Objective To construct radiomics-semantic models to predict the treatment resistance of chemoradiotherapy in brain gliomas based on MRI and clinical data of multicenter patients.Methods Among 2 108 brain gliomas patients from five medical institutions,132 patients had residual gliomas after surgery.The clinical risk factors and multimodal MRI were collected.All patients were divided into training set(n=95)and validation set(n=37).The treatment response of gliomas after standardized chemoradiotherapy were divided into resistant and non-resistant types.The semantic features of MRI were evaluated by two radiologists.Three different segmentation regions of interest(ROI)were delineated to extract radiomics features.And that three groups of radiomics models were con-structed based on different sequence MRIs.The radiomics model with the best predictive efficacy in each group was selected and combined with MRI semantic features,three radiomics-semantic models(combined models)were established.Finally,a MRI semantic model,three groups of radiomics models and three combined models were developed.Results Comparisons between the different models showed that the radiomics-semantic model based on pre-operative T2-fluid attenuated inversion recovery(FLAIR)sequence,had the best predictive efficacy,the area under the curve(AUC)in the training and validation sets were 0.866[95％confidence interval(CI)0.790-0.942]and 0.810(95％CI 0.667-0.952),respectively.The radiomics-semantic model based on postoperative T1 WI sequence performed the second best,with the AUC of the training and validation sets being 0.812(95％CI 0.726-0.898)and 0.711(95％CI 0.541-0.881),respectively.Conclusion The combined models based on MRI radiomics and semantic features are able to predict the treatment resistance of chemoradiotherapy in brain gliomas patients,and may be used as an important basis for optimizing treatment.

As the most common phenotype of type 1 neurofibromatosis(NF1),plexiform neurofibromas(pNF)exhibit early asymptomatic presentation but multisite involvement,with a risk of progression.Imaging serves as vital tool for evaluation and management of NF1-associated pNF.The progresses of imaging for evaluating NF1-related pNF were reviewed in this article.

Objective To construct radiomics-semantic models to predict the treatment resistance of chemoradiotherapy in brain gliomas based on MRI and clinical data of multicenter patients.Methods Among 2 108 brain gliomas patients from five medical institutions,132 patients had residual gliomas after surgery.The clinical risk factors and multimodal MRI were collected.All patients were divided into training set(n=95)and validation set(n=37).The treatment response of gliomas after standardized chemoradiotherapy were divided into resistant and non-resistant types.The semantic features of MRI were evaluated by two radiologists.Three different segmentation regions of interest(ROI)were delineated to extract radiomics features.And that three groups of radiomics models were con-structed based on different sequence MRIs.The radiomics model with the best predictive efficacy in each group was selected and combined with MRI semantic features,three radiomics-semantic models(combined models)were established.Finally,a MRI semantic model,three groups of radiomics models and three combined models were developed.Results Comparisons between the different models showed that the radiomics-semantic model based on pre-operative T2-fluid attenuated inversion recovery(FLAIR)sequence,had the best predictive efficacy,the area under the curve(AUC)in the training and validation sets were 0.866[95％confidence interval(CI)0.790-0.942]and 0.810(95％CI 0.667-0.952),respectively.The radiomics-semantic model based on postoperative T1 WI sequence performed the second best,with the AUC of the training and validation sets being 0.812(95％CI 0.726-0.898)and 0.711(95％CI 0.541-0.881),respectively.Conclusion The combined models based on MRI radiomics and semantic features are able to predict the treatment resistance of chemoradiotherapy in brain gliomas patients,and may be used as an important basis for optimizing treatment.

Spinal cord injury(SCI)is a serious central nervous system traumatic disease that usually leads to severe neurological dysfunction below the site of injury.Studies have shown that SCI is closely related to oxidative stress,inflammatory responses,and mitochondrial dynamic imbalance.Mitochondrial dysfunction,such as accumulation of mitochondrial DNA damage,depletion of ATP,and impairment of oxidative stress regulation,can lead to dysfunction or death of neurons.Mitochondrial fusion plays a key role in maintaining energy balance and promoting neurological recovery after SCI.This review summarizes the changes in mitochondrial dynamics after SCI and the neuroprotective mechanisms mediated by mitochondrial fusion,with aim to provide new ideas on the treatment of SCI.

Objective To explore the functional connectivity(FC)changes of hippocampus and amygdala in type 2 diabetes mellitus(T2DM)patients with erectile dysfunction(DMED),and the central pathological neural mechanisms underlying DMED.Methods 61 T2DM patients who visited Department of Endocrinology,Nanjing First Hospital,Nanjing Medical University from January 2020 to December 2021 were selected and divided into a simple T2DM group(n=30)and a combined DMED group(n=31).Another 47 healthy individuals were selected as control group(NC).The international erectile function scale(IIEF-5)was used to evaluate the erectile function.Resting-state functional magnetic resonance imaging(rs-fMRI)data were acquired from all participants.MRI data were preprocessed by the DPABI software package.Bilateral hippocampus and amygdala were selected as regions of interest(ROI)and the whole brain FC values were calculated.The FC values of brain regions between groups were tested by two-sample t-test with REST software package.Results Left hippocampus as ROI:compared with the NC group,FC values of the left superior temporal gyrus increased in the T2DM group,FC values of the left superior frontal gyrus,left inferior temporal gyrus,left posterior central gyrus and rectus gyrus decreased in the DMED group.Compared with the T2DM group,FC values of the left inferior parietal gyrus,left supramarginal gyrus,left middle occipital gyrus and right posterior central gyrus decreased in the DMED group.Right hippocampus as ROI:compared with the NC group,FC values of the right middle temporal gyrus and right rolandic operculum increased while FC values of the right calcarine fissure decreased in the T2DM group;FC values of bilateral anterior cingulate gyrus,right middle temporal gyrus and left rectus gyrus decreased in the DMED group.Compared with the T2DM group,FC values of the left middle frontal gyrus,left inferior parietal gyrus and right inferior temporal gyrus decreased in the DMED group.Left amygdala as ROI:compared with the NC group,FC values in the left parahippocampal gyrus,left fusiform gyrus and right insula increased in the T2DM group;FC value of the left middle temporal gyrus decreased in the DMED group.Compared with the T2DM group,FC values of the left middle frontal gyrus and left supramarginal gyrus decreased in the DMED group.Right amygdala as ROI:compared with the NC group,FC values of the left insula,right parahippocampal gyrus,right superior temporal gyrus and right supramarginal gyrus increased while FC values in the right caudate decreased in the T2DM group;FC values of the right middle frontal gyrus,left rectus gyrus and left middle occipital gyrus decreased in the DMED group.Compared with the T2DM group,FC values of the left middle frontal gyrus and left inferior parietal gyrus decreased in the DMED group.Conclusions DMED patients have abnormalities in the hippocampus,amygdala and other brain regions,especially the frontal lobe functional cortex,which may be related to changes in their brain function.

As the most common phenotype of type 1 neurofibromatosis(NF1),plexiform neurofibromas(pNF)exhibit early asymptomatic presentation but multisite involvement,with a risk of progression.Imaging serves as vital tool for evaluation and management of NF1-associated pNF.The progresses of imaging for evaluating NF1-related pNF were reviewed in this article.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

In recent years, gastrointestinal stromal tumors (GIST) have increased incidence and mortality, and most GIST is caused by the activation mutation of the c-KIT gene. Therefore, c-KIT has become a promising therapeutic target of GIST. At present, the drugs approved for the treatment of GIST including imatinib, sunitinib, regorafenib and ripretinib, are mostly prone to developing resistance and accompanied by various degrees of adverse reactions. Therefore, there is an urgent need to develop new c-KIT inhibitors to solve the problem of resistance. In this study, we investigated the anti-tumor effect of a novel c-KIT inhibitor PN17-1 on gastrointestinal stromal tumor GIST-882 cells in vitro. We found that PN17-1 significantly inhibited the proliferation, colony formation and migration ability of GIST-882 cells, and significantly downregulated the protein expression levels of p-c-KIT and its downstream signals p-AKT, p-STAT5 and p-ERK in GIST-882 cells. In addition, PN17-1 induced apoptosis in GIST-882 cells, and the apoptosis may be mainly related to the mitochondrial-dependent endogenous pathway. In conclusion, the novel c-KIT inhibitor PN17-1 is a promising anti-GIST drug, and this study provides new ideas for further development of c-KIT inhibitors in the future.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.