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Intercalation has attracted considerable attention due to its extensive ability to modify the electronic, optical, and magnetic properties of two-dimensional (2D) layered nanomaterials. Typically, dispersed atoms or molecules are inserted into the van der Waals gap of the 2D materials. Recently, Guo et al. experimentally reported the novel VS2–VS superlattice, where the intercalation takes the form of atomic chain arrays. In this study, we employed the first-principles calculations based on density functional theory to investigate a series of analogous 2D MX2–MX–MX2 nanomaterials, which, consisting of 2D transition metal dichalcogenide bilayers, intercalated with a one-dimensional transition metal chalcogenide MX chain array, forming a hotdog-like structure. Some of the 2D MX2–MX–MX2 are thermally and dynamically stable, suggesting their potential for experimental fabrication similar to VS2–VS–VS2. MnS2–MnS–MnS2 and MnSe2–MnSe–MnSe2 have been found to exhibit ferromagnetic half-metallic properties. In addition, VSe2–VSe–VSe2, CrS2–CrS–CrS2, and CrSe2–CrSe–CrSe2 have been found to be thermally and dynamically stable. Under appropriate external stress, doping, or bias, they could become ferromagnetic half-metals, revealing their potential for spintronic applications. [ABSTRACT FROM AUTHOR]

“All in one” organic–inorganic nanocomposites with high biocompatibility and excellent physicochemical properties have recently attracted special attention in cancer imaging and therapy. Combination of organic and inorganic materials confers the nanocomposites with superior biocompatibility and biodegradability of organic materials, as well as magnetic, mechanical, and optical properties of inorganic materials. Increased endeavors have been made to produce diverse organic–inorganic nanocomposites and investigate their potential applications in cancer treatment. Thus, a systematic review of research progresses of diverse organic–inorganic nanocomposites in cancer imaging and therapy is indispensable. Following a brief overview of nanocomposites synthesis, classification, and functionalization, the current review is focused on comprehensively summarizing representatives of both organic–inorganic nanoscale nanocomposites (including organic-silica, organic-carbon, organic-quantum dots, organic-platinum family metals, organic-gold, organic metal oxides, and other nanocomposites) and organic–inorganic molecular nanocomposites (including metal-organic frameworks, organosilica nanoparticles, and amorphous metal coordination polymer particles), and further analyzing their working mechanism in cancer imaging and therapy. Finally, the challenges and future perspectives of organic–inorganic nanocomposites are addressed for promoting their developments and clinical application in cancer treatment.

The emergence of microfluidic devices integrated with nanostructures enables highly efficient, flexible and controllable biosensing, among which zinc oxide (ZnO) nanostructure-based fluorescence detection has been demonstrated to be a promising methodology due to its high electrical point and unique fluorescence enhancement properties. The optimization of microfluidic synthesis of ZnO nanostructures for biosensing on chip has been in demand due to its low cost and high efficiency, but still the flow-induced growth of ZnO nanostructures is not extensively studied. Here, we report a simple and versatile strategy that could manipulate the local flow field by creating periodically arranged micropillars within a straight microchannel. We have explored the effects of perfusion speed and flow direction of seed solution, localized flow variation of growth solution and growth time on the morphology of nanostructures. This provided a comprehensive understanding which governs nanostructure fabrication controlled by flow. The results demonstrated that localized flow in microfluidic devices was essential for the initiation and growth of zinc oxide crystals, enabling precise control over their properties and morphology. Furthermore, a model protein was used to demonstrate the intrinsic fluorescence enhancement of ZnO nanostructures as an example to reveal the morphology-related enhancement properties. [ABSTRACT FROM AUTHOR]

Background: Gut microbiota is involved in the development of psoriatic arthritis (PsA), but until now, there has been a lack of understanding of the PsA host–bacteria interaction. Objectives: To reveal the labels of gut microbiota in PsA patients and the species and functions related to disease activity. Design: Observational research (cross-sectional) with an exploratory nature. Methods: Metagenomics sequencing was used to analyze stool samples from 20 treatment-naïve PsA patients and 10 age-matched healthy individuals. All samples were qualified for subsequent analysis. Results: Compared with the healthy group, α-diversity was reduced in the PsA group, and β-diversity could distinguish the two groups. Two bacteria with high abundance and correlation with PsA disease activity were identified, Bacteroides sp. 3_1_19 and Blautia AF 14-40. In different functions, K07114 (calcium-activated chloride channel (CaCC) homolog) showed a positive correlation with PsA disease activity (disease activity in psoriatic arthritis, DAPSA) and Tet32 (an antibiotic-resistant gene), and carbohydrate-binding module family 50 was negatively correlated with erythrocyte sedimentation rate. A bacterial co-expression network associated with DAPSA was constructed. The network was centered on the bacteria in the Bacteroides genus, which formed a closely related network and were positively correlated with DAPSA. As another core of the network, K07114 was closely related to multiple bacteria in the Bacteroides genus and is also positively correlated with disease activity. Conclusion: The network composed of Bacteroides is associated with PsA disease activity, and its therapeutic value needs to be further explored. CaCCs may be a key channel for the interaction between Bacteroides and PsA-host. [ABSTRACT FROM AUTHOR]

Background: Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18F‐FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi‐center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR‐related research involved model visualization and interpretation, and multi‐center trial. Purpose: To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi‐center 18F‐FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD. Methods: Baseline 18F‐FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross‐combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis. Results: STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort). Conclusions: Explainable machine learning models combined with radiomics features extracted from multi‐center/scanner 18F‐FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD. [ABSTRACT FROM AUTHOR]