Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors.

The association between immune checkpoint inhibitors (ICIs) and immune gene networks in squamous lung cancer (LUSC) and lung adenocarcinoma (LUAD) was studied. Immune gene networks were constructed using RNA-seq data from the gene expression omnibus (GEO) database. Datasets with more than 10 samples of normal control and tumor tissues were selected; of these, GSE87340, GSE120622, and GSE111907 were suitable for analysis. Gene set enrichment for pathway analysis was performed. For immune gene network construction, 998 unique immune genes were selected from 21 pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene function annotation was performed based on the KEGG, Gene Ontology, and Reactome databases. Tumor tissues showed decreased coagulation, hematopoiesis, and innate immune pathways, whereas complement- and coagulation-related genes were prominent in the tumor immune gene network. The average numbers of neighbors, clustering coefficients, network diameters, path lengths, densities, and heterogeneities were highest for normal tissue, followed by LUAD and LUSC. Decreased coagulation genes, which were prominent in tumor immune networks, imply functional attenuation. LUAD was deviated from normal tissue, based on network parameters. Tumor tissues showed decreased immune function, and the deviation of LUSC from normal tissue might explain LUSC's better therapeutic response to ICI treatment.

GeoT: A Geometry-Aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning.

In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric information on molecular structures, resulting in less intuitive representations. Moreover, the widely used message passing mechanism is limited to providing the interpretation of experimental results from a chemical perspective. To address these challenges, we introduce a novel transformer-based framework for molecular representation learning, named the geometry-aware transformer (GeoT). The GeoT learns molecular graph structures through attention-based mechanisms specifically designed to offer reliable interpretability as well as molecular property prediction. Consequently, the GeoT can generate attention maps of the interatomic relationships associated with training objectives. In addition, the GeoT demonstrates performance comparable to that of MPNN-based models while achieving reduced computational complexity. Our comprehensive experiments, including an empirical simulation, reveal that the GeoT effectively learns chemical insights into molecular structures, bridging the gap between artificial intelligence and molecular sciences.

Toughening self-healing elastomer crosslinked by metal-ligand coordination through mixed counter anion dynamics.

Mechanically tough and self-healable polymeric materials have found widespread applications in a sustainable future. However, coherent strategies for mechanically tough self-healing polymers are still lacking due to a trade-off relationship between mechanical robustness and viscoelasticity. Here, we disclose a toughening strategy for self-healing elastomers crosslinked by metal-ligand coordination. Emphasis was placed on the effects of counter anions on the dynamic mechanical behaviors of polymer networks. As the coordinating ability of the counter anion increases, the binding of the anion leads to slower dynamics, thus limiting the stretchability and increasing the stiffness. Additionally, multimodal anions that can have diverse coordination modes provide unexpected dynamicity. By simply mixing multimodal and non-coordinating anions, we found a significant synergistic effect on mechanical toughness ( > 3 fold) and self-healing efficiency, which provides new insights into the design of coordination-based tough self-healing polymers.

Smartphone Authentication System Using Personal Gaits and a Deep Learning Model.

In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. In this paper, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method of using the smartphone as the medium. After learning human gait features with a convolutional neural network deep learning model, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 1.8 s while carrying the smartphone. The accuracy, precision, recall, and F1-score were measured as evaluation indicators of the proposed model. These measures all achieved an average of at least 90%. The analysis results show that the proposed system has high reliability. Therefore, this study demonstrates the possibility of using human gait as a new user authentication method. In addition, compared to our previous studies, the gait data collection time for user authentication of the proposed model was reduced from 7 to 1.8 s. This reduction signifies an approximately four-fold performance enhancement through the implementation of filtering techniques and confirms that gait data collected over a short period of time can be used for user authentication.

Highly conductive tissue-like hydrogel interface through template-directed assembly.

Over the past decade, conductive hydrogels have received great attention as tissue-interfacing electrodes due to their soft and tissue-like mechanical properties. However, a trade-off between robust tissue-like mechanical properties and good electrical properties has prevented the fabrication of a tough, highly conductive hydrogel and limited its use in bioelectronics. Here, we report a synthetic method for the realization of highly conductive and mechanically tough hydrogels with tissue-like modulus. We employed a template-directed assembly method, enabling the arrangement of a disorder-free, highly-conductive nanofibrous conductive network inside a highly stretchable, hydrated network. The resultant hydrogel exhibits ideal electrical and mechanical properties as a tissue-interfacing material. Furthermore, it can provide tough adhesion (800 J/m2) with diverse dynamic wet tissue after chemical activation. This hydrogel enables suture-free and adhesive-free, high-performance hydrogel bioelectronics. We successfully demonstrated ultra-low voltage neuromodulation and high-quality epicardial electrocardiogram (ECG) signal recording based on in vivo animal models. This template-directed assembly method provides a platform for hydrogel interfaces for various bioelectronic applications.

MYC determines lineage commitment in KRAS-driven primary liver cancer development.

BACKGROUND & AIMS: Primary liver cancer (PLC) comprises hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA), two frequent and lethal tumour types that differ regarding their tumour biology and responses to cancer therapies. Liver cells harbour a high degree of cellular plasticity and can give rise to either HCC or iCCA. However, little is known about the cell-intrinsic mechanisms directing an oncogenically transformed liver cell to either HCC or iCCA. The scope of this study was to identify cell-intrinsic factors determining lineage commitment in PLC. METHODS: Cross-species transcriptomic and epigenetic profiling was applied to murine HCCs and iCCAs and to two human PLC cohorts. Integrative data analysis comprised epigenetic Landscape In Silico deletion Analysis (LISA) of transcriptomic data and Hypergeometric Optimization of Motif EnRichment (HOMER) analysis of chromatin accessibility data. Identified candidate genes were subjected to functional genetic testing in non-germline genetically engineered PLC mouse models (shRNAmir knockdown or overexpression of full-length cDNAs). RESULTS: Integrative bioinformatic analyses of transcriptomic and epigenetic data pinpointed the Forkhead-family transcription factors FOXA1 and FOXA2 as MYC-dependent determination factors of the HCC lineage. Conversely, the ETS family transcription factor ETS1 was identified as a determinant of the iCCA lineage, which was found to be suppressed by MYC during HCC development. Strikingly, shRNA-mediated suppression of FOXA1 and FOXA2 with concomitant ETS1 expression fully switched HCC to iCCA development in PLC mouse models. CONCLUSIONS: The herein reported data establish MYC as a key determinant of lineage commitment in PLC and provide a molecular explanation why common liver-damaging risk factors such as alcoholic or non-alcoholic steatohepatitis can lead to either HCC or iCCA. IMPACT AND IMPLICATIONS: Liver cancer is a major health problem and comprises hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA), two frequent and lethal tumour types that differ regarding their morphology, tumour biology, and responses to cancer therapies. We identified the transcription factor and oncogenic master regulator MYC as a switch between HCC and iCCA development. When MYC levels are high at the time point when a hepatocyte becomes a tumour cell, an HCC is growing out. Conversely, if MYC levels are low at this time point, the result is the outgrowth of an iCCA. Our study provides a molecular explanation why common liver-damaging risk factors such as alcoholic or non-alcoholic steatohepatitis can lead to either HCC or iCCA. Furthermore, our data harbour potential for the development of better PLC therapies.

Indirect Method for Estimation of Reference Intervals of Inflammatory Markers.

Background: The direct method for reference interval (RI) estimating is limited due to the requirement of resources, difficulties in defining a non-diseased population, or ethical problems in obtaining samples. We estimated the RI for inflammatory biomarkers using an indirect method (RII). Methods: C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and presepsin (PSEP) data of patients visiting a single hospital were retrieved from April 2009 to April 2021. Right-skewed data were transformed using the Box-Cox transformation method. A mixed population of non-diseased and diseased distributions was assumed, followed by latent profile analysis for the two classes. The intersection point of the distribution curve was estimated as the RI. The influence of measurement size was evaluated as the ratio of abnormal values and adjustment (n×bandwidth) of the distribution curve. Results: The RIs estimated by the proposed RII method (existing method) were as follows: CRP, 0-4.1 (0-4.7) mg/L; ESR, 0-10.2 (0-15) mm/hr and PSEP, 0-411 (0-300) pg/mL. Measurement sizes ≥2,500 showed stable results. An abnormal-to-normal value ratio of 0.5 showed the most accurate result for CRP. Adjustment values ≤5 or >5 were applicable for a measurement size <25,000 or ≥25,000, respectively. Conclusions: The proposed RII method could provide additional information for RI verification or estimation with some limitations.

Orthogonally-tunable and ER-targeting fluorophores detect avian influenza virus early infection.

Cell-based assays can monitor virus infection at a single-cell level with high sensitivity and cost-efficiency. For this purpose, it is crucial to develop molecular probes that respond selectively to physiological changes in live cells. We report stimuli-responsive light-emitters built on a T-shaped benzimidazole platform, and consecutive borylation reactions to produce a library of homologs displaying systematic changes in fluorescence quantum yield and environmental sensitivity. We find that certain fluorophores localize selectively at the endoplasmic reticulum, and interact with proteins involved in the stress signaling pathways. Notably, the mono-borylated compound responds selectively to the stress conditions by enhancing fluorescence, and detects avian influenza virus infection at the single-cell level. Our findings demonstrate the unprecedented practical utility of the stress-responsive molecular probes to differentiate cellular states for early diagnosis.

Performance evaluation of presepsin using a Sysmex HISCL-5000 analyzer and determination of reference interval.

BACKGROUND: Analytical evaluation of newly developed presepsin by a Sysmex HISCL-5000 (Sysmex, Japan) automated immune analyzer was performed. METHODS: For evaluation, sepsis patient samples were collected before treatment in an emergency department. Precision, linearity, limit of blank/limit of detection, method comparisons, and reference intervals were evaluated. Method comparisons were performed using a PATHFAST immune analyzer (LSI Medience Corporation, Japan). RESULTS: Precision using a 20x2x2 protocol for low (306 pg/mL) and high (1031 pg/mL) levels resulted in within-laboratory standard deviation (95% confidence interval [CI]) and coefficient of variation (CV) %, which were as follows: 15.3 (13.1-18.7), 5.5% and 47.7, (40.5-58.1), 6.4%, respectively. Linearity using patient samples and calibrators were measured from 201 to 16,177 and 188 to 30,000 pg/mL, respectively. The regression equation was y = -23.2 + 1.008x (SE = 162.4) for low levels and y = 779.9 + 1.006x (SE = 668) for high levels. Method comparison by Passing-Bablock analysis was as follows: y = -209.77 + 1.047x (Syx  = 335.3). The correlation coefficient (95% CI) was 0.869 (0.772-0.927) with statistical significance (p < 0.001). Reference intervals from 120 normal healthy subjects showed that 300 pg/mL was the cut off. Presepsin tended to show a higher value at higher ages and in males. Presepsin showed correlation with some parameters, and the correlation coefficient (p value) were as follows: hematocrit, 0.198 (0.03); eGFR (CKD-EPI), -0.240 (0.0129); MDRD-eGFR, -0.194 (0.048), respectively. CONCLUSION: Presepsin measurement by HISCL-5000 showed reliable performance. Further clinical studies are required for the diagnosis and prognosis of sepsis.

Changes in regional knowledge bases and its effect on local labour markets in the midst of transition: Evidence from France over 1985-2015.

In the 2000s, the European labour market experienced a number of significant changes including the transition to a more knowledge-intensive economy as well as the introduction of various economic policies (e.g. Eurozone, subsidized jobs, and social tax cuts). In times like these, the role of knowledge, which is essentially the driving force of innovation and thus promoting technological change and economic growth, is shifting due to new labour market conditions. The present study aims to explore how processes of local knowledge bases have been altered in this transformative environment and how these have impacted on local employment growth. The investigation considers three different knowledge bases in conjunction, incl. knowledge size, knowledge creation, and knowledge application. The study is based on an econometric analysis of a panel of 94 France NUTS-3 regions covering the period 1985-2015, utilizing patent data from European Patent Office (EPO) Statistical Patent Database (PATSTAT), and regional data from European Regional Database (ERD). The result shows that the role of knowledge for employment growth has indeed changed towards more specialized inputs in applications while the importance of greater knowledge size remains still important.

CLUSTER guide RNAs enable precise and efficient RNA editing with endogenous ADAR enzymes in vivo.

RNA base editing represents a promising alternative to genome editing. Recent approaches harness the endogenous RNA-editing enzyme adenosine deaminase acting on RNA (ADAR) to circumvent problems caused by ectopic expression of engineered editing enzymes, but suffer from sequence restriction, lack of efficiency and bystander editing. Here we present in silico-optimized CLUSTER guide RNAs that bind their target messenger RNAs in a multivalent fashion, achieve editing with high precision and efficiency and enable targeting of sequences that were not accessible using previous gRNA designs. CLUSTER gRNAs can be genetically encoded and delivered using viruses, and are active in a wide range of cell lines. In cell culture, CLUSTER gRNAs achieve on-target editing of endogenous transcripts with yields of up to 45% without bystander editing. In vivo, CLUSTER gRNAs delivered to mouse liver by hydrodynamic tail vein injection edited reporter constructs at rates of up to 10%. The CLUSTER approach opens avenues for drug development in the field of RNA base editing.

Exploring the Intra Entrepreneurship-Employee Engagement-Creativity Linkage and the Diverse Effects of Gender and Marital Status.

This research examines which of the sub-dimensions of intra entrepreneurship (innovativeness, pro-activeness, risk-taking), and corporate social responsibility (CSR) support affects employee engagement (organizational and job engagement), which leads to employee creativity. The study uses survey data from SME employees in South Korea and applies the Structural Equation Modeling (SEM)-Artificial Neural Network (ANN) approach, to find that innovativeness and CSR support affect creativity through mediating roles of organizational engagement and job engagement, where job engagement plays a mediating role in the relationship between organizational engagement and creativity. The study also examines how employee gender and marital status effects the relative importance of intra entrepreneurship, organizational engagement, and job engagement on creativity. Findings of ANN analysis evaluates the effects per group (male-unmarried, male-married, female-unmarried, female-married) and shows how the importance of organizational engagement, job engagement, CSR support and innovativeness differ for each group. Contribution to theory and practice are discussed.

A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning.

This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generated by RRT-connect. To check the proposed algorithm's performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples.

Non-stackable molecules assemble into porous crystals displaying concerted cavity-changing motions.

With small molecules, it is not easy to create large void spaces. Flat aromatics stack tightly, while flexible chains fold to fill the cavities. As an intuitive design to make open channels inside molecularly constructed solids, we employed propeller-shaped bicyclic triazoles to prepare a series of aromatic-rich three-dimensional (3D) building blocks. This modular approach has no previous example, but is readily applicable to build linear, bent, and branched arrays of non-stackable architectural motifs from existing flat aromatics by single-pot reactions. A letter H-shaped molecule thus prepared self-assembles into porous crystals, the highly unusual stepwise gas sorption behaviour of which prompted in-depth studies. A combination of single-crystal and powder X-ray diffraction analysis revealed multiple polymorphs, and sterically allowed pathways for their reversible interconversions that open and close the pores in response to external stimuli.

Thin-Film Composite Nanofiltration Membranes for Non-Polar Solvents.

Organic solvent nanofiltration (OSN) has been recognized as an eco-friendly separation system owing to its excellent cost and energy saving efficiency, easy scale-up in the narrow area and mild operation conditions. Membrane properties are the key part in terms of determining the separation efficiency in the OSN system. In this review paper, the recently reported OSN thin-film composite (TFC) membranes were investigated to understand insight of membrane materials and performance. Especially, we highlighted the representative study concepts and materials of the selective layer of OSN TFC membranes for non-polar solvents. The proper choice of monomers and additives for the selective layer forms much more interconnected voids and the enhanced microporosity, which can improve membrane performance of the OSN TFC membrane with reducing the transport resistance. Therefore, this review paper could be an important bridge to connect with the next-generation OSN TFC membranes for non-polar solvents.

LXRα activation and Raf inhibition trigger lethal lipotoxicity in liver cancer.

The success of molecular therapies targeting specific metabolic pathways in cancer is often limited by the plasticity and adaptability of metabolic networks. Here we show that pharmacologically induced lipotoxicity represents a promising therapeutic strategy for the treatment of hepatocellular carcinoma (HCC). LXRα-induced liponeogenesis and Raf-1 inhibition are synthetic lethal in HCC owing to a toxic accumulation of saturated fatty acids. Raf-1 was found to bind and activate SCD1, and conformation-changing DFG-out Raf inhibitors could disrupt this interaction, thereby blocking fatty acid desaturation and inducing lethal lipotoxicity. Studies in genetically engineered and nonalcoholic steatohepatitis-induced HCC mouse models and xenograft models of human HCC revealed that therapies comprising LXR agonists and Raf inhibitors were well tolerated and capable of overcoming therapy resistance in HCC. Conceptually, our study suggests pharmacologically induced lipotoxicity as a new mode for metabolic targeting of liver cancer.

Author Correction: Necroptosis microenvironment directs lineage commitment in liver cancer.

In this Article, the pCaMIN construct consisted of 'mouse MYC and mouse NrasG12V' instead of 'mouse Myc and human NRASG12V; and the pCAMIA construct consisted of 'mouse Myc and human AKT1' instead of 'mouse Myc and Akt1' this has been corrected online.

Necroptosis microenvironment directs lineage commitment in liver cancer.

Primary liver cancer represents a major health problem. It comprises hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), which differ markedly with regards to their morphology, metastatic potential and responses to therapy. However, the regulatory molecules and tissue context that commit transformed hepatic cells towards HCC or ICC are largely unknown. Here we show that the hepatic microenvironment epigenetically shapes lineage commitment in mosaic mouse models of liver tumorigenesis. Whereas a necroptosis-associated hepatic cytokine microenvironment determines ICC outgrowth from oncogenically transformed hepatocytes, hepatocytes containing identical oncogenic drivers give rise to HCC if they are surrounded by apoptotic hepatocytes. Epigenome and transcriptome profiling of mouse HCC and ICC singled out Tbx3 and Prdm5 as major microenvironment-dependent and epigenetically regulated lineage-commitment factors, a function that is conserved in humans. Together, our results provide insight into lineage commitment in liver tumorigenesis, and explain molecularly why common liver-damaging risk factors can lead to either HCC or ICC.

TNFα sensitizes hepatocytes to FasL-induced apoptosis by NFκB-mediated Fas upregulation.

Although it is well established that TNFα contributes to hepatitis, liver failure and associated hepatocarcinogenesis via the regulation of inflammation, its pro-apoptotic role in the liver has remained enigmatic. On its own, TNFα is unable to trigger apoptosis. However, when combined with the transcriptional inhibitor GaLN, it can cause hepatocyte apoptosis and liver failure in mice. Moreover, along with others, we have shown that TNFα is capable of sensitizing cells to FasL- or drug-induced cell death via c-Jun N-terminal kinase (JNK) activation and phosphorylation/activation of the BH3-only protein Bim. In this context, TNFα could exacerbate hepatocyte cell death during simultaneous inflammatory and T-cell-mediated immune responses in the liver. Here we show that TNFα sensitizes primary hepatocytes, established hepatocyte cell lines and mouse embryo fibroblasts to FasL-induced apoptosis by the transcriptional induction and higher surface expression of Fas via the NFκB pathway. Genetic deletion, diminished expression or dominant-negative inhibition of the NFκB subunit p65 resulted in lower Fas expression and inhibited TNFα-induced Fas upregulation and sensitization to FasL-induced cell death. By hydrodynamic injection of p65 shRNA into the tail vein of mice, we confirm that Fas upregulation by TNFα is also NFκB-mediated in the liver. In conclusion, TNFα sensitization of FasL-induced apoptosis in the liver proceeds via two parallel signaling pathways, activation of JNK and Bim phosphorylation and NFκB-mediated Fas upregulation.

Triazoliptycenes: A Twist on Iptycene Chemistry for Regioselective Cross-Coupling To Build Nonstacking Fluorophores.

Triazoliptycene fluorophores have been designed and synthesized, in which a three-dimensional propeller-like iptycene motif is employed to suppress intermolecular π-π stacking in the solid state. Key to the success of this modular synthesis is a stereoelectronic bias imposed by the iptycene scaffold, which assists the desired regioselectivity in the C-N cross-coupling step as the last-stage structure diversification from a common precursor.