A 40-nm 169mW Ultrasound Imaging Processor Supporting Advanced Modes for Hand-Held Devices.

Hand-held ultrasound devices have been widely used in the field of healthcare and power-efficient, real-time imaging is essential. This work presents the world's first ultrasound imaging processor supporting advanced modes, including vector flow imaging and elastography imaging. Plane-wave beamforming is utilized to ensure that the pulse repetition frequency (PRF) is sufficiently high for the advanced mode.

Abatacept retention and clinical effectiveness in patients with rheumatoid arthritis in a real-world setting in Taiwan.

Aim: To evaluate real-world abatacept retention and clinical outcomes in patients with rheumatoid arthritis in Taiwan.

Methods: This prospective, observational study enrolled patients with rheumatoid arthritis aged ≥20 years who received abatacept in real-world practice. The primary endpoint was the abatacept retention rate at 24 months.

Improve BBB Penetration and Cytotoxicity of Palbociclib in U87-MG Glioblastoma Cells Delivered by Dual Peptide Functionalized Nanoparticles.

Palbociclib (PBC) is an FDA-approved CDK4/6 inhibitor used for breast cancer treatment. PBC has been demonstrated its ability to suppress the proliferation of glioma cells by inducing cell cycle arrest. However, the efflux transporters on the blood-brain barrier (BBB) restricts the delivery of PBC to the brain.

tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.

Summary: While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface.

Availability And Implementation: {tidytof} is available at https://github.

Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers.

Childhood cancer is the second leading cause of death in children aged 1 to 14. Although survival rates have vastly improved over the past 40 years, cancer resistance and relapse remain a significant challenge. Advances in single-cell technologies enable dissection of tumors to unprecedented resolution.

CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors.

The increasing use of mass cytometry for analyzing clinical samples offers the possibility to perform comparative analyses across public datasets. However, challenges in batch normalization and data integration limit the comparison of datasets not intended to be analyzed together. Here, we present a data integration strategy, CytofIn, using generalized anchors to integrate mass cytometry datasets from the public domain.

Benefit of a Short Chain Peptide as a Targeting Ligand of Nanocarriers for a Brain-Driven Purpose.

Treatment of glioma remains a critical challenge worldwide, since the therapeutic effect is greatly hindered by poor transportation across the blood brain barrier (BBB) and low penetration into tumor cells. In this study, a peptide-conjugated nano-delivery system was explored for the purpose of glioma therapy. A peptide-decorated copolymer was used to prepare nanoparticles (NPs) by a solvent evaporation method.

Regulation of Iron Homeostasis through Parkin-Mediated Lactoferrin Ubiquitylation.

Somatic mutations that perturb Parkin ubiquitin ligase activity and the misregulation of iron homeostasis have both been linked to Parkinson's disease. Lactotransferrin (LTF) is a member of the family of transferrin iron binding proteins that regulate iron homeostasis, and increased levels of LTF and its receptor have been observed in neurodegenerative disorders like Parkinson's disease. Here, we report that Parkin binds to LTF and ubiquitylates LTF to influence iron homeostasis.

A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry.

The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data.

Homology modeling of TMPRSS2 yields candidate drugs that may inhibit entry of SARS-CoV-2 into human cells.

The most rapid path to discovering treatment options for the novel coronavirus SARS-CoV-2 is to find existing medications that are active against the virus. We have focused on identifying repurposing candidates for the transmembrane serine protease family member II (TMPRSS2), which is critical for entry of coronaviruses into cells. Using known 3D structures of close homologs, we created seven homology models.

Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site.

Taxanes are a family of natural products with a broad spectrum of anticancer activity. This activity is mediated by interaction with the taxane site of beta-tubulin, leading to microtubule stabilization and cell death. Although widely used in the treatment of breast cancer and other malignancies, existing taxane-based therapies including paclitaxel and the second-generation docetaxel are currently limited by severe adverse effects and dose-limiting toxicity.

Computational analysis of kinase inhibitor selectivity using structural knowledge.

Motivation: Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling.

Machine learning in chemoinformatics and drug discovery.

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis.

Microtubins: a novel class of small synthetic microtubule targeting drugs that inhibit cancer cell proliferation.

Microtubule targeting drugs like taxanes, vinca alkaloids, and epothilones are widely-used and effective chemotherapeutic agents that target the dynamic instability of microtubules and inhibit spindle functioning. However, these drugs have limitations associated with their production, solubility, efficacy and unwanted toxicities, thus driving the need to identify novel antimitotic drugs that can be used as anticancer agents. We have discovered and characterized the Microtubins (Microtubule inhibitors), a novel class of small synthetic compounds, which target tubulin to inhibit microtubule polymerization, arrest cancer cells predominantly in mitosis, activate the spindle assembly checkpoint and trigger an apoptotic cell death.

Computational Cell Cycle Profiling of Cancer Cells for Prioritizing FDA-Approved Drugs with Repurposing Potential.

Discovery of first-in-class medicines for treating cancer is limited by concerns with their toxicity and safety profiles, while repurposing known drugs for new anticancer indications has become a viable alternative. Here, we have developed a new approach that utilizes cell cycle arresting patterns as unique molecular signatures for prioritizing FDA-approved drugs with repurposing potential. As proof-of-principle, we conducted large-scale cell cycle profiling of 884 FDA-approved drugs.

Fatostatin Inhibits Cancer Cell Proliferation by Affecting Mitotic Microtubule Spindle Assembly and Cell Division.

The sterol regulatory element-binding protein (SREBP) transcription factors have become attractive targets for pharmacological inhibition in the treatment of metabolic diseases and cancer. SREBPs are critical for the production and metabolism of lipids and cholesterol, which are essential for cellular homeostasis and cell proliferation. Fatostatin was recently discovered as a specific inhibitor of SREBP cleavage-activating protein (SCAP), which is required for SREBP activation.

3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping.

Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds.

The X-Linked-Intellectual-Disability-Associated Ubiquitin Ligase Mid2 Interacts with Astrin and Regulates Astrin Levels to Promote Cell Division.

Mid1 and Mid2 are ubiquitin ligases that regulate microtubule dynamics and whose mutation is associated with X-linked developmental disorders. We show that astrin, a microtubule-organizing protein, co-purifies with Mid1 and Mid2, has an overlapping localization with Mid1 and Mid2 at intercellular bridge microtubules, is ubiquitinated by Mid2 on lysine 409, and is degraded during cytokinesis. Mid2 depletion led to astrin stabilization during cytokinesis, cytokinetic defects, multinucleated cells, and cell death.

Tctex1d2 associates with short-rib polydactyly syndrome proteins and is required for ciliogenesis.

Short-rib polydactyly syndromes (SRPS) arise from mutations in genes involved in retrograde intraflagellar transport (IFT) and basal body homeostasis, which are critical for cilia assembly and function. Recently, mutations in WDR34 or WDR60 (candidate dynein intermediate chains) were identified in SRPS. We have identified and characterized Tctex1d2, which associates with Wdr34, Wdr60 and other dynein complex 1 and 2 subunits.

Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens.

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling.

A unique insertion in STARD9's motor domain regulates its stability.

STARD9 is a largely uncharacterized mitotic kinesin and putative cancer target that is critical for regulating pericentriolar material cohesion during bipolar spindle assembly. To begin to understand the mechanisms regulating STARD9 function and their importance to cell division, we took a multidisciplinary approach to define the cis and trans factors that regulate the stability of the STARD9 motor domain. We show that, unlike the other ∼50 mammalian kinesins, STARD9 contains an insertion in loop 12 of its motor domain (MD).

The STARD9/Kif16a kinesin associates with mitotic microtubules and regulates spindle pole assembly.

During cell division, cells form the microtubule-based mitotic spindle, a highly specialized and dynamic structure that mediates proper chromosome transmission to daughter cells. Cancer cells can show perturbed mitotic spindles and an approach in cancer treatment has been to trigger cell killing by targeting microtubule dynamics or spindle assembly. To identify and characterize proteins necessary for spindle assembly, and potential antimitotic targets, we performed a proteomic and genetic analysis of 592 mitotic microtubule copurifying proteins (MMCPs).