Number Of Seconds Research Articles

Integrated pulmonary index during procedural sedation and analgesia: A cluster-randomized trial.

To evaluate the effectiveness of utilizing the integrated pulmonary index for capnography implementation during sedation administered by nurses. Cluster-randomized trial. Participants were enrolled from the interventional radiology department at an academic hospital in Canada. Nurses were randomized to either enable or disable the Integrated Pulmonary Index feature of the capnography monitor. Procedures were observed by a research assistant to collect information about alarm performance characteristics. The primary outcome was the number of seconds in an alert condition state without an intervention being applied. The number of seconds in an alarm state without intervention was higher in the group that enabled the integrated pulmonary index compared to the group that disabled this feature, but this difference did not reach statistical significance. Likewise, the difference between groups for the total alarm duration, total number of alarms and the total number of appropriate alarms was not statistically significant. The number of inappropriate alarms was higher in the group that enabled the Integrated Pulmonary Index, but this estimate was highly imprecise. There was no difference in the odds of an adverse event (measured by the Tracking and Reporting Outcomes of Procedural Sedation tool) occurring between groups. Desaturation events were uncommon and brief in both groups but the area under the SpO2 90% desaturation curve scores were lower for the group that enabled the integrated pulmonary index. Enabling the integrated pulmonary index during nurse-administered procedural sedation did not reduce nurses' response times to alarms. Therefore, integrating multiple physiological parameters related to respiratory assessment into a single index did not lower the threshold for intervention by nurses. The time it takes to respond to capnography monitor alarms will not be reduced if the integrated pulmonary Iindex feature of capnography monitors is enabled during nurse-administered procedural sedation. Results do not support the routine enabling of the integrated pulmonary index when nurses use capnography to monitor patients during procedural sedation as a strategy to reduce the time it takes to initiate responses to alarms. CONSORT. There was no patient or public contribution. This study was prospectively registered at ClinicalTrials.gov (ID: NCT05068700).

Semantic Segmentation of Solar Radio Spikes at Low Frequencies

Solar radio spikes are short lived, narrow bandwidth features in low frequency solar radio observations. The timing of their occurrence and the number of spikes in a given observation is often unpredictable. The high temporal and frequency of resolution of modern radio telescopes such as NenuFAR mean that manually identifying radio spikes is an arduous task. Machine learning approaches to data exploration in solar radio data is on the rise. Here we describe a convolutional neural network to identify the per pixel location of radio spikes as well as determine some simple characteristics of duration, spectral width and drift rate. The model, which we call SpikeNet, was trained using an Nvidia Tesla T4 16GB GPU with ~100000 sample spikes in a total time of 2.2 hours. The segmentation performs well with an intersection over union in the test set of ~0.85. The root mean squared error for predicted spike properties is of the order of 23%. Applying the algorithm to unlabelled data successfully generates segmentation masks although the accuracy of the predicted properties is less reliable, particularly when more than one spike is present in the same 64 X 64 pixel time-frequency range. We have successfully demonstrated that our convolutional neural network can locate and characterise solar radio spikes in a number of seconds compared to the weeks it would take for manual identification.

Research on the Advocacy for Water Conservation in the Promotion of Water Resource Protection

The question to pose is how to use an assumption to encourage water conservation in a situation where water is scarce. First, the washing time and water consumption are recorded in this article using a stopwatch and measuring cup. Second, we utilized a formula to record the duration of each person's hand washing session. We multiplied the number of seconds by the water flow rate per second to determine the total volume of water each person used. This paper then examined the change in people's hand-washing time following the poster by contrasting the change in people's hand-washing time with the control variable technique. It is possible to draw the conclusion that posters in women's restrooms use the least amount of water, followed by posters in men's restrooms and none at all in women's restrooms. These statistics demonstrate that posters have an effect on how long people spend washing their hands. As a result, after seeing the signs, people will wash their hands less frequently, fulfilling the goal of conserving water.

Detection Swell Situation using LED by Pulse Changing-state of Porous Matter on Skin

Changes in fluid within the vitreous chamber were examined using a non-invasive technique using LED. The vitreous chamber was irradiated with a certain amount of LED as an elliptical circular sphere to confirm the acute-angle vitreous chamber status with continuous pulsation transition. Dazzling-divergence knowledge level (DDKL) was determined as the number generated by pulsation knowledge figuration by receiving the scattered signal generated from the irradiated LED for a certain number of seconds. The knowledge level was configured as a system that converts pulsation generated in a vitreous chamber, and the uneven conveyance technique was used to confirm the experimental conditions of dazzling halftone-free-vector-dot. The degree of scattering around micro-vessels and foreign substances was found to occur at a halftone-free-vector-dot level within the vitreous chamber. The maximum average value of DDKL was calculated as the result of the tide-beating status as a result of the output formed in the vitreous chamber status. In Pu-KF-FA-αAVG-MIN, the pulsation far transformation due to pulsation halftone-free-vector-dot beating was shown as 10.90±12.69 units. In pulsation convenient transformation value, Pu-KF-CO-αAVG-MIN was found to be 4.08±3.45. In pulsation flank transformation value, Pu-KF-PU-αAVG-MIN was found to be 0.85±0.76 units. In pulsation vicinage transformation value, Pu-KF-VI-αAVG-MIN was found to be 0.19±0.17 units. By expanding the knowledge level in DDKL, a vitreous chamber status was formed and halftone-free-vector-dot was developed, and the pulsation knowledge figuration ability was evaluated by scattering the vitreous chamber figuration according to the acute-angle. Examining the vitreous chamber using the uneven conveyance technique as a non-invasive technique confirmed that dazzling-divergence figuration, which requires a knowledge level system, appeared due to uneven conveyance. These physical findings enable the functional peculiarity of pulsation knowledge figuration in a vitreous chamber and provide a data base that can be utilized at the vitreous chamber level in a tide knowledge system.

Effect of vibratory device on the time of administration of vaccines and on patient satisfaction measures.

Previous studies have demonstrated that Buzzy® is effective for pain reduction during vaccination. This study aimed to determine if Buzzy® would have an effect on either duration of vaccine administration and/or patient satisfaction. Pediatric patients aged birth to 18 years old receiving a vaccination were randomized to either a control group receiving no intervention, or the experimental group, utilizing Buzzy®. Time of administration was measured by the number of seconds required by nursing to administer vaccines. Patient satisfaction was measured with a survey given to guardians. Time required was reduced by almost 2 min when utilizing Buzzy®, with median time dropping to 190, 95% CI [26.99, 415.92] seconds from 333, 95% CI [51.35, 627.21] seconds. Patient satisfaction surveys showed positive impacts of using the device, with 100% that used the device reporting that it "made a difference in the pain level experienced," but did not demonstrate statistical significance. This study shows that use of Buzzy® increases efficiency of appointments with possible positive effect on patient satisfaction.

Feasibility of a Novel Fluoroscopic Trainer for the Orthopaedic Trainee

We sought to create a novel method of teaching orthopedic trainees to efficiently obtain intraoperative radiographs using nonfluoroscopic digital cameras. Specifically, teaching them to make minor, uniplanar, adjustments while limiting the number of fluoroscopy images obtained during placement of a guidewire "start-point," for intramedullary nailing. Prospective cohort study including medical students from 2 academic centers. Two nonfluoroscopic digital cameras simulating orthogonal fluoroscopic images were utilized. A sponge was used to simulate soft tissue resistance while navigating a guidewire to the desired starting point. Three cannulated parallel cylinders in a triangular configuration are used to simulate our "start point." Students completed 4 phases; trial and error, teaching, testing and retention. The protocol was completed at a single academic teaching hospital at the primary authors institution. We utilized medical students from 2 GME accredited medical schools to complete the protocol. Students were selected from orthopedic surgery interest groups at their respective institutions and participation was voluntary. Twenty-one medical students completed the protocol. The number of seconds to achieve each target along with the number of pictures to achieve each target were recorded and averaged. The paired t-test was used to compare the difference between phases. There is a statistically significant difference in the mean number of seconds to achieve each target between phase 1 (baseline) and phase 3 (testing) (p < 0.0001). This statistically significant difference was retained in phase 4 (retention) (p < 0.0001). We were able to demonstrate a statistically significant decrease in the number of images and time to obtain the correct "start point." This could theoretically decrease operative time and morbidity while teaching students in a low-stress training environment without exposure to radiation.

Energy Usage Forecasting Model Based on Long Short-Term Memory (LSTM) and eXplainable Artificial Intelligence (XAI)

The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to financial consequences for the company, since it will generate a high cost of energy production. Therefore, in this study, we proposed an energy usage forecasting model and parameter analysis using long short-term memory (LSTM) and explainable artificial intelligence (XAI), respectively. A public energy usage dataset from a steel company was used in this study to evaluate our models and compare them with previous study results. The results showed that our models achieved the lowest root mean squared error (RMSE) scores by up to 0.08, 0.07, and 0.07 for the single-layer LSTM, double-layer LSTM, and bi-directional LSTM, respectively. In addition, the interpretability analysis using XAI revealed that two parameters, namely the leading current reactive power and the number of seconds from midnight, had a strong influence on the model output. Finally, it is expected that our study could be useful for industry practitioners, providing LSTM models for accurate energy forecasting and offering insight for policymakers and industry leaders so that they can make more informed decisions about resource allocation and investment, develop more effective strategies for reducing energy consumption, and support the transition toward sustainable development.

Time to Die 2: Improved in-game death prediction in Dota 2

Competitive video game playing, an activity called esports, is increasingly popular to the point that there are now many professional competitions held for a variety of games. These competitions are broadcast in a professional manner similar to traditional sports broadcasting. Esports games are generally fast paced, and due to the virtual nature of these games, camera positioning can be limited. Therefore, knowing ahead of time where to position cameras, and what to focus a broadcast and associated commentary on, is a key challenge in esports reporting. This gives rise to moment-to-moment prediction within esports matches which can empower broadcasters to better observe and process esports matches. In this work we focus on this moment-to-moment prediction and in particular present techniques for predicting if a player will die within a set number of seconds for the esports title Dota 2. A player death is one of the most consequential events in Dota 2. We train our model on ‘telemetry’ data gathered directly from the game itself, and position this work as a novel extension of our previous work on the challenge. We use an enhanced dataset covering 9,822 Dota 2 matches. Since the publication of our previous work, new dataset parsing techniques developed by the WEAVR project enable the model to track more features, namely player status effects, and more importantly, to operate in real time. Additionally, we explore two new enhancements to the original model: one data-based extension and one architectural. Firstly we employ learnt embeddings for categorical features, e.g. which in game character a player has selected, and secondly we explicitly model the temporal element of our telemetry data using recurrent neural networks. We find that these extensions and additional features all aid the predictive power of the model achieving an F1 score of 0.54 compared to 0.17 for our previous model (on the new data). We improve this further by experimenting with the length of the time-series in the input data and find using 15 time steps further improves the F1 score to 0.62. This compares to F1 of 0.1 for a standard RNN on the same task. Additionally a deeper analysis of the Time to Die model is carried out to assess its suitability as a broadcast aid.

DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

In recent years, artificial intelligence technology has exhibited great potential in seismic signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research. In this study, based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center, we constructed an artificial intelligence seismological training dataset (“DiTing”) with the largest known total time length. Data were recorded using broadband and short-period seismometers. The obtained dataset included 2,734,748 three-component waveform traces from 787,010 regional seismic events, the corresponding P- and S-phase arrival time labels, and 641,025 P-wave first-motion polarity labels. All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake. Each three-component waveform contained a considerable amount of descriptive information, such as the epicentral distance, back azimuth, and signal-to-noise ratios. The magnitudes of seismic events, epicentral distance, signal-to-noise ratio of P-wave data, and signal-to-noise ratio of S-wave data ranged from 0 to 7.7, 0 to 330 km, –0.05 to 5.31 dB, and –0.05 to 4.73 dB, respectively. The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection, seismic phase picking, first-motion polarity determination, earthquake magnitude prediction, early warning systems, and strong ground-motion prediction. Such research will further promote the development and application of artificial intelligence in seismology.

Enhanced Carotid Plaque Echolucency Is Associated with Reduced Cognitive Performance in Elderly Patients with Atherosclerotic Disease Independently on Metabolic Profile.

Vulnerable carotid atherosclerotic plaques are related to an increased risk of cognitive impairment and dementia in advanced age. In this study, we investigated the relationship between the echogenicity of carotid plaques and cognitive performance in patients with asymptomatic carotid atherosclerotic plaques. We enrolled 113 patients aged 65 years or more (72.4 ± 5.9 years) who underwent carotid duplex ultrasound to evaluate plaque echogenicity by grey-scale median (GSM) and neuropsychological tests to assess cognitive function. The GSM values at baseline were inversely correlated with the number of seconds required to complete Trail Makin Test (TMT) A (rho: -0.442; p < 0.0001), TMT B (rho: -0.460; p < 0.0001) and TMT B-A (rho: -0.333; p < 0.0001) and directly correlated with Mini Mental State Examination (MMSE) and Verbal Fluency Test (VFT) score (rho: 0.217; p = 0.021 and rho: 0.375; p < 0.0001, respectively) and the composite cognitive z-score (rho: 0.464; p < 0.0001). After a mean period of 3.5 ± 0.5 years, 55 patients were reevaluated according to the same baseline study protocol. Patients with baseline GSM value higher than the median value of 29 did not show any significant variation in the z-score. Instead, those with GSM ≤ 29 showed a significant worsening of z-score (-1.2; p = 0.0258). In conclusion, this study demonstrates the existence of an inverse relationship between the echolucency of carotid plaques and cognitive function in elderly patients with atherosclerotic carotid disease. These data suggest that the assessment of plaque echogenicity if used appropriately, might aid in identifying subjects at increased risk for cognitive dysfunction.

Shared emotions in shared lives: Moments of co-experienced affect, more than individually experienced affect, linked to relationship quality.

Motivated by collective emotions theories that propose emotions shared between individuals predict group-level qualities, we hypothesized that co-experienced affect during interactions is associated with relationship quality, above and beyond the effects of individually experienced affect. Consistent with positivity resonance theory, we also hypothesized that co-experienced positive affect would have a stronger association with relationship quality than would co-experienced negative affect. We tested these hypotheses in 150 married couples across 3 conversational interactions: a conflict, a neutral topic, and a pleasant topic. Spouses continuously rated their individual affective experience during each conversation while watching video-recordings of their interactions. These individual affect ratings were used to determine, for positive and negative affect separately, the number of seconds of co-experienced affect and individually experienced affect during each conversation. In line with hypotheses, results from all 3 conversational topics suggest that more co-experienced positive affect is associated with greater marital quality, whereas more co-experienced negative affect is associated with worse marital quality. Individual level affect factors added little explanatory value beyond co-experienced affect. Comparing co-experienced positive affect and co-experienced negative affect, we found that co-experienced positive affect generally outperformed co-experienced negative affect, although co-experienced negative affect was especially diagnostic during the pleasant conversational topic. Findings suggest that co-experienced positive affect may be an integral component of high-quality relationships and highlight the power of co-experienced affect for individual perceptions of relationship quality. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A-112 Cognitive Reliability and Time to Failure Rate Theory and Genetics

Abstract Objective: In this study, we utilized the cognitive reliability intact the failure theory toward an understanding of the impact of genetics and more specifically APOE-4 on cognitive decline. This method allows for the identification of the threshold as well as the decline rate in groups with different levels of education. Method: Data was obtained from the National Alzheimer’s coordination center(NACC). Following Brickell et al, 2020 we focused on the Trail Making Test part B (number of seconds to complete the task) and semantic fluency (animals) tests. Results: The study shows higher performance on the TMT B among those without the E4 allele emerging already at the age of 65. The decline rate however remains identical across the 3 groups. On the Semantic Fluency, Taskwe compared high school and college-educated subjects. Those without copies of the E4 allele exhibit stable functioning until about the age of 75 and then show a rapid exponential decline. Those with one copy exhibit an earlier threshold at about the age of 55. Those with a college education have a higher asymptote and show a decline before the age of 45 With the rate of decline has a linear characteristic. Conclusion(s): The present study demonstrates the utility of the reliability intent of failure theory for the study of cognitive decline across the lifespan. They show that the distinction between threshold decline rates could serve as an important factor in examining the impact of genetics.

Time-off-pick Assay to Measure Caenorhabditis elegans Motility.

Caenorhabditis elegans is a simple metazoan that is often used as a model organism to study various human ailments with impaired motility phenotypes, including protein conformational diseases. Numerous motility assays that measure neuro-muscular function have been employed using C. elegans . Here, we describe "time-off-pick" (TOP), a novel assay for assessing motility in C. elegans . TOP is conducted by sliding an eyebrow hair under the mid-section of the worm and counting the number of seconds it takes for the worm to crawl completely off. The time it takes for the worm to crawl off the eyebrow hair is proportional to the severity of its motility defect. Other readouts of motility include crawling or swimming phenotypes, and although widely established, have some limitations. For example, worms that are roller mutants are less suitable for crawling or swimming assays. We demonstrated that our novel TOP assay is sensitive to age-dependent changes in motility, thus, providing another more inclusive method to assess motor function in C. elegans . Graphical abstract: Conceptual overview of the "time-off-pick" (TOP) assay. Various C. elegans models exhibit age-dependent defects in motility. The time it takes for a worm to crawl off of an eyebrow pick that is slid under its mid-section is measured in TOP seconds. A greater TOP is indicative of a greater motility defect. Eventually, worms with phenotypes that lead to paralysis will not be able to leave the pick.

Regression Analysis to Estimate the Factor VIII Activity of Patients with Hemophilia A Without Inhibitor who Received Emicizumab Therapy.

Background Emicizumab, a bispecific monoclonal antibody for hemophilia A (HA), has strong pharmacodynamic effects in several coagulation assays resulting in dosing difficulties with Factor VIII (FVIII) concentrates during bleeding emergencies.Materials and MethodsSingle and multiple regression models were studied to estimate FVIII activity using 27 archived plasma samples from three patients with HA without inhibitor under emicizumab treatment. Explanatory variables were FVIII chromogenic assay (CSA), Ad|min1|, Ad|min2|, the number of seconds of APTT, and the FVIII one-stage assay (OSA), which were measured without idiotype antibodies. The response variable was FVIII OSA measured with idiotype antibodies.ResultsIn the simple linear model, the FVIII CSA regression coefficient was 1.04 and the intercept was −14.55 (r2 = 0.95; p < 0.001). In the multiple regression model, FVIII OSA and FVIII CSA were selected based on the Akaike Information Criterion, with regression coefficients of 1.74 and 1.15, respectively, and an intercept of −92.03 (r2 = 0.96, p < 0.001).ConclusionsThe regression models can estimate the FVIII:C levels in patients with HA receiving emicizumab and would be useful in a bleeding emergency and/or surgery.

The Effect of Adding a Training Device and Smartphone Application to Traditional Verbal Counseling in Asthmatic Children.

IntroductionNew training devices have been introduced to help in inhaler counseling by addressing the inspiratory flow through the metered-dose inhaler (MDI), which is the most important problem of the MDI inhalation technique. This study aims to compare the effects of MDI traditional verbal counseling and advanced counseling using training devices with a smartphone application in pediatric asthmatic patients.MethodsA total of 201 pediatric asthmatic subjects (8–18 years) were divided into two groups: a verbal counseling group, who received only MDI verbal counseling training (n = 101), and an advanced counseling group who received counseling using a training device (Flo-Tone with Trainhaler smartphone application) in addition to the traditional MDI verbal counseling (n = 100). Every patient in the two groups attended three counseling visits, 1 month apart. At each visit, pulmonary functions [peak expiratory flow (PEF), forced expiratory volume in 1 s (FEV1) as % of predicted] were measured. Also, patients were asked to perform their normal inhalation technique using their MDI, and mistakes were detected and recorded by the investigator. Then, patients were trained on the correct steps of the MDI inhalation technique using either verbal counseling or advanced counseling depending on their study group. In the advanced group, the Flo-Tone was connected to the mouthpiece of the MDI to blow a whistle while the patient inhaled from the MDI. That whistle was detected by the Trainhaler smartphone application and the duration of inhalation determined by the application was recorded.ResultsBoth groups showed a gradual significant decrease (p < 0.05) in the total mean number of MDI inhalation technique mistakes from the second visit of counseling, and the improvement continued in the third visit, with a lower number of mistakes in the advanced group especially in inhaling at a slow rate until the lungs are a full step. Also, the advanced counseling group showed a gradual significant increase (p < 0.05) in lung function (PEF and FEV1% of predicted) from the second visit of counseling, particularly (FEV1% predicted) results which showed a greater and more rapid overall improvement in the advanced group compared to the limited overall improvements that occurred in the control group, while significant improvement (p < 0.05) of lung function was obtained at the third visit in the verbal counseling group. In the advanced group, the number of seconds measured by the smartphone application, which represents the duration of inhalation, increased significantly (p < 0.05) in the second and third visits.ConclusionsThe addition of training devices and smartphone applications to traditional verbal counseling of MDI inhalation technique in asthmatic children resulted in significant improvements in lung function (especially in FEV1% of predicted results), and duration of inhalation, and progressive decreases in the average number of MDI inhalation techniques errors compared to the verbal counseling group.

PSX-B-19 Behaviour of cows while being tested for methane emissions using the greenfeed system

Abstract Increased focus on sustainability is driving a need for environmental efficiency traits in dairy cattle breeding. Breeding for reduced emission of methane, an inevitable product of fermentation in ruminants, is increasingly being explored. Methods to measure methane emissions vary but can be impacted by cow behaviour. As part of an on-going project to develop genomic tools for breeding resilient dairy cows, we explored changes in cow behaviour over time during methane emission measurements. First lactation heifers (n = 49) were tested in tie-stall housing at a research herd in Ontario, Canada. Animals were tested over 5 consecutive days at 08:00h, 12:00h, and 16:00h each day for a 10-min period using the GreenFeed system (C-Lock Inc., Rapid City, SD, USA). The frequency of movement (body shifts and leg lifts) and the number of seconds the cow removed her head from the machine were recorded. The effect of day on the average frequency of movements or time the cow’s head was outside of the machine was assessed using a repeated measures model. In general, cows moved their legs the most on day 1 of testing (76 ± 5.0 movements per 10 min), after which it numerically decreased (e.g., day 5: 68 ± 5.0 movements per 10 min, P = 0.1110). A similar effect was observed for seconds the cow had her head out of the machine (P = 0.0650). Cows spent an average of 39 ± 5.7 sec with their head outside of the machine on day 1 versus 25 ± 3.6 sec on day 5 (P = 0.0499). These preliminary results suggest that cows adapt to the testing conditions; however, changes in their behaviour were minor and do not intervene with recording of methane emissions using the GreenFeed system.

Predicting video engagement using heterogeneous DeepWalk

Video engagement is important in online advertisements where there is no physical interaction with the consumer. Engagement can be directly measured as the number of seconds after which a consumer skips an advertisement. In this paper, we propose a model to predict video engagement of an advertisement using only a few samples. This allows for early identification of poor quality videos. This can also help identify advertisement frauds where a robot runs fake videos behind the name of well-known brands. We leverage on the fact that videos with high engagement have similar viewing patterns over time. Hence, we can create a similarity network of videos and use a graph-embedding model called DeepWalk to cluster videos into significant communities. The learned embedding is able to identify viewing patterns of fraud and popular videos. In order to assess the impact of a video, we also consider how the view counts increase or decrease over time. This results in a heterogeneous graph where an edge indicates similar video engagement or history of view counts between two videos. Since it is difficult to find labelled samples for ‘fraud’ video, we leverage on a one-class model that can determine ‘fraud’ videos with outlier or abnormal behavior. The proposed model outperforms baselines in F-measure by over 20%.

Aberrant Interoceptive Accuracy in Patients With Schizophrenia Performing a Heartbeat Counting Task

Abstract Introduction Although self-disturbances and emotional disturbances are common in schizophrenia, there is no integrated understanding to explain these symptoms. Interoception has a crucial role in the development of self and emotion, and interoceptive abnormality could lead to such symptoms. Methods We compared interoceptive accuracy between controls and patients with schizophrenia. Forty-two patients and 30 healthy controls were recruited and their interoceptive accuracy was assessed using a heartbeat counting task. Participants were instructed to count the number of times they felt their own heartbeat during various measurement periods. Interoceptive accuracy was calculated based on the discrepancy between the number of reported and actual heartbeats during the measurement period. Participants also performed a time estimation task and were instructed to count the number of seconds there were during the same period. Time accuracy was calculated in a similar manner to that for the heartbeat. Participants also completed a questionnaire regarding interoception to assess their subjective experiences. Results Interoceptive accuracy was significantly lower among patients with schizophrenia than in healthy controls (P = .017), even after controlling for age, sex, time accuracy, anxiety, depression, educational level, and heart rate (HR). In addition, patients’ positive and negative symptoms were significantly associated with their HR-adjusted interoceptive accuracy, especially hallucination. The discrepancy between HR-adjusted interoceptive accuracy and the score of the questionnaire was significantly associated with positive symptoms, especially delusion, but not negative symptoms. Discussion These findings suggest that patients with schizophrenia have aberrant interoception. Aberrant interoception in schizophrenia could be a novel therapeutic target in future.

Independent risk factors for acute hospital patients' reduced ability to hear electronic axillary thermometer alarms.

Hearing loss prevalence increases dramatically with age. However, no prior studies exist regarding the prevalence and factors related to reduced ability to hear alarm sounds using electronic axillary thermometers. This study aimed to investigate the number of individuals with a reduced ability to hear the alarm sound of the electronic axillary thermometers among participants over 20 and 65 years old (Objective 1) and to identify the factors associated with reduced ability to hear the alarm sound (Objective 2). In this 5-month cross-sectional study, the participants, aged ≥20 years, were recruited from samples of patients at an acute hospital. The outcome measured was the average number of seconds for each participant between the axillary placement of a thermometer tip and their report of hearing the alarm, performed three times per patient. Complete data were obtained from 107 participants, with 58 (54.2%) showing a reduced ability to hear the alarm. The prevalence of reduced ability to hear the alarm was 69.5% (>65 years older). Multivariate analysis demonstrated that age was the factor most significantly associated with reduced hearing ability. Age 70 was determined as the cut-off point for ability to hear the alarm. This is a serious issue, as many individuals with the inability to hear the alarm sound use the electronic axillary thermometer with the alarm sound. For a reliable measurement of body temperature, digital thermometers need alternative alarm signals, (e.g., vibration) for self-use by adults ≥70 years to correctly measure body temperature.

Progress Update and Challenges on O2max Testing and Interpretation.

The maximal oxygen uptake (O2max) is the primary determinant of endurance performance in heterogeneous populations and has predictive value for clinical outcomes and all-cause mortality. Accurate and precise measurement of O2max requires the adherence to quality control procedures, including combustion testing and the use of standardized incremental exercise protocols with a verification phase preceded by an adequate familiarization. The data averaging strategy employed to calculate the O2max from the breath-by-breath data can change the O2max value by 4–10%. The lower the number of breaths or smaller the number of seconds included in the averaging block, the higher the calculated O2max value with this effect being more prominent in untrained subjects. Smaller averaging strategies in number of breaths or seconds (less than 30 breaths or seconds) facilitate the identification of the plateau phenomenon without reducing the reliability of the measurements. When employing metabolic carts, averaging intervals including 15–20 breaths or seconds are preferable as a compromise between capturing the true O2max and identifying the plateau. In training studies, clinical interventions and meta-analysis, reporting of O2max in absolute values and inclusion of protocols and the averaging strategies arise as imperative to permit adequate comparisons. Newly developed correction equations can be used to normalize O2max to similar averaging strategies. A lack of improvement of O2max with training does not mean that the training program has elicited no adaptations, since peak cardiac output and mitochondrial oxidative capacity may be increased without changes in O2max.