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The use of #digitalpowerups is a technique that involves associating keywords with prompts in online discussion forums, which enables students to have more choice and voice. These powerups not only help structure responses but also enrich discussions and develop academic skills necessary for online assignments. The approach leverages the social media interaction space of discussion forums by introducing hashtags that remind students of the prompt being addressed and indicate the level of Bloom's being engaged. By using the powerups, students can engage in mid-levels and higher-order levels of Bloom's, along with the lower levels that they typically engage in based on the design and facilitation of the discussion. Students typically participate in the lower levels of Bloom's taxonomy (#remember, #understand) due to the way the discussion is structured and facilitated. However, the use of #digitalpowerups encourages students to move beyond these levels and engage in mid-levels (#apply, #analyze, #evaluate) and higher-order levels (#create, #connect) during discussions. The powerups also scaffold or frame student responses with habits of mind skills. This article examines how the #digitalpowerups strategy supports learning in a virtual community for Environmental Concerns in the Environmental Sociology Course through content analysis of student discussion postings. The primary data (total of 375 postings) were collected from the Environmental Sociology Class throughout the following academic years: Fall 2020 (96 postings), Fall 2021 (67 postings), Spring 2022 (92 postings) and Spring 2023 (120 postings).

Local leaders are essential for helping Michigan achieve its 30-year water strategy goals. The Michigan Water School is an Extension nonformal educational program to address the knowledge gap of local leaders. We evaluated programs conducted from 2017 to 2019. Results revealed program outcomes in knowledge, attitudes, perceptions of criticalness to work, and stewardship and behavioral intentions aligned to water quality, water quantity, and field experience units. Growth areas for program improvement include water policy, economics, planning, and finance as well as expanding the focus of the program for transformational leadership and to broaden recruitment efforts.

Introduction: Fatal and non-fatal occupational injuries are one of the most important public health problems all over the world. Its number greatly varies from country to country over the years. Worldwide approximately 4% of global gross domestic product (GDP) is lost due to occupational injuries. It has also been estimated that daily, approximately 1 million workers get injured, and more than 5000 workers die due to their occupation. In this research work, using modern data analytics techniques, we have tried to explore the current situation and time trend of fatal and non-fatal occupational injuries globally and in India. Materials and Methods: Using the open-source data available at the International Labour Organization (ILO) database and the Government of India open database, analysis was performed systematically using R and Python programming languages. Data have been accessed by December 31, 2022. To make a representative sample population for this article, four countries from Europe and Central Asia region (Russia, France, Germany, and United Kingdom), four countries from the Asia-Pacific region (India, Australia, Japan, and Republic of Korea), and two countries from Americas (United States and Argentina) are selected for a meaningful comparison purpose. Results: Since 2000, the time trend of fatal and non-fatal occupational injuries has been increasing trend globally. The yearly average (± standard deviation) of fatal and non-fatal injuries per country per million population was 256 (±68) and 83380 (±41280). The number of fatal injuries in India has increased over the years, but the number of non-fatal injuries in India is decreasing trend. Discussion: According to the ILO database, it has been revealed in this article that, on average yearly, 83380 per million population total occupational injuries are reported worldwide. Among them, yearly, 256 per million population (0.3%) workers per country succumbed to injuries. According to Government of India database, there was an average of 271 deaths per year between 2000 and 2021. For evidence-based decision and policy making and subsequent program implementation, the availability of timely, good-quality data from every industrial sector of society is most important. It can only be achieved by implementing integrated occupational injury surveillance programs at national and global levels. Keywords: Asia, Europe, gross domestic product, ILO, India, occupational injuries, United States, Author(s): Arkaprabha Sau (corresponding author) [1,2]; Santanu Phadikar [2]; Ishita Bhakta [3]; Arindam Chatterjee [4] INTRODUCTION An occupational injury is defined by the International Labour Organization (ILO) as 'any personal [...]

While early efforts by researchers, advocates and practitioners on improving menstrual health focused on menstrual hygiene management, recent initiatives have advanced an understanding of menstrual health (1) within a comprehensive [...]

Vibrational polaritons have shown potential in influencing chemical reactions, but the exact mechanism by which they impact vibrational energy redistribution, crucial for rational polariton chemistry design, remains unclear. In this work, we shed light on this aspect by revealing the role of solvent phonon modes in facilitating the energy relaxation process from the polaritons formed of a T 1u mode of W(CO)6 to an IR inactive E g mode. Ultrafast dynamic measurements indicate that along with the direct relaxation to the dark T 1u modes, lower polaritons also transition to an intermediate state, which then subsequently relaxes to the T 1u mode. We reason that the intermediate state could correspond to the near-in-energy Raman active E g mode, which is populated through a phonon scattering process. This proposed mechanism finds support in the observed dependence of the IR-inactive states population on the factors influencing phonon density of states, e.g., solvents. The significance of the Raman modes involvement emphasizes the importance of non-IR active modes in modifying chemical reactions and ultrafast molecular dynamics.

In study after study, marijuana use has been found to be associated with increased forced vital capacity (FVC). This is puzzling, because marijuana is commonly consumed by inhalation of its smoke, and smoke exposure of any kind is not generally considered a cause of increased FVC. Although this observation was first made decades ago, a satisfactory explanation remains elusive. In this review we survey the evidence supporting the relationship between marijuana use and increased FVC, discuss potential threats to validity when inferring causation, and, presupposing a possible causal relationship, pose two key questions. First, what are possible physiologic or pathophysiologic mechanisms by which marijuana use might increase FVC? Second, why might this effect be consistently observed with marijuana use but not with tobacco use? Explanations for the first question include lung and chest growth and remodeling from strenuous marijuana smoke inhalation and reductions in lung elastic recoil from marijuana smoke exposure. Explanations for the second include differences between marijuana and tobacco in smoke composition and patterns of consumption, such as smoking topography. Finally, the possibility that smoke, whether from marijuana or tobacco, might have nonmonotonic effects on FVC depending on the degree of exposure is explored. In synthesizing a curated breadth of epidemiologic and physiologic science, we leverage a perplexing observation to generate potential insights and avenues for further research into the biological effects of smoke, from marijuana or otherwise, on the respiratory system.

Brittany E Levy,1 Jennifer T Castle,1 Roshmita Bardhan,2 Mark Dignan,3 Avinash Bhakta1 1Division of Colorectal Surgery, University of Kentucky College of Medicine, Lexington, KY, USA; 2Mercy Health – The Jewish Hospital, Cincinnati, OH, USA; 3Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY, USACorrespondence: Mark Dignan, Department of Internal Medicine, University of Kentucky College of Medicine, 760 Press Avenue, Room 335, Lexington, KY, 40536-0679, USA, Tel +1 859 913 0207, Email Mark.dignan@uky.eduPurpose: Appalachian Kentucky, a 32-county region in the eastern part of the state, has elevated colon cancer mortality rates. While recommended as the standard of care, access to adjuvant chemotherapy treatment is limited in this region due to scarce health services and significant social and geographical barriers. The purpose of this investigation was to improve understanding of barriers that cancer patients residing in rural areas not served directly by tertiary medical systems must overcome in completing adjuvant therapy.Methods: Participants were recruited from two medical centers: A tertiary care NCI designated Cancer Center and a regional hospital. Participants underwent a 15– 20 minute interview to assess factors associated with adherence to adjuvant treatment recommendations. Grounded theory identified themes related to patient behaviors and non-adherence to standard of care recommendations.Results: Data were collected in 45 telephone and in-person patient interviews, 26 from an NCI-designated cancer center and 19 from a rural hospital. Statistically the two groups were equivalent in terms of age, subjective health status, and medical comorbidities. Six themes were identified from analysis of the transcribed interviews including: confidence in my care provider, communication, treatment issues, distrust, faith, and barriers to obtaining healthcare. Participants completing adjuvant therapy were more likely to express trust in their provider and describe fewer barriers to obtaining healthcare than those not completing adjuvant therapy.Conclusion: Barriers to completing adjuvant therapy may differ between rural and urban healthcare systems which may yield opportunities for targeted interventions to improve rates of completion of colon cancer adjuvant chemotherapy.Keywords: cancer, rural, barriers to care, adjuvant therapy

Abstract Nanoparticles (NPs) have been successfully used as drug delivery systems. To develop and optimize NP-based drug delivery systems, it is essential to understand the dynamics of cell-NP interactions. Quantitative phase imaging techniques enable label-free imaging and have the potential to reveal how cells interact with NPs. To measure the subtle motion at cellular and subcellular scales, it requires a high phase sensitivity and a high spatial resolution. However, phase imaging techniques are limited by an intrinsic tradeoff between sensitivity and resolution. To overcome the tradeoff, we develop a technology termed as modulated optically computed phase microscopy (M-OCPM) based on low coherence interferometry and optical computation. The key innovation of M-OCPM is to utilize optical computation that performs Fourier transform of the interferometric spectra, imposes temporal modulation on the interference signal, and circumvents the sensitivity-resolution tradeoff. We evaluated the performance of M-OCPM using various samples, and demonstrated its label-free imaging capability, high sensitivity (nanometer scale displacement sensitivity) and high resolution (~ 250 nm). Particularly, we imaged NPs along with cultured cells and showed different signal characteristics for NPs that were adhered to the cells or in the cell culture medium. Our results clearly demonstrated the feasibility of M-OCPM in studying cell-NP interactions.

In pandemic scenarios involving novel human pathogenic viruses, it is highly desirable that vaccines induce strong neutralizing antibodies as quickly as possible. However, current vaccine strategies require multiple immunization doses to produce high titers of neutralizing antibodies and are poorly protective after a single vaccination. We therefore wished to design a vaccine candidate that would induce increased protective immune responses following the first vaccine dose. We hypothesized that antibodies against the receptor-binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein could be increased by drawing upon immunity to a previous infection. We generated a fusion protein containing the influenza H1N1 PR8 virus nucleoprotein (NP) and the SARS-CoV-2 spike RBD. Mice with or without preexisting immunity to PR8 were then vaccinated with NP/RBD. We observed significantly increased SARS-CoV-2 neutralizing antibodies in mice with PR8 immunity compared to mice without preexisting PR8 immunity. Vaccination with NP/RBD protected mice from SARS-CoV-2-induced morbidity and mortality after a single dose. Additionally, we compared SARS-CoV-2 virus titers in the lungs and nasal turbinates 4 days post-challenge of mice vaccinated with NP/RBD. SARS-CoV-2 virus was detectable in the lungs and nasal turbinate of mice without preexisting PR8 immunity, while SARS-CoV-2 virus was completely undetectable in mice with preexisting PR8 immunity. We also found that CD4-positive T cells in mice with preexisting immunity to PR8 play an essential role in producing the increased antibody response against RBD. This vaccine strategy potentially can be modified to target other pathogens of concern and offers extra value in future pandemic scenarios.IMPORTANCEIncreased globalization and changes in human interactions with wild animals has increased the likelihood of the emergence of novel viruses with pandemic potential. Vaccines can be effective in preventing severe disease caused by pandemic viruses. However, it takes time to develop protective immunity via prime-boost vaccination. More effective vaccine designs should quickly induce protective immunity. We propose leveraging preexisting immunity to a different pathogen to boost protection against emerging viruses. We targeted SARS-CoV-2 as a representative pandemic virus and generated a fusion protein vaccine that combines the nucleoprotein from influenza A virus and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Our vaccine design significantly increased the production of RBD-specific antibodies in mice that had previously been exposed to influenza virus, compared to those without previous exposure. This enhanced immunity reduced SARS-CoV-2 replication in mice. Our results offer a vaccine design that could be valuable in a future pandemic setting.

BACKGROUNDInformation about the size, airway location, and longitudinal behavior of mucus plugs in asthma is needed to understand their role in mechanisms of airflow obstruction and to rationally design muco-active treatments.METHODSCT lung scans from 57 patients with asthma were analyzed to quantify mucus plug size and airway location, and paired CT scans obtained 3 years apart were analyzed to determine plug behavior over time. Radiologist annotations of mucus plugs were incorporated in an image-processing pipeline to generate size and location information that was related to measures of airflow.RESULTSThe length distribution of 778 annotated mucus plugs was multimodal, and a 12 mm length defined short ("stubby", ≤12 mm) and long ("stringy", >12 mm) plug phenotypes. High mucus plug burden was disproportionately attributable to stringy mucus plugs. Mucus plugs localized predominantly to airway generations 6-9, and 47% of plugs in baseline scans persisted in the same airway for 3 years and fluctuated in length and volume. Mucus plugs in larger proximal generations had greater effects on spirometry measures than plugs in smaller distal generations, and a model of airflow that estimates the increased airway resistance attributable to plugs predicted a greater effect for proximal generations and more numerous mucus plugs.CONCLUSIONPersistent mucus plugs in proximal airway generations occur in asthma and demonstrate a stochastic process of formation and resolution over time. Proximal airway mucus plugs are consequential for airflow and are in locations amenable to treatment by inhaled muco-active drugs or bronchoscopy.TRIAL REGISTRATIONClinicaltrials.gov; NCT01718197, NCT01606826, NCT01750411, NCT01761058, NCT01761630, NCT01716494, and NCT01760915.FUNDINGAstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Sanofi-Genzyme-Regeneron, and TEVA provided financial support for study activities at the Coordinating and Clinical Centers beyond the third year of patient follow-up. These companies had no role in study design or data analysis, and the only restriction on the funds was that they be used to support the SARP initiative.

Research on neutrophil biology has been limited by the short life span and limited genetic manipulability of these cells, driving the need for representative and efficient model cell lines. The promyelocytic cell line HL-60 and its subline PLB-985 can be differentiated into neutrophil-like cells (NLCs) and have been used to study neutrophil functions including chemotaxis, phagocytosis, endocytosis, and degranulation. Compared to neutrophils derived from hematopoietic stem cells, NLCs serve as a cost-effective neutrophil model. NLCs derived from both HL-60 and PLB-985 cells have been shown to perform degranulation, an important neutrophil function. However, no study has directly compared the two lines as models for degranulation including their release of different types of mobilizable organelles. Furthermore, Nutridoma, a commercially available supplement, has recently been shown to improve the chemotaxis, phagocytosis, and oxidative burst abilities of NLCs derived from promyelocytic cells, however it is unknown whether this reagent also improves the degranulation ability of NLCs. Here, we show that NLCs derived from both HL-60 and PLB-985 cells are capable of degranulating, with each showing markers for the release of multiple types of secretory organelles, including primary granules. We also show that differentiating HL-60 cells using Nutridoma does not enhance their degranulation activity over NLCs differentiated using Dimethyl Sulfoxide (DMSO) plus Granulocyte-colony stimulating factor (G-CSF). Finally, we show that promyelocytic cells can be genetically engineered and differentiated using these methods, to yield NLCs with a defect in degranulation. Our results indicate that both cell lines serve as effective models for investigating the mechanisms of neutrophil degranulation, which can advance our understanding of the roles of neutrophils in inflammation and immunity.

Introduction: Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population.Methods: To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23–May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12–June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort.Results: In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747–0.822) performed better than gradient boosting (0.774, 95% CI 0.739–0.811) and extra trees classifier (0.72, 95% CI 0.677–0.762). There was no significant difference between the constructed models.Conclusion: Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.