Despite the rarity of NCS, its recognition and management are important. This article has explored the evidence basis for conservative, medical and surgical options.

In the field of cardio-oncology, it is well recognised that despite the benefits of chemotherapy in treating and possibly curing cancer, it can cause catastrophic damage to bystander tissues resulting in a range of potentially of life-threatening cardiovascular toxicities, and leading to a number of damaging side effects including heart failure and myocardial infarction. Cardiotoxicity is responsible for significant morbidity and mortality in the long-term in oncology patients, specifically due to left ventricular dysfunction. There is increasing emphasis on the early use of biomarkers in order to detect the cardiotoxicity at a stage before it becomes irreversible. The most important markers of cardiac injury are cardiac troponin and natriuretic peptides, whilst markers of inflammation such as interleukin-6, C-reactive protein, myeloperoxidase, Galectin-3, growth differentiation factor-15 are under investigation for their use in detecting cardiotoxicity early. In addition, microRNAs, genome-wide association studies and proteomics are being studied as novel markers of cardiovascular injury or inflammation. The aim of this literature review is to discuss the evidence base behind the use of these biomarkers for the detection of cardiotoxicity.

Background: Artificial intelligence (AI) for echocardiography requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of such techniques. Methods: The training dataset consisted of 2056 individual frames drawn at random from 1265 parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015 to 2016. Nine experts labeled these images using our online platform. From this, we trained a convolutional neural network to identify keypoints. Subsequently, 13 experts labeled a validation dataset of the end-systolic and end-diastolic frame from 100 new video-loops, twice each. The 26-opinion consensus was used as the reference standard. The primary outcome was precision SD, the SD of the differences between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5 mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4 mm. Intraclass correlation coefficient between AI and expert consensus was 0.926 (95% CI, 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8 mm for AI (intraclass correlation coefficient, 0.809; 0.729–0.967), versus 2.0 mm for individuals (intraclass correlation coefficient, 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4 mm for AI (intraclass correlation coefficient, 0.535 [95% CI, 0.379–0.661]), versus 2.2 mm for individuals (0.366 [0.288–0.462]). We present all images and annotations. This highlights challenging cases, including poor image quality and tapered ventricles. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations, and results openly.

The quest for possible targets for the development of novel analgesics has identified the activation of the cannabinoid type 1 (CB1) receptor outside the CNS as a potential means of providing relief from persistent pain, which currently constitutes an unmet medical need. Increasing tissue levels of the CB1 receptor endogenous ligand N-arachidonoylethanolamine (anandamide), by inhibiting anandamide degradation through blocking the anandamide-hydrolysing enzyme fatty acid amide hydrolase, has been suggested to be used to activate the CB1 receptor. However, recent clinical trials revealed that this approach does not deliver the expected relief from pain. Here, we discuss one of the possible reasons, the activation of the transient receptor potential vanilloid type 1 ion channel (TRPV1) on nociceptive primary sensory neurons (PSNs) by anandamide, which may compromise the beneficial effects of increased tissue levels of anandamide. We conclude that better design such as concomitant blocking of anandamide hydrolysis and anandamide uptake into PSNs, to inhibit TRPV1 activation, could overcome these problems.

Antibiotic use in intensive care units (ICUs) can promote antimicrobial resistance. Outbreaks of multi-resistant bacteria significantly affect patient outcomes and delivery of care. Antibiotic stewardship programmes (ASPs), combining root-cause analyses and multi-faceted prevention strategies, are necessary, often at significant cost and time. Which elements of such strategies have the largest impact on antibiotic usage following an outbreak is unclear. The aim of this study was to investigate how antibiotic usage in a university hospital ICU changed with a non-protocolised ASP following a disruptive outbreak of multi-resistant Acinetobacter baumannii (MRAB). This was a three time-period observational cohort study. The primary endpoint was the change in overall antibiotic usage (daily defined dose, DDD, antibiotic-days, antibiotic-courses) for consecutive ICU patients staying >48 h, over three 6-month study time periods pre-MRAB (2008, n = 84) and post-MRAB (2010, n = 88; 2012, n = 122). Secondary endpoints were changes in antibiotic usage and patient demographics, in predefined admission categories (Medical Emergency, ME; Surgical Elective, SEL; and Surgical Emergency, SE). The mean age (54.6 ± 17.7, 58.1 ± 17.9, 62.8 ± 19.1 years*) and severity of illness (APACHE 14.8 ± 8.0, 16.7 ± 6.8, 18.3 ± 6.1*) increased, particularly medical admissions. There was a sustained reduction in DDD antibiotic usage [1895.1 (2008), 1224.2 (2010), 1236.6 (2012) per 1000 patient-days] but no overall change in antibiotic-days or antibiotic-courses. Antibiotic usage (antibiotic-days) fell significantly in surgical emergency admissions [20.2 ± 32.1, 4.6 ± 7.4*, 5.9 ± 7.3]. There was a sustained drop in beta-lactam, quinolone, glycopeptide and macrolide usage. Following an MRAB outbreak, and subsequent operational changes including enhanced ASPs (non-protocolised), there was a sustained overall fall in antibiotic usage in spite of an increase in disease severity over 5 years.

In preparing a recent presentation on the nutcracker phenomenon (NCP), I benefitted greatly from the review by Ananthan and colleagues. 1 In addition to their recognition of NCP as independent from nutcracker syndrome (NCS), I would like to suggest an additional point of discrimination on the subject. I propose a distinction between the complex of associated symptoms putatively arising from NCP, and that of the pathological signs which manifest from it, the former comprising a clinical NCS and the latter a pathological NCS. There will be some overlap, of course, in patient presentation, but such a division may be conceptually useful and is justifiable on pathogenetic grounds. Whereas vascular manifestations such as peri-calyceal haematuria, gonadal varices, and pelvic venous congestion might arise from left renal venous hypertension by an intuitive mechanism, the same cannot be said of the various symptoms often ascribed to NCS. Although complaints of flank or pelvic pain can be plausibly examined through analogy with cutaneous varicosities, explanations for complex symptoms such as fatigue and orthostatic intolerance 2 are tenuous 3 or lacking. The idiosyncrasy that "urologists don't believe in nutcracker syndrome" and similar scepticism may reflect the speculative pathophysiology of the so called "urological presentation" 2 of clinical NCS, wherein (apart from varicocele) patients have only non-specific symptoms. Distinguishing between these syndromic phenotypes may thus prove valuable by highlighting differences in pathogenetic theory.

Background and purpose Artificial intelligence (AI) has the potential to greatly improve efficiency and reproducibility of quantification in echocardiography, but to gain widespread use it must both meet expert standards of excellence and have a transparent methodology. We developed an online platform to enable multiple collaborators to annotate medical images for training and validating neural networks. Methods Using our online collaborative platform 9 expert echocardiographers labelled 2056 images that comprised the training dataset. They labelled the four points from where the standard parasternal long axis (PLAX) measurements (interventricular septum, posterior wall, left ventricular dimension) would be made. Using these labelled images we trained a 2d convolutional neural network to replicate these labels. Separately, we curated an external validation dataset of the systolic and diastolic frames of 100 PLAX acquisitions. Each of these images were labelled twice by 13 different experts, and the average of the 26 measurements was taken as the consensus standard. We then compared the individual experts and the AI measurements on the external validation dataset to the consensus standard, and calculated the precision standard deviation (SD) of the signed differences from the consensus standard. Results For diastolic septum thickness, the AI had a precision SD of 1.8 mm (ICC 0.81; 95% CI 0.73 to 0.97), compared with 2.0 mm for the individual experts (ICC 0.64; 95% CI 0.57 to 0.72). For diastolic posterior wall thickness, the AI had a precision SD 1.4 mm (ICC 0.54; 95% CI 0.38 to 0.66), and the individual experts 2.2 mm (ICC 0.37; 95% CI 0.29 to 0.46). The AI's precision SD for left ventricular internal dimension was 3.5 mm (ICC 0.93, 95% CI 0.90 to 0.94), and for individual experts was 4.4mm (ICC 0.82, 95% CI 0.78 to 0.95). Both the experts and AI performed better in diastole than systole (precision SD AI 2.5mm vs 4.3mm, p<0.0001; experts 3.3mm vs 5.3mm, p<0.0001). Conclusions AI trained by a group of echocardiography experts was able to perform PLAX measurements which matched the reference standard more closely than any individual expert's own measurements. This open, collaborative approach may be a model for the development of AI that is explainable to, and trusted by clinicians. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIHR Imperil BRC ITMATDr Howard was additionally funded by Wellcome. Online collaborative platform Results of AI and experts

patients on SLGT2 inhibitor. Average HbA1c, T2DM patients = 62.7 mmol/mol. HFrEF medication 3 classes: Beta Blockers/ Angiotensin-converting-enzyme inhibitors, Angiotensin II Receptor Blockers or Sacubitril Valsartan/ Mineralocorticoid receptor antagonists. No therapy, 2 (0.62%), Monotherapy: 22 (6.85%), Dual-therapy 156 (48.60%), Triple-therapy 141 (43.93%). Conclusion Applying DAPA HF inclusion criteria, 292 (90.97%) should be considered for introduction of SGLT2 inhibitors. Renal function isn't a significant barrier to SGLT2 inhibitor introduction. SGLT2 inhibitors aren't widely prescribed in patients with T2DM and HFrEF. Following recent NICE approval, there is scope for local and regional guidelines, directed at primary and secondary care for the prescribing of SLGT2 inhibitors in a HFrEF population. Conflict of Interest Nil