Electrochemical biosensors for early detection of breast cancer.

Breast cancer continues to be a significant contributor to global cancer deaths, particularly among women. This highlights the critical role of early detection and treatment in boosting survival rates. While conventional diagnostic methods like mammograms, biopsies, ultrasounds, and MRIs are valuable tools, limitations exist in terms of cost, invasiveness, and the requirement for specialized equipment and trained personnel. Recent shifts towards biosensor technologies offer a promising alternative for monitoring biological processes and providing accurate health diagnostics in a cost-effective, non-invasive manner. These biosensors are particularly advantageous for early detection of primary tumors, metastases, and recurrent diseases, contributing to more effective breast cancer management. The integration of biosensor technology into medical devices has led to the development of low-cost, adaptable, and efficient diagnostic tools. In this framework, electrochemical screening platforms have garnered significant attention due to their selectivity, affordability, and ease of result interpretation. The current review discusses various breast cancer biomarkers and the potential of electrochemical biosensors to revolutionize early cancer detection, making provision for new diagnostic platforms and personalized healthcare solutions.

Application of MinION Long-Read Sequencer for Semi-targeted Detection of Foodborne Pathogens.

Foodborne pathogens remain a serious health issue in developed and developing countries. Safeness of food products has been assured for years with culture-based microbiological methods; however, these present several limitations such as turnaround time and extensive hands-on work, which have been typically address taking advantage of DNA-based methods such as real-time PCR (qPCR). These, and other similar techniques, are targeted assays, meaning that they are directed for the specific detection of one specific microbe. Even though reliable, this approach suffers from an important limitation that unless specific assays are design for every single pathogen potentially present, foods may be considered erroneously safe. To address this problem, next-generation sequencing (NGS) can be used as this is a nontargeted method; thus it has the capacity to detect every potential threat present. In this chapter, a protocol for the simultaneous detection and preliminary serotyping of Salmonella enterica serovar Enteritidis, Salmonella enterica serovar Typhimurium, Listeria monocytogenes, and Escherichia coli O157:H7 is described.

Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions.

The presence of aluminum (Al3+) and fluoride (F-) ions in the environment can be harmful to ecosystems and human health, highlighting the need for accurate and efficient monitoring. In this paper, an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum (Al3+) and fluoride (F-) ions in aqueous solutions. The proposed method involves the synthesis of sulfur-functionalized carbon dots (C-dots) as fluorescence probes, with fluorescence enhancement upon interaction with Al3+ ions, achieving a detection limit of 4.2 nmol/L. Subsequently, in the presence of F- ions, fluorescence is quenched, with a detection limit of 47.6 nmol/L. The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python, followed by data preprocessing. Subsequently, the fingerprint data is subjected to cluster analysis using the K-means model from machine learning, and the average Silhouette Coefficient indicates excellent model performance. Finally, a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions. The results demonstrate that the developed model excels in terms of accuracy and sensitivity. This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment, making it a valuable tool for safeguarding our ecosystems and public health.

Enhanced tomato detection in greenhouse environments: a lightweight model based on S-YOLO with high accuracy.

Introduction: Efficiently and precisely identifying tomatoes amidst intricate surroundings is essential for advancing the automation of tomato harvesting. Current object detection algorithms are slow and have low recognition accuracy for occluded and small tomatoes. Methods: To enhance the detection of tomatoes in complex environments, a lightweight greenhouse tomato object detection model named S-YOLO is proposed, based on YOLOv8s with several key improvements: (1) A lightweight GSConv_SlimNeck structure tailored for YOLOv8s was innovatively constructed, significantly reducing model parameters to optimize the model neck for lightweight model acquisition. (2) An improved version of the α-SimSPPF structure was designed, effectively enhancing the detection accuracy of tomatoes. (3) An enhanced version of the ß-SIoU algorithm was proposed to optimize the training process and improve the accuracy of overlapping tomato recognition. (4) The SE attention module is integrated to enable the model to capture more representative greenhouse tomato features, thereby enhancing detection accuracy. Results: Experimental results demonstrate that the enhanced S-YOLO model significantly improves detection accuracy, achieves lightweight model design, and exhibits fast detection speeds. Experimental results demonstrate that the S-YOLO model significantly enhances detection accuracy, achieving 96.60% accuracy, 92.46% average precision (mAP), and a detection speed of 74.05 FPS, which are improvements of 5.25%, 2.1%, and 3.49 FPS respectively over the original model. With model parameters at only 9.11M, the S-YOLO outperforms models such as CenterNet, YOLOv3, YOLOv4, YOLOv5m, YOLOv7, and YOLOv8s, effectively addressing the low recognition accuracy of occluded and small tomatoes. Discussion: The lightweight characteristics of the S-YOLO model make it suitable for the visual system of tomato-picking robots, providing technical support for robot target recognition and harvesting operations in facility environments based on mobile edge computing.

Enhancing brain tumor detection in MRI images using YOLO-NeuroBoost model.

Brain tumors are diseases characterized by abnormal cell growth within or around brain tissues, including various types such as benign and malignant tumors. However, there is currently a lack of early detection and precise localization of brain tumors in MRI images, posing challenges to diagnosis and treatment. In this context, achieving accurate target detection of brain tumors in MRI images becomes particularly important as it can improve the timeliness of diagnosis and the effectiveness of treatment. To address this challenge, we propose a novel approach-the YOLO-NeuroBoost model. This model combines the improved YOLOv8 algorithm with several innovative techniques, including dynamic convolution KernelWarehouse, attention mechanism CBAM (Convolutional Block Attention Module), and Inner-GIoU loss function. Our experimental results demonstrate that our method achieves mAP scores of 99.48 and 97.71 on the Br35H dataset and the open-source Roboflow dataset, respectively, indicating the high accuracy and efficiency of this method in detecting brain tumors in MRI images. This research holds significant importance for improving early diagnosis and treatment of brain tumors and provides new possibilities for the development of the medical image analysis field.

Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer's spectrum.

BACKGROUND: Digital cognitive assessments, particularly those that can be done at home, present as low-burden biomarkers for participants and patients alike, but their effectiveness in the diagnosis of Alzheimer's disease (AD) or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk of cognitive decline. METHODS: We analyzed >500 Alzheimer's Disease Neuroimaging Initiative participants who underwent a brief digital cognitive assessment and amyloid beta (Aß)/tau positron emission tomography scans, examining their ability to distinguish cognitive status and predict cognitive decline. RESULTS: Performance on the digital cognitive assessment was superior to both cortical Aß and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation. DISCUSSION: Digital assessments are effective at identifying at-risk individuals, supporting their utility as low-burden tools for early AD detection and monitoring. HIGHLIGHTS: Performance on digital cognitive assessments predicts progression to mild cognitive impairment at a higher proficiency compared to amyloid beta and tau. Deficits in mnemonic discrimination are indicative of future cognitive decline. Impaired mnemonic discrimination predicts future entorhinal and inferior temporal tau.

Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses.

BACKGROUND: Metagenomics is a powerful approach for the detection of unknown and novel pathogens. Workflows based on Illumina short-read sequencing are becoming established in diagnostic laboratories. However, high sequencing depth requirements, long turnaround times, and limited sensitivity hinder broader adoption. We investigated whether we could overcome these limitations using protocols based on untargeted sequencing with Oxford Nanopore Technologies (ONT), which offers real-time data acquisition and analysis, or a targeted panel approach, which allows the selective sequencing of known pathogens and could improve sensitivity. METHODS: We evaluated detection of viruses with readily available untargeted metagenomic workflows using Illumina and ONT, and an Illumina-based enrichment approach using the Twist Bioscience Comprehensive Viral Research Panel (CVRP), which targets 3153 viruses. We tested samples consisting of a dilution series of a six-virus mock community in a human DNA/RNA background, designed to resemble clinical specimens with low microbial abundance and high host content. Protocols were designed to retain the host transcriptome, since this could help confirm the absence of infectious agents. We further compared the performance of commonly used taxonomic classifiers. RESULTS: Capture with the Twist CVRP increased sensitivity by at least 10-100-fold over untargeted sequencing, making it suitable for the detection of low viral loads (60 genome copies per ml (gc/ml)), but additional methods may be needed in a diagnostic setting to detect untargeted organisms. While untargeted ONT had good sensitivity at high viral loads (60,000 gc/ml), at lower viral loads (600-6000 gc/ml), longer and more costly sequencing runs would be required to achieve sensitivities comparable to the untargeted Illumina protocol. Untargeted ONT provided better specificity than untargeted Illumina sequencing. However, the application of robust thresholds standardized results between taxonomic classifiers. Host gene expression analysis is optimal with untargeted Illumina sequencing but possible with both the CVRP and ONT. CONCLUSIONS: Metagenomics has the potential to become standard-of-care in diagnostics and is a powerful tool for the discovery of emerging pathogens. Untargeted Illumina and ONT metagenomics and capture with the Twist CVRP have different advantages with respect to sensitivity, specificity, turnaround time and cost, and the optimal method will depend on the clinical context.

Establishment of LAMP-CRISPR/Cas12a for rapid detection of Escherichia coli O157:H7 and one-pot detection.

Escherichia coli O157:H7 is a pathogenic serotype of Escherichia coli. Consumption of food contaminated with E. coli O157:H7 could cause a range of diseases. Therefore, it is of great importance to establish rapid and accurate detection methods for E. coli O157:H7 in food. In this study, based on LAMP and combined with the CRISPR/cas12a system, a sensitive and specific rapid detection method for E. coli O157:H7 was established, and One-Pot detection method was also constructed. The sensitivity of this method could stably reach 9.2 × 10° CFU/mL in pure culture, and the whole reaction can be completed within 1 h. In milk, E. coli O157:H7 with an initial contamination of 7.4 × 10° CFU/mL only needed to be cultured for 3 h to be detected. The test results can be judged by the fluorescence curve or by visual observation under a UV lamp, eliminating instrument limitations and One-Pot detection can effectively prevent the problem of false positives. In a word, the LAMP-CRISPR/cas12a system is a highly sensitive and convenient method for detecting E. coli O157:H7.

Deep Learning Based Shear Wave Detection and Segmentation Tool for Use in Point-of-Care for Chronic Liver Disease Assessments.

OBJECTIVE: As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this study is to test the performance of a new software tool implemented in Velacur (Sonic Incytes), a liver stiffness and ultrasound attenuation measurement device, on patients with MASLD. This tool employs a deep learning-based method to detect and segment shear waves in the liver tissue for subsequent analysis to improve tissue characterization for patient diagnosis. METHODS: This new tool consists of a deep learning based algorithm, which was trained on 15,045 expert-segmented images from 103 patients, using a U-Net architecture. The algorithm was then tested on 4429 images from 36 volunteers and patients with MASLD. Test subjects were scanned at different clinics with different Velacur operators. Evaluation was performed on both individual images (image based) and averaged across all images collected from a patient (patient based). Ground truth was defined by expert segmentation of the shear waves within each image. For evaluation, sensitivity and specificity for correct wave detection in the image were calculated. For those images containing waves, the Dice coefficient was calculated. A prototype of the software tool was also implemented on Velacur and assessed by operators in real world settings. RESULTS: The wave detection algorithm had a sensitivity of 81% and a specificity of 84%, with a Dice coefficient of 0.74 and 0.75 for image based and patient-based averages respectively. The implementation of this software tool as an overlay on the B-Mode ultrasound resulted in improved exam quality collected by operators. CONCLUSION: The shear wave algorithm performed well on a test set of volunteers and patients with metabolic dysfunction-associated steatotic liver disease. The addition of this software tool, implemented on the Velacur system, improved the quality of the liver assessments performed in a real world, point of care setting.

Corrigendum: PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images.

[This corrects the article DOI: 10.3389/fonc.2024.1347123.].

Benzimidazolium Salt Modified Microporous Silica-Coupled Iron Oxide Nanoparticles: Material Engineered for Nitrate Removal.

Today's extensive use of inorganic fertilizers in agricultural techniques has increased the concentration of nitrate in drinking water beyond safety limits, causing serious health problems in humans such as thyroidism and methemoglobinemia. Therefore, the present work describes the synthesis of a benzimidazolium salt-based fluorescent chemosensor (KG3) via a multistep synthesis which detects nitrate ions in aqueous medium. This was validated using various analytical techniques such as fluorescence spectroscopy, UV-visible spectroscopy, and electrochemical studies with a detection limit of 0.032 µM without any interference from other active water pollutants. Subsequently, KG3 is further modified with the help of iron oxide nanoparticles (Fe3O4 NPs) and silica to obtain the SiO2@Fe3O4-KG3 nanocomposite, which was immobilized over a polyether sulfone membrane and evaluated for removal of nitrate ions from groundwater with a removal efficiency of 96%. Moreover, the engineered composite membrane can serve as a solid-state fluorescence sensor to detect NO3- ions, which was demonstrated through a portable mobile-based prototype employing a hue, saturation, and value parameter model.

Time-resolved NMR detection of prolyl-hydroxylation in intrinsically disordered region of HIF-1α.

Prolyl-hydroxylation is an oxygen-dependent posttranslational modification (PTM) that is known to regulate fibril formation of collagenous proteins and modulate cellular expression of hypoxia-inducible factor (HIF) α subunits. However, our understanding of this important but relatively rare PTM has remained incomplete due to the lack of biophysical methodologies that can directly measure multiple prolyl-hydroxylation events within intrinsically disordered proteins. Here, we describe a real-time 13C-direct detection NMR-based assay for studying the hydroxylation of two evolutionarily conserved prolines (P402 and P564) simultaneously in the intrinsically disordered oxygen-dependent degradation domain of hypoxic-inducible factor 1α by exploiting the "proton-less" nature of prolines. We show unambiguously that P564 is rapidly hydroxylated in a time-resolved manner while P402 hydroxylation lags significantly behind that of P564. The differential hydroxylation rate was negligibly influenced by the binding affinity to prolyl-hydroxylase enzyme, but rather by the surrounding amino acid composition, particularly the conserved tyrosine residue at the +1 position to P564. These findings support the unanticipated notion that the evolutionarily conserved P402 seemingly has a minimal impact in normal oxygen-sensing pathway.

Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies.

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants' subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent's intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Phosphorylated Tau at T181 accumulates in the serum of hibernating Syrian hamsters and rapidly disappears after arousal.

The search for biomarkers for the early diagnosis of neurodegenerative diseases is a growing area. Numerous investigations are exploring minimally invasive and cost-effective biomarkers, with the detection of phosphorylated Tau (pTau) protein emerging as one of the most promising fields. pTau is the main component of the paired helical filaments found in the brains of Alzheimer's disease cases and serves as a precursor in the formation of neurofibrillary tangles (NFTs). Recent research has revealed that analysis of p-Tau181, p-Tau217 and p-Tau231 in blood may be an option for detecting the preclinical stage of Alzheimer's disease. In this study, we have analyzed the values of pTau 181 in the serum of Syrian hamsters during hibernation. Naturally, over the course of hibernation, these animals exhibit a reversible accumulation of pTau in the brain tissue, which rapidly disappears upon awakening. A biosensing system based on the interferometric optical detection method was used to measure the concentration of pTau181 protein in serum samples from Syrian hamsters. This method eliminates the matrix effect and amplifies the signal obtained by using silicon dioxide nanoparticles (SiO2 NPs) biofunctionalized with the αpTau181 antibody. Our results indicate a substantial increase in the serum concentration of pTau in threonine-181 during hibernation, which disappears completely 2-3 h after awakening. Investigating the mechanism by which pTau protein appears in the blood non-pathologically may enhance current diagnostic techniques. Furthermore, since this process is reversible, and no tangles are detected in the brains of hibernating hamsters, additional analysis may contribute to the discovery of improved biomarkers. Additionally, exploring drugs targeting pTau to prevent the formation of tangles or studying the outcomes of any pTau-targeted treatment could be valuable.

Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm.

Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees' face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees' fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models.

Flying foxes optimization with reinforcement learning for vehicle detection in UAV imagery.

Intelligent transportation systems (ITS) are globally installed in smart cities, which enable the next generation of ITS depending on the potential integration of autonomous and connected vehicles. Both technologies are being tested widely in various cities across the world. However, these two developing technologies are vital in allowing a fully automatic transportation system; it is necessary to automate other transportation and road components. Unmanned aerial vehicles (UAVs) or drones are utilized for many surveillance applications in the ITS. Detecting on-ground vehicles in drone images is significant for disaster rescue operations, traffic and parking management, and navigating uneven territories. This study presents a flying foxes optimization with deep learning-based vehicle detection and classification model on aerial images (FFODL-VDCAI) technique for ITS application. The main objective of the FFODL-VDCAI technique is to automate and accurately classify vehicles that exist in aerial images. Three primary processes are involved in the presented FFODL-VDCAI technique. Initially, the FFODL-VDCAI approach utilizes YOLO-GD (Ghost-Net and Depthwise convolution) for vehicle detection, where the YOLO-GD uses lightweight Ghost Net in place on the backbone network of YOLO-v4 and interchanges the conventional convolutional with depthwise separable convolutional and pointwise convolutional. Next, the FFO technique is used for hyperparameter tuning the Ghost Net technique. Finally, a deep Q-network (DQN) based reinforcement learning technique is used to classify detected vehicles effectively. A comprehensive simulation analysis of the FFODL-VDCAI methodology is conducted on the UAV image dataset. The performance validation of the FFODL-VDCAI methodology exhibited superior values of 96.15% and 92.03% under PSU and Stanford datasets concerning various aspects.

A two-stage defect detection method for unevenly illuminated self-adhesive printed materials.

The process of printing defect detection usually suffers from challenges such as inaccurate defect extraction and localization, caused by uneven illumination and complex textures. Moreover, image difference-based defect detection methods often result in numerous small-scale pseudo defects. To address these challenges, this paper proposes a comprehensive defect detection approach that integrates brightness correction and a two-stage defect detection strategy for self-adhesive printed materials. Concretely, a joint bilateral filter coupled with brightness correction corrects uneven brightness properly, meanwhile smoothing the grid-like texture in complex printed material images. Then, in the first detection stage, an image difference method based on a bright-dark difference template group is designed to effectively locate printing defects despite slight brightness fluctuations. Afterward, a discriminative method based on feature similarity is employed to filter out small-scale pseudo-defects in the second detection stage. The experimental results show that the improved difference method achieves an average precision of 99.1% in defect localization on five different printing pattern samples. Furthermore, the second stage reduces the false detection rate to under 0.5% while maintaining the low missed rate.

Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.

OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and human visual detection-based motion analysis (Human-MA), including costly equipment, time-consuming analysis, and restricted experimental settings. This study aims to assess the precision of OpenPose-MA in comparison to Human-MA, using 3D-MA as the reference standard. The study involved a cohort of 21 young and healthy adults. OpenPose-MA employed the OpenPose algorithm, a deep learning-based open-source two-dimensional (2D) pose estimation method. Human-MA was conducted by a skilled physiotherapist. The knee valgus angle during a drop vertical jump task was computed by OpenPose-MA and Human-MA using the same frontal-plane video image, with 3D-MA serving as the reference standard. Various metrics were utilized to assess the reproducibility, accuracy and similarity of the knee valgus angle between the different methods, including the intraclass correlation coefficient (ICC) (1, 3), mean absolute error (MAE), coefficient of multiple correlation (CMC) for waveform pattern similarity, and Pearson's correlation coefficients (OpenPose-MA vs. 3D-MA, Human-MA vs. 3D-MA). Unpaired t-tests were conducted to compare MAEs and CMCs between OpenPose-MA and Human-MA. The ICCs (1,3) for OpenPose-MA, Human-MA, and 3D-MA demonstrated excellent reproducibility in the DVJ trial. No significant difference between OpenPose-MA and Human-MA was observed in terms of the MAEs (OpenPose: 2.4° [95%CI: 1.9-3.0°], Human: 3.2° [95%CI: 2.1-4.4°]) or CMCs (OpenPose: 0.83 [range: 0.99-0.53], Human: 0.87 [range: 0.24-0.98]) of knee valgus angles. The Pearson's correlation coefficients of OpenPose-MA and Human-MA relative to that of 3D-MA were 0.97 and 0.98, respectively. This study demonstrated that OpenPose-MA achieved satisfactory reproducibility, accuracy and exhibited waveform similarity comparable to 3D-MA, similar to Human-MA. Both OpenPose-MA and Human-MA showed a strong correlation with 3D-MA in terms of knee valgus angle excursion.

Comparative analysis of electrophoresis and interferometric optical detection method for molecular weight determination of proteins.

Analytical detection methods play a pivotal role in scientific research, enabling the identification and quantification of specific analytes in various disciplines. This scientific report aims to compare two very different methodologies for determining the Molecular Mass (MM, also known as Molecular Weight, MW) of proteins: electrophoresis gel and the Interferometric Optical Detection Method (IODM). For this purpose, several proteins with different MM were selected. The electrophoresis technique was employed to validate the structure and MM of different parts or fragments of the Matrix Metallopeptidase 9 antibody (anti-MMP9), antibody against S100 calcium binding protein A6 (anti-S100A6) and Cystatin S4 antibody (anti-CST4) by examining the presence of bands with expected sizes. The IODM was applied to study the above-mentioned proteins (part of the antibodies) together with the protein G, as a reference to correlate the MM and protein sizes with the measured signal. We report the evidence of IODM as a competitive analytical approach for the determination of the MM of proteins for the first time. This innovative method allows for accurate MM determination using minimal sample volumes and concentrations, employing a simple experimental procedure that eliminates the requirement for protein denaturation.

Advances in the synthesis and properties of sulfur quantum dots for food safety detection and antibacterial applications.

Food safety is closely related to human health and has become a worldwide, pressing concern. Food safety analysis is essential for ensuring food safety. Sulfur quantum dots (SQDs), a new type of zero-dimensional metal-free nanomaterials, have recently become the focus of scientific research due to their good luminescence properties, dispersibility, biocompatibility, and inherent antibacterial properties. This review focuses on recent advances in SQDs, with emphasis on their practical applications in the food field. First, commonly used methods for the synthesis of SQDs are presented, including traditional and emerging strategies. The properties of SQDs are then analyzed in detail, particularly their luminescence properties, catalytic activities, and reducing properties. Next, the use of SQDs in food safety detection and antibacterial fields are elaborated. Finally, this review discusses the challenges associated with the use of SQDs in food safety detection and antimicrobial applications.