Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images

Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three different diseases from features extracted from Optical Coherence Tomography (OCT) images. The deep-learning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases

Isoflavonoid Glycosides and Rotenoids from Pongamia pinnata Leaves

Chromatographic separation of a 70% aqueous methanol extract (AME) of Pongamia pinnata (Linn.) Pierre (Leguminosae) leaves has led to the isolation of two new isoflavonoi

MS/MS-based molecular networking for mapping the chemical diversity of the pulp and peel extracts from Citrus japonica Thunb.; in vivo evaluation of their anti-inflammatory and anti-ulcer potential

Although inflammation is a beneficial response to harmful triggers, the associated diseases develop the potential for death-threatening conditions. Citrus species are valuable sources of chemical compounds with diverse structural properties that could alleviate damaging inflammation and reduce serious side effects of synthetic drugs. Kumquats are the smallest trees among the citrus family widely distributed in Asia, Europe, and North America, with little cultivation in Africa. The current study aims to conduct comprehensive chemical, anti-inflammatory and anti-ulcer studies of Citrus japonica, thus focusing attention on extensive cultivation of these species in Africa to enhance their beneficial uses. A comparative chemical profiling of peel and pulp extracts was performed via HPLC-MS/MS analysis, 164 metabolites were annotated aided by the spectral similarity networks. Around 148 of which were visualized as a species-first documentation. Phenolics were the predominant classes including methoxylated flavonoids, O/C-glycosylated flavones, and flavanones with the less common O- or C-O-triglycosyl methoxylated flavones among the genus Citrus. Moreover, the anti-inflammatory study demonstrated the significant activity of the pulp and peel extracts (200 and 400 mg/kg, p.o.) via reducing paw swelling induced by carrageenan at all-time points and decreasing the formation of TNF-α and IL-1β. Moreover, in ethanol-induced gastric ulcer rat model, the high doses of both extracts significantly improved ulcer indexes and suppressed gastric inflammation by inhibiting myeloperoxidase activity and possessed an antioxidant effect via increasing reduced glutathione, decreasing malondialdehyde, and nitric oxide. Additionally, histopathological investigations confirmed the anti-inflammatory and anti-ulcer effects. Considering the two fruit tissues, peels markedly improved inflammatory and gastroprotective properties associated with the high diversity of their flavonoid structures

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

BackgroundWe previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in similar to 80% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1x10(-4)) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1x10(-4)). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4x10(-3)), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7x10(-8)). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68x10(-5)).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old