presented approaches, first, the performance of MDE is validated using 30 benchmark functions, and then the proposed segmentation method is empirically compared with other comparable methods using standard pictures. On the basis of breast cancer and skin cancer pathology images, the developed segmentation method is compared to other competing methods and experimentally validated in further detail. By analyzing experimental data, the key compensations of MDE are proven, and it is experimentally shown that the unique MDE-based MLTS approach can achieve good performance in terms of many performance assessment indices. Consequently, the proposed method may offer an efficient segmentation procedure for pathological medical images.

Oncolytic Activity of Wild-type Newcastle Disease Virus HK84 Against Hepatocellular Carcinoma Associated with Activation of Type I Interferon Signaling.

Background and Aims: Hepatocellular carcinoma (HCC) is listed as one of the most common causes of cancer-related death. Oncolytic therapy has become a promising treatment because of novel immunotherapies and gene editing technology, but biosafety concerns remain the biggest limitation for clinical application. We studied the the antitumor activity and biosafety of the wild-type Newcastle disease virus HK84 strain (NDV/HK84) and 10 other NDV strains. Methods: Cell proliferation and apoptosis were determined by cell counting Kit-8 and fluorescein isothiocyanate Annexin V apoptosis assays. Colony formation, wound healing, and a xenograft mouse model were used to evaluate in vivo and in vitro oncolytic effectiveness. The safety of NDV/HK84 was tested in nude mice by an in vivo luciferase imaging system. The replication kinetics of NDV/HK84 in normal tissues and tumors were evaluated by infectious-dose assays in eggs. RNA sequencing analysis was performed to explore NDV/HK84 activity and was validated by quantitative real-time PCR. Results: The cell counting Kit-8 assays of viability found that the oncolytic activity of the NDV strains differed with the multiplicity of infection (MOI). At an MOI of 20, the oncolytic activity of all NDV strains except the DK/JX/21358/08 strain was >80%. The oncolytic activities of the NDV/HK84 and DK/JX/8224/04 strains were >80% at both MOI=20 and MOI=2. Only NDV/HK84 had >80% oncolytic activities at both MOI=20 and MOI=2. We chose NDV/HK84 as the candidate virus to test the oncolytic effect of NDV in HCC in the in vitro and in vivo experiments. NDV/HK84 killed human SK-HEP-1 HCC cells without affecting healthy cells. Conclusions: Intratumor infection with NDV/HK84 strains compared with vehicle controls or positive controls indicated that NDV/HK84 strain specifically inhibited HCC without affecting healthy mice. High-throughput RNA sequencing showed that the oncolytic activity of NDV/HK84 was dependent on the activation of type I interferon signaling.

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis.

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.

An efficient multilevel thresholding image segmentation method based on the slime mould algorithm with bee foraging mechanism: A real case with lupus nephritis images.

To improve the diagnosis of Lupus Nephritis (LN), a multilevel LN image segmentation method is developed in this paper based on an improved slime mould algorithm. The search of the optimal threshold set is key to multilevel thresholding image segmentation (MLTIS). It is well known that swarm-based methods are more efficient than the traditional methods because of the high complexity in finding the optimal threshold, especially when performing image partitioning at high threshold levels. However, swarm-based methods tend to obtain the poor quality of the found segmentation thresholds and fall into local optima during the process of segmentation. Therefore, this paper proposes an ASMA-based MLTIS approach by combining an improved slime mould algorithm (ASMA),  where ASMA is mainly implemented by introducing the position update mechanism of the artificial bee colony (ABC) into the SMA. To prove the superiority of the ASMA-based MLTIS method, we first conducted a comparison experiment between ASMA and 11 peers using 30 test functions. The experimental results fully demonstrate that ASMA can obtain high-quality solutions and almost does not suffer from premature convergence. Moreover, using standard images and LN images, we compared the ASMA-based MLTIS method with other peers and evaluated the segmentation results using three evaluation indicators called PSNR, SSIM, and FSIM. The proposed ASMA can be an excellent swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of LN images, and thus the ASMA-based MLTIS method has great potential to be used as an image segmentation method for LN images. The lastest updates for the SMA algorithm are available in https://aliasgharheidari.com/SMA.html.

Exploring the Prognosis of Breast Cancer with Synchronous Distant Nonregional Lymph Node Metastasis and Establishing a Predictive Model: A Population-Based Study.

BACKGROUND: We aimed to explore the prognosis of breast cancer patients with synchronous isolated distant-lymph node metastasis (SDLNM). METHODS: We extracted information from the Surveillance, Epidemiology, and End Results Program. Kaplan-Meier and Cox regression analyses were used to compare overall survival (OS). Fine-Gray test was utilized to compare breast cancer-specific survival (BCSS). We applied propensity score matching (PSM) to balance confounders. In total, 692 SDLNM patients were allocated into training and validation cohorts. Univariate and multivariate analyses were implemented to determine independent prognostic variables. A nomogram predicting OS of SDLNM patients was constructed. Calibration curves and receiver operating characteristic curves were utilized to access the predictive model. RESULTS: Cox regression and PSM analysis showed that the prognosis of SDLNM patients was similar to breast cancer patients in stage TnN3cM0 and superior to patients with other oligometastasis (SDLNM vs. TnN3cM0, p = 0.778; SDLNM vs. other oligometastasis: HR 0.767, 95% CI, 0.672-0.875, p < 0.001). A nomogram was established to predict 1-, 3-, and 5-year OS for SDLNM patients. All C-indexes and AUCs were greater than 0.7. Calibration curves implied accurate prediction. For patients receiving mastectomy, postoperative chemotherapy and radiotherapy were significant. CONCLUSIONS: Breast cancer with SDLNM has a similar OS and BCSS with locally advanced disease. Comprehensive treatment was associated with better prognosis compared with palliative therapy. We constructed a predictive model for SDLNM breast cancer. It will be necessary to design large-scale prospective trials to confirm our results and validate the predictive model.

Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine.

Introduction: Although tuberculous pleural effusion (TBPE) is simply an inflammatory response of the pleura caused by tuberculosis infection, it can lead to pleural adhesions and cause sequelae of pleural thickening, which may severely affect the mobility of the chest cavity. Methods: In this study, we propose bGACO-SVM, a model with good diagnostic power, for the adjunctive diagnosis of TBPE. The model is based on an enhanced continuous ant colony optimization (ACOR) with grade-based search technique (GACO) and support vector machine (SVM) for wrapped feature selection. In GACO, grade-based search greatly improves the convergence performance of the algorithm and the ability to avoid getting trapped in local optimization, which improves the classification capability of bGACO-SVM. Results: To test the performance of GACO, this work conducts comparative experiments between GACO and nine basic algorithms and nine state-of-the-art variants as well. Although the proposed GACO does not offer much advantage in terms of time complexity, the experimental results strongly demonstrate the core advantages of GACO. The accuracy of bGACO-predictive SVM was evaluated using existing datasets from the UCI and TBPE datasets. Discussion: In the TBPE dataset trial, 147 TBPE patients were evaluated using the created bGACO-SVM model, showing that the bGACO-SVM method is an effective technique for accurately predicting TBPE.

An effective model for predicting serum albumin level in hemodialysis patients.

Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients.

Protective Effects of ACEI/ARB on Left Ventricular Function in Anthracycline-Induced Chronic Cardiotoxicity: A Meta-Analysis of Randomized Controlled Trials.

PURPOSE: Cardiotoxicity is an important side effect of anthracycline. Cardioprotective drugs for anthracycline remain inconclusive. We attempted to determine the role of angiotensin-converting enzyme inhibitors (ACEI) and angiotensin-receptor blockers (ARB) in the prevention of anthracycline-induced cardiotoxicity. HYPOTHESIS: Prophylactic use of ACEI/ARB reduces the clinical or subclinical cardiotoxicity of anthracycline. METHODS: Randomized controlled trials (RCTs) of ACEI/ARB in the prevention of anthracycline-induced cardiotoxicity were obtained by searching Pubmed, Embase, Web of Science, and Cochrane databases. 7 studies were finally included. A meta-analysis was performed on the 7 studies. The end points were changes in left ventricle ejection fraction (LVEF), early and late diastolic peak velocity ratio (E/A), and occurrence of hypotensive events. RESULTS: Prophylactic use of ACEI/ARB has potential benefits for anthracycline-induced cardiotoxicity. LVEF was better preserved in the experimental group than in the control group (weighted mean difference [WMD] -3.16%, 95% confidence interval [CI] [-5.78, -0.54], p = 0.02). Follow-up time, tumor type, drug type, and geographical region did not affect the results. There was no significant benefit of E/A in the experimental group (WMD 0.02, 95% CI [-0.06, 0.11], p = 0.58), and no increase in the incidence of hypotension (risk ratio 3.79, 95% CI [0.44, 32.89], p = 0.23). CONCLUSIONS: We found that prophylactic use of ACEI/ARB reduced the clinical or subclinical cardiotoxicity of anthracycline, and the increase in hypotensive events was not significant. Due to the relatively small number of clinical studies and participants, more related studies are necessary to further verify our results.

Rapid, ultrasensitive and non-enzyme electrochemiluminescence detection of hydrogen peroxide in food based on the ssDNA/g-C3N4 nanosheets hybrid.

Hydrogen peroxide (H2O2) is usually used as a fungicide in food, it is carcinogenic, accelerates aging or inducing toxic effects such as cardiovascular disease. Herein, to meet the demand for effective and fast detection of H2O2 in food, a novel non-enzymatic electrochemiluminescence (ECL) sensor based on single-stranded DNA (ssDNA)/g-C3N4 nanosheets (NS) was established. The ssDNA/g-C3N4 NS hybrid was prepared by simple mixing g-C3N4 NS and ssDNA solution together. The prepared ssDNA/g-C3N4 NS exhibited improved peroxidase-like activity and was modified on a glassy carbon electrode to catalyze the ECL reaction of luminol-H2O2 to amplify the luminescence signal. Under the optimized conditions, the proposed sensor exhibits high sensitivity with a limit of detection (LOD) as low as 33 aM H2O2, which is much lower than the vast majority of reported methods. This method enables the reliable responding to H2O2 from the milk samples within 1 min.