Biologics have transformed the management of severe asthma, establishing clinical remission as a realistic treatment objective. Moreover, they offer the potential to reduce background treatment with inhaled corticosteroids (ICS). Although guidelines recommend ICS dose reduction in patients achieving disease control, they provide limited guidance on how to implement tapering. In this context, we developed Step-DAWN (De-escalation of ICS in Asthma With a Novel algorithm), a consensus-based protocol that guides ICS tapering in adults with severe asthma receiving biologic therapy. Step-DAWN was ideated through a modified Delphi process; a Steering Committee consisting of two Italian specialists and two pharmaceutical Medical Affairs professionals drafted 30 statements, which were voted on by an Expert Panel comprising 12 specialists. Consensus was reached on 21 of 30 statements in the first round; three other statements reached consensus in the second round; six statements did not reach consensus. The Step-DAWN protocol was designed based on 24 statements that achieved consensus. Patients eligible for Step-DAWN should have maintained the four criteria of complete clinical remission (cCR) for at least 6 months. ICS dose should be reduced gradually, with follow-up assessments not exceeding 6 months. In case of loss of eligibility criteria, potential causes should be addressed before reattempting tapering. Fractional exhaled nitric oxide (FeNO) and blood eosinophil count (BEC) were identified as key biomarkers for monitoring airway inflammation. Step-DAWN is the first consensus-based protocol designed by Italian specialists to provide guidance for ICS tapering in patients with severe asthma treated with biologics.
At dawn, many bird communities exhibit a rapid and highly synchronous transition from silence to vocal activity. Although light levels increase gradually, the onset of this dawn chorus can appear abrupt, suggesting the possibility of tipping behaviour. While such dynamics are frequently described using catastrophe theory, direct mechanistic links between individual behavioural rules and macroscopic bifurcation structure remain limited. We develop a stochastic threshold model for this phenomenon in which each individual sings when a combination of light and social stimulation exceeds its activation threshold. Under mean-field scaling, the population dynamics converge to a deterministic equation for the proportion of singing individuals, with nonlinearity determined entirely by the cumulative distribution of thresholds. We show that for a broad class of threshold distributions, this system exhibits cusp bifurcations and hysteresis. Thus, the abrupt onset of singing can emerge directly from heterogeneity and social feedback, without imposing nonlinearities at the population level. Extending the model to spatially structured populations yields travelling waves, providing an explanation for the spatial variation of song across a landscape. Our results demonstrate how heterogeneity in behavioural thresholds can generate hysteresis in socially coupled populations, offering a general framework for linking individual decision rules to emergent ecological transitions.
The Dawn Phenomenon (DP) denotes the occurrence of spontaneous early-morning hyperglycemia in the absence of overnight hypoglycemia, or that necessitating an elevation in insulin to sustain normal morning blood glucose levels. Studies exhibit considerable variability, with some indicating that DP is a spontaneous occurrence reported in a substantial percentage of individuals. Patients with DP demonstrate increased average blood glucose variability over a 24-h period compared to those without DP, presenting considerable problems for clinical diabetes pharmacotherapy and nutritional regulation. Moreover, given the ambiguity surrounding the precise pathophysiology and targeted therapies for DP, therapeutic care predominantly emphasizes symptom alleviation. Conventional perspectives ascribe DP to physiological surges in the secretion of counterregulatory hormones, including cortisol, catecholamines, and growth hormone, in the early morning hours. These hormones enhance hepatic glucose production while concurrently diminishing peripheral tissue sensitivity to insulin. This article examines the contemporary pathophysiological underpinnings of DP to offer insights for clinical diagnosis and treatment in diabetes. This article focuses mainly on the dawn phenomenon in type 2 diabetes (T2DM), while evidence related to type 1 diabetes (T1DM) is addressed independently.
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In recent years, the dual regulatory role of adipocytes in the tumor microenvironment (TME) has emerged as a critical focus in cancer research. Advances in the fields of metabolic biology and tumor immunology have shown that adipocytes play a crucial role in tumor occurrence, progression, and treatment outcome by secreting adipokines, regulating metabolic reprogramming, and mediating immunosuppressive networks. This article systematically reviews the physiological functions of adipose tissue and its intricate crosstalk with cancer cells, with particular emphasis on dual metabolic and immune mechanisms by which adipocytes dynamically modulate tumor progression within the TME. Furthermore, by integrating preclinical research and translational medicine evidence, a precise treatment framework targeting the interaction between fat and tumor is proposed, providing theoretical basis for the development of metabolic immune combination therapy.
Parkinson's disease (PD), a prevalent neurodegenerative disorder, is characterized by progressive loss of dopaminergic neurons in the midbrain. While dopamine replacement therapy effectively manages early symptoms, its long-term use leads to motor complications, highlighting the urgent need for treatments that directly address the underlying pathological changes. Cell transplantation, which aims to replace the lost dopaminergic neurons, has emerged as a promising approach. Early attempts using fetal ventral mesencephalic (fVM) tissue showed proof-of-concept, with some patients experiencing long-term motor improvement. However, these trials have been hampered by inconsistent results, graft-induced dyskinesia (GID), and significant ethical and logistical issues related to tissue supply. These challenges have shifted the focus to pluripotent stem cells (PSCs), including human-induced pluripotent stem cells (iPSCs) and embryonic stem cells (ESCs), which offer a stable, ethically sound, and scalable source of high-quality cells. Recent clinical trials using PSCs suggest a turning point. All reported clinical trials demonstrated the safety and feasibility of this approach. The need for long-term safety and efficacy data, patient stratification, and techniques to improve graft survival are key areas of future research. Nevertheless, recent clinical trial successes suggest that cell transplantation is moving beyond symptomatic relief to become a truly restorative therapy for PD.
Overcoming the traditional trade-off between spatial control and high quality factors, a new class of multifunctional photonic crystals bridges the gap between local and nonlocal light manipulation. This work opens a new frontier in flat optics: the realm of partially nonlocal metasurfaces, paving the way for advanced imaging, communication, and analog optical computing.
Pavlovian-Instrumental Transfer (PIT) exemplifies how Pavlovian-motivational influences modulate goal-directed behavior, yielding outcome-specific (specific PIT) and general (general PIT) transfer. General PIT is commonly interpreted as outcome-general invigoration and is sensitive to stress. However, human PIT research typically uses visual, appetitive procedures, whereas rodent PIT research often uses auditory cues, limiting translation. We tested whether cue modality, along with additional factors such as immersive threat contexts, modulates PIT, and whether virtual reality (VR) enhances general transfer. Across three experiments (N = 196), participants completed a PIT task: (1) two-dimensional (2D) appetitive PIT with auditory vs. visual cues (Experiment 1; n = 60); (2) VR PIT comparing appetitive (positive reinforcement) vs. aversive (negative reinforcement; "zombie") contexts (Experiment 2; n = 40); and (3) aversive VR PIT preceded by immersive compound threat scenarios (neutral, spiders, contamination) in individuals stratified by contamination fear (CF) (Experiment 3; n = 96). Specific and general PIT were computed from baseline-corrected response rates. Stress induction (Experiment 3) was assessed using photoplethysmography-derived heart rate variability (HRV), salivary alpha-amylase (sAA), and self-report measures. Robust specific (η p 2 > 0.45) and general PIT (η p 2 > 0.68) were observed across all experiments. In Experiment 1, PIT magnitude did not differ by cue modality; specific PIT exceeded general PIT across auditory and visual conditions. In Experiment 3, threat scenario type and contamination fear did not significantly alter transfer effects. Nevertheless, increased stress indices were observed, including phase-dependent HRV changes, elevated sAA from pre- to post-test, and higher self-reported anxiety (with stronger subjective fear/disgust in the contamination condition). Across experiments, general PIT was larger in the VR studies than in the 2D study, whereas specific PIT remained stable; however, VR was not manipulated independently of reinforcement context and other procedural differences, precluding strong causal inference about its effect on general PIT. Despite this, human PIT appeared robust across cue modality and reinforcement context, and there was an indication that VR immersion may selectively amplify general invigoration while sparing specific PIT across all three experiments, albeit with a small effect (pseudo-R 2 = 0.06).
Brain-computer interface (BCI) technology has emerged as a crucial interdisciplinary advancement in the field of neuropsychiatric disease treatment. With the global rise in the prevalence of neurological and psychiatric disorders, which impose a substantial burden on society, BCI offers a novel approach. Since the discovery of bioelectric phenomena in the 19th century, various classification frameworks have been developed based on signal paradigms, invasiveness, and feedback mechanisms. BCI applications span multiple disease areas. In movement disorders, it aids in restoring motor function through prosthetic control, functional electrical stimulation, and brain stimulation-based therapies. For patients with communication barriers, it enables alternative communication methods and speech-related neural signal decoding. In psychiatric conditions, BCI shows growing potential in both diagnosis and treatment, particularly in conditions like autism and depression. Despite significant progress, BCI faces challenges. The long-term biocompatibility of electrodes and the resolution of neural signals remain to be improved. To address these limitations, research on new electrode materials, such as carbon nanomaterials and composites, is ongoing. Emerging BCI technologies, including endovascular BCI and optogenetics BCI, present new possibilities. The integration of multimodal technologies and artificial intelligence in BCI systems is expected to enhance performance and enable more personalized treatment. Overall, BCI technology holds great promise for improving the quality of life of patients with neuropsychiatric disorders and driving innovation in the medical and neuroscience fields.
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This article comments on: Liu X-D, Ren Y-L, Li Y-L, Zeng Y-Y, Zhang X-Y, Li Y-R, Du Y-X, Fang X-W. 2026. Lineage-specific response to melatonin in stomatal regulation across vascular plants. Journal of Experimental Botany 77, 4668–4678. https://doi.org/10.1093/jxb/erag146
Class imbalance is a fundamental challenge in medical image analysis, where certain disease categories occur far less frequently than others. This uneven data distribution often causes learning algorithms to favor common conditions while underperforming on rare but clinically significant cases. In chest X-ray analysis, the deployment of artificial intelligence is particularly hindered by the long-tailed distribution of thoracic diseases, as conventional deep learning models exhibit optimization bias toward majority classes, leading to reduced sensitivity for rare yet critical pathologies. To address this challenge, this paper presents Dynamic Adaptive Weighting with Hybrid Networks (DAWN-Net), a unified framework that synergizes data-level and algorithm-level interventions. Unlike conventional approaches that treat augmentation and re-weighting in isolation, DAWN-Net introduces a Hybrid Synergy mechanism. At the data level, we propose Augmentation-Aware Manifold Smoothing, which generates synthetic variations in the local tangent space of minority samples to densify their feature representation. Architecturally, the model employs a dual-stream design comprising a Hierarchical Feature Propagation Network (HFPN) to capture high-frequency local textural details, and a Semantic Context Modeling Network (SCMN) to enforce global anatomical consistency. These components are jointly optimized using a novel Momentum-Adjusted Gradient Harmonization loss, which dynamically recalibrates gradient contributions based on batch-wise class statistics and augmentation intensity. Validation on three large-scale benchmarks-NIH ChestXray14, CheXpert, and PadChest-demonstrates that DAWN-Net consistently outperforms state-of-the-art baselines, particularly in the detection of rare diseases such as Hernia and Fibrosis. By mitigating the optimization bias and improving sensitivity for rare yet critical pathologies, DAWN-Net overcomes the limitations of conventional deep learning models, thereby offering a more reliable solution for safety-critical radiological diagnosis.
Some plants emit floral scents at night and are pollinated by nocturnal bees, yet their scent dynamics, composition, and emitting tissues remain poorly understood. Here, we study Passiflora pohlii, a passionflower whose blossoms open at dawn, are pollinated by crepuscular Ptiloglossa bees, and emit a strong citrus-like scent to (i) identify the chemical composition of the floral scent, (ii) quantify the rhythm of volatile emissions, and (iii) localize and characterize the ultrastructure of osmophores, to understand better their role in synchronizing pollination within a restricted temporal window. Osmophores were localized using morphochemical techniques and characterized by cellular structure. Volatile compounds were collected by dynamic headspace at early (dawn; complete and dissected flowers) and late (early morning; complete flowers) anthesis, and analysed by gas chromatography-mass spectrometry (GC-MS). Sequential ultrastructural analyses tracked subcellular changes correlated with scent emission. The outer corona filaments were the main scent source, containing osmophores and releasing volatiles. Scent emission peaked at dawn, temporally aligned with the activity of Ptiloglossa pollinators, before declining by 90% in late anthesis, despite unchanged floral turgor. TEM revealed organelle degradation, correlating with the decline in scent emission. The scent profile was dominated by monoterpenoids, primarily 'citronelloids' (geraniol and derivatives thereof). We identify a coordinated system of structural, chemical, and temporal characteristics in a species of passionflower that appear to be tailored to the sensory characteristics, body size, and crepuscular flight activities of the pollinating Ptiloglossa bees. These findings emphasize the importance of chemical communication in narrow plant-pollinator associations involving crepuscular bees.
Most bird species are diurnal but drastically change their diel cycle of activity to migrate at night. Nocturnal migration has been documented using different methods (e.g., experiments, radar and radio tracking, and acoustic monitoring), but accurately quantifying the proportion of nocturnal versus diurnal flight at the individual and species levels, and understanding how this behavior evolved across the avian tree, has remained methodologically challenging.1,2,3 Such uncertainty is not only of theoretical importance but also limits our ability to mitigate conservation threats, particularly from light pollution and building collisions.4,5,6 With multi-sensor geolocators recording light, barometric pressure, and activity,7,8 we reconstructed high-resolution migratory trajectories9,10 for 411 individuals from 56 small- or medium-sized landbird species across four continents and measured the proportion of each flight that occurred during night or day. Our species-level quantification confirmed nocturnal migration as the dominant strategy among small landbirds, while also providing precise flight proportion estimates across a broad taxonomic sample and refining the classification of several species previously described as partial or facultative diurnal migrants. We found that birds initiated and ended migratory flights near civil dusk and dawn, thereby maximizing nocturnal travel. The phylogenetic signal we detected indicates that nocturnal migration is largely conserved within lineages. While nocturnal migration confers multiple advantages, the relative importance of the proposed drivers remains to be determined.11,12 Our study provides species-specific quantification of nocturnal migration, highlights tracking gaps across taxa and regions, and opens new avenues for studying the evolutionary, ecological, and sensory drivers of nocturnal migratory flights.
Alcohol-impaired driving (DUI) remains a major contributor to traffic fatalities, yet most evidence on DUI risk factors is derived from surveys, crash records, or simulator studies. This study aims to quantify driver, behavioral, and environmental risk factors associated with DUI using real-world naturalistic driving data and to assess their joint effects on DUI risk. Data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) were used, comprising over 50 million miles of driving from more than 3,000 drivers across six U.S. sites. A total of 47 alcohol-impaired events were identified and matched with 435 sober events from the same drivers. Driver demographics, risk-taking propensity (risk score), environmental conditions, traffic characteristics, and driving behaviors were analyzed. Associations were evaluated using chi-squared tests with Cramér's V, odds ratios (ORs), and logistic regression models. A Bayesian network was developed to capture conditional dependencies among significant risk factors and to estimate marginal and joint effects using conditional probability ratios (CPRs). Driver risk-taking propensity and age were strongly associated with DUI involvement. Risk score is associated with higher the odds of DUI by a factor of 7.47 (95% CI: 2.91-18.07), while teen and young drivers had approximately six times higher odds of DUI involvement compared to senior drivers. Environmental and behavioral factors showed substantial associations with DUI. Lighting condition was the strongest risk factor, with dawn/dusk/dark conditions increasing DUI odds by 9.90 compared to daylight. Unsafe maneuvers (OR = 3.73), illegal maneuvers (OR = 3.09), performance errors (OR = 3.09), non-divided roadways (OR = 2.39), adverse weather (OR = 2.98), low traffic density (OR = 2.57), and improper seatbelt use (OR = 2.13) were also significantly associated with DUI. Bayesian network analysis identified lighting condition and driver risk score as direct parent nodes of DUI. High-risk drivers had a CPR of 29.6 relative to low-risk drivers, nighttime conditions had a CPR of 13.7 relative to daylight, and their joint effect elevated DUI risk by a factor of 308.0. This study revealed that DUI risk is associated with a combination of driver personality, age-related risk-taking, environmental factors, and observable unsafe behaviors. These findings indicate opportunities for population-level prevention strategies, risk-based public health interventions, and early identification approaches, such as targeted enforcement and driver monitoring, to mitigate impairment-related driving errors and alcohol-related injuries and fatalities.
Acoustic communication in anurans is energetically costly and constrained by environmental factors, especially in open savanna ecosystems where extreme temperature fluctuations, desiccation risks, and wind-induced noise pose significant challenges to signal propagation and caller physiology. We analyzed the acoustic ecology of the widespread bubbling Kassina (Kassina senegalensis) in Mozambique, and compared it with that of other locations. We examined the influence of temperature, photoperiod and moonlight on calling phenology. Calling phenology was strongly seasonal driven, while daily patterns showed a bimodal strategy with peaks at dawn and dusk. This crepuscular activity suggests a behavioral adaptation to optimize signal transmission during stable atmospheric conditions and/or reducing desiccation. We identified a thermal window for vocalization (~ 23-27 °C), with activity sharply declining above 30 °C, suggesting a physiological upper limit. Moonlight also had an effect on calling activity. These findings highlight the behavioral and physiological plasticity of K. senegalensis in navigating savanna constraints, offering insights into ectotherm neuroethological adaptations amid climate change.
Diurnal changes in light availability are a defining feature of life on Earth. Photoautotrophic organisms therefore store reduced carbon during the day to sustain energy metabolism at night. In cyanobacteria, glycogen is the primary carbon storage compound and supports both energy homeostasis and stress responses. Although glycogen-deficient Synechocystis strains have been studied previously, how these mutants cope with the loss of the major daytime carbon sink and can sustain themselves during the night remains unclear. Using single-cell microfluidics, transcriptomics, and metabolomics, we show that ΔglgC mutants exhibit pronounced light sensitivity. At sub-lethal light intensities, daytime transcriptional responses are dominated by downregulation of photosynthesis-related genes, likely preventing NADPH overaccumulation in the absence of a carbon sink. During the night, mutants display severe energy limitation, characterized by reduced ATP levels, altered redox balance, and depletion of central carbon intermediates. In contrast, fumarate and malate accumulate, indicating enhanced respiratory flux through succinate dehydrogenase. These metabolic constraints lead to extended lag phases and delayed cell divisions after the onset of light, demonstrating that glycogen-deficient cells fail to efficiently reinitiate growth after dawn. Overall, our results as a snapshot of the initial response to diurnal regimes highlight glycogen as a central integrator of diurnal physiology in Synechocystis, coordinating energy metabolism, redox balance, and cell division, with implications for metabolic robustness and the evolutionary constraints shaping (endo)symbiosis.
Bacterial plant pathogens have ravaged crops since the dawn of agriculture and continue to pose a serious threat today. Bacteria and their plant hosts have co-evolved in an evolutionary arms race, with artificial selection due to agriculture tipping the scale in favor of the pathogen. This review gives an overview of plant pathogenic bacterial diversity, showing that pathogenicity has independently evolved numerous times, and that there is not one unifying trait determining plant pathogenicity. Instead, these bacteria represent repeated, independent evolutionary transitions driven by life in complex ecological networks that include plant hosts, insect vectors, microbial competitors, and highly heterogeneous abiotic environments. Their genomes reflect this interplay through a dynamic balance of architecture and flux. These structural features, along with highly variable pangenomes, capture the balance between genome stability and flux imposed by ecological constraints and epidemiological dynamics. Horizontal gene transfer via conjugative plasmids, prophages, integrative and conjugative elements, transposons, and in some lineages, natural competence, remains the major source of adaptive novelty, enabling rapid remodeling of virulence repertoires, metabolic capabilities, and antibiotic or heavy metal resistance genes. These changes create distinct selective landscapes. Agricultural practices such as chemical use, host resistance deployment, or seed trade, can drive recurrent bottlenecks, expansions, and admixture events that leave strong genomic signatures in pathogens. Finally, this review explores the genomic differences enabling the divergence of lifestyles, while also acknowledging knowledge gaps and future directions of research on the evolution of bacterial plant pathogens.