Predicting Health Misperceptions: The Role of eHealth Literacy and Situational Perceptions.

This study sought to understand how health misperceptions develop among individuals after exposure to misinformation messages, and how eHealth literacy and situational motivation in problem solving are associated with the negative effects of misinformation exposure. We also sought to understand the differentiated effects of misinformation exposure on the four misinformation-susceptible publics. Results from two studies revealed that situational motivation was positively associated with the formation of misperceptions after misinformation exposure as well as individuals' likelihood of amplifying the misinformation message. However, eHealth literacy does not reduce misperceptions, as had been hypothesized. In fact, eHealth literacy was not significantly associated with misperceptions or with misinformation amplification likelihood. Results also provide support for the typology of misinformation-susceptible publics as misinformation-amplifying publics were the most susceptible to misinformation messages.

Trivariate Joint Modeling for Family Data with Longitudinal Counts, Recurrent Events and a Terminal Event with Application to Lynch Syndrome.

Trivariate joint modeling for longitudinal count data, recurrent events, and a terminal event for family data has increased interest in medical studies. For example, families with Lynch syndrome (LS) are at high risk of developing colorectal cancer (CRC), where the number of polyps and the frequency of colonoscopy screening visits are highly associated with the risk of CRC among individuals and families. To assess how screening visits influence polyp detection, which in turn influences time to CRC, we propose a clustered trivariate joint model. The proposed model facilitates longitudinal count data that are zero-inflated and over-dispersed and invokes individual-specific and family-specific random effects to account for dependence among individuals and families. We formulate our proposed model as a latent Gaussian model to use the Bayesian estimation approach with the integrated nested Laplace approximation algorithm and evaluate its performance using simulation studies. Our trivariate joint model is applied to a series of 18 families from Newfoundland, with the occurrence of CRC taken as the terminal event, the colonoscopy screening visits as recurrent events, and the number of polyps detected at each visit as zero-inflated count data with overdispersion. We showed that our trivariate model fits better than alternative bivariate models and that the cluster effects should not be ignored when analyzing family data. Finally, the proposed model enables us to quantify heterogeneity across families and individuals in polyp detection and CRC risk, thus helping to identify individuals and families who would benefit from more intensive screening visits.

Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box.

Aims: The spatiotemporal deep convolutional neural network (DCNN) helps reduce echocardiographic readers' erroneous 'judgement calls' on Takotsubo syndrome (TTS). The aim of this study was to improve the interpretability of the spatiotemporal DCNN to discover latent imaging features associated with causative TTS pathophysiology. Methods and results: We applied gradient-weighted class activation mapping analysis to visualize an established spatiotemporal DCNN based on the echocardiographic videos to differentiate TTS (150 patients) from anterior wall ST-segment elevation myocardial infarction (STEMI, 150 patients). Forty-eight human expert readers interpreted the same echocardiographic videos and prioritized the regions of interest on myocardium for the differentiation. Based on visualization results, we completed optical flow measurement, myocardial strain, and Doppler/tissue Doppler echocardiography studies to investigate regional myocardial temporal dynamics and diastology. While human readers' visualization predominantly focused on the apex of the heart in TTS patients, the DCNN temporal arm's saliency visualization was attentive on the base of the heart, particularly at the atrioventricular (AV) plane. Compared with STEMI patients, TTS patients consistently showed weaker peak longitudinal displacement (in pixels) in the basal inferoseptal (systolic: 2.15 ± 1.41 vs. 3.10 ± 1.66, P < 0.001; diastolic: 2.36 ± 1.71 vs. 2.97 ± 1.69, P = 0.004) and basal anterolateral (systolic: 2.70 ± 1.96 vs. 3.44 ± 2.13, P = 0.003; diastolic: 2.73 ± 1.70 vs. 3.45 ± 2.20, P = 0.002) segments, and worse longitudinal myocardial strain in the basal inferoseptal (-8.5 ± 3.8% vs. -9.9 ± 4.1%, P = 0.013) and basal anterolateral (-8.6 ± 4.2% vs. -10.4 ± 4.1%, P = 0.006) segments. Meanwhile, TTS patients showed worse diastolic mechanics than STEMI patients (E'/septal: 5.1 ± 1.2 cm/s vs. 6.3 ± 1.5 cm/s, P < 0.001; S'/septal: 5.8 ± 1.3 cm/s vs. 6.8 ± 1.4 cm/s, P < 0.001; E'/lateral: 6.0 ± 1.4 cm/s vs. 7.9 ± 1.6 cm/s, P < 0.001; S'/lateral: 6.3 ± 1.4 cm/s vs. 7.3 ± 1.5 cm/s, P < 0.001; E/E': 15.5 ± 5.6 vs. 12.5 ± 3.5, P < 0.001). Conclusion: The spatiotemporal DCNN saliency visualization helps identify the pattern of myocardial temporal dynamics and navigates the quantification of regional myocardial mechanics. Reduced AV plane displacement in TTS patients likely correlates with impaired diastolic mechanics.

Accommodating misclassification effects on optimizing dynamic treatment regimes with Q-learning.

Research on dynamic treatment regimes has enticed extensive interest. Many methods have been proposed in the literature, which, however, are vulnerable to the presence of misclassification in covariates. In particular, although Q-learning has received considerable attention, its applicability to data with misclassified covariates is unclear. In this article, we investigate how ignoring misclassification in binary covariates can impact the determination of optimal decision rules in randomized treatment settings, and demonstrate its deleterious effects on Q-learning through empirical studies. We present two correction methods to address misclassification effects on Q-learning. Numerical studies reveal that misclassification in covariates induces non-negligible estimation bias and that the correction methods successfully ameliorate bias in parameter estimation.

Gastric epithelial stem cells in development, homeostasis and regeneration.

The stem/progenitor cell pool is indispensable for the development, homeostasis and regeneration of the gastric epithelium, owing to its defining ability to self-renew whilst supplying the various functional epithelial lineages needed to digest food efficiently. A detailed understanding of the intricacies and complexities surrounding the behaviours and roles of these stem cells offers insights, not only into the physiology of gastric epithelial development and maintenance, but also into the pathological consequences following aberrations in stem cell regulation. Here, we provide an insightful synthesis of the existing knowledge on gastric epithelial stem cell biology, including the in vitro and in vivo experimental techniques that have advanced such studies. We highlight the contributions of stem/progenitor cells towards patterning the developing stomach, specification of the differentiated cell lineages and maintenance of the mature epithelium during homeostasis and following injury. Finally, we discuss gaps in our understanding and identify key research areas for future work.

Unveiling the Influence of Tumor Microenvironment and Spatial Heterogeneity on Temozolomide Resistance in Glioblastoma Using an Advanced Human In Vitro Model of the Blood-Brain Barrier and Glioblastoma.

Glioblastoma (GBM) is the most common primary malignant brain cancer in adults with a dismal prognosis. Temozolomide (TMZ) is the first-in-line chemotherapeutic; however, resistance is frequent and multifactorial. While many molecular and genetic factors have been linked to TMZ resistance, the role of the solid tumor morphology and the tumor microenvironment, particularly the blood-brain barrier (BBB), is unknown. Here, the authors investigate these using a complex in vitro model for GBM and its surrounding BBB. The model recapitulates important clinical features such as a dense tumor core with tumor cells that invade along the perivascular space; and a perfusable BBB with a physiological permeability and morphology that is altered in the presence of a tumor spheroid. It is demonstrated that TMZ sensitivity decreases with increasing cancer cell spatial organization, and that the BBB can contribute to TMZ resistance. Proteomic analysis with next-generation low volume sample workflows of these cultured microtissues revealed potential clinically relevant proteins involved in tumor aggressiveness and TMZ resistance, demonstrating the utility of complex in vitro models for interrogating the tumor microenvironment and therapy validation.

Formation and Assessment of a Laryngology Pathology Video Atlas for Resident Education.

OBJECTIVE: Conventional reference images of laryngeal pathologies may provide educational value for Otolaryngology-Head & Neck Surgery (OHNS) residents, but observing dynamic vocal fold function is critical for diagnosis. Our aim was to develop and validate a video atlas of laryngeal pathologies for resident education in OHNS. DESIGN: A multi-institution, prospective case-control study. SETTING/PARTICIPANTS: Ten videos showing 10 representative laryngeal pathologies were verified by two laryngologists. Six videos per category with kappa>0.8 were included in the video database. A collection of the videos was shown to a group of OHNS residents in a quiz fashion to determine if senior trainees would score higher than junior trainees. Another group of residents in OHNS was recruited and randomized to control or intervention. The control group was shown a quiz of 10 laryngeal videos at baseline and 24 weeks later. The intervention group was shown quizzes at baseline and every 6 weeks, ending at 24 weeks. Free-text diagnoses were scored for accuracy. Descriptive statistics, two-tailed tests, and analysis of covariance were performed. RESULTS: Twenty-nine residents participated, with 14 (48.3%) randomized to control, and 15 (51.7%) to the intervention. The postgraduate year (PGY) level had a significant impact on diagnostic performance. PGY1 and 2 had a significantly lower score than PGY5 (P = 0.017 and P = 0.035, respectively). PGY3 and PGY4 scores were not statistically different from PGY5 scores. The mean score difference between groups decreases as the PGY level increases (mean difference between groups = 0.87, P = 0.153), but this was not significant. CONCLUSIONS: The current study has created a validated collection of videos that are representative of common laryngeal pathologies and can be easily incorporated into resident video-based learning. Future directions include larger multi-site studies to further elucidate whether repeated viewing of this video atlas can improve OHNS resident laryngology knowledge.

The astrocytic Na+ -HCO3 - cotransporter, NBCe1, is dispensable for respiratory chemosensitivity.

The interoceptive homeostatic mechanism that controls breathing, blood gases and acid-base balance in response to changes in CO2 /H+ is exquisitely sensitive, with convergent roles proposed for chemosensory brainstem neurons in the retrotrapezoid nucleus (RTN) and their supporting glial cells. For astrocytes, a central role for NBCe1, a Na+ -HCO3 - cotransporter encoded by Slc4a4, has been envisaged in multiple mechanistic models (i.e. underlying enhanced CO2 -induced local extracellular acidification or purinergic signalling). We tested these NBCe1-centric models by using conditional knockout mice in which Slc4a4 was deleted from astrocytes. In GFAP-Cre;Slc4a4fl/fl mice we found diminished expression of Slc4a4 in RTN astrocytes by comparison to control littermates, and a concomitant reduction in NBCe1-mediated current. Despite disrupted NBCe1 function in RTN-adjacent astrocytes from these conditional knockout mice, CO2 -induced activation of RTN neurons or astrocytes in vitro and in vivo, and CO2 -stimulated breathing, were indistinguishable from NBCe1-intact littermates; hypoxia-stimulated breathing and sighs were likewise unaffected. We obtained a more widespread deletion of NBCe1 in brainstem astrocytes by using tamoxifen-treated Aldh1l1-Cre/ERT2;Slc4a4fl/fl mice. Again, there was no difference in effects of CO2 or hypoxia on breathing or on neuron/astrocyte activation in NBCe1-deleted mice. These data indicate that astrocytic NBCe1 is not required for the respiratory responses to these chemoreceptor stimuli in mice, and that any physiologically relevant astrocytic contributions must involve NBCe1-independent mechanisms. KEY POINTS: The electrogenic NBCe1 transporter is proposed to mediate local astrocytic CO2 /H+ sensing that enables excitatory modulation of nearby retrotrapezoid nucleus (RTN) neurons to support chemosensory control of breathing. We used two different Cre mouse lines for cell-specific and/or temporally regulated deletion of the NBCe1 gene (Slc4a4) in astrocytes to test this hypothesis. In both mouse lines, Slc4a4 was depleted from RTN-associated astrocytes but CO2 -induced Fos expression (i.e. cell activation) in RTN neurons and local astrocytes was intact. Likewise, respiratory chemoreflexes evoked by changes in CO2 or O2 were unaffected by loss of astrocytic Slc4a4. These data do not support the previously proposed role for NBCe1 in respiratory chemosensitivity mediated by astrocytes.

Gender Differences in North American and International Otolaryngology Clinical Practice Guideline Authorship: A 17-Year Analysis.

OBJECTIVE: To analyze gender differences in authorship of North American (Canadian and American) and international published otolaryngology-head and neck surgery (OHNS) clinical practice guidelines (CPG) over a 17-year period. METHODS: Clinical practice guidelines published between 2005 and 2022 were identified through the Canadian Agency for Drugs and Technology in Health (CADTH) search strategy in MEDLINE and EMBASE. Studies were included if they were original studies, published in the English language, and encompassed Canadian, American, or international OHNS clinical practice guidelines. RESULTS: A total of 145 guidelines were identified, encompassing 661 female authors (27.4%) and 1756 male authors (72.7%). Among OHNS authors, women and men accounted for 21.2% and 78.8% of authors, respectively. Women who were involved in guideline authorship were 31.0% less likely to be an otolaryngologist compared to men. There were no gender differences across first or senior author and by subspeciality. Female otolaryngologist representation was the greatest in rhinology (28.3%) and pediatrics (26.7%). American guidelines had the greatest proportion of female authors per guideline (34.1%) and the greatest number of unique female authors (33.2%). CONCLUSION: Despite the increasing representation of women in OHNS, gender gaps exist with regards to authorship within clinical practice guidelines. Greater gender diversity and transparency is required within guideline authorship to help achieve equitable gender representation and the development of balanced guidelines with a variety of viewpoints.

Gender representation in leadership & research: a 13-year review of the Annual Canadian Society of Otolaryngology Meetings.

BACKGROUND: The gender disparity in surgical disciplines, specifically in speakers across North American medical and surgical specialty conferences, has been highlighted in recent literature. Improving gender diversity at society meetings and panels may provide many benefits. Our aim was to determine the state of gender diversity amongst presenters and speakers at the annual Canadian Society of Otolaryngology-Head and Neck Surgery (CSO) meetings. METHODS: Scientific programs for the CSO annual meetings from 2008 to 2020 were obtained from the national society website. Participant name, role, gender, location, and subspecialty topic were recorded for all roles other than poster presenter. Gender (male or female) was determined using an online search. The total number of opportunity spots and proportion of women was then calculated. Gender differences were analyzed using chi-square test and logistic regression with odds ratios. Four categories were analyzed: Society Leadership, Invited Speaker Opportunities, Workshop Composition (male-only panels or "manels", female-only panels, or with at least one female speaker), and Oral Paper Presenters (first authors). RESULTS: There were 1874 leadership opportunity spots from 2008 to 2020, of which 18.6% were filled by women. Among elected leadership positions in the society, only 92 unique women filled 738 leadership opportunity spots. 13.2% of workshop chairs, 20.8% of panelists and 22.7% of paper session chairs were female. There was an overall increase in the proportion of leadership positions held by women, from 13.9% of leadership spots in 2008 to 30.1% in 2020. Of the 368 workshops, 61.1% were led by men only, 36.4% by at least 1 female surgeon, and 2.5% by women only. "Manels" have comprised at least 37.5% of workshops each year. CONCLUSIONS: The proportion of women in speaking roles at the annual CSO meetings has generally increased over time, particularly among panelists, leading to fewer male-only speaking panels. However, there has been a slower rate of growth in the proportion of unique women in speaker roles. There remains an opportunity to increase gender/sex diversity at the major Canadian otolaryngology meeting.

Sensitivity analysis of error-contaminated time series data under autoregressive models with the application of COVID-19 data.

Autoregressive (AR) models are useful in time series analysis. Inferences under such models are distorted in the presence of measurement error, a common feature in applications. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We consider two measurement error models to describe different data contamination scenarios. We propose an estimating equation approach to estimate the AR model parameters with measurement error effects accounted for. We further discuss forecasting using the proposed method. Our work is inspired by COVID-19 data, which are error-contaminated due to multiple reasons including those related to asymptomatic cases and varying incubation periods. We implement the proposed method by conducting sensitivity analyses and forecasting the fatality rate of COVID-19 over time for the four most populated provinces in Canada. The results suggest that incorporating or not incorporating measurement error effects may yield rather different results for parameter estimation and forecasting.

Role of Wnt signaling in the maintenance and regeneration of the intestinal epithelium.

The intestinal epithelium plays a key role in digestion and protection against external pathogens. This tissue presents a high cellular turnover with the epithelium being completely renewed every 5days, driven by intestinal stem cells (ISCs) residing in the crypt bases. To sustain this dynamic renewal of the intestinal epithelium, the maintenance, proliferation, and differentiation of ISCs must be precisely controlled. One of the central pathways supporting ISC maintenance and dynamics is the Wnt pathway. In this chapter, we examine the role of Wnt signaling in intestinal epithelial homeostasis and tissue regeneration, including mechanisms regulating ISC identity and fine-tuning of Wnt pathway activation. We extensively discuss the contribution of the stem cell niche in maintaining Wnt signaling in the intestinal crypts that support ISC functions. The integration of these findings highlights the complex interplay of multiple niche signals and cellular components sustaining ISC behavior and maintenance, which together supports the immense plasticity of the intestinal epithelium.

Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines.

While the impact of the COVID-19 pandemic has been widely studied, relatively fewer discussions about the sentimental reaction of the public are available. In this article, we scrape COVID-19 related tweets on the microblogging platform, Twitter, and examine the tweets from February 24, 2020 to October 14, 2020 in four Canadian cities (Toronto, Montreal, Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago, and Seattle). Applying the RoBERTa, Vader and NRC approaches, we evaluate sentiment intensity scores and visualize the results over different periods of the pandemic. Sentiment scores for the tweets concerning three anti-epidemic measures, "masks", "vaccine", and "lockdown", are computed for comparison. We explore possible causal relationships among the variables concerning tweet activities and sentiment scores of COVID-19 related tweets by integrating the echo state network method with convergent cross-mapping. Our analyses show that public sentiments about COVID-19 vary from time to time and from place to place, and are different with respect to anti-epidemic measures of "masks", "vaccines", and "lockdown". Evidence of the causal relationship is revealed for the examined variables, assuming the suggested model is feasible.

Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders.

In the framework of causal inference, the inverse probability weighting estimation method and its variants have been commonly employed to estimate the average treatment effect. Such methods, however, are challenged by the presence of irrelevant pre-treatment variables and measurement error. Ignoring these features and naively applying the usual inverse probability weighting estimation procedures may typically yield biased inference results. In this article, we develop an inference method for estimating the average treatment effect with those features taken into account. We establish theoretical properties for the resulting estimator and carry out numerical studies to assess the finite sample performance of the proposed estimator.

Generalized network structured models with mixed responses subject to measurement error and misclassification.

Research of complex associations between a gene network and multiple responses has attracted increasing attention. A great challenge in analyzing genetic data is posited by the presence of the genetic network that is typically unknown. Moreover, mismeasurement of responses introduces additional complexity to distort usual inferential procedures. In this paper, we consider the problem with mixed binary and continuous responses that are subject to mismeasurement and associated with complex structured covariates. We first start with the case where data are precisely measured. We propose a generalized network structured model and develop a two-step inferential procedure. In the first step, we employ a Gaussian graphical model to facilitate the covariates network structure, and in the second step, we incorporate the estimated graphical structure of covariates and develop an estimating equation method. Furthermore, we extend the development to accommodating mismeasured responses. We consider two cases where the information on mismeasurement is either known or estimated from a validation sample. Theoretical results are established and numerical studies are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to analyze the outbred Carworth Farms White mice data arising from a genome-wide association study.

Zero-inflated Poisson models with measurement error in the response.

Zero-inflated count data arise frequently from genomics studies. Analysis of such data is often based on a mixture model which facilitates excess zeros in combination with a Poisson distribution, and various inference methods have been proposed under such a model. Those analysis procedures, however, are challenged by the presence of measurement error in count responses. In this article, we propose a new measurement error model to describe error-contaminated count data. We show that ignoring the measurement error effects in the analysis may generally lead to invalid inference results, and meanwhile, we identify situations where ignoring measurement error can still yield consistent estimators. Furthermore, we propose a Bayesian method to address the effects of measurement error under the zero-inflated Poisson model and discuss the identifiability issues. We develop a data-augmentation algorithm that is easy to implement. Simulation studies are conducted to evaluate the performance of the proposed method. We apply our method to analyze the data arising from a prostate adenocarcinoma genomic study.

Spatial and neighborhood-level correlates of lay naloxone reversal events and service availability.

BACKGROUND: The opioid epidemic in the United States continues to surge, reaching record deaths from opioid and fentanyl overdoses in 2020. This study analyzes spatial and neighborhood correlates of free naloxone distribution sites as well as overdose and naloxone reversal events in Baltimore, Maryland, which has one of the highest overdose rates in the country. METHODS: Using data from a randomized clinical trial on HIV prevention among people using substances in Baltimore, Maryland, as well as demographic data from the US Census Bureau, we conducted: (1) exploratory spatial visualizations of census tracts' minimum distance to naloxone distribution sites, (2) univariable Wilcoxon rank-sum tests to compare census tracts on demographic metrics, and (3) bivariable and multivariable negative binomial regression models to assess associations between census tract characteristics and naloxone reversal events. RESULTS: Valid geographic data were provided for 518 overdose events involving either fentanyl or heroin in this study. Of these, 190 (37%) attempted naloxone reversal events were reported. Exploratory spatial visualization techniques suggest that most distribution sites are appropriately located near populations at high risk of overdose, but study findings also identify areas where drug use and overdoses occur that are located farther from distribution sites. In multivariable analyses, naloxone administration was significantly and inversely associated with distance to the nearest distribution site (incidence rate ratio (IRR)=0.72 per 1000m increase, 95% CI 0.59-0.89, p=0.002). CONCLUSION: Study findings emphasize the correlation between proximity to naloxone sites and utilization of resources, highlighting that physical proximity to harm reduction resources may contribute to uptake. Results further underscore that research on service accessibility and utilization must consider the spatial distribution of health services.

Characterizing the COVID-19 dynamics with a new epidemic model: Susceptible-exposed-asymptomatic-symptomatic-active-removed.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread stealthily and presented a tremendous threat to the public. It is important to investigate the transmission dynamics of COVID-19 to help understand the impact of the disease on public health and the economy. In this article, we develop a new epidemic model that utilizes a set of ordinary differential equations with unknown parameters to delineate the transmission process of COVID-19. The model accounts for asymptomatic infections as well as the lag between symptom onset and the confirmation date of infection. To reflect the transmission potential of an infected case, we derive the basic reproduction number from the proposed model. Using the daily reported number of confirmed cases, we describe an estimation procedure for the model parameters, which involves adapting the iterated filter-ensemble adjustment Kalman filter (IF-EAKF) algorithm. To illustrate the use of the proposed model, we examine the COVID-19 data from Quebec for the period from 2 April 2020 to 10 May 2020 and carry out sensitivity studies under a variety of assumptions. Simulation studies are used to evaluate the performance of the proposed model under a variety of settings.

La maladie à coronavirus 2019 (COVID­19), causée par le coronavirus 2 du syndrome respiratoire aigu sévère (SARS­CoV­2), s'est rapidement propagée et représente une grande menace pour le public. Pour mieux comprendre l'impact de cette maladie sur la santé publique et l'économie, il est important d'étudier la dynamique de sa transmission. A cette fin, les auteurs de cet article proposent un nouveau modèle épidémiologique basé sur un ensemble d'équations différentielles ordinaires avec des paramètres inconnus et qui tient compte des infections asymptomatiques ainsi que du décalage entre l'apparition des symptômes et la date de confirmation de l'infection. Ils en déduisent le taux de reproduction de base qui traduit le potentiel de transmission d'un cas infecté. En utilisant le nombre rapporté de cas confirmés, les auteurs décrivent une procédure d'estimation des paramètres du modèle qui repose sur une adaptation de l'algorithme filtre itéré ­ filtre de Kalman énsemble àjustement (IF­EAKF). Une mise en application du modèle proposé est illustrée à travers l'examen des données COVID­19 du Québec pour la période du 2 avril 2020 au 10 mai 2020. Une analyse de sensibilité du modèle construit est explorée sous diverses hypothèses. Enfin, les auteurs ont fait appel à des études de simulation pour évaluer la performance du modèle proposé et ce sous différents scénarios.

Targeted ablation of Lgr5-expressing intestinal stem cells in diphtheria toxin receptor-based mouse and organoid models.

Intestinal cells marked by Lgr5 function as tissue-resident stem cells that sustain the homeostatic replenishment of the epithelium. By incorporating a diphtheria toxin receptor (DTR) cassette linked to the Lgr5 coding region, native Lgr5-expressing cells are susceptible to ablation upon DT administration in vivo. A similar strategy can be used for Lgr5-expressing cells within organoids established from DTR models. Together, these in vivo and in vitro approaches will facilitate dissection of the roles of Lgr5-expressing cells residing in different tissue compartments. For complete details on the use and execution of this protocol, please refer to Tan et al. (2021).