Arsenic speciation in saliva of acute promyelocytic leukemia patients undergoing arsenic trioxide treatment

Arsenic trioxide has been successfully used as a therapeutic in the treatment of acute promyelocytic leukemia (APL). Detailed monitoring of the therapeutic arsenic and its metabolites in various accessible specimens of APL patients can contribute to improving treatment efficacy and minimizing arsenic-induced side effects. This article focuses on the determination of arsenic species in saliva samples from APL patients undergoing arsenic treatment. Saliva samples were collected from nine APL patients over three consecutive days. The patients received 10 mg arsenic trioxide each day via intravenous infusion. The saliva samples were analyzed using high-performance liquid chromatography coupled with inductively coupled plasma mass spectrometry. Monomethylarsonous acid and monomethylmonothioarsonic acid were identified along with arsenite, dimethylarsinic acid, monomethylarsonic acid, and arsenate. Arsenite was the predominant arsenic species, accounting for 71.8 % of total arsenic in the saliva. Following the arsenic infusion each day, the percentage of methylated arsenicals significantly decreased, possibly suggesting that the arsenic methylation process was saturated by the high doses immediately after the arsenic infusion. The temporal profiles of arsenic species in saliva following each arsenic infusion over 3 days have provided information on arsenic exposure, metabolism, and excretion. These results suggest that saliva can be used as an appropriate clinical biomarker for monitoring arsenic species in APL patients. [Figure: see text

Research on Construction and Application of Individual Knowledge Management Maturity Evaluation Model

Individual knowledge management is basic component of organizational knowledge management, and the maturity of individual knowledge management has a significant impact on organizational knowledge management. This research introduces scientific idea of capability maturity model into individual knowledge management, building corresponding assessment criteria combined with features of individual knowledge management, and constructing an individual knowledge management maturity model with gray comprehensive evaluation method. In the fourth part of this paper, the validity of the model has been verified by applying the model on an instance. This research is made in order to provide references and suggestions on improving the level of individual knowledge management in knowledge-based organizations.</p

Childhood Sexual Abuse and the Development of Recurrent Major Depression in Chinese Women

Background Our prior study in Han Chinese women has shown that women with a history of childhood sexual abuse (CSA) are at increased risk for developing major depression (MD). Would this relationship be found in our whole data set? Method Three levels of CSA (non-genital, genital, and intercourse) were assessed by self-report in two groups of Han Chinese women: 6017 clinically ascertained with recurrent MD and 5983 matched controls. Diagnostic and other risk factor information was assessed at personal interview. Odds ratios (ORs) were calculated by logistic regression. Results We confirmed earlier results by replicating prior analyses in 3,950 new recurrent MD cases. There were no significant differences between the two data sets. Any form of CSA was significantly associated with recurrent MD (OR 4.06, 95% confidence interval (CI) [3.19–5.24]). This association strengthened with increasing CSA severity: non-genital (OR 2.21, 95% CI 1.58–3.15), genital (OR 5.24, 95% CI 3.52–8.15) and intercourse (OR 10.65, 95% CI 5.56–23.71). Among the depressed women, those with CSA had an earlier age of onset, longer depressive episodes. Recurrent MD patients those with CSA had an increased risk for dysthymia (OR 1.60, 95%CI 1.11–2.27) and phobia (OR 1.41, 95%CI 1.09–1.80). Any form of CSA was significantly associated with suicidal ideation or attempt (OR 1.50, 95% CI 1.20–1.89) and feelings of worthlessness or guilt (OR 1.41, 95% CI 1.02–2.02). Intercourse (OR 3.47, 95%CI 1.66–8.22), use of force and threats (OR 1.95, 95%CI 1.05–3.82) and how strongly the victims were affected at the time (OR 1.39, 95%CI 1.20–1.64) were significantly associated with recurrent MD

Cross-Vendor CT Image Data Harmonization Using CVH-CT

While remarkable advances have been made in Computed Tomography (CT), most of the existing efforts focus on imaging enhancement while reducing radiation dose. How to harmonize CT image data captured using different scanners is vital in cross-center large-scale radiomics studies but remains the boundary to explore. Furthermore, the lack of paired training image problem makes it computationally challenging to adopt existing deep learning models. We propose a novel deep learning approach called CVH-CT for harmonizing CT images captured using scanners from different vendors. The generator of CVH-CT uses a self-attention mechanism to learn the scanner-related information. We also propose a VGG feature based domain loss to effectively extract texture properties from unpaired image data to learn the scanner based texture distributions. The experimental results show that CVH-CT is clearly better than the baselines because of the use of the proposed domain loss, and CVH-CT can effectively reduce the scanner-related variability in terms of radiomic features

Framework for Hyperspectral Image Processing and Quantification for Cancer Detection During Animal Tumor Surgery

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor