Reassessing Trabeculectomy: A Long-Term Study with Stringent Success Criteria.

Background: The aim was to evaluate the long-term outcome and efficacy of primary trabeculectomy with adjunctive mitomycin c (MMC) for treating glaucoma. Methods: We examined the medical records of 286 eyes that underwent trabeculectomy between 2008 and 2009 at the University Eye Hospital in Freiburg, Germany. Preoperative and follow-up data were collected, including intraocular pressure (IOP) measurements, surgical glaucoma interventions, and prescribed glaucoma medication. The first success criterion was defined as IOP ≤ 15 mmHg with no use of pressure-lowering medication by the patient, the second criterion was defined as the absence of surgical revision, and the third criterion as no further IOP-lowering surgery excluding early revisions following trabeculectomy. Statistical analyses comprised Cox regression and Kaplan-Meier survival estimations. Results: The mean follow-up duration was 1841 days (5 years). The mean preoperative IOP was 26.1 mmHg. Evaluating the success criteria at the time of average follow-up yielded a success rate of only 25% for the first criterion but 80% for both the second and third success criteria. Conclusions: The findings suggest that trabeculectomy with adjunctive MMC can be an effective procedure for permanently lowering IOP. However, surgical revisions and/or further glaucoma surgeries might still be needed. The long-term success rate is lower in comparison to previous research, which may be explained by the stricter success criteria in our study.

Retinal vessel volume reference database derived from volume-rendered optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) enables three-dimensional reconstruction of the functional blood vessels in the retina. Therefore, it enables the quantification of 3D retinal vessel parameters such as surface area and vessel volume. In spite of the widespread use of OCTA, no representative volume-rendered vessel volume (VV) data are published to date. In this study, OCTA 3 × 3 mm macular cubes were processed with volume-rendering techniques to measure VV in 203 eyes from 107 healthy volunteers. Generalized linear models (GLM) were constructed to assess the impact of age, gender, visual acuity (VA), spherical equivalent (SE), and axial length (AL) on VV. Overall mean VV was 0.23 ± 0.05mm3. Age and axial length showed a negative correlation with VV. However, GLM model analysis found that AL exerted the most pronounced influence on VV. No statistically significant associations were identified between gender or between left and right eyes. This is the first study to assess 3D OCTA VV and its naturally occurring variations in a large series of healthy subjects. It offers novel insights into the characterization of normal retinal vascular anatomy in healthy individuals, contributing to a valuable reference for future research in this field.

ZEISS PLEX Elite 9000 Widefield Optical Coherence Tomography Angiography as Screening Method for Early Detection of Retinal Hemangioblastomas in von Hippel-Lindau Disease.

Purpose: To evaluate early detection of retinal hemangioblastomas (RHs) in von Hippel-Lindau disease (VHLD) with widefield optical coherence tomography angiography (wOCTA) compared to the standard of care in ophthalmologic VHLD screening in a routine clinical setting. Methods: We conducted prospective comparisons of three screening methods: wOCTA, standard ophthalmoscopy, and fluorescein angiography (FA), which was performed only in uncertain cases. The numbers of detected RHs were compared among the three screening methods. The underlying causes for the lack of detection were investigated. Results: In 91 eyes (48 patients), 67 RHs were observed (mean, 0.74 ± 1.59 RH per eye). FA was performed in eight eyes. Ophthalmoscopy overlooked 25 of the 35 RHs detected by wOCTA (71.4%) due to the background color of the choroid (n = 5), small tumor size (n = 13), masking by a bright fundus reflex (n = 2), and masking by surrounding retinal scars (n = 5). However, wOCTA missed 29 RHs due to peripheral location (43.3%). The overall detection rates were up to 37% on the basis of ophthalmoscopy alone, up to 52% for wOCTA, and 89% for FA. Within the retinal area covered by wOCTA, the detection rates were up to 46.7% for ophthalmoscopy alone, up to 92.1% for wOCTA, and 73.3% for FA. Conclusions: The overall low detection rate of RHs using wOCTA is almost exclusively caused by its inability to visualize the entire peripheral retina. Therefore, in unclear cases, FA is necessary after ophthalmoscopy. Translational Relevance: Within the imageable retinal area, wOCTA shows a high detection rate of RHs and therefore may be suitable to improve screening for RHs in VHLD.

Human selection bias drives the linear nature of the more ground truth effect in explainable deep learning optical coherence tomography image segmentation.

Supervised deep learning (DL) algorithms are highly dependent on training data for which human graders are assigned, for example, for optical coherence tomography (OCT) image annotation. Despite the tremendous success of DL, due to human judgment, these ground truth labels can be inaccurate and/or ambiguous and cause a human selection bias. We therefore investigated the impact of the size of the ground truth and variable numbers of graders on the predictive performance of the same DL architecture and repeated each experiment three times. The largest training dataset delivered a prediction performance close to that of human experts. All DL systems utilized were highly consistent. Nevertheless, the DL under-performers could not achieve any further autonomous improvement even after repeated training. Furthermore, a quantifiable linear relationship between ground truth ambiguity and the beneficial effect of having a larger amount of ground truth data was detected and marked as the more-ground-truth effect.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

Deciphering the Functional Roles of Individual Cancer Alleles Across Comprehensive Cancer Genomic Studies.

Cancer genome data has been growing in both size and complexity, primarily driven by advances in next-generation sequencing technologies, such as Pan-cancer data from TCGA, ICGC, and single-cell sequencing. Yet, discerning the functional role of individual genomic lesions remains a substantial challenge due to the complexity and scale of the data. Previously, we introduced REVEALER, which identifies groups of genomic alterations that significantly associate with target functional profiles or phenotypes, such as pathway activation, gene dependency, or drug response. In this paper, we present a new mathematical formulation of the algorithm. This version (REVEALER 2.0) is considerably more powerful than the original, allowing for rapid processing and analysis of much larger datasets and facilitating higher-resolution discoveries at the level of individual alleles. REVEALER 2.0 employs the Conditional Information Coefficient (CIC) to pinpoint features that are either complementary or mutually exclusive but still correlate with the target functional profile. The aggregation of these features provides a better explanation for the target functional profile than any single alteration on its own. This is indicative of scenarios where several activating genomic lesions can initiate or stimulate a key pathway or process. We replaced the initial three-dimensional kernel estimation with multiple precomputed one-dimensional kernel estimations, resulting in an approximate 150x increase in speed and efficiency. This improvement, combined with its efficient execution, makes REVEALER 2.0 suitable for much larger datasets and a more extensive range of genomic challenges.

NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration.

Non-negative Matrix Factorization (NME) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using Cupy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePatten gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelnes on high performance computing (HPC) culsters that enable reproducible in silco research for non-programmers.

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that although developers recognize the utility of analyzing data related to the impact or usage of their software, they struggle to find the time or funding to support such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of the evaluations of their software so that they can more fully benefit from such assessments.

Politics and Policies of Health Systems: Reflections on 50 Years of Observing Protests, Leaders, and Political Analysis.

This commentary presents reflections on my work over the past five decades related to the politics and policies of health systems from various perspectives. The essay is based on a plenary lecture at the Seventh Global Symposium on Health Systems Research in Bogotá, Colombia, in November 2022. The commentary examines a central concern in many of my writings-and a persistent challenge for people working to improve public health: How can the powerless influence policy? Using examples drawn from my past writings, I discuss three broad themes related to this question: the role of social protest movements, the impact of political leadership, and the relevance of political analysis. These reflections are offered in the hope of expanding the use of applied political analysis in public health, and thus contributing to improved health and health equity in the world.

Genomics to Notebook (g2nb): extending the electronic notebook to address the challenges of bioinformatics analysis.

We present Genomics to Notebook (g2nb), an environment that combines the JupyterLab notebook system with widely-used bioinformatics platforms. Galaxy, GenePattern, and the JavaScript versions of IGV and Cytoscape are currently available within g2nb. The analyses and visualizations within those platforms are presented as cells in a notebook, making thousands of genomics methods available within the notebook metaphor and allowing notebooks to contain workflows utilizing multiple software packages on remote servers, all without the need for programming. The g2nb environment is, to our knowledge, the only notebook-based system that incorporates multiple bioinformatics analysis platforms into a notebook interface.

Incidence and long-term outcome of laser pointer maculopathy in children.

PURPOSE: Single center study to evaluate the incidence and long-term outcome of laser pointer maculopathy (LPM). METHODS: Medical records of 909,150 patients visiting our institution between 2007 and 2020 were screened in our electronic patient record system using the keywords "laserpointer," "laser pointer," and "solar." RESULTS: Eight patients (6/2 male/female, 11 eyes) with a history of LPM were identified by fundoscopy and optical coherence tomography (OCT), all of whom were children (6/2 male/female). Mean age at injury was 12.1 years (range 6-16). Five children (62.5%) were injured between 2019 and 2020, three (37.5%) between 2007 and 2018. Median best-corrected visual acuity (BCVA) of affected eyes at first presentation was 20/25 (range 20/50-20/16). Follow-up examination was performed in seven children (10 eyes) with a median follow-up period of 18 months (range 0.5-96). BCVA improved in 4 children (5 eyes; BCVA at follow-up 20/22.5, range 20/40-20/16). Three of these four children were treated with oral steroids. OCT revealed acute signs such as intraretinal fluid to resolve quickly, while outer retinal disruption persisted until the last follow-up in eight of eleven eyes. These lesions resembled lesions of patients with solar retinopathy of which seven cases (11 eyes) were identified between 2007 and 2020. CONCLUSION: Readily available consumer laser pointers can damage the retina and the underlying retinal pigment epithelium, possibly leading to long-lasting visual impairments. The number of laser pointer injuries has increased over the last years. Therefore, access to laser pointers for children should be strictly controlled.

NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.

Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers.

Retinal Thinning in Adults with Autism Spectrum Disorder.

Since the retina shares its embryological origin with the central nervous system, optical coherence tomography (OCT), an imaging technique frequently employed in ophthalmology to analyze the macula and intraretinal layer thicknesses and volumes, has recently become increasingly important in psychiatric research. We examined 34 autistic and 31 neurotypical adults (NT) using OCT. Autistic adults had reduced overall macular and outer nuclear layer (ONL) thickness and volume compared to NT. Both macular and ONL thickness showed significant inverse associations with the severity of autistic symptoms measured with the Social Responsiveness Scale 2 (SRS-2). Longitudinal studies across different age groups are required to clarify whether retinal changes may represent a possible trait marker.

A Predictive Machine Learning Tool for Asthma Exacerbations: Results from a 12-Week, Open-Label Study Using an Electronic Multi-Dose Dry Powder Inhaler with Integrated Sensors.

Purpose: Machine learning models informed by sensor data inputs have the potential to provide individualized predictions of asthma deterioration. This study aimed to determine if data from an integrated digital inhaler could be used to develop a machine learning model capable of predicting impending exacerbations. Patients and Methods: Adult patients with poorly controlled asthma were enrolled in a 12-week, open-label study using ProAir® Digihaler®, an electronic multi-dose dry powder inhaler (eMDPI) with integrated sensors, as reliever medication (albuterol, 90 µg/dose; 1-2 inhalations every 4 hours, as needed). Throughout the study, the eMDPI recorded inhaler use, peak inspiratory flow (PIF), inhalation volume, inhalation duration, and time to PIF. A model predictive of impending exacerbations was generated by applying machine learning techniques to data downloaded from the inhalers, together with clinical and demographic information. The generated model was evaluated by receiver operating characteristic area under curve (ROC AUC) analysis. Results: Of 360 patients included in the predictive analysis, 64 experienced a total of 78 exacerbations. Increased albuterol use preceded exacerbations; the mean number of inhalations in the 24-hours preceding an exacerbation was 7.3 (standard deviation 17.3). The machine learning model, using gradient-boosting trees with data from the eMDPI and baseline patient characteristics, predicted an impending exacerbation over the following 5 days with an ROC AUC of 0.83 (95% confidence interval: 0.77-0.90). The feature of the model with the highest weight was the mean number of daily inhalations during the 4 days prior to the day the prediction was made. Conclusion: A machine learning model to predict impending asthma exacerbations using data from the eMDPI was successfully developed. This approach may support a shift from reactive care to proactive, preventative, and personalized management of chronic respiratory diseases.

Comprehensive Assessment of Health System Performance in Odisha, India.

India has recently implemented several major health care reforms at national and state levels, yet the nation continues to face significant challenges in achieving better health system performance. These challenges are particularly daunting in India's poorer states, like Odisha. The first step toward overcoming these challenges is to understand their root causes. Toward this end, the Harvard T.H. Chan School of Public Health conducted a comprehensive study in Odisha based on ten new field surveys of the system's performance to provide a multi-perspective analysis. This article reports on the assessment of the performance of Odisha's health system and the preliminary diagnosis of underlying causes of the strengths and challenges. This comprehensive health system assessment is aimed toward the overarching goals of informing and supporting efforts to improve the performance of health systems in Odisha and other similar contexts.

Structural and functional retinal alterations in patients with paranoid schizophrenia.

Ophthalmological methods have increasingly raised the interest of neuropsychiatric specialists. While the integrity of the retinal cell functions can be evaluated with the electroretinogram (ERG), optical coherence tomography (OCT) allows a structural investigation of retinal layer thicknesses. Previous studies indicate possible functional and structural retinal alterations in patients with schizophrenia. Twenty-five patients with paranoid schizophrenia and 25 healthy controls (HC) matched for age, sex, and smoking status participated in this study. Both, ERG and OCT were applied to obtain further insights into functional and structural retinal alterations. A significantly reduced a-wave amplitude and thickness of the corresponding para- and perifoveal outer nuclear layer (ONL) was detected in patients with paranoid schizophrenia with a positive correlation between both measurement parameters. Amplitude and peak time of the photopic negative response (PhNR) and thickness of the parafoveal ganglion cell layer (GCL) were decreased in patients with schizophrenia compared to HC. Our results show both structural and functional retinal differences between patients with paranoid schizophrenia and HC. We therefore recommend the comprehensive assessment of the visual system of patients with schizophrenia, especially to further investigate the effect of antipsychotic medication, the duration of illness, or other factors such as inflammatory or neurodegenerative processes. Moreover, longitudinal studies are required to investigate whether the functional alterations precede the structural changes.

Tangent normalization for somatic copy-number inference in cancer genome analysis.

MOTIVATION: Somatic copy-number alterations (SCNAs) play an important role in cancer development. Systematic noise in sequencing and array data present a significant challenge to the inference of SCNAs for cancer genome analyses. As part of The Cancer Genome Atlas, the Broad Institute Genome Characterization Center developed the Tangent normalization method to generate copy-number profiles using data from single-nucleotide polymorphism (SNP) arrays and whole-exome sequencing (WES) technologies for over 10 000 pairs of tumors and matched normal samples. Here, we describe the Tangent method, which uses a unique linear combination of normal samples as a reference for each tumor sample, to subtract systematic errors that vary across samples. We also describe a modification of Tangent, called Pseudo-Tangent, which enables denoising through comparisons between tumor profiles when few normal samples are available. RESULTS: Tangent normalization substantially increases signal-to-noise ratios (SNRs) compared to conventional normalization methods in both SNP array and WES analyses. Tangent and Pseudo-Tangent normalizations improve the SNR by reducing noise with minimal effect on signal and exceed the contribution of other steps in the analysis such as choice of segmentation algorithm. Tangent and Pseudo-Tangent are broadly applicable and enable more accurate inference of SCNAs from DNA sequencing and array data. AVAILABILITY AND IMPLEMENTATION: Tangent is available at https://github.com/broadinstitute/tangent and as a Docker image (https://hub.docker.com/r/broadinstitute/tangent). Tangent is also the normalization method for the copy-number pipeline in Genome Analysis Toolkit 4 (GATK4). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.