MDAN-21: A Bivalent Opioid Ligand Containing mu-Agonist and Delta-Antagonist Pharmacophores and Its Effects in Rhesus Monkeys

MDAN-21, , a bivalent opioid ligand containing a mu-opioid receptor agonist (derived from oxymorphone) linked to the delta-opioid receptor antagonist (related to naltrindole) by a spacer of 21 atoms, was reported to have potent analgesic properties in mice. Tolerance, physical dependence, and conditioned place preference were not evident in that species. The finding that bivalent ligands in this series, with spacers 19 atoms or greater, were devoid of tolerance and dependence led to the proposal that MDAN-21 targets heteromeric mu-delta-opioid receptors. The present study focused on its effects in nonhuman primates (Macaca mulatta), a species with a physiology and behavioral repertoire not unlike humans. With regard to opioids, this species usually better predicts clinical outcomes. MDAN-21 substituted for morphine in morphine-dependent monkeys in the remarkably low dose range 0.006–0.032 mg/kg, subcutaneously. Although MDAN-21 failed to produce reliable thermal analgesia in the dose range 0.0032–0.032 mg/kg, intramuscularly, it was active in the same dose range and by the same route of administration, in the capsaicin-induced thermal allodynia assay. The results suggest that MDAN-21 may be useful in the treatment of opioid dependence and allodynia. The data provide additional evidence that opioid withdrawal is associated with sensitized pain

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Monitoring of fibre optic links with a machine learning-assisted low-cost polarimeter

The optical fibres widely used in telecommunication can be simultaneously used for (distributed) sensing or fibre network self-monitoring. In our work, we monitor changes in the fibre environment via monitoring changes in the state of light polarization without the utilization of methods based on back-scattered light. These changes can generate a vast amount of data, but it is generally not straightforward to extract useful information from them, e.g., future fibre break predictions or earthquake monitoring. We suggest using machine learning to solve this problem. However, since the measured data events are not labelled(i.e., we do not know in advance what fingerprint in the measured data corresponds to a future fibre break), unsupervised machine learning methods must be used. Here, we report a proof-of-concept approach in which we use a simple polarimetric technique and installed optical fibre, which we disturb with controlled vibrations, knocking on the fibre, and rack door closing near the fibre. Using a machine learning K-means algorithm, we distinguish between data generated with these controlled disturbances and data generated by noise due to common traffic. These results are the first step along the way to automated data labelling, which can be used for the classification of events.<br/