Unveiling stress markers: A systematic review investigating psychological stress biomarkers

Psychological stress is a ubiquitous facet of modern life, impacting individuals across diverse contexts and demographics. Understanding its physiological manifestations through biomarkers has gained substantial attention within the scientific community. A comprehensive search was conducted across multiple databases for peer-reviewed articles published within the past decade. Preliminary findings reveal many biomarkers associated with psychological stress across different biological systems, including the hypothalamic-pituitary-adrenal axis, immune system, cardiovascular system, and central nervous system. This systematic review explores psychological, physiological, and biochemical biomarkers associated with stress. Analyzing recent literature, it synthesizes findings across these three categories, elucidating their respective roles in stress response mechanisms. Psychological markers involve subjective assessments like self-reported stress levels, perceived stress scales, or psychometric evaluations measuring anxiety, depression, or coping mechanisms. Physiological markers include heart rate variability, blood pressure, and immune system responses such as cytokine levels or inflammatory markers. Biochemical markers involve hormones or chemicals linked to stress. It includes cortisol, catecholamines, copeptin, salivary amylase, IL-6, and C-reactive protein.

Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson's disease

In Parkinson's disease, imbalances between "antikinetic" and "prokinetic" patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (65-90 Hz). Excessive narrowband gamma in the motor cortex promotes dyskinesia in rodent models, but the relationship between narrowband gamma and dyskinesia in humans has not been well established. To assess this relationship, we used a sensing-enabled deep brain stimulator system, attached to both motor cortex and basal ganglia (subthalamic or pallidal) leads, paired with wearable devices that continuously tracked motor signs in the contralateral upper limbs. We recorded 984 hours of multisite field potentials in 30 hemispheres of 16 subjects with Parkinson's disease (2/16 female, mean age 57 ± 12 years) while at home on usual antiparkinsonian medications. Recordings were done two to four weeks after implantation, prior to starting therapeutic stimulation. Narrowband gamma was detected in the precentral gyrus, subthalamic nucleus, or both structures on at least one side of 92% of subjects with a clinical history of dyskinesia. Narrowband gamma was not detected in the globus pallidus. Narrowband gamma spectral power in both structures co-fluctuated similarly with contralateral wearable dyskinesia scores (mean correlation coefficient of ρ=0.48 with a range of 0.12-0.82 for cortex, ρ=0.53 with a range of 0.5-0.77 for subthalamic nucleus). Stratification analysis showed the correlations were not driven by outlier values, and narrowband gamma could distinguish "on" periods with dyskinesia from "on" periods without dyskinesia. Time lag comparisons confirmed that gamma oscillations herald dyskinesia onset without a time lag in either structure when using 2-minute epochs. A linear model incorporating the three oscillatory bands (beta, theta/alpha, and narrowband gamma) increased the predictive power of dyskinesia for several subject hemispheres. We further identified spectrally distinct oscillations in the low gamma range (40-60 Hz) in three subjects, but the relationship of low gamma oscillations to dyskinesia was variable. Our findings support the hypothesis that excessive oscillatory activity at 65-90 Hz in the motor network tracks with dyskinesia similarly across both structures, without a detectable time lag. This rhythm may serve as a promising control signal for closed-loop deep brain stimulation using either cortical or subthalamic detection.

A scoping review of physiological biomarkers in autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by pervasive deficits in social interaction, communication impairments, and the presence of restricted and repetitive behaviors. This complex disorder is a significant public health concern due to its escalating incidence and detrimental impact on quality of life. Currently, extensive investigations are underway to identify prospective susceptibility or predictive biomarkers, employing a physiological biomarker-based framework. However, knowledge regarding physiological biomarkers in relation to Autism is sparse. We performed a scoping review to explore putative changes in physiological activities associated with behaviors in individuals with Autism. We identified studies published between January 2000 and June 2023 from online databases, and searched keywords included electroencephalography (EEG), magnetoencephalography (MEG), electrodermal activity markers (EDA), eye-tracking markers. We specifically detected social-related symptoms such as impaired social communication in ASD patients. Our results indicated that the EEG/ERP N170 signal has undergone the most rigorous testing as a potential biomarker, showing promise in identifying subgroups within ASD and displaying potential as an indicator of treatment response. By gathering current data from various physiological biomarkers, we can obtain a comprehensive understanding of the physiological profiles of individuals with ASD, offering potential for subgrouping and targeted intervention strategies.

Effects of exposure to sediment-associated fipronil on cardiac function of Neotropical armored catfish Hypostomus regani

Fipronil is widely used as a broad-spectrum insecticide in agriculture, urban environments, and veterinary medicine. Fipronil can enter aquatic ecosystems and spread to sediment and organic matter, representing a risk to non-target species. This study aimed to evaluate the effects of short-term (96 h) exposure to a low and realistic concentration of sediment-associated fipronil (4.2 µg.kg^-1 of Regent® 800 WG) on myocardial contractility of armored catfish Hypostomus regain, a benthic fish species. Fipronil exposure induced increased inotropism and acceleration of contractile kinetics, although no alterations in the relative ventricular mass were observed. This better cardiac function was associated with an elevated expression and/or function of the Na⁺/Ca²⁺ exchanger and its marked contribution to contraction and relaxation, probably due to a stress-induced adrenergic stimulation. Ventricle strips of exposed fish also exhibited a faster relaxation and a higher cardiac pumping capacity, indicating that armored catfish were able to perform cardiac adjustments to face the exposure. However, a high energetic cost to maintain an increased cardiac performance can make fish more susceptible to other stressors, impairing developmental processes and/or survival. These findings highlight the need for regulations of emerging contaminants, such as fipronil, to ensure adequate protection of the aquatic system.

Effects of spinetoram and glyphosate on physiological biomarkers and gut microbes in Bombus terrestris

The sublethal effects of pesticide poisoning will have significant negative impacts on the foraging and learning of bees and bumblebees, so it has received widespread attention. However, little is known about the physiological effects of sublethal spinetoram and glyphosate exposure on bumblebees. We continuously exposed Bombus terrestris to sublethal (2.5 mg/L) spinetoram or glyphosate under controlled conditions for 10 days. The superoxide dismutase, glutathione-S-transferase, carboxylesterase, prophenoloxidase, α-amylase and protease activities, and changes in gut microbes were measured to understand the effects of sublethal pesticide exposure on the physiology and gut microbes of bumblebees. Sublethal pesticide exposure to significantly increased superoxide dismutase activity and significantly decreased gut α-amylase activity in bumblebees but had no significant effect on glutathione-S-transferase, carboxylesterase or gut protease activities. In addition, glyphosate increased the activity of prophenoloxidase. Interestingly, we observed that neither of the two pesticides had a significant effect on dominant gut bacteria, but glyphosate significantly altered the structure of the dominant gut fungal community, and reduced the relative abundance of Zygosaccharomyces associated with fat accumulation. These results suggest that sublethal spinetoram and glyphosate do not significantly affect the detoxification system of bumblebees, but may affect bumblebee health by inhibiting energy acquisition. Our results provide information on the sublethal effects of exposure to low concentrations of glyphosate and spinetoram on bumblebees in terms of physiology and gut microbes.

Physiological biomarkers of chronic stress: A systematic review

OBJECTIVE

The basic objective of this systematic review was to identify potential biomarkers for chronic stress.

METHODS

A systematic review of studies linking biomarkers in people with chronic stress was conducted using PRISMA guidelines. The last 40 years' studies were included in the systematic review with no age restrictions; animal studies were excluded from the study. Electronic databases including PubMed, Embase, and Google Scholar were searched for the study purpose. The studies were searched using the combinations of search terms that comprised chronic stress together with the keywords hypothalamic-pituitary-adrenal axis (HPA axis), autonomic nervous system (ANS), immune system, metabolic biomarkers, cortisol, hair cortisol, salivary cortisol, urinary cortisol, epinephrine, norepinephrine, adrenocorticotropic hormone (ACTH), brain-derived neurotropic factor (BDNF), metabolic biomarkers, antioxidants, glucose, hemoglobin, C-reactive protein (CRP), cytokines, pro-inflammatory cytokines, anti-inflammatory cytokines, and tumor necrosis factor (TNF).

RESULTS

A total of 37 studies out of 671 studies met the eligibility criteria and were included in this review. Potential diagnostic biomarkers of chronic stress included cortisol, ACTH, BDNF, catecholamines, glucose, HbA1c, triglycerides, cholesterol, prolactin, oxytocin, dehydroepiandrosterone sulfate (DHEA-S), CRP, and interleukin - 6 and 8. While the others including antioxidants and natural killer (NK) cells require further validation. Taken together, addition, these stress biomarkers have critical prognostic capacities for stress-associated diseases and therapeutic guidance.

CONCLUSION

This systematic review provides an update to the literature by highlighting the role of physiological biomarkers in chronic stress and describing their prognostic and therapeutic values.

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

Differentiating Females with Rett Syndrome and Those with Multi-Comorbid Autism Spectrum Disorder Using Physiological Biomarkers: A Novel Approach

This study explored the use of wearable sensor technology to investigate autonomic function in children with autism spectrum disorder (ASD) and Rett syndrome (RTT). We aimed to identify autonomic biomarkers that can correctly differentiate females with ASD and Rett Syndrome using an innovative methodology that applies machine learning approaches. Our findings suggest that we can predict (95%) the status of ASD/Rett. We conclude that physiological biomarkers may be able to assist in the differentiation between patients with RTT and ASD and could allow the development of timely therapeutic strategies.

Automatic Detection of Target Engagement in Transcutaneous Cervical Vagal Nerve Stimulation for Traumatic Stress Triggers

Transcutaneous cervical vagal nerve stimulation (tcVNS) devices are attractive alternatives to surgical implants, and can be applied for a number of conditions in ambulatory settings, including stress-related neuropsychiatric disorders. Transferring tcVNS technologies to at-home settings brings challenges associated with the assessment of therapy response. The ability to accurately detect whether tcVNS has been effectively delivered in a remote setting such as the home has never been investigated. We designed and conducted a study in which 12 human subjects received active tcVNS and 14 received sham stimulation in tandem with traumatic stress, and measured continuous cardiopulmonary signals including the electrocardiogram (ECG), photoplethysmogram (PPG), seismocardiogram (SCG), and respiratory effort (RSP). We extracted physiological parameters related to autonomic nervous system activity, and created a feature set from these parameters to: 1) detect active (vs. sham) tcVNS stimulation presence with machine learning methods, and 2) determine which sensing modalities and features provide the most salient markers of tcVNS-based changes in physiological signals. Heart rate (ECG), vasomotor activity (PPG), and pulse arrival time (ECG+PPG) provided sufficient information to determine target engagement (compared to sham) in addition to other combinations of sensors. resulting in 96% accuracy, precision, and recall with a receiver operator characteristics area of 0.96. Two commonly utilized sensing modalities (ECG and PPG) that are suitable for home use can provide useful information on therapy response for tcVNS. The methods presented herein could be deployed in wearable devices to quantify adherence for at-home use of tcVNS technologies.

Materials and Wearable Devices for Autonomous Monitoring of Physiological Markers

Wearable devices are gaining considerable attention owing to the ease with which they can collect crucial information in real-time, both continuously and noninvasively, regarding a wearer's health. A concise summary is given of the three main elements that enable autonomous detection and monitoring of the likelihood or the existence of a health-risk state in continuous and real-time modes, with an emphasis on emerging materials and fabrication techniques in the relevant fields. The first element is the sensing technology used in the noninvasive detection of physiological markers relevant to the state of health. The second element is self-powered devices for longer periods of use by drawing energy from bodily movement and temperature. The third element is the self-healing properties of the materials used in the wearable devices to extended usage if they become scratched or cut. Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further improvement of skin-based wearable devices are also presented and discussed.