Fluid resuscitation is the recommended management of shock, but increased mortality in febrile African children in the FEAST trial. We hypothesised that fluid bolus-induced deaths in FEAST would be associated with detectable changes in cardiovascular, neurological, or respiratory function, oxygen carrying capacity, and blood biochemistry.We developed composite scores for respiratory, cardiovascular, and neurological function using vital sign data from the FEAST trial, and used them to compare participants from FEAST with those from four other cohorts and to identify differences between the bolus (n=2097) and no bolus (n=1044) groups of FEAST. We calculated the odds of adverse outcome for each ten-unit increase in baseline score using logistic regression for each cohort. Within FEAST participants, we also compared haemoglobin and plasma biochemistry between bolus and non-bolus patients, assessed the effects of these factors along with the vital sign scores on the contribution of bolus to mortality using Cox proportional hazard models, and used Bayesian clustering to identify subgroups that differed in response to bolus. The FEAST trial is registered with ISRCTN, number ISRCTN69856593.Increasing respiratory , neurological , and cardiovascular scores  were associated with death in FEAST , and with adverse outcomes for specific scores in the four other cohorts. In FEAST, fluid bolus increased respiratory and neurological scores and decreased cardiovascular score at 1 h after commencement of the infusion. Fluid bolus recipients had mean 0·33 g/dL (95% CI 0·20–0·46) reduction in haemoglobin concentration after 8 h (p<0·0001), and at 24 h had a decrease of 1·41 mEq/L  in mean base excess and increase of 1·65 mmol/L  in mean chloride, and a decrease of 0·96 mmol/L  in bicarbonate. There were similar effects of fluid bolus in three patient subgroups, identified on the basis of their baseline characteristics. Hyperchloraemic acidosis and respiratory and neurological dysfunction induced by saline or albumin bolus explained the excess mortality due to bolus in Cox survival models.In the resuscitation of febrile children, albumin and saline boluses can cause respiratory and neurological dysfunction, hyperchloraemic acidosis, and reduction in haemoglobin concentration. The findings support the notion that fluid resuscitation with unbuffered electrolyte solutions may cause harm and their use should be cautioned. The effects of lower volumes of buffered solutions should be evaluated further.Medical Research Council, Department for International Development, National Institute for Health Research, Imperial College Biomedical Research Centre. Both adult and paediatric resuscitation guidelines recommend boluses of intravenous fluid, most commonly crystalloid or colloid solutions, with 20–60 mL/kg bodyweight being recommended in the first hour of resuscitation.Evidence before this studyFluid resuscitation has been recommended initial treatment of sepsis and septic shock for more than 40 years. The practice of fluid resuscitation in sepsis has strong theoretical and physiological justification, because volume expansion is believed to correct the hypovolaemia occurring in sepsis due to capillary leak and pathological vasodilatation, and thus increases cardiac output by achieving a more favourable position on the Starling curve.We searched PubMed from Sept 1, 1974, to July 31, 2018, using the search term “fluid resuscitation in sepsis” restricting the search to human studies, and English language.We identified 1827 publications, including reviews, observational studies, and clinical trials. We found numerous observational studies and randomised trials comparing different types of fluid resuscitation, including colloids  and different crystalloids . We identified only one randomised controlled trial  in which volume resuscitation by itself was compared with maintenance fluids alone. In the FEAST trial, more than 3000 children with fever and clinical signs of impaired perfusion were randomly assigned to volume resuscitation with boluses of 5% albumin or normal saline, or maintenance fluids alone. The trial was stopped because of increased mortality in the bolus groups. Since FEAST is the only randomised controlled trial comparing bolus volume expansion with a no bolus control group, and showed harm from fluid expansion, it provides unique data through which to understand the physiological effects of fluid resuscitation.Added value of this studyThe findings of the FEAST trial—that volume expansion with 20-40 mL/kg of normal saline or albumin increased mortality in critically ill children, compared with no bolus—has been intensively debated by the intensive care and resuscitation community, and has left practitioners uncertain as to what the best resuscitation practice should be. To date, there has been no clear explanation as to why volume resuscitation was harmful in FEAST. Because FEAST was done in resource-poor hospitals in Africa, where other aspects of intensive care were not available, including mechanical ventilation and inotropic drugs, it provides unique data about the physiological effects of bolus volume expansion without the interference of other intensive care methods.We compared sequential haemodynamic and vital sign data, haemoglobin, and blood chemistry in the bolus and no bolus groups of FEAST. Using novel composite scores to compare changes in respiratory, cardiovascular, and neurological function between the bolus and no bolus groups, we found that volume expansion with either 5% albumin or normal saline caused worsening of respiratory function and increased signs of raised intracranial pressure, but improved cardiovascular function. Bolus recipients had lower haemoglobin concentrations at 8 h, lower bicarbonate, and increased base deficit and chloride at 24 h after the bolus infusion. Regression modelling using the physiological variables significantly altered by bolus suggest that the increased mortality in FEAST can be explained by bolus-induced worsening in respiratory and neurological function, haemodilution, and hyperchloraemic acidosis.Implications of all the available evidenceDespite the universal belief that volume expansion with crystalloid or colloidal fluid is beneficial in sepsis, and the inclusion of bolus volume expansion in current treatment recommendations, the FEAST trial showed fluid bolus increased mortality. The results of our analysis of the vital signs, haematological data, and biochemical data from FEAST provide a biologically plausible explanation for the adverse outcomes associated with bolus fluids. A modest bolus (20-40 mL/kg) of normal saline or albumin resulted in prolonged hyperchloraemic acidosis, reduced haemoglobin concentration, and worse respiratory and neurological function in the early hours after infusion, despite transient improvement in cardiovascular function, and explained the increase mortality in bolus recipients. Of these effects, saline-induced hyperchloraemic acidosis was a major contributor to the adverse outcome in bolus recipients. Our data suggest that normal saline and other unbuffered crystalloid solutions should be avoided in resuscitating seriously ill patients. Because volume resuscitation is associated with deterioration of respiratory function and neurological function in some patients, caution in use of fluids might be needed in patients with respiratory or CNS compromise, and trials of lower volumes of buffered solutions are now needed to establish whether the beneficial effects of fluid on cardiovascular function can be achieved with less risk of respiratory and neurological compromise.By contrast with practice in resource-rich countries, fluid bolus resuscitation is not routine in Africa and other resource-poor regions. There has been an equipoise in many African hospitals between clinicians who recommend fluid resuscitation and those who avoid rapid fluid bolus resuscitation for fear that it might result in pulmonary or cerebral oedema. It was this equipoise in the use of fluids for resuscitation that motivated the FEAST (Fluid Expansion As Supportive Treatment) trial.Since publication of the FEAST trial in 2011, there has been extensive debate about the generalisability of the findings and their implications for practice.Research ethics committees in Africa and other resource-poor regions are unlikely to approve further trials of fluid resuscitation in light of the increased mortality shown in FEAST. Conversely, neither clinicians nor ethics committees in most resource-rich countries are likely to approve a trial in which fluid bolus is withheld from critically ill patients, because of the widely held belief in the benefits of fluid resuscitation. Many clinicians involved in emergency care in Europe and North America have chosen to disregard the results of the FEAST trial as being unique to the African context and argue that the findings are not relevant in well resourced settings, where the availability of ventilators and inotropes can ameliorate any negative effects of bolus fluids.In light of the unresolved questions raised by FEAST, we believed that there was both a need and an obligation to fully use all the available data from the FEAST trial to understand why fluid bolus was associated with increased mortality. We postulated that the increased mortality in bolus recipients resulted from a measurable adverse effect of bolus fluids on cardiovascular function, respiratory function, raised intracranial pressure or neurological function, oxygen carrying capacity, or the biochemical and acid base status of bolus recipients. To test this hypothesis we developed composite scores to measure respiratory function, cardiovascular function, and identify raised intracranial pressure, using sequential vital sign data from FEAST, and compared the function of each system, as well as plasma biochemistry and acid-base balance, in the bolus and no bolus groups of FEAST.We hypothesised that fluid bolus-induced deaths in FEAST would be associated with detectable changes in cardiovascular, neurological, or respiratory function, oxygen carrying capacity, or blood biochemistry. Several scoring systems exist to predict severity and outcome in paediatric intensive care.We analysed data from FEAST and two published paediatric studies of critically ill children In the FEAST study,In the UK meningococcal disease study,In the Malawian cerebral malaria cohort, between Jan 4, 2010, and June 5, 2011, 448 children with cerebral malaria were admitted to Queen Elizabeth Central Hospital, Blantyre, Malawi, and reported in a study of raised intracranial pressure.In the South African cohort, 61 children receiving fluid resuscitation for presumed sepsis and gastroenteritis at Red Cross Children's Hospital, Cape Town, South Africa were recruited between Feb 1, 2013, and June 1, 2013. Vital signs and other clinical details were recorded on admission to hospital and related to outcome (admission to intensive care or death).In the St Mary's cohort, vital sign data and outcome  were collected for 18 863 children attending the emergency department at St Mary's Hospital, London, UK, from June 10, 2014, to March 9, 2015.All studies were approved by the institutional review board of the relevant centre. Details of the recruitment procedure, consent procedures, ethics, and institutional approvals for the FEAST, meningococcal, and Malawian cerebral malaria studies have been reported.We analysed the distribution of respiratory, cardiovascular, and neurological function scores among FEAST participants and within each of the four cohorts, and for FEAST compared scores between those who received bolus and those who did not. We used logistic regression to calculate the odds of adverse outcome  for each ten-unit increase in baseline score. Within FEAST participants, we examined the effect of bolus on the proportion of patients with extreme derangement of vital sign scores or physiological parameters, and assessed the effect of volume of fluid administered. Finally, we used Bayesian analysis to identify subgroups with differing physiological derangement who might differ in response to fluids, and to explore how the multiple simultaneous physiological changes caused by fluid bolus were associated with excess deaths in bolus recipients.The physiological basis and development of composite scores for respiratory function, cardiovascular function, and detection of raised intracranial pressure and neurological function are described in detail in the web To assess respiratory compromise we used respiratory rate (which increases as respiratory function worsens) and oxygen saturation (which decreases as respiratory function worsens). Vital signs were normalised as described in the Respiratory score=(respiratory rate – mean respiratory rate for age) + 5 × (100 – oxygen saturation)Cardiovascular responses to developing shock maintain blood pressure with vasoconstriction and increasing heart rate. If these fail, blood pressure falls. We combined age-adjusted degree of tachycardia, age-adjusted degree of hypotension, and weighted peripheral capillary refill time into a measure of cardiovascular compromise:Cardiovascular score=(heart rate – mean heart rate for age) + (mean systolic blood pressure for age –systolic blood pressure) + 25 × (capillary refill time)Raised intracranial pressure is associated with decreasing conscious level, bradycardia, and hypertension (the latter two are components of Cushing's triad).Neurological score=(blood pressure – mean blood pressure for age) + (mean heart rate for age – heart rate) + 25 × In FEAST, scores were calculated at baseline (before commencing fluid administration) and 1 h, 4 h, 8 h, 24 h, and 48 h after commencing fluid bolus or maintenance fluids. In the other cohorts, only baseline data  were used.In FEAST, haemoglobin concentration and plasma lactate were measured at baseline, 8 h, and 24 h; base excess, pH, and electrolytes were measured at baseline and 24 h.All available data in each dataset were used , with thWe calculated the odds of adverse outcome for each ten-unit increase in baseline score using logistic regression. In FEAST and the UK meningococcal disease cohorts the outcome was death; in the Malawi cohort the outcome was neurological sequelae or death; in the South African cohort the outcome was admission to intensive care, or death; in the St Mary's Hospital cohort the outcome was admission to hospital or intensive care.To show the association of each physiological score with disease severity, and to gain insight into the association of the scores with specific pathophysiological phenotypes such as cerebral malaria and septic shock, we compared the distribution of scores between cohorts using the Mann-Whitney test.Saline and albumin bolus were equally associated with increase in mortality in FEAST.To examine whether bolus was associated with an increased proportion of patients with extreme derangement of physiological parameters we created separate bins across the distribution of each variable, and then calculated the relative risk for bolus recipients versus no bolus recipients being in each bin. We did this for both change from baseline value, and for absolute values of each parameter.To assess the effect of volume of fluid administered, we did a secondary analysis based on the actual volume of fluid boluses received within the first 2 h. On the basis of the bimodal distribution, we categorised low volume as less than 30 mL/kg and high volume as 30 mL/kg or more. Because the decision to administer more than 20 mL/kg  was not randomised but was based on clinical assessment of ongoing impaired perfusion, we anticipated some bias due to severity of illness at baseline. We therefore analysed both change from baseline of each physiological score, biochemical variable, and haemoglobin, as well as the actual distribution observed in high-volume and low-volume bolus groups.To identify subgroups with differing physiological derangement who might differ in response to fluids, we used Bayesian Dirichlet process clustering as implemented in the R package PreMiuM,To explore how the multiple simultaneous physiological changes caused by fluid bolus were associated with excess deaths in bolus recipients (the primary finding in FEAST) we examined the relationship between death and the combination of physiological scores, base excess, bicarbonate, chloride, and haemoglobin concentration in principal component analysis and Cox proportional hazard regression models . We builThe FEAST trial is registered with ISRCTN, number ISRCTN69856593.The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.Details of each cohort are shown in Physiological scores in FEAST were highest in the first hours following admission, and decreased over the next 48 h . Median In FEAST, odds of death for each ten-unit increase in baseline score was 1·09 (95% CI 1·07–1·11) for respiratory score, 1·26 (1·21–1·31) for neurological score, and 1·09 (95% CI 1·05-1·14) for cardiovascular score  bolus . HoweverThe biochemical changes we observed in FEAST suggested that boluses of albumin or saline could cause hyperchloraemic metabolic acidosis. To explore this further, we did a post-hoc analysis of the effect of bolus on pH and respiratory compensation. 719 (35%) of 2082 FEAST participants with pH measurements at baseline had acidosis pH<7·35; , and theWe identified three clusters of patients on the basis of their baseline characteristics . ClusterThe clusters differed markedly in mortality . In clusr=–0·56, p<0·0001; Considering all participants, there was a negative correlation between haemoglobin and lactate concentrations ; base deficit and chloride concentrations increased, and plasma bicarbonate decreased (changes consistent with hyperchloraemic acidosis). Although the linear regression analysis showed a modest, but significant, change in the mean value of each variable, there were much greater changes observed in some individuals, with increased proportions of bolus recipients having extreme adverse physiological derangement in physiological scores, haemoglobin concentration, and blood chemistry. Most of these changes were greater in patients receiving higher fluid volumes. The changes in physiological scores were most evident at early timepoints, but early deaths and the increased mortality in bolus recipients makes evaluation of later timepoints difficult. The only beneficial physiological change observed was a reduction of cardiovascular score, in keeping with the impression of clinicians that perfusion improved during fluid resuscitation.When we included the bolus-induced changes in respiratory function, cardiovascular function, neurological function, and biochemical changes in Cox survival models, the increased mortality in bolus recipients in FEAST was explained by the physiological changes we identified. Analysis of the effect of each component in the models showed that the excess deaths in bolus recipients were largely explained by worsening neurological score , increased base deficit, and worsening respiratory score. Of the individual components analysed, bolus-induced increased base deficit, worsening conscious level, and oxygen saturation were the major contributors to the excess mortality associated with bolus.Since the 1990s, there have been increasing reports, including small case studies, randomised trials in healthy volunteers and patients, and experimental studies in animals that have documented hyperchloraemic acidosis after infusion of unbuffered salt solutions .30, 31, Worsening respiratory function and increase in signs of raised intracranial pressure, which we documented in bolus recipients, are well known effects of fluid administration in some critically ill patients. Indeed, many guidelines for management of shock recommend intubation, and elective mechanical positive pressure ventilation for patients who remain with signs of shock after initial bolus volume infusion because of the recognition that continued fluid resuscitation is likely to cause respiratory deterioration.The worsening neurological score in bolus recipients and the contribution of deteriorating neurological score to the excess mortality in bolus recipients is also an expected effect of bolus fluids in patients who have increased intracranial pressure. More than 50% of the patients included in FEAST had severe malaria, and many of these patients had evidence of cerebral malaria. Evidence of raised intracranial pressure has been well documented in cerebral malaria by direct measurement,Our cluster analysis revealed heterogeneity within FEAST. The majority of patients (cluster one), had least severe derangement of all three physiological scores, adequate haemoglobin concentrations, and remarkably low mortality in the absence of bolus. Fluid bolus in this cluster caused worsening of respiratory and neurological function, reduced haemoglobin concentration and increased acidosis. The second cluster, characterised by low haemoglobin concentration, high lactate concentration, and high cardiovascular score, identified patients with severe anaemia. The inverse relationship observed between haemoglobin and lactate concentrations, and lower bicarbonate and base excess suggests that the baseline hyperlactataemia and metabolic acidosis is largely driven by impaired oxygen carrying capacity. In this cluster, bolus fluids caused worsening of respiratory and neurological function and increased acidosis. The third cluster, characterised by high respiratory and neurological scores had high mortality (23–26%). Patients in the bolus group had lower haemoglobin concentrations and higher chloride concentrations than those in the non-bolus group.Identification of clusters within the large FEAST cohort of ill children has important therapeutic implications. The low mortality in the absence of bolus fluids in cluster one suggests that this group did not require any additional intervention, and most would recover with treatment of the infection alone. In cluster two, in which anaemia was the major problem, we believe blood transfusion was required, and bolus probably worsened tissue oxygen delivery. In cluster three, in which respiratory failure and neurological impairment were the main physiological derangements, we believe that respiratory support was needed to reduce mortality.Combination of the physiological derangements caused by bolus explained mortality better than the pre-bolus values of the same parameters, indicating that through their combined effects, the bolus-induced changes plausibly explain the effect of bolus on mortality in FEAST . BecauseThe findings in our analysis differ from the previous analysis of modes of death in FEASTOur study has several limitations. Although our physiological scores are associated with outcome in multiple different cohorts, they have not been independently validated. In particular, the weightings were chosen to reflect the perceived importance of each variable as a measure of organ system function. They were not derived from the data or from previous studies. The scores should be seen as means of studying changes in physiology between arms of a trial, rather than as outcome predictors, for which there are already a number of scoring systems available.The FEAST trial included children with widely differing diseases and underlying physiology. We found that fluid bolus achieved the intended improvement in cardiovascular function at the cost of worsening respiratory and neurological function, acidosis, and reduced haemoglobin. Our finding that within FEAST there are clusters with differing physiological derangements suggests particular need for caution in fluid administration to patients with established respiratory failure, neurological failure, severe acidosis, or anaemia, and the need for better detection and specific management of the physiological derangements in each individual patient. The physiological scores we have developed might be useful in identifying derangement in specific organ systems.The combination of adverse physiological effects we observed, associated with both albumin and saline bolus, raises questions about both the volume and type of fluid used in resuscitation. Balanced salt solutions such as Ringer's lactate or plasmalyte  are associated with more rapid resolution of acidosis than 0·9% saline, and in recent clinical trials they were associated with improved outcomes.


Adoptive cell transfer (ACT) is a form of personalised immunotherapy which has shown promising results in metastasised cancer. For this treatment, autologous T lymphocytes are selected and stimulated in vitro before re-administration in large numbers. However, only a fraction of patients benefit from ACT, and it is not yet known what biomarkers can predict treatment outcome. In this review, we describe what tumour characteristics are associated with response to ACT. Based on the current knowledge, the best candidate biomarker for a good anti-tumour response seems to be a large number of neoantigens with a homogeneous distribution across the tumour in combination with sufficient MHC-I expression level. Additionally, it is necessary to be able to isolate a diverse population of T cells reactive to these neoantigens from tumour tissue or peripheral blood. Additional promising candidate biomarkers shared with other cancer immunotherapies are a large number of tumour-infiltrating cytotoxic and memory T cells, normal levels of glycolysis, and a pro-inflammatory cytokine profile within the tumour. Intense research in this field will hopefully result in identification of more biomarkers for cancers with low mutational load. A cell needs to successively acquire characteristics via genetic modifications in order to become cancerous and escape the mechanisms that control cell proliferation and tissue integrity  is a personalised cancer immunotherapy where lymphocytes acquired from the patient are used. These lymphocytes are selected based on how well they recognise the tumour and are restimulated in vitro, in order to revert them back to efficient immune cells. Depletion of lymphocytes in the patient prior to readministration of specific lymphocytes considerably improves the efficacy of ACT  that are either secreted or present in tumour-derived exosomes   before and during treatment of 29 NSCLC patients with anti-PD-1 or anti-PD-L1 antibodies , which can also be present in healthy cells, but often expressed in much higher levels by tumour cells; and tumour-specific antigens (TSAs) which are unique for tumours (Kelderman and Kvistborg Viruses like human papillomavirus (HPV) and Epstein-Barr virus (EBV) have the potential to cause cancer. As these viral antigens are rather different than self-peptides, they form excellent targets for ACT. This was also illustrated with successful ACT trials using CTLs selected for their reactivity to these viruses Table . In a stThe identification of neoantigens on a large scale requires whole exome sequencing (WES), in combination with algorithms predicting possible immunogenic mutations    help to prevent autoimmunity and maintain peripheral tolerance. Tregs carry TCRs specific for self-peptides and can suppress the proliferation of naive T cells which respond to the same epitope. The presence of Tregs in the TME is widely studied, leading to varying conclusions regarding their influence on the anti-tumour immune response   with gene expression of immune markers in 12 cancer types  the entire tumour can be considered the most strict criterion, since this cannot be replaced by or improved with additional treatment Fig. .This review is limited to application of adoptive cell transfer to treat solid tumours with the patient’s cytotoxic CD8+ T cells. However, ACT can also be combined with other immunotherapies or conducted with different types of immune cells, for example CD4+ cells (Friedman et al. Using a combination of different biomarkers is advisable, especially because of the complexity of immunological interactions. Before the biomarkers discussed in this review can be used as biomarker, they need to be validated for specific tumour types and in multiple large patient cohorts to overcome the diversity caused by intrinsic differences between tissues that contain the tumours and their interactions with the immune system. In addition, for a biomarker to be useful in a therapeutic setting, it is important that assays are standardised, reproducible and fast (Willis and Lord


Micro magnetic field (MMF) sensors developed employing complementary metal oxide semiconductor (CMOS) technology are investigated. The MMF sensors, which are a three-axis sensing type, include a magnetotransistor and four Hall elements. The magnetotransistor is utilized to detect the magnetic field (MF) in the x-axis and y-axis, and four Hall elements are used to sense MF in the z-axis. In addition to emitter, bases and collectors, additional collectors are added to the magnetotransistor. The additional collectors enhance bias current and carrier number, so that the sensor sensitivity is enlarged. The MMF sensor fabrication is easy because it does not require post-CMOS processing. Experiments depict that the MMF sensor sensitivity is 0.69 V/T in the x-axis MF and its sensitivity is 0.55 V/T in the y-axis MF. Magnetic field (MF) sensors, which are important components, are applied in industrial apparatuses, automation equipment, cable-stayed bridges, electrical devices and portable electronic instruments ,2,3,4. TA one-axis magnetotransistor MMF sensor, presented by Tseng , was fabVarious MEMS actuators and sensors, which are manufactured utilizing a CMOS process, are called CMOS-MEMS devices ,25,26,271, B2, B3 and B4 are the bases, C1, C2, C3 and C4 are the collectors, AC1, AC2, AC3 and AC4 are the additional collectors and H1, H2, H3, H4, H5, H6, H7 and H8 are the electrodes of the Hall elements. C1, VC2, VC3 and VC4 are the bias voltage of the collectors, VAC1, VAC2, VAC3 and VAC4 are the bias voltage of the additional collectors, VB1, VB2, VB3, and VB4 are the bias voltage of the bases, Vout-AC1, Vout-AC2, Vout-AC3 and Vout-AC4 are the output voltages and the symbol (4) denotes the corresponding circuit repeated four times.The MMF sensor includes a magnetotransistor and four Hall elements. The magnetotransistor is employed to detect MF in the x-axis and y-axis, and the four hall elements are utilized to sense MF in the z-axis. In addition to the emitter, bases and collectors, the additional collectors are introduced into the magnetotransistor. The additional collectors can increase bias current, so that the emitter induces more electron carriers. Shallow trench isolation (STI) oxide is used to confine the current direction, so that leakage current reduces. 1/AC3, the bases B1/B3 and the collectors C1/C3. Given a magnetic field in the y-axis, carriers (on the right in 1 to the base B1 and collector C1. Most of carriers move to the additional collector AC1, resulting in the current increment of the additional collector AC1. At the same time, carriers (on the left in 3 to the base B3 and collector C3, leading to the current decrement of the additional collector AC3. Therefore, this action produces a voltage difference between the additional collectors AC1 and AC3 in the y-direction MF. As shown in 1 and AC3 respectively connect to a resistor R, so the voltage difference of the additional collectors AC1 and AC3 is obtained by Vout-AC1 − Vout-AC3, which is the sensor output voltage in the y-axis MF. Similarly, when the bias voltages are applied to the collectors, the bases and the additional collectors, carriers produce a movement from the emitter to the additional collectors AC2/AC4, the bases B2/B4 and the collectors C2/C4. Given a magnetic field in the x-axis, carriers that move to the additional collector AC2 are bent downward by Lorentz force, leading to the current decrement of the additional collector AC2. Additionally, carriers that move to the additional collector AC4 are bent upward by the Lorentz force, resulting in the current increment of the additional collector AC4. The current between both additional collectors AC2 and AC4 generates an imbalance, so the additional collectors AC2 and AC4 produce a voltage difference. As shown in 2 and AC4 can be obtained by Vout-AC4 − Vout-AC2, which is the sensor output voltage in the x-axis MF.1, AC2, AC3 and AC4. Given an MF in the z-axis, carriers are bent toward the Hall electrodes H1, H3, H5 and H7 by the Lorentz force. The current causes an imbalance between the electrodes H1 and H2, leading to the generation of a Hall voltage between the electrodes H1 and H2. Similarly, the Hall voltages between the electrodes H3/H4, H5/H6 and H7/H8, respectively, are generated in z-direction MF. The Hall voltages in series are the sensor output voltages in z-axis MF.The MMF sensor has four Hall elements used to detect z-direction MF. The Sentaurus TCAD, which is a finite element method software, was utilized to simulate the MMF sensor performance. According to the structure in In this simulation, bias voltages of 1.5 V, 4.5 V and 4.5 V were supplied to bases, collectors and additional collectors, respectively. A magnetic field of 250 mT was given in the y-axis. The carrier density distribution of the MMF sensor in the z-direction magnetic field was computed with the same simulation approach. In this computation, bias voltages of 1.5 V, 4.5 V and 4.5 V were supplied to bases, collectors and additional collectors, respectively. A magnetic field of 250 mT was applied in the z-axis. The layout of the MMF sensor was designed in accordance with the structure in A magnetic testing system was employed to measure the MMF sensor performance. 1/AC3 in the MMF sensor. B = 1 V, VC = 0.6 V and VAC = 0.6 V, the sensor was insensitive to MF. The sensor became more sensitive to MF at VB = 1.25 V, VC = 2.04 V and VAC =2.04 V, and its output voltage changed from −43.7 mV at −220 mT to 38.6 mV at 220 mT. When VB = 1.5 V, VC = 3.38 V and VAC = 3.38 V, the sensor output voltage obviously increased under different MF. When VB = 1.75 V, VC = 4.58 V and VAC = 4.58 V, the sensor output voltage varied from −120 mV at −220 mT to 119 mV at 220 mT, and the method of least squares was adopted to evaluate the linear regression of the curve. The evaluation obtained that the regression line had a slope of 0.55 V/T and a standard deviation of 5.4 mV. Thereby, the MMF sensor sensitivity in the y-direction MF was 0.55 V/T at bias voltage VB = 1.75 V, VC = 4.58 V and VAC = 4.58 V. The MMF sensor was composed of a magnetotransistor and four Hall elements. The magnetotransistor was used to detect MF in the x- and y-directions, and the Hall elements were utilized to measure the magnetic field in the z-direction. First, the MMF sensor performance in the x-direction MF was tested. The MMF sensor was set in the magnetic testing system. An MF range of −220–220 mT generated by the MF generator was supplied to the MMF sensor, and the MF magnitude was calibrated using the Gauss-meter. Bias voltages were applied to the bases, collectors and additional collectors. An MF in the y-direction was applied to the MMF sensor. The digital multimeter measured the voltage difference of the additional collectors AC2/AC4 in the MMF sensor. B = 1 V, VC = 0.6 V and VAC = 0.6 V. The sensor was more sensitive to MF at VB = 1.25 V, VC = 2.04 V and VAC = 2.04 V, and it output voltage varied from −46.8 mV at −220 mT to 39 mV at 220 mT. When VB = 1.5 V, VC = 3.38 V and VAC = 3.38 V, the output voltage enlarged under different MF. When VB = 1.75 V, VC = 4.58 V and VAC = 4.58 V, the output voltage changed from −162 mV at −220 mT to 140 mV at 220 mT, and the method of least squares was used to calculate the linear regression of the curve. The calculation obtained that the regression line had a slope of 0.69 V/T and a standard deviation of 12 mV. Thereby, the MMF sensor sensitivity in the x-direction MF was 0.69 V/T at bias voltage VB = 1.75 V, VC = 4.58 V and VAC = 4.58 V. With the same testing approach, the sensing performance of the MMF sensor in the x-direction MF was measured. A MF in the x-direction was applied to The MMF sensor. The digital multimeter recorded the voltage difference of the additional collectors ACB = 1 V, VC = 0.6 V and VAC = 0.6 V. When bias voltage VB, V and VAC increased, the sensor output voltage became large. When VB = 1.75 V, VC = 4.58 V and VAC = 4.58 V, the MMF sensor output voltage varied from −20.5 mV at −220 mT to 20 mV at 220 mT, the curve slope of which was about 0.09 V/T. Thereby, the MMF sensor sensitivity in the z-direction MF was 0.09 V/T at bias voltage VB = 1.75 V, VC = 4.58 V and VAC = 4.58 V.The sensing performance of the MMF sensor in the z-direction MF was tested. An MF in the z-direction was applied to the MMF sensor. The digital multimeter measured the output voltage of the Hall electrodes in the MMF sensor. The Hall voltage of the MMF sensor in the z-direction MF was recorded. The characteristics of the MMF sensor in the x- and y-direction MF should be the same owing to the MMF sensor being a symmetric structure. Actually, as shown in B = 1.75 V, VC = 4.58 V and VAC = 4.58 V, and its sensitivity was 0.55 V/T in the y-direction MF at bias voltage VB = 1.75 V, VC = 4.58 V and VAC = 4.58 V. The sensitivity was 0.09 V/T in the z-direction MF at bias voltage VB = 1.75 V, VC = 4.58 V and VAC = 4.58 V.Micro magnetic field sensors were developed employing the CMOS-MEMS technique. The MMF sensors included a magnetotransistor and four Hall elements, where the magnetotransistor could sense MF in the x-axis and y-axis and four Hall elements could detect MF in the z-axis. Additional collectors were added to the magnetotransistor, so that bias current increased and the emitter induced more electron carriers, resulting in enhancing the sensor sensitivity. Manufacturing of the MMF sensors was simple as they did not require post-CMOS processing. Experiments showed that the MMF sensor sensitivity was 0.69 V/T in the x-direction MF at bias voltage V


Chemical probes are of central relevance for chemical biology. To unambiguously explore the role of target proteins in triggering or mediating biological functions, small molecules used as probes should ideally be target-specific; at least, they should have sufficiently high selectivity for a primary target. We present a thorough analysis of currently available activity data for designated chemical probes to address several key questions: How well defined are chemical probes? What is their level of selectivity? Is there evidence for additional activities? Are some probes “better” than others? Therefore, highly curated chemical probes were collected and their selectivity was analyzed on the basis of publicly available compound activity data. Different selectivity patterns were observed, which distinguished designated high-quality probes. In 2003, the Human Genome Project was completed ,2, whichChemical probes have stringent requirements. They must be capable of selectively binding to targets and modulating protein functions in their physiological context; ideally, probes should be target-specific. Furthermore, if they are used in a phenotypic context, it must ultimately be possible to deconvolute and identify targets that are responsible for an interesting biological readout ,12,13,14In addition to defining key requirements for chemical probes, the scientific community has set out to evaluate and rate probes and validate their use in cellular or in vivo model systems. For example, this is attempted through submission of candidate compounds to the Chemical Probes Portal ,14, whicDespite ongoing efforts to set high standards for chemical probes, the scientific community is still facing problems due to poor characterization of small molecule modulators, improper use of probes, and outdated recommendations ,12,13. TIn this study, we report a comprehensive analysis of highly curated chemical probes from the Chemical Probes Portal on the basis of compound activity data available in ChEMBL , the majBased on the classification in Protein kinases share an adenosine triphosphate (ATP) (cofactor)-binding site that is highly conserved across the human kinome . The majFor each chemical probe, the promiscuity degree (PD) was defined as the number of its unique targets on the basis of ChEMBL activity records or target annotations of the Chemical Probes Portal. Activity of a compound against multiple targets including unrelated targets is generally rationalized as promiscuity, whereas specificity implies exclusive activity against a single target. Furthermore, selectivity is best understood as activity against very few related targets, for example, a primary target and one or two others from the same family. Hence, formally it is difficult to draw a line between low levels of compound promiscuity and selectivity. However, since promiscuity also applies to increasingly large numbers of targets, it is advantageous to introduce the promiscuity degree as a measure of multi-target activity, rather than selectivity degree.PDs were calculated on the basis of medium-confidence activity data  and high-confidence data (level 2) applying two different potency thresholds . In the following, the ≤10,000 nM threshold is referred to as ≤10μM. For each probe, four PDs were obtained by combining data confidence level 1 and 2 with the two potency thresholds, and a fifth value was calculated on the basis of target annotations provided by the Chemical Probes Portal. The results obtained for the 67 probes are reported in Based on the information provided by the Chemical Probes Portal, probes were active against one to four targets, with on average 1.6 targets per probe. The majority of probes only had a single target annotation, consistent with proposed high-quality probe characteristics. On the basis of activity data from ChEMBL, a somewhat different picture emerged. For the data confidence level 2/≤100 nM threshold combination, most probes retained their Portal-based PD value, yielding a similar average of 1.7 targets per probe. For the confidence level 2/≤10 μM threshold combination, nearly half of the probes retained their PD. However, for others, an increase in promiscuity was detected, yielding an average of 2.6 targets per probe. In some cases, a PD > 5 was observed. These findings indicated that a subset of probes were at least weakly active against multiple targets.Next, data confidence criteria were relaxed and PD values calculated at confidence level 1 applying a potency threshold ≤10 μM. In this case, about half of the qualifying probes also retained their Portal-based PD value. However, for other probes a substantial increase in promiscuity was observed, resulting in an average PD of 6.3 targets per probe. When applying the more rigorous ≤100 nM threshold at level 1, the mean PD decreased again to 2.2 targets per probe, revealing that reported weak activities at medium data confidence were largely responsible for the significant increase in the average PD.Taken together, the results in NVS-PAK1-1  is an alRuxolitinib  is an ATChemical Probes Portal also reports a class of small molecules termed “historic compounds” . As the For the confidence level 2 and 1/≤10 μM threshold combinations, activity annotations for 127 of the 164 historic compounds of Chemical Probes Portal were identified in ChEMBL, applying the same criteria as for chemical probes. For the level 2 and 1/≤100 nM threshold combinations, activity annotations were detected for 94 historic compounds. In general, historic compounds had higher PD values than current chemical probes on the basis of available activity data, consistent with the Portal assessment. Hence, shortcomings of historic compounds were attributable to limited target selectivity.Applying the confidence level 2/≤10 μM threshold combination, 67 chemical probes, combined with qualifying 233,675 bioactive compounds from ChEMBL and Bemis-Murcko (BM) scaffolds   analysis . An MMP MMPs were obtained for 49 of 67 chemical probes and 738 bioactive analogs from ChEMBL. These compounds were active against a total of 135 targets. A tripartite network was constructed to capture all structural and activity relationships. The network revealed 47 previously unobserved probe-target associations involving a subset of 16 chemical probes and 40 targets from ChEMBL. New relationships can be studied in a structure-activity context by focusing on network neighborhoods of chemical probes. An example is provided in Small molecular probes are of central importance to chemical biology. However, many currently investigated probes remain to be fully characterized. To these ends, important contributions are made by the Chemical Probes Portal, which carefully assesses candidate probes and prioritizes a set of highly curated chemical probes. Herein, we have further investigated designated high-quality probes by systematic analysis of available activity data for probes and closely related bioactive compounds. Our analysis adds another layer to the characterization of probes for chemical biology. Taking different data confidence and potency criteria into account, we show that ~50% of designated high-quality probes are target-selective when all available activity data are considered, consistent with expert curation; an encouraging finding. This applies to chemical probes directed against kinase or non-kinase targets. On the other hand, activity data analysis also differentiates between probes and identifies a subset of putative high-quality probes for which selectivity cannot be supported on the basis of currently available data, as summarized in Chemical probes were extracted from Chemical Probes Portal (accessed in July 2018) ,14, whici or IC50 values with “=” standard relation), use of the nanomolar (“nM”) activity unit, and presence of fully consistent “activity comments” in ChEMBL. To investigate selectivity characteristics of chemical probes, two potency thresholds were applied to activity data at confidence levels 1 and 2, i.e., ≤10,000 nM (≤10μM) and ≤100 nM. For each chemical probe, PD values were calculated for each combination of a confidence level and potency threshold, yielding four PD values per probe from ChEMBL data. An additional PD value was calculated on the basis of target annotations reported by the Chemical Probes Portal. At confidence level 2 applying the ≤10 μM potency threshold, at least one activity record for a human target was obtained for 67 of 80 pre-selected chemical probes. These 67 probes provided our basis set for subsequent analysis. We also searched ChEMBL for 164 historic compounds designated by the Chemical Probes Portal on the basis of the same criteria applied to chemical probes.For selected chemical probes, available activity data for human targets were extracted from ChEMBL (release 24). Activity data were evaluated at two different confidence levels including level 1 (medium confidence) and level 2 (high confidence) according to . For levScaffold analysis of chemical probes was performed applying the Bemis-Murcko (BM) scaffold concept . BM scaf


Fluid satiation, or quenching of thirst, is a critical homeostatic signal to stop drinking; however, its underlying neurocircuitry is not well characterized. Cutting‐edge genetically encoded tools and techniques are now enabling researchers to pinpoint discrete neuronal populations that control fluid satiation, revealing that hindbrain regions, such as the nucleus of the solitary tract, area postrema, and parabrachial nucleus, primarily inhibit fluid intake. By contrast, forebrain regions such as the lamina terminalis, primarily stimulate thirst and fluid intake. One intriguing aspect of fluid satiation is that thirst is quenched tens of minutes before water reaches the circulation, and the amount of water ingested is accurately calibrated to match physiological needs. This suggests that ‘preabsorptive’ inputs from the oropharyngeal regions, esophagus or upper gastrointestinal tract anticipate the amount of fluid required to restore fluid homeostasis, and provide rapid signals to terminate drinking once this amount has been consumed. It is likely that preabsorptive signals are carried via the vagal nerve to the hindbrain. In this review, we explore our current understanding of the fluid satiation neurocircuitry, its inputs and outputs, and its interconnections within the brain, with a focus on recent studies of the hindbrain, particularly the parabrachial nucleus. Fluid satiation is a key homeostatic signal which limits the amount of fluid ingested and prevents the potentially lethal consequences of overhydration, such as severe hyponatremia and cerebral edema, which can occur in patients with psychogenic polydipsia Goldman . Tight cThe sensation or ‘homeostatic emotion’ of fluid satiation is more than just the absence of thirst, at least initially. Based on human reports, fluid satiation is inherently pleasant, particularly after extreme dehydration  accurately replace fluid deficits immediately upon gaining access to water following dehydration; whereas other species  replace fluid more gradually and may take several hours to restore body fluid volume and composition, which is termed ‘voluntary dehydration’ McKinley . This isWhile fluid satiation generally refers to satiation for water, another key component for restoring fluid homeostasis is salt (NaCl), which is the major osmotic component of the extracellular fluid (ECF)  Input signals; (2) Neurocircuitry within the central nervous system; and (3) Outputs. The input signals for terminating water ingestion are not well understood, but likely involve a combination of preabsorptive and postabsorptive factors  thirst‐related neurons, even before plasma osmolality or volume have been restored. The input signal likely arises from receptors in preabsorptive regions which send projections to the brain , and many animals will continue to drink until they appear fatigued, suggesting that the effort of drinking and swallowing water per se is insufficient to satiate thirst Towbin . Inputs If an animal with an open gastric fistula is co‐infused with water into its intestine, there is a dose‐related decrease in sham‐drinking, but not a complete halt, suggesting that postabsorptive receptors mediate part, but not all, of the fluid satiation effect , osmoreceptors , and sodium sensors, which signal to prevent or diminish further fluid and salt intake. For example, increased blood pressure inhibits drinking, whereas lowered blood pressure enhances drinking   receptor antagonist; although only when accompanied by additional facilitatory treatments that promote fluid intake, such as angiotensin II , which also plays a key role in inhibiting fluid and NaCl intake , which selectively decreased noncaloric fluid intake, but not food intake after fasting, or salt intake after salt depletion. Calcium imaging studies demonstrated that OxtrPBN neurons rapidly increased calcium activity when drinking water after 24‐h dehydration, but not after drinking from an empty bottle or ingesting a high‐caloric liquid diet, Ensure®, indicating a selective role for OxtrPBN neurons in fluid satiation , while more robust activation suppresses both water and NaCl intake.By contrast, inactivation of OxtrPBN neurons is similar to intra‐PBN injections of methysergide (nonselective serotonin receptor antagonist) and proglumide (nonselective CCK receptor antagonist)  decreases food intake  to both the central nucleus of the amygdala (CeA) and BNST, which are heavily interconnected and which receive inputs from multiple other visceral and somatic sensory regions, suggesting an integrative centre  revealed that during elevated osmolality, VPpp neurons decrease activity within seconds after being presented with water‐predicting cues, with activity decreasing even further upon drinking water, suggesting a method of anticipating correction of osmolality  decrease water intake when activated, although the effect is relatively mild compared to OxtrPBN activation. This milder effect is likely due to activating only a subset of OxtrPBN neurons , the organum vasculosum of the lamina terminalis (OVLT) and the median preoptic nucleus (MnPO)  response in certain brain regions during water ingestion as the subjects shifted to the nonthirsty state , which demonstrates firing rates time‐locked to individual licks (Rossi et al., The endocrine and autonomic responses predominantly target the kidney to influence sodium and water excretion, such as by decreasing vasopressin secretion to limit the antidiuretic response, or by decreasing sympathetic nervous system effects on the renal tubules to prevent increased reabsorption of sodium and water Verbalis . The sym  The fluid satiation neural circuitry comprises several highly interconnected forebrain and hindbrain regions, which together act to decrease or terminate fluid intake when fluid satiated. This neural circuitry substantially overlaps with the neural circuitry for thirst, and interacts with other neural circuits, including feeding circuits and motivational circuits (Zimmerman et al. While key brain regions have already been identified, an overarching goal in understanding fluid satiation is to complete the neural circuit, tracing from input through to output, in order to understand the different components modulating fluid satiation. An essential part of understanding the fluid satiation neural circuitry will be to chemically phenotype each distinct neuronal population, which can then allow selective manipulation using genetically‐encoded technologies.To understand how fluid intake is coordinated, it will also be important to understand how the fluid‐related neural circuitry interacts with the neural circuitry for other ingestive behaviors, for example, how feeding circuits interact with fluid intake circuits to increase thirst after feeding (Zimmerman et al. Overall, genetically encoded techniques for analysing neural circuits are enabling novel insights into the fluid satiation neural circuitry, which is essential for understanding fluid homeostasis. Clinically, this is important for better managing blood pressure and hemodynamic stability, and fluid overload in conditions such as cardiac failure, renal failure, and cirrhosis with ascites, as well as overdrinking in conditions such as psychogenic polydipsia. By using genetically encoded techniques, we expect further novel insights into the fluid satiation neural circuitry in the coming years.I have no conflicts of interest to disclose.


The purpose of this work is to define fiducial points in the seismocardiogram (SCG) and to correlate them with physiological events identified in ultrasound images. For 45 healthy subjects the SCG and the electrocardiogram (ECG) were recorded simultaneously at rest. Immediately following the SCG and ECG recordings ultrasound images of the heart were also obtained at rest. For all subjects a mean SCG signal was calculated and all fiducial points  were identified and labeled in the same way across all signals. Eight physiologic events, including the valve openings and closings, were annotated from ultrasound as well and the fiducial points were correlated with those physiologic events. A total of 42 SCG signals were used in the data analysis. The smallest mean differences (±SD) between the eight events found in the ultrasound images and the fiducial points, together with their correlation coefficients (r) were: atrial systolic onset: −2 (±16) ms, r = 0.75 (p < 0.001); peak atrial inflow: 13 (±19) ms, r = 0.63 (p < 0.001); mitral valve closure: 4 (±11) ms, r = 0.71 (p < 0.01); aortic valve opening: −3 (±11) ms, r = 0.60 (p < 0.001); peak systolic inflow: 13 (±23) ms, r = 0.42 (p < 0.01); aortic valve closure: −5 (±12) ms, r = 0.94 (p < 0.001); mitral valve opening: −7 (±19) ms, r = 0.87 (p < 0.001) and peak early ventricular filling: −18 (±28 ms), r = 0.79 (p < 0.001). In conclusion eight physiologic events characterizeing the cardiac cycle, are associated with reproducible, well-defined fiducial points in the SCG. The first use of the word “seismocardiography” was in 1961 by Bozhenko3 and later by Baevski in 19644 who investigated the use of SCG in relation to space flights, as an alternative to BCG, the latter being a measure of the recoil forces of the body as response to the intermittent blood flow5 and which, at the time, could not be used in space6.Seismocardiography (SCG) is a technique of measuring the vibrations produced by the beating heart, where those vibrations are recorded from the chest using an accelerometer. The technique was introduced by Mounsey in 19578, but even though SCG promised to provide measures of cardiac contractivity and timing of events in the cardiac cycle, the technique never made it to the clinic and was again largely abandoned throughout the 1990s9. This was in part due to the advances in echocardiography and other medical imaging techniques9 and to the fact that the accelerometers used were large and heavy (up to 1 kg10) and thus not easy to use.After a period of relative silence, in 1990 Zanetti and Salerno introduced the technology in the USA11. Wearable systems have been studied in relation to changes in the SCG related to posture, walking and the assessment of heart failure. In 2016 De Rienzo et al. found that the pre-ejection period (PEP) and left ventricular ejection time (LVET), estimated from the SCG, were significantly different between supine and standing positions12 with recordings from the MagIC-SCG vest developed in 201011.The emergence of micro electro mechanical systems (MEMS) technology has made it possible to produce much smaller and lighter accelerometers, capable of recording with a high sensitivity, which transformed SCG from a measuring method mainly used in supine position, to a wearable technology14, although there is some disagreement as well16. Accurate determination of events, including AO, could make SCG a diagnostic tool for cardiac disease, comparable with the ECG. In addition, SCG could become an important tool in e.g., arterial pulse transit time (PTT) analysis for the assessment of vascular status20.SCG is thus being revised as an easy to use, non-invasive measuring technique of the cardiac performance and health status. The signal is deterministic and even though it is more detailed than, for instance, the electrocardiogram (ECG), the SCG fiducial points related to characteristic events in the cardiac cycle can be found among subjects. Especially the Aortic Valve Opening (AO) point has been investigated extensively and is consistent among most studies21 and, even though this imaging technique is limited in temporal resolution, echocardiography is indeed a helpful tool to determine the occurrence of cardiac events.However, the relation between SCG fiducial points and the physiological events in the cardiac cycle still lack sufficient evidence. Moreover, the literature is inconsistent on the location and even the definition of the fiducial points, which limits the use of the SCG. In some studies echocardiography was used to correlate some features in the SCG with cardiac eventsThe aims of this study were to define all waves in the SCG that are common among normal subjects, to define the fiducial points related to those waves, and to relate fiducial points and intervals between fiducial points to events in the cardiac cycle as observed in echocardiograms of the same subjects.In this study we collected SCGs and ultrasound data from a total of 45 healthy subjects without known heart diseases and correlated the fiducial points in the SCG with physiological events from the ultrasound images. The following sections describe the approach in detail.The subjects were recruited via posters and flyers placed at Aalborg University (Denmark), and in local shopping malls in the city of Aalborg.Diagnosed with any cardiovascular diseaseReceiving cardiovascular related medications such as medication for hypertension and hyperlipidaemiaInability to co-operate during the trial.Forty-five subjects were recruited. Inclusion criteria were: Healthy male and females at the age of 20–80 years. Exclusion criteria were:The study protocol was approved by the scientific ethical committee of Northern Jutland (N-20120069). Subjects signed a written inform consent before participating in the study. All methods were performed in accordance with relevant guidelines and regulations.For each subject basic demographic data  was recorded before the trial started. The subject was placed in a supine position and two accelerometers were attached to the subject’s skin with double adhesive tape. The accelerometers were Silicon Designs 1521 placed in a 3D-printed plastic housing .Three-lead ECG recording was performed with four electrodes, placed on the left and right shoulder and on the left and right iliac crests. The accelerometers and ECG were connected to an iWorx 228 data acquisition system and the signals were sampled at 5000 Hz to a PC using LabScribe software. Ultrasound images were recorded with a Vivid E9 . The ECG electrodes from the Vivid E9 were placed in the same locations as the electrodes used to obtain ECG via the iWorx 228. This ensured alignment of the data recorded via iWorx and the echocardiograph using the ECG signals recorded on the two devices. The Vivid E9 ultrasound system was tested for a potential delay between the ECG signal and the image. This delay was found to be less than one frame (with an acquisition rate of 70 frames per second).The subject lay supine for three minutes while recording baseline measurements of ECG and SCG. One accelerometer was placed in the xiphoid process for recording of SCG signal while one was placed in the inter costal space 4 (IC4). As described later, the signal recorded in IC4 was used in the signal processing step as a proxy for heart sound. Afterwards the three ultrasound images were recorded: Apical 4 and 5-chamber view with Pulsed Wave Doppler (PWD) and M-mode Tissue Doppler Image (TDI) of the mitral valve leaflets. During the echocardiography the iWorx system was still recording. Following the echocardiographic procedure data was recorded for 5 more minutes before the trial ended.et al.22. This method uses ECG lead I or II to divide the signal into individual heart beats based on the ECG R-peak. A mean SCG beat was calculated as well as a mean ECG beat, based on these individual beats, using the approach described in the following.All recorded data from the iWorx system was exported and processed in MATLAB . The signals were segmented into beats using the method described by Jensen st order low-pass Butterworth filter with a cutoff frequency of 90 Hz and then forward-backward filtered with a 3rd order high-pass Butterworth filter at 0.05 Hz. Using the algorithm described in22 allowed for alignment of the beats used to calculate the mean beat with respect to either the R-peak of the ECG or the diastolic complex. Annotation of fiducial points in the systolic complex was thus done with the beats aligned to the R-peak of the ECG. For fiducial points in the diastolic complex the SCG signals were aligned in time to second heart sound (S2). The heart sounds were obtained by filtering the acceleration signal from IC4 with a Butterworth bandpass filter of 1st order and a cutoff frequency of 50 and 500 Hz. S2 was located as the second largest peak in the autocorrelation of the median heart sound.For the annotation process a mean SCG beat for each subject was calculated, where the systolic part and the diastolic part were separately aligned and averaged to consider the variable duration of the systole. The SCG signals were first filtered forward-backward with a 122.The lag between the individual beats and the peak of the envelope was calculated using the cross correlation and by aligning the beats with respect to the median heart sound. A new median beat was then computed as well as the envelope to find the peak of the S2 peak again and to realign the individual beats once more using the cross correlation. This ensured that all beats were aligned to a median heart sound, that was not influenced by the initial alignment to the R-peak22. This ensured that the mean beat is composed of beats with similar characteristics.As part of the segmentation method noisy beats were rejectedBecause of the variability of the S1-S2 interval this procedure gave significantly different averages as compared with alignment based on the R-peak. The autocorrelation alignment thus ensured that no events were neglected to be marked: see Fig. All SCG signals were manually annotated to ensure that fiducial points of the same characteristics were labeled the same way. During the labeling of the fiducial points the operator was blinded for the corresponding ultrasound images. A custom tool was developed in MATLAB for this purpose. The SCG signals were annotated in a process of two steps. First, all significant peaks and valleys in all mean beats were marked. In the second step the different events in the SCG signals were labeled using a simple naming scheme of the letters from A to M followed by subscript to indicate the systolic (s) or the diastolic (d) complex. Thus, As is fiducial point A in the systolic complex and Ad is fiducial point A in the diastolic complex. In this step fiducial points of similar characteristics were labeled with the same letter across the subjects. Thus, after this step the mean beats for some subjects could have fewer points labeled than for other subjects, depending on how many points could be identified in each subject. To a large extent the annotation process was iterative, and the operator went through the mean beats multiple times. Figure t = 0. This negative peak was labeled Fs .In three cases a shoulder near the peak of the systolic complex was labeled as fiducial point Gs was labeled Js. The last valley marking the end of the systolic period was labeled Ms. The peak and valley before Ms were labeled Ls and Ks respectively.The peak following the fiducial point Id. This point was not always present as a distinct valley in which case the onset of the positive acceleration in the diastolic complex was labeled Ad, see Fig. In the diastolic complex, the first valley was labeled Ad. This fiducial point was in some cases present as the shoulder of a steep negative acceleration towards the valley labeled Cd , MC, AO, aortic valve closure (AC) and mitral valve opening (MO), in accordance withDuring the process of measuring the timings the operators were blinded for the corresponding SCG signals. Thus, operator bias was minimized.Onset of the A-wave in the 4-chamber PWD Images and Atrial Systole found in the TDI ImagesOnset of the aortic flow profile found in the 5-chamber view PWD versus the AO from TDIEnd systolic flow profile from the 5-chamber view and the AC from the TDIOnset of the E-wave from the 4-chamber PWD Images and the Mitral Valve Opening from TDI.The Pulsed Wave Doppler images and the Tissue Doppler images were analyzed by two different operators respectively. To ensure that the operators agreed on where to place the markers for the different events, the following points were compared:Differences in timing >15 ms between the two images were reevaluated by the operators, although a reevaluation of a particular point did not always yield in a better agreement. For each of these four physiological events a mean time was calculated between the two image modalities to mitigate potential errors. Thus, each time point for e.g. Aortic Valve Opening consists of the mean of the times from both the TDI and PWD images.All the time points of the fiducial points in the SCG and of physiological events in the ultrasound images were tested for normality by visualizing the histogram and probability plot of the data points. As normal distribution was observed among all data points Pearson’s Linear Correlation Coefficient was used to calculate the correlation between the fiducial points in the SCG and the timing of the physiological events found in the ultrasound images. All events from the systolic complex of the SCG signal were correlated with all physiological events during that complex and likewise for the diastolic complex. P-values < 0.05 was considered significant for rejecting the null hypothesis of no correlation between fiducial points and physiological events.Besides the correlation, the mean differences between the fiducial points from the SCG and their corresponding time points from the ultrasound images were calculated together with their standard deviations. The time difference in combination with the correlation should give an indication as to which fiducial point from the SCG best corresponds to a certain physiological event found in the ultrasound images. For these points the beta coefficients for the linear regression is presented.A total of 45 subjects participated in the study. One subject from the original 45 subjects decided to withdraw. Two subjects were excluded due to abnormal SCG signals with unidentifiable fiducial points. Thus, a total of 42 SCG signals were included in the study. For one subject the signal recording prior to the ultrasound scanning was interrupted by noise thus the signal recording following the ultrasound scanning was used instead. The mean age for the included subjects was 46.8 (±17.4) years with a mean weight of 72.9 (±12) kg and mean height of 174.9 (±8) cm and 52% of the subjects were female. The mean RR-interval was 950 (±177) ms.Table s and point Hs.Table s with a mean difference of −2 ms (±16 ms). The correlation between the SCG fiducial point and the ultrasound image time location is 0.75 with p < 0.001.The lowest difference between A-wave-onset and AS in the echocardiograms and any fiducial point in the SCG was found for point Bs with a mean difference of 13 ms (±19 ms) and a correlation of 0.63 (p < 0.001).The shortest time difference between the peak of the A-wave and the fiducial points was found for point Cs with a mean difference = 4 ms (±11 ms). The correlation for this fiducial point is 0.71 with a p-value < 0.001.The mean difference between the fiducial points and the MC event in the images is lowest for fiducial point Es has the lowest mean and absolute mean time difference to the mean time point (mean difference = −3 ms (±11 ms)). The correlation is 0.60; p < 0.001. Figure s and Aortic Valve Opening is 0.70 instead of 0.60 – still with a p < 0.001.The mean time point from the onset of the systolic profile in the tissue Doppler images and the aortic valve opening found on the pulsed Doppler flow images were correlated with the fiducial points from the systolic complex of the SCG. Fiducial point Gs has the shortest mean time difference of 13 ms (±23 ms) to the event from the ultrasound image. The correlation is 0.42 (p < 0.01).The fiducial point KTable d. The correlation between the fiducial point and time point is 0.94 (p < 0.001).The time point for AC was found in both the tissue Doppler image and the pulsed wave Doppler as the end of systolic flow. The shortest time difference between the fiducial point of the SCG and the time point for aortic valve closure is −5 ms (±12 ms) and is located at point Bd. The correlation is 0.87 (p < 0.001).For the MO time point a combination of the tissue Doppler image and the onset of the E-wave in the pulsed wave Doppler was used. The shortest mean time difference of −7 ms (±19 ms) is to the fiducial point Ss with a correlation of 0.79 (p < 0.001).For the peak of the E-wave the fiducial point with the shortest time difference (mean difference = −18 ms (±28 ms)) was UAll the points evaluated above with a short time interval between a particular fiducial point and the corresponding physiologic event in the ultrasound image, are listed in Table s, Cs, Ds, Es, Fs, Gs, Ks, Bd, Fd and Hd were used as fixed points in a time warping approach to define one overall mean SCG and ECG signal. The signals were segmented into parts based on the aforementioned fiducial points. A segment from signal start to fiducial point Bs and a segment from Hd to the end of the signal was also created, making a total of 11 segments across those 34 signals that contained all the fiducial points. For each segment the mean length was calculated, and all segments were resampled to fit this length with Matlab’s “spline” function. Thus, following this step all signal parts of the same segment were of the same length. Using the original length of each signal part and the new length of the same part a “resampling factor” was calculated and used to reposition the location of the ultrasound event to the new length of the signal parts. An overall mean SCG signal was computed based on the parts of signals, see Fig. The fiducial points BIn this work we propose a nomenclature for the various waves and wave complexes in the normal seismocardiogram and we have identified the main physiological events in the cardiac cycle relative to those waves. A total of 42 SCG signals were manually annotated and the times of occurrence of the fiducial points were correlated to the occurrences of the physiological events in ultrasound images.21. Only the original SCG paper describes cardiac events taking place on the slopes of the signal and not at the fiducial points1. Our results points towards the same as Mounsey’s: the physiologic event does not always occur at the fiducial point but before or after.Current literature visualizes cardiac events as occurring at the local extrema of the SCG signals is related to the atrial systole according to our results, see Table s marks the beginning of the negative slope describing the atrial systole in Mounseys paper. Likewise, the following positive slope in Mounseys paper is the same as the positive slope from our results. This corresponds to the positive slope following fiducial point Cs. According to the ultrasound images the onset of the echocardiographic A-wave  is just prior to Bs whereas the beginning of the slope following fiducial point Cs marks the peak of the A-wave . The acceleration deflection corresponds to an initial outwards deflection in displacement followed by an inwards deflection  ms after the R-peak, thus not at the same location as suggested by Mounsey.Fiducial point Es is the isovolumetric contraction segment. Before the aortic valve opens the heart start to contract to build enough pressure in the left ventricle to exceed the pressure in the aorta. The acceleration is first negative, then positive. The corresponding displacement signal is an outward deflection. This could be due to a rounding of the ventricles as they contract, and pressure is built. The outward displacement deflection stops as the acceleration deflection is at the valley prior to fiducial point Gs. From this point the positive acceleration describes the inward displacement, possibly due to the blood being forced out of the ventricles thus causing the heart to flatten. This corresponds with the finding by Mounsey.Following Es is caused by an inward displacement during what Mounsey calls “reduced ejection” period1.The positive acceleration starting at fiducial point Kd is associated with the closing of the aortic valve1. Just before this event a positive deflection of displacement starts. This deflection continues to fiducial point Ed. The outward and following inward displacement could be due to the forces created by the closure of the aortic valve. Blood in the aorta forces the valves to close causing a movement of the heart towards the apex of the heart. Mounsey explains this further by comparing two recordings of the SCG, one from the upper sternal area and one from the apical area. The deflections in the two recordings are opposite to each other. In the upper sternal area the displacement was upward instead of downward, resulting from an inward displacement over the area of the valves1.Fiducial point Bd is located in the interval containing of the mitral valve opening. The positive acceleration leading up to this fiducial point is associated with an inward displacement deflection. Mounsey explains these changes with a possible change in position of the heart in this period.Fiducial Point Fd is associated with early ventricular filling. This positive deflection in acceleration is associated with a positive displacement possibly caused by blood filling the ventricles as the mitral valve and tricuspid valves opens. Mounsey proposes the same explanation for this positive displacement deflection.The last fiducial point HFor the three locations peak atrial inflow, peak systolic inflow and peak early ventricular filling the difference between these physiologic events and the closest fiducial points were significantly different from zero.With regard to accurately determining the events in the ultrasound images it should be noted that this process is not perfect. The quality of the profile of the flow or tissue movement was varying, such that an accurate determination of the occurrence of the events still is a subjective task. In this study, the operator was blinded for the corresponding SCG signal when determining the events in the ultrasound image. This eliminates the bias towards selecting events that could fit any hypothesis the operator might have with regard to where to locate echocardiographic events relative to the SCG.et al.27. This would result in a relation between the SCG signal and the evolution of the shape of the contractions, instead of the locations defined in this paper.A different approach for a study like this, could be to use homologous time points as described by Piras The main limitation in this study is the low temporal resolution of the ultrasound modalities compared to that of the SCG signal. When analyzing the ultrasound images, the low resolution limits the operator’s ability to accurately mark the events. Moreover, the ultrasound images consist of only 1–4 consecutive beats whereas the SCG signals are composed of more than 20 beats, thus a more general representation of the SCG signal from a subject compared to the ultrasound image. This could lead to inconsistency between the SCGs and the ultrasound images due to variations of the heartbeat, for instance introduced by the respiration component of the signals.We have defined fiducial points in the SCG and associated them with physiologic events obtained from cardiac ultrasound images and we found a consistent relationship between SCG fiducial points and the events. Furthermore, this study introduces a new location for the MO event.All SCG Signals from Dataset