DART.2: bidirectional synaptic pharmacology with thousandfold cellular specificity.

Precision pharmacology aims to manipulate specific cellular interactions within complex tissues. In this pursuit, we introduce DART.2 (drug acutely restricted by tethering), a second-generation cell-specific pharmacology technology.

Knockdown siRNA Targeting GPR55 Reveals Significant Differences Between the Anti-inflammatory Actions of KLS-13019 and Cannabidiol.

KLS-13019 was reported previously to reverse paclitaxel-induced mechanical allodynia in a mouse model of chemotherapy-induced peripheral neuropathy (CIPN). Recent studies demonstrated that paclitaxel-induced increases in inflammatory markers (GPR55, NLRP3, and IL-1β) of dorsal root ganglion (DRG) cultures were shown to be reversed by KLS-13019 treatment. The mechanism of action for KLS-13019-mediated reversal of paclitaxel-induced neuroinflammation now has been explored using GPR55 siRNA.

Knockdown siRNA targeting GPR55 reveals significant differences between the anti-inflammatory actions of KLS-13019 and cannabidiol.

KLS-13019 was reported previously to reverse paclitaxel-induced mechanical allodynia in a mouse model of chemotherapy-induced peripheral neuropathy (CIPN). Recent studies demonstrated that paclitaxel-induced increases in inflammatory markers (GPR55, NLRP3 and IL-1b) of dorsal root ganglion (DRG) cultures were shown to be reversed by KLS-13019 treatment. The mechanism of action for KLS-13019-mediated reversal of paclitaxel-induced neuroinflammation now has been explored using GPR55 siRNA.

Global challenges in access to diagnostics and treatment for neuroendocrine tumor (NET) patients.

SCAN, an online survey, measured access to diagnosis, treatments and monitoring of neuroendocrine tumor (NET) patients globally. Between September and November 2019, NET patients and healthcare professionals (HCPs) completed an online, semi-standardized survey with 54 patient questions and 33 HCP questions. A total of 2359 patients with NETs and 436 HCPs responded.

Efficient Syntheses of KLS-13019 Using Palladium Mediated Cross Couplings.

KLS-13019 is a structural analogue of cannabidiol, that shows improved bioavailability and potency in both preventing and reversing paclitaxel-induced neurotoxicity in vitro and in vivo. KLS-13019 was selected as a development candidate and attention was turned to development of a scalable synthesis. The original synthesis of KLS-13019 was not efficient, regioselective, or high yielding.

Four-dimensional electrical resistivity imaging for monitoring pumping-induced saltwater intrusion in a coastal aquifer.

Conventional views of saltwater intrusion (SWI), where a basal saline wedge extends inland below fresh groundwater, can be complicated by the influence of saltwater cells in the upper part of aquifers in areas affected by tidal cycles. Distinguishing the contribution of each saltwater source may prove fundamental for well design and resource management. Application of time-lapse electrical resistivity imaging (ERI) during a 32-h pumping test in a pristine unconfined coastal sand aquifer, affected by strong tidal ranges (>2 m), aimed to evaluate the potential of the method to characterize the source of induced SWI in four dimensions (three dimensions and time).

PRESTO 2: An International Survey to Evaluate Patients' Injection Experiences with the Latest Devices/Formulations of Long-Acting Somatostatin Analog Therapies for Neuroendocrine Tumors or Acromegaly.

Introduction: Real-world data evaluating patients' injection experiences using the latest devices/formulations of the long-acting (LA) somatostatin analogs (SSAs) lanreotide Autogel/Depot (LAN; Somatuline®) and octreotide LA release (OCT; Sandostatin®) are limited.

Methods: PRESTO 2 was a 2020/2021 e-survey comparing injection experience of adults with neuroendocrine tumors (NETs) or acromegaly treated with LAN prefilled syringe versus OCT syringe for > 3 months in Canada, Ireland, the UK and the USA (planned sample size, 304).

Primary Endpoint: the proportion of patients with injection-site pain lasting > 2 days after their most recent injection, analyzed using a multivariate logistic regression model.

Anti-Inflammatory Properties of KLS-13019: a Novel GPR55 Antagonist for Dorsal Root Ganglion and Hippocampal Cultures.

KLS-13019, a novel devised cannabinoid-like compound, was explored for anti-inflammatory actions in dorsal root ganglion cultures relevant to chemotherapy-induced peripheral neuropathy (CIPN). Time course studies with 3 µM paclitaxel indicated > 1.9-fold increases in immunoreactive (IR) area for cell body GPR55 after 30 min as determined by high content imaging.

Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study.

We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation.

Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging.

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.

Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.

Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities.

The quest for better clinical word vectors: Ontology based and lexical vector augmentation versus clinical contextual embeddings.

Background: Word vectors or word embeddings are n-dimensional representations of words and form the backbone of Natural Language Processing of textual data. This research experiments with algorithms that augment word vectors with lexical constraints that are popular in NLP research and clinical domain constraints derived from the Unified Medical Language System (UMLS). It also compares the performance of the augmented vectors with Bio + Clinical BERT vectors which have been trained and fine-tuned on clinical datasets.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma.

Background: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.

Methods: We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA).

Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications.

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities.

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey.

In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

The discovery of azetidine-piperazine di-amides as potent, selective and reversible monoacylglycerol lipase (MAGL) inhibitors.

Monoacylglycerol lipase (MAGL) is the enzyme that is primarily responsible for hydrolyzing the endocannabinoid 2-arachidononylglycerol (2-AG) to arachidonic acid (AA). It has emerged in recent years as a potential drug target for a number of diseases. Herein, we report the discovery of compound 6g from a series of azetidine-piperazine di-amide compounds as a potent, selective, and reversible inhibitor of MAGL.

Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis.

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted.

Non-NAD-like PARP-1 inhibitors in prostate cancer treatment.

In our previous studies of the molecular mechanisms of poly(ADP-ribose) polymerase 1 (PARP-1)-mediated transcriptional regulation we identified a novel class of PARP-1 inhibitors targeting the histone-dependent route of PARP-1 activation. Because histone-dependent activation is unique to PARP-1, non-NAD-like PARP-1 inhibitors have the potential to bypass the off-target effects of classical NAD-dependent PARP-1 inhibitors, such as olaparib, veliparib, and rucaparib. Furthermore, our recently published studies demonstrate that, compared to NAD-like PARP-1 inhibitors that are used clinically, the non-NAD-like PARP-1 inhibitor 5F02 exhibited superior antitumor activity in cell and animal models of human prostate cancer (PC).

A roadmap for urban evolutionary ecology.

Urban ecosystems are rapidly expanding throughout the world, but how urban growth affects the evolutionary ecology of species living in urban areas remains largely unknown. Urban ecology has advanced our understanding of how the development of cities and towns change environmental conditions and alter ecological processes and patterns. However, despite decades of research in urban ecology, the extent to which urbanization influences evolutionary and eco-evolutionary change has received little attention.

Simulation of electromyographic recordings following transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is a technique that enables noninvasive manipulation of neural activity and holds promise in both clinical and basic research settings. The effect of TMS on the motor cortex is often measured by electromyography (EMG) recordings from a small hand muscle. However, the details of how TMS generates responses measured with EMG are not completely understood.

Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance.

Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics.