DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has become one of the most reliable and sensitive biomarkers of DNA DSBs. However, the γ-H2AX foci assay still has limitations in the time consumed for manual scoring and possible variability between scorers. This study proposed a novel automated foci scoring method using a deep convolutional neural network based on a You-Only-Look-Once (YOLO) algorithm to quantify γ-H2AX foci in peripheral blood samples. FociRad, a two-stage deep learning approach, consisted of mononuclear cell (MNC) and γ-H2AX foci detections. Whole blood samples were irradiated with X-rays from a 6 MV linear accelerator at 1, 2, 4 or 6 Gy. Images were captured using confocal microscopy. Then, dose–response calibration curves were established and implemented with unseen dataset. The results of the FociRad model were comparable with manual scoring. MNC detection yielded 96.6% accuracy, 96.7% sensitivity and 96.5% specificity. γ-H2AX foci detection showed very good F1 scores (> 0.9). Implementation of calibration curve in the range of 0–4 Gy gave mean absolute difference of estimated doses less than 1 Gy compared to actual doses. In addition, the evaluation times of FociRad were very short (< 0.5 min per 100 images), while the time for manual scoring increased with the number of foci. In conclusion, FociRad was the first automated foci scoring method to use a YOLO algorithm with high detection performance and fast evaluation time, which opens the door for large-scale applications in radiation triage.
This study determined the extent of heavy metal contamination of local fruit in Rayong, Thailand, an area where an industrial base is adjacent to agricultural areas. Dietary exposure to agricultural products grown in contaminated areas can cause multiple adverse effects to the human body. In order to avoid such undesirable effects, concentrations of heavy metals [arsenic (As), cadmium (Cd), copper (Cu), mercury (Hg), lead (Pb), and zinc (Zn)] were investigated in popular tropical fruits from three districts of Rayong, namely Wang Chan, Klang and Mueang. The levels of heavy metals were determined by inductively coupled plasma-mass spectrometry (ICP-MS) and cold vapor-atomic absorption spectrometry (CV-AAS). Levels of the six heavy metals in sampled fruits (durian, jackfruit, pineapple, rambutan, long kong, and mangosteen) were in the range of 0.0004-6.7095 mg/kg; 16.7% of fruit samples exceeded maximum permissible limits of Pb. Based on health risk assessments, values of estimated daily intake (EDI) were less than those of maximum tolerable daily intake. However, for non-carcinogenic risks, high hazard index (HI) values were found in some markets while for carcinogenic risks (CRs), CR values of three fruits (durian, jackfruit, and mangosteen) exceeded acceptable levels. Therefore, long-term fruit consumption could impact health of local consumers. These results provided insight into the need for regular monitoring of heavy metal concentrations in potentially contaminated fruits and for prevention of its potential effects.
A colorimetric liquid sensor based on a poly(vinyl alcohol)/silver nanoparticle (PVA/AgNPs) hybrid nanomaterial was developed for gamma radiation in the range of 0–100 Gy. In this study, gamma rays (Cobalt-60 source) triggered the aggregation of AgNPs in a PVA/silver nitrate (AgNO3) hybrid solution. The color of this solution visibly changed from colorless to dark yellow. Absorption spectra of the PVA/AgNPs solution were analyzed by UV-Vis spectrophotometry in the range of 350–800 nm. Important parameters, such as pH and AgNO3 concentration were optimized. The accuracy, sensitivity, stability, and uncertainty of the sensor were investigated and compared to the reference standard dosimeter. Based on the spectrophotometric results, an excellent positive linear correlation (r = 0.998) between the absorption intensity and received dose was found. For the accuracy, the intra-class correlation coefficient (ICC) between the PVA/AgNPs sensor and the standard Fricke dosimeter was 0.998 (95%CI). The sensitivity of this sensor was 2.06 times higher than the standard dosimeter. The limit of detection of the liquid dosimeter was 13.4 Gy. Moreover, the overall uncertainty of this sensor was estimated at 4.962%, in the acceptable range for routine standard dosimeters (<6%). Based on its dosimetric performance, this new PVA/AgNPs sensor has potential for application as an alternative gamma sensor for routine dose monitoring in the range of 13.4–100 Gy.
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