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Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer

  • K. Sudhakar , D. Saravanan , G. Hariharan , M. S. Sanaj , Santosh Kumar , Maznu Shaik , Jose Luis Arias Gonzales EMAIL logo and Khursheed Aurangzeb
Published/Copyright: November 30, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably slower, and in some instances, the incidence has increased. Implementing routine screenings for cervical cancer is something that has to be done to protect the health of women. Cervical cancer is famously difficult to diagnose and cure due to the slow rate at which it spreads and develops into more advanced stages of the disease. Screening for cervical cancer using a Pap smear, more often referred to as a Pap test, has the potential to detect the illness in its earlier stages. For the purpose of selecting features for this article, a gray level co-occurrence matrix (GLCM) technique was used. Following this step, classification is performed with methods such as convolutional neural network (CNN), support vector machine, and auto encoder. According to the findings of this experiment, the GLCM-CNN classifier proved to be the one with the highest degree of precision.

Keywords: cervical cancer detection; convolutional neural network; accuracy; gray level co-occurrence matrix; feature selection; pep test images

1 Introduction

Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. It is also one of the most common cancers in women. In 2008, around 530,000 new cases of obstructive cervical cancer were detected in women all over the world. As a direct result of these diagnoses, 275,000 women lost their lives to the disease. There is a significant disparity in the distribution of the disease burden across different nations due to the fact that developing countries are responsible for 86% of all cervical cancer diagnoses and 88% of all cervical cancer deaths worldwide [1]. There is a higher than average prevalence of cervix cancer in India, which accounts for one-quarter of the global total prevalence rate. This disease is present in more than 80% of the world’s countries. In recent years, the proportion of women in industrialised nations who have been given a diagnosis of cervical cancer has significantly decreased. This decline may be traced back to the direct outcome of vigorous public education programmes [2]. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably slower, and in some instances, the incidence has actually increased.

An imbalance in the rate of cell proliferation in the cervix is the primary contributor to the development of cervical cancer. Cervical cancer is an extremely rare kind of cancer. Cancer that develops in the cervix has the risk of becoming deadly. The lowest section of the uterus, often known as the belly, is referred to as the cervix in medical terminology. This structure is also known as the cervix of the uterus from time to time. It is located at the end of the uterus. The uterus, sometimes referred to as the “womb,” is the organ that is responsible for the development of an embryo into a newborn (upper half). During pregnancy, the cervix serves as a structural link between the vaginal area and the uterus. This connection is essential for the development of the baby. This connection must exist in order for the baby to be delivered successfully (birth trench). The term “endo-cervix” refers to the portion of the cervix that is located in close proximity to the inside of the uterus and is located within the cervix. There are two types of cells that may be seen on the surface of the cervix: epithelial cells and squamous cells. On the exocervix, squamous cells account for around 50% of the entire mass of the cervix. In the human body, there is an area known as the change zone that is located at the point where two separate populations of cells meet and interact with one another. The transition zone is the part of the cervix where the great majority of instances of cervical cancer originate. Malignancies that originate in the cervical area often begin in the cervix’s epithelium as the first stage of development. They do not mysteriously transform into cancerous cells all of a sudden for no apparent cause. The formation of cancer is the end consequence of precancerous modifications that take place in the normal cells of the cervix [3,4].

It is an example of a risk factor that could have a role in determining the possibility of one being afflicted with a disease such as cancer. Cancer comes in many distinct forms, and each of those forms has its own unique set of risk factors. Spending a lot of time in the sun may increase the likelihood that you may acquire skin cancer at some point in your life. People who smoke have an increased risk of developing a number of different forms of cancer. Even if a person has just one risk factor, this does not always suggest that they will get cancer as a direct result of that factor alone. When many risk factors are present, there is an increased likelihood of developing cervical cancer. When none of these risk factors are present, a woman has a very low chance of developing cervical cancer. Even while possessing these risk factors significantly increases a woman’s probability of developing cervical cancer, there are still a significant number of women who do not end up having the disease. If a woman is diagnosed with cervical cancer or pre-cancerous abnormalities in her cervix, it is possible that they will not be connected to a single risk factor with a certainty of 100%. Everyone who can quit the habit of smoking or prevent themselves from being infected with the human papillomavirus (HPV) is considered a member of this group (HPV). In spite of this, it is essential to take into consideration the risk factors that cannot be altered. This is for the simple reason that it is much more vital for women who have these risk factors to have frequent Pap tests to detect cervical cancer at an earlier stage. The stages are shown in Figure 1.

Figure 1 Cervical cancer stages.
Figure 1

Cervical cancer stages.

According to the World Health Organization (WHO), death rates and incidence rates for cervical cancer vary from country to country [5,6]. In 2008, cervical cancer was responsible for the deaths of 275,000 women all over the globe. Eighty-eight percent of the deaths took place in poor nations, with Asia being responsible for 159,800 of those deaths. When discussed in the context of medicine, cervical cancer is sometimes described as an infection of the singularity. According to the findings of the recent study, the created world has a greater rate of cervical cancer, which also has a higher mortality rate. According to the Crisis Card, the greatest rate of death in the world may be found in Africa. The number of women in Australia who have died due to cervical cancer is at an all-time low. One reason for this is the widespread use of HPV antibodies, treatment, and public expectations.

In the United Kingdom, cervical cancer accounts for around 2% of all newly diagnosed instances of cancer in females, placing it as the tenth most prevalent illness in females. In 2010, 2,851 women in the United Kingdom were diagnosed with cervical cancer. According to the yearly incidence rate, there are around nine newly diagnosed cases of cervical cancer for every one hundred thousand females in the United Kingdom. In this computation, unprocessed incidence rates are utilised. The International Agency for Cancer Research estimates that yearly 275,000 women lose their lives as a result of cervical cancer. In addition, it is the most common cause of mortality from cancer in women all over the globe (266,000 deaths in 2012) [7,8].

To safeguard the health of women, it is imperative that tests for cervical cancer become standard practice and become part of regular healthcare. A big additional barrier to the physical and mental well-being of modern women is the manner in which they live their life, the food that they consume, and the way that they behave in the current society. Due to the sluggish pace at which it spreads and develops into more severe stages of the illness, cervical cancer is notoriously difficult to identify and treat until it has progressed to an advanced stage. The screening for cervical cancer known as a Pap smear, which is more often referred to as a Pap test, offers the ability to identify the condition in its earliest stages. There is a multi-step process that must be completed before one may detect anything and then decide what that detection means. It is imperative that you get the advice of an expert before deciding on a certain plan of action. Doing so will ensure that you make the best decision possible. Experience is much more significant than intellectual aptitude when it comes to determining the best course of action to take in a given situation. To be able to determine if a slide of pap smear results includes positive or negative cells, one has to possess the specialised knowledge of microscopy as well as the capacity to put that information into practice. Due to the fact that there are seven different kinds of cells, the task at hand is far more difficult than it would be otherwise if there were just two different kinds of cells.

Image processing algorithms have come a long way in recent years, which has been of considerable help to modern machine vision systems. On the other hand, machine learning has made possible a whole new set of approaches to the resolution of problems. In circumstances in which the application of human thought is unable to solve a problem, a learning algorithm may be used to train a computer on how to discover a solution on its own to solve the issue. This is the objective that is sought for by the machine vision field. It is possible that the rule-based systems that were in existence in the past, which were formed via the use of hand-crafted tools and processes and were based on characteristics that were observed, were able to sort through obstacles and arrive at conclusions. On the other hand, the program does not cope with potentially dangerous situations since it is unable to react to them or discover a solution to them. Because of this, adaptive learning would not be possible until computers are able to learn from their own mistakes. Until then, it would not be possible. On the other hand, the term “machine learning” refers to the process by which a computer imitates human intelligence by learning about a problem and various aspects of the problem’s underlying knowledge to use that information to solve issues in real time and arrive at a final solution to the problem. In other words, a computer mimics human intelligence by learning about a problem and various aspects of the problem’s underlying knowledge. The term for this kind of activity is “machine learning.”

Gray level co-occurrence matrix (GLCM) is used to extract features. GLCM shows space and grey size distribution in an area. This example uses only four of many texture options. ECH – energy, contrast, correlation, and homogeneity – is their acronym. The photo shows these features. When analysing stochastic textures, the GLCM is typically utilised. This version improves the image and adds explanation. A photo can be analysed using the GLCM metric with various pixel brightness values. For instance, the co-occurrence matrix shows that two pixels might appear in several spatial orientations based on their distance and angular spatial relationships. Image texture, colour, contrast, and shape are obtained using the GLCM. Texture analysis is very important in machine learning and human visual perception. It emphasizes key diagnostic system components to demonstrate its reliability. GLCM texturing is vital for image processing and used frequently.

Deep learning is reliant on the representation of data acquired from the real world so that it can automate the learning process. This dependency is for the aim of making deep learning more efficient. It is more challenging to design specialised software when a picture comprises additional items in addition to the one that has to be found. This is because there are more things for the program to take into consideration. To find anything inside an image, one has to have a broad notion of where that object is positioned in relation to the rest of the picture. This is necessary to find anything. There is a possibility that the deep learning technique, which is one that makes use of neural networks (NNs), will be used here. The concept of “deep learning” has been receiving a significant amount of attention in the mainstream media as of late.

For the purpose of selecting features for this article, a GLCM technique was used. It is possible to obtain high-level information about a picture by employing the GLCM, such as the image’s texture, colour, contrast, and shape. In addition, both human visual perception and machine learning make extensive use of texture analysis as an important component in their respective processes. It is a useful instrument for demonstrating the reliability of the diagnostic system by directing attention to the various essential parts of the system. The textural feature that goes by the name GLCM is one that is vital for image analysis and is utilised quite frequently. Following this step, classification is performed with methods such as convolutional neural network (CNN), support vector machine (SVM), and auto encoder. The results of this experiment show that GLCM-CNN had the highest accuracy of all the classifiers that were examined.

2 Literature survey

Alyafeai and Ghouti [9] discovered and differentiated malignant spots in cervical pictures by making use of a classification strategy that relied on deep learning. Through the use of this approach, cancerous regions were identified. Deep learning is the term that describes this kind of instruction. CNNs were used by researchers to recognise photographs of the cervical region to make a cancer diagnosis. Morphological processes were used so that the cancerous parts of cervical pictures could be identified and segmented. In this particular instance, cancerous cells taken from the cervix were utilised. The authors’ ground truth images were accurate 97.2% of the time, 98.3% of the time, and 98.5% of the time, according to the Guanacase Dataset 2005.

CNNs are a type of artificial intelligence that can be used to classify cervical cell segmentations. Allehaibi [10] presented the Mask R-CNN, which is a visual geometry group-like network with a more condensed training set (VGG-like Net). The network that makes up the Mask R-neural CNN is constructed on top of ResNet10, which gives it the ability to make use of both spatial input and information that it has previously learned. The data from the Herlev Pap Smear are utilised to validate our methodology. The preceding segmentation technique is surpassed by Mask R-CNN in terms of performance metrics such as accuracy (0.910.05), recall (0.910.05), and ZSI (0.91).

According to Gupta et al. [11], for instance, data mining has the potential to be used to assist in the prevention of cervical cancer. Researchers were able to narrow down the potential risk factors for the development of cancer in patients by using the Boruta analysis. All of the demographic, historical, and medical aspects of this study are taken into account in this research. The “Hospital Universitario de Caracas” in Caracas did not make the “missing values” in this dataset of 858 patients available to the general public because the hospital wanted to protect the patients’ right to privacy. The construct of an evaluation of the same thing may be constructed using the criteria that were supplied, provided that they are relevant.

Treatment of the virus that causes the sickness was shown to be an effective technique for preventing the development of cervical cancer in patients, according to Deswal et al. research [12]. Because there are many different ways in which cervical cancer may spread, there are also many different strategies to diagnose and treat it at an early stage.

Karthiga Jaya and Senthil Kumar [13] identified and isolated regions of photos harbouring cervical cancer by using the artificial neural network fuzzy inference system (ANFIS) classification approach. Researchers were able to classify cervical photos by making use of an image registration strategy and an ANFIS classification method. Both of these techniques matched non-correlated photographs and made it possible for them to be categorised for cancer diagnosis. Therefore, it is feasible to detect and separate the cervical malignant patches using the morphological methodologies that are utilised to categorise images of cancer’s effects. When the authors’ methodology was compared against images taken on the ground, the Guanacase Dataset 2005 was used, which resulted in the discovery of an accuracy rate of 99.12%. The authors’ methodology had an average sensitivity, specificity, and accuracy rate of 97.42%.

Anousouya Devi et al. [14] looked at photographs of the cervical region by doing calculations based on the structure of the nervous system. The developers of the plan connected their computation to the district development division plan so that the results would be more accurate when applied to their proposed cancer division.

It was proposed by Kumar and Srivastava [15] that a method may be established to determine the reason why the cervixes of certain women are covered with unusual growths. The cervical regions of the patients were scanned via computer-aided detection, which stands for computer-assisted detection. There is potential for improvements in the accuracy of cervical cancer diagnosis to be made via the use of soft computing and computer-aided methods. When compared to the photographs that served as the ground truth, they found that their method had a sensitivity of 86% and a tumour segmentation accuracy of 91%.

Pap smear cell screening was shown to be the most effective method for detecting cervical cancer in female patients in a study that was conducted by Chen et al. [16]. On the image of the cervical region, an SVM classifier was utilised to extract information on the picture’s surface characteristics and lifelikeness. The authors had a Pap smear cell classification percentage of 96.12% on average across all of their samples. As a direct consequence of this, the identification of the great majority of cells was successful. You are going to want to use this procedure with high-quality photographs to get the most out of it.

The categorisation of cervical pictures was accomplished by the use of feature extraction, which was carried out by Mouelhi et al. [17]. The authors were successful in obtaining information on the texture and gradient of both healthy and diseased populations. These attributes were trained and classified with the help of subfold classifiers in preparation for their usage in machine learning applications.

When trying to detect whether a patient had cervical cancer, Bergmeir et al. [18] utilised a classification system that was based on feature extraction. To train the texture and energy characteristics that were obtained from the cervical picture, they used a technique that is known as quasi supervised learning. In the dataset that was utilised to evaluate the effectiveness of the proposed method, cervical photographs that had previously been seen as well as those that had not been seen before were included. Based on the 132 cervical photos, there were only 19% that were considered to be false positives, which resulted in an 88.0% true positive rate.

Classifiers based on SVM were created by Huang and Lai [19] for the early diagnosis of cervical cancer. In the beginning, the researchers began by using image analysis tools to extract textural characteristics from histopathology photos. These attributes were used in the training and classification processes of the SVM classifier. Researchers had a success rate of 92.8% when it came to accurately classifying instances of cervical cancer.

The innovative method that Chen et al. have devised [20] makes it feasible to perform cell segmentation in the cervical regions of patients. In the light of the radiologist’s previously segmented nucleus areas, the characteristics of the nucleus that had been segmented were analysed. By adhering to the procedures that the authors advocated, the authors were able to achieve an overall accuracy in categorisation that was 93% on average.

Researchers looked at numerous different classification approaches, such as the one developed by Rama Praba and Ranganathan [21], to determine how to detect and classify cervical pictures. Researchers enhanced cervical imaging and classified the acquired textural information using SVM and NNs. Within the scope of this investigation, extracted characteristics and NN classification were combined to accomplish the goal of achieving the highest possible classification accuracy of 89%.

3 Methodology

This section presents optimised feature selection-driven CNN using GLCM for the detection of cervical cancer. It is shown in Figure 2. The GLCM approach for feature selection is included in this methodology section. After that, classification is carried out using techniques such as CNN, SVM, and auto encoder.

Figure 2 Optimised feature selection-driven CNN using gray level co-occurrence matrix for detection of cervical cancer.
Figure 2

Optimised feature selection-driven CNN using gray level co-occurrence matrix for detection of cervical cancer.

To extract features, GLCM [22] is used. The GLCM is a representation of the space and grey size distribution in a certain location. Only four of the many different texture possibilities are used in this particular illustration. In summary, they are referred to as ECH, which stands for energy, contrast, correlation, and homogeneity. These traits may be gleaned from the photo. The GLCM is one of the strategies that is used rather often when stochastic textures are being analysed (GLCM). In this version, the image has been improved, and the explanation has been included. When analysing a photo using the GLCM metric, there are several distinct permutations of the pixel brightness values that may be used. According to the calculation of the co-occurrence matrix, for instance, two pixels may simultaneously exist in a variety of spatial orientations with regard to the distance and angular spatial interactions between the two pixels. The GLCM is used to get high-level information about an image, such as its texture, colour, contrast, and shape. In addition, both human visual perception and machine learning make significant use of texture analysis as a key component. It is an effective tool for showing the soundness of the diagnostic system by focusing attention on important components. The texture characteristic known as GLCM is an essential one for image analysis and is used rather often.

Auto-encoders are a kind of unsupervised NN that attempts to copy input to output. Auto encoders are used in datasets to cut down on the total number of dimensions included in the data. AEs are constructed by using NNs’ encoder and decoder layers. The encoder layer is the one that is responsible for taking in the image data points. The encoder function will use this input to produce a hidden representation space denoted by the letter z. Encoding and decoding a Gaussian distribution allows for the calculation of the distribution’s mean as well as its variance. Because of this, a decoder takes the latent representation into consideration as an input and then generates distribution parameters for the data points that are chosen from the input [23,24].

It is possible to use SVM to classify both linear and nonlinear data. It does this by first using a nonlinear mapping to elevate the original training data up a dimensionality inside this new dimension. After doing this, it searches for the linear optimal separation hyper plane and decision boundary that divides tuples of one class from those of another class. An example of a constructive learning algorithm is the SVM, which is based on statistical learning theory and the Vapnik Chervonenkis dimensions. This strategy, which is founded on the principle of structural risk reduction, places its major emphasis not on the square of the mean square error over the whole dataset but rather on the generalisation error. Because of this mistake, the likelihood of these mistakes having a significant impact on the situation is significantly increased. If the SVM is able to generalise well to data that were not included in the training set, then it will have a good performance when it is applied to data that were not included in the training set. A clear and succinct explanation for the learned model was found as a direct consequence of employing the SVM. Even if the data used for training SVMs are biased, the models’ flexibility and resilience will continue to improve. Elements in the training set that are located close to or inside the decision limits of the classifier are referred to as support vectors. The classifier is able to form its decision lines thanks to the use of these support vectors in the decision-making process. The classifier is able to differentiate between normal and abnormal by making use of examples that are on the verge of falling into either category. A hyperplane may be used to construct SVMs by partitioning many data sets in the appropriate manner. Because hyper planes are created, the SVM is able to recognise the boundaries between input class categories. The establishment of these boundaries is the responsibility of the individual bits of input data that are together referred to as support vectors.

It is a common practise for computer vision technologies to make use of CNN, which is a kind of architecture for a deep NN. A CNN is composed of many layers, the most important of which are the convolutional, activation, pooling, and connected convolutional layers. Each convolutional layer of a deep CNN is linked to the subsequent layers in the network and so on. The filters in the convolutional layer are the most important component of that layer. A very small number of pixels from the input picture, maybe only three by three in size, are subjected to the filter that the convolutional layer employs [25].

The pixel values are processed by the filter using a dot operation, and the weights for each dot are supplied individually. To obtain a single output that is representative of all of the pixels that were entered into the filter, the sum of all of the weights is added together and utilised. As a direct result of this, the picture that is produced by the convolutional layer has a lot smaller matrix of data points than it had when it was first created. After the activation layer, which offers nonlinearity, has been provided to the activation layer, backpropagation is used to train the network. Backpropagation is used to train the network.

Sample production is cut down due to the layer-down sample pooling technique, which also results in the matrix of the filter being of a smaller size. It is referred to as the “max layer” when the pooling layer chooses the most essential feature from each group and then picks that characteristic. Calculating the label probabilities of an image may be accomplished by utilising the output of a linked layer that is connected to the maximum layers of the picture. The classification is done based on the response that is most likely to occur.

4 Result analysis and discussion

The Guanacaste collection was used to obtain the cervical photographs that were analysed for this inquiry [26]. This dataset has been annotated by experienced radiologists with images that depict the actual findings of each cervical scan. These photographs represent the ground truth. The investigation makes use of a total of 250 photographs. A model is initially taught with 175 unique photos, and then it is evaluated with 75 unique photographs. This component of the methodology includes a way for selecting features called the GLCM. By using this method, you can choose which characteristics to use. A GLCM [22] is utilised to extract features from a given dataset. A depiction of the space and grey size distribution in a particular area can be found in the GLCM. Following this step, classification is performed with methods such as CNN, SVM, and auto encoder. Encoder and decoder layers, which are found in NNs, are utilised in the construction of AEs. A CNN, often known as a CNN, is made up of multiple layers, the convolutional, activation, pooling, and connected convolutional layers being the most crucial ones. Every single convolutional layer that makes up a deep CNN is connected to the layers that come after it in the network and so on. The filters that make up the convolutional layer are the most vital part of that layer.

Four parameters such as accuracy, sensitivity, specificity, and precision are used in this study to compare the performance of different algorithms.

  • Accuracy

    The proportion of all predictions that are predicted correctly (accurately) is referred to as accuracy.

    Accuracy = ( TP + TN ) / ( TP + TN + FP + FN ) .

  • Sensitivity

    The proportion of positive cases projected as positive is known as sensitivity. It is also known as “Recall.”

    Sensitivity = ( TP ) / ( TP + FN ) .

  • Specificity

    The proportion of actual negative cases projected as negative is known as sensitivity.

    Specificity = ( TN ) / ( TN + FP ) .

  • Precision

Precision = TP / ( TP + FP ) ,

where TP is true positive, TN is true negative, FP is false positive, and FN is false negative.

Figures 3–6 illustrate the accuracy, sensitivity, specificity, and precision of the GLCM-SVM, GLCM-auto encoder, and GLCM-CNN in detecting cervical cancer. The GLCM-CNN classifier had the highest accuracy of all the classifiers that were utilised in this experimental investigation. The accuracy of the GLCM-CNN has been measured at 99%; this is 3% better than the accuracy of the GLCM-SVM and 10% better than the accuracy of the GLCM-auto encoder. The sensitivity of the GLCM-CNN is also 98%, which is a larger percentage than the sensitivity of the GLCM-auto encoder and the GLCM-encoder combined. The specificity of the GLCM-CNN is 97%, which is higher than the specificity of both the GLCM-auto encoder and the GLCM-CNN. The GLCM-CNN, the GLCM-auto encoder, and the GLCM-SVM all have a precision of 99%.

Figure 3 Accuracy of classifiers for cervical cancer detection.
Figure 3

Accuracy of classifiers for cervical cancer detection.

Figure 4 Sensitivity of classifiers for cervical cancer detection.
Figure 4

Sensitivity of classifiers for cervical cancer detection.

Figure 5 Specificity of classifiers for cervical cancer detection.
Figure 5

Specificity of classifiers for cervical cancer detection.

Figure 6 Precision of classifiers for cervical cancer detection.
Figure 6

Precision of classifiers for cervical cancer detection.

5 Conclusion

Cervical cancer is one of the deadliest and most common forms of cancer that affects women all over the world, but mainly in East Africa and South Asia. In addition, it is one of the tumours that affects women at the highest rate. The number of people diagnosed with cervical cancer has been steadily declining over the course of the last several decades in industrialised countries. In contrast, the rate of incidence decline has been much more gradual in emerging nations; indeed, there is some evidence to suggest that the incidence rate may even be rising in certain cases. To safeguard the health of women, it is imperative that tests for cervical cancer become standard practise and become part of regular healthcare. Due to the sluggish pace at which it spreads and develops into more severe stages of the illness, cervical cancer is notoriously difficult to identify and treat until it has progressed to an advanced stage. The screening for cervical cancer known as a Pap smear, which is more often referred to as a Pap test, offers the ability to identify the condition in its earliest stages. There is a multi-step process that must be completed before one may detect anything and then decide what that detection means. A method called a GLCM was used for the aim of picking characteristics for this piece of writing. After completing this stage, the classification process is carried out using techniques such as CNN, SVM, and auto encoder. The results of this experiment demonstrated that the GLCM-CNN classifier was the one that achieved the best level of accuracy.

  1. Funding information: Authors state no funding involved.

  2. Conflict of interest: Authors state no conflict of interest.

  3. Data availability statement: The datasets generated during and/or analysed during the current study are available in the Guanacaste dataset 2005, available at: http://www.cse.lehigh.edu/idealab/cervitor/downloads.htm.

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Received: 2023-08-05
Revised: 2023-10-09
Accepted: 2023-10-23
Published Online: 2023-11-30

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0770
Keywords for this article
cervical cancer detection; convolutional neural network; accuracy; gray level co-occurrence matrix; feature selection; pep test images
Creative Commons
BY 4.0

Articles in the same Issue

  1. Biomedical Sciences
  2. Systemic investigation of inetetamab in combination with small molecules to treat HER2-overexpressing breast and gastric cancers
  3. Immunosuppressive treatment for idiopathic membranous nephropathy: An updated network meta-analysis
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  26. Experimental study on the optimization of ANM33 release in foam cells
  27. Primary retroperitoneal angiosarcoma: A case report
  28. Metabolomic analysis-identified 2-hydroxybutyric acid might be a key metabolite of severe preeclampsia
  29. Malignant pleural effusion diagnosis and therapy
  30. Effect of spaceflight on the phenotype and proteome of Escherichia coli
  31. Comparison of immunotherapy combined with stereotactic radiotherapy and targeted therapy for patients with brain metastases: A systemic review and meta-analysis
  32. Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation
  33. Association between the VEGFR-2 -604T/C polymorphism (rs2071559) and type 2 diabetic retinopathy
  34. The role of IL-31 and IL-34 in the diagnosis and treatment of chronic periodontitis
  35. Triple-negative mouse breast cancer initiating cells show high expression of beta1 integrin and increased malignant features
  36. mNGS facilitates the accurate diagnosis and antibiotic treatment of suspicious critical CNS infection in real practice: A retrospective study
  37. The apatinib and pemetrexed combination has antitumor and antiangiogenic effects against NSCLC
  38. Radiotherapy for primary thyroid adenoid cystic carcinoma
  39. Design and functional preliminary investigation of recombinant antigen EgG1Y162–EgG1Y162 against Echinococcus granulosus
  40. Effects of losartan in patients with NAFLD: A meta-analysis of randomized controlled trial
  41. Bibliometric analysis of METTL3: Current perspectives, highlights, and trending topics
  42. Performance comparison of three scaling algorithms in NMR-based metabolomics analysis
  43. PI3K/AKT/mTOR pathway and its related molecules participate in PROK1 silence-induced anti-tumor effects on pancreatic cancer
  44. The altered expression of cytoskeletal and synaptic remodeling proteins during epilepsy
  45. Effects of pegylated recombinant human granulocyte colony-stimulating factor on lymphocytes and white blood cells of patients with malignant tumor
  46. Prostatitis as initial manifestation of Chlamydia psittaci pneumonia diagnosed by metagenome next-generation sequencing: A case report
  47. NUDT21 relieves sevoflurane-induced neurological damage in rats by down-regulating LIMK2
  48. Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis
  49. Exosomal HBV-DNA for diagnosis and treatment monitoring of chronic hepatitis B
  50. Shear stress leads to the dysfunction of endothelial cells through the Cav-1-mediated KLF2/eNOS/ERK signaling pathway under physiological conditions
  51. Interaction between the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count in rats with LPS-induced pulmonary infection
  52. Meta-analysis of the rs231775 locus polymorphism in the CTLA-4 gene and the susceptibility to Graves’ disease in children
  53. Cloning, subcellular localization and expression of phosphate transporter gene HvPT6 of hulless barley
  54. Coptisine mitigates diabetic nephropathy via repressing the NRLP3 inflammasome
  55. Significant elevated CXCL14 and decreased IL-39 levels in patients with tuberculosis
  56. Whole-exome sequencing applications in prenatal diagnosis of fetal bowel dilatation
  57. Gemella morbillorum infective endocarditis: A case report and literature review
  58. An unusual ectopic thymoma clonal evolution analysis: A case report
  59. Severe cumulative skin toxicity during toripalimab combined with vemurafenib following toripalimab alone
  60. Detection of V. vulnificus septic shock with ARDS using mNGS
  61. Novel rare genetic variants of familial and sporadic pulmonary atresia identified by whole-exome sequencing
  62. The influence and mechanistic action of sperm DNA fragmentation index on the outcomes of assisted reproduction technology
  63. Novel compound heterozygous mutations in TELO2 in an infant with You-Hoover-Fong syndrome: A case report and literature review
  64. ctDNA as a prognostic biomarker in resectable CLM: Systematic review and meta-analysis
  65. Diagnosis of primary amoebic meningoencephalitis by metagenomic next-generation sequencing: A case report
  66. Phylogenetic analysis of promoter regions of human Dolichol kinase (DOLK) and orthologous genes using bioinformatics tools
  67. Collagen changes in rabbit conjunctiva after conjunctival crosslinking
  68. Effects of NM23 transfection of human gastric carcinoma cells in mice
  69. Oral nifedipine and phytosterol, intravenous nicardipine, and oral nifedipine only: Three-arm, retrospective, cohort study for management of severe preeclampsia
  70. Case report of hepatic retiform hemangioendothelioma: A rare tumor treated with ultrasound-guided microwave ablation
  71. Curcumin induces apoptosis in human hepatocellular carcinoma cells by decreasing the expression of STAT3/VEGF/HIF-1α signaling
  72. Rare presentation of double-clonal Waldenström macroglobulinemia with pulmonary embolism: A case report
  73. Giant duplication of the transverse colon in an adult: A case report and literature review
  74. Ectopic thyroid tissue in the breast: A case report
  75. SDR16C5 promotes proliferation and migration and inhibits apoptosis in pancreatic cancer
  76. Vaginal metastasis from breast cancer: A case report
  77. Screening of the best time window for MSC transplantation to treat acute myocardial infarction with SDF-1α antibody-loaded targeted ultrasonic microbubbles: An in vivo study in miniswine
  78. Inhibition of TAZ impairs the migration ability of melanoma cells
  79. Molecular complexity analysis of the diagnosis of Gitelman syndrome in China
  80. Effects of maternal calcium and protein intake on the development and bone metabolism of offspring mice
  81. Identification of winter wheat pests and diseases based on improved convolutional neural network
  82. Ultra-multiplex PCR technique to guide treatment of Aspergillus-infected aortic valve prostheses
  83. Virtual high-throughput screening: Potential inhibitors targeting aminopeptidase N (CD13) and PIKfyve for SARS-CoV-2
  84. Immune checkpoint inhibitors in cancer patients with COVID-19
  85. Utility of methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses
  86. Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma
  87. Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy
  88. DUSP2 inhibits the progression of lupus nephritis in mice by regulating the STAT3 pathway
  89. Lung abscess by Fusobacterium nucleatum and Streptococcus spp. co-infection by mNGS: A case series
  90. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis
  91. Granulomatous polyangiitis involving the fourth ventricle: Report of a rare case and a literature review
  92. Studying infant mortality: A demographic analysis based on data mining models
  93. Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review
  94. Protein Z modulates the metastasis of lung adenocarcinoma cells
  95. Inhibition of pyroptosis and apoptosis by capsaicin protects against LPS-induced acute kidney injury through TRPV1/UCP2 axis in vitro
  96. TAK-242, a toll-like receptor 4 antagonist, against brain injury by alleviates autophagy and inflammation in rats
  97. Primary mediastinum Ewing’s sarcoma with pleural effusion: A case report and literature review
  98. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents
  99. Tanshinone IIA alleviates chondrocyte apoptosis and extracellular matrix degeneration by inhibiting ferroptosis
  100. Study on the cytokines related to SARS-Cov-2 in testicular cells and the interaction network between cells based on scRNA-seq data
  101. Effect of periostin on bone metabolic and autophagy factors during tooth eruption in mice
  102. HP1 induces ferroptosis of renal tubular epithelial cells through NRF2 pathway in diabetic nephropathy
  103. Intravaginal estrogen management in postmenopausal patients with vaginal squamous intraepithelial lesions along with CO2 laser ablation: A retrospective study
  104. Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients
  105. Effects of physical exercise on biomarkers of oxidative stress in healthy subjects: A meta-analysis of randomized controlled trials
  106. Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
  107. Development of an instrument-free and low-cost ELISA dot-blot test to detect antibodies against SARS-CoV-2
  108. Research progress on gas signal molecular therapy for Parkinson’s disease
  109. Adiponectin inhibits TGF-β1-induced skin fibroblast proliferation and phenotype transformation via the p38 MAPK signaling pathway
  110. The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma
  111. α-Fetoprotein contributes to the malignant biological properties of AFP-producing gastric cancer
  112. CXCL12/CXCR4/CXCR7 axis in placenta tissues of patients with placenta previa
  113. Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
  114. Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction
  115. Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression
  116. Research progress of AMP-activated protein kinase and cardiac aging
  117. TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5
  118. Cross-talk between gut microbiota and liver steatosis: Complications and therapeutic target
  119. Metastasis from small cell lung cancer to ovary: A case report
  120. The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury
  121. The effect of NK cell therapy on sepsis secondary to lung cancer: A case report
  122. Erianin alleviates collagen-induced arthritis in mice by inhibiting Th17 cell differentiation
  123. Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
  124. Signalling pathways in the osteogenic differentiation of periodontal ligament stem cells
  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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