In the last decade, the thermostatted kinetic theory has been proposed as a general paradigm for the modeling of complex systems of the active matter and, in particular, in biology. Homogeneous and inhomogeneous frameworks of the thermostatted kinetic theory have been employed for modeling phenomena that are the result of interactions among the elements, called active particles, composing the system. Functional subsystems contain heterogeneous active particles that are able to perform the same task, called activity. Active matter living systems usually operate out-of-equilibrium; accordingly, a mathematical thermostat is introduced in order to regulate the fluctuations of the activity of particles. The time evolution of the functional subsystems is obtained by introducing the conservative and the nonconservative interactions which represent activity-transition, natural birth/death, induced proliferation/destruction, and mutation of the active particles. This review paper is divided in two parts: In the first part the review deals with the mathematical frameworks of the thermostatted kinetic theory that can be found in the literature of the last decade and a unified approach is proposed; the second part of the review is devoted to the specific mathematical models derived within the thermostatted kinetic theory presented in the last decade for complex biological systems, such as wound healing diseases, the recognition process and the learning dynamics of the human immune system, the hiding-learning dynamics and the immunoediting process occurring during the cancer-immune system competition. Future research perspectives are discussed from the theoretical and application viewpoints, which suggest the important interplay among the different scholars of the applied sciences and the desire of a multidisciplinary approach or rather a theory for the modeling of every active matter system.

Parkinson\'s disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops-a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems.

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

This qualitative study aimed to explore the experiences of 11 adults with chronic kidney disease (CKD) undergoing evaluation for kidney transplant (KT) and examine the role played by the nurse in the process. Employing a descriptive phenomenology approach, semi-structured interviews were conducted between October 2022 and July 2023. Thematic analysis, facilitated by Atlas. ti software, revealed a systemic management diagram with \"The candidate for kidney transplant and their reality\" at the center, followed by \"The process of chronic kidney disease and kidney transplantation,\" and concluding with the most distal category centered on \"The kidney transplant access nurse.\" This organizational framework provided insights into the layers of relationships between emerging themes. The findings underscored the complexity and multidimensionality of the CKD and KT process, emphasizing the nurse\'s pivotal role as a guide and protector throughout the evaluation process for accessing kidney transplantation. The convergence of results with existing literature highlighted the need to address challenges such as lack of time, resources, and emotional support to enhance the quality of care. Recognizing the nurse\'s crucial importance in this process, the study emphasizes the significance of addressing these challenges to improve patient care and calls for attention to the nurse\'s role in guiding individuals through the intricate journey of CKD and kidney transplantation.

BACKGROUND: The Black, Indigenous, and People of Colour populations in the United States are disproportionately affected by the emerging health threat SARS-CoV-2, which causes COVID-19.
OBJECTIVE: This paper aims to demonstrate the usefulness of critical systems thinking by using scenario planning based on epidemiological data and tying epidemiology with soft systems methodology to investigate COVID-19 disparities among disproportionately affected Black, Indigenous, and People of Colour populations.
METHODS: Using a review of the COVID-19 literature and publicly available US COVID-19 data, critical systems thinking is applied in a scenario planning example and a call to link soft systems methodology with epidemiology.
RESULTS: According to the four plausible Endgame scenarios, levels of community transmission as well as the current state transmission are based on the driving forces of the scenarios. In addition, soft systems methodology explores the effect on stakeholders and strengthens the picture of disease burden beyond sole reliance on traditional data sources.
CONCLUSIONS: This analysis underscores employing critical systems thinking to critically assess diverse methods appropriate for the ongoing complexity of global crises. It is argued that critically engaged subjectivity should be given space alongside data-dependent objectivity. COVID-19 disparities are reliant on the social determinants of health\'s effects as driving forces on disease transmission in Black, Indigenous, and People of Colour populations. It is moreover argued that critical systems thinking is demonstrated by linking epidemiological evidence with scenario planning and soft systems methodology. This in turn supports a critical systems thinking approach to uncover the state of health disparities among minoritized communities under COVID-19.

Addressing Diversity, Equity and Inclusion in health care is a multidimensional challenge. From a US perspective, the third-party payment system has disempowered and depersonalized health-care delivery. The net result is wasteful and inefficient use of human and financial resources, burnout among providers, as well as care inequities. Financial integration at the point of patient care is essential to aligning the needs of patients with advances in medical technology. Complexity theory offers valuable insights into the roles of government, intermediaries and patients. The government must focus on equity as a rule compiler and referee of the system. Patient and providers who are actively engaged in shared decision-making will naturally address the diverse needs of multitudinous communities. Intermediaries address inclusion by connecting resources with the point of care. In a dynamic, emerging health-care system that serves diverse communities, patient and community-based financing, vouchers and defined contributions are necessary first steps in addressing cultural diversity, inclusion and equity.

BACKGROUND: Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients.
METHODS: We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting.
RESULTS: The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results.
CONCLUSIONS: The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.

Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.
In this study, we investigated the dynamics of information transfer among functionally identified neural modules during cognitive motor control. Our focus was on mesoscopic neural activities in the dorsal premotor cortex of rhesus monkeys engaged in a Stop-signal task. Leveraging multivariate transfer entropy and graph theory, we uncovered insights on how behavioral control shapes the topology of information transmission in a local brain network. Task phases modulated the strength and hierarchy of information exchange between modules, revealing the nuanced interplay between neural populations during generated and canceled movements. Notably, during successful inhibition, the network displayed a distinctive configuration, unveiling a novel mechanism for motor inhibition in the premotor cortex: a topological reshuffle of information among neuronal ensembles.

UNASSIGNED: Social prescribing involves connecting individuals to community groups and activities, often to support their mental health and well-being. It has received increasing support in recent years across the NHS. There is a strong evidence base for the benefits of different types of community activities, including exercise groups, arts groups and nature interventions, on mental health outcomes, however, less is known about how these groups impact mental health and well-being. This study explores through what individual-level mechanisms (the \'how\') these groups support psychosocial well-being.
UNASSIGNED: An ethnographic study was conducted over 12-months to explore key shared, individual-level mechanisms across 4 social prescribing community groups: football, singing, gardening and reading. This study focused mostly on those with severe mental illness, whereas previously most social prescribing studies have focused on mild to moderate mental health problems. To frame the findings, a \'multi-level theoretical framework of mechanisms of action\' of leisure activities was used.
UNASSIGNED: Key shared psychological mechanisms were: increased self-confidence and self-esteem, increased purpose/meaning, increased sense of achievement, experience of pleasure; social mechanisms included: increased social support, formation of friendships and reduced loneliness, enhanced sense of community and belonging; behavioural mechanisms were: increased independence and openness to experience, reduction in addictive behaviours and building healthier habits, increased work-seeking behaviour, and provision of structure & routine.
UNASSIGNED: It is hoped that the findings of this study can help referring professionals increase their understanding of exactly how such groups support individuals\' mental health, thus enhancing referring practices.
This study uses ethnographic methods, wherein the lead researcher spent over a year participating in 4 different community groups, using interviews, conversations and observation to explore the mental health impact of such groups on individuals\' lives. The participants had moderate to severe mental health conditions, and the groups consisted of a reading, gardening, singing and football group. The study explored \'mechanisms\' underlying the mental health benefits of these groups. Key shared psychological mechanisms were: increased self-confidence and self-esteem, increased purpose/meaning, increased sense of achievement, experience of pleasure; social mechanisms included: increased social support, formation of friendships and reduced loneliness, enhanced sense of community and belonging; behavioural mechanisms were: increased independence and openness to experience, reduction in addictive behaviours and building healthier habits, increased work-seeking behaviour, and provision of structure & routine. It is hoped that the findings of this study can help referring professionals (e.g. GPs, social workers, link workers) increase their understanding of exactly how such groups support individuals’ mental health, thus improving referring skills.