Oxidative stress (OS) is a chemical imbalance between an oxidant and an antioxidant, causing damage to redox signaling and control or causing molecular damage. Unbalanced oxidative metabolism can produce excessive reactive oxygen species (ROS). These excess ROS can cause drastic changes in platelet metabolism and further affect platelet function. It will also lead to an increase in platelet procoagulant phenotype and cell apoptosis, which will increase the risk of thrombosis. The creation of ROS and subsequent platelet activation, adhesion, and recruitment are then further encouraged in an auto-amplifying loop by ROS produced from platelets. Meanwhile, cancer cells produce a higher concentration of ROS due to their fast metabolism and high proliferation rate. However, excessive ROS can result in damage to and modification of cellular macromolecules. The formation of cancer and its progression is strongly associated with oxidative stress and the resulting oxidative damage. In addition, platelets are an important part of the tumor microenvironment, and there is a significant cross-communication between platelets and cancer cells. Cancer cells alter the activation status of platelets, their RNA spectrum, proteome, and other properties. The "cloaking" of cancer cells by platelets providing physical protection，avoiding destruction from shear stress and the attack of immune cells, promoting tumor cell invasion.We explored the vicious circle interaction between ROS, platelets, and cancer in this review, and we believe that ROS can play a stimulative role in tumor growth and metastasis through platelets.

The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.

Organelles do not act as autonomous discrete units but rather as interconnected hubs that engage in extensive communication by forming close contacts called “membrane contact sites (MCSs)”. And many proteins have been identified as residing in MCS and playing important roles in maintaining and fulfilling specific functions within these microdomains. However, a comprehensive compilation of these MCS proteins is still lacking. Therefore, we developed MCSdb, a manually curated resource of MCS proteins and complexes from publications. MCSdb documents 7010 MCS protein entries and 263 complexes, involving 24 organelles and 44 MCSs across 11 species. Additionally, MCSdb orchestrates all data into different categories with multitudinous information for presenting MCS proteins. In summary, MCSdb provides a valuable resource for accelerating MCS functional interpretation and interorganelle communication deciphering.

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available.

Background: Systemic immune-inflammation index (SII) is calculated by the value which is based on peripheral lymphocyte, neutrophil, and platelet counts, has been recently investigated as a valuable prognostic marker in several types of tumors. However, the combined prognostic value of SII for these tumors remains unclear. Here, we quantify the prognostic impact of this index and assess its consistency in a motley of cancers. Methods: We conducted a systematic review of PubMed/Medline, Cochrane Library, American Society of Clinical Oncology (ASCO), and European Society for Medical Oncology (ESMO) with a final date of April 20, 2017. Data from relevant studies reporting a hazard ratio (HR) and 95% confidence interval (CI) were pooled in a meta-analysis. The meta-analysis was conducted by using RevMan 5.3 and STATA software. Results: Thirteen studies comprising 4,104 patients were included in the final analysis. The median cutoff for SII was 575. The aggregate results indicated that elevated SII was associated with poor overall survival (OS) (HR =1.80; 95% CI: 1.43–2.28), poor cancer-specific survival (CSS) (HR =1.44; 95% CI: 1.04–1.99), time to recurrence (TTR) (HR =1.91; 95% CI: 1.53–2.43) and poor progression-free survival (PFS) (HR =1.18; 95% CI: 0.55–2.52). Further analysis found similar results in various subgroups stratified by cancer type, sample size, and ethnicity. Conclusions: Our meta-analysis shows that SII may be a potentially useful prognostic biomarker in patients with cancer. We suggest the need for further investigations to delineate the utility of SII for clinical decision making in this population.

Platelets are vital in many pathophysiological processes, yet there is a lack of a comprehensive resource dedicated specifically to platelet research. To fill this gap, we have developed PlateletBase, a knowledge base aimed at enhancing the understanding and study of platelets and related diseases. Our team retrieved information from various public databases, specifically extracting and analyzing RNA sequencing (RNA-seq) data from 3711 samples across 41 different conditions available on the National Center for Biotechnology Information (NCBI). PlateletBase offers six analytical and visualization tools, enabling users to perform gene similarity analysis, pair correlation, multi-correlation, expression ranking, clinical information association, and gene annotation for platelets. The current version of PlateletBase includes 10,278 genomic entries, 31,758 transcriptomic entries, 4869 proteomic entries, 2614 omics knowledge entries, 1833 drugs, 97 platelet resources, 438 diseases/traits, and six analysis modules. Each entry has been carefully curated and supported by experimental evidence. Additionally, PlateletBase features a user-friendly interface designed for efficient querying, manipulation, browsing, visualization, and analysis of detailed platelet protein and gene information. The case studies on gray platelet syndrome and angina pectoris demonstrate that PlateletBase is a suitable tool for identifying diagnostic biomarkers and exploring disease mechanisms, thereby advancing research in platelet functionality. PlateletBase is accessible at http://plateletbase.clinlabomics.org.cn/.