Influence of cataract and small incision cataract surgery on the macular thickness measurements: an optical coherence tomography-based study

Background: Optical Coherence Tomography (OCT) is a noninvasive imaging technique for analysing retinal architecture. It is a common investigation for various optic disc and macular diseases like glaucoma and diabetic macular oedema nowadays. OCT image quality is affected by many factors especially media opacity due to cataract. This study was done to compare macular thickness measurements by OCT in the presence of cataract and after removal of the cataract by Small Incision Cataract Surgery (SICS).Methods: A prospective observational study was designed which included 99 eyes of 99 patients with no optic disc and retinal pathology who underwent uncomplicated small incision cataract surgery. Routine ophthalmological evaluation including scans using macular analysis protocols of Cirrus HD OCT were done on the first visit to outpatient department and repeated on the day of surgery, one week and three weeks after surgery. The difference between the visits were analysed by Student’s t-test for paired samples.Results: The best corrected visual acuity and signal strength of OCT scans improved significantly after surgery. Among the macular parameters the temporal inner, nasal inner and nasal outer area thicknesses showed significant improvement from preoperative to postoperative values. The foveal thickness, nasal inner and nasal outer thicknesses changed significantly between two postoperative visits.Conclusions: The presence of cataract and small incision cataract surgery affects the macular measurements performed with Cirrus HD OCT. This should be taken into consideration while managing macular diseases like diabetic macular oedema

Streaming histogram sketching for rapid microbiome analytics

Background: The growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for the compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time. Results: We apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed ‘histosketch’ that can efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using the pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme. Furthermore, we use a ‘real life’ example to show that histosketches can train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a random forest classifier that could accurately predict whether the neonate had received antibiotic treatment (97% accuracy, 96% precision) and could subsequently be used to classify microbiome data streams in less than 3 s. Conclusions: Our method offers a new approach to rapidly process microbiome data streams, allowing samples to be rapidly clustered, indexed and classified. We also provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2 GB microbiome in 50 s on a standard laptop using four cores, with the sketch occupying 3000 bytes of disk space