Nanomaterials for Cortisol Sensing

Headaches in Healthcare Workers: A Prospective Study of Precipitating and Maintenance Variables and Their Relationship with Burnout as a Post-COVID Syndrome

BACKGROUND

Headaches are a common symptom in healthcare workers (HCWs), mainly associated with high levels of stress. Different research has studied their incidence during the COVID-19 pandemic, most of them with correlational designs, and at the beginning of the pandemic and focused on the associated occupational variables.

AIMS

(1) To analyze the incidence of headaches in HCWs at the beginning of the COVID-19 pandemic and their maintenance six months later. (2) To explore the risk factors associated with their onset and maintenance, including sociodemographic, occupational, emotional symptomatology, and personality variables. (3) To propose a model to explain the chronification of stress in burnout, including the moderating role of chronic headaches.

METHODS

A prospective study (n = 259 HCWs) at three points in time during the COVID-19 pandemic, from the alarm state phase (T1: May-June 2020) to the post-pandemic stage (T3: April-July 2022), including an intermediate measure six months after T1 (T2). Descriptive analyses, Pearson's chi-square, Student's t, logistic regressions, and moderated mediation models were conducted using the Process package for SPSS. In addition to headaches, socio-demographic, occupational, emotional symptomatology, and personality variables were included.

RESULTS

At T1 the prevalence of headaches was 69.9%. At T2 the prevalence was 73.7%. Of these, 59.5% are T1-T2 sustained headaches. Headaches at T1 were associated with age (p = 0.010) (younger HCWs), professional category (p = 0.049) (nurses), service (p = 0.023) (ICU, COVID hospitalization), non-availability of PPE (p = 0.010), additional COVID-19 symptomatology (p < 0.001), and concern for contagion of family members (p < 0.001) (higher scores). In addition, HCWs with headaches had higher levels of stress (p = 0.001), anxiety (p = 0.001), depression (p = 0.041), and sleep disorders (p < 0.001). A subsequent logistic regression analysis showed that of the above variables, the presence of additional COVID-19 symptoms (p < 0.001) and depression (p = 0.010) were the predictor variables. With regard to the maintenance of headaches (T1-T2), anxiety (p = 0.035), stress (p = 0.001), and cognitive fusion (p = 0.013) were found to be the significant variables. The tested model proposes anxiety (T1) as antecedent, cognitive fusion (T2) as mediator, burnout (T3) as consequent, and chronic headaches (yes/no) as the moderating variable between anxiety and burnout (model 5). The model is significant (F = 19.84, p < 0.001) and contributes to the explanation of 36% of the variance of burnout. The relationships in the model are all statistically significant, and specifically chronic headaches contribute to a 6-fold increase in the likelihood of burnout.

CONCLUSIONS

The present research differentiates between precipitating and maintenance factors of headaches in HCWs. The former, more studied in previous research, are usually related to sociodemographic and occupational variables and levels of anxiety and stress. Maintenance factors, scarcely explored, are related to the maintenance of emotional symptomatology and the inability to manage intrusive thoughts (i.e., cognitive fusion). Of particular interest is that the presence of chronic headaches itself is capable of producing burnout as a post-COVID syndrome.

Dynamic Real-Time Biosensing Enabled Biorhythm Tracking for Psychiatric Disorders

This review article explores the transformative potential of dynamic, real-time biosensing in biorhythm tracking for psychiatric disorders. Psychiatric diseases, characterized by a complex, heterogeneous, and multifactorial pathophysiology, pose challenges in both diagnosis and treatment. Common denominators in the pathophysiology of psychiatric diseases include disruptions in the stress response, sleep-wake cycle, energy metabolism, and immune response: all of these are characterized by a strong biorhythmic regulation (e.g., circadian), leading to dynamic changes in the levels of biomarkers involved. Technological and practical limitations have hindered the analysis of such dynamic processes to date. The integration of biosensors marks a paradigm shift in psychiatric research. These advanced technologies enable multiplex, non-invasive, and near-continuous analysis of biorhythmic biomarkers in real time, overcoming the constraints of conventional approaches. Focusing on the regulation of the stress response, sleep/wake cycle, energy metabolism, and immune response, biosensing allows for a deeper understanding of the heterogeneous and multifactorial pathophysiology of psychiatric diseases. The potential applications of nanobiosensing in biorhythm tracking, however, extend beyond observation. Continuous monitoring of biomarkers can provide a foundation for personalized medicine in Psychiatry, and allow for the transition from syndromal diagnostic entities to pathophysiology-based psychiatric diagnoses. This evolution promises enhanced disease tracking, early relapse prediction, and tailored disease management and treatment strategies. As non-invasive biosensing continues to advance, its integration into biorhythm tracking holds promise not only to unravel the intricate etiology of psychiatric disorders but also for ushering in a new era of precision medicine, ultimately improving the outcomes and quality of life for individuals grappling with these challenging conditions.

Cortisol: Biosensing and detection strategies

Cortisol, a crucial steroid hormone synthesized by the adrenal glands, has diverse impacts on multiple physiological processes, such as metabolism, immune function, and stress management. Disruption in cortisol levels can result in conditions like Cushing's syndrome and Addison's disease. This review provides an in-depth exploration of cortisol, covering its structure, various forms in the body, detection methodologies, and emerging trends in cancer treatment and detection. Various techniques for cortisol detection, including electrochemical, chromatographic, and immunoassay methods were discussed and highlighted for their merits and applications. Electrochemical immunosensing emerges as a promising approach, which offered high sensitivity and low detection limits. Moreover, the review delves into the intricate relationship between cortisol and cancer, emphasizing cortisol's role in cancer progression and treatment outcomes. Lastly, the utilization of biomarkers, in-silico modeling, and machine learning for electrochemical cortisol detection were explored, which showcased innovative strategies for stress monitoring and healthcare advancement.

Nanomaterials for Potential Uses in Extraterrestrial Environments

Over the past decades, the development of nanomaterials has played an important role in the most intriguing aspects of new technologies in several scientific fields, such as nanoelectronics, nanomedicine [...].

Cortisol sensing by optical sensors

During a stress condition, the human body synthesizes catecholamine neurotransmitters and specific hormones (called "stress hormones"), the most important of which is cortisol. The monitoring of cortisol levels is extremely important for controlling the stress levels. For this reason, it has important medical applications. Common analytical methods (HPLC, GC-MS) cannot be used in real life due to the bulkiness of the instruments and the necessity of specialized operators. Molecular probes solve this problem. This review aims to provide a description of recent developments in this field, focusing on the analytical aspects and the possibility to obtain real practical devices from these molecular probes.

Electrochemical Detection of Cortisol by Silver Nanoparticle-Modified Molecularly Imprinted Polymer-Coated Pencil Graphite Electrodes

The sensitive cortisol detection by an electrochemical sensor based on silver nanoparticle-doped molecularly imprinted polymer was successfully improved. This study describes the method development for cortisol detection in both aqueous solution and biological samples using molecularly imprinted poly(hydroxyethyl methacrylate-N-methacryloyl-(l)-histidine methyl ester)-coated pencil graphite electrodes modified with silver nanoparticles (AgNPs) by differential pulse voltammetry (DPV). The cortisol-imprinted pencil graphite electrode (PGE) has a large surface area because of doped AgNPs with enhanced electroactivity. The prepared molecularly imprinted polymer was characterized by scanning electron microscopy. The DPV response of the synthesized electrode with outstanding electrical conductivity was clarified. Cortisol-imprinted polymer-coated PGEs (MIP), cortisol-imprinted polymer-coated PGEs with AgNPs (MIP@AgNPs), and nonimprinted polymer-coated PGEs with AgNPs (NIP@AgNPs) were evaluated for sensitive and selective detection of cortisol in aqueous solution. Five different cortisol concentrations (0.395, 0.791, 1.32, 2.64, and 3.96 nM) were applied to the MIP@AgNPs, and signal responses were detected by the DPV with a regression coefficient (R2) value of 0.9951. The modified electrode showed good electrocatalytic activity toward cortisol for the linear concentration range from 0.395 to 3.96 nM, and a low limit of detection was recorded as 0.214 nM. The results indicate that the MIP@AgNPs sensor has great potential for sensitive and selective cortisol determination in biological samples.