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Membrane aerated biofilm reactor (MABR) is challenged by biofilm thickness control and phosphorus removal. Air scouring aided by computational fluid dynamics (CFD) was employed to detach outer biofilm in sequencing batch MABR treating low C/N wastewater. Biofilm with 177-285 µm thickness in cycle 5-15 achieved over 85 % chemical oxygen demand (COD) and total inorganic nitrogen (TIN) removals at loading rate of 13.2 gCOD/m2/d and 2.64 gNH4+-N/m2/d. Biofilm rheology measurements in cycle 10-25 showed yield stress against detachment of 2.8-7.4 Pa, which were equal to CFD calculated shear stresses under air scouring flowrate of 3-9 L/min. Air scouring reduced effluent NH4+-N by 10 % and biofilm thickness by 78 µm. Intermittent aeration (4h off, 19.5h on) and air scouring (3 L/min, 30 s before settling) in one cycle achieved COD removal over 90 %, TIN and PO43--P removals over 80 %, showing great potential for simultaneous carbon, nitrogen and phosphorus removals.

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The current study presents the development process and initial validation of the Engagement in Athletic Training Scale (EATS), which was designed to evaluate athletes' engagement in athletic training. In study 1, item generation and initial content validity of the EATS were achieved. In study 2, the factor structure of the EATS was examined using exploratory factor analysis (EFA) and exploratory structural equation modeling (ESEM). Internal consistency reliabilities of the subscales were examined (N = 460). In study 3, factor structure, discriminant validity, internal consistency reliability, and nomological validity of the EATS were further examined in an independent sample (N = 513). Meanwhile, measurement invariance of the EATS across samples (study 2 and study 3) and genders was evaluated. Overall, results from the 3 rigorous studies provided initial psychometric evidence for the 19-item EATS and suggested that the EATS could be used as a valid and reliable measure to evaluate athletes' engagement in athletic training.

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The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

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DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

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Meiotic recombination plays a pivotal role in genetic evolution. Genetic variation induced by recombination is a crucial factor in generating biodiversity and a driving force for evolution. At present, the development of recombination hotspot prediction methods has encountered challenges related to insufficient feature extraction and limited generalization capabilities. This paper focused on the research of recombination hotspot prediction methods. We explored deep learning-based recombination hotspot prediction and scrutinized the shortcomings of prevalent models in addressing the challenge of recombination hotspot prediction. To addressing these deficiencies, an automated machine learning approach was utilized to construct recombination hotspot prediction model. The model combined sequence information with physicochemical properties by employing TF-IDF-Kmer and DNA composition components to acquire more effective feature data. Experimental results validate the effectiveness of the feature extraction method and automated machine learning technology used in this study. The final model was validated on three distinct datasets and yielded accuracy rates of 97.14%, 79.71%, and 98.73%, surpassing the current leading models by 2%, 2.56%, and 4%, respectively. In addition, we incorporated tools such as SHAP and AutoGluon to analyze the interpretability of black-box models, delved into the impact of individual features on the results, and investigated the reasons behind misclassification of samples. Finally, an application of recombination hotspot prediction was established to facilitate easy access to necessary information and tools for researchers. The research outcomes of this paper underscore the enormous potential of automated machine learning methods in gene sequence prediction.

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OBJECTIVE: To investigate the expression level and clinical correlation of microRNA-144/451 gene cluster (miR-144/451) in different types of anemia. METHODS: The peripheral blood of patients with aplastic anemia (AA), myelodysplastic syndrome (MDS) and diffuse large B-cell lymphoma (DLBCL) who had been diagnosed with anemia for the first time and after chemotherapy were collected. The expression levels of miR-144 and miR-451 were measured by RT-qPCR, and the correlation between the expression levels of miR-144 and miR-451 and routine laboratory indexes was analyzed by Spearman correlation analysis. RESULTS: The expression levels of miR-144 and miR-451 in the peripheral blood of AA and MDS patients were significantly lower than those in normal controls (all P < 0.01). No statistical differences were observed in the expression level of miR-144 in three subgroups of DLBCL patients (P >0.05), while the expression level of miR-451 in peripheral blood of three subgroups of DLBCL patients were significantly higher than those in normal controls (all P < 0.05). Correlation analysis showed that the expression levels of miR-144 and miR-451 in AA patients were positively correlated with red blood cell distribution width-coefficient of variation (RDW-CV) (r =0.629, 0.574). There were no significant correlations between the expression levels of miR-144 and miR-451 and laboratory parameters in MDS and DLBCL patients. CONCLUSION: Different types of anemia disorders have varying levels of miR-144 and miR-451 expression, which is anticipated to develop into a secondary diagnostic and differential diagnostic indicator for clinical anemia diseases.

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Cancer immunotherapy has demonstrated significant efficacy in various tumors, but its effectiveness in treating Hepatocellular Carcinoma (HCC) remains limited. Therefore, there is an urgent need to identify a new immunotherapy target and develop corresponding intervention strategies. Bioinformatics analysis has revealed that growth differentiation factor 15 (GDF15) is highly expressed in HCC and is closely related to poor prognosis of HCC patients. The previous study revealed that GDF15 can promote immunosuppression in the tumor microenvironment. Therefore, knocking out GDF15 through gene editing could potentially reverse the suppressive tumor immune microenvironment permanently. To deliver the CRISPR/Cas9 system specifically to HCC, nanocapsules (SNC) coated with HCC targeting peptides (SP94) on their surface is utilized. These nanocapsules incorporate disulfide bonds (SNCSS) that release their contents in the tumor microenvironment characterized by high levels of glutathione (GSH). In vivo, the SNCSS target HCC cells, exert a marked inhibitory effect on HCC progression, and promote HCC immunotherapy. Mechanistically, CyTOF analysis showed favorable changes in the immune microenvironment of HCC, immunocytes with killer function increased and immunocytes with inhibitive function decreased. These findings highlight the potential of the CRISPR-Cas9 gene editing system in modulating the immune microenvironment and improving the effectiveness of existing immunotherapy approaches for HCC.

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BACKGROUND: The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS: In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS: This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.

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Ferroptosis induction is an emerging strategy for tumor therapy. Reactive oxygen species (ROS) can induce ferroptosis but are easily consumed by overexpressed glutathione (GSH) in tumor cells. Therefore, achieving a large amount of ROS production in tumor cells without being consumed is key to efficiently inducing ferroptosis. In this study, a self-amplifying ferroptosis-inducing therapeutic agent, Pd@CeO2-Fe-Co-WZB117-DSPE-PEG-FA (PCDWD), is designed for tumor therapy. PCDWD exhibits excellent multi-enzyme activities due to the loading of Fe-Co dual atoms with abundant active sites, including peroxidase-like enzymes, catalase-like enzymes, and glutathione oxidases (GSHOx), which undergo catalytic reactions in the tumor microenvironment to produce ROS, thereby inducing ferroptosis. Furthermore, PCDWD can also deplete GSH in tumor cells, thus reducing the consumption of ROS by GSH and inhibiting the expression of GSH peroxidase 4. Moreover, the photothermal effect of PCDWD can not only directly kill tumor cells but also further enhance its own enzyme activities, consequently promoting ferroptosis in tumor cells. In addition, WZB117 can reduce the expression of heat shock protein 90 by inhibiting glucose transport, thereby reducing the thermal resistance of tumor cells and further improving the therapeutic effect. Finally, X-ray computed tomography imaging of PCDWD guides it to achieve efficient tumor therapy.

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Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

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Radiation-Induced Pulmonary Fibrosis (RIPF) frequently arises as a delayed complication following radiation therapy for thoracic cancers, encompassing lung, breast, and esophageal malignancies. Characterized by a relentless and irreversible accumulation of extracellular matrix (ECM) proteins within the lung parenchyma, RIPF presents a significant clinical challenge. While the modulation of gene expression by transcription factors is a recognized aspect in various pathologies, their specific role in the context of RIPF has been less clear. This study elucidates that ionizing radiation prompts the translocation of the transcription factor GATA3 into the nucleus. This translocation facilitates GATA3's binding to the NRP1 promoter, thereby enhancing the transcription and subsequent translation of NRP1. Further investigations demonstrate that the TGF-ß pathway agonist, SRI-011381, can mitigate the effects of NRP1 knockdown on epithelial-mesenchymal transition (EMT) and ECM deposition, suggesting a pivotal role of the GATA3/NRP1/TGF-ß axis in the pathogenesis of RIPF. In conclusion, our findings not only underscore the critical involvement of GATA3 in RIPF but also highlight the GATA3/NRP1/TGF-ß signaling pathway as a promising target for therapeutic intervention in RIPF management.

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Given the widespread presence of Pseudomonas aeruginosa in water and its threat to human health, the metabolic changes in Pseudomonas aeruginosa when exposed to polystyrene microplastics (PS-MPs) exposure were studied, focusing on molecular level. Through non-targeted metabolomics, a total of 64 differential metabolites were screened out under positive ion mode and 44 under negative ion mode. The content of bacterial metabolites changed significantly, primarily involving lipids, nucleotides, amino acids, and organic acids. Heightened intracellular oxidative damage led to a decrease in lipid molecules and nucleotide-related metabolites. The down-regulation of amino acid metabolites, such as L-Glutamic and L-Proline, highlighted disruptions in cellular energy metabolism and the impaired ability to synthesize proteins as a defense against oxidation. The impact of PS-MPs on organic acid metabolism was evident in the inhibition of pyruvate and citrate, thereby disrupting the cells' normal participation in energy cycles. The integration of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that PS-MPs mainly caused changes in metabolic pathways, including ABC transporters, Aminoacyl-tRNA biosynthesis, Purine metabolism, Glycerophospholipid metabolism and TCA cycle in Pseudomonas aeruginosa. Most of the differential metabolites enriched in these pathways were down-regulated, demonstrating that PS-MPs hindered the expression of metabolic pathways, ultimately impairing the ability of cells to synthesize proteins, DNA, and RNA. This disruption affected cell proliferation and information transduction, thus hampering energy circulation and inhibiting cell growth. Findings of this study supplemented the toxic effects of microplastics and the defense mechanisms of microorganisms, in turn safeguarding drinking water safety and human health.

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Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.

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The number of people with Alzheimer's disease (AD) is increasing annually, with the nidus mainly concentrated in the cortex and hippocampus. Despite of numerous efforts, effective treatment of AD is still facing great challenges due to the blood brain barrier (BBB) and limited drug distribution in the AD nidus sites. Thus, in this study, using vinpocetine (VIN) as a model drug, the objective is to explore the feasibility of tackling the above bottleneck via intranasal drug delivery in combination with a brain guider, borneol (BOR), using nanoemulsion (NE) as the carrier. First of all, the NE were prepared and characterized. In vivo behavior of the NE after intranasal administration was investigated. Influence of BOR dose, BOR administration route on drug brain targeting behavior was evaluated, and the influence of BOR addition on drug brain subregion distribution was probed. It was demonstrated that all the NE had comparable size and similar retention behavior after intranasal delivery. Compared to intravenous injection, improved brain targeting effect was observed by intranasal route, and drug targeting index (DTI) of the VIN-NE group was 154.1%, with the nose-to-brain direct transport percentage (DTP) 35.1%. Especially, remarkably enhanced brain distribution was achieved after BOR addition in the NE, with the extent depending on BOR dose. VIN brain concentration was the highest in the VIN-1-BOR-NE group at BOR dose of 1 mg/kg, with the DTI reaching 596.1% and the DTP increased to 83.1%. BOR could exert better nose to brain delivery when administrated together with the drug via intranasal route. Notably, BOR can remarkably enhance drug distribution in both hippocampus and cortex, the nidus areas of AD. In conclusion, in combination with intranasal delivery and the intrinsic brain guiding effect of BOR, drug distribution not only in the brain but also in the cortex and hippocampus can be enhanced significantly, providing the perquisite for improved therapeutic efficacy of AD.

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Aneurysmal subarachnoid hemorrhage (aSAH) develops quickly once it occurs and threatens the life of patients. We aimed to use machine learning to predict mortality for SAH patients at an early stage which can help doctors make clinical decisions. In our study, we applied different machine learning methods to an aSAH cohort extracted from a national EHR database, the Cerner Health Facts EHR database (2000-2018). The outcome of interest was in-hospital mortality, as either passing away while still in the hospital or being discharged to hospice care. Machine learning-based models were primarily evaluated by the area under the receiver operating characteristic curve (AUC). The population size of the SAH cohort was 6728. The machine learning methods achieved an average of AUCs of 0.805 for predicting mortality with only the initial 24 hours' EHR data. Without losing the prediction power, we used the logistic regression to identify 42 risk factors, -examples include age and serum glucose-that exhibit a significant correlation with the mortality of aSAH patients. Our study illustrates the potential of utilizing machine learning techniques as a practical prognostic tool for predicting aSAH mortality at the bedside.

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Liver cancer has high incidence and mortality rates and its treatment generally requires the use of a combination treatment strategy. Therefore, the early detection and diagnosis of liver cancer is crucial to achieving the best treatment effect. In addition, it is imperative to explore multimodal combination therapy for liver cancer treatment and the synergistic effect of two liver cancer treatment drugs while preventing drug resistance and drug side effects to maximize the achievable therapeutic effect. Gold nanoparticles are used widely in applications related to optical imaging, CT imaging, MRI imaging, biomarkers, targeted drug therapy, etc., and serve as an advanced platform for integrated application in the nano-diagnosis and treatment of diseases. Dual-drug-delivery nano-diagnostic and therapeutic agents have drawn great interest in current times. Therefore, the present report aims to review the effectiveness of dual-drug-delivery nano-diagnostic and therapeutic agents in the field of anti-tumor therapy from the particular perspective of liver cancer diagnosis and treatment.

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The mitotic regulator, Aurora kinase B (AURKB), is frequently overexpressed in malignancy and is a target for therapeutic intervention. The compound, LXY18, is a potent, orally available small molecule that inhibits the proper localization of AURKB during late mitosis, without affecting its kinase activity. In this study, we demonstrate that LXY18 elicits apoptosis in cancer cells derived from various indications, but not in non-transformed cell lines. The apoptosis is p53-independent, triggered by a prolonged mitotic arrest and occurs predominantly in mitosis. Some additional cells succumb post-mitotic slippage. We also demonstrate that cancer cell lines refractory to AURKB kinase inhibitors are sensitive to LXY18. The mitotic proteins MKLP2, NEK6, NEK7 and NEK9 are known regulators of AURKB localization during the onset of anaphase. LXY18 fails to inhibit the catalytic activity of these AURKB localization factors. Overall, our findings suggest a novel activity for LXY18 that produces a prolonged mitotic arrest and lethality in cancer cells, leaving non-transformed cells healthy. This new activity suggests that the compound may be a promising drug candidate for cancer treatment and that it can also be used as a tool compound to further dissect the regulatory network controlling AURKB localization.

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Nanotechnology, an emerging and promising therapeutic tool, may improve the effectiveness of phototherapy (PT) in antitumor therapy because of the development of nanomaterials (NMs) with light-absorbing properties. The tumor-targeted PTs, such as photothermal therapy (PTT) and photodynamic therapy (PDT), transform light energy into heat and produce reactive oxygen species (ROS) that accumulate at the tumor site. The increase in ROS levels induces oxidative stress (OS) during carcinogenesis and disease development. Because of the localized surface plasmon resonance (LSPR) feature of copper (Cu), a vital trace element in the human body, Cu-based NMs can exhibit good near-infrared (NIR) absorption and excellent photothermal properties. In the tumor microenvironment (TME), Cu2+ combines with H2O2 to produce O2 that is reduced to Cu1+ by glutathione (GSH), causing a Fenton-like reaction that reduces tumor hypoxia and simultaneously generates ROS to eliminate tumor cells in conjunction with PTT/PDT. Compared with other therapeutic modalities, PTT/PDT can precisely target tumor location to kill tumor cells. Moreover, multiple treatment modalities can be combined with PTT/PDT to treat a tumor using Cu-based NMs. Herein, we reviewed and briefly summarized the mechanisms of actions of tumor-targeted PTT/PDT and the role of Cu, generated from Cu-based NMs, in PTs. Furthermore, we described the Cu-based NMs used in PTT/PDT applications.

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Microplastic pollution in the water environment is becoming increasingly serious, impacting the growth and development of aquatic organisms. There are limited studies on the mechanisms of microplastic effects on biofilm formation. Therefore, in this study, the effects of polystyrene microplastics (PS-MPs) were investigated on the biofilm formation and development of Pseudomonas aeruginosa. Different concentrations and particle sizes of PS-MPs were selected for exposure tests to explore the effects on biofilm biomass, oxidative stress levels, biofilm structure, and population sensing system. The results showed that PS-MPs induced severe oxidative stress and inhibited biofilm formation and development, and the smaller the particle size, the stronger the inhibitory effect was. The inhibition effect was 0.1 µm>0.5 µm≈1 µm>5 µm. PS-MPs caused severe physical damage through contact with bacteria. The thickness of the biofilm was significantly reduced, damaging the structural stability. The bacteria in the biofilm secreted extracellular polymers to resist the stress of PS-MPs. Meanwhile, PS-MPs interfered with the QS system of P. aeruginosa; down-regulated the expression levels of key genes lasI, lasR, rhlI, and rhlR; inhibited the synthesis and secretion of signal molecules and related virulence factors; and ultimately affected the formation and structural stability of biofilms.

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Objectives: The present study aimed to examine the psychometric properties of the Chinese version of the Career Adapt-Abilities Scale-Short Form (CAAS-SF) among a sample of Chinese elite athletes. Methods: A sample of Chinese elite athletes (n = 770) was invited to participate in this study. First, the factor structure of the Chinese version of the CAAS-SF was examined, and six measurement models (CFA, H-CFA, B-CFA, ESEM, H-ESEM, and B-ESEM) were constructed and compared. Second, the internal consistency reliability of the Chinese version of the CAAS-SF was examined. Finally, structural equation modeling (SEM) was employed to assess the nomological validity of the Chinese version of the CAAS-SF. Results: The results showed that the hierarchical ESEM (H-ESEM) model best represented the factor structure of the CAAS-SF among Chinese elite athletes. It suggests that the higher-order factor of career adaptability explains the four distinctive but interrelated specific factors of concern, control, curiosity, and confidence. Cronbach's alpha coefficients (0.84-0.90), composite reliability (0.81-0.96), and coefficient omega hierarchical (0.855-0.94) of the Chinese version of the CAAS-SF were larger than the cutoff values, which suggest satisfactory reliability. The results of the SEM revealed that the higher-order factor of career adaptability was positively associated with career decision self-efficacy (ß = 0.676, p < 0.001). This result is consistent with previous findings (r = 0.65, p < 0.01) and provided support for the nomological validity of the CAAS-SF among Chinese elite athletes. Conclusion: The findings of the present study indicated that the Chinese version of the CAAS-SF displayed satisfactory reliability and validity and could be used to assess the career adaptability of Chinese elite athletes. In addition, the total score of the CAAS-SF is suggested to be used in future research and practical works.