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.

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.

Aptamer based electrochemiluminescent determination of bisphenol A by using carboxylated graphitic carbon nitride.

An electrochemiluminescence (ECL) based assay is described for the determination of the endocrine disruptor bisphenol A (BPA). The method is based on the use of carboxylated graphitic carbon nitride (C-g-C3N4) carrying an immobilized aptamer against BPA. In the presence of BPA, the ECL signal decreases due to ECL energy transfer from excited-state C-g-C3N4 to the BPA oxidation product. Under the optimal conditions, ECL intensity increases linearly in the 0.1 pM to 1 nM BPA concentration range. The detection limit is as low as 30 fM. The assay has excellent sensitivity, outstanding stability and high selectivity. It was applied to the determination of BPA in spiked water samples. Graphical abstract Aptamer modified carboxylated graphitic carbon nitride was synthesized and applied in an electrochemiluminescence-based aptasensor for bisphenol A.

A fluorometric aptasensor for bisphenol a based on the inner filter effect of gold nanoparticles on the fluorescence of nitrogen-doped carbon dots.

An aptamer-based fluorometric assay is described for the determination of bisphenol A (BPA). The aptamer against BPA is first attached to the surface of the red AuNPs, and this prevents the AuNPs from salt-induced formation of a blue-colored aggregate. Hence, the blue fluorescence of added nitrogen-doped carbon dots (NCDots) is quenched via an inner filter effect (IFE) caused by the red AuNPs. After addition of BPA, the BPA/aptamer complex is formed, and the AuNPs are no longer stabilized agains aggregation. This weakens the IFE and results in the recovery of the fluorescence of the NCDots which is measured best at excitation/emission wavelengths of 300/420 nm. The recovered fluorescence increases linearly in the 10 to 250 nM and 250 to 900 nM BPA concentration ranges, and the detection limit is 3.3 nM. The method was successfully applied to the determination of BPA in spiked environmental tap water samples. Graphical abstract Schematic presentation of a fluorometric aptamer based assay for bisphenol A (BPA). It is based on the inner filter effect of gold nanoparticles (AuNPs) on the fluorescence of nitrogen-doped carbon dots (NCDots).

Magnetic mixed hemimicelles solid-phase extraction coupled with high-performance liquid chromatography for the extraction and rapid determination of six fluoroquinolones in environmental water samples.

In this study, a mixed hemimicelle solid-phase extraction method based on Fe3 O4 nanoparticles coated with sodium dodecyl sulfate was applied for the preconcentration and fast isolation of six fluoroquinolones in environmental water samples before high-performance liquid chromatography determination. The main factors affecting the extraction efficiency of the analytes, such as amount of surfactant, amount of Fe3 O4 nanoparticles, extraction time, sample volume, sample pH, ionic strength, and desorption conditions, were investigated and optimized. The method has detection limits from 0.05 to 0.1 ng/mL and good linearity (r ≥ 09948) in the range 0.1-200 ng/mL depending on the fluoroquinolone. The enrichment factor is ∼200. The recoveries (at spiked levels of 1, 5, and 50 ng/mL) are in the range of 79-120%.

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.

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.

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.

IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection.

Rime optimization algorithm (RIME) encounters issues such as an imbalance between exploitation and exploration, susceptibility to local optima, and low convergence accuracy when handling problems. This paper introduces a variant of RIME called IRIME to address these drawbacks. IRIME integrates the soft besiege (SB) and composite mutation strategy (CMS) and restart strategy (RS). To comprehensively validate IRIME's performance, IEEE CEC 2017 benchmark tests were conducted, comparing it against many advanced algorithms. The results indicate that the performance of IRIME is the best. In addition, applying IRIME in four engineering problems reflects the performance of IRIME in solving practical problems. Finally, the paper proposes a binary version, bIRIME, that can be applied to feature selection problems. bIRIMR performs well on 12 low-dimensional datasets and 24 high-dimensional datasets. It outperforms other advanced algorithms in terms of the number of feature subsets and classification accuracy. In conclusion, bIRIME has great potential in feature selection.

N-acetylglucosamine biofunctionalized CdSeTe quantum dots as fluorescence probe for specific protein recognition.

Hsp70 proteins are implicated in resistance to chemotherapy in cancers, the detection of which is important for cancer treatment and prognosis. In this work, we report the study on the detection of specific intracellular target protein in fixed cells using GlcNAc-conjugated CdSeTe QDs. The QDs were coupled with Con A via a carbodiimide reaction and then were further assembled with GlcNAc by lectin-carbohydrate interaction between Con A and GlcNAc. The obtained QDs-Con A-GlcNAc conjugates have an emission wavelength at 650 nm that is close to the near-infrared (NIR) regions and a specific recognition for Hsp70. These results show that the QDs-Con A-GlcNAc probe can be a promising tool for direct localization of the Hsp70 protein.

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.

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.

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.

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.

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.