Search

Background: During cardiopulmonary resuscitation (CPR), end-tidal carbon dioxide (EtCO2) is primarily determined by pulmonary blood flow, thereby reflecting the blood flow generated by CPR. We aimed to develop an EtCO2 trajectory-based prediction model for prognostication at specific time points during CPR in patients with out-of-hospital cardiac arrest (OHCA).Methods: We screened patients receiving CPR between 2015–2021 from a prospectively collected database of a tertiary-care medical center. The primary outcome was survival to hospital discharge. We used group-based trajectory modeling to identify the EtCO2 trajectories. Multivariable logistic regression analysis was used for model development and internally validated using bootstrapping. We assessed performance of the model using the area under the receiver operating characteristic curve (AUC).Results: The primary analysis included 542 patients with a median age of 68.0 years. Three distinct EtCO2 trajectories were identified in patients resuscitated for 20 minutes (min): low (average EtCO2 10.0 millimeters of mercury [mm Hg]; intermediate (average EtCO2 26.5 mm Hg); and high (average EtCO2: 51.5 mm Hg). Twenty-min EtCO2 trajectory was fitted as an ordinal variable (low, intermediate, and high) and positively associated with survival (odds ratio 2.25, 95% confidence interval [CI] 1.07–4.74). When the 20-min EtCO2 trajectory was combined with other variables, including arrest location and arrest rhythms, the AUC of the 20-min prediction model for survival was 0.89 (95% CI 0.86–0.92). All predictors in the 20-min model remained statistically significant after bootstrapping.Conclusion: Time-specific EtCO2 trajectory was a significant predictor of OHCA outcomes, which could be combined with other baseline variables for intra-arrest prognostication. For this purpose, the 20-min survival model achieved excellent discriminative performance in predicting survival to hospital discharge.

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.

Employers agree that high school graduates lack teamwork skills needed for workplace productivity. The lag in student readiness to meet employer expectations urges school reforms that implicate group interaction, empirical assessment, and reporting of teamwork competencies. The "Teamwork Skills Inventory" (TSI) evaluated how well individual students performed in cooperative learning teams as observed by peers and self with anonymous feedback. Field-testing involved 303 high school students in the United States from middle class and racially diverse families. Psychometric indicators of reliability, validity, exploratory factor analysis, and group analytics were determined. Qualitative responses by participants to TSI methodology were highly favorable.

Aging results in a decline in neural stem cells (NSCs), neurogenesis, and cognitive function, and evidence is emerging to demonstrate disrupted adult neurogenesis in the hippocampus of patients with several neurodegenerative disorders. Here, single-cell RNA sequencing of the dentate gyrus of young and old mice shows that the mitochondrial protein folding stress is prominent in activated NSCs/neural progenitors (NPCs) among the neurogenic niche, and it increases with aging accompanying dysregulated cell cycle and mitochondrial activity in activated NSCs/NPCs in the dentate gyrus. Increasing mitochondrial protein folding stress results in compromised NSC maintenance and reduced neurogenesis in the dentate gyrus, neural hyperactivity, and impaired cognitive function. Reducing mitochondrial protein folding stress in the dentate gyrus of old mice improves neurogenesis and cognitive function. These results establish the mitochondrial protein folding stress as a driver of NSC aging and suggest approaches to improve aging-associated cognitive decline.

This article examines the persistent problem of student literacy, the range of tutoring solutions, and implications for peer teaching in cooperative learning teams. Possible school reforms that reflect practices in other countries with high student test scores and peer norms in support of tutoring are described. An online poll was completed by 190 adolescents to share perceptions about tutoring at their middle school. Given the powerful impact of peers on adolescent socialization, it was not surprising that findings showed students who needed tutoring most denied their academic problem, tried to avoid embarrassment from peers, and did not request assistance. Poll responses revealed absence of a peer norm to motivate engagement in tutoring without sacrifice to personal status. If a lesson or curriculum subject was difficult, the students asked friends for help more often than they asked teachers. Analysis of gender differences showed that if students told friends they planned to request tutoring, females were more likely to give encouragement than males. Female students also showed greater willingness than males to volunteer as tutors for assisting learning disabled classmates, English as second language learners, and peers in their cooperative learning groups. Results of student voice as expressed in the online Tutoring Poll contributed to the school continuous improvement plan. Tutoring recommendations for school districts to consider are explained.

Most college students have grown up using technology and consequently, they are proficient with its many uses and applications. The use of this technology provides many benefits to college students' learning, both in and out of the classroom. However, despite the numerous benefits of technology, these digital activities can also lead to much digital distraction. Digital distractions may include Internet surfing, watching movies, checking texts, reading, and sending emails, and perusing social media. These digital distractions often result in limited attention and engagement during class, as well as less learning and academic achievement overall. Digital distraction can result from such factors as anxiety and depression, motivational variables, the need to keep up and the fear of missing out, emotional numbing and procrastination as well as an overreliance on multitasking. It is suggested here that the use of self-regulated learning (SRL) strategies may be effective in assisting college students in avoiding digital distractions, both in and out of the classroom. SRL strategies include forethought, planning, activation; monitoring, control and reflection on the learner's cognition, motivation/affect, and behavior. Through the use of these SRL strategies, college students may be taught to decrease digital distractions and, thus, experience higher levels of learning and academic performance.

Enabling students, teachers, and parents to become more informed about stress during adolescence can improve student emotional and social support. This article documents the growing problem of student stress and resulting effects on mental health. Students (Grades 9-12) at one public high school in the southern United States were invited by the principal to complete an anonymous online poll about sources of stress in their lives. The intent of the school principal was to use the findings to strengthen student support and contribute to the Continuous School Improvement Plan. Results of the 17-item Stress Poll are presented by frequency and percentages for the 349 students and compared by gender responses (females n = 172, males n = 177). Results indicated the most prominent student stresses at school involved worrying about getting good grades in required courses, not understanding some of their courses, being unable to concentrate, and having poor time management practices. Students felt that teachers should collaborate to reduce undue student stress by avoiding overloading and letting students make mistakes without affecting grades. Outside of school the main stress was getting along with relatives; in addition, parents should set more reasonable expectations for academic achievement. Students felt that workshops about stress could improve understanding and influence their parents and teachers.

International students who pursue their academic goals in United States are prone to difficulties when attempting to build social resources and adjust to the new culture. Social media is a practical means of connection due to its ease of use and accessibility. Previous research has indicated contradictory effects of social media use on academic engagement. In addition to the direct effect, this research examined social media use influences on international students' learning engagement by mediating social capital and cultural adjustment. A total of 209 international students completed a web-based survey distributed via e-mail and social media between November 2021 and May 2022. Data were analyzed using Structural Equation Model. Results showed that only purposely using social media to collaborate with learning counterparts or materials directly improves international students' learning engagement. Other uses of social media (e.g., expanding "new" resources, solidifying "close" relationships) have no significant direct effects. Nonetheless, they are essential to improving levels of learning engagement via the mediation of "bridging" capital (social resources attributed to expanding relationships) and students' cultural adjustment in the U.S. International students' "bonding" capital (social resources available through trustworthy relationships) and "home" cultural retention showed little direct or indirect effects on learning engagement. This study recognizes the importance of social resources and cultural adjustment for international students. Also, this study provides valuable information to educators and administrators, as there is a need to identify the underlying mechanisms to contribute feasible learning intervention approaches and alleviate negative effects for international students.

Introduction: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians.Methods: We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10–June 20, 2021, where we recruited physicians, nurses, and nurse practitioners. All participants wore a commercially available smartwatch capable of measuring various physiological data during the experiment. Participants completed the Multidimensional Fatigue Inventory (MFI) web form before and after each of their work shifts. Wecalculated and labeled the before-and-after-shift score differences between each pair of scores. Using several tree-based algorithms, we constructed the prediction models based on features collected from the smartwatch. Records were split into training/validation and testing sets at a 70∶30 ratio, and we evaluated the performances using the area under the curve (AUC) measure of receiver operating characteristic on the test set.Results: In total, 110 participants were included in this study, contributing to a set of 1,542 effective records. Of these records, 85 (5.5%) were labeled as having work-related fatigue when setting the MFI difference between two standard deviations as the threshold. The mean age of the participants was 29.6. Most of the records were collected from nurses (87.7%) and females (77.5%). We selected a union of 31 features to construct the models. For total participants, CatBoost classifier achieved the best performances of AUC (0.838, 95% confidence interval [CI] 0.742–0.918) to identify work-related fatigue. By focusing on a subgroup of nurses

Introduction: The return of spontaneous circulation after cardiac arrest (RACA) score is a well-validated model for estimating the probability of return of spontaneous circulation (ROSC) in patients with out-of-hospital cardiac arrest (OHCA) by incorporating several variables, including gender, age, arrest aetiology, witness status, arrest location, initial cardiac rhythms, bystander cardiopulmonary resuscitation (CPR), and emergency medical services (EMS) arrival time. The RACA score was initially designed for comparisons between different EMS systems by standardising ROSC rates. End-tidal carbon dioxide (EtCO2) is a quality indicator of CPR. We aimed to improve the performance of the RACA score by adding minimum EtCO2 measured during CPR to develop the EtCO2 + RACA score for OHCA patients transported to an emergency department (ED). Methods: This was a retrospective analysis using prospectively collected data for OHCA patients resuscitated at an ED during 2015–2020. Adult patients with advanced airways inserted and available EtCO2 measurements were included. We used the EtCO2 values recorded in the ED for analysis. The primary outcome was ROSC. In the derivation cohort, we used multivariable logistic regression to develop the model. In the temporally split validation cohort, we assessed the discriminative performance of the EtCO2 + RACA score by the area under the receiver operating characteristic curve (AUC) and compared it with the RACA score using the DeLong test. Results: There were 530 and 228 patients in the derivation and validation cohorts, respectively. The median measurements of EtCO2 were 8.0 times (interquartile range [IQR] 3.0-12.0 times), with the median minimum EtCO2 of 15.5 millimeters of mercury (mm Hg) (IQR 8.0-26.0 mm Hg). The median RACA score was 36.4% (IQR 28.9-48.0%), and a total of 393 patients (51.8%) achieved ROSC. The EtCO2 + RACA score was validated with good discriminative performance (AUC, 0.82, 95% CI 0.77-0.88), outperforming the RACA score (AUC, 0.71, 95% CI 0.65-0.78) (DeLong test: P < 0.001). Conclusion: The EtCO2 + RACA score may facilitate the decision-making process regarding allocations of medical resources in EDs for OHCA resuscitation.

Nonalcoholic fatty liver disease (NAFLD) can be ameliorated by calorie restriction, which leads to the suppressed somatotroph axis. Paradoxically, the suppressed somatotroph axis is associated with patients with NAFLD and is correlated with the severity of fibrosis. How the somatotroph axis becomes dysregulated and whether the repressed somatotroph axis impacts liver damage during the progression of NAFLD are unclear. Here, we identify a regulatory branch of the hepatic integrated stress response (ISR), which represses the somatotroph axis in hepatocytes through ATF3, resulting in enhanced cell survival and reduced cell proliferation. In mouse models of NAFLD, the ISR represses the somatotroph axis, leading to reduced apoptosis and inflammation but decreased hepatocyte proliferation and exacerbated fibrosis in the liver. NAD+ repletion reduces the ISR, rescues the dysregulated somatotroph axis, and alleviates NAFLD. These results establish that the hepatic ISR suppresses the somatotroph axis to control cell fate decisions and liver damage in NAFLD.