搜索结果：Physics of life reviews

Natural hot spring water has been known for its healing and wellness properties for thousands of years. Hot spring hydrotherapy a form of natural therapy, the medical effects of it mainly involves a comprehensive effect of multiple aspects such as physics, chemistry, biology and psychology. This paper starts from the current stage of the application of hot spring hydrotherapy in the field of medicine, analyses the recent research on hot spring hydrotherapy in the medical field in order to form a comprehensive understanding of hot spring hydrotherapy. The recent literature and systematic reviews were surveyed and summarized, hot spring hydrotherapy is very effective in solving skin diseases, rheumatism, digestive system diseases, respiratory system diseases, nervous system diseases and cardiovascular diseases. In addition, the selection of hot spring and treatments should be considered for different conditions as hot spring hydrotherapy is not suitable for all diseases. Hot spring hydrotherapy can be considered a safe and generally well-accepted intervention in health care to alleviate symptoms and improve quality of life, and hopes that it can provide a certain reference basis for the research of the relevant content in the future.

The multifaceted nature of subjective experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global state of consciousness and construct a theory around the relevant empirical paradigms and findings. Theories of consciousness are, therefore, often difficult to compare; indeed, there might be little overlap in the phenomena such theories aim to explain. Here, we take a different approach: starting with active inference, a first principles framework for modelling behaviour as (approximate) Bayesian inference, and building up to a minimal theory of consciousness, which emerges from the shared features of computational models derived under active inference. We review a body of work applying active inference models to the study of consciousness and argue that there is implicit in all these models a small set of theoretical commitments that point to a minimal (and testable) theory of consciousness.

Meningitis remains the leading infectious cause of neurological disabilities globally, disproportionately affecting children younger than 5 years and populations in the African meningitis belt. Whereas previous global estimates focused on ten pathogen categories, this study presents the most comprehensive analysis to date, assessing the meningitis burden attributable to 17 causative pathogens based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework. GBD is a systematic, scientific effort aimed at quantifying the comparative magnitude of health loss caused by diseases, injuries, and risk factors across age groups, sexes, and geographical locations over time. We estimated meningitis mortality using the Cause of Death Ensemble model (CODEm) and morbidity using DisMod-MR 2.1, incorporating data from vital registration, verbal autopsy, surveillance, hospital data, and systematic reviews. Aetiology-specific estimates were generated with pathogen-linked case-fatality ratios and splined binomial regression models. Risk factor attribution was based on established risk-outcome pairs and population attributable fractions. In 2023, there were 259 000 (95% uncertainty interval 202 000-335 000) global deaths and 2·54 million (2·20-2·93) incident cases of meningitis. Children younger than 5 years accounted for more than a third of deaths (86 600 [53 300-149 000]). Streptococcus pneumoniae, Neisseria meningitidis, non-polio enteroviruses, and other viruses were the leading causes of death, while non-polio enteroviruses caused the most cases. The four WHO-defined preventable meningitis pathogens of interest (S pneumoniae, N meningitidis, Haemophilus influenzae, and Group B streptococcus) contributed to 98 700 deaths (77 000-127 000) and 594 000 cases (514 000-686 000). Low birthweight, short gestation, and household air pollution were the top risk factors for meningitis-related mortality. Although mortality and incidence have declined significantly since 1990, progress is insufficient to meet WHO 2030 targets. Despite marked progress in reducing bacterial meningitis via global vaccination campaigns, a substantial meningitis burden persists, attributable both to common pathogens such as S pneumoniae and N meningitidis and to emerging non-bacterial pathogens such as Candida spp and drug-resistant fungi. Achieving WHO goals will require sustained investment in surveillance, vaccination, maternal screening, and health-system strengthening, especially in high-burden settings. Gates Foundation, Wellcome Trust, and UK Department of Health and Social Care.

The essential role of non-coding RNAs (ncRNAs) in cardiovascular disease (CVD) research instigates a shift from empirical studies to those based on molecular mechanisms, targeted interventions, and personalized health management in the world of precision medicine. This review systematically summarizes the expression profiles and multilayered regulatory mechanisms of various ncRNAs such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and small nucleolar RNAs (snoRNAs) in a variety of cardiovascular diseases (CVDs) ranging from congenital heart disease to atherosclerosis (AS), cardiomyopathies, heart failure (HF), and arrhythmias. The central roles of key pathological pathways like epigenetic changes, competing endogenous RNA (ceRNA), inflammation and cell fate determination will be highlighted. From a diagnostic point of view, ncRNAs have good potentials as early-stage biomarkers, in applications such as exosomal liquid biopsy, disease classification, and prognosis. Emerging technologies, notably locked nucleic acid (LNA) oligonucleotides, adeno-associated virus serotype 9 (AAV9)-based delivery systems and engineered exosomes, are unlocking new avenues for intervention on the therapeutic front. These developments, coupled with drug repurposing strategies and tissue-specific delivery platforms, can make ncRNA-targeted therapies more specific and controllable. At the same time, interdisciplinary innovations, like single-cell multi-omics, spatial transcriptomics, CRISPR-dCas9 systems, and deep learning assist clinical translation greatly. However, the real-world application of ncRNA-based therapies is constrained by many challenges like low delivery efficiency, functional redundancy, and microenvironmental dependence. Future directions must aim to create integrative platforms that can dynamically identify and modulate ncRNA functions to link mechanistic studies to personalized therapies and subsequently expedite clinical translation of ncRNA discoveries. In this sense, three cross-scale principles-network topology and ceRNA competition dynamics; spatiotemporal gradients modeled by exosome transport and tissue microenvironments; and energetic/stoichiometric constraints like Dicer processing capacity and miRNA-target ratios- provide an analytical framework that appears recurrently across diverse CVD phenotypes and tighten the mechanistic unity of this review.

Metastasis-directed stereotactic body radiotherapy (SBRT) may be a viable strategy to defer systemic therapy in patients with oligometastatic cancer due to comorbidities, patient preferences, or concerns about adverse effects. To date, the evidence supporting this approach has not been comprehensively assessed. To evaluate systemic therapy-free survival (STFS) for different types of oligometastatic cancer after SBRT alone and to quantify adverse events, quality of life, and oncological outcomes. A systematic search of PubMed and EMBASE was conducted on July 10, 2024, to identify potentially eligible studies published after 2009. Forward and backward citation tracking was performed to identify additional relevant studies. Studies eligible for inclusion in the systematic review were retrospective or prospective with at least 10 patients who received metastasis-directed SBRT and no up-front systemic therapy for oligometastatic cancer (≤5 metastases) irrespective of primary tumor histology. Required outcomes being reported were STFS, progression-free survival, or overall survival. The subgroup of studies reporting STFS at 1 or 2 years were included in a meta-analysis of these pooled outcomes. Two reviewers independently extracted data using predefined forms and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A random-effects model was used for meta-analysis. The primary outcome of the meta-analysis was the pooled STFS rate at 1 or 2 years. Of 29 unique studies (2074 unique patients) included in the systematic review, 13 (984 patients) contributed data to the meta-analysis. The pooled 1- or 2-year STFS rate was 69.7% (95% CI, 57.4%-80.9%) across cancer types. Renal cell cancer demonstrated the highest STFS rate (87.0%; 95% CI, 76.2%-95.2%), followed by prostate cancer (78.1%; 95% CI, 67.4%-87.3%), with lower rates in other cancer types. Among 20 studies reporting adverse events, rates of adverse effects of grade 3 or higher were absent in 15 of 19 studies (79%) and ranged from 2 of 103 (1.9%) to 3 of 34 patients (8.8%) among those that observed severe adverse events. In this systematic review and meta-analysis, metastasis-directed SBRT alone was associated with meaningful STFS, particularly in patients with oligometastatic prostate or renal cell cancer, with low risks of treatment-related adverse events. These findings suggest that SBRT alone might be an acceptable option instead of immediate systemic therapy in selected patients with oligometastatic cancer.

Bilateral elective nodal irradiation (ENI) remains standard for treating most head and neck squamous cell carcinomas (HNSCC) but is associated with significant toxicity. Advances in lymphatic mapping, particularly with SPECT/CT-guided sentinel lymph node (SLN) identification, have enabled more personalized radiotherapy strategies. This systematic review evaluates the efficacy and quality-of-life impact of ENI strategies using SPECT/CT-guided SLN mapping. This systematic review, conducted according to PRISMA guidelines, included ten studies published between January 2014 and March 2024, including prospective, retrospective studies, randomized trials, and systematic reviews, examining oncologic outcomes and toxicity in patients undergoing SPECT/CT-guided SLN mapping or individualized ENI. Findings show that in well-lateralized, early stage carcinomas, SPECT/CT-guided ENI safely allows for unilateral treatment in up to 82% of patients, with a low contralateral regional failure rate. This approach significantly reduces radiation exposure to organs at risk and rates of xerostomia, dysphagia, and hypothyroidism, leading to improved quality of life. However, its applicability to advanced or midline tumors remains limited. SPECT/CT-guided SLN mapping and individualized ENI offer a promising, less toxic alternative for selected patients. Further prospective, multicenter, and randomized studies are needed to confirm these benefits and support broader clinical adoption.

Collective behavior, emerging from interactions among individuals, is a ubiquitous phenomenon observed across a wide range of biological systems-from cellular dynamics to animal ecology. Network science offers powerful tools for understanding the structure and functional properties underlying such systems. Despite significant progress in modeling, data-driven analysis, and interdisciplinary approaches, several critical challenges persist. How do complex social interactions among individuals influence the emergence of collective behavior? Moreover, in what ways do individual information and social interactions jointly shape collective decision-making? To address these challenges, this review synthesizes recent advances in network science, statistical physics, and artificial intelligence as applied to the study of collective phenomena. We focus on collective patterns in animals and cells, highlighting the interplay between structure and dynamics. The review begins with an overview of basic forms of collective behavior, followed by discussions of microscopic and macroscopic modeling approaches, structural and functional features of social interaction networks, mechanisms of information transmission, decision-making processes, and emergent collective intelligence. We also explore cutting-edge applications, including bio-inspired robotic swarms and coordination systems for unmanned aerial vehicles. We conclude by outlining open questions and potential research directions, aiming to provide insights into the design, control, and understanding of complex collective systems across biology, physics, and artificial intelligence.

Network science models have transformed our understanding of complex systems across biology, technology, and society, proving valuable in neuroscience. However, modeling biological complexity poses specific challenges, calling for expansions of traditional network frameworks. This paper explores constructive ways to enhance models, highlighting opportunities such as incorporating time-varying connections, adaptive topologies, and multilayer structures to better represent the temporal dynamics and multilevel interactions characteristic of biological systems. Additionally, it addresses deeper conceptual challenges, notably the substantial context dependence, open-endedness, and history sensitivity often observed in biology. By reviewing concepts such as Kauffman's "adjacent possible," the discussion emphasizes how biological state spaces themselves may dynamically evolve, suggesting the need for modeling strategies beyond static or pre-specified assumptions. Rather than undermining network science, these considerations highlight areas where traditional formalisms can fruitfully adapt and grow, ultimately deepening their explanatory power. The paper advocates integrating data-driven approaches that dynamically infer system properties from empirical observations, balancing modeling generality with biological specificity. Overall, this synthesis provides an assessment of both the strengths of network science and the challenges it faces, proposing constructive avenues for methodological and conceptual innovation that advance our ability to capture the nuanced complexity inherent in biological phenomena.

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In our target article "Dark Brain Energy: Toward an Integrative Model of Spontaneous Slow Oscillations", we proposed a three-layer hierarchical framework to model the spontaneous slow oscillations (SSOs) across six frequency bands to delineate their spectral architecture, functional roles, and neurocognitive basis. We are grateful for the seven insightful commentary articles from colleagues across neuroscience, physics, mathematics, psychology and clinics, which collectively enrich our framework. In this review, we address five key themes emerging from and inspired by these commentaries: (1) the relationship between SSOs and consciousness, (2) the geometric foundations of SSOs, (3) evolutionary, developmental, and metabolic mechanisms of SSOs, (4) mathematical modeling of traveling waves underlying SSOs, and (5) neuroscientific implications for brain-inspired intelligence. We finally integrate these perspectives to refine our original model and outline future research directions.

Protein post-translational modifications (PTMs), which involve the covalent attachment of specific chemical groups to amino acid residues, significantly reshape protein structure and function. These modifications play a crucial role in fundamental physiological processes such as signal transduction, metabolic regulation, and protein homeostasis. In the context of pan-cancer, various types of PTMs, including phosphorylation, acetylation, glycosylation, and ubiquitination, create an intricate crosstalk network that finely tunes the stability and function of immune checkpoint molecules, directly influencing tumor immune evasion and immune cell recognition. Additionally, PTMs exert multilayered regulation over the functional states of key immune cells, such as T cells, macrophages, and dendritic cells (DCs), thereby determining the intensity and nature of immune responses within the tumor microenvironment (TME). Furthermore, PTMs are pivotal in antigen processing and presentation by influencing antigen diversity and epitope display, which facilitates tumor cell escape from immune surveillance. Dynamic analyses reveal that PTM landscapes exhibit spatiotemporal specificity during tumor initiation, progression, and metastasis, closely correlating with tumor stage and the establishment of an immunosuppressive microenvironment. Based on these findings, immunotherapeutic strategies targeting key PTM-modifying enzymes, such as kinases, deacetylases, and deubiquitinases, are rapidly emerging. However, these approaches still face challenges, including drug specificity, resistance, and off-target effects. The exploration of synergistic effects through the combinational targeting of distinct PTM pathways, along with a deeper understanding of the interactive regulatory networks among PTMs, opens promising avenues for the development of next-generation precision immunotherapies. This review aims to systematically elucidate the multifaceted roles and dynamic regulation of PTMs in tumor immunity, providing a theoretical foundation and research direction for identifying novel immunotherapeutic targets and optimizing therapeutic strategies.

Chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease (ILD) and pulmonary sarcoidosis, are major global causes of mortality and morbidity. Although the COVID-19 pandemic has influenced acute respiratory health, its impact on chronic respiratory conditions remains unclear. We estimated the global, regional and national burden of chronic respiratory diseases from 1990 to 2023, including risk factors, and evaluated how these burdens have shifted during the COVID-19 pandemic using the Global Burden of Disease Study 2023. In 2023, chronic respiratory diseases accounted for 569.2 million (95% uncertainty interval (UI), 508.8-639.8) cases and 4.2 million (3.6-5.1) deaths. The age-standardized death rate declined by 25.7% globally from 1990 to 2023 despite an increase in ILD and pulmonary sarcoidosis. Mortality declined in younger males, especially for asthma, whereas older adults experienced a rise in ILD and pulmonary sarcoidosis. Smoking was the primary risk factor for COPD, whereas high body mass index and silica exposure were key risk factors for asthma and pneumoconiosis. During the pandemic, the incidence of chronic respiratory diseases increased modestly, but the decline in mortality rates became more pronounced, highlighting the need for sustained global attention and action to address their long-term burden.

This commentary commends Evers et al.'s multidimensional heuristic for structuring artificial consciousness research while arguing it cannot, as stated, adjudicate the nomological possibility of phenomenal consciousness, which is at stake in current debates. Behavioral-cognitive "profiles" lack a justified principle linking function to experience, and the awareness case study illustrates how externally specified goals can just as well underwrite as-if (pseudo-intentional) control rather than original intentionality. Moreover, the proposed heuristic overlooks that substrate similarity is currently indispensable for justifiably inferring the presence of consciousness beyond the validated case of the adult human brain. Given all this, the framework seems to provide a blueprint for building a more sophisticated philosophical zombie; it does not-and cannot-tell us whether anyone is there.

Recent discussions about the nature of meaning and concepts focus on abstract semantic knowledge including key information about inner states of the individual. Classic cognitive approaches anchor the meaning of words in universal concepts, semantic networks or semantic features encapsulated in the individual's own mind. However, this does not explain how symbols become interpretable during language development. Embodiment theorists acknowledge the relevance of semantic grounding of concrete referential symbols in perceptions and actions during learning, but, similar to classic cognitivism, assume internal anchoring of mental terms in introspection, thus once again implicating a main role of privileged access to 'private' inner states in language learning. This raises the basic question as to how a public language can be founded in private inner access. Here, we argue that, in semantic learning, a purely introspection-based classification of inner states is neither possible nor required, and even less so a first-person privilege in accessing these states. Rather, classification and semantic learning of symbols for mentalistic concepts is an interactive process between the learner and an external observer who can employ contextual knowledge and behavioral information for recognizing and categorizing the learner's mental states. In support of this 'extrospective' mental grounding account, we review observations that, in case of doubt about internal states, third-person evidence can play a decisive role. We also highlight supporting empirical studies showing that individuals in whom the link between first-person experience and externally observable behavior is broken may suffer from deficits in processing and understanding the related mentalistic vocabulary.

In a world dominated by large-scale numerical simulations and applications of machine learning to biology, medicine and neuroscience (and complex systems, in general), each driven by the availability of 'big data' and decreasing costs of computation, the art form of constructing simple models is no longer in the focus of scientific attention. Yet, the most successful of these 'minimal models' have brought about a revolution of our understanding of complex systems, in particular in the life sciences, and are still shaping our view of the world today. It is hard to look at a power-law distribution without thinking of the Bak-Tang-Wiesenfeld sandpile model. And it is near impossible to reflect on synchronization without imagining coupled phase oscillators. Here we have put together a few ideas on how to formulate and analyze simple models and to draw conclusions from them.

The intervertebral disc (IVD) and the articular cartilage (AC) are specialized, load-bearing tissues critical for spinal flexibility and joint mobility, respectively. Both tissues are characterized by their avascular nature and abundant extracellular matrix (ECM). They heavily rely on precisely regulated anabolic and catabolic processes to maintain structural integrity and functional performance. Disturbances can contribute to IVD degeneration and osteoarthritis that represent leading global causes of disability and pose substantial challenges to healthcare systems worldwide. One of the key regulators of IVD and AC homeostasis is mechanotransduction, the process through which mechanical cues are translated into biological responses. More recently, daily oscillations of genes and proteins implicated in mechanotransduction-related intracellular pathways started to gain attention. Such oscillations are driven by circadian rhythms and seem to affect the IVD and AC in health and degeneration. Circadian rhythms regulate the oscillatory expression of genes essential for matrix homeostasis, including those involved in nutrient transport, inflammation control, and cellular metabolism. Alterations of these rhythms, due to aging, inflammation, or lifestyle, might impair tissue homeostasis. Mechanotransduction and circadian rhythms interact reciprocally, as daily patterns of mechanical stimuli can entrain circadian rhythms, and circadian rhythms modulate cellular mechanosensitivity, optimizing responses to daily activity-rest cycles. This review synthesizes recent advances in understanding these intertwined mechano-circadian interactions within IVD and AC. It discusses the implications for degenerative disease progression and highlights potential therapeutic strategies leveraging chronotherapeutics and mechanobiology to preserve tissue function and improve the management of musculoskeletal disorders.

Biosensing with solid-state nanopores (SSNPs) is a versatile technique suitable for the analysis of double-stranded DNA, RNA, and their hybrids, complementing the use of biological nanopores for single-stranded nucleic acids and proteins. In this review, we systematically explain how operational parameters - including bias voltage, pore geometry, and buffer properties - affect translocation of double-stranded nucleic acids through SSNPs. We focus on key performance metrics for resolution, characterised by translocation time and signal-to-noise ratio, and throughput, described by event frequency and unfolded fraction. Beyond summarizing empirical observations, we dissect the underlying physical mechanisms that mediate these dependencies, including electrophoretic driving, hydrodynamic friction, electro-osmotic flow, and entropic effects. By integrating experimental findings with physical models, we aim to provide a practical guide for optimizing and interpreting SSNP experiments. The concepts discussed are broadly transferable to other SSNP targets and nanoscale transport applications.

Understanding and promoting cooperative behaviour among self-interested individuals is a critical concern in physical, biological, and social sciences. Numerous foundational mechanisms for the evolution of cooperation have been identified, and these mechanisms have served as the basis for developing tools and interventions designed to sustain and enhance cooperative behaviour. However, since both foundational mechanisms and the derived tools and interventions often involve costs affecting individuals or institutions, striving for maximum cooperation can sometimes harm social welfare, defined as the total population payoff. Herein, we review existing evolutionary mechanisms for the evolution of cooperation as well as tools and interventions based on these mechanisms, emphasising the often-overlooked hidden costs that may lead to a misalignment between cooperation and social welfare. By explicitly incorporating these hidden factors into the models, we analyse the conditions under which they reduce social welfare, across a broad range of social dilemma games and evolutionary forces. Additionally, we review experimental studies that support and inform mathematical models and agent-based simulations. We highlight when considering social welfare is crucial, as misalignment is most likely to occur. Ultimately, we argue that social welfare, not just cooperation, should be the primary optimisation objective when designing interventions for social good. We also suggest several key directions to further explore this often-overlooked issue in the literature. Overall, we reveal that hidden costs often influence the alignment between cooperation and social welfare, challenging the common prioritisation of cooperation alone.