Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.

The explosion of next-generation sequencing (NGS) has enabled the widespread use of genomic data in precision medicine. Currently, several neonatal genome projects have emerged to explore the advantages of NGS to diagnose or screen for rare genetic disorders. These projects have made remarkable achievements, but still the genome data could be further explored with the assistance of phenotype collection. In contrast, longitudinal birth cohorts are great examples to record and apply phenotypic information in clinical studies starting at the neonatal period, especially the trajectory analyses for health development or disease progression. It is obvious that efficient integration of genotype and phenotype benefits not only the clinical management of rare genetic disorders but also the risk assessment of complex diseases. Here, we first summarize the recent neonatal genome projects as well as some longitudinal birth cohorts. Then, we propose two simplified strategies by integrating genotypic and phenotypic information in precision medicine based on current studies. Finally, research collaborations, sociological issues, and future perspectives are discussed. How to maximize neonatal genomic information to benefit the pediatric population remains an area in need of more research and effort.

Cartilage, an important connective tissue, provides structural support to other body tissues, and serves as a cushion against impacts throughout the body. Found at the end of the bones, cartilage decreases friction and averts bone-on-bone contact during joint movement. Therefore, defects of cartilage can result from natural wear and tear, or from traumatic events, such as injuries or sudden changes in direction during sports activities. Overtime, these cartilage defects which do not always produce immediate symptoms, could lead to severe clinical pathologies. The emergence of induced pluripotent stem cells (iPSCs) has revolutionized the field of regenerative medicine, providing a promising platform for generating various cell types for therapeutic applications. Thus, chondrocytes differentiated from iPSCs become a promising avenue for non-invasive clinical interventions for cartilage injuries and diseases. In this review, we aim to highlight the current strategies used for in vitro chondrogenic differentiation of iPSCs and to explore their multifaceted applications in disease modeling, drug screening, and personalized regenerative medicine. Achieving abundant functional iPSC-derived chondrocytes requires optimization of culture conditions, incorporating specific growth factors, and precise temporal control. Continual improvements in differentiation methods and integration of emerging genome editing, organoids, and 3D bioprinting technologies will enhance the translational applications of iPSC-derived chondrocytes. Finally, to unlock the benefits for patients suffering from cartilage diseases through iPSCs-derived technologies in chondrogenesis, automatic cell therapy manufacturing systems will not only reduce human intervention and ensure sterile processes within isolator-like platforms to minimize contamination risks, but also provide customized production processes with enhanced scalability and efficiency.

BACKGROUND: Breast cancer (BC) is the world\'s largest tumor species in which hormone receptor-positive patients have relatively good prognosis. However, majority of patients will develop late resistance, one of the important factors is due to the loss of the original estrogen receptor (ER) expression.
METHODS: We conducted this study in 115 patients with BC who experienced second biopsy at Jiangsu Province Hospital (JSPH) and divided patients into two subgroups ER + to - and ER + to + . First, clinicopathological characteristics between two groups were evaluated. Second, we explored candidate genes related to BC ER intratumor heterogeneity by applying next-generation sequencing (NGS) in 42 patients. Multi-omics integrative analysis of tumor transcriptomic, cancer-related pathway, diagnostic and prognostic value and immune profile were conducted. Besides, preliminary assay were also used to evaluate the correlation between KMT2C and ERα (ESR1) expression. The CCK-8, 5-Ethynyl-2\'-deoxyuridine (EdU) assays, Transwell assays and the wound scratch tests were applied to explore the cellular interactions between KMT2C and BC.
RESULTS: We find the histological type (p = 0.008) and disease-free survival (DFS) (p = 0.004) were significantly different in two subgroups. In Cox survival analysis, metastasis (Hazard ratio (HR) > 1, p = 0.007) and neo-adjuvant (HR < 1, p < 0.001) are independent prognostic factors of DFS. Besides, by analyzing NGS results, we found four genes KMT2C, FGFR19, FGF1 and FGF4 were highly mutated genes in ER + to - subgroup. Furthermore, the gene KMT2C displayed significant diagnostic value and prognostic value in BC and pan-cancer. In addition, a positive correlation between KMT2C expression and immune infiltrating levels of T cell CD4 + , macrophage and neutrophil was found. In the end, Western blot and RT-qPCR assay were used and found KMT2C and ERα (ESR1) expressions are strongly positive correlated in mRNA and protein level. Inhibition of KMT2C significantly reduced proliferation, invasion, and migration of MCF7 cells.
CONCLUSIONS: People in two cohorts from JSPH presented different clinical characteristics and prognosis. The gene KMT2C may affect the progression of BC by regulating the molecular, epigenetic activity and immune infiltration. It may also serve as a novel prognostic biomarker for BC patients who underwent ER status converted from positive to negative.

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

It remains a substantial challenge to balance treatment efficacy and toxicity in geriatric patients with multiple myeloma (MM), primarily due to the dynamic nature of frailty. Here, we conducted a prospective study to evaluate the feasibility and benefits of dynamic frailty-tailored therapy (DynaFiT) in elderly patients. Patients with newly diagnosed MM (aged ≥ 65 years) received eight induction cycles of bortezomib, lenalidomide, and dexamethasone (daratumumab was recommended for frail patients), with treatment intensity adjusted according to longitudinal changes in the frailty category (IMWG-FI) at each cycle. Of 90 patients, 33 (37%), 16 (18%), and 41 (45%) were fit, intermediate fit, and frail at baseline, respectively. Of 75 patients who had geriatric assessment at least twice, 28 (37%) experienced frailty category changes at least once. At analysis, 15/26 (58%) frail patients improved (27% became fit and 31% became intermediate fit), 4/15 (27%) intermediate fit patients either improved or deteriorated (two for each), and 6/30 (20%) fit patients deteriorated. During induction, 34/90 (38%) patients discontinued treatment, including 10/33 (30%) fit, 4/16 (25%) intermediate fit, and 20/41 (49%) frail; 14/40 (35%) frail patients discontinued treatment within the first two cycles, mainly because of non-hematologic toxicity (mostly infections). For fit, intermediate-fit, and frail patients, the overall response rate was 100%, 93%, and 73%, respectively; one-year overall survival was 90%, 75%, and 54%, respectively. Therefore, the individualized DynaFiT is feasible and promising for heterogeneous elderly patients.

In this study, we aimed to elucidate the role of mitochondrial calcium uptake 1/2 (MiCU1/2) in breast cancer (BRCA) by employing a comprehensive multi-omics approach. Unlike previous research, we utilized a novel web application tailored for whole tumor tissue, single-cell, and spatial transcriptomics analysis to investigate the association between MiCU1/2 and the tumor immune microenvironment (TIME). Our gene set enrichment analysis (GSEA) provided insights into the primary biological effects of MiCU1/2, while our CRISPR-based drug screening repository identified potential effective drugs. Our study revealed that high MiCU1/2 expression serves as an independent diagnostic biomarker, correlating with advanced clinical status and indicating poorer recurrence-free survival (RFS) rates in BRCA patients. Additionally, spatial transcriptome analysis highlighted the heightened expression of MiCU1/2 in tumors and its relevance in surrounding immune cells. Furthermore, using the CIBERSORT algorithm, we discovered a positive correlation between MiCU1/2 levels and macrophage infiltration, underscoring their potential impact on immune infiltration. We also identified expression patterns of immune-related genes associated with responses against various immune cell types, including CXCL, MIF, GDF, SPP1, and IL16. Finally, our pharmacogenomic screening identified potential small molecule drugs capable of effectively targeting breast cancer cells with elevated MiCU1/2 expression. Overall, our study establishes MiCU1/2 as a promising novel biomarker for BRCA diagnosis and prognostic prediction, as well as a potential therapeutic target, highlighting the importance of exploring these pathways to advance patient care and outcomes in BRCA treatment.

This review presents a comprehensive exploration of gene editing technologies and their potential applications in the treatment of liver fibrosis, a condition often leading to serious complications such as liver cancer. Through an in-depth review of current literature and critical analysis, the study delves into the intricate signaling pathways underlying liver fibrosis development and examines the promising role of gene editing in alleviating this disease burden. Gene editing technologies offer precise, efficient, and reproducible tools for manipulating genetic material, holding significant promise for basic research and clinical practice. The manuscript highlights the challenges and potential risks associated with gene editing technology. By synthesizing existing knowledge and exploring future perspectives, this study aims to provide valuable insights into the potential of precision gene editing to combat liver fibrosis and its associated complications, ultimately contributing to advances in liver fibrosis research and therapy.

Breast cancer, a prevalent disease with significant mortality rates, often presents treatment challenges due to its complex genetic makeup. This review explores the potential of combining Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) gene knockout strategies with immunotherapeutic approaches to enhance breast cancer treatment. The CRISPR/Cas9 system, renowned for its precision in inducing genetic alterations, can target and eliminate specific cancer cells, thereby minimizing off-target effects. Concurrently, immunotherapy, which leverages the immune system\'s power to combat cancer, has shown promise in treating breast cancer. By integrating these two strategies, we can potentially augment the effectiveness of immunotherapies by knocking out genes that enable cancer cells to evade the immune system. However, safety considerations, such as off-target effects and immune responses, necessitate careful evaluation. Current research endeavors aim to optimize these strategies and ascertain the most effective methods to stimulate the immune response. This review provides novel insights into the integration of CRISPR/Cas9-mediated knockout strategies and immunotherapy, a promising avenue that could revolutionize breast cancer treatment as our understanding of the immune system\'s interplay with cancer deepens.

Microbial therapies have promising applications in the treatment of a broad range of diseases. However, effective colonization of the target region by therapeutic microorganisms remains a significant challenge owing to the complexity of the intestinal system. Here, we developed surface nanocoating-based universal platform (SNUP), which enabled the manipulation of controlled release and targeted colonization of therapeutic microbes in the digestive tract without the utilization of any targeting molecules. The system controlled the decomposition time of SNUP in the gut by regulating different modification layers and modification sequences on the microorganism\'s surface, so that the microorganism was released at a predetermined time and space. With the SNUP nanomodification technology, we could effectively deliver therapeutic microorganisms to specific complex intestinal regions such as the small intestine and colon, and protect the bioactivity of therapeutic microorganisms from destruction by both strong acids and digestive enzymes. In this study, we found that two layers SNUP-encapsulated Liiliilactobacillus salivarius (LS@CCMC) could efficiently colonize the small intestine and significantly improve the symptoms of a mouse model of Parkinson\'s disease through sustained secretion of γ-aminobutyric acid (GABA). This surface nanocoating-based universal platform system does not require the design of specific targeting molecules, providing a simple and universal method for colonized microbial therapy, target theranostics, precision medicine, and personalized medicine.