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Abstract: Interpenetrating polymer network hydrogels composed of poly(ethylene glycol) macromer (PEGM) and chitosan were synthesised by UV irradiation of solutions in a mild aqueous media. The IPN hydrogels exhibited the equilibrium water content (EWC) in the range of 86–94%. The hydrogels were characterised using FT-IR, FT-Raman spectroscopy and differential scanning calorimetry (DSC). The results from DSC measurements indicate that the melting endotherms of PEGM, within the hydrogels, decreased in intensities and shifted to lower temperatures comparing with a linear PEGM. This was due to the decrease of the crystallinity in the IPN hydrogels with higher contents of PEGM. The electrical response of the IPN hydrogels was also investigated by applying electrical current to the hydrogels immersed in a NaCl solution. The extent of a bending degree of the IPN hydrogel depends on the IPN hydrogel composition and applied electric field strength. [Copyright &y& Elsevier]

The study involves the preparation and characterization of crosslinked-carboxymethyl cellulose (CMC) films using varying amounts of citric acid (CA) within the range 5 %–20 %, w/w, relative to the dry weight of CMC. Through techniques such as Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, carbonyl content analysis, and gel fraction measurements, the successful crosslinking between CMC and CA is confirmed. The investigation includes an analysis of chemical structure, physical and optical characteristics, swelling behavior, water vapor transmission rate, moisture content, and surface morphologies. The water resistance of the cross-linked CMC films exhibited a significant improvement when compared to the non-crosslinked CMC film. The findings indicated that films crosslinked with 10 % CA demonstrated favorable properties for application as edible coatings. These transparent films, ideal for packaging, prove effective in preserving the quality and sensory attributes of fresh bananas, including color retention, minimized weight loss, slowed ripening through inhibiting amyloplast degradation, and enhanced firmness during storage. [Display omitted] • Water insolubility of CMC was improved by citric acid crosslink. • The properties of CMC films can be altered by variation of CA content. • Basil and citronella essential oils effectively suppressed the growth of S. aureus and E. coli. • CMC-based hydrogels coatings inhibited banana ripening. • CMC-based hydrogels coatings prevented amyloplasts from degradation. [ABSTRACT FROM AUTHOR]

This study explores the modification of silk fibroin films for hydrophilic coating applications using various sugar alcohols. Films, prepared via solvent casting, incorporated glycerol, sorbitol, and maltitol, revealing distinctive transparency and UV absorption characteristics based on sugar alcohol chemical structures. X-ray diffraction confirmed a silk I to silk II transition influenced by sugar alcohols. Glycerol proved most effective in enhancing the β-sheet structure. The study also elucidated a conformational shift towards a β-sheet structure induced by sugar alcohols. Silk fibroin–sugar alcohol blind docking and sugar alcohol-sugar alcohol blind docking investigations were conducted utilizing the HDOCK Server. The computer simulation unveiled the significance of size and hydrogen bonding characteristics inherent in sugar alcohols, emphasizing their pivotal role in influencing interactions within silk fibroin matrices. Hydrophilicity of ozonized silicone surfaces improved through successful coating with silk fibroin films, particularly glycerol-containing ones, resulting in reduced contact angles. Strong adhesion between silk fibroin films and ozonized silicone surfaces was evident, indicating robust hydrogen bonding interactions. This comprehensive research provides crucial insights into sugar alcohols' potential to modify silk fibroin film crystalline structures, offering valuable guidance for optimizing their design and functionality, especially in silicone coating applications. [ABSTRACT FROM AUTHOR]

Eco-friendly starch biofoams derived from natural rubber were fabricated in this study. To overcome their two main shortcomings, i.e., lack of flexibility and susceptibility to moisture, cotton fiber and concentrated natural rubber latex (CNRL) were incorporated into the biofoam products. Cotton-fiber-reinforced cassava starch biofoams were successfully prepared by compression molding using water as the solvent, a processing aid, and a blowing agent. The morphology and the physical, flexural, and thermal properties of the starch biofoams as a function of the CNRL content were investigated. The moisture adsorption capacity of the foam decreased with increasing CNRL content up to 5 phr (−73.4% and −41.78% at 0 and 100% RH, respectively). With increasing CNRL content, the hydrophobicity of the biofoam increased, as evidenced by contact angle measurements; this result suggested better moisture resistance and dimensional stability. The flexural properties of the biofoams were tuned by adjusting the CNRL content. With the addition of the CNRL content of 2.5 phr, the elongation of the biofoam clearly improved by 24%, with an acceptable decrease (−2.2%) in the bending modulus. Biodegradation by the soil burial test revealed that the degradation of the biofoam mainly proceeds by hydrolysis, and the addition of CNRL retards the degradation of the biofoam. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics. [ABSTRACT FROM AUTHOR]

Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance. [ABSTRACT FROM AUTHOR]

Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or absence of hemagglutination in the sample zones after immobilizing the antibodies targeting each Rh antigen. The PAD was optimized in terms of filter paper type, antibodies, and distance of the visualization zone. In this study, the optimal conditions were Whatman filter paper Grade 4; anti-D, –C, -E, -c, and -e antibodies; RBC suspension of 30%; and a visualization zone of 1 cm above the sample zone. The accuracy of simultaneously phenotyping the five Rh RBC antigens in the blood samples (n = 4692) was 99.19%, comparable with the accuracy of the gold-standard tube method used by blood bank laboratories in several regions of Thailand. Furthermore, decision making based on this method can be assisted by deep learning. After implementing a two-stage objective detection algorithm (YOLO v4-tiny) and classification model (DenseNet-201), the ambiguous images (n = 48) were interpreted with 100% accuracy. The PAD integrated with customized-region convolutional neural networks can reduce the interpretation discrepancies in RBC antigen phenotyping in any laboratory. [Display omitted] • Five Rh antigens typing PADs combined with deep learning algorithm for an assisted human-decision. • Rh antigens typing PADs were tested with real blood samples and compared with the standard tube method. • Dataset of photographs following Rh PADs testing were trained and evaluated by the in-house deep learning platform CiRA CORE. • Misinterpretation caused by weak or ambiguous reactions was solved by the CiRA CORE platform. [ABSTRACT FROM AUTHOR]

This paper outlines a system for detecting printing errors and misidentifications on hard disk drive sliders, which may contribute to shipping tracking problems and incorrect product delivery to end users. A deep-learning-based technique is proposed for determining the printed identity of a slider serial number from images captured by a digital camera. Our approach starts with image preprocessing methods that deal with differences in lighting and printing positions and then progresses to deep learning character detection based on the You-Only-Look-Once (YOLO) v4 algorithm and finally character classification. For character classification, four convolutional neural networks (CNN) were compared for accuracy and effectiveness: DarkNet-19, EfficientNet-B0, ResNet-50, and DenseNet-201. Experimenting on almost 15,000 photographs yielded accuracy greater than 99% on four CNN networks, proving the feasibility of the proposed technique. The EfficientNet-B0 network outperformed highly qualified human readers with the best recovery rate (98.4%) and fastest inference time (256.91 ms). [ABSTRACT FROM AUTHOR]