After procured rat cortical slices had equilibrated with room temperature, some slices were used for viable imaging experiments (described below) and some slices from each rat were immersed in various aldehyde fixative solutions. This study design used immersion fixation methods to control for the effects of slice procurement in the viable, unfixed treatment group and to model the treatment of human autopsy or biopsy samples. However, it should be noted that perfusion and immersion fixation methods may affect the MRI properties of tissue differently. The fixative solutions consisted of phosphate-buffered saline (PBS) (290 mOsm/kg) with 4% formaldehyde, 4% glutaraldehyde, or 2% formaldehyde plus 2% glutaraldehyde (referred to as Karnovsky's solution) (24 ). All solutions had a pH of 7.4. The cortical slice samples were immersed in a volume excess of their respective fixative solutions (>100:1) at room temperature for 3–4 h, then stored in a similar volume of fresh fixative solution at 4°C for 10+ days to complete the chemical reactions of fixation. After this period, the slices were gradually equilibrated to room temperature, then imaged in the perfusion chamber while immersed in their respective fixative solutions. After these MRI measurements (described below), the samples were washed over 12 h with four to five PBS solution changes at room temperature and then reimaged using the perfusion chamber setup while immersed in PBS.
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Body Temperature Changes
Body Temperature Changes
Body Temperature Changes refers to the physiological adjustments in an organism's core temperature in response to internal or external factors.
This may include increases (hyperthermia) or decreases (hypothermia) in temperature, which can have significant impacts on health and function.
Factors that can influence body temperature include environmental conditions, physical activity, illness, and underlying medical conditions.
Understanding the mechanisms and patterns of body temperature changes is crucial for research and clinical applications related to thermoregulation, thermal stress, and temperature-related disorders.
This MeSH term provides a comprehensive overview of the topic to guide reasearch and clinical practice.
This may include increases (hyperthermia) or decreases (hypothermia) in temperature, which can have significant impacts on health and function.
Factors that can influence body temperature include environmental conditions, physical activity, illness, and underlying medical conditions.
Understanding the mechanisms and patterns of body temperature changes is crucial for research and clinical applications related to thermoregulation, thermal stress, and temperature-related disorders.
This MeSH term provides a comprehensive overview of the topic to guide reasearch and clinical practice.
Most cited protocols related to «Body Temperature Changes»
Aldehydes
Autopsy
Biopsy
Body Temperature Changes
Fixatives
Formaldehyde
Glutaral
Homo sapiens
Kidney Cortex
Perfusion
Phosphates
Saline Solution
Submersion
Tissues
Anabolism
Biological Assay
Body Temperature Changes
Buffers
Cations
Cations, Divalent
Diamines
Fluorescence
Generic Drugs
inhibitors
Ligands
Molecular Probes
Nucleotides
prisma
Protein Denaturation
Proteins
Pyrimidines
Real-Time Polymerase Chain Reaction
Sodium Chloride
Sulfoxide, Dimethyl
Titrimetry
Tromethamine
As the distribution of survival times may undergo complex changes across a range of temperatures we also chose to construct a non-parametric model that would not share the same restrictions as the aforementioned parametric models. For this we chose regression spline generalised additive models (GAMs) which apply a smoothing variable to the explanatory variables in order to model the response variable [58 ]. This method has the advantage of being able to model unknown and non-linear effects of covariates and thus elucidate the potentially complex effect of temperature on adult mosquito survival.
To evaluate the improvement of using GAMs over the parametric alternatives, we fitted parametric and GAM models to each laboratory experiment and calculated the difference in AIC between parametric and non-parametric models across all experiments.
A second GAM was then formulated to use the data from all experiments in one model to recreate the relationship between survival, time and temperature. The GAM was formulated as follows:
Sij = number of mosquitoes surviving at observation i in experiment jNij = number of mosquitoes at start of time step at i, jPij = survival probability for a mosquito at i, jf() = smooth term
Di = day of observation iTi = temperature of observation iϵj = random error term for experiment jϵd = random error term for mosquito diet dθj2 = variance across experiments
θd2 = variance across mosquito diets
Smoothing parameters were selected by restricted maximum likelihood with a data-driven basis dimension choice of kD = 8 and 5 and kT = 5 and 5 for Ae. aegypti and Ae. albopictus respectively [59 ]. Confidence intervals for the interquartile range of predictions were obtained by bootstrapping with 200 repeats, each the size of the original dataset. This model was fit using D ≥ 1 to be consistent with the experimental observations that record mortality. Extrapolated model predictions for 0 ≤ D < 1 were scaled proportionally to ensure 100% survival at D = 0. All GAMs were implemented using the “mgcv” package in R [60 (link)].
For the model to fit biologically appropriate responses, additional data defining the limits of prediction were required. Observations from Christophers [18 ], suggest 4°C and 42-43°C as suitable minimum and maximum critical temperatures at which survival of Ae. aegypti is minimal (<24 hours). Similar observations for Ae. albopictus suggest values between -5°C and 40–40.6°C [61 ,62 (link)]. To constrain mortality in the model, all non right censored experimental observations were extended to 120 days at 0% survival and a maximum lifetime of 120 days was imposed at all temperatures, the maximum longevity observed in our dataset. Furthermore, to produce meaningful estimates of longevity, survival of less than 0.1% of the initial mosquito population was considered sufficient to indicate complete mortality.
To evaluate the improvement of using GAMs over the parametric alternatives, we fitted parametric and GAM models to each laboratory experiment and calculated the difference in AIC between parametric and non-parametric models across all experiments.
A second GAM was then formulated to use the data from all experiments in one model to recreate the relationship between survival, time and temperature. The GAM was formulated as follows:
Sij = number of mosquitoes surviving at observation i in experiment jNij = number of mosquitoes at start of time step at i, jPij = survival probability for a mosquito at i, jf() = smooth term
Di = day of observation iTi = temperature of observation iϵj = random error term for experiment jϵd = random error term for mosquito diet dθj2 = variance across experiments
θd2 = variance across mosquito diets
Smoothing parameters were selected by restricted maximum likelihood with a data-driven basis dimension choice of kD = 8 and 5 and kT = 5 and 5 for Ae. aegypti and Ae. albopictus respectively [59 ]. Confidence intervals for the interquartile range of predictions were obtained by bootstrapping with 200 repeats, each the size of the original dataset. This model was fit using D ≥ 1 to be consistent with the experimental observations that record mortality. Extrapolated model predictions for 0 ≤ D < 1 were scaled proportionally to ensure 100% survival at D = 0. All GAMs were implemented using the “mgcv” package in R [60 (link)].
For the model to fit biologically appropriate responses, additional data defining the limits of prediction were required. Observations from Christophers [18 ], suggest 4°C and 42-43°C as suitable minimum and maximum critical temperatures at which survival of Ae. aegypti is minimal (<24 hours). Similar observations for Ae. albopictus suggest values between -5°C and 40–40.6°C [61 ,62 (link)]. To constrain mortality in the model, all non right censored experimental observations were extended to 120 days at 0% survival and a maximum lifetime of 120 days was imposed at all temperatures, the maximum longevity observed in our dataset. Furthermore, to produce meaningful estimates of longevity, survival of less than 0.1% of the initial mosquito population was considered sufficient to indicate complete mortality.
Adult
Body Temperature Changes
Culicidae
Diet
The methodology for measuring endothelial function and vascular reactivity using DTM has been previously described [21 (link)–25 (link)]. All DTM tests were performed using a VENDYS® 6000 Portable System (Endothelix, Houston, TX), a PC-based system that fully automates the cuff reactive hyperemia protocol. The general test setup and a sample VENDYS test report are shown in Figure 1 . During subject preparation, blood pressure cuffs were placed on both of the subject's upper arms, and VENDYS skin temperature sensors were affixed to both of the subject's index fingers. The software-driven DTM test began with an automated measurement of blood pressure and heart rate obtained from the left arm cuff. Following a 5-minute period of patient and temperature stabilization, a 5-minute cuff occlusion (cuff inflated to 30 mmHg above systolic BP) of the right arm was performed. During the cuff occlusion period, fingertip temperature in the right hand decreased because of the absence of warm circulating blood. When the cuff was released after the 5-minute occlusion, hyperemic blood flow to the forearm and hand was restored, and this resulted in a “temperature rebound” in the fingertip that is directly related to the subject's hyperemic blood flow response, endothelial function, and vascular reactivity [21 (link), 22 (link)]. Using the recorded fingertip temperatures, the ambient temperature of the testing room, the observed slope of temperature decline, and a multivariate bioheat formula, the VENDYS software calculated and plotted a zero reactivity curve (ZRC). The ZRC served as an internal control and showed the expected temperature rebound curve, if zero vascular reactivity was present and the other variables remained the same. In other words, the ZRC is the expected temperature curve, if no vasodilatation and subsequent reactive hyperemia had occurred [21 (link)]. Vascular reactivity index (VRI) was determined by taking the maximum difference between the observed temperature rebound curve and the ZRC during the reactive hyperemia period. VRI ranged from 0.0 to 3.5 and was classified as being indicative of poor (0.0 to <1.0), intermediate (1.0 to <2.0), or good (≥2.0) vascular reactivity.
The VENDYS DTM Test Registry includes age, sex, blood pressure, heart rate, VRI, and fingertip temperature measurements recorded during DTM tests. The Registry does not include other health related information. All DTM tests were performed in ambulatory care clinical settings. This study includes a total of 6,084 patients from 18 clinics that volunteered to submit their data to the Registry. The number of each type of medical practice is as follows: cardiology = 9, general/family practice = 4, antiaging = 3, and internal medicine = 2.
Statistical analyses were performed using MATLAB (The MathWorks, Inc., Natick, MA). Variable data were expressed as mean ± SD. VRI scores in men and women were compared using unpaired Student's t-test. Comparisons of categorical data (e.g., proportion of subjects with good VRI in men versus women) were performed using Fisher's exact test. Pairwise correlations were examined using Pearson's correlation coefficient, and correlations between VRI and multiple patient characteristics (i.e., age, sex, blood pressure, and heart rate) were evaluated using multiple linear regression analysis. p value < 0.05 was considered significant. When performing statistical comparisons, tests with missing data were excluded from the comparison. “Cold Finger Flag” was defined as the condition in which the right finger temperature at start of cuff occlusion (time 300 s) is ≤27°C. Previous DTM testing had shown that right finger t300 temperatures < 27°C often resulted in technically poor results. “Sympathetic Response Flag” was defined as the condition in which left finger temperature continuously declines (>0.5°C temperature drop over a 5-minute time period) after right arm-cuff occlusion. When evaluating VRI, tests that exhibited “Cold Finger Flag” (n = 353) or “Sympathetic Response Flag” (n = 294) were excluded from the analyses. In addition to monitoring temperature at the index finger of the right arm, we studied temperature changes at the index finger of the left (nonoccluded) arm and observed interesting signals that are currently under further investigations and not included in the results below.
The VENDYS DTM Test Registry includes age, sex, blood pressure, heart rate, VRI, and fingertip temperature measurements recorded during DTM tests. The Registry does not include other health related information. All DTM tests were performed in ambulatory care clinical settings. This study includes a total of 6,084 patients from 18 clinics that volunteered to submit their data to the Registry. The number of each type of medical practice is as follows: cardiology = 9, general/family practice = 4, antiaging = 3, and internal medicine = 2.
Statistical analyses were performed using MATLAB (The MathWorks, Inc., Natick, MA). Variable data were expressed as mean ± SD. VRI scores in men and women were compared using unpaired Student's t-test. Comparisons of categorical data (e.g., proportion of subjects with good VRI in men versus women) were performed using Fisher's exact test. Pairwise correlations were examined using Pearson's correlation coefficient, and correlations between VRI and multiple patient characteristics (i.e., age, sex, blood pressure, and heart rate) were evaluated using multiple linear regression analysis. p value < 0.05 was considered significant. When performing statistical comparisons, tests with missing data were excluded from the comparison. “Cold Finger Flag” was defined as the condition in which the right finger temperature at start of cuff occlusion (time 300 s) is ≤27°C. Previous DTM testing had shown that right finger t300 temperatures < 27°C often resulted in technically poor results. “Sympathetic Response Flag” was defined as the condition in which left finger temperature continuously declines (>0.5°C temperature drop over a 5-minute time period) after right arm-cuff occlusion. When evaluating VRI, tests that exhibited “Cold Finger Flag” (n = 353) or “Sympathetic Response Flag” (n = 294) were excluded from the analyses. In addition to monitoring temperature at the index finger of the right arm, we studied temperature changes at the index finger of the left (nonoccluded) arm and observed interesting signals that are currently under further investigations and not included in the results below.
BLOOD
Blood Circulation
Blood Pressure
Blood Vessel
Body Temperature Changes
Cardiovascular System
Care, Ambulatory
Cold Temperature
Dental Occlusion
Determination, Blood Pressure
Endothelium
Fingers
Forearm
Hyperemia
Patients
Rate, Heart
Reactive Hyperemia
Skin Temperature
Systolic Pressure
Test Preparation
Vasodilation
Woman
Age Groups
Air Pollutants
Body Temperature Changes
Child
Cuboid Bone
Diagnosis
Humidity
Hypersensitivity
Physiological Stress
Pressure
Vorinostat
Wind
Most recents protocols related to «Body Temperature Changes»
TRPV4 agonist GSK1016790A (GSK) was purchased from Selleckchem (Houston, TX, Cat. #S6637). TRPV4 antagonist HC067047 (HC) was purchased from Sigma (Saint Louis, MO; Cat. # SML0143). Because TRPV4 can be activated by several stimuli, including heat and mechanical stress (Nilius et al., 2004 (link)), we avoided temperature changes and fast superfusion to study the separate effects of the chemicals. Thus, experiments were conducted at room temperature (22°C–24°C); agonist and antagonist were diluted in external solution and gently dripped onto the recording chamber for final concentrations of 10 nM GSK1016790A and 10 µM HC067047. For TRPV4 inhibition, cells were preincubated with HC067047 at room temperature for at least 30 min before the start of recordings.
Body Temperature Changes
Cells
GSK 1016790A
Psychological Inhibition
Stress, Mechanical
TRPV4 protein, human
The simultaneous determination of methane, carbon dioxide and nitrous oxide fluxes was calculated by using static black chamber method. We used closed box-gas chromatography (Zhang et al., 2015 (link)). Before transplanting rice to the plot, a PVC flux loop was permanently embedded in each plot to continuously monitor GHG emissions during the experiment period. A 5cm deep groove on the edge of each base was used to inject water and seal the gas chamber during gas production, in order to prevent gas exchange. The cross-sectional area of the gas tank was 0.25m2 (50cm* 50cm) and the height was 50cm. Once the rice grew taller, the height was increased to 1m.
Sponge and aluminum foil were wrapped around the exterior of the gas box to prevent drastic changes in temperature inside the chamber. The gas chamber was also equipped with a small fan to ensure that the gas in the box was fully mixed. Sampling was conducted 9:00 a.m. and 11:00 a.m. every time, using a 60ml medical syringe to collect gas from the top of the chamber, once every 5 minutes, for a total of four times (Hao et al., 2001 (link)).
Before gas chromatography analysis, the gas was transferred to a vacuum airbag for less than a day to ensure that the gas was not mixed with the outside environment. A gas chromatograph was equipped with an electron capture detector (ECD) and a flame ionization detector (Agilent 7890A network gas chromatograph, Gow Mac instruments, Bethlehem, PA, USA). It was used for the simultaneous analysis of methane, carbon dioxide, and nitrous oxide gas concentrations (Liu S. et al., 2016 (link)). The linear regression slope for the greenhouse gas concentration of the continuous samples was calculated, and the data with the linear regression value r2<0.9 was removed from the dataset for gas flux calculation (Liu et al., 2013 (link)). The gas flux calculation formula is as follows:
Where F is the gas emission flux (mg m-2 h-1), H is the height of the sampling chamber, ρ is the gas density in the standard state, and dC/dt is the slope of the concentration growth of the gas concentration fitted by a linear equation (mg m-3 h-1). T is the temperature in the sampling chamber at the time of sampling(°C). During the test, cumulative methane, carbon dioxide, and nitrous oxide emissions were sequentially accumulated from the flux of each two adjacent intervals. For N2O cumulative emission, the final value was multiplied by atomic mass of N2 divided by atomic mass of N2O (Liu et al., 2013 (link); Liu S. et al., 2016 (link); Iqbal et al., 2021 (link)). The gas flux calculation formula and the total cumulative GHG emission during one growing season was also calculated according to protocol given in (Iqbal et al., 2021 (link)).
Sponge and aluminum foil were wrapped around the exterior of the gas box to prevent drastic changes in temperature inside the chamber. The gas chamber was also equipped with a small fan to ensure that the gas in the box was fully mixed. Sampling was conducted 9:00 a.m. and 11:00 a.m. every time, using a 60ml medical syringe to collect gas from the top of the chamber, once every 5 minutes, for a total of four times (Hao et al., 2001 (link)).
Before gas chromatography analysis, the gas was transferred to a vacuum airbag for less than a day to ensure that the gas was not mixed with the outside environment. A gas chromatograph was equipped with an electron capture detector (ECD) and a flame ionization detector (Agilent 7890A network gas chromatograph, Gow Mac instruments, Bethlehem, PA, USA). It was used for the simultaneous analysis of methane, carbon dioxide, and nitrous oxide gas concentrations (Liu S. et al., 2016 (link)). The linear regression slope for the greenhouse gas concentration of the continuous samples was calculated, and the data with the linear regression value r2<0.9 was removed from the dataset for gas flux calculation (Liu et al., 2013 (link)). The gas flux calculation formula is as follows:
Where F is the gas emission flux (mg m-2 h-1), H is the height of the sampling chamber, ρ is the gas density in the standard state, and dC/dt is the slope of the concentration growth of the gas concentration fitted by a linear equation (mg m-3 h-1). T is the temperature in the sampling chamber at the time of sampling(°C). During the test, cumulative methane, carbon dioxide, and nitrous oxide emissions were sequentially accumulated from the flux of each two adjacent intervals. For N2O cumulative emission, the final value was multiplied by atomic mass of N2 divided by atomic mass of N2O (Liu et al., 2013 (link); Liu S. et al., 2016 (link); Iqbal et al., 2021 (link)). The gas flux calculation formula and the total cumulative GHG emission during one growing season was also calculated according to protocol given in (Iqbal et al., 2021 (link)).
Aluminum
Body Temperature Changes
Carbon dioxide
Electrons
Flame Ionization
Gas Chromatography
Greenhouse Gases
Methane
Oryza sativa
Phocidae
Porifera
Syringes
Vacuum
The magnitude of change in resonance frequency of an element is indicated by the average difference in frequency (MHz) and SD (shown in figures as the error bar). From the start of culture to the end of measurement, the entire region where the bacterial suspension was present on the element and for which no change of − 10 MHz or less was observed before or after injection of the drug solution was used as the measurement area. Of the 1488 elements on the sensor array, 745–1260 elements (average: 1039; SD: 150) were automatically selected by the computer program for use in analysis. In accordance with these criteria, sensor elements that lost bacteria from the near-field after the injection of antibacterial drug or liquid medium were removed by an automated program. The growth ability was indicated by the mean difference in resonance frequency (MHz) starting from 0 h and the SD. DST was performed at two concentrations for some drugs (Supplementary Table 1 ). There were three control experiments, and one experiment for each drug. Because the temperature was controlled within ± 0.1 °C, the effect of each drug on BCG was determined from the time course of the difference (MHz) of each sensor element without correcting for changes in temperature. A histogram analysis was performed at 16 h after drug administration by using elemental measurements from each sensor (Fig. 3 ). The data analysis and graphs were prepared by using GraphPad Prism™ (version 9.2.0, GraphPad Software, Inc., San Diego, CA).
Aftercare
Anti-Bacterial Agents
Bacteria
Body Temperature Changes
Indium
Investigational New Drugs
Pharmaceutical Preparations
Pharmaceutical Solutions
prisma
Vibration
We first explored the changes in the intraoperative core body temperature. The body temperature trend was estimated using linear mixed models, regressing core temperature against time from anesthesia induction, using a compound-symmetric correlation matrix, and adjusting for the baseline temperature of the patients. The nonlinearity of the effect of time on the core temperature was explained using B-splines. Since the core temperature trend was changed by surgical duration, we separately plotted the curves for patients with surgical durations of 2–3 h, 3–4 h, and 4–6 h.
Although the blood loss, infusion, transfusion and irrigation are important factors for hypothermia, it cannot be accurately predicted before surgery. Therefore, we just described the data as median, first quartile (Q1), third quartile (Q3) instead of considering these factors when constructing the model.
To assess the potential risk factors for IOH in patients undergoing robotic surgery, we first summarized patient profiles by the incidence of IOH via standardized summary statistics as means ± standard deviation or n (%). Furthermore, univariate comparisons for patients with or without IOH were performed using the t-test and chi-square for continuous and categorical variables, respectively.
Subsequently, the selection of risk factors in a multivariable model was performed using backward elimination, retaining variables with P-values < 0.05. A fivefold cross-validated AUROC and its 95% CI were reported for the internal validation of the model. In addition, we compared our model’s predictability to that proposed by Yi et al.22 (link), and the AUROC was compared using the DeLong’s method23 (link). The final model, which included all patients, was reported and summarized into a nomogram.
Although the blood loss, infusion, transfusion and irrigation are important factors for hypothermia, it cannot be accurately predicted before surgery. Therefore, we just described the data as median, first quartile (Q1), third quartile (Q3) instead of considering these factors when constructing the model.
To assess the potential risk factors for IOH in patients undergoing robotic surgery, we first summarized patient profiles by the incidence of IOH via standardized summary statistics as means ± standard deviation or n (%). Furthermore, univariate comparisons for patients with or without IOH were performed using the t-test and chi-square for continuous and categorical variables, respectively.
Subsequently, the selection of risk factors in a multivariable model was performed using backward elimination, retaining variables with P-values < 0.05. A fivefold cross-validated AUROC and its 95% CI were reported for the internal validation of the model. In addition, we compared our model’s predictability to that proposed by Yi et al.22 (link), and the AUROC was compared using the DeLong’s method23 (link). The final model, which included all patients, was reported and summarized into a nomogram.
Anesthesia
Blood Transfusion
Body Temperature Changes
Hemorrhage
Operative Surgical Procedures
Patients
Robotic Surgical Procedures
Four temperature-controlled chambers (6m×3m×2.5m) were used to study the impact of high night temperature. According to the average daily and nightly temperatures (28°C and 18°C) and the sunlight duration (12.5 h) during seed filling stage (September) in Beijing, all the plants were exposed to 18°C or 28°C night temperature with a natural day temperature and 12-h light (7 pm - 7 am) treatment. Each night temperature treatment corresponded to a chamber installed with two platform trailers, and the pots were placed on the trailers during the experimental period. During the day time, the trailers were pushed outside the chambers, and all the plants grew under the natural light and day temperature, and at night they were pushed into the chambers with different temperature treatment.
Temperature and humidity data were recorded every 30 min from the seed filling stage to the maturity stage using a temperature and humidity recorder (OM-EL-WIFI-TH, OMEGA Engineering, USA). The temperature changes during the treatment period (R5-R7) are shown inFigure S1
Temperature and humidity data were recorded every 30 min from the seed filling stage to the maturity stage using a temperature and humidity recorder (OM-EL-WIFI-TH, OMEGA Engineering, USA). The temperature changes during the treatment period (R5-R7) are shown in
Body Temperature Changes
Fever
Humidity
Light
Marijuana Abuse
Plants
Sunlight
Top products related to «Body Temperature Changes»
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More about "Body Temperature Changes"
Thermoregulation, Thermal Stress, Temperature Homeostasis, Hyperthermia, Hypothermia, Fever, Core Body Temperature, Pyrexia, Thermal Regulation, Metabolic Rate, Thermogenesis, Evaporative Cooling, Vasodilation, Vasoconstriction, Shivering, Sweating, Heat Stroke, Frostbite, Raynaud's Phenomenon, Malignant Hyperthermia, Neuroendocrine Control, Autonomic Nervous System, Environmental Factors, Exercise, Illness, Medical Conditions.
Leverage tools like Prometheus NT.48, FLIR ONE, MATLAB, GraphPad Prism 5, Prism 8, Prism 6, Ti400, CFX96 real-time PCR cycler, and SAS 9.4 to optimize your body temperature research and identify the best protocols for your studies.
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Leverage tools like Prometheus NT.48, FLIR ONE, MATLAB, GraphPad Prism 5, Prism 8, Prism 6, Ti400, CFX96 real-time PCR cycler, and SAS 9.4 to optimize your body temperature research and identify the best protocols for your studies.
PubCompare.ai's AI-powered protocol comparison can help you find the top techniques from literature, pre-prints, and patents, ensuring reproducible, accurate results.
Stop wasting time - let our AI do the heavy lifting and discover the optimal body temperature protocols for your research!