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Abstract Engineered nanoparticles have emerged as new types of materials for a wide range of applications from therapeutics to energy. Still, fabricating nanomaterials presenting complex inner morphologies and shapes in a simple manner remains a great challenge. Herein, we report the template‐free one‐pot continuous gradient nanoprecipitation of different types of non‐compatible polymers to spontaneously form nanostructured particles. The continuous addition of antisolvent induces precipitation and (re)organization of polymer chains at the forming particle interface, ultimately and naturally developing complex inner morphologies and shapes while particle grows. This low‐energy‐cost bottom‐up assembly approach applies to various functional polymers, possibly embedded with metal nanoparticles, for continuous growth into well‐organized nanoparticles. UV crosslinking of the particles and core removal allows both confirming the building process and leading to hollow or multivoid nanomaterials.

2,5‐bis(hydroxymethyl)furan (BHMF) derived from 5‐Hydroxymethylfurfural (HMF) through a hydrogenation process has extensive applications in the production of resins, polymers, and artificial fibers. However, screening out the candidate and then modulating the active site to optimize the catalyst for high yield of BHMF are currently insufficient. In this study, Gibbs free energy diagrams of the reduction of HMF on 13 metals were presented, along with the identification of the rate‐determining step (RDS) with the highest reaction barrier for each metal. We attempted to construct a volcano plot for HMFRR reaction. Additionally, a strategy was proposed to adjust the reaction barriers of RDS by combining two appropriate metals. Further experiments confirmed that Pb with the lowest energy barrier exhibited the highest HMF conversion (BHMF selectivity) among single metals. The modified catalyst by doping Ag on Pb, further boosted the HMF conversion (BHMF selectivity) from 42.1 % (59.4 %) to 80.8 % (80.9 %), respectively. These results provide an approach to rationally design and construct the catalyst system for efficient conversion of HMF. [ABSTRACT FROM AUTHOR]

Carbon quantum dots (CQDs), as a novel category of zero-dimensional fluorescent materials, have garnered significant interest because of their distinctive optical characteristics, low toxicity, and easy surface modification. However, most techniques used to manufacture CQDs involve costly raw materials, intricate production processes, or hazardous chemicals. In this study, innovatively, low-cost, renewable, and green biomass raw material, camphor tree waste leaves, were employed to prepare CQDs through a simple one-step hydrothermal approach under relatively mild conditions. The results indicate that the prepared CQDs had a size of around 1.6 nm and a thickness of one to six layers of graphene. They can function as a reliable fluorescent probe for detecting Fe3+, exhibiting a limit of detection (LOD) of 2.03 μM. The full utilization of renewable resources, combined with relatively simple hydrothermal processing, enables the environmentally friendly and economical production of CQDs that have broad prospects for metal ion sensing applications. [ABSTRACT FROM AUTHOR]

A nitrogen-fixing strain designated SG130T was isolated from paddy soil in Fujian Province, China. Strain SG130T was Gram-staining-negative, rod-shaped, and strictly anaerobic. Strain SG130T showed the highest 16S rRNA gene sequence similarities with the type strains Dendrosporobacter quercicolus DSM 1736T (91.7%), Anaeroarcus burkinensis DSM 6283T (91.0%) and Anaerospora hongkongensis HKU 15T (90.9%). Furthermore, the phylogenetic and phylogenomic analysis also suggested strain SG130T clustered with members of the family Sporomusaceae and was distinguished from other genera within this family. Growth of strain SG130T was observed at 25–45 °C (optimum 30 °C), pH 6.0–9.5 (optimum 7.0) and 0–1% (w/v) NaCl (optimum 0.1%). The quinones were Q-8 and Q-9. The polar lipids were phosphatidylserine (PS), phosphatidylethanolamine (PE), glycolipid (GL), phospholipid (PL) and an unidentified lipid (UL). The major fatty acids (> 10%) were iso-C13:0 3OH (26.6%), iso-C17:1 (15.6%) and iso-C15:1 F (11.4%). The genomic DNA G + C content was 50.7%. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between strain SG130T and the most closely related type strain D. quercicolus DSM 1736T (ANI 68.0% and dDDH 20.3%) were both below the cut-off level for species delineation. The average amino acid identity (AAI) between strain SG130T and the most closely related type strain D. quercicolus DSM 1736T was 63.2%, which was below the cut-off value for bacterial genus delineation (65%). Strain SG130T possessed core genes (nifHDK) involved in nitrogen fixation, and nitrogenase activity (106.38 μmol C2H4 g−1 protein h−1) was examined using the acetylene reduction assay. Based on the above results, strain SG130T is confirmed to represent a novel genus of the family Sporomusaceae, for which the name Azotosporobacter soli gen. nov., sp. nov. is proposed. The type strain is SG130T (= GDMCC 1.3312T = JCM 35641T). [ABSTRACT FROM AUTHOR]

In this research, two novel Fe(III)-reducing bacteria, SG10T and SG198T of genus Geothrix, were isolated from the rice field of Fujian Agriculture and Forestry University in Fuzhou, Fujian Province, China. Strains SG10T and SG198T were strictly anaerobic, rod-shaped and Gram-stain-negative. The two novel strains exhibited iron reduction ability, utilizing various single organic acid as the elector donor and Fe(III) as a terminal electron acceptor. Strains SG10T and SG198T showed the highest 16S rRNA sequences similarities to the type strains of Geothrix oryzisoli SG189T (99.0–99.5%) and Geothrix paludis SG195T (99.0–99.7%), respectively. The phylogenetic trees based on the 16S rRNA gene and genome 120 conserved core genes showed that strains SG10T and SG198T belong to the genus Geothrix. Average nucleotide identity (ANI) and digital DNA–DNA hybridization (dDDH) values between the phylogenetic neighbors and the two isolated strains were 86.1–94.3% and 30.7–59.5%, respectively. The major fatty acids were iso-C15:0, anteiso-C15:0, C16:0 and iso-C13:0 3OH, and MK-8 was the main respiratory quinone. According to above results, the two strains were assigned to the genus Geothrix with the names Geothrix campi sp. nov. and Geothrix mesophila sp. nov. Type strains are SG10T (= GDMCC 1.3406 T = JCM 39331 T) and SG198T (= GDMCC 62910 T = KCTC 25635 T), respectively. [ABSTRACT FROM AUTHOR]

Alkali-activated concrete (AAC), produced from industrial by-products like fly ash and slag, offers a promising alternative to traditional Portland cement concrete by significantly reducing carbon emissions. Yet, the inherent variability in AAC formulations presents a challenge for accurately predicting its compressive strength using conventional approaches. To address this, we leverage machine learning (ML) techniques, which enable more precise strength predictions based on a combination of material properties and cement mix design parameters. In this study, we curated an extensive dataset comprising 1756 unique AAC mixtures to support robust ML-based modeling. Four distinct input variable schemes were devised to identify the optimal predictor set, and a comparative analysis was performed to evaluate their effectiveness. After this, we investigated the performance of several popular ML algorithms, including random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression trees (GBRTs), and extreme gradient boosting (XGBoost). Among these, the XGBoost model consistently outperformed its counterparts. To further enhance the predictive accuracy of the XGBoost model, we applied four state-of-the-art optimization techniques: the Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), beetle antennae search (BAS), and Bayesian optimization (BO). The optimized XGBoost model delivered superior performance, achieving a remarkable coefficient of determination (R2) of 0.99 on the training set and 0.94 across the entire dataset. Finally, we employed SHapely Additive exPlanations (SHAP) to imbue the optimized model with interpretability, enabling deeper insights into the complex relationships governing AAC formulations. Through the lens of ML, we highlight the benefits of the multi-faceted synergistic approach for AAC strength prediction, which combines careful input parameter selection, optimal hyperparameter tuning, and enhanced model interpretability. This integrated strategy improves both the robustness and scalability of the model, offering a clear and reliable prediction of AAC performance. [ABSTRACT FROM AUTHOR]

Background: Topoisomerase II α(TOP2A) is usually highly expressed in rapidly proliferating cells, and its expression is regulated by cell cycle. The relationship between TOP2A and oral squamous cell carcinoma (OSCC) needs further study. Methods: TOP2A immunoreactivity was analyzed using immunohistochemical (IHC) staining analysis in specimens from 20 OSCC patients. Based on the high-throughput sequencing and gene microarray database, the expression of TOP2A mRNA in OSCC was calculated and its ability to identify OSCC tissues was evaluated by diagnostic analysis. CRISPR screen was used to select the genes necessary for OSCC cell growth, and the gene set was analyzed for function enrichment. Single-cell RNA sequencing analysis was conducted to evaluate the expression level of TOP2A mRNA in OSCC cells. Results: Compared with normal oral tissues, the expression of TOP2A protein was up-regulated in OSCC tissues. A total of 1240 OSCC and 428 non-OSCC oral tissue samples were included based on high-throughput dataset retrieval, and it was confirmed that TOP2A mRNA was highly expressed in OSCC tissues [SMD = 1.51 (95% CI 0.94–2.07), sROC AUC = 0.96 (95% CI 0.94–0.98)]. As an essential gene for OSCC cell growth, single-cell RNA sequencing data also confirmed that TOP2A mRNA expression was up-regulated in OSCC cells. Conclusion: The up-regulation of TOP2A may play a pivotal role in OSCC. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate how the impact of preoperative sarcopenia and inflammatory markers for laryngeal cancer patients and develop a new scoring system to predict their prognosis. Materials and methods: Patients who underwent laryngectomy for laryngeal cancer (LC) from December 2015 to December 2020 at the Second Affiliated Hospital of Fujian Medical University were included. Independent prognostic factors were determined using univariate and multivariate analyses. A new scoring system (SFAR) was established based on FAR and preoperative sarcopenia, and statistically analyzed. Results: 198 cases included in this study that met the admission criteria. Multivariate analysis shown that preoperative sarcopenia, pTNM stage, and FAR were independent prognostic factors for laryngeal cancer. Based on these three indicators, we developed the SFAR scoring system. Multivariate analysis showed that SFAR was an independent predictor of laryngeal cancer (p < 0.001). SFAR was then incorporated into a prognostic model that included T-stage and N-stage, and a column-line graph was generated to accurately predict its survival. Conclusion: Systemic inflammation and sarcopenia are significantly associated with postoperative prognosis in laryngeal cancer. A new scoring system (SFAR) had implications for improving the prognosis of patients undergoing surgery for laryngeal cancer. [ABSTRACT FROM AUTHOR]