Class B Scavenger Receptor CD36 as a Potential Therapeutic Target in Inflammation Induced by Danger-Associated Molecular Patterns.

The class B scavenger receptor CD36 is known to bind and mediate the transport of lipid-related ligands and it functions as a pattern recognition receptor (PRR) for a variety of pathogens, including bacteria and viruses. In this study, we assessed CD36's role as a PRR mediating pro-inflammatory effects of several known Danger-Associated Molecular Patterns (DAMPs) used either as a single preparation or as a combination of DAMPs in the form of total cell/skeletal muscle tissue lysates. Our data demonstrated that multiple DAMPs, including HMGB1, HSPs, histone H3, SAA, and oxPAPC, as well as cell/tissue lysate preparations, induced substantially higher (~7-10-fold) IL-8 cytokine responses in HEK293 cells overexpressing CD36 compared to control WT cells.

Case Report of Herpes Zoster Infection of the Cervical Dermatome - A Rare Cause of Acute Shoulder Pain.

Introduction: Acute shoulder pain is among the most common presenting complaints in the clinic. Clinicians may find it challenging with the myriad of potential etiologies to explain for the presenting complaint. We present a unique case, where a herpes zoster infection in the C6 dermatome mimicked the presentation of rotator cuff pathology and was admitted to the care of orthopedics.

Macrophage depletion protects against cisplatin-induced ototoxicity and nephrotoxicity.

Cisplatin is a widely used anticancer drug with notable side effects including ototoxicity and nephrotoxicity. Macrophages, the major resident immune cells in the cochlea and kidney, are important drivers of both inflammatory and tissue repair responses. To investigate the roles of macrophages in cisplatin-induced toxicities, we used PLX3397, a U.

Urinary MicroRNA biomarkers of nephrotoxicity in Macaca fascicularis.

Drug-induced kidney injury (DIKI) refers to kidney damage resulting from the administration of medications. The aim of this project was to identify reliable urinary microRNA (miRNAs) biomarkers that can be used as potential predictors of DIKI before disease diagnosis. This study quantified a panel of six miRNAs (miRs-210-3p, 423-5p, 143-3p, 130b-3p, 486-5p, 193a-3p) across multiple time points using urinary samples from a previous investigation evaluating effects of a nephrotoxicant in cynomolgus monkeys.

BindingSiteDTI: differential-scale binding site modelling for drug-target interaction prediction.

Motivation: Enhanced by contemporary computational advances, the prediction of drug-target interactions (DTIs) has become crucial in developing de novo and effective drugs. Existing deep learning approaches to DTI prediction are frequently beleaguered by a tendency to overfit specific molecular representations, which significantly impedes their predictive reliability and utility in novel drug discovery contexts. Furthermore, existing DTI networks often disregard the molecular size variance between macro molecules (targets) and micro molecules (drugs) by treating them at an equivalent scale that undermines the accurate elucidation of their interaction.

CD9 shapes glucocorticoid sensitivity in pediatric B-cell precursor acute lymphoblastic leukemia.

Resistance to glucocorticoids (GC), the common agents for remission induction in pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL), poses a significant therapeutic hurdle. Therefore, dissecting the mechanisms shaping GC resistance could lead to new treatment modalities. Here, we showed that CD9- BCP-ALL cells were preferentially resistant to prednisone and dexamethasone over other standard cytotoxic agents.

Allosteric Feature Collaboration for Model-Heterogeneous Federated Learning.

Although federated learning (FL) has achieved outstanding results in privacy-preserved distributed learning, the setting of model homogeneity among clients restricts its wide application in practice. This article investigates a more general case, namely, model-heterogeneous FL (M-hete FL), where client models are independently designed and can be structurally heterogeneous. M-hete FL faces new challenges in collaborative learning because the parameters of heterogeneous models could not be directly aggregated.

Effectiveness of telehealth in preventive care: a study protocol for a randomised controlled trial of tele-exercise programme involving older people with possible sarcopenia or at risk of fall.

Background: Continuous loss of muscle mass and strength are the consequences of the ageing process, which increase the risk of falls among older people. Falls can lead to severe consequences such as bone fractures and hampered physical and psychological well-being. Regular exercise is the key to reversing muscle atrophy and relieving sarcopenia.

Macrophage Depletion Protects Against Cisplatin-Induced Ototoxicity and Nephrotoxicity.

Cisplatin is a widely used and highly effective anti-cancer drug with significant side effects including ototoxicity and nephrotoxicity. Macrophages, the major resident immune cells in the cochlea and kidney, are important drivers of both inflammatory and tissue repair responses. To investigate the roles of macrophages in cisplatin-induced ototoxicity and nephrotoxicity, we used PLX3397, an FDA-approved inhibitor of the colony-stimulating factor 1 receptor (CSF1R), to eliminate tissue-resident macrophages during the course of cisplatin administration.

A Liver Stiffness-Based Etiology-Independent Machine Learning Algorithm to Predict Hepatocellular Carcinoma.

Background & Aims: The existing hepatocellular carcinoma (HCC) risk scores have modest accuracy, and most are specific to chronic hepatitis B infection. In this study, we developed and validated a liver stiffness-based machine learning algorithm (ML) for prediction and risk stratification of HCC in various chronic liver diseases (CLDs).

Methods: MLs were trained for prediction of HCC in 5155 adult patients with various CLDs in Korea and further tested in 2 prospective cohorts from Hong Kong (HK) (N = 2732) and Europe (N = 2384).

Robust Remote Photoplethysmography Estimation With Environmental Noise Disentanglement.

Remote Photoplethysmography (rPPG) has been attracting increasing attention due to its potential in a wide range of application scenarios such as physical training, clinical monitoring, and face anti-spoofing. On top of conventional solutions, deep-learning approach starts to dominate in rPPG estimation and achieves top-level performance. However, most of them try to integrate preprocessing steps such as the ROI selection into an end-to-end network, which may diverge the attention and also limit the generalization in other scenarios with different input skin regions.

Local Style Transfer via Latent Space Manipulation for Cross-Disease Lesion Segmentation.

Automatic lesion segmentation is important for assisting doctors in the diagnostic process. Recent deep learning approaches heavily rely on large-scale datasets, which are difficult to obtain in many clinical applications. Leveraging external labelled datasets is an effective solution to tackle the problem of insufficient training data.

Snare Tip Soft Coagulation vs Argon Plasma Coagulation vs No Margin Treatment After Large Nonpedunculated Colorectal Polyp Resection: a Randomized Trial.

Background & Aims: Thermal treatment of the defect margin after endoscopic mucosal resection (EMR) of large nonpedunculated colorectal lesions reduces the recurrence rate. Both snare tip soft coagulation (STSC) and argon plasma coagulation (APC) have been used for thermal margin treatment, but there are few data directly comparing STSC with APC for this indication.

Methods: We performed a randomized 3-arm trial in 9 US centers comparing STSC with APC with no margin treatment (control) of defects after EMR of colorectal nonpedunculated lesions ≥15 mm.

BAM15 treats mouse sepsis and kidney injury, linking mortality, mitochondrial DNA, tubule damage, and neutrophils.

Sepsis pathogenesis is complex and heterogeneous; hence, a precision-medicine strategy is needed. Acute kidney injury (AKI) following sepsis portends higher mortality. Overproduction of mitochondrial ROS (mtROS) is a potential mediator of sepsis and sepsis-induced AKI.

Sidelobe Suppression Techniques for Near-Field Multistatic SAR.

Multirotor Unmanned Air Systems (UAS) represent a significant improvement in capability for Synthetic Aperture Radar (SAR) imaging when compared to traditional, fixed-wing, platforms. In particular, a swarm of UAS can generate significant measurement diversity through variation of spatial and frequency collections across an array of sensors. In such imaging schemes, the image formation step is challenging due to strong extended sidelobe; however, were this to be effectively managed, a dramatic increase in image quality is theoretically possible.

Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future.

Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability.

IL-18 deficiency ameliorates the progression from AKI to CKD.

Inflammation is an important factor in the progression from acute kidney injury (AKI) to chronic kidney disease (CKD). The role of interleukin (IL)-18 in this progression has not been examined. We aimed to clarify whether and how IL-18 limits this progression.

Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation.

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate pneumonia infection segmentation is important for assisting doctors in diagnosing COVID-19. Deep learning-based methods can be developed for automatic segmentation, but the lack of large-scale well-annotated COVID-19 training datasets may hinder their performance.

Pharmacogenomic Profiling of Pediatric Acute Myeloid Leukemia to Identify Therapeutic Vulnerabilities and Inform Functional Precision Medicine.

Unlabelled: Despite the expanding portfolio of targeted therapies for adults with acute myeloid leukemia (AML), direct implementation in children is challenging due to inherent differences in underlying genetics. Here we established the pharmacologic profile of pediatric AML by screening myeloblast sensitivity to approved and investigational agents, revealing candidates of immediate clinical relevance. Drug responses ex vivo correlated with patient characteristics, exhibited age-specific alterations, and concorded with activities in xenograft models.

The effect of continuous intravenous norepinephrine infusion on systemic hemodynamics in a telemetrically-monitored mouse model of sepsis.

Sepsis, a life-threatening organ dysfunction, results from dysregulated host responses to infection and still has a high incidence and mortality. Although administration of vasopressors to treat septic shock is standard of care, the benefits are not well established. We evaluated the effect of continuous intravenous norepinephrine infusion in a septic cecal ligation and puncture (CLP) mouse model, evaluating systemic hemodynamics and body temperature post-hoc.

Federated Generalized Face Presentation Attack Detection.

Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. An fPAD model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks. In reality, training data (both real face images and spoof images) are not directly shared between data owners due to legal and privacy issues.

Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation.

Automatic liver tumor segmentation could offer assistance to radiologists in liver tumor diagnosis, and its performance has been significantly improved by recent deep learning based methods. These methods rely on large-scale well-annotated training datasets, but collecting such datasets is time-consuming and labor-intensive, which could hinder their performance in practical situations. Learning from synthetic data is an encouraging solution to address this problem.