Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery.

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through Gaussian process surrogates. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.

Impact of Needle Selection on Survival of Muscle-Derived Cells When Used for Laryngeal Injections.

Objective: To describe how differing injector needles and delivery vehicles impact Autologous Muscle-Derived Cell (AMDC) viability when used for laryngeal injection. Methods: In this study, adult porcine muscle tissue was harvested and used to create AMDC populations. While controlling cell concentration (1 × 107 cells/ml), AMDCs including Muscle Progenitor Cells (MPCs) or Motor Endplate Expressing Cells (MEEs) were suspended in either phosphate-buffered saline or polymerizable (in-situ scaffold forming) type I oligomeric collagen solution. Cell suspensions were then injected through 23- and 27-gauge needles of different lengths at the same rate (2 ml/min) using a syringe pump. Cell viability was measured immediately after injection and 24- and 48-hours post-injection, and then compared to baseline cell viability prior to injection. Results: The viability of cells post-injection was not impacted by needle length or needle gauge but was significantly impacted by the delivery vehicle. Overall, injection of cells using collagen as a delivery vehicle maintained the highest cell viability. Conclusion: Needle gauge, needle length, and delivery vehicle are important factors that can affect the viability of injected cell populations. These factors should be considered and adapted to improve injectable MDC therapy outcomes when used for laryngeal applications.

Assessment of San Antonio refugee health clinic telehealth response and refugees' needs during the COVID-19 pandemic.

In March of 2020, Texas declared a statewide public health emergency in response to the rapidly spreading COVID-19 virus, forcing the shutdown of many critical operations across the state. The pandemic has had a massive impact on the refugee population worldwide, increasing displacement and limiting opportunities for resettlement, work, and aid. In an effort to evaluate and address the holistic needs of the San Antonio's vulnerable refugee community during the pandemic, the San Antonio Refugee Health Clinic (SARHC) created a COVID-19 response team that screened and triaged the population, collected data, and provided telemedicine and other urgent teleservices. The SARHC clinic has served the mostly uninsured and underserved refugee population of San Antonio, Texas for over 10 years as a Student-Faculty Collaborative Practice (SFCP). With the collaboration of the Center for Refugee Services in San Antonio, the clinic utilizes the site of a local church on a weekly basis to serve refugees via teams of nursing, dental, and medical students and faculty. At the height of the COVID-19 lockdown, teams of student and faculty volunteers conducted a cross-sectional study of patients' needs by systematically calling and screening patients. Qualitative data on COVID-19 risk, mental wellness, financial needs, food security, dental needs, and medical needs was collected. Quantitative data on number of patients contacted, country of origin, interpreter use, insurance access, internet access, referrals, appointments, and prescriptions carried out was also collected and analyzed. Of the patients called (N = 216), 57% (n = 123) were successfully reached and completed the survey. 61% (n = 75) required language interpreter services. Only 9% (n = 11) of individuals had health insurance. 46% (n = 52) expressed the need for telemedicine services, and 34% (n = 42) reported access to WiFi. 41% (n = 50) reported a medical concern, 18% (n = 22) reported dental concerns, 41% (n = 51) reported social needs, and 11% (n = 14) reported mental health concerns. 24% (n = 30) of patients requested medication refills. Our snapshot of the San Antonio refugee community during the COVID-19 pandemic captures their social, mental, and physical struggles; the pandemic left many families without access to medications, health services, social services, job, and food security. The telemedicine campaign proved to be an effective method of assessing and addressing a variety of patient needs in a virtual setting. Of concern is the high rates of uninsured families and limited Internet access. These findings reveal important considerations for equitable healthcare delivery to vulnerable populations in the face of prolonged unforeseen events, like the COVID-19 pandemic.

Computational Mechanobiology Model Evaluating Healing of Postoperative Cavities Following Breast-Conserving Surgery.

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through the Gaussian Process. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.

Cultural Variations of Areca Nut Utilization and Its Availability in San Antonio, Texas.

Unknown in the U.S., areca nut (AN) is the fourth most used psychoactive substance in the world and is associated with oral cancers. We investigated the availability of AN in San Antonio ethnic grocery stores and assessed AN practices in immigrant communities. Grocery stores were contacted to assess AN availability. A survey on AN knowledge and utilization were administered at four community sites with large immigrant populations. 13 of the 19 identified grocery stores carried AN. Most survey participants (n = 66) recognized AN. Most Southeast Asians and South Asians knew what AN is, knew someone who uses it, and knew where to buy it. Most South Asian participants knew its harmful effects. AN usage is associated with older age, male sex, and recent immigration. AN is widely available and utilized among immigrant populations in San Antonio. Further work is needed to raise AN awareness among healthcare workers.

Exploring the relationship between electronic health records and provider burnout: A systematic review.

OBJECTIVE: Stress and burnout due to electronic health record (EHR) technology has become a focus for burnout intervention. The aim of this study is to systematically review the relationship between EHR use and provider burnout. MATERIALS AND METHODS: A systematic literature search was performed on PubMed, EMBASE, PsychInfo, ACM Digital Library in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. Inclusion criterion was original research investigating the association between EHR and provider burnout. Studies that did not measure the association objectively were excluded. Study quality was assessed using the Medical Education Research Study Quality Instrument. Qualitative synthesis was also performed. RESULTS: Twenty-six studies met inclusion criteria. The median sample size of providers was 810 (total 20 885; 44% male; mean age 53 [range, 34-56] years). Twenty-three (88%) studies were cross-sectional studies and 3 were single-arm cohort studies measuring pre- and postintervention burnout prevalence. Burnout was assessed objectively with various validated instruments. Insufficient time for documentation (odds ratio [OR], 1.40-5.83), high inbox or patient call message volumes (OR, 2.06-6.17), and negative perceptions of EHR by providers (OR, 2.17-2.44) were the 3 most cited EHR-related factors associated with higher rates of provider burnout that was assessed objectively. CONCLUSIONS: The included studies were mostly observational studies; thus, we were not able to determine a causal relationship. Currently, there are few studies that objectively assessed the relationship between EHR use and provider burnout. The 3 most cited EHR factors associated with burnout were confirmed and should be the focus of efforts to improve EHR-related provider burnout.