Myelodysplastic syndrome (MDS), a myeloid tumor derived from the malignant clones of hematopoietic stem cells, has an annually increasing incidence. The contemporary research direction has shifted to analyzing the synergistic effect of immune surveillance collapse and abnormal bone marrow microenvironment in the pathological process of MDS. Against this backdrop, the immune checkpoint molecule TIM3 has emerged as a key target because of its persistently high expression on the surface of important immune cells such as T and NK cells. The abnormal activation of the TIM3 pathway is the mechanism by which solid tumors and hematological malignancies achieve immune escape and is a key hub in the formation of immune exhaustion phenotypes. This work integrates the original discoveries of our team with the latest international progress, systematically demonstrating the bidirectional regulatory network of TIM3 between the malignant clone proliferation of MDS and the immunosuppressive microenvironment. Integrating the evidence from emerging clinical trials allows us to consider the clinical significance of TIM3-targeted blocking for MDS, providing a transformative path to overcome the resistance of traditional treatments and marking a new chapter in the active immune reconstitution of MDS treatment.

Microglia, the resident immune cells in the central nervous system (CNS), rapidly transition from a resting to an active state in the acute phase of ischemic brain injury. This active state mediates a pro-inflammatory response that can exacerbate the injury. Targeting the pro-inflammatory response of microglia in the semi-dark band during this acute phase may effectively reduce brain injury. Shionone (SH), an active ingredient extracted from the dried roots and rhizomes of the genus Aster (Asteraceae), has been reported to regulate the inflammatory response of macrophages in sepsis-induced acute lung injury. However, its function in post-stroke neuroinflammation, particularly microglia-mediated neuroinflammation, remains uninvestigated. This study found that SH significantly inhibited lipopolysaccharide (LPS)-induced elevation of inflammatory cytokines, including interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and inducible nitric oxide synthase (iNOS), in microglia in vitro. Furthermore, the results demonstrated that SH alleviated infarct volume and improved behavioral performance in middle cerebral artery occlusion (MCAO) mice, which may be attributed to the inhibition of the microglial inflammatory response induced by SH treatment. Mechanistically, SH potently inhibited the phosphorylation of serine-threonine protein kinase B (AKT), mammalian target of rapamycin (mTOR), and signal transducer and activator of transcription 3 (STAT3). These findings suggest that SH may be a potential therapeutic agent for relieving ischemic stroke (IS) by alleviating microglia-associated neuroinflammation.
Animals ; Microglia/immunology* ; Mice ; Male ; Mice, Inbred C57BL ; Brain Ischemia/immunology* ; Neuroinflammatory Diseases/drug therapy* ; Neuroprotective Agents/administration & dosage* ; Interleukin-1beta/genetics* ; STAT3 Transcription Factor/genetics* ; TOR Serine-Threonine Kinases/genetics* ; Tumor Necrosis Factor-alpha/genetics* ; Proto-Oncogene Proteins c-akt/immunology* ; Nitric Oxide Synthase Type II/genetics* ; Lipopolysaccharides

Objective To investigate the clinical efficacy and aesthetic outcome of liposuction combined with mammary adenectomy through a periareolar small incision in the management of gynecomastia(GYN).Methods From January 2019 to June 2023,18 patients with GYN were admitted.All of them were treated with small incision through the areola combined with liposuction.The postoperative aesthetic effect,occurrence of complications and patient satisfaction of the patients were evaluated.Results All 18 patients in this study were follwed up for a period of 3 to 18 months.No serious complications such as wound infection or necrosis of the nipple-areola occurred.Pathological examinations were consistent with the diagnosis of GYN.Except for one patient,who exhibited slight skin folds in the surgical area at the 12-month follow-up,the other patients all achieved symmetrical and smooth chest contours with noticeable aesthetic improvement,resulting in a 100％patient satisfaction rate.Conclusion The combined approach of liposuction combined with mammary adenectomy through a periareolar small incision for the treatment of GYN is straightforward,minimally invasive,and yields satisfactory therapeutic and aesthetic outcomes.

Objective:To explore the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET-CT dual-modality habitat imaging technology in predicting the epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma. Methods:This study was designed as a cross-sectional study. Clinical and imaging data of 403 patients with lung adenocarcinoma who underwent 18F-FDG PET-CT imaging with definitive EGFR results from January 2018 to April 2022 at the Third Affiliated Hospital of Soochow University were retrospectively analyzed.The patients were divided into a development set (282 cases) and a validation set (121 cases) using a stratified random sampling method at a 7∶3 ratio. An adaptive clustering algorithm was used to segment the regions of interest, forming different habitats and obtaining derived parameters. Independent samples t-test or Mann-Whitney U test were used to compare clinical, imaging indicators, and habitat-derived parameters between EGFR mutant and wild-type patient. The clinical, imaging indicators, and habitat-derived parameters that showed statistically significant differences in univariate analysis were included in multivariate logistic regression to construct clinical and clinical-habitat combined models, respectively. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the model′s ability to predict EGFR mutations in lung adenocarcinoma. Additionally, the net reclassification index (NRI) was employed to assess the model′s classification improvement capability. Results:There were 249 cases of EGFR mutation and 154 cases of wild type. The optimal number of habitats was two, namely Habitat 1 and Habitat 2. The parameters included in the clinical model were smoking history, bronchial sign, pleural indentation sign, and tumor diameter. The parameters incorporated into the clinical-habitat combined model were smoking history, bronchial sign, pleural indentation sign, Habitat 2, and Habitat 1 voxel count. In the development set, the AUCs for predicting EGFR mutations in lung adenocarcinoma using the clinical model and the clinical-habitat combined model were 0.723 and 0.733, respectively, with no statistically significant difference ( Z=0.60, P=0.549); In the validation set, the AUCs were 0.684 and 0.715, respectively, with no statistically significant difference ( Z=1.32, P=0.186). The accuracy (0.694) and specificity (0.609) of the clinical-habitat combined model in the validation set were slightly higher than those of the clinical model (0.686 and 0.565, respectively). NRI analysis confirmed that the clinical-habitat combined model improved the correct classification of EGFR wild-type lung adenocarcinoma by 10.9% compared to the clinical model ( P=0.018). Conclusion:18F-FDG PET-CT dual-modality habitat imaging technology can be used to analyze the microenvironment of lung adenocarcinoma and has the potential in non-invasively predicting EGFR mutation status, providing an important basis for personalized and accurate treatment of patients with lung adenocarcinoma.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.

With the rapid development of generative adversarial networks (GAN), learning the mapping between CT and PET images enables cross-modality generation. This not only integrates anatomical and functional information to improve image quality, but also helps reduce the radiation exposure to some extent. Based on a review of representative GAN architectures such as conditional GAN and CycleGAN, this paper discusses their research progress and limitations in various application scenarios, including initial tumor diagnosis and staging, treatment evaluation and follow-up, as well as methods for reducing PET/CT radiation dose. Additionally, challenges related to small-sample learning, model interpretability, and cross-institutional standardization are highlighted, and the clinical application prospects of GAN-based cross-modality generation technology are explored.

Oncolytic virotherapy is a promising therapeutic approach treating tumors, where oncolytic viruses (OVs) can selectively infect and lyse tumor cells through replication, while also triggering long-lasting anti-tumor immune responses. Vaccinia virus (VV) has emerged as a leading candidate for use as an OV due to its broad cytophilicity and robust capacity to express exogenous genes. Consequently, oncolytic vaccinia virus (OVV) has entered clinical trials. This review provides an overview of the key strategies used in the development of OVV, summarizes the findings from clinical trials, and addresses the challenges that must be overcome in the advancement of OVV-based therapies. Furthermore, it explores potential future strategies for enhancing the development and clinical application of OVV, intending to improve tumor treatment outcomes. The review aims to facilitate the further development and clinical adoption of OVV, thereby advancing tumor therapies.
Vaccinia virus/physiology* ; Humans ; Oncolytic Virotherapy/methods* ; Oncolytic Viruses/physiology* ; Neoplasms/therapy* ; Animals

Objective To investigate the clinical efficacy and aesthetic outcome of liposuction combined with mammary adenectomy through a periareolar small incision in the management of gynecomastia(GYN).Methods From January 2019 to June 2023,18 patients with GYN were admitted.All of them were treated with small incision through the areola combined with liposuction.The postoperative aesthetic effect,occurrence of complications and patient satisfaction of the patients were evaluated.Results All 18 patients in this study were follwed up for a period of 3 to 18 months.No serious complications such as wound infection or necrosis of the nipple-areola occurred.Pathological examinations were consistent with the diagnosis of GYN.Except for one patient,who exhibited slight skin folds in the surgical area at the 12-month follow-up,the other patients all achieved symmetrical and smooth chest contours with noticeable aesthetic improvement,resulting in a 100％patient satisfaction rate.Conclusion The combined approach of liposuction combined with mammary adenectomy through a periareolar small incision for the treatment of GYN is straightforward,minimally invasive,and yields satisfactory therapeutic and aesthetic outcomes.

With the rapid development of generative adversarial networks (GAN), learning the mapping between CT and PET images enables cross-modality generation. This not only integrates anatomical and functional information to improve image quality, but also helps reduce the radiation exposure to some extent. Based on a review of representative GAN architectures such as conditional GAN and CycleGAN, this paper discusses their research progress and limitations in various application scenarios, including initial tumor diagnosis and staging, treatment evaluation and follow-up, as well as methods for reducing PET/CT radiation dose. Additionally, challenges related to small-sample learning, model interpretability, and cross-institutional standardization are highlighted, and the clinical application prospects of GAN-based cross-modality generation technology are explored.

Objective:To develop and assess a three-class machine learning model for predicting wild-type, 19 del, and 21 L858R mutations of the epidermal growth factor receptor (EGFR) in lung adenocarcinoma using 18F-FDG PET/CT radiomic features and clinical features. Methods:The retrospective data was collected from 703 patients (346 males, 357 females; age (64.3±9.0) years) with lung adenocarcinoma at the First People′s Hospital of Changzhou from January 2018 to June 2023. Patients were divided into the training set (563 cases) and test set (140 cases) at the ratio of 8∶2. Clinical features were selected using recursive feature elimination (RFE). Radiomic features were extracted from PET and CT images, and the optimal feature sets were selected using minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. Base models were constructed by using random forest (RF), logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron (MLP), and the stacking method was applied to establish the CT and PET ensemble models. Delong test was used to compare the AUC differences between the PET/CT combined model and the clinical + PET/CT integrated model.Results:Among 703 patients, 273 were with EGFR wild-type, 202 were with 19 del mutation, and 228 were with 21 L858R mutation. In the single-modal analysis, the AUCs of CT ensemble model in the training and test sets were 0.893 and 0.667, respectively, while the AUCs of PET ensemble model were 0.692 and 0.660. The AUC of PET/CT combined model were 0.897 in training set and 0.672 in test set. The AUC of clinical + PET/CT integrated model showed further improvement, with AUCs of 0.902 and 0.721 in training and test sets, respectively. Notably, the clinical + PET/CT integrated model outperformed PET/CT combined model in predicting wild-type EGFR (test set AUC: 0.784 vs 0.707; Z=3.28, P=0.001). Conclusion:The three-class model (clinical + PET/CT integrated model) based on 18F-FDG PET/CT radiomics and clinical features effectively predicts EGFR mutation subtypes in lung adenocarcinoma.