Phenomics (Cham, Switzerland)最新文献

With thousands of years of application history, traditional Chinese medicine (TCM) has unique advantages in the prevention of various chronic diseases, and in recent years, the development of TCM has presented a situation where opportunities and challenges coexist. Phenomics is an emerging area of life science research, which has numerous similarities to the cognitive perspective of TCM. Thus, how to carry out the interdisciplinary research between TCM and phenomics deserves in-depth discussion. Diabetes is one of the most common chronic non-communicable diseases around the world, and TCM plays an important role in all stages of diabetes treatment, but the molecular mechanisms are difficult to elucidate. Phenomics research can not only reveal the hidden scientific connotations of TCM, but also provide a bridge for the confluence and complementary between TCM and Western medicine. Facing the challenges of the TCM phenomics research, we suggest applying the State-target theory (STT) to overall plan relevant researches, namely, focusing on the disease development, change trends, and core targets of each stage, and to deepen the understanding of TCM disease phenotypes and the therapeutic mechanisms of herbal medicine.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00146-6.

The collection of big data on skin and appendage phenotypes has revolutionized the field of personalized diagnosis and treatment by enabling the evaluation of individual characteristics and early detection of abnormalities. To establish a standardized system for collecting and measuring big data on phenotypes, a systematic categorization of measurement entries has been undertaken, accompanied by recommendations on measurement entries, environmental equipment requirements, and collection processes, tailored to the needs of different usage scenarios. Specific collection sites have also been recommended based on different index characteristics. A multi-center, multi-regional collaboration has been initiated to collect big date on phenotypes of healthy and diseased skin in the Chinese population. This data will be correlated with patient disease information, exploring the factors influencing skin phenotype, analyzing the phenotypic data features that can predict prognosis, and ultimately promoting the exploration of the pathophysiology and pathogenesis of skin diseases and therapeutic approaches. Non-invasive skin measurement robots are also in development. This consensus aims to provide a reference for the study of phenomics and the standardization of phenotypic measurements of skin and appendages in China.

Pyroptosis is a newly recognized type of programmed cell death mediated by the gasdermin family and caspase. It is characterized by the formation of inflammasomes and the following inflammatory responses. Recent studies have elucidated the value of pyroptosis induction in cancer treatment. The inflammatory cytokines produced during pyroptosis can trigger immune responses to suppress malignancy. Physical approaches for cancer treatment, including radiotherapy, light-based techniques (photodynamic and photothermal therapy), ultrasound-based techniques (sonodynamic therapy and focused ultrasound), and electricity-based techniques (irreversible electroporation and radiofrequency ablation), are effective in clinical application. Recent studies have reported that pyroptosis is involved in the treatment process of physical approaches. Manipulating pyroptosis using physical approaches can be utilized in combating cancer, according to recent studies. Pyroptosis-triggered immunotherapy can be combined with the original anti-tumor methods to achieve a synergistic therapy and improve the therapeutic effect. Studies have also revealed that enhancing pyroptosis may increase the sensitivity of cancer cells to some physical approaches. Herein, we present a comprehensive review of the literature focusing on the associations between pyroptosis and various physical approaches for cancer and its underlying mechanisms. We also discussed the role of pyroptosis-triggered immunotherapy in the treatment process of physical manipulation.

Phenome has become a consensus as the next innovation source of biomedicine. As the global network dedicated to large-scale research efforts on human phenome and promoting the Human Phenome Project, the Board of International Human Phenome Consortium (IHPC) plays an essential role to guide the strategy and implementation of international human phenome project and to ensure coordination across the IHPC members. The 4th International Human Phenome Consortium Board Meeting was held virtually on December 13, 2022. During the meeting, the keynote speeches highlighted the latest advancements in phenomics. The construction and discoveries of the first human phenome Atlas had shown promising potential in limb development, disease prevention, and early diagnosis. Combining genome-phenome sequencing, analysis, and wellness coaching enhanced individual wellness. Phenomics trajectories from healthy to diseased states and recovery provided insight into the metabolic risk spaces associated with COVID-19. Board members from Ghana, Malaysia, India, and Russia presented their own plans and research progress. The IHPC Board deliberated on the "Framework Guidelines for Human Phenome-related Measurements" and "Proposal of the PhenoBank Initiative". The meeting also featured a presentation of the annual report of the IHPC Journal Phenomics. Laboratory coordination, interoperable databases, and standardized platforms were productively discussed, which would enable concerted research efforts of the Human Phenome Project.

It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00136-8.

Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.

The polymorphic microbiome has been defined as one of the "Hallmarks of Cancer". Extensive studies have now uncovered the role of oral microbiota in cancer development and progression. Bacteria, fungi, archaea, and viruses in the oral cavity interact dynamically with the oral microenvironment to maintain the oral micro-ecological homeostasis. This complex interaction is influenced by many factors, such as maternal transmission, personal factors and environmental factors. Dysbiosis of oral microbiota can disturbed this host-microbiota interaction, leading to systemic diseases. Numerous studies have shown the potential associations between oral microbiota and a variety of cancers. However, the underlying mechanisms and therapeutic insights are still poorly understood. In this review, we mainly focus on the following aspects: (1) the factors affect oral microbiota composition and function; (2) the interaction between microenvironment and oral microbiota; (3) the role of multi-kingdom oral microbiota in human health; (4) the potential underlying mechanisms and therapeutic benefits of oral microbiota against cancer. Finally, we aim to describe the impact of oral microbiota on cancer progression and provide novel therapeutic insights into cancer prevention and treatment by targeting oral microbiota.

Evidence for the influence of chronic inflammation induced by microbial dysbiosis on aberrant DNA methylation supports a plausible connexion between disordered microbiota and precancerous lesions of gastric cancer (PLGC). Here, a comprehensive study including multi-omics data was performed to estimate the relationships amongst the gastric microbiome, inflammatory proteins and DNA methylation alterations and their roles in PLGC development. The results demonstrated that gastric dysbacteriosis increased the risk of PLGC and DNA methylation alterations in related tumour suppressor genes. Seven inflammatory biomarkers were identified for antrum and corpus tissues, respectively, amongst which the expression levels of several biomarkers were significantly correlated with the microbial dysbiosis index (MDI) and methylation status of specific tumour suppressor genes. Notably, mediation analysis revealed that microbial dysbiosis partially contributed to DNA methylation changes in the stomach via the inflammatory cytokines C-C motif chemokine 20 (CCL20) and tumour necrosis factor receptor superfamily member 9 (TNFRSF9). Overall, these results may provide new insights into the mechanisms that might link the gastric microbiome to PLGC.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00118-w.

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).