Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study.

INTRODUCTION: To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC). METHODS: A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models. RESULTS: Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives. CONCLUSION: We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.

Ubiquitinome Analysis Uncovers Alterations in Synaptic Proteins and Glucose Metabolism Enzymes in the Hippocampi of Adolescent Mice Following Cold Exposure.

Cold exposure exerts negative effects on hippocampal nerve development in adolescent mice, but the underlying mechanisms are not fully understood. Given that ubiquitination is essential for neurodevelopmental processes, we attempted to investigate the effects of cold exposure on the hippocampus from the perspective of ubiquitination. By conducting a ubiquitinome analysis, we found that cold exposure caused changes in the ubiquitination levels of a variety of synaptic-associated proteins. We validated changes in postsynaptic density-95 (PSD-95) ubiquitination levels by immunoprecipitation, revealing reductions in both the K48 and K63 polyubiquitination levels of PSD-95. Golgi staining further demonstrated that cold exposure decreased the dendritic-spine density in the CA1 and CA3 regions of the hippocampus. Additionally, bioinformatics analysis revealed that differentially ubiquitinated proteins were enriched in the glycolytic, hypoxia-inducible factor-1 (HIF-1), and 5'-monophosphate (AMP)-activated protein kinase (AMPK) pathways. Protein expression analysis confirmed that cold exposure activated the mammalian target of rapamycin (mTOR)/HIF-1α pathway. We also observed suppression of pyruvate kinase M2 (PKM2) protein levels and the pyruvate kinase (PK) activity induced by cold exposure. Regarding oxidative phosphorylation, a dramatic decrease in mitochondrial respiratory-complex I activity was observed, along with reduced gene expression of the key subunits NADH: ubiquinone oxidoreductase core subunit V1 (Ndufv1) and Ndufv2. In summary, cold exposure negatively affects hippocampal neurodevelopment and causes abnormalities in energy homeostasis within the hippocampus.

Mucin-producing tumors of the ovary--preoperative differentiation between metastatic ovarian mucinous carcinoma and primary mucinous malignant tumors.

OBJECTIVE: To investigate the clinical and magnetic resonance imaging (MRI) features for preoperatively discriminating  primary ovarian mucinous malignant tumors (POMTs) and metastatic mucinous carcinomas involving the ovary (MOMCs). METHODS: This retrospective multicenter study enrolled 61 patients with 22 POMTs and 49 MOMCs, which were pathologically proved between November 2014 to Jane 2023. The clinical and MRI features were evaluated and compared between POMTs and MOMCs. Univariate and multivariate analyses were performed to identify the significant variables between the two groups, which were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: 35.9% patients with MOMCs were discovered synchronously with the primary carcinomas; 25.6% patients with MOMCs were bilateral, and all of the patients with POMTs were unilateral. The biomarker CEA was significantly different between the two groups (p = 0.002). There were significant differences in the following MRI features: tumor size, configuration, enhanced pattern, the number of cysts, honeycomb sign, stained-glass appearance, ascites, size diversity ratio, signal diversity ratio. The locular size diversity ratio (p = 0.005, OR = 1.31), and signal intensity diversity ratio (p = 0.10, OR = 4.01) were independent predictors for MOMCs. The combination of above independent criteria yielded the largest area under curve of 0.922 with a sensitivity of 82.3% and specificity of 88.9%. CONCLUSIONS: Patients with MOMCs were more commonly bilaterally and having higher levels of CEA, but did not always had a malignant tumor history. For ovarian mucin-producing tumors, the uniform locular sizes and signal intensities were more predict MOMCs.

The interaction of ammonia and manganese in abnormal metabolism of minimal hepatic encephalopathy: A comparison metabolomics study.

This study was to investigate the effects of ammonia and manganese in the metabolism of minimal hepatic encephalopathy (MHE). A total of 32 Sprague-Dawley rats were divided into four subgroups: chronic hyperammonemia (CHA), chronic hypermanganese (CHM), MHE and control group (CON). 1H-NMR-based metabolomics was used to detect the metabolic changes. Sparse projection to latent structures discriminant analysis was used for identifying and comparing the key metabolites. Significant elevated blood ammonia were shown in the CHA, CHM, and MHE rats. Significant elevated brain manganese (Mn) were shown in the CHM, and MHE rats, but not in the CHA rats. The concentrations of γ-amino butyric acid (GABA), lactate, alanine, glutamate, glutamine, threonine, and phosphocholine were significantly increased, and that of myo-inositol, taurine, leucine, isoleucine, arginine, and citrulline were significantly decreased in the MHE rats. Of all these 13 key metabolites, 10 of them were affected by ammonia (including lactate, alanine, glutamate, glutamine, myo-inositol, taurine, leucine, isoleucine, arginine, and citrulline) and 5 of them were affected by manganese (including GABA, lactate, myo-inositol, taurine, and leucine). Enrichment analysis indicated that abnormal metabolism of glutamine and TCA circle in MHE might be affected by the ammonia, and abnormal metabolism of GABA might be affected by the Mn, and abnormal metabolism of glycolysis and branched chain amino acids metabolism might be affected by both ammonia and Mn. Both ammonia and Mn play roles in the abnormal metabolism of MHE. Chronic hypermanganese could lead to elevated blood ammonia. However, chronic hyperammonemia could not lead to brain Mn deposition.

Development and Validation of a Risk Nomogram Model for Predicting Constipation in Patients with Type 2 Diabetes Mellitus.

Purpose: Constipation is a common complication of diabetic patients, which has a negative impact on their own health. This study aims to establish and internally validate the risk nomogram of constipation in patients with type 2 diabetes mellitus (T2DM) and to test its predictive ability. Patients and Methods: This retrospective study included 746 patients with T2DM at two medical centers. Among the 746 patients with T2DM, 382 and 163 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University were enrolled in the training cohort and the validation cohort, respectively. A total of 201 patients in the First Affiliated Hospital of Nanchang University were enrolled in external validation cohorts. The nomogram was established by optimizing the predictive factors through univariate and multivariable logistic regression analysis. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA). Furthermore, its applicability was internally and independently validated. Results: Among the 16 clinicopathological features, five variables were selected to develop the prediction nomogram, including age, glycated hemoglobin (HbA1c), calcium, anxiety, and regular exercise. The nomogram revealed good discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.908 (95% CI = 0.865-0.950) in the training cohort, and 0.867 (95% CI = 0.790-0.944) and 0.816 (95% CI = 0.751-0.881) in the internal and external validation cohorts, respectively. The calibration curve presented a good agreement between the prediction by the nomogram and the actual observation. The DCA revealed that the nomogram had a high clinical application value. Conclusion: In this study, the nomogram for pretreatment risk management of constipation in patients with T2DM was developed which could help in making timely personalized clinical decisions for different risk populations.

Ferrous sulfate reverses cerebral metabolic abnormality induced by minimal hepatic encephalopathy.

Orally administered ferrous iron was previously reported to significantly improve the cognition and locomotion of patients with minimal hepatic encephalopathy (MHE). However, the metabolic mechanisms of the therapeutic effect of ferrous iron are unknown. In this study, MHE was induced in rats by partial portal vein ligation (PPVL), and was treated with ferrous sulfate. The Morris water maze was used to evaluate the cognitive condition of the rats. The metabolites observed by NMR and validated by liquid chromatography-mass spectrometry were defined as the key affected metabolites. The enzyme activities and trace element contents in the rat brains were also investigated. The Mn content was found to be increased but the ferrous iron content decreased in the cortex and striatum in MHE. Decreased oxoglutarate dehydrogenase activity and increased glutamine synthetase (GS) and pyruvate carboxylase (PC) activity were observed in the cortex of MHE rats. Decreased pyruvate dehydrogenase activity and increased GS and PC activity were observed in the striatum of MHE rats. The levels of BCAAs and taurine were significantly decreased, and the contents of GABA, lactate, arginine, aspartate, carnosine, citrulline, cysteine, glutamate, glutamine, glycine, methionine, ornithine, proline, threonine and tyrosine were significantly increased. These metabolic abnormalities described above were restored after treatment with ferrous sulfate. Pathway enrichment analysis suggested that urea cycle, aspartate metabolism, arginine and proline metabolism, glycine and serine metabolism, and glutamate metabolism were the major metabolic abnormalities in MHE rats, but these processes could be restored and cognitive impairment could be improved by ferrous sulfate administration.

Magnetic resonance spectroscopy and liquid chromatography-mass spectrometry metabolomics study may differentiate pre-eclampsia from gestational hypertension.

OBJECTIVE: To investigate the findings of magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and serum metabolomics for differentiating pre-eclampsia (PE) from gestational hypertension (GH). METHODS: This prospective study enrolled 176 subjects including a primary cohort with healthy non-pregnant women (HN, n = 35), healthy pregnant women (HP, n = 20), GH (n = 27), and PE (n = 39) and a validation cohort with HP (n = 22), GH (n = 22), and PE (n = 11). T1 signal intensity index (T1SI), apparent diffusion coefficient (ADC) value, and the metabolites on MRS were compared. The differentiating performances of single and combined MRI and MRS parameters for PE were evaluated. Serum liquid chromatography-mass spectrometry (LC-MS) metabolomics was investigated by sparse projection to latent structures discriminant analysis. RESULTS: Increased T1SI, lactate/creatine (Lac/Cr), and glutamine and glutamate (Glx)/Cr and decreased ADC value and myo-inositol (mI)/Cr in basal ganglia were found in PE patients. T1SI, ADC, Lac/Cr, Glx/Cr, and mI/Cr yielded an area under the curves (AUC) of 0.90, 0.80, 0.94, 0.96, and 0.94 in the primary cohort, and of 0.87, 0.81, 0.91, 0.84, and 0.83 in the validation cohort, respectively. A combination of Lac/Cr, Glx/Cr, and mI/Cr yielded the highest AUC of 0.98 in the primary cohort and 0.97 in the validation cohort. Serum metabolomics analysis showed 12 differential metabolites, which are involved in pyruvate metabolism, alanine metabolism, glycolysis, gluconeogenesis, and glutamate metabolism. CONCLUSIONS: MRS is expected to be a noninvasive and effective tool for monitoring GH patients to avoid the development of PE. KEY POINTS: • Increased T1SI and decreased ADC value in the basal ganglia were found in PE patients than in GH patients. • Increased Lac/Cr and Glx/Cr, and decreased mI/Cr in the basal ganglia were found in PE patients than in GH patients. • LC-MS metabolomics showed that the major differential metabolic pathways between PE and GH were pyruvate metabolism, alanine metabolism, glycolysis, gluconeogenesis, and glutamate metabolism.

Magnetic Resonance Imaging and Diffusion Weighted Imaging-Based Histogram in Predicting Mesenchymal Transition High-Grade Serous Ovarian Cancer.

RATIONALE AND OBJECTIVES: To investigate the value of magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) findings in predicting mesenchymal transition (MT) high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: Patients with HGSOC were enrolled from May 2017 to December 2020, who underwent pelvic MRI including DWI (b = 0,1000 s/mm2) before surgery, and were assigned to the MT HGSOC or non-MT HGSOC group according to histopathology results. Clinical characteristics and MRI features including DWI-based histogram metrics were assessed and compared between the two groups. Univariate and multivariate analyses were performed to identify the significant variables associated with MT HGSOC - these variables were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: A total of 81 consecutive patients were recruited for pelvic MRI before surgery, including 37 (45.7%) MT patients and 44 (54.3%) non-MT patients. At univariate analysis, the features significantly related to MT HGSOC were identified as absence of discrete primary ovarian mass, pouch of Douglas implants, ovarian mass size, tumor volume, mean, SD, median, and 95th percentile apparent diffusion coefficient (ADC) values (all p < 0.05). At multivariate analysis, the absence of discrete primary ovarian mass {odds ratio (OR): 46.477; p = 0.025}, mean ADC value ≤ 1.105 (OR: 1.023; p = 0.009), and median ADC value ≤ 1.038 (OR: 0.982; p = 0.034) were found to be independent risk factors associated with MT HGSOC. The combination of all independent criteria yielded the largest AUC of 0.82 with a sensitivity of 83.87% and specificity of 66.67%, superior to any of the single predictor alone (p ≤ 0.012). The predictive C-index nomogram performance of the combination was 0.82. CONCLUSION: The combination of absence of discrete primary ovarian mass, lower mean ADC value, and median ADC value may be helpful for preoperatively predicting MT HGSOC.

C-reactive protein as a non-linear predictor of prolonged length of intensive care unit stay after gastrointestinal cancer surgery.

BACKGROUND: The relationship between C-reactive protein (CRP) levels and prolonged intensive care unit (ICU) length of stay (LoS) has not been well defined. AIM: To explore the association between CRP levels at ICU admission and prolonged ICU LoS in gastrointestinal cancer (GC) patients after major surgery. METHODS: A retrospective study was performed to quantify serum CRP levels and to establish their association with prolonged ICU LoS (≥ 72 h) in GC patients admitted to the ICU. Univariate and multivariate regression analyses were conducted, and restricted cubic spline curves with four knots (5%, 35%, 65%, 95%) were used to explore non-linearity assumptions. RESULTS: A total of 408 patients were enrolled. Among them, 83 (20.3%) patients had an ICU LoS longer than 72 h. CRP levels were independently associated with the risk of prolonged ICU LoS [odds ratio (OR) 1.47, 95% confidence interval (CI) 1.00-2.17]. Restricted cubic spline analysis revealed a non-linear relationship between CRP levels and OR for the prolonged ICU LoS (P = 0.035 for non-linearity). After the cut-off of 2.6 (log transformed mg/L), the OR for prolonged ICU LoS significantly increased with CRP levels. The adjusted regression coefficient was 0.70 (95%CI 0.31-1.57, P = 0.384) for CRP levels less than 2.6, whereas it was 2.43 (95%CI 1.39-4.24, P = 0.002) for CRP levels higher than 2.6. CONCLUSION: Among the GC patients, CRP levels at ICU admission were non-linearly associated with prolonged ICU LoS in survivors. An admission CRP level > 2.6 (log transformed mg/L) was associated with increased risk of prolonged ICU LoS.

Prediction of pre-eclampsia by using radiomics nomogram from gestational hypertension patients.

Background: Pre-eclampsia (PE) is the main cause of death in maternal and prenatal morbidity. No effective clinical tools could be used for the prediction of PE. A radiomics nomogram based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps was established to predict PE from gestational hypertension (GH). Materials and methods: A total of 138 patients with hypertensive disorders of pregnancy were continuously enrolled in the study prospectively, namely, 58 patients with PE and 80 patients with GH. The patients were randomly divided into a training cohort (n = 97) and a test cohort (n = 41). Radiomics features were extracted from DWI and ADC maps. The radiomics signature was constructed using a least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. A radiomics nomogram was developed by combining the radiomics signature with the selected clinical risk factors. The area under the receiver operating characteristic (ROC) curves (AUC), specificity, sensitivity, accuracy, positive predictive value, and negative predictive values of the radiomics signature, clinical risk factors, and radiomics nomogram were calculated. Decision curve analysis (DCA) was performed to determine the clinical usefulness of the radiomics nomogram. Results: The LASSO analysis finally included 11 radiomics features, which were defined as the radiomics signature. The individualized prediction nomogram was constructed by integrating the radiomics signature, maternal age, and body mass index (BMI). The nomogram exhibited a good performance both in the training cohort [AUC of 0.89 (95% CI, 0.82-0.95)] and test cohort [AUC of 0.85 (95% CI, 0.73-0.97)] for predicting PE from GH. The DCA indicated that clinicians and patients could benefit from the use of radiomics nomogram. Conclusion: The radiomics nomogram could individually predict PE from GH. The nomogram could be conveniently used to facilitate the treatment decision for clinicians and patients.

Bioinformatics analysis and experimental verification of the prognostic and biological significance mediated by fatty acid metabolism related genes for hepatocellular carcinoma.

Background: Liver cancer is among the leading causes of death related to cancer around the world. The most frequent type of human liver cancer is hepatocellular carcinoma (HCC). Fatty acid (FA) metabolism is an emerging hallmark that plays a promoting role in numerous malignancies. This study aimed to discover a FA metabolism-related risk signature and formulate a better model for HCC patients' prognosis prediction. Methods: We collected mRNA expression data and clinical parameters of patients with HCC using the TCGA databases, and the differential FA metabolism-related genes were explored. To create a risk prognostic model, we carried out the consensus clustering as well as univariate and multivariate Cox regression analyses. 16 genes were used to establish a prognostic model, which was then validated in the ICGC dataset. The accuracy of the model was performed using receiver operating characteristic (ROC) analyses, decision curve analysis (DCA) and nomogram. The immune cell infiltration level of risk genes was evaluated with single-sample GSEA (ssGSEA) algorithm. To reflect the response to immunotherapy, immunophenoscore (IPS) was obtained from TCGA-LIHC. Then, the expression of the candidate risk genes (p < 0.05) was validated by qRT-PCR, Western blotting and single-cell transcriptomics. Cellular function assays were performed to revealed the biological function of HAVCR1. Results: According to the TCGA-LIHC cohort analysis, the majority of the FA metabolism-related genes were expressed differentially in the HCC and normal tissues. The prognosis of patients with high-risk scores was observed to be worse. Multivariate COX regression analysis confirmed that the model can be employed as an independent prognosis factor for HCC patients. Furthermore, ssGSEA analysis revealed a link between the model and the levels of immune cell infiltration. Our model scoring mechanism also provides a high predictive value in HCC patients receiving anti-PDL1 immunotherapy. One of the FA metabolism-related genes, HAVCR1, displays a significant differential expression between normal and HCC cell lines. Hepatocellular carcinoma cells (Huh7, and HepG2) proliferation, motility, and invasion were all remarkably inhibited by HAVCR1 siRNA. Conclusion: Our study identified a novel FA metabolism-related prognostic model, revealing a better potential treatment and prevention strategy for HCC.

Gross perianal embryonal rhabdomyosarcoma with severe multiple bone metastases throughout the body: a case report.

In adults, embryonal rhabdomyosarcoma (ERMS) is rare and has a poor prognosis. Giant perianal ERMS with severe multiple bone metastases at initial diagnosis has not been reported and lacks effective treatment options. This current case report describes a 31-year-old female patient that presented with a large lump on the right side of the anus. ERMS was diagnosed, accompanied by multiple bone metastases throughout the body and severe thrombocytopenia. She had an extremely low platelet count at initial diagnosis, making systemic chemotherapy inappropriate. Genetic testing did not help identify effective targeted drugs. A multi-target tyrosine kinase inhibitor, anlotinib, was selected to control the tumours combined with local radiotherapy to relieve pain. The lump became smaller and this reduction was maintained for 5 months. At 7 months after the diagnosis, the patient died of thrombocytopenia. This current case may provide supportive evidence for a potential treatment for patients with advanced ERMS, especially those not suitable for chemotherapy or surgery.

Scale- and Slice-aware Net (S2 aNet) for 3D segmentation of organs and musculoskeletal structures in pelvic MRI.

PURPOSE: MRI of organs and musculoskeletal structures in the female pelvis presents a unique display of pelvic anatomy. Automated segmentation of pelvic structures plays an important role in personalized diagnosis and treatment on pelvic structures disease. Pelvic organ systems are very complicated, and it is a challenging task for 3D segmentation of massive pelvic structures on MRI. METHODS: A new Scale- and Slice-aware Net ( S2 aNet) is presented for 3D dense segmentation of 54 organs and musculoskeletal structures in female pelvic MR images. A Scale-aware module is designed to capture the spatial and semantic information of different-scale structures. A Slice-aware module is introduced to model similar spatial relationships of consecutive slices in 3D data. Moreover, S2 aNet leverages a weight-adaptive loss optimization strategy to reinforce the supervision with more discriminative capability on hard samples and categories. RESULTS: Experiments have been performed on a pelvic MRI cohort of 27 MR images from 27 patient cases. Across the cohort and 54 categories of organs and musculoskeletal structures manually delineated, S2 aNet was shown to outperform the UNet framework and other state-of-the-art fully convolutional networks in terms of sensitivity, Dice similarity coefficient and relative volume difference. CONCLUSION: The experimental results on the pelvic 3D MR dataset show that the proposed S2 aNet achieves excellent segmentation results compared to other state-of-the-art models. To our knowledge, S2 aNet is the first model to achieve 3D dense segmentation for 54 musculoskeletal structures on pelvic MRI, which will be leveraged to the clinical application under the support of more cases in the future.

Polysaccharides from Tetrastigma hemsleyanum Diels et Gilg: optimum extraction, monosaccharide compositions, and antioxidant activity.

The optimization of extraction of Tetrastigma hemsleyanum Diels et Gilg polysaccharides (THP) using ultrasonic with enzyme method and its monosaccharide compositions and antioxidant activity were investigated in this work. Single-factor experiments and response surface methodology (RSM) were performed to optimize conditions for extraction, and the independent variables were (XA) dosage of cellulase, (XB) extraction time, (XC) ultrasonic power, and (XD) ratio of water to the material. The extraction rate of THP was increased effectively under the optimum conditions, and the maximum (4.692 ± 0.059%) was well-matched the predicted value from RSM. THP was consisted of mannose, glucuronic acid, rhamnose, galacturonic acid, glucose, galactose, and arabinose, while glucose was the dominant (26.749 ± 0.634%). According to the total antioxidant capacity assay with the FRAP method, DPPH, and hydroxyl radical scavenging assay, THP showed strong antioxidant activity with a dose-dependent behavior. The results indicated that THP has the potential to be a novel antioxidant and could expand its application in food and medicine.

Elevated expression of nuclear receptor-binding SET domain 3 promotes pancreatic cancer cell growth.

The nuclear receptor-binding SET domain 3 (NSD3) catalyzes methylation of histone H3 at lysine 36 (H3K36), and promotes malignant transformation and progression of human cancer. Its expression, potential functions and underlying mechanisms in pancreatic cancer are studied. Bioinformatics studies and results from local human tissues show that NSD3 is upregulated in human pancreatic cancer tissues, which is correlated with poor overall survival. In primary and established pancreatic cancer cells, NSD3 silencing (by shRNAs) or CRISPR/Cas9-induced NSD3 knockout potently inhibited cell proliferation, migration and invasion, while provoking cell cycle arrest and apoptosis. Conversely, ectopic expression of NSD3-T1232A mutation significantly accelerated proliferation, migration, and invasion of pancreatic cancer cells. H3K36 dimethylation, expression of NSD3-dependent genes (Prkaa2, Myc, Irgm1, Adam12, and Notch3), and mTOR activation (S6K1 phosphorylation) were largely inhibited by NSD3 silencing or knockout. In vivo, intratumoral injection of adeno-associated virus (AAV)-packed NSD3 shRNA potently inhibited pancreatic cancer xenograft growth in nude mice. These results suggest that elevated NSD3 could be an important driver for the malignant progression of pancreatic cancer.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers.

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the past few years, many researchers have attempted to develop and apply artificial intelligence (AI) techniques to multiple clinical scenarios of ovarian cancer, especially in the field of medical imaging. AI-assisted imaging studies have involved computer tomography (CT), ultrasonography (US), and magnetic resonance imaging (MRI). In this review, we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer, and bring up the advances in terms of four clinical aspects, including medical diagnosis, pathological classification, targeted biopsy guidance, and prognosis prediction. Meanwhile, current status and existing issues of the researches on AI application in ovarian cancer are discussed.

External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images.

Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation. Methods The ULDor system consists of a convolutional neural network (CNN) trained on around 80K lesion annotations from about 12K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets. During the validation process, the test sets include two parts: the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital, and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial (NLST). We ran the model on the two test sets to output lesion detection. Three board-certified radiologists read the CT scans and verified the detection results of ULDor. We used positive predictive value (PPV) and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images, including liver, kidney, pancreas, adrenal, spleen, esophagus, thyroid, lymph nodes, body wall, thoracic spine, etc. Results In the external validation, the lesion-level PPV and sensitivity of the model were 57.9% and 67.0%, respectively. On average, the model detected 2.1 findings per set, and among them, 0.9 were false positives. ULDor worked well for detecting liver lesions, with a PPV of 78.9% and a sensitivity of 92.7%, followed by kidney, with a PPV of 70.0% and a sensitivity of 58.3%. In internal validation with NLST test set, ULDor obtained a PPV of 75.3% and a sensitivity of 52.0% despite the relatively high noise level of soft tissue on images. Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs. With further optimisation and iterative upgrades, ULDor may be well suited for extensive application to external data.

Identification of histone methyltransferase NSD2 as an important oncogenic gene in colorectal cancer.

Colorectal cancer (CRC) is the second common cause of cancer-related human mortalities. Dysregulation of histone 3 (H3) methylation could lead to transcriptional activation of multiple oncogenes, which is closely associated with CRC tumorigenesis and progression. Nuclear receptor-binding SET Domain protein 2 (NSD2) is a key histone methyltransferase catalyzing histone H3 lysine 36 dimethylation (H3K36me2). Its expression, the potential functions, and molecular mechanisms in CRC are studied here. Gene Expression Profiling Interactive Analysis (GEPIA) bioinformatics results showed that the NSD2 mRNA expression is elevated in both colon cancers and rectal cancers. Furthermore, NSD2 mRNA and protein expression levels in local colon cancer tissues are significantly higher than those in matched surrounding normal tissues. In primary human colon cancer cells and established CRC cell lines, shRNA-induced silencing or CRISPR/Cas9-induced knockout of NSD2 inhibited cell viability, proliferation, cell cycle progression, migration, and invasion. Furthermore, NSD2 shRNA or knockout induced mitochondrial depolarization, DNA damage, and apoptosis in the primary and established CRC cells. Contrarily, ectopic NSD2 overexpression in primary colon cancer cells further enhanced cell proliferation, migration, and invasion. H3K36me2, expressions of multiple oncogenes (ADAM9, EGFR, Sox2, Bcl-2, SYK, and MET) and Akt activation were significantly decreased after NSD2 silencing or knockout in primary colon cancer cells. Their levels were however increased after ectopic NSD2 overexpression. A catalytic inactive NSD2 (Y1179A) also inhibited H3K36me2, multiple oncogenes expression, and Akt activation, as well as cell proliferation and migration in primary colon cancer cells. In vivo, intratumoral injection of adeno-associated virus (AAV)-packed NSD2 shRNA largely inhibited primary colon cancer cell xenograft growth in nude mice. Together, NSD2 exerted oncogenic functions in CRC and could be a promising therapeutic target.

[Potential targets of Euodiae Fructus in treatment of insomnia based on network pharmacology].

The acupoint application of Euodiae Fructus at Yongquan(KI1) can significantly improve the sleep quality of patients with insomnia with berberine as the main effective component for the efficacy. Nineteen active compounds and 203 drug targets were screened out from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). After comparison with GeneCards and Online Mendelian Inheritance in Man(OMIM), 24 common genes of diseases and drugs were obtained. STRING 11.0 was used to construct a protein-protein interaction(PPI) network of the overlapping genes, and Matthews correlation coefficient(MCC) was employed to screen the core genes, which were then subjected to enrichment analysis with gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG). The results revealed that the main compounds of Euodiae Fructus, such as berberine and rutaecarpine, participated in the biological processes(such as neurotransmitter receptor activity) by regulating C-reactive protein(CRP), estrogen receptor 1(ESR1), 5-hydroxytryptamine(5-HT) receptor, and interleukin-6(IL-6) to exert sedative, anxiolytic, and antidepressant effects. Sixty 4-week-old SPF mice were randomly divided into a control group, a model group, a positive drug(diazepam tablets) group, and low-, medium-, and high-dose berberine groups. Medication with corresponding drugs was performed for one week. The results demonstrated that berberine was potent in reducing the activities and standing times of mice, down-regulating the levels of CRP and IL-6 mRNA in the hypothalamus, and up-regulating the expression of 5-HT(P<0.01); however, no significant effect on ESR1 was observed. The network of Euodiae Fructus in treating insomnia was constructed by network pharmacology and verified by tests. The findings indicated that the therapeutic efficacy of Euodiae Fructus in treating insomnia was achieved by participating in multiple biological processes, such as neurotransmitter receptor activity, which provided a scientific basis for its clinical application.

A Nomogram for Predicting Risk of Thromboembolism in Gastric Cancer Patients Receiving Chemotherapy.

Purpose: The aims of this study were to develop and validate a novel nomogram to predict thromboembolism (TE) in gastric cancer (GC) patients receiving chemotherapy and to test its predictive ability. Methods: This retrospective study included 544 GC patients who received chemotherapy as the initial treatment at two medical centers. Among the 544 GC patients who received chemotherapy, 275 and 137 patients in the First Affiliated Hospital of Nanchang University from January 2014 to March 2019 were enrolled in the training cohort and the validation cohort, respectively. A total of 132 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University from January 2015 to August 2019 were enrolled in external validation cohorts. The nomogram was based on parameters determined by univariate and multivariate logistic analyses. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis (DCA). The applicability of the nomogram was internally and independently validated. Results: The predictors included the Eastern Cooperative Oncology Group Performance Status (ECOG), presence of an active cancer (AC), central venous catheter (CVC), and D-dimer levels. These risk factors are shown on the nomogram and verified. The nomogram demonstrated good discrimination and fine calibration with an AUROC of 0.875 (0.832 in internal validation and 0.807 in independent validation). The DCA revealed that the nomogram had a high clinical application value. Conclusions: We propose the nomogram for predicting TE in patients with GC receiving chemotherapy, which can help in making timely personalized clinical decisions for different risk populations.