Scientific Reports最新文献

The construction of large reservoirs has modified the process of water and sediment transport downstream, resulting in changes in the morphology of the river cross-section. Changes in water and sand transport and cross-sectional morphology are reflected in the rating curve at the cross-section. This study analyzed the variations in the rating curve at the Huayuankou (HYK) section and their influencing factors, and conducted water level predictions based on this relationship. The findings revealed that while the annual mean water level has shown a declining tendency over the past 20 years, the annual mean discharge has shown a constant pattern. The rating curve at this stretch narrowed from a rope-loop type curve in its natural condition to a more stable single curve as a result of the construction of the dam upstream of the HYK section. The effect of pre-flood section morphology and the water-sediment process on the scattering degree of the rating curve is inverse; increasing roughness and hydraulic radius decreases scattering degree, while increasing sand content and sand transport rate increases scattering degree. Using the measured data from 2020 as an example, the feasibility of predicting cross-sectional water levels using the rating curve was verified. The prediction results were accurate when the flow was between 1000 and 2800 m³/s; However, when the flow was between 2800 and 4000 m³/s, the forecast results were typically slightly lower than the measured values. Overall, the method demonstrates good predictive accuracy. Insight from the method can be used to predict water levels to better inform decision making about water resources management, and flood emergency response in the lower Yellow River.

Heatwaves pose a serious threat and are projected to amplify with changing climate and social demographics. A comprehensive understanding of heatwave exposure to the communities is imperative for the development of effective strategies and mitigation plans. This study explores spatiotemporal characterization of heatwaves across the historically vulnerable communities in Mississippi, United States. We derive multiple heatwave metrics including frequency, duration, and magnitude based on temperature data for urban-specific daytime, nighttime, and day-night combined conditions. Our analysis depicts a rising heatwave trend across all counties, with the most extreme shifts observed in prolonged day-night events lacking overnight relief. We integrate physical heatwave hazards with a socioeconomic vulnerability index to develop an integrated urban heatwave risk index. Integrated metric identifies the counties in northwest Mississippi as heat-prone areas, exhibiting an urgent need to prioritize heat resilience and adaptive strategies in these regions. The compounding urban heatwave and vulnerability risks in these communities highlights an environmental justice imperative to implement equitable policies that protect disadvantaged populations. Although this study is focused on Mississippi, our framework is scalable and can be employed to urban regions globally. This study provides a solid foundation for developing timely heatwave preparedness and mitigation to avert preventable heat-related tragedies as extremes intensify with climate change.

In March 2023, our pediatric intensive care unit (PICU) retrospectively examined six cases of pediatric necrotizing tracheobronchitis (NTB), focusing on co-infections with influenza A virus (IAV) and Staphylococcus aureus (S. aureus). This study aimed to elucidate NTB's clinical characteristics, diagnostics, and therapeutic approaches. Diagnostics included symptom assessment, microbiological testing that confirmed all patients were positive for IAV H1N1 with a predominant S. aureus co-infection, and bronchoscopy. The patients predominantly exhibited fever, cough, and dyspnea. Laboratory analysis revealed decreased lymphocyte counts and elevated infection markers like C-reactive protein and procalcitonin. Chest computed tomography (CT) scans detected tracheobronchial obstructions in half of the cases, while bronchoscopy showed severe mucosal congestion, edema, necrosis, and purulent-hemorrhagic exudates. Treatments encompassed comprehensive strategies like oxygen therapy, intubation, bronchoscopic interventions, thoracentesis, oseltamivir, and a regimen of antibiotics. Our findings suggested potential correlations between clinical markers, notably lymphocyte count and procalcitonin, and clinical interventions such as the number of rescues and intensive care unit (ICU) duration. This research highlights the importance of early detection and the role of bronchoscopy and specific markers in assessing NTB, advocating for continued research in larger cohorts to better understand its clinical trajectory and refine treatment approaches for this challenging pediatric disease.

Non-small cell lung cancer (NSCLC) remains a significant challenge, as it is one of the leading causes of cancer-related deaths, and the development of resistance to anticancer therapy makes it difficult to treat. In this study, we investigated the anticancer mechanism of deoxybouvardin (DB), a cyclic hexapeptide, in gefitinib (GEF)-sensitive and -resistant NSCLC HCC827 cells. DB inhibited the viability and growth of HCC827 cells in a concentration- and time-dependent manner. In vitro kinase assay showed DB inhibited epidermal growth factor receptor (EGFR), mesenchymal-epithelial transition (MET), and AKT, and their phosphorylation was suppressed in HCC827 cells treated with DB. A molecular docking model suggested that DB interacts with these kinases in the ATP-binding pockets. DB induces ROS generation and cell cycle arrest. DB treatment of HCC827 cells leads to mitochondrial membrane depolarization. The induction of apoptosis through caspase activation was confirmed by Z-VAD-FMK treatment. Taken together, DB inhibited the growth of both GEF-sensitive and GEF-resistant NSCLC cells by targeting EGFR, MET, and AKT and inducing ROS generation and caspase activation. Further studies on DB can improve the treatment of chemotherapy-resistant NSCLC through the development of effective DB-based anticancer agents.

The preservation of microorganisms is pivotal in microbiological practice. Currently, cryopreservation is assumed to be an effective and inexpensive approach for the storage of microorganisms, including bacteria. The key point of cryopreservation is optimal cryoprotectant selection. In the present study, different cryoprotectant compositions were tested for long-term storage of 15 Enterobacterales bacterial strains at - 20 °C. The survival rates of the bacterial strains were evaluated in four different cryoprotectant solutions containing 70% glycerin only (cryoprotectants 1 and 4), 10% dimethyl sulfoxide (DMSO) with 70% glycerin (cryoprotectant 2), and 10% DMSO (cryoprotectant 3). In addition, cryoprotectants 1 and 2 contained peptone and yeast extract as nutritional supplements. The general survival rates of the bacterial strains were evaluated after 12 months of storage. After 12 months, the survival rates of the different cryoprotectants were as follows: cryoprotectant 1-88.87%; cryoprotectant 2-84.85%; cryoprotectant 3-83.50%; and cryoprotectant 4-44.81%. Thus, the composition of cryoprotectant 1 (70% glycerin with nutrient supplements) was optimal for preserving 15 tested strains of the order Enterobacterales. Despite these findings, the biochemical properties of the tested strains changed after cryopreservation for 12 months in the presence of 1 or 3 cryoprotectants. Alterations in the biochemical profile could be related to changes in environmental conditions and cold adaptation. We assume that the composition of cryoprotectant 1 can be optimal for storing the order Enterobacterales at - 20 °C. However, further investigations are needed to elucidate the problem of cryopreservation and to support our assumption.

During pregnancy the immune system needs to maintain immune tolerance of the foetus while also responding to infection, which can cause premature activation of the inflammatory pathways leading to the onset of labour and preterm birth. The vaginal microbiome is an important modifier of preterm birth risk, with Lactobacillus dominance during pregnancy associated with term delivery while high microbial diversity is associated with an increased risk of preterm birth. Glycans on glycoproteins along the lower female reproductive tract are fundamental to microbiota-host interactions and the mediation of inflammatory responses. However, the specific glycan epitopes involved in these processes are not well understood. To address this, we conducted glycomic analyses of cervicovaginal fluid (CVF) from 36 pregnant women at high risk of preterm birth and 4 non-pregnant women. Our analysis of N- and O-glycans revealed a rich CVF glycome. While O-glycans were shown to be the main carriers of ABO blood group epitopes, the main features of N-glycans were the presence of abundant paucimannose and high mannose glycans, and a remarkable diversity of complex bi-, tri-, and tetra-antennary glycans decorated with fucose and sialic acid. We identified immuno-regulatory epitopes, such as Lewis antigens, and found that fucosylation was negatively correlated to pro-inflammatory factors, such as IL-1β, MMP-8, C3a and C5a, while glycans with only sialylated antennae were mainly positively correlated to those. Similarly, paucimannose glycans showed a positive correlation to pro-inflammatory factors. We revealed a high abundance of glycans which have previously been identified as hallmarks of cancer and viral glycosylation, such as Man8 and Man9 high mannose glycans. Although each pregnant woman had a unique glycomic profile, longitudinal studies showed that the main glycosylation features were consistent throughout pregnancy in women who delivered at term, whereas women who experienced extreme preterm birth exhibited sharp changes in the CVF glycome shortly before delivery. These findings shed light on the processes underlying the role of glycosylation in maintaining a healthy vaginal microbiome and associated host immune responses. In addition, these discoveries facilitate our understanding of the lower female reproductive tract which has broad implications for women's health.

A new type of hybrid polymer particles capable of carrying the cytostatic drug doxorubicin and labeled with a gallium compound was prepared. These microparticles consist of a core and a hydrogel shell, which serves as the structural matrix. The shell can be employed to immobilize gallium oxide hydroxide (GaOOH) nanoparticles and the drug, resulting in hybrid beads with sizes of approximately 3.81 ± 0.09 μm. The microparticles exhibit the ability to incorporate a remarkably large amount of doxorubicin, approximately 0.96 mg per 1 mg of the polymeric carrier. Additionally, GaOOH nanoparticles can be deposited within the hydrogel layer at an amount of 0.64 mg per 1 mg of the carrier. These nanoparticles, resembling rice grains with an average size of 593 nm by 155 nm, are located on the surface of the polymer carrier. In vitro studies on breast and colon cancer cell lines revealed a pronounced cytotoxic effect of the hybrid polymer particles loaded with doxorubicin, indicating their potential for cancer therapies. Furthermore, investigations on doping the hybrid particles with the Ga-68 radioisotope demonstrated their potential application in positron emission tomography (PET) imaging. The proposed structures present a promising theranostic platform, where particles could be employed in anticancer therapies while monitoring their accumulation in the body using PET.

With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descriptors, which represent the structural, physicochemical, and electronic properties of a particular compound. Deviating from the conventional approach, in this investigation, we have employed the classification Read-Across Structure-Activity Relationship (c-RASAR), which involves the amalgamation of the concepts of classification-based quantitative structure-activity relationship (QSAR) and Read-Across to incorporate Read-Across-derived similarity and error-based descriptors into a statistical and machine learning modeling framework. ML models developed from these RASAR descriptors use similarity-based information from the close source neighbors of a particular query compound. We have employed different classification modeling algorithms on the selected QSAR and RASAR descriptors to develop predictive models for efficient prediction of query compounds' hepatotoxicity. The predictivity of each of these models was evaluated on a large number of test set compounds. The best-performing model was also used to screen a true external data set. The concepts of explainable AI (XAI) coupled with Read-Across were used to interpret the contributions of the RASAR descriptors in the best c-RASAR model and to explain the chemical diversity in the dataset. The application of various unsupervised dimensionality reduction techniques like t-SNE and UMAP and the supervised ARKA framework showed the usefulness of the RASAR descriptors over the selected QSAR descriptors in their ability to group similar compounds, enhancing the modelability of the dataset and efficiently identifying activity cliffs. Furthermore, the activity cliffs were also identified from Read-Across by observing the nature of compounds constituting the nearest neighbors for a particular query compound. On comparing our simple linear c-RASAR model with the previously reported models developed using the same dataset derived from the US FDA Orange Book ( https://www.accessdata.fda.gov/scripts/cder/ob/index.cfm ), it was observed that our model is simple, reproducible, transferable, and highly predictive. The performance of the LDA c-RASAR model on the true external set supersedes that of the previously reported work. Therefore, the present simple LDA c-RASAR model can efficiently be used to predict the hepatotoxicity of query chemicals.

Understanding the human milk metabolome can help inform infant nutrition and health. Untargeted metabolomics was used to study breast milk from 31 healthy participants to assess the shared metabolites in milk from participants with various backgrounds and understand how different demographic, health, and environmental factors impact the milk metabolome. Breast milk samples were analyzed by four separate UPLC-MS/MS methods. Metabolite Set Enrichment Analysis was used to study the most and least variable metabolites. The associations between participant factors and the metabolome were assessed with redundancy analyses. Among all 31 participants and between each untargeted UPLC-MS/MS method, 731 metabolites were detected, of which 389 were shared among all participants. Of the shared metabolites, lactose was the least and lactobionate the most variable metabolite. In the biological super pathway analysis, xenobiotics were the most variable metabolites. Infant age, maternal age, number of live births, and pre-pregnancy BMI were associated with the milk metabolome. In conclusion, the most variable metabolites originate from environmental exposures while the well-conserved core metabolites are linked to cell metabolism or are crucial for infant nutrition and osmoregulation. Understanding the variability of the breast milk metabolome can help identify components that are crucial for infant nutrition, growth, and development.