Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection.

Glaucoma is a progressive neurodegenerative disease characterized by the gradual degeneration of retinal ganglion cells, leading to irreversible blindness worldwide. Therefore, timely and accurate diagnosis of glaucoma is crucial, enabling early intervention and facilitating effective disease management to mitigate further vision deterioration. The advent of optical coherence tomography (OCT) has marked a transformative era in ophthalmology, offering detailed visualization of the macula and optic nerve head (ONH) regions. In recent years, both 2D and 3D convolutional neural network (CNN) algorithms have been applied to OCT image analysis. While 2D CNNs rely on post-prediction aggregation of all B-scans within OCT volumes, 3D CNNs allow for direct glaucoma prediction from the OCT data. However, in the absence of extensively pre-trained 3D models, the comparative efficacy of 2D and 3D-CNN algorithms in detecting glaucoma from volumetric OCT images remains unclear. Therefore, this study explores the efficacy of glaucoma detection through volumetric OCT images using select state-of-the-art (SOTA) 2D-CNN models, 3D adaptations of these 2D-CNN models with specific weight transfer techniques, and a custom 5-layer 3D-CNN-Encoder algorithm. The performance across two distinct datasets is evaluated, each focusing on the macula and the ONH, to provide a comprehensive understanding of the models' capabilities in identifying glaucoma. Our findings demonstrate that the 2D-CNN algorithm consistently provided robust results compared to their 3D counterparts tested in this study for glaucoma detection, achieving AUC values of 0.960 and 0.943 for the macular and ONH OCT test images, respectively. Given the scarcity of pre-trained 3D models trained on extensive datasets, this comparative analysis underscores the overall utility of 2D and 3D-CNN algorithms in advancing glaucoma diagnostic systems in ophthalmology and highlights the potential of 2D algorithms for volumetric OCT image-based glaucoma detection.

Automated vertical cup-to-disc ratio determination from fundus images for glaucoma detection.

Glaucoma is the leading cause of irreversible blindness worldwide. Often asymptomatic for years, this disease can progress significantly before patients become aware of the loss of visual function. Critical examination of the optic nerve through ophthalmoscopy or using fundus images is a crucial component of glaucoma detection before the onset of vision loss. The vertical cup-to-disc ratio (VCDR) is a key structural indicator for glaucoma, as thinning of the superior and inferior neuroretinal rim is a hallmark of the disease. However, manual assessment of fundus images is both time-consuming and subject to variability based on clinician expertise and interpretation. In this study, we develop a robust and accurate automated system employing deep learning (DL) techniques, specifically the YOLOv7 architecture, for the detection of optic disc and optic cup in fundus images and the subsequent calculation of VCDR. We also address the often-overlooked issue of adapting a DL model, initially trained on a specific population (e.g., European), for VCDR estimation in a different population. Our model was initially trained on ten publicly available datasets and subsequently fine-tuned on the REFUGE dataset, which comprises images collected from Chinese patients. The DL-derived VCDR displayed exceptional accuracy, achieving a Pearson correlation coefficient of 0.91 (P = 4.12 × 10-412) and a mean absolute error (MAE) of 0.0347 when compared to assessments by human experts. Our models also surpassed existing approaches on the REFUGE dataset, demonstrating higher Dice similarity coefficients and lower MAEs. Moreover, we developed an optimization approach capable of calibrating DL results for new populations. Our novel approaches for detecting optic discs and optic cups and calculating VCDR, offers clinicians a promising tool that significantly reduces manual workload in image assessment while improving both speed and accuracy. Most importantly, this automated method effectively differentiates between glaucoma and non-glaucoma cases, making it a valuable asset for glaucoma detection.

Application of supramolecular solvent based on the surface-active ionic liquid in dispersive liquid-liquid microextraction of triazine herbicides in tea samples.

In this study, a novel supramolecular solvent based on surface-active ionic liquid was prepared and used as an extraction solvent for dispersive liquid-liquid microextraction of four triazine herbicides in tea samples. The formation mechanism, microstructure and physicochemical properties of supramolecular solvent were studied. Some parameters, including the molar ratio of surface-active ionic liquid to tetrahydrofuran, volume of supramolecular solvent, vortex time, pH of sample solution, type and amount of salt, were investigated and optimized. The good linearities (r > 0.9990) for the analytes were obtained. The limits of detection and quantification for triazine herbicides were in the range of 1.7-2.1 µg kg-1 and 5.6-7.1 µg kg-1, respectively. The spiked recoveries were 80.0-119.9 %. The supramolecular solvent prepared in this study has the advantages of simple preparation process, low viscosity and good dispersibility. It can be used for the extraction and enrichment of trace triazine herbicides in tea samples.

A Polysiloxane Delivery Vehicle of Cyclic N-Halamine for Biocidal Coating of Cellulose in Supercritical CO2.

Cyclic N-halamines are highly antimicrobial, very stable, and not susceptible to bacterial resistance. A polysiloxane delivery vehicle was synthesized to deliver cyclic imide N-halamine onto cellulose via a benign and universal procedure that does not require a harmful solvent or chemical bonding. In brief, Knoevenagel condensation between barbituric acid and 4-hydroxybenzaldehyde furnished 5-(4-hydroxybenzylidene)pyrimidine-2,4,6-trione, whose phenolic O-H was subsequently reacted with the Si-H of poly(methylhydrosiloxane) (PMHS) via silane alcoholysis. The product of silane alcoholysis was interpenetrated into cellulose in supercritical CO2 (scCO2) at 50 °C, to form a continuous modification layer. The thickness of the modification layer positively correlated with interpenetration pressure in the experimental range of 10 to 28 MPa and reached a maximum value of 76.5 nm, which demonstrates the ability for tunable delivery, to control the loading of the imide N-H bond originating from barbituric acid unit. The imide N-H bonds on cellulose with the thickest modifier were then chlorinated into N-Cl counterparts using tert-butyl hypochlorite, to exert a powerful biocidability, providing ~7 log reductions of both S. aureus and E. coli in 20 min. The stability and rechargeability of the biocidability were both very promising, suggesting that the polysiloxane modifier has a satisfactory chemical structure and interlocks firmly with cellulose via scCO2 interpenetration.

Effects of Different Conditions on Co-Pyrolysis Behavior of Corn Stover and Polypropylene.

The pyrolysis behavior of corn stover and polypropylene during co-pyrolysis was studied using a tube furnace reactor. The effects of mixing ratio of corn stover and polypropylene, pyrolysis temperature, addition amount of catalyst (HZSM-5) and reaction atmosphere (N2 and CO2) on the properties of pyrolysis products were studied. The results showed that co-pyrolysis of corn stover and polypropylene can increase the yield of pyrolysis oil. When corn stover:polypropylene = 1:3, the yield of pyrolysis oil was as high as 52.1%, which was 4.5% higher than the theoretical value. With the increase of pyrolysis temperature, the yield of pyrolysis oil increased first and then decreased, and reached the optimal yield at 550 °C. The addition of catalyst (HZSM-5) reduced the proportion of oxygenates and promoted the generation of aromatic hydrocarbons. CO2 has a certain oxidation effect on the components of pyrolysis oil, which promoted the increase of oxygen-containing aromatics and the reduction of deoxy-aromatic hydrocarbons. This study identified the theoretical basis for the comprehensive utilization of plastic and biomass energy.

A Comprehensive Characterization of Pyrolysis Oil from Softwood Barks.

Pyrolysis of raw pine bark, pine, and Douglas-Fir bark was examined. The pyrolysis oil yields of raw pine bark, pine, and Douglas-Fir bark at 500 °C were 29.18%, 26.67%, and 26.65%, respectively. Both energy densification ratios (1.32-1.56) and energy yields (48.40-54.31%) of char are higher than pyrolysis oils (energy densification ratios: 1.13-1.19, energy yields: 30.16-34.42%). The pyrolysis oils have higher heating values (~25 MJ/kg) than bio-oils (~20 MJ/kg) from wood and agricultural residues, and the higher heating values of char (~31 MJ/kg) are comparable to that of many commercial coals. The elemental analysis indicated that the lower O/C value and higher H/C value represent a more valuable source of energy for pyrolysis oils than biomass. The nuclear magnetic resonance results demonstrated that the most abundant hydroxyl groups of pyrolysis oil are aliphatic OH groups, catechol, guaiacol, and p-hydroxy-phenyl OH groups. The aliphatic OH groups are mainly derived from the cleavage of cellulose glycosidic bonds, while the catechol, guaiacol, and p-hydroxy-phenyl OH groups are mostly attributed to the cleavage of the lignin ß-O-4 bond. Significant amount of aromatic carbon (~40%) in pyrolysis oils is obtained from tannin and lignin components and the aromatic C-O bonds may be formed by a radical reaction between the aromatic and aliphatic hydroxyl groups. In this study, a comprehensive analytical method was developed to fully understand and evaluate the pyrolysis products produced from softwood barks, which could offer valuable information on the pyrolysis mechanism of biomass and promote better utilization of pyrolysis products.