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ObjectivesThis study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value.MethodsFor this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients.ResultsThe radiomics signature comprised eight LN-status–related features and showed significant association with LN metastasis in both cohorts (p < 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (both p < 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival.ConclusionsOur radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making.Key Points• The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup.• Prognosis of high-risk patients remains dismal after curative-intent resection.• The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.

Lip language is an effective method of voice-off communication in daily life for people with vocal cord lesions and laryngeal and lingual injuries without occupying the hands. Collection and interpretation of lip language is challenging. Here, we propose the concept of a novel lip-language decoding system with self-powered, low-cost, contact and flexible triboelectric sensors and a well-trained dilated recurrent neural network model based on prototype learning. The structural principle and electrical properties of the flexible sensors are measured and analysed. Lip motions for selected vowels, words, phrases, silent speech and voice speech are collected and compared. The prototype learning model reaches a test accuracy of 94.5% in training 20 classes with 100 samples each. The applications, such as identity recognition to unlock a gate, directional control of a toy car and lip-motion to speech conversion, work well and demonstrate great feasibility and potential. Our work presents a promising way to help people lacking a voice live a convenient life with barrier-free communication and boost their happiness, enriches the diversity of lip-language translation systems and will have potential value in many applications. Lip-language decoding systems are a promising technology to help people lacking a voice live a convenient life with barrier-free communication. Here, authors propose a concept of such system integrating self-powered triboelectric sensors and a well-trained dilated RNN model based on prototype learning.

We aimed to build a deep learning-based pathomics model to predict the early recurrence of non-muscle-infiltrating bladder cancer (NMIBC) in this work. A total of 147 patients from Xuzhou Central Hospital were enrolled as the training cohort, and 63 patients from Suqian Affiliated Hospital of Xuzhou Medical University were enrolled as the test cohort. Based on two consecutive phases of patch level prediction and WSI-level predictione, we built a pathomics model, with the initial model developed in the training cohort and subjected to transfer learning, and then the test cohort was validated for generalization. The features extracted from the visualization model were used for model interpretation. After migration learning, the area under the receiver operating characteristic curve for the deep learning-based pathomics model in the test cohort was 0.860 (95% CI 0.752–0.969), with good agreement between the migration training cohort and the test cohort in predicting recurrence, and the predicted values matched well with the observed values, with p values of 0.667766 and 0.140233 for the Hosmer–Lemeshow test, respectively. The good clinical application was observed using a decision curve analysis method. We developed a deep learning-based pathomics model showed promising performance in predicting recurrence within one year in NMIBC patients. Including 10 state prediction NMIBC recurrence group pathology features be visualized, which may be used to facilitate personalized management of NMIBC patients to avoid ineffective or unnecessary treatment for the benefit of patients.

Micro-nano biorobots based on bacteria have demonstrated great potential for tumor diagnosis and treatment. The bacterial gene expression and drug release should be spatiotemporally controlled to avoid drug release in healthy tissues and undesired toxicity. Herein, we describe an alternating magnetic field-manipulated tumor-homing bacteria developed by genetically modifying engineered Escherichia coli with Fe 3 O 4 @lipid nanocomposites. After accumulating in orthotopic colon tumors in female mice, the paramagnetic Fe 3 O 4 nanoparticles enable the engineered bacteria to receive and convert magnetic signals into heat, thereby initiating expression of lysis proteins under the control of a heat-sensitive promoter. The engineered bacteria then lyse, releasing its anti-CD47 nanobody cargo, that is pre-expressed and within the bacteria. The robust immunogenicity of bacterial lysate cooperates with anti-CD47 nanobody to activate both innate and adaptive immune responses, generating robust antitumor effects against not only orthotopic colon tumors but also distal tumors in female mice. The magnetically engineered bacteria also enable the constant magnetic field-controlled motion for enhanced tumor targeting and increased therapeutic efficacy. Thus, the gene expression and drug release behavior of tumor-homing bacteria can be spatiotemporally manipulated in vivo by a magnetic field, achieving tumor-specific CD47 blockage and precision tumor immunotherapy. Several strategies have been employed to enhance the tumor-targeting and anti-cancer properties of engineered bacteria. Here the authors describe the design of alternating magnetic field-manipulated bacteria engineered to release an anti-CD47 nanobody, promoting anti-tumor immune response in preclinical cancer models.

When operating within the environments rich with sodium chloride, steel bars of reinforced concrete structures are often subject to corrosion caused by surrounding erosive materials, and the associated rust expansion force due to corrosion takes a critical role in determining the durability of relevant reinforced concrete structures. By investigating the corrosion course of steel reinforcement with theory of elasticity, a numerical rust expansion model is established for the moment of concrete surface rupture based on non-uniform sin function. Cuboid reinforced concrete specimen with squared cross sections is tested to analyze the rust expansion when concrete cracks due to corrosive forces. The utility of the established expansion model is validated by numerical simulation with Abaqus through the comparison between the associated outcomes. The impacts of steel bar diameter and concrete cover thickness on the magnitude of rust expansion force are discussed.

Since May 2022, cases of monkeypox, a zoonotic disease caused by the monkeypox virus (MPXV), have been increasingly reported worldwide. There are, however, no proven therapies or vaccines available for monkeypox. In this study, several multi-epitope vaccines were designed against the MPXV using immunoinformatics approaches. Three target proteins, A35R and B6R, enveloped virion (EV) form-derived antigens, and H3L, expressed on the mature virion (MV) form, were selected for epitope identification. The shortlisted epitopes were fused with appropriate adjuvants and linkers to vaccine candidates. The biophysical andbiochemical features of vaccine candidates were evaluated. The Molecular docking and molecular dynamics(MD) simulation were run to understand the binding mode and binding stability between the vaccines and Toll-like receptors (TLRs) and major histocompatibility complexes (MHCs). The immunogenicity of the designed vaccines was evaluated via immune simulation. Five vaccine constructs (MPXV-1-5) were formed. After the evaluation of various immunological and physicochemical parameters, MPXV-2 and MPXV-5 were selected for further analysis. The results of molecular docking showed that the MPXV-2 and MPXV-5 had a stronger affinity to TLRs (TLR2 and TLR4) and MHC (HLA-A*02:01 and HLA-DRB1*02:01) molecules, and the analyses of molecular dynamics (MD) simulation have further confirmed the strong binding stability of MPXV-2 and MPXV-5 with TLRs and MHC molecules. The results of the immune simulation indicated that both MPXV-2 and MPXV-5 could effectively induce robust protective immune responses in the human body. The MPXV-2 and MPXV-5 have good efficacy against the MPXV in theory, but further studies are required to validate their safety and efficacy.

Transition-metal-catalyzed alkene hydrosilylation is one of the most important homogeneous catalytic reactions, and the development of methods that use base metals, especially iron, as catalysts for this transformation is a growing area of research. However, the limited number of ligand scaffolds applicable for base-metal-catalyzed alkene hydrosilylation has seriously hindered advances in this area. Herein, we report the use of 1,10-phenanthroline ligands in base-metal catalysts for alkene hydrosilylation. In particular, iron catalysts with 2,9-diaryl-1,10-phenanthroline ligands exhibit unexpected reactivity and selectivity for hydrosilylation of alkenes, including unique benzylic selectivity with internal alkenes, Markovnikov selectivity with terminal styrenes and 1,3-dienes, and excellent activity toward aliphatic terminal alkenes. According to the mechanistic studies, the unusual benzylic selectivity of this hydrosilylation initiates from π – π interaction between the phenyl of the alkene and the phenanthroline of the ligand. This ligand scaffold and its unique catalytic model will open possibilities for base-metal-catalyzed hydrosilylation reactions. Hydrosilylation of alkenes poses substantial challenges in terms of regioselectivity. Here, the authors report iron complexes with 1,10-phenantroline ligand scaffolds which  display benzylic selectivity in the hydrosilylation of internal alkenes and Markovnikov selectivity with terminal styrenes and 1,3-dienes.

In view of the influence of variability of low-frequency noise frequency on noise prevention in real life, we present a novel two-dimensional tunable phononic crystal plate which is consisted of lead columns deposited in a silicone rubber plate with periodic holes and calculate its bandgap characteristics by finite element method. The low-frequency bandgap mechanism of the designed model is discussed simultaneously. Accordingly, the influence of geometric parameters of the phononic crystal plate on the bandgap characteristics is analyzed and the bandgap adjustability under prestretch strain is further studied. Results show that the new designed phononic crystal plate has lower bandgap starting frequency and wider bandwidth than the traditional single-sided structure, which is due to the coupling between the resonance mode of the scatterer and the long traveling wave in the matrix with the introduction of periodic holes. Applying prestretch strain to the matrix can realize active realtime control of low-frequency bandgap under slight deformation and broaden the low-frequency bandgap, which can be explained as the multiple bands tend to be flattened due to the localization degree of unit cell vibration increases with the rise of prestrain. The presented structure improves the realtime adjustability of sound isolation and vibration reduction frequency for phononic crystal in complex acoustic vibration environments.

• Molybdenum (Mo) is an essential micronutrient for most living organisms, including humans. Cereals such as rice (Oryza sativa) are the major dietary source of Mo. However, little is known about the genetic basis of the variation in Mo content in rice grain. • We mapped a quantitative trait locus (QTL) qGMo8 that controls Mo accumulation in rice grain by using a recombinant inbred line population and a backcross introgression line population. • We identified a molybdate transporter, OsMOT1;1, as the causal gene for this QTL. OsMOT1;1 exhibits transport activity for molybdate, but not sulfate, when heterogeneously expressed in yeast cells. OsMOT1;1 is mainly expressed in roots and is involved in the uptake and translocation of molybdate under molybdate-limited condition. Knockdown of OsMOT1;1 results in less Mo being translocated to shoots, lower Mo concentration in grains and higher sensitivity to Mo deficiency. We reveal that the natural variation of Mo concentration in rice grains is attributed to the variable expression of OsMOT1;1 due to sequence variation in its promoter. • Identification of natural allelic variation in OsMOT1;1 may facilitate the development of rice varieties with Mo-enriched grain for dietary needs and improve Mo nutrition of rice on Modeficient soils.

Synthesis of inorganic nanomaterials such as metal nanoparticles (MNPs) using various biological entities as smart nanofactories has emerged as one of the foremost scientific endeavors in recent years. The biosynthesis process is environmentally friendly, cost-effective and easy to be scaled up, and can also bring neat features to products such as high dispersity and biocompatibility. However, the biomanufacturing of inorganic nanomaterials is still at the trial-and-error stage due to the lack of understanding for underlying mechanism. Dissimilatory metal reduction bacteria, especially Shewanella and Geobacter species, possess peculiar extracellular electron transfer (EET) features, through which the bacteria can pump electrons out of their cells to drive extracellular reduction reactions, and have thus exhibited distinct advantages in controllable and tailorable fabrication of inorganic nanomaterials including MNPs and graphene. Our aim is to present a critical review of recent state-of-the-art advances in inorganic biosynthesis methodologies based on bacterial EET using Shewanella and Geobacter species as typical strains. We begin with a brief introduction about bacterial EET mechanism, followed by reviewing key examples from literatures that exemplify the powerful activities of EET-enabled biosynthesis routes towards the production of a series of inorganic nanomaterials and place a special emphasis on rationally tailoring the structures and properties of products through the fine control of EET pathways. The application prospects of biogenic nanomaterials are then highlighted in multiple fields of (bio-) energy conversion, remediation of organic pollutants and toxic metals, and biomedicine. A summary and outlook are given with discussion on challenges of bio-manufacturing with well-defined controllability.