Structural Classification Research Articles

Comparative analysis of sleep parameters and structures derived from wearable flexible electrode sleep patches and polysomnography in young adults.

Polysomnography (PSG) is the gold standard for clinical sleep monitoring, but its cost, discomfort, and limited suitability for continuous use present challenges. The flexible electrode sleep patch (FESP) emerges as an economically viable and patient-friendly solution, offering lightweight, simple operation, and self-applicable. Nevertheless, its utilization in young individuals remains uncertain. The objective of this study was to compare sleep data obtained by FESP and PSG in healthy young individuals and analyze agreement for sleep parameters and structure classification. Overnight monitoring with FESP and PSG recordings in 48 participants (mean age: 23 yr) was done. Correlation analysis, Bland-Altman plots, and Cohen's kappa coefficient assessed consistency. Sensitivity, specificity, and predictive values compared classification against PSG. FESP showed strong correlation and consistency with PSG for sleep monitoring. Bland-Altman plots indicated small errors and high consistency. Kappa values (0.70-0.84) suggested substantial agreement for sleep stage classification. Pearson correlation coefficient values for sleep stages (0.75-0.88) and sleep parameters (0.80-0.96) confirm that FESP has a strong application. Intraclass correlation coefficient yielded values between 0.65 and 0.97. In addition, FESP demonstrated an impressive accuracy range of 84.12-93.47% for sleep stage classification. The FESP also features a wearable self-test program with an error rate of no more than 8% for both deep sleep and wake. In young adults, FESP demonstrated reliable monitoring capabilities comparable to PSG. With its low cost and user-friendly design, FESP is a potential alternative for portable sleep assessment in clinical and research applications. Further studies involving larger populations are needed to validate its diagnostic potential.NEW & NOTEWORTHY By comparison with PSG, this study confirmed the reliability of an efficient, objective, low-cost, and noninvasive portable automatic sleep-monitoring device FESP, which provides effective information for long-term family sleep disorder diagnosis and sleep quality monitoring.

EARLY SUPPORT OF GIFTEDNESS THROUGH A PHYSICS EXPERIMENT IN THE APPLIED PROPAEDEUTIC MODEL OF TEACHING AT PHYSICS AND MATHEMATICS SCHOOLS

The purpose of this theoretical study is to develop a model of giftedness support that allows us to move to the practical application of knowledge about giftedness, that is, to introduce it into applied work with gifted students. The methods of this study are the analysis of literary sources on the problem of giftedness and its support, as well as sign simulation. Based on the works of J. Renzulli and the working concept of giftedness, an applied propaedeutic model for giftedness support is proposed and the possibilities of practical application of this model in the framework of propaedeutic courses in physics are considered. It is shown that a physics experiment included in propaedeutic courses plays a significant role in supporting giftedness. The dynamic classification of educational structures is developed, which allows us to determine the “place of application” of the applied propaedeutic model. It is shown that for comprehensive support of giftedness it is necessary to involve the resources of both basic and additional education.

Plant chitinases: Types, structural classification, antifungal potential and transgenic expression in plants for enhanced disease resistance

Plant chitinases: Types, structural classification, antifungal potential and transgenic expression in plants for enhanced disease resistance

Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index

In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety of all kinds of large buildings, especially underground engineering buildings. As a quantitative classification index of rock mass, the rock block index is very common in the classification of borehole and dam foundation rock mass structures. However, there are few studies on the classification of underground engineering rock masses. Moreover, their classification criteria have disadvantages in spatial dimension. Therefore, this paper takes the long exploratory cave CPD1 in the water transmission and power generation system of the Qingtian pumped storage power station in Zhejiang Province as the research object and launches a study on the structural classification of the rock mass of a flat cave based on the 3D rock block index. According to the group distribution of joints, the sections are statistically homogeneous. Additionally, the Monte Carlo method is used to carry out random simulations to generate a three-dimensional joint network model. The virtual survey lines are arranged along the center of the shape of the three different orthogonal planes of the 3D joint network model to represent the boreholes, and the RBI values of the virtual survey lines on each orthogonal plane are counted to classify the rock mass structure of the flat cave in a refined manner using the rock block index of the rock mass in 3D. The above method realizes the application of the 3D rock block index in underground engineering and overcomes the limitations of traditional rock mass classification methods in terms of classification index and dimension. The results show that: (1) Three-dimensional joint network simulations built on statistical and probabilistic foundations can visualize the structure of the rock mass and more accurately reflect the structural characteristics of the actual rock mass. (2) Based on the 3D rock block index, the rock mass structure of the long-tunnel CPD1 is classified, from that of a continuous structure to a blocky structure, corresponding to the integrity of the rock mass from complete to relatively complete. The classification results are consistent with the evaluation results of horizontal tunnel seismic wave geophysical exploration. (3) Based on the 3D joint network model, it is reasonable and feasible to use the 3D rock block index as a quantitative evaluation index to determine the structure type of flat cave rock masses. The above method is helpful and significant in the classification of underground engineering rock mass structures.

Learning Flow-Based Disentanglement.

Face reenactment aims to generate the talking face images of a target person given by a face image of source person. It is crucial to learn latent disentanglement to tackle such a challenging task through domain mapping between source and target images. The attributes or talking features due to domains or conditions become adjustable to generate target images from source images. This article presents an information-theoretic attribute factorization (AF) where the mixed features are disentangled for flow-based face reenactment. The latent variables with flow model are factorized into the attribute-relevant and attribute-irrelevant components without the need of the paired face images. In particular, the domain knowledge is learned to provide the condition to identify the talking attributes from real face images. The AF is guided in accordance with multiple losses for source structure, target structure, random-pair reconstruction, and sequential classification. The random-pair reconstruction loss is calculated by means of exchanging the attribute-relevant components within a sequence of face images. In addition, a new mutual information flow is constructed for disentanglement toward domain mapping, condition irrelevance, and condition relevance. The disentangled features are learned and controlled to generate image sequence with meaningful interpretation. Experiments on mouth reenactment illustrate the merit of individual and hybrid models for conditional generation and mapping based on the informative AF.

Regulation of Transcription by RNA Polymerase III Promotors in the Norm and Pathology

RNA polymerase III synthesizes a wide range of noncoding RNAs shorter than 400 nucleotides in length. These RNAs are involved in protein synthesis (tRNA, 5S rRNA, and 7SL RNA), maturation, and splicing of different types of RNA (RPR, MRP RNA, and U6 snRNA), regulation of transcription (7SK RNA), replication (Y RNA), and intracellular transport (vault RNA). BC200 and BC1 RNA genes are transcribed by RNA polymerase III in neurons only where these RNAs regulate protein synthesis. Mutations in the regulatory elements of the genes transcribed by RNA polymerase III as well as in transcription factors of this RNA polymerase are associated with the development of a number of diseases, primarily oncological and neurological. In this regard, the mechanisms of regulation of the expression of the genes containing various RNA polymerase III promoters were actively studied. This review describes the structural and functional classification of polymerase III promoters, as well as the factors involved in the regulation of promoters of different types. A number of examples demonstrate the role of the described factors in the pathogenesis of human diseases.

Elucidating Dissolved Organic Sulfur in the Coastal Environment by Improved Online Liquid Chromatography Coupled to FT‐ICR Mass Spectrometry

AbstractDissolved organic sulfur (DOS) is an important component of dissolved organic matter (DOM) and widely exists at the land‐atmosphere interface. However, the characterization of DOS at the molecular level among different environmental samples in coastal regions has not been sufficiently focused. Here, we investigated DOS in aerosol, rainwater, and river water samples in Shanghai using an improved method–online liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC‐FT‐ICR MS). This system is utilized to separate DOS from complicated environmental matrices. The results showed that the average relative abundance of DOS in three different environmental media (aerosol, rainwater, and river water samples) accounted for 16.8%, 21.9% and 13.1%, respectively. The common molecules of the three mediums in coastal Shanghai contain an abundance of aromatic and aliphatic DOS of anthropogenic origin in addition to the natural sources of sea spray. Notably, results from a new structural classification scheme for DOS demonstrate that anthropogenic sulfonates and their derivatives are widely spread, although their molecular diversity in different medium was recorded due to complex environmental conditions and diverse sources.

An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images

Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes. Analyzing multi-label samples, which contain clusters of cells, is challenging due to difficulties in separating individual cells, such as touching or overlapping cells. High-performance biomedical imaging and several medical applications are made possible by advanced biosensors. We develop an intelligent neural network model that can automatically identify and categorize red blood cells from microscopic medical images using region-based convolutional neural networks (RCNN) and cutting-edge biosensors. Our model successfully navigates obstacles like touching or overlapping cells and accurately recognizes various blood structures. Additionally, we utilized data augmentation as a pre-processing method on microscopic images to enhance the model's computational efficiency and expand the sample size. To refine the data and eliminate noise from the dataset, we utilized the Radial Gradient Index filtering algorithm for imaging data equalization. We exhibit improved detection accuracy and a reduced model loss rate when using medical imagery datasets to apply our proposed model in comparison to existing ResNet and GoogleNet models. Our model precisely detected red blood cells in a collection of medical images with 99% training accuracy and 91.21% testing accuracy. Our proposed model outperformed earlier models like ResNet-50 and GoogleNet by 10-15%. Our results demonstrated that Artificial intelligence (AI)-assisted automated red blood cell detection has the potential to revolutionize and speed up blood cell analysis, minimizing human error and enabling early illness diagnosis.

Examining the Relationship Between Internationalization Strategies and the Structural Classification of Knowledge-based Companies

Examining the Relationship Between Internationalization Strategies and the Structural Classification of Knowledge-based Companies

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The research methodology entails several key steps. A grid-based network structure is established with IoT nodes and root nodes, where a DODAG process incorporating DIO messages is utilized for node ranking. To enhance energy efficiency, the Trickle algorithm is employed for control message optimization. Grid head nodes are chosen based on metrics such as root node fairness, residual energy, and load influence index. The novel Middle Order Optimal Routing (MOOR) objective function is utilized to optimize routing decisions. ADNN is implemented for precise medical data classification. The proposed model's performance is evaluated through simulation in a Python environment. Findings: The research findings demonstrate that the ARPLO model yields notable benefits compared to existing models. It achieves higher energy efficiency, improved throughput, enhanced packet delivery ratio (PDR), and an extended network lifespan. The Trickle algorithm contributes to efficient control message optimization. The MOOR-based routing approach improves multimedia medical data transfer. Moreover, the integration of ADNN enhances the accuracy of data classification, particularly in healthcare applications. The research outcomes align with the broader field's existing values and reports while offering novel insights that contribute to enhancing the existing knowledge base. ARPLO protocol performance reveals that there is increase of throughput of 31.2%, PDR by 7.12%, lifetime of 10.7 % with reduction of energy consumption by 12.72%, control overhead by 31.01% and end-to-end delay by 33.01%. Novelty: The novelty of this research lies in its comprehensive approach that integrates a grid-based network structure, MOOR-based optimization, and ADNN-based classification. The incorporation of the Trickle algorithm for energy-efficient communication is an innovative feature. The introduction of new metrics for grid head node selection, along with the application of the MOOR objective function for multimedia medical data routing, showcases the research's innovative contributions. Keywords: Internet of Things (IoT), RPL (Routing Protocol for Low­Power and Lossy Networks), Optimization, Routing, Multimedia, Healthcare

Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks.

Skin cancer is a severe and potentially lethal disease, and early detection is critical for successful treatment. Traditional procedures for diagnosing skin cancer are expensive, time-intensive, and necessitate the expertise of a medical practitioner. In recent years, many researchers have developed artificial intelligence (AI) tools, including shallow and deep machine learning-based approaches, to diagnose skin cancer. However, AI-based skin cancer diagnosis faces challenges in complexity, low reproducibility, and explainability. To address these problems, we propose a novel Grid-Based Structural and Dimensional Explainable Deep Convolutional Neural Network for accurate and interpretable skin cancer classification. This model employs adaptive thresholding for extracting the region of interest (ROI), using its dynamic capabilities to enhance the accuracy of identifying cancerous regions. The VGG-16 architecture extracts the hierarchical characteristics of skin lesion images, leveraging its recognized capabilities for deep feature extraction. Our proposed model leverages a grid structure to capture spatial relationships within lesions, while the dimensional features extract relevant information from various image channels. An Adaptive Intelligent Coney Optimization (AICO) algorithm is employed for self-feature selected optimization and fine-tuning the hyperparameters, which dynamically adapts the model architecture to optimize feature extraction and classification. The model was trained and tested using the ISIC dataset of 10,015 dermascope images and the MNIST dataset of 2357 images of malignant and benign oncological diseases. The experimental results demonstrated that the model achieved accuracy and CSI values of 0.96 and 0.97 for TP 80 using the ISIC dataset, which is 17.70% and 16.49% more than lightweight CNN, 20.83% and 19.59% more than DenseNet, 18.75% and 17.53% more than CNN, 6.25% and 6.18% more than Efficient Net-B0, 5.21% and 5.15% over ECNN, 2.08% and 2.06% over COA-CAN, and 5.21% and 5.15% more than ARO-ECNN. Additionally, the AICO self-feature selected ECNN model exhibited minimal FPR and FNR of 0.03 and 0.02, respectively. The model attained a loss of 0.09 for ISIC and 0.18 for the MNIST dataset, indicating that the model proposed in this research outperforms existing techniques. The proposed model improves accuracy, interpretability, and robustness for skin cancer classification, ultimately aiding clinicians in early diagnosis and treatment.

Role of post-translational modification of basic leucine zipper transcription factors in response to abiotic stresses in plants

Abiotic stresses substantially affect the growth and development of plants. Plants have evolved multiple strategies to cope with the environmental stresses, among which transcription factors play an important role in regulating the tolerance to abiotic stresses. Basic leucine zipper transcription factors (bZIP) are one of the largest gene families. The stability and activity of bZIP transcription factors could be regulated by different post-translational modifications (PTMs) in response to various intracellular or extracellular stresses. This paper introduces the structural feature and classification of bZIP transcription factors, followed by summarizing the PTMs of bZIP transcription factors, such as phosphorylation, ubiquitination and small ubiquitin-like modifier (SUMO) modification, in response to abiotic stresses. In addition, future perspectives were prospected, which may facilitate cultivating excellent stress-resistant crop varieties by regulating the PTMs of bZIP transcription factors.

Classifications of anterior segment structure of congenital corneal opacity in infants and toddlers by ultrasound biomicroscopy and slit-lamp microscopic photographs: an observational study

BackgroundThe structural features have an impact on the surgical prognosis for congenital corneal opacity (CCO). The structural classification system of CCO, however, is lacking. Based on data from ultrasound biomicroscopy (UBM) findings in infants and toddlers with CCO, this research proposed a classification system for the anterior segment structure severity.MethodsMedical records, preoperative UBM images and slit-lamp photographs of infants and toddlers diagnosed with CCO at University Third Hospital between December 2018 and June 2022 were reviewed. According to the anterior segment structural features observed in UBM images, eyes were classified as follows: U1, opaque cornea only; U2, central anterior synechia; U3, peripheral anterior synechia combined with angle closure; and U4, aniridia or lens anomaly. The opacity appearance and corneal vascularization density observed in slit-lamp photographs were assigned grades according to previous studies. The extent of vascularization was also recorded. The corresponding intraocular anomaly classifications and ocular surface lesion severity were analysed.ResultsAmong 81 eyes (65 patients), 41 (50.6%) were right eyes, and 40 (49.4%) were left eyes. The median age at examination was 6.91 months (n = 81, 1.00, 34.00). Two (2.5%) of the 81 eyes were classified as U1, 20 (24.7%) as U2, 22 (27.2%) as U3a, 11 (13.6%) as U3b and 26 (32.1%) as U4. Bilateral CCO eyes had more severe UBM classifications (P = 0.019), more severe dysgenesis (P = 0.012) and a larger angle closure (P = 0.009). Eyes with more severe UBM classifications had higher opacity grades (P = 0.003) and vascularization grades (P = 0.014) and a larger vascularization extent (P = 0.001). Eyes with dysgenesis had higher haze grades (P = 0.012) and more severe vascularization (P = 0.003 for density; P = 0.008 for extent), while the angle closure range was related to haze grade (P = 0.013) and vascularization extent (P = 0.003).ConclusionsThis classification method based on UBM and slit-lamp photography findings in the eyes of CCO infants and toddlers can truly reflect the degree of abnormality of the ocular surface and anterior segment and is correlated with the severity of ocular surface anomalies. This method might provide meaningful guidance for surgical procedure design and prognostic determinations for keratoplasty in CCO eyes.

Improved SVM Algorithm Financial Management Model for Data Mining

Financial crisis early warning is an important link in financial management. With its relatively simple structure and excellent classification performance, support vector machine is often used in intelligent warning of financial crises. A financial warning model based on improved support vector machine is proposed to address the shortcomings of current financial warning methods that cannot handle large-scale data well, and the accuracy and efficiency are not ideal. In response to the excessive indicators in the financial early warning indicator system, the principal component analysis method is studied for dimensionality reduction, reducing the computational complexity of the model, and improving the training and operational efficiency of the model. In response to the limited practicality of support vector machine, which can only search for the optimal solution under constraint conditions, the use of smooth support vector machine to replace support vector machine for predictive classification is studied. Finally, a financial crisis early warning model is constructed based on smooth support vector machine using principal component analysis for dimensionality reduction of input data. The performance of the model is tested using an internet listed company as an example. The findings demonstrate that Model 1 outperforms the other three models in terms of accuracy rate (95%), MAE, MSE and [Formula: see text] values (0.162, 0.174 and 0.169, respectively). Therefore, the financial crisis early warning model constructed by the research can properly forecast the company’s financial situation, thereby helping the enterprise to develop better.

Understanding AP2/ERF Transcription Factor Responses and Tolerance to Various Abiotic Stresses in Plants: A Comprehensive Review.

Abiotic stress is an adverse environmental factor that severely affects plant growth and development, and plants have developed complex regulatory mechanisms to adapt to these unfavourable conditions through long-term evolution. In recent years, many transcription factor families of genes have been identified to regulate the ability of plants to respond to abiotic stresses. Among them, the AP2/ERF (APETALA2/ethylene responsive factor) family is a large class of plant-specific proteins that regulate plant response to abiotic stresses and can also play a role in regulating plant growth and development. This paper reviews the structural features and classification of AP2/ERF transcription factors that are involved in transcriptional regulation, reciprocal proteins, downstream genes, and hormone-dependent signalling and hormone-independent signalling pathways in response to abiotic stress. The AP2/ERF transcription factors can synergise with hormone signalling to form cross-regulatory networks in response to and tolerance of abiotic stresses. Many of the AP2/ERF transcription factors activate the expression of abiotic stress-responsive genes that are dependent or independent of abscisic acid and ethylene in response to abscisic acid and ethylene. In addition, the AP2/ERF transcription factors are involved in gibberellin, auxin, brassinosteroid, and cytokinin-mediated abiotic stress responses. The study of AP2/ERF transcription factors and interacting proteins, as well as the identification of their downstream target genes, can provide us with a more comprehensive understanding of the mechanism of plant action in response to abiotic stress, which can improve plants' ability to tolerate abiotic stress and provide a more theoretical basis for increasing plant yield under abiotic stress.

Graph-Neural-Network-Based Unsupervised Learning of the Temporal Similarity of Structural Features Observed in Molecular Dynamics Simulations.

Classification of molecular structures is a crucial step in molecular dynamics (MD) simulations to detect various structures and phases within systems. Molecular structures, which are commonly identified using order parameters, were recently identified using machine learning (ML), that is, the ML models acquire structural features using labeled crystals or phases via supervised learning. However, these approaches may not identify unlabeled or unknown structures, such as the imperfect crystal structures observed in nonequilibrium systems and interfaces. In this study, we proposed the use of a novel unsupervised learning framework, denoted temporal self-supervised learning (TSSL), to learn structural features and design their parameters. In TSSL, the ML models learn that the structural similarity is learned via contrastive learning based on minor short-term variations caused by perturbations in MD simulations. This learning framework is applied to a sophisticated architecture of graph neural network models that use bond angle and length data of the neighboring atoms. TSSL successfully classifies water and ice crystals based on high local ordering, and furthermore, it detects imperfect structures typical of interfaces such as the water-ice and ice-vapor interfaces.

Sheathless inertial particle focusing methods within microfluidic devices: a review.

The ability to manipulate and focus particles within microscale fluidic environments is crucial to advancing biological, chemical, and medical research. Precise and high-throughput particle focusing is an essential prerequisite for various applications, including cell counting, biomolecular detection, sample sorting, and enhancement of biosensor functionalities. Active and sheath-assisted focusing techniques offer accuracy but necessitate the introduction of external energy fields or additional sheath flows. In contrast, passive focusing methods exploit the inherent fluid dynamics in achieving high-throughput focusing without external actuation. This review analyzes the latest developments in strategies of sheathless inertial focusing, emphasizing inertial and elasto-inertial microfluidic focusing techniques from the channel structure classifications. These methodologies will serve as pivotal benchmarks for the broader application of microfluidic focusing technologies in biological sample manipulation. Then, prospects for future development are also predicted. This paper will assist in the understanding of the design of microfluidic particle focusing devices.

Classification of the Syntactic Structure of English and Uzbek Languages

This work gives information about classification of the syntactic structure of English and Uzbek languages. The study of the syntactic typology of the English and Uzbek languages is a current and important topic that opens up new perspectives for understanding linguistic features, translation, language teaching and intercultural communication.

Kepribadian Dan Emosi Tokoh Saka Dalam Novel Egosentris Karya Syahid Muhammad : Kajian Psikologi Sastra

This research discusses the personality and emotions of the character Saka in the novel Egocentric by Syahid Muhammad. The aim of the research is to explain an in-depth and detailed understanding of the personality structure and emotional classification of the Saka character. This research is a qualitative descriptive study. The approach used is literary psychology using Sigmund Freud's personality theory and David Krech's emotional classification theory. The data for this research is a quote from the novel Egocentric by Syahid Muhammad. The data collection method in this research uses the literature study method. The results and discussion show that Saka's character has an id, ego and superego personality structure and shows Saka's emotional classification.

Fluorescent small molecule donors.

Small molecule donors (SMDs) play subtle roles in the signaling mechanism and disease treatments. While many excellent SMDs have been developed, dosage control, targeted delivery, spatiotemporal feedback, as well as the efficiency evaluation of small molecules are still key challenges. Accordingly, fluorescent small molecule donors (FSMDs) have emerged to meet these challenges. FSMDs enable controllable release and non-invasive real-time monitoring, providing significant advantages for drug development and clinical diagnosis. Integration of FSMDs with chemotherapeutic, photodynamic or photothermal properties can take full advantage of each mode to enhance therapeutic efficacy. Given the remarkable properties and the thriving development of FSMDs, we believe a review is needed to summarize the design, triggering strategies and tracking mechanisms of FSMDs. With this review, we compiled FSMDs for most small molecules (nitric oxide, carbon monoxide, hydrogen sulfide, sulfur dioxide, reactive oxygen species and formaldehyde), and discuss recent progress concerning their molecular design, structural classification, mechanisms of generation, triggered release, structure-activity relationships, and the fluorescence response mechanism. Firstly, from the large number of fluorescent small molecular donors available, we have organized the common structures for producing different types of small molecules, providing a general strategy for the development of FSMDs. Secondly, we have classified FSMDs in terms of the respective donor types and fluorophore structures. Thirdly, we discuss the mechanisms and factors associated with the controlled release of small molecules and the regulation of the fluorescence responses, from which universal guidelines for optical properties and structure rearrangement were established, mainly involving light-controlled, enzyme-activated, reactive oxygen species-triggered, biothiol-triggered, single-electron reduction, click chemistry, and other triggering mechanisms. Fourthly, representative applications of FSMDs for trackable release, and evaluation monitoring, as well as for visible in vivo treatment are outlined, to illustrate the potential of FSMDs in drug screening and precision medicine. Finally, we discuss the opportunities and remaining challenges for the development of FSMDs for practical and clinical applications, which we anticipate will stimulate the attention of researchers in the diverse fields of chemistry, pharmacology, chemical biology and clinical chemistry. With this review, we hope to impart new understanding thereby enabling the rapid development of the next generation of FSMDs.