Evaluating the impact of a decision-making game on empathy development in pharmacy students from the dual perspectives of the patient and pharmacist.

INTRODUCTION: Doctor of Pharmacy programs are charged with developing students' empathy by the 2016 Accreditation Council for Pharmacy Education (ACPE) Standard 3 and the 2022 Curriculum Outcomes and Entrustable Professional Activities (COEPA). Although empathy is essential to optimal patient care, its subjective nature makes it challenging to teach and therefore literature is lacking on best teaching practices. The authors of this paper describe a novel simulated approach to elicit and assess empathy in a pharmacy classroom. This study evaluated the impact of a decision-making game in a pharmacy skills lab course on the development of students' empathy using a validated empathy scale. METHODS: This is a cohort-based quality improvement project in which third year pharmacy students participated in a 3-h classroom empathy game experience that simulated a month in a patient's life including issues related to the cycle of poverty. Prior to the game, students completed a voluntary, anonymous baseline demographics survey. They also completed a pre- and post-survey of the validated empathy tool, the Kiersma-Chen Empathy Scale (KCES-R), to assess change in the empathy score following the decision-making game. Students also provided narrative comments in the post-survey. Statistical tests used included descriptive statistics for demographic data, Shapiro-Wilk test of normality, and Wilcoxon Signed-Rank test for survey scores (SPSS Version 29). RESULTS: Pharmacy students (n = 37) showed an overall increase in composite KCES-R scores after participating in the empathy game class session (z = -5.071, p < 0.001). The scores of each of the 14 KCES-R items also increased after the learning experience (p < 0.05). Students' narrative comments were all positive and indicated that the activity offered new insights on self-perceived empathy development. CONCLUSION: The empathy game simulation was a successful approach to increase empathy scores in third-year pharmacy students.

Enabling quantitative comparison of wastewater surveillance data across methods through data standardization without method standardization.

Wastewater surveillance for COVID-19 and other pathogens has expanded globally. Rapid development and availability of various assays has facilitated swift adoption of wastewater surveillance in localities with diverse requirements. However, it presents challenges in comparing data due to methodological variations. Using surrogates for recovery control to address quantification biases has limitations as the recovery of surrogates and target pathogens often diverges significantly. Using non-spiked field-obtained wastewater samples as reference samples in an inter-lab study, this article proposes a straightforward, inexpensive, and most representative way of measuring relative quantification biases that occurs in analyzing field wastewater samples. Five labs participated in the study, testing five types of assays, resulting in a total of seven methods of lab-assay combinations. Each method quantified the concentration of SARS-CoV-2 and pepper mild mottle virus (PMMoV) RNAs in two types of reference samples. The results showed significant variations in quantification among methods, but the relative quantification biases were consistent across reference samples. This suggests that relative quantification biases measured with the reference samples are contingent on methods rather than wastewater samples, and that the once-determined method-specific factors can be used to correct for quantification biases in routine wastewater surveillance results. Subsequent data standardization was performed on year-long observational data from seven cities, serving as a preliminary validation of the proposed approach. This process demonstrated the potential for quantitative data comparison through the bias correction factors obtained in this inter-lab study.

A future classroom lab with active and gamified STEAM proposal for mathematics and science disciplines: Analyzing the effects on pre-service teacher's affective domain.

The recent emergence of innovative learning spaces, Future Classroom Lab (FCL), provides educators the use of physical learning spaces (to research, interact, exchange, develop, create, and present) and diverse technological tools to work according to active methodologies. Learners become more active in the learning process with the introduction of innovative learning environments that enable the application of interdisciplinary STEAM methodology and foster the development of 21st century competences. This study aims to uncover the probable link between application active and gamified STEAM educational interventions in the FCL and Pre-Service Teachers' (PSTs) affective domain. The findings obtained showed statistically significant variations and, therefore, positive effects on the PSTs' affective domain (self-efficacy, attitude, and emotion) after performing the intervention. The sample consisted of a total of 54 PSTs enrolled in the second year of Primary Education. Limited studies regarding the affective domain in the FCL were found, which restricted the comparison with prior research. This study has several implications, such as the introduction of innovative educational proposals to PSTs at the university level and, consequently, the implementation of similar interventions in elementary schools. This research intended to reveal how the different variables work as a support system for students' learning process in mathematics and science disciplines.

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers.

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations: A Retrospective Cohort Study.

OBJECTIVES: Data suggest patients suffering acute coronary occlusion myocardial infarction (OMI) benefit from prompt primary percutaneous intervention (PPCI). Many emergency medical services (EMS) activate catheterization labs to reduce time to PPCI, but suffer a high burden of inappropriate activations. Artificial intelligence (AI) algorithms show promise to improve electrocardiogram (ECG) interpretation. The primary objective was to evaluate the potential of AI to reduce false positive activations without missing OMI. METHODS: Electrocardiograms were categorized by 1) STEMI criteria, 2) ECG integrated device software and 3) a proprietary AI algorithm (Queen of Hearts (QOH), Powerful Biomedical). If multiple ECGs were obtained and any one tracing was positive for a given method, that diagnostic method was considered positive. The primary outcome was OMI defined as an angiographic culprit lesion with either TIMI 0-2 flow; or TIMI 3 flow with either peak high sensitivity troponin-I > 5000 ng/L or new wall motion abnormality. The primary analysis was per-patient proportion of false positives. RESULTS: A total of 140 patients were screened and 117 met criteria. Of these, 48 met the primary outcome criteria of OMI. There were 80 positives by STEMI criteria, 88 by device algorithm, and 77 by AI software. All approaches reduced false positives, 27% for STEMI, 22% for device software, and 34% for AI (p < 0.01 for all). The reduction in false positives did not significantly differ between STEMI criteria and AI software (p = 0.19) but STEMI criteria missed 6 (5%) OMIs, while AI missed none (p = 0.01). CONCLUSIONS: In this single-center retrospective study, an AI-driven algorithm reduced false positive diagnoses of OMI compared to EMS clinician gestalt. Compared to AI (which missed no OMI), STEMI criteria also reduced false positives but missed 6 true OMI. External validation of these findings in prospective cohorts is indicated.

The harms of promoting the lab leak hypothesis for SARS-CoV-2 origins without evidence.

Science is humanity's best insurance against threats from nature, but it is a fragile enterprise that must be nourished and protected. The preponderance of scientific evidence indicates a natural origin for SARS-CoV-2. Yet, the theory that SARS-CoV-2 was engineered in and escaped from a lab dominates media attention, even in the absence of strong evidence. We discuss how the resulting anti-science movement puts the research community, scientific research, and pandemic preparedness at risk.

Endovascular Treatment of May-Thurner Syndrome in an Office-Based Laboratory.

May-Thurner syndrome (MTS) is a rare condition that increases the risk of left-sided iliofemoral venous thrombosis due to compression of the left common iliac vein by the right common iliac artery. Treatment for symptomatic MTS typically includes combined anticoagulation and endovascular therapy. This patient presented to the emergency department with acute left lower extremity pain and swelling. After imaging confirmed MTS, the patient was discharged from the ED and expeditiously treated in an office-based lab (OBL) setting with venous thrombectomy, angioplasty, and stenting. The setting where endovascular therapy is performed may significantly impact access to care for patients. Additionally, cost-effectiveness is a factor that should be considered when deciding the treatment site of service. We demonstrate the safety and cost-viability of performing venous thrombectomy, angioplasty, and stenting in an outpatient setting for the treatment of acute iliofemoral venous thrombosis.

Lab-in-a-Vial Rapid Test for Internet of Things-Embedded Point-of-Healthcare Protein Biomarker Detection in Bodily Fluids.

Amateurs often struggle with detecting and quantifying protein biomarkers in body fluids due to the high expertise required. This study introduces a Lab-in-a-Vial (LV) rapid diagnostic platform, featuring hydrangea-like platinum nanozymes (PtNH), for rapid, accurate detection and quantification of protein biomarkers on-site within 15 min. This method significantly enhances detection sensitivity for various biomarkers in body fluids, surpassing traditional methods such as enzyme-linked immunosorbent assays (ELISA) and lateral flow assays (LFA) by ≈250 to 1300 times. The LV platform uses a glass vial coated with specific bioreceptors such as antigens or antibodies, enabling rapid in vitro evaluation of disease risk from small fluid samples, similar to a personal ELISA-like point-of-care test (POCT). It overcomes challenges in on-site biomarker detection, allowing both detection and quantification through a portable wireless spectrometer for healthcare internet of things (H-IoT). The platform's effectiveness and adaptability are confirmed using IgG/IgM antibodies from SARS-CoV-2 infected patients and nuclear matrix protein (NMP22) from urothelial carcinoma (UC) patients as biomarkers. These tests demonstrated its accuracy and flexibility. This approach offers vast potential for diverse disease applications, provided that the relevant protein biomarkers in bodily fluids are identified.

Ergonomic design of computer laboratory furniture: Mismatch analysis utilizing anthropometric data of university students.

Many studies have shown that ergonomically designed furniture improves productivity and well-being. As computers have become a part of students' academic lives, they will continue to grow in the future. We propose anthropometric-based furniture dimensions that are suitable for university students to improve computer laboratory ergonomics. We collected data from 380 participants and analyzed 11 anthropometric measurements, correlating them with 11 furniture dimensions. Two types of furniture were found and studied in different university computer laboratories: (1) a non-adjustable chair with a non-adjustable table and (2) an adjustable chair with a non-adjustable table. The mismatch calculation showed a significant difference between existing furniture dimensions and anthropometric measurements, indicating that 7 of the 11 existing furniture dimensions need improvement. The one-way ANOVA test with a significance level of 5% also showed a significant difference between the anthropometric data and existing furniture dimensions. All 11 dimensions were determined to match students' anthropometric data. The proposed dimensions were found to be more compatible and showed reduced mismatch percentages for nine furniture dimensions and nearly zero mismatches for seat width, backrest height, and under the hood for both males and females compared to the existing furniture dimensions. The proposed dimensions of the furniture set with adjustable seat height showed slightly improved match results for seat height and seat-to-table clearance, which showed zero mismatches compared with the non-adjustable furniture set. The table width and table depth dimensions were suggested according to Barnes and Squires' ergonomic work envelope model, considering hand reach. The positions of the keyboard and mouse are also suggested according to the work envelope. The monitor position and viewing angle were proposed according to OSHA guidelines. This study suggests that the proposed dimensions can improve comfort levels, reducing the risk of musculoskeletal disorders among students. Further studies on the implementation and long-term effects of the proposed dimensions in real-world computer laboratory settings are recommended.

Harnessing the power of clustered regularly interspaced short palindromic repeats (CRISPR) based microfluidics for next-generation molecular diagnostics.

CRISPR-based (Clustered regularly interspaced short palindromic repeats-based) technologies have revolutionized molecular biology and diagnostics, offering unprecedented precision and versatility. However, challenges remain, such as high costs, demanding technical expertise, and limited quantification capabilities. To overcome these limitations, innovative microfluidic platforms are emerging as powerful tools for enhancing CRISPR diagnostics. This review explores the exciting intersection of CRISPR and microfluidics, highlighting their potential to revolutionize healthcare diagnostics. By integrating CRISPR's specificity with microfluidics' miniaturization and automation, researchers are developing more sensitive and portable diagnostic tools for a range of diseases. These microfluidic devices streamline sample processing, improve diagnostic performance, and enable point-of-care applications, allowing for rapid and accurate detection of pathogens, genetic disorders, and other health conditions. The review discusses various CRISPR/Cas systems, including Cas9, Cas12, and Cas13, and their integration with microfluidic platforms. It also examines the advantages and limitations of these systems, highlighting their potential for detecting DNA and RNA biomarkers. The review also explores the key challenges in developing and implementing CRISPR-driven microfluidic diagnostics, such as ensuring robustness, minimizing cross-contamination, and achieving robust quantification. Finally, it highlights potential future directions for this rapidly evolving field, emphasizing the transformative potential of these technologies for personalized medicine and global health.

Design and implementation of a lab-on-a-chip for assisted reproductive technologies.

Introduction: The microfluidic device is highly optimized to remove oocytes from the cumulus-corona cell mass surrounding them. Additionally, it effectively captures and immobilizes the oocytes, aiding in assessing their quality and facilitating the injection of sperm into the oocyte. In this study, a novel microfluidic chip was designed and manufactured using conventional soft lithography methods. Methods: This research proposes the utilization of a microfluidic chip as a substitute for the conventional manual procedures involved in oocyte denudation, trapping, and immobilization. The microfluidic chip was modeled and simulated using COMSOL Multiphysics® 5.2 software to optimize and enhance its design and performance. The microfluidic chip was fabricated using conventional injection molding techniques on a polydimethylsiloxane substrate by employing soft lithography methods. Results: A hydrostatic force was applied to guide the oocyte through predetermined pathways to eliminate the cumulus cells surrounding the oocyte. The oocyte was subsequently confined within the designated trap region by utilizing hydraulic resistance along the paths and immobilized by applying vacuum force. Conclusion: The application of this chip necessitates a lower level of operator expertise compared to enzymatic and mechanical techniques. Moreover, it is feasible to continuously monitor the oocyte's state throughout the procedure. There is a reduced need for cultural media compared to more standard approaches.

Discrimination of Microplastics and Phytoplankton Using Impedance Cytometry.

Both microplastics and phytoplankton are found together in the ocean as suspended microparticles. There is a need for deployable technologies that can identify, size, and count these particles at high throughput to monitor plankton community structure and microplastic pollution levels. In situ analysis is particularly desirable as it avoids the problems associated with sample storage, processing, and degradation. Current technologies for phytoplankton and microplastic analysis are limited in their capability by specificity, throughput, or lack of deployability. Little attention has been paid to the smallest size fraction of microplastics and phytoplankton below 10 µm in diameter, which are in high abundance. Impedance cytometry is a technique that uses microfluidic chips with integrated microelectrodes to measure the electrical impedance of individual particles. Here, we present an impedance cytometer that can discriminate and count microplastics sampled directly from a mixture of phytoplankton in a seawater-like medium in the 1.5-10 µm size range. A simple machine learning algorithm was used to classify microplastic particles based on dual-frequency impedance measurements of particle size (at 1 MHz) and cell internal electrical composition (at 500 MHz). The technique shows promise for marine deployment, as the chip is sensitive, rugged, and mass producible.