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Introduction Objective Methods Results Conclusions Primary premature ejaculation (PPE) is a common male neurobiological disorder. Currently, there is consensus that the impairment in central serotonin (5‐HT) neurotransmission constitutes a key pathogenic factor in PPE. Selective serotonin reuptake inhibitors (SSRIs) serve as the primary pharmacological intervention; however, a comprehensive elucidation of their mechanism of action remains incomplete. Owing to significant individual variability in efficacy, SSRIs exhibit a high discontinuation rate. Hence, there is an urgent need to address the selection of SSRIs for PPE treatment.This study aims to investigate the characteristics of tryptophan (TRP) metabolism in patients with PPE and to assess its influence on the efficacy of SSRIs.The exploratory study included a total of 16 patients with PPE and 16 control subjects who were healthy men without any sexual dysfunction. Upon enrollment in the study, all participants underwent a thorough medical history review and physical examination. Subsequently, their serum levels of TRP, its metabolites, large neutral amino acids (LNAAs), and metabolite ratios were assessed using a liquid chromatography–mass spectrometry (LC–MS) assay. After a period of 4 weeks of dapoxetine treatment, all patients with PPE underwent reassessment using the Premature Ejaculation Diagnostic Tool (PEDT) score and intravaginal ejaculatory latency time (IELT) test.The ratio of serum TRP to other LNAAs (TRP/LNAAs) in patients with PPE was found to be significantly lower compared to the control group (P < 0.05). Conversely, the ratio of kynurenine to TRP (KYN/TRP) was observed to be significantly higher in the PPE patients compared to the control group (P < 0.05). Including the serum TRP/LNAAs ratio and KYN/TRP ratio in the prediction model yielded the highest prediction efficiency for PPE. There was a significant negative correlation between the ratio of TRP/LNAAs before the treatment and the IELT after 4 weeks of the treatment. Additionally, there was a significant positive correlation observed between the ratio of TRP/LNAAs before the treatment and the PEDT score after 4 weeks of the treatment.This study demonstrates that the reduction in the TRP/LNAAs ratio and the elevation of the KYN/TRP ratio are significant characteristics associated with PPE. These findings suggest that diminished tryptophan availability in the brain and the activation of the kynurenine (KYN) pathway may play a role in the pathogenesis of PPE. The TRP/LNAAs ratio has potential as a reliable indicator of central serotonin (5‐HT) levels. Considering the TRP/LNAAs ratio when selecting SSRIs for the treatment of PPE may enhance the response rate of this medication. [ABSTRACT FROM AUTHOR]

Biointerfacing nanomaterials with cell membranes has been successful in the functionalization of nanoparticles or nanovesicles, but microbubble functionalization remains challenging due to the unique conformation of the lipid monolayer structure at the gas‐liquid interface that provides insufficient surfactant activity. Here, we describe a strategy to rationally regulate the surfactant activity of platelet membrane vesicles by adjusting the ratio of proteins to lipids through fusion with synthetic phospholipids (i.e. liposomes). A "platesome" with the optimized protein‐to‐lipid ratio can be assembled at the gas‐liquid interface in the same manner as pulmonary surfactants to stabilize a microsized gas bubble. Platesome microbubbles (PMBs) inherited 61.4 % of the platelet membrane vesicle proteins and maintained the active conformation of integrin αIIbβ3 without the talin 1 for fibrin binding. We demonstrated that the PMBs had good stability, long circulation, and superior functionality both in vitro and in vivo. Moreover, by molecular ultrasound imaging, the PMBs provide up to 11.8 dB of ultrasound signal‐to‐noise ratio enhancement for discriminating between acute and chronic thrombi. This surface tension regulating strategy may provide a paradigm for biointerfacing microbubbles with cell membranes, offering a potential new approach for the construction of molecular ultrasound contrast agents for the diagnosis of different diseases. [ABSTRACT FROM AUTHOR]

Background: Non‐coding RNAs (ncRNAs) are a group of RNAs that cannot synthesize proteins, but are critical in gene expression regulation. Long non‐coding RNAs (lncRNAs) and microRNAs (miRNAs), the two major family members, are intimately involved in controlling immune response, cell proliferation, apoptosis, differentiation and polarization, and cytokine secretion. Their interactions significantly influence lung inflammatory diseases and could be potential therapeutic targets. Objectives: The review aims to elucidate the role of ncRNAs, especially the interactions between lncRNA and miRNA in lung diseases, including acute and chronic lung inflammatory diseases, as well as lung cancer. And provide novel insights into disease mechanisms and potential therapeutic methods. Methods: We conducted a comprehensive review of the latest studies on lncRNA and miRNA in lung inflammatory diseases. Our research involved searching through electronic databases like PubMed, Web of Science, and Scopus. Results: We explain the fundamental characteristics and functions of miRNA and lncRNA, their potential interaction mechanisms, and summarize the newly explorations on the role of lncRNA and miRNA interactions in lung inflammatory diseases. Conclusions: Numerous lncRNAs and miRNAs have been found to partipicate in all stages of lung inflammatory diseases. While ncRNA‐based therapies have been validated and developed, there remain challenges in developing more stable and effective drugs for clinical use. [ABSTRACT FROM AUTHOR]

The role of the gut microbiota in modulating the risk of respiratory infections has garnered increasing attention. However, conventional clinical trials have faced challenges in establishing the precise relationship between the two. In this study, we conducted a Mendelian randomization analysis with single nucleotide polymorphisms employed as instrumental variables to assess the causal links between the gut microbiota and respiratory infections. Two categories of bacteria, family Lactobacillaceae and genus Family XIII AD3011, were causally associated with the occurrence of upper respiratory tract infections (URTIs). Four categories of gut microbiota existed that were causally associated with lower respiratory tract infections (LRTIs), with order Bacillales and genus Paraprevotella showing a positive association and genus Alistipes and genus Ruminococcaceae UCG009 showing a negative association. The metabolites and metabolic pathways only played a role in the development of LRTIs, with the metabolite deoxycholine acting negatively and menaquinol 8 biosynthesis acting positively. The identification of specific bacterial populations, metabolites, and pathways may provide new clues for mechanism research concerning therapeutic interventions for respiratory infections. Future research should focus on elucidating the potential mechanisms regulating the gut microbiota and developing effective strategies to reduce the incidence of respiratory infections. These findings have the potential to significantly improve global respiratory health. [ABSTRACT FROM AUTHOR]

Over 70% land plants live in mutualistic symbiosis with arbuscular mycorrhizal (AM) fungi, and maintenance of symbiosis requires transcriptional and post‐transcriptional regulation. The former has been widely studied, whereas the latter mediated by symbiotic microRNAs (miRNAs) remains obscure, especially in woody plants. Here, we performed high‐throughput sequencing of the perennial woody citrus plant Poncirus trifoliata and identified 3750 differentially expressed genes (DEGs) and 42 miRNAs (DEmiRs) upon AM fungal colonization. By analyzing cis‐regulatory elements in the promoters of the DEGs, we predicted 329 key AM transcription factors (TFs). A miRNA‐mRNA regulatory network was then constructed by integrating these data. Several candidate miRNA families of P. trifoliata were identified whose members target known symbiotic genes, such as miR167h‐AMT2;3 and miR156e‐EXO70I, or key TFs, such as miR164d‐NAC and miR477a‐GRAS, thus are involved in AM symbiotic processes of fungal colonization, arbuscule development, nutrient exchange and phytohormone signaling. Finally, analysis of selected miRNA family revealed that a miR159b conserved in mycorrhizal plant species and a Poncirus‐specific miR477a regulate AM symbiosis. The role of miR477a was likely to target GRAS family gene RAD1 in citrus plants. Our results not only revealed that miRNA‐mRNA network analysis, especially miRNA‐TF analysis, is effective in identifying miRNA family regulating AM symbiosis, but also shed light on miRNA‐mediated post‐transcriptional regulation of AM symbiosis in woody citrus plants. Summary statement: In our study, we integrated miRNA with mRNA analysis, especially specific miRNA‐TF co‐expression node analysis, and attempted to reveal the regulatory links between transcriptional and post‐transcriptional regulatory mechanisms involved in AM symbiosis of woody citrus plants. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing image (HSI) clustering can be defined as the process of segmenting pixels into different sets that satisfy the requirement that the differences between sets are much greater than the differences within sets. According to the fast density peak-based clustering algorithm, we propose an unsupervised HSI clustering method based on the density of pixels in the spectral space and the distance between pixels. For the metric of the density, we present an adaptive-bandwidth probability density function using pixel numbers as the input and the calculated pixel local density as the output, which determines the bandwidth on the basis of the Gaussian assumption. For the metric of the distance, in order to obtain a pixel-level spectral distance, we calculate the Euclidean distance between pixel vectors from the multiple bands. In the proposed approach: 1) use the least-squares method for the curve fitting of the two results; 2) eliminate outliers based on the Pauta criterion; 3) adopt regression calculation; and 4) obtain the cluster centers according to the classification criteria of the local density and the distance between pixel vectors. The other noncluster center points are clustered based on their similarities with the cluster centers by iteration. Finally, we compare the results with those of other unsupervised clustering methods and the reference data sets. [ABSTRACT FROM PUBLISHER]

The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89–19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios. [ABSTRACT FROM AUTHOR]

Biointerfacing nanomaterials with cell membranes has been successful in the functionalization of nanoparticles or nanovesicles, but microbubble functionalization remains challenging due to the unique conformation of the lipid monolayer structure at the gas-liquid interface that provides insufficient surfactant activity. Here, we describe a strategy to rationally regulate the surfactant activity of platelet membrane vesicles by adjusting the ratio of proteins to lipids through fusion with synthetic phospholipids (i.e., liposomes). A "platesome" with the optimized protein-to-lipid ratio can be assembled at the gas-liquid interface in the same manner as pulmonary surfactants to stabilize a microsized gas bubble. Platesome microbubbles (PMBs) inherited 61.4 % of the platelet membrane vesicle proteins and maintained the active conformation of integrin αIIbβ3 without the talin 1 for fibrin binding. We demonstrated that the PMBs had good stability, long circulation, and superior functionality both in vitro and in vivo. Moreover, by molecular ultrasound imaging, the PMBs provide up to 11.8 dB of ultrasound signal-to-noise ratio enhancement for discriminating between acute and chronic thrombi. This surface tension regulating strategy may provide a paradigm for biointerfacing microbubbles with cell membranes, offering a potential new approach for the construction of molecular ultrasound contrast agents for the diagnosis of different diseases., (© 2024 Wiley-VCH GmbH.)

Introduction: Burns are a global public health problem. Major burns can stimulate the body to enter a stress state, thereby increasing the risk of infection and adversely affecting the patient's prognosis. Recently, it has been discovered that cuproptosis, a form of cell death, is associated with various diseases. Our research aims to explore the molecular clusters associated with cuproptosis in major burns and construct predictive models., Methods: We analyzed the expression and immune infiltration characteristics of cuproptosis-related factors in major burn based on the GSE37069 dataset. Using 553 samples from major burn patients, we explored the molecular clusters based on cuproptosis-related genes and their associated immune cell infiltrates. The WGCNA was utilized to identify cluster-specific genes. Subsequently, the performance of different machine learning models was compared to select the optimal model. The effectiveness of the predictive model was validated using Nomogram, calibration curves, decision curves, and an external dataset. Finally, five core genes related to cuproptosis and major burn have been was validated using RT-qPCR., Results: In both major burn and normal samples, we determined the cuproptosis-related genes associated with major burns through WGCNA analysis. Through immune infiltrate profiling analysis, we found significant immune differences between different clusters. When K=2, the clustering number is the most stable. GSVA analysis shows that specific genes in cluster 2 are closely associated with various functions. After identifying the cross-core genes, machine learning models indicate that generalized linear models have better accuracy. Ultimately, a generalized linear model for five highly correlated genes was constructed, and validation with an external dataset showed an AUC of 0.982. The accuracy of the model was further verified through calibration curves, decision curves, and modal graphs. Further analysis of clinical relevance revealed that these correlated genes were closely related to time of injury., Conclusion: This study has revealed the intricate relationship between cuproptosis and major burns. Research has identified 15 cuproptosis-related genes that are associated with major burn. Through a machine learning model, five core genes related to cuproptosis and major burn have been selected and validated., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Xiong, Hong, Liao, Yang, Jiang, Jing, Huang, Fu and Zhu.)

Single-Cell Sequencing (SCS) technology plays an important role in the field of Mesenchymal Stem Cells (MSCs) research. This paper comprehensively describes the application of SCS technology in the field of MSCs research, including (1) SCS enables more precise MSCs characterization and biomarker definition. (2) SCS reveals the prevalent gene expression heterogeneity among different subclusters within MSCs, which contributes to a more comprehensive understanding of MSCs function and diversity in developmental, regenerative, and pathological contexts. (3) SCS provides insights into the dynamic transcriptional changes experienced by MSCs during differentiation and the complex web of important signaling pathways and regulatory factors controlling key processes within MSCs, including proliferation, differentiation and regulation, and interactions mechanisms. (4) The analytical methods underpinning SCS data are rapidly evolving and converging with the field of histological research to systematically deconstruct the functions and mechanisms of MSCs. This review provides new perspectives for unraveling the biological properties, heterogeneity, differentiation potential, biological functions, and clinical potential of MSCs at the single-cell level., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Wang, Zhu, Ma, Huang, Guo and Zhu.)

In this paper, considering the aggregation effect and Allee effect of cyanobacteria populations and the harvesting of both cyanobacteria and fish by human beings, a new cyanobacteria-fish model with two harvesting terms and a modified Holling type Ⅳ functional response function is proposed. The main purpose of this paper is to further elucidate the influence of harvesting terms on the dynamic behavior of a cyanobacteria-fish model. Critical conditions for the existence and stability of several interior equilibria are given. The economic equilibria and the maximum sustainable total yield problem are also studied. The model exhibits several bifurcations, such as transcritical bifurcation, saddle-node bifurcation, Hopf bifurcation and Bogdanov-Takens bifurcation. It is concluded from a biological perspective that the survival mode of cyanobacteria and fish can be determined by the harvesting terms. Finally, concrete examples of our model are given through numerical simulations to verify and enrich the theoretical results.

In the paper, under the stress of aggregation and reproduction mechanism of algae, we proposed a modified algae and fish model with aggregation and Allee effect, its main purpose was to further ascertain the dynamic relationship between algae and fish. Several critical conditions were investigated to guarantee the existence and stabilization of all possible equilibrium points, and ensure that the model could undergo transcritical bifurcation, saddle-node bifurcation, Hopf bifurcation and B-T bifurcation. Numerical simulation results of related bifurcation dynamics were provided to verify the feasibility of theoretical derivation, and visually demonstrate the changing trend of the dynamic relationship. Our results generalized and improved some known results, and showed that the aggregation and Allee effect played a vital role in the dynamic relationship between algae and fish.