Modelling count data with partial differential equation models in biology.

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth-death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction-diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.

Formation and Growth of Co-Culture Tumour Spheroids: New Compartment-Based Mathematical Models and Experiments.

Co-culture tumour spheroid experiments are routinely performed to investigate cancer progression and test anti-cancer therapies. Therefore, methods to quantitatively characterise and interpret co-culture spheroid growth are of great interest. However, co-culture spheroid growth is complex. Multiple biological processes occur on overlapping timescales and different cell types within the spheroid may have different characteristics, such as differing proliferation rates or responses to nutrient availability. At present there is no standard, widely-accepted mathematical model of such complex spatio-temporal growth processes. Typical approaches to analyse these experiments focus on the late-time temporal evolution of spheroid size and overlook early-time spheroid formation, spheroid structure and geometry. Here, using a range of ordinary differential equation-based mathematical models and parameter estimation, we interpret new co-culture experimental data. We provide new biological insights about spheroid formation, growth, and structure. As part of this analysis we connect Greenspan's seminal mathematical model to co-culture data for the first time. Furthermore, we generalise a class of compartment-based spheroid mathematical models that have previously been restricted to one population so they can be applied to multiple populations. As special cases of the general model, we explore multiple natural two population extensions to Greenspan's seminal model and reveal biological mechanisms that can describe the internal dynamics of growing co-culture spheroids and those that cannot. This mathematical and statistical modelling-based framework is well-suited to analyse spheroids grown with multiple different cell types and the new class of mathematical models provide opportunities for further mathematical and biological insights.

Association Between Antibiotic Prescribing and Time to Diagnosis of Head and Neck Cancer.

Importance: Diagnostic delay can negatively affect patient outcomes in head and neck cancer (HNC). Neck mass and other symptoms of undiagnosed HNC may be treated with antibiotics, delaying diagnosis and treatment, despite current clinical practice guidelines. Objective: To investigate temporal trends, associated factors, and time from symptom onset to antibiotic prescribing before an HNC diagnosis. Design, Setting, and Participants: A retrospective cohort study was conducted using data obtained from a deidentified electronic health records data set from January 1, 2011, to December 31, 2018. Patients with HNC enrolled in the data set for at least 1 year before diagnosis date determined by either 1 inpatient encounter or first of 2 outpatient encounters within 6 months were included. Data analysis was conducted from May 1 to November 9, 2022. Exposure: Antibiotic prescription within 3 months before HNC diagnosis date. Main Outcomes and Measures: The primary outcome was days from the first documented symptom to HNC diagnosis. Results: The cohort included 7811 patients with HNC (4151 [53.1%] men, mean [SD] age, 60.2 [15.8] years). At least 1 antibiotic was prescribed for 1219 patients (15.6%) within 3 months before HNC diagnosis. This represented an increase over the 8.9% prescribing rate during the baseline period 12 to 9 months before diagnosis. The rate of antibiotic prescribing within 3 months before diagnosis did not change significantly over time (quarterly percent change, 0.49%; 95% CI, -3.06% to 4.16%). Patients receiving an antibiotic prescription within 3 months of an HNC diagnosis had a 21.1% longer time between symptom onset and HNC diagnoses (adjusted rate ratio [ARR], 1.21; 95% CI, 1.14-1.29). Compared with diagnosis by otolaryngologists, primary care/internal medicine physicians were most likely to prescribe antibiotics for patients who were diagnosed with a presenting symptom (adjusted prevalence ratio, 1.60; 95% CI, 1.27-2.02). In patients presenting with neck mass/swelling, those presenting with other symptoms were more likely to have longer intervals from symptom onset to diagnosis (ARR, 1.31; 95% CI, 1.08-1.59). Conclusions and Relevance: The findings of this cohort study suggest there is an increased rate of antibiotic prescription in the 3 months before HNC diagnosis, which is associated with an increased time to diagnosis. These findings identify an area for improvement in HNC care and guidelines.

Approach to sialadenitis.

OBJECTIVE: To provide family physicians with a practical evidence-based approach to the management of patients with sialadenitis. SOURCES OF INFORMATION: MEDLINE and PubMed databases were searched for English-language research on sialadenitis and other salivary gland disorders, as well as for relevant review articles and guidelines published between 1981 and 2021. MAIN MESSAGE: Sialadenitis refers to inflammation or infection of the salivary glands and is a condition that can be caused by a broad range of processes including infectious, obstructive, and autoimmune. History and physical examination play important roles in directing management, while imaging is often useful to establish a diagnosis. Red flags such as suspected abscess formation, signs of respiratory obstruction, facial paresis, and fixation of a mass to underlying tissue should prompt urgent referral to head and neck surgery or a visit to the emergency department. CONCLUSION: Family physicians can play an important role in the diagnosis and management of sialadenitis. Prompt recognition and treatment of the condition can prevent the development of complications.

The "Medicare effect" on head and neck cancer diagnosis and survival.

BACKGROUND: Uninsured individuals age 55-64 experience disproportionately poor outcomes compared to their insured counterparts. Adequate coverage may prevent these delays. This study investigates a "Medicare-effect" on head and neck squamous cell carcinoma (HNSCC) diagnosis and treatment. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for persons ages 60-70 years in the United States from 2000 to 2016 with HNSCC. A "Medicare effect" was defined as an increase in incidence, reduction in advanced stage presentation, and/or decrease in cancer-specific mortality (CSM). RESULTS: Compared to their Medicaid or uninsured counterparts, patients age 65 have an increased incidence of HNSCC diagnosis, reduction in advanced stage presentation, decrease in cancer-specific mortality, and higher likelihood of receiving cancer-specific surgery. CONCLUSIONS: Patients age 65 with Medicare have decreased incidence of HNSCC, less hazard of late-stage diagnosis, and lower cancer-specific mortality than their Medicaid or uninsured counterparts, supporting the idea of a "Medicare effect" in HNSCC.

High-risk human papillomavirus 16/18 associated with improved survival in sinonasal squamous cell carcinoma.

BACKGROUND: There has been conflicting evidence on the independent prognostic role of human papillomavirus (HPV) status in sinonasal cancer. The objective of this study was to assess whether the survival of patients with sinonasal cancer differs based on various HPV statuses, including HPV-negative, positive for the high-risk HPV-16 and HPV-18 (HPV16/18) subtypes, and positive for other high-risk and low-risk HPV subtypes. METHODS: In this retrospective cohort study, data from the National Cancer Database were extracted from the years 2010-2017 for patients who had primary sinonasal cancer (N = 12,009). The outcome of interest was overall survival based on HPV tumor status. RESULTS: Study included an analytic cohort of 1070 patients with sinonasal cancer who had confirmed HPV tumor status (732 [68.4%] HPV-negative; 280 [26.2%] HPV16/18-positive; 40 [3.7%] positive for other high-risk HPV; and 18 [1.7%] positive for low-risk HPV). HPV-negative patients had the lowest all-cause survival probability at 5 years postdiagnosis (0.50). After controlling for covariates, HPV16/18-positive patients had a 37% lower mortality hazard than HPV-negative patients (adjusted hazard ratio, 0.63; 95% confidence interval [CI], 0.48-0.82). Patients aged 64-72 years (crude prevalence ratio, 0.66; 95% CI, 0.51-0.86) and 73 years and older (crude prevalence ratio, 0.43; 95% CI, 0.31-0.59) presented with lower rates of HPV16/18-positive sinonasal cancer than those aged 40-54 years. In addition, Hispanic patients had a 2.36 times higher prevalence of non-HPV16/18 sinonasal cancer than non-Hispanic White patients. CONCLUSIONS: These data suggest that, for patients with sinonasal cancer, HPV16/18-positive disease may confer a significant survival advantage compared with HPV-negative disease. Other high-risk and low-risk HPV subtypes have survival rates similar to the rates for HPV-negative disease. HPV status might be an important independent prognostic factor in sinonasal cancer that could be used in patient selection and clinical decisions.

Geometric analysis enables biological insight from complex non-identifiable models using simple surrogates.

An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have been developed to harmonise this juxtaposition, yet cannot always resolve the mismatch between available data and the granularity required in mathematical models to answer important biological questions. Often, it is only simple phenomenological models, such as the logistic and Gompertz growth models, that are identifiable from standard experimental measurements. To draw insights from complex, non-identifiable models that incorporate key biological mechanisms of interest, we study the geometry of a map in parameter space from the complex model to a simple, identifiable, surrogate model. By studying how non-identifiable parameters in the complex model quantitatively relate to identifiable parameters in surrogate, we introduce and exploit a layer of interpretation between the set of non-identifiable parameters and the goodness-of-fit metric or likelihood studied in typical identifiability analysis. We demonstrate our approach by analysing a hierarchy of mathematical models for multicellular tumour spheroid growth experiments. Typical data from tumour spheroid experiments are limited and noisy, and corresponding mathematical models are very often made arbitrarily complex. Our geometric approach is able to predict non-identifiabilities, classify non-identifiable parameter spaces into identifiable parameter combinations that relate to features in the data characterised by parameters in a surrogate model, and overall provide additional biological insight from complex non-identifiable models.

Growth and adaptation mechanisms of tumour spheroids with time-dependent oxygen availability.

Tumours are subject to external environmental variability. However, in vitro tumour spheroid experiments, used to understand cancer progression and develop cancer therapies, have been routinely performed for the past fifty years in constant external environments. Furthermore, spheroids are typically grown in ambient atmospheric oxygen (normoxia), whereas most in vivo tumours exist in hypoxic environments. Therefore, there are clear discrepancies between in vitro and in vivo conditions. We explore these discrepancies by combining tools from experimental biology, mathematical modelling, and statistical uncertainty quantification. Focusing on oxygen variability to develop our framework, we reveal key biological mechanisms governing tumour spheroid growth. Growing spheroids in time-dependent conditions, we identify and quantify novel biological adaptation mechanisms, including unexpected necrotic core removal, and transient reversal of the tumour spheroid growth phases.

Factors Shifting Preference Toward Telemedicine in the Delivery of Skin Cancer Reconstruction Care.

OBJECTIVES: Identify which delivery modality for skin reconstruction care, face-to-face (FTF) in-person versus two telemedicine modalities, store-and-forward (S&F) and live video chat (LVC), is patient preferred and how cost, access, wait time, and demographics influence this preference. STUDY DESIGN: Cross-sectional survey. METHODS: A 16-question survey querying demographics and five scenario-specific preferences questions for the delivery of skin cancer reconstruction care was created and distributed via Amazon Mechanical Turk (MTurk), a crowdsourcing online marketplace, and in-person to Mohs micrographic surgery patients. RESULTS: 1394 MTurk and 55 in-person responses were included. While 82.1% of online respondents prefer FTF clinic visits, this decreases to 58.3% with an in-person visit cost (p < 0.01) and furthermore to a minority 43.5% with both an in-person visit cost and wait time (p < 0.01) despite 77.8% believing that usefulness to the surgeon would improve FTF. Both the MTurk and in-person cohorts demonstrated similar response patterns despite considerable demographic differences. Multivariable analyses revealed that telemedicine was preferred by MTurk respondents with Medicaid (adjusted OR [95% CI]: 1.97 [1.18-3.31]) or Medicare (1.69 [1.10-2.59]) versus private insurance, and prior skin cancer (2.01 [1.18-3.42]) and less preferred by those earning $140,000+ per year (0.49 [0.29-0.82]) compared to those earning <$20,000 per year. CONCLUSIONS: FTF visits are preferred for skin cancer reconstruction care; this shifts toward virtual care with a cost and wait time in spite of the perceived quality of care. Individuals with socioeconomic barriers to access prefer telemedicine. MTurk can be a valuable tool for behavioral research in FPRS. LEVEL OF EVIDENCE: NA Laryngoscope, 133:294-301, 2023.

Epidemiology and Prognostic Indicators of Survival in Tongue Lymphoma.

OBJECTIVES: Lymphoma, categorized as either non-Hodgkin's lymphoma or Hodgkin's lymphoma, is the second most common malignancy in the head and neck. Primary tongue lymphoma is exceedingly rare, with only case reports or small case series in the literature. This population-based analysis is the first to report the epidemiology and prognostic factors of survival in patients with primary tongue lymphoma. METHODS: The Surveillance, Epidemiology, and End Results 18 database from the National Cancer Institute was queried for patients diagnosed between the years 2000 and 2016 with tongue lymphoma. Outcomes of interest were overall and disease-specific survival. Independent variables included age at diagnosis, sex, race, marital status, primary subsite, histologic subtype, stage, and treatment type. RESULTS AND CONCLUSION: Seven hundred forty patients met criteria; the male-female ratio was 1.5:1 and the mean age at diagnosis was 67.8 years. The majority of lesions localized to the base of tongue (90.0%), were histologically diffuse large B-cell lymphoma (59.5%), and presented at stage I or II (77.9%). Most early-stage lymphomas were treated with chemotherapy only (40.5%) or a combination of both chemotherapy and radiation (31.3%), while late-stage cancers were primarily treated with chemotherapy alone (68.5%). In multivariate analysis, younger age at diagnosis, female sex, married/partnered marital status, mucosa-associated lymphoid tissue histologic subtype, and earlier cancer stage were found to be associated with improved survival. Chemotherapy treatment with or without radiation was also associated with better survival compared to no treatment or radiation alone, though data regarding immunotherapy was unavailable.

Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models.

BACKGROUND: Radiomics is an approach to medical imaging that quantifies the features normally translated into visual display. While both radiomic and clinical markers have shown promise in predicting response to neoadjuvant chemoradiation therapy (nCRT) for rectal cancer, the interrelationship is not yet clear. METHODS: A retrospective, single-institution study of patients treated with nCRT for locally advanced rectal cancer was performed. Clinical and radiomic features were extracted from electronic medical record and pre-treatment magnetic resonance imaging, respectively. Machine learning models were created and assessed for complete response and positive treatment effect using the area under the receiver operating curves. RESULTS: Of 131 rectal cancer patients evaluated, 68 (51.9%) were identified to have a positive treatment effect and 35 (26.7%) had a complete response. On univariate analysis, clinical T-stage (OR 0.46, p = 0.02), lymphovascular/perineural invasion (OR 0.11, p = 0.03), and statin use (OR 2.45, p = 0.049) were associated with a complete response. Clinical T-stage (OR 0.37, p = 0.01), lymphovascular/perineural invasion (OR 0.16, p = 0.001), and abnormal carcinoembryonic antigen level (OR 0.28, p = 0.002) were significantly associated with a positive treatment effect. The clinical model was the strongest individual predictor of both positive treatment effect (AUC = 0.64) and complete response (AUC = 0.69). The predictive ability of a positive treatment effect increased by adding tumor and mesorectal radiomic features to the clinical model (AUC = 0.73). CONCLUSIONS: The use of a combined model with both clinical and radiomic features resulted in the strongest predictive capability. With the eventual goal of tailoring treatment to the individual, both clinical and radiologic markers offer insight into identifying patients likely to respond favorably to nCRT.

Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth.

Throughout the life sciences, biological populations undergo multiple phases of growth, often referred to as biphasic growth for the commonly encountered situation involving two phases. Biphasic population growth occurs over a massive range of spatial and temporal scales, ranging from microscopic growth of tumours over several days, to decades-long regrowth of corals in coral reefs that can extend for hundreds of kilometres. Different mathematical models and statistical methods are used to diagnose, understand and predict biphasic growth. Common approaches can lead to inaccurate predictions of future growth that may result in inappropriate management and intervention strategies being implemented. Here, we develop a very general computationally efficient framework, based on profile likelihood analysis, for diagnosing, understanding and predicting biphasic population growth. The two key components of the framework are as follows: (i) an efficient method to form approximate confidence intervals for the change point of the growth dynamics and model parameters and (ii) parameter-wise profile predictions that systematically reveal the influence of individual model parameters on predictions. To illustrate our framework we explore real-world case studies across the life sciences.

Defining the safe corridor for transcondylar screw placement in the feline humeral condyle.

OBJECTIVES: The aim of this study was to develop guidelines for the optimal location of drill entry and exit points for insertion of a transcondylar screw across the feline humeral condyle. METHODS: Multiplanar reconstruction of feline elbow CT scans performed between 2016 and 2021 at one referral institution were reviewed. The optimum medial and lateral epicondylar entry and exit points for transcondylar screw placement were determined. These values were normalised to the humeral condylar diameter (HCD) for each elbow to determine the transcondylar screw placement guidelines. These guidelines were applied to each elbow and tolerance angles were determined in the transverse and frontal plane CT images to determine the safe corridor for screw placement. RESULTS: Twenty elbows from 12 cats were evaluated in this study. The guidelines for transcondylar screw placement were as follows: the medial entry/exit point was 0.38 × HCD cranial and 0.16 × HCD distal to the medial epicondyle, and the lateral entry/exit point was 0.3 × HCD cranial and 0.16 × HCD distal to the lateral epicondyle. Tolerance angles were statistically significantly (P <0.05) larger in both frontal (34.5% larger) and transverse (21.1% larger) planes when drilled from a lateral to medial direction compared with drilling from a medial to lateral direction. CONCLUSIONS AND RELEVANCE: The guidelines determined from this study may aid clinicians in the placement of humeral transcondylar screws in cats. Where possible, drilling from a lateral to medial direction is recommended owing to the higher tolerance angles reducing the likelihood of articular surface damage. Further studies are warranted to determine whether these guidelines are clinically useful and result in the safe insertion of a transcondylar screw in the clinical setting.

Temporal trends in oropharyngeal cancer incidence, survival, and cancer-directed surgery among elderly Americans.

OBJECTIVE: This study assesses longitudinal epidemiologic trends in the oldest head and neck cancer (HNC) patients, comparing the oropharynx to other mucosal HNC sites. MATERIALS AND METHODS: Using data from the Surveillance, Epidemiology, and End Results database, trends in incidence, two-year cancer specific mortality, and percent of cases recommended for and which received surgery from 2000 to 2018 in patients ages ≥85 years were assessed using Joinpoint analysis by HNC site. Trends were quantified as annual percentage change (APC) with 95% confidence intervals (CI). RESULTS: Among older adults, oropharyngeal cancer (OPC) incidence increased (APC = 1.80% [95% CI: 0.94-2.67]), while mortality decreased (APC = -2.01% [95% CI: -3.26--0.74]) from 2000 to 2018. At other mucosal HNC sites, incidence and mortality remained stable. Percentage of patients who received surgery significantly changed for oropharyngeal (APC = -15.34% from 2000 to 2005 [95% CI: -24.37 to -4.79]) and laryngeal (APC = -4.61% from 2000 to 2008 [95% CI -8.28 to -0.80]) cancers. Trends in recommendation for surgery varied by site with significant decreases at the larynx, oral cavity, and oropharynx. CONCLUSION: OPC incidence is increasing among the oldest HNC patients. An increasing proportion of HPV-associated tumors could account for associated mortality improvement. There has been a shift towards non-surgical therapy possibly due to known favorable response of HPV-associated OPC to radiation therapy and/or poor surgical candidacy in this age group. The evolving treatment approach has not been detrimental to population-level survival outcomes, but optimal treatment has yet to be established. Future studies with pathologically confirmed HPV status are needed to better understand older adult OPC burden.

Medical students' knowledge of HPV, HPV vaccine, and HPV-associated head and neck cancer.

On the basis of their training, medical students are considered "the best case scenario" among university students in knowledge of the human papillomavirus (HPV). We evaluated differences in knowledge of HPV, HPV vaccine, and head and neck cancer (HNC) among medical students. A previously validated questionnaire was completed by 247 medical students at a Midwestern university. Outcomes of interest were knowledge score for HPV and HPV vaccine, and HNC, derived from combining questionnaire items to form HPV knowledge and HNC scores, and analyzed using multivariate linear regression. Mean scores for HPV knowledge were 19.4 out of 26, and 7.2 out of 12 for HNC knowledge. In the final multivariate linear regression model, sex, race, and year of study were independently associated with HPV and HPV vaccine knowledge. Males had significantly lower HPV vaccine knowledge than females (ß = -1.53; 95% CI: -2.53, -0.52), as did nonwhite students (ß = -1.05; 95% CI: -2.07, -0.03). There was a gradient in HPV vaccine knowledge based on the year of study, highest among fourth year students (ß = 6.75; 95% CI: 5.17, 8.33). Results were similar for factors associated with HNC knowledge, except for sex. HNC knowledge similarly increased based on year of study, highest for fourth year students (ß = 2.50; 95% CI: 1.72, 3.29). Among medical students, gaps remain in knowledge of HPV, HPV vaccine, and HPV-linked HNC. Male medical students have significantly lower knowledge of HPV. This highlights the need to increase medical student knowledge of HPV and HPV-linked HNC.

A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling.

In vitro tumour spheroids have been used to study avascular tumour growth and drug design for over 50 years. Tumour spheroids exhibit heterogeneity within the growing population that is thought to be related to spatial and temporal differences in nutrient availability. The recent development of real-time fluorescent cell cycle imaging allows us to identify the position and cell cycle status of individual cells within the growing spheroid, giving rise to the notion of a four-dimensional (4D) tumour spheroid. We develop the first stochastic individual-based model (IBM) of a 4D tumour spheroid and show that IBM simulation data compares well with experimental data using a primary human melanoma cell line. The IBM provides quantitative information about nutrient availability within the spheroid, which is important because it is difficult to measure these data experimentally.

Parameter identifiability and model selection for sigmoid population growth models.

Sigmoid growth models, such as the logistic, Gompertz and Richards' models, are widely used to study population dynamics ranging from microscopic populations of cancer cells, to continental-scale human populations. Fundamental questions about model selection and parameter estimation are critical if these models are to be used to make practical inferences. However, the question of parameter identifiability - whether a data set contains sufficient information to give unique or sufficiently precise parameter estimates - is often overlooked. We use a profile-likelihood approach to explore practical parameter identifiability using data describing the re-growth of hard coral. With this approach, we explore the relationship between parameter identifiability and model misspecification, finding that the logistic growth model does not suffer identifiability issues for the type of data we consider whereas the Gompertz and Richards' models encounter practical non-identifiability issues. This analysis of parameter identifiability and model selection is important because different growth models are in biological modelling without necessarily considering whether parameters are identifiable. Standard practices that do not consider parameter identifiability can lead to unreliable or imprecise parameter estimates and potentially misleading mechanistic interpretations. For example, using the Gompertz model, the estimate of the time scale of coral re-growth is 625 days when we estimate the initial density from the data, whereas it is 1429 days using a more standard approach where variability in the initial density is ignored. While tools developed here focus on three standard sigmoid growth models only, our theoretical developments are applicable to any sigmoid growth model and any appropriate data set. MATLAB implementations of all software are available on GitHub.

Designing and interpreting 4D tumour spheroid experiments.

Tumour spheroid experiments are routinely used to study cancer progression and treatment. Various and inconsistent experimental designs are used, leading to challenges in interpretation and reproducibility. Using multiple experimental designs, live-dead cell staining, and real-time cell cycle imaging, we measure necrotic and proliferation-inhibited regions in over 1000 4D tumour spheroids (3D space plus cell cycle status). By intentionally varying the initial spheroid size and temporal sampling frequencies across multiple cell lines, we collect an abundance of measurements of internal spheroid structure. These data are difficult to compare and interpret. However, using an objective mathematical modelling framework and statistical identifiability analysis we quantitatively compare experimental designs and identify design choices that produce reliable biological insight. Measurements of internal spheroid structure provide the most insight, whereas varying initial spheroid size and temporal measurement frequency is less important. Our general framework applies to spheroids grown in different conditions and with different cell types.

Cholecystectomy in 23 cats (2005-2021).

OBJECTIVE: To describe the clinical presentation, treatments, and long-term outcomes following cholecystectomy in cats. STUDY DESIGN: Clinical retrospective study. ANIMALS: Twenty-three client-owned cats. METHODS: Medical records of all cats undergoing cholecystectomy between 2005 and 2021 at a single referral hospital were retrospectively reviewed. No cats were excluded. An owner questionnaire assessed long-term outcomes. RESULTS: Vomiting, jaundice, and abdominal pain were the most common clinical signs; median duration of signs was 4 days (range 1-21). Cholelithiasis was the major indication for cholecystectomy followed by cholecystitis. Intraoperative hypotension and postoperative anemia were commonly encountered. Nine cats required a postoperative blood product transfusion. Cardiopulmonary arrest and death occurred in five cats. Eighteen cats (78.3%) survived to discharge. Long-term follow up (>60 days) was available for 16 cats at a median of 1003 days (range 81-4995). Fifteen cats survived over 6 months with eight cats (44.4%) surviving over 3 years. The most common short-term and long-term postoperative complication was vomiting. Owners assessed postoperative outcome as excellent in all cats and quality of life as excellent or good. CONCLUSION: The most common indication for cholecystectomy was cholelithiasis. Perioperative complications were commonly encountered. Perioperative mortality rate was 21.7%. Long-term owner evaluation of clinical outcome was considered excellent. CLINICAL SIGNIFICANCE: Cats undergoing cholecystectomy for non-neoplastic causes can have a favorable prognosis for recovery and quality of life. Concurrent extrahepatic biliary duct obstruction is not a contraindication for cholecystectomy provided that patency of the common bile duct is restored.

Radiation-Associated Sarcoma of the Head and Neck: Incidence, Latency, and Survival.

OBJECTIVES/HYPOTHESIS: Radiation-associated sarcomas of the head and neck (RASHN) are known but rare sequelae after radiation for squamous cell carcinoma. The purpose of this study was to characterize RASHN, estimate the risk of RASHN in head and neck squamous cell patients after therapeutic radiation, and compare their survival to that of patients with de novo sarcomas of the head and neck (dnSHN). STUDY DESIGN: Retrospective database analysis. METHODS: RASHN and dnSHN cases were collected from the Surveillance, Epidemiology, and End Results Database to identify risk factors and calculate incidence and latency. Survival was compared between RASHN and dnSHN. RESULTS: The risk of RASHN was 20.0 per 100,000 person-years. The average latency period was 124.2 months (range 38-329). The cumulative incidence of RASHN at 20 years was 0.13%. Oral cavity and oropharynx primaries demonstrate increased risk. Five-year overall survival of RASHN was 22.4% compared to 64.5% for dnSHN. CONCLUSIONS: RASHN are confirmed to be rare. RASHN have poor overall survival and worse survival compared to dnSHN. The impact of intensity-modulated radiation therapy protocols on this risk is unknown. Modifiable risk factors of smoking and alcohol consumption continue to dwarf radiation therapy as risk factors of second primary head and neck cancers. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:1034-1041, 2022.