Good Application Research Articles

Ginseng-DF ameliorates intestinal mucosal barrier injury and enhances immunity in immunosuppressed mice by regulating MAPK/NF-κB signaling pathways.

Dietary fiber (DF) has a good application prospect in effectively restoring the integrity of the intestinal mucosal barrier. Ginseng-DF has good physicochemical properties and physiological activity and shows positive effects in enhancing immunity. The aim of this study was to investigate the protective effect of Ginseng-DF on intestinal mucosal barrier injury induced by cyclophosphamide (CTX) in immunosuppressed mice and its possible mechanism. The effects of Gginseng-DF on immune function in mice were studied by delayed-type hypersensitivy, lymphocyte proliferation assay and NK cytotoxicity assay, the T lymphocyte differentiation and intestinal barrier integrity were analyzed by flow cytometry and western blot. Ginseng-DF (2.5% and 5%) could attenuate the inhibition of DTH response by CTX, promote the transformation and proliferation of lymphocytes, and stimulate NK effector cell activity. At the same time, Ginseng-DF could restore the proportion of CD4+/CD8+ T lymphocytes induced by CTX to different extents, improved spleen tissue damage, promoted the secretion of immunoglobulin IgG, and enhanced body immunity. More importantly, Ginseng-DF could up-regulate the contents of TNF-α, IFN-γ, IL-6 and IL-1β in serum and intestine of immunosuppressed mice to maintain the balance between Th1/Th2 cytokines, and improve the permeability of intestinal mucosal barrier. Meanwhile, Ginseng-DF could reduce intestinal epithelial cell apoptosis and improve intestinal adaptive immunity in CTX-induced immunosuppressed mice by regulating MAPK/NF-κB signaling pathway. Ginseng-DF can be used as a safe dietary supplement to enhance body immunity and reduce intestinal mucosal injury caused by CTX.

Transforming Adolescent Smiles: Correcting Skeletal Mandibular Retrusion and Bimaxillary Protrusion with Clear Aligners.

Maxillary protrusion combined with mandibular retraction is a highly prevalent but extremely complex maxillofacial deformity that can have a serious negative impact on patients' facial aesthetics and mental health. The traditional orthodontic treatment strategy often involves extracting 4 first premolars and conventional fixed techniques, combined with mini-implant screws, to retract the anterior teeth and improve facial protrusion. In recent years, an invisible orthodontic technique, without brackets, has become increasingly popular. However, while an invisible aligner has been used in some cases with reasonable results, there remain significant challenges in achieving a perfect outcome. This case report presents an adolescent patient with bimaxillary protrusion and mandibular retrognathia. Based on the characteristics of the invisible aligners and the growth characteristics of the adolescent's teeth and jawbone, we designed precise three-dimensional tooth movement and corresponding resistance/over-correction for each tooth, while utilizing the patient's jawbone growth potential to promote rapid development of the mandible, accurately and efficiently correcting bimaxillary protrusion and skeletal mandibular retrognathia. The patient's facial aesthetics, especially the lateral morphology, have been greatly improved, and various aesthetic indicators have also shown significant changes, and to the patient's great benefit, invasive mini-implant screws were not used during the treatment. This case highlights the advantages of using invisible aligners in adolescent maxillary protrusion combined with mandibular retraction patients. Furthermore, comprehensive and accurate design combined with good application of growth potential can also enable invisible orthodontic technology to achieve perfect treatment effects in tooth extractions, providing clinical guidance for orthodontists.

Electronic maternal and child health application usability, feasibility and acceptability among healthcare providers in Amhara region, Ethiopia.

An innovative electronic Maternal and Child Health (eMCH) application was developed to support operational and clinical decision-making in maternal and child health services. End-user-based evaluation of eHealth application is a critical step to ascertain how successfully users can learn and use it, and improve the technology. Therefore, this study aimed to evaluate the eMCH tool usability, feasibility, and acceptability among healthcare providers (HCPs) in the Amhara region, Ethiopia. A cross-sectional study was conducted among HCPs working in six public healthcare facilities. The usability evaluation was done on 24 HCPs across three professional categories using the ISO 9241-11 usability guideline. One hundred nine HCPs were participated in the feasibility and acceptability study. Data were collected using a standard usability tool, think-aloud protocol, a self-administered approach, and Open Broadcaster Software Studio version 26.1.1 video recorder. Descriptive statistics were used to describe the data. A Kruskal-Wallis test was used to measure the association between mean scores and categories of HCPs. The recorded videos were used for the log file analysis method. None of the HCP categories were able to complete all the tasks without errors. The average number of errors and restarts were 7.5 and 2.8, respectively. The average number of restarts was directly proportional to the average number of errors. The participants successfully completed more than 70% of the tasks without requiring any assistance or guidance. Forty-seven comments or errors were identified from the think-aloud analysis and 22 comments from the usability metrics analysis. Overall, statistically significant performance differences were observed among the three HCP groups across the majority of the usability evaluation metrics. Fifty-seven percent of HCPs scored higher than the mean on the feasibility study. Slightly higher than half, 56 (51.4%), of the HCPs scored higher than the mean score on the acceptability study. The usability evaluation identified vital comments and usability flaws that were essential for the eMCH tool to be upgraded. The tool was feasible and acceptable as reported by end-users. Therefore, the errors and usability flaws of the tool should be fixed before deployment to other healthcare settings.

Polarization‐independent liquid crystal device with microdomain twisted structure

AbstractThe polarization‐independent liquid crystal (LC) phase modulators can greatly improve the efficiency and reduce the complexity of the optical system. However, it is difficult for the LC phase modulator to achieve both good low‐loss modulation and polarization‐independent properties. A LC phase modulator of single layer microdomain twisted structure has been proposed to achieve good polarization independence and low loss in phase modulation. However, the multi‐exposure in fabrication is difficult to realize in production process. To simplify the fabrication process, an alternate LC (ALC) mask is proposed with the alternate arrangement of twist‐nematic (TN) LC and nematic LC. The fabrication process is optimized to two steps exposure with the usage of ALC and is suitable for large‐amount‐production. The measurement results showed that the single layer microdomain twisted LC phase modulator may get very low polarization dependence, about 3.52% under 2π phase modulation depth, with low loss. The device shows good potential application in optical communication, wearable device, and display.

Sometimes simple is good enough: An analysis of methods for residential building population estimation

AbstractResidential building population data can be useful in a breadth of urban planning, health, transportation, and business applications. Unfortunately, complete datasets of residential building populations are not widely available for use in Canada, and therefore either larger census geographies are used or residential building populations must be estimated. This research explores four different methods of estimating residential building populations, including: an equal allocation method, two measures based on building volume, and a novel method that integrates census data at the dissemination area level to calibrate a population estimation model. This work comprises three parts: 1) a description of these approaches, 2) an evaluation of their validity in a case study in Hamilton, Ontario, and 3) an application of these methods in measuring spatial accessibility to schools. Our results show that most methods yield very similar results, and most provide reasonable estimates of building populations that could be useful for some analytical tasks. However, all methods resulted in instances of error, particularly for the largest population buildings. We conclude that while more complex methods do not significantly outperform simpler methods based on building volume alone, the blend of these methods could yield more accurate population predictions.

Clinical applications and challenges of metagenomic next-generation sequencing in the diagnosis of pediatric infectious disease

Abstract Infectious diseases seriously threaten the lives of children. Timely and accurate detection of pathogenic microorganisms and targeted medication are the keys to the diagnosing and treatment of infectious diseases in children. The next-generation metagenomic sequencing technology has attracted great attention in infectious diseases because of its characteristics such as no culture, high throughput, short detection cycle, wide coverage, and a good application prospect. In this paper, we review the studies of metagenomic next-generation sequencing in pediatric infectious diseases and analyze the challenges of its application in pediatric diseases.

A one-dimensional computational model for blood flow in an elastic blood vessel with a rigid catheter.

Strokes are one of the leading causes of death in the United States. Stroke treatment involves removal or dissolution of the obstruction (usually a clot) in the blocked artery by catheter insertion. A computer simulation to systematically plan such patient-specific treatments needs a network of about 105 blood vessels including collaterals. The existing computational fluid dynamic (CFD) solvers are not employed for stroke treatment planning as they are incapable of providing solutions for such big arterial trees in a reasonable amount of time. This work presents a novel one-dimensional mathematical formulation for blood flow modeling in an elastic blood vessel with a centrally placed rigid catheter. The governing equations are first-order hyperbolic partial differential equations, and the hypergeometric function needs to be computed to obtain the characteristic system of these hyperbolic equations. We employed the Discontinuous Galerkin method to solve the hyperbolic system and validated the implementation by comparing it against a well-established 3D CFD solver using idealized vessels and a realistic truncated arterial network. The results showed clinically insignificant differences in steady flow cases, with overall variations between 1D and 3D models remaining below 10%. Additionally, the solver accurately captured wave reflection phenomena at domain discontinuities in unsteady cases. A primary advantage of this model over 3D solvers is its ease in obtaining a discretized geometry of complex vasculatures with multiple arterial branches. Thus, the 1D computational model offers good accuracy and applicability in simulating complex vasculatures, demonstrating promising potential for investigating patient-specific endovascular interventions in strokes.

A STOCHASTIC MODEL FOR LENGTH BIASED LOAI DISTRIBUTION WITH PROPERTIES AND ITS APPLICATIONS

One of the most important applications of statistical analysis is in health research and application. Cancer studies are mostly required special statistical considerations in order to find the appropriate model for fitting the survival data. In this paper, we examine some general models leading to a weighted distribution. The developed distribution, also known as the length biased model, is referred to as the length biased Loai distribution. This distribution is a specific type of basic distribution, the Loai distribution. The length biased distribution has been compared with the original distribution. Some statistical properties of this distribution are derived, such as moments, the moment generating function, the reliability analysis, and the included functions. Also, the distribution of order statistics, quantile function, and likelihood ratio test are presented. The Bonferroni and Lorenz carvers, as well as the Rnyi and Tsallis entropies, are derived. The method of maximum likelihood estimation is used to estimate the distribution parameters. A simulation study is performed to investigate the performance of the estimation. The real data applications demonstrate that the proposed distribution can provide better results than several well-known distributions.

Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection

Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANS and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90–15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility–privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements.

Optically controlled variable damping system based on PLZT ceramic/ERF in rectangular microchannel

Variable damping control technology based on intelligent materials of electromagnetic excitation has been widely used in the field of (semi-) active vibration control and fluid control. Unfortunately, a major drawback is the electromagnetic noise interference and low response speed. In this paper, a new optically controlled variable damping system based on PLZT ceramic/electrorheological fluid (ERF) is proposed. The mathematical models of the photovoltage generated by PLZT ceramics and the pressure difference between the two ends of the microchannel are established and verified by numerical simulation in COMSOL Multiphysics. Meanwhile, with the increase of light intensity, liquid flow rate and decrease of microchannel height and width, the pressure difference shows an uptrend. Optically controlled variable damping system based on PLZT ceramic/ERF is a control method with the advantages of a clean excitation source, remote control, no electromagnetic interference, and fast response speed, and has a good application prospect in the field of vibration control and fluid control.

Investigation of a novel biofilm model close to the original oral microbiome

A more optimized culture medium used in vitro to mimic the bacterial composition of original oral flora as similar as possible remains difficult at present, and the goal of this study is to develop a novel oral biofilm medium to restore the original oral microbiome. Firstly, we conducted a systematic literature review by searching PubMed and summarized the current reported culture media in vitro. Seven culture media were found. We used mixed saliva as the origin of oral species to compare the effects of the above media in culturing oral multispecies biofilms. Results indicated that among the seven media brain heart infusion containing 1% sucrose (BHIs) medium, PG medium, artificial saliva (AS) medium, and SHI medium could obviously gain large oral biofilm in vitro. The nutrients contained in different culture media may be suitable for the growth of different oral bacteria; therefore, we optimized several novel media accordingly. Notably, results of crystal violet staining showed that the biofilm cultured in our modified artificial saliva (MAS) medium had the highest amount of biofilm biomass. 16S rRNA gene sequencing showed that the operational taxonomic units (OTUs) and Shannon index of biofilm cultured in MAS medium were also the highest among all the tested media. More importantly, the 16S rRNA gene sequencing analysis indicated that the biofilm cultured in MAS medium was closer to the original saliva species. Besides, biofilm cultured by MAS was denser and produced more exopolysaccharides. MAS supported stable biofilm formation on different substrata. In conclusion, this study demonstrated a novel MAS medium that could culture oral biofilm in vitro closer to the original oral microbiome, showing a good application prospect.Key points• We compare the effects of different media in culturing oral biofilms• A novel modified artificial saliva (MAS) medium was obtained in our study• The MAS medium could culture biofilm that was closer to oral microbiome

Exploring the digital divide: results of a survey informing mobile application development

IntroductionMobile health apps risk widening health disparities if they overlook digital inclusion. The digital divide, encompassing access, familiarity, and readiness, poses a significant barrier to medical interventions. Existing literature lacks exploration of the digital divide's contributing factors. Hence, data are needed to comprehend the challenges in developing inclusive health apps.MethodsWe created a survey to gauge internet and smartphone access, smartphone familiarity, and readiness for using mobile health apps among caregivers of pediatric patients in tertiary care. Open-ended questions solicited feedback and suggestions on mobile health applications. Responses were categorized by similarity and compared. Developed with patient partners, the survey underwent cognitive testing and piloting for accuracy.ResultsData from 209 respondents showed that 23% were affected by the digital divide, mainly due to unfamiliarity with digital skills. Among 49 short text responses about health app concerns, 31 mentioned security and confidentiality, with 7 mentioning the impersonal nature of such apps. Desired features included messaging healthcare providers, scheduling, task reminders, and simplicity.ConclusionsThis study underscores a digital divide among caregivers of pediatric patients, with nearly a quarter affected primarily due to a lack of digital comfort. Respondents emphasized user-friendliness and online security for health apps. Future apps should prioritize digital inclusion by addressing the significant barriers and carefully considering patient and family concerns.

A look into the neighboring discipline: eHealth in oncology

Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show anoticeable benefit in oncology.

Valorization of Wine Lees: Assessment of Antioxidant, Antimicrobial and Enzyme Inhibitory Activity of Wine Lees Extract and Incorporation in Chitosan Films

AbstractWine lees, as one of the main by-products of winery wastes, is considered a rich source of bioactive compounds such as polyphenols. Recovery of such bioactive compounds would amplify the valorization of winery by-products, as well as promote their use in cosmetics and health applications. Towards this aim, the objective of this work is the recovery of phenolic compounds from wine lees by ultrasound-assisted extraction. The effect of extraction time, and ethanol content on the extraction yield of total phenolics was investigated, while the phenolic profile of the extract was monitored by GC-MS. Under the optimized conditions, the total phenolic content was calculated at 302.6 mg gallic acid equivalents g− 1 dry extract; the dry extract was investigated for its biological activities, exhibiting excellent antioxidant and antimicrobial activity. Moreover, the extract was found to be a potent inhibitor of tyrosinase, elastase, collagenase, α-glucosidase, and α-amylase with IC50 values of 53.3, 83.5, 82.7, 19.0, and 31.3 µg mL− 1, respectively. Finally, the wine lees dry extract was incorporated into chitosan films, reinforcing the properties of the films, and providing them with excellent antioxidant activity. The results support that wine lees extract exhibits high antioxidant activity and can be used as a highly efficient inhibitor of enzymes with pharmaceutical and health interest. Moreover, the chitosan film enriched with wine lees extract is a promising material with high antioxidant potential and can lead to the development of novel food packaging films in the food industry. Graphical Abstract

Optimization of electrical transport and magnetoresistance of La0.72Ca0.28-xPbxMnO3 ceramics by Pb2+ doping

Here, La0.72Ca0.28-xPbxMnO3 (LCPMO, 0 ≤ x ≤ 0.06) polycrystalline ceramics are prepared by the sol-gel method and the influence of Pb2+ doping on microstructure, chemical composition, and electrical transport properties of LCPMO ceramics is systematically investigated. The study was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), Energy-Dispersive Spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and four-probe method. The four-probe test shows that all samples have MIT (metal-insulator transition) behavior, and the peak resistance gradually decreases with the increase of Pb2+ doping amount. With the increase of Pb2+ doping amount, the temperature coefficient of resistance (TCR) and magnetoresistance (MR) first increased and then decreased, the TCR increased from 24.6 % to 36.5 %·K−1, and the MR value increased from 47 % to 71.5 %, and both corresponded to high temperature direction. Referring to previous studies, higher TCR and MR values near room temperature are what researchers have been pursuing for a long time. it can be seen that the LCPMO ceramics prepared in this article can have good application prospects in infrared detection and magnetic storage.

Preparation of complex physicochemical synergistic inhibitors and study on inhibition of spontaneous combustion of coal

ABSTRACT To efficiently isolate oxygen and block free radical reactions， sodium alginate (SA), sodium polyacrylate (PAAS), attapulgite (ATP), and melatonin (MLT) were selected to develop an oxygen-insulating physicochemical synergistic inhibitor. The water loss experiment, EPR experiment, and programmed temperature rise experiment were used to characterize the performance of the inhibitor. The results show that the optimal ratio of the oxygen barrier physicochemical synergistic inhibitor is 2.5 wt% SA, 1.5 wt‰ PAAS, 9 wt% ATP, 6 wt% MLT, and 3 wt% CaCl2. In the oxygen-insulating physicochemical synergistic inhibitor, the MLT can react with oxidizing radicals in the coal to slow down the process of the oxidation reaction of the coal, and the SA gel can fill the pore space to prevent oxygen adsorption on the coal surface, the inhibition rate of the inhibitor reaches 70% at 120°C. Compared with the original coal, the CO concentration of the treated coal samples is reduced by 253.9ppm compared with that of the original coal, the generation rate is reduced by 1. 13 × 10−11 mol·cm−3·s−1, and the rate of oxygen consumption is reduced by 90. 65 × 10−11 mol·cm−3·s−1 at 120°C. The above results show that the oxygen-insulating physicochemical synergistic inhibitor developed in this paper can effectively improve the fire extinguishing efficiency is environmentally friendly and pollution-free, and has good application prospects.

Dress protective potential of naturally green-colored domestic silk against ultraviolet-induced skin cell damage

Naturally green-colored domestic silk demonstrates remarkable antioxidant activity due to the presence of intrinsic antioxidant and pigmented substance flavonoids. Oxidative damage is one of the main causes of sun-induced skin damage. To investigate the potential protective effect of naturally green-colored domestic silk on skin cells against ultraviolet damage, the antioxidant was extracted and analyzed. An in vitro experiment which involves introducing ultraviolet damage to the human keratinocyte HaCaT cell line was used for the research. By adding the extract into HaCaT culture medium and then subjecting to ultraviolet irradiation, the data evidently showed that the cell apoptosis and growth inhibition of HaCaT were alleviated, indicating the protective effect of the extract to the HaCaT cell against ultraviolet damage. Meanwhile, a large number of the surviving damaged cells stagnated in the diploid (2n) phase, which is the main phase of gene repair during the whole cell cycle. The survival and repair of damaged cells also produced fewer gene fragments caused by ultraviolet damage. The detected reduction of reactive oxygen species and malondialdehyde induced by ultraviolet irradiation in HaCaT confirmed that the antioxidant extract from green silk cocoons does have intracellular and extracellular antioxidant effects. These findings suggest that naturally green silk has the potential to be utilized as a functional textile material for skin sunscreen products, and its extract can be further developed as a source of antioxidants in medical and safety health applications.

FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data—Study protocol of a multicenter trial

Introduction Endometriosis is a chronic disease that affects up to 190 million women and those assigned female at birth and remains unresolved mainly in terms of etiology and optimal therapy. It is defined by the presence of endometrium-like tissue outside the uterine cavity and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) intended to replace the need for invasive surgery, the time to diagnosis remains in the range of 4 to 11 years. Aims This study aims to create a large prospective data bank using the Lucy mobile health application (Lucy app) and analyze patient profiles and structured clinical data. In addition, we will investigate the association of removed or restricted dietary components with quality of life, pain, and central pain sensitization. Methods A baseline and a longitudinal questionnaire in the Lucy app collects real-world, self-reported information on symptoms of endometriosis, socio-demographics, mental and physical health, economic factors, nutritional, and other lifestyle factors. 5,000 women with confirmed endometriosis and 5,000 women without diagnosed endometriosis in a control group will be enrolled and followed up for one year. With this information, any connections between recorded symptoms and endometriosis will be analyzed using machine learning. Conclusions We aim to develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, we may identify dietary components that worsen the quality of life and pain in women with endometriosis, upon which we can create real-world data-based nutritional recommendations.

Construction of a painting image classification model based on AI stroke feature extraction

Abstract A large number of digital painting image resources cannot be directly converted into electronic form due to their differences in painting techniques and poor preservation of paintings. Moreover, the difficulty of extracting classification features can also lead to the consumption of human time and misclassification problems. The aim of this research is to address the challenges of converting various digital painting image resources into electronic form and the difficulties of accurately extracting classification features. The goal is to improve the usefulness and accuracy of painting image classification. Converting various digital painting image resources directly into electronic format and accurately extracting classification features are challenging due to differences in painting techniques and painting preservation, as well as the complexity of accurately extracting classification features. Overcoming these adjustments and improving the classification of painting features with the help of artificial intelligence (AI) techniques is crucial. The existing classification methods have good applications in different fields. But their research on painting classification is relatively limited. In order to better manage the painting system, advanced intelligent algorithms need to be introduced for corresponding work, such as feature recognition, image classification, etc. Through these studies, unlabeled classification of massive painting images can be carried out, while guiding future research directions. This study proposes an image classification model based on AI stroke features, which utilizes edge detection and grayscale image feature extraction to extract stroke features; and the convolutional neural network (CNN) and support vector machine are introduced into image classification, and an improved LeNet-5 CNN is proposed to achieve comprehensive assurance of image feature extraction. Considering the diversity of painting image features, the study combines color features with stroke features, and uses weighted K-means clustering algorithm to extract sample features. The experiment illustrates that the K-CNN hybrid model proposed in the study achieved an accuracy of 94.37% in extracting image information, which is higher than 78.24, 85.69, and 86.78% of C4.5, K-Nearest Neighbor (KNN), and Bi directional Long short-term Memory (BiLSTM) algorithms. In terms of image classification information recognition, the algorithms with better performance from good to poor are: the mixed model > BiLSTM > KNN > C4.5 model, with corresponding accuracy values of 0.938, 0.897, 0.872, and 0.851, respectively. And the number of fluctuation nodes in the mixed model is relatively small. And the sample search time is significantly shorter than other comparison algorithms, with a maximum recognition accuracy of 92.64% for the style, content, color, texture, and direction features of the image, which can effectively recognize the contrast and discrimination of the image. This method effectively provides a new technical means and research direction for digitizing image information.

Dual-ligand lanthanide metal-organic framework based ratiometric fluorescent platform for visual monitoring of aminoglycoside residues in food samples

Herein, a dual-emission Eu metal-organic framework (Eu-MOF) is prepared and used as the ratiometric fluorescence probe for ultrasensitive detection of aminoglycoside antibiotics (AGs). Due to the strong hydrogen bond interactions between AGs and Eu-MOF, the blue emission is enhanced while the red emission has little fluctuation in Eu-MOF with the addition of AGs, thus a good linear relationship with the logarithm of AGs concentrations from 0.001 to 100 μg/mL can be established for quantitative analysis. Good sensitivity with the detection limit of 0.33 ng/mL for apramycin, 0.32 ng/mL for amikacin and 0.30 ng/mL for kanamycin is achieved. The proposed assay demonstrates good selectivity and applicability for determination of AGs in real milk and honey samples. The Eu-MOF materials are further fabricated as fluorescent test papers for facile visual detection. The as-established ratio fluorescence platform offers a portable and economical way for rapid monitoring AGs residues in complex food samples.