Feasibility of a new ultrasound guided procedure to ensure the correct position of the central venous catheter tip.

BACKGROUND: Safety of central venous catheter (CVC) placement relies on some general aspects, including selection of the right vessel, correct lumen targeting while inserting the needle, check the position of catheter tip, and post-procedure check for complications. All these four points can be guided by bedside ultrasound, but the best technique to ensure the position of the CVC tip is still uncertain. METHODS: We investigated feasibility of a novel ultrasound technique consisting of focused view of guidewire tip in the cavoatrial junction (CAJ) to calculate the CVC depth in adult patients needing CVC placement in emergency. Direct visualization of the guidewire in the CAJ was used to calculate how deep the CVC needed to be inserted. In those patients without a valid CAJ window, a bubble test in the right atrium was performed to position the CVC tip. In all cases chest radiography confirmed the CVC position. RESULTS: The procedure was performed in 37 patients and CVC was correctly placed in all cases. Within the group, in 25 patients the CVC depth (21.5 ± 6.0 cm) was successfully measured. In other 11 patients the correct CVC tip position was confirmed by the bubble test. In only one case it was not possible to use ultrasound for incomplete CAJ and right atrium views. CONCLUSIONS: This study confirms the feasibility of a new ultrasound method to ensure the correct CVC tip position. This protocol could potentially become a standard method reducing costs, post-procedural irradiation, and time of CVC placement in emergency.

Nephrology Partnership for Advancing Technology in Healthcare (N-PATH) program: the teachers' perspective.

The N-PATH (Nephrology Partnership for Advancing Technology in Healthcare) program concluded with the 60th European Renal Association 2023 Congress in Milan, Italy. This collaborative initiative aimed to provide advanced training in interventional nephrology to young European nephrologists. Funded by Erasmus+ Knowledge Alliance, N-PATH addressed the global burden of chronic kidney disease (CKD) and the shortage of nephrologists. CKD affects >850 million people worldwide, yet nephrology struggles to attract medical talent, leading to unfilled positions in residency programs. To address this, N-PATH focused on enhancing nephrology education through four specialized modules: renal expert in renal pathology (ReMAP), renal expert in vascular access (ReVAC), renal expert in medical ultrasound (ReMUS) and renal expert in peritoneal dialysis (RePED). ReMAP emphasized the importance of kidney biopsy in nephrology diagnosis and treatment, providing theoretical knowledge and hands-on training. ReVAC centred on vascular access in haemodialysis, teaching trainees about different access types, placement techniques and managing complications. ReMUS recognized the significance of ultrasound in nephrology, promoting interdisciplinary collaboration and preparing nephrologists for comprehensive patient care. RePED addressed chronic peritoneal dialysis, offering comprehensive training in patient selection, prescription, monitoring, complications and surgical techniques for catheter insertion. Overall, N-PATH's strategy involved collaborative networks, hands-on training, mentorship, an interdisciplinary approach and the integration of emerging technologies. By bridging the gap between theoretical knowledge and practical skills, N-PATH aimed to revitalize interest in nephrology and prepare proficient nephrologists to tackle the challenges of kidney diseases. In conclusion, the N-PATH program aimed to address the shortage of nephrologists and improve the quality of nephrology care in Europe. By providing specialized training, fostering collaboration and promoting patient-centred care, N-PATH aimed to inspire future nephrology professionals to meet the growing healthcare demands related to kidney diseases and elevate the specialty's status within the medical community.

Multiple drug-delivery strategies to enhance the pharmacological and toxicological properties of Mefenamic acid.

OBJECTIVE: To improve the biological and toxicological properties of Mefenamic acid (MA), the galactosylated prodrug of MA named MefeGAL was included in polymeric solid dispersions (PSs) composed of poly(glycerol adipate) (PGA) and Pluronic® F68 (MefeGAL-PS). MefeGAL-PS was compared with polymeric solid formulations of MA (MA-PS) or a mixture of equal ratio of MefeGAL/MA (Mix-PS). METHODS: The in vitro and in vivo pharmacological and toxicological profiles of PSs have been investigated. In detail, we evaluated the anti-inflammatory (carrageenan-induced paw edema test), analgesic (acetic acid-induced writhing test) and ulcerogenic activity in mice after oral treatment. Additionally, the antiproliferative activity of PSs was assessed on in vitro models of colorectal and non-small cell lung cancer. RESULTS: When the PSs were resuspended in water, MefeGAL's, MA's and their mixture's apparent solubilities improved due to the interaction with the polymeric formulation. By comparing the in-vivo biological performance of MefeGAL-PS with that of MA, MefeGAL and MA-PS, it was seen that MefeGAL-PS exhibited the same sustained and delayed analgesic and anti-inflammatory profile as MefeGAL but did not cause gastrointestinal irritation. The pharmacological effect of Mix-PS was present from the first hours after administration, lasting about 44 hours with only slight gastric mucosa irritation. In-vitro evaluation indicated that Mix-PS had statistically significant higher cytotoxicity than MA-PS and MefeGAL-PS. CONCLUSIONS: These preliminary data are promising evidence that the galactosylated prodrug approach in tandem with a polymer-drug solid dispersion formulation strategy could represent a new drug delivery route to improve the solubility and biological activity of NSAIDs.

Verbal lie detection using Large Language Models.

Human accuracy in detecting deception with intuitive judgments has been proven to not go above the chance level. Therefore, several automatized verbal lie detection techniques employing Machine Learning and Transformer models have been developed to reach higher levels of accuracy. This study is the first to explore the performance of a Large Language Model, FLAN-T5 (small and base sizes), in a lie-detection classification task in three English-language datasets encompassing personal opinions, autobiographical memories, and future intentions. After performing stylometric analysis to describe linguistic differences in the three datasets, we tested the small- and base-sized FLAN-T5 in three Scenarios using 10-fold cross-validation: one with train and test set coming from the same single dataset, one with train set coming from two datasets and the test set coming from the third remaining dataset, one with train and test set coming from all the three datasets. We reached state-of-the-art results in Scenarios 1 and 3, outperforming previous benchmarks. The results revealed also that model performance depended on model size, with larger models exhibiting higher performance. Furthermore, stylometric analysis was performed to carry out explainability analysis, finding that linguistic features associated with the Cognitive Load framework may influence the model's predictions.