Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture.

Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomography (OCT) images. First, a scale-adaptive neural network is developed to produce multi-scale inputs for feature extraction and ensemble learning. The larger input sizes yield more global information, while the smaller input sizes focus on local details. Then, a feature-rich pyramidal architecture is designed to extract multi-scale features as inputs using DenseNet as the backbone. The advantage of the hierarchical structure is that it allows the system to extract multi-scale, information-rich features for the accurate classification of retinal disorders. Evaluation on two public OCT datasets containing normal and abnormal retinas (e.g., diabetic macular edema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and Drusen) and comparison against recent networks demonstrates the advantages of the proposed architecture's ability to produce feature-rich classification with average accuracy of 97.78%, 96.83%, and 94.26% for the first (binary) stage, second (three-class) stage, and all-at-once (four-class) classification, respectively, using cross-validation experiments using the first dataset. In the second dataset, our system showed an overall accuracy, sensitivity, and specificity of 99.69%, 99.71%, and 99.87%, respectively. Overall, the tangible advantages of the proposed network for enhanced feature learning might be used in various medical image classification tasks where scale-invariant features are crucial for precise diagnosis.

Outpatient medications associated with protection from COVID-19 hospitalization.

The COVID-19 pandemic remains the pre-eminent global health problem, and yet after more than three years there is still no prophylactic agent against the disease aside from vaccines. The objective of this study was to evaluate whether pre-existing, outpatient medications approved by the US Food and Drug Administration (FDA) reduce the risk of hospitalization due to COVID-19. This was a retrospective cohort study of patients from across the United States infected with COVID-19 in the year 2020. The main outcome was adjusted odds of hospitalization for COVID-19 amongst those positive for the infection. Outcomes were adjusted for known risk factors for severe disease. 3,974,272 patients aged 18 or older with a diagnosis of COVID-19 in 2020 met our inclusion criteria and were included in the analysis. Mean age was 50.7 (SD 18). Of this group, 290,348 patients (7.3%) were hospitalized due to COVID-19, similar to the CDC's reported estimate (7.5%). Four drugs showed protective effects against COVID-19 hospitalization: rosuvastatin (aOR 0.91, p = 0.00000024), empagliflozin-metformin (aOR 0.69, p = 0.003), metformin (aOR 0.97, p = 0.017), and enoxaparin (aOR 0.88, p = 0.0048). Several pre-existing medications for outpatient use may reduce severity of disease and protect against COVID-19 hospitalization. Well-designed clinical trials are needed to assess the efficacy of these agents in a therapeutic or prophylactic setting.

Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning.

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the University of Louisville Medical Center. The use of the feature selection method based on analysis of variance is demonstrated in the paper. Furthermore, a dimensionality reduction method called principal component analysis is used. XGBoost classifier achieves the best classification accuracy (84%) in the first stage. It also achieved optimal performance in the second stage, with a classification accuracy of 83%.

Virus Dynamics and Decay in Evaporating Human Saliva Droplets on Fomites.

The transmission of most respiratory pathogens, including SARS-CoV-2, occurs via virus-containing respiratory droplets, and thus, factors that affect virus viability in droplet residues on surfaces are of critical medical and public health importance. Relative humidity (RH) is known to play a role in virus survival, with a U-shaped relationship between RH and virus viability. The mechanisms affecting virus viability in droplet residues, however, are unclear. This study examines the structure and evaporation dynamics of virus-containing saliva droplets on fomites and their impact on virus viability using four model viruses: vesicular stomatitis virus, herpes simplex virus 1, Newcastle disease virus, and coronavirus HCoV-OC43. The results support the hypothesis that the direct contact of antiviral proteins and virions within the "coffee ring" region of the droplet residue gives rise to the observed U-shaped relationship between virus viability and RH. Viruses survive much better at low and high RH, and their viability is substantially reduced at intermediate RH. A phenomenological theory explaining this phenomenon and a quantitative model analyzing and correlating the experimentally measured virus survivability are developed on the basis of the observations. The mechanisms by which RH affects virus viability are explored. At intermediate RH, antiviral proteins have optimal influence on virions because of their largest contact time and overlap area, which leads to the lowest level of virus activity.

The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey.

Diabetic retinopathy (DR) is a devastating condition caused by progressive changes in the retinal microvasculature. It is a leading cause of retinal blindness in people with diabetes. Long periods of uncontrolled blood sugar levels result in endothelial damage, leading to macular edema, altered retinal permeability, retinal ischemia, and neovascularization. In order to facilitate rapid screening and diagnosing, as well as grading of DR, different retinal modalities are utilized. Typically, a computer-aided diagnostic system (CAD) uses retinal images to aid the ophthalmologists in the diagnosis process. These CAD systems use a combination of machine learning (ML) models (e.g., deep learning (DL) approaches) to speed up the diagnosis and grading of DR. In this way, this survey provides a comprehensive overview of different imaging modalities used with ML/DL approaches in the DR diagnosis process. The four imaging modalities that we focused on are fluorescein angiography, fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). In addition, we discuss limitations of the literature that utilizes such modalities for DR diagnosis. In addition, we introduce research gaps and provide suggested solutions for the researchers to resolve. Lastly, we provide a thorough discussion about the challenges and future directions of the current state-of-the-art DL/ML approaches. We also elaborate on how integrating different imaging modalities with the clinical information and demographic data will lead to promising results for the scientists when diagnosing and grading DR. As a result of this article's comparative analysis and discussion, it remains necessary to use DL methods over existing ML models to detect DR in multiple modalities.

Automated Diagnosis of Optical Coherence Tomography Angiography (OCTA) Based on Machine Learning Techniques.

Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a three-dimensional matrix to be used as an input to a convolutional neural network (CNN). The main motivation for using this data representation is that it combines the low-level data as well as high-level processed data to allow the CNN to capture significant features to increase its ability to distinguish DR from the normal retina. This has been applied on multi-scale levels to include the original full dimension images as well as sub-images extracted from the original OCTA images. The proposed approach was tested on in-vivo data using about 91 patients, which were qualitatively graded by retinal experts. In addition, it was quantitatively validated using datasets based on three metrics: sensitivity, specificity, and overall accuracy. Results showed the capability of the proposed approach, outperforming the current deep learning as well as features-based detecting DR approaches.

A Novel Computer-Aided Diagnostic System for Early Detection of Diabetic Retinopathy Using 3D-OCT Higher-Order Spatial Appearance Model.

Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage to the retina and/or vision loss. In this study, an optical coherence tomography (OCT)-based computer-aided diagnosis (CAD) method is proposed to detect DR early using structural 3D retinal scans. This system uses prior shape knowledge to automatically segment all retinal layers of the 3D-OCT scans using an adaptive, appearance-based method. After the segmentation step, novel texture features are extracted from the segmented layers of the OCT B-scans volume for DR diagnosis. For every layer, Markov-Gibbs random field (MGRF) model is used to extract the 2nd-order reflectivity. In order to represent the extracted image-derived features, we employ cumulative distribution function (CDF) descriptors. For layer-wise classification in 3D volume, using the extracted Gibbs energy feature, an artificial neural network (ANN) is fed the extracted feature for every layer. Finally, the classification outputs for all twelve layers are fused using a majority voting schema for global subject diagnosis. A cohort of 188 3D-OCT subjects are used for system evaluation using different k-fold validation techniques and different validation metrics. Accuracy of 90.56%, 93.11%, and 96.88% are achieved using 4-, 5-, and 10-fold cross-validation, respectively. Additional comparison with deep learning networks, which represent the state-of-the-art, documented the promise of our system's ability to diagnose the DR early.

Long-Term Follow-Up of Outer Retinal Layers in Patients with Epiretinal Membranes.

PURPOSE: This study aimed to investigate the long-term effect of observed epiretinal membranes on the outer retinal layers and visual acuity. METHODS: It is a retrospective observational study. Subjects with an epiretinal membrane and consecutive optical coherence tomography scans were followed for changes in visual acuity, central macular thickness, ellipsoid zone loss, and outer foveal thickness (OFT). RESULTS: The study consisted of 24 eyes of 22 patients, with a mean follow-up of 5 ± 1.6 years. The mean visual acuity was slightly worse at the last follow-up (0.22 ± 0.36 LogMAR [20/33] vs. 0.27 ± 0.36 LogMAR [20/36], p = 0.05). Ellipsoid zone loss was found in 37.5% of eyes. Vision loss was associated with initial size of ellipsoid disruption (p = 0.048) and age (p = 0.027). A decrease in OFT was associated with an initially larger zone of ellipsoid disruption (p = 0.006) and an initially thicker OFT (p = 0.011). An epiretinal membrane associated with vitreomacular adhesion within 1,000 µm of the foveal center at baseline was associated with ellipsoid zone loss (p = 0.012) but not with a change in visual acuity. CONCLUSIONS: Ellipsoid zone changes were common in this study and tended to enlarge over time. Epiretinal membranes associated with vitreomacular adhesion within 1,000 µm of the foveal center may be a risk factor for ellipsoid zone loss.

Role of Optical Coherence Tomography Imaging in Predicting Progression of Age-Related Macular Disease: A Survey.

In developed countries, age-related macular degeneration (AMD), a retinal disease, is the main cause of vision loss in the elderly. Optical Coherence Tomography (OCT) is currently the gold standard for assessing individuals for initial AMD diagnosis. In this paper, we look at how OCT imaging can be used to diagnose AMD. Our main aim is to examine and compare automated computer-aided diagnostic (CAD) systems for diagnosing and grading of AMD. We provide a brief summary, outlining the main aspects of performance assessment and providing a basis for current research in AMD diagnosis. As a result, the only viable alternative is to prevent AMD and stop both this devastating eye condition and unwanted visual impairment. On the other hand, the grading of AMD is very important in order to detect early AMD and prevent patients from reaching advanced AMD disease. In light of this, we explore the remaining issues with automated systems for AMD detection based on OCT imaging, as well as potential directions for diagnosis and monitoring systems based on OCT imaging and telemedicine applications.

An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images.

Uveitis is one of the leading causes of severe vision loss that can lead to blindness worldwide. Clinical records show that early and accurate detection of vitreous inflammation can potentially reduce the blindness rate. In this paper, a novel framework is proposed for automatic quantification of the vitreous on optical coherence tomography (OCT) with particular application for use in the grading of vitreous inflammation. The proposed pipeline consists of two stages, vitreous region segmentation followed by a neural network classifier. In the first stage, the vitreous region is automatically segmented using a U-net convolutional neural network (U-CNN). For the input of U-CNN, we utilized three novel image descriptors to account for the visual appearance similarity of the vitreous region and other tissues. Namely, we developed an adaptive appearance-based approach that utilizes a prior shape information, which consisted of a labeled dataset of the manually segmented images. This image descriptor is adaptively updated during segmentation and is integrated with the original greyscale image and a distance map image descriptor to construct an input fused image for the U-net segmentation stage. In the second stage, a fully connected neural network (FCNN) is proposed as a classifier to assess the vitreous inflammation severity. To achieve this task, a novel discriminatory feature of the segmented vitreous region is extracted. Namely, the signal intensities of the vitreous are represented by a cumulative distribution function (CDF). The constructed CDFs are then used to train and test the FCNN classifier for grading (grade from 0 to 3). The performance of the proposed pipeline is evaluated on a dataset of 200 OCT images. Our segmentation approach documented a higher performance than related methods, as evidenced by the Dice coefficient of 0.988 ± 0.01 and Hausdorff distance of 0.0003 mm ± 0.001 mm. On the other hand, the FCNN classification is evidenced by its average accuracy of 86%, which supports the benefits of the proposed pipeline as an aid for early and objective diagnosis of uvea inflammation.

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images.

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support.

Automated Diagnosis of Diabetic Retinopathy Using Clinical Biomarkers, Optical Coherence Tomography, and Optical Coherence Tomography Angiography.

PURPOSE: To determine if combining clinical, demographic, and imaging data improves automated diagnosis of nonproliferative diabetic retinopathy (NPDR). DESIGN: Cross-sectional imaging and machine learning study. METHODS: This was a retrospective study performed at a single academic medical center in the United States. Inclusion criteria were age >18 years and a diagnosis of diabetes mellitus (DM). Exclusion criteria were non-DR retinal disease and inability to image the macula. Optical coherence tomography (OCT) and OCT angiography (OCTA) were performed, and data on age, sex, hypertension, hyperlipidemia, and hemoglobin A1c were collected. Machine learning techniques were then applied. Multiple pathophysiologically important features were automatically extracted from each layer on OCT and each OCTA plexus and combined with clinical data in a random forest classifier to develop the system, whose results were compared to the clinical grading of NPDR, the gold standard. RESULTS: A total of 111 patients with DM II were included in the study, 36 with DM without DR, 53 with mild NPDR, and 22 with moderate NPDR. When OCT images alone were analyzed by the system, accuracy of diagnosis was 76%, sensitivity 85%, specificity 87%, and area under the curve (AUC) was 0.78. When OCT and OCTA data together were analyzed, accuracy was 92%, sensitivity 95%, specificity 98%, and AUC 0.92. When all data modalities were combined, the system achieved an accuracy of 96%, sensitivity 100%, specificity 94%, and AUC 0.96. CONCLUSIONS: Combining common clinical data points with OCT and OCTA data enhances the power of computer-aided diagnosis of NPDR.

Diagnostic Utility of Quantitative Polymerase Chain Reaction versus Culture in Endophthalmitis and Uveitis.

Purpose: To compare genetic testing for microbes in infectious endophthalmitis or uveitis to culture.Methods: This was a retrospective, single-center case series that enrolled patients with clinically suspected endophthalmitis or uveitis of unknown etiology. Aqueous humor or vitreous was collected and sent for routine cultures and genetic testing.Results: In total, 46 patients were enrolled. Genetic testing was positive in 32/46 (70%) cases and culture 6/46 cases (13%). Five of 16 uveitis cases had a final clinical diagnosis of infectious uveitis, and polymerase chain reaction (PCR) was positive in 4/5 cases (80%), versus 0% for culture. In uveitis cases, PCR was 80% sensitive and 82% specific, and culture had 0% sensitivity. The overall sensitivity and specificity of PCR for all cases were 85% and 67%, respectively, compared with 17% and 100% for culture.Conclusion: Genetic assays are inexpensive ($25/case) and more sensitive than culture for identifying intraocular pathogens in endophthalmitis and uveitis.

Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography.

Purpose: We determine the feasibility and accuracy of a computer-assisted diagnostic (CAD) system to diagnose and grade nonproliferative diabetic retinopathy (NPDR) from optical coherence tomography (OCT) images. Methods: A cross-sectional, single-center study was done of type II diabetics who presented for routine screening and/or monitoring exams. Inclusion criteria were age 18 or older, diagnosis of diabetes mellitus type II, and clear media allowing for OCT imaging. Exclusion criteria were inability to image the macula, posterior staphylomas, proliferative diabetic retinopathy, and concurrent retinovascular disease. All patients underwent a full dilated eye exam and spectral-domain OCT of a 6 × 6 mm area of the macula in both eyes. These images then were analyzed by a novel CAD system that segments the retina into 12 layers; quantifies the reflectivity, curvature, and thickness of each layer; and ultimately uses this information to train a neural network that classifies images as either normal or having NPDR, and then further grades the level of retinopathy. A first dataset was tested by "leave-one-subject-out" (LOSO) methods and by 2- and 4-fold cross-validation. The system then was tested on a second, independent dataset. Results: Using LOSO experiments on a dataset of images from 80 patients, the proposed CAD system distinguished normal from NPDR subjects with 93.8% accuracy (sensitivity = 92.5%, specificity = 95%) and achieved 97.4% correct classification between subclinical and mild/moderate DR. When tested on an independent dataset of 40 patients, the proposed system distinguished between normal and NPDR subjects with 92.5% accuracy and between subclinical and mild/moderate NPDR with 95% accuracy. Conclusions: A CAD system for automated diagnosis of NPDR based on macular OCT images from type II diabetics is feasible, reliable, and accurate.

Automated diabetic retinopathy detection using optical coherence tomography angiography: a pilot study.

BACKGROUND: Optical coherence tomography angiography (OCTA) is increasingly being used to evaluate diabetic retinopathy, but the interpretation of OCTA remains largely subjective. The purpose of this study was to design a computer-aided diagnostic (CAD) system to diagnose non-proliferative diabetic retinopathy (NPDR) in an automated fashion using OCTA images. METHODS: This was a two-centre, cross-sectional study. Adults with type II diabetes mellitus (DMII) were eligible for inclusion. OCTA scans of the macula were taken, and the five vascular maps generated per eye were analysed by a novel CAD system. For the purpose of classification/diagnosis, three different local features-blood vessel density, blood vessel calibre and the size of the foveal avascular zone (FAZ)-were segmented from these images and used to train a new, automated classifier. RESULTS: One hundred and six patients with DMII were included in the study, 23 with no DR and 83 with mild NPDR. When using features of the superficial retinal map alone, the system demonstrated an accuracy of 80.0% and area under the curve (AUC) of 76.2%. Using the features of the deep retinal map alone, accuracy was 91.4% and AUC 89.2%. When data from both maps were combined, the presented CAD system demonstrated overall accuracy of 94.3%, sensitivity of 97.9%, specificity of 87.0%, area under curve (AUC) of 92.4% and dice similarity coefficient of 95.8%. CONCLUSION: Automated diagnosis of NPDR using OCTA images is feasible and accurate. Combining this system with OCT data is a plausible next step that would likely improve its robustness.

Oral Fluoroquinolones and the Risk of Uveitis.

IMPORTANCE: Fluoroquinolones are the most commonly prescribed antibiotic class in the outpatient setting. Recent reports have implicated an association between oral fluoroquinolones and an increased risk of uveitis. OBJECTIVE: To determine the hazard of uveitis with oral fluoroquinolone use. DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study was conducted using medical claims data from a large national US insurer (N = 4,387,651). Cohorts from ambulatory care centers across the United States were created including every new user of an oral fluoroquinolone or ß-lactam antibiotic prescription with at least 24 months of data prior to the date of the prescription from January 1, 2000, to January 30, 2013. Exclusion criteria consisted of any previous diagnosis of uveitis or a uveitis-associated systemic illness. Participants were censored for a new diagnosis of a uveitis-associated systemic illness, the end of an observation period, use of the other class of antibiotic, or removal from the insurance plan. Data analysis was performed from January 2 through March 15, 2015. MAIN OUTCOMES AND MEASURES: The hazard of a uveitis diagnosis after a fluoroquinolone prescription compared with a ß-lactam prescription using multivariate regression with Cox proportional hazards models. RESULTS: Of the 4,387,651 patients in the database, 843,854 individuals receiving a fluoroquinolone and 3,543,797 patients receiving a ß-lactam were included in the analysis. After controlling for age, race, and sex using multivariate analysis, no hazard for developing uveitis at the 30-, 60-, or 90-day observation windows was seen (hazard ratio [HR] range, 0.96; 95% CI, 0.82-1.13; to 1.05; 95% CI, 0.95-1.16; P > .38 for all comparisons). The 365-day observation period showed a small increase in the HR for the fluoroquinolone cohort (1.11; 95% CI, 1.05-1.17; P < .001). Moxifloxacin produced an increased hazard for uveitis at every time point (HR range, 1.47-1.75; 95% CI, 1.27-2.37; P < .001 for all comparisons). Secondary analysis demonstrated a similar hazard at 365 days for a later diagnosis of a uveitis-associated systemic illness after fluoroquinolone use (HR range, 1.46-1.96; 95% CI, 1.42-2.07; P < .001 for all comparisons). CONCLUSIONS AND RELEVANCE: These data do not support an association between oral fluoroquinolone use and uveitis. Instead, this study shows an association between oral fluoroquinolone use and the risk for uveitis-associated systemic illnesses, which is a possible source of bias that could explain the findings of previous studies.

Evaluation of lipid peroxidation and antioxidant status on fenvalerate, nitrate and their co-exposure in Bubalus bubalis.

The toxic effects of pesticides and minerals have been explored in different species, but still there is paucity of information regarding their combined toxicological effects. The present investigation reports oxidative stress induced by oral subacute exposure to fenvalerate (1 mg/kg) and sodium nitrate (20 mg/kg) alone, as well as in combination daily for 21 days in buffalo calves. Fenvalerate exposure produced significant elevation in lipid peroxidation (LPO), glutathione peroxidase (GPx), while it produced significant decline in blood glutathione (GSH) levels, superoxide dismutase (SOD) and catalase (CAT). No significant alteration was evidenced in nitric oxide (NOx) levels. Oral exposure to sodium nitrate produced significant inclination in LPO and NOx, while on the other hand significant depreciation in SOD and CAT with no significant change in GPx activity. Combined exposure to fenvalerate and sodium nitrate produced severe effects with an appreciably more prominent elevation in extent of LPO and decline in blood GSH levels.

Alterations in Hematological Profile of Experimentally Induced Subchronic Thiacloprid Toxicosis in Gallus domesticus.

OBJECTIVES: Thiacloprid, a novel neonicotinoid insecticide is chiefly used as a crop protectant therefore it is likely to cause indirect exposure to poultry through contaminated feed and water because this species is occasionally supplied with feed that is, declared unfit for human consumption. The current study was performed to explore the nonlethal toxic effects of thiacloprid in Gallus domesticus on hematological parameters. MATERIALS AND METHODS: Fifty-two birds were randomly divided into nine groups. Groups I to IV of four birds each were kept as healthy control. The Groups V, VI, VII, VIII, IX, and X contained six birds each and were administered thiacloprid at 1 mg/kg/day for 15, 30, 45, 60, 75, and 90 days, respectively. RESULTS: Thiacloprid caused variable changes in the hematological parameters. There was a significant decline in the packed cell volume (PCV), hemoglobin (Hb) concentration, and total erythrocyte count (TEC). The PCV declined to the extent of 23.33 ± 0.76% on day 90 from the 0 day value of 29.75 ± 1.26% of experiment. The Hb concentration decreased from 9.93 ± 0.57 g/dl (0 day) to 7.52 ± 0.62 g/dl (90 days). The TEC declined from the 0 day value of 2.41 ± 0.08 × 10(6)/mm(3) to 90 days value of 2.08 ± 0.05 × 10(6)/mm(3). The total leukocyte count on 0 day was 12.50 ± 0.76 × 10(3)/mm(3) and it showed a significant increase from day 45 (17.80 ± 2.67 × 10(3)/mm(3)) to day 90 (21.33 ± 1.48 × 10(3)/mm(3)) of thiacloprid treatment. There was a significant rise in value of erythrocyte sedimentation rate to 19.25 ± 1.22 mm/24 h on day 90 of treatment from the 14.42 ± 1.09 mm/24 h on 0 day. The long-term oral administration of thiacloprid produced no significant alterations in the values of erythrocytic indices. CONCLUSIONS: The repeated oral toxicity on thiacloprid in present investigation suggested that it has an adverse effect on health of birds and is moderately risk insecticide in G. domesticus.