Synthesis and Quantitative Structure–Activity Relationship of Imidazotetrazine Prodrugs with Activity Independent of O6-Methylguanine-DNA-methyltransferase, DNA Mismatch Repair and p53.

The antitumor prodrug Temozolomide is compromised by its dependence for activity on DNA mismatch repair (MMR) and the repair of the chemosensitive DNA lesion, O6-methylguanine (O6-MeG), by O6-methylguanine-DNA-methyltransferase (EC 2.1.1.63, MGMT). Tumor response is also dependent on wild-type p53. Novel 3-(2-anilinoethyl)-substituted imidazotetrazines are reported that have activity independent of MGMT, MMR and p53. This is achieved through a switch of mechanism so that bioactivity derives from imidazotetrazine-generated arylaziridinium ions that principally modify guanine-N7 sites on DNA. Mono- and bi-functional analogs are reported and a quantitative structure-activity relationship (QSAR) study identified the p-tolyl-substituted bi-functional congener as optimized for potency, MGMT-independence and MMR-independence. NCI60 data show the tumor cell response is distinct from other imidazotetrazines and DNA-guanine-N7 active agents such as nitrogen mustards and cisplatin. The new imidazotetrazine compounds are promising agents for further development and their improved in vitro activity validates the principles on which they were designed

Receptor activity-modifying proteins 2 and 3 generate adrenomedullin receptor subtypes with distinct molecular properties

Adrenomedullin (AM) is a peptide hormone with numerous effects in the vascular systems. AM signals through the AM1 and AM2 receptors formed by the obligate heterodimerization of a G protein-coupled receptor, the calcitonin receptor-like receptor (CLR), and receptor activity-modifying proteins (RAMP) 2 and 3, respectively. These different CLR-RAMP interactions yield discrete receptor pharmacology and physiological effects. The effective design of therapeutics that target the individual AM receptors is dependent on understanding the molecular details of the effects of RAMPs on CLR. To understand the role of RAMPs 2 and 3 on the activation and conformation of the CLR subunit of AM receptors we mutated 68 individual amino acids in the juxtamembrane region of CLR, a key region for activation of AM receptors and determined the effects on cAMP signalling. Sixteen CLR mutations had differential effects between the AM1 and AM2 receptors. Accompanying this, independent molecular modelling of the full-length AM-bound AM1 and AM2 receptors predicted differences in the binding pocket, and differences in the electrostatic potential of the two AM receptors. Druggability analysis indicated unique features that could be used to develop selective small molecule ligands for each receptor. The interaction of RAMP2 or RAMP3 with CLR induces conformational variation in the juxtamembrane region, yielding distinct binding pockets, probably via an allosteric mechanism. These subtype-specific differences have implications for the design of therapeutics aimed at specific AM receptors and for understanding the mechanisms by which accessory proteins affect G protein-coupled receptor function

Lyssavirus in Indian Flying Foxes, Sri Lanka

A novel lyssavirus was isolated from brains of Indian flying foxes (Pteropus medius) in Sri Lanka. Phylogenetic analysis of complete virus genome sequences, and geographic location and host species, provides strong evidence that this virus is a putative new lyssavirus species, designated as Gannoruwa bat lyssavirus

IUHPE Position statement on health literacy: a practical vision for a health literate world

Since the 1990s, there has been a steep and steady rise in studies published, and national and international policies adopted, on health literacy. This surge in interest has focused on the definition of health literacy and its various measures, the relationship between health literacy, health promotion and a wide range of health and social outcomes, and increasingly, investment in policy and programs to improve health literacy in populations. The Position Statement is a mechanism by which we describe what we believe to be the current state of the art and how it can be promoted through adoption by key stakeholders

Influence of O6-benzylguanine on the anti-tumour activity and normal tissue toxicity of 1,3-bis(2-chloroethyl)-1-nitrosourea and molecular combinations of 5-fluorouracil and 2-chloroethyl-1-nitrosourea in mice

Previous studies have demonstrated that novel molecular combinations of 5-fluorouracil (5FU) and 2-chloroethyl-1-nitrosourea (CNU) have good preclinical activity and may exert less myelotoxicity than the clinically used nitrosoureas such as 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU). This study examined the effect of O6-alkylguanine-DNA-alkyltransferase (ATase) depletion by the pseudosubstrate O6-benzylguanine (BG) on the anti-tumour activity and normal tissue toxicity in mice of three such molecular combinations, in comparison with BCNU. When used as single agents at their maximum tolerated dose, all three novel compounds produced a significant growth retardation of BCNU-resistant murine colon and human breast xenografts. This in vivo anti-tumour effect was potentiated by BG, but was accompanied by severe myelotoxicity as judged by spleen colony forming assays. However, while tumour resistance to BCNU was overcome using BG, this was at the expense of enhanced bone marrow, gut and liver toxicity. Therefore, although this ATase-depletion approach resulted in improved anti-tumour activity for all three 5-FU:CNU molecular combinations, the potentiated toxicities in already dose-limiting tissues indicate that these types of agents offer no therapeutic advantage over BCNU when they are used together with BG. © 1999 Cancer Research Campaig

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0–5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm(3) or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70–16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07–5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99–1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction

Patient-specific myocardial infarction risk thresholds from AI-enabled coronary plaque analysis

Background: Plaque quantification from coronary computed tomography angiography (CTA) has emerged as a valuable predictor of cardiovascular risk. Deep learning (DL) can provide automated quantification of coronary plaque from CTA. We determined per-patient age and sex-specific distributions of DL-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples.Methods: In this international, multicenter study of 2803 patients, a previously validated DL system was used to quantify coronary plaque from CTA. Age and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing CTA for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction.Results: Quantitative DL plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1,847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio 2.65, 95% confidence interval 1.47-4.78, p=0.001) were at increased risk of myocardial infarction compared to patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the ≥75th percentile of total plaque volume. Conclusions: Per-patient age and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.Keywords: Deep learning; coronary plaque; risk prediction; coronary CT Angiography; sex-specific analysis; myocardial infarction<br/

Prevalence of inherited blood disorders and associations with malaria and anemia in Malawian children

In sub-Saharan Africa, inherited causes of anemia are common, but data are limited regarding the geographical prevalence and coinheritance of these conditions and their overall contributions to childhood anemia. To address these questions in Malawi, we performed a secondary analysis of the 2015-2016 Malawi Micronutrient Survey, a nationally and regionally representative survey that estimated the prevalence of micronutrient deficiencies and evaluated both inherited and noninherited determinants of anemia. Children age 6 to 59 months were sampled from 105 clusters within the 2015-2016 Malawi Demographic Health Survey. Hemoglobin, ferritin, retinol binding protein, malaria, and inflammatory biomarkers were measured from venous blood. Molecular studies were performed using dried blood spots to determine the presence of sickle cell disease or trait, α-thalassemia trait, and glucose-6-phosphate dehydrogenase (G6PD) deficiency. Of 1279 eligible children, 1071 were included in the final analysis. Anemia, iron deficiency, and malaria were common, affecting 30.9%, 21.5%, and 27.8% of the participating children, respectively. α-Thalassemia trait was common (>40% of children demonstrating deletion of 1 [33.1%] or 2 [10.0%] α-globin genes) and associated with higher prevalence of anemia (P < .001). Approximately 20% of males had G6PD deficiency, which was associated with a 1.0 g/dL protection in hemoglobin decline during malaria infection (P = .02). These data document that inherited blood disorders are common and likely play an important role in the prevalence of anemia and malaria in Malawian children

The power of comparative and developmental studies for mouse models of Down syndrome

Since the genetic basis for Down syndrome (DS) was described, understanding the causative relationship between genes at dosage imbalance and phenotypes associated with DS has been a principal goal of researchers studying trisomy 21 (Ts21). Though inferences to the gene-phenotype relationship in humans have been made, evidence linking a specific gene or region to a particular congenital phenotype has been limited. To further understand the genetic basis for DS phenotypes, mouse models with three copies of human chromosome 21 (Hsa21) orthologs have been developed. Mouse models offer access to every tissue at each stage of development, opportunity to manipulate genetic content, and ability to precisely quantify phenotypes. Numerous approaches to recreate trisomic composition and analyze phenotypes similar to DS have resulted in diverse trisomic mouse models. A murine intraspecies comparative analysis of different genetic models of Ts21 and specific DS phenotypes reveals the complexity of trisomy and important considerations to understand the etiology of and strategies for amelioration or prevention of trisomic phenotypes. By analyzing individual phenotypes in different mouse models throughout development, such as neurologic, craniofacial, and cardiovascular abnormalities, greater insight into the gene-phenotype relationship has been demonstrated. In this review we discuss how phenotype-based comparisons between DS mouse models have been useful in analyzing the relationship of trisomy and DS phenotypes