SOX4 inhibits ferroptosis and promotes proliferation of endometrial cancer cells via the p53/SLC7A11 signaling.

AIM: Sex-determining region Y-related high-mobility group box 4 (SOX4) has been reported to play a carcinogenic role in endometrial cancer (EC). However, the biological function and regulatory mechanisms of SOX4 in ferroptosis during the progression of EC are still unknown. METHODS: The mRNA and protein levels were scrutinized by quantitative reverse-transcription polymerase chain reaction and western blot, respectively. The cell viability and proliferative capability were determined by cell counting kit-8 assay and 5-ethynyl-2'-deoxyuridine (EdU) assay. Transcriptional regulation of gene expression was investigated by dual-luciferase reporter assay and chromatin immunoprecipitation. Ferroptosis was evaluated by detection of reactive oxygen species, malondialdehyde, Fe2+, and ferroptosis-related proteins. The mice test was implemented to confirm the influence of SOX4 on EC tumor growth and ferroptosis in vivo. RESULTS: We here discovered the elevation of SOX4 in EC tissues and cells. Functionally, SOX4 knockdown hampered proliferation and promoted ferroptosis of EC cells. Mechanistically, SOX4 bound to p53 promoter and inhibited its transcriptional activity in EC cells. In addition, p53 transcriptionally suppressed SLC7A11 expression in EC cells. Downregulation of p53 reverses the effect of SOX4 knockdown on proliferation and ferroptosis of EC cells. Finally, in vivo experiments demonstrated that SOX4 depletion hindered tumor growth and triggered ferroptosis in EC. CONCLUSIONS: These findings collectively suggested that SOX4 inhibited ferroptosis and promoted proliferation of EC cells via the p53/SLC7A11 signaling. Our research unveiled a novel regulatory mechanism of ferroptosis in EC, offering promising perspectives for the development of EC therapies.

Identification and analysis of biomarkers associated with oxidative stress and ferroptosis in recurrent miscarriage.

Recurrent miscarriage (RM) has a huge impact on women. Both oxidative stress and ferroptosis play an important role in the pathogenesis of RM. Hence, it was vital to screen the ferroptosis oxidation-related biomarkers for the diagnosis and treatment of RM. We introduced transcript data to screen out differentially expressed genes (DEGs) in RM. Ferroptosis oxidation-related differentially expressed genes were obtained by overlapping DEGs and oxidative stress related genes with correlations >0.9 with ferroptosis-related genes. Least Absolute Shrinkage and Selectionator operator regression and support vector machine based recursive feature elimination algorithm were implemented to screen feature genes. The biomarkers associated with ferroptosis oxidation were screened via receiver operating characteristic curve analysis. We finally analyzed the competing endogenous RNAs regulatory network and potential drugs of biomarkers. We identified 1047 DEGs in RM. Then, 9 ferroptosis oxidation-related differentially expressed genes were obtained via venn diagram. Subsequently, 8 feature genes (PTPN6, GJA1, HMOX1, CPT1A, CREB3L1, SNCA, EPAS1, and TGM2) were identified via machine learning. Moreover, 4 biomarkers associated with ferroptosis oxidation, including PTPN6, GJA1, CPT1A, and CREB3L1, were screened via receiver operating characteristic curve analysis. We constructed the '227 long noncoding RNAs-4 mRNAs-36 microRNAs' network, in which hsa-miR-635 was associated with CREB3L1 and PTPN6. There were 11 drugs with therapeutic potential on 3 biomarkers associated with ferroptosis oxidation. We also observed higher expression of CPT1A and CREB3L1 in RM group compared to the healthy control group by quantitative real-time reverse transcription polymerase chain reaction. Overall, we obtained 4 biomarkers (PTPN6, GJA1, CPT1A, and CREB3L1) associated with ferroptosis and oxidative stress, which laid a theoretical foundation for the diagnosis and treatment of RM.

Examining the relationship between alterations in plasma cholesterol, vascular endothelin-1 levels, and the severity of sepsis in children: An observational study.

Considering the significant impact of total cholesterol (TC) and vascular endothelin-1 (ET-1) on children sepsis outcomes, this research aimed to explore the association between the levels of plasma cholesterol and vascular endothelin-1 and the severity of sepsis and evaluated its clinical implications. In this study, we examined 250 pediatric patients diagnosed with sepsis between February 2019 and April 2021, collecting data on their plasma levels of TC and ET-1. Depending on the observed outcomes, the participants were divided into 2 categories: a group with a positive prognosis (control group, n = 100) and a group with a negative prognosis (n = 50). We assessed the significance of plasma TC and ET-1 levels in forecasting the outcomes for these pediatric patients. Patients in the group with a poor prognosis experienced notably longer hospital stays and higher treatment expenses than those in the control group (P < .05). Within the first 24 hours of admission and again on days 3 and 7, the levels of ET-1 were significantly higher in the poor prognosis group, whereas plasma TC levels were notably lower in comparison to the control group (P < .05). A Spearman correlation analysis identified a significant correlation between the levels of plasma TC and ET-1 and the severity of sepsis among the children (P < .05). The diagnostic performance for the severity of sepsis in children, as measured by the area under the curve (AUC), was 0.805 for plasma TC, 0.777 for ET-1 levels, and 0.938 when both were combined. This investigation underscores a meaningful relationship between the levels of plasma TC and ET-1 in pediatric sepsis patients, suggesting these biomarkers are highly valuable in predicting patient outcomes. High levels of ET-1 and low levels of TC in these patients signify a grave condition and a poor prognosis.

A tabular data generation framework guided by downstream tasks optimization.

Recently, generative models have been gradually emerging into the extended dataset field, showcasing their advantages. However, when it comes to generating tabular data, these models often fail to satisfy the constraints of numerical columns, which cannot generate high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. Responding to the challenge, we propose a tabular data generation framework guided by downstream task optimization (TDGGD). It incorporates three indicators into each time step of diffusion generation, using gradient optimization to align the generated fake data. Unlike the traditional strategy of separating the downstream task model from the upstream data synthesis model, TDGGD ensures that the generated data has highly focused columns feasibility in upstream real tabular data. For downstream task, TDGGD strikes the utility of tabular data over solely pursuing statistical fidelity. Through extensive experiments conducted on real-world tables with explicit column constraints and tables without explicit column constraints, we have demonstrated that TDGGD ensures increasing data volume while enhancing prediction accuracy. To the best of our knowledge, this is the first instance of deploying downstream information into a diffusion model framework.

Correction: Assessing the dynamic impacts of non-pharmaceutical and pharmaceutical intervention measures on the containment results against COVID-19 in Ethiopia.

[This corrects the article DOI: 10.1371/journal.pone.0271231.].

Through virtual saturation mutagenesis and rational design for superior substrate conversion in engineered d-amino acid oxidase.

The d-amino acid oxidase (DAAO) is pivotal in obtaining optically pure l-glufosinate (l-PPT) by converting d-glufosinate (d-PPT) to its deamination product. We screened and designed a Rasamsonia emersonii DAAO (ReDAAO), making it more suitable for oxidizing d-PPT. Using Caver 3.0, we delineated three substrate binding pockets and, via alanine scanning, identified nearby key residues. Pinpointing key residues influencing activity, we applied virtual saturation mutagenesis (VSM), and experimentally validated mutants which reduced substrate binding energy. Analysis of positive mutants revealed elongated side-chain prevalence in substrate binding pocket periphery. Although computer-aided approaches can rapidly identify advantageous mutants and guide further design, the mutations obtained in the first round may not be suitable for combination with other advantageous mutations. Therefore, each round of combination requires reasonable iteration. Employing VSM-assisted screening multiple times and after four rounds of combining mutations, we ultimately obtained a mutant, N53V/F57Q/V94R/V242R, resulting in a mutant with a 5097% increase in enzyme activity compared to the wild type. It provides valuable insights into the structural determinants of enzyme activity and introduces a novel rational design procedure.

Identification of ASF1A and HJURP by global H3-H4 histone chaperone analysis as a prognostic two-gene model in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignancy with poor prognosis. Abnormal expression of H3-H4 histone chaperones has been identified in many cancers and holds promise as a biomarker for diagnosis and prognosis. However, systemic analysis of H3-H4 histone chaperones in HCC is still lacking. Here, we investigated the expression of 19 known H3-H4 histone chaperones in HCC. Integrated analysis of multiple public databases indicated that these chaperones are highly expressed in HCC tumor tissues, which was further verified by immunohistochemistry (IHC) staining in offline samples. Additionally, survival analysis suggested that HCC patients with upregulated H3-H4 histone chaperones have poor prognosis. Using LASSO and Cox regression, we constructed a two-gene model (ASF1A, HJURP) that accurately predicts prognosis in ICGC-LIRI and GEO HCC data, which was further validated in HCC tissue microarrays with follow-up information. GSEA revealed that HCCs in the high-risk group were associated with enhanced cell cycle progression and DNA replication. Intriguingly, HCCs in the high-risk group exhibited increased immune infiltration and sensitivity to immune checkpoint therapy (ICT). In summary, H3-H4 histone chaperones play a critical role in HCC progression, and the two-gene (ASF1A, HJURP) risk model is effective for predicting survival outcomes and sensitivity to immunotherapy for HCC patients.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. METHODS: This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. RESULTS: In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. CONCLUSION: This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

The Implication of Diabetes-Specialized Nurses in Aiming for the Better Treatment and Management of Patients with Diabetes Mellitus: A Brief Narrative Review.

Diabetes mellitus (DM) is regarded as one of the most critical public health challenges of the 21st century. It has evolved into a burgeoning epidemic since the last century, and today ranks among the major causes of mortality worldwide. Diabetes specialist nurses (DSNs) are central to good patient care and outcomes including confident self-care management. Evidence shows that DSNs are cost-effective, improve clinical outcomes, and reduce length of stay in hospital. In this brief narrative review, we aim to describe the roles of DSNs and their contribution in the treatment and management of patients with DM. This narrative review describes the importance of DSNs in healthcare practice, in the inpatient and outpatient departments, in the pediatrics department, in managing diabetic foot ulcers, in the treatment and management of gestational diabetes, in prescribing medications for DM and in diabetes self-management education on glycosylated hemoglobin, and cardiovascular risk factors. To conclude, DSNs have a crucial role in the treatment and management of patients with DM and its complications. DSNs have a great impact on diabetes therapy, and hence implementation of DSNs and nurse-led diabetic clinics might be beneficial for the health care system. Finally, having DSNs might significantly contribute to good healthcare practice and support. Even though DSNs are not available in several regions around the globe, and even though this post is still new to several health care institutions, the presence of DSNs recognized and certified by the various healthcare systems would be very useful.

Sustainable Cellulose-Derived Organic Photonic Gels with Tunable and Dynamic Structural Color.

Structural color is a fascinating optical phenomenon arising from intricate light-matter interactions. Biological structural colors from natural polymers are invaluable in biomimetic design and sustainable construction. Here, we report a renewable, abundant, and biodegradable cellulose-derived organic gel that generates stable cholesteric liquid crystal structures with vivid structural colors. We construct the chromatic gel using a 68 wt % hydroxypropyl cellulose (HPC) matrix, incorporating distinct polyethylene glycol (PEG) guest molecules. The PEGs contain peculiar end groups with tailored polarity, allowing for precise positioning on the HPC helical backbone through electrostatic repulsion between the PEG and HPC chains. This preserves the HPC's chiral nematic phase without being disrupted. We demonstrate that the PEGs' polarity tunes the HPC gel's reflective color. Additionally, gels with variable polarities are highly sensitive to temperature, pressure, and stretching, resulting in rapid, continuous, and reversible color changes. These exceptional dynamic traits establish the chiral nematic gel as an outstanding candidate for next-generation applications across displays, wearables, flexible electronics, health monitoring, and multifunctional sensors.

Li Dynamics in Mixed Ionic-Electronic Conducting Interlayer of All-Solid-State Li-metal Batteries.

Lithium-metal (Li0) anodes potentially enable all-solid-state batteries with high energy density. However, it shows incompatibility with sulfide solid-state electrolytes (SEs). One strategy is introducing an interlayer, generally made of a mixed ionic-electronic conductor (MIEC). Yet, how Li behaves within MIEC remains unknown. Herein, we investigated the Li dynamics in a graphite interlayer, a typical MIEC, by using operando neutron imaging and Raman spectroscopy. This study revealed that intercalation-extrusion-dominated mechanochemical reactions during cell assembly transform the graphite into a Li-graphite interlayer consisting of SE, Li0, and graphite-intercalation compounds. During charging, Li+ preferentially deposited at the Li-graphite|SE interface. Upon further plating, Li0-dendrites formed, inducing short circuits and the reverse migration of Li0. Modeling indicates the interface has the lowest nucleation barrier, governing lithium transport paths. Our study elucidates intricate mechano-chemo-electrochemical processes in mixed conducting interlayers. The behavior of Li+ and Li0 in the interlayer is governed by multiple competing factors.

Highly Ionic Conductive, Stretchable, and Tough Ionogel for Flexible Solid-State Supercapacitor.

The increasing demand for wearable electronics calls for advanced energy storage solutions that integrate high  electrochemical performances and mechanical robustness. Ionogel is a promising candidate due to its stretchability combined with high ionic conductivity. However, simultaneously optimizing both the electrochemical and mechanical performance of ionogels remains a challenge. This paper reports a tough and highly ion-conductive ionogel through ion impregnation and solvent exchange. The fabricated ionogel consists of double interpenetrating networks of long polymer chains that provide high stretchability. The polymer chains are crosslinked by hydrogen bonds that induce large energy dissipation for enhanced toughness. The resultant ionogel possesses mechanical stretchability of 26, tensile strength of 1.34 MPa, and fracture toughness of 4175 J m-2. Meanwhile, due to the high ion concentrations and ion mobility in the gel, a high ionic conductivity of 3.18 S m-1 at room temperature is achieved. A supercapacitor of this ionogel sandwiched with porous fiber electrodes provides remarkable areal capacitance (615 mF cm-2 at 1 mA cm-2), energy density (341.7 µWh cm-2 at 1 mA cm-2), and power density (20 mW cm-2 at 10 mA cm-2), offering significant advantages in applications where high efficiency, compact size, and rapid energy delivery are crucial, such as flexible and wearable electronics.

Structural Characterization and Anti-Osteoporosis Effects of a Novel Sialoglycopeptide from Tuna Eggs.

Several sialoglycopeptides were isolated from several fish eggs and exerted anti-osteoporosis effects. However, few papers have explored sialoglycopeptide from tuna eggs (T-ES). Here, a novel T-ES was prepared through extraction with KCl solution and subsequent enzymolysis. Pure T-ES was obtained through DEAE-Sepharose ion exchange chromatography and sephacryl S-300 gel filtration chromatography. The T-ES was composed of 14.07% protein, 73.54% hexose, and 8.28% Neu5Ac, with a molecular weight of 9481 Da. The backbone carbohydrate in the T-ES was →4)-ß-D-GlcN-(1→3)-α-D-GalN-(1→3)-ß-D-Glc-(1→2)-α-D-Gal-(1→2)-α-D-Gal-(1→3)-α-D-Man-(1→, with two branches of ß-D-GlcN-(1→ and α-D-GalN-(1→ linking at o-4 in →2,4)-α-D-Gal-(1→. Neu5Ac in the T-ES was linked to the branch of α-D-GlcN-(1→. A peptide chain, Ala-Asp-Asn-Lys-Ser*-Met-Ile that was connected to the carbohydrate chain through O-glycosylation at the -OH of serine. Furthermore, in vitro data revealed that T-ES could remarkably enhance bone density, bone biomechanical properties, and bone microstructure in SAMP mice. The T-ES elevated serum osteogenesis-related markers and reduced bone resorption-related markers in serum and urine. The present study's results demonstrated that T-ES, a novel sialoglycopeptide, showed significant anti-osteoporosis effects, which will accelerate the utilization of T-ES as an alternative marine drug or functional food for anti-osteoporosis.

Lithiation Gradients and Tortuosity Factors in Thick NMC111-Argyrodite Solid-State Cathodes.

Achieving high energy density in all-solid-state lithium batteries will require the design of thick cathodes, and these will need to operate reversibly under normal use conditions. We use high-energy depth-profiling X-ray diffraction to measure the localized lithium content of Li1-xNi1/3Mn1/3Co1/3O2 (NMC111) through the thickness of 110 µm thick composite cathodes. The composite cathodes consisted of NMC111 of varying mass loadings mixed with argyrodite solid electrolyte Li6PS5Cl (LPSC). During cycling at C/10, substantial lithiation gradients developed, and varying the NMC111 loading altered the nature of these gradients. Microstructural analysis and cathode modeling showed this was due to high tortuosities in the cathodes. This was particularly true in the solid electrolyte phase, which experienced a marked increase in tortuosity factor during the initial charge. Our results demonstrate that current distributions are observed in sulfide-based composites and that these will be an important consideration for practical design of all-solid-state batteries.

Enhancing Lithium Stripping Efficiency in Anode-Free Solid-State Batteries through Self-Regulated Internal Pressure.

Anode-free all-solid-state lithium metal batteries (ASLMBs) promise high energy density and safety but suffer from a low initial Coulombic efficiency and rapid capacity decay, especially at high cathode loadings. Using operando techniques, we concluded these issues were related to interfacial contact loss during lithium stripping. To address this, we introduce a conductive carbon felt elastic layer that self-adjusts the pressure at the anode side, ensuring consistent lithium-solid electrolyte contact. This layer simultaneously provides electronic conduction and releases the plating pressure. Consequently, the first Coulombic efficiency dramatically increases from 58.4% to 83.7% along with a >10-fold improvement in cycling stability. Overall, this study reveals an approach for enhancing anode-free ASLMB performance and longevity by mitigating lithium stripping inefficiency through self-adjusting interfacial pressure enabled by a conductive elastic interlayer.

Enhancement of the substrate specificity of D-amino acid oxidase based on tunnel-pocket engineering.

D-Amino acid oxidase (DAAO) selectively catalyzes the oxidative deamination of  D-amino acids, making it one of the most promising routes for synthesizing optically pure  L-amino acids, including  L-phosphinothricin ( L-PPT), a chiral herbicide with significant market potential. However, the native DAAOs that have been reported have low activity against unnatural acid substrate  D-PPT. Herein, we designed and screened a DAAO from Rhodotorula taiwanensis (RtwDAAO), and improved its catalytic potential toward  D-PPT through protein engineering. A semirational design approach was employed to create a mutation library based on the tunnel-pocket engineering. After three rounds of iterative saturation mutagenesis, the optimal variant M3rd -SHVG was obtained, exhibiting a >2000-fold increase in relative activity. The kinetic parameters showed that M3rd -SHVG improved the substrate binding affinity and turnover number. This is the optimal parameter reported so far. Further, molecular dynamics simulation revealed that the M3rd -SHVG reshapes the tunnel-pocket and corrects the direction of enzyme-substrate binding, allowing efficiently catalyze unnatural substrates. Our strategy demonstrates that the redesign of tunnel-pockets is effective in improving the activity and kinetic efficiency of DAAO, which provides a valuable reference for enzymatic catalysis. With the M3rd -SHVG as biocatalyst, 500 mM D, L-PPT was completely converted and the yield reached 98%. The results laid the foundation for further industrial production.

Efficient Production of 3-Amino-2-Hydroxy Acetophenone by Multi-Enzyme Biosynthesis.

We developed a synthetic route for producing 3-amino-2-hydroxy acetophenone (3AHAP) from m-nitroacetophenone (3NAP) using an in vitro approach. Various reaction systems were evaluated, and a direct reaction method with crude enzyme and supersaturated substrates for optimal catalytic efficiency was chosen. The reaction system included three enzymes and was enhanced by adjusting enzyme molar ratios and optimizing ribosomal binding sites. We performed substrate docking and alanine scanning to identify key sites in the enzymes nitrobenzene nitroreductase (nbzA) and hydroxylaminobenzene mutase (habA). The optimal mutant was obtained through site-directed mutagenesis, and incorporated into the reaction system, resulting in increased product yield. After optimization, the yield of 3AHAP increased from 75 mg/L to 580 mg/L within 5 hours, the highest reported yield using biosynthesis. This work provides a promising strategy for the efficient and sustainable production of 3AHAP, which has critical applications in the chemical and pharmaceutical industries.

Structural Characterization and Effects on Insulin Resistance of a Novel Chondroitin Sulfate from Halaelurus burgeri Skin.

Several studies have isolated chondroitin sulphate (CHS) from sharks' jaws or cartilage. However, there has been little research on CHS from shark skin. In the present study, we extracted a novel CHS from Halaelurus burgeri skin, which has a novel chemical structure and bioactivity on improvement in insulin resistance. Results using Fourier transform-infrared spectroscopy (FT-IR), 1H-nuclear magnetic resonance spectroscopy (1H-NMR), and methylation analysis showed that the structure of the CHS was [4)-ß-D-GlcpA-(1→3)-ß-D-GlcpNAc-(1→]n with 17.40% of sulfate group concentration. Its molecular weight was 238.35 kDa, and the yield was 17.81%. Experiments on animals showed that this CHS could dramatically decrease body weight, reduce blood glucose and insulin levels, lower lipid concentrations both in the serum and the liver, improve glucose tolerance and insulin sensitivity, and regulate serum-inflammatory factors. These results demonstrated that the CHS from H. burgeri skin has a positive effect in reducing insulin resistance because of its novel structure, which provides a significant implication for the polysaccharide as a functional food.