Xiujing He’s research while affiliated with Sichuan University and other places

Idiopathic granulomatous mastitis (IGM) is a noncancerous, chronic inflammatory disorder of breast with unknown causes, posing significant challenges to the quality of life due to its high refractoriness and local aggressiveness. The typical symptoms of this disease involve skin redness, a firm and tender breast mass and mastalgia; others may include swelling, fistula, abscess (often without fever), nipple retraction, and peau d’orange appearance. IGM often mimics breast abscesses or malignancies, particularly inflammatory breast cancer, and is characterized by absent standardized treatment options, inconsistent patient response and unknown mechanism. Definite diagnosis of this disease relies on core needle biopsy and histopathological examination. The prevailing etiological theory suggests that IGM is an autoimmune disease, as some patients respond well to steroid treatment. Additionally, the presence of concurrent erythema nodosum or other autoimmune conditions supports the autoimmune nature of the disease. Based on current knowledge, this review aims to elucidate the autoimmune-favored features of IGM and explore its potential etiologies. Furthermore, we discuss the immune-mediated pathogenesis of IGM using existing research and propose immunotherapeutic strategies for managing this condition.

The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of “omics” promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients’ cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.

Increasing evidence has proved that microbiota is not only the target of small molecule drugs but also an underexplored resource for developing small molecule drugs. Meanwhile, microbiota as a critical modulator of the immune system impacts the efficacy and toxicity of cancer immunotherapy. Harnessing microbiota or developing microbiota-derived medications provide novel therapeutic strategies to overcome resistance to cancer immunotherapy and immune-related adverse events (irAEs). In this review, we elucidate how microbiota and their metabolites impact anti-tumor immunity and immunotherapy efficacy and highlight the potential of microbiota and their metabolites as a resource for small molecule drug discovery. We further overview the current landscape of clinical trials evaluating the potential effect of microbiota and their metabolites on immunotherapy outcomes, presenting future trends in the field of microbiota-based therapies. Microbiota-based therapies are promising therapeutic options to promote therapeutic efficacy and diminish the toxicity of immunotherapy.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technology.

Editorial on the Research Topic Linking cellular metabolism to hematological malignancies Convincing evidence has revealed that metabolic reprogramming orchestrates tumor initiation and progression, immune evasion, and drug resistance. Targeting metabolic vulnerabilities of tumors has remarkable advantages as a therapeutic strategy that exerts prominent antitumor effects, without affecting normal cell physiology. Indeed, the current treatment options based on metabolic reprogramming present impressive curative effects in solid tumors and hematological malignancies. A better understanding of tumor metabolic remodeling and immunometabolism will deepen the insights into disease mechanisms and promote the development of promising therapeutic strategies. This Research Topic intends to highlight the current understanding of the role of cellular metabolism in hematological malignancies, with the purpose of identifying new therapeutic targets and rational metabolic therapies alone or in combination with other regimens. The mechanism underlying metabolic reprogramming Arginine plays a multifaceted role in numerous crucial biological processes and exerts a significant impact on carcinogenesis and immune response. Arginine auxotrophic tumors, including many hematological malignancies, lose the ability to endogenously synthesize arginine; thus, arginine depletion therapy can serve as a promising antitumor therapeutic approach. Arginase is a prevalent arginine-depleting agent in clinical practice Frontiers in Oncology He X, Kashyap MK, Zhao ZJ, Yu J and Shi H (2022) Editorial: Linking cellular metabolism to hematological malignancies.

https://doi.org/10.1093/bjd/ljac035 As the most common melanoma subtype in patients with skin of colour, acral melanoma (AM) presents with an aggressive character and poor prognosis in clinics. Encouraged by the revolutionary achievement of immune checkpoint blockade in cutaneous melanoma, scientists have made an attempt to transfer this regimen to AM. In this issue of the BJD, Zhou et al. systematically characterize response patterns in patients with AM treated with anti-programmed death (PD)-1 monotherapy and they provide a prognostic model with six potential key factors.¹ Compared with cutaneous melanoma, AM shows lower tumour mutational burden but higher frequency of structural rearrangement and copy-number variations.² These characteristics imply a relatively weak and diversified response to T-cell-based immunotherapy. To provide guidance for precision treatment, the clinical response needs to be investigated comprehensively. In this article, the heterogeneous responses to immune checkpoint blockade are dissected at multiple levels, including interpatient, intrapatient and interlesion. Malignancies at different metastatic anatomical sites showed variant clinical responses. The best response rates were observed for metastases in lymph nodes whereas the worst were observed for liver metastases. Although diversified prognosis has been documented in the whole melanoma population previously,³ this study specified the outcomes in patients with AM.