Miracle-dz/UniFAR-FTUs huggingface dataset:
The Delta-Opioid Receptor; a Target for the Treatment of Pain

Nowadays, pain represents one of the most important societal burdens. Current treatments are, however, too often ineffective and/or accompanied by debilitating unwanted effects for patients dealing with chronic pain. Indeed, the prototypical opioid morphine, as many other strong analgesics, shows harmful unwanted effects including respiratory depression and constipation, and also produces tolerance, physical dependence, and addiction. The urgency to develop novel treatments against pain while minimizing adverse effects is therefore crucial. Over the years, the delta-opioid receptor (DOP) has emerged as a promising target for the development of new pain therapies. Indeed, targeting DOP to treat chronic pain represents a timely alternative to existing drugs, given the weak unwanted effects spectrum of DOP agonists. Here, we review the current knowledge supporting a role for DOP and its agonists for the treatment of pain. More specifically, we will focus on the cellular and subcellular localization of DOP in the nervous system. We will also discuss in further detail the molecular and cellular mechanisms involved in controlling the cellular trafficking of DOP, known to differ significantly from most G protein-coupled receptors. This review article will allow a better understanding of how DOP represents a promising target to develop new treatments for pain management as well as where we stand as of our ability to control its cellular trafficking and cell surface expression.
Bioluminescence Imaging-Based Assessment of the Anti-Triple-Negative Breast Cancer and NF-Kappa B Pathway Inhibition Activity of Britanin

Britanin has been reported to have therapeutic effects on neurodegenerative and inflammation-based diseases. However, whether it is involved in the regulation of triple-negative breast cancer development has not been elucidated. In this study, we investigated the anti-tumor activity against triple-negative breast cancer tumor of Britanin by bioluminescence imaging in vivo using athymic (nu/nu) mice implanted with MDA-MB-231 and SUM-159 cells expressing a luciferase reporter gene, and explored the anti-tumor mechanism of Britanin. The results showed that Britanin treatment inhibited triple-negative breast cancer cell proliferation in vivo, and Cell Counting Kit-8 (IC50 values are 4.27 and 5.05 μM) and colony formation tests (P < 0.001) confirmed this result. Transwell assays were conducted to verify that Britanin treatment inhibited cell migration and invasion (P < 0.001). Apoptosis was determined by TdT-mediated dUTP nick-end labeling method. Western blot and qRT-PCR analysis showed that Britanin treatment caused a decrease in the member expression of NF-kappa B signaling pathway. Computational modeling showed that Britanin could directly bind to a p-65 core region composed of Cys38, Cys120, and Gln128 residues. The results showed that the inhibitory mechanisms of Britanin on cancer cells may be by ways of inhibiting the NF-kappa B pathway. In addition, bioluminescence imaging screening system is useful for accelerating the application of Britanin in the antitumor field, and provides a useful tool for evaluating the phytochemicals efficacy in inhibiting cancer cell proliferation in animal models.
The Network Structure of Personality Pathology in Adolescence With the 100-Item Personality Inventory for DSM-5 Short-Form (PID-5-SF)

There is currently a lack of understanding of the structure of personality disorder (PD) trait facets. The network approach may be useful in providing additional insights, uncovering the unique association of each PD trait facet with every other facet. A unique feature of network analysis is centrality, which indicates the importance of the role a trait facet plays in the context of other trait facets. Using data from 1,940 community Dutch adolescents, we applied network analysis to the 25 trait facets from the 100-item Personality Inventory for DSM-5 Short-Form (PID-5-SF) to explore their associations. We found that some trait facets only seem to be core indicators of their pre-ordained domains, whereas we observed that other trait facets were strongly associated with trait facets outside of their hypothesized domains. Importantly, anxiousness and callousness were identified as highly central facets, being uniquely associated with many other trait facets. Future longitudinal network studies could therefore further examine the possibility of anxiousness and callousness as risk marker trait facets among other PD trait facets.
Resection vs. Sorafenib for Hepatocellular Carcinoma With Macroscopic Vascular Invasion: A Real World, Propensity Score Matched Analytic Study

Background: Macroscopic vascular invasion (MVI) commonly occurs in patients with advanced hepatocellular carcinoma (HCC) for which resection and sorafenib are the common therapies prescribed. Here, we aimed to compare the survival outcomes of these two therapies in HCC patients with MVI. Methods: In total, 496 patients diagnosed with HCC and MVI without extrahepatic metastasis, treated with resection (resection-based group, n = 388) and sorafenib (sorafenib-based group, n = 108) were included in this study. A one-to-one propensity score-matching analysis (PSM) was performed to minimize the effect of potential confounders. Results: The median OS in the resection- and sorafenib-based group was 20.7 months (95% CI: 16.9–24.5) and 11.6 months (95% CI: 8.4–14.9) (p < 0.001), respectively. The median PFS was 4.7 months (95% CI: 3.8–5.5) in the resection-based group and 4.4 months (95% CI: 3.6–5.2) in the sorafenib-based group (p < 0.001). After PSM, 72 patients from each group were matched. The median OS was 27.2 months (95% CI: 16.4–38.0) in the resection-based group and 13.0 months (95% CI: 9.6–16.3) in the sorafenib-based group (p < 0.001). The median PFS was 5.3 months (95% CI: 3.2–7.4) in the resection-based group and 4.8 months (95% CI: 3.6–6.0) in the sorafenib-based group (p = 0.061). Conclusion: Findings from this study showed that, compared with sorafenib-based treatment, surgical resection might be associated with better survival benefits to HCC patients with MVI.
Role of the Hippocampus During Logical Reasoning and Belief Bias in Aging

Reasoning requires initial encoding of the semantic association between premises or assumptions, retrieval of these semantic associations from memory, and recombination of information to draw a logical conclusion. Currently-held beliefs can interfere with the content of the assumptions if not congruent and inhibited. This study aimed to investigate the role of the hippocampus and hippocampal networks during logical reasoning tasks in which the congruence between currently-held beliefs and assumptions varies. Participants of younger and older age completed a series of syllogistic reasoning tasks in which two premises and one conclusion were presented and they were required to decide if the conclusion logically followed the premises. The belief load of premises was manipulated to be either congruent or incongruent with currently-held beliefs. Our whole-brain results showed that older adults recruited the hippocampus during the premise integration stage more than their younger counterparts. Functional connectivity using a hippocampal seed revealed that older, but not younger, adults recruited a hippocampal network that included anterior cingulate and inferior frontal regions when premises were believable. Importantly, this network contributed to better performance in believable inferences, only in older adults group. Further analyses suggested that, in older adults group, the integrity of the left cingulum bundle was associated with the higher rejection of believable premises more than unbelievable ones. Using multimodal imaging, this study highlights the importance of the hippocampus during premise integration and supports compensatory role of the hippocampal network during a logical reasoning task among older adults.
Effects of BMPER, CXCL10, and HOXA9 on Neovascularization During Early-Growth Stage of Primary High-Grade Glioma and Their Corresponding MRI Biomarkers

Neovascularization is required in high-grade glioma (HGG). The objective of this study was to explore neovascularization-related genes and their corresponding MRI biomarkers during the early-growth stage of HGG. Tumor tissues from 30 HGG patients underwent perfusion MRI scanning prior to surgery were used to establish orthotopic xenograft models, pathologically analyze the tumor vasculature and perform transcriptome sequencing. The cases were divided into two groups based on whether the xenograft was successfully established. Microvascular density and BMPER, CXCL10, and HOXA9 expression of surgical specimens in the xenograft-forming group was significantly elevated and the microvascular diameter was significantly reduced, in vitro inhibition of BMPER, CXCL10, or HOXA9 in the glioma stem cell significantly suppressed its tube formation abilities. The in vivo experiment showed that BMPER was highly expressed in the early tumor growth phase (20 days), CXCL10 and HOXA9 expression was elevated with tumor progress, and spatially associated with tumor vasculature. Perfusion weighted MRI (PWI-MRI) derived parameters, rCBV, rCBF, Ktrans, and Vp, were also increased in the xenograft-forming group. In conclusion BMPER, CXCL10, and HOXA9 promote early tumor growth and progression by stimulating neovascularization of primary HGG. The rCBV, rCBF, Ktrans, and Vp can be used as imaging biomarkers to predict the expression statuses of these genes.
Neoplastic and Non-neoplastic Acute Intracerebral Hemorrhage in CT Brain Scans: Machine Learning-Based Prediction Using Radiomic Image Features

Background: Early differentiation of neoplastic and non-neoplastic intracerebral hemorrhage (ICH) can be difficult in initial radiological evaluation, especially for extensive ICHs. The aim of this study was to evaluate the potential of a machine learning-based prediction of etiology for acute ICHs based on quantitative radiomic image features extracted from initial non-contrast-enhanced computed tomography (NECT) brain scans. Methods: The analysis included NECT brain scans from 77 patients with acute ICH (n = 50 non-neoplastic, n = 27 neoplastic). Radiomic features including shape, histogram, and texture markers were extracted from non-, wavelet-, and log-sigma-filtered images using regions of interest of ICH and perihematomal edema (PHE). Six thousand and ninety quantitative predictors were evaluated utilizing random forest algorithms with five-fold model-external cross-validation. Model stability was assessed through comparative analysis of 10 randomly drawn cross-validation sets. Classifier performance was compared with predictions of two radiologists employing the Matthews correlation coefficient (MCC). Results: The receiver operating characteristic (ROC) area under the curve (AUC) of the test sets for predicting neoplastic vs. non-neoplastic ICHs was 0.89 [95% CI (0.70; 0.99); P < 0.001], and specificities and sensitivities reached >80%. Compared to the radiologists' predictions, the machine learning algorithm yielded equal or superior results for all evaluated metrics. The MCC of the proposed algorithm at its optimal operating point (0.69) was significantly higher than the MCC of the radiologist readers (0.54); P = 0.01. Conclusion: Evaluating quantitative features of acute NECT images in a machine learning algorithm provided high discriminatory power in predicting non-neoplastic vs. neoplastic ICHs. Utilized in the clinical routine, the proposed approach could improve patient care at low risk and costs.
Methodology for Measurement of in vivo Tibiotalar Kinematics After Total Ankle Replacement Using Dual Fluoroscopy

Biomechanical data could improve our clinical understanding of failures in total ankle replacement (TAR) patients, leading to better surgical approaches and implant designs. Kinematics of the prosthetic tibiotalar joint in TAR patients have yet to be measured using dual fluoroscopy. With dual fluoroscopy, computed tomography (CT) images are acquired to track bone motion. One challenge with this approach is dealing with metal artifact in the CT images that distorts implant visualization and the surrounding bone to implant interfaces. The aim of this study was to develop a methodology to measure in vivo TAR kinematics using inputs of computer-aided design (CAD) models, dual fluoroscopy and CT imaging with metal artifact reduction. To develop this methodology, we created a hybrid three-dimensional (3D) model that contained both: (1) the segmented bone; and (2) the CAD models of the TAR components. We evaluated a patient following total ankle replacement to demonstrate feasibility. The patient performed a self-selected overground walk during which dual fluoroscopy images were collected at 200 Hz. In vivo tracking verifications were performed during overground walking using a distance calculation between the implant articular surfaces to evaluate the model-based tracking 3D solution. Tracking verification indicated realistic alignment of the hybrid models with an evenly distributed distance map pattern during the trial. Articular surface distance calculations were reported as an average of 1.3 mm gap during the entirety of overground walking. The successful implementation of our new tracking methodology with a hybrid model presents a new approach to evaluate in vivo TAR kinematics. Measurements of in vivo kinematics could improve our clinical understanding of failures in TAR patients, leading to better long-term surgical outcomes.
Clinical Features and Experimental Models of Cerebral Small Vessel Disease

Cerebral small vessel disease (SVD) refers to a group of disease conditions affecting the cerebral small vessels, which include the small arteries, arterioles, capillaries, and postcapillary venules in the brain. SVD is the primary cause of vascular cognitive impairment and gait disturbances in aged people. There are several types of SVD, though arteriolosclerosis, which is mainly associated with hypertension, aging, and diabetes mellitus, and cerebral amyloid angiopathy (CAA) comprise most SVD cases. The pathology of arteriolosclerosis-induced SVD is characterized by fibrinoid necrosis and lipohyalinosis, while CAA-associated SVD is characterized by progressive deposition of amyloid beta (Aβ) protein in the cerebral vessels. Brain magnetic resonance imaging (MRI) has been used for examination of SVD lesions; typical lesions are characterized by white matter hyperintensity, lacunar infarcts, enlargement of perivascular spaces (EPVS), microbleeds, cortical superficial siderosis (cSS), and cortical microinfarcts. The microvascular changes that occur in the small vessels are difficult to identify clearly; however, these consequent image findings can represent the SVD. There are two main strategies for prevention and treatment of SVD, i.e., pharmacotherapy and lifestyle modification. In this review, we discuss clinical features of SVD, experimental models replicating SVD, and treatments to further understand the pathological and clinical features of SVD.
Identification of a Conserved Prophenoloxidase Activation Pathway in Cotton Bollworm Helicoverpa armigera

Melanization is a prominent insect humoral response for encapsulation of and killing invading pathogens. It is mediated by a protease cascade composed of a modular serine protease (SP), and clip domain SPs (cSPs), which converts prophenoloxidase (PPO) into active phenoloxidase (PO). To date, melanization pathway in cotton bollworm Helicoverpa armigera, an important agricultural pest, remains largely unclear. To biochemically reconstitute the pathway in vitro, the putative proteases along with modified proteases containing the factor Xa cleavage site were expressed by Drosophila S2 cell expression system. Purified recombinant proteins were used to examine their role in activating PPO. It is revealed that cascade is initiated by a modular SP-SP41, followed by cSP1 and cSP6. The three-step SP41/cSP1/cSP6 cascade could further activate PPO, and the PO activity was significantly enhanced in the presence of two cSP homologs (cSPHs), cSPH11 and cSPH50, suggesting the latter are cofactors for PPO activation. Moreover, baculovirus infection was efficiently blocked by the reconstituted PPO activation cascade, and the effect was boosted by cSPH11 and cSPH50. Taken together, we unraveled a conserved PPO activation cascade in H. armigera, which is similar to that exists in lepidopteran biochemical model Manduca sexta and highlighted its role in antagonizing viral infection.
Adversarial Deconfounding Autoencoder for Learning Robust Gene Expression Embeddings

Motivation Increasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in large numbers, inherently contain variations introduced by technical artifacts (e.g., batch effects) and uninteresting biological variables (e.g., age) in addition to the true signals of interest. These sources of variations, called confounders, produce embeddings that fail to transfer to different domains, i.e., an embedding learned from one dataset with a specific confounder distribution does not generalize to different distributions. To remedy this problem, we attempt to disentangle confounders from true signals to generate biologically informative embeddings. Results In this paper, we introduce the AD-AE (Adversarial Deconfounding AutoEncoder) approach to deconfounding gene expression latent spaces. The AD-AE model consists of two neural networks: (i) an autoencoder to generate an embedding that can reconstruct original measurements, and (ii) an adversary trained to predict the confounder from that embedding. We jointly train the networks to generate embeddings that can encode as much information as possible without encoding any confounding signal. By applying AD-AE to two distinct gene expression datasets, we show that our model can (1) generate embeddings that do not encode confounder information, (2) conserve the biological signals present in the original space, and (3) generalize successfully across different confounder domains. We demonstrate that AD-AE outperforms standard autoencoder and other deconfounding approaches. Availability Our code and data are available at https://gitlab.cs.washington.edu/abdincer/ad-ae. Contact abdincer@cs.washington.edu; suinlee@cs.washington.edu
The CRISPR-Cas9 crATIC HeLa transcriptome: Characterization of a novel cellular model of ATIC deficiency and ZMP accumulation

In de novo purine biosynthesis (DNPS), 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase (2.1.2.3) / inosine monophosphate cyclohydrolase (3.5.4.10) (ATIC) catalyzes the last two reactions of the pathway: conversion of 5-aminoimidazole-4-carboxamide ribonucleotide [aka Z-nucleotide monophosphate (ZMP)] to 5-formamido-4-imidazolecarboxamide ribonucleotide (FAICAR) then to inosine monophosphate (IMP). ZMP is an adenosine monophosphate (AMP) mimetic and a known activator of AMP-activated protein kinase (AMPK). Recently, a HeLa cell line null mutant for ATIC was constructed via CRISPR-Cas9 mutagenesis. This mutant, crATIC, accumulates ZMP during purine starvation. Given that the mutant can accumulate ZMP in the absence of treatment with exogenous compounds, crATIC is likely an important cellular model of DNPS inactivation and ZMP accumulation. In the current study, we characterize the crATIC transcriptome versus the HeLa transcriptome in purine-supplemented and purine-depleted growth conditions. We report and discuss transcriptome changes with particular relevance to Alzheimer’s disease and in genes relevant to lipid and fatty acid synthesis, neurodevelopment, embryogenesis, cell cycle maintenance and progression, extracellular matrix, immune function, TGFβ and other cellular processes.
Semi-automated annotation of known and novel cancer long noncoding RNAs with the Cancer LncRNA Census 2 (CLC2)

Long noncoding RNAs (lncRNAs) can promote or repress the cellular hallmarks of cancer. Understanding their molecular roles and realising their therapeutic potential depend on high-quality catalogues of cancer lncRNA genes. Presently, such catalogues depend on labour-intensive curation of heterogeneous data with permissive criteria, resulting in unknown numbers of genes without direct functional evidence. Here, we present an approach for semi-automated curation focused exclusively on pathogenic functionality. The result is Cancer LncRNA Census 2 (CLC2), comprising 492 gene loci in 33 cancer types. To complement manual literature curation, we develop an automated pipeline, CLIO-TIM, to identify novel cancer lncRNAs based on functional evolutionary conservation with mouse. This yields 95 novel lncRNAs, which display characteristics of known cancer genes and include LINC00570 ( ncRNA-a5 ), which we demonstrate experimentally to promote cell proliferation. The clinical importance and curation accuracy of CLC2 lncRNAs is highlighted by a range of features, including evolutionary selection, expression in tumours, and both somatic and germline polymorphisms. The entire dataset is available in a highly-curated format facilitating the widest range of downstream applications. In summary, we show how manual and automated methods can be integrated to catalogue known and novel functional cancer lncRNAs with unique genomic and clinical properties.
Collateral Sensitivity Interactions between Antibiotics Depend on Local Abiotic Conditions

When bacteria become resistant to an antibiotic, the genetic changes involved sometimes increase (cross-resistance) or decrease (collateral sensitivity) their resistance to other antibiotics. Antibiotic combinations showing repeatable collateral sensitivity could be used in treatment to slow resistance evolution. ABSTRACT Mutations conferring resistance to one antibiotic can increase (cross-resistance) or decrease (collateral sensitivity) resistance to others. Antibiotic combinations displaying collateral sensitivity could be used in treatments that slow resistance evolution. However, lab-to-clinic translation requires understanding whether collateral effects are robust across different environmental conditions. Here, we isolated and characterized resistant mutants of Escherichia coli using five antibiotics, before measuring collateral effects on resistance to other paired antibiotics. During both isolation and phenotyping, we varied conditions in ways relevant in nature (pH, temperature, and bile). This revealed that local abiotic conditions modified expression of resistance against both the antibiotic used during isolation and other antibiotics. Consequently, local conditions influenced collateral sensitivity in two ways: by favoring different sets of mutants (with different collateral sensitivities) and by modifying expression of collateral effects for individual mutants. These results place collateral sensitivity in the context of environmental variation, with important implications for translation to real-world applications. IMPORTANCE When bacteria become resistant to an antibiotic, the genetic changes involved sometimes increase (cross-resistance) or decrease (collateral sensitivity) their resistance to other antibiotics. Antibiotic combinations showing repeatable collateral sensitivity could be used in treatment to slow resistance evolution. However, collateral sensitivity interactions may depend on the local environmental conditions that bacteria experience, potentially reducing repeatability and clinical application. Here, we show that variation in local conditions (pH, temperature, and bile salts) can influence collateral sensitivity in two ways: by favoring different sets of mutants during bacterial resistance evolution (with different collateral sensitivities to other antibiotics) and by modifying expression of collateral effects for individual mutants. This suggests that translation from the lab to the clinic of new approaches exploiting collateral sensitivity will be influenced by local abiotic conditions.