Respiratory Pattern Research Articles

Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime.

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.

GAN-Based Motion Artifact Correction of 3D MR Volumes Using an Image-to-Image Translation Algorithm.

The quality of brain MRI volumes is often compromised by motion artifacts arising from intricate respiratory patterns and involuntary head movements, manifesting as blurring and ghosting that markedly degrade imaging quality. In this study, we introduce an innovative approach employing a 3D deep learning framework to restore brain MR volumes afflicted by motion artifacts. The framework integrates a densely connected 3D U-net architecture augmented by generative adversarial network (GAN)-informed training with a novel volumetric reconstruction loss function tailored to 3D GAN to enhance the quality of the volumes. Our methodology is substantiated through comprehensive experimentation involving a diverse set of motion artifact-affected MR volumes. The generated high-quality MR volumes have similar volumetric signatures comparable to motion-free MR volumes after motion correction. This underscores the significant potential of harnessing this 3D deep learning system to aid in the rectification of motion artifacts in brain MR volumes, highlighting a promising avenue for advanced clinical applications.

Changing respiratory pathogens infection patterns after COVID-19 pandemic in Shanghai, China.

To assess the positive rate of 11 respiratory pathogens in 2023, providing a comprehensive summary and analysis of the respiratory infection patterns after COVID-19 pandemic. The study comprised 7544 inpatients suspected of respiratory infections who underwent respiratory pathogen multiplex polymerase chain reaction tests from July 2022 to December 31, 2023. We analyzed the positive rate of 11 pathogens over 18 months and the characterization of infection patterns among different age groups and immune states. Among 7544 patients (age range 4 months to 104 years, 44.99% female), the incidence of infected by at least one of the 11 pathogens was 26.07%. Children (55.18%, p < 0.05) experienced a significantly higher infection probability than adults (20.88%) and old (20.66%). Influenza A virus (8.63%), Mycoplasma pneumoniae (5.47%), and human rhinovirus (5.12%) were the most common pathogens. In children, M. pneumoniae (35.96%) replaced the predominant role of human respiratory syncytial virus (HRSV) (5.91%) in the pathogen spectrum. Age, immunosuppressed state, and respiratory chronic conditions were associated with a significantly higher risk of mixed infection. Immunosuppressed patients were more vulnerable to human coronavirus (4.64% vs. 1.65%, p < 0.05), human parainfluenza virus (3.46% vs. 1.69%, p < 0.05), and HRSV (2.27% vs. 0.55%, p < 0.05). Patterns in respiratory infections changed following regional epidemic control measures and the COVID-19 pandemic.

Quantifying depressive symptoms on incidence of common chronic diseases and multimorbidity patterns in middle-aged and elderly Chinese adults

BackgroundDepressive symptoms are highly prevalent and increase risks of various morbidities. However, the extent to which depressive symptoms could account for incidence of these chronic conditions, in particular multimorbidity patterns, remains to be examined and quantified. MethodsFor this cohort analysis, we included 9024–14,093 participants aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the longitudinal associations between depressive symptoms and 13 common chronic diseases and 4 multimorbidity patterns. Population attributable fractions (PAFs) combining the information on both exposure prevalence and risk association were estimated to quantify the magnitude of the burden of these conditions attributable to depressive symptoms. ResultsDepressive symptoms were associated with increased risks of liver disease, stroke, heart problem, asthma, diabetes, arthritis, kidney disease, chronic lung disease, digestive disease, dyslipidemia, and memory-related disease, and the adjusted HRs (95% CIs) and PAFs (95% CIs) ranged from 1.15 (1.05–1.26) to 1.64 (1.38–1.96) and 5% (0–10%) to 17% (6–28%), respectively. In addition, individuals with depressive symptoms had elevated risks of the cardiometabolic-cancer pattern, the cerebrovascular-memory pattern, the articular-visceral organ pattern, and the respiratory pattern, with respective HRs (95% CIs) of 1.26 (1.11–1.42), 1.34 (1.07–1.69), 1.45 (1.29–1.63), and 2.01 (1.36–2.96), and respective PAFs (95% CIs) of 5% (0–10%), 8% (˗4–21%), 12% (7–17%), and 20% (5–35%). ConclusionDepressive symptoms contribute substantially to the burden across a broad range of chronic diseases as well as different multimorbidity patterns in middle-aged and older Chinese.

A fast response humidity sensor based on MXene-SWCNTs for the monitoring of respiration

Two-dimensional materials have attracted great interest in humidity sensing due to their excellent sensitivity and response time. Herein, we propose a humidity sensor consisting of MXene and single-walled carbon nanotubes (SWCNTs), using the Marangoni effect to self-assemble two-dimensional materials on the liquid surface, and then preparing them on the interdigital substrate through the layer-by-layer dip-coating method. Due to the expanded interlayer spacing of MXene and more exposed hydrogen active sites provided by SWCNTs, the sensor showed an excellent dynamic property with the response and recovery time of 2 s and 1.75 s Besides, the sensitivity is 0.53 pF/% RH (10–60% RH) and 7.6 pF/% RH (60–90% RH), the maximum humidity hysteresis is 6% of full scale (60% RH), and the reproducibility is ＞200cycles. Then, a flexible humidity breathing detection system has been developed based-on the sensor. Result demonstrated that it can detect human respiration and be successfully used to distinguish different human respiratory patterns. This work not only optimizes the dynamic property of sensors, but also provides a solution for the production of high-performance flexible humidity monitoring systems on this basis.

Waterproof, stretchable and wearable corrugated conductive carbon fiber strain sensors for underwater respiration monitoring and swimming instruction

Respiration is one of crucial vital signs, which can be used for health assessment and sports management. Flexible resistive strain sensors provide an opportunity to monitor personal respiratory behaviors in real time due to high sensitivity, good linearity and fast response. However, there are some critical issues with the resistive strain sensor, such as strain mismatch, poor contact properties and moisture sensitivity, which easily cause unexpected electrical response, limiting its application scenarios. Here, a novel stretchable carbon fiber strain sensor is developed by a simple structural design, which endow the device with a remarkable linearity (R2=0.99601 in 0–40 % tensile strain), excellent anti-jamming performance, long-term durability, and waterproofness. Moreover, a sophisticated wearable respiratory monitoring system is further developed, which can not only precisely distinguish diverse respiratory patterns to enhance the accuracy in early illness diagnosis but also remotely capture real-time breathing information during swimming for guiding swimming training. It demonstrates that our stretchable resistive strain sensor has an enormous application potential in wearable electronics and smart clothing for health monitoring and training guide.

Dynamic hyperinflation is a risk factor for mortality and severe exacerbations in COPD patients

ObjectiveTo assess if dynamic hyperinflation is an independent risk factor for mortality and severe exacerbations in COPD patients. MethodsA cohort of 141 patients with stable COPD and moderate to very severe airflow limitation, treated according to conventional guidelines, was followed for a median of 9 years. Clinical characteristics were recorded and arterial blood gases, pulmonary function tests, 6-min walk and incremental exercise test with measurement of respiratory pattern and operative lung volumes were performed. Endpoints were all-cause mortality and hospitalization for COPD exacerbation. Results58 patients died during the follow-up period (1228 patients x year). The mortality rate was higher in patients with dynamic hyperinflation (n = 106) than in those without it (n = 35) (14.6; 95% CI, 14.5–14.8 vs. 7.2; 95% CI, 7.1–7.4 per 1000 patients-year). After adjusting for sex, age, body mass index, pack-years and treatment with inhaled corticosteroids, dynamic hyperinflation was associated with a higher mortality risk (adjusted hazard ratio [aHR], 2.725; 95% CI, 1.010–8.161), and in a multivariate model, comorbidity, peak oxygen uptake and dynamic hyperinflation were retained as independent predictors of mortality. The time until first severe exacerbation was shorter for patients with dynamic hyperinflation (aHR, 3.961; 95% CI, 1.385–11.328), and dynamic hyperinflation, FEV1 and diffusing capacity were retained as independent risk factors for severe exacerbation. Moreover, patients with dynamic hyperinflation had a higher hospitalization risk than those without it (adjusted incidence rate ratio, 1.574; 95% CI, 1.087–2.581). ConclusionIn stable COPD patients, dynamic hyperinflation is an independent prognostic factor for mortality and severe exacerbations.

Quantitative Characterization of Respiratory Patterns on Dynamic Higher Temporal Resolution MRI to Stratify Postacute Covid-19 Patients by Cardiopulmonary Symptom Burden.

Postacute Covid-19 patients commonly present with respiratory symptoms; however, a noninvasive imaging method for quantitative characterization of respiratory patterns is lacking. To evaluate if quantitative characterization of respiratory pattern on free-breathing higher temporal resolution MRI stratifies patients by cardiopulmonary symptom burden. Prospective analysis of retrospectively acquired data. A total of 37 postacute Covid-19 patients (25 male; median [interquartile range (IQR)] age: 58 [42-64] years; median [IQR] days from acute infection: 335 [186-449]). 0.55 T/two-dimensional coronal true fast imaging with steady-state free precession (trueFISP) at higher temporal resolution. Patients were stratified into three groups based on presence of no (N = 11), 1 (N = 14), or ≥2 (N = 14) cardiopulmonary symptoms, assessed using a standardized symptom inventory within 1 month of MRI. An automated lung postprocessing workflow segmented each lung in each trueFISP image (temporal resolution 0.2 seconds) and respiratory curves were generated. Quantitative parameters were derived including tidal lung area, rates of inspiration and expiration, lung area coefficient of variability (CV), and respiratory incoherence (departure from sinusoidal pattern) were. Pulmonary function tests were recorded if within 1 month of MRI. Qualitative assessment of respiratory pattern and lung opacity was performed by three independent readers with 6, 9, and 23 years of experience. Analysis of variance to assess differences in demographic, clinical, and quantitative MRI parameters among groups; univariable analysis and multinomial logistic regression modeling to determine features predictive of patient symptom status; Akaike information criterion to compare the quality of regression models; Cohen and Fleiss kappa (κ) to quantify inter-reader reliability. Two-sided 5% significance level was used. Tidal area and lung area CV were significantly higher in patients with two or more symptoms than in those with one or no symptoms (area: 15.4 cm2 vs. 12.9 cm2 vs. 12.8 cm2 ; CV: 0.072, 0.067, and 0.058). Respiratory incoherence was significantly higher in patients with two or more symptoms than in those with one or no symptoms (0.05 vs. 0.043 vs. 0.033). There were no significant differences in patient age (P = 0.19), sex (P = 0.88), lung opacity severity (P = 0.48), or pulmonary function tests (P = 0.35-0.97) among groups. Qualitative reader assessment did not distinguish between groups and showed slight inter-reader agreement (κ = 0.05-0.11). Quantitative respiratory pattern measures derived from dynamic higher-temporal resolution MRI have potential to stratify patients by symptom burden in a postacute Covid-19 cohort. 3 TECHNICAL EFFICACY: Stage 3.

Single neurons in the thalamus and subthalamic nucleus process cardiac and respiratory signals in humans

Visceral signals are constantly processed by our central nervous system, enable homeostatic regulation, and influence perception, emotion, and cognition. While visceral processes at the cortical level have been extensively studied using non-invasive imaging techniques, very few studies have investigated how this information is processed at the single neuron level, both in humans and animals. Subcortical regions, relaying signals from peripheral interoceptors to cortical structures, are particularly understudied and how visceral information is processed in thalamic and subthalamic structures remains largely unknown. Here, we took advantage of intraoperative microelectrode recordings in patients undergoing surgery for deep brain stimulation (DBS) to investigate the activity of single neurons related to cardiac and respiratory functions in three subcortical regions: ventral intermedius nucleus (Vim) and ventral caudalis nucleus (Vc) of the thalamus, and subthalamic nucleus (STN). We report that the activity of a large portion of the recorded neurons (about 70%) was modulated by either the heartbeat, the cardiac inter-beat interval, or the respiration. These cardiac and respiratory response patterns varied largely across neurons both in terms of timing and their kind of modulation. A substantial proportion of these visceral neurons (30%) was responsive to more than one of the tested signals, underlining specialization and integration of cardiac and respiratory signals in STN and thalamic neurons. By extensively describing single unit activity related to cardiorespiratory function in thalamic and subthalamic neurons, our results highlight the major role of these subcortical regions in the processing of visceral signals.

High-performance electrochemical power generation humidity sensor based on NaCl/sodium alginate humidity sensing electrolyte and Cu/Zn electrodes for visual humidity indication and respiratory patterns detection

Electrochemical power generation humidity sensors have received great attention recently, but it is challenging for achieving good humidity sensing and power generation performances simultaneously. Herein, we construct a high-performance power generation humidity sensor based on NaCl/sodium alginate (SA) humidity sensing electrolyte and Cu/Zn electrodes. Benefiting from the reasonable selection of humidity sensing electrolyte and electrode materials and the design of sensor structure, the optimized NaCl/SA humidity sensor exhibits wide humidity sensing detection range (10.9−91.5% relative humidity (RH)), short response time (5.6 s), and good repeatability (20 cycles) at 25°C. Importantly, the humidity sensor has high response voltage (∼0.7 V), large short circuit current (∼24 μA) and loading power (∼2.63 μW) at 91.5% RH. The NaCl/SA humidity sensor can be used for passive visual humidity indication by connecting it to a pointer microammeter. In addition, we designed signal processing circuit and software program to recognize respiratory patterns using NaCl/SA humidity sensor. This work not only achieves a high-performance electrochemical humidity sensor, but also provides a useful reference for its applications in passive visual humidity and respiratory patterns detections.

Acid rain reduced soil carbon emissions and increased the temperature sensitivity of soil respiration: A comprehensive meta-analysis

Soil respiration the second-largest carbon flux in terrestrial ecosystems, has been extensively studied across a wide range of biomes. Surprisingly, no consensus exist on how acid rain (AR) impacts the spatiotemporal pattern of soil respiration. Therefore, we conducted a meta-analysis using 318 soil respiration and 263 soil respiration temperature sensitivity (Q10) data points obtained from 48 studies to assess the impact of AR on soil respiration components and their Q10. The results showed that AR reduced soil total respiration (Rt) and soil autotrophic respiration (Ra) by 7.41 % and 20.75 %, respectively. As the H+ input increased, the response rates of Ra to AR (RR-Ra) and soil heterotrophic respiration (Rh) to AR (RR-Rh) decreased and increased, respectively. With increased AR duration, the RR-Ra increased, whereas the RR-Rh did not change. AR increased the Q10 of Rt (Rt-Q10) and Rh (Rh-Q10) by 1.92 % and 9.47 %, respectively, and decreased the Q10 of Ra (Ra-Q10) by 2.77 %. Increased mean annual temperature, mean annual precipitation, and initial soil organic carbon increased the response rate of Ra-Q10 to AR (RR-Ra-Q10) and decreased the response rate of Rh-Q10 to AR (RR-Rh-Q10). However, as the AR frequency and initial soil pH increased, both RR-Ra-Q10 and RR-Rh-Q10 also increased. In summary, AR decreased Rt but increased Q10, likely due to soil acidification (soil pH decreased by 7.84 %), reducing plant root biomass (decreased by 5.67 %) and soil microbial biomass (decreased by 5.67 %), changing microbial communities (increased fungi to bacteria ratio of 15.91 %), and regulated by climate, vegetation, soil and AR regimes. To the best of our knowledge, this is the first study to reveal the large-scale, varied response patterns of soil respiration components and their Q10 to AR. It highlights the importance of applying the reductionism theory in soil respiration research to enhance our understanding of soil carbon cycling processes with in the context of global climate change.

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.

Bioenergetic signatures of neurodevelopmental regression.

Background: Studies have linked autism spectrum disorder (ASD) to physiological abnormalities including mitochondrial dysfunction. Mitochondrial dysfunction may be linked to a subset of children with ASD who have neurodevelopmental regression (NDR). We have developed a cell model of ASD which demonstrates a unique mitochondrial profile with mitochondrial respiration higher than normal and sensitive to physiological stress. We have previously shown similar mitochondrial profiles in individuals with ASD and NDR. Methods: Twenty-six ASD individuals without a history of NDR (ASD-NoNDR) and 15 ASD individuals with a history of NDR (ASD-NDR) were recruited from 34 families. From these families, 30 mothers, 17 fathers and 5 typically developing (TD) siblings participated. Mitochondrial respiration was measured in peripheral blood mononuclear cells (PBMCs) with the Seahorse 96 XF Analyzer. PBMCs were exposed to various levels of physiological stress for 1h prior to the assay using 2,3-dimethoxy-1,4-napthoquinone. Results: ASD-NDR children were found to have higher respiratory rates with mitochondria that were more sensitive to physiological stress as compared to ASD-NoNDR children, similar to our cellular model of NDR. Differences in mitochondrial respiration between ASD-NDR and TD siblings were similar to the differences between ASD-NDR and ASD-NoNDR children. Interesting, parents of children with ASD and NDR demonstrated patterns of mitochondrial respiration similar to their children such that parents of children with ASD and NDR demonstrated elevated respiratory rates with mitochondria that were more sensitive to physiological stress. In addition, sex differences were seen in ASD children and parents. Age effects in parents suggested that mitochondria of older parents were more sensitive to physiological stress. Conclusion: This study provides further evidence that children with ASD and NDR may have a unique type of mitochondrial physiology that may make them susceptible to physiological stressors. Identifying these children early in life before NDR occurs and providing treatment to protect mitochondrial physiology may protect children from experiencing NDR. The fact that parents also demonstrate mitochondrial respiration patterns similar to their children implies that this unique change in mitochondrial physiology may be a heritable factor (genetic or epigenetic), a result of shared environment, or both.

Falls in oxygen saturations accompany electrographic seizures in term neonates: an observational study

BackgroundEffective seizure detection is important however, clinical signs of seizure activity may be subtle in neonates. This study aimed to systematically investigate SpO2 and respiratory pattern changes associated with EEG seizures in term-born neonates.MethodAn observational study in term neonates at risk of seizures admitted to a single tertiary level neonatal intensive care unit. Synchronised high-resolution physiological data (ECG, pulse oximetry, respiration) and EEG/amplitude-integrated EEG (aEEG) monitoring were recorded. Sections of traces with evidence of clear EEG seizure activity were compared with physiological data recorded at the same time.Results22/44 (50%) neonates who had aEEG monitoring were noted to have electrographic seizures. Physiologic download measurements were available for 11 of these neonates. In nine of these, an acute drop in oxygen saturation (SpO2) of at least 5% was noted in at least one seizure. Accompanying apnoeas were noted in three neonates.ConclusionAcute decreases in SpO2 were seen in term neonates associated with seizures and these were not always accompanied by an apnoeic episode. Physiologic download in association with EEG monitoring may assist in improving seizure detection. Unexplained drops in SpO2 could indicate further investigation for possible seizures in at-risk neonates.ImpactA decrease in blood oxygen saturation (SpO2) associated with EEG seizures can occur in term infants with HIE or perinatal stroke.Drops in SpO2 associated with EEG seizures in term infants with HIE or stroke may occur in the absence of apnoeas.Unexplained acute falls in SpO2 in sick neonates may suggest possible seizures.Drops in SpO2 associated with seizures in term infants can occur over less than 3 minutes.Physiological monitoring alongside EEG monitoring could help to improve seizure detection.

Long-term organic amendments increase the vulnerability of microbial respiration to environmental changes: Evidence from field and laboratory studies

Organic amendments can improve soil fertility and microbial diversity, making agroecosystems more resilient to stress. However, it is uncertain whether organic amendments will enhance the functional capacity of soil microbial communities, thereby mitigating fluctuations in microbial respiration caused by environmental changes. Here, we examined the impacts of long-term organic amendments on the dynamics of microbial catabolic capacity (characterized by enzyme activities and carbon source utilization) and microbial respiration, as well as their interrelationships during a period with fluctuating temperature and rainfall in the field. We then subjected the field soil samples to laboratory heating disturbances to further evaluate the importance of microbial catabolic capacity in explaining patterns of microbial respiration. In both field and laboratory experiments, organic amendments tended to increase the stability of microbial catabolic capacity, but significantly increased the vulnerability of microbial respiration to environmental changes. However, the direction and driving factors of microbial respiration affected by environmental changes differed between the field and laboratory experiments. Environmental changes in the field suppressed the promotional effects of organic amendments on microbial respiration mainly through reducing microbial catabolic capacity, while laboratory heating further enhanced microbial respiration mainly due to increased soil resource availability. Together, these findings suggest that increased microbial respiration variations under organic amendments may potentially increase the uncertainty in predicting soil carbon emissions in the scenario of ongoing climate/anthropogenic changes, and highlight the necessity of linking laboratory studies on environmental changes to field conditions.

Improving Pediatric Critical Care Nurses' Practice in Implementing the Full Outline of Unresponsiveness Coma Scale.

Clinical estimation of consciousness is the most fundamental and crucial component of neurological examinations for pediatric patients in the pediatric intensive care unit (PICU). The full outline of unresponsiveness (FOUR) Coma Scale assesses four variables: eye response, motor response, brainstem reflexes, and respiratory pattern. A score of 0 represents non-function in each category, whereas a score of 4 indicates normal functioning. The FOUR Coma Scale is easy to apply and is currently the most widely used scale for assessing consciousness, particularly in intubated children. This study aimed to improve pediatric critical care nurses' practice in implementing the FOUR Coma Scale. A quasi-experimental research design (pre/post-test design) was employed on a convenience sample composed of 45 male and female nurses who participated in the study and provided direct care for critically ill children at the Medical Pediatric Intensive Care Unit affiliated with the Mansoura University Children's Hospital, Mansoura University, Egypt. All the participants completed the FOUR Coma Scale knowledge and practice assessment questionnaire. Majority of the nurses were females and had bachelors' degrees in nursing (94.6% and 71.1%, respectively). Moray Coma Scale was used to assess level of consciousness (LOC) by more than half the number of nurses (55.6%) in the PICU. All PICU nurses had poor knowledge and practice scores in the pre-intervention phase, and this percentage statistically significantly improved in the post-intervention phase (P = .001). The mean score of nurses' knowledge and practice on the FOUR Coma Scale significantly improved after the implementation of instructional intervention.

Weekday Surgery Associated With Short-Term Outcomes in Patients With Colorectal Cancers

IntroductionThe “weekday effect” on elective surgery remains controversial. We aimed to examine the association between the day of surgery and short-term outcomes after elective surgery for stage I-III colorectal cancer (CRC). MethodsWe performed a multicenter retrospective analysis of 2574 patients who underwent primary colorectal resection for CRC between January 2017 and December 2019 at 15 institutions belonging to the Hiroshima Surgical Study Group of Clinical Oncology. Patients were divided into two groups according to the day of surgery: Friday and non-Friday (Monday to Thursday). After propensity score matching (PSM), we compared 30-day mortality and postoperative outcomes. ResultsOut of the total, 368 patients underwent surgery on Fridays, and the remaining 2206 underwent surgery on non-Fridays. The overall mortality rate was 0.04% (n = 1). In 1685 patients with colon cancer, the proportion of American Society of Anesthesiologists scores was significantly lower in the Friday group than in the non-Friday group before PSM. After PSM of patient, tumor, and operative characteristics, operative time was slightly more prolonged and blood loss was slightly greater in the Friday group; however, these differences were not clinically meaningful. In the 889 patients with rectal cancer, the proportion of patients with abnormal respiratory patterns was significantly lower in the Friday group than in the non-Friday group before PSM. After PSM, the Friday group had a higher incidence of morbidity (≥ Clavien–Dindo 3a), higher incidence of digestive complications, and prolonged postoperative hospital stay. ConclusionsThe results may be useful in determining the day of the week for CRC surgery, which requires more advanced techniques and higher skills.

Development and validation of a respiratory pattern analysis system for Post-COVID-19 patients

Introduction: Due to advancements in vaccination, the morbidity and lethality rates of Covid-19 have diminished significantly. Consequently, there has been a substantial decline in severe cases, underscoring the importance of long-term monitoring for individuals. In response to this imperative, a prototype device for evaluating the respiratory patterns of Covid-19-affected individuals has been conceptualized, necessitating specialized software for data analysis and processing. Objective: To develop a system for analyzing variables of the respiratory pattern for application in post-Covid-19 patients. Methodology: The desktop application of the device was developed using the Electron framework, incorporating the React graphical interface library and JavaScript for algorithm development to analyze respiratory flow and volume curves. HTML and CSS were employed for screen structuring and styling. The measured respiratory flow signal underwent numerical calculation techniques and algorithms for time-series analysis based on respiratory cycle intervals. Derived variables included respiratory rate, inspiratory, expiratory, and total time, inspiratory and expiratory flow and volume, minute inspiratory and expiratory volume, inspiratory capacity, and vital capacity. System validation involved comparing the flow signal acquired by the device with that of a Hans Rudolph Pneumotachograph (standard method) using Bland-Altman plots. Results: The RDA Analysis software, integrated with interfaces for patient records and flow/volume vs. time graphs, captured respiratory cycles during rest breathing and incorporated slow inspiratory and vital lung capacities. The RDA Sync software was developed as an auxiliary program, synchronizing and simultaneously analyzing multiple patient exams. Bland-Altman analysis revealed a bias of 0.48 L/min, with agreement limits of -10.7 and 11.6 L/min (p-value &lt; 0.0001). Conclusion: The respiratory flow measured by the device exhibits high concordance with the gold standard. The developed software strengthens the device as a minimum viable product, currently employed to monitor respiratory pattern dysfunctions in post-Covid patients. This enhances the precision of the examination, providing quantitative and qualitative information for diagnostic assessment of respiratory functionality.

Seasonal variations in respiratory morbidity in primary care and its correlation with the quality of air in urban Odisha, India.

Poor air quality, especially in urban regions among low-and middle-income countries such as India poses a significant healthcare challenge. Amongst urban areas, metropolitan cities garner the utmost importance for air quality related policies and studies with limited studies from tier II cities which are thought to be relatively immune to air pollution. Hence, we aimed to identify the most frequent respiratory morbidities and explore its correlation with exposure to ambient PM2.5 particles in Bhubaneswar (a tier II city in coastal India), Odisha. A chart review was carried out through data extracted from the records of urban health centres. Data on PM2.5 concentrations were obtained from Odisha State Pollution Control Board. The morbidities were coded by using the International Classification of Primary Care‑2 system (ICPC-2). Descriptive statistics such as incidence of respiratory illnesses was computed across seasons. The ecological correlation between respiratory morbidity patterns and corresponding concentration of PM2.5 in air was analysed for each season. A positive correlation (r = o.94) between PM2.5 and respiratory morbidities was observed. The incidence of respiratory morbidities was 183.31 per 1000 person year. We identified 21 out of 43 respiratory diseases classified under ICPC-2. Upper Respiratory Tract Infection was the most commonly (116.8 per 1000 person year) incident condition. We observed one-fourth increase in the incidence of respiratory illnesses during winters. Respiratory morbidities are common in urban Bhubaneswar which follows a seasonal pattern and are possibly linked with the seasonal variations in levels of PM2.5 particles. Our study highlights that tier II cities are equally prone to health effects of air pollution. Future programmes and policies should take these cities into consideration too.

Neuroanatomical frameworks for volitional control of breathing and orofacial behaviors

Breathing is the only vital function that can be volitionally controlled. However, a detailed understanding how volitional (cortical) motor commands can transform vital breathing activity into adaptive breathing patterns that accommodate orofacial behaviors such as swallowing, vocalization or sniffing remains to be developed. Recent neuroanatomical tract tracing studies have identified patterns and origins of descending forebrain projections that target brain nuclei involved in laryngeal adductor function which is critically involved in orofacial behavior. These nuclei include the midbrain periaqueductal gray and nuclei of the respiratory rhythm and pattern generating network in the brainstem, specifically including the pontine Kölliker-Fuse nucleus and the pre-Bötzinger complex in the medulla oblongata. This review discusses the functional implications of the forebrain-brainstem anatomical connectivity that could underlie the volitional control and coordination of orofacial behaviors with breathing.