Analysis Of Variability Research Articles

Effect of listening music (Rabindra Sangeet) on autonomic dysfunction in patients with generalized anxiety disorder: A power spectral analysis of heart rate variability

Background: Power spectral analysis of heart rate variability (HRV) is a complex method for assessing cardiac autonomic nerve function (CANF) in generalized anxiety disorder (GAD) patients which is a common mental disorder in the world. Recently, music therapy is used as an adjunct to pharmacotherapy in clinical psychiatry for GAD. Objective: To observe the effect of music therapy on CANF by power spectral analysis of HRV in GAD patients was designed. Methods: This prospective non randomized intervention study was done in 2019 on 60 newly diagnosed GAD patients aged 20- 40 years, both male and female. Among them 30 patients were under music therapy with listening of Raga Bhairabi based Rabindra sangeet for 3 months and 30 patients were without music therapy for three months. Age, sex and BMI matched healthy control were also enrolled. HRV of all GAD patients and control were recorded at baseline and after three months of follow up. HRV were recorded by Power Lab 8/35. For statistical analysis Bonferroni’s Post Hoc test and paired sample‘t’ test were done. Results: Total power, LF power, HF power, LF n.u, HF n.u and LF/HF and were significantly lower in all GAD patients compared to healthy control at baseline. After 3 months of music therapy, significant increment of Total power, HF power, HF n.u and significant decrement of LF n.u and LF/HF occurred whereas no change of these except HF n.u were noted in patients without music after 3 months. Moreover, after three months of music therapy, Total power, HF n.u, and LF/HF were almost same to control. Conclusion: So this study concluded that music therapy improve autonomic dysfunction in GAD patients and can be used as an adjunct to pharmacotherapy in clinical psychiatry for good cardiac health in GAD patients. J Bangladesh Soc Physiol 2022;17(2): 91-100

Studies on Genetic Variability, Correlation and Path Coefficient Analysis for Yield and Yield Contributing Traits in Bread Wheat (Triticum aestivum L.)

A study was undertaken to analyse the genetic variability, correlation and estimates path coefficient analysis of yield and yield contributing traits in twenty-eight wheat genotypes grown at BAU Agricultural Farm, ranchi during Rabi season of 2019-2020 in three different dates of sowing with an interval of 15 days which is considered as three environments E1, E2, E3. In this investigation the pooled analysis of variance revealed that the treatments were highly significant for all the characters. Wide genetic variation was observed among genotypes for plant height, number of grains per spike, 1000 kernel weight, days to maturity and flag leaf area. Considering genetic parameters, high genotypic co-efficient of variation (GCV), was observed for 1000 kernel weight (11.95), no of grains per spike (12.73), flag leaf area (13.74), spike length (15.83), grain yield per plant (17.64), no of effective tillers (18.38) Whereas, low GCV was observed for plant height (3.49), days to 50% flowering (1.46), days to maturity (2.54) and harvest index (6.90). In most of the cases, phenotypic variances were higher than the genotypic variances. Environmental variance is much higher in harvest index (35.64), days to 50% flowering (33.47). low environmental variance was observed in plant height (16.75), flag leaf area (10.29), days to maturity (13.93), no of grains per spike (14.07), spike length (0.49) and biological yield (35.64). High heritability with low genetic advance in per cent of mean was observed for plant height, days to maturity, days to 50% flowering & harvest index which indicated the involvement of non-additive gene action for the expression of this character and selection for such trait might not be rewarding. High heritability with high genetic advance in per cent of mean was observed for grain yield per plant (27.73), no of grains per spike (20.93), 1000 kernel weight (23.58) and no of effective tillers (27.91) indicating that these traits were under additive gene control and selection for genetic improvement for these traits would be effective. Biological yield had high positive and significant correlation with plant height, number of effective tillers and number of grains per spike. Path coefficient analysis revealed maximum direct contribution towards yield per plot with sheath length followed by grains per spike.

Changes in autonomic regulation in workers under conditions of long-term fluoride intoxication

Introduction. Long-term intake and accumulation of fluoride compounds in the body, in addition to pathological changes in the bronchopulmonary system, bone tissue, liver, leads to changes in the regulatory systems, primarily the autonomic ones, in the conditions of chronic fluoride intoxication. The aim of the study was to assess the nature and severity of the changes in autonomic regulation in workers of the main occupations in aluminum production under exposure to chronic fluoride intoxication. Material and methods. We examined fifty two male workers of aluminum production over the age range from 40 to 56 years; work experience was more than 15 years. Autonomic regulation was assessed using spectral analysis of the heart rate variability, highlighting oscillations in the ranges of very low frequency (0.004–0.08 Hz), low (0.09–0.16 Hz), and high frequencies (0.17–0.5 Hz). Results. Analysis of the heart rate variability made it possible to identify 4 groups of subjects: 16 individuals had a moderate decrease in variability and a predominance of low-frequency oscillations, 12 subjects examined cases had a pronounced decline in variability in all frequency ranges; a decrease in the capabilities of autonomic regulation was manifested during a test with hyperventilation by an increase in very low frequency oscillations frequency indicating the need for activation of suprasegmental autonomic centers. In 24 subjects, the stable predominance of low-frequency oscillations indicates an enhancement in sympathetic influence. In response to the hyperventilation test, most of them (18 subjects were included in the group 3) showed an increase in low and very low frequency oscillations as a sign of a violation of baroreceptor regulation. Limitations. The study was limited by the number of workers with long-term work experience in aluminum production who underwent an in-depth medical examination. Conclusion. Changes in autonomic regulation are observed in the majority of aluminum production workers with long-term work experience, which gives grounds to recommend a study of neurovegetative status using heart rate variability analysis during preventive examinations to identify individuals at high risk of developing comorbid diseases.

How reliable are femoropelvic kinematics during deep squats? The influence of subject-specific skeletal modelling on measurement variability

BackgroundBiplanar radiography displays promising results in the production of subject-specific (S.specific) biomechanical models. However, the focus has predominantly centred on methodological investigations in gait analysis. Exploring the influence of such models on the analysis of high range of motion tasks linked to hip pathologies is warranted. The aim of this study is to investigate the effect of S.Specific modelling techniques on the reliability of deep squats kinematics in comparison to generic modelling. Methods8 able-bodied male participants attended 5 motion capture sessions conducted by 3 observers and performed 5 deep squats in each. Prior to each session a biplanar scan was acquired with the reflective-markers attached. Inverse kinematics of pelvis and thigh segments were calculated based on S.specific and Generic model definition. Agreement between the two models femoropelvic orientation in standing was assessed with Bland-Altman plots and paired t- tests. Inter-trial, inter-session, inter-observer variability and observer/trial difference and ratio were calculated for squat kinematic data derived from the two modelling approaches. ResultsCompared to the Generic model, the S.Specific model produced a calibration trial that is significantly offset into more posterior pelvis tilt (–2.8±2.7), hip extension (–2.2±3.8), hip abduction (–1.2±3.6) and external rotation (–13.8±11.4). The S.specific model produced significantly different squat kinematics in the sagittal plane of the pelvis (entire squat cycle) and hip (between 40 % and 60 % of the squat cycle). Variability analysis indicated that the error magnitude between the two models was comparable (difference<2°). The S.specific model exhibited a lower variability in the observer/trial ratio in the sagittal pelvis and hip, the frontal hip, but showed a higher variability in the transverse hip. SignificanceS.specific modelling appears to introduce a calibration offset that primarily translates into an effect in the sagittal plane kinematics. However, the clinical added value of S.specific modelling in terms of reducing experimental sources of kinematic variability was limited.

Fetal magnetocardiographic recordings with a prototype bed-based array system of optically-pumped magnetometers

ObjectiveTo record and extract features of fetal cardiac activities with a semi-rigid prototype optically-pumped magnetometers (OPM) sensor array. MethodsFetal magnetocardiography (fMCG) data were collected from 15 pregnant women between 28 and 40 weeks gestation. Mothers were lying flat in a customized bed with sensors touching their abdomen from below using a prototype grid. fMCG was extracted to perform standard fetal heart rate variability (FHRV) analysis. ResultsfMCG was observed in 13 of the 15 pregnant women. OPM FHRV indicators were in the range of previous SQUID studies. ConclusionSemi-rigid prototype OPM system has the ability to record quality fMCG. fMCG is capable of identifying lethal cardiac rhythm disturbances in the fetus. Our novel application of OPM technology may lower costs and increase maternal comfort, thus expanding fMCG's generalizability.

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

Re-evaluating soil moisture-based drought criteria for rainfed crops in peninsular India

Background: Peninsular India, being completely under the influence of monsoonal climate, suffers crop yield variability due to rainfall distribution-induced soil moisture constraints. Timely and appropriate assessment of this rainfall and soil moisture-induced crop yield variability serves as a key for exemplary relief assistance. Per cent available soil moisture (PASM) is one among several drought declaration indices followed by stakeholders in India for declaration of drought, needs re-evaluation as the existing criteria in unable to capture the yield loss due to ineffective classification of PASM categories. This study attempts to revise the agricultural drought classes by PASM based on relationships established between yield of major rainfed crops of the study region and PASM.Methods: Analysis of yield variability due to PASM was carried out based on long term observations in experiments conducted at five dry farming locations (Akola, Parbhani, Kovilpatti, Ananthapuramu and Bengaluru) of peninsular India. The average yield for each category of PASM was calculated and tabulated for regression analysis. The PASM versus yield in each group was correlated and regression equations were developed if significant positive correlations were established.Results: The range of available soil moisture to obtain at least 50 percent of optimum yield in cereals (maize: 26 and finger millet: 52.9 PASM), pulses (pigeon pea: 37.2 PASM), oilseeds (soybean: 26.8 to 30.5, groundnut: 53.8 to 61.7 PASM) and commercial crops (cotton: 26.3 PASM) was 26–61 percent.Conclusion: The revised PASM-based drought classes (0–50 severe; 51–75 mild and 76–100 no drought) would help in drought declaration and precise identification of drought-hit areas for meaningful relief assistance. However, there is further investigation is needed to include a soil component for further fine-tuning of the criteria.

Winter “warm Arctic-cold Eurasia” pattern and its statistical linkages to oceanic precursors during the era of satellite observations

AbstractA striking recurrent feature of winter climate variability is the “warm Arctic-cold Eurasia” (WACE) pattern of opposite sign anomalies of surface air temperature (SAT) in the Barents Sea region and midlatitude Eurasia. Its origins and mechanisms are hotly debated, and its predictability remains unknown. This study investigates statistical relationships of the winter WACE dipole with concurrent anomalies of atmospheric circulation and oceanic precursors during the era of satellite observations. The results reveal a high potential for seasonal predictability of not only the WACE dipole but also several related indicators of winter climate variability, including the Arctic and Eurasian SAT anomalies. During subperiods of extreme covariability between the Arctic and Eurasian SATs around the early 1980s and late 2000s, most of the WACE variability is explained by ocean temperature and surface turbulent heat flux anomalies in the Barents Sea region during the preceding months. Anomalies in summer Atlantic water temperature (AWT) and autumnal sea surface temperature (SST) in this region explain about 70–80% of the variance of the following winter WACE variability during all events of strong Arctic-Eurasian SAT covariability. Analysis of SST variability in the Arctic-North Atlantic region suggests that the winter WACE link to the summer AWT anomalies reflects an atmospheric response to a large-scale surface reemergence of ocean temperature anomalies. However, this linkage had been robust only until the early 2000s. Since then, the winter WACE variability has been strongly related to autumnal SST anomalies in the Barents Sea region and the North Pacific.

Efficient Verilog-A based time-domain variability analysis method for passive photonic integrated circuits

The performance of silicon photonic integrated circuits (PICs), especially wavelength filters, can be highly sensitive to variations in the fabrication process due to the large refractive index contrast of the silicon on insulator platform. This paper proposes an easy-to-implement and efficient time-domain variability analysis method for passive PICs. The method utilizes the polynomial chaos expansion technique to construct Verilog-A based models for estimating the statistical information of stochastic passive PICs. In comparison to existing methods, this approach is considerably easy to implement, efficient, and exhibits superior scalability, particularly as the numbers of ports and random parameters in the studied PICs increase. The technique is demonstrated via the time-domain variability analysis of a ring-resonator-based wavelength filter and a Mach-Zehnder interferometer-based demultiplexer filter.

Reconstructing long-term natural flows by ensemble machine learning

Large-scale human activities have caused significant disruption to the natural flow regime of rivers worldwide, leading to substantial deviations from river flows’ natural state. The natural hydrological regime of rivers holds great significance for the conservation and management of riverine ecosystems, which relies on the accurate reconstruction of natural river flows. However, the application of existing high spatiotemporal resolution large-scale natural flow datasets at the regional scale has not been adequately assessed, thereby limiting their regional-scale utility. In this study, utilizing global reanalysis datasets and observation data, in combination with ensemble machine learning models, we constructed long-term natural flow time series for inflows into the Pearl River Delta and evaluated the performance of these reconstructed inflows based on observed flow records during undisturbed periods. The reconstruction shows that the ensemble model, the AutoGluon model, indicates the best performance when compared with other machine learning models and outperforms the existing publicly available large-scale datasets in terms of evaluation metrics for the corresponding natural flows. The results of Indicator of Hydrologic Alteration and Range of Variability Analysis (IHA-RVA) indicate that climate variability, reservoir regulation and human activities have significantly altered the natural flow regime of the Pearl River Delta. Specifically, human activities generally tend to smooth natural flow variability in the Pearl River Delta. This research is expected to provide an efficient and reliable method for reconstructing natural flows and offers guidance for water resource management and ecosystem conservation.

Assessment of Stress and Well-Being of Japanese Employees Using Wearable Devices for Sleep Monitoring Combined With Ecological Momentary Assessment: Pilot Observational Study.

Poor sleep quality can elevate stress levels and diminish overall well-being. Japanese individuals often experience sleep deprivation, and workers have high levels of stress. Nevertheless, research examining the connection between objective sleep assessments and stress levels, as well as overall well-being, among Japanese workers is lacking. This study aims to investigate the correlation between physiological data, including sleep duration and heart rate variability (HRV), objectively measured through wearable devices, and 3 states (sleepiness, mood, and energy) assessed through ecological momentary assessment (EMA) and use of rating scales for stress and well-being. A total of 40 office workers (female, 20/40, 50%; mean age 40.4 years, SD 11.8 years) participated in the study. Participants were asked to wear a wearable wristband device for 8 consecutive weeks. EMA regarding sleepiness, mood, and energy levels was conducted via email messages sent by participants 4 times daily, with each session spaced 3 hours apart. This assessment occurred on 8 designated days within the 8-week timeframe. Participants' stress levels and perception of well-being were assessed using respective self-rating questionnaires. Subsequently, participants were categorized into quartiles based on their stress and well-being scores, and the sleep patterns and HRV indices recorded by the Fitbit Inspire 2 were compared among these groups. The Mann-Whitney U test was used to assess differences between the quartiles, with adjustments made for multiple comparisons using the Bonferroni correction. Furthermore, EMA results and the sleep and HRV indices were subjected to multilevel analysis for a comprehensive evaluation. The EMA achieved a total response rate of 87.3%, while the Fitbit Inspire 2 wear rate reached 88.0%. When participants were grouped based on quartiles of well-being and stress-related scores, significant differences emerged. Specifically, individuals in the lowest stress quartile or highest subjective satisfaction quartile retired to bed earlier (P<.001 and P=.01, respectively), whereas those in the highest stress quartile exhibited greater variation in the midpoint of sleep (P<.001). A multilevel analysis unveiled notable relationships: intraindividual variability analysis indicated that higher energy levels were associated with lower deviation of heart rate during sleep on the preceding day (β=-.12, P<.001), and decreased sleepiness was observed on days following longer sleep durations (β=-.10, P<.001). Furthermore, interindividual variability analysis revealed that individuals with earlier midpoints of sleep tended to exhibit higher energy levels (β=-.26, P=.04). Increased sleep variabilities, characterized by unstable bedtime or midpoint of sleep, were correlated with elevated stress levels and diminished well-being. Conversely, improved sleep indices (eg, lower heart rate during sleep and earlier average bedtime) were associated with heightened daytime energy levels. Further research with a larger sample size using these methodologies, particularly focusing on specific phenomena such as social jet lag, has the potential to yield valuable insights. UMIN-CTR UMIN000046858; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053392.

Analysis of sea level variability and its contributions in the Bohai, Yellow Sea, and East China Sea

Understanding the sea level variability of the Bohai, Yellow Sea, and East China Sea (BYECS) is crucial for the socio-cultural and natural ecosystems of the coastal regions. In this study, based on satellite altimetry data, selected time range from 1993 to 2020, using the cyclostationary empirical orthogonal function (CSEOF) analysis method distinguishes the primary sea level variability modes. The analysis encompasses the seasonal signal, trend, and El Niño-Southern Oscillation (ENSO) associated mode of sea level anomaly. The amplitude of the annual cycle demonstrates a non-stationary signal, fluctuating between -15% and 15% from the average. Monsoons, atmospheric forcing, ocean circulation, wind-driven Ekman transport, and the Kuroshio emerge as the primary factors influencing BYECS variability on seasonal scales. The satellite altimetry sea level exhibits an average trend within the range of 3-4 mm/year, while the steric sea level trend is generally smaller, falling within the range of 0-2 mm/year. Throughout the entire period, the contribution of steric sea level to the mean sea level trend consistently remains below 25%. Furthermore, BYECS sea level variations have a sensitive response to strong El Niño years, with a clear regionalization of the response, which is related to the intricate atmospheric circulation and local wind pressures, as well as the influence of ocean circulation. In conclusion, we gained a more comprehensive understanding of sea level variability in the BYECS, especially the annual cycle of sea level amplitude and the response of ENSO. However, more studies still need to be done to differentiate the various factors in sea level variations.

Does cardiovascular autonomic modulation correspond to the increase in dim-light onset of melatonin? A preliminary analysis

Introduction: The endogenous circadian system promotes the rise in melatonin approximately every 24 hours. In the laboratory, dim light melatonin onset (DLMO) is used as the gold standard method to assess circadian phase. Exogenous melatonin affects cardiac sympathovagal balance and vasomotor sympathetic modulation. Whether or not the endogenous increase in melatonin corresponds to changes in cardiovascular autonomic modulation is unclear. Objective: To evaluate if the timing of DLMO corresponds to the timing of changes in cardiovascular autonomic indices. Methods: Five healthy adults (3 females, age 23.8±SD1.8 y) completed a minimum of four nights of sleep regularization by maintaining a self-selected 8h in bed each night before the experiment. The day after regularization, they reported to the laboratory ~8 hours before bedtime. Measurements for melatonin and cardiovascular autonomic modulation started 6 hours before bedtime and continued 1 hour after. Saliva samples were collected every 30 minutes. At the end of every hour, 10 minutes of continuous R-R intervals (R-R) and systolic blood pressure (SBP) were recorded via electrocardiogram and beat-to-beat finger photoplethysmography, respectively. As currently established, DLMO was defined as the interpolated time for melatonin increase over 3pg/ml. Cardiovascular autonomic indexes were run through CardioSeries v.2 to create a temporal series for R-R interval and systolic blood pressure. Cardiac autonomic modulation was assessed by applying the spectral analysis using the fast Fourier transformation method and time domain analysis of heart rate variability (HRV) and spectral analysis of blood pressure variability (BPV). For each autonomic index, data were plotted and fitted with a segmental linear regression to reveal the intercept of the two segments to define the autonomic onset (AO). A paired t-test compared the difference between DLMO and AO times. Bland-Altman plots with a 95% limit of agreement (LOA) identified the distribution of differences between the methods. Additionally, a one-sample t-test compared the differences between methods to a fixed time clock range. Data are shown as mean ± SD and significance was set as p&lt;0.05. Results: AO time calculated for all indexes from HRV and BPV were not statistically different compared to DLMO time (R-R: +16±86 mins, P=0.70; root mean square of successive differences (RMSSD): -20±91 mins, P=0.67; low frequency (LF) nu: +29±52 mins, P=0.74; high frequency (HF) nu: -43±91 mins, P=0.23; LFHF: -26±73 mins, P=0.47; SBP: -14±104 mins, P=0.78; LFSBP: -6±107 mins, P=0.90). The Bland Altman plots did not identify any systematic difference between AO and DLMO times. The smallest difference was LFnu with a LOA 95% from -73 to +131 minutes, while the largest difference was LFSBP with LOA 95% from -217 to +204 minutes. Additionally, the difference between DLMO and AO LFnu times (LFnu: +29±52) was statistically similar from -36 to +95 minutes. Conclusion: In young healthy adults, our preliminary data shows that AO times of cardiovascular autonomic indexes correspond to salivary DLMO. A larger dataset with more frequent simultaneous measurements of melatonin and cardiovascular autonomic modulation are essential to confirm our preliminary findings. Medical Research Foundation of Oregon, OHSU OFDIR, R01HL163232. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Effect of Breathing Room Air Through a Two-Way Non-Rebreathing Valve on HRV in BI and NBI College-Aged Students

Behavioral inhibition (BI) is a temperament associated with increased vulnerability to stress compared to non-behaviorally inhibited (NBI) individuals. Heart-rate variability (HRV) analysis to assess physiological stress has been growing in popularity due to its association with crucial physiological functions, including cardiac activity, autonomic nervous system activity, and physiologic stress. Previous studies in our lab have demonstrated that HRV in female college-age students expressing a BI temperament is significantly lower than in NBI individuals while breathing room air through a two-way non-rebreathing valve. These findings suggested that the parasympathetic nervous system is partially inhibited at rest in BI females; however, it is unknown if the parasympathetic nervous system in these individuals is inhibited prior to or after breathing through a breathing valve. Our current study will address this question by measuring HRV in BI and NBI individuals while breathing room air for 15 minutes without a breathing valve and for 10 minutes with a two-way breathing valve. Raw electrocardiogram signals will be collected throughout the study protocol and will be used to calculate HRV, which will be measured using a BIOPAC data acquisition system during the experiment. BI individuals will be identified through a series of self-reported questionnaires. Our preliminary results indicate that BI individuals have a lower HRV and are stressed throughout the duration of the study. Thank you to Carthage College's Biology and Neuroscience Department for allowing this research project to be conducted. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Relation between heart rate variability and spectral analysis of electroencephalogram in chronic neuropathic pain patients.

Chronic neuropathic pain (CNP) is a complex condition often arising from neural maladaptation after nerve injury. Understanding CNP complications involves the intricate interplay between brain-heart dynamics, assessed through quantitative electroencephalogram (qEEG) and heart rate variability (HRV). However, insights into their interaction in chronic pain are limited. Resting EEG and simultaneous electrocardiogram (lead II) of the participants were recorded for qEEG and HRV analysis. Correlations between HRV and qEEG parameters were calculated and compared with age, sex, and body mass index (BMI)-matched controls. CNP patients showed reduced HRV and significant increases in qEEG power spectral densities within delta, theta, and beta frequency ranges. A positive correlation was found between low frequency/ high frequency (LF/HF) ratio in HRV analysis and theta, alpha, and beta frequency bands in qEEG among CNP patients. However, no significant correlation was observed between parasympathetic indices and theta, beta bands in qEEG within CNP group, unlike age, sex, and BMI-matched healthy controls. CNP patients display significant HRV reductions and distinctive qEEG patterns. While healthy controls exhibit significant correlations between parasympathetic HRV parameters and qEEG spectral densities, these relationships are diminished or absent in CNP individuals. LF/HF ratio, reflecting sympathovagal balance, correlates significantly with qEEG frequency bands (theta, alpha, beta), illuminating autonomic dysregulation in CNP. These findings emphasize the intricate brain-heart interplay in chronic pain, warranting further exploration.

Genetic variability, correlation studies and path analysis in sugarcane early clones

Genetic variability, correlation studies and path analysis in sugarcane early clones

Factors Influencing Quetiapine Pharmacokinetic Variability: A Review of Population Pharmacokinetics

Abstract: Atypical antipsychotic quetiapine (QTP) exhibits high pharmacokinetic variability and population pharmacokinetic (PopPK) analysis is one of the approaches used to characterize factors influencing QTP pharmacokinetic variability. Though QTP is not regarded as a narrow therapeutic index drug, knowledge of this area is of importance. Thus, this review was conducted to summarize significant predictors for QTP pharmacokinetic variability identified using a PopPK analysis and to explore any knowledge gaps to be investigated. PubMed, Scopus, and CINAHL Complete databases were searched for eligible studies, and 75 articles were identified. Of these, only five studies were included as they were conducted using a nonlinear mixed-effects approach. This review found that only limited predictors for QTP pharmacokinetics were identified, with body weight being a predictor for the volume of distribution and age and γ-glutamyl transpeptidase being predictors for QTP clearance. None of the studies included elderly patients aged &gt;65 years, and thus factors associated with aging were not investigated. Also, most of the participants in the PopPK analyses were from clinical trials which might not reflect real-world patients e.g., the impacts of polypharmacy may not be available. Moreover, while a population pharmacokinetic-pharmacodynamic model explaining QTP exposure and clinical response using the scores of the Brief Psychiatric Rating Scale is available, knowledge relevant to the relationship between exposure and QTP side effects has not been explored. Based on this limited information, future PopPK research encompassing a wide range of patient characteristics is required.

Diagnostic performance of artificial intelligence in interpreting thyroid nodules on ultrasound images: a multicenter retrospective study.

Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis. This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers. We included patients who underwent thyroid ultrasonography complemented by ultrasound-guided fine needle aspiration (FNA) between January 2015 and March 2021. Participants formed a consecutive series, enhancing the study's validity. A comparative analysis was conducted between the AI model's diagnostic performance and that of both an experienced radiologist and a third-year radiology resident, using a dataset of 600 ultrasound images from three distinguished Thai medical institutions, each verified with cytological findings. The AI system demonstrated superior diagnostic performance, with an overall sensitivity of 80% [95% confidence interval (CI): 59.3-93.2%] and specificity of 71.4% (95% CI: 53.7-85.4%). At Siriraj Hospital, the AI achieved a sensitivity of 90.0% (95% CI: 55.5-99.8%), specificity of 100.0% (95% CI: 69.2-100%), positive prediction value (PPV) of 100.0%, negative prediction value (NPV) of 90.9%, and an overall accuracy of 95.0%, indicating the benefits of AI's extensive training across diverse datasets. The experienced radiologist's sensitivity was 40.0% (95% CI: 21.1-61.3%), while the specificity was 80.0% (95% CIs: 63.6-91.6%), respectively, showing that the AI significantly outperformed the radiologist in terms of sensitivity (P=0.043) while maintaining comparable specificity. The inter-observer variability analysis indicated a moderate agreement (K=0.53) between the radiologist and the resident, contrasting with fair agreement (K=0.37/0.33) when each was compared with the AI system. Notably, 95% CIs for these diagnostic indexes highlight the AI system's consistent performance across different settings. The findings advocate for the integration of AI into clinical settings to enhance the diagnostic accuracy of radiologists in assessing thyroid nodules. The AI system, designed as a supportive tool rather than a replacement, promises to revolutionize thyroid nodule diagnosis and management by providing a high level of diagnostic precision.

Cardiorespiratory Disturbances in Alzheimer's Disease: A Focus on the Contribution of Sympathetic Premotor Neurons

While therapeutic approaches targeting neurodegenerative mechanisms associated with cognitive impairment in Alzheimer's disease (AD) have been intensively explored, other comorbidities closely linked to AD progression, such as sleep-disordered breathing and autonomic dysfunction, have not been thoroughly investigated. In preliminary studies, we showed an increase in neural activity of catecholaminergic premotor neurons located in the rostral ventrolateral medulla region (RVLM-C1), a main region involved in cardiorespiratory regulation, in experimental AD. Therefore, in the present study, we aimed first to characterize cardiorespiratory function in AD patients and then to determine the role of RVLM-C1 neurons in autonomic and sleep-disordered breathing in APP/PS1 double transgenic mice, an experimental model showing AD-like pathology. Sixteen subjects (8 AD and 8 age-matched controls) were enrolled to assess sleep breathing disorders and to record cardiac autonomic function. Whole-body plethysmography and blood pressure monitoring in freely moving mice were used to study sleep-associated cardiorespiratory disorders. Bilateral stereotaxic injections of anti-dopamine β-hydroxylase-saporin (DβH-SAP) into the RVLM were used to selectively destroy C1 neurons. Compared to age-matched healthy subjects, patients with AD showed larger increases in neural sympathetic discharges during autonomic testing. In addition, heart rate variability analysis in AD patients showed a shift in spectral components towards a more sympathetic influence. Remarkably, we found that autonomic function impairment in the APP/PS1 mice resembles what we found in AD patients. Compared to wild type (WT), APP/PS1 mice displayed sympatho-excitation (ΔHR: -80±5 vs -125±10, WT vs APP/PS1, respectively), a higher incidence of cardiac arrhythmias (events/h 6.0±1.0 vs 12±1.5, WT vs APP/PS1, respectively), and sleep-disordered breathing (AHI: 8.5±2.0 vs 18.0±3.0, WT vs APP/PS1, respectively). Partial ablation of RVLM-C1 neurons (~70%) in APP/PS1 mice resulted in a two-fold and three-fold reduction in cardiac sympathetic drive and arrhythmogenesis, respectively. Furthermore, APP/PS1 mice treated with DβH-SAP showed marked improvements in breathing regularity (IS: 7.9±1.1 vs 16.9±2.2, APP/PS1DβH-SAP vs APP/PS1, respectively) and a restoration of normal sleep effciency (SE: 75.5±4.3 vs 58.1±6.4, APP/PS1DβH-SAP vs APP/PS1, respectively) compared to untreated APP/PS1 mice. Our results show that RVLM-C1 neurons play a main role in the development/maintenance of cardiorespiratory disorders in experimental AD. FONDECYT 11220962, 1220950. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position and self-reported sleep

Objective. This study aimed to examine differences in heart rate variability (HRV) across accelerometer-derived position, self-reported sleep, and different summary measures (sleep, 24 h HRV) in free-living settings using open-source methodology. Approach. HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate (HR) and accelerometry data were collected using single-lead electrocardiography and trunk- and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files were processed and analysed in the RHRV R package. Start time and duration spent in physical behaviours were extracted, and time and frequency analysis for each episode was performed. Differences in HRV estimates across activities were compared using linear mixed models adjusted for age and sex with subject ID as random effect. Next, repeated-measures Bland–Altman analysis was used to compare 24 h RMSSD estimates to HRV during self-reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the approach of employing accelerometer-determined episodes to examine activity-independent HRV was described. Main results. HRV was estimated for 31 289 episodes in 160 individuals (53.1% female) at a mean age of 41.4 years. Significant differences in HR and most markers of HRV were found across positions [Mean differences RMSSD: Sitting (Reference) − Standing (−2.63 ms) or Lying (4.53 ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor agreement between 24 h estimates compared to sleep HRV was detected. Sensitivity analyses confirmed that removing the first and last 30 s of accelerometry-determined HR episodes was an accurate strategy to account for orthostatic effects. Significance. Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep. The proposed approach for free-living HRV analysis may be an effective strategy to remove confounding by physical activity when the aim is to monitor general autonomic stress.