Bone Metastases Lesion Segmentation on Breast Cancer Bone Scan Images with Negative Sample Training.

The use of deep learning methods for the automatic detection and quantification of bone metastases in bone scan images holds significant clinical value. A fast and accurate automated system for segmenting bone metastatic lesions can assist clinical physicians in diagnosis. In this study, a small internal dataset comprising 100 breast cancer patients (90 cases of bone metastasis and 10 cases of non-metastasis) and 100 prostate cancer patients (50 cases of bone metastasis and 50 cases of non-metastasis) was used for model training. Initially, all image labels were binary. We used the Otsu thresholding method or negative mining to generate a non-metastasis mask, thereby transforming the image labels into three classes. We adopted the Double U-Net as the baseline model and made modifications to its output activation function. We changed the activation function to SoftMax to accommodate multi-class segmentation. Several methods were used to enhance model performance, including background pre-processing to remove background information, adding negative samples to improve model precision, and using transfer learning to leverage shared features between two datasets, which enhances the model's performance. The performance was investigated via 10-fold cross-validation and computed on a pixel-level scale. The best model we achieved had a precision of 69.96%, a sensitivity of 63.55%, and an F1-score of 66.60%. Compared to the baseline model, this represents an 8.40% improvement in precision, a 0.56% improvement in sensitivity, and a 4.33% improvement in the F1-score. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Clinical evaluation of a multitarget fecal immunochemical test-sDNA test for colorectal cancer screening in a high-risk population: a prospective, multicenter clinical study.

Colorectal cancer (CRC) is a major malignancy threatening the health of people in China and screening could be effective for preventing the occurrence and reducing the mortality of CRC. We conducted a multicenter, prospective clinical study which recruited 4,245 high-risk CRC individuals defined as having positive risk-adapted scores or fecal immunochemical test (FIT) results, to evaluate the clinical performance of the multitarget fecal immunochemical and stool DNA (FIT-sDNA) test for CRC screening. Each participant was asked to provide a stool sample prior to bowel preparation, and FIT-sDNA test and FIT were performed independently of colonoscopy. We found that 186 (4.4%) were confirmed to have CRC, and 375 (8.8%) had advanced precancerous neoplasia among the high CRC risk individuals. The sensitivity of detecting CRC for FIT-sDNA test was 91.9% (95% CI, 86.8-95.3), compared with 62.4% (95% CI, 54.9-69.3) for FIT (P < 0.001). The sensitivity for detecting advanced precancerous neoplasia was 63.5% (95% CI, 58.3-68.3) for FIT-sDNA test, compared with 30.9% (95% CI, 26.3-35.6) for FIT (P < 0.001). Multitarget FIT-sDNA test detected more colorectal advanced neoplasia than FIT. Overall, these findings indicated that in areas with limited colonoscopy resources, FIT-sDNA test could be a promising further risk triaging modality to select patients for colonoscopy in CRC screening.

Skeleton Segmentation on Bone Scintigraphy for BSI Computation.

Bone Scan Index (BSI) is an image biomarker for quantifying bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related research focus on binary classification in bone scintigraphy: having metastasis or none. Rare studies focus on pixel-wise segmentation. This study compares three advanced convolutional neural network (CNN) based models to explore bone segmentation on a dataset in-house. The best model is Mask R-CNN, which reaches the precision, sensitivity, and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, and 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability of clinical use on bone segmentation.

Distinct subpopulations of hepatitis C virus infectious cells with different levels of intracellular hepatitis C virus core protein.

Chronic infection by hepatitis C virus (HCV) is a major risk factor for the development of hepatocellular carcinoma (HCC). Despite the clear clinical importance of virus-associated HCC, the underlying molecular mechanisms remain largely unclarified. Oxidative stress, in particular, DNA lesions associated with oxidative damage, plays a major role in carcinogenesis, and is strongly linked to the development of many cancers, including HCC. However, in identifying hepatocytes with HCV viral RNA, estimates of the median proportion of HCV-infected hepatocytes have been found as high as 40% in patients with chronic HCV infection. In order to explore the gene alternation and association between different viral loads of HCV-infected cells, we established a method to dissect high and low viral load cells and examined the expression of DNA damage-related genes using a quantitative polymerase chain reaction array. We found distinct expression patterns of DNA damage-related genes between high and low viral load cells. This study provides a new method for future study on virus-associated gene expression research.

Gold nanoparticle-based colorimetric assays for coagulation-related proteins and their inhibition reactions.

In this paper, we describe two simple, label-free, homogenous assays using gold nanoparticles (Au NPs)-one to detect coagulation-related proteins and the other to screen inhibition reactions. The first nanosensor functions on the basis of the fact that thrombin catalyzes fibrinogen to form long-chain fibrins, which then induce aggregation of Au NPs. We applied this sensor to study the interactions of thrombin, inhibitors, cofactors, and antidotes. We further used thrombin-conjugated Au NPs (Thr-Au NPs) to analyze the levels of fibrinogen in plasma samples via fibrinogen-induced aggregation of Thr-Au NPs. The limit of detection (LOD; S/N=3) of this sensor for fibrinogen in plasma was 10nM. The Thr-Au NP probe provided quantitative results for fibrinogen in plasma samples that correlated (R(2)=0.97) with those obtained using a clinical von Clauss clotting rate assay. In addition, the Thr-Au NP-based sensor could be used to monitor thrombin concentrations in plasma samples under physiological conditions. Compared with conventional assays, these label-free assays offer several advantages, such as rapid and simple readout by the naked eye or by UV-vis absorption spectroscopy.

A label-free colorimetric detection of lead ions by controlling the ligand shells of gold nanoparticles.

We have developed a simple, colorimetric and label-free gold nanoparticle (Au NP)-based probe for the detection of Pb(2+) ions in aqueous solution, operating on the principle that Pb(2+) ions change the ligand shell of thiosulfate (S(2)O(3)(2-))-passivated Au NPs. Au NPs reacted with S(2)O(3)(2-) ions in solution to form Au(+).S(2)O(3)(2-) ligand shells on the Au NP surfaces, thereby inhibiting the access of 4-mercaptobutanol (4-MB). Surface-assisted laser desorption/ionization time-of-flight ionization mass spectrometry (SALDI-TOF MS) and inductively coupled plasma mass spectrometry (ICP-MS) measurements revealed that PbAu alloys formed on the surfaces of the Au NPs in the presence of Pb(2+) ions; these alloys weakened the stability of the Au(+).S(2)O(3)(2-) ligand shells, enhancing the access of 4-MB to the Au NP surfaces and, therefore, inducing their aggregation. As a result, the surface plasmon resonance (SPR) absorption of the Au NPs red-shifted and broadened, allowing quantitation of the Pb(2+) ions in the aqueous solution. This 4-MB/S(2)O(3)(2-)-Au NP probe is highly sensitive (linear detection range: 0.5-10 nM) and selective (by at least 100-fold over other metal ions) toward Pb(2+) ions. This cost-effective sensing system allows the rapid and simple determination of the concentrations of Pb(2+) ions in real samples (in this case, river water, Montana soil and urine samples).

Colorimetric assay for lead ions based on the leaching of gold nanoparticles.

A colorimetric, label-free, and nonaggregation-based gold nanoparticles (Au NPs) probe has been developed for the detection of Pb(2+) in aqueous solution, based on the fact that Pb(2+) ions accelerate the leaching rate of Au NPs by thiosulfate (S(2)O(3)(2-)) and 2-mercaptoethanol (2-ME). Au NPs reacted with S(2)O(3)(2-) ions in solution to form Au(S(2)O(3))(2)(3-) complexes on the Au NP surfaces, leading to slight decreases in their surface plasmon resonance (SPR) absorption. Surface-assisted laser desorption/ionization time-of-flight ionization mass spectrometry (SALDI-TOF MS) data reveals the formation of Pb-Au alloys on the surfaces of the Au NPs in the presence of Pb(2+) ions and 2-ME. The formation of Pb-Au alloys accelerated the Au NPs rapidly dissolved into solution, leading to dramatic decreases in the SPR absorption. The 2-ME/S(2)O(3)(2-)-Au NP probe is highly sensitive (LOD = 0.5 nM) and selective (by at least 1000-fold over other metal ions) toward Pb(2+) ions, with a linear detection range (2.5 nM-10 muM) over nearly 4 orders of magnitude. The cost-effective probe allows rapid and simple determination of the concentrations of Pb(2+) ions in environmental samples (Montana soil and river), with results showing its great practicality for the detection of lead in real samples.