Logic Ciucuits Using Solution-processed Single-walled Carbon Nanotue Transistors

This letter reports on the realization of logic circuits employing solution-processed networks of single-walled carbon nanotubes. We constructed basic logic gates (inverter, NAND and NOR) with n- and p-type field-effect transistors fabricated by solution-based chemical doping. Complementary metal-oxide-semiconductor inverters exhibited voltage gains of up to 20, which illustrates the great potential of carbon nanotube networks for printable flexible electronics.Comment: 12 PAGES, 3 FIGURE

A preoperative simulation of medial open-wedge high tibial osteotomy for predicting postoperative realignment

Three-dimensional preoperative surgical simulation of the medial open-wedge high tibial osteotomy (OWHTO), simplified as the rigid rotation around the hinge axis, has been performed to predict postoperative realignment. However, the practicality of this highly simplified simulation method has not been verified. This study aimed to investigate the validity of realignment simulation simplified as a rotation around a hinge axis compared with a postoperative CT model. A three-dimensional surface model of the tibia and femur was created from preoperative computed tomography (CT) images (preoperative model) of three patients. The simulation of medial OWHTO created sixty computer simulation models in each patient simplified as the rigid rotation of the proximal part of the tibia relative to the distal part from 1° to 20° around three types of hinge axes. The simulation models were compared with the actual postoperative model created from postoperative CT images to assess the reality of the simulation model. The average surface distance between the two models was calculated as an index representing the similarity of the simulation model to the postoperative model. The minimum value of average surface distances between the simulation and postoperative CT models was almost 1 mm in each patient. The rotation angles at which the minimum value of average surface distances was represented were almost identical to the actual correction angles. We found that the posterior tibial tilt and the axial rotation of the proximal tibia of the simulation model well represented those of the postoperative CT model, as well as the valgus correction. Therefore, the realignment simulation of medial OWHTO can generate realistic candidates for postoperative realignment that includes the actual postoperative realignment, suggesting the efficacy of the preoperative simulation method.Konda S., Ishibashi T., Tamaki M., et al. A preoperative simulation of medial open-wedge high tibial osteotomy for predicting postoperative realignment. Frontiers in Bioengineering and Biotechnology 11, 1278912 (2023); https://doi.org/10.3389/fbioe.2023.1278912

A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma

The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with the overall survival and other clinicopathological variables; and included a combination of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) artificial neural networks, gene-set enrichment analysis (GSEA), Cox regression and other machine learning and predictive analytics modeling [C5.0 algorithm, logistic regression, Bayesian Network, discriminant analysis, random trees, tree-AS, Chi-squared Automatic Interaction Detection CHAID tree, Quest, classification and regression (C&R) tree and neural net)]. From an initial 54,613 gene-probes, a set of 488 genes and a final set of 16 genes were defined. Secondly, two identified markers of the immune checkpoint, PD-L1 (CD274) and IKAROS (IKZF4), were validated in an independent series from Tokai University, and the immunohistochemical expression was quantified, using a machine-learning-based Weka segmentation. High PD-L1 associated with poor overall and progression-free survival, non-GCB phenotype, Epstein–Barr virus infection (EBER+), high RGS1 expression and several clinicopathological variables, such as high IPI and absence of clinical response. Conversely, high expression of IKAROS was associated with a good overall and progression-free survival, GCB phenotype and a positive clinical response to treatment. Finally, the set of 16 genes (PAF1, USP28, SORT1, MAP7D3, FITM2, CENPO, PRCC, ALDH6A1, CSNK2A1, TOR1AIP1, NUP98, UBE2H, UBXN7, SLC44A2, NR2C2AP and LETM1), in combination with PD-L1, IKAROS, BCL2, MYC, CD163 and TNFAIP8, predicted the survival outcome of DLBCL with an overall accuracy of 82.1%. In conclusion, building predictive models of DLBCL is a feasible analytical strategy

Artificial Intelligence Analysis of the Gene Expression of Follicular Lymphoma Predicted the Overall Survival and Correlated with the Immune Microenvironment Response Signatures

Follicular lymphoma (FL) is the second most common lymphoma in Western countries. FL is characterized by being incurable, usually having an indolent clinical course with frequent relapses, and an eventual patient’s death or transformation to Diffuse Large B-cell Lymphoma. The immune response and the tumoral immune microenvironment, including FOXP3+Tregs, PD-1+TFH cells, TNFRSF14 (HVEM), and BTLA play a role in the pathogenesis. We aimed to analyze the gene expression of FL by Artificial Intelligence (machine learning, deep learning), to identify genes associated with the prognosis of the patients and with the microenvironment in terms of overall survival (OS). A series of 184 cases of the GSE16131 dataset was analyzed by multilayer perceptron (MLP) and radial basis function (RBF) neural networks. In the analysis, MLP and RBF had a synergistic effect. From an initial set of 22,215 genes probes, a final set of 43 genes was highlighted. These 43 genes predicted the OS and correlated with the immune microenvironment: in a multivariate Cox analysis, 18 genes were associated with a poor prognosis (namely, MED8, KRT19, CDC40, SLC24A2, PRB1, KIAA0100, EVA1B, KLK10, TMEM70, BTN2A3P, TRPM4, MED6, FRYL, CBFA2T2, RANBP9, BNIP2, PTP4A2 and ALDH1L1) and 25 genes were associated with a good prognosis of the patients. Gene set enrichment analysis (GSEA) confirmed these findings and showed a typical sinusoidal-like shape. Some of the most relevant genes for poor OS were EVA1B, KRT19, BTN2A3P, KLK10, TRPM4, TMEM70, and SLC24A2 (hazard risk = from 1.7 to 4.3, p < 0.005) and for good OS, these were TDRD12 and ZNF230 (HR = 0.34 and 0.28, p < 0.001). EVA1B, KRT19, BTN2AP3, KLK10, and TRPM4 also associated with M2-like macrophage markers including CD163, MRC1 (CD206), and IL10 in the core enrichment for dead OS outcome by GSEA and to poor OS by Kaplan–Meier with Log rank test. The scientific literature showed that some of these genes also play a role in other types of cancer. In conclusion, by Artificial Intelligence, we have identified new biomarkers with prognostic relevance in FL

The Use of the Random Number Generator and Artificial Intelligence Analysis for Dimensionality Reduction of Follicular Lymphoma Transcriptomic Data

Follicular lymphoma (FL) is one of the most frequent subtypes of non-Hodgkin lymphomas. This research predicted the prognosis of 184 untreated follicular lymphoma patients (LLMPP GSE16131 series), using gene expression data and artificial intelligence (AI) neural networks. A new strategy based on the random number generation was used to create 120 different and independent multilayer perceptron (MLP) solutions, and 22,215 gene probes were ranked according to their averaged normalized importance for predicting the overall survival. After dimensionality reduction, the final neural network architecture included (1) newly identified predictor genes related to cell adhesion and migration, cell signaling, and metabolism (EPB41L4B, MOCOS, SPIN2A, BTD, SRGAP3, CTNS, PRB1, L1CAM, and CEP57); (2) the international prognostic index (IPI); and (3) other relevant immuno-oncology, immune microenvironment, and checkpoint markers (CD163, CSF1R, FOXP3, PDCD1, TNFRSF14 (HVEM), and IL10). The performance of this neural network was good, with an area under the curve (AUC) of 0.89. A comparison with other machine learning techniques (C5 tree, logistic regression, Bayesian network, discriminant analysis, KNN algorithms, LSVM, random trees, SVM, tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network) was also made. In conclusion, the overall survival of follicular lymphoma was predicted with a neural network with high accuracy

Copy Number Alteration and Mutational Profile of High-Grade B-Cell Lymphoma with MYC and BCL2 and/or BCL6 Rearrangements, Diffuse Large B-Cell Lymphoma with MYC-Rearrangement, and Diffuse Large B-Cell Lymphoma with MYC-Cluster Amplification.

The authors thank the technicians and scientists of the Genomics Core Facility of the IDIBAPS for their assistance in performing the NGS analysis.Diffuse large B-cell lymphoma (DLBCL) with MYC alteration is classified as high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (double/triple-hit lymphoma; DHL/THL), DLBCL with MYC rearrangement (single-hit lymphoma; SHL) and DLBCL with MYC-cluster amplification (MCAD). To elucidate the genetic features of DHL/THL, SHL, and MCAD, 23 lymphoma cases from Tokai University Hospital were analyzed. The series included 10 cases of DHL/THL, 10 cases of SHL and 3 cases of MCAD. The analysis used whole-genome copy number microarray analysis (OncoScan) and a custom-made next-generation sequencing (NGS) panel of 115 genes associated with aggressive B-cell lymphomas. The copy number alteration (CNA) profiles were similar between DHL/THL and SHL. MCAD had fewer CNAs than those of DHL/THL and SHL, except for +8q24. The NGS profile characterized DHL/THL with a higher "mutation burden" than SHL (17 vs. 10, p = 0.010), and the most relevant genes for DHL/THL were BCL2 and SOCS1, and for SHL was DTX1. MCAD was characterized by mutations of DDX3X, TCF3, HLA-A, and TP53, whereas MYC was unmutated. In conclusion, DHL/THL, SHL, and MCAD have different profiles.S

A Single Gene Expression Set Derived from Artificial Intelligence Predicted the Prognosis of Several Lymphoma Subtypes; and High Immunohistochemical Expression of TNFAIP8 Associated with Poor Prognosis in Diffuse Large B-Cell Lymphoma

OBJECTIVES: We have recently identified using multilayer perceptron analysis (artificial intelligence) a set of 25 genes with prognostic relevance in diffuse large B-cell lymphoma (DLBCL), but the importance of this set in other hematological neoplasia remains unknown. METHODS AND RESULTS: We tested this set of genes (i.e., ALDOB, ARHGAP19, ARMH3, ATF6B, CACNA1B, DIP2A, EMC9, ENO3, GGA3, KIF23, LPXN, MESD, METTL21A, POLR3H, RAB7A, RPS23, SERPINB8, SFTPC, SNN, SPACA9, SWSAP1, SZRD1, TNFAIP8, WDCP and ZSCAN12) in a large series of gene expression comprised of 2029 cases, selected from available databases, that included chronic lymphocytic leukemia (CLL, n = 308), mantle cell lymphoma (MCL, n = 92), follicular lymphoma (FL, n = 180), DLBCL (n = 741), multiple myeloma (MM, n = 559) and acute myeloid leukemia (AML, n = 149). Using a risk-score formula we could predict the overall survival of the patients: the hazard-ratio of high-risk versus low-risk groups for all the cases was 3.2 and per disease subtype were as follows: CLL (4.3), MCL (5.2), FL (3.0), DLBCL not otherwise specified (NOS) (4.5), multiple myeloma (MM) (5.3) and AML (3.7) (all p values 60 years, high serum levels of soluble IL2RA, a non-GCB phenotype (cell-of-origin Hans classifier), moderately higher MYC and Ki67 (proliferation index), and high infiltration of the immune microenvironment by CD163-positive tumor associated macrophages (CD163+TAMs). CONCLUSIONS: It is possible to predict the prognosis of several hematological neoplasia using a single gene-set derived from neural network analysis. High expression of TNFAIP8 is associated with poor prognosis of the patients in DLBCL

A phosphorylation-deficient mutant of Sik3, a homolog of Sleepy, alters circadian sleep regulation by PDF neurons in Drosophila

Sleep behavior has been observed from non-vertebrates to humans. Sleepy mutation in mice resulted in a notable increase in sleep and was identified as an exon-skipping mutation of the salt-inducible kinase 3 (Sik3) gene, conserved among animals. The skipped exon includes a serine residue that is phosphorylated by protein kinase A. Overexpression of a mutant gene with the conversion of this serine into alanine (Sik3-SA) increased sleep in both mice and the fruit fly Drosophila melanogaster. However, the mechanism by which Sik3-SA increases sleep remains unclear. Here, we found that Sik3-SA overexpression in all neurons increased sleep under both light–dark (LD) conditions and constant dark (DD) conditions in Drosophila. Additionally, overexpression of Sik3-SA only in PDF neurons, which are a cluster of clock neurons regulating the circadian rhythm, increased sleep during subjective daytime while decreasing the amplitude of circadian rhythm. Furthermore, suppressing Sik3-SA overexpression specifically in PDF neurons in flies overexpressing Sik3-SA in all neurons reversed the sleep increase during subjective daytime. These results indicate that Sik3-SA alters the circadian function of PDF neurons and leads to an increase in sleep during subjective daytime under constant dark conditions