Accurate Clinical Detection of Vitamin D by Mass Spectrometry: A Review

Vitamin D deficiency is thought to be associated with a wide range of diseases, including diabetes, cancer, depression, neurodegenerative diseases, and cardiovascular and cerebrovascular diseases. This vitamin D deficiency is a global epidemic affecting both developing and developed countries and therefore qualitative and quantitative analysis of vitamin D in a clinical context is essential. Mass spectrometry has played an increasingly important role in the clinical analysis of vitamin D because of its accuracy, sensitivity, specificity, and the ability to detect multiple substances at the same time. Despite their many advantages, mass spectrometry-based methods are not without analytical challenges. Front-end and back-end challenges such as protein precipitation, analyte extraction, derivatization, mass spectrometer functionality, must be carefully considered to provide accurate and robust analysis of vitamin D through a well-designed approach with continuous control by internal and external quality control. Therefore, the aim of this review is to provide a comprehensive overview of the development of mass spectrometry methods for vitamin D accurate analysis, including emphasis on status markers, deleterious effects of biological matrices, derivatization reactions, effects of ionization sources, contribution of epimers, standardization of assays between laboratories.

Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method

Breast cancer can develop when breast cells replicate abnormally. It is now a worldwide issue that concerns people’s safety all around the world. Every day, women die from breast cancer, which is especially common in the United States. Mammography, CT, MRI, ultrasound, and biopsies may all be used to detect breast cancer. Histopathology (biopsy) is often carried out to examine the image and discover breast cancer. Breast cancer detection at an early stage saves lives. Deep and machine learning models aid in the detection of breast cancer. The aim of the research work is to encourage medical research and the development of technology by employing deep learning models to recognize cancer cells that are small in size. For histological annotation and diagnosis, the proposed technique makes use of the BreCaHAD dataset. Color divergence is caused by differences in slide scanners, staining procedures, and biopsy materials. To avoid overfitting, we used data augmentation with 19 factors, such as scale, rotation, and gamma. The proposed hybrid dilation deep learning model is of two sorts. It illustrates edges, curves, and colors, and it improves the key traits. It utilizes dilation convolution and max pooling for multi-scale information. The proposed dilated unit processes the image and sends the processed features to the Alexnet, and it can recognize minute objects and thin borders by using the dilated residual expanding kernel model. An AUC of 96.15 shows that the new strategy is better than the old one.

Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors’ data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper’s methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to ‘simulate’ the sensors’ reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations.

Accurate Detection and Evaluation of the Gene-Editing Frequency in Plants Using Droplet Digital PCR

Gene-editing techniques are becoming powerful tools for modifying target genes in organisms. Although several methods have been reported that detect mutations at targeted loci induced by the CRISPR/Cas system in different organisms, they are semiquantitative and have difficulty in the detection of mutants in processed food samples containing low initial concentrations of DNA and may not accurately quantify editing frequency, especially at very low frequencies in a complex polyploid plant genome. In this study, we developed a duplexed dPCR-based method for the detection and evaluation of gene-editing frequencies in plants. We described the design, performance, accurate quantification, and comparison with other detection systems. The results show that the dPCR-based method is sensitive to different kinds of gene-editing mutations induced by gene-editing. Moreover, the method is applicable to polyploid plants and processed food samples containing low initial concentrations of DNA. Compared with qPCR and NGS-based methods, the dPCR method has a lower limit of detection (LOD) of the editing frequency and a better relationship with the expected editing frequency in detecting the edited region of gene-edited rice samples. Taken together, the duplexed dPCR assay is accurate and precise, and it will be a powerful tool for the detection and evaluation of gene-editing frequencies in plants in gene-editing technology.

Validity of an Accelerometer-Based Activity Monitor System for Measuring Physical Activity in Frail Older Adults

This study aimed to investigate the validity of the accelerometer-based DynaPort system to detect physical activity in frail, older subjects. Eighteen home-dwelling subjects (Groningen Frailty Indicator [GFI] score ≥ 4, ≥ 75 years) were included. Activities in their home environment were simultaneously observed by two researchers and measured with the DynaPort system during six consecutive hours. Primary outcome measures were the sensitivity and specificity of the DynaPort for locomotion (90% considered as sufficient agreement). Other outcome measures were overall agreement, and sensitivity and specificity for other activities. Sensitivity and specificity for locomotion were 83.3% and 100.0%, respectively. Overall agreement was 74.6%. Sensitivity was sufficient for sitting (94.4%), but not for lying and standing (59.2% and 69.6%, respectively). Specificity was sufficient for lying and standing (100.0% and 93.3%, respectively), but not for sitting (80.7%). In conclusion, the DynaPort system is not a valid method for assessing physical activity in frail, older subjects.

A novel anti-MUC1 antibody against the MUC1 cytoplasmic tail domain: use in sensitive identification of poorly differentiated cells in adenocarcinoma of the stomach

BACKGROUND

Isolated cancer cells of non-solid type poorly differentiated adenocarcinoma (por2) or signet-ring cell carcinoma (sig) are frequently seen in scirrhous gastric cancers with a very poor prognosis. These cells are often scattered in granulation tissue or desmoplastic fibrotic tissue and tend to be overlooked in routine pathological examination. We aimed to raise a novel antibody that can identify the isolated cancer cells easily.

METHODS

Because the MUC1 cytoplasmic tail domain (CTD) has many biological roles including tumor progression and cell adhesion disturbance and is expected to be expressed in isolated cancer cells, we raised a novel monoclonal antibody (MAb) MUC1-014E against an intracellular nonrepeating 19-amino-acid sequence (RYVPPSSTDRSPYEKVSAG: N-1217-1235-C) of the MUC1 CTD, using a synthetic peptide including the 7-amino-acid epitope (STDRSPY: N-1223-1229-C).

RESULTS

In the immunohistochemical staining of 107 gastrectomy specimens including 48 por2 and 31 sig lesions, the MAb MUC1-014E showed high rates of positive staining (≥5% of carcinoma cells stained) for por2 (100%) and sig (97%), and of the highest intensity staining (4+, ≥75% of carcinoma cells stained) for por2 (100%) and sig (90%). In the 89 biopsy specimens including 82 por2 and 38 sig lesions, the MAb MUC1-014E showed high rates of positive staining for por2 (100%) and sig (100%) and of 4+ staining for por2 (87%) and sig (84%). All the rates were significantly higher than those with cytokeratins (AE1/AE3 or CAM5.2).

CONCLUSIONS

The MAb MUC1-014E is very useful for accurate detection of isolated cancer cells in scirrhous gastric cancers.