Recommended timing of medications that impact sleep and wakefulness: A review of the American Prescribers' Digital Reference.

An appreciable number of medicines have a recommended unique single time-of-day or asymmetrical or unequal-interval multiple-daily administration schedule. Many prescription and over-the-counter (OTC) products, according to administration time, can exert positive or negative impact on nighttime sleep and daytime wakefulness. Intuitively, medicines used to manage nighttime sleep and daytime wake disorders should be taken, respectively, at night before bedtime and morning after arising. However, some utilized for other medical conditions, if improperly timed, may compromise nocturnal sleep and diurnal attentiveness. We conducted a comprehensive review of the American Prescribers' Digital Reference, internet version of the Physician's Desk Reference, for the recommended scheduling of medications and OTC remedies that can impact sleep and wakefulness. The search revealed several hundred therapies of various classes -- α2-receptor agonists, antidepressants, barbiturates, central nervous system stimulants, benzodiazepines, dopamine agonists, dopamine norepinephrine reuptake inhibitors, selective norepinephrine reuptake inhibitors, eugeroics, γ-aminobutyric acid modulators, H1 and H3-receptor antagonists, melatonin analogues, OTC melatonin-containing products, non-benzodiazepine benzodiazepine-receptor agonists, dual orexin-receptor antagonists, and serotonin modulators -- that have a recommended unique dosing schedule. The tables and text of this article are intended to guide the proper scheduling of these medicines to optimize desired and/or minimize undesired effects.

Interdependency of Core Temperature and Glabrous Skin Blood Flow in Human Thermoregulation Function: A Pilot Study.

Human thermoregulation is governed by a complex, nonlinear feedback control system. The system consists of thermoreceptors, a controller, and effector mechanisms for heat exchange that coordinate to maintain a central core temperature. A principal route for heat flow between the core and the environment is via convective circulation of blood to arteriovenous anastomoses located in glabrous skin of the hands and feet. This paper presents new human experimental data for thermoregulatory control behavior along with a coupled, detailed control system model specific to the interdependent actions of core temperature and glabrous skin blood flow (GSBF) under defined transient environmental thermal stress. The model was tuned by a nonlinear least-squared curve fitting algorithm to optimally fit the experimental data. Transient GSBF in the model is influenced by core temperature, nonglabrous skin temperature, and the application of selective thermal stimulation. The core temperature in the model is influenced by integrated heat transfer across the nonglabrous body surface and GSBF. Thus, there is a strong cross-coupling between GSBF and core temperature in thermoregulatory function. Both variables include a projection term in the model based on the average rates of their change. Six subjects each completed two thermal protocols to generate data to which the common model was fit. The model coefficients were unique to each of the twelve data sets but produced an excellent agreement between the model and experimental data for the individual trials. The strong match between the model and data confirms the mathematical structure of the control algorithm.

Novel temperature-controlled sleep system to improve sleep: a proof-of-concept study.

The sleep-wake cycle is regulated by circadian Process C and homeostatic Process S. Selective thermal stimulation (STS) of the cervical spine region enhances glabrous skin blood flow (GSBF) and augments body heat dissipation to increase distal-to-proximal skin gradient (DPG) causing decrease of core body temperature (CBT), which can shorten sleep onset latency (SOL) and improve sleep quality. A total of 11 young healthy/normal sleeper males challenged to go to bed (lights-off) 2 h earlier than usual were subjected in a randomised order to non-consecutive treatment and control night-time sleep sessions. The treatment night entailed activation of a dual-temperature zone mattress with a cooler centre and warmer periphery plus STS pillow that applied mild heating to the cervical spinal skin for 30 min after lights-off for sleep. During the first 30 min after lights-off, GSBF (mean [standard error (SE)] Δ = 49.77 [19.13] perfusion units, p = 0.013) and DPG (mean [SE] Δ = 2.05 [0.62] °C, p = 0.005) were significantly higher and CBT (mean [SE] Δ = -0.15 [0.07] °C, p = 0.029) was significantly lower in the treatment than control night, while there was no significant difference in these variables during the 45 min prior to lights-off (baseline). Moreover, SOL was significantly reduced (mean [SE] Δ = -48.6 [23.4] min, p = 0.032) and subjective sleep quality significantly better (p < 0.001) in the treatment than control night. In conclusion, the novel sleep facilitating system comprised of the STS pillow plus dual-temperature zone mattress induced earlier increase in GSBF and DPG and earlier decline in CBT. This resulted in statistically significant shortened SOL and improved overall sleep quality, thereby reducing sleep pressure of Process S, even under the challenging investigative protocol requiring participants to go to sleep 2 h earlier than customary.

Bioheat Transfer Basis of Human Thermoregulation: Principles and Applications.

Thermoregulation is a process that is essential to the maintenance of life for all warm-blooded mammalian and avian species. It sustains a constant core body temperature in the face of a wide array of environmental thermal conditions and intensity of physical activities that generate internal heat. A primary component of thermoregulatory function is the movement of heat between the body core and the surface via the circulation of blood. The peripheral vasculature acts as a forced convection heat exchanger between blood and local peripheral tissues throughout the body enabling heat to be convected to the skin surface where is may be transferred to and from the environment via conduction, convection, radiation, and/or evaporation of water as local conditions dictate. Humans have evolved a particular vascular structure in glabrous (hairless) skin that is especially well suited for heat exchange. These vessels are called arteriovenous anastomoses (AVAs) and can vasodilate to large diameters and accommodate high flow rates. We report herein a new technology based on a physiological principle that enables simple and safe access to the thermoregulatory control system to allow manipulation of thermoregulatory function. The technology operates by applying a small amount of heating local to control tissue on the body surface overlying the cerebral spine that upregulates AVA perfusion. Under this action, heat exchangers can be applied to glabrous skin, preferably on the palms and soles, to alter the temperature of elevated blood flow prior to its return to the core. Therapeutic and prophylactic applications are discussed.

The Circadian Rhythm of Thermoregulation Modulates both the Sleep/Wake Cycle and 24 h Pattern of Arterial Blood Pressure.

Borbély proposed an interacting two-component model of sleep regulation comprising a homeostatic Process S and a circadian Process C. The model has provided understanding of the association between core body temperature (CBT) as a key element of Process C that is deterministic of sleep onset and offset. However, it additionally provides a new perspective of the importance of the thermoregulatory mechanisms of Process C in modulating the circadian rhythm of arterial blood pressure (ABP). Herein, we examine the circadian physiology of thermoregulation, including at the end of the activity span the profound redistribution of cardiac output from the systemic circulation to the arteriovenous anastomoses of the glabrous skin that markedly enhances convective transfer of heat from the body to the environment to cause (i) decrease of the CBT as a pathway to sleep onset and (ii) attenuation of the asleep ABP mean and augmentation of the ABP decline (dipping) from the wake-time mean, in combination the strongest predictors of the risk for blood vessel and organ pathology and morbid and mortal cardiovascular disease events. We additionally review the means by which blood perfusion to the glabrous skin can be manipulated on demand by selective thermal stimulation, that is, mild warming, on the skin of the cervical spinal cord to intensify Process C as a way to facilitate sleep induction and promote healthy asleep ABP. © 2021 American Physiological Society. Compr Physiol 11:1-14, 2021.

A Conformable Two-Dimensional Resistance Temperature Detector for Measuring Average Skin Temperature.

Thermoregulation research and various medical procedures are accomplished by manipulating skin temperature in a nonuniform pattern. Skin temperature monitoring is essential to assess conformance to protocol specifications and to prevent thermal injury. Existing solutions for skin temperature monitoring include single point sensors, such as thermocouples, and two-dimensional methods of sensing surface temperature, such as infrared thermography, and wearable technology. Single point sensors cannot detect the average temperature and consequently their measurements cannot be representative of average surface temperature in a nonuniform temperature field. Infrared thermography requires optical access, and existing ambulatory sensors may require complex manufacturing processes and impede the heat exchange with a source by including a structural substrate layer. Our solution is a two-dimensional resistance temperature detector (two-dimensional (2D) RTD) created by knitting copper magnet wire into custom shapes. The 2D RTDs were calibrated, compared to one-dimensional sensors and wearable sensors, and analyzed for hysteresis, repeatability, and surface area conformation. Resistance and temperature were correlated with an R2 of 0.99. The 2D RTD proved to be a superior device for measuring average skin temperature over a defined area exposed to a nonuniform temperature boundary in the absence of optical access such as when a full body thermal control garment is worn.

Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.

Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3-6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2-34.3% higher than comparator IAs), and 58.7% Kappa agreement (16-23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters-sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs.

Application of deep learning to improve sleep scoring of wrist actigraphy.

BACKGROUND: Estimation of sleep parameters by wrist actigraphy is highly dependent on performance of the interpretative algorithm (IA) that converts movement data into sleep/wake scores. RESEARCH QUESTIONS: (1) Does the actigraphy mode of operation -Proportional Integrating Measure (PIM) or Zero Crossing Mode (ZCM), responsive respectively to intensity and frequency of movements- impact sleep scoring; and (2) Can a better performing sleep scoring IA be developed by a deep learning approach combining PIM/ZCM data. STUDY DESIGN AND METHODS: ZCM and PIM plus electroencephalographic (EEG) data of 40 healthy adults (17 female, mean age: 26.7 years) were obtained from a single in-home nighttime sleep study. Effect of mode of operation was first evaluated by applying several classic deep learning models to PIM only, ZCM only, and combined ZCM/PIM data. After, a novel deep learning model was developed incorporating combined ZCM/PIM data, and its performance was compared with existing Cole-Kripke, rescored Cole-Kripke, Sadeh, and UCSD IAs. RESULTS: Relative to the EEG reference, ZCM/PIM combined mode produced higher agreement of scoring sleep/wake epochs than only ZCM or PIM modes. The proposed novel deep learning model showed 87.7% accuracy (0.2-1% higher than the other IAs), 94.1% sensitivity (0.7-4.3% lower than the other IAs), 64.0% specificity (9.9-21.5% higher than the other IAs), and 59.9% Kappa agreement (∼6.9-11.6% higher than other IAs) in detecting sleep epochs. The proposed deep learning model did not differ significantly from the reference EEG in estimating sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE). Amount of bias and minimum detectable change in estimating SOL, WASO, TST and SE by the deep learning model was smaller than other four IAs. INTERPRETATION: The proposed novel deep learning algorithm simultaneously incorporating ZCM/PIM mode data performs significantly better in assessing sleep than existing conventional IAs.

Insights and Experiences in Academic Mentoring and Education.

It is a great honor to be named the awardee of the 2019 ASME Robert M. Nerem Education and Mentorship Medal. Bob Nerem has been a mentor to me since the beginning of my faculty career and has been a model to me for effectively dealing with the many dimensions of the interpersonal side of an academic career. This brief paper presents a summary of some of my personal insights and practices in this arena as gained during 46 years on the faculty of the University of Texas at Austin.

A comprehensive guideline for Bland-Altman and intra class correlation calculations to properly compare two methods of measurement and interpret findings.

The rapid emergence of new measurement instruments and methods requires personnel and researchers of different disciplines to know the correct statistical methods to utilize to compare their performance with reference ones and properly interpret findings. We discuss the often-made mistake of applying the inappropriate correlation and regression statistical approaches to compare methods and then explain the concepts of agreement and reliability. Then, we introduce the intraclass correlation as a measure of inter-rater reliability, and the Bland-Altman plot as a measure of agreement, and we provide formulae to calculate them along with illustrative examples for different types of study designs, specifically single measurement per subject, repeated measurement while the true value is constant, and repeated measurement when the true value is not constant. We emphasize the requirement to validate the assumptions of these statistical approaches, and also how to deal with violations and provide formulae on how to calculate the confidence interval for estimated values of agreement and intraclass correlation. Finally, we explain how to interpret and report the findings of these statistical analyses.

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages.

We compared performance in deriving sleep variables by both Fitbit Charge 2™, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing 'Sadeh' IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.

Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis.

BACKGROUND: Wearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way. OBJECTIVE: We conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages. METHODS: In adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria. RESULTS: The search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. CONCLUSIONS: Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data.

We compared performance of four popular interpretative algorithms (IAs), i.e., Cole-Kripke, Rescored Cole-Kripke, Sadeh, and UCSD, utilized to derive sleep parameters from wrist actigraphy data. We conducted in-home sleep study of 40 healthy adults (17 female/23 male; age 26.7 ± 12.1 years), assessing sleep variables both by Motionlogger® Micro Watch Actigraphy (MMWA) and Zmachine® Insight+ electroencephalography (EEG). Data of MMWA were separately scored per 30 sec epochs by each of the four popular IAs, and data of the Zmachine were also scored per 30 sec epochs by its proprietary IA. In reference to the EEG Zmachine method, all four of the MMWA algorithms showed high (~94 to 98%) sensitivity and moderate (~42 to 54%) specificity in detecting Sleep epochs. All of them significantly underestimated Sleep Onset Latency (SOL: ~9 to 20 min), and all of them, except the Sadeh IA, significantly underestimated Wake After Sleep Onset (WASO: ~22 to 25 min) and overestimated Total Sleep Time (TST: ~32 to 45 min) and Sleep Efficiency (SE: ~7 to 9%). The Sadeh IA showed significantly smaller bias than the other three IAs in deriving WASO, TST, and SE. Overall, application of 'Rescoring Rules' improved performance of the Cole-Kripke IA. The Sadeh and Rescored Cole-Kripke IAs exhibited highest agreement with the EEG Zmachine method (Cohen's Kappa: ~51%), while the UCSD IA exhibited lowest agreement (Cohen's kappa: ~47%). However, minimum detectable change across all sleep parameters was smallest with use of the UCSD IA and, except for SOL, largest with use of the Sadeh algorithm. Findings of this study indicate the Sadeh IA is most appropriate for deriving sleep parameters of healthy adults, while the UCSD IA is most appropriate for evaluating change in sleep parameters over time or in response to medical intervention.

Selective Thermal Stimulation Delays the Progression of Vasoconstriction During Body Cooling.

The objective of this study was to test the feasibility of selective thermal stimulation (STS) as a method to upregulate glabrous skin blood flow. STS is accomplished by mild surface heating along the spinal cord. Four healthy subjects were tested in this study. Each participated in a control experiment and an intervention experiment (STS). Both experiments included establishing a maximum level of vasodilation, considered unique to a subject on a test day, and then cooling to a maximum level of vasoconstriction. Perfusion was measured by a laser Doppler flow probe on the index fingertip. The percent of perfusion in the range of minimum to maximum was the primary outcome variable. The data were fit to a linear mixed effects model to determine if STS had a significant influence on perfusion during whole body cooling. STS had a statistically significant effect on perfusion and increased glabrous skin blood flow by 16.3% (P < 0.001, CI (13.1%, 19.5%)) as skin temperature was decreased. This study supports the theory that STS improves the heat exchanger efficiency of palmar and plantar surfaces by increasing the blood flow.

Before-bedtime passive body heating by warm shower or bath to improve sleep: A systematic review and meta-analysis.

Water-based passive body heating (PBHWB) as a warm shower or bath before bedtime is often recommended as a simple means of improving sleep. We searched PubMed, CINAHL, Cochran, Medline, PsycInfo, and Web of Science databases and extracted pertinent information from publications meeting predefined inclusion and exclusion criteria to explore the effects of PBHWB on sleep onset latency (SOL), wake after sleep onset, total sleep time, sleep efficiency (SE), slow wave sleep, and subjective sleep quality. The search yielded 5322 candidate articles of which 17 satisfied inclusion criteria after removing duplicates, with 13 providing comparable quantitative data for meta-analyses. PBHWB of 40-42.5 °C was associated with both improved self-rated sleep quality and SE, and when scheduled 1-2 h before bedtime for little as 10 min significant shortening of SOL. These findings are consistent with the mechanism of PBHWB effects being the extent of core body temperature decline achieved by increased blood perfusion to the palms and soles that augments the distal-to-proximal skin temperature gradient to enhance body heat dissipation. Nonetheless, additional investigation is required because the findings regarding PBHWB are limited by the relative scarcity of reported research, especially its optimal timing and duration plus exact mechanisms of effects.

Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep.

Elevated asleep heart rate (HR) is a risk factor for cardiovascular disease and other-cause morbidity and mortality. We assessed the accuracy of Fitbit Inc. PurePulse® photoplethysmography with reference to three-lead electrocardiography (ECG) in determining HR during sleep. HR of 35 (17 female) healthy adults 25.1 ± 10.6 years of age (mean ± SD) was continuously recorded throughout a single night of sleep. There was no significant difference in asleep HR mean (0.09 beats per minute [bpm], P = 0.426) between Fitbit photoplethysmography and ECG; plus, there was excellent intraclass correlation (0.998) and narrow Bland-Altman agreement range (2.67 bpm). The regression analysis of Bland-Altman plot of mean asleep HR indicates Fitbit tends to slightly overestimate reference values in the lower range of HR (HR < 50 bpm) by 0.51 bpm and slightly underestimate reference values in the higher range of HR (HR > 80 bpm) by 0.63 bpm. Mixed model analysis of epoch-by-epoch (5-min epochs) asleep HR showed significant "U" shape trend (P < 0.001) in amount of Fitbit error (absolute amount of difference between ECG and Fitbit values regardless of overestimation or underestimation) in regard to HR, i.e. smaller error in the medium range of HR (60-80 bpm) and slightly larger error for lower (<60 bpm) and higher (>80 bpm) ranges of HR. However, effect of age, body mass index, gender, and subjective sleep quality measured by Pittsburgh sleep quality index (good/poor sleepers) on error in estimating HR by the Fitbit method was not significant. It is concluded that Fitbit photoplethysmography suitably tracks HR during sleep in healthy young adults.

From Nanowarming to Thermoregulation: New Multiscale Applications of Bioheat Transfer.

This review explores bioheat transfer applications at multiple scales from nanoparticle (NP) heating to whole-body thermoregulation. For instance, iron oxide nanoparticles are being used for nanowarming, which uniformly and quickly rewarms 50-80-mL (≤5-cm-diameter) vitrified systems by coupling with radio-frequency (RF) fields where standard convective warming fails. A modification of this approach can also be used to successfully rewarm cryopreserved fish embryos (∼0.8 mm diameter) by heating previously injected gold nanoparticles with millisecond pulsed laser irradiation where standard convective warming fails. Finally, laser-induced heating of gold nanoparticles can improve the sensitivity of lateral flow assays (LFAs) so that they are competitive with laboratory tests such as the enzyme-linked immunosorbent assay. This approach addresses the main weakness of LFAs, which are otherwise the cheapest, easiest, and fastest to use point-of-care diagnostic tests in the world. Body core temperature manipulation has now become possible through selective thermal stimulation (STS) approaches. For instance, simple and safe heating of selected areas of the skin surface can open arteriovenous anastomosis flow in glabrous skin when it is not already established, thereby creating a convenient and effective pathway to induce heat flow between the body core and environment. This has led to new applications of STS to increase or decrease core temperatures in humans and animals to assist in surgery (perioperative warming), to aid ischemic stress recovery (cooling), and even to enhance the quality of sleep. Together, these multiscale applications of nanoparticle heating and thermoregulation point to dramatic opportunities for translation and impact in these prophylactic, preservative, diagnostic, and therapeutic applications of bioheat transfer.

Pronounced and sustained cutaneous vasoconstriction during and following cyrotherapy treatment: Role of neurotransmitters released from sympathetic nerves.

Cryotherapy is a therapeutic technique using ice or cold water applied to the skin to manage soft tissue trauma and injury. While beneficial, there are some potentially detrimental side effects, such as pronounced vasoconstriction and tissue ischemia that are sustained for hours post-treatment. This study tested the hypothesis that this vasoconstriction is mediated by 1) activation of post-synaptic α-adrenergic receptors and/or 2) activation of post-synaptic neuropeptide Y1 (NPY Y1) receptors. 8 subjects were fitted with a commercially available cryotherapy unit with a water perfused bladder on the lateral portion of the right calf. Participants were instrumented with four intradermal microdialysis probes beneath the bladder. The following conditions were applied at the four treatment sites: 1) control (Ringer solution), 2) combined post-synaptic ß-adrenergic receptors and neuropeptide (NPY) Y1 receptors blockade (P+B site), 3) combined post-synaptic α-adrenergic receptor, ß-adrenergic receptor, and NPY Y1 receptor blockade (Y+P+B site), and 4) blockade of pre-synaptic release of all neurotransmitters from the sympathetic nerves (BT site). Following thermoneutral baseline data collection, 1°C water was perfused through the bladder for 30min, followed by passive rewarming for 60min. Skin temperature (Tskin) fell from ~34°C to ~18.5°C during active cooling across all sites and there was no difference between sites (P>0.05 vs. control for each site). During passive rewarming Tskin rose to a similar degree in all sites (P>0.05 relative to the end of cooling). In the first 20min of cooling %CVC was reduced at all sites however, this response was blunted in the BT and the Y+P+B sites (P>0.05 for all comparisons). By the end of cooling the degree of vasoconstriction was similar between sites with the exception that the reduction in %CVC in the Y+B+P site was less relative to the reduction in the control site. %CVC was unchanged in any of the sites during passive rewarming such that each remained similar to values obtained at the end of active cooling. These findings indicate that the initial vasoconstriction (i.e. within the 1st 20min) that occurs during cryotherapy induced local cooling is achieved via activation of post-synaptic α-adrenergic receptors; whereas nonadrenergic mechanisms predominate as the duration of cooling continues. The sustained vasoconstriction that occurs following cessation of the cooling stimulus does not appear to be related to activation of post-synaptic α-adrenergic receptors or NPY Y1 receptor.

Level of Cutaneous Blood Flow Depression During Cryotherapy Depends on Applied Temperature: Criteria for Protocol Design.

Cryotherapy is commonly used for the management of soft tissue injury. The dose effect of the applied cooling temperature has not been quantified previously. Six subjects were exposed during five different experiments to local skin temperatures of 16.6 °C, 19.8 °C, 24.7 °C, 27.3 °C, and 37.2 °C for 1 h of active heat transfer followed by 2 h of passive environmental interaction. Skin blood perfusion and temperature were measured continuously at treatment and control sites. All treatments resulted in significant changes in cutaneous vascular conductance (CVC, skin perfusion/mean arterial pressure) compared to baseline values. The drop in CVC for cooling to both 19.8 °C and 16.6 °C was significantly larger than for 27.3 °C (P < 0.05 and P < 0.0005, respectively). The depression of CVC for cooling to 16.6 °C was significantly larger than at 24.7 °C (P < 0.05). Active warming at 37.2 °C produced more than a twofold increase in CVC (P < 0.05). A simulation model was developed to describe the coupled effects of exposure time and temperature on skin perfusion. The model was applied to define an equivalent cooling dose defined by exposure time and temperature that produced equivalent changes in skin perfusion. The model was verified with data from 22 independent cryotherapy experiments. The equivalent doses were applied to develop a nomogram to identify therapeutic time and temperature combinations that would produce a targeted vascular response. The nomogram may be applied to design cryotherapy protocols that will yield a desired vascular response history that may combine the benefits of tissue temperature reduction while diminishing the risk of collateral ischemic injury.