Startseite Embedded monitoring system and teaching of artificial intelligence online drug component recognition
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Embedded monitoring system and teaching of artificial intelligence online drug component recognition

  • Li Ding , Zhengrong Wu , Junmin Zhang , Quanyi Zhao , Xiaoling Chen EMAIL logo , Zhong Jia und Dian He EMAIL logo
Veröffentlicht/Copyright: 11. Juni 2024
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Open Life Sciences
Aus der Zeitschrift Open Life Sciences Band 19 Heft 1

Abstract

Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market and ensure drug safety. The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the use of the monitoring system. With the continuous development of computer and measurement technologies, various biochemical data are increasing at an unprecedented speed, and numerous databases are emerging. Extracting patterns from considerable known data and experimental facts is an essential task for a wide range of biological and chemical workers. Pattern recognition is one of the essential technologies for data mining. It is widely used in industry, agriculture, national defense, biomedicine, meteorology, astronomy, and other fields. To improve the effect of the online drug ingredient recognition system, this study used AI to design an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study constructed the framework of the AI online drug ingredient recognition-embedded monitoring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern recognition system, and presented the pattern recognition algorithm and the algorithm evaluation index. Afterward, it used pattern recognition to conduct a qualitative analysis of the infrared spectrum of drug components and introduced the overall process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universities, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensitivity, specificity, and Matthew correlation coefficient of the online drug component recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85. Students in five majors reported high levels of satisfaction with the embedded system teaching, which is better for delivering college instruction.

Keywords: drug composition identification; embedded monitoring system; drug teaching; artificial intelligence

1 Introduction

With the rapid development of modern spectral analysis technology, the analysis and identification of effective components have become more practical than before. Present drug analysis technology mainly includes microbiology, high-performance liquid chromatography, and infrared spectroscopy. The microbiological method is to use the inhibition of drugs on the growth of microorganisms. High-performance liquid chromatography is to transport a specific liquid dissolved in a test substance to a chromatographic column for separation and measurement through a high-pressure transmission system. Infrared spectroscopy is a widely used analytical method that analyzes substances by measuring the interaction between substances and spectra.

Many studies have been conducted on drug recognition. Khan et al. adopted a drug reuse method to identify 3C-like protease and 2′-O-ribomethyltransferase inhibitors [1]. Chang et al. used single-cell RNA sequencing to track differential cloning reactions [2]. Islam et al. performed molecular simulation to identify the best candidate for antiviral phytochemicals that can be used as effective inhibitors of major protease [3]. Drug identification is an essential step in drug research, but studies on drug identification are not deep enough and need to be further strengthened.

Artificial intelligence (AI) has been applied in drug research. Zeng et al. provided a powerful network-based deep learning method for target recognition to accelerate drug reuse [4]. Zhao et al. used convolutional and deep neural networks to identify drug target interaction [5]. Kobylka et al. outlined the molecular basis of drug inhibitor binding, including insights into the mechanism of AcrB in drug efflux. They also summarized mutation analysis of the acrB gene and reported the impact of each substitution on antibiotic resistance in Escherichia coli [6]. Gupta et al. studied the application of machine and deep learning algorithms in drug design and development [7]. Magge et al. evaluated the role of the deep learning pharmacovigilance pipeline in adverse drug events [8]. Dara et al. reviewed the application of machine learning tools and technologies in drug discovery [9]. Chandrasekaran et al. studied the role of image analysis in drug discovery [10]. Wang applied computer-aided drug design technology to identify promising drug reuse candidates quickly [11]. Monteiro et al. examined how drugs can be used in animal clinical trials to successfully identify and treat pain in animals [12]. However, these scholars did not consider privacy and security issues, as well as the excessive reliance on AI, in drug testing. The system designed in this study encrypts data to prevent data leakage, and automatic analysis technology for the chemical composition of drugs is added to the system. AI systems are regularly evaluated and validated to ensure their accuracy and reliability.

To improve the effectiveness of online drug component recognition systems, this study used AI to conduct online drug component recognition research. First, this study constructed an online drug component recognition-embedded monitoring system and a pattern recognition system. Then, it used the pattern recognition method to analyze the infrared spectrum of drug components qualitatively. Finally, this work introduced the shortcomings and improvement methods of embedded system teaching under the background of AI. The overall structure of this study is shown in Figure 1.

Figure 1 Overall structure of this study.
Figure 1

Overall structure of this study.

2 Embedded monitoring system for online drug component recognition based on AI

The embedded monitoring system of AI online drug ingredient recognition is shown in Figure 2.

Figure 2 AI online drug ingredient recognition-embedded monitoring system.
Figure 2

AI online drug ingredient recognition-embedded monitoring system.

The monitoring system consists of the following modules: user connection, information upload, real-time monitoring, data receiving, data processing, general control, and information transmission. After detecting the user information, the user connection module activates the connection. With the help of the information download module, users can download standard drug information. Information on the real-time monitoring of drugs is gathered using this module. The data receiving module is used to receive the standard information about the drug that was downloaded from the information download module and the drug information from the real-time monitoring module. It instantly transmits the usual drug information that was received by the data-processing module.

3 Pattern recognition method based on AI

3.1 Pattern recognition system

Pattern recognition is based on the attributes of the object to be tested centered on the computer system. It uses a specific analysis algorithm to define the category, which can display the classification and identification results as accurately as possible. Its composition and environment determine the characteristics of matter, and the understanding of things depends on the understanding of such characteristics. The characteristics of things with diverse properties differ in quantity and quality. Therefore, as long as people find the properties related to the properties of things, they can classify things with different properties. Then, things with unknown attributes can be distinguished to predict their attributes [13]. The principle of the pattern recognition system and recognition process is shown in Figure 3.

Figure 3 Principle of the pattern recognition system and recognition process.
Figure 3

Principle of the pattern recognition system and recognition process.

In small spaces, such as 2D or 3D, pattern recognition can be used for all recognition tasks, such as face recognition and speech recognition. The measurements obtained with modern analyzers are high-dimensional and contain much relevant information. People need help to intuitively access pattern information, which requires the assistance of mathematics and computer technology. Although the level of machine recognition remains considerably behind the human brain, with the development of pattern recognition theory and other related disciplines, its functions would be more powerful and extensive.

Feature extraction is an essential stage of pattern recognition. It has been widely studied but without universal theory to follow. Generally, it should select the features suitable for classification in accordance with the research issues and topics, combined with experience. The principle of feature extraction is to obtain good classification results. Sometimes, when meeting the requirements of classification and recognition accuracy, it can select features that play an essential role in correct classification and recognition in accordance with specific criteria and try to complete classification and recognition tasks with fewer features [14].

Machines with classification and recognition functions must first input the classification and recognition knowledge of objects into the machine to create classification and recognition rules and classification processes. This step usually requires constant repetition to correct errors and improve defects, as well as change feature extraction methods, including feature selection schemes, decision rules, and parameter variations. Finally, the accuracy of system identification can be improved to meet the design requirements. At present, this machine learning process requires human intervention [15].

The classification rules and procedures generated during and after learning are used to detect unknown objects. The more classification and recognition knowledge and methods entered by the machine and the more understanding of relevant objects, the stronger the recognition ability of the system and the higher the recognition accuracy.

3.2 Pattern recognition algorithm

3.2.1 Mode similarity measurement

In pattern recognition, the relationship between samples or variables is usually necessary to describe, and two basic methods can be used to describe these relationships. One is the similarity coefficient. Similar samples have high similarity, whereas heterogeneous samples have low similarity. Standard similarity measures are correlation coefficient and square cosine. The other is to test the relationship between samples or variables through the distance of sampling points in multidimensional space in accordance with the principle of similar clustering [16]. Given a specific type of substance to be measured, the substance must keep a small distance from the aggregation of such substances in the n-dimensional space and a considerable distance from the aggregation of substances that are not similar in the n-dimensional space. The mode similarity measurement is carried out using the angle similarity coefficient.

The angle similarity coefficient is as follows:

(1) alsas a ij = ∑ k = 1 n x ik x jk ∑ k = 1 n x ik 2 ∑ k = 1 n x jk 2 .

The smaller the included angle is, the more similar it is, similar to comparing two spectra. The two spectra are completely the same when cos a ij = 1 , and they are completely different when cos a ij = 0 .

3.2.2 Feature extraction and optimization

Genetic algorithm is an effective nonlinear global optimization search method. It simulates the evolution process of natural organisms, randomly searches the optimization space, and generates global optimization. The numerical genetic algorithm sets the different parameters to be optimized as chromosomes. The parameters are all genes of chromosomes. In accordance with the adaptability of a chromosome to the environment, it can control its reproduction through various genetic operations, remove wrong parameters, and keep suitable parameters. The individuals that undergo genetic operation form a new population of the next generation. The next series of evolution of this new population ends after obtaining the best chromosome.

The basic process of genetic algorithm includes initialization, evaluation, selection, heredity (gene exchange), and population mutation. Two conditions are considered to terminate the process of the genetic variation cycle. One is that the solution is already the best solution to the problem or a satisfactory solution, and the other is the control of genetic algebra [17].

Genetic algorithm uses simple coding methods and genetic mechanism to represent complex phenomena so that very complex problems can be solved. In particular, because it is not restricted by the limited assumptions of the search blank problem, it is not necessary to comply with the assumptions of continuity, compatibility, and single mode. It has considerable application potential in chemistry.

3.2.3 Classification recognition algorithm

Classification includes searching a group of models (or functions) in the learning data, describing the typical characteristics of the dataset to classify and identify the distribution or category of unknown data, and assigning unknown situations to discrete categories. Current classification methods include support vector machines and artificial neural networks.

Small-sample, nonlinear, and high-dimensional learning issues can be solved using support vector machines, a unique tool for implementing statistical learning theory that does not require enough data to create feature space. A classification interface can only be created with a minimal amount of examples.

The basic idea of vector machines is to first transform the input space into a high-dimensional feature space through nonlinear transformation and then find the best linear classification surface in the new space by defining the corresponding integral function. A hyperplane is defined between the midpoints of the feature space, and the kernel function k can subsequently be specified. This technology avoids the computational load that obviously represents the eigenvector. The discriminant functions of the support vector include the following:

(2) f ( x ) = sgn ∑ i = 1 n α i × y i K ( x i · x ) + b * .

Among them, ( x i , y i ) is the sample set of linear segmentation, sgn ( ) is the sign function, α i is the Lagrange coefficient, and b * is the classification threshold.

3.3 Evaluation index of pattern recognition algorithm

The commonly used taxonomic performance indicators include accuracy (ACC), sensitivity (SEN), specificity (SPE), and Matthew correlation coefficient (MCC).

ACC only reflects the general predictive effect and does not describe the predictive effect of positive (or negative) samples [18]. Differences exist between the two. SEN and SPE can reflect the expected impact of positive and negative samples. However, a single SEN or SPE does not indicate the validity of the prediction, so the composite indicator MCC is usually used.

(3) ACC = TP + TN TP + TN + FP + FN ,

(4) SEN = TP TP + FN ,

(5) SPE = TN TN + FP ,

(6) MCC = TP · TN − FN · FP ( TP + FN ) · ( TP + FP ) · ( TN + FN ) · ( TN + FP ) .

TP represents true positive, predicted positive, and actual positive; FP stands for false positive, the predicted value is positive, and the actual value is negative; FN stands for false negative, the predicted value is negative, and the actual value is positive; TN represents true negative, predicted negative, and actual negative.

4 Qualitative analysis of the infrared spectrum of drug components based on pattern recognition

4.1 Infrared spectrum steps for drug components

For the infrared spectrum data of drugs, the data refer to a fuzzy collection. The differences in the basic characteristics of samples cannot be identified by spectral data, and the samples to be identified are usually fuzzy sets in the application process. Therefore, the model is established in the spectral data of the samples with known attributes. Then, the approximate principle of fuzzy pattern recognition technology is used to calculate the classification of unknown patterns. Combined with fuzzy pattern recognition technology, the steps for qualitative analysis of the infrared spectrum of active components are as follows:

  1. The infrared spectrometer can be used to measure the spectral data in the sample to be measured, preprocess the spectral data, and eliminate the spectral noise.

  2. The original spectral data can be dimensioned down.

    Because excessive original spectral variables exist, the complexity of the problem analysis is undoubtedly increased, and a certain correlation occurs between multiple spectral variables, which often leads to low accuracy of the recognition model. When the original spectral data are used as the input data of fuzzy morphological recognition, the stability is reduced, and long-term modeling may lead to defects. Therefore, before the infrared spectrum detection model is created, the size of spectral variables should be reduced first. Before fuzzy pattern recognition technology is used to model, traditional principal component analysis is employed to reduce the dimension of the infrared spectrum [19]. The principal component analysis method allows a small number of new variables to be selected instead of the original spectral variables without losing the basic spectral information. In other words, the space of high-dimensional original spectral variables can be replaced with a low-dimensional spectral variable space to achieve the goal of reducing dimensions.

  3. New variables are used as indicators for each known category sample in the model, and statistical software is adopted to determine the maximum proximity of each known category and each category to be tested.

  4. To analyze the identification results, the abnormal data are cleared, and step 3 is repeated until the results are satisfactory.

4.2 Qualitative analysis of drug components by infrared spectroscopy

4.2.1 Dimension reduction of spectral variables via principal component analysis

When analyzing data, complex datasets often need to be simplified. The p-measurement system composed of p indicators would be simplified to m(m < p) – measurement. Basic component analysis is a statistical analysis method that converts several indicators into several comprehensive indicators. In the process of studying variables, excessive variables often exist with a certain correlation between them. The measured data overlap with the information to a certain extent. If many variables are available, the sampling rules in the high-dimensional space are difficult to study. Principal component analysis uses the method of reducing measurement to identify multiple elements. Most of the information of these elements reflects the content of the original variables, and they are not related to each other to achieve the goal of simplification.

4.2.2 Fuzzy pattern recognition of drug infrared spectrum

The point matrix of principal components is introduced into the recognition model. The Data Processing System (DPS) statistical analysis software is used to calculate the proximity of drug samples, followed by the category.

5 Embedded system teaching under the background of AI

5.1 Current challenges in embedded system teaching

Embedded systems are useful and place great emphasis on learning fundamental technological abilities and using technology in the teaching process. As a result, relevant research projects and competitions are covered in class and during the break. The mastery of theoretical information and the abilities used in practical competitions must be encouraged during the educational process. However, numerous universities still use traditional methods of instruction, which emphasize theory over practice almost exclusively. No real connection exists between theory and practice, which is primarily reflected in the following areas:

  1. Many theoretical courses but relatively few practical courses exist in the learning process.

  2. Although some colleges and universities pay considerable attention to practical teaching and increase the experimental time, the organization of the experimental content is unreasonable, the experimental task is extremely simple, and few comprehensive design experiments are available. The experimental class is only a course to complete the course tasks. Students only need to learn step by step and cannot use knowledge after learning.

  3. When selecting textbooks, colleges and universities mainly focus on the expression of theoretical knowledge. The practice part is relatively small, lacking examples and design tasks, which is not in line with the current goal of talent training in colleges and universities.

  4. Teachers lack of professional and technical abilities, product-related comprehensive project experience, and backward teaching methods; the teaching process is only theoretical explanation; and students lack learning interest and enthusiasm.

  5. The evaluation and promotion of teachers mainly depend on their work and scientific research. Hence, teachers spend most of their time on projects and work, and spending the rest of their time and energy on training talents is difficult.

  6. The existing courses are outdated and cannot keep up with the current pace of relevant industries and technologies.

  7. The enthusiasm of students to participate in various competitions is low, the number of students who can complete tasks and win prizes is small, and students lose the opportunity to exercise in competitions, which has a negative impact on the formation of applied talents.

5.2 Teaching reform of embedded system course under the background of AI

5.2.1 Course reform of embedded software foundation

As one of the programming languages, C language is an entry-level language for software development and one of the leading development languages for embedded systems and AI technology. Therefore, the C programming course is particularly important for students studying embedded system programs.

For each programming language, data structure is the prerequisite for realizing software functions. Especially for the low- and middle-level embedded systems with insufficient internal resources, the running speed is relatively slow, and the various links of software design are closely related. They are small and simple data structures. The data structure of software is different from that of traditional computers.

Database is the core of AI and an important part of embedded systems. It is a useful tool for developing and managing user applications. Students have mastered database programming technology and can use capacitors to manage data in large embedded and artificial applications.

5.2.2 Revising the training plan

Revising the curriculum, adjusting the time and proportion of the curriculum, and correspondingly increasing the proportion of comprehensive, creative, and open textbooks are necessary. In view of the rapid development of application technology, the teaching of application functions in talent development planning has been increased.

5.2.3 Strengthening the development of practical learning platform

A comprehensive practical learning platform should be actively implemented, and the construction of infrastructure, such as competition bases, comprehensive learning centers, and innovation and entrepreneurship practice learning bases, should be strengthened. This article introduces relevant research projects for students to practice on the platform.

5.2.4 Improving teaching methods and curriculum evaluation system

Practical training materials suitable for applied learning, including practical case studies and special training manuals for open experiments, can be chosen. The analysis of students’ current learning level and motivation, the improvement of the curriculum, and the introduction of effective learning methods are also possible. The project method is introduced into the curriculum to realize knowledge transfer and technology development, achieve comprehensive education objectives, guide and encourage students to apply for different types of innovative science and technology projects, and develop students’ research and technical skills.

To develop evaluation methods that can help students learn, the focus is to mobilize their enthusiasm and improve their practical and innovative skills. Some practical evaluations can be added to the traditional graduation evaluation mechanism to reduce the proportion of theoretical achievements and increase the proportion of practical achievements. This approach can stimulate students’ learning motivation and lay a good foundation for the development of applied engineering talents.

6 Online drug ingredient recognition-embedded monitoring system and teaching experiment

To identify the components of five drugs, the recognition method used is the online drug component recognition-embedded monitoring system proposed in this study and other drug component monitoring systems. The teaching effect of the embedded system under the background of AI is also studied.

6.1 Identification of drug components

The ACC of drug component identification results is shown in Figure 4.

Figure 4 ACC of drug composition identification results. (a) ACC of the drug component identification results of the online drug component identification-embedded monitoring system proposed in this study. (b) ACC of the drug composition identification results from other drug composition monitoring systems.
Figure 4

ACC of drug composition identification results. (a) ACC of the drug component identification results of the online drug component identification-embedded monitoring system proposed in this study. (b) ACC of the drug composition identification results from other drug composition monitoring systems.

Figure 4(a) shows the ACC of the online drug component identification-embedded monitoring system proposed in this study, and Figure 4(b) presents the ACC of other drug component monitoring systems.

When the online drug component identification-embedded monitoring system proposed in this study is used for drug component identification, the result for drug 1 (ACC) is 0.879. The ACC for drug 2 is 0.854, the ACC for drug 3 is 0.892, and the ACC for drug 4 is 0.847. The ACC for drug 5 is 0.886, and the average ACC of the online drug component identification-embedded monitoring system presented in this study is 0.872.

The ACC for drug 1 is 0.687, and the ACC for drug 2 is 0.724 when other drug component monitoring systems identify drug components. The component identification for drug 3 has an ACC of 0.739, and that for drug 4 has an ACC of 0.692. The ACC for drug 5 is 0.715, and the average ACC obtained by other drug component monitoring systems is 0.711.

The SEN of drug component identification results is shown in Figure 5.

Figure 5 SEN of drug component identification results. (a) SEN of the drug component recognition results obtained by the online drug component recognition embedded monitoring system presented in this study. (b) SEN of the drug component identification results of other drug component monitoring systems.
Figure 5

SEN of drug component identification results. (a) SEN of the drug component recognition results obtained by the online drug component recognition embedded monitoring system presented in this study. (b) SEN of the drug component identification results of other drug component monitoring systems.

Figure 5(a) shows the drug component identification result SEN of the online drug component identification-embedded monitoring system presented in this study, and Figure 5(b) demonstrates the drug component identification result SEN of other drug component monitoring systems.

For drug 1, when using the online drug component identification-embedded monitoring system proposed in this work, the SEN of the drug component identification result is 0.814; when using other drug component monitoring systems, the SEN of the drug component identification result is 0.597. For drug 2, the SEN of drug component identification is 0.823 when using the proposed system and 0.623 when using other systems. For drug 3, the SEN of the drug component identification result is 0.796 when using the proposed system and 0.619 when using other systems. For drug 4, the SEN is 0.785 when using the presented system and 0.583 when using other systems. For drug 5, the SEN is 0.818 when using the presented system and 0.627 when using other systems.

The average SEN of the online drug component recognition-embedded monitoring system proposed in this work is 0.807, and the average SEN of other drug component monitoring systems is 0.61.

The SPE of drug component identification results is shown in Figure 6.

Figure 6 SPE of drug component identification results. (a) SPE of the drug component recognition results from the online drug component recognition-embedded monitoring system proposed in this work. (b) SPE of the drug component identification results of other drug component monitoring systems.
Figure 6

SPE of drug component identification results. (a) SPE of the drug component recognition results from the online drug component recognition-embedded monitoring system proposed in this work. (b) SPE of the drug component identification results of other drug component monitoring systems.

Figure 6(a) shows the SPE of the drug component identification results of the online drug component identification-embedded monitoring system proposed in this work, and Figure 6(b) depicts the SPE of the drug component identification results of other drug component monitoring systems.

The SPE of the drug component identification results for drugs 1 and 2 are 0.917 and 0.923, respectively, when the online drug component identification-embedded monitoring system in this work performs drug component identification. The component identification for drug 3 has an SPE of 0.894, and that for drug 4 has an SPE of 0.908. The SPE for drug 5 is 0.916, and the average SPE using the proposed system is 0.912.

When other drug component monitoring systems identify drug components, the SPE for drug 1 is 0.823, and the SPE for drug 2 is 0.846. The SPE for drug 3 is 0.819, and the SPE for drug 4 is 0.833. The SPE for drug 5 is 0.829, and the average SPE of the drug component identification results of other systems is 0.83.

The MCC of drug component identification results is shown in Figure 7.

Figure 7 MCC of drug component identification results. (a) MCC of the drug component recognition result obtained by the online drug component recognition-embedded monitoring system in this work. (b) MCC of the drug component identification results from other drug component monitoring systems.
Figure 7

MCC of drug component identification results. (a) MCC of the drug component recognition result obtained by the online drug component recognition-embedded monitoring system in this work. (b) MCC of the drug component identification results from other drug component monitoring systems.

Figure 7(a) shows the MCC of the online drug component identification-embedded monitoring system in this work, and Figure 7(b) indicates the MCC using other drug component monitoring systems.

The drug component recognition result for drug 1 is 0.71 when the online drug component recognition-embedded monitoring system in this study performs drug component recognition. For drugs 2 and 3, the MCCs of drug component identification are 0.69 and 0.7, respectively. The MCC for drug 4 is 0.72. The component identification result for drug 5 has an MCC of 0.69, and the average MCC using the proposed system is 0.702.

When other drug component monitoring systems perform drug component identification, the MCC for drug 1 is 0.59, and that for drug 2 is 0.61. The MCC for drug 3 is 0.62, that for drug 4 is 0.6, and that for drug 5 is 0.58. The average MCC of the drug component identification results of other systems is 0.6.

The comprehensive results of drug component identification are shown in Table 1.

Table 1

Comprehensive results of drug component identification

Target Proposed system Other systems
ACC 0.872 0.711
SEN 0.807 0.61
SPE 0.912 0.83
MCC 0.702 0.6

The comparative data indicate that the ACC, SEN, SPE, and MCC of the drug component identification system in this study are higher than those of other systems. The average accuracy of the presented system is above 0.85, which is a high value.

6.2 Embedded system teaching under the background of AI

This study surveys the students of five majors in a university and compares their satisfaction with embedded system teaching and traditional teaching. The results are shown in Figure 8.

Figure 8 Comparison of teaching satisfaction. (a) Students’ satisfaction with embedded system teaching. (b) Students’ satisfaction with traditional teaching.
Figure 8

Comparison of teaching satisfaction. (a) Students’ satisfaction with embedded system teaching. (b) Students’ satisfaction with traditional teaching.

Figure 8(a) shows students’ satisfaction with embedded system teaching, and Figure 8(b) illustrates students’ satisfaction with traditional teaching.

Students in major 1 are 93.6% satisfied with embedded system teaching and 85.2% satisfied with traditional teaching. Students in Major 2 are 94.2% satisfied with embedded system teaching and 87.4% satisfied with traditional teaching. Students in Major 3 are 95.7% satisfied with embedded system teaching and 81.3% satisfied with traditional teaching. Students in major 4 are 92.3% satisfied with embedded system teaching and 84.9% satisfied with traditional teaching. Students in Major 5 are 93.9% satisfied with embedded system teaching and 83.8% satisfied with traditional teaching.

In comparison with traditional teaching, which has an average student satisfaction rating of 84.5, embedded system teaching has a rating of 93.9. More than 90% of the students in the five majors are satisfied with embedded system teaching, which is greater than the percentage of students who are satisfied with traditional teaching. This finding suggests that embedded system teaching is more effective and can pique students’ interest in learning.

7 Conclusion

In this study, AI was used to assess the online drug ingredient recognition-embedded monitoring system, and the teaching mode under the background of AI was discussed. This work constructed an embedded monitoring system framework for online drug component identification and selected pattern recognition methods for drug component identification. It introduced the measurement of pattern similarity, feature extraction and optimization, and classification recognition algorithm and selected ACC, SEN, SPE, and MCC as the evaluation indicators for pattern recognition algorithm. Pattern recognition was used to qualitatively analyze the infrared spectrum of drug components, and the original spectral data were preprocessed and dimensionally reduced. The DPS statistical analysis software was utilized to calculate the proximity of drug samples and determine their category. Then, this study analyzed the problems existing in the teaching of embedded system in colleges and universities and suggested teaching reform methods for embedded systems in view of these problems. The experimental part evaluated the online drug ingredient recognition-embedded monitoring system and the embedded system teaching. The research results showed that the online drug ingredient recognition-embedded monitoring system had high ACC, SEN, SPE, and MCC, and the embedded system teaching gained high student satisfaction.

  1. Funding information: This work was supported by the National Science and Technology Ministry (2017ZX09101001), Gansu Science and Technology Fund Grant (17ZD2FA009 and 18JR3RA417), Lanzhou Science and Technology Bureau Program Funds (2021-1-141, 2021-RC-86), and the fund grant of NMPA Key Laboratory for Quality Control of Traditional Chinese Medicine (2021GSMPA-KL11 and 2021GSMPA-AJ01).

  2. Author contributions: D.H. and X.C. conceived the work and designed the online pharmaceutical recognition embedded monitoring system and the teaching application based on AI. L.D. and X.C. collected the data, analyzed the results, and drafted the initial manuscript. Z.W. and J.Z. selected the literature and helped to prepare the figures 2–8. Q.Z. and Z.J. provided some valuable suggestions and amendments to the discussion, supporting technologically this work. AIl authors read, edited, and approved the final version of the manuscript.

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

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-08-24
Revised: 2023-10-25
Accepted: 2023-11-05
Published Online: 2024-06-11

© 2024 the author(s), published by De Gruyter

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

Artikel in diesem Heft

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  2. Constitutive and evoked release of ATP in adult mouse olfactory epithelium
  3. LARP1 knockdown inhibits cultured gastric carcinoma cell cycle progression and metastatic behavior
  4. PEGylated porcine–human recombinant uricase: A novel fusion protein with improved efficacy and safety for the treatment of hyperuricemia and renal complications
  5. Research progress on ocular complications caused by type 2 diabetes mellitus and the function of tears and blepharons
  6. The role and mechanism of esketamine in preventing and treating remifentanil-induced hyperalgesia based on the NMDA receptor–CaMKII pathway
  7. Brucella infection combined with Nocardia infection: A case report and literature review
  8. Detection of serum interleukin-18 level and neutrophil/lymphocyte ratio in patients with antineutrophil cytoplasmic antibody-associated vasculitis and its clinical significance
  9. Ang-1, Ang-2, and Tie2 are diagnostic biomarkers for Henoch-Schönlein purpura and pediatric-onset systemic lupus erythematous
  10. PTTG1 induces pancreatic cancer cell proliferation and promotes aerobic glycolysis by regulating c-myc
  11. Role of serum B-cell-activating factor and interleukin-17 as biomarkers in the classification of interstitial pneumonia with autoimmune features
  12. Effectiveness and safety of a mumps containing vaccine in preventing laboratory-confirmed mumps cases from 2002 to 2017: A meta-analysis
  13. Low levels of sex hormone-binding globulin predict an increased breast cancer risk and its underlying molecular mechanisms
  14. A case of Trousseau syndrome: Screening, detection and complication
  15. Application of the integrated airway humidification device enhances the humidification effect of the rabbit tracheotomy model
  16. Preparation of Cu2+/TA/HAP composite coating with anti-bacterial and osteogenic potential on 3D-printed porous Ti alloy scaffolds for orthopedic applications
  17. Aquaporin-8 promotes human dermal fibroblasts to counteract hydrogen peroxide-induced oxidative damage: A novel target for management of skin aging
  18. Current research and evidence gaps on placental development in iron deficiency anemia
  19. Single-nucleotide polymorphism rs2910829 in PDE4D is related to stroke susceptibility in Chinese populations: The results of a meta-analysis
  20. Pheochromocytoma-induced myocardial infarction: A case report
  21. Kaempferol regulates apoptosis and migration of neural stem cells to attenuate cerebral infarction by O‐GlcNAcylation of β-catenin
  22. Sirtuin 5 regulates acute myeloid leukemia cell viability and apoptosis by succinylation modification of glycine decarboxylase
  23. Apigenin 7-glucoside impedes hypoxia-induced malignant phenotypes of cervical cancer cells in a p16-dependent manner
  24. KAT2A changes the function of endometrial stromal cells via regulating the succinylation of ENO1
  25. Current state of research on copper complexes in the treatment of breast cancer
  26. Exploring antioxidant strategies in the pathogenesis of ALS
  27. Helicobacter pylori causes gastric dysbacteriosis in chronic gastritis patients
  28. IL-33/soluble ST2 axis is associated with radiation-induced cardiac injury
  29. The predictive value of serum NLR, SII, and OPNI for lymph node metastasis in breast cancer patients with internal mammary lymph nodes after thoracoscopic surgery
  30. Carrying SNP rs17506395 (T > G) in TP63 gene and CCR5Δ32 mutation associated with the occurrence of breast cancer in Burkina Faso
  31. P2X7 receptor: A receptor closely linked with sepsis-associated encephalopathy
  32. Probiotics for inflammatory bowel disease: Is there sufficient evidence?
  33. Identification of KDM4C as a gene conferring drug resistance in multiple myeloma
  34. Microbial perspective on the skin–gut axis and atopic dermatitis
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  36. Highly specific vaginal microbiome signature for gynecological cancers
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  205. Erratum
  206. Erratum to “Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM”
  207. Erratum to “Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation”
  208. Retraction
  209. Retraction to “MiR-223-3p regulates cell viability, migration, invasion, and apoptosis of non-small cell lung cancer cells by targeting RHOB”
  210. Retraction to “A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis”
  211. Special Issue on Advances in Neurodegenerative Disease Research and Treatment
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  213. Special Issue on Multi-omics
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https://doi.org/10.1515/biol-2022-0795
Schlagwörter für diesen Artikel
drug composition identification; embedded monitoring system; drug teaching; artificial intelligence
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Biomedical Sciences
  2. Constitutive and evoked release of ATP in adult mouse olfactory epithelium
  3. LARP1 knockdown inhibits cultured gastric carcinoma cell cycle progression and metastatic behavior
  4. PEGylated porcine–human recombinant uricase: A novel fusion protein with improved efficacy and safety for the treatment of hyperuricemia and renal complications
  5. Research progress on ocular complications caused by type 2 diabetes mellitus and the function of tears and blepharons
  6. The role and mechanism of esketamine in preventing and treating remifentanil-induced hyperalgesia based on the NMDA receptor–CaMKII pathway
  7. Brucella infection combined with Nocardia infection: A case report and literature review
  8. Detection of serum interleukin-18 level and neutrophil/lymphocyte ratio in patients with antineutrophil cytoplasmic antibody-associated vasculitis and its clinical significance
  9. Ang-1, Ang-2, and Tie2 are diagnostic biomarkers for Henoch-Schönlein purpura and pediatric-onset systemic lupus erythematous
  10. PTTG1 induces pancreatic cancer cell proliferation and promotes aerobic glycolysis by regulating c-myc
  11. Role of serum B-cell-activating factor and interleukin-17 as biomarkers in the classification of interstitial pneumonia with autoimmune features
  12. Effectiveness and safety of a mumps containing vaccine in preventing laboratory-confirmed mumps cases from 2002 to 2017: A meta-analysis
  13. Low levels of sex hormone-binding globulin predict an increased breast cancer risk and its underlying molecular mechanisms
  14. A case of Trousseau syndrome: Screening, detection and complication
  15. Application of the integrated airway humidification device enhances the humidification effect of the rabbit tracheotomy model
  16. Preparation of Cu2+/TA/HAP composite coating with anti-bacterial and osteogenic potential on 3D-printed porous Ti alloy scaffolds for orthopedic applications
  17. Aquaporin-8 promotes human dermal fibroblasts to counteract hydrogen peroxide-induced oxidative damage: A novel target for management of skin aging
  18. Current research and evidence gaps on placental development in iron deficiency anemia
  19. Single-nucleotide polymorphism rs2910829 in PDE4D is related to stroke susceptibility in Chinese populations: The results of a meta-analysis
  20. Pheochromocytoma-induced myocardial infarction: A case report
  21. Kaempferol regulates apoptosis and migration of neural stem cells to attenuate cerebral infarction by O‐GlcNAcylation of β-catenin
  22. Sirtuin 5 regulates acute myeloid leukemia cell viability and apoptosis by succinylation modification of glycine decarboxylase
  23. Apigenin 7-glucoside impedes hypoxia-induced malignant phenotypes of cervical cancer cells in a p16-dependent manner
  24. KAT2A changes the function of endometrial stromal cells via regulating the succinylation of ENO1
  25. Current state of research on copper complexes in the treatment of breast cancer
  26. Exploring antioxidant strategies in the pathogenesis of ALS
  27. Helicobacter pylori causes gastric dysbacteriosis in chronic gastritis patients
  28. IL-33/soluble ST2 axis is associated with radiation-induced cardiac injury
  29. The predictive value of serum NLR, SII, and OPNI for lymph node metastasis in breast cancer patients with internal mammary lymph nodes after thoracoscopic surgery
  30. Carrying SNP rs17506395 (T > G) in TP63 gene and CCR5Δ32 mutation associated with the occurrence of breast cancer in Burkina Faso
  31. P2X7 receptor: A receptor closely linked with sepsis-associated encephalopathy
  32. Probiotics for inflammatory bowel disease: Is there sufficient evidence?
  33. Identification of KDM4C as a gene conferring drug resistance in multiple myeloma
  34. Microbial perspective on the skin–gut axis and atopic dermatitis
  35. Thymosin α1 combined with XELOX improves immune function and reduces serum tumor markers in colorectal cancer patients after radical surgery
  36. Highly specific vaginal microbiome signature for gynecological cancers
  37. Sample size estimation for AQP4-IgG seropositive optic neuritis: Retinal damage detection by optical coherence tomography
  38. The effects of SDF-1 combined application with VEGF on femoral distraction osteogenesis in rats
  39. Fabrication and characterization of gold nanoparticles using alginate: In vitro and in vivo assessment of its administration effects with swimming exercise on diabetic rats
  40. Mitigating digestive disorders: Action mechanisms of Mediterranean herbal active compounds
  41. Distribution of CYP2D6 and CYP2C19 gene polymorphisms in Han and Uygur populations with breast cancer in Xinjiang, China
  42. VSP-2 attenuates secretion of inflammatory cytokines induced by LPS in BV2 cells by mediating the PPARγ/NF-κB signaling pathway
  43. Factors influencing spontaneous hypothermia after emergency trauma and the construction of a predictive model
  44. Long-term administration of morphine specifically alters the level of protein expression in different brain regions and affects the redox state
  45. Application of metagenomic next-generation sequencing technology in the etiological diagnosis of peritoneal dialysis-associated peritonitis
  46. Clinical diagnosis, prevention, and treatment of neurodyspepsia syndrome using intelligent medicine
  47. Case report: Successful bronchoscopic interventional treatment of endobronchial leiomyomas
  48. Preliminary investigation into the genetic etiology of short stature in children through whole exon sequencing of the core family
  49. Cystic adenomyoma of the uterus: Case report and literature review
  50. Mesoporous silica nanoparticles as a drug delivery mechanism
  51. Dynamic changes in autophagy activity in different degrees of pulmonary fibrosis in mice
  52. Vitamin D deficiency and inflammatory markers in type 2 diabetes: Big data insights
  53. Lactate-induced IGF1R protein lactylation promotes proliferation and metabolic reprogramming of lung cancer cells
  54. Meta-analysis on the efficacy of allogeneic hematopoietic stem cell transplantation to treat malignant lymphoma
  55. Mitochondrial DNA drives neuroinflammation through the cGAS-IFN signaling pathway in the spinal cord of neuropathic pain mice
  56. Application value of artificial intelligence algorithm-based magnetic resonance multi-sequence imaging in staging diagnosis of cervical cancer
  57. Embedded monitoring system and teaching of artificial intelligence online drug component recognition
  58. Investigation into the association of FNDC1 and ADAMTS12 gene expression with plumage coloration in Muscovy ducks
  59. Yak meat content in feed and its impact on the growth of rats
  60. A rare case of Richter transformation with breast involvement: A case report and literature review
  61. First report of Nocardia wallacei infection in an immunocompetent patient in Zhejiang province
  62. Rhodococcus equi and Brucella pulmonary mass in immunocompetent: A case report and literature review
  63. Downregulation of RIP3 ameliorates the left ventricular mechanics and function after myocardial infarction via modulating NF-κB/NLRP3 pathway
  64. Evaluation of the role of some non-enzymatic antioxidants among Iraqi patients with non-alcoholic fatty liver disease
  65. The role of Phafin proteins in cell signaling pathways and diseases
  66. Ten-year anemia as initial manifestation of Castleman disease in the abdominal cavity: A case report
  67. Coexistence of hereditary spherocytosis with SPTB P.Trp1150 gene variant and Gilbert syndrome: A case report and literature review
  68. Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells
  69. Exploratory evaluation supported by experimental and modeling approaches of Inula viscosa root extract as a potent corrosion inhibitor for mild steel in a 1 M HCl solution
  70. Imaging manifestations of ductal adenoma of the breast: A case report
  71. Gut microbiota and sleep: Interaction mechanisms and therapeutic prospects
  72. Isomangiferin promotes the migration and osteogenic differentiation of rat bone marrow mesenchymal stem cells
  73. Prognostic value and microenvironmental crosstalk of exosome-related signatures in human epidermal growth factor receptor 2 positive breast cancer
  74. Circular RNAs as potential biomarkers for male severe sepsis
  75. Knockdown of Stanniocalcin-1 inhibits growth and glycolysis in oral squamous cell carcinoma cells
  76. The expression and biological role of complement C1s in esophageal squamous cell carcinoma
  77. A novel GNAS mutation in pseudohypoparathyroidism type 1a with articular flexion deformity: A case report
  78. Predictive value of serum magnesium levels for prognosis in patients with non-small cell lung cancer undergoing EGFR-TKI therapy
  79. HSPB1 alleviates acute-on-chronic liver failure via the P53/Bax pathway
  80. IgG4-related disease complicated by PLA2R-associated membranous nephropathy: A case report
  81. Baculovirus-mediated endostatin and angiostatin activation of autophagy through the AMPK/AKT/mTOR pathway inhibits angiogenesis in hepatocellular carcinoma
  82. Metformin mitigates osteoarthritis progression by modulating the PI3K/AKT/mTOR signaling pathway and enhancing chondrocyte autophagy
  83. Evaluation of the activity of antimicrobial peptides against bacterial vaginosis
  84. Atypical presentation of γ/δ mycosis fungoides with an unusual phenotype and SOCS1 mutation
  85. Analysis of the microecological mechanism of diabetic kidney disease based on the theory of “gut–kidney axis”: A systematic review
  86. Omega-3 fatty acids prevent gestational diabetes mellitus via modulation of lipid metabolism
  87. Refractory hypertension complicated with Turner syndrome: A case report
  88. Interaction of ncRNAs and the PI3K/AKT/mTOR pathway: Implications for osteosarcoma
  89. Association of low attenuation area scores with pulmonary function and clinical prognosis in patients with chronic obstructive pulmonary disease
  90. Long non-coding RNAs in bone formation: Key regulators and therapeutic prospects
  91. The deubiquitinating enzyme USP35 regulates the stability of NRF2 protein
  92. Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as potential diagnostic markers for rebleeding in patients with esophagogastric variceal bleeding
  93. G protein-coupled receptor 1 participating in the mechanism of mediating gestational diabetes mellitus by phosphorylating the AKT pathway
  94. LL37-mtDNA regulates viability, apoptosis, inflammation, and autophagy in lipopolysaccharide-treated RLE-6TN cells by targeting Hsp90aa1
  95. The analgesic effect of paeoniflorin: A focused review
  96. Chemical composition’s effect on Solanum nigrum Linn.’s antioxidant capacity and erythrocyte protection: Bioactive components and molecular docking analysis
  97. Knockdown of HCK promotes HREC cell viability and inner blood–retinal barrier integrity by regulating the AMPK signaling pathway
  98. The role of rapamycin in the PINK1/Parkin signaling pathway in mitophagy in podocytes
  99. Laryngeal non-Hodgkin lymphoma: Report of four cases and review of the literature
  100. Clinical value of macrogenome next-generation sequencing on infections
  101. Overview of dendritic cells and related pathways in autoimmune uveitis
  102. TAK-242 alleviates diabetic cardiomyopathy via inhibiting pyroptosis and TLR4/CaMKII/NLRP3 pathway
  103. Hypomethylation in promoters of PGC-1α involved in exercise-driven skeletal muscular alterations in old age
  104. Profile and antimicrobial susceptibility patterns of bacteria isolated from effluents of Kolladiba and Debark hospitals
  105. The expression and clinical significance of syncytin-1 in serum exosomes of hepatocellular carcinoma patients
  106. A histomorphometric study to evaluate the therapeutic effects of biosynthesized silver nanoparticles on the kidneys infected with Plasmodium chabaudi
  107. PGRMC1 and PAQR4 are promising molecular targets for a rare subtype of ovarian cancer
  108. Analysis of MDA, SOD, TAOC, MNCV, SNCV, and TSS scores in patients with diabetes peripheral neuropathy
  109. SLIT3 deficiency promotes non-small cell lung cancer progression by modulating UBE2C/WNT signaling
  110. The relationship between TMCO1 and CALR in the pathological characteristics of prostate cancer and its effect on the metastasis of prostate cancer cells
  111. Heterogeneous nuclear ribonucleoprotein K is a potential target for enhancing the chemosensitivity of nasopharyngeal carcinoma
  112. PHB2 alleviates retinal pigment epithelium cell fibrosis by suppressing the AGE–RAGE pathway
  113. Anti-γ-aminobutyric acid-B receptor autoimmune encephalitis with syncope as the initial symptom: Case report and literature review
  114. Comparative analysis of chloroplast genome of Lonicera japonica cv. Damaohua
  115. Human umbilical cord mesenchymal stem cells regulate glutathione metabolism depending on the ERK–Nrf2–HO-1 signal pathway to repair phosphoramide mustard-induced ovarian cancer cells
  116. Electroacupuncture on GB acupoints improves osteoporosis via the estradiol–PI3K–Akt signaling pathway
  117. Renalase protects against podocyte injury by inhibiting oxidative stress and apoptosis in diabetic nephropathy
  118. Review: Dicranostigma leptopodum: A peculiar plant of Papaveraceae
  119. Combination effect of flavonoids attenuates lung cancer cell proliferation by inhibiting the STAT3 and FAK signaling pathway
  120. Renal microangiopathy and immune complex glomerulonephritis induced by anti-tumour agents: A case report
  121. Correlation analysis of AVPR1a and AVPR2 with abnormal water and sodium and potassium metabolism in rats
  122. Gastrointestinal health anti-diarrheal mixture relieves spleen deficiency-induced diarrhea through regulating gut microbiota
  123. Myriad factors and pathways influencing tumor radiotherapy resistance
  124. Exploring the effects of culture conditions on Yapsin (YPS) gene expression in Nakaseomyces glabratus
  125. Screening of prognostic core genes based on cell–cell interaction in the peripheral blood of patients with sepsis
  126. Coagulation factor II thrombin receptor as a promising biomarker in breast cancer management
  127. Ileocecal mucinous carcinoma misdiagnosed as incarcerated hernia: A case report
  128. Methyltransferase like 13 promotes malignant behaviors of bladder cancer cells through targeting PI3K/ATK signaling pathway
  129. The debate between electricity and heat, efficacy and safety of irreversible electroporation and radiofrequency ablation in the treatment of liver cancer: A meta-analysis
  130. ZAG promotes colorectal cancer cell proliferation and epithelial–mesenchymal transition by promoting lipid synthesis
  131. Baicalein inhibits NLRP3 inflammasome activation and mitigates placental inflammation and oxidative stress in gestational diabetes mellitus
  132. Impact of SWCNT-conjugated senna leaf extract on breast cancer cells: A potential apoptotic therapeutic strategy
  133. MFAP5 inhibits the malignant progression of endometrial cancer cells in vitro
  134. Major ozonated autohemotherapy promoted functional recovery following spinal cord injury in adult rats via the inhibition of oxidative stress and inflammation
  135. Axodendritic targeting of TAU and MAP2 and microtubule polarization in iPSC-derived versus SH-SY5Y-derived human neurons
  136. Differential expression of phosphoinositide 3-kinase/protein kinase B and Toll-like receptor/nuclear factor kappa B signaling pathways in experimental obesity Wistar rat model
  137. The therapeutic potential of targeting Oncostatin M and the interleukin-6 family in retinal diseases: A comprehensive review
  138. BA inhibits LPS-stimulated inflammatory response and apoptosis in human middle ear epithelial cells by regulating the Nf-Kb/Iκbα axis
  139. Role of circRMRP and circRPL27 in chronic obstructive pulmonary disease
  140. Investigating the role of hyperexpressed HCN1 in inducing myocardial infarction through activation of the NF-κB signaling pathway
  141. Characterization of phenolic compounds and evaluation of anti-diabetic potential in Cannabis sativa L. seeds: In vivo, in vitro, and in silico studies
  142. Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence
  143. Ecology and Environmental Science
  144. Screening of different growth conditions of Bacillus subtilis isolated from membrane-less microbial fuel cell toward antimicrobial activity profiling
  145. Degradation of a mixture of 13 polycyclic aromatic hydrocarbons by commercial effective microorganisms
  146. Evaluation of the impact of two citrus plants on the variation of Panonychus citri (Acari: Tetranychidae) and beneficial phytoseiid mites
  147. Prediction of present and future distribution areas of Juniperus drupacea Labill and determination of ethnobotany properties in Antalya Province, Türkiye
  148. Population genetics of Todarodes pacificus (Cephalopoda: Ommastrephidae) in the northwest Pacific Ocean via GBS sequencing
  149. A comparative analysis of dendrometric, macromorphological, and micromorphological characteristics of Pistacia atlantica subsp. atlantica and Pistacia terebinthus in the middle Atlas region of Morocco
  150. Macrofungal sporocarp community in the lichen Scots pine forests
  151. Assessing the proximate compositions of indigenous forage species in Yemen’s pastoral rangelands
  152. Food Science
  153. Gut microbiota changes associated with low-carbohydrate diet intervention for obesity
  154. Reexamination of Aspergillus cristatus phylogeny in dark tea: Characteristics of the mitochondrial genome
  155. Differences in the flavonoid composition of the leaves, fruits, and branches of mulberry are distinguished based on a plant metabolomics approach
  156. Investigating the impact of wet rendering (solventless method) on PUFA-rich oil from catfish (Clarias magur) viscera
  157. Non-linear associations between cardiovascular metabolic indices and metabolic-associated fatty liver disease: A cross-sectional study in the US population (2017–2020)
  158. Knockdown of USP7 alleviates atherosclerosis in ApoE-deficient mice by regulating EZH2 expression
  159. Utility of dairy microbiome as a tool for authentication and traceability
  160. Agriculture
  161. Enhancing faba bean (Vicia faba L.) productivity through establishing the area-specific fertilizer rate recommendation in southwest Ethiopia
  162. Impact of novel herbicide based on synthetic auxins and ALS inhibitor on weed control
  163. Perspectives of pteridophytes microbiome for bioremediation in agricultural applications
  164. Fertilizer application parameters for drip-irrigated peanut based on the fertilizer effect function established from a “3414” field trial
  165. Improving the productivity and profitability of maize (Zea mays L.) using optimum blended inorganic fertilization
  166. Application of leaf multispectral analyzer in comparison to hyperspectral device to assess the diversity of spectral reflectance indices in wheat genotypes
  167. Animal Sciences
  168. Knockdown of ANP32E inhibits colorectal cancer cell growth and glycolysis by regulating the AKT/mTOR pathway
  169. Development of a detection chip for major pathogenic drug-resistant genes and drug targets in bovine respiratory system diseases
  170. Exploration of the genetic influence of MYOT and MB genes on the plumage coloration of Muscovy ducks
  171. Transcriptome analysis of adipose tissue in grazing cattle: Identifying key regulators of fat metabolism
  172. Comparison of nutritional value of the wild and cultivated spiny loaches at three growth stages
  173. Transcriptomic analysis of liver immune response in Chinese spiny frog (Quasipaa spinosa) infected with Proteus mirabilis
  174. Disruption of BCAA degradation is a critical characteristic of diabetic cardiomyopathy revealed by integrated transcriptome and metabolome analysis
  175. Plant Sciences
  176. Effect of long-term in-row branch covering on soil microorganisms in pear orchards
  177. Photosynthetic physiological characteristics, growth performance, and element concentrations reveal the calcicole–calcifuge behaviors of three Camellia species
  178. Transcriptome analysis reveals the mechanism of NaHCO3 promoting tobacco leaf maturation
  179. Bioinformatics, expression analysis, and functional verification of allene oxide synthase gene HvnAOS1 and HvnAOS2 in qingke
  180. Water, nitrogen, and phosphorus coupling improves gray jujube fruit quality and yield
  181. Improving grape fruit quality through soil conditioner: Insights from RNA-seq analysis of Cabernet Sauvignon roots
  182. Role of Embinin in the reabsorption of nucleus pulposus in lumbar disc herniation: Promotion of nucleus pulposus neovascularization and apoptosis of nucleus pulposus cells
  183. Revealing the effects of amino acid, organic acid, and phytohormones on the germination of tomato seeds under salinity stress
  184. Combined effects of nitrogen fertilizer and biochar on the growth, yield, and quality of pepper
  185. Comprehensive phytochemical and toxicological analysis of Chenopodium ambrosioides (L.) fractions
  186. Impact of “3414” fertilization on the yield and quality of greenhouse tomatoes
  187. Exploring the coupling mode of water and fertilizer for improving growth, fruit quality, and yield of the pear in the arid region
  188. Metagenomic analysis of endophytic bacteria in seed potato (Solanum tuberosum)
  189. Antibacterial, antifungal, and phytochemical properties of Salsola kali ethanolic extract
  190. Exploring the hepatoprotective properties of citronellol: In vitro and in silico studies on ethanol-induced damage in HepG2 cells
  191. Enhanced osmotic dehydration of watermelon rind using honey–sucrose solutions: A study on pre-treatment efficacy and mass transfer kinetics
  192. Effects of exogenous 2,4-epibrassinolide on photosynthetic traits of 53 cowpea varieties under NaCl stress
  193. Comparative transcriptome analysis of maize (Zea mays L.) seedlings in response to copper stress
  194. An optimization method for measuring the stomata in cassava (Manihot esculenta Crantz) under multiple abiotic stresses
  195. Fosinopril inhibits Ang II-induced VSMC proliferation, phenotype transformation, migration, and oxidative stress through the TGF-β1/Smad signaling pathway
  196. Antioxidant and antimicrobial activities of Salsola imbricata methanolic extract and its phytochemical characterization
  197. Bioengineering and Biotechnology
  198. Absorbable calcium and phosphorus bioactive membranes promote bone marrow mesenchymal stem cells osteogenic differentiation for bone regeneration
  199. New advances in protein engineering for industrial applications: Key takeaways
  200. An overview of the production and use of Bacillus thuringiensis toxin
  201. Research progress of nanoparticles in diagnosis and treatment of hepatocellular carcinoma
  202. Bioelectrochemical biosensors for water quality assessment and wastewater monitoring
  203. PEI/MMNs@LNA-542 nanoparticles alleviate ICU-acquired weakness through targeted autophagy inhibition and mitochondrial protection
  204. Unleashing of cytotoxic effects of thymoquinone-bovine serum albumin nanoparticles on A549 lung cancer cells
  205. Erratum
  206. Erratum to “Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM”
  207. Erratum to “Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation”
  208. Retraction
  209. Retraction to “MiR-223-3p regulates cell viability, migration, invasion, and apoptosis of non-small cell lung cancer cells by targeting RHOB”
  210. Retraction to “A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis”
  211. Special Issue on Advances in Neurodegenerative Disease Research and Treatment
  212. Transplantation of human neural stem cell prevents symptomatic motor behavior disability in a rat model of Parkinson’s disease
  213. Special Issue on Multi-omics
  214. Inflammasome complex genes with clinical relevance suggest potential as therapeutic targets for anti-tumor drugs in clear cell renal cell carcinoma
  215. Gastroesophageal varices in primary biliary cholangitis with anti-centromere antibody positivity: Early onset?
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Heruntergeladen am 17.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/biol-2022-0795/html
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