TY - JOUR AB - CAR T cells are genetically modified T cells that target specific epitopes. CAR T cell therapy has proven effective in difficult-to-treat B cell cancers and is now expanding into hematology and solid tumors. To date, approved CAR therapies target only two specific epitopes on cancer cells. Identifying more suitable targets is challenged by the lack of truly cancer-specific structures and the potential for on-target off-tumor toxicity. We analyzed gene expression of potential targets in single-cell data from cancer and healthy tissues. Because safety and efficacy can ultimately only be defined clinically, we selected approved and investigational targets for which clinical trail data are available. We generated atlases using >300,000 cells from 48 patients with follicular lymphoma, multiple myeloma, and B-cell acute lymphoblastic leukemia, and integrated over 3 million cells from 35 healthy tissues, harmonizing datasets from over 300 donors. To contextualize findings, we compared target expression patterns with outcome data from clinical trials, linking target profiles to efficacy and toxicity, and ranked 15 investigational targets based on their similarity to approved ones. Target expression did not significantly correlate with reported clinical toxicities in patients undergoing therapy. This may be attributed to the intricate interplay of patient-specific variables, the limited amount of metadata, and the complexity underlying toxicity. Nevertheless, our study serves as a resource for retrospective and prospective target evaluation to improve the safety and efficacy of CAR therapies. AU - Thomas, M. AU - Brabenec, R. AU - Gregor, L.* AU - Andreu-Sanz, D.* AU - Carlini, E.* AU - Müller, P.J.* AU - Gottschlich, A.* AU - Simnica, D.* AU - Kobold, S. AU - Marr, C. C1 - 74580 C2 - 57534 TI - The role of single cell transcriptomics for efficacy and toxicity profiling of chimeric antigen receptor (CAR) T cell therapies. JO - Comput. Biol. Med. VL - 192 IS - Pt B PY - 2025 SN - 0010-4825 ER - TY - JOUR AB - Latent diffusion models (LDMs) have emerged as a state-of-the-art image generation method, outperforming previous Generative Adversarial Networks (GANs) in terms of training stability and image quality. In computational pathology, generative models are valuable for data sharing and data augmentation. However, the impact of LDM-generated images on histopathology tasks compared to traditional GANs has not been systematically studied. We trained three LDMs and a styleGAN2 model on histology tiles from nine colorectal cancer (CRC) tissue classes. The LDMs include 1) a fine-tuned version of stable diffusion v1.4, 2) a Kullback-Leibler (KL)-autoencoder (KLF8-DM), and 3) a vector quantized (VQ)-autoencoder deploying LDM (VQF8-DM). We assessed image quality through expert ratings, dimensional reduction methods, distribution similarity measures, and their impact on training a multiclass tissue classifier. Additionally, we investigated image memorization in the KLF8-DM and styleGAN2 models. All models provided a high image quality, with the KLF8-DM achieving the best Frechet Inception Distance (FID) and expert rating scores for complex tissue classes. For simpler classes, the VQF8-DM and styleGAN2 models performed better. Image memorization was negligible for both styleGAN2 and KLF8-DM models. Classifiers trained on a mix of KLF8-DM generated and real images achieved a 4% improvement in overall classification accuracy, highlighting the usefulness of these images for dataset augmentation. Our systematic study of generative methods showed that KLF8-DM produces the highest quality images with negligible image memorization. The higher classifier performance in the generatively augmented dataset suggests that this augmentation technique can be employed to enhance histopathology classifiers for various tasks. AU - Niehues, J.M.* AU - Müller-Franzes, G.* AU - Schirris, Y.* AU - Wagner, S. AU - Jendrusch, M.* AU - Kloor, M.* AU - Pearson, A.T.* AU - Muti, H.S.* AU - Hewitt, K.J.* AU - Veldhuizen, G.P.* AU - Zigutyte, L.* AU - Truhn, D.* AU - Kather, J.N.* C1 - 70612 C2 - 55598 TI - Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance. JO - Comput. Biol. Med. VL - 175 PY - 2024 SN - 0010-4825 ER - TY - JOUR AB - Obesity in children is related to the development of cardiometabolic complications later in life, where molecular changes of visceral adipose tissue (VAT) and skeletal muscle tissue (SMT) have been proven to be fundamental. The aim of this study is to unveil the gene expression architecture of both tissues in a cohort of Spanish boys with obesity, using a clustering method known as weighted gene co-expression network analysis. For this purpose, we have followed a multi-objective analytic pipeline consisting of three main approaches; identification of gene co-expression clusters associated with childhood obesity, individually in VAT and SMT (intra-tissue, approach I); identification of gene co-expression clusters associated with obesity-metabolic alterations, individually in VAT and SMT (intra-tissue, approach II); and identification of gene co-expression clusters associated with obesity-metabolic alterations simultaneously in VAT and SMT (inter-tissue, approach III). In both tissues, we identified independent and inter-tissue gene co-expression signatures associated with obesity and cardiovascular risk, some of which exceeded multiple-test correction filters. In these signatures, we could identify some central hub genes (e.g., NDUFB8, GUCY1B1, KCNMA1, NPR2, PPP3CC) participating in relevant metabolic pathways exceeding multiple-testing correction filters. We identified the central hub genes PIK3R2, PPP3C and PTPN5 associated with MAPK signaling and insulin resistance terms. This is the first time that these genes have been associated with childhood obesity in both tissues. Therefore, they could be potential novel molecular targets for drugs and health interventions, opening new lines of research on the personalized care in this pathology. This work generates interesting hypotheses about the transcriptomics alterations underlying metabolic health alterations in obesity in the pediatric population. AU - Bustos-Aibar, M.* AU - Aguilera, C.M.* AU - Alcalá-Fdez, J.* AU - Ruiz Ojeda, F.J. AU - Plaza-Díaz, J.* AU - Plaza-Florido, A.* AU - Tofe, I.* AU - Gil-Campos, M.* AU - Gacto, M.J.* AU - Anguita-Ruiz, A.* C1 - 68438 C2 - 54634 CY - The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England TI - Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity. JO - Comput. Biol. Med. VL - 163 PB - Pergamon-elsevier Science Ltd PY - 2023 SN - 0010-4825 ER - TY - JOUR AB - Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections generate approximately one million virions per day, and the majority of available antivirals are ineffective against it due to the virus's inherent genetic mutability. This necessitates the investigation of concurrent inhibition of multiple SARS-CoV-2 targets. We show that fortunellin (acacetin 7-O-neohesperidoside), a phytochemical, is a promising candidate for preventing and treating coronavirus disease (COVID-19) by targeting multiple key viral target proteins. Fortunellin supports protective immunity while inhibiting pro-inflammatory cytokines and apoptosis pathways and protecting against tissue damage. Fortunellin is a phytochemical found in Gojihwadi kwath, an Indian traditional Ayurvedic formulation with an antiviral activity that is effective in COVID-19 patients. The mechanistic action of its antiviral activity, however, is unknown. The current study comprehensively evaluates the potential therapeutic mechanisms of fortunellin in preventing and treating COVID-19. We have used molecular docking, molecular dynamics simulations, free-energy calculations, host target mining of fortunellin, gene ontology enrichment, pathway analyses, and protein-protein interaction analysis. We discovered that fortunellin reliably binds to key targets that are necessary for viral replication, growth, invasion, and infectivity including Nucleocapsid (N-CTD) (−54.62 kcal/mol), Replicase-monomer at NSP-8 binding site (−34.48 kcal/mol), Replicase-dimer interface (−31.29 kcal/mol), Helicase (−30.02 kcal/mol), Papain-like-protease (−28.12 kcal/mol), 2′-O-methyltransferase (−23.17 kcal/mol), Main-protease (−21.63 kcal/mol), Replicase-monomer at dimer interface (−22.04 kcal/mol), RNA-dependent-RNA-polymerase (−19.98 kcal/mol), Nucleocapsid-NTD (−16.92 kcal/mol), and Endoribonuclease (−16.81 kcal/mol). Furthermore, we identify and evaluate the potential human targets of fortunellin and its effect on the SARS-CoV-2 infected tissues, including normal-human-bronchial-epithelium (NHBE) and lung cells and organoids such as pancreatic, colon, liver, and cornea using a network pharmacology approach. Thus, our findings indicate that fortunellin has a dual role; multi-target antiviral activities against SARS-CoV-2 and immunomodulatory capabilities against the host. AU - Agrawal, S.* AU - Pathak, E. AU - Mishra, R.* AU - Parveen, A.* AU - Mishra, S.K.* AU - Byadgi, P.S.* AU - Dubey, S.K.* AU - Chaudhary, A.K.* AU - Singh, V.* AU - Chaurasia, R.N.* AU - Atri, N.* C1 - 66213 C2 - 52627 TI - Computational exploration of the dual role of the phytochemical fortunellin: Antiviral activities against SARS-CoV-2 and immunomodulatory abilities against the host. JO - Comput. Biol. Med. VL - 149 PY - 2022 SN - 0010-4825 ER - TY - JOUR AB - Numerical simulations of the dispersion and deposition of poly-disperse particles in a patient-specific human nasal configuration are performed. Computed tomography (CT) images are used to create a realistic configuration of the nasal cavity and paranasal sinuses. The OpenFOAM software is used to perform unsteady Large Eddy Simulations (LES) with the dynamic sub-grid scale Smagorinsky model. For the numerical analysis of the particle motion, a Lagrangian particle tracking method is implemented. Two different nosepieces with clockwise inclinations of 45 degrees and 90 degrees with respect to the horizontal axis are connected to the nostrils. A sinusoidal pulsating airflow profile with a frequency of 45 Hz is imposed on the airflow which carries the particles. Flow partition analysis inside the sinuses show that ventilation of the sinuses is improved slightly when the 45 degrees nosepiece is used instead of the 90 degrees nosepiece. The flow partition into the right maxillary is improved from 0.22% to 0.25%. It is observed that a closed soft palate increases the aerosol deposition efficiency (DE) in the nasal cavity as compared to an open soft palate condition. The utilization of pulsating inflow leads to more uniform deposition pattern in the nasal airway and enhances the DE by 160% and 44.6%, respectively, for the cases with clockwise 45 degrees and 90 degrees nosepieces, respectively. The bi-directional pulsating drug delivery with the same particle size distribution and inflow rates as the PARI SINUS device results in higher total DEs with 45 degrees nosepiece than with the 90 degrees. Thus, the numerical simulation suggests that the 45 degrees nosepiece is favorable in terms of the delivered dose. AU - Farnoud, A. AU - Baumann, I.* AU - Rashidi, M.M.* AU - Schmid, O. AU - Gutheil, E.* C1 - 59691 C2 - 49013 CY - The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England TI - Simulation of patient-specific bi-directional pulsating nasal aerosol dispersion and deposition with clockwise 45 degrees and 90 degrees nosepieces. JO - Comput. Biol. Med. VL - 123 PB - Pergamon-elsevier Science Ltd PY - 2020 SN - 0010-4825 ER - TY - JOUR AB - In this work we provide an up-to-date short review of computational magnetic resonance imaging (MRI) and software tools that are widely used to process and analyze diffusion-weighted MRI data. A review of different methods used to acquire, model and analyze diffusion-weighted imaging data (DWI) is first provided with focus on diffusion tensor imaging (DTI). The major preprocessing, processing and post-processing procedures applied to DTI data are discussed. A list of freely available software packages to analyze diffusion MRI data is also provided. AU - Hasan, K.M.* AU - Walimuni, I.S.* AU - Abid, H.* AU - Hahn, K.R. C1 - 6849 C2 - 29352 SP - 1062-1072 TI - A review of diffusion tensor magnetic resonance imaging computational methods and software tools. JO - Comput. Biol. Med. VL - 41 IS - 12 PB - Elsevier PY - 2011 SN - 0010-4825 ER - TY - JOUR AB - The clinical relevance of platelet function assessment with stagnation point flow adhesio-aggregometry (SPAA) has been verified. Quantitative analysis of platelet adhesion and aggregation is possible by means of mathematical analysis of the dark-field, light intensity curves (growth curves) obtained during the SPAA experiment. We present a computational procedure for evaluating these curves, which was necessitated by, and is based on, actual clinical application. A qualitative growth curve classification, corresponding to a basic and distinct pattern of platelet deposition and characteristic of a regularly occurring clinical state is also presented. AU - Reininger, C.B.* AU - Lasser, R. AU - Rumitz, M.* AU - Böger, C.* AU - Schweiberer, L.* C1 - 20841 C2 - 18896 SP - 1-18 TI - Computational analysis of platelet adhesion and aggregation under stagnation point flow conditions. JO - Comput. Biol. Med. VL - 29 IS - 1 PY - 1999 SN - 0010-4825 ER -