A Longitudinal Analysis about the Effect of Air Pollution on Astigmatism for Children and Young Adults

Purpose: This study aimed to investigate the correlation between air pollution and astigmatism, considering the detrimental effects of air pollution on respiratory, cardiovascular, and eye health. Methods: A longitudinal study was conducted with 127,709 individuals aged 4-27 years from 9 cities in Guangdong Province, China, spanning from 2019 to 2021. Astigmatism was measured using cylinder values. Multiple measurements were taken at intervals of at least 1 year. Various exposure windows were used to assess the lagged impacts of air pollution on astigmatism. A panel data model with random effects was constructed to analyze the relationship between pollutant exposure and astigmatism. Results: The study revealed significant associations between astigmatism and exposure to carbon monoxide (CO), nitrogen dioxide (NO2), and particulate matter (PM2.5) over time. A 10 {\mu}g/m3 increase in a 3-year exposure window of NO2 and PM2.5 was associated with a decrease in cylinder value of -0.045 diopters and -0.017 diopters, respectively. A 0.1 mg/m3 increase in CO concentration within a 2-year exposure window correlated with a decrease in cylinder value of -0.009 diopters. No significant relationships were found between PM10 exposure and astigmatism. Conclusion: This study concluded that greater exposure to NO2 and PM2.5 over longer periods aggravates astigmatism. The negative effect of CO on astigmatism peaks in the exposure window of 2 years prior to examination and diminishes afterward. No significant association was found between PM10 exposure and astigmatism, suggesting that gaseous and smaller particulate pollutants have easier access to human eyes, causing heterogeneous morphological changes to the eyeball

Object Detection for Caries or Pit and Fissure Sealing Requirement in Children's First Permanent Molars

Dental caries is one of the most common oral diseases that, if left untreated, can lead to a variety of oral problems. It mainly occurs inside the pits and fissures on the occlusal/buccal/palatal surfaces of molars and children are a high-risk group for pit and fissure caries in permanent molars. Pit and fissure sealing is one of the most effective methods that is widely used in prevention of pit and fissure caries. However, current detection of pits and fissures or caries depends primarily on the experienced dentists, which ordinary parents do not have, and children may miss the remedial treatment without timely detection. To address this issue, we present a method to autodetect caries and pit and fissure sealing requirements using oral photos taken by smartphones. We use the YOLOv5 and YOLOX models and adopt a tiling strategy to reduce information loss during image pre-processing. The best result for YOLOXs model with tiling strategy is 72.3 mAP.5, while the best result without tiling strategy is 71.2. YOLOv5s6 model with/without tiling attains 70.9/67.9 mAP.5, respectively. We deploy the pre-trained network to mobile devices as a WeChat applet, allowing in-home detection by parents or children guardian

Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary Resuscitation Instruction via Temporal Action Segmentation

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a custom CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, thereby devising an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone of the model preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which accounts for the excavation of the segmentation pipeline on the CPR dataset. In general, we unprecedentedly probe into a feasible pipeline that genuinely elevates the CPR instruction qualification via action segmentation in conjunction with cutting-edge deep learning techniques. Associated experiments advocate our implementation with multiple metrics surpassing 91.0%.Comment: Transformer for Cardiopulmonary Resuscitatio

Smartphone-Based Quantitative Detection of Ochratoxin A in Wheat via a Lateral Flow Assay

Ochratoxin A (OTA) poses a severe health risk to livestock along the food chain. Moreover, according to the International Agency for Research on Cancer, it is also categorized as being possibly carcinogenic to humans. The lack of intelligent point-of-care test (POCT) methods restricts its early detection and prevention. This work establishes a smartphone-enabled point-of-care test for OTA detection via a fluorescent lateral flow assay within 6 min. By using a smartphone and portable reader, the assay allows for the recording and sharing of the detection results in a cloud database. This intelligent POCT provided (iPOCT) a linearity range of 0.1–3.0 ng/mL and a limit of detection (LOD) of 0.02 ng/mL (0.32 µg/kg in wheat). By spiking OTA in blank wheat samples, the recoveries were 89.1–120.4%, with a relative standard deviation (RSD) between 3.9–9.1%. The repeatability and reproducibility were 94.2–101.7% and 94.6–103.4%, respectively. This work provides a promising intelligent POCT method for food safety

Evaluation and comparison of in vitro antioxidant activities of unsaponifiable fraction of 11 kinds of edible vegetable oils

The radical scavenging capabilities of the extracts from eleven edible vegetable oils were investigated by using 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH ), 2,2′‐azino‐bis‐3‐ ethylbenzothiazoline‐6‐sulfonic acid (ABTS ), and ferric reducing ability of plasma (FRAP ) assays. The results indicated that rapeseed oil and sesame oil showed higher radical scavenging abilities than other vegetable oils. When the radical scavenging capabilities of the extracts from virgin camellia oils and commercially available refined camellia oils were evaluated by FRAP assay, the results showed that the antioxidant capabilities of the former were higher than the latter. Therefore, it is recommended that moderate refining processes should be taken to minimize the loss of antioxidant components and people consume virgin oils or less processed edible vegetable oils for higher antioxidant activities

A Rapid and Nondestructive Detection Method for Rapeseed Quality Using NIR Hyperspectral Imaging Spectroscopy and Chemometrics

In this study, a fast and non-destructive method was proposed to analyze rapeseed quality parameters with the help of NIR hyperspectral imaging spectroscopy and chemometrics. Hyperspectral images were acquired in the reflectance mode. Meanwhile, the region of interest was extracted from each image by the regional growth algorithm. The kernel partial least square regression was used to build prediction models for crude protein content, oil content, erucic acid content, and glucosinolate content of rapeseed. The results showed that the correlation coefficients were 0.9461, 0.9503, 0.9572, and 0.9335, whereas the root mean square errors of prediction were 0.5514%, 0.5680%, 2.8113%, and 10.3209 µmol/g for crude protein content, oil content, erucic acid content, and glucosinolate content, respectively. It demonstrated that NIR hyperspectral imaging is a promising tool to determine rapeseed quality parameters in a rapid and non-invasive manner

Risk Assessment on Dietary Exposure to Aflatoxin B1 in Post-Harvest Peanuts in the Yangtze River Ecological Region

Based on the 2983 peanut samples from 122 counties in six provinces of China’s Yangtze River ecological region collected between 2009–2014, along with the dietary consumption data in Chinese resident nutrition and health survey reports from 2002 and 2004, dietary aflatoxin exposure and percentiles in the corresponding statistics were calculated by non-parametric probability assessment, Monte Carlo simulation and bootstrap sampling methods. Average climatic conditions in the Yangtze River ecological region were calculated based on the data from 118 weather stations via the Thiessen polygon method. The survey results found that the aflatoxin contamination of peanuts was significantly high in 2013. The determination coefficient (R2) of multiple regression reflected by the aflatoxin B1 content with average precipitation and mean temperature in different periods showed that climatic conditions one month before harvest had the strongest impact on aflatoxin B1 contamination, and that Hunan and Jiangxi provinces were greatly influenced. The simulated mean aflatoxin B1 intake from peanuts at the mean peanut consumption level was 0.777–0.790 and 0.343–0.349 ng/(kg·d) for children aged 2–6 and standard adults respectively. Moreover, the evaluated cancer risks were 0.024 and 0.011/(100,000 persons·year) respectively, generally less than China’s current liver cancer incidence of 24.6 cases/(100,000 persons·year). In general, the dietary risk caused by peanut production and harvest was low. Further studies would focus on the impacts of peanut circulation and storage on aflatoxin B1 contamination risk assessment in order to protect peanut consumers’ safety and boost international trade

Development of an Ultrasensitive and Rapid Fluorescence Polarization Immunoassay for Ochratoxin A in Rice

Ochratoxin A (OTA) is a known food contaminant that affects a wide range of food and agricultural products. The presence of this fungal metabolite in foods poses a threat to human health. Therefore, various detection and quantification methods have been developed to determine its presence in foods. Herein, we describe a rapid and ultrasensitive tracer-based fluorescence polarization immunoassay (FPIA) for the detection of OTA in rice samples. Four fluorescent tracers OTA-fluorescein thiocarbamoyl ethylenediamine (EDF), OTA-fluorescein thiocarbamoyl butane diamine (BDF), OTA-amino-methyl fluorescein (AMF), and OTA-fluorescein thiocarbamoyl hexame (HDF) with fluorescence polarization values (δFP = FPbind-FPfree) of 5, 100, 207, and 80 mP, respectively, were synthesized. The tracer with the highest δFP value (OTA-AMF) was selected and further optimized for the development of an ultrasensitive FPIA with a detection range of 0.03–0.78 ng/mL. A mean recovery of 70.0% to 110.0% was obtained from spiked rice samples with a relative standard deviation of equal to or less than 20%. Good correlations (r2 = 0.9966) were observed between OTA levels in contaminated rice samples obtained by the FPIA method and high-performance liquid chromatography (HPLC) as a reference method. The rapidity of the method was confirmed by analyzing ten rice samples that were analyzed within 25 min, on average. The sensitivity, accuracy, and rapidity of the method show that it is suitable for screening and quantification of OTA in food samples without the cumbersome pre-analytical steps required in other mycotoxin detection methods

Time-Resolved Fluorescence Immunochromatographic Assay Developed Using Two Idiotypic Nanobodies for Rapid, Quantitative, and Simultaneous Detection of Aflatoxin and Zearalenone in Maize and Its Products

Aflatoxins and zearalenone (ZEN) are highly common mycotoxins in maize and maize-based products. This study aimed to report a time-resolved fluorescence immunochromatographic assay (TRFICA) developed using two idiotypic nanobodies for rapid, quantitative, and simultaneous detection of aflatoxin B₁ (AFB₁) and ZEN in maize and its products. A novel Eu/Tb­(III) nanosphere with enhanced fluorescence was prepared as a label and conjugated to anti-idiotypic nanobody (AIdnb) and monoclonal antibody (mAb). On the basis of nanosphere–antibody conjugation, two patterns of competitive time-resolved strip methods (AIdnb–TRFICA and mAb–TRFICA) were established and compared. The half inhibition concentration of AIdnb–TRFICA was 0.46 and 0.86 ng·mL^–1 for AFB₁ and ZEN, which was 18.3- and 20.3-fold more sensitive than that of mAb–TRFICA for AFB₁ and ZEN, respectively. Under optimal conditions, AIdnb–TRFICA for dual mycotoxin was established and provided a quantitative relationship ranging from 0.13 to 4.54 ng·mL^–1 for AFB₁ and 0.20 to 2.77 ng·mL^–1 for ZEN, with a detection limit of 0.05 and 0.07 ng·mL^–1 in the buffer solution, respectively. AIdnb–TRFICA showed good recoveries (72.6%–106.6%) in samples and was applied to detect dual mycotoxin in maize samples with satisfying results. To the best of our knowledge, it is the first report about a time-resolved strip method based on AIdnbs for dual mycotoxin