Amoxicillin Loaded Chitosan–Alginate Polyelectrolyte Complex Nanoparticles as Mucopenetrating Delivery System for H. Pylori

The present study has been undertaken to apply the concept of nanoparticulate mucopenetrating drug delivery system for complete eradication of Helicobacter pylori (H. pylori), colonised deep into the gastric mucosal lining. Most of the existing drug delivery systems have failed on account of either improper mucoadhesion or mucopenetration and no dosage form with dual activity of adhesion and penetration has been designed till date for treating H. pylori induced disorders. In the present study, novel chitosan-alginate polyelectrolyte complex (CS-ALG PEC) nanoparticles of amoxicillin have been designed and optimized for various variables such as pH and mixing ratio of polymers, concentrations of polymers, drug and surfactant, using 33 Box-Behnken design. Various studies like particle size, surface charge, percent drug entrapment, in-vitro mucoadhesion and in-vivo mucopenetration of nanoparticles on rat models were conducted. The optimised FITC labelled CS-ALG PEC nanoparticles have shown comparative low in-vitro mucoadhesion with respect to plain chitosan nanoparticles, but excellent mucopenetration and localization as observed with increased fluorescence in gastric mucosa continuously over 6 hours, which clinically can help in eradication of H. pylori

Reporting trends, practices, and resource utilization in neuroendocrine tumors of the prostate gland: a survey among thirty-nine genitourinary pathologists

Background: Neuroendocrine differentiation in the prostate gland ranges from clinically insignificant neuroendocrine differentiation detected with markers in an otherwise conventional prostatic adenocarcinoma to a lethal high-grade small/large cell neuroendocrine carcinoma. The concept of neuroendocrine differentiation in prostatic adenocarcinoma has gained considerable importance due to its prognostic and therapeutic ramifications and pathologists play a pivotal role in its recognition. However, its awareness, reporting, and resource utilization practice patterns among pathologists are largely unknown. Methods: Representative examples of different spectrums of neuroendocrine differentiation along with a detailed questionnaire were shared among 39 urologic pathologists using the survey monkey software. Participants were specifically questioned about the use and awareness of the 2016 WHO classification of neuroendocrine tumors of the prostate, understanding of the clinical significance of each entity, and use of different immunohistochemical (IHC) markers. De-identified respondent data were analyzed. Results: A vast majority (90%) of the participants utilize IHC markers to confirm the diagnosis of small cell neuroendocrine carcinoma. A majority (87%) of the respondents were in agreement regarding the utilization of type of IHC markers for small cell neuroendocrine carcinoma for which 85% of the pathologists agreed that determination of the site of origin of a high-grade neuroendocrine carcinoma is not critical, as these are treated similarly. In the setting of mixed carcinomas, 62% of respondents indicated that they provide quantification and grading of the acinar component. There were varied responses regarding the prognostic implication of focal neuroendocrine cells in an otherwise conventional acinar adenocarcinoma and for Paneth cell-like differentiation. The classification of large cell neuroendocrine carcinoma was highly varied, with only 38% agreement in the illustrated case. Finally, despite the recommendation not to perform neuroendocrine markers in the absence of morphologic evidence of neuroendocrine differentiation, 62% would routinely utilize IHC in the work-up of a Gleason score 5 + 5 = 10 acinar adenocarcinoma and its differentiation from high-grade neuroendocrine carcinoma. Conclusion: There is a disparity in the practice utilization patterns among the urologic pathologists with regard to diagnosing high-grade neuroendocrine carcinoma and in understanding the clinical significance of focal neuroendocrine cells in an otherwise conventional acinar adenocarcinoma and Paneth cell-like neuroendocrine differentiation. There seems to have a trend towards overutilization of IHC to determine neuroendocrine differentiation in the absence of neuroendocrine features on morphology. The survey results suggest a need for further refinement and development of standardized guidelines for the classification and reporting of neuroendocrine differentiation in the prostate gland

Safe Data Gathering in Physical Spaces

Reliable and efficient acquisition of data from physical spaces has widespread applications in industry, policy, defense, and humanitarian work. Unmanned Aerial Vehicles (UAVs) are an excellent choice for data gathering applications, due to their capability of gaining information at multiple scales. A robust data gathering system needs to reason about multi-resolution nature of information gathering while being safe, and cognizant of its sensory and battery limitations. The state of the art algorithms with provable worst-case guarantees are unable to present an efficient solution online. This thesis addresses three critical aspects of enabling safe, efficient, and multi-resolution data gathering: (1) online budgeted multi-resolution informative path planning (IPP), (2) guaranteeing safety and, (3) optimization of sensing bandwidth for implicit and explicit data gathering requirements. First, we present an online navigation algorithm to guarantee the safety of the robot via an Emergency Maneuver Library (EML). We define a vehicle to be safe in static environments if it can stay in known unoccupied space while operating in partially known environments. Finding an optimal solution online for a non-holonomic system with non-linear dynamic constraints, in an online fashion is computationally infeasible. We present an efficient method to construct an EML that fully exploits the vehicle ’s dynamics capabilities and known unoccupied space available to ensure safety at high speeds. Another advantage of the EML is that it defines a pertinent volume from which uncertainty needs to be removed, to ensure UAV’s safety. Further, we present a sensor motion planning approach that optimizes mission costs while using EML to ensure vehicle safety by gaining information relevant to the mission. Second, we prove that for a specific class of information gathering problems, which consist of informative actions, such as gaining elevation to gather low-resolution data, traditional Markov Decision Process-based approximate solvers are not optimal. In such cases, the belief space dynamics need to be modeled to obtain efficient solutions to the multi-resolution IPP problems.Third, we present Randomized Anytime Orienteering (RAOr), an anytime, asymptotically near-optimal algorithm,that enables solving aforementioned multi-resolution IPP problems online by taking heuristically guided random walksin the space of near-optimal routes. Although the work focusses on developing motion planning algorithms, we alsodescribe representations that enable these planning algorithms to run on-board on computationally constraint platforms. The algorithms developed, form a framework for safe, efficient, multi-resolution data gathering that has enabled UAVs to operate in diverse environments, scales, and applications. Further, we evaluate our algorithms on multiple UAVs varying from full-scale helicopters to small quad-rotors, running closed-loop autonomous missions that cumulatively span hundreds of kilometers

Cross-sectional study about perioperative management of Blood Pressure and effects of anaesthesia in hypertensive patients undergoing general & orthopaedic surgery

Introduction: Hypertension is regarded as an additional risk factor during anaesthesia. There is not enough Indian evidence in literature regarding antihypertensive usage and its implications during perioperative period in patients undergoing general & orthopedic surgery. This drove us to conduct this study. Methods: Single centre cross-sectional observational study conducted in a General Surgery and Orthopaedics wards of a Tertiary care hospital. The data was collected from the period of first visit by the anaesthetist to 24 h’ postoperative period of the operated hypertensive patients. The variables accounted were of antihypertensive medications, anaesthesia drugs, hemodynamics, blood loss and fluids used. The data was analysed by using descriptive statistics, nonparametric tests and P < 0.05 was considered as significant. Results: 180 patients had median age of 65 years (Interquartile range – 20). Calcium channel Blockers were the most common class and Amlodipine was most common antihypertensive medication used during perioperative period. 132 patients took antihypertensive medication on the morning of the surgery. In 136 patients’ antihypertensive medication was started within 24 h’ postoperative period. Bupivacaine was most common anaesthetic drug used. Intraoperative fall in SBP (Systolic Blood Pressure) was found in 31 patients, and rises were found in 7 patients. Also, intraoperative fall in DBP (Diastolic Blood Pressure) was found in 13 patients, and rises were found in 9 patients. Intraoperative hemodynamic changes were managed appropriately by the anaesthetist. The median IV fluids given intraoperatively was 1375 ml (Interquartile range – 700). Median blood loss was 272 ml (Interquartile range – 250). Conclusion: Antihypertensive medications use during perioperative period were not associated with major hemodynamic changes. Keywords: General surgery, Orthopaedics, Hemodynamic

Infrastructure-free Shipdeck Tracking for Autonomous Landing

Abstract — Shipdeck landing is one of the most challenging tasks for a rotorcraft. Current autonomous rotorcraft use shipdeck mounted transponders to measure the relative pose of the vehicle to the landing pad. This tracking system is not only expensive but renders an unequipped ship unlandable. We address the challenge of tracking shipdeck without additional infrastructure on the deck. We present two methods based on video and lidar that are able to track the shipdeck starting at a considerable distance from the ship. This redundant sensor design enables us to have two independent tracking systems. We show the results of the tracking algorithms in 3 different environments, 1. field testing results on actual helicopter flights, 2. in simulation with a moving shipdeck for lidar based tracking and 3. in laboratory using an occluded and moving scaled model of a landing deck for camera based tracking. The complimentary modalities allow shipdeck tracking under varying conditions. I

Infrastructure-free Shipdeck Tracking for Autonomous Landing

<p>Shipdeck landing is one of the most challenging tasks for a rotorcraft. Current autonomous rotorcraft use shipdeck mounted transponders to measure the relative pose of the vehicle to the landing pad. This tracking system is not only expensive but renders an unequipped ship unlandable. We address the challenge of tracking shipdeck without additional infrastructure on the deck. We present two methods based on video and lidar that are able to track the shipdeck starting at a considerable distance from the ship. This redundant sensor design enables us to have two independent tracking systems. We show the results of the tracking algorithms in 3 different environments, 1. field testing results on actual helicopter flights, 2. in simulation with a moving shipdeck for lidar based tracking and 3. in laboratory using an occluded and moving scaled model of a landing deck for camera based tracking. The complimentary modalities allow shipdeck tracking under varying conditions.</p

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images

The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches