Generalized additive models to analyze nonlinear trends in biomedical longitudinal data using R: Beyond repeated measures ANOVA and linear mixed models.

In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA as they can work with unbalanced data and non-constant correlation between observations, both methodologies assume a linear trend in the measured response. It is common in biomedical research that the true trend response is nonlinear and in these cases the linearity assumption of rm-ANOVA and LMEMs can lead to biased estimates and unreliable inference. In contrast, GAMs relax the linearity assumption of rm-ANOVA and LMEMs and allow the data to determine the fit of the model while also permitting incomplete observations and different correlation structures. Therefore, GAMs present an excellent choice to analyze longitudinal data with non-linear trends in the context of biomedical research. This paper summarizes the limitations of rm-ANOVA and LMEMs and uses simulated data to visually show how both methods produce biased estimates when used on data with non-linear trends. We present the basic theory of GAMs and using reported trends of oxygen saturation in tumors, we simulate example longitudinal data (2 treatment groups, 10 subjects per group, 5 repeated measures for each group) to demonstrate their implementation in R. We also show that GAMs are able to produce estimates with non-linear trends even when incomplete observations exist (with 40% of the simulated observations missing). To make this work reproducible, the code and data used in this paper are available at: https://github.com/aimundo/GAMs-biomedical-research.

Longitudinal examination of perfusion and angiogenesis markers in primary colorectal tumors shows distinct signatures for metronomic and maximum-tolerated dose strategies.

Metronomic chemotherapy (MET) has been developed to address the shortcomings of maximum-tolerated chemotherapy (MTD) in regard to toxicity and development of resistance mechanisms in the tumor. In colorectal cancer (CRC), MET is a promising novel strategy to treat locally advanced malignancies when used as neoadjuvant chemotherapy (NAC). However, so far there are no preclinical studies to assess the impact of MET NAC in CRC to assess the benefits and challenges of this approach. Here, we used a primary model of CRC (via azoxymethane) to analyze longitudinal changes in angiogenesis in primary tumors under MET and MTD NAC using a combination of diffuse reflectance spectroscopy and mRNA expression (via qPCR). Our results show that MET and MTD NAC lead to increased mean tissue oxygen saturation (8% and 5%, respectively) and oxyhemoglobin (15% and 10%) between weeks 2 and 5 of NAC, and that such increases are caused by distinct molecular signatures in the angiogenic program. Specifically, we find that in the MET group there is a sustained increase in Hif-1a, Aldoa, and Pgk1 expression, suggesting upregulated glycolysis, whereas MTD NAC causes a significant reduction in the expression of the aforementioned genes and of Vegf, leading to vascular remodeling in MTD-treated tumors. Taken together, this study demonstrates the ability of combined optical and molecular methodologies to provide a holistic picture of tumor response to therapy in CRC in a minimally invasive manner.

Minimally invasive in vivo endoscopic monitoring of dextran sulfate sodium-induced murine colitis.

Ulcerative colitis (UC) is a gastrointestinal, autoimmune disease that causes ulceration and inflammation of the colon with an incidence of 10 out of every 100,000 people in North America and Western Europe. Though the specific cause is unknown, several studies have demonstrated that inflammatory cells as well as environmental variables, genetics, and lifestyle behaviors can play a role in the long-term inflammatory response. Researchers have commonly used immunohistochemistry, western blotting and gene sequencing to establish the cellular processes behind UC relapse and remission. However, because these destructive methods necessitate the removal of a sample, they can only be used on non-living tissues. The use of minimally invasive approaches to evaluate the in vivo, longitudinal effects of UC on the mucosa in the colon is gaining popularity among clinicians and researchers. We have created a dextran sulfate sodium-induced model of UC in C57 mice based on the work of Wirtz et al., and a minimally invasive imaging modality to explore the changes in mucosal tissue during "active" and "in remission" UC. Briefly, C57 mice were given dextran sulfate sodium (DSS) dissolved in water in 5-day cycles with a remission/recovery period of 10 days. After 7 days post-DSS treatment and 7 days post-recovery, mice were anesthetized and exploratory endoscopies were performed to assess the mucosal changes that occur during the "active" and "remission" periods of UC. Value of protocol:•Minimally invasive induction of ulcerative colitis in a murine mouse model.•Minimally invasive longitudinal monitoring of "active" and "in remission" ulcerative colitis.•Our endoscopic based imaging modality can be used to validate the induction of ulcerative colitis and the potential treatment response for pre-clinical trials.

Molecular and Serologic Diagnostic Technologies for SARS-CoV-2.

The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic.

Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response (Erratum).

The erratum notes a correction to Fig. 5 for the published article.

Diffuse reflectance spectroscopy to monitor murine colorectal tumor progression and therapeutic response.

SIGNIFICANCE: Many studies in colorectal cancer (CRC) use murine ectopic tumor models to determine response to treatment. However, these models do not replicate the tumor microenvironment of CRC. Physiological information of treatment response derived via diffuse reflectance spectroscopy (DRS) from murine primary CRC tumors provide a better understanding for the development of new drugs and dosing strategies in CRC. AIM: Tumor response to chemotherapy in a primary CRC model was quantified via DRS to extract total hemoglobin content (tHb), oxygen saturation (StO2), oxyhemoglobin, and deoxyhemoglobin in tissue. APPROACH: A multimodal DRS and imaging probe (0.78 mm outside diameter) was designed and validated to acquire diffuse spectra longitudinally-via endoscopic guidance-in developing colon tumors under 5-fluoruracil (5-FU) maximum-tolerated (MTD) and metronomic regimens. A filtering algorithm was developed to compensate for positional uncertainty in DRS measurements Results: A maximum increase in StO2 was observed in both MTD and metronomic chemotherapy-treated murine primary CRC tumors at week 4 of neoadjuvant chemotherapy, with 21 ± 6 % and 17 ± 6 % fold changes, respectively. No significant changes were observed in tHb. CONCLUSION: Our study demonstrates the feasibility of DRS to quantify response to treatment in primary CRC models.

Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts.

Diffuse reflectance spectroscopy (DRS) is a probe-based spectral biopsy technique used in cancer studies to quantify tissue reduced scattering (µs') and absorption (µa) coefficients and vary in source-detector separation (SDS) to fine-tune sampling depth. In subcutaneous murine tumor allografts or xenografts, a key design requirement is ensuring that the source light interrogates past the skin layer into the tumor without significantly sacrificing signal-to-noise ratio (target of ≥15 dB). To resolve this requirement, a DRS probe was designed with four SDSs (0.75, 2.00, 3.00, and 4.00 mm) to interrogate increasing tissue volumes between 450 and 900 nm. The goal was to quantify percent errors in extracting µa and µs', and to quantify sampling depth into subcutaneous Balb/c-CT26 colon tumor allografts. Using an optical phantom-based experimental method, lookup-tables were constructed relating µa,µs', diffuse reflectance, and sampling depth. Percent errors were <10 % and 5% for extracting µa and µs', respectively, for all SDSs. Sampling depth reached up to 1.6 mm at the first Q-band of hemoglobin at 542 nm, the key spectral region for quantifying tissue oxyhemoglobin concentration. This work shows that the DRS probe can accurately extract optical properties and the resultant physiological parameters such as total hemoglobin concentration and tissue oxygen saturation, from sufficient depth within subcutaneous Balb/c-CT26 colon tumor allografts. Methods described here can be generalized for other murine tumor models. Future work will explore the feasibility of the DRS in quantifying volumetric tumor perfusion in response to anticancer therapies.