Integrating Network Pharmacology and Experimental Validation to Investigate the Mechanism of Qushi Huatan Decoction Against Coronary Heart Disease.

Purpose: This study was designed to evaluate the effect and mechanism of the Qushi Huatan (QSHT) decoction against coronary heart disease (CHD) through network pharmacology and experimental verification. Methods: In the present study, the active ingredients of the QSHT decoction were identified by ultra performance liquid chromatography/tandem mass spectrometry (UPLC/MS), then the potential ingredients and coronary heart disease targets were predicted using the SwissTarget Prediction database and the database of Genecards and OMIM database, respectively. A herb-compound-target network was constructed using Cytoscape. GO and KEGG enrichment analysis were performed using the ClusterProfiler data package of R software. Molecular docking was used to predict the core targets of QSHT against CHD. In addition, we used a myocardial infarction (MI) and high-fat diet ApoE-/- mice model to investigate the cardioprotective effects of QSHT. Western blotting and immunochemistry were used to verify the core targets and the signaling pathway. Results: A total of 68 active ingredients were found in the QSHT decoction. Network pharmacology indicated 28 targets and 147 signal pathways, including AKT1, HIF-1α, GSK-3ß, TLR4 and NF-κB, those key targets were also verified by molecular docking. The results of GO and KEGG enrichment analysis showed that the targets of QSHT against CHD were largely associated with inflammatory and oxidative stress, and AKT/HIF-1α and TLR4/NF-κB pathways might be key functional pathways. In vivo, QSHT significantly improved cardiac function and attenuated fibrosis and inflammation. Furthermore, QSHT could significantly inhibit the expression of HIF-1α, TLR4, phosphorylation of AKT1, GSK-3ß and NF-κB after MI in ApoE-/- mice. Conclusion: Based on network pharmacology, molecular docking and experimental verification, this study demonstrated that QSHT could improve cardiac function and attenuate cardiac fibrosis by regulating TLR4/NF-κB and AKT/HIF-1α signaling pathway in post- MI and high-fat diet ApoE-/- mice.

GPRASP2 deficiency contributes to apoptosis in the spiral ganglion cells via the AMPK/DRP1 signaling pathway.

G protein-coupled receptor-associated sorting protein 2 (GPRASP2) deficiency has been implicated in immunological inflammation, cancers, and neurological disorders. Our previous work revealed that the pathogenic mutation in GPRASP2 was responsible for X-linked recessive syndromic hearing loss (SHL). Given the specific high expression of GPRASP2 in the spiral ganglion, GPRASP2 likely contributes to the maintenance and functionality of neurons, potentially playing a role in synaptic transmission. The impact of GPRASP2 deficiency on spiral ganglion cells (SGCs) and their underlying pathogenic mechanisms will be investigated in this study. The primary culture of SGCs obtained from mouse cochleae was treated with Gprasp2-targeting short hairpin RNA (Gprasp2-shRNA) via lentivirus infection. The results showed that GPRASP2 deficiency enhanced SGCs apoptosis and decreased cell viability. Meanwhile, a significant abnormality of mitochondrial morphology and decreased membrane potential were observed in GPRASP2-deficient SGCs. These effects could be mitigated by treatment with the mitochondrial division inhibitor 1 (Mdivi-1). In addition to enhancing SGCs apoptosis and decreasing cell viability, GPRASP2 deficiency also inhibited the development of SGCs in mouse cochlear explant culture. Our study further revealed that this deficiency resulted in increased phosphorylation of AMPK and activation of the AMPK/DRP1 pathway, promoting SGCs apoptosis. These findings provide insight into the pathogenic mechanisms by which GPRASP2 deficiency is implicated in auditory dysfunction.

AOPPs induces EMT and fibrosis by activating oxidative stress through ERK/p38 MAPK signaling pathway in endometriosis.

Epithelial-mesenchymal transition (EMT) is known to play a crucial role in the development of endometriosis (EMs). However, the exact mechanisms involved in EMT regulation in EMs are not well understood. In this study, we performed comprehensive research using clinical samples, single-cell sequencing, and in vivo/in vitro models to investigate the effects of advanced oxidation protein products (AOPPs) on EMT and the underlying mechanisms in EMs. Combining bioinformatics analysis with experimental validation, our results show that AOPPs accumulate in EMs tissues, and their levels positively correlate with the expression of EMT markers in fibrotic lesions of EMs patients. Stimulation with AOPPs leads to a concentration- and time-dependent alteration of EMT markers expression in both in vitro and in vivo models. These effects are mainly mediated by the generation of reactive oxygen species and nitrite, along with the activation of the ERK and P38 signaling pathways. In chronic administration studies using normal rats, AOPPs induce EMT and enhance collagen deposition. These findings significantly contribute to our understanding of the molecular mechanisms of EMs and provide a foundation for future research and therapeutic development in this field.

Exploring the Frontier: Antisense Long Non-Coding RNAs as Key Regulators in Alzheimer's Disease.

Alzheimer's Disease (AD) is the most prevalent, costly, and fatal neurodegenerative disorder of this century. Two hallmark features of AD are the anomalous cleavage of amyloid precursor protein (APP), which leads to the accumulation of amyloid-beta (Aß), and the hyperphosphorylation of tau protein. Despite extensive research efforts, the pathology and pathogenesis of AD remain elusive. Recent investigations have highlighted the close association between antisense long non-coding RNAs (AS-lncRNAs) and various biological and functional aspects of AD. However, many AS-lncRNAs implicated in AD have not yet been comprehensively compiled and discussed. This paper reviews the role of AS-lncRNAs in neurodegenerative diseases, outlines their association with AD, and offers novel insights into the potential applications of antisense RNAs in the diagnosis and treatment of AD.

Maternal exposure to ambient particulate matter on the growth of twin fetuses after in vitro fertilization.

BACKGROUND: While ambient air pollution has been associated with fetal growth in singletons, its correlation among twins is not well-established due to limited research in this area. METHODS: The effects of exposure to PM2.5 particulate matter and its main components during pregnancy on birth weight and the incidence of large for gestational age (LGA) were investigated in 6177 twins born after in vitro fertilization at the Center for Reproductive Medicine of Shanghai Ninth People's Hospital (Shanghai, China) between 2007 and 2021. Other birth weight-related outcomes included macrosomia, low birth weight, very low birth weight, and small for gestational age (SGA). The associations of PM2.5 exposure with birth weight outcomes were analyzed using linear mixed-effect models and random-effect logistic regression models. Distributed lag models were incorporated to estimate the time-varying associations. RESULTS: The findings revealed that an interquartile range (IQR) increase (18 µg/m3) in PM2.5 exposure over the entire pregnancy was associated with a significant increase (57.06 g, 95 % confidence interval [CI]: 30.91, 83.22) in the total birth weight of twins. The effect was more pronounced in larger fetuses (34.93 g, 95 % CI: 21.13, 48.72) compared to smaller fetuses (21.77 g, 95 % CI: 6.94, 36.60) within twin pregnancies. Additionally, an IQR increase in PM2.5 exposure over the entire pregnancy was associated with a 34 % increase in the risk of LGA (95 % CI: 11 %, 63 %). Furthermore, specific chemical components of PM2.5, such as sulfate (SO42-), exhibited effect estimates comparable to the PM2.5 total mass. CONCLUSION: Overall, the findings indicate that exposures to PM2.5 and its specific components are associated with fetal overgrowth in twins.

Pseudo-CT synthesis in adaptive radiotherapy based on a stacked coarse-to-fine model: Combing diffusion process and spatial-frequency convolutions.

BACKGROUND: Cone beam computed tomography (CBCT) provides critical anatomical information for adaptive radiotherapy (ART), especially for tumors in the pelvic region that undergo significant deformation. However, CBCT suffers from inaccurate Hounsfield Unit (HU) values and lower soft tissue contrast. These issues affect the accuracy of pelvic treatment plans and implementation of the treatment, hence requiring correction. PURPOSE: A novel stacked coarse-to-fine model combining Denoising Diffusion Probabilistic Model (DDPM) and spatial-frequency domain convolution modules is proposed to enhance the imaging quality of CBCT images. METHODS: The enhancement of low-quality CBCT images is divided into two stages. In the coarse stage, the improved DDPM with U-ConvNeXt architecture is used to complete the denoising task of CBCT images. In the fine stage, the deep convolutional network model jointly constructed by fast Fourier and dilated convolution modules is used to further enhance the image quality in local details and global imaging. Finally, the accurate pseudo-CT (pCT) images consistent with the size of the original data are obtained. Two hundred fifty paired CBCT-CT images from cervical and rectal cancer, combined with 200 public dataset cases, were used collectively for training, validation, and testing. RESULTS: To evaluate the anatomical consistency between pCT and real CT, we have used the mean(std) of structure similarity index measure (SSIM), peak signal to noise ratio (PSNR), and normalized cross-correlation (NCC). The numerical results for the above three metrics comparing the pCT synthesized by the proposed model against real CT for cervical cancer cases were 87.14% (2.91%), 34.02 dB (1.35 dB), and 88.01% (1.82%), respectively. For rectal cancer cases, the corresponding results were 86.06% (2.70%), 33.50 dB (1.41 dB), and 87.44% (1.95%). The paired t-test analysis between the proposed model and the comparative models (ResUnet, CycleGAN, DDPM, and DDIM) for these metrics revealed statistically significant differences (p < 0.05). The visual results also showed that the anatomical structures between the real CT and the pCT synthesized by the proposed model were closer. For the dosimetric verification, mean absolute error of dosimetry (MAEdoes) values for the maximum dose (Dmax), the minimum dose (Dmin), and the mean dose (Dmean) in the planning target volume (PTV) were analyzed, with results presented as mean (lower quartile, upper quartile). The experimental results show that the values of the above three dosimetry indexes (Dmin, Dmax, and Dmean) for the pCT images synthesized by the proposed model were 0.90% (0.48%, 1.29%), 0.82% (0.47%, 1.17%), and 0.57% (0.44%, 0.67%). Compared with 10 cases of the original CBCT image by Mann-Whitney test (p < 0.05), it also proved that pCT can significantly improve the accuracy of HU values for the dose calculation. CONCLUSION: The pCT synthesized by the proposed model outperforms the comparative models in numerical accuracy and visualization, promising for ART of pelvic cancers.

Effect of freeze-thaw treatments with different conditions on frost fatigue in three diffuse-porous trees.

None declared.Conflict of interestIn addition to inducing xylem embolism, freeze-thaw events can cause frost fatigue phenomena. Freezing temperature, freezing times, number of freeze-thaw cycles, and frost drought can affect the level of freeze-thaw-induced embolism, but it is unknown whether there is an effect on frost fatigue. We assessed whether these frost-related factors changed frost fatigue in the three diffuse-porous species by simulating freeze-thaw treatments under different conditions. We also proposed a new metric, embolism area, in place of embolism resistance, to more accurately quantify the shift of the vulnerability curve after experiencing freeze-thaw-induced embolism and refilling. Frost fatigue caused VCs of all species to change from S-shaped to double S-shaped or even R-shaped curves. When exposed to a freeze-thaw event, Acer truncatum showed strong resistance to frost fatigue, in contrast, Populus (I-101 × 84 K) and Liriodendron chinense were more vulnerable. Changing freezing temperature and times did not impact the response to frost fatigue in the three species, but a greater number of freeze-thaw cycles and more severe frost drought significantly exacerbated their fatigue degree. Considering that frost fatigue may be a widespread phenomenon among temperate diffuse-porous species, more work is needed in the future to reveal the mechanisms of frost fatigue.

Body image and social anxiety in hemifacial spasm: Examining self-esteem and fear of negative evaluation as mediators.

BACKGROUND: Individuals diagnosed with hemifacial spasm (HFS) frequently undergo noticeable alterations in their facial appearance. Such changes can detrimentally influence both their physical and psychological well-being. While prior studies have identified self-esteem and fear of negative evaluation (FNE) as key elements in social anxiety, their role in studies concerning body image and its correlation with social anxiety has been seldom explored. This research seeks to explore how self-esteem and FNE concurrently mediate the relationship between body image and social anxiety among Chinese individuals with HFS. METHODS: Chinese patients with HFS (n=151) completed a cross-sectional questionnaire on the first day of admission that assessed body image, social anxiety, self-esteem, and FNE over the past week. Path analysis was used to test the hypothesis of the mediation model. RESULTS: The hypothesized model showed that FNE was positively correlated with body image and social anxiety, while negative associations were found among body image, self-esteem and social anxiety. Self-esteem and FNE play a mediating role between body image and social anxiety. CONCLUSION: Our findings suggest that self-esteem and FNE may be important psychological pathways that affect body image and social anxiety in Chinese patients with HFS. Supplementing mental health services that help increase self-esteem and reduce FNE should be considered to improve the psychological quality of patients with HFS.

Deep learning approach for unified recognition of driver speed and lateral intentions using naturalistic driving data.

Driver intention recognition is a critical component of advanced driver assistance systems, with significant implications for improving vehicle safety, intelligence, and fuel economy. However, previous research on driver intention recognition has not fully considered the influence of the driving environment on speed intentions and has not exploited the temporal dependency inherent in the lateral intentions to prevent erroneous changes in recognition. Furthermore, the coupling of speed and lateral intentions was overlooked; they were generally considered separately. To address these limitations, a unified recognition approach for speed and lateral intentions based on deep learning is presented in this study. First, extensive naturalistic driving data are collected, and information related to road slope and driving trajectories is extracted. A comprehensive classification of driver intentions is then performed. Toeplitz inverse covariance-based clustering and trajectory clustering methods are applied separately to label speed and lateral intentions, so that the influence of driving environments and the coupling of speed and lateral intentions are integrated into intention recognition. Finally, a deep-learning-based unified recognition model for driver intention is developed. This model uses a hierarchical recognition approach for speed intentions and includes a double-layer networks architecture with long short-term memory for the recognition of lateral intention. The validation results show that the created driver intention recognition model can accurately and stably recognize both speed and lateral intentions in complex driving environments.

Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI.

Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in the diagnosis and prognosis of oncological conditions. However, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address the paucity of discussion regarding the factors that influence the reproducibility and repeatability of radiomic features and their subsequent impact on the application of radiomic models. We provide a synthesis of the literature on the repeatability and reproducibility of CT/MR-based radiomic features, examining sources of variation, the number of reproducible features, and the availability of individual feature repeatability indices. We differentiate sources of variation into random effects, which are challenging to control but can be quantified through simulation methods such as perturbation, and biases, which arise from scanner variability and inter-reader differences and can significantly affect the generalizability of radiomic model performance in diverse settings. Four suggestions for repeatability and reproducibility studies are suggested: (1) detailed reporting of variation sources, (2) transparent disclosure of calculation parameters, (3) careful selection of suitable reliability indices, and (4) comprehensive reporting of reliability metrics. This review underscores the importance of random effects in feature selection and harmonizing biases between development and clinical application settings to facilitate the successful translation of radiomic models from research to clinical practice.

Non-contrasted computed tomography (NCCT) based chronic thromboembolic pulmonary hypertension (CTEPH) automatic diagnosis using cascaded network with multiple instance learning.

Objective.The diagnosis of chronic thromboembolic pulmonary hypertension (CTEPH) is challenging due to nonspecific early symptoms, complex diagnostic processes, and small lesion sizes. This study aims to develop an automatic diagnosis method for CTEPH using non-contrasted computed tomography (NCCT) scans, enabling automated diagnosis without precise lesion annotation.Approach.A novel cascade network (CN) with multiple instance learning (CNMIL) framework was developed to improve the diagnosis of CTEPH. This method uses a CN architecture combining two Resnet-18 CNN networks to progressively distinguish between normal and CTEPH cases. Multiple instance learning (MIL) is employed to treat each 3D CT case as a 'bag' of image slices, using attention scoring to identify the most important slices. An attention module helps the model focus on diagnostically relevant regions within each slice. The dataset comprised NCCT scans from 300 subjects, including 117 males and 183 females, with an average age of 52.5 ± 20.9 years, consisting of 132 normal cases and 168 cases of lung diseases, including 88 cases of CTEPH. The CNMIL framework was evaluated using sensitivity, specificity, and the area under the curve (AUC) metrics, and compared with common 3D supervised classification networks and existing CTEPH automatic diagnosis networks.Main results. The CNMIL framework demonstrated high diagnostic performance, achieving an AUC of 0.807, accuracy of 0.833, sensitivity of 0.795, and specificity of 0.849 in distinguishing CTEPH cases. Ablation studies revealed that integrating MIL and the CN significantly enhanced performance, with the model achieving an AUC of 0.978 and perfect sensitivity (1.000) in normal classification. Comparisons with other 3D network architectures confirmed that the integrated model outperformed others, achieving the highest AUC of 0.8419.Significance. The CNMIL network requires no additional scans or annotations, relying solely on NCCT. This approach can improve timely and accurate CTEPH detection, resulting in better patient outcomes.

Comparative Analysis of Repeatability in CT Radiomics and Dosiomics Features under Image Perturbation: A Study in Cervical Cancer Patients.

This study aims to evaluate the repeatability of radiomics and dosiomics features via image perturbation of patients with cervical cancer. A total of 304 cervical cancer patients with planning CT images and dose maps were retrospectively included. Random translation, rotation, and contour randomization were applied to CT images and dose maps before radiomics feature extraction. The repeatability of radiomics and dosiomics features was assessed using intra-class correlation of coefficient (ICC). Pearson correlation coefficient (r) was adopted to quantify the correlation between the image characteristics and feature repeatability. In general, the repeatability of dosiomics features was lower compared with CT radiomics features, especially after small-sigma Laplacian-of-Gaussian (LoG) and wavelet filtering. More repeatable features (ICC > 0.9) were observed when extracted from the original, Large-sigma LoG filtered, and LLL-/LLH-wavelet filtered images. Positive correlations were found between image entropy and high-repeatable feature number in both CT and dose (r = 0.56, 0.68). Radiomics features showed higher repeatability compared to dosiomics features. These findings highlight the potential of radiomics features for robust quantitative imaging analysis in cervical cancer patients, while suggesting the need for further refinement of dosiomics approaches to enhance their repeatability.

Substantial contribution of transported emissions to organic aerosol in Beijing.

Haze in Beijing is linked to atmospherically formed secondary organic aerosol, which has been shown to be particularly harmful to human health. However, the sources and formation pathways of these secondary aerosols remain largely unknown, hindering effective pollution mitigation. Here we have quantified the sources of organic aerosol via direct near-molecular observations in central Beijing. In winter, organic aerosol pollution arises mainly from fresh solid-fuel emissions and secondary organic aerosols originating from both solid-fuel combustion and aqueous processes, probably involving multiphase chemistry with aromatic compounds. The most severe haze is linked to secondary organic aerosols originating from solid-fuel combustion, transported from the Beijing-Tianjing-Hebei Plain and rural mountainous areas west of Beijing. In summer, the increased fraction of secondary organic aerosol is dominated by aromatic emissions from the Xi'an-Shanghai-Beijing region, while the contribution of biogenic emissions remains relatively small. Overall, we identify the main sources of secondary organic aerosol affecting Beijing, which clearly extend beyond the local emissions in Beijing. Our results suggest that targeting key organic precursor emission sectors regionally may be needed to effectively mitigate organic aerosol pollution.

Bridging MRI Cross-Modality Synthesis and Multi-Contrast Super-Resolution by Fine-Grained Difference Learning.

In multi-modal magnetic resonance imaging (MRI), the tasks of imputing or reconstructing the target modality share a common obstacle: the accurate modeling of fine-grained inter-modal differences, which has been sparingly addressed in current literature. These differences stem from two sources: 1) spatial misalignment remaining after coarse registration and 2) structural distinction arising from modality-specific signal manifestations. This paper integrates the previously separate research trajectories of cross-modality synthesis (CMS) and multi-contrast super-resolution (MCSR) to address this pervasive challenge within a unified framework. Connected through generalized down-sampling ratios, this unification not only emphasizes their common goal in reducing structural differences, but also identifies the key task distinguishing MCSR from CMS: modeling the structural distinctions using the limited information from the misaligned target input. Specifically, we propose a composite network architecture with several key components: a label correction module to align the coordinates of multi-modal training pairs, a CMS module serving as the base model, an SR branch to handle target inputs, and a difference projection discriminator for structural distinction-centered adversarial training. When training the SR branch as the generator, the adversarial learning is enhanced with distinction-aware incremental modulation to ensure better-controlled generation. Moreover, the SR branch integrates deformable convolutions to address cross-modal spatial misalignment at the feature level. Experiments conducted on three public datasets demonstrate that our approach effectively balances structural accuracy and realism, exhibiting overall superiority in comprehensive evaluations for both tasks over current state-of-the-art approaches. The code is available at https://github.com/papshare/FGDL.

Increasing methane production in an anaerobic membrane bioreactor for treating landfill leachate: Impact of organic concentration and HRT.

The anaerobic biological treatment of landfill leachate frequently encounters the souring problems because of the high concentration of organic in landfill leachate. Nonetheless, the performance of anaerobic membrane bioreactor (AnMBR) is commendable in terms of removal of organic compounds. Hence, this study explored the effect of organic concentration and hydraulic retention time(HRT) on the removal performance of actual landfill leachate, additionally, carbon conversion through carbon mass balance analysis was analyzed, in order to determine the optimal treatment potential of AnMBR in treating landfill leachate. For HRT values between 14.5 h and 34.6 h, and the influent COD (Chemical Oxygen Demand) range of 12,773.33-15706.67 mg/L, AnMBR could efficiently treat landfill leachate. As HRT was fixed at 14.5 h and influent COD was around 12,206.7-15,373.33 mg/L, AnMBR achieved a maximum organic removal rate of 18.22 ± 0.51 kg COD/(m3∙d) with methane yield of 0.24 ± 0.01 m3 CH4/kg COD and methane content of 88.26%. Based on carbon mass balance, increasing COD concentration in the influent (less than 16,000 mg/L) boosted the conversion of organic compounds (45.19 ± 4.24%) into CH4; while decreasing HRT (more than 27.0 h) also promoted the conversion of organic compounds into CH4 (38.36-60.93%) resulting in a decreased TOC (Total Organic Carbon) loss by 2.02-7.19% with outflow. AnMBR may efficiently produce methane while treating landfill leachate by assessing the random forest model (RF) and adjusting the balance between HRT and influent COD concentration.

Dopaminergic Neurons in Zona Incerta Drives Appetitive Self-Grooming.

Dopaminergic (DA) neurons are known to play a key role in controlling behaviors. While DA neurons in other brain regions are extensively characterized, those in zona incerta (ZITH or A13) receive much less attention and their function remains to be defined. Here it is shown that optogenetic stimulation of these neurons elicited intensive self-grooming behaviors and promoted place preference, which can be enhanced by training but cannot be converted into contextual memory. Interestingly, the same stimulation increased DA release to periaqueductal grey (PAG) neurons and local PAG antagonism of DA action reduced the elicited self-grooming. In addition, A13 neurons increased their activity in response to various external stimuli and during natural self-grooming episodes. Finally, monosynaptic retrograde tracing showed that the paraventricular hypothalamus represents one of the major upstream brain regions to A13 neurons. Taken together, these results reveal that A13 neurons are one of the brain sites that promote appetitive self-grooming involving DA release to the PAG.

Nanoparticle-mediated cell pyroptosis: a new therapeutic strategy for inflammatory diseases and cancer.

Pyroptosis, a lytic form of cell death mediated by the gasdermin family, is characterized by cell swelling and membrane rupture. Inducing pyroptosis in cancer cells can enhance antitumor immune responses and is a promising strategy for cancer therapy. However, excessive pyroptosis may trigger the development of inflammatory diseases due to immoderate and continuous inflammatory reactions. Nanomaterials and nanobiotechnology, renowned for their unique advantages and diverse structures, have garnered increasing attention owing to their potential to induce pyroptosis in diseases such as cancer. A nano-delivery system for drug-induced pyroptosis in cancer cells can overcome the limitations of small molecules. Furthermore, nanomedicines can directly induce and manipulate pyroptosis. This review summarizes and discusses the latest advancements in nanoparticle-based treatments with pyroptosis among inflammatory diseases and cancer, focusing on their functions and mechanisms and providing valuable insights into selecting nanodrugs for pyroptosis. However, the clinical application of these strategies still faces challenges owing to a limited understanding of nanobiological interactions. Finally, future perspectives on the emerging field of pyroptotic nanomaterials are presented.

Seasonality in embolism resistance and hydraulic capacitance jointly mediate hydraulic safety in branches and leaves of oriental cork oak (Quercus variabilis Bl.).

Seasonality in temperate regions is prominent during the era of increased climatic variability. A hydraulic trait that can adjust to seasonally changing climatic conditions is crucial for tree safety. However, little attention has been paid to the intraspecific seasonality of drought-related traits and hydraulic safety of keystone forest trees. We examined seasonal variations in the key morphological and physiological traits as well as multiple hydraulic safety margins (SMs) at the branch and leaf levels in oriental cork oak (Quercus variabilis Bl.), which is predominant in Chinese temperate forests. Pneumatic measurements indicated that, as seasons progressed, the water potential at which 50% of branch embolisms occur (P50_branch) decreased from -3.34 MPa to -4.23 MPa, with a coefficient of variation (CV) of 9.08%. Sapwood capacitance ranged from 48.19-248.08 kg m-3 MPa-1, peaking in autumn and reaching minimum in winter (CV 60.58%). Rehydration kinetics confirmed higher leaf embolism vulnerability (P50_leaf) in spring and autumn than those in summer, with values ranging from -1.06 MPa to -3.02 MPa (CV 39.85%). All leaf pressure-volume (PV) traits shifted with growth, with CVs ranging from 6.95-46.69%. Sapwood density had significant negative correlations with P50_branch and hydraulic capacitance for elastic water storage, whereas leaf mass per area was linearly associated with PV traits but not with P50_leaf. Furthermore, the branch typical SMs (difference between branch midday water potential and P50_branch) were consistently > 1.84 MPa, and vulnerability segmentation was prevalent throughout, implying a plausible hydraulic foundation for the dominance of Q. variabilis. Diverse hydraulic response patterns existed across seasons, leading to positive safety margins mediated by the aforementioned physiological traits. Although Q. variabilis exhibits a high level of hydraulic safety, its susceptibility to sudden summer droughts may increase due to global climate change.

The Potential Mechanisms Involved in the Disruption of Spermatogenesis in Mice by Nanoplastics and Microplastics.

BACKGROUND: Plastic-based products are ubiquitous due to their tremendous utility in our daily lives. Nanoplastic (NP) and microplastic (MP) pollution has become a severe threat to the planet and is a growing concern. It has been widely reported that polystyrene (PS) MPs are severely toxic to the male reproduction system, with effects including decreased sperm parameters, impaired spermatogenesis, and damaged testicular structures. However, the molecular mechanisms for impaired spermatogenesis remain poorly understood. METHODS: C57BL/6 male mice were treated with PS-NPs (80 nm) and PS-MPs (5 µm) by oral gavage every day for 60 days. A series of morphological analyses were completed to explore the influence of PS-NP and PS-MP exposure on the testes. Compared to other cell types in the seminiferous tubule, PS-NP and PS-MP exposure can lead to decreased spermatocytes. Then, more refined molecular typing was further performed based on gene expression profiles to better understand the common and specific molecular characteristics after exposure to PS-NPs and PS-MPs. RESULTS: There were 1794 common DEGs across the PS-NP groups at three different doses and 1433 common DEGs across the PS-MP groups at three different doses. GO and KEGG analyses of the common DEGs in the PS-NP and PS-MP groups were performed to enrich the common and specific functional progress and signaling pathways, including 349 co-enriched GO entries and 13 co-enriched pathways. Moreover, 348 GO entries and 33 pathways were specifically enriched in the PS-NP group, while 526 GO entries and 15 pathways were specifically enriched in the PS-MPs group. CONCLUSIONS: PS-NPs were predominantly involved in regulating retinoic acid metabolism, whereas PS-MPs primarily influenced pyruvate metabolism and thyroid hormone metabolism. Our results highlight the different molecular mechanisms of PS-NPs and PS-MPs in the impairment of spermatogenesis in male mammals for the first time, providing valuable insights into the precise mechanisms of PS-NPs and PS-MPs in male reproduction.

An Integrated Inertial-Magnetophoresis Microfluidic Chip Online-Coupled with ICP-MS for Rapid Separation and Precise Detection of Circulating Tumor Cells.

Circulating tumor cells (CTCs) are recognized as promising targets for liquid biopsy, which play an important role in early diagnosis and efficacy monitoring of cancer. However, due to the extreme scarcity of CTCs and partial size overlap between CTCs and white blood cells (WBCs), the separation and detection of CTCs from blood remain a big challenge. To address this issue, we fabricated a microfluidic chip by integrating a passive contraction-expansion array (CEA) inertial sorting zone and an active magnetophoresis zone with the trapezoidal groove and online coupled it with inductively coupled plasma mass spectrometry (ICP-MS) for rapid separation and precise detection of MCF-7 cells (as a model CTC) in blood samples. In the integrated microfluidic chip, most of the small-sized WBCs can be rapidly removed in the circular CEA inertial sorter, while the rest of the magnetically labeled WBCs can be further captured in the trapezoidal groove under the magnetic field. As a result, the rapid separation of MCF-7 cells from blood samples was achieved with an average recovery of 91.6% at a sample flow rate of 200 µL min-1. The developed online integrated inertial-magnetophoresis microfluidic chip-ICP-MS system has been applied for the detection of CTCs in real clinical blood samples with a fast analysis speed (5 min per 1 mL blood). CTCs were detected in all 24 blood samples from patients with different types of cancer, exhibiting excellent application potential in clinical diagnosis.