Women at Altitude: Menstrual-Cycle Phase, Menopause, and Exogenous Progesterone Are Not Associated with Acute Mountain Sickness.

Gardner, Laurel, Linda E. Keyes, Caleb Phillips, Elan Small, Tejaswi Adhikari, Nathan Barott, Ken Zafren, Rony Maharjan, and James Marvel. Women at altitude: Menstrual-cycle phase, menopause, and exogenous progesterone are not associated with acute mountain sickness. High Alt Med Biol. 00:000-000, 2024. Background: Elevated progesterone levels in women may protect against acute mountain sickness (AMS). The impact of hormonal contraception (HC) on AMS is unknown. We examined the effect of natural and exogenous progesterone on the occurrence of AMS. Methods: We conducted a prospective observational convenience study of female trekkers in Lobuche (4,940 m) and Manang (3,519 m). We collected data on last menstrual period, use of exogenous hormones, and development of AMS. Results: There were 1,161 trekkers who met inclusion criteria, of whom 307 (26%) had AMS. There was no significant difference in occurrence of AMS between women in the follicular (28%) and the luteal (25%) phases of menstruation (p = 0.48). The proportion of premenopausal (25%) versus postmenopausal women (30%) with AMS did not differ (p = 0.33). The use of HC did not influence the occurrence of AMS (HC 23% vs. no HC 26%, p = 0.47), nor did hormonal replacement therapy (HRT) (HRT 11% vs. no HRT 31%, p = 0.13). Conclusion: We found no relationship between menstrual-cycle phase, menopausal status, or use of exogenous progesterone and the occurrence of AMS in trekkers and conclude that hormonal status is not a risk factor for AMS. Furthermore, women should not be excluded from future AMS studies based on hormonal status.

Prior Ambulatory Mild Coronavirus Disease 2019 Does Not Increase Risk of Acute Mountain Sickness.

Small, Elan, Caleb Phillips, William Bunzel, Lakota Cleaver, Nishant Joshi, Laurel Gardner, Rony Maharjan, and James Marvel. Prior ambulatory mild coronavirus disease 2019 does not increase risk of acute mountain sickness. High Alt Med Biol. 24:201-208, 2023. Background: Given its long-term morbidity, understanding how prior coronavirus disease 2019 (COVID-19) may affect acute mountain sickness (AMS) susceptibility is important for preascent risk stratification. The objective of this study was to examine if prior COVID-19 impacts risk of AMS. Materials and Methods: This was a prospective observational study conducted in Lobuje (4,940 m) and Manang (3,519 m), Nepal, from April to May 2022. AMS was defined by the 2018 Lake Louise Questionnaire criteria. COVID-19 severity was defined using the World Health Organization-developed criteria. Results: In the Lobuje cohort of 2,027, 46.2% of surveyed individuals reported history of COVID-19, with 25.7% AMS point-prevalence. There was no significant relationship between prior ambulatory mild COVID-19 and AMS (p = 0.6) or moderate AMS (p = 1.0). In the Manang cohort of 908, 42.8% reported history of COVID-19, with 14.7% AMS point-prevalence. There was no significant relationship between prior ambulatory mild COVID-19 and AMS (p = 0.3) or moderate AMS (p = 0.4). Average months since COVID-19 was 7.4 (interquartile range [IQR] 3-10) for Lobuje, 6.2 (IQR 3-6) for Manang. Both cohorts rarely exhibited moderate COVID-19 history. Conclusions: Prior ambulatory mild COVID-19 was not associated with increased risk of AMS and should not preclude high-altitude travel.

The Microbiome of Osteoarthritic Hip and Knee Joints: A Prospective Multicenter Investigation.

BACKGROUND: Recent advances in high-throughput DNA sequencing technologies have made it possible to characterize the microbial profile in anatomical sites previously assumed to be sterile. We used this approach to explore the microbial composition within joints of osteoarthritic patients. METHODS: This prospective multicenter study recruited 113 patients undergoing hip or knee arthroplasty between 2017 and 2019. Demographics and prior intra-articular injections were noted. Matched synovial fluid, tissue, and swab specimens were obtained and shipped to a centralized laboratory for testing. Following DNA extraction, microbial 16S-rRNA sequencing was performed. RESULTS: Comparisons of paired specimens indicated that each was a comparable measure for microbiological sampling of the joint. Swab specimens were modestly different in bacterial composition from synovial fluid and tissue. The 5 most abundant genera were Escherichia, Cutibacterium, Staphylococcus, Acinetobacter, and Pseudomonas. Although sample size varied, the hospital of origin explained a significant portion (18.5%) of the variance in the microbial composition of the joint, and corticosteroid injection within 6 months before arthroplasty was associated with elevated abundance of several lineages. CONCLUSIONS: The findings revealed that prior intra-articular injection and the operative hospital environment may influence the microbial composition of the joint. Furthermore, the most common species observed in this study were not among the most common in previous skin microbiome studies, suggesting that the microbial profiles detected are not likely explained solely by skin contamination. Further research is needed to determine the relationship between the hospital and a "closed" microbiome environment. These findings contribute to establishing the baseline microbial signal and identifying contributing variables in the osteoarthritic joint, which will be valuable as a comparator in the contexts of infection and long-term arthroplasty success. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects.

In silico models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, parameter space exploration is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance of model parameters. One widely used computational platform in the mathematical biology community is PhysiCell, which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. Nonetheless, one limitation of PhysiCell is the lack of a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Here, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines for PhysiCell models.

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis.

The goal of this study is to calibrate a multiscale model of tumor angiogenesis with time-resolved data to allow for systematic testing of mathematical predictions of vascular sprouting. The multi-scale model consists of an agent-based description of tumor and endothelial cell dynamics coupled to a continuum model of vascular endothelial growth factor concentration. First, we calibrate ordinary differential equation models to time-resolved protein concentration data to estimate the rates of secretion and consumption of vascular endothelial growth factor by endothelial and tumor cells, respectively. These parameters are then input into the multiscale tumor angiogenesis model, and the remaining model parameters are then calibrated to time resolved confocal microscopy images obtained within a 3D vascularized microfluidic platform. The microfluidic platform mimics a functional blood vessel with a surrounding collagen matrix seeded with inflammatory breast cancer cells, which induce tumor angiogenesis. Once the multi-scale model is fully parameterized, we forecast the spatiotemporal distribution of vascular sprouts at future time points and directly compare the predictions to experimentally measured data. We assess the ability of our model to globally recapitulate angiogenic vasculature density, resulting in an average relative calibration error of 17.7% ± 6.3% and an average prediction error of 20.2% ± 4% and 21.7% ± 3.6% using one and four calibrated parameters, respectively. We then assess the model's ability to predict local vessel morphology (individualized vessel structure as opposed to global vascular density), initialized with the first time point and calibrated with two intermediate time points. In this study, we have rigorously calibrated a mechanism-based, multiscale, mathematical model of angiogenic sprouting to multimodal experimental data to make specific, testable predictions.

Molecular testing with next-generation sequencing appears to identify biofilm on penile prostheses better than traditional cultures: The new gold standard?

INTRODUCTION: Traditional culture is the current standard-of-care to determine therapeutic antibiotics for patients suffering from penile prostheses (PP) infections. However, approximately 50% of PPs removed for infection are culture negative. Next-generation sequencing (NGS) compares DNA sequences to reference sequences with known microbial taxonomies to identify isolates and report relative abundances. We aim to compare the ability for standard culture and NGS techniques to identify microorganisms and biofilm composition on PPs. MATERIALS AND METHODS: Ninety-one PPs explanted for mechanical malfunction were included in this study. Devices removed for infection or erosion were excluded. During revision surgery, two specimens were collected and sent for culture testing at institutional laboratory and for NGS testing (MicroGenDx, Lubbock, TX, USA). Species' relative abundances, sample diversity and richness, and compositional differences among samples were analyzed. RESULTS: NGS had a higher rate of microbial detection (n = 72, 79.1%) compared to culture results (n = 3, 3.3%). Some of the bacteria identified using both methods were known prosthetic infectious pathogens, with NGS producing more isolates (mean: 11) than culture (mean: 1). Escherichia coli was the most abundant and most frequently occurring bacteria detected on NGS. Coagulase-negative Staphylococci were the most common bacteria detected on traditional culture. CONCLUSIONS: NGS appears to be beneficial in its thorough analysis of PP biofilm composition when compared to culture methods. We hope that further research will be able to demonstrate a clinical benefit of NGS in characterizing distinct microbiomes and biofilms of infected PP, which can aid in tailoring antimicrobial therapy and improving patient outcomes.

Internal Hemorrhoids: A Source of Massive Obscure Lower Gastrointestinal Bleeding in Cirrhosis.

Anorectal bleeding is the second most common site of lower gastrointestinal bleeding. Colonoscopy remains the gold standard test to localize sources of lower gastrointestinal bleeding, but it can miss left-sided colon pathologies such as diverticula, rectal varices, and internal hemorrhoids. We report an unusual case of a male cirrhotic patient with massive hemorrhoidal bleeding which went undiagnosed despite multiple imaging and endoscopic evaluations. He underwent urgent sigmoidoscopy that identified grade III internal hemorrhoids and sclerotherapy which resolved the hematochezia. Decompensated cirrhosis complicates patient candidacy for surgical hemorrhoidectomy, but sclerotherapy is a viable option even for high-risk patients. Urgent sigmoidoscopy during active bleeding should be considered if hemorrhoidal bleeding is suspected but inconclusive by colonoscopy.

An Enhanced Understanding of Culture-Negative Periprosthetic Joint Infection with Next-Generation Sequencing: A Multicenter Study.

BACKGROUND: The challenges of culture-negative periprosthetic joint infection (PJI) have led to the emergence of molecular methods of pathogen identification, including next-generation sequencing (NGS). While its increased sensitivity compared with traditional culture techniques is well documented, it is not fully known which organisms could be expected to be detected with use of NGS. The aim of this study was to describe the NGS profile of culture-negative PJI. METHODS: Patients undergoing revision hip or knee arthroplasty from June 2016 to August 2020 at 14 institutions were prospectively recruited. Patients meeting International Consensus Meeting (ICM) criteria for PJI were included in this study. Intraoperative samples were obtained and concurrently sent for both routine culture and NGS. Patients for whom NGS was positive and standard culture was negative were included in our analysis. RESULTS: The overall cohort included 301 patients who met the ICM criteria for PJI. Of these patients, 85 (28.2%) were culture-negative. A pathogen could be identified by NGS in 56 (65.9%) of these culture-negative patients. Seventeen species were identified as common based on a study-wide incidence threshold of 5%. NGS revealed a polymicrobial infection in 91.1% of culture-negative PJI cases, with the set of common species contributing to 82.4% of polymicrobial profiles. Escherichia coli, Cutibacterium acnes, Staphylococcus epidermidis, and Staphylococcus aureus ranked highest in terms of incidence and study-wide mean relative abundance and were most frequently the dominant organism when occurring in polymicrobial infections. CONCLUSIONS: NGS provides a more comprehensive picture of the microbial profile of infection that is often missed by traditional culture. Examining the profile of PJI in a multicenter cohort using NGS, this study demonstrated that approximately two-thirds of culture-negative PJIs had identifiable opportunistically pathogenic organisms, and furthermore, the majority of infections were polymicrobial. LEVEL OF EVIDENCE: Diagnostic Level II . See Instructions for Authors for a complete description of levels of evidence.

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Diets Containing Egg or Whey Protein and Inulin Fiber Improve Energy Balance and Modulate Gut Microbiota in Exercising Obese Rats.

SCOPE: Dietary protein, prebiotic fiber, and exercise individually have been shown to aid in weight loss; however less is known of their combined effects on energy balance. The effects of diets high in protein and fiber, with exercise, on energy balance, hormones, and gut microbiota, were determined. METHODS AND RESULTS: Obese male rats were fed high-fat diets with high protein and fiber contents from egg protein and cellulose, egg protein and inulin, whey protein and cellulose, or whey protein and inulin, together with treadmill exercise. We found that inulin enriched diets decreased energy intake and respiratory quotient (RQ), increased energy expenditure (EE), and upregulated transcripts for cholecystokinin (CCK), peptide YY, and proglucagon in distal gut. Notably, CCK1-receptor blockade attenuated the hypophagic effects of diets and in particular whey-inulin diet, and ß-adrenergic blockade reduced EE across all diets. Egg-cellulose, egg-inulin, and whey-inulin diets decreased weight gain, adiposity, and hepatic lipidosis; decreased lipogenic transcripts, improved glycemic control, and upregulated hepatic glucose metabolism transcripts; and decreased plasma insulin and leptin. Importantly, diet was linked to altered gut microbial composition and plasma metabolomics, and a subset of predicted metagenome pathways and plasma metabolites significantly correlated, with plasma butyric acid the most strongly associated to metagenome function. CONCLUSION: Combination of dietary egg or whey protein with inulin and exercise improved energy balance, glucose metabolism, upregulated anorectic hormones, and selectively modulated gut microbiota and plasma metabolites.

Microorganism Profiles of Penile Prosthesis Removed for Infection, Erosion, and Mechanical Malfunction Based on Next-Generation Sequencing.

BACKGROUND: Next-generation sequencing (NGS) is an emerging technology that may allow for more sensitive and sophisticated microbial testing of the microbiota of penile prostheses (PP). AIM: To describe the microorganism profiles of PP explanted for infection, erosion, and mechanical malfunction using NGS. METHODS: All patients who underwent PP removal by two physicians at two institutions were identified. Differences in alpha diversity (ie, number of species detected, species diversity across samples) and microbiome compositional profiles (Bray-Curtis community dissimilarities) across samples were assessed using ANOVA and PERMANOVA, respectively. OUTCOMES: Number of species detected, species diversity across samples, and microbiome compositional profiles. RESULTS: A total of 83 patients who underwent device removal for infection (n = 8, 10%), erosion (n = 5, 6%), and mechanical malfunction (n = 70, 84%) were included. When considering all devices, 56% (n = 48) of NGS and 29% (n = 24) of standard cultures resulted positive for presence of microorganisms. Culture only detected the most abundant NGS species in 62.5% (n = 5) of infected devices. Species richness and microbiome compositional profiles varied by surgical indication, but not by age, race, diabetes status, or implant duration. Most frequent organisms by surgical indication were Pseudomonas aeruginosa (infection), Staphylococcus epidermidis (erosion), and Escherichia coli (mechanical malfunction). The highest relative abundance organisms were P aeruginosa (infection), Corynebacterium jeikeium (erosion), and E coli (mechanical malfunction). CLINICAL IMPLICATIONS: Identifying microbiome profiles of PP removed for infection, erosion, and mechanical malfunction may guide the selection of peri-operative antibiotics and PP antibiotic coatings or hydrophilic dip solutions for each individual scenario. STRENGTHS AND LIMITATIONS: While this is the first study to utilize next-generation sequencing to evaluate penile prosthesis biofilm, the clinical significance of these findings has yet to be determined. A prospective, randomized trial aimed at evaluating the clinical significance of NGS in patients with PP infection is currently underway. CONCLUSION: NGS testing identified distinct microbiome profiles of PP removed for infection, erosion, and mechanical malfunction. Chung PH, Leong JY, Phillips CD, Henry GD. Microorganism Profiles of Penile Prosthesis Removed for Infection, Erosion, and Mechanical Malfunction Based on Next-Generation Sequencing. J Sex Med 2022;19:356-363.

The effect of local estrogen therapy on the urinary microbiome composition of postmenopausal women with and without recurrent urinary tract infections.

INTRODUCTION AND HYPOTHESIS: Recurrent urinary tract infections (rUTIs) occur in 2-10% of postmenopausal women. Local estrogen therapy (LET) has been shown to reduce UTIs. This study aimed to compare the urinary microbiome between patients with and without a history of rUTIs and to examine whether treatment with LET influences the diversity and richness of microbiome species in two groups. METHODS: Postmenopausal women with and without rUTIs attending the urogynecology clinic between April 2019 and December 2020 were recruited. Participant baseline characteristics and demographics were recorded. Aseptic transurethral urine samples were collected at recruitment and at 3-6 months following treatment with LET. The V1-V2 and ITS regions of the 16S rRNA gene were sequenced to identify bacteria. RESULTS: A total of 37 women were recruited, 20 controls and 17 patients with rUTI. During follow-up, symptomatic UTIs occurred in 3/17 (17.6%) and 0/20 in the rUTI group and control group, respectively. Klebsiella aerogenes was present in 80% of rUTI samples and in 53.3% of control samples before LET. Abundance of Finegoldia magna was present in 33.3% of samples before LET, but only in 6.7% after LET. There was no change in relative abundance of lactobacillus species following LET in both groups. CONCLUSIONS: Treatment with vaginal LET altered the local hormonal environment of the urinary bladder and likely protected women from development of rUTI by decreasing the presence of F. magna. To confirm the significance of this bacterial species in rUTI symptomatology, our finding needs to be validated on a larger patient cohort.

Dietary whey and egg proteins interact with inulin fiber to modulate energy balance and gut microbiota in obese rats.

Diets supplemented with protein and fiber are well known to reduce food intake and weight gain; however, less is known about the combined effects of protein and prebiotic fiber on energy balance and gut microbiota composition. We compared effects of diets containing high egg or whey protein with cellulose or prebiotic (inulin) fiber on energy balance, gut microbiota, hormones, and metabolites. Male obese rats (n=8/group) were allocated to four diets: Egg albumen+Cellulose (EC), Egg albumen+Inulin (EI), Whey protein+Cellulose (WC), and Whey protein+Inulin (WI). Results revealed that diet-induced hypophagia was transient with EC and prolonged with EI and WI, compared to WC. Importantly, CCK-1 receptor antagonist (Devazepide) attenuated the hypophagic effects of EC, EI, and WI. Further, EC, EI and WI decreased respiratory quotient, energy expenditure, weight and adiposity gains, and improved glycemia, relative to WC. Propranolol (ß1-ß2-receptor blocker) attenuated diet-induced changes in energy expenditure. Transcript abundance of thermogenic markers in brown adipose tissue, plasma hormones, and metabolites especially acyl-carnitines and glycerophospholipids, were differentially altered by diets. Diet explained 25% of compositional differences in cecal microbiomes, but diets with same fiber type did not differ. Microbiota differing between groups also strongly correlated with gut hormones and metabolites. Species most strongly correlated to a marker for butyrate production were in highest abundance in inulin diets. Together, these findings indicate that inulin enriched diets containing egg or whey protein improved energy balance, decreased adiposity, and modulated gut microbiota and metabolites, with CCK signaling partly mediating the satiety effects of diets.

Older Age as a Predictive Risk Factor for Acute Mountain Sickness.

BACKGROUND: Older populations are increasing and comprise a substantial portion of high-altitude travelers. Aging physiology may influence susceptibility to acute mountain sickness, though prior research remains inconclusive. The goal of this study was to investigate the relationship between increasing age and acute mountain sickness. METHODS: This study was a pooled analysis of 5 prospective randomized controlled trials conducted at White Mountain, California from 2010, 2016-2019 with identical 4-hour rapid ascent from 1242 m to overnight sojourn at 3810 m. Acute mountain sickness was defined by the 2018 Lake Louise Questionnaire criteria. RESULTS: There were 491 participants analyzed, 234 (48%) diagnosed with acute mountain sickness and 71 (14%) with moderate acute mountain sickness. Mean age was 37 years (±13). There was no significant correlation between Lake Louise Questionnaire severity and age (r = -0.02; 95% confidence interval [CI], -0.11-0.07, P = .7), 40-year-old dichotomy (t = -0.6; 95% CI, -0.53-0.28, P = .6), or decade of life (P = .4). Logistic regression found no increased odds of acute mountain sickness for increasing age by decade of life (odds ratio [OR] 1.0; 95% CI, 0.97-1.0) or 40-year-old dichotomy (OR 1.4; 95% CI, 0.97-2.1). A history of acute mountain sickness increased odds of acute mountain sickness (OR 3.2; 95% CI, 1.5-7.7). CONCLUSIONS: Older age was not associated with incidence nor severity of acute mountain sickness. A history of altitude illness increased odds of acute mountain sickness and should be used for pre-ascent risk stratification.

Research data infrastructure for high-throughput experimental materials science.

The High-Throughput Experimental Materials Database (HTEM-DB, htem.nrel.gov) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain.

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth.

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.

Weight Bias 2.0: The Effect of Perceived Weight Change on Performance Evaluation and the Moderating Role of Anti-fat Bias.

Overweight employees are viewed as lazy, slow, inactive, and even incapable. Even if such attributes are false, this perspective can seriously undermine others' evaluation of their work performance. The current study explores a broader phenomenon of weight bias that has an effect on weight change. In a longitudinal study with a time lag of 6 months, we surveyed 226 supervisor-employee dyads. We found supervisor perceptions of employee weight change notably altered their evaluation of the employee performance from Time 1, especially following low vs. high Time-1 performance evaluation. Meanwhile, the moderating effects among different levels of supervisor anti-fat bias functioned as boundary conditions for such performance evaluation alteration. In particular, the interaction between the Time-1 performance evaluation and the impact of supervisor perception of employee weight change on the Time-2 performance evaluation was significant only if supervisors held a stronger anti-fat bias.

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.

Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.

Predictive Capacity of Pulmonary Function Tests for Acute Mountain Sickness.

Small, Elan, Nicholas Juul, David Pomeranz, Patrick Burns, Caleb Phillips, Mary Cheffers, and Grant S. Lipman. Predictive capacity of pulmonary function tests for acute mountain sickness. High Alt Med Biol. 22: 193-200, 2021. Background: Pulmonary function as measured by spirometry has been investigated at altitude with heterogenous results, though data focused on spirometry and acute mountain sickness (AMS) are limited. The objective of this study was to investigate the capacity of pulmonary function tests (PFTs) to predict the development of AMS. Materials and Methods: This study was a blinded prospective observational study run during a randomized controlled trial comparing acetazolamide, budesonide, and placebo for AMS prevention on White Mountain, CA. Spirometry measurements of forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and peak expiratory flow were taken at a baseline altitude of 1,250 m, and the evening of and morning after ascent to 3,810 m. Measurements were assessed for correlation with AMS. Results: One hundred three participants were analyzed with well-matched baseline demographics and AMS incidence of 75 (73%) and severe AMS of 48 (47%). There were no statistically significant associations between changes in mean spirometry values on ascent to high altitude with incidence of AMS or severe AMS. Lake Louise Questionnaire scores were negatively correlated with FVC (r = -0.31) and FEV1 (r = -0.29) the night of ascent. Baseline PFT had a predictive accuracy of 65%-73% for AMS, with a receiver operating characteristic of 0.51-0.65. Conclusions: Spirometry did not demonstrate statistically significant changes on ascent to high altitude, nor were there significant associations with incidence of AMS or severe AMS. Low-altitude spirometry did not accurately predict development of AMS, and it should not be recommended for risk stratification.