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Cellular senescence serves as a fundamental and underlying activity that drives the aging process, and it is intricately associated with numerous age-related diseases, including Alzheimer's disease (AD), a neurodegenerative aging-related disorder characterized by progressive cognitive impairment. Although increasing evidence suggests that senescent microglia play a role in the pathogenesis of AD, their exact role remains unclear. In this study, we quantified the levels of lactic acid in senescent microglia, and hippocampus tissues of naturally aged mice and AD mice models (FAD 4T and APP/PS1). We found lactic acid levels were significantly elevated in these cells and tissues compared to their corresponding counterparts, which increased the level of pan histone lysine lactylation (Kla). We aslo identified all histone Kla sites in senescent microglia, and found that both the H3K18 lactylation (H3K18la) and Pan-Kla were significantly up-regulated in senescent microglia and hippocampus tissues of naturally aged mice and AD modeling mice. We demonstrated that enhanced H3K18la directly stimulates the NFκB signaling pathway by increasing binding to the promoter of Rela (p65) and NFκB1(p50), thereby upregulating senescence-associated secretory phenotype (SASP) components IL-6 and IL-8. Our study provides novel insights into the physiological function of Kla and the epigenetic regulatory mechanism that regulates brain aging and AD. Specifically, we have identified the H3K18la/NFκB axis as a critical player in this process by modulating IL-6 and IL-8. Targeting this axis may be a potential therapeutic strategy for delaying aging and AD by blunting SASP.

Melatonin is a multifunctional molecule that has been widely discovered in most plants. An increasing number of studies have shown that melatonin plays essential roles in plant growth and stress tolerance. It has been extensively applied to alleviate the harmful effects of abiotic stresses. In view of its role in regulating aspects of plant growth and development, we ponder and summarize the scientific discoveries about seed germination, root development, flowering, fruit maturation, and senescence. Under abiotic and biotic stresses, melatonin brings together many pathways to increase access to treatments for the symptoms of plants and to counteract the negative effects. It has the capacity to tackle regulation of the redox, plant hormone networks, and endogenous melatonin. Furthermore, the expression levels of several genes and the contents of diverse secondary metabolites, such as polyphenols, terpenoids, and alkaloids, were significantly altered. In this review, we intend to examine the actions of melatonin in plants from a broader perspective, explore the range of its physiological functions, and analyze the relationship between melatonin and other metabolites and metabolic pathways.

With the increasing frequency and intensity of extreme precipitation events globally, it is imperative to study the characteristics of extreme precipitation for disaster prevention and mitigation. Traditional statistical analyses to study extreme precipitation often tend to separate temporal and spatial features, and spatial features are often studied based on complex networks, with few studies conducted from a combined temporal and spatial perspective. In this study, we aimed to analyze the spatiotemporal evolution characteristics of extreme precipitation based on visible graph network and state transition networks at different percentile thresholds, and to analyze and compare with the topology, network type, anomalous year, trend of the change stage, key modes, and the future evolution trend. The results indicated that the extreme precipitation networks exhibited high clustering coefficients and short average path lengths, identifying the key year around 1990, and the community was divided into 4–6 stages with an overall increasing trend. The Hangzhou-Wuhan dual-core driving mode, and displayed a consistent trend in the future. This study innovatively reveals the spatiotemporal evolution characteristics of extreme precipitation from the perspective of complex networks, which provides a reference point and basis for preventing, mitigating and predicting extreme precipitation.

With the gradual demographic shift toward an aging and obese society, an increasing number of patients are suffering from bone and cartilage injuries. However, conventional therapies are hindered by the defects of materials, failing to adequately stimulate the necessary cellular response to promote sufficient cartilage regeneration, bone remodeling and osseointegration. In recent years, the rapid development of nanomedicine has initiated a revolution in orthopedics, especially in tissue engineering and regenerative medicine, due to their capacity to effectively stimulate cellular responses on a nanoscale with enhanced drug loading efficiency, targeted capability, increased mechanical properties and improved uptake rate, resulting in an improved therapeutic effect. Therefore, a comprehensive review of advancements in nanomedicine for bone and cartilage diseases is timely and beneficial. This review firstly summarized the wide range of existing nanotechnology applications in the medical field. The progressive development of nano delivery systems in nanomedicine, including nanoparticles and biomimetic techniques, which are lacking in the current literature, is further described. More importantly, we also highlighted the research advancements of nanomedicine in bone and cartilage repair using the latest preclinical and clinical examples, and further discussed the research directions of nano-therapies in future clinical practice. Graphical Abstract

Hydrocephalus is one of the most common neurological disorders, but pharmacotherapy options are currently lacking due to the complex pathogenesis. The blood-CSF barrier (BCSFB), consisting of choroid plexus (ChP) epithelial cells, is a crucial gate for the entry of peripheral immune cells and its dysfunction emerges as an important contributor to hydrocephalus pathology. Meanwhile, SPAK-mediated CSF hypersecretion in ChP epithelial cells plays an important role in hydrocephalus. Here, we fabricated a transferrin receptor-targeted nano-drug (siR/RSV@TNP) that can intelligently navigate to the blood-CSF barrier and prepared for combined delivery of resveratrol (RSV) and SPAK siRNA (siSPAK) for synergetic hydrocephalus therapy. As expected, siR/RSV@TNP fulfilled its function of knocking down SPAK expression, relieving inflammation and oxidative stress, retrieving blood-CSF barrier integrity, and ultimately preventing ventriculomegaly and hydrocephalus in male mice. Here, we demonstrate that targeting the choroid plexus blood-CSF barrier and cerebrospinal fluid hypersecretion offers a promising approach for alleviating hydrocephalus. Hydrocephalus, a disorder characterized by cerebrospinal fluid (CSF) overproduction and impaired blood-CSF barrier function, lacks effective non-invasive treatments. Here, authors show that a multi-functional nanomedicine (siR/RSV@TNP) combining anti-inflammatory resveratrol and SPAK siRNA restores blood-CSF barrier integrity, reduces CSF overproduction, and prevents hydrocephalus in mice, offering a promising therapeutic strategy.

To understand the applicability of the temperature profile product of the FENGYUN-4A (FY-4A) geostationary interferometric infrared detector (GIIRS) in summer over the Qinghai–Tibet Plateau and to improve product quality, the error of GIIRS temperature products was analyzed based on radiosonde data. A long short-term memory network model (LSTM) was used to correct the GIIRS temperature profile product in summer over the plateau at a high altitude (above 500 hPa), and further evaluation of the corrected product was conducted. The results show that summertime GIIRS temperature retrievals over the Qinghai–Tibet Plateau had a positive bias above 150 hPa and a negative bias below 150 hPa, resulting in an overall negative bias. The root mean square error was between 2 and 2.9 K, and the root mean square error was relatively large at 100 hPa and above. The LSTM established in this study could effectively correct the GIIRS temperature over the plateau. The correlation and root mean square error of the corrected GIIRS temperature and the radiosonde observation temperature were significantly improved. Using a trained LSTM correction model to correct the hourly GIIRS temperature can improve the accuracy and usability of the product. After correction, the average bias of the GIIRS temperature compared to the ERA5 temperature product was reduced from −0.26 K to 0.06 K, and the root mean square error was reduced from 2.25 K to 1.25 K. The correction model can be applied to different seasons, and it can also correct the GIIRS temperature in larger areas based on other high-precision and high-resolution data, achieving good results, thus indicating that the correction model has universal applicability.

Using the daily maximum temperature from the 0.5° × 0.5° grid datasets (V2.0) covering China during 1961-2020, this study conducts a decadal-scale spatio-temporal analysis of summer daily maximum temperatures across eastern China (east of 110°E, excluding Inner Mongolia and Northeast China). The results indicate that daily maximum temperatures during the summer months (June-August) exhibited a significant warming trend across three statistical measures-minimum, mean, and maximum values. The minimum temperature showed the most pronounced increase, rising by over 2 °C in the past six decades and accompanied by a broader temperature distribution. This trend was especially evident in central and eastern regions, indicating a marked intensification in both the frequency and severity of extreme heat events. Additionally, the study evaluated the performance of four operational forecasting models-CMA, ECMWF, NCEP, and UKMO-which participate in the Subseasonal to Seasonal (S2S) Prediction Project. The assessment found that short-term forecasts (1-10 days) had relatively small biases and high accuracy. UKMO is more reliable for short-term applications, while ECMWF shows greater potential for extended-range forecasting. In terms of heatwave prediction, all four models performed well at the 5-day lead time, with CMA producing the most accurate forecasts.

Osteoarthritis (OA), the commonest arthritis, is characterized by the progressive destruction of cartilage, leading to disability. The Current early clinical treatment strategy for OA often centers on anti‐inflammatory or analgesia medication, weight loss, improved muscular function and articular cartilage repair. Although these treatments can relieve symptoms, OA tends to be progressive, and most patients require arthroplasty at the terminal stages of OA. Recent studies have shown a close correlation between joint pain, inflammation, cartilage destruction and synovial cells. Consequently, understanding the potential mechanisms associated with the action of synovial cells in OA could be beneficial for the clinical management of OA. Therefore, this review comprehensively describes the biological functions of synovial cells, the synovium, together with the pathological changes of synovial cells in OA, and the interaction between the cartilage and synovium, which is lacking in the present literature. Additionally, therapeutic approaches based on synovial cells for OA treatment are further discussed from a clinical perspective, highlighting a new direction in the treatment of OA.

In order to enhance the prediction accuracy and computational efficiency of chaotic sequence data, issues such as gradient explosion and the long computation time of traditional methods need to be addressed. In this paper, an improved Particle Swarm Optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network are proposed for chaotic prediction. The temporal pattern attention mechanism (TPA) is introduced to extract the weights and key information of each input feature, ensuring the temporal nature of chaotic historical data. Additionally, the PSO algorithm is employed to optimize the hyperparameters (learning rate, number of iterations) of the LSTM network, resulting in an optimal model for chaotic data prediction. Finally, the validation is conducted using chaotic data generated from three different initial values of the Lorenz system. The root mean square error (RMSE) is reduced by 0.421, the mean absolute error (MAE) is reduced by 0.354, and the coefficient of determination (R2) is improved by 0.4. The proposed network demonstrates good adaptability to complex chaotic data, surpassing the accuracy of the LSTM and PSO-LSTM models, thereby achieving higher prediction accuracy.

Recently, terahertz waves of higher frequencies compared to microwave and radio frequency have shown great potential in radar detection and high-speed wireless communication. To spatially control the wavefront of terahertz beams, various novel components, such as terahertz filters, polarization converters and lenses, have been investigated. Metamaterials and metasurfaces have become the most promising technique for the free manipulation of terahertz waves. Metadevices integrated with liquid crystals have been widely used in active terahertz devices. In this review, the birefringence of liquid crystals in the terahertz band and terahertz devices based on liquid crystals are summarized. By integrating liquid crystals with plasmonic metamaterials, the functions become dynamically adjustable and are reconstructed. Utilizing liquid crystals to change the resonance of metamaterials, tunable filters, absorbers, and programmable metasurfaces are realized. To solve the problem of low efficiency, terahertz wavefront shaping devices based on dielectric metasurfaces and liquid crystals, such as a variable deflection angle grating and zoom metalenses, are presented. Finally, we discuss and anticipate the future developments of liquid-crystal-integrated meta-devices, which will inspire broad applications in terahertz communication and imaging.