The innate immune system can be boosted in response to subsequent triggers by pre-exposure to microbes or microbial products, known as “trained immunity”. Compared to classical immune memory, innate trained immunity has several different features. Firstly, the molecules involved in trained immunity differ from those involved in classical immune memory. Innate trained immunity mainly involves innate immune cells (e.g., myeloid immune cells, natural killer cells, innate lymphoid cells) and their effector molecules (e.g., pattern recognition receptor (PRR), various cytokines), as well as some kinds of non-immune cells (e.g., microglial cells). Secondly, the increased responsiveness to secondary stimuli during innate trained immunity is not specific to a particular pathogen, but influences epigenetic reprogramming in the cell through signaling pathways, leading to the sustained changes in genes transcriptional process, which ultimately affects cellular physiology without permanent genetic changes (e.g., mutations or recombination). Finally, innate trained immunity relies on an altered functional state of innate immune cells that could persist for weeks to months after initial stimulus removal. An appropriate inducer could induce trained immunity in innate lymphocytes, such as exogenous stimulants (including vaccines) and endogenous stimulants, which was firstly discovered in bone marrow derived immune cells. However, mature bone marrow derived immune cells are short-lived cells, that may not be able to transmit memory phenotypes to their offspring and provide long-term protection. Therefore, trained immunity is more likely to be relied on long-lived cells, such as epithelial stem cells, mesenchymal stromal cells and non-immune cells such as fibroblasts. Epigenetic reprogramming is one of the key molecular mechanisms that induces trained immunity, including DNA modifications, non-coding RNAs, histone modifications and chromatin remodeling. In addition to epigenetic reprogramming, different cellular metabolic pathways are involved in the regulation of innate trained immunity, including aerobic glycolysis, glutamine catabolism, cholesterol metabolism and fatty acid synthesis, through a series of intracellular cascade responses triggered by the recognition of PRR specific ligands. In the view of evolutionary, trained immunity is beneficial in enhancing protection against secondary infections with an induction in the evolutionary protective process against infections. Therefore, innate trained immunity plays an important role in therapy against diseases such as tumors and infections, which has signature therapeutic effects in these diseases. In organ transplantation, trained immunity has been associated with acute rejection, which prolongs the survival of allografts. However, trained immunity is not always protective but pathological in some cases, and dysregulated trained immunity contributes to the development of inflammatory and autoimmune diseases. Trained immunity provides a novel form of immune memory, but when inappropriately activated, may lead to an attack on tissues, causing autoinflammation. In autoimmune diseases such as rheumatoid arthritis and atherosclerosis, trained immunity may lead to enhance inflammation and tissue lesion in diseased regions. In Alzheimer’s disease and Parkinson’s disease, trained immunity may lead to over-activation of microglial cells, triggering neuroinflammation even nerve injury. This paper summarizes the basis and mechanisms of innate trained immunity, including the different cell types involved, the impacts on diseases and the effects as a therapeutic strategy to provide novel ideas for different diseases.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

ObjectivePhotoacoustic pump-probe imaging can effectively eliminate the interference of blood background signal in traditional photoacoustic imaging, and realize the imaging of weak phosphorescence molecules and their triplet lifetimes in deep tissues. However, background differential noise in photoacoustic pump-probe imaging often leads to large fitting results of phosphorescent molecule concentration and triplet lifetime. Therefore, this paper proposes a novel triplet lifetime fitting method for photoacoustic pump-probe imaging. By extracting the phase of the triplet differential signal and the background noise, the fitting bias caused by the background noise can be effectively corrected. MethodsThe advantages and feasibility of the proposed algorithm are verified by numerical simulation, phantom and in vivo experiments, respectively. ResultsIn the numerical simulation, under the condition of noise intensity being 10% of the signal amplitude, the new method can optimize the fitting deviation from 48.5% to about 5%, and has a higher exclusion coefficient (0.88>0.79), which greatly improves the fitting accuracy. The high specificity imaging ability of photoacoustic pump imaging for phosphorescent molecules has been demonstrated by phantom experiments. In vivo experiments have verified the feasibility of the new fitting method proposed in this paper for fitting phosphoometric lifetime to monitor oxygen partial pressure content during photodynamic therapy of tumors in nude mice. ConclusionThis work will play an important role in promoting the application of photoacoustic pump-probe imaging in biomedicine.

ObjectiveIn the realm of forensic science, dust is a valuable type of trace evidence with immense potential for intricate investigations. With the development of DNA sequencing technologies, there is a heightened interest among researchers in unraveling the complex tapestry of microbial communities found within dust samples. Furthermore, striking disparities in the microbial community composition have been noted among dust samples from diverse geographical regions, heralding new possibilities for geographical inference based on microbial DNA analysis. The pivotal role of microbial community data from dust in geographical inference is significant, underscoring its critical importance within the field of forensic science. This study aims to delve deeply into the nuances of fungal community composition across the urban landscapes of Beijing, Fuzhou, Kunming, and Urumqi in China. It evaluates the accuracy of biogeographic inference facilitated by the internal transcribed spacer 2 (ITS2) fungal sequencing while concurrently laying a robust foundation for the operational integration of environmental DNA into geographical inference mechanisms. MethodsITS2 region of the fungal genomes was amplified using universal primers known as 5.8S-Fun/ITS4-Fun, and the resulting DNA fragments were sequenced on the Illumina MiSeq FGx platform. Non-metric multidimensional scaling analysis (NMDS) was employed to visually represent the differences between samples, while analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) were utilized to statistically evaluate the dissimilarities in community composition across samples. Furthermore, using Linear Discriminant Analysis Effect Size (LEfSe) analysis to identify and filter out species that exhibit significant differences between various cities. In addition, we leveraged SourceTracker to predict the geographic origins of the dust samples. ResultsAmong the four cities of Beijing, Fuzhou, Kunming and Urumqi, Beijing has the highest species richness. The results of species annotation showed that there were significant differences in the species composition and relative abundance of fungal communities in the four cities. NMDS analysis revealed distinct clustering patterns of samples based on their biogeographic origins in multidimensional space. Samples from the same city exhibited clear clustering, while samples from different cities showed separation along the first axis. The results from ANOSIM and PERMANOVA confirmed the significant differences in fungal community composition between the four cities, with the most pronounced distinctions observed between Fuzhou and Urumqi. Notably, the biogeographic origins of all known dust samples were successfully predicted. ConclusionSignificant differences are observed in the fungal species composition and relative abundance among the cities of Beijing, Fuzhou, Kunming, and Urumqi. Employing fungal ITS2 sequencing on dust samples from these urban areas enables accurate inference of biogeographical locations. The high feasibility of utilizing fungal community data in dust for biogeographical inferences holds particular promise in the field of forensic science.

ObjectiveThe primary objective of this study was to investigate the effects of carbohydrate intake order on post-exercise recovery and metabolic regulation under heat stress, particularly in models of exercise induced fatigue. Given the increasing significance of optimizing nutritional strategies to support performance in extreme environmental conditions, this study aimed to provide experimental evidence that contributes to a better understanding of how the sequence in which carbohydrates are consumed impacts exercise recovery, metabolic homeostasis, and fatigue alleviation in a high-temperature environment. MethodsA mouse model of exercise-induced fatigue was established under high-temperature (35°C) to simulate heat stress. The subjects were divided into 3 distinct groups based on their carbohydrate intake order: the “mixed intake” group (HOT_MIX), where all macronutrients (carbohydrates, proteins, and fats) were consumed in a balanced ratio; the “carbohydrate-first intake” group (HOT_CHO), where carbohydrates were consumed first followed by other macronutrients; the “carbohydrate-later intake” group (HOT_PRO), where proteins and fats were consumed prior to carbohydrates. Each group underwent a 7 d intervention period with daily intake according to their designated group. Exercise performance was assessed using rotarod retention time test, and biomarkers of muscle damage, such as lactate dehydrogenase (LDH), creatine kinase (CK), lactate (LD), alanine aminotransferase (ALT), and non-esterified fatty acids (NEFA), were measured. Furthermore, targeted metabolomics analyses were conducted to investigate metabolic shifts in response to different dietary strategies, and KEGG pathway enrichment analysis was employed to explore the biological mechanisms underlying these changes. ResultsThe findings demonstrated that the HOT_PRO group exhibited a significantly improved performance in the rotarod test, with a longer retention time compared to both the HOT_MIX and HOT_CHO groups (P<0.05). Additionally, this group showed significantly reduced levels of muscle damage markers such as LDH and CK, indicating that the carbohydrate-later intake strategy helped alleviate exercise-induced muscle injury. Metabolomic profiling of the HOT_PRO group showed marked increases in alanine, creatine, and flavin adenine dinucleotide (FAD), indicating shifts in amino acid metabolism and oxidative metabolism. Conversely, metabolites such as spermidine, cholesterol sulfate, cholesterol, and serine were significantly reduced in the HOT_PRO group, pointing to alterations in lipid and sterol metabolism. Further analysis of the differential metabolites revealed that these changes were primarily associated with key metabolic pathways, including glycine-serine-threonine metabolism, primary bile acid biosynthesis, taurine and hypotaurine metabolism, and steroid hormone biosynthesis. These pathways are essential for energy production, antioxidant defense, and muscle recovery, suggesting that the carbohydrate-later feeding strategy may promote metabolic homeostasis and improve exercise recovery by enhancing these critical metabolic processes. ConclusionThe results of this study support the hypothesis that consuming carbohydrates after proteins and fats during exercise recovery enhances metabolic homeostasis and accelerates recovery under heat stress. This strategy effectively modulates energy, amino acid, and lipid-related pathways, which are crucial for improving endurance performance and mitigating fatigue in high-temperature environments. The findings suggest that carbohydrate-later intake could be a promising nutritional strategy for athletes and individuals exposed to heat during physical activity. Furthermore, the study provides valuable insights into how different nutrient timing strategies can impact exercise recovery and metabolic regulation, paving the way for more personalized and effective nutritional interventions in extreme environmental conditions.

ObjectiveBased on functional near-infrared spectroscopy (fNIRS), we investigated the brain activity characteristics of gross motor tasks in children with autism spectrum disorder (ASD) and motor dysfunctions (MDs) to provide a theoretical basis for further understanding the mechanism of MDs in children with ASD and designing targeted intervention programs from a central perspective. MethodsAccording to the inclusion and exclusion criteria, 48 children with ASD accompanied by MDs were recruited into the ASD group and 40 children with typically developing (TD) into the TD group. The fNIRS device was used to collect the information of blood oxygen changes in the cortical motor-related brain regions during single-handed bag throwing and tiptoe walking, and the differences in brain activation and functional connectivity between the two groups of children were analyzed from the perspective of brain activation and functional connectivity. ResultsCompared to the TD group, in the object manipulative motor task (one-handed bag throwing), the ASD group showed significantly reduced activation in both left sensorimotor cortex (SMC) and right secondary visual cortex (V2) (P<0.05), whereas the right pre-motor and supplementary motor cortex (PMC&SMA) had significantly higher activation (P<0.01) and showed bilateral brain region activity; in terms of brain functional integration, there was a significant decrease in the strength of brain functional connectivity (P<0.05) and was mainly associated with dorsolateral prefrontal cortex (DLPFC) and V2. In the body stability motor task (tiptoe walking), the ASD group had significantly higher activation in motor-related brain regions such as the DLPFC, SMC, and PMC&SMA (P<0.05) and showed bilateral brain region activity; in terms of brain functional integration, the ASD group had lower strength of brain functional connectivity (P<0.05) and was mainly associated with PMC&SMA and V2. ConclusionChildren with ASD exhibit abnormal brain functional activity characteristics specific to different gross motor tasks in object manipulative and body stability, reflecting insufficient or excessive compensatory activation of local brain regions and impaired cross-regions integration, which may be a potential reason for the poorer gross motor performance of children with ASD, and meanwhile provides data support for further unraveling the mechanisms underlying the occurrence of MDs in the context of ASD and designing targeted intervention programs from a central perspective.

Humans ; Vascular Stiffness ; Coal Mining ; Occupational Diseases/epidemiology* ; Risk Factors ; Coal ; Miners

Air Pollution/analysis* ; Environmental Exposure/analysis* ; China/epidemiology* ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Monitoring

Calcium-based biomaterials have been intensively studied in the field of drug delivery owing to their excellent biocompatibility and biodegradability. Calcium-based materials can also deliver contrast agents, which can enhance real-time imaging and exert a Ca²⁺-interfering therapeutic effect. Based on these characteristics, amorphous calcium carbonate (ACC), as a brunch of calcium-based biomaterials, has the potential to become a widely used biomaterial. Highly functional ACC can be either discovered in natural organisms or obtained by chemical synthesis However, the standalone presence of ACC is unstable in vivo. Additives are required to be used as stabilizers or core-shell structures formed by permeable layers or lipids with modified molecules constructed to maintain the stability of ACC until the ACC carrier reaches its destination. ACC has high chemical instability and can produce biocompatible products when exposed to an acidic condition in vivo, such as Ca²⁺ with an immune-regulating ability and CO₂ with an imaging-enhancing ability. Owing to these characteristics, ACC has been studied for self-sacrificing templates of carrier construction, targeted delivery of oncology drugs, immunomodulation, tumor imaging, tissue engineering, and calcium supplementation. Emphasis in this paper has been placed on the origin, structural features, and multiple applications of ACC. Meanwhile, ACC faces many challenges in clinical translation, and long-term basic research is required to overcome these challenges. We hope that this study will contribute to future innovative research on ACC.

The objective of this study was to analyze the effects on cell viability, apoptosis, and cell cycle of non-small cell lung cancer (NSCLC) A549 cells after intervention with Agrimonia pilosa (AP) and investigate Agrimonia pilosa anti-tumor activity in vitro. Meanwhile, liquid chromatography mass spectrometry (LC-MS) metabolomics technology was used to analyze the changes of cellular metabolites and metabolic pathways. The results of this study will provide a theoretical and experimental basis for investigating the mechanism of the effect of Agrimonia pilosa on non-small cell lung cancer A549 cells. The results showed that the cell nucleus of A549 cells crumpled and apoptosis occurred with the increase of drug concentration. The survival rate of the cells decreased, and the inhibition rate reached 21.5% and 91.74% under the low and high dose conditions, respectively. Lactate dehydrogenase (LDH) content increased (P < 0.05). Metabolomics results showed significant differences in metabolism between groups, thirty-three distinct metabolites including LysoPC(24:0/0:0), LysoPC(17:0/0:0) and PC(O-40:5) were deduced. The pathway enrichment showed that the Agrimonia pilosa plays an anti-tumor role mainly by regulating the metabolism of glycerophosphate and purine in A549 cells, in which the effect on glycerophosphate metabolism pathway was most significant. The results of combined pharmacodynamics suggested that Agrimonia pilosa might induce apoptosis and inhibit the growth of A549 cells by regulating LysoPC(24:0/0:0), LysoPC(17:0/0:0) and PC(O-40:5) metabolites in A549 cells.