Effects of pollen concentration on allergic rhinitis in children: A retrospective study from Beijing, a Chinese megacity.

With global climate change and rapid urbanization, the prevalence of allergic diseases caused by pollen is rising dramatically worldwide with unprecedented complexity and severity, especially for children in mega-cities. However, because of the lack of long time-series pollen concentrations data, the accurate evaluation of the impact of pollen on allergic rhinitis (AR) was scarce in the Chinese metropolis. A generalized additive model was used to assess the effect of pollen concentration on pediatric AR outpatient visits in Beijing from 2014 to 2019. A stratified analysis of 10 pollen species and age-gender-specific groups was also conducted during the spring and summer-autumn peak pollen periods separately. Positive associations between pollen concentration and pediatric AR varied with the season and pollen species were detected. Although the average daily pollen concentration is higher during the spring tree pollen peak, the influence was stronger at the summer-autumn weed pollen peak with the maximum relative risk 1.010 (95% CI 1.009, 1.011), which was higher than the greatest relative risk, 1.003 (95% CI 1.002, 1.004) in the spring peak. The significant adverse effects can be sustained to lag10 during the study period, and longer in the summer-autumn peak (lag13) than in the spring peak (lag8). There are thresholds for the health effects and they varied between seasons. The significant effect appeared when the pollen concentration was higher than 3.74 × 105 grain·m-2·d-1 during the spring tree pollen peaks and 4.70 × 104 grain·m-2·d-1 during the summer-autumn weed pollen peaks. The stratified results suggested that the species-specific effects were heterogeneous. It further highlights that enough attention should be paid to the problem of pollen allergy in children, especially school-aged children aged 7-18 years and weed pollen in the summer-autumn peak pollen period. These findings provide a more accurate reference for the rational coordination of medical resources and improvement of public health.

In Situ Self-Assembled Phytopolyphenol-Coordinated Intelligent Nanotherapeutics for Multipronged Management of Ferroptosis-Driven Alzheimer's Disease.

Ferroptosis is a vital driver of pathophysiological consequences of Alzheimer's disease (AD). High-efficiency pharmacological inhibition of ferroptosis requires comprehensive coordination of diverse abnormal intracellular events, which is an urgent problem and great challenge for its application in AD treatment. Herein, a triphenylphosphonium-modified quercetin-derived smart nanomedicine (TQCN) is developed for multipronged anti-ferroptosis therapy in AD. Taking advantage of the favorable brain-targeting and mitochondria-locating properties, TQCN can efficiently chelate iron through phytopolyphenol-mediated spontaneous coordination and self-assemble into metal-phenolic nanocomplexes in situ, exerting escalating exogenous offensive effects to attenuate iron overload and its induced free radical burst. Meanwhile, the Nrf2 signaling-mediated endogenous defensive system is reconstituted to restore iron metabolism homeostasis represented by iron export and storage and enhance cytoprotective antioxidant cascades represented by lipid peroxidation detoxification. Benefiting from the multifaceted regulation of pathogenic processes triggering ferroptosis, TQCN treatment can ameliorate various neurodegenerative manifestations associated with brain iron deposition and rescue severe cognitive decline in AD mice. This work displays great promise of in situ self-assembled phytopolyphenol-coordinated intelligent nanotherapeutics as advanced candidates against ferroptosis-driven AD progression.

Sensitization characteristics in allergic rhinitis and transport pathway for Artemisia pollen in northern Beijing, China.

The genus Artemisia, an important allergen related to Allergic Rhinitis (AR), is widespread in temperate regions. However, the sensitization rate of Artemisia pollen varies significantly, and the source of Artemisia pollen is not clear. Based on continuous daily airborne pollen monitoring in the summer and autumn of 2019 and 2020 in northern Beijing, the daily number of AR patient visits during the same period, and the detection of allergen serum-specific immunoglobulin E (sIgE) in some AR patients, this study discusses the sensitization rate of Artemisia pollen and its transmission pathway and possible source area. The results show that (1) Artemisia pollen is the most important airborne pollen in summer and autumn in northern Beijing, and the pollen concentration is significantly related to the daily number of AR patient visits; (2) the rate of AR patients testing positive for Artemisia pollen allergens is 32.35 %, which is the first risk allergen and is consistent with the high sensitization rate of Artemisia pollen in northern China; and (3) in addition to local sources, central and southern Inner Mongolia, southern Mongolia and northwestern China are potential source areas of Artemisia pollen within the study area. This study provides first-hand data for accurately understanding the allergenic characteristics and sources of Artemisia pollen in northern Beijing and provides a scientific basis for the prevention of AR induced by Artemisia pollen in patients in China.

Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images.

Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and classification in a weakly supervised manner. It first locates pollen regions from the raw images based on the detection model. To improve the classification performance, we segmented the pollen grains through a pre-trained U-Net using unsupervised pollen contour features. The segmented pollen regions were fed into a deep convolutional neural network to obtain the activation maps, which were used to further refine the segmentation masks. In this way, both segmentation and classification models can be collaboratively trained, supervised by just pollen contour features and class-specific information. Extensive experiments on real-world datasets were conducted, and the results prove that our method effectively avoids impurity interference and improves pollen identification accuracy (86.6%) under the limited supervision (around 1000 images with image-level labels).

Engineered macrophage-biomimetic versatile nanoantidotes for inflammation-targeted therapy against Alzheimer's disease by neurotoxin neutralization and immune recognition suppression.

Immune recognition of excessive neurotoxins by microglia is a trigger for the onset of neuroinflammation in the brain, leading to neurodegeneration in Alzheimer's disease (AD). Blocking active recognition of microglia while removing neurotoxins holds promise for fundamentally alleviating neurotoxin-induced immune responses, but is very challenging. Herein, an engineered macrophage-biomimetic versatile nanoantidote (OT-Lipo@M) is developed for inflammation-targeted therapy against AD by neurotoxin neutralization and immune recognition suppression. Coating macrophage membranes can not only endow OT-Lipo@M with anti-phagocytic and inflammation-tropism capabilities to target inflammatory lesions in AD brain, but also efficiently reduce neurotoxin levels to prevent them from activating microglia. The loaded oxytocin (OT) can be slowly released to downregulate the expression of immune recognition site Toll-like receptor 4 (TLR4) on microglia, inhibiting TLR4-mediated pro-inflammatory signalling cascade. Benefiting from this two-pronged immunosuppressive strategy, OT-Lipo@M exhibits outstanding therapeutic effects on ameliorating cognitive deficits, inhibiting neuronal apoptosis, and enhancing synaptic plasticity in AD mice, accompanied by the delayed hippocampal atrophy and brain microstructural disruption by in vivo 9.4T MR imaging. This work provides new insights into potential AD therapeutics targeting microglia-mediated neuroinflammation at the source.

Oxytocin Nanogels Inhibit Innate Inflammatory Response for Early Intervention in Alzheimer's Disease.

Prevention of Alzheimer's disease (AD) is a global imperative, but reliable early interventions are currently lacking. Microglia-mediated chronic neuroinflammation is thought to occur in the early stage of AD and plays a critical role in AD pathogenesis. Here, oxytocin (OT)-loaded angiopep-2-modified chitosan nanogels (AOC NGs) were designed for early treatment of AD via inhibiting innate inflammatory response. Through the effective transcytosis of angiopep-2, AOC NGs were driven intravenously to cross the blood-brain barrier, enter the brain, and enrich in brain areas affected by AD. A large amount of OT was then released and specifically bound to the pathological upregulated OT receptor, thus effectively inhibiting microglial activation and reducing inflammatory cytokine levels through blocking the ERK/p38 MAPK and COX-2/iNOS NF-κB signaling pathways. Consecutive weekly intravenous administration of AOC NGs into 12-week-old young APP/PS1 mice, representing the early stage of AD, remarkably slowed the progression of Aß deposition and neuronal apoptosis in the APP/PS1 mice as they aged and ultimately prevented cognitive impairment and delayed hippocampal atrophy. Together, the findings suggest that AOC NGs, which show good biosafety, can serve as a promising therapeutic candidate to combat neuroinflammation for early prevention of AD.

Abnormal Regional Spontaneous Brain Activities in White Matter in Patients with Autism Spectrum Disorder.

Previous studies have demonstrated patients with autism spectrum disorder (ASD) are accompanied by alterations of spontaneous brain activity in gray matter. However, whether the alterations of spontaneous brain activity exist in white matter remains largely unclear. In this study, 88 ASD patients and 87 typical controls (TCs) were included and regional homogeneity (ReHo) was calculated to characterize spontaneous brain activity in white matter. Voxel-wise two-sample t-tests were performed to investigate ReHo alterations, and cluster-level analyses were conducted to examine structural-functional coupling changes. Compared with TCs, the ASD group showed significantly decreased ReHo in the left superior corona radiata and left posterior limb of internal capsule, and decreased ReHo in the left anterior corona radiata with a trend level of significance. In addition, significantly weaker structural-functional coupling was observed in the left superior corona radiata and left posterior limb of internal capsule in ASD patients. Taken together, these findings highlighted abnormalities of white matter's regional spontaneous brain activity in ASD, which may provide new insights into the pathophysiological mechanisms of this disorder.

Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images.

Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at "pollen localization problem") and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at "pollen classification problem"). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.