Volume 3, Issue 6

Systemic lupus erythematosus (SLE) is a complex autoimmune disease. Current SLE therapies include immunosuppressants, antimalarial drugs, non-steroidal anti-inflammatory drugs (NSAIDs), and corticosteroids, but these treatments can cause substantial toxicities to organs and may not be effective for all patients. In recent years, significant progress has been made in the treatment of SLE using immunotherapy, including Benlysta and Saphnelo. These advances in immunotherapy hold promise for SLE patients, providing new therapeutic options that may offer better clinical benefit and effectiveness. Simultaneously, several new biological therapies focusing on cytokines, peptides, targeted antibodies, and cell-based approaches are under clinical evaluation and have shown immense potential for the treatment of SLE. However, the complexity of SLE immunopathogenesis and disease heterogeneity present significant challenges in the development of effective immunological therapies. This review aims to discuss past experiences and understanding of diverse immunological targeting therapies for SLE and highlight future perspectives for the development of novel immunological therapies.

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

This perspective discussed the available evidence on the involvement of mTOR pathway in antiphospholipid syndrome (APS), from the aspects of endothelial cells, platelets, monocytes and anti-phospholipid antibodies (PLs), which may lead to future therapeutic applications of mTOR inhibition in APS.

The kidneys are susceptible to a range of insults that can cause damage to them. Early diagnosis, timely prevention, and proper treatment are crucial for improving the outcome of kidney injury. However, the complexity of renal structure and function makes it difficult to reach the demand of early detection and comprehensive evaluation of kidney injury. No successful drug therapy caused by the elaborate pathogenesis mechanism network of kidney injury calls for a systematical interpretation in mechanism researches. Recent advances in renal imaging and omics studies have provided novel views and deeper insights into kidney injury, but also raise challenges in reaching a comprehensive cellular and molecular atlas of kidney injury. Progresses in imaging and omics of kidney injury are being made in various directions, with the initiative of construction a high-resolution structural atlas of kidney, dynamic and non-invasive evaluation of renal function, and systematic establishment of spatially resolved molecular atlas by transcriptomics and metabolomics. With the limitations of a single modality, novel multimodal integration technologies of imaging and omics are being attempted to achieve a systematic description of nephropathy mechanisms. Further extensive efforts in renal multimodal imaging and omics studies are extremely required to deepen our understanding on kidney injury in the context of diagnostic, mechanistic and therapeutic perspectives.

The success and safety seen in treating complement-mediated hemolysis conditions has sparked the development of targeted therapies for rare autoimmune diseases, with expansion to more common autoimmune conditions. Various classes of drugs, including small molecules, peptides, monoclonal antibodies, and small interfering RNA (siRNA), are undergoing development to specifically address complement activity. A dual approach targeting both complement and other immune components may be required for autoimmune diseases characterized by inflammation and complex pathogenic mechanisms. siRNA, which suppresses complement production, is emerging as a potent therapeutic tool. Combining a complement-blocking siRNA drug with a treatment that reduces autoantibodies could prove clinically feasible and impactful in managing these conditions.

Considering the main factor that causes or triggers depression in humans is stress. Several stress factors are applied to form depression-like symptoms in rodents. Depression tests are used to analyze the nature and patterns of depression. Well-founded modeling and versatile evaluation of tests are necessary to investigate a hypothesis that is related to depression. It is impossible to model or test all aspects of depression in humans by using experimental animals. As a result, the aims of the study should be determined specifically in depression models. The correct interpretation of the tests that are suitable for these aims is indispensable for the reliability of the data. To achieve this goal, the biological basis of the depression-related behaviors of animals should be well known. In this review, model and test concepts related to depression are discussed and behavioral patterns of rodents are explained with several examples.