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Background: We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly. Methods: We retrospectively analyzed CT studies from 139 patients (age range 26–93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were considered to be significant. Results: Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. Conclusions: CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the generalizability of transfer learning (TL) of automated tumor segmentation from cervical cancers toward a universal model for cervical and uterine malignancies in diffusion-weighted magnetic resonance imaging (DWI). Methods: In this retrospective multicenter study, we analyzed pelvic DWI data from 169 and 320 patients with cervical and uterine malignancies and divided them into the training (144 and 256) and testing (25 and 64) datasets, respectively. A pretrained model was established using DeepLab V3 + from the cervical cancer dataset, followed by TL experiments adjusting the training data sizes and fine-tuning layers. The model performance was evaluated using the dice similarity coefficient (DSC). Results: In predicting tumor segmentation for all cervical and uterine malignancies, TL models improved the DSCs from the pretrained cervical model (DSC 0.43) when adding 5, 13, 26, and 51 uterine cases for training (DSC improved from 0.57, 0.62, 0.68, 0.70, p < 0.001). Following the crossover at adding 128 cases (DSC 0.71), the model trained by combining data from adding all the 256 patients exhibited the highest DSCs for the combined cervical and uterine datasets (DSC 0.81) and cervical only dataset (DSC 0.91). Conclusions: TL may improve the generalizability of automated tumor segmentation of DWI from a specific cancer type toward multiple types of uterine malignancies especially in limited case numbers. Key points: Transfer learning (TL) improves performance of tumor segmentation on diffusion-weighted imaging (DWI) especially in limited case numbers. Training a model by combining sufficient data of different cancers exhibited the highest performance for segmenting mixed cervical and uterine datasets and also improved the pretrained cervical model. The TL model with fine-tuning the early layers of the encoder part outperformed those by fine-tuning the other layers. [ABSTRACT FROM AUTHOR]

Despite numerous treatment methods, there is no gold standard for the treatment of peri-implantitis—an infectious peri-implant disease. Here, we examined selenium nanoparticles (SeNPs) at a wide range of concentrations to investigate their cytotoxicity, regulation of osteoblastic differentiation, and assessed the antibacterial effect against Porphyromonas gingivalis. SeNPs (mean size: 70 nm; shape: near-spherical; concentration: 0–2048 ppm) were tested against the MC3T3-E1 osteoblast precursor cell line and P. gingivalis red complex pathogen. Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) analysis was used to evaluate the bone morphogenetic protein 2 (BMP-2) signaling pathway. SeNPs at concentrations of 2–16 ppm showed no obvious cytotoxicity and promoted good mineralization and calcification. SeNPs at concentrations 64 ppm and below influenced gene expression promoting osteoblastic differentiation, whereas at high concentrations inhibited the expression of Runt-related transcription factor 2 (Runx2). The growth of P. gingivalis was significantly inhibited at SeNP concentrations of more than 4 ppm. SeNPs at low concentrations promoted osteoblastic differentiation while strongly inhibiting peri-implantitis pathogen growth. This study represents one of the few in vitro assessments of SeNPs against a red complex pathogen and the regulatory effect on osteoblastic differentiation. The findings demonstrate SeNPs could potentially be used for future application on implant coating. [ABSTRACT FROM AUTHOR]

Purpose: Nuclear factor (NF)-κB signaling in cancer cells has been reported to be involved in tumorigenesis. Phosphorylation and degradation of inhibitor of NF-κBα (IκBα) is a canonical pathway of NF-κB signaling. Here, we aimed to identify and characterize noncanonical activation of NF-κB signaling by ubiquitin-conjugating enzyme E2S (UBE2S) in lung adenocarcinoma cells. Methods: TCGA and the Human Atlas Protein Database were used to analyze the survival rate of lung adenocarcinoma patients in conjunction with UBE2S expression. In addition, PC9, H460, H441 and A549 lung adenocarcinoma cells were used in this study. PC9 and H460 cells were selected for further analysis because they expressed different UBE2S protein levels. Specific IKK inhibitors, PS1145 and SC514, were used to assess IκBα phosphorylation. Western blot analysis was used to assess protein levels in PC9 and H460 cells. A scratch wound-healing assay was used to analyze the migrative abilities of PC9 and H460 cells. Overexpression and knockdown of UBE2S in H460 and PC9 cells were used to analyze their effects on downstream protein levels. Immunoprecipitation, immunofluorescent staining, glutathione S transferase (GST) pull-down and in vitro binding assays were used to analyze the interaction between UBE2S and IκBα. A luciferase assay was used to analyze activation of NF-κB signaling regulated by UBE2S. An in vivo zebrafish xenograft model was used to assess metastasis of PC9 cells regulated by UBE2S. Results: We found that UBE2S expression in lung adenocarcinoma patients was negatively related to survival rate. The protein level of UBE2S was higher in PC9 cells than in H460 cells, which was opposite to that observed for IκBα. PC9 cells showed a higher UBE2S expression and migrative ability than H460 cells. Phosphorylation of IκBα was not changed by treatment with the IKK-specific inhibitors PS1145 and SC514 in PC9 and H460 cells. Overexpression and knockdown of UBE2S in H460 and PC9 cells revealed that the protein levels of IκBα were inversely regulated. Immunoprecipitation, immunofluorescent staining, GST pull-down and in vitro binding assays revealed direct binding of UBE2S with IκBα. Nuclear P65 protein levels and luciferase assays showed that NF-κB signaling was regulated by UBE2S. The expression of epithelial-to-mesenchymal (EMT) markers and the migrative ability of lung adenocarcinoma cells were also regulated by UBE2S. A zebrafish xenograft tumor model showed a reduction in the metastasis of PC9 cells that was induced by UBE2S knockdown. Conclusions: Higher UBE2S expression in lung adenocarcinomas may lead to increased binding with IκBα to activate NF-κB signaling and promote adenocarcinoma cell metastasis. UBE2S may serve as a potential therapeutic target for lung adenocarcinomas. [ABSTRACT FROM AUTHOR]

Graph: Figure 5 GE-1 cells cytotoxicity test. (A) Lactate dehydrogenase assay of the materials against GE-1 cells. On day 3, GP showed the highest level of LDH cytotoxicity, indicating toxicity to the GE-1 cells in comparison to cotton. In Figure 5 GE-1 cells cytotoxicity test, there was statistical mistake as published. [Extracted from the article]

Objective: To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics features.Methods: This retrospective study involved analysis of MR images from 169 patients with cervical cancer stage IB-IVA captured; among them, diffusion-weighted (DW) images from 144 patients were used for training, and another 25 patients were recruited for testing. A U-Net convolutional network was developed to perform automated tumor segmentation. The manually delineated tumor region was used as the ground truth for comparison. Segmentation performance was assessed for various combinations of input sources for training. ADC radiomics were extracted and assessed using Pearson correlation. The reproducibility of the training was also assessed.Results: Combining b0, b1000, and ADC images as a triple-channel input exhibited the highest learning efficacy in the training phase and had the highest accuracy in the testing dataset, with a dice coefficient of 0.82, sensitivity 0.89, and a positive predicted value 0.92. The first-order ADC radiomics parameters were significantly correlated between the manually contoured and fully automated segmentation methods (p < 0.05). Reproducibility between the first and second training iterations was high for the first-order radiomics parameters (intraclass correlation coefficient = 0.70-0.99).Conclusion: U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images. First-order radiomics features extracted from whole tumor volume demonstrate the potential robustness for longitudinal monitoring of tumor responses in broad clinical settings. U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images.Key Points: • U-Net-based deep learning can perform accurate fully automated localization and segmentation of cervical cancer in diffusion-weighted MR images. • Combining b0, b1000, and apparent diffusion coefficient (ADC) images exhibited the highest accuracy in fully automated localization. • First-order radiomics feature extraction from whole tumor volume was robust and could thus potentially be used for longitudinal monitoring of treatment responses. [ABSTRACT FROM AUTHOR]

Background: A triage test to assist clinical decision-making on choosing primary chemoradiation for cervical carcinomas or primary surgery for endometrial carcinomas is important.Purpose or Hypothesis: To develop and validate a multiparametric prediction model based on MR imaging and spectroscopy in distinguishing adenocarcinomas of uterine cervical or endometrial origin.Study Type: Prospective diagnostic accuracy study.Population: Eighty-seven women: 25 cervical and 62 endometrial adenocarcinomas divided into training (n = 43; cervical/endometrial adenocarcinomas = 11/32) and validation (n = 44; 14/30) datasets.Field Strength/sequence: The 3T diffusion-weighted (DW) MR imaging and MR spectroscopy.Assessment: Morphology, volumetric DW MR imaging and spectroscopy (MDS) scoring system with total points 0-5, based on presence of the following MR features assessed independently by two radiologists: (a) epicenter at the cervix, (b) rim enhancement, (c) disrupted cervical stromal integrity, (d) mean volumetric apparent diffusion coefficient values (ADCmean) higher than 0.98 × 10-3 mm2 /s, (e) fatty acyl δ 1.3 ppm more than 161.92 mM. Histopathology as gold standard.Statistical Tests: Logistic regression and receiver operator characteristic (ROC) curves analysis.Results: For both the training and validation datasets, the MDS score achieved an accuracy of 93.0% and 84.1%, significantly higher than that of morphology (88.4% and 79.5%), ADC value (74.4% and 68.2%), and spectroscopy (81.4% and 68.2%; P < 0.05 for all). The performances of the scoring were superior to the morphology in the training dataset (areas under the receiver operating characteristics curve [AUC] = 0.95 vs. 0.89; P = 0.046), but not in the validation dataset (AUC = 0.90 vs. 0.85; P = 0.289).Data Conclusion: MDS score has potentials to improve distinguishing adenocarcinomas of cervical or endometrial origin, and warrants large-scale studies for further validation.Level Of Evidence: 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1654-1666. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) has served as the first-line imaging modality but its role for detecting recurrent ovarian cancer should be revisited in the era of whole-body CT. Purpose: To study anatomical distributions and imaging patterns of recurrent ovarian epithelial cancer through whole-body CT and their associations with serum cancer antigen 125 (CA 125) levels. Material and Methods: This retrospective study approved by the institutional review board comprised a cohort of 497 patients with ovarian cancer. The specific anatomical locations and imaging features of recurrent lesions on CT at the time of recurrence were analyzed. Results: Fifty-nine patients had ovarian cancer recurrence. The median time from diagnosis to recurrence was 17 months (range = 8–51). Among the 144 recurrent lesions, the leading recurrent sites were the intraperitoneal cavity and lymph nodes. Cystic lesions were significantly more frequently located intraperitoneally (P = 0.026), in particular with locoregional recurrence (P = 0.006). Serum CA 125 levels in the patients were significantly higher in the presence of peritoneal metastases (P = 0.010), locoregional lesions at the cul-de-sac or vaginal stump (P = 0.002), and metastases to infradiaphragmatic organs (P = 0.013). Among the 18 patients with CA 125 levels ≤ 35 U/mL at recurrence, 10 patients had supradiaphragmatic lesions in the lung and brain. None of the studied patients had bony metastasis at recurrence. Conclusion: In patients with suspected recurrence, extended-field CT to cover the thoracic region might be useful during follow-up of patients with ovarian cancer, regardless of CA 125 levels. [ABSTRACT FROM AUTHOR]

Background: Most manufacturer manuals do not verify the use of dual energy X-ray absorptiometry for body composition analysis in subjects with a metal implant. This study aimed to quantify the effects of a spinal implant on body composition, and to determine whether unadjusted lean mass estimates are valid for patients with a spinal implant. Methods: A total of 30 healthy subjects were recruited. Three consecutive scans were performed for each participant, one with and two without extraneous spinal implant, without repositioning between scans. Lean, fat and bone estimates in the total body, trunk and limb were measured. Results: Precision errors for all total and regional body compositions were within the recommended ranges. Bone masses in the trunk and total body were significantly increased with spinal implant, and the increases exceeded the least significant change. For total and regional lean and fat estimates, the measurements between subjects with and without metal implants were in substantial to almost perfect agreement and the differences were not significant and did not exceed the least significant change. Conclusions: Spinal metal artifacts significantly increased the total body and trunk bone mass but the differences in lean- and fat-related estimates at total and regional body levels and all estimates in the extremity remained within the clinical acceptable range. Thus, a spinal implant may not compromise screening of patients for fat and lean masses using dual energy X-ray absorptiometry. Application of image reconstruction or a filtering algorithm may help reduce the effect of metallic artifacts and further study is needed. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a prognostic model of integrating whole-tumour apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging with human papillomavirus (HPV) genotyping in predicting the overall survival (OS) and disease-free survival (DFS) for women with stage IB-IV cervical cancer following concurrent chemoradiotherapy (CCRT).Methods: We retrospectively analysed three prospectively collected cohorts comprising 300 patients with stage IB-IV cervical cancer treated with CCRT in 2007-2014 and filtered 134 female patients who underwent MR imaging at 3.0 T for final analysis (age, 24-92 years; median, 54 years). Univariate and multivariate Cox regression analyses were used to evaluate the whole-tumour ADC histogram parameters, HPV genotyping and relevant clinical variables in predicting OS and DFS. The dataset was randomly split into training (n = 88) and testing (n = 46) datasets for construction and independent bootstrap validation of the models.Results: The median follow-up time for surviving patients was 69 months (range, 9-126 months). Non-squamous cell type, ADC10 <0.77 × 10-3 mm2/s, T3-4, M1 stage and high-risk HPV status were selected to generate a model, in which the OS and DFS for the low, intermediate and high-risk groups were significantly stratified (p < 0.0001). The prognostic model improved the prediction significantly compared with the International Federation of Gynaecology and Obstetrics (FIGO) stage for both the training and independent testing datasets (p < 0.0001).Conclusions: The prognostic model based on integrated clinical and imaging data could be a useful clinical biomarker to predict OS and DFS in patients with stage IB-IV cervical cancer treated with CCRT.Key Points: • ADC 10 is the best prognostic factor among ADC parameters in cervical cancer treated with CCRT • A novel prognostic model was built based on histology, ADC 10 , T and M stage and HPV status • The prognostic model outperforms FIGO stage in the survival prediction. [ABSTRACT FROM AUTHOR]

Objectives: To compare the diagnostic accuracy of diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging for detecting cervical stromal invasion in endometrial cancer.Methods: Eighty-three consecutive women with endometrial cancer underwent preoperative evaluation in a 3-T unit, including T2-weighted, DW (b = 0 and 1000 s/mm2), and DCE MR imaging. Two radiologists independently assessed presence of cervical stromal invasion, with histopathological reference as gold standard.Results: For assessing cervical stromal invasion, the diagnostic accuracy, sensitivity, and specificity, respectively for Reader 1/Reader 2, were as follows: DW MR imaging- 95.2 %/91.6 %, 91.7 %/100 %, and 95.8 %/90.1 %; DCE MR imaging- 91.6 %/88 %, 58.3 %/50 %, and 97.2 %/94.4 %. The diagnostic performance of DW MR imaging (Reader 1: areas under the receiver operating characteristic curve (AUC) = 0.98; Reader 2: AUC = 0.97) was significantly higher than that of DCE MR imaging (p = 0.009 for Reader 2) or T2-weighted MR imaging (Reader 1: p = 0.006; Reader 2: p = 0.013). Patients with cervical stromal invasion showed a significantly greater canal width (p < 0.0001) and myometrial invasion extent (p = 0.006).Conclusions: DW MR imaging has superior diagnostic performance compared with DCE MR imaging in the detection of cervical stromal invasion.Key Points: • DWI demonstrates a higher accuracy than DCE in detecting cervical stromal invasion. • Tumour ADC values are similar between patients without or with cervical invasion. • Canal widening causes false-negativity on DCE and T2W but not on DWI. [ABSTRACT FROM AUTHOR]

Purpose: To assess the clinical value of proton (1 H) MR spectroscopy in cervical carcinomas, in the prediction of poor prognostic human papillomavirus (HPV) genotypes as well as persistent disease following concurrent chemoradiotherapy (CCRT).Materials and Methods: 1 H MR spectroscopy using external phase array coil was performed in 52 consecutive cervical cancer patients at 3 Tesla (T). Poor prognostic HPV genotypes (alpha-7 species or absence of HPV infection) and persistent cervical carcinoma after CCRT were recorded. Statistical significance was calculated with the Mann-Whitney two-sided nonparametric test and areas under the receiver operating characteristics curve (AUC) analysis.Results: A 4.3-fold (P = 0.032) increased level of methyl resonance at 0.9 ppm was found in the poor prognostic HPV genotypes, mainly attributed to the presence of HPV18, with a sensitivity of 75%, a specificity of 81%, and an AUC of 0.76. Poor prognostic HPV genotypes were more frequently observed in patients with adeno-/adenosquamous carcinoma (Chi-square, P < 0.0001). In prediction of the four patients with persistent disease after CCRT, elevated methyl resonance demonstrated a sensitivity of 100%, a specificity of 74%, and an AUC of 0.82.Conclusion: 1 H MR spectroscopy at 3T can be used to depict the elevated lipid resonance levels in cervical carcinomas, as well as help to predict the poor prognostic HPV genotypes and persistent disease following CCRT. Further large studies with longer follow up times are warranted to validate our initial findings.Level Of Evidence: 1 J. Magn. Reson. Imaging 2017;45:899-907. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm (AAA) is a life-threatening situation affecting almost 10% of elders. There has been no effective medication for AAA other than surgical intervention. Dipeptidyl peptidase-4 (DPP-4) inhibitors have been shown to have a protective effect on cardiovascular disease. Whether DPP-4 inhibitors may be beneficial in the treatment of AAA is unclear. We investigated the effects of DPP-4 inhibitor sitagliptin on the angiotensin II (Ang II)-infused AAA formation in apoE-deficient (apoE-/-) mice. Mice with induced AAA were treated with placebo or 2.5, 5 or 10 mg/kg/day sitagliptin. Ang II-infused apoE-/- mice exhibited a 55.6% incidence of AAA formation, but treatment with sitagliptin decreased AAA formation. Specifically, administered sitagliptin in Ang II-infused mice exhibited decreased expansion of the suprarenal aorta, reduced elastin lamina degradation of the aorta, and diminished vascular inflammation by macrophage infiltration. Treatment with sitagliptin decreased gelatinolytic activity and apoptotic cells in aorta tissues. Sitaglipitn, additionally, was associated with increased levels of plasma active glucagon-like peptide-1 (GLP-1). In vitro studies, GLP-1 decreased reactive oxygen species (ROS) production, cell migration, and MMP-2 as well as MMP-9 activity in Ang II-stimulated monocytic cells. The results conclude that oral administration of sitagliptin can prevent abdominal aortic aneurysm formation in Ang II-infused apoE-/-mice, at least in part, by increasing of GLP-1 activity, decreasing MMP-2 and MMP-9 production from macrophage infiltration. The results indicate that sitagliptin may have therapeutic potential in preventing the development of AAA. [ABSTRACT FROM AUTHOR]

The article deals with a study which successfully synthesized and characterized a poly(ethylene succinate) copolyester containing 7 molecular percentage butylene succinate. Differential scanning calorimetry was employed to examine the melting behavior after isothermal crystallization. Meanwhile, the morphology of melt-crystallized samples was investigated using scanning electron microscopy and polarized light microscopy.

The article presents a study that analyzes the isothermal crystallization kinetics and melting behavior of poly[(trimethylene terephthalate)-co-(29 mol % ethylene terephthalate)] or PIT copolyester. The purpose was to find means of hastening the crystallization of recycled polyethylene terepththalates (PET) in a range of applications. It says that the melting behavior of PIT involved a melting-recrystallization-remelting cycle and the production of different lamellar crystals.

The article presents a study on the synthesis and characterization of poly(ethylene succinate) and its copolyesters containing minor amounts of butylene succinate (BS). The distribution of ethylene succinate and BS units was found to be random. It used a differential scanning calorimeter and a thermogravimetric analyzer to determine thermal properties, finding no significant difference among the polyesters' thermal stabilities. All the copolymers exhibited a single glass transition temperature.

Background and Objectives: Oral implant restorations are an excellent treatment option for edentulous patients; however, periodontopathogenic bacteria have been found in the microgaps between implant−abutment junctions. Implant designs to limit the microgaps have been extensively studied. However, studies have shown microgaps continue to exist, allowing for the leakage of bacteria into the implant system. Screw access hole materials are used to fill the access hole void. The use of materials with beneficial properties could provide bacterial leakage prevention. The aim of this study was to examine the surface free energy, cytotoxicity, and bacterial adhesion of selected screw access hole materials such as cotton, polytetrafluoroethylene (PTFE) tape, paraffin wax−polyolefin thermoplastic (PF), paraffin wax (Wax), gutta-percha (GP), and caviton EX (CE). Materials and Methods: A sessile drop test was performed to observe the contact angle and calculate the surface free energy of each material in order to determine the level of hydrophobicity. Cytotoxicity was examined in a mouse gingival epithelial cell line for day 1 and day 3. Bacterial adhesion was tested with Porphyromonas gingivalis, Fusobacterium nucleatum, and Aggregatibacter actinomycetemcomitans. Results: PTFE, PF, and wax presented low surface free energies of 19.34, 23.041, and 24.883 mN.m-1, respectively. No cytotoxicity was observed, except for GP and CE. Concurrently, the bacterial adhesion was also the lowest in PTFE and PF. Conclusions: Within the limits of this study, PTFE and PF showed an excellent biocompatibility with few bacterial adhesions. These materials could be potential screw access hole materials in clinical settings. [ABSTRACT FROM AUTHOR]

Aortic dissection (AD) is a highly lethal vascular disease characterized by separation of the constituent layers of the aortic wall. An increasing body of research indicates that inflammatory response and oxidative stress are implicated in vascular remodeling, which plays a key role in the development of AD. Hydrogen sulfide (H2S) has been found to protect against various types of cardiovascular disease, including myocardial infarction, arthrosclerosis, and hypertension. However, research on the effect of H2S on AD is insufficient. This study therefore elucidated the effect of H2S on the development and progression of AD, and the potential mechanism involved. Using β-aminopropionitrile fumarate (BAPN) and angiotensin II (Ang-II)-induced AD animal models, the administration of NaHS (as H2S donor, 56 μmol/kg body weight/day) was found to retard the development of AD. Murine VSMCs (Movas) exposed to interleukin-6 (IL-6) (20 ng/mL) to induce phenotypic switch. Histological analyses indicated that H2S administration inhibited the accumulation of inflammatory cells in the aortic wall and the related expression of inflammatory genes. Additionally, H2S treatment elevated aortic superoxide dismutase (SOD) activity and ablated malonaldehyde (MDA) and nitric oxide (NO) levels. In mechanistic terms, H2S attenuated IL-6 induced a pathological VSMC phenotypical switch through NO modulation by N(G)-monomethyl-L-arginine acetate salt (L-NMMA) stimulation. H2S inhibits AD formation by decreasing the inflammatory response, and oxidative stress, and by positively participating in vascular remodeling. These findings suggest a role for H2S as a novel and promising therapeutic strategy to prevent AD development. [ABSTRACT FROM AUTHOR]

Simple Summary: Computer-aided segmentation and machine learning added values of clinical parameters and diffusion-weighted imaging radiomics for predicting nodal metastasis in endometrial cancer, with a diagnostic performance superior to criteria based on lymph node size or apparent diffusion coefficient. Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10−3 mm2/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10−2), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria. [ABSTRACT FROM AUTHOR]

Galectin-3 (Gal-3) is a 26-kDa lectin that regulates many aspects of inflammatory cell behavior. We assessed the hypothesis that increased levels of Gal-3 contribute to abdominal aortic aneurysm (AAA) progression by enhancing monocyte chemoattraction through macrophage activation. We analyzed the plasma levels of Gal-3 in 76 patients with AAA (AAA group) and 97 controls (CTL group) as well as in angiotensin II (Ang-II)-infused ApoE knockout mice. Additionally, conditioned media (CM) were used to polarize THP-1 monocyte to M1 macrophages with or without Gal-3 inhibition through small interfering RNA targeted deletion to investigate whether Gal-3 inhibition could attenuate macrophage-induced inflammation and smooth muscle cell (SMC) apoptosis. Our results showed a markedly increased expression of Gal-3 in the plasma and aorta in the AAA patients and experimental mice compared with the CTL group. An in vitro study demonstrated that the M1 cells exhibited increased Gal-3 expression. Gal-3 inhibition markedly decreased the quantity of macrophage-induced inflammatory regulators, including IL-8, TNF-α, and IL-1β, as well as messenger RNA expression and MMP-9 activity. Moreover, Gal-3-deficient CM weakened SMC apoptosis through Fas activation. These findings prove that Gal-3 may contribute to AAA progression by the activation of inflammatory macrophages, thereby promoting SMC apoptosis. [ABSTRACT FROM AUTHOR]

Introduction: Endometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers. Objectives: We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy. Methods: We screened 100 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testing datasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms—support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection. Results: A total of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, the top-ranking metabolites chosen through SVM, RF and PLS-DA which existed in independent metabolic pathways, i.e. phosphocholine, malate, and asparagine, were selected to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively. Conclusion: Phosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy. [ABSTRACT FROM AUTHOR]