Os níveis plasmáticos, farmacocinética e regime de dosagem de gatifloxacina administrado por via intravenosa em bezerros búfalos (Bubalus bubalis) na administração concomitante com meloxicam

The pharmacokinetics of intravenously administered gatifloxacin, upon concomitant administration with meloxicam was investigated in buffalo calves. Meloxicam was administered subcutaneously (0.5 mg.kg-1) immediately followed by intravenous administration of Gatifloxacin (4 mg.kg-1). The concentration of gatifloxacin was estimated in plasma by microbiological assay. Pharmacokinetic parameters were calculated and appropriate dosage schedule was computed. The therapeutic plasma drug concentration was maintained up to 12 h. Gatifloxacin was rapidly distributed from blood to tissue compartment, which was evident from the high values of distribution rate constant, α1 (11.9 ± 0.52 h-1) and the ratio of rate constant of transfer of drug from central to peripheral compartments and vice versa, K12/K21 (3.05 ± 0.36) and K13/K31 (2.04 ± 0.12). The area under the plasma drug concentration-time curve and apparent volume of distribution were 12.0 ± 0.68 µg.ml-1.h and 2.69 ± 0.14 L.kg-1, respectively. The elimination half-life (t1/2β), total body clearance (ClB) and the ratio of drug present in peripheral to central compartment (P/C) were 5.59 ± 0.40 h, 337.6 ± 19.9 ml.kg-1.h-1 and 8.04 ± 0.50, respectively. The present study revealed that the most suitable dosage regimen of gatifloxacin when concomitantly administered with meloxicam in buffalo calves would be 2.5 mg.kg-1 followed by 2.0 mg.kg-1 at 12 h intervals.Investigou-se a farmacocinética da gatifloxacina, administrada por via intravenosa, concomitante à aplicação de meloxicam em bezerros búfalos. O meloxicam foi administrado por via subcutânea (0,5 mg.kg-1), imediatamente seguido pela administração intravenosa de gatifloxacina (4 mg.kg-1). A concentração plasmática de gatifloxacina foi estimada por ensaio microbiológico. Os parâmetros farmacocinéticos foram calculados e a posologia adequada foi computada. A concentração plasmática do fármaco-terapêutico foi mantida por 12 h. A gatifloxacina foi rapidamente distribuída a partir de sangue para o compartimento de tecido, o que ficou evidente a partir dos valores elevados da taxa constante de distribuição, α1 (11.9 ± 0.52 h-1) e a proporção de velocidade constante de transferência de droga a partir de centrais para os compartimentos periféricos e vice-versa, K12/K21 (3.05 ± 0.36) e K13/K31 (2.04 ± 0.12). A área sob a curva plasmática de concentração-tempo da droga e o volume aparente de distribuição foi de 12.0 ± 0.68 µg.ml-1.h e 2.69 ± 0.14 L.kg-1, respectivamente. A meia-vida (t1/2β), a depuração corporal total (ClB) e relação da droga presente no sangue periférico para o compartimento central (P/C) foram 5.59 ± 0.40 h, 337.6 ± 19.9 ml.kg-1.h-1 e 8.04 ± 0.50, respectivamente. O presente estudo revelou que o regime de dosagem mais adequado de gatifloxacina quando administrada concomitantemente com meloxicam em bezerros búfalos seria 2,5 mg.kg-1 seguida de 2,0 mg.kg-1 em intervalos de 12 h

Farmakokinetika gatifloksacina u bivolje teladi nakon jednokratne intramuskularne primjene

The pharmacokinetics and in vivo plasma protein binding of gatifloxacin after a single intramuscular injection of 4 mg/kg were studied in buffalo calves. The minimum therapeutic concentration of drug was maintained in plasma from 1 min to 12 h. Gatifloxacin was rapidly absorbed from the extravascular site of injection, as evident from the high value of absorption rate constant (4.91 ± 0.22 /h) and attained a Cmax of 2.98 ± 0.08 μg/mL at 1h. The area under the plasma concentration-time curve and apparent volume of distribution were 10.8 ± 0.64 μg/mL/h and 3.2 ± 0.08 L/kg, respectively. Elimination half-life and total body clearance were 7.45 ± 0.55 h and 301.5 ± 34.4 mL/kg/h, respectively. Cmax/MIC ratio was 14.9 ± 0.3 and systemic bioavailability was 79.7 ± 3.35 per cent. Gatifloxacin was bound to plasma proteins of buffalo calves to the extent of 25.0 ± 1.05 per cent. A suitable intramuscular dosage regimen of gatifloxacin in buffalo calves would be 6.0 mg/kg followed by 5.3 mg/kg at 24 h intervals.Farmakokinetika i in vivo vezanje gatifloksacina na proteine plazme istraživani su u bivolje teladi nakon jednokratne intramuskularne primjene u dozi od 4 mg/kg. Minimalna terapijska koncentracija lijeka održavana je u plazmi u tijeku od 1 minute do 12 sati. Gatifloksacin se brzo resorbirao s mjesta ubrizgavanja što je vidljivo po visokoj vrijednosti konstante brzine apsorpcije (4,91 ± 0,22 sati) i postignute Cmax 2,98 ± 0,08 μg/mL/sat. Površina ispod koncentracijske krivulje u plazmi iznosila je 10,8 ± 0,64 μg/mL/sat, a prividni volumen raspodjele 3,2 ± 0,08 L/kg. Poluvrijeme izlučivanja iznosilo je 7,45 ± 0,55 sati, a ukupni tjelesni klirens 301,5 ± 34,4 mL/kg/sat. Omjer Cmax/MIC bio je 14,9 ± 0,3, a sustavna bioraspoloživost iznosila je 79,7 ± 3,35%. Gatifloksacin je bio vezan na bjelančevine plazme do 25,0 ± 1,05%. Prikladno intramuskularno doziranje u bivolje teladi bilo bi 6,0 mg/kg, a u sljedećim dozama treba davati 5,3 mg/kg u razmacima od 24 sata

Farmakokinetika gatifloksacina u bivolje teladi nakon jednokratne intramuskularne primjene

The pharmacokinetics and in vivo plasma protein binding of gatifloxacin after a single intramuscular injection of 4 mg/kg were studied in buffalo calves. The minimum therapeutic concentration of drug was maintained in plasma from 1 min to 12 h. Gatifloxacin was rapidly absorbed from the extravascular site of injection, as evident from the high value of absorption rate constant (4.91 ± 0.22 /h) and attained a Cmax of 2.98 ± 0.08 μg/mL at 1h. The area under the plasma concentration-time curve and apparent volume of distribution were 10.8 ± 0.64 μg/mL/h and 3.2 ± 0.08 L/kg, respectively. Elimination half-life and total body clearance were 7.45 ± 0.55 h and 301.5 ± 34.4 mL/kg/h, respectively. Cmax/MIC ratio was 14.9 ± 0.3 and systemic bioavailability was 79.7 ± 3.35 per cent. Gatifloxacin was bound to plasma proteins of buffalo calves to the extent of 25.0 ± 1.05 per cent. A suitable intramuscular dosage regimen of gatifloxacin in buffalo calves would be 6.0 mg/kg followed by 5.3 mg/kg at 24 h intervals.Farmakokinetika i in vivo vezanje gatifloksacina na proteine plazme istraživani su u bivolje teladi nakon jednokratne intramuskularne primjene u dozi od 4 mg/kg. Minimalna terapijska koncentracija lijeka održavana je u plazmi u tijeku od 1 minute do 12 sati. Gatifloksacin se brzo resorbirao s mjesta ubrizgavanja što je vidljivo po visokoj vrijednosti konstante brzine apsorpcije (4,91 ± 0,22 sati) i postignute Cmax 2,98 ± 0,08 μg/mL/sat. Površina ispod koncentracijske krivulje u plazmi iznosila je 10,8 ± 0,64 μg/mL/sat, a prividni volumen raspodjele 3,2 ± 0,08 L/kg. Poluvrijeme izlučivanja iznosilo je 7,45 ± 0,55 sati, a ukupni tjelesni klirens 301,5 ± 34,4 mL/kg/sat. Omjer Cmax/MIC bio je 14,9 ± 0,3, a sustavna bioraspoloživost iznosila je 79,7 ± 3,35%. Gatifloksacin je bio vezan na bjelančevine plazme do 25,0 ± 1,05%. Prikladno intramuskularno doziranje u bivolje teladi bilo bi 6,0 mg/kg, a u sljedećim dozama treba davati 5,3 mg/kg u razmacima od 24 sata

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 x 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

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%

Remodeling of Retinal Architecture in Diabetic Retinopathy: Disruption of Ocular Physiology and Visual Functions by Inflammatory Gene Products and Pyroptosis

Diabetic patients suffer from a host of physiological abnormalities beyond just those of glucose metabolism. These abnormalities often lead to systemic inflammation via modulation of several inflammation-related genes, their respective gene products, homocysteine metabolism, and pyroptosis. The very nature of this homeostatic disruption re-sets the overall physiology of diabetics via upregulation of immune responses, enhanced retinal neovascularization, upregulation of epigenetic events, and disturbances in cells’ redox regulatory system. This altered pathophysiological milieu can lead to the development of diabetic retinopathy (DR), a debilitating vision-threatening eye condition with microvascular complications. DR is the most prevalent cause of irreversible blindness in the working-age adults throughout the world as it can lead to severe structural and functional remodeling of the retina, decreasing vision and thus diminishing the quality of life. In this manuscript, we attempt to summarize recent developments and new insights to explore the very nature of this intertwined crosstalk between components of the immune system and their metabolic orchestrations to elucidate the pathophysiology of DR. Understanding the multifaceted nature of the cellular and molecular factors that are involved in DR could reveal new targets for effective diagnostics, therapeutics, prognostics, preventive tools, and finally strategies to combat the development and progression of DR in susceptible subjects

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