Preformulation study of Levofloxacin

oai:ojs.pkp.sfu.ca:article/1398Levofloxacin is the L-isomer of the racemate ofloxacin, a quinolone antimicrobial agent. Chemically levofloxacin, a chiral fluorinated carboxyquinolone, is the pure (S)-enantiomer of the racemic drug substance Ofloxacin.Preformulation studies are needed to ensure the development of a stable as well as therapeutically effective and safe dosage form. The Preformulation studies, performed in this research include identification of drug, solubility analysis, partition coefficient and drug compatibility.In present work complete preformulation study was carried out, which include identification of drug, quantitative estimation of drug, solubility determination, melting point determination,partition coefficient determination etc

Assessment of Diara land under Bhagalpur district using remote sensing and GIS tools

The Diara land is found in between the natural levees of the river and formed due to its meandering and course changing behavior. The topography of Diara land is mostly undulating and intersected with numerous dead and disconnected channels, Remote sensing and Geographical Information System (GIS) is a reliable technique to prepare a comprehensive inventory of land use pattern of an area. The present study was carried out to prepare a complete digital map of diara land of Bhagalpur district using spatial software (TNT Mips). On the basis of visual interpretation of the satellite image and physiographic pattern of the land escape, polygons were digitized for area delineation and mapping for diara land. Out of sixteen blocks of Bhagalpur district, only six blocks were identified as an old Diara land (203.26 km2) and thirteen blocks were identified as a new diara land (869.78 km2). Occupied areas viz. Narayanpur, Bihpur, Kharik, Naugachhiya, Ismailpur, Rangra Chowk and Gopalpur blocks were identified under complete diara land. No any one Diara land characteristics ware marked in Shahkund, Goradih and Sanhaula blocks

Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach

Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks in recent years. Existing efforts propose metrics that allow a user to choose one model from a pool of pre-trained models without having to fine-tune each model individually and identify one explicitly. With the growth in the number of available pre-trained models and the popularity of model ensembles, it also becomes essential to study the transferability of multiple-source models for a given target task. The few existing efforts study transferability in such multi-source ensemble settings using just the outputs of the classification layer and neglect possible domain or task mismatch. Moreover, they overlook the most important factor while selecting the source models, viz., the cohesiveness factor between them, which can impact the performance and confidence in the prediction of the ensemble. To address these gaps, we propose a novel Optimal tranSport-based suBmOdular tRaNsferability metric (OSBORN) to estimate the transferability of an ensemble of models to a downstream task. OSBORN collectively accounts for image domain difference, task difference, and cohesiveness of models in the ensemble to provide reliable estimates of transferability. We gauge the performance of OSBORN on both image classification and semantic segmentation tasks. Our setup includes 28 source datasets, 11 target datasets, 5 model architectures, and 2 pre-training methods. We benchmark our method against current state-of-the-art metrics MS-LEEP and E-LEEP, and outperform them consistently using the proposed approach.Comment: To appear at ICCV 202

MADG: Margin-based Adversarial Learning for Domain Generalization

Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training. In recent years, numerous methods have been proposed to address the DG setting, among which one popular approach is the adversarial learning-based methodology. The main idea behind adversarial DG methods is to learn domain-invariant features by minimizing a discrepancy metric. However, most adversarial DG methods use 0-1 loss based $\mathcal{H}\Delta\mathcal{H}$ divergence metric. In contrast, the margin loss-based discrepancy metric has the following advantages: more informative, tighter, practical, and efficiently optimizable. To mitigate this gap, this work proposes a novel adversarial learning DG algorithm, MADG, motivated by a margin loss-based discrepancy metric. The proposed MADG model learns domain-invariant features across all source domains and uses adversarial training to generalize well to the unseen target domain. We also provide a theoretical analysis of the proposed MADG model based on the unseen target error bound. Specifically, we construct the link between the source and unseen domains in the real-valued hypothesis space and derive the generalization bound using margin loss and Rademacher complexity. We extensively experiment with the MADG model on popular real-world DG datasets, VLCS, PACS, OfficeHome, DomainNet, and TerraIncognita. We evaluate the proposed algorithm on DomainBed's benchmark and observe consistent performance across all the datasets

Validity and reliability of a novel 3D scanner for assessment of the shape and volume of amputees’ residual limb models

Objective assessment methods to monitor residual limb volume following lower-limb amputation are required to enhance practitioner-led prosthetic fitting. Computer aided systems, including 3D scanners, present numerous advantages and the recent Artec Eva scanner, based on laser free technology, could potentially be an effective solution for monitoring residual limb volumes. The aim of this study was to assess the validity and reliability of the Artec Eva scanner (practical measurement) against a high precision laser 3D scanner (criterion measurement) for the determination of residual limb model shape and volume. Three observers completed three repeat assessments of ten residual limb models, using both the scanners. Validity of the Artec Eva scanner was assessed (mean percentage error <2%) and Bland-Altman statistics were adopted to assess the agreement between the two scanners. Intra and inter-rater reliability (repeatability coefficient <5%) of the Artec Eva scanner was calculated for measuring indices of residual limb model volume and shape (i.e. residual limb cross sectional areas and perimeters). Residual limb model volumes ranged from 885 to 4399 ml. Mean percentage error of the Artec Eva scanner (validity) was 1.4% of the criterion volumes. Correlation coefficients between the Artec Eva and the Romer determined variables were higher than 0.9. Volume intra-rater and inter-rater reliability coefficients were 0.5% and 0.7%, respectively. Shape percentage maximal error was 2% at the distal end of the residual limb, with intra-rater reliability coefficients presenting the lowest errors (0.2%), both for cross sectional areas and perimeters of the residual limb models. The Artec Eva scanner is a valid and reliable method for assessing residual limb model shapes and volumes. While the method needs to be tested on human residual limbs and the results compared with the current system used in clinical practice, it has the potential to quantify shape and volume fluctuations with greater resolution

Extracellular volume quantification in isolated hypertension - changes at the detectable limits?

The funding source (British Heart Foundation and UK National Institute for Health Research) provided salaries for research training (FZ, TT, DS, SW), but had no role in study design, collection, analysis, interpretation, writing, or decisions with regard to publication. This work was undertaken at University College London Hospital, which received a proportion of funding from the UK Department of Health National Institute for Health Research Biomedical Research Centres funding scheme. We are grateful to King’s College London Laboratories for processing the collagen biomarker panel

Neutrophil-Derived MMP-8 Drives AMPK-Dependent Matrix Destruction in Human Pulmonary Tuberculosis.

Pulmonary cavities, the hallmark of tuberculosis (TB), are characterized by high mycobacterial load and perpetuate the spread of M. tuberculosis. The mechanism of matrix destruction resulting in cavitation is not well defined. Neutrophils are emerging as key mediators of TB immunopathology and their influx are associated with poor outcomes. We investigated neutrophil-dependent mechanisms involved in TB-associated matrix destruction using a cellular model, a cohort of 108 patients, and in separate patient lung biopsies. Neutrophil-derived NF-kB-dependent matrix metalloproteinase-8 (MMP-8) secretion was up-regulated in TB and caused matrix destruction both in vitro and in respiratory samples of TB patients. Collagen destruction induced by TB infection was abolished by doxycycline, a licensed MMP inhibitor. Neutrophil extracellular traps (NETs) contain MMP-8 and are increased in samples from TB patients. Neutrophils lined the circumference of human pulmonary TB cavities and sputum MMP-8 concentrations reflected TB radiological and clinical disease severity. AMPK, a central regulator of catabolism, drove neutrophil MMP-8 secretion and neutrophils from AMPK-deficient patients secrete lower MMP-8 concentrations. AMPK-expressing neutrophils are present in human TB lung biopsies with phospho-AMPK detected in nuclei. These data demonstrate that neutrophil-derived MMP-8 has a key role in the immunopathology of TB and is a potential target for host-directed therapy in this infectious disease

Dialysis-associated peritonitis in children

Peritonitis remains a frequent complication of peritoneal dialysis in children and is the most common reason for technique failure. The microbiology is characterized by a predominance of Gram-positive organisms, with fungi responsible for less than 5% of episodes. Data collected by the International Pediatric Peritonitis Registry have revealed a worldwide variation in the bacterial etiology of peritonitis, as well as in the rate of culture-negative peritonitis. Risk factors for infection include young age, the absence of prophylactic antibiotics at catheter placement, spiking of dialysis bags, and the presence of a catheter exit-site or tunnel infection. Clinical symptoms at presentation are somewhat organism specific and can be objectively assessed with a Disease Severity Score. Whereas recommendations for empiric antibiotic therapy in children have been published by the International Society of Peritoneal Dialysis, epidemiologic data and antibiotic susceptibility data suggest that it may be desirable to take the patient- and center-specific history of microorganisms and their sensitivity patterns into account when prescribing initial therapy. The vast majority of patients are treated successfully and continue peritoneal dialysis, with the poorest outcome noted in patients with peritonitis secondary to Gram-negative organisms or fungi and in those with a relapsing infection

Muc2 Protects against Lethal Infectious Colitis by Disassociating Pathogenic and Commensal Bacteria from the Colonic Mucosa

Despite recent advances in our understanding of the pathogenesis of attaching and effacing (A/E) Escherichia coli infections, the mechanisms by which the host defends against these microbes are unclear. The goal of this study was to determine the role of goblet cell-derived Muc2, the major intestinal secretory mucin and primary component of the mucus layer, in host protection against A/E pathogens. To assess the role of Muc2 during A/E bacterial infections, we inoculated Muc2 deficient (Muc2−/−) mice with Citrobacter rodentium, a murine A/E pathogen related to diarrheagenic A/E E. coli. Unlike wildtype (WT) mice, infected Muc2−/− mice exhibited rapid weight loss and suffered up to 90% mortality. Stool plating demonstrated 10–100 fold greater C. rodentium burdens in Muc2−/− vs. WT mice, most of which were found to be loosely adherent to the colonic mucosa. Histology of Muc2−/− mice revealed ulceration in the colon amid focal bacterial microcolonies. Metabolic labeling of secreted mucins in the large intestine demonstrated that mucin secretion was markedly increased in WT mice during infection compared to uninfected controls, suggesting that the host uses increased mucin release to flush pathogens from the mucosal surface. Muc2 also impacted host-commensal interactions during infection, as FISH analysis revealed C. rodentium microcolonies contained numerous commensal microbes, which was not observed in WT mice. Orally administered FITC-Dextran and FISH staining showed significantly worsened intestinal barrier disruption in Muc2−/− vs. WT mice, with overt pathogen and commensal translocation into the Muc2−/− colonic mucosa. Interestingly, commensal depletion enhanced C. rodentium colonization of Muc2−/− mice, although colonic pathology was not significantly altered. In conclusion, Muc2 production is critical for host protection during A/E bacterial infections, by limiting overall pathogen and commensal numbers associated with the colonic mucosal surface. Such actions limit tissue damage and translocation of pathogenic and commensal bacteria across the epithelium