BACKGROUND: Asthma is a common chronic inflammatory airway disease and intermittent hypoxia is increasingly recognized as a factor that may impact disease progression. The present study investigated whether intermittent hypoxia (IH) could aggravate asthma by promoting hypoxia-inducible factor-1α (HIF-1α)/nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain-containing protein 3 (NLRP3)/interleukin (IL)-1β-dependent pyroptosis and the inflammatory response and further elucidated the underlying molecular mechanisms involved. METHODS: A total of 49 patients diagnosed with severe bronchial asthma and diagnosed by polysomnography were enrolled at Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, between January 2022 and December 2022, and their general data and induced sputum were collected. BEAS-2B cells were treated with IL-13 and subjected to IH. An ovalbumin (OVA)-treated mouse model was also used to assess the effects of chronic intermittent hypoxia (CIH) on asthma. Pyroptosis, the inflammatory response, and related signalling pathways were assessed in vivo and in vitro . RESULTS: In this study, as the apnoea and hypopnea index (AHI) increased, the proportion of patients with uncontrolled asthma increased. The proportions of neutrophils and the levels of IL-6, IL-8, HIF-1α and NLRP3 in induced sputum were related to the AHI. NLRP3-mediated pyroptosis, which could be mediated by the HIF-1α signalling pathway, was activated in IL-13 plus IH-treated BEAS-2B cells and in the lungs of OVA/CIH mice. HIF-1α downregulation significantly reduced lung pyroptosis and ameliorated neutrophil inflammation by modulating the NLRP3/IL-1β pathway both in vitro and in vivo . Similarly, pretreatment with LW6, an inhibitor of HIF-1α, effectively blocked the generation of inflammatory cytokines in neutrophils. In addition, administration of the NLRP3 activator nigericin obviously increased lung neutrophil inflammation. CONCLUSIONS Obstructive sleep apnoea-hypopnea syndrome (OSAHS) is a risk factor for asthma exacerbation. IH aggravates neutrophil inflammation in asthma via NLRP3/IL-1β-dependent pyroptosis mediated by the HIF-1α signalling pathway, which should be considered a potential therapeutic target for the treatment of asthma with OSAHS.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Humans ; Asthma/metabolism* ; Animals ; Pyroptosis/physiology* ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Mice ; Signal Transduction/physiology* ; Male ; Hypoxia/metabolism* ; Female ; Interleukin-1beta/metabolism* ; Adult ; Inflammation/metabolism* ; Middle Aged ; Mice, Inbred C57BL

In recent years, the application of artificial intelligence technology in rectal cancer radiotherapy has become increasingly significant. By constructing models from patient clinical information, accurate prediction of dose distribution, treatment effect, and toxic side effects of rectal cancer can be achieved. This allows optimizing the radiotherapy plan, ensuring the dose is focused on the tumor target area while reducing the radiation damage to the bladder, rectum, and other surrounding tissues. Thus, it can achieve precision and personalization in radiotherapy. In this review, the construction method of artificial intelligence predictive models was described, and the value of different predictive factors to the model was systematically analyzed, including patient clinical data, radiomics, and dosimetry. Moreover, the application and limitations of artificial intelligence predictive models in radiotherapy were summarized. This information can serve as a reference for the clinical application of artificial intelligence predictive models in rectal cancer radiotherapy.

Pancreatic cancer is a highly lethal malignancy that is usually diagnosed at an advanced stage because of its absence of early symptoms and lack of effective screening tools. Although existing treatments (e.g., surgery, chemotherapy, and radiotherapy) can provide temporary relief, the therapeutic effect of pancreatic cancer remains limited, with major issues related to drug resistance and recurrence. In recent years, immunotherapy and targeted therapy have provided renewed hope to the treatment of pancreatic cancer. The immune microenvironment of pancreatic cancer is complex and strongly immunosuppressive. Immune cells such as tumor-associated macrophages and regulatory T cells can weaken the antitumor function of the immune system by secreting inhibitory factors, thereby enabling the tumor to evade immune surveillance. Meanwhile, immune escape is exacerbated by the infiltration of hypo-immune cells and the role of tumor-associated fibroblasts in the tumor microenvironment of pancreatic cancer. In response to these immune escape mechanisms, combining immunotherapy with targeted therapy has emerged as a focal point of current research. This review compiles the characteristics of the immune microenvironment in pancreatic cancer based on current literature, aiming to provide a basis and insights for related drug development.

Pancreatic cancer patients are at the highest risk of malnutrition among all cancer patients,and radiochemotherapy may further exacerbate the risk of malnutrition in these patients.Malnutrition can reduce the therapeutic efficacy of radiochemotherapy and increase adverse reactions,thereby adversely impacting patients'quality of life and prognosis.Consequently,nutrition intervention is particularly crucial in the management of pancreatic cancer.Researches have shown that nutrition intervention can effectively ameliorate cachexia associated with pancreatic cancer,reduce the risk of malnutrition,and thereby maintain the sensitivity and tolerance to radiochemotherapy,alleviate adverse reactions,and reduce the complications of adjuvant therapy and surgical treatment.The combination of nutrition intervention and radiochemotherapy can disrupt the metabolism of pancreatic cancer and inhibit tumor growth,reduce the hospitalization rate of patients,and improve their quality of life.This review discusses the methods of nutritional assessment,nutrition intervention strategies for pancreatic cancer patients,and the impact of nutrition intervention on the efficacy and adverse reactions of radiochemotherapy in pancreatic cancer patients.

Objective To investigate the feasibility and accuracy of convolutional neural networks for automatically delineating the pancreatic cancer gross target volume(GTV)in pancreatic enhanced CT.Methods The localizable enhanced CT images of 114 patients with pancreatic cancer were retrospectively selected,in which the GTV was manually delineated using AccuContour.The imaging data were then import to AccuLearning and randomly divided as the training set,validation set and test set at a ratio of 8:1:1.Flex and Segresnet were used to train the automatic segmentation model,with each network structure trained continuously 3 times using fixed training parameters.The model was evaluated in terms of Dice similarity coefficient(DSC),95%Hausdorff distance(HD95),average symmetric surface distance(ASSD)and relative volume difference(RVD).Results In the model training phase,Flex-3 test results in Flex group were the worst,with a minimum DSC of 0.14%and an average DSC of 56.30%,while Flex-1 performed well,achieving a minimum DSC of 47.90%and an average DSC of 67.35%.Meanwhile,Segresnet-2 in Segresnet group had the worst test results,with a minimum DSC of 0.00%and an average DSC of 42.46%,while Segresnet-3 test results were better,with a minimum DSC of 42.65%and an average DSC of 63.28%.In the fixed testing phase,the best results among all were as follows:average DSC and RVD values of 63.88%and 29.41%in Segresnet-3 group,average ASSD value of 4.43 mm in Segresnet-2 group,and average HD95 value of 12.87 mm in Segresnet-1 group.Conclusion Both Flex and Segresnet architectures of convolutional neural network can be used for the automatic pancreatic tumor GTV segmentation training,with Segresnet performing better in comprehensive evaluation.

Prostate cancer is the second most common malignancy among men worldwide. Owing to its unique biological characteristics (a low α/β ratio), hypofractionated radiotherapy can improve tumor control in prostate cancer. Consequently, the American National Comprehensive Cancer Network (NCCN) guidelines have recommended hypofractionated radiotherapy as the preferred treatment option for localized prostate cancer. However, the use of hypofractionated radiotherapy in pelvic irradiation after radical prostatectomy remains limited, and its safety and efficacy are yet to be fully established. Investigating the feasibility of moderate-hypofractionated post-prostatectomy radiotherapy has therefore become a recent focus of clinical research. In this review, moderate-hypofractionated post-prostatectomy radiotherapy was categorized according to the per-fraction dose and current evidence was summarized from retrospective studies, prospective studies, and ongoing clinical trials.

Objective To investigate the feasibility and accuracy of convolutional neural networks for automatically delineating the pancreatic cancer gross target volume(GTV)in pancreatic enhanced CT.Methods The localizable enhanced CT images of 114 patients with pancreatic cancer were retrospectively selected,in which the GTV was manually delineated using AccuContour.The imaging data were then import to AccuLearning and randomly divided as the training set,validation set and test set at a ratio of 8:1:1.Flex and Segresnet were used to train the automatic segmentation model,with each network structure trained continuously 3 times using fixed training parameters.The model was evaluated in terms of Dice similarity coefficient(DSC),95%Hausdorff distance(HD95),average symmetric surface distance(ASSD)and relative volume difference(RVD).Results In the model training phase,Flex-3 test results in Flex group were the worst,with a minimum DSC of 0.14%and an average DSC of 56.30%,while Flex-1 performed well,achieving a minimum DSC of 47.90%and an average DSC of 67.35%.Meanwhile,Segresnet-2 in Segresnet group had the worst test results,with a minimum DSC of 0.00%and an average DSC of 42.46%,while Segresnet-3 test results were better,with a minimum DSC of 42.65%and an average DSC of 63.28%.In the fixed testing phase,the best results among all were as follows:average DSC and RVD values of 63.88%and 29.41%in Segresnet-3 group,average ASSD value of 4.43 mm in Segresnet-2 group,and average HD95 value of 12.87 mm in Segresnet-1 group.Conclusion Both Flex and Segresnet architectures of convolutional neural network can be used for the automatic pancreatic tumor GTV segmentation training,with Segresnet performing better in comprehensive evaluation.

Prostate cancer is the second most common malignancy among men worldwide. Owing to its unique biological characteristics (a low α/β ratio), hypofractionated radiotherapy can improve tumor control in prostate cancer. Consequently, the American National Comprehensive Cancer Network (NCCN) guidelines have recommended hypofractionated radiotherapy as the preferred treatment option for localized prostate cancer. However, the use of hypofractionated radiotherapy in pelvic irradiation after radical prostatectomy remains limited, and its safety and efficacy are yet to be fully established. Investigating the feasibility of moderate-hypofractionated post-prostatectomy radiotherapy has therefore become a recent focus of clinical research. In this review, moderate-hypofractionated post-prostatectomy radiotherapy was categorized according to the per-fraction dose and current evidence was summarized from retrospective studies, prospective studies, and ongoing clinical trials.

Objective To analyze the correlation between morphological and dosimetric parameters in patients with esophageal cancer at different locations using multi-to-multi double screening stepwise regression method,and to make simple predictions.Methods A retrospective analysis was conducted on 105 patients with advanced esophageal cancer who underwent radiotherapy at the First Affiliated Hospital of Fujian Medical University from 2019 to 2021.Morphological parameters of organs-at-risk were collected from CT images,and intensity-modulated radiotherapy plans were developed using Raystation4.7.The prescription doses for PTV-G and PTV-C were 60 Gy/30 F and 54 Gy/30 F,respectively.Multi-to-multi double screening stepwise regression method was employed to analyze the correlation between morphological and dosimetric parameters in esophageal cancer patients,and some preliminary predictions were provided.Results The dosimetric volume parameters of the lungs and heart were correlated with PTV-G volume,PTV-G length,PTV-G cross-sectional area,left and right lung volumes,lung length and total lung volume(P<0.05).For upper thoracic esophageal cancer,dosimetric volume parameters of the lungs and heart were correlated with PTV-G volume,PTV-G length,and right lung volume(P<0.05).For middle thoracic esophageal cancer,dosimetric volume parameters of the lungs,heart,and spinal cord were correlated with PTV-G volume,PTV-G length,PTV-G cross-sectional area,left and right lung volumes,and lung length(P<0.05).For lower thoracic esophageal cancer,dosimetric volume parameters of the lungs,heart,and spinal cord were correlated with PTV-G volume,PTV-G length,right lung volume,and lung length(P<0.05).Conclusion For patients with tumors at different locations,both overall and segmental analyses should be considered to balance therapeutic effect and side effects of radiotherapy,thereby maximizing the benefits for tumor patients.

Organoids are in vitro-cultured three-dimensional (3D) miniature structures derived from human pluripotent stem cells or adult stem cells from healthy individuals or patients. Compared to traditional two-dimensional (2D) cell lines or animal models, organoids are regarded as more promising high-fidelity models, possessing unique advantages in terms of disease modeling, drug development, the establishment of living biobanks, and the exploration of personalized treatment. Over recent years, the rapid development of organoid technology has brought new hopes for innovating preclinical experimental tumor models and promoting clinical personalized diagnosis and treatment. This review is intended to introduce the development status and latest progress of organoids in the field of radiotherapy for tumors, explore the advantages and limitations of organoid models for cancer, and prospect for its application in the field of radiotherapy.