The International Performance, Resilience and Efficiency Program Protocol for the Application of HRV Biofeedback in Applied Law Enforcement Settings.

Law enforcement officers are routinely exposed to high-threat encounters that elicit physiological stress responses that impact health, performance, and safety. Therefore, self-regulation using evidence-based approaches is a priority in police research and practice. This paper describes a five-module heart rate variability biofeedback (HRVB) protocol that is part of a larger resilience program (the International Performance Resilience and Efficiency Program - iPREP) established in 2014. Supported by 10 years of user-informed research and development, our methods are tailored to address occupational stressors and the practical realities of training and resource availability in operational settings. Building on existing clinical methods that comprise five to six weekly sessions and up to 40-min of daily practice, our iPREP HRVB protocol is typically delivered in a condensed format across 2-3 days and is seamlessly integrated with reality-based training scenarios commonly employed in policing. By combining best practices in clinical HRVB with police-specific pedagogical frameworks, officers receive accelerated and job-relevant training to adaptively modulate autonomic responses to acute and chronic stress. Efficacy of the iPREP HRVB protocol is supported by several research studies of various methodological designs (i.e., randomized control trial, longitudinal cohort) that demonstrate immediate and sustained improvements in police performance and physiological health outcomes. We conclude with a critical appraisal of the available empirical evidence contrasting common and emerging breathing techniques proposed for use in operational policing contexts. The critical appraisal guide is intended to serve as a resource for law enforcement agencies, governing bodies, and operators when choosing appropriate and effective self-regulation training approaches.

Rapid Compensation for Noisy Voluntary Movements in Adults with Primary Tic Disorders.

BACKGROUND: It has been proposed that tics and premonitory urges in primary tic disorders (PTD), like Tourette syndrome, are a manifestation of sensorimotor noise. However, patients with tics show no obvious movement imprecision in everyday life. One reason could be that patients have strategies to compensate for noise that disrupts performance (ie, noise that is task-relevant). OBJECTIVES: Our goal was to unmask effects of elevated sensorimotor noise on the variability of voluntary movements in patients with PTD. METHODS: We tested 30 adult patients with PTD (23 male) and 30 matched controls in a reaching task designed to unmask latent noise. Subjects reached to targets whose shape allowed for variability either in movement direction or extent. This enabled us to decompose variability into task-relevant versus less task-relevant components, where the latter should be less affected by compensatory strategies than the former. In alternating blocks, the task-relevant target dimension switched, allowing us to explore the temporal dynamics with which participants adjusted movement variability to changes in task demands. RESULTS: Both groups accurately reached to targets, and adjusted movement precision based on target shape. However, when task-relevant dimensions of the target changed, patients initially produced movements that were more variable than controls, before regaining precision after several reaches. This effect persisted across repeated changes in the task-relevant dimension across the experiment, and therefore did not reflect an effect of novelty, or differences in learning. CONCLUSIONS: Our results suggest that patients with PTD generate noisier voluntary movements compared with controls, but rapidly compensate according to current task demands. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study.

BACKGROUND: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. OBJECTIVE: The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. METHODS: To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. RESULTS: In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. CONCLUSIONS: In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.

Identification of a novel GNAS mutation in a family with pseudohypoparathyroidism type 1A.

BACKGROUND: Pseudohypoparathyroidism (PHP) is caused by loss-of-function mutations at the GNAS gene (as in the PHP type 1A; PHP1A), de novo or inherited at heterozygous state, or by epigenetic alterations at the GNAS locus (as in the PHP1B). The condition of PHP refers to a heterogeneous group of disorders that share common clinical and biological features of PTH resistance. Manifestations related to resistance to other hormones are also reported in many patients with PHP, in association with the phenotypic picture of Albright hereditary osteodystrophy characterized by short stature, round facies, subcutaneous ossifications, brachydactyly, mental retardation and, in some subtypes, obesity. The purpose of our study is to report a new mutation in the GNAS gene and to describe the significant phenotypic variability of three sisters with PHP1A bearing the same mutation. CASE PRESENTATION: We describe the cases of three sisters with PHP1A bearing the same mutation but characterized by a significantly different phenotypic picture at onset and during follow-up in terms of clinical features, auxological pattern and biochemical changes. Clinical exome sequencing revealed a never before described heterozygote mutation in the GNAS gene (NM_000516.5 c.118_139 + 51del) of autosomal dominant maternal transmission in the three siblings, confirming the diagnosis of PHP1A. CONCLUSIONS: This study reported on a novel mutation of GNAS gene and highlighted the clinical heterogeneity of PHP1A characterized by wide genotype-phenotype variability. The appropriate diagnosis has crucial implications for patient care and long-term multidisciplinary follow-up.

Direct Observation of Particle-To-Particle Variability in Ambient Aerosol pH Using a Novel Analytical Approach Based on Surface-Enhanced Raman Spectroscopy.

The pH of atmospheric aerosols is a key characteristic that profoundly influences their impacts on climate change, human health, and ecosystems. Despite widely performed aerosol pH research, determining the pH levels of individual atmospheric aerosol particles has been a challenge. This study presents a novel analytical technique that utilizes surface-enhanced Raman spectroscopy to assess the pH of individual ambient PM2.5-10 aerosol particles in conjunction with examining their hygroscopic behavior, morphology, and elemental compositions. The results revealed a substantial pH variation among simultaneously collected aerosol particles, ranging from 3.3 to 5.7. This variability is likely related to each particle's unique reaction and aging states. The extensive particle-to-particle pH variability suggests that atmospheric aerosols present at the same time and location can exhibit diverse reactivities, reaction pathways, phase equilibria, and phase separation properties. This pioneering study paves the way for in-depth investigations into particle-to-particle variability, size dependency, and detailed spatial and temporal variations of aerosol pH, thus deepening our understanding of atmospheric chemistry and its environmental implications.

Implementing Meta-Session Autoregulation Strategies for Exercise - A Scoping Review.

Meta-session autoregulation, a person-adaptive form of exercise prescription that adjusts training variables according to daily fluctuations in performance considering an individual's daily fitness, fatigue, and readiness-to-exercise is commonly used in sports-related training and may be beneficial for non-athlete populations to promote exercise adherence. To guide refinement of meta-session autoregulation, it is crucial to examine the existing literature and synthesize how these procedures have been practically implemented. Following PRIMSA guidelines a scoping review of two databases was conducted from August 2021 to September 2021 to identify and summarize the selected measures of readiness-to-exercise and decision-making processes used to match workload to participants in meta-session autoregulatory strategies, while also evaluating the methodological quality of existing study designs using a validated checklist. Eleven studies reported utilizing a form of meta-session autoregulation for exercise. Primary findings include: (i) readiness-to-exercise measures have been divided into either objective or subjective measures, (ii) measures of subjective readiness measures lacked evidence of validity, and (iii) fidelity to autoregulatory strategies was not reported. Results of the risk of bias assessment indicated that 45% of the studies had a poor-quality score. Existing implementations of meta-session autoregulation are not directly translatable for use in health promotion and disease prevention settings. Considerable refinement research is required to optimize this person-adaptive strategy prior to estimating effects related to exercise adherence and/or health and fitness outcomes. Based on the methodological deficits uncovered, researchers implementing autoregulation strategies would benefit reviewing existing models and frameworks created to guide behavioral intervention development.

Influence of Ambient Temperature on Autonomic Nerve Function and Peripheral Sensation from Moderate-Intensity Treadmill Exercise.

Objective: The main objective was to ascertain the acute responses in autonomic nervous activity and peripheral sensation induced by moderate-intensity treadmill exercise performed under different ambient temperatures. Methods: Twelve young healthy subjects underwent three sessions of moderate-intensity treadmill exercise (warming, 5 min and running, 25 min), on different days under 10°C, 20°C and 30°C room temperatures. Pre- and post-intervention, heart rate variability (HRV) and plantar vibrotactile perception threshold (VPT) were measured. Additionally, rate of perceived exertion (RPE) was recorded after intervention. Results: In comparison with the corresponding baseline values, after intervention, low frequency power (LF) and LF/high frequency power (HF) of HRV increased significantly and HF decreased significantly under the condition of 10°C only (p < .005). Following intervention, VPT increased significantly at the hallux for 31.5 Hz test frequency under 30°C and at the heel for 31.5 Hz test frequency under 10°C (both p < .05). In contrast, VPT decreased significantly at the hallux for 125 Hz test frequency under 10°C (p < .005). Exposure under the temperature of 20°C did not result in any significant change in VPT. After intervention, RPE under 30°C showed significantly higher values than those under 20°C (p < .01) and 10°C (p < .005) conditions with no difference between the latter two conditions. Conclusions: Treadmill exercise under 20°C ambient temperature did not exert any negative impacts on autonomic and peripheral nerve function and resulted in a perceived exertion of moderate intensity among the study participants. Therefore, an ambient temperature around 20°C might be recommended for the mentioned purpose.

Enhancing emotion regulation: investigating the efficacy of transcutaneous electrical acupoint stimulation at PC6 in reducing fear of heights.

This study investigated the impact of transcutaneous electrical acupoint stimulation (TEAS) at Neiguan acupoint (PC6) on the physiological and behavioral responses of participants exposed in virtual height. 40 participants were included in the study and were randomly assigned to either a control group or an intervention group. Participants had an immersive experience with a VR interactive platform that provided somatosensory interaction in height stimulation scenes. Psychological scores, behavioral and cognitive performance, and physiological responses were recorded and analyzed. The results indicated that the intervention group had significantly lower fear scores compared to the control group. Analysis of heart rate variability revealed that the intervention group exhibited improved heart rate variability, indicating enhanced cardiovascular function and emotion regulation. The behavioral and cognitive results demonstrated that the intervention group exhibited higher left eye openness, faster reaction times, and greater movement distance, suggesting enhanced attentional focus, cognitive processing, and reduced avoidance behaviors. These findings suggest that TEAS at PC6 can effectively reduce fear and improve the regulation of physiological and behavioral responses to negative emotional stimuli.

Association of Arterial Stiffness With Mid- to Long-Term Home Blood Pressure Variability in the Electronic Framingham Heart Study: Cohort Study.

BACKGROUND: Short-term blood pressure variability (BPV) is associated with arterial stiffness in patients with hypertension. Few studies have examined associations between arterial stiffness and digital home BPV over a mid- to long-term time span, irrespective of underlying hypertension. OBJECTIVE: This study aims to investigate if arterial stiffness traits were associated with subsequent mid- to long-term home BPV in the electronic Framingham Heart Study (eFHS). We hypothesized that higher arterial stiffness was associated with higher home BPV over up to 1-year follow-up. METHODS: At a Framingham Heart Study research examination (2016-2019), participants underwent arterial tonometry to acquire measures of arterial stiffness (carotid-femoral pulse wave velocity [CFPWV]; forward pressure wave amplitude [FWA]) and wave reflection (reflection coefficient [RC]). Participants who agreed to enroll in eFHS were provided with a digital blood pressure (BP) cuff to measure home BP weekly over up to 1-year follow-up. Participants with less than 3 weeks of BP readings were excluded. Linear regression models were used to examine associations of arterial measures with average real variability (ARV) of week-to-week home systolic (SBP) and diastolic (DBP) BP adjusting for important covariates. We obtained ARV as an average of the absolute differences of consecutive home BP measurements. ARV considers not only the dispersion of the BP readings around the mean but also the order of BP readings. In addition, ARV is more sensitive to measurement-to-measurement BPV compared with traditional BPV measures. RESULTS: Among 857 eFHS participants (mean age 54, SD 9 years; 508/857, 59% women; mean SBP/DBP 119/76 mm Hg; 405/857, 47% hypertension), 1 SD increment in FWA was associated with 0.16 (95% CI 0.09-0.23) SD increments in ARV of home SBP and 0.08 (95% CI 0.01-0.15) SD increments in ARV of home DBP; 1 SD increment in RC was associated with 0.14 (95% CI 0.07-0.22) SD increments in ARV of home SBP and 0.11 (95% CI 0.04-0.19) SD increments in ARV of home DBP. After adjusting for important covariates, there was no significant association between CFPWV and ARV of home SBP, and similarly, no significant association existed between CFPWV and ARV of home DBP (P>.05). CONCLUSIONS: In eFHS, higher FWA and RC were associated with higher mid- to long-term ARV of week-to-week home SBP and DBP over 1-year follow-up in individuals across the BP spectrum. Our findings suggest that higher aortic stiffness and wave reflection are associated with higher week-to-week variation of BP in a home-based setting over a mid- to long-term time span.

Immune response stability to the SARS-CoV-2 mRNA vaccine booster is influenced by differential splicing of HLA genes.

Many molecular mechanisms that lead to the host antibody response to COVID-19 vaccines remain largely unknown. In this study, we used serum antibody detection combined with whole blood RNA-based transcriptome analysis to investigate variability in vaccine response in healthy recipients of a booster (third) dose schedule of the mRNA BNT162b2 vaccine against COVID-19. The cohort was divided into two groups: (1) low-stable individuals, with antibody concentration anti-SARS-CoV IgG S1 below 0.4 percentile at 180 days after boosting vaccination; and (2) high-stable individuals, with antibody values greater than 0.6 percentile of the range in the same period (median 9525 [185-80,000] AU/mL). Differential gene expression, expressed single nucleotide variants and insertions/deletions, differential splicing events, and allelic imbalance were explored to broaden our understanding of the immune response sustenance. Our analysis revealed a differential expression of genes with immunological functions in individuals with low antibody titers, compared to those with higher antibody titers, underscoring the fundamental importance of the innate immune response for boosting immunity. Our findings also provide new insights into the determinants of the immune response variability to the SARS-CoV-2 mRNA vaccine booster, highlighting the significance of differential splicing regulatory mechanisms, mainly concerning HLA alleles, in delineating vaccine immunogenicity.

Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models.

BACKGROUND: The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain. METHODS: Participants were from the UK Biobank. The primary outcome was a "lifetime" history of depression. The model's performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot). RESULTS: Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a "lifetime" history of depression was 45.7% and varied (25.0-66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a "lifetime" history of depression was 30.2% and varied (21.4-70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI. CONCLUSIONS: There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients' treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them.

Reproducibility and prognostic ability of chronicity parameters in kidney biopsy - Comprehensive evaluation comparing microscopy and artificial intelligence in digital pathology.

INTRODUCTION: Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognostication. There are concerns on the impact of interobserver variability in its prognostic utility, generating a need for computerized quantification. METHODS: We studied 94 patients with renal biopsies, 45 with native diseases and 49 transplant patients with index biopsies for Polyomavirus nephropathy. Chronicity scores were evaluated using two methods. A standard definition diagram was agreed after international consultation and four renal pathologists scored each parameter in a double-blinded manner. Interstitial fibrosis (IF) score was assessed with five different computerized and AI-based algorithms on trichrome and PAS stains. RESULTS: There was strong prognostic correlation with renal function and graft outcome at a median follow-up ranging from 24 to 42 months respectively, independent of moderate concordance for pathologists scores. IF scores with two of the computerized algorithms showed significant correlation with estimated glomerular filtration rate (eGFR) at biopsy but not at the end of follow-up. There was poor concordance for AI based platforms. CONCLUSION: Chronicity scores are robust prognostic tools despite interobserver reproducibility. AI-algorithms have absolute precision but are limited by significant variation when different hardware and software algorithms are used for quantification.

Intraspecific variation in fine-root traits is larger than in aboveground traits in European herbaceous species regardless of drought.

Differences within species (Intraspecific trait variation - ITV) contribute substantially to overall trait variability and environmental harshness can reduce among-species variation. While aboveground traits have received considerable attention, knowledge about ITV in fine-root traits and how it differs from ITV in aboveground traits remains limited. This study examined the partitioning of trait variation aboveground and fine-root traits in 52 European herbaceous species and how such proportions change in response to drought, offering valuable insights for accurate functional species characterization and inter-species comparisons. We studied seven morphological aboveground and fine-root traits under drought and well-watered conditions in a greenhouse experiment. Linear mixed effect models and permutational multivariate analysis of variance (PERMANOVA) were employed to decompose trait variation, ensuring the robustness of our results. We also calculated variance partitioning for the combination of aboveground traits and the combination of fine-root traits, as well as pairs of analogous leaf and fine-root traits (i.e., traits that fulfill similar functions) for each treatment (control and drought). Among-species trait differences explained a greater proportion of overall variance than within-species variation, except for root dry matter content (RDMC). Height and leaf area stood out, with species' identity accounting for 87-90% of total trait variation. Drought had no significant effect on the proportions of variation in any of the traits. However, the combination of fine-root traits exhibited higher intraspecific variability (44-44%) than aboveground traits (19-21%) under both drought and control. Analogous root traits also showed higher ITV (51-50%) than analogous leaf traits (27-31%). Our findings highlight substantial within-species variation and the nuanced responses of fine-root traits, particularly RDMC, suggesting root traits' flexibility to soil heterogeneity that fosters less differentiation among species. Among-species trait differences, especially aboveground, may underscore distinct strategies and competitive abilities for resource acquisition and utilization. This study contributes to elucidate the mechanisms underlying the multifunctionality of the above- and belowground plants compartments.

Characterize and analysis of meteorological and hydrological drought trends under future climate change conditions in South Wollo, North Wollo, and Oromia Zones, in Ethiopia.

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

A statistical shape analysis for the assessment of the main geometrical features of the distal femoral medullary canal.

Statistical Shape Models (SSMs) are widely used in orthopedics to extract the main shape features from bone regions (e.g., femur). This study aims to develop an SSM of the femoral medullary canal, investigate its anatomical variability, and assess variations depending on canal length. The canals were isolated from 72 CT femur scans, through a threshold-based segmentation. A region of interest (ROI) was selected; sixteen segments were extracted from the ROI, ranging from 25% of the full length down to the most distal segment. An SSM was developed to identify the main modes of variation for each segment. The number of Principal Components (PCs) needed to explain at least 90% of the shape variance were three/four based on the length of the canal segment. The study examined the relationship between the identified PCs and geometric parameters like length, radius of curvature, ellipticity, mean diameter, and conicity, reporting range and percentage variation of these parameters for each segment. The SSMs provide insights into the anatomical variability of the femoral canal, emphasizing the importance of considering different segments to capture shape variations at various canal length. These findings can contribute for the design of personalized orthopedic implants involving the distal femur.

Your blush gives you away: detecting hidden mental states with remote photoplethysmography and thermal imaging.

Multimodal emotion recognition techniques are increasingly essential for assessing mental states. Image-based methods, however, tend to focus predominantly on overt visual cues and often overlook subtler mental state changes. Psychophysiological research has demonstrated that heart rate (HR) and skin temperature are effective in detecting autonomic nervous system (ANS) activities, thereby revealing these subtle changes. However, traditional HR tools are generally more costly and less portable, while skin temperature analysis usually necessitates extensive manual processing. Advances in remote photoplethysmography (r-PPG) and automatic thermal region of interest (ROI) detection algorithms have been developed to address these issues, yet their accuracy in practical applications remains limited. This study aims to bridge this gap by integrating r-PPG with thermal imaging to enhance prediction performance. Ninety participants completed a 20-min questionnaire to induce cognitive stress, followed by watching a film aimed at eliciting moral elevation. The results demonstrate that the combination of r-PPG and thermal imaging effectively detects emotional shifts. Using r-PPG alone, the prediction accuracy was 77% for cognitive stress and 61% for moral elevation, as determined by a support vector machine (SVM). Thermal imaging alone achieved 79% accuracy for cognitive stress and 78% for moral elevation, utilizing a random forest (RF) algorithm. An early fusion strategy of these modalities significantly improved accuracies, achieving 87% for cognitive stress and 83% for moral elevation using RF. Further analysis, which utilized statistical metrics and explainable machine learning methods including SHapley Additive exPlanations (SHAP), highlighted key features and clarified the relationship between cardiac responses and facial temperature variations. Notably, it was observed that cardiovascular features derived from r-PPG models had a more pronounced influence in data fusion, despite thermal imaging's higher predictive accuracy in unimodal analysis.

Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation.

Background: The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability. Methods: Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process. Results: The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection. Conclusions: Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities.

Effect of manual osteopathic techniques on the autonomic nervous system, respiratory system function and head-cervical-shoulder complex-a systematic review.

Background: Osteopathic manual techniques are now widely used in medicine worldwide. At present, there are no clear conclusions regarding the possibility of affecting the function of the autonomic nervous system (ANS), respiratory system and head-cervical-shoulder complex by manual osteopathic techniques. Objectives: The aim of the study was to review the current literature regarding the possible impact of osteopathic manual techniques on the state of the autonomic nervous system, spirometric parameters of the respiratory system and the state of the head-collar-shoulder complex. Methods: Publications have been searched in the following databases: PubMed, Virtual Health Library and Cochrane Central Register of Controlled Trials. The search strategy included keywords related to manual osteopathic treatment, autonomic nervous system, spirometry, respiratory function and head, neck and shoulder pain. The methodological quality of the included studies was assessed. The PRISMA guidelines were used for the systematic review. Studies from 2010 to 2023 were selected. Results: Using the proposed descriptions and manual searches from the literature of other works, 40 studies were found, out of which 22 were rejected because they did not meet the inclusion criteria. The analysis included: 15 randomized controlled trials, 3 pilot studies. Conclusion: Studies clearly show the effect of OMT on both spirometric parameters and the condition of the head-collar-shoulder complex. Most often this translates into improved ANS performance, but there are exceptions. Systematic review registration: https://www.crd.york.ac.uk/prospero/, CRD42023476963.

Pattern analysis using lower body human walking data to identify the gaitprint.

All people have a fingerprint that is unique to them and persistent throughout life. Similarly, we propose that people have a gaitprint, a persistent walking pattern that contains unique information about an individual. To provide evidence of a unique gaitprint, we aimed to identify individuals based on basic spatiotemporal variables. 81 adults were recruited to walk overground on an indoor track at their own pace for four minutes wearing inertial measurement units. A total of 18 trials per participant were completed between two days, one week apart. Four methods of pattern analysis, a) Euclidean distance, b) cosine similarity, c) random forest, and d) support vector machine, were applied to our basic spatiotemporal variables such as step and stride lengths to accurately identify people. Our best accuracy (98.63%) was achieved by random forest, followed by support vector machine (98.40%), and the top 10 most similar trials from cosine similarity (98.40%). Our results clearly demonstrate a persistent walking pattern with sufficient information about the individual to make them identifiable, suggesting the existence of a gaitprint.

Histological analysis of the medial gastrocnemius muscle in young healthy children.

Introduction: Histological data on muscle fiber size and proportion in (very) young typically developing (TD) children is not well documented and data on capillarization and satellite cell content are also lacking. Aims: This study investigated the microscopic properties of the medial gastrocnemius muscle in growing TD children, grouped according to age and gender to provide normal reference values in healthy children. Methods: Microbiopsies of the medial gastrocnemius (MG) muscle were collected in 46 TD boys and girls aged 2-10 years subdivided into 4 age groups (2-4, 4-6, 6-8 and 8-10 years). Sections were immunostained to assess fiber type cross-sectional area (fCSA) and proportion, the number of satellite cells (SC), capillary to fiber ratio (C/F), capillary density for type I and II fiber (CFD), capillary domain, capillary-to-fiber perimeter exchange index (CFPE) and heterogeneity index. fCSA was normalized to fibula length2 and the coefficient of variation (CV) was calculated to reflect fCSA intrasubject variability. Results: Absolute fCSA of all fibers increased with age (r = 0.72, p < 0.001) but more in boys (+112%, p < 0.05) than in girls (+48%, p > 0.05) Normalized fCSA, CV and fiber proportion did not differ between age groups and gender. C/F was strongly correlated with age in boys (r = 0.83, p < 0.001), and to a lesser extent in girls (r = 0.37, p = 0.115), while other capillary parameters as well as the number of SC remained stable with increasing age in boys and girls. Discussion: This study provides reference values of histological measures in MG according to age in normally growing boys and girls. These data may be used as a reference to determine disease impact and efficacy of therapeutic approach on the muscle.