Single Time Point Research Articles

The dynamics of biofilm development and dispersal should be taken into account when quantifying biofilm via the crystal violet microtiter plate assay

The crystal violet microtiter plate biofilm assay is often used to compare the amount of biofilm formed by a mutant versus wild-type or a compound-treated biofilm versus the non-treatment control. In many of these studies the amount of biofilm is assessed only at one single time point. However, if the dynamics of biofilm development of the mutant (or compound-treated biofilm) is different than that of the wild-type (or non-treatment control), then biofilm quantification at a single time point may give misleading results. To overcome this shortcoming of the common biofilm quantification technique, we recommend to use a serial dilution-based crystal violet microtiter plate biofilm assay for easy assessment of the dynamics of biofilm development and dispersal. We demonstrate that the dilution-resolved crystal violet assay displays the dynamics of Pseudomonas aeruginosa biofilm development and dispersal as efficient as a time-resolved crystal violet assay. In addition, focusing on mutants of different parts of the c-di-GMP signaling system in P. aeruginosa, we provide an example illustrating the need to assess biofilm dynamics instead of quantifying biofilm biomass at a single time point.

Case Report: Longitudinal Evaluation and Treatment of a Melanoma-Associated Retinopathy Patient.

Melanoma-associated retinopathy (MAR) is a paraneoplastic syndrome associated with cutaneous metastatic melanoma in which patients develop vision deficits that include reduced night vision, poor contrast sensitivity, and photopsia. MAR is caused by autoantibodies targeting TRPM1, an ion channel found in melanocytes and retinal ON-bipolar cells (ON-BCs). The visual symptoms arise when TRPM1 autoantibodies enter ON-BCs and block the function of TRPM1, thus detection of TRPM1 autoantibodies in patient serum is a key criterion in diagnosing MAR. Electroretinograms are used to measure the impact of TRPM1 autoantibodies on ON-BC function and represent another important diagnostic tool for MAR. To date, MAR case reports have included one or both diagnostic components, but only for a single time point in the course of a patient's disease. Here, we report a case of MAR supported by longitudinal analysis of serum autoantibody detection, visual function, ocular inflammation, vascular integrity, and response to slow-release intraocular corticosteroids. Integrating these data with the patient's oncological and ophthalmological records reveals novel insights regarding MAR pathogenesis, progression, and treatment, which may inform new research and expand our collective understanding of the disease. In brief, we find TRPM1 autoantibodies can disrupt vision even when serum levels are barely detectable by western blot and immunohistochemistry; intraocular dexamethasone treatment alleviates MAR visual symptoms despite high levels of circulating TRPM1 autoantibodies, implicating antibody access to the retina as a key factor in MAR pathogenesis. Elevated inflammatory cytokine levels in the patient's eyes may be responsible for the observed damage to the blood-retinal barrier and subsequent entry of autoantibodies into the retina.

Changes in Online Sexual Activities During the Lockdown Caused by COVID-19 in Spain: “INSIDE” Project

Abstract Introduction The lockdown, as a measure to stop the spread of COVID-19, has had an impact on different areas of our life, including sexuality. This study aims to analyze its impact on online sexual activities (OSAs) in people who lived in Spain during confinement. Methods This study involved 1448 people aged 18–60 years who were assessed through an online survey during confinement in Spain (April, 2020). The design of this study was a cross-sectional design, in which information on OSA before confinement and OSA, physical and social environment, and other variables related to gender and negative mood during confinement were collected at a single point in time. Results Our results show that both men and women, regardless of their sexual orientation, have experienced an increase in time and frequency invested in OSAs. In addition, individuals have also innovated their OSAs, for example, contacting sex workers. Factors such as high sexual desire, being a man, and consuming pornography were positively related to the frequency of ASOs during confinement, while high sexual desire and sexting were related to the time spent on ASOs during confinement. Conclusions The COVID-19 lockdown has had an impact on online sexuality. Both men and women have experienced an increase in their online sexual activity during confinement. In addition, some factors related to the increase in the time and frequency of OSAs are identified. Policy Implications Identifying how these changes have been during this very restrictive period and what factors related to the increase in OSAs can help us prevent possible consequences in similar scenarios.

Housing amenity and affordability shape floodplain development

Flood damage has severe and long-term repercussions for households and communities, and continued housing development in floodplains escalates damages over time. Policies and interventions to reduce damage depend on assumptions about housing stock and residents, but assessments of flood exposure to date largely focus on community-scale characteristics at a single point in time, masking potential within-community differences and their evolution through time. We measure residential development in the floodplain nationwide over time to characterize the type and value of U.S. floodplain housing stock and to assess how new development contributes to flood exposure. Over 4M U.S. residences built from 1700 to 2019 (4.8% of all residences built during that time) are located within current regulatory floodplains. These residences are concentrated at the affordable and expensive extremes of the housing value spectrum, reflecting deep differences in the social vulnerability of floodplain residents. Floodplain housing stock often differs substantially from the local market, with coastal floodplains containing relatively expensive housing and inland floodplains containing relatively affordable housing. New housing development has not occurred equally across these contexts. In the past two decades, more floodplain development has occurred in communities with relatively expensive floodplain housing, and mobile home construction in floodplains has slowed. The bifurcated patterns in floodplain housing, across values and geographies, demonstrate the importance of considering the specific population at risk and how it may differ from the broader community when tailoring flood risk management approaches.

Learning Optimal Dynamic Treatment Regimes from Longitudinal Data.

Studies often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR). Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments' benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments' benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly-robust unbiased transformation of the conditional average treatment effect. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone (BUP-NX) dose to minimize return-to-regular-opioid-use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision making: the learned ODTR outperforms a clinically defined strategy.

Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability.

The majority of deep learning models in medical image analysis concentrate on single snapshot timepoint circumstances, such as the identification of current pathology on a given image or volume. This is often in contrast to the diagnostic methodology in radiology where presumed pathologic findings are correlated to prior studies and subsequent changes over time. For multiple sclerosis (MS), the current body of literature describes various forms of lesion segmentation with few studies analyzing disability progression over time. For the purpose of longitudinal time-dependent analysis, we propose a combinatorial analysis of a video vision transformer (ViViT) benchmarked against traditional recurrent neural network of Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architectures and a hybrid Vision Transformer-LSTM (ViT-LSTM) to predict long-term disability based upon the Extended Disability Severity Score (EDSS). The patient cohort was procured from a two-site institution with 703 patients' multisequence, contrast-enhanced MRIs of the cervical spine between the years 2002 and 2023. Following a competitive performance analysis, a VGG-16-based CNN-LSTM was compared to ViViT with an ablation analysis to determine time-dependency of the models. The VGG16-LSTM predicted trinary classification of EDSS score in 6years with 0.74 AUC versus the ViViT with 0.84 AUC (p-value < 0.001 per 5 × 2 cross-validation F-test) on an 80:20 hold-out testing split. However, the VGG16-LSTM outperformed ViViT when patients with only 2years of MRIs (n = 94) (0.75 AUC versus 0.72 AUC, respectively). Exact EDSS classification was investigated for both models using both classification and regression strategies but showed collectively worse performance. Our experimental results demonstrate the ability of time-dependent deep learning models to predict disability in MS using trinary stratification of disability, mimicking clinical practice. Further work includes external validation and subsequent observational clinical trials.

CellPLATO - an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data.

Advances in imaging, segmentation and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this 'phenomics' type data. Here, we present 'Cell PLasticity Analysis Tool' (cellPLATO), a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a 'behavioural ID' at each timepoint, and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human natural killer (NK) cell migration on ICAM-1 or VCAM-1. We find eight behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and four trajectories based on sequences of these behaviours over time. Therefore, by using cellPLATO, we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours.

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

Individual differences in working memory impact the trajectory of non-native speech category learning.

What is the role of working memory over the course of non-native speech category learning? Prior work has predominantly focused on how working memory might influence learning assessed at a single timepoint. Here, we substantially extend this prior work by examining the role of working memory on speech learning performance over time (i.e., over several months) and leverage a multifaceted approach that provides key insights into how working memory influences learning accuracy, maintenance of knowledge over time, generalization ability, and decision processes. We found that the role of working memory in non-native speech learning depends on the timepoint of learning and whether individuals learned the categories at all. Among learners, across all stages of learning, working memory was associated with higher accuracy as well as faster and slightly more cautious decision making. Further, while learners and non-learners did not have substantially different working memory performance, learners had faster evidence accumulation and more cautious decision thresholds throughout all sessions. Working memory may enhance learning by facilitating rapid category acquisition in initial stages and enabling faster and slightly more careful decision-making strategies that may reduce the overall effort needed to learn. Our results have important implications for developing interventions to improve learning in naturalistic language contexts.

Neighborhood Trust, Cohesion, and Diversity: How Demographic Patterns and Processes Shape Their Relationships

AbstractCompelling evidence from around the world suggests that ethnic diversity is detrimental to neighborhood trust and cohesion. Much of this evidence derives from empirical associations found with static indicators of diversity, measuring the mix of ethnic groups at a single point in time, and controlling for a varied mix of individual and community‐level covariates. Less attention though has sought to understand how dynamic processes of migration and residential settlement both create diversity and influence trust and cohesion. In this study, census and survey data are used to analyze the demographic and economic characteristics and dynamics coinciding with diversity in local communities in Australia, and the implications for neighborhood trust and cohesion. Diverse communities are heavily concentrated in moderately and highly disadvantaged communities in large immigrant gateway cities. Highly diverse communities experience continual high levels of immigration and population turnover and contain young age structures and more diverse and transient housing and household structures. Survey analyses reveal that these characteristics substantially explain observed negative associations between diversity and neighborhood trust and cohesion. The results point to the near‐intractable difficulties in isolating the effects of diversity and the importance of understanding the dynamic demographic processes in the construction of neighborhood diversity, trust, and cohesion.

Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study

BackgroundAutomatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time. MethodsWe included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing–based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation. ResultsThe dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97–1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months). ConclusionsThese findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.

Longitudinal soluble marker profiles reveal strong association between cytokine storms resulting from macrophage activation and disease severity in COVID-19 disease

SARS-CoV2 infection results in a range of disease severities, but the underlying differential pathogenesis is still not completely understood. At presentation it remains difficult to estimate and predict severity, in particular, identify individuals at greatest risk of progression towards the most severe disease-states. Here we used advanced models with circulating serum analytes as variables in combination with daily assessment of disease severity using the SCODA-score, not only at single time points but also during the course of disease, to correlate analyte levels and disease severity. We identified a remarkably strong pro-inflammatory cytokine/chemokine profile with high levels for sCD163, CCL20, HGF, CHintinase3like1 and Pentraxin3 in serum which correlated with COVID-19 disease severity and overall outcome. Although precise analyte levels differed, resulting biomarker profiles were highly similar at early and late disease stages, and even during convalescence similar biomarkers were elevated and further included CXCL3, CXCL6 and Osteopontin. Taken together, strong pro-inflammatory marker profiles were identified in patients with COVID-19 disease which correlated with overall outcome and disease severity.

An efficient and high-throughput method for the evaluation of mitochondrial dysfunction in frozen brain samples after traumatic brain injury.

Mitochondrial function analysis is a well-established method used in preclinical and clinical investigations to assess pathophysiological changes in various disease states, including traumatic brain injury (TBI). Although there are multiple approaches to assess mitochondrial function, one common method involves respirometric assays utilizing either Clark-type oxygen electrodes or fluorescent-based Seahorse analysis (Agilent). However, these functional analysis methods are typically limited to the availability of freshly isolated tissue samples due to the compromise of the electron transport chain (ETC) upon storage, caused by freeze-thaw-mediated breakdown of mitochondrial membranes. In this study, we propose and refine a method for evaluating electron flux through the ETC, encompassing complexes I, II, and IV, in frozen homogenates or mitochondrial samples within a single well of a Seahorse plate. Initially, we demonstrate the impact of TBI on freshly isolated mitochondria using the conventional oxidative phosphorylation protocol (OxPP), followed by a comparison with ETC analysis conducted on frozen tissue samples within the context of a controlled cortical impact (CCI) model of TBI. Additionally, we explore the effects of mitochondrial isolation from fresh versus snap-frozen brain tissues and their storage at -80°C, assessing its impact on electron transport chain protocol (ETCP) activity. Our findings indicate that while both sets of samples were frozen at a single time point, mitochondria from snap-frozen tissues exhibited reduced injury effects compared to preparations from fresh tissues, which were either homogenized or isolated into mitochondria and subsequently frozen for later use. Thus, we demonstrate that the preparation of homogenates or isolated mitochondria can serve as an appropriate method for storing brain samples, allowing for later analysis of mitochondrial function, following TBI using ETCP.

The Grip Strength Loss Rate and the Subsequent Cognitive Decline Rate in Older Adults: The Moderating Role of Social Isolation.

Accumulating evidence suggests that low grip strength (GS) is associated with a faster cognitive decline, but most previous studies have measured GS at a single time point, ignoring changes in GS. We aimed to explore the association of the GS loss rate with the sequent cognitive decline, as well as the moderating role of social isolation in older adults. Data were from the English Longitudinal Study of Ageing. Absolute and relative GS loss rates were calculated as the annual losses from Wave 2 (2004-05) to Wave 4 (2008-09). Participants were divided into 3 groups according to the tertiles of GS loss rates. Linear mixed models were used to assess the association of the GS loss rate during Waves 2-4 with the cognitive decline rate during Waves 4-9 (Wave 9,2018-19). Of the 4356 participants included in analyses, 1938 (44.5%) were men, with a mean age of 68.4 (SD: 8.4) years. Compared with Tertile 1 of the absolute GS loss rate, Tertile 2 (β = -0.009 [95% CI: -0.018 to -0.001] SD/year) and Tertile 3 (β = -0.018 [95% CI: -0.027 to -0.010] SD/year) were associated with a faster cognitive decline rate. The results of relative GS were similar to those of absolute GS. Social isolation was a significant modifier in the associations of the absolute GS loss rate with decline rates in global cognition and episodic memory, but not in temporal orientation. We did not observe that social isolation moderated the association of the relative GS loss rate with the cognitive decline rate. Both absolute and relative GS loss rates were positively associated with the cognitive decline rate in older adults. Low social isolation scores attenuated the association of the absolute GS loss rate with the cognitive decline rate.

A Microfluidic Chip-Based Automated System for Whole-Course Monitoring the Drug Responses of Organoids.

Tumor patients-derived organoids, as a promising preclinical prediction model, have been utilized to evaluate ex vivo drug responses for formulating optimal therapeutic strategies. Detecting adenosine triphosphate (ATP) has been widely used in existing organoid-based drug response tests. However, all commercial ATP detection kits containing the cell lysis procedure can only be applied for single time point ATP detection, resulting in the neglect of dynamic ATP variations in living cells. Meanwhile, due to the limited number of viable organoids from a single patient, it is impractical to exhaustively test all potential time points in search of optimal ones. In this work, a multifunctional microfluidic chip was developed to perform all procedures of organoid-based drug response tests, including establishment, culturing, drug treatment, and ATP monitoring of organoids. An ATP sensor was developed to facilitate the first successful attempt on whole-course monitoring the growth status of fragile organoids. To realize a clinically applicable automatic system for the drug testing of lung cancer, a microfluidic chip based automated system was developed to perform entire organoid-based drug response test, bridging the gap between laboratorial manipulation and clinical practices, as it outperformed previous methods by improving data repeatability, eliminating human error/sample loss, and more importantly, providing a more accurate and comprehensive evaluation of drug effects.

The Gundersen Precision Oncology Cohort: A biospecimen resource for mutation characterization and target discovery.

e15184 Background: The generation and widespread sharing of large somatic mutation datasets has revolutionized our understanding of genetic mechanisms driving cancer initiation and progression. For well-understood mutations, this has created the opportunity for individualized therapeutic intervention in a wide range of malignancies. Beyond these well-characterized mutations, there exists a wide range of alterations that remain poorly understood and which require some kind of functional genomics analysis to determine whether such patients are eligible for the targeted therapy used for more typical mutations in that gene, whether these alterations are passenger mutations that don’t affect throughput on the associated signaling pathway, or reflect another more nuanced phenomenon. Whether using ectopic overexpression in cell lines or generation of genetically modified mice, addressing this problem at scale using functional genomics approaches is challenging. A key limitation is the absence of the original tissue context in which that variant co-evolved with other genetic alterations as well as data on the clinical response of the patient to successive lines of therapy. This can be addressed, in part, by the banking of tissue from patient cohorts who have had comprehensive genomic profiling as part of their routine clinical care. For this purpose, we have established the Gundersen Precision Oncology Cohort. Methods: The Gundersen Precision Oncology Cohort is a retrospective collection of tissue specimens, NGS reports and associated clinical data from deceased patients who received cancer care at the Gundersen Health System. The cohort is updated twice per year and banked tissue specimens are available for collaborative research studies. Results: At November 20, 2023 there were data from 780 patients in the cohort, with specimens tested between 2015 and 2023. 54 tumor types were represented, of which lung (n = 196), colorectal (n = 79), pancreas (n = 65), esophageal (n = 58) and breast (n = 46) were most common. The most common diagnostic companies were STRATA Oncology (n = 463) and Tempus (n = 248). Most patients had testing at a single timepoint, but longitudinal testing is available for 13 patients. We present data from some of the studied subgroups, including atypical point mutations in BRAF as well as fusions affecting BRAF and ROS1. Conclusions: This cohort, available for collaborative studies via the Gundersen Cancer Biobank, illustrates the value of banking well-annotated biospecimens to address outstanding questions in oncology.

Biomechanical improvements in gait following medial pivot knee implant surgery

BackgroundTotal knee replacements are used to improve function and reduce pain in patients with advanced osteoarthritis. The medially stabilising implant is designed to mimic a healthy knee. This study aims to provide a comprehensive analysis of the kinematics and kinetics of a medially stabilising knee implant, comparing it to a healthy control group, as well as to its pre-operative state and the contralateral limb. MethodsSixteen total knee replacement patients and ten healthy participants were recruited. Patients underwent testing 4–6 weeks before surgery and repeated the same tests 12 months after surgery. Healthy participants completed the same tests at a single time point. All participants completed three walking trials: kinematics was captured with eight cameras; kinetics with in-ground force plates. Subject-specific musculoskeletal models were developed in OpenSim. Inverse kinematics and inverse dynamics were used to determine gait parameters. Joint angles and joint moments were evaluated using Statistical Parametric Mapping. Patient-reported outcome measures were also collected at both time points. FindingsSpatiotemporal results indicate significant differences in velocity and step length between pre-operative patients and control participants. Differences are observed in the adduction angles between the contralateral and ipsilateral limbs pre-operatively. Postoperatively, there was an increase in the 1st peak flexion moment, reduced adduction moment and reduced internal rotation moment. In PROMs, patients all report improvements in pain levels and high satisfaction levels following surgery. InterpretationsFollowing medial stabilising total knee arthroplasty, patients displayed improved clinical parameters and joint moments reflecting a shift towards more normal, healthy gait.

RBD mutations at the residues K417, E484, N501 reduced immunoreactivity with antisera from vaccinated and COVID-19 recovered patients.

It is unclear whether induced spike protein-specific antibodies due to infections with SARS-CoV-2 or to the prototypic Wuhan isolate-based vaccination can immune-react with the emerging variants of SARS-CoV-2. The main objective of the study was to measure the immunoreactivity of induced antibodies postvaccination with Covishield™ (ChAdOx1 nCoV-19 coronavirus vaccines) or infections with SARS-CoV-2 by using selected peptides of the spike protein of wild type and variants of SARS-CoV-2. Thirty patients who had recovered from SARS-CoV-2 infections and 30 individuals vaccinated with both doses of Covishield™ were recruited for the study. Venous blood samples (5 mL) were collected at a single time point from patients within 3-4 weeks of recovery from SARS-CoV-2 infections or receiving both doses of Covishield™ vaccines. The serum levels of total immunoglobulin were measured in both study groups. A total of 12 peptides of 10 to 24 amino acids length spanning to the receptor-binding domain (RBD) of wild type of SARS-CoV-2 and their variants were synthesized. The serum levels of immune-reactive antibodies were measured using these peptides. The serum levels of total antibodies were found to be significantly (p<0.001) higher in the vaccinated individuals as compared to COVID-19 recovered patients. Our study reported that the mutations in the RBD at the residues K417, E484, and N501 have been associated with reduced immunoreactivity with anti-sera of vaccinated people and COVID-19 recovered patients. The amino acid substitutions at the RBD of SARS-CoV-2 have been associated with a higher potential to escape the humoral immune response.

Assessing heterogeneity in surrogacy using censored data.

Determining whether a surrogate marker can be used to replace a primary outcome in a clinical study is complex. While many statistical methods have been developed to formally evaluate a surrogate marker, they generally do not provide a way to examine heterogeneity in the utility of a surrogate marker. Similar to treatment effect heterogeneity, where the effect of a treatment varies based on a patient characteristic, heterogeneity in surrogacy means that the strength or utility of the surrogate marker varies based on a patient characteristic. The few methods that have been recently developed to examine such heterogeneity cannot accommodate censored data. Studies with a censored outcome are typically the studies that could most benefit from a surrogate because the follow-up time is often long. In this paper, we develop a robust nonparametric approach to assess heterogeneity in the utility of a surrogate marker with respect to a baseline variable in a censored time-to-event outcome setting. In addition, we propose and evaluate a testing procedure to formally test for heterogeneity at a single time point or across multiple time points simultaneously. Finite sample performance of our estimation and testing procedure are examined in a simulation study. We use our proposed method to investigate the complex relationship between change in fasting plasma glucose, diabetes, and sex hormones using data from the diabetes prevention program study.

Digital "flipbooks" for enhanced visual assessment of simple and complex brain tumors.

Typical longitudinal radiographic assessment of brain tumors relies on side-by-side qualitative visualization of serial magnetic resonance images (MRIs) aided by quantitative measurements of tumor size. However, when assessing slowly growing tumors and/or complex tumors, side-by-side visualization and quantification may be difficult or unreliable. Whole-brain, patient-specific "digital flipbooks" of longitudinal scans are a potential method to augment radiographic side-by-side reads in clinical settings by enhancing the visual perception of changes in tumor size, mass effect, and infiltration across multiple slices over time. In this approach, co-registered, consecutive MRI scans are displayed in a slide deck, where one slide displays multiple brain slices of a single timepoint in an array (eg, 3 × 5 "mosaic" view of slices). The flipbooks are viewed similarly to an animated flipbook of cartoons/photos so that subtle radiographic changes are visualized via perceived motion when scrolling through the slides. Importantly, flipbooks can be created easily with free, open-source software. This article describes the step-by-step methodology for creating flipbooks and discusses clinical scenarios for which flipbooks are particularly useful. Example flipbooks are provided in Supplementary Material.