Proteomic insights from extracellular vesicles into the molecular mechanisms of health effects induced by Per- and polyfluoroalkyl substances.

Per- and polyfluoroalkyl substances (PFASs) can induce a range of adverse health effects, with the precise molecular mechanisms remaining elusive. Extracellular vesicles (EVs) have demonstrated their potential to elucidate unknown molecular mechanisms. Building upon the close alignment of their biological functions with the observed health effects of PFASs, this study innovatively focuses on proteomic insights from EVs into the molecular mechanisms underlying the systemic health effects of PFASs. Through rat exposure experiments and proteomics technology, it not only demonstrated the occurrence of PFASs in EVs but also revealed the alterations in the serum EVs and the expression of their protein cargos following mixed exposure to PFASs, leading to changes in related pathways. These changes encompass various biological processes, including proteasome activity, immune response, cytoskeletal organization, oxidative stress, cell signaling, and nervous system function. Particularly noteworthy is the uncovering of the activation of the proteasome pathway, highlighting significant key contributing proteins. These novel findings provide a new perspective for exploring the molecular mechanism underlying the systemic health effects of PFASs and offer reliable screening for potential biomarkers. Additionally, comparisons with serum confirmed the potential of serum EVs as biological responders and measurable endpoints for evaluating PFASs-induced toxicity.

Complete response to disitamab vedotin in HER2-low metastatic endometrial carcinoma: a case report and review of the literature.

Endometrial cancer (EC) is one of the most common gynecologic malignancies with increasing morbidity. The prognosis for patients diagnosed with early-stage EC remains favorable; however, for patients with recurrent or metastatic EC, the prognosis is poor and treatment options, until recently, are limited. Antibody drug conjugates (ADCs) represent innovative strategies in cancer treatment; however, there are less investigations regarding their efficacy in EC. This report describes an EC case with low human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) expression score (IHC 2+) that experienced recurrent metastasis in the abdominal and peritoneal following post-surgical chemotherapy and radiotherapy. Subsequently, the commencement of HER2-targeted ADC, disitamab vedotin (RC48; 2.5 mg/kg), administered intravenously every two weeks, was initiated. The tumor lesions shrunk markedly after three cycles of treatment and disappeared by the completion of ten cycles of therapy. The patient is still in remission at present. The current findings imply the potential efficacy of HER2-targeted ADCs for patients with HER2-low metastatic EC.

Anti-inflammatory effects of Tao Hong Si Wu Tang in mice with lung cancer and chronic obstructive pulmonary disease.

BACKGROUND: Lung cancer (LC) combined with chronic obstructive pulmonary disease (COPD) is a common combination of comorbidities. Anti-inflammation and modulation of oxidative/antioxidative imbalance may prevent COPD-induced LC, and are also crucial to the treatment of LC combined with COPD. Modern studies have shown that Tao Hong Si Wu Tang (THSW) has vasodilatory, anti-inflammatory, anti-fatigue, anti-shock, immunoregulatory, lipid-reducing, micronutrient-supplementing, and anti-allergy effects. AIM: To observe the effects of THSW on COPD and LC in mice. METHODS: A total of 100 specific pathogen-free C57/BL6 mice were randomly divided into five groups: Blank control group (group A), model control group (group B), THSW group (group C), IL-6 group (group D), and THSW + IL-6 group (group E), with 20 mice in each group. A COPD mouse model was established using fumigation plus lipopolysaccharide intra-airway drip, and an LC model was replicated by in situ inoculation using the Lewis cell method. RESULTS: The blank control group exhibited a clear alveolar structure. The model control and IL-6 groups had thickened alveolar walls, with smaller alveolar lumens, interstitial edema, and several inflammatory infiltrating cells. Histopathological changes in the lungs of the THSW and THSW + IL-6 groups were less than those of the model control group. The serum IL-1ß, IL-6, and TNF-α levels and IL-6R, JAK, p-JAK, STAT1/3, p-STAT1/3, FOXO, p-FOXO, and IL-7R expression levels in lung tissues of mice in the rest of the groups were significantly higher than those of the blank control group (P < 0.01). Compared with the model control group, the IL-6 group demonstrated significantly higher levels for the abovementioned proteins in the serum and lung tissues (P < 0.01), and the THSW group had significantly higher serum IL-1ß, IL-6, and TNF-α levels and IL-7R expression levels in lung tissues (P < 0.01) but significantly decreased IL-6R, JAK, p-JAK, STAT1/3, p-STAT1/3, FOXO, p-FOXO, and IL-7R levels (P < 0.01). CONCLUSION: THSW reduces the serum IL-1ß, IL-6, and TNF-α levels in the mouse model with anti-inflammatory effects. Its anti-inflammatory mechanism lies in inhibiting the overactivation of the JAK/STAT1/3 signaling pathway.

Targeting EZH2 attenuates the ferroptosis-mediated osteoblast-osteoclast imbalance in rheumatoid arthritis.

OBJECTIVE: The enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) can regulate osteogenesis and osteoclastogenesis. This study aimed to further explore the effects of EZH2 modification on ferroptosis and the osteoblast-osteoclast balance in rheumatoid arthritis (RA) in vitro and in vivo. METHODS: Bone marrow mesenchymal stromal cells were transfected with EZH2 overexpression (oeEZH2) and EZH2 shRNA (shEZH2) plasmids with or without ferrostatin-1 (Fer-1) treatment and subjected to an osteoblast differentiation assay. The cells were then cocultured with bone marrow-derived macrophages and subjected to an osteoclast differentiation assay. Collagen-induced arthritis (CIA) mice were generated and injected with shEZH2 adeno-associated virus (AAV). RESULTS: OeEZH2 repressed osteoblast differentiation, as reflected by decreased ALP and Alizarin Red S staining and increased OPN, RUNX2, OPG and RANKL; however, shEZH2 had the opposite effects. Besides, oeEZH2 promoted osteoblast ferroptosis, as suggested by increased MDA, Fe2+, ROS, and PTGS2 but decreased GPX4 and SLC7A11; these effects could be attenuated by Fer-1 treatment. In contrast, shEZH2 ameliorated osteoblast ferroptosis. OeEZH2 subsequently increased osteoclast differentiation, as indicated by increased TRAP+ multinucleated cells, NFATC1, CTSK, and c-FOS; however, shEZH2 had the opposite effect, except that it did not regulate CTSK. In CIA mice, shEZH2 AAV decreased the clinical symptom score, histological score of cartilage, and systemic inflammation (TNF-α and IL-6) and repressed bone ferroptosis and restored the osteoblast-osteoclast balance to some extent, as reflected by immunohistochemical staining of related markers. CONCLUSION: Targeting EZH2 attenuates the ferroptosis-mediated osteoblast-osteoclast imbalance in RA, revealing its potential as a treatment target.

In silico prediction of drug-induced nephrotoxicity: current progress and pitfalls.

INTRODUCTION: Due to its role in absorption and metabolism, the kidney is an important target for drug toxicity. Drug-induced nephrotoxicity (DIN) presents a significant challenge in clinical practice and drug development. Conventional methods for assessing nephrotoxicity have limitations, highlighting the need for innovative approaches. In recent years, in silico methods have emerged as promising tools for predicting DIN. AREAS COVERED: A literature search was performed using PubMed and Web of Science, from 2013 to February 2023 for this review. This review provides an overview of the current progress and pitfalls in the in silico prediction of DIN, which discusses the principles and methodologies of computational models. EXPERT OPINION: Despite significant advancements, this review identified issues accentuates the pivotal imperatives of data fidelity, model optimization, interdisciplinary collaboration, and mechanistic comprehension in sculpting the vista of DIN prediction. Integration of multiple data sources and collaboration between disciplines are essential for improving predictive models. Ultimately, a holistic approach combining computational, experimental, and clinical methods will enhance our understanding and management of DIN.

Macro-Nanoporous Film with Cauliflower-Shaped Fibers for Highly Efficient Passive Daytime Radiative Cooling.

Passive daytime radiative cooling (PDRC) is a simple and effective cooling approach that does not consume any extra energy just by highly reflecting shortwave sunlight and highly radiating infrared heat through the atmospheric windows. In recent years, the application of photonic coolers and metamaterials for PDRC has been studied. However, they usually have complex processes and high precision requirements, which seriously limit large-scale fabrication. In this paper, a high-performance polyvinylidene difluoride-hexafluoropropylene (PVDF-HFP) PDRC fiber film was prepared by a simple and efficient electrospinning method combined with phase separation, and the obtained PVDF-HFP fibers have a cauliflower-shaped macro-nanoporous structure. The cauliflower-shaped structure provides more scattering sites of the fibers, and the fiber film with the macro-nanoporous morphology has a high scattering ability in the solar region, resulting in a high solar reflectivity of 99.65%. The PVDF-HFP porous film possesses an emissivity of 90.44% in the atmospheric window, and it can reach a maximum cooling temperature of 10.2 °C during the daytime. In addition, the excellent mechanical strength provides a guarantee for its large-scale practical application. This study offers an effective improvement strategy for the spectral performance of polymer fiber films, which is meaningful for green cooling management and reduction of carbon emission.

Retracted: MicroRNA-495 Confers Increased Sensitivity to Chemotherapeutic Agents in Gastric Cancer via the Mammalian Target of Rapamycin (mTOR) Signaling Pathway by Interacting with Human Epidermal Growth Factor Receptor 2 (ERBB2).

The Editors of Medical Science Monitor wish to inform you that the above manuscript has been retracted from publication due to concerns with the credibility and originality of the study, the manuscript content, and the Figure images. Reference: Na Li, Mei Han, Ning Zhou, Yong Tang, Xu-Shan Tang. MicroRNA-495 Confers Increased Sensitivity to Chemotherapeutic Agents in Gastric Cancer via the Mammalian Target of Rapamycin (mTOR) Signaling Pathway by Interacting with Human Epidermal Growth Factor Receptor 2 (ERBB2). Med Sci Monit, 2018; 24: CLR5990-5972. DOI: 10.12659/MSM.909458.

Exploring the bioactive ingredients of three traditional Chinese medicine formulas against age-related hearing loss through network pharmacology and experimental validation.

Traditional Chinese medicine (TCM) formulas, including the Er-Long-Zuo-Ci pill, Tong-Qiao-Er-Long pill, and Er-Long pill, have long been utilized in China for managing age-related hearing loss (ARHL). However, the specific bioactive compounds, pharmacological targets, and underlying mechanisms remain elusive. This study aims to find the shared bioactive ingredients among these three formulas, uncover the molecular pathways they regulate, and identify potential therapeutic targets for ARHL. Furthermore, it seeks to validate the efficacy of these major components through both in vivo and in vitro experiments. Common bioactive ingredients were extracted from the TCMSP database, and their putative target proteins were predicted using the Swiss Target Prediction database. ARHL-related target proteins were collected from GeneCards and OMIM databases. Our approach involved constructing drug-target networks and drug-disease-specific protein-protein interaction networks and conducting clustering, topological property analyses, and functional annotation through GO and KEGG enrichment analysis. Molecular docking analysis was utilized to delineate interaction mechanisms between major bioactive ingredients and key target proteins. Finally, in vivo and in vitro experiments involving ABR recording, immunofluorescent staining, HE staining, and quantitative PCR were conducted to validate the treatment effects of flavonoids on the declining auditory function in DBA/2 J mice. We identified 11 common chemical compounds across the three formulas and their associated 276 putative targets. Additionally, 3350 ARHL-related targets were compiled. As an intersection of the putative targets of the common compounds and ARHL-related proteins, 145 shared targets were determined. Functional enrichment analysis indicated that these compounds may modulate various biological processes, including cell proliferation, apoptosis, inflammatory response, and synaptic connections. Notably, potential targets such as TNFα, MAPK1, SRC, AKT, EGFR, ESR1, and AR were implicated. Flavonoids emerged as major bioactive components against ARHL based on target numbers, with molecular docking demonstrating diverse interaction models between these flavonoids and protein targets. Furthermore, baicalin could mitigate the age-related cochlear damage and hearing loss of DBA/2 J mice through its multi-target and multi-pathway mechanism, involving anti-inflammation, modulation of sex hormone-related pathways, and activation of potassium channels. This study offers an integrated network pharmacology approach, validated by in vivo and in vitro experiments, shedding light on the potential mechanisms, major active components, and therapeutic targets of TCM formulas for treating ARHL.

Prediction model for spontaneous preterm birth less than 32 weeks of gestation in low-risk women with mid-trimester short cervical length: a retrospective cohort study.

BACKGROUND: A short cervix in mid-trimester pregnancy is a risk factor for spontaneous preterm birth. However, there is currently a lack of predictive models and classification systems for predicting spontaneous preterm birth in these patients, especially those without additional risk factors for spontaneous preterm birth. METHODS: A retrospective observational cohort study of low-risk singleton pregnant women with a short cervix (≤ 25 mm) measured by transvaginal ultrasonography between 22 and 24 weeks was conducted. A multivariate logistic regression model for spontaneous preterm birth < 32 weeks in low-risk pregnant women with a short cervix was constructed. Moreover, we developed a nomogram to visualize the prediction model and stratified patients into three risk groups (low-, intermediate-, and high-risk groups) based on the total score obtained from the nomogram model. RESULTS: Between 2020 and 2022, 213 low-risk women with a short cervix in mid-trimester pregnancy were enrolled in the study. Univariate logistic analysis revealed that a high body mass index, a history of three or more miscarriages, multiparity, a short cervical length, leukocytosis, and an elevated C-reactive protein level were associated with spontaneous preterm birth < 32 weeks, but multivariate analysis revealed that multiparity (OR, 3.31; 95% CI, 1.13-9.68), leukocytosis (OR, 3.96; 95% CI, 1.24-12.61) and a short cervical length (OR, 0.88; 95% CI, 0.82-0.94) were independent predictors of sPTB < 32 weeks. The model incorporating these three predictors displayed good discrimination and calibration, and the area under the ROC curve of this model was as high as 0.815 (95% CI, 0.700-0.931). Patients were stratified into low- (195 patients), intermediate- (14 patients) and high-risk (4 patients) groups according to the model, corresponding to patients with scores ≤ 120, 121-146, and > 146, respectively. The predicted probabilities of spontaneous preterm birth < 32 weeks for these groups were 6.38, 40.62, and 71.88%, respectively. CONCLUSIONS: A noninvasive and efficient model to predict the occurrence of spontaneous preterm birth < 32 weeks in low-risk singleton pregnant women with a short cervix and a classification system were constructed in this study and can provide insight into the optimal management strategy for patients with different risk stratifications according to the score chart.

A Two-Photon-Active Zr-Based Metal-Organic Framework-Based Orthogonal Nanoprobe for Recognition of Cellular Senescence.

A luminescent nanoprobe capable of orthogonal sensing of two independent events is highly significant for unbiased disease-related detection such as the detection of senescent cells. Moreover, it is invaluable that the nanoprobe possesses a two-photon excitable characteristic that is highly suitable for imaging living cells and tissues. Herein, we present a two-photon-excitable multiluminescent orthogonal-sensing nanoprobe (OS nanoprobe) capable of detecting both pH elevation and ß-galactosidase (ß-gal) overexpression in senescent cells. In the design, Zr-based dual-emissive metal-organic frameworks prepared from two mixed amino linkers, referred to as NH2-MU, were used as the component for the ratiometric sensing of pH; additionally, fluorogenic resorufin-ß-d-galactopyranoside, linked to the NH2-MU framework, enables ß-gal detection. In the OS nanoprobe, the signals for pH and ß-gal sensing remain independent while maintaining high colocalization. The two-photon excitable organic linkers of NH2-MU impart the OS nanoprobe with a bioimaging capability, allowing for the differentiation of senescent human foreskin fibroblast (HFF) cells from younger HFF cells or LacZ positive cells with the 800 nm laser excitation. This study marks the first instance of achieving the multiplexed orthogonal fluorescent sensing of cellular senescence using a two-photon excitation strategy, suggesting the potential of using versatile metal-organic framework (MOFs)-based fluorophores to realize the orthogonal multiplexing of disease-related biomarkers through multiphoton excitation.

Involvement of INS15 in the development and pathogenicity of the zoonotic pathogen Cryptosporidium parvum.

BACKGROUND: Cryptosporidium parvum is a common protozoan pathogen responsible for moderate to severe diarrhea in humans and animals. The C. parvum genome contains 22 genes encoding insulinase-like M16 proteases (INS) with diverse structures and sequences, suggesting that members of the protein family may have distinct biological functions in the life cycle of parasites. Here, we investigated the role of INS15 and INS16, two proteases encoded by neighboring genes with high sequence identity, in the growth and development of C. parvum in vivo and in vitro. METHODOLOGY/PRINCIPAL FINDINGS: INS15 and INS16 genes were tagged and knocked out using CRISPR/Cas9 technology in C. parvum IIdA20G1-HLJ isolate. The expression of INS15 and INS16 was determined by immunofluorescence analysis and immunoelectron microscopy. The effect of depletion of INS15 and INS16 on parasite growth and pathogenicity were assessed on HCT-8 cells and in interferon-γ knockout mice. Endogenous tagging showed that INS15 and INS16 expressed in the oocyst, trophozoite, meront and female gametes. INS15 also expressed in male gamonts, while INS16 was not detected in the male gamonts. Although depletion of the INS15 or INS16 gene affected late development of C. parvum in vitro, only depletion of INS15 significantly reduced parasite burden in infected mice. Mice infected with the INS15-depleted strain had reduced clinical signs, body weight, intestinal villus length to crypt height ratio, and survival time compared to infected with the tagging mutant. CONCLUSIONS/SIGNIFICANCE: The results of this study indicate that INS15 is mainly involved in the late development of C. parvum. Depletion of this gene attenuates the pathogenicity of this important zoonotic parasite.

Harnessing molecular probes for imaging of human epidermal growth factor receptor (HER) family.

The human epidermal growth factor receptor (HER) family plays a critical role in the development, migration, and invasion of various cancers. Currently, the FDA has approved numerous targeting therapies for the HER family consist of small molecule drugs, monoclonal antibodies and antibody-drug conjugates. To facilitate precision therapy using currently approved targeted agents, early detection and quantification of each HER receptor are essential for assessment, treatment, and prognostic purposes. This study provides a comprehensive review of the latest advancements in detection and quantification of HER receptors, including traditional biopsies, liquid biopsies, and non-invasive detection methods. Although traditional histological methods, such as immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), have yielded valuable insights, advancements in real-time and non-invasive detection technologies necessitate improved methods for the dynamic evaluation of HER status. This article also reviews several emerging real-time techniques for detecting and quantifying HER status in circulating tumor cells (CTCs) extracted from blood samples, as well as in vivo assessments using positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging. This review emphasizes the importance of continuous innovation in the application of HER receptor imaging technologies, with the goal of enhancing treatment outcomes and prognoses for cancer patients.

Federated learning via multi-attention guided UNet for thyroid nodule segmentation of ultrasound images.

Accurate segmentation of thyroid nodules is essential for early screening and diagnosis, but it can be challenging due to the nodules' varying sizes and positions. To address this issue, we propose a multi-attention guided UNet (MAUNet) for thyroid nodule segmentation. We use a multi-scale cross attention (MSCA) module for initial image feature extraction. By integrating interactions between features at different scales, the impact of thyroid nodule shape and size on the segmentation results has been reduced. Additionally, we incorporate a dual attention (DA) module into the skip-connection step of the UNet network, which promotes information exchange and fusion between the encoder and decoder. To test the model's robustness and effectiveness, we conduct the extensive experiments on multi-center ultrasound images provided by 17 local hospitals. The model is trained using the federal learning mechanism to ensure privacy protection. The experimental results show that the Dice scores of the model on the data sets from the three centers are 0.908, 0.912 and 0.887, respectively. Compared to existing methods, our method demonstrates higher generalization ability on multi-center datasets and achieves better segmentation results.

Analysis of electrode locations on limb condition effect for myoelectric pattern recognition.

BACKGROUND: Gesture recognition using surface electromyography (sEMG) has garnered significant attention due to its potential for intuitive and natural control in wearable human-machine interfaces. However, ensuring robustness remains essential and is currently the primary challenge for practical applications. METHODS: This study investigates the impact of limb conditions and analyzes the influence of electrode placement. Both static and dynamic limb conditions were examined using electrodes positioned on the wrist, elbow, and the midpoint between them. Initially, we compared classification performance across various training conditions at these three electrode locations. Subsequently, a feature space analysis was conducted to quantify the effects of limb conditions. Finally, strategies for group training and feature selection were explored to mitigate these effects. RESULTS: The results indicate that with the state-of-the-art method, classification performance at the wrist was comparable to that at the middle position, both of which outperformed the elbow, consistent with the findings from the feature space analysis. In inter-condition classification, training under dynamic limb conditions yielded better results than training under static conditions, especially at the positions covered by dynamic training. Additionally, fast and slow movement speeds produced similar performance outcomes. To mitigate the effects of limb conditions, adding more training conditions reduced classification errors; however, this reduction plateaued after four conditions, resulting in classification errors of 22.72%, 22.65%, and 26.58% for the wrist, middle, and elbow, respectively. Feature selection further improved classification performance, reducing errors to 19.98%, 19.75%, and 27.14% at the respective electrode locations, using three optimal features derived from single-condition training. CONCLUSIONS: The study demonstrated that the impact of limb conditions was mitigated when electrodes were placed near the wrist. Dynamic limb condition training, combined with feature optimization, proved to be an effective strategy for reducing this effect. This work contributes to enhancing the robustness of myoelectric-controlled interfaces, thereby advancing the development of wearable intelligent devices.

Arginine as a regulator of antioxidant and gel formation in yak Myofibrillar proteins: Efficacy and mechanistic insights.

Arginine (Arg), a safe basic amino acid, modulates interprotein interactions and impacts the processing characteristics of myofibrillar proteins (MP) in meat products, as numerous studies have demonstrated. This study aimed to explore the effects of varying concentrations of Arg (0.025, 0.050, 0.100, 0.200 %) on the physicochemical properties and gel behavior of yak MP. Utilizing yak MP as the substrate, we assessed and analyzed the physicochemical attributes and gel performance of the MP-Arg composite system. The findings revealed that Arg facilitates MP unfolding and internal group exposure, effectively mitigating oxidative tertiary structure alterations. Arg exerts potent antioxidant activity on MP, augmenting their water-holding capacity, which ameliorates gel properties. In this experiment, 0.05 % Arg maximally inhibited oxidative damage to MP, with protection being concentration-dependent. Collectively, these findings suggest that Arg effectively inhibits the oxidative degradation of MP structure and promotes the formation of enhanced gel characteristics.

Fusarium as potential pathogenic fungus of Ginger (Zingiber officinale Roscoe) wilt disease.

The wilt disease of ginger, caused by various Fusarium species, imperils the cultivation of this valuable crop. However, the pathogenic mechanisms and epidemiology of ginger wilt remain elusive. Here, we investigate the association between ginger rhizome health and the prevalence of Fusarium conidia, as well as examine fungal community composition in symptomatic and asymptomatic ginger tissues. Our findings show that diseased rhizomes have reduced tissue firmness, correlating negatively with Fusarium conidia counts. Pathogenicity assays confirmed that both Fusarium oxysporum and Fusarium solani are capable of inducing wilt symptoms in rhizomes and sterile seedlings. Furthermore, Fungal community profiling revealed Fusarium to be the dominant taxon across all samples, yet its relative abundance was significantly different between symptomatic and asymptomatic tissues. Specifically, there is a higher incidence of Fusarium amplicon sequence variants (ASVs) in symptomatic above-ground parts. Our results unequivocally implicate F. oxysporum or F. solani as the etiological agents responsible for ginger wilt and demonstrate that Fusarium is the principal fungal pathogen associated with this disease. These findings provide critical insights for efficacious disease management practices within the ginger industry.

An integrated fast-slow plant and nematode economics spectrum predicts soil organic carbon dynamics during natural restoration.

Aboveground and belowground attributes of terrestrial ecosystems interact to shape carbon (C) cycling. However, plants and soil organisms are usually studied separately, leading to a knowledge gap regarding their coordinated contributions to ecosystem C cycling. We explored whether integrated consideration of plant and nematode traits better explained soil organic C (SOC) dynamics than plant or nematode traits considered separately. Our study system was a space-for-time natural restoration chronosequence following agricultural abandonment in a subtropical region, with pioneer, early, mid and climax stages. We identified an integrated fast-slow trait spectrum encompassing plants and nematodes, demonstrating coordinated shifts from fast strategies in the pioneer stage to slow strategies in the climax stage, corresponding to enhanced SOC dynamics. Joint consideration of plant and nematode traits explained more variation in SOC than by either group alone. Structural equation modeling revealed that the integrated fast-slow trait spectrum influenced SOC through its regulation of microbial traits, including microbial C use efficiency and microbial biomass. Our findings confirm the pivotal role of plant-nematode trait coordination in modulating ecosystem C cycling and highlight the value of incorporating belowground traits into biogeochemical cycling under global change scenarios.

Machine learning-enabled mental health risk prediction for youths with stressful life events: A modelling study.

BACKGROUND: Youths face significant mental health challenges exacerbated by stressful life events, particularly in the context of the COVID-19 pandemic. Immature coping strategies can worsen mental health outcomes. METHODS: This study utilised a two-wave cross-sectional survey design with data collected from Chinese youth aged 14-25 years. Wave 1 (N = 3038) and Wave 2 (N = 539) datasets were used for model development and external validation, respectively. Twenty-five features, encompassing dimensions related to demographic information, stressful life events, social support, coping strategies, and emotional intelligence, were input into the model to predict the mental health status of youth, which was considered their coping outcome. Shapley additive explanation (SHAP) was used to determine the importance of each risk factor in the feature selection. The intersection of top 10 features identified by random forest and XGBoost were considered the most influential predictors of mental health during the feature selection process, and was then taken as the final set of features for model development. Machine learning models, including logistic regression, AdaBoost, and a backpropagation neural network (BPNN), were trained to predict the outcomes. The optimum model was selected according to the performance in both internal and external validation. RESULTS: This study identified six key features that were significantly associated with mental health outcomes: punishment, adaptation issues, self-regulation of emotions, learning pressure, use of social support, and recognition of others' emotions. The BPNN model, optimized through feature selection methods like SHAP, demonstrated superior performance in internal validation (C-index [95 % CI] = 0.9120 [0.9111, 0.9129], F-score [95 % CI] = 0.8861 [0.8853, 0.8869]). Additionally, external validation showed the model had strong discrimination (C-index = 0.9749, F-score = 0.8442) and calibration (Brier score = 0.029) capabilities. LIMITATIONS: Although the clinical prediction model performed well, the study it still limited by self-reported data and representativeness of samples. Causal relationships need to be established to interpret the coping mechanism from multiple perspectives. Also, the limited data on minority groups may lead to algorithmic unfairness. CONCLUSIONS: Machine learning models effectively identified and predicted mental health outcomes among youths, with the SHAP+BPNN model showing promising clinical applicability. These findings emphasise the importance and effectiveness of targeted interventions with the help of clinical prediction model.

The influence of organizational caring on mobile phone addiction in undergraduate nursing students: The chain mediating role of perceived stress and self-control.

Background: The incidence of mobile phone addiction (MPA) is increasing in undergraduates and may lead to depression, anxiety, and sleep disorders. Undergraduate nursing students are important group for clinical services; therefore poor mental health has an important implications for the quality of future nursing work and the relationship between nurses and patients. Objectives: To explore the connection between organizational caring and MPA in Chinese undergraduate nursing students and to investigate the mechanisms of perceived stress and self-control in this relationship by constructing a chain mediation model. Design: A cross-sectional study. Methods: A total of 900 participants (age range 18-25 years, M = 20.99, SD = 1.58, 94.0 % female) were recruited from 10 universities in China. Data were collected using an online survey between February and March 2023. Organizational caring, subjective stress, self-control ability, and MPA among undergraduate nursing students were assessed. Results: (1) The prevalence of MPA was 34.9 % (314/900). (2) MPA in undergraduate nursing students was negatively correlated with self-control (r = -0.468, p < 0.001) and organizational caring (r = -0.156, p < 0.001), and positively correlated with perceived stress (r = 0.362, p < 0.001). (3) Perceived stress and self-control mediated the relationship between organizational caring and MPA, and the relative mediating effect values were 16.6 % and 17.2 %, respectively. (4) Organizational caring had an indirect effect on MPA through the chain mediation effect of perceived stress and self-control, and the relative mediating effect value was 19.1 %. Conclusion: Organizational caring, perceived stress and self-control directly influenced MPA among undergraduate nursing students. Additionally, organizational caring indirectly affected MPA through perceived stress and self-control. To further mitigate MPA among students, nursing managers and educators should enhance organizational caring, reduce perceived stress, and improve self-control abilities.

Efficient Solid-Phase Extraction of Neonicotinoid Insecticides from Environmental Water and Drink Samples Using a Postmodified Metal-Organic Framework.

Neonicotinoid insecticides (NNIs) are extensively utilized globally because of their efficient and broad-spectrum properties. However, their residues are also extensively distributed in the environment. Herein, MIL-101-SO3Na with abundant -NH- and sulfonate groups was synthesized via chloromethylation and nucleophilic substitution postmodification strategies and used to extract NNIs via solid-phase extraction. MIL-101-SO3Na was enhanced by introducing C-H···N hydrogen bonds to strengthen interaction forces and -SO3Na groups to adjust surface charge and enhance electrostatic attraction. This modification and the substantial specific surface area (998 m2·g-1) of the metal-organic framework markedly enhanced the enrichment efficiency of MIL-101. The proposed method based on MIL-101-SO3Na exhibited a minimal detection threshold (0.04-0.87 ng·L-1), an extensive linear spectrum (1-2000 ng·L-1), and notable accuracy (a variation of 3.02-11.8%) in water and drink samples. NNI concentrations between 0.25 and 24.2 ng·L-1 in fruit juice and tea samples were accurately identified using the proposed method, demonstrating its feasibility in practical applications. The postmodification of MIL-101-SO3Na is an exceptional and promising approach for the sensitive detection of ultratrace NNI levels in complex matrices.