Diallyl trisulfide alleviates chemotherapy sensitivity of ovarian cancer via the AMPK/SIRT1/PGC1α pathway.

Platinum-based chemotherapy promotes drug resistance in ovarian cancer. We investigated the antichemoresistance characteristics of diallyl trisulfide (DATS) in cisplatin-resistant ovarian cancer cells, in vitro and in vivo. Previous preclinical studies have revealed that DATS regulates distinct hallmark cancer-signaling pathways. The cell cycle pathway is the most investigated signaling pathway in DATS. Additionally, post-DATS treatment has been found to promote proapoptotic capacity through the regulation of intrinsic and extrinsic apoptotic pathway components. In the present study, we found that treating cisplatin-sensitive and cisplatin-resistant ovarian cell lines with DATS inhibited their proliferation and reduced their IC50. It induced cell apoptosis and promoted oxidative phosphorylation through the regulation of the AMPK/SIRT1/PGC1α pathway, OXPHOS, and enhanced chemotherapy sensitivity. DATS treatment alleviated glutamine consumption in cisplatin-resistant cells. Our findings highlight the role of DATS in overcoming drug resistance in ovarian cancer in vitro and in vivo. In addition, we elucidated the role of the AMPK/SIRT1/PGC1α signaling pathway as a potential target for the treatment of drug-resistant ovarian cancer.

Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors.

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

Sub-Pixel Extraction of Laser Stripe Center Using an Improved Gray-Gravity Method.

Laser stripe center extraction is a key step for the profile measurement of line structured light sensors (LSLS). To accurately obtain the center coordinates at sub-pixel level, an improved gray-gravity method (IGGM) was proposed. Firstly, the center points of the stripe were computed using the gray-gravity method (GGM) for all columns of the image. By fitting these points using the moving least squares algorithm, the tangential vector, the normal vector and the radius of curvature can be robustly obtained. One rectangular region could be defined around each of the center points. Its two sides that are parallel to the tangential vector could alter their lengths according to the radius of the curvature. After that, the coordinate for each center point was recalculated within the rectangular region and in the direction of the normal vector. The center uncertainty was also analyzed based on the Monte Carlo method. The obtained experimental results indicate that the IGGM is suitable for both the smooth stripes and the ones with sharp corners. The high accuracy center points can be obtained at a relatively low computation cost. The measured results of the stairs and the screw surface further demonstrate the effectiveness of the method.

Lower Expression of SPRY4 Predicts a Poor Prognosis and Regulates Cell Proliferation in Colorectal Cancer.

BACKGROUND/AIMS: Colorectal cancer (CRC) is the third most common type of cancer worldwide. Sprouty proteins are modulators of mitogeninduced signal transduction processes and therefore can influence the process of cancerogenesis. The encoded protein of Sprouty homolog 4 (SPRY4) is associated with various human cancers. However, its biological role and clinical significance in CRC development and progression are unknown. METHODS: The aim of this study was to evaluate the expression and biological role of SPRY4 in colorectal cancer. qRT-PCR was performed to investigate the expression of SPRY4 in tumor tissues and corresponding non tumor colorectal tissues from 70 patients. The effect of SPRY4 on proliferation was evaluated by MTT and colony formation assays. CRC cells transfected with SPRY4 were injected into nude mice to study the effect of SPRY4 on tumorigenesis in vivo. RESULTS: The lower expression of SPRY4 was remarkably correlated with deep tumor invasion and advanced TNM stage. Multivariate analyses revealed that SPRY4 expression served as an independent predictor for overall survival. Using 5-aza treatment, we also observed that SPRY4 expression can be affected by DNA methylation. Further experiments revealed that overexpressed SPRY4 significantly inhibited CRC cell proliferation both in vitro and in vivo. CONCLUSION: Our study demonstrated that SPRY4 is involved in the development and progression of colorectal cancer by regulating cell proliferation and shows that SPRY4 may be a potential diagnostic and prognostic target in patients with colorectal cancer.

Research on an Online Monitoring Device for the Powder Laying Process of Laser Powder Bed Fusion.

Improving the quality of metal additive manufacturing parts requires online monitoring of the powder bed laying procedure during laser powder bed fusion production. In this article, a visual online monitoring tool for flaws in the powder laying process is examined, and machine vision technology is applied to LPBF manufacture. A multiscale improvement and model channel pruning optimization method based on convolutional neural networks is proposed, which makes up for the deficiencies of the defect recognition method of small-scale powder laying, reduces the redundant parameters of the model, and enhances the processing speed of the model under the premise of guaranteeing the accuracy of the model. Finally, we developed an LPBF manufacturing process laying powder defect recognition algorithm. Test experiments show the performance of the method: the minimum size of the detected defects is 0.54 mm, the accuracy rate of the feedback results is 98.63%, and the single-layer laying powder detection time is 3.516 s, which can realize the effective detection and control of common laying powder defects in the additive manufacturing process, avoids the breakage of the scraper, and ensures the safe operation of the LPBF equipment.

Process Study of Selective Laser Sintering of PS/GF/HGM Composites.

To address the issues of insufficient strength and poor precision in polystyrene forming parts during the selective laser sintering process, a ternary composite of polystyrene/glass fiber/hollow glass microbeads was prepared through co-modification by incorporating glass fiber and hollow glass microbeads into polystyrene using a mechanical mixing method. The bending strength and dimensional accuracy of the sintered composites were investigated by conducting an orthogonal test and analysis of variance to study the effects of laser power, scanning speed, scanning spacing, and delamination thickness. The process parameters were optimized and selected to determine the optimal combination. The results demonstrated that when considering bending strength and Z-dimensional accuracy as evaluation criteria for terpolymer sintered parts, the optimum process parameters are as follows: laser power of 24 W, scanning speed of 1600 mm/s, scanning spacing of 0.24 mm, and delamination thickness of 0.22 mm. Under these optimal process parameters, the bending strength of sintered parts reaches 6.12 MPa with a relative error in the Z-dimension of only 0.87%. The bending strength of pure polystyrene sintered parts is enhanced by 15.69% under the same conditions, while the relative error in the Z-dimension is reduced by 63.45%. It improves the forming strength and precision of polystyrene in the selective laser sintering process and achieves the effect of enhancement and modification, which provides a reference and a new direction for exploring polystyrene-based high-performance composites and expands the application scope of selective laser sintering technology.

Parametric Design of Porous Structure and Optimal Porosity Gradient Distribution Based on Root-Shaped Implants.

Porous structures can reduce the elastic modulus of implants, decrease stress shielding, and avoid bone loss in the alveolar bone and aseptic loosening of implants; however, there is a mismatch between yield strength and elastic modulus as well as biocompatibility problems. This study aimed to investigate the parametric design method of porous root-shaped implants to reduce the stress-shielding effect and improve the biocompatibility and long-term stability and effectiveness of the implants. Firstly, the porous structure part was parametrically designed, and the control of porosity gradient distribution was achieved by using the fitting relationship between porosity and bias and the position function of bias. In addition, the optimal distribution law of the porous structure was explored through mechanical and hydrodynamic analyses of the porous structure. Finally, the biomechanical properties were verified using simulated implant-bone tissue interface micromotion values. The results showed that the effects of marginal and central porosity on yield strength were linear, with the elastic modulus decreasing from 18.9 to 10.1 GPa in the range of 20-35% for marginal porosity, with a maximum decrease of 46.6%; the changes in the central porosity had a more consistent effect on the elastic modulus, ranging from 18.9 to 15.3 GPa in the range of 50-90%, with a maximum downward shift of 19%. The central porosity had a more significant effect on permeability, ranging from 1.9 × 10-7 m2 to 4.9 × 10-7 m2 with a maximum enhancement of 61.2%. The analysis showed that the edge structure had a more substantial impact on the mechanical properties. The central structure could increase the permeability more effectively. Hence, the porous structure with reasonable gradient distribution had a better match between mechanical properties and flow properties. The simulated implantation results showed that the porous implant with proper porosity gradient distribution had better biomechanical properties.

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

Background: In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening. Methods: A cohort of 64 patients undergoing both LDCT and NDCT was randomly divided into training (n=46) and testing (n=18) sets. A two-stage training approach was adopted. First, Gaussian noise was added to NDCT data to create simulated LDCT data for generator training. Then, the model was trained on a clinically structure-unaligned paired data set using a Wasserstein generative adversarial network (WGAN) framework with the initial generator weights obtained during the first stage of training. An attention mechanism was also incorporated into the network. Results: Validated on a clinical CT data set, our proposed method outperformed other available methods [CycleGAN, Pixel2Pixel, block-matching and three-dimensional filtering (BM3D)] in noise removal and detail retention tasks in terms of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) metrics. Compared with the results produced by BM3D, our method yielded an average improvement of approximately 7% in terms of the three evaluation indicators. The probability density profile of the denoised CT output produced using our method best fit the reference NDCT scan. Additionally, our two-stage model outperformed the one-stage WGAN-based model in both objective and subjective evaluations, further demonstrating the higher effectiveness of our two-stage training approach. Conclusions: The proposed method performed the best in removing noise from LDCT scans and exhibited good detail retention, which could potentially enhance the lesion detection and characterization effects obtained for soft tissues in the scanning scope of LDCT lung cancer screening.

A Case-Control Study on Factors of HPV Vaccination for Mother and Daughter in China.

(1) Background: To explore the influencing factors of human papillomavirus (HPV) vaccination among mothers and daughters so as to provide evidence and strategies for improving the HPV vaccination rate of 9-18-years-old girls. (2) A questionnaire survey was conducted among the mothers of 9-18-year-old girls from June to August 2022. The participants were divided into the mother and daughter vaccinated group (M1D1), the mother-only vaccinated group (M1D0), and the unvaccinated group (M0D0). Univariate tests, the logistic regression model, and the Health Belief Model (HBM) were employed to explore the influencing factors. (3) Results: A total of 3004 valid questionnaires were collected. According to the regions, Totally 102, 204, and 408 mothers and daughters were selected from the M1D1, M1D0, and M0D0 groups, respectively. The mother having given her daughter sex education (OR = 3.64; 95%CI 1.70, 7.80), the mother's high perception of disease severity (OR = 1.79; 95%CI 1.02, 3.17), and the mother's high level of trust in formal information (OR = 2.18; 95%CI 1.26, 3.78) were all protective factors for both the mother and her daughter's vaccination. The mother's rural residence (OR = 0.51; 95%CI 0.28, 0.92) was a risk factor for vaccination of both mother and daughter. The mother's education of high school or above (OR = 2.12; 95%CI 1.06, 4.22), the mother's high level of HPV and HPV vaccine knowledge (OR = 1.72; 95%CI 1.14, 2.58), and the mother's high level of trust in formal information (OR = 1.72; 95%CI 1.15, 2.57) were protective factors of mother-only vaccination. The older the mother (OR = 0.95; 95%CI 0.91, 0.99) was classed as a risk factor for mother-only vaccination. "Waiting until the daughters are older to receive the 9-valent vaccine" is the main reason why the daughters of M1D0 and M0D0 are not vaccinated". (4) Chinese mothers had a high willingness to vaccinate their daughters with the HPV vaccine. The higher education level of the mother, giving sex education to the daughter, the older ages of mothers and daughters, the mother's high level of HPV and HPV vaccine knowledge, a high level of perception of the disease severity, and a high level of trust in formal information were promoting factors of HPV vaccination for mother and daughter, and rural residence was a risk factor to vaccination. To promote HPV vaccination in girls from 9-18 years old, communities could provide health education to rural mothers with low education levels; the government could advocate for HPV vaccination through issuing policy documents; and doctors and the CDC could popularize the optimal age for HPV vaccination to encourage mothers to vaccinate their daughters at the age of 9-14 years old.

Improved GAN: Using a transformer module generator approach for material decomposition.

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps. This is not a straightforward process, and it potentially introduces many inaccuracies after multiple steps. In this work, we proposed a generative adversarial network (GAN) framework as the base and improved its generator; this approach combines convolutional neural networks (CNNs) and a transformer module to directly generate material decomposition maps from conventional single-energy CT images. Our model pays attention to both local and global information. Then, we compared our method with 6 competitive deep learning methods on water (calcium) and calcium (water) substrate density image datasets. The average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the water (calcium) substrate density images were 32.7207, 0.9685, 0.0323, and 0.0555, respectively. Furthermore, the average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the calcium (water) substrate density images were 30.2823, 0.9449, 0.0652, and 0.0715, respectively. Our model achieved better performance and stronger stability than competing approaches.

Highly efficient drug delivery nanosystem via L-phenylalanine triggering based on supramolecular polymer micelles.

An intelligent drug delivery nanosystem has been developed based on biodegradable supramolecular polymer micelles (SMPMs). The drug release can be triggered from SMPMs responsively by a bioactive agent, L-phenylalanine in a controlled fashion. The SMPMs are constructed from ethylcellulose-graft-poly(ε-caprolactone) (EC-g-PCL) and α-cyclodextrin (α-CD) derivate via host-guest and hydrophobic interactions. It has been found that these SMPMs have disassembled rapidly in response to an additional L-phenylalanine, due to great affinity discrepancy to α-CD between L-phenylalanine and PCL. Experiments have been carried out on trigger-controlled in vitro drug release of the SMPMs loaded with a model porphyrin based photosensitizer THPP. The result shows that the SMPMs released over 85% THPP in 6 h, which is two orders magnitudes faster than that of control. Also investigated is the photodynamic therapy (PDT) of THPP-loaded SMPMs with and without L-phenylalanine on MCF-7 carcinoma cell line. An effective trigger-concentration dependent lethal effect has been found showing promise in clinical photodynamic therapy.

Contrast-enhanced to noncontrast CT transformation via an adjacency content-transfer-based deep subtraction residual neural network.

To reduce overall patient radiation exposure in some clinical scenarios (since cancer patients need frequent follow-ups), noncontrast CT is not used in some institutions. However, although less desirable, noncontrast CT could provide additional important information. In this article, we propose a deep subtraction residual network based on adjacency content transfer to reconstruct noncontrast CT from contrast CT and maintain image quality comparable to that of a CT scan originally acquired without contrast. To address the slight structural dissimilarity of the paired CT images (noncontrast CT and contrast CT) due to involuntary physiological motion, we introduce a contrastive loss network derived from the adjacency content-transfer strategy. We evaluate the results of various similarity metrics (MSE, SSIM, NRMSE, PSNR, MAE) and the fitting curve (HU distribution) of the output mapping to estimate the reconstruction performance of the algorithm. To build the model, we randomly select a total of 15,405 CT paired images (noncontrast CT and contrast-enhanced CT) for training and 10,270 CT paired images for testing. The proposed algorithm preserves the robust structures from the contrast-enhanced CT scans and learns the noncontrast attenuation pattern from the noncontrast CT scans. During the evaluation, the deep subtraction residual network achieves higher MSE, MAE, NRMSE, and PSNR scores (by 30%) than those of the baseline models (BEGAN, CycleGAN, Pixel2Pixel) and better simulates the HU curve of noncontrast CT attenuation. After validation based on an analysis of the experimental results, we can report that the noncontrast CT images reconstructed by our proposed algorithm not only preserve the high-quality structures from the contrast-enhanced CT images, but also mimic the CT attenuation of the originally acquired noncontrast CT images.

Association between H. pylori infection and health Outcomes: an umbrella review of systematic reviews and meta-analyses.

OBJECTIVE: Systematic reviews and meta-analyses have revealed the associations between H. pylori infection and various health outcomes. We aimed to evaluate the strength and breadth of evidence on the associations. DESIGN: Umbrella review of systematic reviews and meta-analyses. SETTING: No settings. PARTICIPANTS: No patients involved. DATA SOURCES: Embase, PubMed, Web of Science, Cochrane Library Databases, CNKI, VIP database and Wangfang database from inception to February 1, 2019. OUTCOMES MEASURES: Diverse diseases (such as cancer and ischaemic heart disease). RESULTS: Sixty articles reporting 88 unique outcomes met the eligible criteria. 74 unique outcomes had nominal significance (p<0.05). Of the outcomes with significance, 61 had harmful associations and 13 had beneficial associations. Furthermore, 73% (64) of the outcomes exhibited significant heterogeneity . Of the these meta-analyses, 32 had moderate to high heterogeneity (I2=50%-75%) and 24 had high heterogeneity (I2>75%). Moreover, 20% exhibited publication bias (p<0.1). In addition, 97% of the methodological qualities were rated 'critically low'. 36% of the evidence qualities of outcomes were rated 'low', 56% of the evidence qualities were rated 'very low' and 8% of the evidence qualities were rated 'moderate'. H. pylori infection may be associated with an increased risk of five diseases and a decreased risk of irritable bowel syndrome. CONCLUSION: Although 60 meta-analyses explored 88 unique outcomes, moderate quality evidence only existed for six outcomes with statistical significance. H. pylori infection may be associated with a decreased risk of irritable bowel syndrome and an increased risk of hypertriglyceridemia, chronic cholecystitis and cholelithiasis, gestational diabetes mellitus, gastric cancer and systemic sclerosis. TRIAL REGISTRATION: CRD42019124680.

MiR-3653 inhibits the metastasis and epithelial-mesenchymal transition of colon cancer by targeting Zeb2.

Aberrant expression of microRNAs (miRNAs) has been widely recognized to play critical roles in the pathogenic processes of colon cancer. However, the expression and functions of miR-3653 in colon cancer remain uncovered. This study revealed for the first time that miR-3653 expression was significantly decreased in colon cancer tissues and cell lines. MiR-3653 overexpression led to decreased migration and invasion of HCT116 cells while miR-3653 knockdown resulted in opposite influence of the metastatic behaviors of HT29 cells. qRT-PCR and western blot demonstrated that miR-3653 suppressed the epithelial-mesenchymal transition (EMT) of colon cancer cells using both gain- and loss- of function assay. Mechanically, miR-3653 was found to interact with the 3'-UTR of Zeb2 through the complementary sequences and inhibited the expression of Zeb2 in colon cancer cells. Rescue experiments demonstrated that the inhibitory effect of miR-3653 overexpression on cell metastasis and EMT was abrogated by forced expression of Zeb2. This study demonstrates that miR-3653 suppresses the metastasis and EMT of colon cells by targeting Zeb2, and serves as a promising biomarker and therapeutic target in colon cancer.

Machine Learning Assisted MRI Characterization for Diagnosis of Neonatal Acute Bilirubin Encephalopathy.

Background: The use of magnetic resonance imaging (MRI) in diagnosis of neonatal acute bilirubin encephalopathy (ABE) in newborns has been limited by its difficulty in differentiating confounding image contrast changes associated with normal myelination. This study aims to demonstrate the feasibility of building a machine learning prediction model based on radiomics features derived from MRI to better characterize and distinguish ABE from normal myelination. Methods: In this retrospective study, we included 32 neonates with clinically confirmed ABE and 29 age-matched controls with normal myelination. Radiomics features were extracted from the manually segmented region of interest (ROI) on T1-weighted spin echo images, followed by the feature selection using two-sample independent t-test, least absolute shrinkage and selection operator (Lasso) regression, and Pearson's correlation matrix. Additional feature quantifying the relative mean intensity of ROI was defined and calculated. A prediction model based on the selected features was built to classify ABE and normal myelination using multiple machine learning classifiers and a leave-one-out cross-validation scheme. Receiver operating characteristics (ROC) analysis was used to evaluate the prediction performance with the area under the curve (AUC) and feature importance ranked based on the Fisher score. Results: Among 1319 radiomics features, one radiologist-defined intensity-based feature and 12 texture features were selected as the most discriminative features. Based on these features, decision trees had the best classification performance with the largest AUC of 0.946, followed by support vector machine (SVM), tree-bagger, logistic regression, Naïve Bayes, discriminant analysis, and k-nearest neighborhood (KNN), which have an AUC of 0.931, 0.925, 0.905, 0.891, 0.883, and 0.817, respectively. The relative mean intensity outperformed other 12 texture features in differentiating ABE from controls. Conclusions: The results from this study demonstrated a new strategy of characterizing ABE-induced intensity and morphological changes in MRI, which are difficult to be recognized, interpreted, or quantified by the routine experience and visual-based reading strategy. With more quantitative and objective measurements, the reported machine learning assisted radiomics features-based approach can improve the diagnosis and support clinical decision-making.

The synthesis of biodegradable graft copolymer cellulose-graft-poly(L-lactide) and the study of its controlled drug release.

According to the concept of green chemistry and sustainable development, a new biodegradable copolymer comprised of hydrophobic poly(l-lactide) (PLLA) segments and hydrophilic cellulose segment (cellulose-g-PLLA) was designed and synthesized. The structure of the copolymer was characterized by (1)H NMR, FT-IR, (13)C NMR, DSC and WAXD. The cytotoxicity study shows that cellulose-g-PLLA exhibits good biocompatibility. The copolymer can self-assemble into micelles in water with the hydrophobic PLLA segments at the cores of micelles and the hydrophilic cellulose segments as the outer shells. Transmission electron microscopy (TEM) shows that the micelles exhibit nanospheric morphology within a size range of 30-80nm. The drug loaded micelles formed by the copolymer in aqueous media show sustained drug release which indicates their potential applicability in drug carrier.

Redetermination of 1-naphthalene-acetic acid.

The crystal structure of the title compound, C(12)H(10)O(2), was originally determined by Rajan [Acta Cryst. (1978). B34, 998-1000] using intensity data estimated from Weissenberg films. This redetermination provides a structure with significantly improved precision with respect to the geometric parameters. In the crystal structure, inter-molecular O-H⋯O hydrogen bonds, weak C-H⋯O hydrogen bonds and C-H⋯π inter-actions link the mol-ecules into a two-dimensional sheet lying parallel to (100).

Primary gastric inflammatory myofibroblastic tumor: A case report.

RATIONALE: Primary gastric inflammatory myofibroblastic tumor is extremely rare. Only a few cases were reported in the domestic and foreign medical literature with corresponding imaging findings of this disease even more rarely reported. PATIENT CONCERNS: We present one case of a 52-year-old female patient with upper abdominal pain, acid reflux, and belching for 2 months. DIAGNOSES AND INTERVENTIONS: Electron ultrasound gastroscopy (EUS) revealed elevation of gastric antrum mucosa. A whole abdominal and pelvic multi-slice spiral computed tomography (CT) detected a round nodule in the gastric antrum with considerably delayed enhancement, with initial suspicion of gastrointestinal stromal tumors (GISTS). Then a laparoscopic assisted distal gastrectomy was performed. Finally, the postoperative pathology confirmed the diagnosis of primary gastric IMT. OUTCOMES: After 6 months of follow-up, the patient was still alive without any evidence of metastasis or recurrence. LESSONS: Familiarizing with the CT features of this rare tumor may raise radiologists' awareness of the disease and potentially could avoid misdiagnosis.

Metabolite Profile of Alzheimer's Disease in the Frontal Cortex as Analyzed by HRMAS 1H NMR.

Background: Investigation on neurochemical changes in the frontal cortex in individuals with Alzheimer's disease (AD) and different Apolipoprotein E (APOE) genotypes, using ex vivo solid-state high-resolution NMR analysis, may lead to a better understanding of the neurochemistry associated with AD as well as new AD-specific metabolite biomarkers that might potentially improve the clinical diagnosis of AD. Methods: Intact tissue samples of the frontal cortex were obtained from 11 patients and 11 age-matched non-demented controls. Metabolite profiles in all samples were analyzed ex vivo, using solid-state high-resolution magic angle spinning (HRMAS) 600 MHz 1H nuclear magnetic resonance (NMR). A logistic regression analysis was used to rank metabolites based on their level of contribution in differentiating the AD patient tissues and the controls, and different AD-associated APOE genotypes (APOE ε4 vs. APOE ε3). Results: Tissue samples from the AD patients showed significantly lower NAA/Cr (p = 0.011), Ace/Cr (p = 0.027), GABA/Cr (p = 0.005), Asp/Cr (p < 0.0001), mI/Cr (p < 0.0001), and Tau/Cr (p = 0.021), and higher PCho/Cr (p < 0.0001), GPCho/Cr (p < 0.0001), and α&ß-Glc/Cr (p < 0.0001) than the controls did. Specifically, a newly observed resonance at 3.71 ppm, referred to as α&ß-Glc, was observed in 90.9% of the AD samples (10/11). Samples with APOE ε4 also exhibited higher PCho/Cr (p = 0.0002), GPCho/Cr (p = 0.0001), α&ß-Glc/Cr (p < 0.0001), and lower Asp/Cr (p = 0.004) and GABA/Cr (p = 0.04) than the samples with APOE ε3 did. In the logistic regression analysis, PCho, GPCho, ASP, and α&ß-Glc were found to be the most relevant metabolites for differentiating the AD patient tissues and the controls, and different APOE genotypes. Conclusion: HRMAS 1H NMR with high spectral resolution and sensitivity offers a powerful tool to gain quantitative information on AD associated neurochemical changes. There are important neurochemical differences in the frontal cortex between the AD patient tissues and the controls, and between those with different APOE genotypes. The resonance (α&ß-Glc) found at 3.71 ppm in the AD patient tissues may be further investigated for its potential in the diagnosis and monitoring of AD.

Degradable cisplatin-releasing core-shell nanogels from zwitterionic poly(beta -aminoester)-graft-PEG for cancer chemotherapy.

Cisplatin conjugated onto macromolecules or loaded in micelles can be preferentially delivered to tumors to minimize its toxicity to healthy tissues and increase its drug efficacy. Herein, we report cisplatin-containing nanogels possibly useful for targeted delivery of cisplatin. Carboxylic acid-functionalized poly(beta -aminoester)graft-poly(ethylene glycol) copolymers were synthesized by cocondensation polymerization of piperazine with 2,2-bis(acryloxymethyl)propionic acid, PEG 2,2-bis(acryloxymethyl)propionate macromonomer (mPEG). The graft copolymers formed 100-200 nm nanogels with low size-distribution by the complexation of their carboxylic groups with cisplatin. The nanogels were negatively charged and had a PEG outer layer. Thus, they had "stealth properties" suitable for in vivo applications. The nanogels had significantly lower in vitro cytotoxicity to SKOV-3 ovarian cancer cells than free cisplatin, but similar anticancer activity toward SKOV-3 tumors xenografted to immunocompromised mice.