Supraventricular tachydysrhythmias (SVTs) are a common presenting complaint, with a national prevalence of 3/1000 persons. While most commonly stable, prolonged paroxysms can deteriorate into haemodynamically unstable subtypes or ventricular dysrhythmias. Early recognition with appropriate management is critical to reducing the morbidity associated with this condition. The American Heart Association holds that vagal manoeuvres are a first-line therapy in the management algorithm of stable SVTs. However, they state that no clear recommendations can be made around which manoeuvre to use, highlighting that future research should examine the efficacy and safety profiles of the various manoeuvres. In the South African primary care setting, clinicians must be at the forefront of pragmatic management strategies in the face of resource limitations, such as the unavailability of adenosine – a second-line therapy when vagal manoeuvres fail. In this article, we begin with a case study and review the literature around vagal manoeuvres. A 35-year-old man presented to our centre during a weekend shift. He reports a 2-h history of experiencing palpitations and anxiety. The symptoms had begun at around 4 o’clock in the afternoon after a Sunday lunch with his family. He had drunk four units of alcohol over a 3-h period and smoked three cigarettes earlier that day.He reported two previous episodes at 29 years and 33 years of age. During the previous episodes, inpatient workup had ruled out any metabolic and/or endocrinological abnormalities, as well as structural heart disease (by electrocardiography [ECG] and echocardiography). He is currently awaiting his cardiology outpatient appointment date. He has no chronic medical illnesses and had an appendectomy at 13 years of age. There was no family history of cardiac disease.He reports occasional alcohol use of around four units of alcohol per weekend, with no use during the week. He is not a regular smoker, smoking up to five cigarettes in a month. The patient did not use any illicit substances and drank three cups of coffee per day. He resides in a four-room house with his life partner of 6 years. Both are employed, have stable jobs, and he reports no psychosocial stressors.The patient’s observations were as follows:Blood pressure (BP): 135/85Pulse: 215 beats per minuteSaturation: 96% (room air)Respiratory rate: 22Examination reveals good peripheral perfusion with a regular tachycardia. Air entry was equal bilaterally, without adventitious sounds. There was no lower limb oedema or jugulovenous distension. The liver was not palpable.1This patient was assessed as having a stable supraventricular tachycardia (SVT). He was moved to the high care area in the emergency centre, placed on monitors, baseline bloods were drawn and an ECG was done that showed a regular, narrow complex tachycardia, similar to the ECG in A modified Valsalva manoeuvre was performed and normal sinus rhythm was restored. A post-reversion ECG was unremarkable.2 Supraventricular tachydysrhythmias are dysrhythmias that originate at, or above, the level of the His-Purkinje system3 from either atrial tissue or the atrioventricular (AV) junction:Tachydysrhythmias are defined as abnormal heart rhythms with a ventricular rate above 100 beats per minute.■Sinus tachycardia■Inappropriate sinus tachycardia■Sinoatrial nodal re-entrant tachycardia (SNRT)■Atrial fibrillation■Atrial flutter■Intra-atrial re-entrant tachycardiaAtrial tissue■Atrioventricular nodal re-entrant tachycardia (AVNRT)■Atrioventricular re-entrant tachycardia (AVRT)■Junctional tachycardiaAtrioventricular junctionSupraventricular tachydysrhythmias are most commonly narrow complex tachydysrhythmias but can have broad complexes when there is pre-existing aberrant conduction (e.g. bundle branch blocks), tachycardia-induced aberrancy , or, in the case of an accessory pathway, with antidromic conduction.4 The majority of SVTs are because of re-entry pathways, without the presence of underlying cardiac disease.3 Common symptoms include syncope/presyncope, palpitations, shortness of breath, diaphoresis, chest pain and lightheadedness/dizziness.While local literature on the incidence of SVT is sparse, evidence shows that the national prevalence approximates international figures at around 3/1000 persons.Most cases of SVT are stable unless there is an underlying cardiac failure, ischaemia or other co-morbidities (such as sepsis or toxin ingestion).HASIA:Determining the haemodynamic stability of the patient is the first step in evaluation. A useful mnemonic for this is HypotensionAltered mentationShockIschaemic chest pain (poor coronary perfusion pressure)Acute pump failure (pulmonary oedema)3 (and treat the underlying cause). If this is ruled out, immediate synchronised cardioversion according to Advanced Cardiac Life Support principles is warranted.If any of these cues are present, attempt to rapidly rule out sinus tachycardia3Provided there are no emergent signs or symptoms that warrant synchronised cardioversion, we regard the SVT as stable and proceed with the evaluation of the SVT:Is it regular or irregular?Are P waves discernible? If so, what is their morphology?Perform the Valsalva maneuver!!:3 although they note that vagal manoeuvres require further refinement to establish their effectiveness and safety.3The American Heart Association describes vagal manoeuvres as a class one recommendation (level of evidence B-R) for first-line therapy of stable SVTs,Studies have recurrently shown the Valsalva manoeuvre (VM) as superior to other options in adults:6Valsalva success rate: 20% – 55%7Carotid sinus massage success rate: 5% – 33%8Diving reflex (cold ice immersion) success rate: 17%3 While the carotid sinus massage is the simplest to perform, studies have reported iatrogenic peril (thromboembolic stroke from dislodged carotid atheroma) in around 1% of cases.7 The greater effectiveness and improved risk profile of the VM have seen it become a first choice vagal manoeuvre for the treatment of stable SVT in adult patients There are several challenges around the use of adenosine in the primary care setting that limit its use Highly selective for adenosine-1 receptor:A1 receptor – induces asystole by inhibiting adenylyl cyclase and reducing intracellular cAMP, which hyperpolarizes cardiac cells by increasing K+ efflux (in turn inhibiting Ca2+ and B-agonist receptors)Rapid intracellular metabolism6However, the adverse effects of adenosine include a high incidence of new and transient cardiac arrhythmia (up to 55%), dyspnoea (12% – 28%), chest tightness (40%), dizziness (< 12%), headache, facial flushing (44%), nausea and an ‘electric shock’ sensation.6Contraindications include hypersensitivity, high-grade heart block, sick sinus rhythm, bronchoconstrictive or bronchospastic disease, including asthma.The success of adenosine is dependent on several technical factors such as the proximity of venous access to heart, elevation of the limb and the rapid use of saline flush with a three-way stop cock.6Note: In South Africa, adenosine is not available at community health centres or clinics. It is not found in the essential drug list and is not mentioned as a step in the management of tachydysrhythmias in the standard treatment guidelines.11A recent modification to the VM was employed in the landmark REVERT trial see  and 2, wConceptually, the aim of the VM is to induce a cascade of four phases vagotonic fluxes that incrementally increase until sufficient vagal tone to induce AV nodal blockade is achieved see .Phase I:■Triggers baroreceptor reflex (either at a carotid bifurcation or within aortic arch)■Afferent impulses via glossopharyngeal (carotid) or vagus (aortic) nerve to vagal nuclei within nucleus tractus solitarius within the medullaIncreased intrathoracic pressure – systolic blood pressure (SBP) rises (> 15 mmHg) following initiation of Valsalva (10 s)Phase II:■Efferent impulses from vagal nuclei descend both right and left vagal nerves to heart, lungs and gastrointestinal tract.■Right vagus nerves stimulate sinoatrial (SA) node – decreased firing■Left vagus innervates av node – decreased conductionDecreased venous return; increased systemic arterial pressure; blood pressure return to (just below) normal while a strain is maintainedPhase III:Decreased intrathoracic pressure. Blood pressure drops when strain is released.Phase IV:Rapid increase in BP as sympathetic surge response to decreased systolic pressure.The response to the hypotension induced by the VM is the final means by which we induce a strong vagal tone to terminate the tachydysrhythmia.10The purpose of a vagal manoeuvre is to impact cardiac physiology. The desired effects are mostly achieved in phases I and IV:SA node – slowed impulse generation in SA node.Atria – normal conduction velocity; prolonged refractory period.AV node – decreased conduction in AV node with prolonging of the refractory period.Ventricles – decreased inotropy; normal conduction in His-Purkinje system.When should we use the VM?Therapeutic – Haemodynamically stable patients: first-line therapy of SVT.Diagnostic – May help distinguish SVT from VT by slowing conduction.As mentioned, the most profound vagotonic effects are induced at the SA and AV nodes, and hence these rhythms undergo the highest reversion success rates. A – Vagal manoeuvres can terminate tachycardias of atrial origin, but they have a lower success rate and care must be taken to not perform AV node blockade in wide-complex tachycardias or if the patient has a known accessory pathway (risk of inducing ventricular fibrillation).B – Vagal manoeuvres are most successful in AV nodal-dependent SVTs (AVRT and AVNRT). At this stage, the clear dichotomy between therapeutic effect (reversion to sinus rhythm) and diagnostic application (slowing the rate to discern the dysrhythmia present and determine the best management approach) can be noted.C – If p waves are present and can be characterised, either can vagal manoeuvres be tried or an effective therapeutic agent selected based on the type of rhythm.Equipment:12 lead ECG placed on patientCardiac monitoring – continuousPulse oximetryNon-invasive BP monitoring – cycling frequentlyIntravenous access with fluids either running or immediately available■Airway■Oxygen■Crash cart with appropriate antiarrhythmic available and procedural sedation should cardioversion be requiredResuscitation trolley availableStaff:Lead clinicianNursing assistant for monitoringIf available, use an assistant to elevate legs3An informed, cooperative patient is paramount. The American Heart Association advocates teaching a patient to cough on command .9Technique:Attach all monitors, have support staff.Patient is seated in an upright seated position.Patient blows against a 10 mL syringe to move plunger (approximately 30–40 mmHg) for 10–20 sImmediately drop patient to supine positionSimultaneously elevate patient’s legs to 45° – 90°Hold for 45–60 sRepeat if necessaryThe ‘modification’ of the Valsalva by dropping the head of the patient, elevating the legs, and finally lifting the head of the patient accentuates the physiological effects of the Valsalva manoeuvre – increasing the likelihood of success.Supraventricular tachydysrhythmias are the most common tachyarrhythmias with an incidence of 3/1000 persons. Most SVTs are benign conditions without underlying structural heart pathology. However, their propensity to deteriorate into unstable SVTs or ventricular fibrillation mandates early and effective treatment. The modified VM is the most effective vagal manoeuvre with minimal documented adverse effects and should be used as the first-line therapy in the management of SVT.The patient in our case study was a 35-year-old man who presented with a recurrent, stable AVNRT in the absence of any underlying structural heart disease. The effective management of his AVNRT avoided unnecessary admission to a higher level of care while awaiting his outpatient cardiology appointment. This case highlights the value of evidence-based practice in the primary care setting to provide appropriate care while avoiding unnecessary medical intervention with its associated costs and potential harm.


The fusion (F) glycoprotein of respiratory syncytial virus (RSV) represents the major neutralizing antigen, and antibodies against the pre-F conformation have the most potent neutralizing activity. This study aimed to assess the correlation between maternal antibody titers against the pre-F, post-F, and G glycoproteins and the child’s risk of developing severe RSV bronchiolitis early in infancy.We identified previously healthy term infants <3 months of age hospitalized with RSV bronchiolitis from December 2015 to March 2016. We measured IgG antibody titers to pre-F, post-F, and G proteins in maternal sera obtained at 9–12 weeks of pregnancy of these hospitalized infants’ mothers (n = 94) and compared them with serum antibody titers of control pregnant mothers (n = 130) whose children were not hospitalized.P = .0026). There were no significant differences in maternal post-F and G antibody titers between hospitalized and nonhospitalized infants.All maternal samples (n = 224) had detectable pre-F antibodies. Pre-F antibody titers were significantly lower in mothers whose infants were hospitalized with RSV bronchiolitis compared with those mothers whose infants were not hospitalized (23.9  µg/L vs 30.6  µg/L; Our findings indicate that maternal pre-F antibodies are fundamental for providing immune protection to the infant. Maternal serum immunoglobulin antibody titers directed to respiratory syncytial virus (RSV) pre-F glycoprotein were lower in infants less than 3 months of age hospitalized with RSV bronchiolitis than in maternal serum samples of age-matched control infants who were not hospitalized. Respiratory syncytial virus (RSV) is the most important cause of bronchiolitis in childhood, leading to 3.4 million annual hospitalizations in children <5 years of age . A substKnowledge gaps exist regarding the immunologic mechanisms associated with immune protection against RSV. Currently, there are no approved strategies for prevention of RSV infections in previously healthy infants . VaccineHigher titers of maternal RSV antibodies reduce the risk of infection with RSV and can delay the onset of severe illness in the first months of life . NeutralThe aim of this study was to assess whether there was a correlation between the titers of maternal antibodies against pre-F, post-F, or G proteins measured early in pregnancy and the infants’ risk of developing severe RSV bronchiolitis requiring hospitalization in the first 3 months of life, during 1 respiratory season.International Statistical Classification of Diseases and Related Health Problems (ICD-10) codes J21.0, J12.1, and/or J20.5 were identified. Helsinki Children’s Hospital is the only pediatric hospital in the area allowing us to capture all of the RSV hospitalizations during this time period. Electronic healthcare records (EHRs) were subsequently reviewed and virology diagnostic results verified. In this retrospective case-control study, severe RSV disease was defined as hospitalization for at least 3 consecutive days for RSV bronchiolitis. We also assessed other parameters of severity such as need for respiratory support and pediatric intensive care unit (ICU) admission. Premature infants  and children with any previously diagnosed major underlying condition documented in their EHR  were excluded to make sure the duration of hospitalization was based only on RSV symptoms and severity.Infants 3 months or younger hospitalized at Helsinki Children’s Hospital between December 2015 and March 2016 with RSV bronchiolitis recognized by the Nearly every pregnant woman in Finland provides a serum sample in the first trimester of pregnancy  for screening of infectious diseases such as human immunodeficiency virus and syphilis. Usually there is a substantial volume (1–2 mL) of individual serum left over that is frozen for future research purposes. After identification of infants who met the inclusion criteria for the study, the mothers were identified from the infants’ records and the maternal screening serum samples were obtained from Helsinki University Central Hospital Laboratory for further analysis. Maternal screening serum samples obtained before April were excluded as we aimed to only include serum samples obtained after the previous RSV season . Serum sAll RSV infections were diagnosed with RSV rapid antigen detection tests (RADTs). The standard of care test used at Helsinki Children’s Hospital is RADT BinaxNOW RSV. RADTs have a high specificity .Serum aliquots stored at –80°C were analyzed for RSV-specific antibody titers at the Abigail Wexner Research Institute at Nationwide Children’s Hospital in Columbus, Ohio. The recombinant RSV surface proteins were prepared as previously described .2 test or Fisher exact test as appropriate. Continuous variables were compared using Kruskal–Wallis or Mann–Whitney tests. Results with P < .05 were considered significant. Statistical analyses were performed using Graph Pad Prism software version 7 .Categorical variables were compared using either the χ We identified 227 infants aged 0–3 months hospitalized in our hospital for RSV bronchiolitis during the study period. Of these infants, 86 were excluded because the duration of hospitalization was <3 days and therefore were most likely less severe cases. In addition, 33 children were excluded because of prematurity (n = 12), a previously diagnosed neurologic disease (n = 4), congenital heart disease (n = 5), congenital anomaly (n = 5), or other underlying conditions (n = 7). A flow chart describing the exclusion process is included in Next, we located the maternal serum samples of these children’s mothers obtained during the first trimester of pregnancy. We excluded samples from mothers whose children were born in September–October (n = 12) because the maternal screening samples were obtained too early for the study design. Two maternal samples could not be located due to missing information in the patient records, leaving a total of serum samples from 94 mothers whose children were subsequently hospitalized with RSV bronchiolitis with <3 months of age (cases). Antibody titers were compared with 130 serum samples obtained from control mothers. Demographic data of the offspring are shown in Infants hospitalized with severe RSV infection were comparable with controls regarding gestational age, birth weight, and age of mother. There were no significant differences in serum antibody titers according to the month of the year  when the maternal samples were obtained.P < .0001). The median age at the time of hospitalization was 35 days . Eight of the 94 hospitalized children (9%) required ICU care. Sixty-nine (73%) required respiratory support with nasal continuous positive airway pressure or high-flow nasal cannula oxygen therapy. Otitis media was diagnosed and treated in 46 (49%) of the hospitalized children. Twenty infants (21%) received antibiotic therapy for suspected bacterial pneumonia.Children hospitalized for RSV infection were more likely than controls to have 1 or more siblings . Next, we compared pre-F antibody titers in the group requiring respiratory support (n = 69) vs the controls (n = 130), and the maternal pre-F titers in the cases were significantly lower than in the controls .All maternal samples evaluated (n = 224) had detectable RSV antibodies against pre-F , with a P = .181) or maternal G antibodies  between the 2 groups , but no significant correlation was identified. A trend toward lower pre-F antibody concentrations was observed in the group requiring longer hospitalizations . No suchLast, we compared the maternal antibody titers of RSV-infected children hospitalized in the ICU (n = 8) or in the inpatient ward (n = 86) and controls (n = 130). Although a trend toward lower pre-F antibody concentrations in children in the ICU group was observed , there wIn the present study, we measured serum antibody titers against the major RSV surface glycoproteins in samples obtained during the first trimester of pregnancy from women whose offspring were subsequently hospitalized with severe RSV bronchiolitis. We also selected control mothers who had provided similar screening serum samples on the same exact day and whose children remained healthy and were not hospitalized with RSV bronchiolitis. In Finland, RSV causes larger epidemics biannually , and theAmong the RSV surface glycoproteins, both the fusion (F) and attachment (G) glycoproteins are essential for viral infectivity in vivo and are the major viral proteins capable of inducing the production of neutralizing antibodies. The G protein is highly glycosylated and the least conserved of the RSV proteins, limiting its ability to induce antibodies that are broadly neutralizing. The F protein is metastable. Transition from its pre-F to post-F form causes its fusion peptide to insert into the target cell membrane, brings the virion and cell membranes together, and causes them to fuse, thereby initiating infection. Because pre-F is the active form of the F protein, antibodies to pre-F have superior neutralizing function compared to antibodies to post-F , 26. In Recent studies have analyzed whether cord blood antibodies confer protection from RSV infection and found no associations. In a prospective study conducted in rural Nepal, cord blood and maternal blood were collected from 310 mother–infant pairs. Children were followed 180 days from birth, and during that time 30 infants had a symptomatic RSV infection. Higher cord blood antibody titers were not found to be protective against developing an earlier or more severe RSV disease . AnotherStudies addressing the role of specific RSV antibodies in infants with acute RSV infection are limited. A small study conducted in 33 infants hospitalized with severe RSV disease found no significant associations between specific anti-RSV antibodies and disease severity . On the Maternal immunization has been a safe and effective approach to prevent infections in young children resulting from pathogens causing tetanus, pertussis, and influenza. The development of new vaccines against other important pathogens of infancy is gaining support as maternal vaccination is being more widely studied and accepted . MaternaPassive immunization through maternal vaccination or administration of extended-half-life monoclonal antibodies are probably the most effective ways to protect RSV-naive infants in the first months of life, as active immunization of this immunologically immature group is challenging. The clinical impact of RSV infection during pregnancy and outcomes of pregnancy with RSV infection are being investigated as maternal vaccination might also decrease the disease burden in pregnant women . In the Our study has limitations. Because of the retrospective study design, we were not able to analyze other factors that may have potentially affected disease severity such as RSV genotypes or viral loads in hospitalized children. Due to limited data regarding the number and age of siblings, we could not perform multivariate analyses to control for the contribution of siblings to the risk of disease. However, a sensitivity analysis including only children with siblings found that controls had significantly higher pre-F antibody titers than RSV cases. The RSV cases were not tested for viral coinfections as this is not a standard procedure at Helsinki Children’s Hospital. In addition, due to limited blood volumes, we did not measure neutralizing antibodies, although current evidence has shown strong correlations between pre-F antibody titers and neutralization , suggestIn conclusion, the findings in this study support the current understanding that maternal pre-F antibodies can protect infants from severe RSV infection. Follow-up studies are needed to explore different epitope-targeting antibodies and antibody titers needed to prevent RSV disease in infants.The Journal of Infectious Diseases online. Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.Supplementary materials are available at jiab315_suppl_Supplementary_Figure_S1Click here for additional data file.jiab315_suppl_Supplementary_MaterialsClick here for additional data file.


Quantitative susceptibility mapping (QSM) is a novel processing method for gradient-echo magnetic resonance imaging (MRI). Higher magnetic susceptibility in cortical veins have been observed on susceptibility maps in the ischemic hemisphere of stroke patients, indicating an increased oxygen extraction fraction (OEF). Our goal was to investigate susceptibility in veins of stroke patients after successful recanalization in order to analyze the value of QSM in predicting tissue prognosis and clinical outcome. We analyzed MR images of 23 patients with stroke due to unilateral middle cerebral artery (MCA)-M1/M2 occlusion acquired 24–72 h after successful thrombectomy. The susceptibilities of veins were obtained from QSM and compared between the stroke territory, the ipsilateral non-ischemic MCA territory and the contralateral MCA territory. As outcome variables, early infarct size and functional disability  after 3–5 months was used. The median susceptibility value of cortical veins in the ischemic core was 41% lower compared to the ipsilateral non-ischemic MCA territory and 38% lower than on the contralateral MCA territory. Strikingly, in none of the patients prominent vessels with high susceptibility signal were found after recanalization. Venous susceptibility values within the infarct did not correlate with infarct volume or functional disability after 3–5 months. Low venous susceptibility within the infarct core after successful recanalization of the occluded vessel likely indicates poor oxygen extraction arising from tissue damage. We did not identify peri-infarct tissue with increased susceptibility values as potential surrogate of former penumbral areas. We found no correlation of QSM parameters with infarct size or outcome. Susceptibility-based tissue contrasts can be deduced by post-processing of the complex-valued signal derived from T2*-weighted gradient-echo scans9. While the technique susceptibility weighted imaging (SWI) has already been established in neuroradiological diagnostics, its successor quantitative susceptibility mapping (QSM) is increasingly gaining consideration in clinical research. Both techniques reveal anatomic and physiologic information about the type of tissue and offer superb visualizations of the cerebral venous vasculature12. As susceptibility reflects the magnetizability of a tissue, it is sensitive to local and global oxygenation. The oxygen extraction fraction (OEF) level in cerebral veins directly relates to the concentration of paramagnetic deoxygenated hemoglobin molecules in the vessels14. Thus, magnetic susceptibility based contrast have been used to describe oxygenation status in the brain15.Magnetic susceptibility is a fundamental physical property of materials that describes their ability to become magnetized. It represents a unique contrast mechanism to image tissue in magnetic resonance imaging (MRI)19. In patients with acute stroke, prominent vessels with high magnetic susceptibility were observed with SWI and QSM23. These have been referred to as asymmetrically prominent cortical veins (APCVs) and were found within the infarcted perfused area, indicating tendency of clinical deterioration in stroke patients18. The appearance of prominent vessels on QSM has been associated with the ischemic penumbra (or “tissue at risk”) which is characterized by an increase in OEF (misery perfusion) and good tissue prognosis after recanalization20. In penumbral areas, increased OEF likely leads to higher deoxyhemoglobin concentrations in cortical veins, which then increases magnetic susceptibility24. Animal models of transient focal ischemia have shown that prominent vessels persist long after recanalization8. However, susceptibility values in cortical veins have so far not been assessed in stroke patients after successfull recanalization.QSM has been applied in the assessment of vessel function and oxygen metabolism in patients and animal models with acute ischemic stroke21. In addition, we correlated susceptibility values of cortical veins with functional disability after 3–5 months as well as infarct volume, an established predictive factor of clinical outcome in patients with middle cerebral artery (MCA) occlusions25.The primary goal of this study was to extend previous preliminary findings with QSM to investigate the differences in susceptibility values of cortical veins between the infarcted area, border and non-infarcted areas in ischemic stroke patients after successful recanalization. We hypothesized that QSM could provide a measure of tissue oxygen extraction and thus potentially aid in discriminating tissues with different fate2*w raw data for QSM analysis could be extracted on site during official working times.During the period of December 2016 until October 2018, we screened MRI and clinical data of patients with ischemic stroke due to MCA-M1 or MCA-M2 occlusion referred for thrombectomy at the Department of Neurology, University Hospital Zurich. Post-treatment MR images were acquired 24–72 h after successful recanalization. Of 263 patients receiving thrombectomy for acute ischemic stroke in 39 cases MCA-M1 or MCA-M2 occlusion was present and T. Informed consent was obtained from all participants as required by the ethics protocol. Only cases with successful recanalization verified by catheter angiography  2b or TICI 3 score26) were included in the study and if the recorded MRI data were of sufficient quality. To further evaluate recanalization, we analyzed flow in the MCA-M1 segment derived from routine transcranial duplex sonography (in cm/s) performed on day 0 to day 1 after thrombectomy.The study was approved by the local ethics committee Out of 39 patients eligible for further analysis, 34 agreed to take part in the data collection for this study. Six patients without or only partially successful recanalization according to the TICI reperfusion grade and five patients with technical issues  were excluded from further analysis, leading to 23 patients with successfully recanalized MCA-M1/M2 occlusion included in the current study. A flowchart illustrating patient selection is shown in Fig. Clinical variables including demographic data, medical history, associated risk factors and stroke etiology were gathered. In addition, information on treatment and outcome was collected, including information on intravenous thrombolysis, onset-to-needle time (ONT), NIH-Stroke Scale (NIHSS) on admission and after 24 h, and modified Rankin Scale (mRS) after 3–5 months. We determined infarct volume after recanalization from diffusion weighted imaging (DWI) which was performed between days 1 and 3 after stroke.2], number of slices 38, voxel size 1.1 × 1.1 × 3.0 [mm3], slice gap 0.9 [mm], repetition time (TR) 7340 [ms], echo time (TE) 68 [ms], B-values 0 respectively 1000 [s/mm2], acquisition time (TA) 4:33 [min:s]), T2*-weighted gradient echo imaging (T2*w) , fluid-attenuated inversion recovery (FLAIR)  1530 [ms], TA 5:59 [min:s]), and T2-weighted imaging (T2w) , field of view 220 × 220 [mm2], number of slices 44, voxel size 0.4 × 0.4 × 3.0 [mm3], slice gap 0.3 [mm], TR 8180 [ms], TE 100 [ms], TA 3:26 [min:s]), sequences were acquired in transverse orientation.All MR images were acquired on a 3 Tesla MRI Scanner . DWI 30 to eliminate background fields and by “homogeneity enabled dipole inversion” (HEIDI)31 to solve the ill-posed inverse problem between local magnetic field and magnetic susceptibility. Residual non-harmonic background field contributions, e.g., resulting from B1-related transceiver phase were implicitly suppressed by applying V-SHARP with high-pass filter regularization (cut-off frequency = 0.012 mm−1)32. Susceptibility maps were referenced to the mean cerebrospinal fluid (CSF) susceptibility. To measure CSF susceptibility, masks were drawn bilaterally in the center of the anterior horn of the lateral ventricles in three axial slices as described in Straub et al.33. Susceptibility values are given in parts-per-million (ppm). Finally, maximum-intensity projections of susceptibility maps were calculated across a slab 12.8 mm thick.Gradient-echo data were processed as outlined in detail below using in-house software that was written in Matlab  and utilized FSL’s brain extraction tool for brain masking34. For each individual, a threshold was applied such that pixel above this threshold were considered to be veins without marking any background tissue. Thresholds were kept constant during analysis of each individual. Susceptibility values from QSM images within semi-automatically drawn masks of individual cortical veins that appeared prominent were analyzed, regardless of their site. An example is demonstrated in Fig. In all patients, the same six slices representative for the MCA territory were selected. MCA territories were anatomically defined as areas supplied by the middle cerebral artery. Massive hemorrhages compromising cortical vein detection (applicable to three patients) were identified and excluded from the analysis considering standard imaging approaches for hemorrhages (SWI and CT scans were checked for each patient). Images were then analyzed using the Image J (NIH) software0.05 was considered as statistically significant.All statistical analyses were perfomed using the IBM SPSS Statistics software version 25. Comparisons between groups were done using the non-parametric Kruskal–Wallis-test. For correlation analyses between susceptibility values, clinical parameters (NIHSS on admission and mRS after 3–5 months), severity of ADC decrease within the infarction and infarct volume, we calculated Pearson’s correlation coefficient. A p-value below The study was approved by the local ethics committee . All procedures performed were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Word Medical Association Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all participants as required by the ethics protocol.During the period of December 2016 until October 2018, out of 263 patients with ischemic stroke referred for thrombectomy at the Department of Neurology, University Hospital Zurich, 23 patients with successfully recanalized MCA-M1/M2 occlusion were included in the study according to study plan.Patients’ clinical characteristics are shown in Table Information on neurological deficit upon admission and 3–5 months after discharge from the hospital is shown in Table 3 . The median number of veins found within the stroke area was 9 .Shown in Table In Fig. Cortical vein susceptibilities within the infarct core did not correlate with early infarct volume  or severity of mean initial ADC decrease within the infarct . We further analyzed a potential relationship between venous susceptibilities within the infarct and the clinical parameters initial stroke severity on NIHSS or 3–5 months functional disability on mRS, and found no such correlation .In patients successfully recanalized by thrombectomy after stroke due to occlusion of the MCA-M1/M2 segment, we found distinct patterns of cortical vein susceptibility in the ipsilateral MCA territory using QSM. While lower venous susceptibility values were found within the infarct core, susceptibilites in the ipsilateral non-infarcted MCA territory were similar to the venous susceptibilities within the unaffected contralateral MCA territory.14. Several papers have reported prominent veins (APCV) on susceptibility maps in patients with hyperacute and acute stroke, i.e. veins that were more visble and had larger diameter and length than those on the contralateral hemisphere or in healthy controls21. The presence of APCV has been hypothesized to be related to increased deoxyhemoglobin resulting from increased OEF35. A previous study found APCV in the infarct territories of hyperacute stroke patients (mean time from symptom onset to MRI acquisition 15 ± 4 h)22. The authors concluded that these prominent vessels could reflect a decrease of oxygen saturation in APCV which was estimated to be 16–44%. Luo et al. demonstrated that patients with APCVs have a tendency of deterioration of the neurological deficit while patients with no apparent APCVs tend to improve18. A study by Kim et al. has shown that APCV are not only observed in the cortex areas but also in deep thalamostriatal areas and that susceptibility values showed a strong inverse correlation with initial NIHSS36. It is expected that APCV disappear upon restoration of tissue perfusion, but this has not been investigated with QSM systematically.QSM has been proven useful to asses vascular oxygenation in the brain35. Further studies are needed to investigate oxygenation status after acute ischemic stroke. Our results are in contrast to two recent preclinical QSM studies that detected increased QSM susceptibility values in veins within the infarct or penumbral areas after reperfusion19. A study observed increased venous susceptibilities after induction of a transient occlusion in the middle cerebral artery in rats. The increased venous susceptibilities were related to lower venous oxygen saturation16. A study in mice found higher susceptibility values in the ischemic hemisphere, particularly in the infarct border zone, after transient middle cerebral artery occlusion19. Immunohistochemistry revealed the presence of compressed capillaries and dilated larger vessels. This suggests that in animal models of transient cerebral ischemia, recanalization of the major feeding artery does not lead to a complete reperfusion of cerebral tissue and the appearance of prominent vessels with an increased OEF may serve compensatory purposes. Such no-reflow or reperfusion failure has been described in both animal models and stroke patients, representing a serious obstacle to recovery37.We selected patients with verified recanalization of the MCA territory by catheter angiography (TICI 2b or TICI 3 score according to the TICI reperfusion grade) as well as duplex sonography measurements, which showed similar flow in the ipsi- and contralateral MCA-M1 segment. APCVs were clearly not detected in cortical regions. We found two vessel types within the affected vascular territory: veins with decreased QSM signal in the infarct core and veins with normal susceptibility values in the non-infarcted MCA territories. While we have not evaluated the presence of APCVs in the acute stage, one possibility is that the absence of prominent vessels represents a normalization of the OEF in the non-ischemic territories after reperfusion and oxygenation is restored38. We did not find a correlation of susceptibility values to stroke severity, functional disability, or final infarct size in our patients. Therefore, we did not find evidence that susceptibility measured after recanalization in patients with stroke due to MCA-M1/M2 indicates severity of tissue damage.DWI is an established imaging method to evaluate early infarct size and predict imaging as well as clinical outcome42. Consequently, non-local artifacts occurring adjacent to vessels with high blod flow might occur on the susceptibility map40.The flow-compensated T2*-weighted data utilized for QSM were acquired in the clinical setting with the SWI acquisition scheme established in-house, providing a familiar view to the radiologists. Thus, venous susceptibility analysis relied on a single echo acquired with TE = 20 and a long echo readout and came without an additional measurement, as only the post-processing needed adjustment. Although multi-echo gradient-echo imaging has been suggested for QSM in particular for studying iron depositions, their application for studying venous vessels is not recommened, as 3D flow-compensation is typically not provided for all echoes. Incomplete flow compensation leads to flow-related phase contributions that are not due to magnetic susceptibility35. While venous ROIs were carefully identified based on MIP and SWI maps, the resulting venous susceptibility values are prone to partial volume effects, which is indicated by the low QSM values in small veins in our study. We calculated the venous susceptibility from the 10% highest signal within the vein mask to overcome the problem of partial volume effects.As a limitation of our study, MRI measurements were done at a single time point, so that transient shifts in QSM signal earlier or later may have been missed. Furthermore, pathophysiological differences between stroke due to MCA occlusion in rodents and MCA stroke in human patients such as stroke duration/severity, collateral flow, or medication  might play a role, causing a different dynamic of tissue oxygenationAnother limitation of our study is the lack of information about reperfusion status on capillary level. While results of catheter angiography and duplex sonography conducted in our patient cohort indicates full reperfusion, this is not comparable to an in-depth  confirmation about reperfusion on a capillary level. Since no perfusion imaging was performed at the time of QSM imaging, there was no assessment of tissue perfusion available to confirm reperfusion status. However, vessels in the peri-infarct region were mainly located in the area of hypoperfusion on the initial (pre-thrombectomy) perfusion images.While we chose a homogenous patient cohort  to draw more valid conclusions according to the affected vascular territory and recanalization the sample size is small. Another limitation is that masks for individual cortical veins were drawn semi-automatically and some vessels might not have been identified because of edema compressing them. However, the semi-automatic approach allowed to carefully inspect and correct blood vessel segmentations. Finally, we did not compare our results to healthy controls, but used the contralateral hemisphere for comparison instead, which may have altered susceptibility as well.In conclusion, we found distinct patterns of cortical vein susceptibility after recanalization of acute ischemic stroke and our findings indicate that the susceptibility signal is highly dynamic in stroke. As QSM reflects tissue oxygenation in stroke patients with recanalized MCA-M1/M2 occlusion, it has potential to aid in understanding the complex processes that follow transient cerebral ischemia.Further studies in larger patient cohorts on QSM vein susceptibilities may derive differences in stroke tissue prognosis with clinical implications for stroke patients.Supplementary Information.


The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line.p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events.We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.The online version contains supplementary material available at 10.1186/s13040-022-00292-z. Animal-based chemical screening methods have evaluated only a fraction of commercial chemicals for human safety . New appOne challenge of leveraging HTTr for chemical screening is the analysis and interpretation of large data sets in which hundreds or thousands of gene expression levels are measured across many chemicals and concentrations. Interpretation of such datasets requires time-intensive and specific application of expert knowledge or, more favorably, a systematic analysis that discounts noise and indicates the probability of “true” biological signals that are relevant to chemical hazard identification. One common strategy for distilling this wealth of expression information is to use gene set enrichment analysis to identify molecular pathways perturbed by chemical treatment . HoweverMachine learning (ML) methods provide a flexible framework to address this problem. To date, ML methods have leveraged chemical structure and gene expression data to predict chemical targets for pharmaceutical repositioning –14, and Here, we predict MIEs through a ML-based classification approach Fig. . We deveClassifiers were trained using one of six algorithms: 1) support vector machine with linear kernel (SVM_L), 2) support vector machine with radial kernel (SVM_R), 3) support vector machine with polynomial kernel (SVM_P), 4) K-nearest neighbor (KNN), 5) multilayer perceptron with multiple hidden layers (MLP), and 6) Naïve Bayes (NB). We tested several sets of input features for model training, including pathway scoring. Binary classifiers were validated by four complementary approaches: 1) 5-fold cross validation, 2) validation on holdout data, 3) empirical significance testing, and 4) evaluation of exemplar chemical activity predictions. Lastly, in the interest of testing whether MIE prediction from transcriptomic profiles is dependent on cell line, we compared the predictive accuracy of classifiers trained on data from the MCF7 breast cancer derived cell line versus classifiers trained on data from the PC3 prostate cancer derived cell line.To predict MIEs from gene expression, we trained binary classifiers using chemical-target labels from RefChemDB  and geneTo train binary classifiers to predict MIEs from gene expression, we leveraged annotations from RefChemDB , a databSome MIEs in RefChemDB are annotated for similar or identical sets of chemicals. For example, if filtered to include only annotations with a support level of at least 5, there are 83 chemicals linked to Carbonic anhydrase 1 (CA1) and Carbonic anhydrase 2 (CA2) inhibition, 82 of which are shared. This was expected, as members of the same gene family that have considerable homology (60% in the case of CA1 and CA2 ) often hTo train classifiers to predict MIEs using gene expression, we required a gene expression data set to train models that had sufficient overlap with chemicals annotated in RefChemDB. To meet this need, we used the LINCS L1000 gene expression database . This cohttps://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0247-6] (https://comptox.epa.gov/dashboard/), a web-based application providing access to chemical property data and in vivo and in vitro toxicity data for nearly 900,000 chemicals. Broad_IDs were matched to DTXSIDs in ChemReg using additional chemical descriptors extracted from LINCS metadata files, including canonical SMILES, InChIKeys, and chemical names  were downloaded from the gene expression omnibus  for LINCcaret with “center” and “scale” methods, as done in previous ML-based investigations using LINCS data [Most chemicals present in the LINCS data set have multiple associated gene expression profiles. Some chemicals were screened at multiple concentrations and with varying durations, resulting in multiple profiles for a single chemical, in addition to replicate experiments generating profiles with identical conditions. The number of profiles associated with each chemical varies from chemical to chemical, with a median of 6 profiles in MCF7 cells for the LINCS chemicals mapped to a DTXSID in RefChemDB. Previous ML-based investigations have limited the heterogeneity of training data by restricting training data sets to a single concentration and exposure duration . We sougNCS data  .caret package v6.0–83 in the R statistical framework [kernlab v0.9–27, RSNNS v0.4-12, and naivebayes v0.9.6 R libraries, respectively. Hyperparameter tuning for model optimization was performed automatically by caret using the grid search default in the ‘train_control’ function.To optimize models, we evaluated the performance of six different classification algorithms for model training. A subset of these algorithms were based on Support Vector Machines (SVMs). SVMs are a popular class of algorithm for biological binary classification problems . SVMs trramework . SVM, MLPrevious ML-based investigations using LINCS L1000 data have shown that pre-processing gene-level expression information into pathway scores can significantly improve multi-classifier performance . We therTo explore how the selection of input features affects classifier performance, we trained models using three types of L1000 derived features: 1) Moderated Z-scores for all genes (LINCS landmark and inferred), 2) Moderated Z-scores for LINCS landmark genes only, and 3) Pathway scores.The classifier training and validation pipeline is outlined in Fig. caret package in R [For each MIE with sufficient training data:Generate list of “MIE-active” chemicals associated with MIE from RefChemDBN MIE-active profiles linked to MIE-active chemical treatments in the selected cell lineGenerate collection of N MIE-inactive profiles linked to chemicals that are not annotated for the MIEGenerate collection of Select 20% each of MIE-active and MIE-inactive profiles as holdout dataRemaining 80% of profiles are designated as training data setFor each algorithm in Split model training data into 5 equal subsetsk:k subsets and train model on these datakTest model on Combine non-For each subset Train classifier using 5-fold cross validation:For each unique combination of hyperparameters (grid search)Test accuracy of model with optimal hyperparameters using holdout dataReturn internal  and holdout accuracies for optimal hyperparameter configurationIdentify optimal hyperparameter configuration that returns the highest average accuracy (proportion of correct assignments) across the 5 foldsFor each MIE, training data sets were formed using RefChemDB chemical-MIE linkages to identify and label suitable LINCS gene expression profiles. First, a collection of “MIE-active” LINCS profiles was generated by selecting LINCS profiles associated with chemicals that are linked to a given MIE. Then, an equally sized collection of “MIE-inactive” profiles was generated by randomly selecting profiles that met two criteria: 1) profiles must not be associated with chemical treatments linked to the MIE being modeled at any support level nor with any mode and 2) profiles must be associated with a different MIE with a support level ≥ 5 , 500 null data sets were generated and corresponding classifiers trained. Then, an empirical significance test was performed by calculating eviously . CandidaTo assess model performance independently of internal and holdout accuracies, we excluded a set of “exemplar” chemicals  from all training and holdout data sets, including for null classifiers. To select exemplar chemicals, we used a data-driven approach. First, we generated a list of candidate chemicals by taking the union of the top 10 chemicals for each MIE ranked by support level. We then iteratively selected one exemplar chemical for each MIE that did not yet have an exemplar selected, with the constraint that the exclusion of the chemical could not reduce the remaining training data to < 5 chemicals or < 50 total profiles for any MIE on the list. If a selected exemplar chemical was in the list of top 10 supported chemicals for multiple MIEs, that exemplar chemical was assigned to all these MIEs, and all were removed from the list for further exemplar selection. This process identified 31 exemplar chemicals assigned to 44 of the 51 MIEs. All profiles associated with these exemplar chemicals were subsequently excluded from all classifier training . A subset of associations at this support level are likely spurious , howeverIn the context of transcriptomic differences between cell lines at baseline, we hypothesized that some MIE classifiers would perform better using data from some cell lines relative to others. To identify MIEs that show cell type specific differences in prediction, we trained a separate set of models using LINCS gene expression profiles derived from the PC3 prostate cancer derived cell line. PC3 cells differ from MCF7 cells in terms of baseline gene expression and tissue of origin, and thus exhibit a different repertoire of proteins that can be perturbed by chemical treatment. Also, the PC3 cell line has the second highest number of associated gene expression profiles in the LINCS data set .Classifiers were trained using LINCS L1000 gene expression data in the form of moderated z-scores (LINCS level 5 data). These values are derived from 978 measurements of landmark genes directly measured by the L1000 assay, and inferred expression of an additional 11,350 genes . To detep < 1.2 × 10− 2). 79% of landmark gene classifiers out-performed corresponding classifiers using all LINCS genes and pathway scores for the same MIE and classification algorithm. Due to the magnitude of difference in accuracies between feature types, and the consistency of these findings across classification algorithms, we focused on landmark gene-based classifiers for further analysis.Previous ML investigations of LINCS data have shown improved performance for models trained on gene expression data that are pre-processed into pathway scores . To testp-value = 10− 8), which were the algorithms with the next highest mean accuracies of 0.67 and 0.66 respectively.Focusing on classifiers trained with landmark genes, we then compared performance across training algorithms Fig. B and C. p-value = 0.031), suggesting overfitting. Therefore, classifiers trained using the SVM_P algorithm were excluded from further analysis. In the absence of SVM_P models, there was not a clear winner among ML algorithms with respect to internal accuracies , without capturing a mechanism of action relevant to the MIE. To explore the possibility, we first compared internal accuracy with the number of chemicals used in classifier training . These linkages were previously excluded from training data sets because a previous comparison to in vitro assay results has shown that these associations are more likely to be spurious relative to linkages with a higher support level . On the To test this, we compared prediction ranks for these moderate support chemicals to the background rank distribution using a one-tailed KS test. Of the 9 confirmed high performance classifiers, 8 generated ranked predictions for moderate-support linkages that were significantly higher than the background distribution Table . Thus, c− 5). Additionally, the classifiers with the highest internal accuracies were the same in both cell types (TUB (−) and MTOR/PI3K (−)). On the other hand, a subset of models showed relatively dissimilar accuracies between cell types. The AR (+) model showed relatively higher internal accuracy when trained on MCF7 data (0.85) compared to training on PC3 data (0.49), while ADRB-1/2 (−) showed relatively higher accuracy when trained on PC3 data (0.80) as opposed to MCF7 (0.53). Because MCF7 cells are known to be estrogen-responsive, we suspected that a greater diversity of estrogen receptor-interacting chemicals may have been assayed in MCF7 cells relative to PC3 cells. Such an imbalance in training data could potentially drive differences in model accuracy between the cell types. However, an inspection of the training data for these classifiers reveals similar numbers of chemicals and profiles were available for ESR-1/2 activation in both cell types  had sufficient training data in PC3 cells. To train PC3 classifiers comparable to MCF7-trained counterparts, we used landmark gene features. In order to maximize comparability between classifiers trained in each cell line, we selected SVM_L-based classifiers for comparison. Classifiers trained with SVM_L on MCF7-derived profiles were compared with algorithm and MIE-matched PC3-derived classifiers based on internal accuracies  values from the Human Protein Atlas v20  for MIE In the current study, we trained binary classifiers to predict MIEs from gene expression profiles and optimized both input feature type and classification algorithm. By integrating LINCS and RefChemDB in a ML framework for MIE prediction, our analysis has the following primary findings: 1) We identified landmark genes as an optimal feature type for MIE prediction based on internal accuracies, 2) Of the 51 MIEs modeled, 9 MIEs generated classifiers that performed significantly better than null models and that were further validated using training-excluded chemicals and profiles, and 3) Classifier accuracy varied based on the cell line from which training data were derived, and may be associated with differences in baseline expression of MIE-linked genes.Previous studies of drug-use category prediction showed that classifiers trained on pathway scores outperformed analogous models trained on individual gene features , prompticaret) are limited to a single hidden layer due to compute time constraints. However, the training and validation framework presented in the current work can be used to test additional models and hyperparameter combinations in the future.Previous ML-based investigations incorporating LINCS L1000 data have shown that deep-learning-based approaches yielded superior accuracy in predicting general chemical use categories relative to SVM-based classifiers . We wereConfirmed high performance classifiers assigned relatively high prediction scores to the corresponding exemplar chemicals, which were completely excluded from the training data. However, some classifiers assigned high ranking prediction scores to chemicals for which they were not annotated. For example, Everolimus received a high ranking prediction for MTOR/PI3K (−), to which it is linked through RefChemDB annotations, but was also a high-ranking prediction for TOP2A (−), for which it is not annotated. These MIEs are related in that induction of either can be linked to decreased cell proliferation. MTOR (Mechanistic Target of Rapamycin) and a downstream pathway that includes Phosphoinositide 3-kinase (PI3K) is a major regulator of cell growth and proliferation, while TOP2A , is a nuclear enzyme that alters topology of DNA to facilitate replication. The high ranking prediction of Everolimus for TOP2A (−) may therefore result from the convergence of distinct MIEs onto the same key events through shared molecular consequences. While Everolimus is known to inhibit MTOR but not TOP2A, the inhibition of either of these targets is known to induce apoptosis , 35. SimAn additional limitation of the current work is sparse data on which to train classifiers. Previous ML-based investigations have leveraged more general labels capturing overall toxicity or chemical use categories to train classifiers using expression profiles associated with hundreds of chemicals. In contrast, models produced by the current work are trained using as few as 5 chemicals associated with much more specific biological activities, such as AR inhibition. One reason for this sparseness in the training data is that currently there are relatively few chemicals associated with certain MIEs with high levels of literature support. RefChemDB is the product of an automated curation process and therefore contains some spurious chemical-target associations. To ensure the validity of our training data, we limited MIE-active chemicals to those with a support level corresponding to at least 5 separate sources of evidence for the association. It is likely that a subset of chemical-MIE annotations with lower support levels are valid and could therefore be added to the available training data as additional evidence becomes available. A second contributor to the sparseness of the training data is limited overlap between chemicals annotated in RefChemDB, and chemicals surveyed in LINCS. Of the 1181 chemicals annotated in RefChemDB with a support level ≥ 5, only 765 (65%) have available data in LINCS.We addressed the sparseness of the data by first clustering similar targets into a single MIE, such as HDAC1 (−) and HDAC2 (−), using a data-driven strategy based on overlapping chemical associations in RefChemDB. This strategy generated MIEs that can easily be associated with AOPs, without unnecessarily distinguishing between gene family members for which there are a limited number of chemicals that display family member specific affinity. We also increased the size of our training data sets by leveraging multiple examples of each chemical treatment in the LINCS data, including those tested at multiple concentrations and exposure durations. We also performed an empirical significance test using null models matched to the number of chemicals and profiles used to train each MIE model. This test should identify cases where the model accuracy is likely to reflect spurious commonalities between profiles for a small number of chemicals, rather than the true transcriptional signature of the MIE.p-value = 0.03) but failed to generate a high-ranking prediction for 17-Methyltestosterone (87.66 percentile). On the other hand, 17-Methyltestosterone did return high-ranking predictions for TUB (−), ESR-1/2 (+), and TOP2A (−), none of which are linked to the chemical in RefChemDB. Consideration of the literature reveals 17-Methyltestosterone is estrogenic due to the ease of its conversion to estrogen by aromatase in MCF7 cells, which may explain the high ranking prediction for ESR-1/2 (+) [Another limitation of the current methodology was the data-driven selection of exemplar chemicals. This selection paradigm considered MIE-chemical support level, frequency of annotation across MIEs, and minimal training data set volume to identify suitable exemplar chemicals (see section 2.8). We used this data-driven approach for exemplar selection to remove our own bias of chemical-MIE linkages from the analysis. However, performing this selection without expert knowledge of metabolism and molecular crosstalk may have resulted in less than ideal exemplar chemicals. For example, our strictly data-driven method selected 17-Methyltestosterone as the exemplar for AR (+). AR (+) passed empirical significance testing (-1/2 (+) .To explore if MIE predictions differed as a function of the cellular context of training data, we compared classifiers trained on MCF7-derived data with models trained on PC3-derived data. Of the models with sufficient gene expression profiles in LINCS for training in both cell types, a subset, such as TUB (−), showed relatively high and comparable internal accuracy across cell lines. For MIEs modeled by classifiers that demonstrate comparable performance across cell lines, it may be advantageous to attempt to improve classifier accuracy further by training classifiers with an expanded set of training data derived from multiple cell lines in LINCS.While some classifiers showed relatively similar results across both cell lines, a subset of MIE classifiers showed markedly different internal accuracies as a function of the cell line on which they were trained Table . This diWhile the relative responsiveness to estrogen receptor modulators in MCF7 cells is well studied , the relThe current study used a binary classification approach to predict MIEs induced by chemical exposure based on gene expression data. We explored a variety of modeling parameters including input feature type, classification algorithm, and cellular context of training data. Candidate high performance classifiers were identified with empirical significance testing, and further validated using training-excluded exemplar reference chemicals.Systematic comparison of models generated with MCF7 and PC3 derived profiles identified MIEs that were modeled with markedly different accuracies in each cell type, emphasizing the importance of cellular context in model training. As demonstrated by training-excluded exemplar chemical predictions, we propose that a subset of classifiers offer utility in predicting MIEs from L1000 gene expression data. Methods developed herein could be integrated into EPA’s current tiered testing paradigm to prioritize chemicals for further study within a framework of new approach methodologies [Additional file 1: Supplemental Fig. 1. Distribution of LINCS gene expression profiles across cell lines. Each slice of the pie chart captures the percent of all LINCS phase I and II chemical perturbagen gene expression profiles that are derived from the indicated cell types. 76 Cell lines associated with < 5% of total profiles each were combined into the “all other cell lines” group.Additional file 2: Supplemental Fig. 2. Original and null model accuracies as a function of the number of training chemicals. A) Linear regression of internal accuracy (Y axis) for each MIE using each of the six classification algorithms as a function of the number of chemicals included in model training for MCF7-derived landmark gene classifiers (X axis). B) Linear regression of mean internal accuracy from 500 corresponding null classifiers (Y axis) for each MIE is as a function of the number of chemicals used in training (X axis).Additional file 3: Supplemental Fig. 3. Enrichment of high-ranking prediction scores among training excluded chemicals for confirmed high performance classifiers. For each of 9 confirmed high performance classifier, an Empirical Cumulative Distribution Function (ECDF) is plotted for LINCS chemicals ranked by median prediction. Solid red lines correspond to the ECDFs for MIE-associated chemicals with a support level of 3 or 4 in RefChemDB. Blue lines correspond to the ECDFs for all chemicals tested in MCF7 cells. Red dots and dashed lines indicate the greatest difference between the ECDF functions, which is used as the test statistic for the Kolmogorov-Smirnov (KS) test p-values shown above each plot.


Foot problems are common in older adults and associated with poorer physical function, falls, frailty and reduced quality of life. Comprehensive Geriatric Assessment (CGA), a multidisciplinary process that is considered the gold standard of care for older adults, does not routinely include podiatry assessment and intervention in hospitalized older adults.To introduce foot assessment to inpatient CGA to determine prevalence of foot disease, foot disease risk factors and inappropriate footwear use, assess inter-rater reliability of foot assessments, determine current podiatry input and examine associations between patient characteristics and foot disease risks.Prospective, observational cohort study of older adults on geriatric rehabilitation wards. Foot assessment completed using the Queensland Foot Disease Form (QFDF) in addition to routine CGA.p = 0.01) and higher median Malnutrition Screening Test scores  than the low-risk group. Patients with foot disease were most likely to see a podiatrist (p < 0.001).Fifty-two patients  were included. Six patients (12%) had foot disease and 13 (25%) had a ‘high risk’ or ‘at risk’ foot. Foot disease risk factor prevalence was peripheral arterial disease 9 (17%); neuropathy 10 (19%) and foot deformity 11 (22%). Forty-one patients (85%) wore inappropriate footwear. Inter-rater agreement was substantial on presence of foot disease and arterial disease, fair to moderate on foot deformity and fair on neuropathy and inappropriate footwear. Eight patients (15%) saw a podiatrist during admission: 5 with foot disease, 1 ‘at risk’ and 2 ‘low risk’ for foot disease. Patients with an at risk foot or foot disease had significantly longer median length of hospital stay (25 [13.7–32.1] vs 15.2 [8–22.1] days, Foot disease, foot disease risk factors and inappropriate footwear are common in hospitalized older adults, however podiatry assessment and intervention is mostly limited to patients with foot disease. Addition of routine podiatry assessment to the multidisciplinary CGA team should be considered. Examination for arterial disease and risk of malnutrition may be useful to identify at risk patients for podiatry review.The online version contains supplementary material available at 10.1186/s12877-021-02164-3. Changes to the feet occur with age, leading to high prevalence of foot problems such as foot pain, deformity, muscle weakness and reduced range of motion , 2. ThisDespite the impact of foot problems and foot disease on older adults, the foot is frequently neglected by both patients and healthcare providers , 24. TheThe aim of this study was to introduce a standardised foot assessment to older adults undergoing inpatient CGA, to: (1) determine the prevalence of foot disease, foot disease risk factors and inappropriate footwear use; (2) assess the inter-rater reliability of foot assessments; (3) determine the frequency of podiatry assessment; and (4) evaluate associations between patient characteristics and foot disease and foot disease risk factors.This study is embedded in REStORing health of acutely unwell adulTs (RESORT), an ongoing prospective, observational, longitudinal inception cohort investigating the characteristics and outcomes of geriatric rehabilitation patients at the Royal Melbourne Hospital . Patients included in the RESORT study are assessed within 48 h of admission and discharge with a multidisciplinary, multi-dimensional CGA. Recruitment for wave 1 of RESORT commenced on October 15, 2017. Each annual RESORT wave recruits approximately 600 patients. This sub study, a pilot study adding foot assessment during the geriatric rehabilitation admission, recruited a convenience sample of inpatients from wave 3 between February 12 and March 19, 2020. A power calculation of sample size for foot disease prevalence was 138 patients  . This stPatient demographics, reason for admission and history of falls were collected from medical records and patient surveys. Physical function was assessed with the Functional Ambulation Classification , a scaleAssessment for foot disease and foot disease risk factors was undertaken by trained clinical researchers using the Queensland Foot Disease Form (QFDF), a 57 item, validated assessment instrument  (AdditioStandardised training in use of the QFDF was undertaken with the assistance of a podiatrist (EL). A sample of foot assessments was repeated by the second assessor, blinded to the initial assessment, to check inter-rater reliability. The patient’s treating team was notified of the outcome of the foot assessment where previously unidentified foot disease was noted, and recommendation for podiatry referral was made where appropriate. A number of patients also underwent foot assessment by a podiatrist as part of their usual care; this was recorded with the hospital podiatry assessment tool   tool. Dap < 0.05.Descriptive statistics were used to summarise the patients’ baseline characteristics. Data with a normal distribution is presented as mean and standard deviation; data with a non-normal distribution is presented as median and interquartile range; and categorical variables are presented as proportions. The baseline characteristics of the foot risk groups were compared using the Kruskal-Wallis H test (for more than 2 groups) or Mann-Whitney U test (for 2 groups) for nonparametric continuous and ordinal data and Fisher’s exact test for categorical data. To reduce type 1 error, the level of significance for all analyses was set at The baseline characteristics of the 52 included patients are given in Table n = 48): the most commonly worn footwear indoors and outdoors was slippers (34%) and moccasins (33%) respectively. Thirteen (25%) patients were assessed as having an ‘at risk’ or ‘high risk’ foot. Eight patients (15%) saw a podiatrist during their hospital admission: 5 had foot disease, 1 was at risk and 2 were low risk of foot disease. All patients that were seen by a podiatrist required an intervention, which included advice, nail care, debridement and wound dressings.Six patients (12%) had foot disease (pressure injury (4/6), neuroischaemia (1/6) and trauma (1/6)). The prevalence of foot disease risk factors was: mild to moderate peripheral arterial disease 9 (17%), neuropathy 10 (19%) and foot deformity 11 (22%). Reported footwear was inappropriate in 41 patients . When risk category was dichotomised , patients with higher foot risk or foot disease had significantly longer median length of hospital stay  and higher median MST scores  than the low-risk group.Differences between patients based on risk foot or foot disease are summarised in Table There was substantial to excellent agreement on presence of foot disease , moderate to substantial agreement on presence of arterial disease , fair to moderate agreement on foot deformity  and fair agreement on neuropathy  and inappropriate footwear .Foot disease risk factors and foot disease were common in this cohort. Podiatry assessment and intervention was undertaken for most patients with foot disease but for few patients with an at risk or low risk foot. Inter-rater reliability testing confirmed that there was substantial agreement on presence of foot disease and arterial disease, however agreement was poorer for neuropathy, deformity and inappropriate footwear. Presence of peripheral arterial disease may therefore be a useful indicator for podiatry referral for patients with an at risk foot. Inter-rater reliability for footwear and neuropathy may have been impacted by high rates of cognitive impairment in older adults on sub-acute wards . DiffereRisk of malnutrition was higher and length of stay on the Geriatric Rehabilitation ward was longer for patients with foot disease, at risk and high risk feet. There was no difference in Cumulative Illness Rating Scale or Clinical Frailty Scale between the groups, suggesting that this is not due to overall ill-health. The difference in length of stay may be explained by differences in nutritional status, as malnutrition is associated with longer length of hospital stay . MalnutrThe rate of foot disease in this cohort is similar to other studies in sub-acute populations in Scotland and Australia that reported prevalence rates of 15%  and 12% Use of inappropriate footwear was very common in this cohort and similar rates have been reported by other inpatient and outpatient studies of older adults , 56, 57.Given the high prevalence of foot disease, foot disease risk factors and inappropriate footwear, as well as evidence for reduced falls  and footThe pragmatic approach of comparing researcher foot assessments using the QFDF with podiatry assessments using the hospital assessment tool may have affected inter-rater reliability measures due to differences in documentation of foot deformity and footwear. Use of the QFDF for foot assessments may have underestimated the prevalence of foot problems, given that single foot deformities and integumentary problems are not captured with this assessment. The study was conducted in late summer, which may have impacted the prevalence of foot disease: studies of diabetes related foot disease have found higher rates of infection and amputation in warmer months, whilst amputations in people without diabetes are most common in winter , 64. TheFoot disease, foot disease risk factors and inappropriate footwear are common in older adults admitted to geriatric rehabilitation wards although podiatry assessment and intervention is mostly limited to patients with foot disease. Assessment for peripheral arterial disease and risk of malnutrition may help to identify at risk patients for podiatry input. Interventional studies incorporating podiatrist assessment and intervention into CGA are needed.Additional file 1: Appendix I. The Queensland Foot Disease Form [Appendix II. Hospital specific podiatry assessment form (2 pages).ase Form . Appendi


Heart failure (HF) continues to be a serious public health challenge despite significant advancements in therapeutics and is often complicated by multiple other comorbidities. Of particular concern is type 2 diabetes mellitus (T2DM) which not only amplifies the risk, but also limits the treatment options available to patients. The sodium-glucose linked cotransporter subtype 2 (SGLT2)-inhibitor class, which was initially developed as a treatment for T2DM, has shown great promise in reducing cardiovascular risk, particularly around HF outcomes – regardless of diabetes status.There are ongoing efforts to elucidate the true mechanism of action of this novel drug class. Its primary mechanism of inducing glycosuria and diuresis from receptor blockade in the renal nephron seems unlikely to be responsible for the rapid and striking benefits seen in clinical trials. Early mechanistic work around conventional therapeutic targets seem to be inconclusive. There are some emerging theories around its effect on myocardial energetics and calcium balance as well as on renal physiology. In this review, we discuss some of the cutting-edge hypotheses and concepts currently being explored around this drug class in an attempt better understand the molecular mechanics of this novel agent. Heart failure (HF) is a growing public health concern with 8.5 million people expected to be living with this condition in the year 2030 at a staggering cost of $70 billion in the United States alone . HF not Interestingly, there appears to be a bidirectional relationship between T2DM and HF; not only can T2DM cause HF, but it can also be the consequence of it . This unA novel class of anti-diabetes therapy known as the sodium glucose-linked cotransporter subtype 2 (SGLT2) inhibitor has shown potential in addressing this area of urgent unmet need, not only by improving glycaemia but also preserving renal function and reducing hard HF outcomes as well. In this review, we discuss the evidence behind the striking CV benefits and unpick some of the unique characteristics of this drug class that could potentially herald a new generation of targeted agents in the treatment of HF.m 2 mM) responsible for approximately 90% reabsorption of filtered plasma glucose, and the remainder is reabsorbed by SGLT1 ), with each component being reduced by 18% and 30% respectively . PatientEmpagliflozin Outcome Trial in Patients with Chronic Heart Failure with Reduced Ejection Fraction (EMPEROR-Reduced) was designed to be more ambitious than DAPA-HF, recruiting a HF population that had on average more severe systolic dysfunction  1907 vs. 1437) . After aThe true CV benefit with SGLT2-inhibition could be benchmarked against the only other novel therapy shown to reduce mortality in a well-treated HF population—the angiotensin receptor-neprilysin inhibitor sacubitril-valsartan, which works by augmenting the natriuretic peptide system and simultaneously inhibiting the renin–angiotensin–aldosterone system . DapagliHF with preserved ejection fraction (HFpEF) makes up approximately half of the HF population. Both HFrEF and HFpEF have similar prognoses but very different pathophysiology . HFrEF iSeveral large dedicated HFpEF studies; Empagliflozin Outcome Trial in Patients with Chronic Heart Failure with Preserved Ejection Fraction (EMPEROR-Preserved) (NCT03057951) and Dapagliflozin Evaluation to Improve the Lives of Patients with Preserved Ejection Fraction Heart Failure (DELIVER) (NCT03619213), are nearing completion and will characterize SGLT2-inhibitor benefit in this cohort.The heart and kidney are inextricably linked, where direct or indirect effects of one dysfunctional organ can initiate and perpetuate the combined disorder of both organs, and is often referred to as the cardiorenal syndrome . Up to 6In patients with T2DM, the increased filtered glucose elicits upregulation of SGLT1 and SGLT2 expression in the proximal convoluted tubule of the nephron. As a consequence of the increased glucose (and sodium) absorption by these transporters in the proximal part of the nephron, there is reduced sodium delivery distally to the macula densa causing afferent renal arteriolar dilatation and hyperfiltration. Excessive hydrostatic damage to the glomerulus from hyperfiltration is thought to be the main driver for diabetic nephropathy . By inhiFollowing consistently positive effects on renal endpoints in early trials, the Evaluation of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Participants with Diabetic Nephropathy (CREDENCE) trial was the first dedicated renal endpoint study in patients with diabetic kidney disease, using canagliflozin. Of the 4401 patients, all had a diagnosis of chronic kidney disease, 50.4% also had established CV disease and 16% had baseline HF . CanagliA Study to Evaluate the Effect of Dapagliflozin on Renal Outcomes and Cardiovascular Mortality in Patients With Chronic Kidney Disease (DAPA-CKD) provides further affirmation for SGLT2-inhibitor use in both diabetic and non-diabetic CKD aetiologies. Dapagliflozin reduced the risk of worsening renal function or death from kidney failure by 44%, hospitalisation for HF or CV death by 29% and all-cause mortality by 31%  – regardless of T2DM status . ResultsDespite multiple guideline recommended disease-modifying therapies, patients with HF continue to have a poor prognosis . ResultsThe question  remains: Should cardiologists and nephrologists be proactively prescribing SGLT2-inhibitors to all patients with CV and renal disease?From a CV perspective, there have now been analyses of three patient groups; 1) primary prevention cohort (part of CANVAS and DECLARE TIMI-58), 2) secondary prevention cohort  and 3) HF cohort (DAPA-HF and EMPEROR-Reduced). As the CV risk profile increases, so does the apparent ‘efficacy’ of SGLT2-inhibition on CV mortality and HF hospitalisation. However, as seen in EMPEROR-Reduced, there is a signal that there may be an upper limit of HF severity beyond which the mortality benefits of SGLT2-inhibition plateaus, whilst morbidity benefits  remain robust throughout the entire spectrum of disease risk and severity.So far only canagliflozin has received regulatory approval for use in patients with T2DM and CKD following data from the CREDENCE trial . In patiA characteristic feature of the pathophysiology of HF is the activation of compensatory pathways; however, sustained activation of the neurohormonal system  results in maladaptive remodelling of the ventricles and myocardial injury, which perpetuate the disease state. Being able to reverse this remodelling is an important determinant of long-term severity of HF and mortality . PreviouThe Research Into the Effect of SGLT2 Inhibition on Left Ventricular Remodeling in Patients With Heart Failure and Diabetes Mellitus (REFORM) trial was the first to try to determine the mechanistic effect of SGLT2-inhibitors specifically in the HF population. At 1 year, no significant difference in LV remodelling , as assessed by cardiac magnetic resonance imaging was seen with dapagliflozin therapy . The absPutting everything together, there is a definite clinical benefit from SGLT2-inhibitor therapy particularly in the context of hard HF outcomes, however the mechanism of that effect remains unclear Table . The glu2+), is a key ion involved in excitation–contraction coupling, cardiac rhythmicity, also acting as a second messenger in regulating gene transcription for myocyte hypertrophy and other pathological remodelling pathways [The ionic balance within the cardiomyocyte is finely modulated by a variety of ion pumps on the cell surface membrane which interact via multiple overlapping pathways. Calcium (Capathways .2+ channels facilitating Ca2+ influx into the cardiomyocyte where it then binds to ryanodine R2 receptors (RyR2) on the sarcoplasmic reticulm (SR) to release intrasarcoplasmic Ca2+. This process  amplifies the cytoplasmic Ca2+ content, which then bind to cardiac troponin, triggering muscle contraction. At the end of systole, the majority of Ca2+ ions are recycled back to the SR for storage via the SR Ca2+-ATPase (SERCA2a), while the rest are extruded extracellularly via the sodium-calcium exchanger (NCX) [Action potentials generated at the cell surface trigger the opening of voltage-dependent L-type Caer (NCX)  Fig. 3)2+ channe2+ reuptake into SR. This prolongs the relaxation phase (causing diastolic dysfunction) and reduces SR Ca2+ content available for use in the next contraction cycle (cauing systolic dysfunction). Likewise, phosphorylation of RyR2 pumps cause diastolic leakage of Ca2+ from the SR further reducing its storage capacity and increasing arrhythmogenicity due to increased cytosolic Ca2+ during diastole [+) concentrations [+ concentrations), resulting in increased cytoplasmic Ca2+ and reduced mitochondrial Ca2+ leading to myocyte hypertrophy, increased oxidative stress and accelerated cell death [HF and T2DM cause structural and functional changes to these pumps, and in doing so, disrupt the finely-balanced calcium homeostasis. There is increased activity of phospholamban which inhibits SERCA2a resulting in reduced Cadiastole . Additiotrations , 75. Thill death , 77  and possibly SGLT1 as well  [SGLT2 is not expressed on the heart , yet theficiency  and redu[2+ leak , 81. Add[2+ leak . SGLT2-idegrees) , 83, 84.without T2DM [2+ and Na+ is independent of extracellular glucose levels and SGLT2 activity [Interestingly, T2DM is not the only trigger for SGLT1 upregulation in the heart; there is a 31% increase in SGLT1 expression seen in obese HF patients out T2DM . Furtheractivity . These fThe healthy heart utilises a variety of fuels to power its function. Under normal conditions, mitochondrial oxidative phosphorylation accounts for 95% of myocardial energy demands, whilst only 5% is met by glycolysis and TCA cycle. Between 70%—90% of the ATP generated by the heart is derived from free fatty acids (FFA) and the remainder from glucose and other substrates such as ketones, branch chain amino acids and lactate . The heaFFAs are the primary energy source for the heart  under most conditions, although its proportion of the overall fuel mix changes situationally. For example in the post prandial state, with higher circulating glucose and insulin, there is increased glucose uptake through myocardial GLUT proteins . FollowiThis shift in metabolism to favour glucose over FFA is a rapid response mechanism to acute changes, however, just like neurohormonal activation in the context of ventricular loading, if left unchecked it can result in ‘metabolic remodelling’ leading to cardiomyocyte dysfunction. Indeed, it is the most consistent metabolic change seen in animal and clinical studies of HF. A chronic overreliance on glucose results in suppression of genes related to FFA beta-oxidation pathways including their upstream regulators such as PPAR-alpha . DownregOn the other hand, insulin resistance and increased proinflammatory cytokinesis (TNF and IL-6) which are hallmarks of T2DM, also induce lipolysis thereby increasing FFA delivery to the cardiomyocyte and worsening lipotoxicity . As a fuGiven the effects of the various fuel substrates on cardiac efficiency and potentially even cellular dysfunction, optimizing myocardial energetics could be a key therapeutic target for patients with HF and T2DM. SGLT2-inhibitors are uniquely poised to fill this area of need as they have consistently shown to increase plasma ketone levels by increased hepatic synthesis from lipolysis and reduced renal loss .  have beneficial cardiac effects including increased survival following ischaemia–reperfusion injury, reduced apoptosis, increased angiogenesis and improved myocardial contractility –104. HowThe sympathetic nervous system is closely linked to the pathophysiology of HF and T2DM. Hypoperfusion from HF results in sympathetic activation, however persistently elevated sympathetic activity results in ventricular remodelling that perpetuates HF . SimilarInterestingly, SGLT2-inhibitors have also shown potential in modulating sympathetic activity, thereby breaking the vicious cycle of chronic sympathetic activation in patients with T2DM and HF. Large clinical trials thus far have shown no reflex increase in heart rate following BP reduction from SGLT2-inhibition; a surrogate marker for sympathetic blockade. Mechanistic studies have supported these findings by demonstrating lower plasma metanephrines  and reduThe pathophysiology of HF involves 2 overarching themes – abnormal loading conditions on the heart (the failing pump) and dysfunctional mechanics at the cellular level (the failing cardiomyocyte) . InsofarNevertheless, there may be a differential effect depending on where the patient is on the HF disease spectrum. In the early stages, with adequate cellular functional reserve, optimizing the molecular milieu within the cardiomyocyte with SGLT2-inhibitor therapy  along with modest improvements in ventricular loading (i.e. diuresis and reduced BP) confers a large benefit. However, that benefit gradually wanes as cellular dysfunction, and indeed, cellular death occurs. In such advanced circumstances, improving haemodynamic loading becomes key, with the heavy lifting being done by neurohormonal modulators and SGLT2-inhibitors playing an important, but secondary role. If this hypothesis is true, then SGLT2-inhibitors will very likely be the first therapeutic agent to be beneficial in patients with HFpEF where cardiomyocyte dysfunction predominates – we will have to wait and see if this is borne out in the large clinical trials currently underway.Some have dubbed treating HF as the “last great battle in the war on cardiovascular disease” . Despite