Risk of Intraocular Pressure Increase With Intravitreal Injections of Vascular Endothelial Growth Factor Inhibitors: A Cohort Study

PURPOSE: Intraocular pressure increase (IOPi) after intravitreal injections of vascular endothelial growth factor inhibitors (VEGFis) might be different among different VEGFis (bevacizumab, aflibercept, ranibizumab). The purpose of this study was to evaluate the risk of IOPi among new users of bevacizumab, ranibizumab, and aflibercept in nondiabetic patients in Tuscany, Italy.DESIGN: Retrospective cohort study.METHODS: Tuscan regional administrative database was used to identify subjects with a first VEGFi intravitreal injection between 2011 and 2020, followed to first incidence of IOPi. Diabetic subjects, those with pre-existing IOPi, or previous use of dexamethasone implants were excluded. Multivariable Cox regression analyses (intentionto-treat and as treated) were conducted to evaluate risk of IOPi among aflibercept, bevacizumab, and ranibizumab, adjusting for potential confounding variables. IOPi was defined as the first record of International Classification of Diseases, Ninth Revision (ICD-9-CM) code 365 or use of 2 glaucoma drugs dispensations within 180 days of each other.RESULTS: We identified 6585 new users of VEGFis: 1749 aflibercept, 1112 bevacizumab, and 3724 ranibizumab. Women made up 60% of the cohort, with a mean age of 73.6 years. In the intention-to-treat analysis, the adjusted hazard ratio (HR) for incident IOPi, compared with aflibercept, was higher for bevacizumab (HR = 2.20, 95% CI = 1.64-2.95) and ranibizumab users (HR = 1.88, 95% CI = 1.46-2.42), respectively.The HRs remained robust after exclusion of patients with proxy of retinal vascular occlusion. As treated analysis confirmed such results (bevacizumab: HR = 3.76, 95% CI = 2.30-6.17; ranibizumab: HR = 2.49, 95% CI = 1.62-3.82).CONCLUSIONS: This study found an increased risk of IOPi among nondiabetic patients with ranibizumab and bevacizumab compared with aflibercept. Future studies are needed to validate these findings. ((c) 2022 Published by Elsevier Inc.

Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome

<div><p>Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. <i>Sinorhizobium meliloti</i> is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF). Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the <i>S. meliloti</i> species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the <i>S. meliloti</i> species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in bacterial pangenomes.</p></div

Regulon downstream genes.

<p>Regulatory network general statistics over the strains used in this study.</p><p>^<i>a</i> Position according to the Rm1021 reference strain;</p><p>^<i>b</i> Mean Absolute Deviation;</p><p>NA: not defined.</p><p>Regulon downstream genes.</p

TFs preferentially associated with a replicon.

<p>a) K-means clustering of the normalized proportion of genes regulated in each of the three main replicons of <i>S. meliloti</i>, visualized in a two-dimensional PCA. The dark blue and cyan clusters contain TFs with no clear replicon preference; b) Variability in the number of regulatory links in the same replicon and between replicons. All differences are significant (t-test p-value < 0.05).</p

Correlations between pangenome diversity and regulatory network distances.

<p>R and S indicate the Pearson’s and Spearman’s correlation coefficients between the regulatory network and each pangenome partition distances (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004478#sec008" target="_blank">Materials and Methods</a> for the definition of the distances metrics used here). Outliers have been defined using a Z-score threshold of 3.5 on the mean absolute deviation of the distances. a) correlations within the <i>S. meliloti</i> species for the accessory genome; b) correlations within the <i>S. meliloti</i> species for coding and upstream regions; and c) correlation between the outgroups.</p

Regulon conservation.

<p>Regulatory network conservation in <i>S. meliloti</i> and near rhizobial species. For each regulator the number of conserved downstream genes over the average regulon size is reported.</p><p>^<i>a</i><i>S. meliloti</i> strain Rm1021 is also considered.</p><p>NA: not defined.</p><p>Regulon conservation.</p

Variability in regulon size.

<p>Color intensity indicates the number of downstream regulated genes in each strain; gray squares indicate the TF absence in the genome of that particular strain. Blue squares indicate that there are more than 64 genes predicted to be under the control of the TF. TFs are colored according to the replicon they belong to: red for chromosome, green for the pSymA megaplasmid and blue for the pSymB chromid.</p

Regulatory network dynamics.

<p>a) Graphical representation of the six states in which each regulatory link (a gene found with a TFBS in at least one genome) can be found in the <i>S. meliloti</i> species and between the outgroup species; b) states probabilities and states transitions probabilities inside the <i>S. meliloti</i> species: nodes and edges sizes are proportional to the probability in the model. For each state, the sum of transition probabilities is one; transition probabilities below 0.1 are not shown; c) states probabilities and states transitions probabilities between the outgroup species.</p

Multi-chamber speckle tracking imaging and diagnostic value of left atrial strain in cardiac amyloidosis

Aims: Cardiac amyloidosis (CA) affects the four heart chambers, which can all be evaluated through speckle-tracking echocardiography (STE). Methods and results: We evaluated 423 consecutive patients screened for CA over 5 years at two referral centres. CA was diagnosed in 261 patients (62%) with either amyloid transthyretin (ATTR; n = 144, 34%) or amyloid light-chain (AL; n = 117, 28%) CA. Strain parameters of all chambers were altered in CA patients, particularly those with ATTR-CA. Nonetheless, only peak left atrial longitudinal strain (LA-PALS) displayed an independent association with the diagnosis of CA or ATTR-CA beyond standard echocardiographic variables and cardiac biomarkers (Model 1), or with the diagnosis of ATTR-CA beyond the validated IWT score in patients with unexplained left ventricular (LV) hypertrophy. Patients with the most severe impairment of LA strain were those most likely to have CA or ATTR-CA. Specifically, LA-PALS and/or LA-peak atrial contraction strain (PACS) in the first quartile (i.e. LA-PALS &lt;6.65% and/or LA-PACS &lt;3.62%) had a 3.60-fold higher risk of CA, and a 3.68-fold higher risk of ATTR-CA beyond Model 1. Among patients with unexplained LV hypertrophy, those with LA-PALS or LA-PACS in the first quartile had an 8.76-fold higher risk for CA beyond Model 1, and a 2.04-fold higher risk of ATTR-CA beyond the IWT score. Conclusions: Among STE measures of the four chambers, PALS and PACS are the most informative ones to diagnose CA and ATTR-CA. Patients screened for CA and having LA-PALS and/or LA-PACS in the first quartile have a high likelihood of CA and ATTR-CA