Reliable Accuracy Research Articles

Assessment of diagnostic accuracy and reliability of reveal fluorescence dental loupes in detecting molar incisor hypomineralization in children

BackgroundMolar-Incisor Hypomineralization (MIH) poses challenges to accurate diagnosis, impacting children's oral health. Traditional methods exhibit limitations, necessitating innovative approaches. This study aimed to evaluate the reliability and diagnostic accuracy Reveal Fluorescence Dental Loupes (RFDLs) for the detection of MIH MethodsThis cross-sectional study, adhering to STRAD guidelines, involved 38 healthy children (age 7–9) with MIH. Ethical approval and informed consent were obtained. Microscope images and Reveal loupes were employed for examinations. Validity was assessed against microscope results, and inter- and intra-examiner reliability were measured using ICC and Kappa coefficients. Sensitivity, specificity, and overall accuracy were calculated, with an AUC-ROC analysis for discriminatory ability. ResultsIntra-examiner reliability scores were excellent (Examiner 1: ICC 0.92, Examiner 2: ICC 0.94). Inter-examiner reliability (Kappa 0.92) indicated almost perfect agreement. Reveal demonstrated high sensitivity (82.61 %) and specificity (92.59 %), with an overall accuracy of 88.70 %. AUC-ROC analysis supported its robust discriminatory ability (AUC: 0.871). ConclusionsReveal Fluorescence Dental Loupes emerged as a promising diagnostic tool for accurate MIH detection, particularly in outreach settings. The study highlights the transformative impact of accessible and reliable diagnostic tools on pediatric oral health outcomes. While acknowledging limitations and the absence of a gold standard, the findings contribute to advancing MIH diagnostic capabilities. Further research in diverse populations is warranted for comprehensive validation.

Utility of the 3Di short version in the identification and diagnosis of autism in children at the Kenyan coast.

The precise epidemiological burden of autism is unknown because of the limited capacity to identify and diagnose the disorder in resource-constrained settings, related in part to a lack of appropriate standardised assessment tools and health care experts. We assessed the reliability, validity, and diagnostic accuracy of the Developmental Diagnostic Dimensional Interview (3Di) in a rural setting on the Kenyan coast. Using a large community survey of neurodevelopmental disorders (NDDs), we administered the 3Di to 2,110 children aged between 6 years and 9 years who screened positive or negative for any NDD and selected 242 who had specific symptoms suggestive of autism based on parental report and the screening tools for review by a child and adolescent psychiatrist. On the basis of recorded video, a multi-disciplinary team applied the Autism Diagnostic Observation Schedule to establish an autism diagnosis. Internal consistency was used to examine the reliability of the Swahili version of the 3Di, tetrachoric correlations to determine criterion validity, structural equation modelling to evaluate factorial structure and receiver operating characteristic analysis to calculate diagnostic accuracy against Diagnostic Statistical Manual of Mental Disorders (DSM) diagnosis. The reliability coefficients for 3Di were excellent for the entire scale {McDonald's omega (ω) = 0.83 [95% confidence interval (CI) 0.79-0.91]}. A higher-order three-factor DSM-IV-TR model showed an adequate fit with the model, improving greatly after retaining high-loading items and correlated items. A higher-order two-factor DSM-5 model also showed an adequate fit. There were weak to satisfactory criterion validity scores [tetrachoric rho = 0.38 (p = 0.049) and 0.59 (p = 0.014)] and good diagnostic accuracy metrics [area under the curve = 0.75 (95% CI: 0.54-0.96) and 0.61 (95% CI: 0.49-0.73] for 3Di against the DSM criteria. The 3Di had a moderate sensitivity [66.7% (95% CI: 0.22-0.96)] and a good specificity [82.5% (95% CI: 0.74-0.89)], when compared with the DSM-5. However, we observed poor sensitivity [38.9% (95% CI: 0.17-0.64)] and good specificity [83.5% (95% CI: 0.74-0.91)] against DSM-IV-TR. The Swahili version of the 3Di provides information on autism traits, which may be helpful for descriptive research of endophenotypes, for instance. However, for accuracy in newly diagnosed autism, it should be complemented by other tools, e.g., observational clinical judgment using the DSM criteria or assessments such as the Autism Diagnostic Observation Schedule. The construct validity of the Swahili 3Di for some domains, e.g., communication, should be explored in future studies.

Analysis of End-to-End Delay in the Transport Segment of Fronthaul 4G/5G Networks Based on TSN Technology

One of the characteristic features of 4G/5G mobile networks is the spatial separation of functional blocks. The corresponding segments of the xHaul transport network are used to connect these blocks. One of them is the Fronthaul front end segment, which connects remote radio equipment with their control equipment. The data streams of standard CPRI/eCPRI radio interfaces in this segment impose strict requirements on the quality of service and, above all, on delays. To meet these requirements, it was proposed to use in the Fronthaul segment Ethernet bridge network based on the technology of time-sensitive networks TSN (Time Sensitive Networking), which provides determinated delays, reliable packet delivery and high accuracy of synchronization of nodes in the network. The IEEE 802.1CM standard describes profiles of TSN networks that defines the functions, options, configurations, default values, protocols and procedures of bridges, stations and local networks required to build the Fronthaul transport segment. The article presents a methodology for determining the maximum end-to-end traffic delays of standard CPRI/eCPRI radio interfaces in the Fronthaul segment of 4G/5G networks, built on the basis of TSN technology, in accordance with the requirements of IEEE 802.1CM standard. Two main components of end-to-end delay are identified ‒ delays in TSN bridges and delays in xEthernet channels. For high-priority traffic flows of CPRI/eCPRI radio interfaces in bridges, characteristic cases of mutual influence of flows arriving simultaneously at different input ports are given. An example of numerical calculation is given, which allowed to determine the permissible physical length of the Fronthaul segment at a given boundary end-to-end delay of transmission of high-priority traffic.

Interleaved Propofol-Ketamine Maintains DBS Physiology and Hemodynamic Stability: A Double-Blind Randomized Controlled Trial.

The gold standard anesthesia for deep brain stimulation (DBS) surgery is the "awake" approach, using local anesthesia alone. Although it offers high-quality microelectrode recordings and therapeutic-window assessment, it potentially causes patients extreme stress and might result in suboptimal surgical outcomes. General anesthesia or deep sedation is an alternative, but may reduce physiological testing reliability and lead localization accuracy. The aim is to investigate a novel anesthesia regimen of ketamine-induced conscious sedation for the physiological testing phase of DBS surgery. Parkinson's patients undergoing subthalamic DBS surgery were randomly divided into experimental and control groups. During physiological testing, the groups received 0.25 mg/kg/h ketamine infusion and normal saline, respectively. Both groups had moderate propofol sedation before and after physiological testing. The primary outcome was recording quality. Secondary outcomes included hemodynamic stability, lead accuracy, motor and cognitive outcome, patient satisfaction, and adverse events. Thirty patients, 15 from each group, were included. Intraoperatively, the electrophysiological signature and lead localization were similar under ketamine and saline. Tremor amplitude was slightly lower under ketamine. Postoperatively, patients in the ketamine group reported significantly higher satisfaction with anesthesia. The improvement in Unified Parkinson's disease rating scale part-III was similar between the groups. No negative effects of ketamine on hemodynamic stability or cognition were reported perioperatively. Ketamine-induced conscious sedation provided high quality microelectrode recordings comparable with awake conditions. Additionally, it seems to allow superior patient satisfaction and hemodynamic stability, while maintaining similar post-operative outcomes. Therefore, it holds promise as a novel alternative anesthetic regimen for DBS. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The Influential People and Memories in the Formation of My Approach to Contextual Functional Interpretation

It will be noted that in all my scholarly writings and studies I have used an approach I call “contextual functional interpretation”, a problem-solving method. Every word has a meaning specific to its cultural context and carries a text on its shoulders. If this text is not correctly understood, the accuracy of the sentence cannot reach a sufficient and necessary level of reliability. By ensuring the correct understanding of each word that makes up the sentence, a reliable accuracy of explanation is achieved. In the genesis of this idea, scientists and mentors who have had an impact on the formation of my mind, identity and embodiment have played a significant role. In this article, I would like to talk to you about the people who influenced me in the birth of the contextual-functional approach to interpretation, and share some memories of them.

How short video marketing influences purchase intention in social commerce: the role of users’ persona perception, shared values, and individual-level factors

AbstractThe purpose of this study is to expand the stimulus‒organism‒response model to explore the impact of short video users’ persona perception on their purchase intention on short video platforms in the context of Chinese short video marketing. The model is empirically tested using structural equation modeling and survey data collected from 350 short video users. The results show that 10 of the 11 hypotheses in the research model are supported and have reliable prediction accuracy. Short video users’ persona perception of each dimension has a significant positive impact on shared value creation. This study thus constructs mediating and moderating variables by exploring the internal correlation and intermediate mechanism between short video users’ persona perception and purchase intention. First, shared value creation plays a mediating role between persona perception and purchase intention on short video platforms. Second, two individual-level moderating variables play a moderating role between shared value creation and purchase intention across short video platforms: Regulatory focus and social presence. This research therefore provides useful implications and guidance for brands and merchants seeking targeted and efficient precision marketing on short video platforms. It also enriches the theoretical research on consumer behavior on short video platforms.

Robust Electrochemical Biosensors Based on Antifouling Peptide Nanoparticles for Protein Quantification in Complex Biofluids.

Peptides with distinct physiochemical properties and biocompatibility hold significant promise across diverse domains including antifouling biosensors. However, the stability of natural antifouling peptides in physiological conditions poses significant challenges to their viability for sustained practical applications. Herein, a unique antifouling peptide FFFGGGEKEKEKEK was designed and self-assembled to form peptide nanoparticles (PNPs), which possessed enhanced stability against enzymatic hydrolysis in biological fluids. The PNP-coated interfaces exhibited superior stability and antifouling properties in preventing adsorption of nonspecific materials, such as proteins and cells in biological samples. Moreover, a highly sensitive and ultralow fouling electrochemical biosensor was developed through the immobilization of the PNPs and specific aptamers onto the polyaniline nanowire-modified electrode, achieving the biomarker carcinoembryonic antigen detection in complex biofluids with reliable accuracy. This research not only addresses the challenge of the poor proteolytic resistance observed in natural peptides but also introduces a universal strategy for constructing ultralow fouling sensing devices.

Diffusion-Based Continuous Real-Time Monitoring System for Total Volatile Organic Compounds

In this study, a total volatile organic compound (TVOC) monitoring system was developed and employed for the continuous and real-time monitoring of TVOCs. Gas chromatography (GC) coupled with thermal desorption (TD) and a commercially available portable VOC detector were simultaneously applied to verify the performance of the developed system. Since the developed system was applicable with and without a pump, the effect of the pump on the monitoring performance was also investigated. Overall, the reliable accuracy and precision of the developed TVOC monitoring system were verified in the TVOC concentration of 50~5000 ppbv. Based on the simultaneous analysis with TD-GC, the TVOC monitoring system showed nearly identical performance to the TD-GC system regardless of the presence of the pump. When compared to the commercially available and portable VOC detector equipped with an internal pump, the maximum delayed response of the TVOC monitoring system was 15 min. In the VOC transport in soil, the breakthrough curve was successfully obtained compared to the case with the TD-GC system. Considering the measurement frequency of the TD-GC system, the TVOC monitoring system as a passive monitoring device can be effectively applied to the subsurface area with the TVOC concentration at or above 50 ppbv.

The Italian version of the 16-item Prodromal Questionnaire (PQ-16) and its psychometric features in help-seeking ultra-high-risk subjects and in the general population.

Increasing attention to the early stages of psychosis and the identification of symptomatic prodromal states have led to the development of a growing number of screening tools. The 16-item version of the Prodromal Questionnaire (PQ-16) is a worldwide used self-administered tool for this purpose. However, to date, fundamental psychometric properties of PQ-16 were not thoroughly investigated. This study aimed to examine the structural validity, measurement invariance, reliability and other psychometrical properties of the Italian version of the PQ-16 (iPQ-16) in help-seeking individuals and in the general population. The iPQ-16 was administered to 449 young outpatients attending six community mental health services and to 318 control participants enrolled in educational environment. Confirmatory factor analyses (CFAs), measurement invariance (MI) between the help-seeking group and the general population sample, convergent validity, test-retest reliability, internal consistency, and prevalence analyses were performed. Lastly, the validity of the adopted PQ-16 cut-offs through Receiver Operating Characteristic (ROC) curves plotted against CAARMS diagnoses was also tested. CFAs confirmed the single-factor structure for the iPQ-16 and scalar MI was reached. The iPQ-16 showed high internal consistency, test-retest reliability, convergent validity, and acceptable diagnostic accuracy. ROC analysis suggested a score of ≥4 as best cut-off. The iPQ-16 represents a valid and reliable questionnaire for the assessment of high mental risk in both Italian outpatients and general student population. It has good psychometric properties and is easy to implement as UHR screening for clinical as well as research purposes.

Development of a Short Version of the ADHD Rating Scale-IV.es (sADHD-RS-IV.es)

Objective: To develop a short version of the Spanish 18-item ADHD-Rating Scale IV.es (sADHD-RS-IV.es) to be used as a potential screening tool in pediatric population. Methods: We recruited 652 subjects, ages 6 to 18 (mean ± SD = 11.14 ± 3.27): 518 patients with ADHD (per DSM-IV criteria); and 134 healthy controls. We performed a stepwise logistic regression to select the best subset of ADHD-RS-IV.es items to create a short-form. We calculated internal consistency reliability (Cronbach’s and ordinal alphas) and diagnostic accuracy using receiver operating characteristic (ROC) curve. Results: Six items were found to enter the stepwise analysis significantly. Internal consistency was high (Cronbach’s alpha = 0.86; ordinal alpha = 0.90) and offered a good concordance with clinician diagnosis and a high discriminatory power (AUC = 0.98) with an optimal cut-off at a score of six points. Conclusions: This shorter questionnaire (six items) was able to discriminate ADHD cases from healthy controls.

Psychosis superspectrum II: neurobiology, treatment, and implications.

Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.

An Efficient GNSS Coordinate Classification Strategy with an Adaptive KNN Algorithm for Epidemic Management

In times of widespread epidemics, numerous individuals are at risk of contracting viruses, such as COVID-19, monkeypox, and pneumonia, leading to a ripple effect of impacts on others. Consequently, the Centers for Disease Control (CDC) typically devises strategies to manage the situation by monitoring and tracing the infected individuals and their areas. For convenience, “targets” and “areas” represent the following individuals and areas. A global navigation satellite system (GNSS) can assist in evaluating the located areas of the targets with pointing-in-polygon (PIP) related technology. When there are many targets and areas, relying solely on PIP technology for classification from targets to areas could be more efficient. The classification technique of k-nearest neighbors (KNN) classification is widely utilized across various domains, offering reliable classification accuracy. However, KNN classification requires a certain quantity of targets with areas (training dataset) for execution, and the size of the training dataset and classification time often exhibit an exponential relationship. This study presents a strategy for applying KNN technology to classify targets into areas. Additionally, within the strategy, we propose an adaptive KNN algorithm to enhance the efficiency of the classification procedure.

Family Fat Talk Questionnaire - Polish adaptation.

One of the dangerous phenomena that reinforces a negative attitude towards one's own body is the so-called family fat talk1, i.e. conversations initiated by carers/siblings selfdevaluing body shapes and sizes. The aim of our own research was to adapt the self-report two-factor tool Family Fat Talk Questionnaire (FFTQ) by MacDonald et al., which may be used in the prophylaxis of maladaptive behaviours towards eating and the body, especially in the adolescent population. The study covered 391 people at the turn of late adolescence and early adulthood. The following were used: (1) personal questionnaire - to control secondary variables (e.g. gender, BMI, difference between the actual and desired body weight), (2) Family Fat Talk Questionnaire in the Polish language version (FFTQ-PL) and (3) Kwestionariusz wizerunku ciała (KWCO) by A. Głębocka to check external accuracy. The research results indicate that the FFTQ-PL has satisfactory reliability and measurement accuracy, and its two-factor structure (factor 1 - "I"; factor 2 - "Family") has been confirmed. This measure is worth using in screening studies to identify people - especially adolescents - engaging in self-devaluing conversations within the family. It turns out that fat talk, especially in the case of people with a negative body image, can become a risk factor for risky eating practices.

A classification method of marine mammal calls based on two-channel fusion network

AbstractMarine mammals are an important part of marine ecosystems, and human intervention seriously threatens their living environments. Few studies exist on the marine mammal call recognition task, and the accuracy of current research needs to improve. In this paper, a novel MG-ResFormer two-channel fusion network architecture is proposed, which can extract local features and global timing information from sound signals almost perfectly. Second, in the input stage of the model, we propose an improved acoustic feature energy fingerprint, which is different from the traditional single feature approach. This feature also contains frequency, energy, time sequence and other speech information and has a strong identity. Additionally, to achieve more reliable accuracy in the multiclass call recognition task, we propose a multigranular joint layer to capture the family and genus relationships between classes. In the experimental section, the proposed method is compared with the existing feature extraction methods and recognition methods. In addition, this paper also compares with the latest research, and the proposed method is the most advanced algorithm thus far. Ultimately, our proposed method achieves an accuracy of 99.39% in the marine mammal call recognition task.

Design optimization of ceramic membrane filters based on a response surface method

Ceramic membrane filters fabricated from local raw materials are excellent for municipal wastewater treatment. This paper studied the fabrication of ceramic membranes using Response Surface Methodology (RSM). It also looked at the influence of clay, palm kernel shell ash, kaolin, and starch on the properties of ceramic membrane filters. Optimal test conditions and procedures were determined by selecting a fitting model based on experimental data. The Box–Behnken design (BBD) was chosen for its ability to reduce the number of experimental trials, thus making it cost-effective. The BBD design was executed using Design-Expert 13 software (Stat-Ease, Inc., Minneapolis, MN, USA), requiring 29 experimental runs. According to the study, responses such as porosity, flow rate, thickness, and density were used in the model. The fabrication of the 29 ceramic membranes includes formulation, shaping (slip casting method), drying, and sintering. Based on RSM, an optimum ceramic membrane design was proposed: Kaolin = 413 g, Clay = 180 g, Starch 147 g, Palm Kernel shell ash = 13 g. Compared with the test results, this combination possesses favorable and reliable accuracy. The addition of kaolin played a role in creating a porous framework within the ceramic membrane. Clay serves as a binding agent, aiding the consolidation of ceramic particles within the membrane matrix. Starch served as a pore-forming agent during the fabrication process. From the study, the palm kernel shell ash served as a binder and a reinforcing agent, bolstering mechanical strength and structural robustness.

Wind speed and wind power forecasting models

Sustainable energy resources have proved to be the best alternative in the wake of environmental degradation, depletion of ozone layer and ever-increasing demand for energy. Though wind energy is a promising resource, the non-linear nature and non-stationary characteristics of wind have remained a formidable challenge. Variability in wind power has posed numerous challenges in managing the power systems, especially in grid evacuation, penetration and integration. Forecasting wind is one of the powerful solutions to solve this problem. As the penetration of renewable energy sources is poised to increase in future, an accurate prediction can go a long way in helping the electricity grid to perform well. This article presents a review of existing research and recent trends in the forecasting of wind power and speed with a critical analysis of the contribution of every researcher. A review of forecasting technologies, data, time horizons, various forecasting approaches and error metrics has been presented in detail. The plethora of research issues that continue to challenge power system operators, wind farm owners and other stakeholders has been highlighted. The development of models for wind power or wind speed forecasting with excellent reliability and outstanding accuracy is the need of the hour.

Identifying virulence factors using graph transformer autoencoder with ESMFold-predicted structures

With the increasing resistance of bacterial pathogens to conventional antibiotics, antivirulence strategies targeting virulence factors (VFs) have become an effective new therapy for the treatment of pathogenic bacterial infections. Therefore, the identification and prediction of VFs can provide ideal candidate targets for the implementation of antivirulence strategies in treating infections caused by pathogenic bacteria. Currently, the existing computational models predominantly rely on the amino acid sequences of virulence proteins while overlooking structural information. Here, we propose a novel graph transformer autoencoder for VF identification (GTAE-VF), which utilizes ESMFold-predicted 3D structures and converts the VF identification problem into a graph-level prediction task. In an encoder-decoder framework, GTAE-VF adaptively learns both local and global information by integrating a graph convolutional network and a transformer to implement all-pair message passing, which can better capture long-range correlations and potential relationships. Extensive experiments on an independent test dataset demonstrate that GTAE-VF achieves reliable and robust prediction accuracy with an AUC of 0.963, which is consistently better than that of other structure-based and sequence-based approaches. We believe that GTAE-VF has the potential to emerge as a valuable tool for assessing VFs and devising antivirulence strategies.

Research on improved black widow algorithm for medical image denoising

Improving the quality of medical images is crucial for accurate clinical diagnosis; however, medical images are often disrupted by various types of noise, posing challenges to the reliability and diagnostic accuracy of the images. This study aims to enhance the Black Widow optimization algorithm and apply it to the task of denoising medical images to improve both the quality of medical images and the accuracy of diagnostic results. By introducing Tent mapping, we refined the Black Widow optimization algorithm to better adapt to the complex features of medical images. The algorithm's denoising capabilities for various types of noise were enhanced through the combination of multiple filters, all without the need for training each time to achieve preset goals. Simulation results, based on processing a dataset containing 1588 images with Gaussian, salt-and-pepper, Poisson, and speckle noise, demonstrated a reduction in Mean Squared Error (MSE) by 0.439, an increase in Peak Signal-to-Noise Ratio (PSNR) by 4.315, an improvement in Structural Similarity Index (SSIM) by 0.132, an enhancement in Edge-to-Noise Ratio (ENL) by 0.402, and an increase in Edge Preservation Index (EPI) by 0.614. Simulation experiments verified that the proposed algorithm has a certain advantage in terms of computational efficiency. The improvement, incorporating Tent mapping and a combination of multiple filters, successfully elevated the performance of the Black Widow algorithm in medical image denoising, providing an effective solution for enhancing medical image quality and diagnostic accuracy.

Non-Destructive Assessment of Microstructural Changes in Kabuli Chickpeas during Storage

The potential of hyperspectral imaging (HSI) and synchrotron phase-contrast micro computed tomography (SR-µCT) was evaluated to determine changes in chickpea quality during storage. Chickpea samples were stored for 16 wk at different combinations of moisture contents (MC of 9%, 11%, 13%, and 15% wet basis) and temperatures (10 °C, 20 °C, and 30 °C). Hyperspectral imaging was utilized to investigate the overall quality deterioration, and SR-µCT was used to study the microstructural changes during storage. Principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used as multivariate data analysis approaches for HSI data. Principal component analysis successfully grouped the samples based on relative humidity (RH) and storage temperatures, and the PLS-DA classification also resulted in reliable accuracy (between 80 and 99%) for RH-based and temperature-based classification. The SR-µCT results revealed that microstructural changes in kernels (9% and 15% MC) were dominant at higher temperatures (above 20 °C) as compared to lower temperatures (10 °C) during storage due to accelerated spoilage at higher temperatures (above 20 °C). Chickpeas which had internal irregularities like cracked endosperm and air spaces before storage were spoiled at lower moisture from 8 wk of storage.

Aggregation-induced emission carbon dots as Al3+-mediated nanoaggregate probe for rapid and selective detection of tetracycline

Worldwide abuse of tetracycline (TC) seriously threatens environmental safety and human health. Metal-TC complexes formed by residual TC in the environment can also contribute to the spread of antibiotic resistance. Therefore, monitoring of TC residues is still required. Here, we report novel aggregation-induced emission carbon dots (AIE-Cdots) as nanoaggregate probes for the rapid and selective detection of TC residue. Riboflavin precursors with rotational functional groups led to the development of AIE-Cdots. The aggregation of AIE-Cdots was induced selectively for Al3+, amplifying the fluorescence signals owing to the restricted rotation of the side chains on the AIE-Cdot surface. The fluorescence signal of such Al3+-mediated nanoaggregates (Al3+-NAs) was further triggered by the structural fixation of TC at the Al3+ active sites, suggesting the formation of TC-coordinated Al3+-NAs. A linear correlation was observed in the TC concentration range of 0–10 μM with a detection limit of 42 nM. In addition, the strong Al3+ binding affinity of AIE-Cdots produced similar NAs and enhanced fluorescence signals in Al3+–TC mixtures. These AIE-Cdots-based nanoplatforms have a rapid response, good selectivity, and reliable accuracy for detecting TC or aluminum complexes, meeting the requirements for hazardous substance monitoring and removal in environmental applications.