Lupus

A new era of psoriasis treatment: Drug repurposing through the lens of nanotechnology and machine learning.

Psoriasis is a persistent inflammatory skin disorder characterized by hyper-proliferation and abnormal epidermal differentiation. Conventional treatments such as; topical therapies, phototherapy, systemic immune modulators, and biologics aim to relieve symptoms and improve patient quality of life. However, challenges like adverse effects, high costs, and individual response variability persist. Th...

Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.

Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory conditions than their natural counterparts. Deep learning-based approaches developed to address this problem have achieved significant success. However, these approaches often do not adequately emphasize the functional properties of proteins. In this stud...

A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control.

This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously d...

Disease diagnostics using machine learning of B cell and T cell receptor sequences.

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use o...

Plasma Cytokine and Chemokine Profiles Predict Efficacy and Toxicity of Anti-CD19 CAR-T Cell Therapy in Large B-Cell Lymphoma.

BACKGROUND: Anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising treatment for large B-cell lymphoma (LBCL); however,...

Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study.

PURPOSE: To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve diabetic macular edema (DME) to a single injection...

Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database.

OBJECTIVE: Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. ...

Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema.

Diabetic Macular Edema (DME) is a major complication of diabetic retinopathy characterized by fluid accumulation in the macula, leading to vision impa...

Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.

Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for c...

Concentration-dependent effects of immunomodulatory cocktails on the generation of leukemia-derived dendritic cells, DC mediated T-cell activation and on-target/off-tumor toxicity.

Acute myeloid leukemia (AML) remains a devastating diagnosis in clear need of therapeutic advances. Both targeted dendritic cells (DC) and particularl...

Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning.

INTRODUCTION: Lupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a ...

Ultrasensitive Detection of Circulating Plasma Cells Using Surface-Enhanced Raman Spectroscopy and Machine Learning for Multiple Myeloma Monitoring.

Multiple myeloma is a hematologic malignancy characterized by the proliferation of abnormal plasma cells in the bone marrow. Despite therapeutic advan...

Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model.

Human organic cation transporter 2 (hOCT2/SLC22A2) is a key drug transporter that facilitates the transport of endogenous and exogenous organic cation...

Therapeutic potential of against monkeypox: antioxidant, anti-inflammatory, and computational insights.

BACKGROUND: Monkeypox (Mpox) is a re-emerging zoonotic disease with limited therapeutic options, necessitating the exploration of novel antiviral agen...

Treatment of neovascular age-related macular degeneration: one year real-life results with intravitreal Brolucizumab.

BACKGROUND: Age-related macular degeneration (AMD) is a prevalent cause of irreversible vision loss worldwide, particularly among the elderly populati...

Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs i...

Opioid growth factor receptor overexpression exerts anti-hepatocellular carcinoma effects by activating P16 and P21 to inhibit proliferation and migration of HepG2 cells.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and the second leading cause of cancer death worldwide [19]. Opio...

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including th...

Machine Learning-Driven Prediction, Preparation, and Evaluation of Functional Nanomedicines Via Drug-Drug Self-Assembly.

Small molecules as nanomedicine carriers offer advantages in drug loading and preparation. Selecting effective small molecules for stable nanomedicine...

Automated Quantification of HER2 Amplification Levels Using Deep Learning.

HER2 assessment is necessary for patient selection in anti-HER2 targeted treatment. However, manual assessment of HER2 amplification is time-costly, l...