Historical perspective on needle development: From the past to the future

Evaluation of short-term hair follicle storage conditions for maintenance of RNA integrity

Hair follicles provide an easily accessible tissue for interrogating gene expression for multiple purposes in mammals. RNAlater® is a liquid storage solution that stabilises and preserves cellular RNA, eliminating the need to immediately process or freeze tissue specimens. The manufacturer advises storage of samples at 2-8°C overnight before transfer to -20°C. This study aimed to evaluate RNA integrity in hair follicle samples collected from horses, stabilized in RNAlater®, and stored under three short-term storage conditions. Mane hair samples complete with follicles were collected from four horses at a single time point. Approximately 15 hairs were placed in each of three 2 mL tubes containing 0.75ml RNAlater® solution. Test group A was stored at 4°C for 24-h, then decanted and stored at -20°C. Test groups B and C were stored at 4°C and 19°C (room temperature) respectively for 7 days, then decanted and stored at -20°C. RNA was isolated from all samples and RNA quantity and quality were measured. One-way ANOVA revealed no difference in RNA concentration (A:516 +/-125 ng/ml, B:273+/-93 ng/ml, C:476+/-176 ng/ml;P = 0.2) or quality (A:9.5 +/-0.19, B:9.8+/-0.09, C:9.2+/-0.35 RIN; P = 0.46) between the test groups. There were no group differences in mean Cycle Threshold values from qPCR validation assays confirming high-quality template cDNA. The results suggest that storage of hair follicles for one week in RNAlater® at cool or room temperature conditions will not compromise RNA integrity and will permit extended transport times from remote sampling locations without the need for freezing.

The application of artificial intelligence in EUS

Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.

Recent Advances and Future Challenges in the Field of Digestive Diseases

Digestive diseases are a rapidly evolving area of clinical and research [...].