Novel potential for optimization of antitubercular therapy:Pulmonary delivery of rifampicin lipospheres

The aim of the present work is to develop rifampicin loaded phospholipid lipospheres containing sulfphobutyl etherβ-cyclodextrin and Vitamin C for inhalation to test their potential for deep lung delivery.The findings of the solid state characterization revealed the amorphous nature of the lipospheres.These exhibited a better flowability,an aerodynamic diameter in the range of 1.76 to 3.99μm.Moreover,the fine particle fraction and emitted dose was found in the range of 68.84–83.73% and 80–93%,respectively.Moreover,lipospheres exhibited enhanced/equivalent efficacy in vitro in H37Rv strain.Hence,the results show the potential of lipospheres for pulmonary delivery of rifampicin.

Acquired Coronary Cameral Fistula Due to Post Stent Balloon Dilatation: Dual Coronary Artery Perforations into the Left Ventricle—What Is the Right Treatment?

A fifty-year-old female with recent history of LAD stent placement for instent restenosis, presented with chest pain and ventricular fibrillatory arrest. Angiography revealed total occlusion of her LAD stent. She underwent IVUS study, balloon angioplasty and stent placements. Post balloon dilatation of the under-deployed distal stent resulted in dual coronary artery perforations with extravasation of contrast into the LV cavity, a Type 4 Ellis coronary artery perforation (CAP). No extravasation was noted into the pericardium. Immediately a covered stent was deployed which completely sealed both perforation sites with resultant TIMI grade 3 flow. Under-deployment of stents is a common occurrence and is underappreciated. It can happen due to various reasons. Not many options exist at that time but to use a high pressure balloon and post dilate the stent. One rare complication is CAP due to post stent dilatation, with incidence reported as 0.1% to 3.0% of PCI procedures. Among the various type of CAP, Ellis Type 4 is of the least frequent however no studies have looked at its exact incidence rate. Prompt recognition and quick intervention are essential to good patient outcome. We chose to deploy a covered stent over the perforation with interim balloon tamponading. Deployment of the stent successfully sealed both the CAPs. Remarkably the patient remained stable and did not complain of chest pain throughout the procedure. The patient did well;she was discharged on dual antiplatelet therapy and is continuing to do well. We report a rare case of 2 distal LAD perforations that drained into the LV (an Ellis Type 4 CAP) caused by post stent dilatation that were successfully treated with a single covered stent. We report successful management of this case along with review of literature about management and dilemmas encountered is such instances.

Predictors of Early Readmission in Heart Failure Patients in an Inner-City Community Hospital

Heart failure (HF) is the most common hospital discharge diagnosis among the elderly. It accounts for nearly 1.4 million hospitalizations and $21 billion in spending per year in the United States. Readmission rates remain high with estimates ranging from 15-day readmission rates of 13%, 30- day readmission rates of 25%, to 6-month readmission rates of 50%. The Center for Medicare and Medicaid Services (CMS) has started penalizing hospitals with higher than expected readmission rates. Objective: To identify factors associated with increased 30-day readmission among heart failure patients in an inner-city community-based teaching hospital. Methods: A retrospective cohort study of patients with principal discharge diagnosis of acute Heart Failure between 2008 and 2010. Demographic, clinical characteristics, length of stay, discharge medications, disposition and all-cause 30-day readmission were abstracted from the hospital’s administrative database and analyzed. Results: Almost 8 out of 10 patients were 65 years or older (mean age 75.4 ± 14.3) and 51% were female. The in-hospital mortality rate was 2.7% (95% confidence interval [CI], 1.6% - 4.3%) with a median length of stay of 5.0 days (Interquartile range of 3 - 7). The all-cause 30-day readmission rate was 17.7% (95% CI 14.9% - 20.8%). By univariate analysis, readmissions were predicted by black race, prior history of HF, length of stay of more than 7 days and discharge to extended care facility (ECF). By logistic regression analysis, black race (OR 2.4, 95% CI 1.4 - 3.8), prior history of HF (OR 1.7, 95% CI 1.5 - 2.6) and discharge to an ECF (OR 2.4, 95% CI 1.5 - 3.7) were the independent predictors of 30-day readmission. HF accounted for 43.7% of the readmissions. Conclusion: Prior diagnosis of HF, black race, and discharge to an ECF were independent predictors of 30-day readmission in this cohort, and over half of the readmissions were for reasons other than HF.

A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting

The yield of cereal crops such as sorghum(Sorghum bicolor L.Moench)depends on the distribution of crop-heads in varying branching arrangements.Therefore,counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field.However,measuring such phenotypic traitsmanually is an extremely labor-intensive process and suffers from low efficiency and human errors.Moreover,the process is almost infeasible for large-scale breeding plantations or experiments.Machine learning-based approaches like deep convolutional neural network(CNN)based object detectors are promising tools for efficient object detection and counting.However,a significant limitation of such deep learningbased approaches is that they typically require a massive amount of hand-labeled images for training,which is still a tedious process.Here,we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images.We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance(𝑅2 between human count and machine count is 0.88)by using a semitrained CNN model(i.e.,trained with limited labeled data)to perform synthetic annotation.In addition,we also visualize key features that the network learns.This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes.

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops.The objective of this study is to develop a machine learning(ML)approach adept at soybean(Glycine max L.(Merr.))pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot.To meet this goal,we developed a multiview image-based yield estimation framework utilizing deep learning architectures.Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions.We used data from controlled imaging environment in field,as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation.Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.

UAS-Based Plant Phenotyping for Research and Breeding Applications

Unmanned aircraft system(UAS)is a particularly powerful tool for plant phenotyping,due to reasonable cost of procurement and deployment,ease and flexibility for control and operation,ability to reconfigure sensor payloads to diversify sensing,and the ability to seamlessly fit into a larger connected phenotyping network.These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications.This paper reviews the state of the art in the deployment,collection,curation,storage,and analysis of data from UAS-based phenotyping platforms.We discuss pressing technical challenges,identify future trends in UAS-based phenotyping that the plant research community should be aware of,and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines.This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.

Soybean Root System Architecture Trait Study through Genotypic,Phenotypic,and Shape-Based Clusters

We report a root system architecture(RSA)traits examination of a larger scale soybean accession set to study trait genetic diversity.Suffering from the limitation of scale,scope,and susceptibility to measurement variation,RSA traits are tedious to phenotype.Combining 35,448 SNPs with an imaging phenotyping platform,292 accessions(replications=14)were studied for RSA traits to decipher the genetic diversity.Based on literature search for root shape and morphology parameters,we used an ideotypebased approach to develop informative root(iRoot)categories using root traits.The RSA traits displayed genetic variability for root shape,length,number,mass,and angle.Soybean accessions clustered into eight genotype-and phenotype-based clusters and displayed similarity.Genotype-based clusters correlated with geographical origins.SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits,while diverse accession could infuse useful genetic variation for these traits.Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods,enabling trait cataloging for breeding and research applications.The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline(SNAP)

Nodules form on plant roots through the symbiotic relationship between soybean(Glycine max L.Merr.)roots and bacteria(Bradyrhizobium japonicum)and are an important structure where atmospheric nitrogen(N2)is fixed into bioavailable ammonia(NH3)for plant growth and development.Nodule quantification on soybean roots is a laborious and tedious task;therefore,assessment is frequently done on a numerical scale that allows for rapid phenotyping,but is less informative and suffers from subjectivity.We report the Soybean Nodule Acquisition Pipeline(SNAP)for nodule quantification that combines RetinaNet and UNet deep learning architectures for object(i.e.,nodule)detection and segmentation.SNAP was built using data from 691 unique roots from diverse soybean genotypes,vegetative growth stages,and field locations and has a good model fit(R2=0:99).SNAP reduces the human labor and inconsistencies of counting nodules,while acquiring quantifiable traits related to nodule growth,location,and distribution on roots.The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors,and their interactions on nodulation from an early development stage.The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars,as well as enhanced insight into the plant-Bradyrhizobium relationship.