Within Clinic Reliability and Usability of a Voice-Based Amazon Alexa Administration of the General Anxiety Disorder 7 (GAD 7).

This is the second in a series of studies assessing the usability and reliability of a novel voice-based delivery system of mental health screening assessments. The previous study demonstrated the reliability and patient preference of a voice-based format of the Patient Health Questionnaire 9 (PHQ 9) for measuring major depression compared to a traditional paper format. Through this study, we further examined the Amazon Alexa tool in the administration of the General Anxiety Disorder 7 (GAD 7). With a replicated methodology to the first study, 40 newly administered patients completed the GAD 7 in one format at their first session and the alternate format at their follow up. Results from the new in clinic population replicated the findings observed in the first PHQ 9 study: GAD 7 assessment scores for the Alexa and paper version showed a high degree of reliability (α = 0.77), patients showed higher overall positive attitudes for the voice-based GAD 7 format, and subscales for attractiveness, stimulation, and novelty were significantly higher for the voiced-based format. Results also demonstrated 42 (84%) of the 50 patients who completed the voice-based format responded as being willing to use the device from home. With new recommendations of universal screening of anxiety disorders for patients below the age of 65 and rapid changes in virtual mental healthcare, convenient screenings are more important than ever. We believe this novel clinical assessment tool has the potential to improve patient behavioral healthcare while mitigating the workload of healthcare professionals.

Impact of age and comorbid heart failure on the utility of smart voice-assistant devices.

Aims: The accuracy of voice-assisted technologies, such as Amazon Alexa, to collect data in patients who are older or have heart failure (HF) is unknown. The aim of this study is to analyse the impact of increasing age and comorbid HF, when compared with younger participants and caregivers, and how these different subgroups classify their experience using a voice-assistant device, for screening purposes. Methods and results: Subgroup analysis (HF vs. caregivers and younger vs. older participants) of the VOICE-COVID-II trial, a randomized controlled study where participants were assigned with subsequent crossover to receive a SARS-CoV2 screening questionnaire by Amazon Alexa or a healthcare personnel. Overall concordance between the two methods was compared using unweighted kappa scores and percentage of agreement. From the 52 participants included, the median age was 51 (34-65) years and 21 (40%) were HF patients. The HF subgroup showed a significantly lower percentage of agreement compared with caregivers (95% vs. 99%, P = 0.03), and both the HF and older subgroups tended to have lower unweighted kappa scores than their counterparts. In a post-screening survey, both the HF and older subgroups were less acquainted and found the voice-assistant device more difficult to use compared with caregivers and younger individuals. Conclusion: This subgroup analysis highlights important differences in the performance of a voice-assistant-based technology in an older and comorbid HF population. Younger individuals and caregivers, serving as facilitators, have the potential to bridge the gap and enhance the integration of these technologies into clinical practice. Study Registration: ClinicalTrials.gov Identifier: NCT04508972.

Characterisation and correction of polarisation effects in fluorescently labelled fibres.

Many biological structures take the form of fibres and filaments, and quantitative analysis of fibre organisation is important for understanding their functions in both normal physiological conditions and disease. In order to visualise these structures, fibres can be fluorescently labelled and imaged, with specialised image analysis methods available for quantifying the degree and strength of fibre alignment. Here we show that fluorescently labelled fibres can display polarised emission, with the strength of this effect varying depending on structure and fluorophore identity. This can bias automated analysis of fibre alignment and mask the true underlying structural organisation. We present a method for quantifying and correcting these polarisation effects without requiring polarisation-resolved microscopy and demonstrate its efficacy when applied to images of fluorescently labelled collagen gels, allowing for more reliable characterisation of fibre microarchitecture.

Probing actin-activated ATP turnover kinetics of human cardiac myosin II by single molecule fluorescence.

Mechanistic insights into myosin II energy transduction in striated muscle in health and disease would benefit from functional studies of a wide range of point-mutants. This approach is, however, hampered by the slow turnaround of myosin II expression that usually relies on adenoviruses for gene transfer. A recently developed virus-free method is more time effective but would yield too small amounts of myosin for standard biochemical analyses. However, if the fluorescent adenosine triphosphate (ATP) and single molecule (sm) total internal reflection fluorescence microscopy previously used to analyze basal ATP turnover by myosin alone, can be expanded to actin-activated ATP turnover, it would appreciably reduce the required amount of myosin. To that end, we here describe zero-length cross-linking of human cardiac myosin II motor fragments (sub-fragment 1 long [S1L]) to surface-immobilized actin filaments in a configuration with maintained actin-activated ATP turnover. After optimizing the analysis of sm fluorescence events, we show that the amount of myosin produced from C2C12 cells in one 60 mm cell culture plate is sufficient to obtain both the basal myosin ATP turnover rate and the maximum actin-activated rate constant (kcat). Our analysis of many single binding events of fluorescent ATP to many S1L motor fragments revealed processes reflecting basal and actin-activated ATPase, but also a third exponential process consistent with non-specific ATP-binding outside the active site.

Interactive virtual assistance for mental health promotion and self-care management in elderly with type 2 diabetes (IVAM-ED): study protocol and statistical analysis plan for a randomized controlled trial.

BACKGROUND: With one in five individuals aged 65 or older living with type 2 diabetes worldwide, it is crucial to acknowledge and address the challenges faced by this population. In this context, our study aims to evaluate the efficacy of a behavioral intervention model delivered through a smart speaker on mental health and diabetes self-care in the elderly with diabetes. METHODS: This is a single-center, pragmatic, parallel two-arm open randomized clinical trial involving elderly patients with type 2 diabetes. We plan to enroll a total of 112 individuals who will be randomized 1:1 to receive the Smart Speaker EchoDot 3rd Gen device (Amazon Echo®) for home use (intervention arm) or to maintain usual care (control arm). The primary outcome is mental distress, assessed using the 20-item Self Reporting Questionnaire (SRQ-20) after a 12-week intervention period. Secondary outcomes include quality of life, adherence to diabetes self-care behaviors, perception of stress, glycemic control, blood pressure, and lipid profile. Analysis of covariance (ANCOVA) will be used to evaluate the effects of the intervention on the outcomes. DISCUSSION: This study assesses the effectiveness of an interactive virtual assistance system for enhancing mental health and glycemic control among elderly individuals with type 2 diabetes. The findings may introduce smart speakers as a valuable tool for promoting diabetes-related self-care in this population. TRIAL REGISTRATION: ClinicalTrials.gov NCT05329376. Registered on 15 April 2022. Enrollment began on 20 June 2023 and the last update of protocol was on 13 December 2023.

Evaluation of Nanomedicine Tissue Distribution and Stability by Alexa Fluor 488 and PEG Immunohistochemistry.

A primary issue with nanomedicine biological evaluation is determination of nanoparticle carrier tissue distribution and stability. Here we present a method to evaluate nanomedicine distribution in tissues that is applicable to most nanomedicine constructs. This method utilizes immunohistochemical (IHC) analysis of an Alexa Fluor 488-tag and/or polyethylene glycol (PEG), a very common nanomedicine component, for tissue localization. Using specific Alexa Fluor 488- and/or PEG antibody-based IHC staining procedures allows evaluation of high-resolution nanoparticle tissue distribution, nanoparticle tissue stability, and also allows correlation of distribution with morphological changes. This protocol outlines the methods to follow to ensure proper tissue collection and optimized immunohistochemical staining of Alexa Fluor 488-tag and PEG in tissues.

Virtual Assistants' Response to Queries About Nicotine Replacement Therapy: A Mixed-Method Analysis.

This study focused on investigating the potential of Artificial Intelligent-powered Virtual Assistants (VAs) such as Amazon Alexa, Apple Siri, and Google Assistant as tools to help individuals seeking information about Nicotine Replacement Treatment (NRT) for smoking cessation. The researchers asked 40 NRT-related questions to each of the 3 VAs and evaluated the responses for voice recognition. The study used a cross-sectional mixed-method design with a total sample size of 360 responses. Inter-rater reliability and differences between VAs' responses were examined by SAS software, and qualitative assessments were conducted using NVivo software. Google Assistant achieved 100% voice recognition for NRT-related questions, followed by Apple Siri at 97.5%, and Amazon Alexa at 83.3%. Statistically significant differences were found between the responses of Amazon Alexa relative to both Google Assistant and Apple Siri. Researcher 1's ratings significantly differed from Researcher 2's (p = .001), but not from Researcher 3's (p = .11). Virtual Assistants occasionally struggled to understand the context or nuances of questions, lacked in-depth information in their responses, and provided generic or unrelated responses. Virtual Assistants have the potential to be incorporated into smoking cessation interventions and tobacco control initiatives, contingent upon improving their competencies.

Patients with floaters: Answers from virtual assistants and large language models.

Objectives: "Floaters," a common complaint among patients of all ages, was used as a query term because it affects 30% of all people searching for eye care. The American Academy of Ophthalmology website's "floaters" section was used as a source for questions and answers (www.aao.org). Floaters is a visual obstruction that moves with the movement of the eye. They can be associated with retinal detachment, which can lead to vision loss. With the advent of large language model (LLM) chatbots ChatGPT, Bard versus virtual assistants (VA), Google Assistant, and Alexa, we analyzed their responses to "floaters." Methods: Using AAO.org, "Public & Patients," and its related subsection, "EyeHealth A-Z": Floaters and Flashes link, we asked four questions: (1) What are floaters? (2) What are flashes? (3) Flashes and Migraines? (4) Floaters and Flashes Treatment? to ChatGPT, Bard, Google Assistant, and Alexa. The American Academy of Ophthalmology (AAO) keywords were identified if they were highlighted. The "Flesch-Kincaid Grade Level" formula approved by the U.S. Department of Education, was used to evaluate the reading comprehension level for the responses. Results: Of the chatbots and virtual assistants, Google Assistant is the only one that uses the term "ophthalmologist." There is no mention of the urgency or emergency nature of floaters. AAO.org shows a lower reading level vs the LLMs and VA (p = .11). The reading comprehension levels of ChatGPT, Bard, Google Assistant, and Alexa are higher (12.3, 9.7, 13.1, 8.1 grade) vs the AAO.org (7.3 grade). There is a higher word count for LLMs vs VA (p < .0286). Conclusion: Currently, ChatGPT, Bard, Google Assistant, and Alexa are similar. Factual information is present but all miss the urgency of the diagnosis of a retinal detachment. Translational relevance: Both the LLM and virtual assistants are free and our patients will use them to obtain "floaters" information. There may be errors of omission with ChatGPT and a lack of urgency to seek a physician's care.

The Impact of Interaction Time and Verbal Engagement with Personal Voice Assistants on Alleviating Loneliness among Older Adults: An Exploratory Study.

BACKGROUND: Loneliness in older adults is a critical issue that negatively affects their well-being. The potential of personal voice assistant (PVA) devices like Amazon's Alexa Echo in reducing loneliness is an emerging area of interest, but it remains under-researched. OBJECTIVE: this study aims to investigate the effect of interaction time and verbal engagement with PVA devices on reducing loneliness among older adults living alone. METHOD: In this experiment, individuals aged 75 and older (n = 15), living alone, were provided with Amazon Alexa Echo devices. They were instructed to interact with the device at least five times a day for a duration of four weeks. The study measured participants' loneliness levels using the UCLA loneliness scale both before and after the study. Additionally, the interaction time and verbal engagement with the device were measured by the total time of use and the total number of intentional commands spoken to Alexa during the four-week period. RESULTS: The findings revealed that the total time spent interacting with Alexa was a significant predictor of loneliness reduction. A mediation analysis indicated an indirect effect, showing that the number of intentional commands spoken to Alexa contributed to loneliness reduction indirectly by increasing the total time spent with the device (verbal engagement → interaction time → loneliness reduction). CONCLUSIONS: This study suggests that the key to reducing loneliness among older adults through PVA devices is not just initiating verbal interaction, but the overall time devoted to these interactions. While speaking to Alexa is a starting point, it is the duration of engagement that primarily drives loneliness alleviation.

Understanding the Effects of Smart-Speaker-Based Surveys on Panelist Experience in Immersive Consumer Testing.

Utilizing immersive technologies to reintroduce the environmental context (i.e., visual, auditory, and olfactory cues) in sensory testing has been one area of research for improving panelist engagement. The current study sought to understand whether pairing smart-speaker questionnaires in immersive spaces could positively affect the panelist experience through enhanced ecological validity. To this end, subjects performed an immersive consumer test in which responses were collected using a traditional computer-based survey, a smart-speaker approach incorporating a direct translation of the computer questionnaire into a verbal survey requiring numeric responses, and an optimized smart-speaker survey with alternative question formatting requiring spoken word-based responses. After testing, participants answered the Engagement Questionnaire (EQ) to assess participant engagement during the test, and the System Usability Scale (SUS) survey to understand the ease, and potential adoption, of using the various survey technologies in the study. Results indicated that the traditional computer-based survey was the most engaging (p < 0.001) and usable (p < 0.001), with no differences found between the two smart-speaker surveys (p = 0.803 and p = 0.577, respectively). This suggests that the proposed optimizations for the smart-speaker surveys were not robust enough to influence engagement and usability, and further research is needed to enhance their conversational capabilities.

Simplifying Barley Leaf Rust Research: An Easy and Reproducible Infection Protocol for Puccinia hordei on a Small Laboratory Scale.

Barley (Hordeum vulgare) is one of the most important agricultural crops in the world, but pathogen infections regularly limit its annual yield. A major threat is the infection with the biotrophic leaf rust fungus, Puccinia hordei. Rust fungi have a complex life cycle, and existing resistances can be easily overcome. To address this problem, it is crucial to develop barley varieties with improved and durable resistance mechanisms. An essential step towards this goal is a simple and reproducible infection protocol to evaluate potential resistance phenotypes in the lab. However, available protocols sometimes lack detailed procedure or equipment information, use spore application methods that are not suitable for uniform spore dispersion, or require special mineral oils or engineered fluids. In addition, they are often optimized for pathogen-dedicated greenhouses or phytochambers, which may not be available to every research institute. Here, we describe an easy and user-friendly procedure to infect barley with Puccinia hordei on a small laboratory scale. This procedure utilizes inexpensive and simple tools to evenly split and apply spores to barley leaves. The treated plants are incubated in affordable and small phytocabinets. Our protocol enables a quick and reproducible infection of barley with leaf rust, a method that can easily be transferred to other rust fungi, including stripe rust, or to other plant species. Key features Step-by-step infection protocol established for barley cv. Golden Promise, the gold standard genotype for genetic transformation Plant age-independent protocol Precise spore application by using inexpensive pipe cleaners for uniform symptom formation and increased reproducibility No specialized equipment needed Includes simple spore harvesting method Protocol is applicable to other biotrophic pathogens (stripe rust or powdery mildew) and other plants (e.g., wheat) Protocol is also applicable for a detached leaf assay Graphical overview.

Alexa Irene Canady: First Female African American Neurosurgeon of the United States.

BACKGROUND: This historical account reviews the course and lasting impact of Dr. Alexa Irene Canady in neurosurgery. METHODS: The writing of this project was sparked by the discovery of original scientific and bibliographical information about Alexa Canady, the first female African-American neurosurgeon in the nation. This article is a thorough review of the literature and information on Canady, reflecting the breadth of these previous publications, and showcasing our viewpoints after comprehensive compilation of information. RESULTS: Our paper begins by introducing Dr. Alexa Irene Canady and her decision to pursue a career in medicine during her years in university; follows her journey through medical school and growing interests in neurosurgery; outlines her journey in residency; discusses her career as an established pediatric neurosurgeon at the University of Michigan; outlines her role in establishing a department of pediatric neurosurgery in Pensacola, Florida; and details the obstacles and challenges she faced throughout her career, as well as the barriers she broke along the way. CONCLUSIONS: Our article provides glimpses into the personal life and achievements of Dr. Alexa Irene Canady and her marked impact on the field of neurosurgery.

Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial.

BACKGROUND: Voice-assisted artificial intelligence-based systems may streamline clinical care among patients with heart failure (HF) and caregivers; however, randomized clinical trials are needed. We evaluated the potential for Amazon Alexa (Alexa), a voice-assisted artificial intelligence-based system, to conduct screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a HF clinic. METHODS AND RESULTS: We enrolled 52 participants (patients and caregivers) from a HF clinic who were randomly assigned with a subsequent cross-over to receive a SARS-CoV-2 screening questionnaire via Alexa or health care personnel. The primary outcome was overall response concordance, as measured by the percentage of agreement and unweighted kappa scores between groups. A postscreening survey evaluated comfort with using the artificial intelligence-based device. In total, 36 participants (69%) were male, the median age was 51 years (range 34-65 years) years and 36 (69%) were English speaking. Twenty-one participants (40%) were patients with HF. For the primary outcome, there were no statistical differences between the groups: Alexa-research coordinator group 96.9% agreement and unweighted kappa score of 0.92 (95% confidence interval 0.84-1.00) vs research coordinator-Alexa group 98.5% agreement and unweighted kappa score of 0.95 (95% confidence interval 0.88-1.00) (P value for all comparisons > .05). Overall, 87% of participants rated their screening experience as good or outstanding. CONCLUSIONS: Alexa demonstrated comparable performance to a health care professional for SARS-CoV-2 screening in a group of patients with HF and caregivers and may represent an attractive approach to symptom screening in this population. Future studies evaluating such technologies for other uses among patients with HF and caregivers are warranted. NCT04508972.

Collecting Food and Drink Intake Data With Voice Input: Development, Usability, and Acceptability Study.

BACKGROUND: Voice-based systems such as Amazon Alexa may be useful for collecting self-reported information in real time from participants of epidemiology studies using verbal input. In epidemiological research studies, self-reported data tend to be collected using short, infrequent questionnaires, in which the items require participants to select from predefined options, which may lead to errors in the information collected and lack of coverage. Voice-based systems give the potential to collect self-reported information "continuously" over several days or weeks. At present, to the best of our knowledge, voice-based systems have not been used or evaluated for collecting epidemiological data. OBJECTIVE: We aimed to demonstrate the technical feasibility of using Alexa to collect information from participants, investigate participant acceptability, and provide an initial evaluation of the validity of the collected data. We used food and drink information as an exemplar. METHODS: We recruited 45 staff members and students at the University of Bristol (United Kingdom). Participants were asked to tell Alexa what they ate or drank for 7 days and to also submit this information using a web-based form. Questionnaires asked for basic demographic information, about their experience during the study, and the acceptability of using Alexa. RESULTS: Of the 37 participants with valid data, most (n=30, 81%) were aged 20 to 39 years and 23 (62%) were female. Across 29 participants with Alexa and web entries corresponding to the same intake event, 60.1% (357/588) of Alexa entries contained the same food and drink information as the corresponding web entry. Most participants reported that Alexa interjected, and this was worse when entering the food and drink information (17/35, 49% of participants said this happened often; 1/35, 3% said this happened always) than when entering the event date and time (6/35, 17% of participants said this happened often; 1/35, 3% said this happened always). Most (28/35, 80%) said they would be happy to use a voice-controlled system for future research. CONCLUSIONS: Although there were some issues interacting with the Alexa skill, largely because of its conversational nature and because Alexa interjected if there was a pause in speech, participants were mostly willing to participate in future research studies using Alexa. More studies are needed, especially to trial less conversational interfaces.

Attitudes About Artificially Intelligent Interactive Voice Response Systems Using Amazon Alexa in Cardiovascular Clinics: Insights from the VOICE-COVID-19 Study.

The acceptability of artificially intelligent interactive voice response (AI-IVR) systems in cardiovascular research settings is unclear. As a result, we evaluated peoples' attitudes regarding the Amazon Echo Show 8 device when used for electronic data capture in cardiovascular clinics. Participants were recruited following the Voice-Based Screening for SARS-CoV-2 Exposure in Cardiovascular clinics study. Overall, 215 people enrolled and underwent screening (mean age 46.1; 55% females) in the VOICE-COVID study and 58 people consented to participate in a post-screening survey. Following thematic analysis, four key themes affecting AI-IVR acceptability were identified. These were difficulties with communication (44.8%), limitations with available interaction modalities (41.4%), barriers with the development of therapeutic relationships (25.9%), and concerns with universality and accessibility (8.6%). While there are potential concerns with the use of AI-IVR technologies, these systems appeared to be well accepted in cardiovascular clinics. Increased development of these technologies could significantly improve healthcare access and efficiency.

Voice Assistants as Consultants for Male Patients with Sexual Dysfunction: A Reliable Option?

The aim of this study was to evaluate the ability of Google Assistant, Alexa, and Siri to recognize and answer questions about male sexual health. Each VA was tested on a smart speaker: Alexa on Amazon Echo Dot 4th Gen., Google Assistant on Google Home Mini, and Siri on Apple HomePod. A pool of patients' frequently asked questions regarding erectile dysfunction (ED), premature ejaculation (PE), Peyronie's disease (PD), male infertility, and other aspects of male sexual health were identified by authors. The recognition of question was evaluated ("yes" or "not"). For each recognized question, the response characteristics (domains) were rated on a scale from 0 to 10 (according to the quality). We chose the recognition rate of the questions as the primary outcome and the quality of the answers as the secondary outcome. Overall, the best VA in recognizing questions was Siri, with a total of 83.3% questions compared with 64.0% for Alexa (p = 0.024) and 74.0% for Google Assistant (p = 0.061). Siri was associated with a significantly higher recognition rate than Alexa for PE (80% vs. 40%; p = 0.002) and PD (66.7% vs. 33.3%; p = 0.010). The quality of the responses was classified as low in 57 out of 105 cases (54.3%), intermediate in 46 cases (43.8%), and high in only 2 cases (1.9%), highlighting an overall intermediate-low quality of the answers. Male infertility was the condition associated with the highest mean scores in "Targeted response to the problem" (7.32 ± 2.57), "Scientific correctness of the answer", (5.9 ± 2.76) "Completeness of the answer" (5.14 ± 2.56), and "Understandability of the response for a patient" (5.3 ± 2.51) domains. Siri was associated with significantly higher scores than Alexa (p < 0.05) in several domains of all conditions evaluated. The question recognition rate of VAs is quite high; however, the quality of the answers is still intermediate-low. Siri seems superior to Alexa in both question recognition and response quality. Male infertility appears to be the sexual dysfunction best addressed by VAs.

Voice-Based Screening for SARS-CoV-2 Exposure in Cardiovascular Clinics (VOICE-COVID-19-II): Protocol for a Randomized Controlled Trial.

BACKGROUND: The COVID-19 pandemic has disrupted the health care system, limiting health care resources such as the availability of health care professionals, patient monitoring, contact tracing, and continuous surveillance. As a result of this significant burden, digital tools have become an important asset in increasing the efficiency of patient care delivery. Digital tools can help support health care institutions by tracking transmission of the virus, aiding in the screening process, and providing telemedicine support. However, digital health tools face challenges associated with barriers to accessibility, efficiency, and privacy-related ethical issues. OBJECTIVE: This paper describes the study design of an open-label, noninterventional, crossover, randomized controlled trial aimed at assessing whether interactive voice response systems can screen for SARS-CoV-2 in patients as accurately as standard screening done by people. The study aims to assess the concordance and interrater reliability of symptom screening done by Amazon Alexa compared to manual screening done by research coordinators. The perceived level of comfort of patients when interacting with voice response systems and their personal experience will also be evaluated. METHODS: A total of 52 patients visiting the heart failure clinic at the Royal Victoria Hospital of the McGill University Health Center, in Montreal, Quebec, will be recruited. Patients will be randomly assigned to first be screened for symptoms of SARS-CoV-2 either digitally, by Amazon Alexa, or manually, by the research coordinator. Participants will subsequently be crossed over and screened either digitally or manually. The clinical setup includes an Amazon Echo Show, a tablet, and an uninterrupted power supply mounted on a mobile cart. The primary end point will be the interrater reliability on the accuracy of randomized screening data performed by Amazon Alexa versus research coordinators. The secondary end point will be the perceived level of comfort and app engagement of patients as assessed using 5-point Likert scales and binary mode responses. RESULTS: Data collection started in May 2021 and is expected to be completed in fall 2022. Data analysis is expected to be completed in early 2023. CONCLUSIONS: The use of voice-based assistants could improve the provision of health services and reduce the burden on health care personnel. Demonstrating a high interrater reliability between Amazon Alexa and health care coordinators may serve future digital tools to streamline the screening and delivery of care in the context of other conditions and clinical settings. The COVID-19 pandemic occurs during the first digital era using digital tools such as Amazon Alexa for disease screening, and it represents an opportunity to implement such technology in health care institutions in the long term. TRIAL REGISTRATION: ClinicalTrials.gov NCT04508972; https://clinicaltrials.gov/ct2/show/NCT04508972. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41209.

Visualization of preimplantation uterine fluid absorption in mice using Alexa Fluor™ 488 Hydrazide†.

Uterine fluid plays important roles in supporting early pregnancy events and its timely absorption is critical for embryo implantation. In mice, its volume is maximum on day 0.5 post-coitum (D0.5) and approaches minimum upon embryo attachment ~D4.0. Its secretion and absorption in ovariectomized rodents were shown to be promoted by estrogen and progesterone (P4), respectively. The temporal mechanisms in preimplantation uterine fluid absorption remain to be elucidated. We have established an approach using intraluminally injected Alexa Fluor™ 488 Hydrazide (AH) in preimplantation control (RhoAf/f) and P4-deficient RhoAf/fPgrCre/+ mice. In control mice, bulk entry (seen as smeared cellular staining) via uterine luminal epithelium (LE) decreases from D0.5 to D3.5. In P4-deficient RhoAf/fPgrCre/+ mice, bulk entry on D0.5 and D3.5 is impaired. Exogenous P4 treatment on D1.5 and D2.5 increases bulk entry in D3.5 P4-deficient RhoAf/fPgrCre/+ LE, while progesterone receptor (PR) antagonist RU486 treatment on D1.5 and D2.5 diminishes bulk entry in D3.5 control LE. The abundance of autofluorescent apical fine dots, presumptively endocytic vesicles to reflect endocytosis, in the LE cells is generally increased from D0.5 to D3.5 but its regulation by exogenous P4 or RU486 is not obvious under our experimental setting. In the glandular epithelium (GE), bulk entry is rarely observed and green cellular dots do not show any consistent differences among all the investigated conditions. This study demonstrates the dominant role of LE but not GE, the temporal mechanisms of bulk entry and endocytosis in the LE, and the inhibitory effects of P4-deficiency and RU486 on bulk entry in the LE in preimplantation uterine fluid absorption.

A decade of blood-brain barrier permeability assays: Revisiting old traumatic brain injury rat data for new insights and experimental design.

Increased microvascular permeability at the level of the blood-brain barrier (BBB) often leads to vasogenic brain edema following traumatic brain injury (TBI). These pathologic conditions compromise the integrity of the neurovascular unit resulting in severe brain dysfunction. To quantify this permeability and assess ionic equillibrium, preclinical researchers have relied on the use of various molecular weight permeable dyes such as Evans Blue that normally cannot enter the brain parenchyma under homeostatic conditions. Evans Blue, the most cited of the molecular weight dyes, has reported reproducibility issues because of harsh extraction processes, suboptimal detection via absorbance, and wide excitation fluorescence spectra associated with the dye. Our laboratory group transitioned to Alexa Fluor 680, a far-red dye with improved sensitivity compared to Evans Blue and thus improved reproducibility to alleviate this issue. To evaluate our reproducibility and increase the rigor of our experimental design, we retrospectively analyzed our controlled cortical impact (CCI) experiments over the past 10 years to evaluate effect size with larger samples and potential sources of variability. All of our BBB permeability experiments were performed with Male, Sprague Dawley rats weighing between 225 and 300 g. Historically, Sprague Dawleys were randomly divided into treatment groups: SHAM, CCI, and a stem cell-based treatment from years 2007-2020. The assessment of microvascular hyperpermeability were evaluated by comparing the mean at minimum threshold, area at 1 k-2 k, and intensity density obtained from Alexa Fluor 680 permeability data. Studies utilizing Evans Blue were further compared by tip depth, diameter size, and the hemisphere of injury. Statistical evaluation utilizing the G Power software analysis did not yield a significant difference in sample size comparing experimental groups for Evans Blue and Alexa Fluor 680 analyzed brain tissue. Our analysis also demonstrated a trend in that recent studies (years 2018-2020) have yielded more compact sample sizes between experimental groups in Alexa Fluor 680 analyzed rats. This retrospective study further revealed that Alexa Fluor 680 image analysis provides greater sensitivity to BBB permeability following TBI in comparison to Evans Blue. Significant differences in sample size were not detected between Evans Blue and Alexa Fluor 680; there were significant differences found throughout year to year analysis at the lower range of thresholds. SUMMARY STATEMENT: This work provides a comparative analysis of BBB permeability assay techniques after CCI model of injury in rats.

I am Alexa, your virtual tutor!: The effects of Amazon Alexa's text-to-speech voice enthusiasm in a multimedia learning environment.

Modern text-to-speech voices can convey social cues ideal for narrating multimedia learning materials. Amazon Alexa has a unique feature among modern text-to-speech vocalizers as she can infuse enthusiasm cues into her synthetic voice. In this first study examining modern text-to-speech voice enthusiasm effects in a multimedia learning environment, a between-subjects online experiment was conducted where learners from a large Asian university (n = 244) listened to either Alexa's: (1) neutral voice, (2) low-enthusiastic voice, (3) medium-enthusiastic voice, or (4) high-enthusiastic voice, narrating a multimedia lesson on distributed denial-of-service attack. While Alexa's enthusiastic voices did not enhance persona ratings compared to Alexa's neutral voice, learners could infer more enthusiasm expressed by Alexa's medium-and high-enthusiastic voices than Alexa's neutral voice. Regarding cognitive load, Alexa's low-and high-enthusiastic voices decreased intrinsic and extraneous cognitive load ratings compared to Alexa's neutral voice. While Alexa's enthusiastic voices did not impact affective-motivational ratings differently from Alexa's neutral voice, learners reported a significant increase of positive emotions from their baseline positive emotions after listening to Alexa's medium-enthusiastic voice. Finally, Alexa's enthusiastic voices did not enhance the learning performance on immediate retention and transfer tests compared to Alexa's neutral voice. This study demonstrates that a modern text-to-speech voice enthusiasm can positively affect learners' emotions and cognitive load during multimedia learning. Theoretical and practical implications are discussed through the lens of the Cognitive Affective Model of E-learning, Integrated-Cognitive Affective Model of Learning with Multimedia, and Cognitive Load Theory. We further outline this study's limitations and recommendations for extending and widening the text-to-speech voice emotions research.