Background: TEXTMEDS (Text Messages to Improve Medication Adherence and Secondary Prevention After Acute Coronary Syndrome) examined the effects of text message–delivered cardiac education and support on medication adherence after an acute coronary syndrome. Methods: TEXTMEDS was a single-blind, multicenter, randomized controlled trial of patients after acute coronary syndrome. The control group received usual care (secondary prevention as determined by the treating clinician); the intervention group also received multiple motivational and supportive weekly text messages on medications and healthy lifestyle with the opportunity for 2-way communication (text or telephone). The primary end point of self-reported medication adherence was the percentage of patients who were adherent, defined as >80% adherence to each of up to 5 indicated cardioprotective medications, at both 6 and 12 months. Results: A total of 1424 patients (mean age, 58 years [SD, 11]; 79% male) were randomized from 18 Australian public teaching hospitals. There was no significant difference in the primary end point of self-reported medication adherence between the intervention and control groups (relative risk, 0.93 [95% CI, 0.84–1.03]; P =0.15). There was no difference between intervention and control groups at 12 months in adherence to individual medications (aspirin, 96% vs 96%; β-blocker, 84% vs 84%; angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 77% vs 80%; statin, 95% vs 95%; second antiplatelet, 84% vs 84% [all P >0.05]), systolic blood pressure (130 vs 129 mm Hg; P =0.26), low-density lipoprotein cholesterol (2.0 vs 1.9 mmol/L; P =0.34), smoking ( P =0.59), or exercising regularly (71% vs 68%; P =0.52). There were small differences in lifestyle risk factors in favor of intervention on body mass index <25 kg/m ² (21% vs 18%; P =0.01), eating ≥5 servings per day of vegetables (9% vs 5%; P =0.03), and eating ≥2 servings per day of fruit (44% vs 39%; P =0.01). Conclusions: A text message–based program had no effect on medical adherence but small effects on lifestyle risk factors. Registration: URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364448 ; Unique identifier: ANZCTR ACTRN12613000793718.

The synthetic α-conotoxin Vc1.1 is a small disulfide bonded peptide currently in development as a treatment for neuropathic pain. Unlike Vc1.1, the native post-translationally modified peptide vc1a does not act as an analgesic in vivo in rat models of neuropathic pain. It has recently been proposed that the primary target of Vc1.1 is the α9α10 nicotinic acetylcholine receptor (nAChR). We show that Vc1.1 and its post-translationally modified analogs vc1a, [P6O]Vc1.1, and [E14γ]Vc1.1 are equally potent at inhibiting ACh-evoked currents mediated by α9α10 nAChRs. This suggests that α9α10 nAChRs are unlikely to be the molecular mechanism or therapeutic target of Vc1.1 for the treatment of neuropathic pain.

Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication. MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302 515 individuals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740–0.806) and 0.759 (95% CI: 0.726–0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139–0.140), P < 0.0001. ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention. This review was registered with PROSPERO (CRD42020220811).

α-Conotoxins that are thought to act as antagonists of nicotinic acetylcholine receptors (nAChRs) containing α3-subunits are efficacious in several preclinical models of chronic pain. Potent interactions of Vc1.1 with other targets have suggested that the pain-relieving actions of α-conotoxins might be mediated by either α9α10 nAChRs or a novel GABA(B) receptor-mediated inhibition of N-type calcium channels. Here we establish that three α-conotoxins, Vc1.1, AuIB and MII have distinct selectivity profiles for these three potential targets. Their potencies after intramuscular administration were then determined for reversal of allodynia produced by partial nerve ligation in rats. Vc1.1, which potently inhibits α9α10 nAChRs and GABA(B)/Ca(2+) channels but weakly blocks α3β2 and α3β4 nAChRs, produced potent, long-lasting reversal of allodynia that were prevented by pre-treatment with the GABA(B) receptor antagonist, SCH50911. α-Conotoxin AuIB, a weak α3β4 nAChR antagonist, inhibited GABA(B)/Ca(2+) channels but did not act on α9α10 nAChRs. AuIB also produced reversal of allodynia. These findings suggest that GABA(B) receptor-dependent inhibition of N-type Ca(2+) channels can mediate the sustained anti-allodynic actions of some α-conotoxins. However, MII, a potent α3β2 nAChR antagonist but inactive on α9α10 and α3β4 nAChRs and GABA(B)/Ca(2+) channels, was demonstrated to have short-acting anti-allodynic action. This suggests that α3β2 nAChRs may also contribute to reversal of allodynia. Together, these findings suggest that inhibition of α9α10 nAChR is neither necessary nor sufficient for relief of allodynia and establish that α-conotoxins selective for GABA(B) receptor-dependent inhibition of N-type Ca(2+) channels relieve allodynia, and could therefore be developed to manage chronic pain.

Cardiovascular disease (CVD) incidence is higher in men with prostate cancer (PC) than without. We describe the rate and correlates of poor cardiovascular risk factor control among men with PC. We prospectively characterized 2,811 consecutive men (mean age 68 ± 8 years) with PC from 24 sites in Canada, Israel, Brazil, and Australia. We defined poor overall risk factor control as ≥3 of the following: suboptimal low-density lipoprotein cholesterol (>2 mmol/L if Framingham Risk Score [FRS] ≥15 and ≥3.5 mmol/L if FRS <15), current smoker, physical inactivity (0.9. Among participants (9% with metastatic PC and 23% with pre-existing CVD), 99% had ≥1 uncontrolled cardiovascular risk factor, and 51% had poor overall risk factor control. Not taking a statin (odds ratio [OR]: 2.55; 95% CI: 2.00-3.26), physical frailty (OR: 2.37; 95% CI: 1.51-3.71), need for BP drugs (OR: 2.36; 95% CI: 1.84-3.03), and age (OR per 10-year increase: 1.34; 95% CI: 1.14-1.59) were associated with poor overall risk factor control after adjustment for education, PC characteristics, androgen deprivation therapy, depression, and Eastern Cooperative Oncology Group functional status. Poor control of modifiable cardiovascular risk factors is common in men with PC, highlighting the large gap in care and the need for improved interventions to optimize cardiovascular risk management in this population.