MedComm - Future medicine最新文献

Intrauterine adhesion (IUA) is a common endometrial disease caused by injury, leading to reproductive health issues. Current treatments have limited effectiveness, side effects, and high recurrence rates, especially, in severe cases. However, the underlying molecular and cellular mechanisms are largely unknown. Here we performed a comprehensive analysis by profiling integrated single-cell transcriptomes of over 72,000 individual endometrial cells, encompassing samples from both patients with IUA and those with normal endometrium. We identified changes in cell type-specific molecular signatures, including the inflammatory activation in immune cells, extensive damage in epithelial subpopulations, and the deposition of collagen secreted by fibroblasts subpopulations. Our results demonstrated activation of the TREM2⁺ macrophages, which displayed properties of inflammatory regulation. Annexin A1⁺ NK subpopulations exhibited the highest susceptibility among NK subtypes, displaying decreased cellular density and the most pronounced differential gene expression. Furthermore, we identified the matrix metallopeptidase 7 (MMP7⁺) and C-C motif chemokine ligand 5 (CCL5⁺) unciliated epithelial subtype originated from pituitary tumor-transforming gene 1 (PTTG1⁺) unciliated epithelium as the most vulnerable subpopulations to epithelial injury. Collectively, our study offers integrated resources of the cellular microenvironment of IUA, serving as a comprehensive cellular map of the disease in affected individuals. The insights gained from this study are expected to provide valuable resources for future diagnostic and therapeutic approaches.

Long coronavirus disease (COVID) is characterized by persistent symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and has emerged as a significant health concern. As SARS-CoV-2 evolved from the wild-type strain to the Alpha, Beta, Delta, and Omicron variants, there may be a variant-specific influence on long COVID akin to the acute disease. This review aims to summarize our current knowledge of variant-specific influences in long COVID incidence, symptom profile as well as mechanisms of pathogenesis. We highlight that long COVID incidence may be lower with the Omicron variants. The symptom profile of long COVID may also show some dependence on the different variants, with a reduction in cardiopulmonary symptoms with more recent SARS-CoV-2 variants. This heterogeneity of long COVID may also be related to the variant-specific differences in affecting the immune system, viral persistence, and autoimmunity. However, emerging data also suggest that vaccinations may play a big role in shaping the presentation of long COVID. We also highlight ongoing work on long COVID incidence and symptom profiles in populations infected only by the Omicron variants. This will be beneficial toward more useful disease definitions and the development of effective diagnostic and therapeutic strategies.

Next-generation sequencing (NGS) has emerged as a transformative technology in oncology, revolutionizing cancer diagnostics and personalized treatment strategies. By providing comprehensive insights into the genetic landscape of tumors, NGS enables the identification of critical somatic and germline mutations, copy number variations (CNVs), and gene fusions. Over the past decade, advancements in NGS platforms have led to greater accuracy, speed, and cost-effectiveness, making it an integral part of cancer research and clinical diagnostics. Despite its widespread adoption, significant challenges remain, including the need for improved methods to detect minimal residual disease (MRD) and accurately profile tumor heterogeneity. This review explores the evolution of NGS technologies and their pivotal role in cancer biology, from early diagnostics to therapeutic guidance. It delves into the application of NGS in identifying CNVs and gene fusions, monitoring MRD, and the increasing relevance of targeted NGS and spatial genomics. Furthermore, the integration of spatial transcriptomics is highlighted as a frontier in understanding the tumor microenvironment. By addressing these critical aspects, this review provides a comprehensive overview of how NGS is shaping the future of cancer research and treatment, offering a complete overview of potential NGS applications in scientific and clinical oncology.

Acute ischemic stroke (AIS) is characterized by high morbidity and mortality, making it crucial to identify the risk factors that influence its occurrence and prognosis. Although individuals with thyroid dysfunction exhibit altered stroke patterns, evidence from observational studies remains inconsistent. Herein, we investigated the influence of thyroid dysfunction on stroke progression and prognosis. We combined Mendelian randomization (MR) and tandem mass tag (TMT)-based quantitative proteomics analysis to study the influence of thyroid dysfunction on AIS. Differentially expression proteins (DEPs) were subsequently identified, functional enrichment analysis was performed, and a protein–protein interaction (PPI) network was constructed. Protein alterations were further validated by western blot. MR analysis revealed a causal association between thyroid disorders and ischemic stroke. DEP analysis identified 38 downregulated proteins and five upregulated proteins. Functional enrichment analysis and PPI network construction highlighted the importance of immune response activation and acute phase pathways, along with the suppression of focal adhesion, regulation of the actin cytoskeleton, and platelet activation pathways. Vasodilator-stimulated phosphoprotein, MYL12B, MYL6, and TPM4 were identified as key DEPs significantly associated with pathological pathways and were verified by western blot. The identification of these key proteins and pathways provides new perspectives for investigating the progression and prognosis of AIS.

Platelets play an irreplaceable role in hemostasis and wound healing. However, beyond these classical roles, as the smallest anucleate cells in the blood stream, they are crucial for immune response which have inflammatory functions through specialized receptors and different signaling pathways, influencing both innate and adaptive immune response. Furthermore, many research have proved that platelets significantly contribute to tumor metastasis and are associated with poor prognoses in cancer patients through its coagulability and supporting an immunosuppressive tumor microenvironment. When tumor cells detach from the primary tumor mass and enter the bloodstream, they rapidly initiate the direct activation and adhesion of platelets, forming a protective microenvironment. This environment shields circulating tumor cells (CTCs) from the mechanical shear forces of blood flow and immune surveillance. Here we delve into the interaction between platelets and immunomodulation and explore the multifaceted roles and underlying mechanisms by which platelets influence tumor cell metastasis and tumor growth. Furthermore, we also discussed the diagnostic role of platelets in cancer occurrence and progression, as well as the feasibility and prospects of targeting platelets for antitumor immunotherapy. This review provides a multidimensional perspective and reference for platelet-related cancer treatment strategies and diagnosis.

The detection of lymph node (LN) involvement is fundamental for staging colorectal cancer (CRC) and aids in clinical decision-making. Traditionally, determining LN status predominantly relies heavily on histological examination of LN specimens, which can occasionally lead to overtreatment. This study aims to develop a clinical prediction model using machine learning algorithms to assess the risk of mesenteric LN metastasis preoperatively, based on computed tomography images and clinicopathological data from CRC patients. Our findings demonstrate that the predictive model based on XGBoost algorithms exhibited the optimal performance, with area under the curve values consistently stable across training (0.836, 95% confidence interval [CI]: 0.750–0.902) and validation (0.831, 95% CI: 0.688–0.927) cohorts. The model was further elucidated using SHapley Additive Explanation values, which ranked predictors in the XGBoost model by their importance, providing insights into the model's decision-making process. Additionally, the force plot visualizes the contribution of each variable to the prediction for individual samples. The as-obtained model may have the potential to aid in clinical treatment planning, optimize the selection of surgical methods, and guide the decision-making process for adjuvant therapy before surgery.

Periodontal disease is a prevalent inflammatory condition affecting the tissues and bone surrounding the teeth, posing significant challenges to oral health. Current treatments often fall short due to incomplete removal of bacteria and plaque, irreversible damage to gums and bone, and their cost and time-consuming nature. Stem cell therapy offers a promising alternative that potentially addresses these limitations. This review delves into the biology, mechanisms, and applications of dental stem cells (DSCs) in alveolar bone and periodontal regeneration. We begin with an overview of various stem cell types and their characteristics, highlighting their role in tissue regeneration and the mechanisms involved. Subsequently, we examine preclinical and clinical studies that demonstrate the efficacy and safety of DSC therapy, showcasing their potential in restoring periodontal health. The discussion also extends to the critical challenges and future directions in the field, emphasizing the necessity for standardized protocols and rigorous research to fully integrate DSC-based therapies into routine clinical practice. This review underscores the transformative potential of DSCs in regenerative dentistry, aiming to provide a comprehensive understanding that can propel further advancements and ultimately improve patient outcomes in periodontal therapy.

Protein structure prediction (PSP) has been a prominent topic in bioinformatics and computational biology, aiming to predict protein function and structure from sequence data. The three-dimensional conformation of proteins is pivotal for their intricate biological roles. With the advancement of computational capabilities and the adoption of deep learning (DL) technologies (especially Transformer network architectures), the PSP field has ushered in a brand-new era of “neuralization.” Here, we focus on reviewing the evolution of PSP from traditional to modern deep learning-based approaches and the characteristics of various structural prediction methods. This emphasizes the advantages of deep learning-based hybrid prediction methods over traditional approaches. This study also provides a summary analysis of widely used bioinformatics databases and the latest structure prediction models. It discusses deep learning networks and algorithmic optimization for model training, validation, and evaluation. In addition, a summary discussion of the major advances in deep learning-based protein structure prediction is presented. The update of AlphaFold 3 further extends the boundaries of prediction models, especially in protein-small molecule structure prediction. This marks a key shift toward a holistic approach in biomolecular structure elucidation, aiming at solving almost all sequence-to-structure puzzles in various biological phenomena.