Purpose: This study aims to explore and characterize healthy eye microbiota. Methods: Healthy subjects older than 18 years were selected for this descriptive cross-sectional study. Samples were collected with an eSwab with 1 mL of Liquid Amies Medium (Copan Brescia, Italy). Following DNA extraction, libraries preparation, and amplification, PCR products were purified and end-repaired for barcode ligation. Libraries were pooled to a final concentration of 26 pM. Template preparation was performed with Ion Chef according to Ion 510, Ion 520, and Ion 530 Kit-Chef protocol. Sequencing of the amplicon libraries was carried out on a 520 or 530 chip using the Ion Torrent S5 system (Thermo Fisher; Waltham, MA, USA). Raw reads were analyzed with GAIA (v 2.02). Results: Healthy eye microbiota is a low-diversity microbiome. The vast majority of the 137 analyzed samples were highly enriched with Staphylococcus, whereas only in a few of them, other genera such as Bacillus, Pseudomonas, and Corynebacterium predominate. We found an average of 88 genera with an average Shannon index of 0.65. Conclusion: We identified nine different ECSTs. A better understanding of healthy eye microbiota has the potential to improve disease diagnosis and personalized regimens to promote health.

Purpose: To evaluate the microbiota of culture negative Corneal Impression Membrane (CIM) microbial keratitis samples with the use of shotgun metagenomics analysis. Methods: DNA of microbial keratitis samples were collected with CIM and extracted using the MasterPure™ Complete DNA and RNA Purification Kit (Epicentre). DNA was fragmented by sonication into fragments of 300 to 400 base pairs (bp) using Bioruptor® (Diagenode, Belgium) and then used as a template for library preparation. DNA libraries were sequenced on Illumina® HiSeq2500. The resulting reads were quality controlled, trimmed and mapped against the human reference genome. The unmapped reads were taxonomically classified using the Kraken software. Results: 18 microbial keratitis samples were included in the study. Brevundimonas diminuta was found in 5 samples while 6 samples showed the presence of viral infections. Cutibacterium acnes, Staphylococcus aureus, Moraxella lacunata and Pseudomonas alcaligenes were also identified as the presumed putative cause of the infection in 7 samples. Conclusions: Shotgun sequencing can be used as a diagnostic tool in microbial keratitis samples. This diagnostic method expands the available tests to diagnose eye infections and could be clinically significant in culture negative samples.

In the field of Ophthalmology, the mNGS 16S rRNA sequencing method of studying the microbiota and ocular microbiome is gaining more and more weight in the scientific community. This study aims to characterize the ocular microbiota of patients diagnosed with keratoconus who have not undergone any prior surgical treatment using the mNGS 16S rRNA sequencing method. Samples of naïve keratoconus patients were collected with an eNAT with 1 mL of Liquid Amies Medium (Copan Brescia, Italy), and DNA was extracted and analyzed with 16S NGS. The microbiota analysis showed a relative abundance of microorganisms at the phylum level in each sample collected from 38 patients with KC and 167 healthy controls. A comparison between healthy control and keratoconus samples identified two genera unique to keratoconus, Pelomonas and Ralstonia. Our findings suggest that alterations in the microbiota may play a role in the complex scenario of KC development.

The increase in the depth of focus (DoF) for the treatment of presbyopia or cataracts is a topic of great interest for anterior segment surgeons who have seen how new surgical possibilities to achieve DoF enlargement have emerged. Nowadays, several technologies to extend the DoF are available, from corneal laser refractive surgery procedures in presbyopia to intraocular lens (IOL) implantation in cataract or refractive lens exchange. Some of these procedures are based on aspheric profiles, either in the cornea or in the IOL, which modulate the spherical aberration (SA) and, therefore, extend the light energy on different focal planes. The aim of this narrative review was to give an overall picture about the reasons why there is not a general solution persistent along time of SA induction to extend DoF, especially considering that SA depends on pupil diameter and this decreases with age.

Purpose: To evaluate the accuracy of 12 intraocular lens (IOL) power formulas; Barrett Universal II, Emmetropia Verifying Optical (EVO), Haigis, Hill-Radial Basis Function (RBF), Hoffer Q, Holladay I, Kane, Ladas Super Formula, Olsen _Lenstar, Panacea, Pearl-DGS, Sanders-Retzlaff-Kraff/theoretical (SRK/T). In addition, an analysis of the efficacy as a function of the axial length was performed. Methods: About 171 from 93 patients: 68 male eyes and 103 female eyes. Twelve IOL power formula calculations were studied with one IOL platform (trifocal hydrophilic IOL, FineVision Micro F), one biometer (Lenstar LS 900), one topographer (CSO Sirius Topographer), one surgeon, and one optometrist. Optimization were determined to be zeroed mean refractive prediction error. Mean error (ME), mean absolute error (MAE), median absolute error (MedAE) and refractive accuracy within ±1.00 D was calculated. Axial length was split in short and medium eyes. Results: One hundred and seventy eyes were included. Formulas were ranked by percentage within ±0.50 diopters and MAE (D). Among all eyes, Olsen 86.55% (0.273 D) and Barrett Universal II 86.55% (0.285D). For short eyes (<22.5 mm), Olsen 90.70% (0.273 D) and Kane 90.70% (0.225 D). For medium eyes, Barrett 89.34% (0.237 D) and Pearl 86.89% (0.263 D). Conclusion: Olsen and Barrett formula obtained excellent accuracy for overall eyes. Kane and Olsen formula obtained the best results in short eyes. For medium axial length Barrett formula achieved the best accuracy results.