Aging is independently associated with an increasing normal respiratory rate among an older adult population in a clinical setting: A cross-sectional study

The emotion paradox in the aging body and brain

With age, parasympathetic activity decreases, while sympathetic activity increases. Thus, the typical older adult has low heart rate variability (HRV) and high noradrenaline levels. Younger adults with this physiological profile tend to be unhappy and stressed. Yet, with age, emotional experience tends to improve. Why does older adults' emotional well-being not suffer as their HRV decreases? To address this apparent paradox, I present the autonomic compensation model. In this model, failing organs, the initial phases of Alzheimer's pathology, and other age-related diseases trigger noradrenergic hyperactivity. To compensate, older brains increase autonomic regulatory activity in the pregenual prefrontal cortex (PFC). Age-related declines in nerve conduction reduce the ability of the pregenual PFC to reduce hyperactive noradrenergic activity and increase peripheral HRV. But these pregenual PFC autonomic compensation efforts have a significant impact in the brain, where they bias processing in favor of stimuli that tend to increase parasympathetic activity (e.g., stimuli that increase feelings of safety) and against stimuli that tend to increase sympathetic activity (e.g., threatening stimuli). In summary, the autonomic compensation model posits that age-related chronic sympathetic/noradrenergic hyperactivity stimulates regulatory attempts that have the side effect of enhancing emotional well-being.

Using explainable AI to investigate electrocardiogram changes during healthy aging-From expert features to raw signals

Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.

Cell orientation under stretch: A review of experimental findings and mathematical modelling

The key role of electro-chemical signals in cellular processes had been known for many years, but more recently the interplay with mechanics has been put in evidence and attracted substantial research interests. Indeed, the sensitivity of cells to mechanical stimuli coming from the microenvironment turns out to be relevant in many biological and physiological circumstances. In particular, experimental evidence demonstrated that cells on elastic planar substrates undergoing periodic stretches, mimicking native cyclic strains in the tissue where they reside, actively reorient their cytoskeletal stress fibres. At the end of the realignment process, the cell axis forms a certain angle with the main stretching direction. Due to the importance of a deeper understanding of mechanotransduction, such a phenomenon was studied both from the experimental and the mathematical modelling point of view. The aim of this review is to collect and discuss both the experimental results on cell reorientation and the fundamental features of the mathematical models that have been proposed in the literature.

Health-Related Telemonitoring Parameters/Signals of Older Adults: An Umbrella Review

Aging is one of the greatest challenges in modern society. The development of wearable solutions for telemonitoring biological signals has been viewed as a strategy to enhance older adults' healthcare sustainability. This study aims to review the biological signals remotely monitored by technologies in older adults. PubMed, the Cochrane Database of Systematic Reviews, the Web of Science, and the Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports were systematically searched in December 2021. Only systematic reviews and meta-analyses of remote health-related biological and environmental monitoring signals in older adults were considered, with publication dates between 2016 and 2022, written in English, Portuguese, or Spanish. Studies referring to conference proceedings or articles with abstract access only were excluded. The data were extracted independently by two reviewers, using a predefined table form, consulting a third reviewer in case of doubts or concerns. Eighteen studies were included, fourteen systematic reviews and four meta-analyses. Nine of the reviews included older adults from the community, whereas the others also included institutionalized participants. Heart and respiratory rate, physical activity, electrocardiography, body temperature, blood pressure, glucose, and heart rate were the most frequently measured biological variables, with physical activity and heart rate foremost. These were obtained through wearables, with the waist, wrist, and ankle being the most mentioned body regions for the device's placement. Six of the reviews presented the psychometric properties of the systems, most of which were valid and accurate. In relation to environmental signals, only two articles presented data on this topic. Luminosity, temperature, and movement were the most mentioned variables. The need for large-scale long-term health-related telemonitoring implementation of studies with larger sample sizes was pointed out by several reviews in order to define the feasibility levels of wearable devices.

A new method for assessing lung tumor motion in radiotherapy using dynamic chest radiography

Dynamic chest radiography (DCR) is a recent advanced modality to acquire dynamic and functional images. We developed a new method using DCR and the free analysis software, Kinovea, to assess lung tumor motion. This study aimed to demonstrate the usefulness of our method. Phantom and clinical studies were performed. In the phantom study, dynamic images of a moving lead sphere were acquired using DCR, and the motion of the phantom was tracked using Kinovea in a DCR video. The amplitude of phantom motion was measured and compared with a predetermined baseline amplitude. In a clinical study, DCR and respiratory‐gated four‐dimensional computed tomography (4D‐CT) were performed on 15 patients who underwent stereotactic body radiation therapy for lung tumors. The amplitudes of tumor motion in DCR and 4D‐CT were measured in the superior‐inferior (SI), left‐right (LR), and anterior‐posterior (AP) directions, and the square root of the sum of squares (SRSS) of the amplitude was calculated in all directions. Spearman's rank correlation and the Wilcoxon signed‐rank test were performed to determine the correlations of the amplitudes of tumor motion obtained using DCR and 4D‐CT. In the phantom study, the absolute mean error between the measured and predetermined amplitudes was 0.60 mm (range: 0.061.53 mm). In the clinical study, the amplitudes of tumor motion obtained using DCR correlated significantly with those of 4D‐CT in the SI and LR directions, as did the SRSS values. The median amplitudes for DCR were significantly higher than those for 4D‐CT in all (SI, LR, and AP) directions, as were the SRSS values. Our proposed method based on DCR and Kinovea is useful for assessing lung tumor motion, visually and quantitatively. Therefore, DCR has potential as a new modality for evaluating lung tumor motion in radiotherapy.

Resting respiration rate predicts all-cause mortality in older outpatients

BACKGROUND

Although respiratory rate has been a sensitive predictor for prognosis in acute settings, resting respiratory rate (RRR) is undervalued in chronic care. The clinical significance of RRR among older people is not well documented.

AIM

We investigated the association between RRR and all-cause mortality among older outpatients.

METHODS

A retrospective cohort study exhaustively included patients who had undergone medical checkups in a facility between April 2017 and March 2018 and followed up for at least 2 years. We excluded patients who were less than 60 years of age or had not undergone regular outpatient appointments. Sex, age, smoking habits, history of hospitalization, polypharmacy, long-term care insurance certification status, Mazzaglia index, pulse rate, systolic blood pressure, and Charlson Comorbidity Index were measured at the baseline medical checkup. Survival was confirmed by chart review and by contacting physicians in charge. The risk ratios were estimated by converting the odds ratios derived from the multivariable logistic regression models.

RESULTS

Of the 853 patients who underwent baseline checkups, 749 were enrolled in the analyses; death occurred in 53 patients (7.1%), with no loss to follow-up. The RRR was independently associated with all-cause mortality after adjusting for covariates [adjusted risk ratio of RRR per 1 bpm = 1.14, 95% confidence interval (CI): 1.06 - 1.22].

DISCUSSION

Given the independent association of RRR for existing predictors, this simple index seems worthy of consideration in further studies aimed at defining its predictive role in older people and in different settings.

CONCLUSION

RRR was independently associated with all-cause mortality.

Heart Rate Variability Changes in Patients With Major Depressive Disorder: Related to Confounding Factors, Not to Symptom Severity?

Background

The aim of this study was to assess the electrophysiological and other influencing factors correlating with symptom severity in patients with major depressive disorder (MDD) under three different conditions: baseline, stress exposure, and relaxation following stress exposure.

Methods

Symptom severity was assessed using the Beck Depression Inventory (BDI-II) in 89 inpatients (37 women; mean age 51 years) with MDD. Resting heart rate (RHR), heart rate variability (HRV), respiration rate (RR), skin conductance (SC), and skin temperature (ST) were recorded at baseline for 300 s, under stress exposure for 60 s, and under self-induced relaxation for 300 s. Age, nicotine consumption, body mass index, and blood pressure were evaluated as influencing factors.

Results

The BDI-II mean score was 29.7 points. Disease severity correlated positively with SC elevation under stress exposure and with a higher RR in the relaxed state, but no association was found between HRV and symptom severity. Age and higher blood pressure were both associated with lower HRV and higher RHR.

Conclusion

The results indicate that, in patients with MDD, changes in the autonomic nervous system (ANS) are complex; and the assessment of ANS reactivity to stressors is useful. Elevated blood pressure might be underdiagnosed, although it is already relevant in patients with MDD in their early 50s.

Monitoring Symptoms of Infectious Diseases: Perspectives for Printed Wearable Sensors

Infectious diseases possess a serious threat to the world's population, economies, and healthcare systems. In this review, we cover the infectious diseases that are most likely to cause a pandemic according to the WHO (World Health Organization). The list includes COVID-19, Crimean-Congo Hemorrhagic Fever (CCHF), Ebola Virus Disease (EBOV), Marburg Virus Disease (MARV), Lassa Hemorrhagic Fever (LHF), Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), Nipah Virus diseases (NiV), and Rift Valley fever (RVF). This review also investigates research trends in infectious diseases by analyzing published research history on each disease from 2000-2020 in PubMed. A comprehensive review of sensor printing methods including flexographic printing, gravure printing, inkjet printing, and screen printing is conducted to provide guidelines for the best method depending on the printing scale, resolution, design modification ability, and other requirements. Printed sensors for respiratory rate, heart rate, oxygen saturation, body temperature, and blood pressure are reviewed for the possibility of being used for disease symptom monitoring. Printed wearable sensors are of great potential for continuous monitoring of vital signs in patients and the quarantined as tools for epidemiological screening.