Differential impacts of nonsteroidal anti-inflammatory drugs on lifespan and healthspan in aged Caenorhabditis elegans.

Aging and age-related diseases are intricately associated with oxidative stress and inflammation. Nonsteroidal anti-inflammatory drugs (NSAIDs) have shown their promise in mitigating age-related conditions and potentially extending lifespan in various model organisms. However, the efficacy of NSAIDs in older individuals may be influenced by age-related changes in drug metabolism and tolerance, which could result in age-dependent toxicities. This study aimed to evaluate the potential risks of toxicities associated with commonly used NSAIDs (aspirin, ibuprofen, acetaminophen, and indomethacin) on lifespan, healthspan, and oxidative stress levels in both young and old Caenorhabditis elegans. The results revealed that aspirin and ibuprofen were able to extend lifespan in both young and old worms by suppressing ROS generation and enhancing the expression of antioxidant SOD genes. In contrast, acetaminophen and indomeacin accelerated aging process in old worms, leading to oxidative stress damage and reduced resistance to heat stress through the pmk-1/skn-1 pathway. Notably, the harmful effects of acetaminophen and indomeacin were mitigated when pmk-1 was knocked out in the pmk-1(km25) strain. These results underscore the potential lack of benefit from acetaminophen and indomeacin in elderly individuals due to their increased susceptibility to toxicity. Further research is essential to elucidate the underlying mechanisms driving these age-dependent responses and to evaluate the potential risks associated with NSAID use in the elderly population.

A Mechanistic Analysis of Metformin's Biphasic Effects on Lifespan and Healthspan in C. elegans: Elixir in Youth, Poison in Elder.

Aging, a complex biological process influenced by genetic, environmental, and pharmacological factors, presents a significant challenge in understanding its underlying mechanisms. In this study, we explored the divergent impacts of metformin treatment on the lifespan and healthspan of young and old C. elegans, demonstrating a intriguing "elixir in youth, poison in elder" phenomenon. By scrutinizing the gene expression changes in response to metformin in young (day 1 of adulthood) and old (days 8) groups, we identified nhr-57 and C46G7.1 as potential modulators of age-specific responses. Notably, nhr-57 and C46G7.1 exhibit contrasting regulation patterns, being up-regulated in young worms but down-regulated in old counterparts following metformin treatment. Functional studies employing knockdown approaches targeting nhr-57, a gene under the control of hif-1 with a documented protective function against pore-forming toxins in C. elegans, and C46G7.1, unveiled their critical roles in modulating lifespan and healthspan, as well as in mediating the biphasic effects of metformin. Furthermore, deletion of hif-1 retarded the influence of metformin, implicating the involvement of hif-1/nhr-57 in age-specific drug responses. These findings underscored the necessity of deciphering the mechanisms governing age-related susceptibility to pharmacological agents to tailor interventions for promoting successful aging.

In vitro studies of factors affecting debridement of dental implants by tricalcium phosphate powder abrasive treatment.

Peri-implantitis is a common complication characterized by inflammation in tissues surrounding dental implants due to plaque accumulation, which can lead to implant failure. While air flow abrasive treatment has been found to be effective for debriding implant surfaces, little is known about the factors that affect its cleaning capacity. This study systematically examined the cleaning capacity of air powder abrasive (APA) treatment with ß-tricalcium phosphate (ß-TCP) powder, using various powder jetting strengths and different particle sizes. Three sizes of ß-TCP powder (S, M, and L) were prepared, and different powder settings (low, medium, and high) were tested. The cleaning capacity was determined by quantifying ink removal, which simulated biofilm removal from the implant surfaces at different time points. The results of the systematic comparisons showed that the most efficient cleaning of implant surfaces was achieved using size M particles with medium setting. Additionally, the amount of powder consumed was found to be critical to cleaning efficiency, and the implant surfaces were altered in all tested groups. These systematically analyzed outcomes may provide insights into the development of potential non-surgical strategies for treating peri-implant diseases.

Deep learning predicts cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma.

OBJECTIVES: Precise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes. METHODS: Preoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation. RESULTS: The ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078). CONCLUSIONS: Deep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts. CRITICAL RELEVANCE STATEMENT: Deep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner. KEY POINTS: • A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.