Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis.

OBJECTIVE: To compare the serum levels of brain-derived neurotrophic factor (BDNF) in type 2 diabetes mellitus (T2DM) patients with healthy controls (HC) and evaluate the BDNF levels in T2DM patients with/without cognitive impairment. METHODS: PubMed, EMBASE, and the Cochrane Library databases were searched for the published English literature on BDNF in T2DM patients from inception to December 2022. The BDNF data in the T2DM and HC groups were extracted, and the study quality was evaluated using the Agency for Healthcare Research and Quality. A meta-analysis of the pooled data was conducted using Review Manager 5.3 and Stata 12.0 software. RESULTS: A total of 18 English articles fulfilled with inclusion criteria. The standard mean difference of the serum BDNF level was significantly lower in T2DM than that in the HC group (SMD: -2.04, z = 11.19, P <0.001). Besides, T2DM cognitive impairment group had a slightly lower serum BDNF level compared to the non-cognitive impairment group (SMD: -2.59, z = 1.87, P = 0.06). CONCLUSION: BDNF might be involved in the neuropathophysiology of cerebral damage in T2DM, especially cognitive impairment in T2DM.

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

The Reduced Gray Matter Volume and Functional Connectivity of the Cerebellum in Type 2 Diabetes Mellitus with High Insulin Resistance.

INTRODUCTION: Insulin resistance is widely thought to be a critical feature in type 2 diabetes mellitus (T2DM), and there is significant evidence indicating a higher abundance of insulin receptors in the human cerebellum than cerebrum. However, the specific structural or functional changes in the cerebellum related to T2DM remain unclear, and the association between cerebellar alterations, insulin resistance, cognition, and emotion is yet to be determined. METHODS: We investigated neuropsychological performance, and structural and functional changes in specific cerebellar subregions in 43 T2DM patients with high insulin resistance (T2DM-highIR), 72 T2DM patients with low insulin resistance (T2DM-lowIR), and 50 controls. Furthermore, the correlation and stepwise multiple linear regression analysis were performed. RESULTS: Compared to the controls, T2DM exhibited lower cognitive scores and higher depressive/anxious scores. Furthermore, T2DM-highIR patients showed reduced gray matter volume (GMV) in the right cerebellar lobules VIIb, Crus I/II, and T2DM showed reduced GMV in left lobules I-IV compared to controls. Additionally, functional connectivity decrease was observed between the right lobules I-V and orbital part of the superior frontal gyrus in T2DM-highIR compared to both T2DM-lowIR and controls. Notably, there were negative correlations between the GMV of the lobules VIIb, Crus I/II, and updated homeostatic model assessment of insulin resistance, and positive correlation with executive/visuospatial performance in T2DM patients. CONCLUSIONS: These results suggest that the cerebellar lobules VIIb, Crus I/II, represent vulnerable brain regions in the context of insulin resistance. Overall, this study offers new insights into the neuropathophysiological mechanisms of brain impairment in patients with T2DM.

Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma.

Purpose: To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes. Method: In this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created. Results: The four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model. Conclusion: The CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.

Alterations of Spontaneous Cortical and Subcortical Activity in Type 2 Diabetes Mellitus with and without Depression.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) patients with depression have a higher risk of complications and mortality than T2DM without depression. However, the exact neuropathophysiological mechanism remains unclear. Consequently, the current study aimed to investigate the alteration of cortical and subcortical spontaneous neural activity in T2DM patients with and without depression. METHODS: The demographic data, clinical variables, neuropsychological tests, and functional and anatomical magnetic resonance imaging of depressed T2DM (n = 47) of non-depressed T2DM (n = 59) and healthy controls (n = 41) were collected and evaluated. The correlation analysis, stepwise multiple linear regression, and receiver operating characteristic curve were performed for further analysis. RESULTS: Abnormal neural activities in the bilateral posterior cingulate cortex (PCC) and hippocampus were observed in depressed and non-depressed T2DM and the right putamen of the depressed T2DM. Interestingly, the subcortical degree centrality (DC) of the right hippocampus and putamen were higher in depressed than non-depressed T2DM. Furthermore, the cortical amplitude of low-frequency fluctuation (ALFF) in PCC, subcortical DC in the putamen of depressed T2DM, and hippocampus of non-depressed T2DM was correlated with cognitive scores. In contrast, the cortical fractional ALFF in PCC of non-depressed T2DM was correlated with depression scores. CONCLUSIONS: The abnormalities of spontaneous cortical activity in PCC and subcortical activity in the hippocampus might represent the neurobiological feature of cerebral dysfunction in T2DM. Notably, the altered subcortical activity in the right putamen might mainly associate with negative emotion in T2DM, which could be a promising biomarker for recognizing early cerebral dysfunction in depressed T2DM. This study provided a novel insight into the neuropathophysiological mechanism of brain dysfunction in T2DM with and without depression.

Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta­analysis.

PURPOSE: The purpose of this study was to evaluate the methodological quality of radiomics-based studies for noninvasive, preoperative prediction of Kirsten rat sarcoma (KRAS) mutations in patients with colorectal cancer; furthermore, we systematically evaluate the diagnostic accuracy of predicting models. METHODS: We systematically searched PubMed, Embase, Cochrane Library and Web of Science databases up to 20 April 2022 for eligible studies. The methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. A meta-analysis of studies on the prediction of KRAS status in colorectal cancer patients was performed. RESULT: Twenty-nine studies were identified in the systematic review, including three studies on the prediction of KRAS status in colorectal cancer liver metastases. All studies had an average RQS score of 9.55 (26.5% of the total score), ranging from 3 to 17. Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. Nineteen studies were included in the meta-analysis, mostly imaged with magnetic resonance imaging (MRI), followed by computed tomography (CT), positron emission tomography-CT (PET/CT). With pooled sensitivity, specificity and area under the curve (AUC) of the training cohorts were 0.80(95% confidence interval(CI), 0.75-0.84), 0.80(95% CI, 0.74-0.85) and 0.87(95% CI, 0.84-0.90),respectively. The pooled sensitivity, specificity, and AUC for the validation cohorts (13 studies) were 0.78(95% CI, 0.71-0.84), 0.84(95% CI, 0.74-0.90), and 0.86(95% CI, 0.83-0.89), respectively. CONCLUSION: Radiomics is a potential noninvasive technology that has a moderate preoperative diagnosis and prediction effect on KRAS mutations. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the methodological quality of the study and further externally validate the model using multicenter datasets.

Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis.

Purpose: The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models. Methods: We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I2 values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity. Results: We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score. Conclusions: Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

[Primary Study on Chronic Myeloid Leukemia in NCG Mice from Qinba Mushroom].

OBJECTIVE: The present study was to evaluate the anti-tumor effects of acidic RNA protein complex (FA-2-b-ß) extracted from the wild edible Qinba mushroom in inducing of apoptosis and immunoregulation of tumor cell. METHODS: Cell proliferation inducing rate of FA-2-b-ß to K562 cell was measured using CCK-8. Apoptosis rate was detected by using flow cytometry. Chronic myeloid leukemia model was developed by tail vein injection/subcutaneous inoculation of K562 cells in NCG mice. The tumor burden of mice was observed. The general condition of the mice was monitored twice daily. The peripherivcal full blood counts of mice was tested daily. RT-qPCR and Western blot was FA-2-b-ß performed to determine involvement of apoptotic-related gene and protenin, Immunofluorescence and immunohistochemistry was used to detected the expression of CD3, CD4 and CD8. RESULTS: The proliferation and apoptosis of K562 cell could be inhibitied and induced by FA-2-b-ß, there was 100% successful in the tumor formation in vivo, after treated by drug for 21 days there were significantly increased peripheral leucocytes, but decreased hemoglobin of mice treated by FA-2-b-ß as compared with those in control group. The CD3, CD4 and CD8 showed positive in mice, and the propotation was imbalance, but it showed reserved after treated by FA-2-b-ß. CONCLUSION: FA-2-b-ß is strong anti-leukemia effect in vitro and in vivo, suggesting the traditional Chinese medicine maybe contribute to the anti-cancer and immunoregulation research.

Computed tomography and magnetic resonance imaging findings of nasal chondromesenchymal hamartoma in a young child: a case report.

Nasal chondromesenchymal hamartoma (NCMH) is a rare destructive benign neoplasm that predominantly develops in infants and young children. The lesion is usually located in the nasal cavity, often in the adjacent paranasal sinuses and orbital region and especially in the ethmoid sinus. Because the imaging characteristics of NCMH often mimic the features of malignant tumors, it is clinically important to study the radiographic appearance of this disease. Therefore, we herein present the computed tomography and magnetic resonance imaging findings of NCMH occurring in a 7-year-old girl. The mass was resected via an endoscopic surgical approach and definitively diagnosed as NCMH based on histologic and immunohistochemical analysis. However, signs of tumor recurrence manifested 45 months after surgery. NCMH can be locally aggressive with an expansive and destructive radiographic appearance, which highly implies a malignant neoplasm. Hence, an accurate diagnosis is essential to avoid potentially harmful therapies, and detailed computed tomography or magnetic resonance imaging should be performed prior to surgery. Selective arterial embolization is also an important part of preoperative management because the degree of enhancement may not be adequate to determine the blood supply of the tumor. Moreover, complete radical excision cannot guarantee that the lesion will not recur.

[Primary Mechanisms of CD34+CD38---KG1a Leukemia Stem Cells Apoptosis Induced by FA-2-b-ß Extracted from Qinba Selenium- Mushroo].

OBJECTIVE: To investigate the apoptosis of CD34+CD38--KG1a leukemia stem cells induced by Qinba selenium-mushroom extract(FA-2-b-ß), and its related mechanism. METHODS: CD34+CD38---KG1a cells were isolated from KG1a cell line by magnetic activated cell sorting. The proliferation ability of KG1a stem cells treatd by various concentration of FA-2-b-ß(1.2-2.4 mg/ml) in vitro for 24 and 48 hours were tested by cell counting Kit-8(CCK8). Flow cytometry was used to detect the apoptosis rate of KG1a stem cells in each group after treated by FA-2-b-ß in vitro. Expression of BAX,BCL-2,Casepase-3 and Cyclin D1 protein were detected by Western blot. RESULTS: The proportion of CD34+CD38---KG1a stem cells was (95.35±2.63）% after immunomagnetic isolation. The proliferation of KG1a stem cells was inhibited significantly by FA-2-b-ß, which shows a time- and dose-dependent manner (24 h,r=0.943; 48 h,r=0.976). Flow cytometry shows that with the increasing of drug concentration, the apoptosis was also increased, when KG1a stem cells was treated by FA-2-b-ß for 24 h. Western blot indicated that the expression of apoptosis-related protein BAX and Casepase-3 were up-regulated, the expression of BCL-2 and Cyclin D1 were down-regulated. CONCLUSION: FA-2-b-ß can regulate proliferation and apoptosis KG1a stem cells, the involved mechanism may be related with the activation of mitochondrial-mediated apoptotic pathway.