Importance of OCT-derived biomarkers for the recurrence of central serous chorioretinopathy using statistics and predictive modelling.

Central serous chorioretinopathy (CSCR) is a retinal disease characterised by the accumulation of subretinal fluid, which often resolves spontaneously in acute cases. However, approximately one-third of patients experience recurrences that may cause severe and irreversible vision. This study aimed to identify parameters derived from optical coherence tomography (OCT) that are associated with CSCR recurrence. Our dataset included 5211 OCT scans from 344 eyes of 255 patients diagnosed with CSCR. 178 eyes were identified as recurrent, 109 as non-recurrent, and 57 were excluded. We extracted parameters using artificial intelligence algorithms based on U-Nets, convolutional kernels, and morphological operators. We applied inferential statistics to evaluate differences between the recurrent and non-recurrent groups, and we used a logistic regression predictive model, reporting the coefficients as a measure of biomarker importance. We identified nine predictive biomarkers for CSCR recurrence: age, intraretinal fluid, subretinal fluid, pigment epithelial detachments, choroidal vascularity index, integrity of photoreceptors and retinal pigment epithelium layer, choriocapillaris and choroidal stroma thickness, and thinning of the outer nuclear layer, and of the inner nuclear layer combined with the outer plexiform layer. These results could enable future developments in the automatic detection of CSCR recurrence, paving the way for translational medical applications.

Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data.

In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant healthcare questions. The ability to automatically extract large-scale patient cohorts from hospital systems is vital in order to unlock the potential of real-world clinical data, and answer pivotal medical questions through retrospective research studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach to access and interoperability. Even when the data are readily accessible, clinical researchers need to sift through Electronic Medical Records, confirm ethical approval, verify status of patient consent, check the availability of imaging data, and filter the data based on disease-specific image biomarkers. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopedics.

Significant Reduction of Radiation Exposure Using Specific Settings of the O-Arm for Percutaneous Cementoplasty in Accordance With the ALARA Principle.

BACKGROUND: Three-dimensional (3D) navigation has become routinely used in spine surgery, allowing more accurate and safe procedures. However, radiation exposure related to the use of imaging is an unresolved issue, and information about it is relatively scarce. The "as low as reasonably achievable" (ALARA) principle aims to reduce the radiation exposure for the patients as low as possible. The objective of this study was to compare the effective dose related to the use of the O-arm in standard settings with adapted features for dose reduction during percutaneous cementoplasty. METHODS: From March 2021 to October 2022, all consecutive patients who underwent navigated percutaneous cementoplasty with the use of the O-arm were prospectively included. Demographic, operative, irradiation, and radiological data were collected. The main outcome was the effective dose (E) in millisievert (mSv). Secondary outcomes were the absolute risk of cancer (AR) in percent equivalent to a whole-body exposition, operative time, and radiological results according to Garnier. In group A, patients were operated on with standard settings of the O-arm, whereas in group B, navigation on the field of view, collimation, and low-dose settings were used. RESULTS: A total of 70 patients were included in the study: 43 in group A and 27 in group B. Also, 109 vertebrae were operated: 59 in group A and 50 in group B. Mean E was significantly higher in group A than in group B (9.94 and 4.34 mSv, respectively; P < 0.01). The 3D-related E followed the same trend (7.82 and 3.97 mSv, respectively), as did 2-dimensional-related E (2.12 and 0.37 mSv, respectively; P < 0.01). Average AR was also significantly higher in group A than in group B (5.10-4% and 2.10-4% respectively; P < 0.01). Operative time was similar in both groups, but the rate of satisfactory radiological results was higher in group A than in group B (95% and 84%, respectively; P = 0.11), and we found similar rates of cement leakage (22% and 24%, respectively; P = 0.71). CONCLUSIONS: The application of settings of the O-arm in accordance with the ALARA principle helped to significantly reduce the radiation exposure and should be routinely used for O-arm-assisted cementoplasty procedures. CLINICAL RELEVANCE: This study details technical aspects and settings that may help users of the O-arm to decrease radiation exposure to patients and surgeons alike, especially in cementoplasty procedures, as well as in other procedures performed under O-arm guidance.

Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases.

Purpose: To identify novel susceptibility loci for retinal vascular tortuosity, to better understand the molecular mechanisms modulating this trait, and reveal causal relationships with diseases and their risk factors. Design: Genome-wide Association Studies (GWAS) of vascular tortuosity of retinal arteries and veins followed by replication meta-analysis and Mendelian randomization (MR). Participants: We analyzed 116 639 fundus images of suitable quality from 63 662 participants from 3 cohorts, namely the UK Biobank (n = 62 751), the Swiss Kidney Project on Genes in Hypertension (n = 397), and OphtalmoLaus (n = 512). Methods: Using a fully automated retina image processing pipeline to annotate vessels and a deep learning algorithm to determine the vessel type, we computed the median arterial, venous and combined vessel tortuosity measured by the distance factor (the length of a vessel segment over its chord length), as well as by 6 alternative measures that integrate over vessel curvature. We then performed the largest GWAS of these traits to date and assessed gene set enrichment using the novel high-precision statistical method PascalX. Main Outcome Measure: We evaluated the genetic association of retinal tortuosity, measured by the distance factor. Results: Higher retinal tortuosity was significantly associated with higher incidence of angina, myocardial infarction, stroke, deep vein thrombosis, and hypertension. We identified 175 significantly associated genetic loci in the UK Biobank; 173 of these were novel and 4 replicated in our second, much smaller, metacohort. We estimated heritability at ∼25% using linkage disequilibrium score regression. Vessel type specific GWAS revealed 116 loci for arteries and 63 for veins. Genes with significant association signals included COL4A2, ACTN4, LGALS4, LGALS7, LGALS7B, TNS1, MAP4K1, EIF3K, CAPN12, ECH1, and SYNPO2. These tortuosity genes were overexpressed in arteries and heart muscle and linked to pathways related to the structural properties of the vasculature. We demonstrated that retinal tortuosity loci served pleiotropic functions as cardiometabolic disease variants and risk factors. Concordantly, MR revealed causal effects between tortuosity, body mass index, and low-density lipoprotein. Conclusions: Several alleles associated with retinal vessel tortuosity suggest a common genetic architecture of this trait with ocular diseases (glaucoma, myopia), cardiovascular diseases, and metabolic syndrome. Our results shed new light on the genetics of vascular diseases and their pathomechanisms and highlight how GWASs and heritability can be used to improve phenotype extraction from high-dimensional data, such as images. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

SOIN - MI Data Lab: Personalized Ophthalmology Through Collaborative Data Collection and Dynamic Patient Consent.

The Swiss Ophthalmic Image Network (SOIN) is part of the Swiss Personalized Health Network (SPHN). SOIN contains a collaborative, clinical research environment, MI Data Lab, which allows privacy-preserving, data-driven, research. Personalized care of chronic ocular disease, based on Machine Learning (ML) and medical imaging, can dramatically improve quality of life and reduce the burden on health and social care systems. MI Data Lab allows research partners to consolidate their data in a space where doctors and data scientists cooperate to design novel ML algorithms, on curated datasets. To date, we have created several algorithms to detect ocular biomarkers automatically, and applied such tools to 100k+ retinal images. MI Data Lab enables the development of predictive models, the extraction novel traits to be explored in terms of -omic associations, treatment outcome, and priors for disease progression.

Isolation and Genetic Characterization of Streptococcus iniae Virulence Factors in Adriatic Sturgeon (Acipenser naccarii).

The first case of infection of Streptococcus iniae in Adriatic sturgeon (Acipenser naccarii) was recently reported in a raceway system located in Northern Italy. A second episode of infection in sturgeons with absence of mortality and evident clinical signs, was registered in November 2020 in the same farm and is reported in this study. Histopathological changes observed in infected organs are described. The strains isolated in the two episodes were compared using molecular analysis based on PCR, phylogeny and virulence factors analysis. Not all the major virulence factors were detected for the two strains; in particular the strains 78697, isolated in November, lacks cpsD, compared to the strains 64844, isolated in September. Moreover, genetic variations were reported for lctO and pmg genes. These findings let us hypothesize a different virulence of the strains in accordance with clinical findings related to the sturgeons.

Relationship between the prevalence of Dibothriocephalus latus (Cestoda: Diphyllobothriidea) and the load of Escherichia coli: New findings in a neglected fish-borne parasitic zoonosis.

The sub-Alpine lakes of Switzerland, Italy and France have long been reported as an endemic area of diphyllobothriosis, a parasitic zoonosis caused by Dibothriocephalus latus. With this study, we explored the hypothesis for a relationship between the prevalence of D. latus in Perca fluviatilis and the Escherichia coli load in lake water. To do this, we identified eleven sampling sites in three areas (north, centre and south) of Lake Iseo (north Italy) to determine E. coli load and the prevalence of D. latus in P. fluviatilis. Prevalence and 95% confidence interval (CI) of D. latus infestation ranged from 0% (95% CI: 0.71-0.0) in Sarnico (southern area) to 20% (95% CI: 33.0-11.2) in Pisogne (northern area). There were significant differences in prevalence between the sites (χ2  = 31.12; p-value = .0006) and in E. coli load (Kruskal-Wallis test; p-value = .0005). There was decreasing gradient of E. coli load and prevalence of D. latus infestation from north to south. A significant positive correlation (r = .881; p-value = .003) was found between E. coli load and prevalence of D. latus. Also, linear regression showed a significant relationship between E. coli load and prevalence of infestation (R2  = .775). Our findings offer an explanation for the link between E. coli load in water and D. latus prevalence. The potential factors in this link are the efficiency of the local wastewater treatment plant, the bathymetric profile of the lake and the life cycle of D. latus, which is mainly affected by light and water temperature.

Investigation of Potential Reservoirs of Non-Tuberculous Mycobacteria in a European Sea Bass (Dicentrarchus labrax) Farm.

Fish mycobacteriosis is a widespread global problem caused by species of non-tuberculous mycobacteria (NTM). Mycobacterium marinum is one of the species most often involved in disease episodes of aquarium and farmed fish. Since there is currently no available effective therapy or vaccine, a prompt search for routes of entry is key to limiting the damage induced by the disease. Here we report a case of mycobacteriosis follow up in a European sea bass (Dicentrarchus labrax) farm located in Northern Italy, in which environmental samples and newly added fish batches were analyzed. Samples from fish present on the farm, sediment, and periphyton all resulted positive for M. marinum, whereas the new fish batches and the water samples resulted negative. The environmental resistance of NTM (alcohol-acid resistance, biofilm formation) and the lack of prophylactic and therapeutic strategies make these diseases difficult to manage. Prompt identification of biotic and abiotic reservoirs, combined with good zootechnical hygiene practices, are the most effective measures to control fish mycobacteriosis in intensive farms.

Gene Sequencing and Phylogenetic Analysis: Powerful Tools for an Improved Diagnosis of Fish Mycobacteriosis Caused by Mycobacterium fortuitum Group Members.

The Mycobacterium fortuitum group (MFG) consists of about 15 species of fast-growing nontuberculous mycobacteria (NTM). These globally distributed microorganisms can cause diseases in humans and animals, especially fish. The increase in the number of species belonging to MFG and the diagnostic techniques panel do not allow to clarify their real clinical significance. In this study, biomolecular techniques were adopted for species determination of 130 isolates derived from fish initially identified through biochemical tests as NTM belonging to MFG. Specifically, gene sequencing and phylogenetic analysis were used based on a fragment of the gene encoding the 65 KDa heat shock protein (hsp65). The analyzes made it possible to confirm that all the isolates belong to MFG, allowing to identify the strains at species level. Phylogenetic analysis substantially confirmed what was obtained by gene sequencing, except for six strains; this is probably due to the sequences present in NCBI database. Although the methodology used cannot represent a univocal identification system, this study has allowed us to evaluate its effectiveness as regards the species of MFG. Future studies will be necessary to apply these methods with other gene fragments and to clarify the real pathogenic significance of the individual species of this group of microorganisms.

The unusual isolation of carnobacteria in eyes of healthy salmonids in high-mountain lakes.

Carnobacteria are common bacteria in cold and temperate environments; they are also reported during fish mortality events. In a previous study, carnobacteria were isolated from the eyes of healthy wild salmonids from a high-mountain lake. To better understand these findings, salmonids were captured from three high-mountain lakes (Lower and Upper Balma Lake, Rouen Lake; northwest Italy) during August 2019 and subjected to bacteriological and histological examination. Although all were healthy, 8.7% (Lower Balma Lake), 24% (Upper Balma Lake), and 32.6% (Rouen Lake) were positive for carnobacteria colonization of the eyes. A Trojan-horse effect was hypothesized to explain carnobacteria isolation in the eye. This immune-escaping macrophage-mediated mechanism has been identified in other Gram-positive bacteria. Biochemical, molecular, and phylogenetic analysis were carried out on isolated bacteria (Carnobacterium maltaromaticum and C. divergens). Based on previous references for carnobacteria isolated from fish, C. maltaromaticum strains were tested for the pisA precursor gene of the bacteriocin piscicolin 126. Carnobacterium maltaromaticum strains were found to display genotypic heterogeneity and a low percentage of pisA positive amplification. Features of geomorphology, geographic isolation, and microbiota common to the three lakes are thought to be possibly related to our findings. Moreover, terrestrial insects collected from the lake shoreline and the stomach contents were screened for the presence of carnobacteria. The salmonids in these high-mountain environments feed mainly on terrestrial insects, which are considered possible vectors for carnobacteria that might catabolize the exoskeleton chitin. All insects tested negative for carnobacteria, but as a small number of samples were analyzed, their role as possible vectors of infection cannot be excluded. Further studies are needed to corroborate our research hypothesis.

Reply to Pavlik et al. Clinical Relevance and Environmental Prevalence of Mycobacterium fortuitum Group Members. Comment on "Mugetti et al. Gene Sequencing and Phylogenetic Analysis: Powerful Tools for an Improved Diagnosis of Fish Mycobacteriosis Caused by Mycobacterium fortuitum Group Members. Microorganisms 2021, 9, 797".

We appreciate the valuable comment of Pavlik et al. [...].

Occurrence and Spatial Distribution of Dibothriocephalus Latus (Cestoda: Diphyllobothriidea) in Lake Iseo (Northern Italy): An Update.

Dibothriocephalus latus (Linnaeus, 1758) (Cestoda: Diphyllobothriidea; syn. Diphyllobothrium latum), is a fish-borne zoonotic parasite responsible for diphyllobothriasis in humans. Although D. latus has long been studied, many aspects of its epidemiology and distribution remain unknown. The aim of this study was to investigate the prevalence, mean intensity of infestation, and mean abundance of plerocercoid larvae of D. latus in European perch (Perca fluviatilis) and its spatial distribution in three commercial fishing areas in Lake Iseo (Northern Italy). A total of 598 specimens of P. fluviatilis were caught in 2019. The total prevalence of D. latus was 6.5%. However, there were significant differences between areas (10.2% North; 7.3% Center; 1.5% South) (Chi-square test, p = 0.0018). The mean intensity of infestation ranged from 1 larva in southern area to 1.2 larvae in both the central and northern (Pisogne) areas. In addition, the mean abundance ranged from 0.02 in the southern area to 0.26 in the northern area (Pisogne). The total number of larvae (anterior dorsal-AD = 21; anterior ventral-AV = 1; posterior dorsal-PD = 15; posterior ventral-PV = 5) differed significantly between the four anatomical quadrants (Kruskal-Wallis test; p = 0.0001). The prevalence of D. latus plerocercoid larvae in European perch from Lake Iseo has long been investigated, but without an appropriate sampling design. With the present study, a broader analysis in spatial distribution has been added to the existing literature, revealing new information about D. latus distribution and occurrence in Lake Iseo, with new data that will be useful for health authorities and future studies.

MONET: a toolbox integrating top-performing methods for network modularization.

SUMMARY: We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful biomarkers. To this end, we launched the 'Disease Module Identification (DMI) DREAM Challenge', a community effort to build and evaluate unsupervised molecular network modularization algorithms. Here, we present MONET, a toolbox providing easy and unified access to the three top-performing methods from the DMI DREAM Challenge for the bioinformatics community. AVAILABILITY AND IMPLEMENTATION: MONET is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/BergmannLab/MONET.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessment of network module identification across complex diseases.

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites.

Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we tested the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of covarying features derived from principal component analysis (PCA), the iterative signature algorithm (ISA), and averaged correlation profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. On the basis of the number and quality of the matches, we concluded that ISA and ACP can robustly identify ten and nine metabolites, respectively, half of which were shared, while PCA did not produce any signatures with robust matches.

PhenoMeNal: processing and analysis of metabolomics data in the cloud.

BACKGROUND: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.

Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data.

BACKGROUND: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learning algorithms trained on unselected, nation-wide population data from Sweden to solve the binary classification problem of predicting survival versus non-survival 2 years after first myocardial infarction (MI). METHODS: This prospective national registry study for prognostic accuracy validation of predictive models used data from 51,943 complete first MI cases as registered during 6 years (2006-2011) in the national quality register SWEDEHEART/RIKS-HIA (90% coverage of all MIs in Sweden) with follow-up in the Cause of Death register (> 99% coverage). Primary outcome was AUROC (C-statistic) performance of each model on the untouched test set (40% of cases) after model development on the training set (60% of cases) with the full (39) predictor set. Model AUROCs were bootstrapped and compared, correcting the P-values for multiple comparisons with the Bonferroni method. Secondary outcomes were derived when varying sample size (1-100% of total) and predictor sets (39, 10, and 5) for each model. Analyses were repeated on 79,869 completed cases after multivariable imputation of predictors. RESULTS: A Support Vector Machine with a radial basis kernel developed on 39 predictors had the highest complete cases performance on the test set (AUROC = 0.845, PPV = 0.280, NPV = 0.966) outperforming Boosted C5.0 (0.845 vs. 0.841, P = 0.028) but not significantly higher than Logistic Regression or Random Forest. Models converged to the point of algorithm indifference with increased sample size and predictors. Using the top five predictors also produced good classifiers. Imputed analyses had slightly higher performance. CONCLUSIONS: Improved mortality prediction at hospital discharge after first MI is important for identifying high-risk individuals eligible for intensified treatment and care. All models performed accurately and similarly and because of the superior national coverage, the best model can potentially be used to better differentiate new patients, allowing for improved targeting of limited resources. Future research should focus on further model development and investigate possibilities for implementation.