Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958-2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000-2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer.

Gastric cancer presents a formidable challenge, marked by its debilitating nature and often dire prognosis. Emerging evidence underscores the pivotal role of tumor stem cells in exacerbating treatment resistance and fueling disease recurrence in gastric cancer. Thus, the identification of genes contributing to tumor stemness assumes paramount importance. Employing a comprehensive approach encompassing ssGSEA, WGCNA, and various machine learning algorithms, this study endeavors to delineate tumor stemness key genes (TSKGs). Subsequently, these genes were harnessed to construct a prognostic model, termed the Tumor Stemness Risk Genes Prognostic Model (TSRGPM). Through PCA, Cox regression analysis and ROC curve analysis, the efficacy of Tumor Stemness Risk Scores (TSRS) in stratifying patient risk profiles was underscored, affirming its ability as an independent prognostic indicator. Notably, the TSRS exhibited a significant correlation with lymph node metastasis in gastric cancer. Furthermore, leveraging algorithms such as CIBERSORT to dissect immune infiltration patterns revealed a notable association between TSRS and monocytes and other cell. Subsequent scrutiny of tumor stemness risk genes (TSRGs) culminated in the identification of CDC25A for detailed investigation. Bioinformatics analyses unveil CDC25A's implication in driving the malignant phenotype of tumors, with a discernible impact on cell proliferation and DNA replication in gastric cancer. Noteworthy validation through in vitro experiments corroborated the bioinformatics findings, elucidating the pivotal role of CDC25A expression in modulating tumor stemness in gastric cancer. In summation, the established and validated TSRGPM holds promise in prognostication and delineation of potential therapeutic targets, thus heralding a pivotal stride towards personalized management of this malignancy.

Study on the response analysis of LID hydrological process to rainfall pattern based on framework for dynamic simulation of urban floods.

An in-depth analysis of the urban flood disaster level in response to different rainfall characteristics and Low Impact Development (LID) measures is of significant importance for addressing unfavorable management conditions and implementing effective flood control measures. This study proposes a dynamic urban flood simulation framework based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS) spatial analysis, incorporating an active inundation seed search algorithm. The framework is calibrated and validated using nine historical urban flood events. Subsequently, the impact of rainfall patterns on urban inundation under LID measures is analyzed based on the dynamic urban flood simulation framework. The results show that the urban flood simulation framework exhibits good applicability, with Nash-Sutcliffe Efficiency (NSE) values of 0.825 and 0.763 during the calibration and validation periods, respectively. The extent of inundation shows little variation for rainfall events with a return period greater than 20 years, and the location of flooding is minimally affected by rainfall patterns. LID measures have a decreasing effect on urban inundation control as the return period of rainfall increases, and there are variations in hydrological responses to different rainfall patterns under the same return period. For single-peak rainfall events with the same return period, the control rates of inundation volume, flow, and infiltration decrease as the rainfall peak coefficient increases, indicating a weakening effect of LID measures on flood control with increasing rainfall peak coefficient. Under the same return period conditions, LID measures exhibit the best runoff control effect for uniform rainfall, while their effectiveness is lower for double-peak rainfall events and single-peak rainfall events with an r = 0.75 coefficient. The findings of this study provide a theoretical basis for urban flood warning and management of Low Impact Development measures.

MAGE-A11 is a potential prognostic biomarker and immunotherapeutic target in gastric cancer.

Gastric cancer poses a serious threat to human health and affects the digestive system. The lack of early symptoms and a dearth of effective identification methods make diagnosis difficult, with many patients only receiving a definitive diagnosis at a malignant stage, causing them to miss out on optimal therapeutic interventions. Melanoma-associated antigen-A (MAGE-A) is part of the MAGE family and falls under the cancer/testis antigen (CTA) category. The MAGE-A subfamily plays a significant role in tumorigenesis, proliferation and migration. The expression, prognosis and function of MAGE-A family members in GC, however, remain unclear. Our research and screening have shown that MAGE-A11 was highly expressed in GC tissues and was associated with poor patient prognosis. Additionally, MAGE-A11 functioned as an independent prognostic factor in GC through Cox regression analysis, and its expression showed significant correlation with both tumour immune cell infiltration and responsiveness to immunotherapy. Our data further indicated that MAGE-A11 regulated GC cell proliferation and migration. Subsequently, our findings propose that MAGE-A11 may operate as a prognostic factor, having potential as an immunotherapy target for GC.

NRP1 regulates autophagy and proliferation of gastric cancer through Wnt/ß-catenin signaling pathway.

Gastric cancer possesses high lethality rate, and its complex molecular mechanisms of pathogenesis lead to irrational treatment outcomes. Autophagy plays a dual role in cancer by both promoting and suppressing the cancer. However, the role of autophagy in gastric cancer is still vague. Therefore, in this study, we first obtained autophagy-related genes from the Human Autophagy Database, and then applied consensus clustering analysis to analyse the molecular subtypes of gastric cancer samples in the TCGA database. The genes obtained after subtyping were then applied to construct risk prognostic model. Following this, PCA and tSNE assessed risk scores with good discriminatory ability for gastric cancer samples. The results of Cox regression analysis and time-dependent ROC curve analysis indicated that the model had good risk prediction ability. Finally, NRP1 was selected as the final study subject in the context of expression pairwise analysis, Kaplan-Meier curves and external validation of the GEO dataset. In vitro experiments showed that NRP1 has the ability to regulate the proliferation and autophagy of gastric cancer cells by affecting the Wnt/ß-catenin signalling pathway. Similarly, in vivo experiments have shown that NRP1 can affect tumour growth in vivo. We therefore propose that NRP1 can be used as both a prognostic factor and a therapeutic target through the regulation of autophagy in gastric cancer.

Superoxide Dismutase-Like Regulated Fe/Ppa@PDA/B for Synergistically Targeting Ferroptosis/Apoptosis to Enhance Anti-Tumor Efficacy.

The cell apoptosis pathway of sonodynamic therapy (SDT) is usually blocked, resulting in limited therapeutic efficacy, therefore, the development of new methods for sensitizing targeted ferroptosis and promoting apoptosis is of great significance to improve the anti-tumor efficacy of SDT. Herein, mesoporous Fe3 O4 nanoparticles (NPs) are synthesized for loading pyropheophorbide-a (ppa), surface-coated by polydopamine (PDA) and further anchored with tumor-targeting moieties of biotin to obtain Fe/ppa@PDA/B NPs. Fe/ppa@PDA/B displayes pH/ultrasound (US) responsive release properties, and magnetic resonance imaging (MRI) functions. Moreover, Fe3 O4 NPs of Fe/ppa@PDA/B as the Fe source for ferroptosis, enhances ferroptosis sensitivity by consuming glutathione (GSH) and producing hydroxyl radical (OH). The quinone groups of PDA layer on Fe/ppa@PDA/B own free electrons, which led to effective superoxide dismutase (SOD) action through superoxide anion (O2 - ) disproportionation to hydrogen peroxide (H2 O2 ) and oxygen (O2 ), thus, overcame hypoxia of SDT and promoted ·OH generation by Fe ions under US trigger, synergistically improves ferroptosis and apoptosis to enhance the anti-tumor efficacy of SDT both in vitro and in vivo. The anti-tumor strategy of synergistic apoptosis and ferroptosis induce by GSH depletion and self-sufficient O2 regulated by SOD provides a new idea for enhancing SDT efficacy.

Mitochondria-Targeted Nanosystem with Reactive Oxygen Species-Controlled Release of CO to Enhance Photodynamic Therapy of PCN-224 by Sensitizing Ferroptosis.

The apoptosis-resistant mechanism of photodynamic therapy (PDT) usually results in limited therapeutic efficacy. The development of new strategies for sensitizing targeted ferroptosis that bypass apoptosis resistance is of great significance to improve the antitumor efficacy of PDT. In this study, a novel amphiphilic copolymer whose main chain contains reactive oxygen species (ROS)-responsive groups and the end of side chains contains triphenylphosphine is synthesized, to encapsulate porphyrinic metal-organic framework PCN-224 via self-assembly which are hydrothermally synthesized by coordination of zirconium (IV) with tetra-kis(4-caboxyphenyl) porphyrin, and loaded carbon monoxide releasing molecule 401 (CORM-401) by their hollow structures (PCN-CORM), and finally, surface-coated with hyaluronic acid. The nanosystem can sequentially localize to mitochondria which is an important target to induce apoptosis and ferroptosis in cancer cells. Upon excitation with near-infrared light, PCN-224 is activated to produce amounts of ROS, and simultaneously triggers the rapid intracellular release of CO. More importantly, the released CO can sensitize ferroptosis and promote apoptosis to significantly enhance the antitumor efficacy of PCN-224 both in vitro and in vivo. These results illustrate that the mitochondria-targeted drug delivery system combined PDT with CO leads to an effective antitumor efficacy, which maybe a promising way to enhance the treatment efficiency of PDT.

MAGE-A3 regulates tumor stemness in gastric cancer through the PI3K/AKT pathway.

Gastric cancer remains a malignant disease of the digestive tract with high mortality and morbidity worldwide. However, due to its complex pathological mechanisms and lack of effective clinical therapies, the survival rate of patients after receiving treatment is not satisfactory. A increasing number of studies have focused on cancer stem cells and their regulatory properties. In this study, we first constructed a co-expression network based on the WGCNA algorithm to identify modules with different degrees of association with tumor stemness indices. After selecting the most positively correlated modules of the stemness index, we performed a consensus clustering analysis on gastric cancer samples and constructed the co-expression network again. We then selected the modules of interest and applied univariate COX regression analysis to the genes in this module for preliminary screening. The results of the screening were then used in LASSO regression analysis to construct a risk prognostic model and subsequently a sixteen-gene model was obtained. Finally, after verifying the accuracy of the module and screening for risk genes, we identified MAGE-A3 as the final study subject. We then performed in vivo and in vitro experiments to verify its effect on tumor stemness and tumour proliferation. Our data supports that MAGE-A3 is a tumor stemness regulator and a potent prognostic biomarker which can help the prediction and treatment of gastric cancer patients.

CENPA regulates tumor stemness in lung adenocarcinoma.

Lung adenocarcinoma is a malignant and fatal respiratory disease. However, due to its complex pathogenesis and poorly effective therapeutic options, accurate early diagnosis and prognosis remain elusive. Now, there is increasing evidence that tumor stem cells are involved in tumorigenesis, metastasis, relapse, resistance to chemotherapy and radiotherapy and are one of the reasons why tumors cannot be cured. The mRNA expression based-stemness index (mRNAsi) is a parameter obtained by Malta and his colleagues applying innovative one-class logistic regression machine learning algorithm (OCLR) on mRNA expression in normal stem cells and their progeny. It is a valid evaluation parameter and is currently employed to evaluate the degree of differentiation of a certain tumor. In this study, we first used WGCNA and the software Cytoscape to obtain key modules and hub genes. We then applied LASSO regression analysis to calculate the genes in the key module to obtain a six-gene risk model. Moreover, the accuracy of this model was validated. Finally, we took the intersection of hub genes and risk genes and validated CENPA as both a tumor stemness regulator and a tumor prognostic factor in lung cancer.

Induction of M-MDSCs with IL6/GM-CSF from adherence monocytes and inhibition by WP1066.

Peripheral blood monocytes acquire the phenotype of myeloid-derived suppressor cells (MDSCs) by induction of cytokine or co-culture with cancer cells and are widely used to model MDSCs for in vitro studies. However, the simplest method of plastic adhesive sorting is poorly described as the purity of monocyte resulting from this method is the lowest compared with flow cytometry cell-sorting and magnetic beads sorting. Therefore, the present study aimed at investigating the effect of the plastic adhesive monocyte isolation techniques on the resulting MDSCs phenotype. Monocytes were allowed to adhere for 1 h and cultured with IL6 and granulocyte-macrophage colony-stimulating factors (GM-CSF) for 7 days. Plastic adhesion sorting resulted in early low monocyte yield and purity, but high purity of MDSCs was obtained by refreshing the induction medium. The resulting MDSCs were the major subpopulation of CD33+CD11b+CD14+CD15-human leukocyte antigen (HLA)-/low cells and provided the potent capacity to suppress T cell proliferation and cytokine IFN-γ production. Moreover, the induced MDSCs were inhibited by STAT3 inhibitor WP1066, resulting in downregulation of phosphorylated-STAT3 and PD-L1 expression and upregulation of apoptosis respectively. In conclusion, the present study described the generation of monocytic MDSCs from adherence monocytes and the inhibition of STAT3 inhibitor WP1066 on the induced MDSCs. The present study contributed to the development of a new clinical drug, WP1066 targeting MDSC.

The skin prick test response after allergen immunotherapy in different levels of tIgE children with mite sensitive Asthma/Rhinitis in South China.

BACKGROUND: At present, the biomarkers which can predict the clinical efficacy of allergen immunotherapy (AIT) are still much debated. IgE levels are often related to allergic severity. Therefore, this study aimed at relating total IgE (tIgE) levels with the efficacy of AIT assessed by symptoms and drug score and skin prick test (SPT) response. METHODS: We evaluated 81 allergic children who had received house-dust mite (HDM) subcutaneous immunotherapy for three years. According to the tIgE levels before treatment, all children were divided into high value, medium value and low value group. Each group according to sIgE/tIgE ratio was divided into subgroups. The efficacy of AIT is assessed by symptoms and drug score. By comparing changes in the grade of SPT in each group, the response of AIT are evaluated. RESULTS: The SPT grade changes to determine efficacy had a high degree of consistency with symptoms and drug score judgment (sensitivity 89.7%, specificity 78.3%, Kappa = 0.670, P < 0.001). Compared to ineffective cases, the effective cases had lower tIgE (P < 0.001) and higher ratio of sIgE/tIgE (P < 0.001). The grades of SPT declined the most in the low value group (low value group vs. medium value group, P < 0.05; low value group vs. high value group, P < 0.001; medium value group vs. high value group, P < 0.05). CONCLUSIONS: The SPT grade change can be used for efficacy evaluation. Children with lower level of tIgE and higher ratio of sIgE/tIgE that obtain a more satisfactory effect.

The influence of inhaled corticosteroid discontinuation in children with well-controlled asthma.

Asthma is a chronic inflammatory disease that requires adherence to both preventative and therapeutic interventions in disease management. Children with asthma are likely to discontinue inhaled corticosteroids (ICS), especially when symptoms are under control. We aimed to investigate the impact of ICS adherence in children whose symptoms were under control.The study is cohort study; 35 children with controlled asthma that had undergone 3 years of follow-up were included. Serum eosinophil count, serum total IgE (tIgE), and lung function (FEV1, FEV1/FVC, PEF, FEF20-75%, and PC20) were evaluated at the beginning and end of the follow-up.At baseline, patients in both the adherent and nonadherent groups were similar. After 3 years, the nonadherent group who had discontinued ICS had a decrease in FEV1 (P < .05), FEV1/FVC (P < .05), PEF (P < .05), and FEF20-75% (P < .05). The nonadherent group had no significant improvement in PC20 compared with their values at the beginning of the follow-up, whereas the adherent group had improvement in PC20. Furthermore, there was an increase in serum eosinophil (P < .001) and tIgE (P < .05) in the nonadherent compared with the adherent group.Despite good asthma control, airway hyperresponsiveness (AHR) was detected in a large proportion of children with asthma. ICS discontinuation affected lung function, serum eosinophil count, tIgE, and AHR. Adequate adherence is important in asthma management. The benefits of ICS and the influence of drug discontinuation despite good asthma control may encourage better adherence from patients.