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Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.

Indolent T-cell lymphoma of the gastrointestinal tract (iTCL-GI) is rare, lacking standardized treatments. We report a successful venetoclax treatment in one patient with indolent T-cell lymphoma of the gastrointestinal tract. A 35-year-old male was admitted due to complaints of anemia and hematochezia. He was diagnosed with iTCL-GI according to histopathology and next-generation sequencing (NGS). He received the first cycle of CHOP-E chemotherapy, but he continued to have intermittent blood in stools. After starting oral Bcl-2 inhibitor venetoclax, the results of peripheral hemogram and the body temperature gradually turned normal, with no symptoms of hematochezia occurring again. In addition, colonoscopy showed improved ulcers in the ascending and transverse colon. Routine blood tests returned to normal without adverse effects. Therefore, venetoclax may represent a potential treatment approach for iTCL-GI. This report might provide clues for the future management of similar cases.

Optimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecular biology. These plans also consider patient-specific factors such as disease stage, age, and genetic mutation status. With the advancement of artificial intelligence (AI), more \"AI + medical\" application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to the diagnosis and treatment of hematological diseases, enhancing precision and efficiency and offering valuable solutions for clinical practice. This study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment. Using PubMed, Web of Science, and other network search engines, we conducted a literature search on studies from the past 5 years using the main keywords \"artificial intelligence\" and \"hematological diseases.\" We classified the clinical applications of AI systems according to the diagnosis and treatment. We outline and summarize the current advancements in AI for optimizing the diagnosis and treatment of hematological diseases, as well as the difficulties and challenges in promoting the standardization of clinical diagnosis and treatment in this field. AI can significantly shorten turnaround times, reduce diagnostic costs, and accurately predict disease outcomes through applications in image-recognition technology, genomic data analysis, data mining, pattern recognition, and personalized medicine. However, several challenges remain, including the lack of AI product standards, standardized data, medical-industrial collaboration, and the complexity and non-interpretability of AI systems. In addition, regulatory gaps can lead to data privacy issues. Therefore, more research and improvements are needed to fully leverage the potential of AI to promote standardization of the clinical diagnosis and treatment of hematological diseases. Our results serve as a reference point for the clinical diagnosis and treatment of hematological diseases and the development of AI-assisted clinical diagnosis and treatment systems. We offer suggestions for further development of AI in hematology and standardization of clinical diagnosis and treatment.

Background: T cell lymphoma is a complex and highly aggressive clinicopathological entity with a poor outcome. The angioimmunoblastic T-cell lymphoma (AITL) tumor immune microenvironment is poorly investigated. Methods: Here, to the best of our knowledge, spatial transcriptomics was applied for the first time to study AITL. Results: Using this method, we observed that AITL was surrounded by cells bearing immune-suppressive markers. CCL17 and CCL22, the dominant ligands for CCR4, were up-regulated, while the expression of natural killer (NK) cell and CD8+ cytotoxic T lymphocyte (CTL) markers decreased. Colocalization of Treg cells with the CD4+ TFH-GC region was also deduced from the bioinformatic analysis. The results obtained with spatial transcriptomics confirm that AITL has a suppressive immune environment. Chemotherapy based on the CHOP regimen (cyclophosphamide, doxorubicin, vincristine plus prednisone) induced complete remission (CR) in this AITL patient. However, the duration of remission (DoR) remains a concern. Conclusions: This study demonstrates that AITL has an immune suppressive environment and suggests that anti-CCR4 therapy could be a promising treatment for this lethal disease.

Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.

The heterogeneity in the health burden and non-infectious risk factors of Burkitt lymphoma (BL) across sex, age, and geographic distribution remain inadequately understood. Based on Global Burden of Disease study 2021, we estimate the health burden of BL from four metrics: incidence, mortality, prevalence, and disability-adjusted life years. Subgroups were stratified by age, sex, region, and socio-demographic index (SDI). Joinpoint regression was used to evaluate the average annual percentage change (AAPC) to quantify trends in the health burden. Predictions were performed using the Bayesian age-period-cohort model. Non-infectious risk factors were identified and analyzed utilizing summary exposure values (SEVs) to assess their impact on BL incidence and mortality. The estimated global incident number of BL was 19,073 (95% CI: 9651 to 32,509) in 2021, nearly threefold that of 1990. The health burden of BL was markedly higher in males than females, especially among individuals aged under 20 years. From 1990 to 2021, the most significant increasing trend in BL health burden was observed in Cabo Verde, while Georgia exhibited the most notable decline. From 2021 to 2040, the global age-standardized incidence and mortality rates were projected to decline by 14.7% and 24.7%, respectively. Conversely, the health burden on individuals aged 20 to 54 years was anticipated to rise through 2040. In our study, low bone mineral density was found to be linked with elevated risk for males aged over 54 years, while childhood sexual abuse exhibited a paramount positive association with BL risk for females, regardless of age. Notably, tobacco use, particularly secondhand smoking, were inversely associated with BL risk across all age groups and sexes. Burkitt lymphoma demonstrated unique distribution patterns in terms of age, sex, and region. Further investigations into the heterogeneity of the risk factors for BL are essential for the development of more effective health policies and clinical practices.

Multidrug resistance (MDR) remains the principal impediment to curative oncology, driven by complex interplays between cancer cells and the tumor microenvironment (TME). While nanomedicines have sought to overcome these delivery barriers, their clinical translation is often hampered by the heterogeneity of the enhanced permeability and retention (EPR) effect and by inefficient intratumoral delivery. In this review, we argue that overcoming MDR requires a transition beyond traditional passive drug delivery, advocating active, localized remodeling of the tumor ecosystem. Next-generation injectable hydrogels are increasingly recognized as localized viscoelastic niches that combine controlled intratumoral retention with the capacity to actively modulate biological responses within tumor TME. By converging principles of mechanobiology and immunometabolism, these hydrogels enable a multi-tiered strategy to dismantle multidimensional MDR. This approach begins with the biomechanical softening of the extracellular matrix to decouple mechanotransduction driven by Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), followed by the metabolic disruption of hypoxia-driven bioenergetics. Beyond the extracellular landscape, nanogel-enabled trafficking allows payloads to circumvent intracellular sequestration and efflux transporters, while immunomodulatory niches mobilize antitumor immunity through in situ vaccination and myeloid reprogramming. Finally, we evaluate the integration of artificial intelligence-driven design and patient-derived organoids as a technical bridge to reconcile laboratory ingenuity with clinical utility, aiming to transform the TME into a vulnerable therapeutic target.

[Purpose/Significance] Gradient theory reveals the causes and evolutionary trends of the uneven development of networks. As one of the most promising urban agglomerations in China, the middle reaches of Yangtze River urban agglomeration is the most representative national central urban agglomeration, which plays a key role in supporting the construction of central rising strategy. Therefore, it has become an important proposition of increasing concern for researchers and policy makers in the management field to study the technology innovation cooperation network in the middle reaches of Yangtze River urban agglomeration, in order to strengthen the innovation cooperation among cities and accelerate the construction of innovative cities and coordinated regional development. [Method/Process] Based on the perspective of gradient theory, this paper explores the structure and evolution of technology innovation cooperation networks in the middle reaches of Yangtze River urban agglomeration at the level of internal dynamics with the help of two important conditions that need to be met by high-gradient cities, evolving city structure stratification from core-edge structure and city role positioning from structural holes and intermediaries. [Results/Conclusions] It is found that the technology gradient in the middle reaches of Yangtze River urban agglomeration is mainly manifested by the polarization effect, and the technology gap between cities is widened. Due to the small number of high-gradient cities, they have not been able to form a driving scale benefit, and the overall regional technology innovation performance is not strong. This paper can provide useful references for the synergistic development of the middle reaches of Yangtze River urban agglomeration from the aspect of technology innovation cooperation.

The PHLDA3 gene encodes a small 127 amino acid protein with a pleckstrin homology (PH)-only domain. The expression and significance of PHLDA3 in lung cancer remain unclear. Here, we investigated the role of PHLDA3 in tumor proliferation and invasion in lung adenocarcinoma. Immunohistochemistry and immunoblotting analyses were used to assess PHLDA3 expression in lung cancer tissues, and its correlation with clinicopathological factors in lung cancer. Plasmids encoding PHLDA3 and small interfering RNA against PHLDA3 were used to regulate the expression of PHLDA3 in lung cancer cells. Furthermore, the effects of PHLDA3 on lung cancer cell proliferation and invasion were investigated using the MTS, colony formation, Matrigel invasion, and wound healing assays. Co-immunoprecipitation analysis and inhibitors of both the Wnt signaling pathway and GSK3β were used to explore the regulatory mechanisms underlying the role of PHLDA3 in lung cancer cells. PHLDA3 was found to be overexpressed in lung cancer tissues, and its expression was correlated with poor outcomes in lung adenocarcinoma patients. PHLDA3 expression promoted the proliferation, invasion, and migration of lung cancer cells. Overexpression of PHLDA3 activated the Wnt signaling pathway and facilitated epithelial–mesenchymal transition. Inhibition of Wnt signaling pathway activity, using XAV-939, reversed the effects of PHLDA3 overexpression in lung cancer cells; moreover, PHLDA3 could bind to GSK3β. Inhibition of GSK3β activity, using CHIR-99021, restored the proliferative and invasive abilities of PHLDA3 knockdown cells. Our findings demonstrate that PHLDA3 is highly expressed in lung adenocarcinomas and is correlated with poor outcomes. Furthermore, it promotes the proliferation and invasion of lung cancer cells by activating the Wnt signaling pathway. This paper demonstrates that PHLDA3 is highly expressed in lung adenocarcinomas and correlates with poor outcome. PHLDA3, in conjunction with GSK3β, promotes the proliferation and invasion of lung cancer cells by activating the Wnt signaling pathway, and facilitates epithelial–mesenchymal transition.