With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and standardized large scale screening of various image modalities to assist clinicians in identifying diseases. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous. The first stage is based on Regions with Convolutional Neural Network (RCNN) and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep Convolutional Neural Network to classify the extracted disc into healthy or glaucomatous. Unfortunately, none of the publicly available retinal fundus image datasets provides any bounding box ground truth required for disc localization. Therefore, in addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization. The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset with healthy and glaucoma labels, for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved Area Under the Receiver Operating Characteristic Curve equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA dataset. Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only Area Under the Curve, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier’s performance and calls for additional performance metrics to substantiate the results.

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Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

Glaucoma is a chronic disease often called “silent thief of sight” as it has no symptoms and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural changes in the retina which aid ophthalmologists to detect glaucoma at an early stage and stop its progression. Fundoscopy is among one of the biomedical imaging techniques to analyze the internal structure of retina. Our proposed technique provides a novel algorithm to detect glaucoma from digital fundus image using a hybrid feature set. This paper proposes a novel combination of structural (cup to disc ratio) and non-structural (texture and intensity) features to improve the accuracy of automated diagnosis of glaucoma. The proposed method introduces a suspect class in automated diagnosis in case of any conflict in decision from structural and non-structural features. The evaluation of proposed algorithm is performed using a local database containing fundus images from 100 patients. This system is designed to refer glaucoma cases from rural areas to specialists and the motivation behind introducing suspect class is to ensure high sensitivity of proposed system. The average sensitivity and specificity of proposed system are 100 and 87 % respectively.

/pdf/automated-detection-of-glaucoma-using-structural-and-non-2vtrb5fcq2.pdf

Automated detection of glaucoma using structural and non structural features.

Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a time-consuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author’s observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community.

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

Glaucoma is a group of eye disorders that damage the optic nerve Considering a single eye condition for the diagnosis of glaucoma has failed to detect all glaucoma cases accurately A reliable computer-aided diagnosis system is proposed based on a novel combination of hybrid structural and textural features The system improves the decision-making process after analysing a variety of glaucoma conditions It consists of two main modules hybrid structural feature-set (HSF) and hybrid texture feature-set (HTF) HSF module can classify a sample using support vector machine (SVM) from different structural glaucoma condition and the HTF module analyses the sample founded on various texture and intensity-based features and again using SVM makes a decision In the case of any conflict in the results of both modules, a suspected class is introduced A novel algorithm to compute the super-pixels has also been proposed to detect the damaged cup This feature alone outperformed the current state-of-the-art methods with 94% sensitivity Cup-to-disc ratio calculation method for cup and disc segmentation, involving two different channels has been introduced increasing the overall accuracy The proposed system has given exceptional results with 100% accuracy for glaucoma referral

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2016.0812

Improved automated detection of glaucoma from fundus image using hybrid structural and textural features

Amphibians and Reptiles of Pakistan.ByMuhammad Sharif Khan.Malabar (Florida): Krieger Publishing. $145.00. xvi + 311 p; ill.; index. ISBN: 0‐89464‐952‐3. 2006.

This paper proposes image processing technique for the early detection of glaucoma. Glaucoma is one of the major causes which cause blindness but it was hard to diagnose it in early stages. In this paper glaucoma is classified by extracting two features using retinal fundus images. (i) Cup to Disc Ratio (CDR). (ii) Ratio of Neuroretinal Rim in inferior, superior, temporal and nasal quadrants i.e. (ISNT quadrants) to check whether it obeys or violates the ISNT rule. The novel technique is implemented on 50 retinal images and an accuracy of 94% is achieved taking an average computational time of 1.42 seconds.

Detection of glaucoma using retinal fundus images

The formation of gas hydrates is a major issue during the operation of oil and gas pipelines, because gas hydrates cause plugging, thereby disrupting the normal oil and gas flows. A solution is to inject gas hydrate inhibitors such as ionic liquids. Contrary to classical inhibitors, ionic liquids act both as thermodynamic inhibitors and hydrate inhibitors, and as anti-agglomerates. Imidazolium-based ionic liquids have been found efficient for the inhibition of CO2 and CH4 hydrates. For CO2 gas hydrates, N-ethyl-N-methylmorpholinium bromide showed an average depression temperature of 1.72 K at 10 wt% concentration. The induction time of 1-ethyl-3-methyl imidazolium bromide is 36.3 h for CO2 hydrates at 1 wt% concentration. For CH4 hydrates, 1-ethyl-3-methyl-imidazolium chloride showed average depression temperature of 4.80 K at 40 wt%. For mixed gas hydrates of CO2 and CH4, only quaternary ammonium salts have been studied. Tetramethyl ammonium hydroxide shifted the hydrate liquid vapour equilibrium to 1.56 K at 10 wt%, while tetrabutylammonium hydroxide showed an induction time of 0.74 h at 1 wt% concentration. 

/pdf/ionic-liquids-for-the-inhibition-of-gas-hydrates-a-review-3o5szpoc.pdf

Ionic liquids for the inhibition of gas hydrates. A review

The coronavirus disease 2019 (COVID-19) pandemic has prompted a lot of questions globally regarding the range of information about the virus’s possible routes of transmission, diagnostics, and therapeutic tools. Worldwide studies have pointed out the importance of monitoring and early surveillance techniques based on the identification of viral RNA in wastewater. These studies indicated the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in human feces, which is shed via excreta including mucus, feces, saliva, and sputum. Subsequently, they get dumped into wastewater, and their presence in wastewater provides a possibility of using it as a tool to help prevent and eradicate the virus. Its monitoring is still done in many regions worldwide and serves as an early “warning signal”; however, a lot of limitations of wastewater surveillance have also been identified.

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COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2

The basic aim of the present study was to find out the vertebrates and invertebrate fauna of Khaisaridam (Ghundi Shahbaz Khan) of district Karak, Khyber Pakhtunkhwa province of Pakistan. A study was conducted from March 2017 to October 2017. Khaisari dam has all the vertebrates, like fishes, amphibians, reptiles, birds and mammals. Fishes include Cyprinus carpio, Labeo rohita, Cirrhinus cirrhosus, Hypophthalmichthys molitrix. Amphibians include Duttaphrynus Melanostictus, Ranatigrina, Euphlyctis cyanophlyctis. Reptiles include Chamaeleo zeylanicus, Xantusia vigilis, Lampropeltis getula. Birds include Passer domesticus, Acridotheres tristis, Anas crecca, Grus grus, Pavo cristatus, Pycnonotus barbatus, Pycnonotus barbatus, Cairina moschata and mammals include Canis adustus, Canis lupus, Feluscatus, Capra hircus, Ovis aries, Equusassinus. Invertebrates include Hirudo medicinalis, Solenopsis invicta, Vespa velutina, Pandinus imperator, Sympetrum flaveolum.

https://www.entomoljournal.com/archives/2018/vol6issue2/PartA/6-1-34-436.pdf

Invertebrates and vertebrates fauna of district, Karak, KP, Pakistan

Phages are highly ubiquitous biological agents, which means they are ideal tools for molecular biology and recombinant DNA technology. The development of a phage display technology was a turning point in the design of phage-based vaccines. Phages are now recognized as universal adjuvant-free nanovaccine platforms. Phages are well-suited for vaccine design owing to their high stability in harsh conditions and simple and inexpensive large-scale production. The aim of this review is to summarize the overall breadth of the antiviral therapeutic perspective of phages contributing to the development of phage-based vaccines for COVID-19. We show that phage vaccines induce a strong and specific humoral response by targeted phage particles carrying the epitopes of SARS-CoV-2. Further, the engineering of the T4 bacteriophage by CRISPR (clustered regularly interspaced short palindromic repeats) presents phage vaccines as a valuable platform with potential capabilities of genetic plasticity, intrinsic immunogenicity, and stability.

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The Breadth of Bacteriophages Contributing to the Development of the Phage-Based Vaccines for COVID-19: An Ideal Platform to Design the Multiplex Vaccine

Ihtisham Ul Haq

Papers

Detection of glaucoma using retinal fundus images

Ionic liquids for the inhibition of gas hydrates. A review

COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2

Invertebrates and vertebrates fauna of district, Karak, KP, Pakistan

The Breadth of Bacteriophages Contributing to the Development of the Phage-Based Vaccines for COVID-19: An Ideal Platform to Design the Multiplex Vaccine