Category: Nevin Manimala

Pharmacoepidemiol Drug Saf. 2023 Apr 17. doi: 10.1002/pds.5631. Online ahead of print.

ABSTRACT

PURPOSE: Heterogeneous results from multi-database studies have been observed, for example in the context of generating background incidence rates (IR) for adverse events of special interest for SARS-CoV-2 vaccines. In this study, we aimed to explore different between-database sources of heterogeneity influencing the estimated background IR of venous thromboembolism (VTE).

METHODS: Through forest plots and random-effects models, we performed a qualitative and quantitative assessment of heterogeneity of VTE background IR derived from 11 databases from six European countries, using age and gender stratified background IR for the years 2017-2019 estimated in two studies. Sensitivity analyses were performed to assess the impact of selection criteria on the variability of the reported IR.

RESULTS: A total of 54,257,284 subjects were included in this study. Age-gender pooled VTE IR varied from 5 to 421/100,000 person-years and IR increased with increasing age for both genders. Wide confidence intervals demonstrated considerable within-data-source heterogeneity. Selecting databases with similar characteristics had only a minor impact on the variability as shown in forest plots and the magnitude of the I² statistic, which remained large. Solely including databases with primary care and hospital data resulted in a noticeable decrease in heterogeneity.

CONCLUSIONS: Large variability in IR between data sources and within age group and gender strata warrants the need for stratification and limits the feasibility of a meaningful pooled estimate. A more detailed knowledge of the data characteristics, operationalisation of case definitions and cohort population might support an informed choice of the adequate databases to calculate reliable estimates. This article is protected by copyright. All rights reserved.

PMID:37068170 | DOI:10.1002/pds.5631

Acc Chem Res. 2023 Apr 17. doi: 10.1021/acs.accounts.3c00030. Online ahead of print.

ABSTRACT

ConspectusThere has been a recent surge of advances in biomolecular assays based on the measurement of discrete molecular targets as opposed to signals averaged across molecular ensembles. Many of these “digital” assay designs derive from now-mature technologies involving single-molecule imaging and microfluidics and provide an assortment of new modalities to quantify nucleic acids and proteins in biospecimens such as blood and tissue homogenates. A primary new benefit is the robust detection of trace analytes at attomolar to femtomolar concentrations for which many ensemble assays cannot distinguish signals above noise levels. In addition, multiple biomolecules can be differentiated within a mixture using optical barcodes, with much faster and simpler readouts compared with sequencing methods. In ideal digital assays, signals should, in theory, further represent absolute molecular counts, rather than relative levels, eliminating the need for calibration standards that are the mainstay of typical assays. Several digital assay platforms have now been commercialized but challenges hinder the adoption and diversification of these new formats, as there are broad needs to balance sensitivity and dynamic range of detection, increase analyte multiplexing, improve sample throughput, and reduce cost. Our lab and others have developed technologies to address these challenges by redesigning molecular probes and labels, improving molecular transport within detection focal volumes, and applying solution-based readout methods in flow.This Account describes the principles, formats, and design constraints of digital biomolecular assays that apply optical labels toward the goal of simple and routine target counting that may ultimately approach absolute readout standards. The primary challenges can be understood from fundamental concepts in thermodynamics and kinetics of association reactions, mass transport, and discrete statistics. Major advances include (1) new inorganic nanocrystal probes for more robust counting compared with dyes, (2) diverse molecular amplification tools that endow attachment of numerous labels to single targets, (3) specialized surfaces with patterned features for electromagnetic coupling to labels for signal amplification, (4) surface capture enhancement methods to concentrate targets through disruption of diffusion depletion zones, and (5) flow counting in which analytes are rapidly counted in solution without pull-down to a surface. Further progress and integration of these tools for biomolecular counting could improve the precision of laboratory measurements in life sciences research and benefit clinical diagnostic assays for low abundance biomarkers in limiting biospecimen volumes that are out of reach of traditional ensemble-level bioassays.

PMID:37068158 | DOI:10.1021/acs.accounts.3c00030

Can Urol Assoc J. 2023 Apr 11. doi: 10.5489/cuaj.8136. Online ahead of print.

ABSTRACT

INTRODUCTION: Renal colic is a common emergency department (ED) presentation. Variations in assessment and management of suspected renal colic may have significant implications on patient and hospital outcomes. We developed a clinical practice guideline to standardize the assessment and management of renal colic in the ED. We subsequently compared outcomes before and after guideline implementation.

METHODS: The guidelines standardized the analgesia regimen, urology consult criteria, imaging modality, patient education, and followup instructions. This is a single-center, observational cohort study of patients presenting to the ED with renal colic prospectively collected after guideline implementation (December 2018 to May 2019), compared to a control group retrospectively collected before guideline implementation (December 2017 to May 2018). A total of 528 patients (pre-guideline n=283, post-guideline n=245) were included. Statistical analysis was performed with SPSS using multivariate linear regression.

RESULTS: ED length of stay (LOS) was significantly shorter after guideline implementation (preguideline 295.82±178.8 minutes vs. post-guideline 253.2±118.2 minutes, p=0.017). The number of computed tomography (CT) scans patients received was significantly less after guideline implementation (pre guideline 1.35±1.34 vs. post-guideline 1.00±0.68, p=0.034). Patients discharged for conservative management had a lower re-presentation rate in the post-guideline group (12.6%) than the pre-guideline group (17.2%); however, this did not reach statistical significance (p=0.18).

CONCLUSIONS: Implementation of a clinical practice guideline for ureteric stones reduces the ED LOS and the total number of CT scan in patients who present with renal colic. Standardizing assessment and management of ureteric stones can potentially improve patient and hospital outcomes without compromising the quality of care.

PMID:37068151 | DOI:10.5489/cuaj.8136

Can Urol Assoc J. 2023 Apr 11. doi: 10.5489/cuaj.8222. Online ahead of print.

ABSTRACT

INTRODUCTION: Patients with chronic scrotal content pain (CSCP) lack effective, non-invasive treatment options. We aimed to determine the local and systemic safety, tolerability, pharmacokinetics (PK), and efficacy of a long-lasting local anesthetic in patients with CSCP.

METHODS: This was a prospective, single-center, open-label, single-arm, phase 1 dose-escalating trial completed between October 2019 and March 2021. Twelve patients ≥19 years old with unilateral scrotal pain lasting ≥3 months reporting an average maximum pain score over seven days of ≥4 on a 0-10 numerical rating scale (NRS) were included. Patients underwent a test spermatic cord block and those reporting a decrease of ≥2 points were included. The investigational drug, ST-01 (sustained-release lidocaine polymer solution), is a long-acting injection of lidocaine around the spermatic cord. Subjects were provided a NRS dairy and recorded their NRS score until day 28. The Chronic Epididymitis Symptom Index (CESI) was completed on days 0, 7, 14, and 28. All patients underwent an examination and assessment for adverse events (AE) on days 0, 1, 7, 14, and 28. Exploratory statistical hypothesis testing was planned for this study due to its investigative nature.

RESULTS: There were no serious adverse events (SAEs) reported. All subjects reported at least one treatment-emergent adverse event (TEAE); 83% of related AEs were injection-site reactions consisting of swelling and bruising. NRS was reduced across all cohorts between baseline and end of study.

CONCLUSIONS: This study provides evidence that the novel ST-01 treatment is safe and well-tolerated.

PMID:37068147 | DOI:10.5489/cuaj.8222

Mol Cancer Res. 2023 Apr 17:OF1-OF12. doi: 10.1158/1541-7786.MCR-22-0623. Online ahead of print.

ABSTRACT

Adult-type granulosa cell tumors (aGCT) are rare ovarian sex cord tumors with few effective treatments for recurrent disease. The objective of this study was to characterize the tumor microenvironment (TME) of primary and recurrent aGCTs and to identify correlates of disease recurrence. Total RNA sequencing (RNA-seq) was performed on 24 pathologically confirmed, cryopreserved aGCT samples, including 8 primary and 16 recurrent tumors. After read alignment and quality-control filtering, DESeq2 was used to identify differentially expressed genes (DEG) between primary and recurrent tumors. Functional enrichment pathway analysis and gene set enrichment analysis was performed using “clusterProfiler” and “GSVA” R packages. TME composition was investigated through the analysis and integration of multiple published RNA-seq deconvolution algorithms. TME analysis results were externally validated using data from independent previously published RNA-seq datasets. A total of 31 DEGs were identified between primary and recurrent aGCTs. These included genes with known function in hormone signaling such as LHCGR and INSL3 (more abundant in primary tumors) and CYP19A1 (more abundant in recurrent tumors). Gene set enrichment analysis revealed that primarily immune-related and hormone-regulated gene sets expression was increased in recurrent tumors. Integrative TME analysis demonstrated statistically significant depletion of cancer-associated fibroblasts in recurrent tumors. This finding was confirmed in multiple independent datasets.

IMPLICATIONS: Recurrent aGCTs exhibit alterations in hormone pathway gene expression as well as decreased infiltration of cancer-associated fibroblasts, suggesting dual roles for hormonal signaling and TME remodeling underpinning disease relapse.

PMID:37068116 | DOI:10.1158/1541-7786.MCR-22-0623

Hosp Pract (1995). 2023 Apr 17:1-14. doi: 10.1080/21548331.2023.2203051. Online ahead of print.

ABSTRACT

OBJECTIVES: This systematic review was conducted to investigate the characteristics and effects of clinical decision support systems (CDSSs) on clinical and process-of-care outcomes of patients with kidney disease.

METHODS: A comprehensive systematic search was conducted in electronic databases to identify relevant studies published until November 2020. Randomized clinical trials evaluating the effects of using electronic CDSS on at least one clinical or process-of-care outcome in patients with kidney disease were included in this study. The characteristics of the included studies, features of CDSSs, and effects of the interventions on the outcomes were extracted. Studies were appraised for quality using the Cochrane risk-of-bias assessment tool.

RESULTS: Out of 8722 retrieved records, 11 eligible studies measured 32 outcomes, including 10 clinical outcomes and 22 process-of-care outcomes. The effects of CDSSs on 45.5% of the process-of-care outcomes were statistically significant, and all the clinical outcomes were not statistically significant. Medication-related process-of-care outcomes were the most frequently measured (54.5%), and CDSSs had the most effective and positive effect on medication appropriateness (18.2%). The characteristics of CDSSs investigated in the included studies comprised automatic data entry, real-time feedback, providing recommendations, and CDSS integration with the Computerized Provider Order Entry system.

CONCLUSION: Although CDSS may potentially be able to improve processes of care for patients with kidney disease, particularly with regard to medication appropriateness, no evidence was found that CDSS affects clinical outcomes in these patients. Further research is thus required to determine the effects of CDSSs on clinical outcomes in patients with kidney diseases.

PMID:37068105 | DOI:10.1080/21548331.2023.2203051

PLoS Comput Biol. 2023 Apr 17;19(4):e1010137. doi: 10.1371/journal.pcbi.1010137. Online ahead of print.

ABSTRACT

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales-precisely the scales at which these processes occur-microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primers sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.

PMID:37068103 | DOI:10.1371/journal.pcbi.1010137

PLoS Comput Biol. 2023 Apr 17;19(4):e1011047. doi: 10.1371/journal.pcbi.1011047. Online ahead of print.

ABSTRACT

Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying de novo DL models in practice and various pathways for future advances are discussed.

PMID:37068100 | DOI:10.1371/journal.pcbi.1011047

PLoS Comput Biol. 2023 Apr 17;19(4):e1011044. doi: 10.1371/journal.pcbi.1011044. Online ahead of print.

ABSTRACT

Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis, merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods.

PMID:37068097 | DOI:10.1371/journal.pcbi.1011044

PLoS One. 2023 Apr 17;18(4):e0282349. doi: 10.1371/journal.pone.0282349. eCollection 2023.

ABSTRACT

Sex hormones can affect cellular physiology and modulate synaptic plasticity, but it is not always clear whether or how sex-dependent differences identified in vitro express themselves as functional dimorphisms in the brain. Historically, most experimental neuroscience has been conducted using only male animals and the literature is largely mute about whether including female mice in will introduce variability due to inherent sex differences or endogenous estrous cycles. Though this is beginning to change following an NIH directive that sex should be included as a factor in vertebrate research, the lack of information raises practical issues around how to design experimental controls and apply existing knowledge to more heterogeneous populations. Various lines of research suggest that visual processing can be affected by sex and estrous cycle stage. For these reasons, we performed a series of in vivo electrophysiological experiments to characterize baseline visual function and experience-dependent plasticity in the primary visual cortex (V1) of male and female mice. We find that sex and estrous stage have no statistically significant effect on baseline acuity measurements, but that both sex and estrous stage have can modulate two mechanistically distinct forms of experience dependent cortical plasticity. We also demonstrate that resulting variability can be largely controlled with appropriate normalizations. These findings suggest that V1 plasticity can be used for mechanistic studies focusing on how sex hormones effect experience dependent plasticity in the mammalian cortex.

PMID:37068089 | DOI:10.1371/journal.pone.0282349