RESUMO
BACKGROUND: The effectiveness of intravenous lidocaine in reducing acute pain after hysterectomy remains uncertain. We conducted a meta-analysis of randomized controlled trials (RCTs) to investigate the impact of intravenous lidocaine on post-hysterectomy recovery. METHODS: This study was completed based on the PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. A systematic search was conducted in PubMed/MEDLINE, the Cochrane Controlled Trials Register (CENTRAL), and Embase up to July 27, 2023. We identified randomized controlled trials (RCTs) involving hysterectomy patients comparing lidocaine to a placebo. Outcome measures included postoperative pain scores at rest and during movement, opioid consumption, postoperative nausea and vomiting (PONV), improvements in functional gastrointestinal recovery, and Quality of Recovery (QoR) scores. RESULTS: Nine RCTs were included in the meta-analysis, comprising 352 patients who received intravenous lidocaine and 354 controls. The analysis revealed that intravenous lidocaine significantly reduced postoperative pain scores at rest at 2, 6, 8, and 24 hours following hysterectomy, as well as postoperative opioid consumption within 24 hours and PONV rates. Furthermore, no observed benefit was noted in shortening the time to first flatus with intravenous lidocaine administration post-hysterectomy. CONCLUSION: Intravenous lidocaine administration effectively reduces acute postoperative pain, opioid consumption, and PONV rates following hysterectomy. Lidocaine serves as an opioid-sparing agent, reducing the morphine equivalent dose while maintaining a similar degree of postoperative pain.
RESUMO
With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human-computer interaction. Due to the high sensitivity (460.1 kPa-1), high linearity (R 2 > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual's blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.
RESUMO
The application of machine learning toward DNA encoded library (DEL) technology is lacking despite obvious synergy between these two advancing technologies. Herein, a machine learning algorithm has been developed that predicts the conversion rate for the DNA-compatible reaction of a building block with a model DNA-conjugate. We exemplify the value of this technique with a challenging reaction, the Pictet-Spengler, where acidic conditions are normally required to achieve the desired cyclization between tryptophan and aldehydes to provide tryptolines. This is the first demonstration of using a machine learning algorithm to cull potential building blocks prior to their purchase and testing for DNA-encoded library synthesis. Importantly, this allows for a challenging reaction, with an otherwise very low building block pass rate in the test reaction, to still be used in DEL synthesis. Furthermore, because our protocol is solution phase it is directly applicable to standard plate-based DEL synthesis.
