Telehealth programs have been successful in reducing 30-day readmissions and emergency department visits. However, such programs often focus on the costliest patients with multiple morbidities and last for only 30 to 60 days postdischarge. Inexpensive monitoring of elderly patients via a personal emergency response system (PERS) to identify those at high risk for emergency hospital transport could be used to target interventions and prevent avoidable use of costly readmissions and emergency department visits after 30 to 60 days of telehealth use.The objectives of this study were to (1) develop and validate a predictive model of 30-day emergency hospital transport based on PERS data; and (2) compare the model’s predictions with clinical outcomes derived from the electronic health record (EHR).We used deidentified medical alert pattern data from 290,434 subscribers to a PERS service to build a gradient tree boosting-based predictive model of 30-day hospital transport, which included predictors derived from subscriber demographics, self-reported medical conditions, caregiver network information, and up to 2 years of retrospective PERS medical alert data. We evaluated the model’s performance on an independent validation cohort . We linked EHR and PERS records for 1815 patients from a home health care program to compare PERS–based risk scores with rates of emergency encounters as recorded in the EHR.In the validation cohort, 2.22%  of patients had 1 or more emergency transports in 30 days. The performance of the predictive model of emergency hospital transport, as evaluated by the area under the receiver operating characteristic curve, was 0.779 (95% CI 0.774-0.785). Among the top 1% of predicted high-risk patients, 25.5% had 1 or more emergency hospital transports in the next 30 days. Comparison with clinical outcomes from the EHR showed 3.9 times more emergency encounters among predicted high-risk patients than low-risk patients in the year following the prediction date.Patient data collected remotely via PERS can be used to reliably predict 30-day emergency hospital transport. Clinical observations from the EHR showed that predicted high-risk patients had nearly four times higher rates of emergency encounters than did low-risk patients. Health care providers could benefit from our validated predictive model by targeting timely preventive interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource utilization. With the worldwide increase in the elderly population , chronicVarious telehealth programs have been demonstrated to be effective in reducing readmissions, ED visits, and mortality for patients with congestive heart failure (CHF), stroke, and chronic obstructive pulmonary disease (COPD) . For insA PERS service enables older adults to get help in a situation that potentially requires emergency transport by ambulance to the hospital, such as a sudden worsening of their chronic condition or a fall ,14. PERSProgress in big-data analysis techniques, such as predictive modeling to identify patients at risk of worsening health conditions, may contribute to cost-effectively reducing potentially avoidable health care utilization . PreviouThe objectives of this study were to (1) develop and validate a predictive model of 30-day emergency hospital transport based on PERS data; and (2) compare the model’s predictions with clinical outcomes derived from the EHR, namely outpatient and inpatient emergency encounters. We used a large, deidentified PERS dataset of more than 580,000 individuals to develop and validate the predictive model. For a subpopulation of 1815 patients, we linked PERS and EHR data so that we could compare PERS–predicted risk of hospital transport with observed clinical outcomes.In this study, we followed the guidelines by Luo et al for developing and reporting machine learning predictive models in biomedical research . This waThis study was approved by the Internal Committee on Biomedical Experiments of Philips Research (ICBE-2-16049). Furthermore, we obtained approval for linkage of PERS and EHR records from the Partners HealthCare institutional review board.We included patients if they were subscribers of the Lifeline PERS service on January 1, 2014 (model development cohort) or February 1, 2014  and were between the ages of 18 and 100 years. We excluded patients if they appeared to be more than 10 years on the service due to the use of a different back-end system prior to 2004 where some patients may have left the service in the meantime without an indication recorded in the database. Also, patients who left the PERS service for any reason in 30 days from the prediction date were excluded from the analysis. Reasons for leaving the service included death, moving to long-term care, and financial reasons. We excluded patients from the model development cohort and validation cohort 1 if they were part of the PPH program, as we included these patients into validation cohort 2. Furthermore, 3.27%  of total patients had missing demographic data and were excluded from the analysis.The primary PERS data source for model development was the Philips Lifeline database, which contained historical data such as demographics, patients’ living situation and caregiver network, self-reported medical conditions, and medical alert data. We collected data on self-reported medical conditions, including medication allergies, using a custom coding system, which consisted of a drop-down list of common disease categories, such as COPD, CHF, and diabetes. Caregiver network information included the number of responders, the number of people who lived with the patients, and persons to be notified after an incident occurred. Medical alert data from the PERS device included all information gathered during the interactions of the patients with the Lifeline call center associates. Call center associates categorized the calls as incidents or nonincidents. For all calls, the situation  and the outcome  was collected by the associate via custom-made software.Validation cohort 2 included PERS users who were on the service on February 1, 2014 and received care at PHH, a homecare management service that offers general care as well as specialized services to help patients manage chronic conditions at home. PHH uses a variety of technological innovations to remotely monitor their patients, including the Philips Lifeline PERS, which is a service routinely recommended for elderly or chronically ill patients who are at risk of falls or other health-related emergencies. We excluded patients if they left the PERS service in the 30 days after the prediction date.The primary data source for comparison of PERS–based predicted risk for emergency hospital transport with clinical outcomes in validation cohort 2 was the Enterprise Data Warehouse, an electronic medical record data repository of hospitals within the Partners HealthCare System. The data include demographic information, medical conditions, and hospital utilization. We combined longitudinal clinical data from the EHR  for 1815 individuals who met the inclusion criteria with the PERS–based predictive score of emergency hospital transport. All data were deidentified before analysis. It should be noted that, in this study, we did not use EHR data to train the predictive model; rather, we compared PERS–based predictions of 30-day emergency hospital transport with clinical observations from the EHR.We processed PERS data for input into the predictive model using the statistical programming language R . DiffereOne table included subscriber demographics, including age, sex, and region in the United States, subscription type, and enrollment information. A few test accounts, used for demonstration purposes and training of call center agents, were removed prior to the predictive analysis. We used enrollment information to determine which subscribers were active on the prediction date. Subscribers with missing enrollment dates  were already deactivated or were pending a PERS installation and were therefore excluded from further analysis. We derived time on the PERS service as the number of days between enrollment and prediction dates.One table consisted of the subscribers’ caregiver network information. This included the number of responders  and number of emergency service providers  listed by the subscriber at the time of enrollment.One table included self-reported medical conditions and medication allergies. Medical conditions and medication allergies were recorded at enrollment by the call center agent via a custom-made drop-down list for the purpose of informing EMSs in case of an incident. We extracted the 50 most frequently occurring medical conditions and medication allergies from the database for use in the prediction model. Up to 3 of the most common medical conditions and medication allergies were listed for various categories .One table consisted of up to 2 years of historical PERS service use data, which totaled to more than 25 million data points. We categorized cases by case type, situation, and outcome . For eacWe derived the dependent variable—having 1 or more emergency hospital transports in the 30 days from the prediction date—from the PERS service use data. As the prediction problem was treated as a classification problem, the dependent variable was rendered binary, with 1 if the patient had 1 or more emergency hospital transports in the 30 days following the prediction date and 0 otherwise.After preprocessing, we merged the data into 1 large table with 129 columns representing predictive model features and 1 column for the dependent variable. Each of the rows corresponded to 1 PERS user. For 1815 PERS users in the PHH program, clinical outcomes from the EHR were available, and we assigned these to validation cohort 2. We randomly assigned the remaining PERS users on a 1:1 basis to the model development cohort and the validation cohort 1.We then developed a predictive model using a boosted regression trees approach called extreme gradient boosting within R . We chosThe frequency and recency features based on medical alert pattern data are highly skewed. While this may pose a problem for certain predictive modeling methods, the tree-based method we used did not make any assumptions on normality of the features. Monotonic transformations of the features to render them more normal did not affect the predictive performance of the models. We determined variable importance according to the gain, a measure of the relative contribution of the corresponding variable to the predictive model, calculated by taking the improvement in accuracy brought by a variable to the branches it is on. We reduced the number of features by selecting only those with non–zero gain values for the final model, resulting in a total of 121 features in the final model. The boosted regression model also involved tuning hyperparameters of the learning algorithm, such as the number of trees, the maximum depth of the trees, and the learning rate. We achieved this optimization using 5-fold cross-validation on the model development set with the optimization metric determined by the AUC in the test fold.We evaluated the discriminatory accuracy of the predictive models using the AUC, which indicates the probability of the predictive model ranking a randomly selected patient with 30-day emergency transport higher than a randomly selected patient without the event. Furthermore, the negative predictive value (NPV) is the percentage of patients not having emergency transport in the group classified as negative, while the positive predictive value (PPV) indicates the percentage of patients having emergency transport in the group classified as positive. We varied the threshold for classifying patients as positive using risk scores above the 90th, 95th, and 99th percentiles such that 10%, 5%, and 1%, respectively, of patients were classified as high risk. For these thresholds, we computed the NPV, PPV, sensitivity, specificity, and accuracy. We derived 95% confidence intervals for performance metrics using a stratified bootstrapping method with 1000 bootstrap replicates.While we used AUC to assess the ability of the predictive model to discriminate between patients with and without transport, regularization, as done in extreme gradient boosting, may have created bias in the predictive model . TherefoWe tested differences in age between the model development and 2 validation cohorts using analysis of variance followed by Tukey post hoc test. Pairwise comparison between pairs of proportions with Holm correction for multiple testing was applied to the patients’ sex, self-reported medical conditions, and 30-day emergency transport.To determine how PERS predicted risk for 30-day emergency hospital transport compared with clinical outcomes, we derived the rates of emergency outpatient and inpatient encounters from the EHR at 30, 60, 90, 180, and 365 days following prediction for validation cohort 2. We split the cohort into low, medium, and high risk according to the risk thresholds that corresponded to the 0 to 50th percentile, 51st to 95th percentile, and greater than 95th percentile. The numbers of emergency encounters in these 3 risk groups were normalized as the number of emergency encounters per 100 patients. Emergency encounters were statistically compared using a pairwise Wilcoxon rank sum test.The distribution of the number of self-reported conditions was different in validation cohort 2, with only 6.89% (125/1815) of users not reporting any conditions, compared with 20.58%  in the model development cohort. One reason may be that these were patients receiving care for 1 or more specific conditions through a home health care program. Regarding the dependent variable of having 1 or more emergency hospital transports in 30 days, 2.20% (40/1815) of patients had emergency hospital transport in the 30 days following the prediction date in validation cohorts 1 and 2. This was not significantly different from the model development cohort . The ratio of positive to negative classes was 0.024 for the model development cohort and 0.023 for both validation cohorts. According to the EHR data, 509/1815 patients of validation cohort 2 (28.04%) had 1 or more emergency encounters in the year following the prediction date.P=.99 for validation cohort 1 and P=.78 for validation cohort 2) showed that predicted probabilities matched observed outcomes.PPVs were on the low side due to the low prevalence of 30-day emergency hospital transport, which was around 2.2% in both validation cohorts . By incrThe predictive model produced a probability (from 0% to 100%) for each patient to assess the risk of 30-day emergency hospital transport. The actually observed percentage of patients with emergency hospital transport increased as the predicted probabilities increased . At predThe predictive model of 30-day emergency hospital transport included 121 variables with nonzero values for the gain. P=.001), 60 (P<.001), 90 (P<.001), 180 (P<.001), and 365 (P<.001) days. The medium-risk group had significantly more emergency encounters at 90 (P=.01), 180 (P<.001), and 365 (P<.001) days. After 365 days, there were 3.9 times more emergency encounters in the high-risk group than in the low-risk group.We then used the predictive model of 30-day emergency hospital transport to segment validation cohort 2 into high risk (>95th percentile), medium risk (51st-95th percentile), and low risk (≤50th percentile) after ranking them according to the predicted probabilities of 30-day emergency hospital transport. We normalized the number of emergency encounters (both outpatient visits and inpatient admissions) in the year following the prediction date to 100 patients . CompareRetrospective validation demonstrated the effectiveness of the PERS medical alert data-based predictive model of 30-day emergency hospital transport in identifying patients at risk of hospital transport with good discrimination (AUC=0.779). A goodness-of-fit test and calibration plot indicated that the model-predicted probabilities matched with observed outcomes across ranges of predicted risk. We evaluated the trade-off between finding many true-positive cases  and reducing the number of false-positives  by varying the threshold to classify patients as positive. At a risk threshold corresponding to the 99th percentile, 1 out of 4 patients flagged at high risk needed hospital transport in the next 30 days PPV=25.5%), while at this threshold the sensitivity was rather low at 11.5%. PPV was nearly 12 times higher than the prevalence of 30-day emergency transport in validation cohort 1, which was 2.20%. The trade-off between sensitivity and PPV was also reported by other predictive modeling studies of emergency health care utilization ,25..5%, whilRecent studies have reported on the development and validation of predictive models that can be used to systematically identify individuals at high risk for an unfavorable outcome in a hospital setting ,27. SuchThis predictive modeling study was, to the best of our knowledge, unique, as it used PERS data to predict emergency hospital transport. Most state laws in the United States require that patients in need of emergency medical care must be taken to the nearest appropriate health care facility capable of treating the patient, which may be an out-of-network facility. Furthermore, in case of ED crowding , ambulanLinkage of PERS and EHR data in a cohort of 1815 PERS users who were patients of an accountable care organization in the greater Boston area group enabled comparison of PERS–based predicted risk of 30-day emergency hospital transport with emergency encounters derived from the clinical EHR. The patients in the high-risk group—that is, those above the 95th percentile range of risk for emergency transport—also had 3.9 times more emergency encounters per 100 patients in the year following the prediction date than did patients classified as having low risk. Here we specifically analyzed rates of health care encounters, as these are the events that could ultimately be avoided with the appropriate interventions. These results suggest that prediction of emergency health care utilization based on PERS data may be a good alternative to EHR-based prediction models, which could be especially helpful for continuous monitoring of patients after discharge and where patients have missing or limited EHR records.Our previous study on health care utilization by PERS users indicated that 21% of emergency admissions were considered potentially avoidable . TherefoThis study had a few limitations. The PERS population is mostly old and primarily female, and the service is predominantly privately paid for by patients and not covered by their health insurance. This may limit the generalizability of the study to older women who can afford the service. Interpretation of the predictive model may have been influenced by confounding of unobserved variables, including when and where users wore the PERS device . While tMore PERS subscribers in validation cohort 2 self-reported on medical conditions than in the development and validation cohorts. It is likely that this cohort was a biased patient group with higher risk and more medical conditions, as they were receiving care from a health care provider. Given that the AUC for 30-day transport was not significantly lower in validation cohort 2, we do not expect that this affected the predictions. Furthermore, patients with emergency transport in validation cohort 2 may not have ended up in the ED of one of the hospitals of the Partners HealthCare network, but in a different hospital system, such that emergency health care utilization was not recorded in the EHR data that we used. Additionally, patients may have initiated emergency hospital transport outside of the PERS service. Both above-mentioned limitations may have affected the correlation between PERS–based risk for emergency hospital transport and emergency encounters derived from the EHR.This study showed that remotely collected patient data using a PERS service can be used to predict 30-day hospital transport. Furthermore, linking these data to clinical observations from the EHR showed that predicted high-risk patients had nearly four times higher rates of emergency encounters in the year following the prediction date compared with low-risk patients. Health care providers could benefit from our validated predictive model by estimating the risk of 30-day emergency hospital transport for individual patients and target timely preventive interventions to high-risk patients. We are testing this hypothesis in a randomized clinical trial where risk predictions are combined with a stepped intervention pathway. This approach could lead to overall improved patient experience, higher quality of care, and more efficient resource utilization. Future studies should explore the impact of combined EHR and PERS data on predictive accuracy.


These models provide a physiologically relevant tool to screen potential neuroprotective drugs in stroke and to study the molecular mechanisms involved in brain ischaemia.It is important to understand the molecular mechanisms underlying neuron death following stroke in order to develop effective neuroprotective strategies. Since studies on human stroke are extremely limited due to the difficulty in collecting post-mortem tissue at different time points after the onset of stroke, brain ischaemia research focuses on information derived from c injury . This re To this end, rigorously conducted in-vitro studies consisting of oxygen and glucose deprivation assays in cell culture  are extremely important to provide a reliable, technically straightforward and physiologically relevant model, useful for the study and screening of potential therapeutic targets of neuroprotection against stroke  assay. They finally selected to use 6 h of OGD as this was the first condition in which a significant decrease (P < 0.001) of cell viability was observed in comparison with the control group (not exposed to OGD) [et al. used the same ischaemic medium and cells but different periods of OGD  to study the involvement of placental growth factor (PIGF) in cerebral ischaemia injury [Li  to OGD) . Similara injury .An also used a rBMEC model but with DMEM glucose-free solution. The cells were submitted to a previous PC protocol consisting of a 30 min exposure to OGD and normoxic incubation for 24 h for reoxygenation before inducing OGD injury for 2.5 h. A second group was studied without the PC protocol to compare the three conditions . The PC procedure was included in order to analyze the effects of PC on the tight junction and cell adhesion of cerebral endothelial cells. Their results demonstrated that OGD preconditioning alleviated the injury and induced protective effects on endothelial cells. Transendothelial electrical resistance was similar to the control, ZO-1 and F-actin localization were maintained as in the control, and a reduction of ICAM-1 and VCAM-1 expression (involved in leukocyte adhesion and activation) was observed. In their preliminary experiments, the authors found on testing different periods of exposure to OGD  that more than 2 h produced irreversible injury, 3 h produced severe damage with apoptosis or necrosis in most cells, whereas 0.5 h of PC did not result in apparent injury but led to ischaemic tolerance . et al., who used EBSS medium and rat cerebromicrovascular endothelial cells (CMVECs), performed ischaemic experiments exposing the cells to 12 h of OGD. The aim of the study was to determine whether primary cultures of rat CMVECs were sensitive to L-glutamate (L-glut) or N-methyl-D-aspartame (NMDA). To this end, they combined L-glut/NMDA (0.1-1 mM) treatment with OGD for 12 h. After OGD, EBSS was replaced with regular culture medium for 12 h before determining cellular viability. The exposure of rat CMVEC cultures to 12 h of OGD was found to significantly reduce viability. However, the addition of 1 mM L-glut or NMDA into the glucose-free EBSS solution did not affect cell death induced by OGD [Domoki d by OGD . On the d by OGD ,58 or imet al., for example, investigated the potential interaction of ataxia telangiectasia mutated (ATM) and NF-KB after OGD in mouse cerebral endothelial cells, exposing cells with deoxygenated glucose-free HBSS to OGD for 4 h. They performed the analysis at 4 h after OGD onset, as this was approximately when 30% of cells had died and when ATM was induced [When the cerebrovascular endothelial cells are from mouse, OGD exposure times seem to be more consistent between the different studies. Yin  induced . The sam induced  to study induced  occured et al. and Narasimhan et al. submitted primary mouse cerebral endothelial cells to 4 h and 8 h of OGD, respectively, with glucose-free buffered salt solution (HBSS) [et al. submitted the cerebral cells to 18 h of OGD using low-glucose medium (1.0 g/L glucose) [et al., also used the b.End3 cell line, but in this case they used serum-free low-glucose medium during larger periods of time  as the OGD protocol [Hu n (HBSS) ,67. Milnglucose) ,125. Othglucose)  and 5 h glucose)  of OGD oglucose)  to mimicprotocol .et al. exposed an immortalized mouse brain endothelial cell line (bEnd.3) and primary cultures of mBMEC to 5 hours of OGD using deoxygenated DMEM glucose-free medium to examine whether cerebral endothelial cells can be preconditioned in vitro in the absence of other cell types. This study consisted of four different sets of experiments comparing different PC duration, the effects of altering the time interval between the PC stimuli and OGD injury, the cell viability of the two types of cerebral endothelial cells and the effect of PC on the endothelial intercellular adhesion molecule (ICAM-1) mRNA and protein after OGD. In all of the experiments cerebral endothelial cells were exposed to a short duration of OGD as a stimulus and, after different time intervals, exposed to a longer duration of OGD (5 h) with or without reoxygenation to examine whether prior PC would reduce cell injury. In the first set of experiments they examined the optimal duration of the PC  stimulus using bEnd.3 using lactate dehydrogenase (LDH) assays. In this study the authors found evidence of protection from 15 min of OGD as the PC stimulus to reach the maximum protection level between 45 and 60 min of OGD . In the second set of experiments, the duration of the PC stimulus was kept constant (1 h), and the time interval between that stimulus and the prolonged OGD was varied . They found that 1 h of PC stimulus and a 24 h interval between the PC stimulus and the OGD injury were the optimal conditions to provide a protective effect on bEnd.3. This delay was considered to be due to the need for new protein synthesis [Finally, Andjelkovic ynthesis , and thiynthesis .et al. and Yan et al. which used significantly longer periods of time .In conclusion, the time of OGD used in the experiments with mouse cerebromicrovascular endothelial cells normally ranges from 4 to 8 h, with the exception of the previously reported studies performed by Lee in-vitro model to mimic ischaemic conditions. Zhang et al. and Stanimirovic et al., who performed experiments exposing cells to glucose-free Krebs solution during 4 h, are a clear example [et al. exposed HBMEC to longer periods of time (8 hours) using HBSS without serum and performing a previous hypoxic preconditioning protocol consisting of two 3 h periods of hypoxia/day  during five days [et al., Guo et al., Allen et al. and Cowan and Easton, have also used human brain endothelial cells in their experiments [et al. and Guo et al., 6 and 24 h of OGD were used, respectively, and in the other cases different periods of in-vitro ischaemia were selected. Allen et al. used either 4 h or 20 h (using HBSS medium), and in the case of Cowan and Easton cells  were exposed for 1 h, 12 h and 24 h to OGD in order to compare results in the different conditions. In the case of Cowan and Easton, two different OGD media, glucose-free PBS and glucose-free DMEM, were used.Human cerebromicrovascular endothelial cells (HCEC) have also been used as an  example ,43. Authive days . Other reriments ,38,41,46Like the experiments performed with rat brain endothelial cells, the OGD experiments with human cerebral endothelial cells exposed the cells to a wide variety of different periods of time, ranging from 4 or 8 h to 24 h.et al., for example, aimed to investigate the effect of hypoxia on permeability changes by specifically exposing cells to serum-lightened (5% v/v serum) and low-glucose (0.2 g/L) medium during 4 h to mimic in-vitro ischaemic conditions [et al. used the same medium as well as the same time of OGD as Brillault et al. [et al. and Paulson et al. used conventional medium without glucose during 6 hours as the OGD protocol in their experiments with bovine brain capillary endothelial cells [et al. used a serum-free and low-glucose medium during 4 h to determine whether vascular tPA crosses the blood-brain barrier (BBB) during cerebral ischaemia [et al. perform the OGD experiments using HBSS medium during different periods of time between 1-8 hours. The aim of the study was to characterize bovine cerebral endothelial cell death in relation to iNOS expression after experimental ischaemia [Some authors have also used bovine brain capillary endothelial cells for their experiments. Brillault nditions . Similart et al. . On the al cells ,85. The schaemia . Finallyschaemia . In summet al. used several periods of hypoxia, none less than 10 h, to measure oxygen free radical production by cerebral endothelial cells and determine its role in hypoxia injury [et al. used periods from 1.5 to 48 h of hypoxia to investigate the effects of methohexital and thiopental on the permeability of the endothelial cell monolayer under these conditions [in-vitro BBB model to evaluate the mechanisms by which hypoxia regulates paracellular permeability. They concluded that the VEGF released leads to delocalization, decreased expression and enhanced phosporylation of ZO-1 (zona occludens) [Several studies have used porcine brain endothelial cells as a BBB cellular model although most perform hypoxia rather than undertaking ischaemic experiments. Beetsch a injury . Fischernditions . In anotAlthough the previously reported studies are based on the OGD experimental model which consisted of replacing the conventional medium and exposing the cerebral endothelial cells from different animal models to OGD conditions, they principally differ in the reperfusion time during which sample collection was performed. All the authors analyzed the samples from between 16 and 20 h of reperfusion, except one who used 72 h of reoxygenation , when a Ischaemic cerebral stroke is produced by arterial occlusion resulting in dramatically reduced levels of oxygen and glucose to the region supplied. In recent years, cerebral endothelium, an important component of the neurovascular unit, has become a potential therapeutic target for neurological diseases including stroke ,129,130.in-vitro experiments, OGD is the closest approximation to the conditions that occur during cerebral ischaemia as only oxygen deprivation is performed in hypoxia experiments. For this reason we have focused on ischaemic models in reviewing the different published studies that have used cerebral vascular endothelial cells as an in-vitro ischaemic cellular model. Different studies aiming to mimic the ischaemic conditions of stroke have used hypoxia as well as ischaemic models. For The studies that have been reviewed here reveal that there is no general consensus as to the conditions  and cellular models to be used in the OGD experiments performed with cerebrovascular endothelial cells. However, the most widely used cellular model in the last thirteen years has been that of the mouse cerebral endothelial cells, principally the bEnd.3. In this case, we have seen that there is a greater correlation in the type of ischaemia medium used than in the OGD exposure time. The most common media used in OGD experiments with these cells are especially glucose-free HBSS but also DMEM glucose-free. The time of OGD varies widely between studies although it is more standardized when glucose-free HBSS is used. In contrast with the studies reported using mouse endothelial cells, in which OGD conditions appear to be more homogeneous, the experiments performed with rat as well as human brain endothelial cells use very different ischaemia media  and a wide range of times of OGD exposure. Therefore, although it appears that for some species there is a consensus in some reviewed studies, when OGD experimental conditions and protocol design are being developed  it is extremely important to take the objectives of the study into account.in-vitro studies. Most authors performed their experiments with brain microvascular endothelial cells to study ischaemia given that they are responsible for angiogenesis in cerebral ischaemia. However, some authors used HUVEC  cultures in their experiments as they are a cellular model in angiogenic studies . These cAlthough most of the studies reviewed used a serum-free media in models of ischaemia, some used a serum-lightened medium in their experimental studies in order to avoid temporary permeability-increasing artefacts due to serum growth factors and improve cell viability and proliferation amongst other reasons. These differences in the media composition should be taken into account in the analysis of the results and in comparing different studies, as serum growth factors has been reported as affecting the barrier properties of an endothelial cell culture, promoting a decrease in the electrical resistance and the opening of the tight junctions, thus affecting the final differentiation of the cells ,135.in-vivo brain conditions (BBB). More reliable results are obtained in this way as they take into account paracrine signalling among the different cell types as occurs in physiological and physiopathologic conditions. Hence, in-vivo ischaemic conditions are better simulated and the repercussions of injury to the different brain cell types can be better analyzed. With regards to the brain endothelial cell differential response in mono versus co-culture conditions, it has been reported that when brain endothelial cells are assayed in co-culture with astrocytes, an earlier increase in the permeability of the ECs (hyperpermeability) is observed in OGD experiments in comparison with EC monocultures. This occurs due to glial cell activation that produces a secretion of soluble factors which make endothelial cells more sensitive to OGD. Some of the reported studies used conditioned media from endothelial cells previously exposed to OGD conditions or from control conditions with cultured neuronal cells to study the effect of this interaction on neuronal [et al. is particularly worthy of mention. This new DIVBBB system is composed of human endothelial cells and human astrocytes, which avoid species differences in the co-culture providing a more reliable physiological model. As this in-vitro model allows cell extravasation analysis under realistic perfusion pressures and shear stress levels, it is a useful tool to study the BBB characteristics in different pathophysiological conditions.Some of the studies reported performed the OGD experiments co-culturing endothelial cells with neurons or astrocytes in order to mimic as closely as possible neuronal  and non-neuronal ,82 brainin-vitro ischaemia. In our opinion, further studies are necessary to better understand the long-term response of microvascular endothelial cells after OGD as part of a broader attempt to unravel the different molecular mechanisms of stroke. Finally, we would like to stress the importance of standardizing the OGD experimental conditions with cerebrovascular endothelial cells in order to provide comparative and reproductive results and to avoid misinterpretation due to the cellular model and experimental conditions used.It should be noted that the literature reviewed here does not provide viability, mortality, gene expression data of cerebral endothelial cells exposed to OGD after longer than 72 h post


Syndecans are a four member multifunctional family of cell surface molecules with diverse biological roles. Syndecan-3 (SDC3) is the largest of these, but in comparison to the other family members relatively little is known about this molecule. SDC3 null mice grow and develop normally, all be it with subtle anatomical phenotypes in the brain. Roles for this molecule in both neuronal and brain tissue have been identified, and is associated with altered satiety responses. Recent studies suggest that SDC3 expression is not restricted to neuronal tissues and has important roles in inflammatory disorders such as rheumatoid arthritis, disease associated processes such as angiogenesis and in the facilitation of infection of dendritic cells by HIV. The purpose of this review article is to explore these new biological insights into SDC3 functions in inflammatory disease. The proteoglycans are a diverse family of molecules with multiple roles in development, health and disease . HeparanThe syndecan-3 (N-syndecan) gene was first cloned from rat Schwann cells and was the third syndecan of the four member family to be identified , 7. It bO-linked glycosylation . Shed SDC3 has been observed in conditioned media in a variety of experimental systems, although the precise cleavage sites and proteases have yet to be confirmed . Predict418, Tyr419 in human SDC3, 180 in human SDC4) was identified as a substrate for Src kinase and this has implications for the trafficking of integrins. It would not be surprising if phosphorylation of these conserved residues impacted on SDC3 functionality in a similar fashion . Studies with syndecan-4 have shown the importance of these lysines for binding phospho-inositides (PIP3) and this not only stabilizes the structure of SDC4 cytoplasmic domain but this interaction is also associated with upregulating the activity of PKCα which promotes many of the downstream cell adhesion pathways associated with SDC4 (The syndecan-3 cytoplasmic domain consists of 33 aa and in common with the rest of the family possesses two conserved domains C1 and C2) flanking a variable sequence (V region), which is unique to each family member . Syndeca flanking fashion . Additionstrated . The V rith SDC4 , 17. It The association with SDC3 and cells of a neuronal lineage is well-established and a number of phenotypes associated with both the brain and nervous tissue have been identified , 19. AltSDC3 is found in other tissues of the brain, immunostaining of wild-type mice revealed SDC3 to be expressed in the hypothalamus, particularly in the paraventricular nucleus and the lateral hypothalamic area . SDC3 exSeveral hypothalamic neuropeptides regulate feeding behaviors; for example, agouti-related protein (AgRP), melanin concentrating hormone (MCH) and neuropeptide Y stimulate increased feeding behaviors, whereas α-melanocyte stimulating hormone (αMSH) and corticotrophin releasing hormone (CRH) serve to inhibit pathways associated with increased feeding . It is kIt is very evident from the studies described above that SDC3 plays a critical role in both brain development and behavior. However, other roles for SDC3 on endothelial cells (ECs) and leukocytes are emerging in inflammatory responses. The purpose of the remainder of this review is to examine the roles of SDC3 in the context of inflammatory disease (rheumatoid arthritis), angiogenesis and also HIV infection.SDC3 is expressed on endothelia in both rheumatoid and non-rheumatoid synovia and is thought to have roles in binding chemokines, specifically CXCL8, during the progression of the disease , 30. Intin cis, as is the case for SDC1  ectodomain have been shown to promote this process in the human lung microvasculature, which has ramifications for conditions such as sepsis or thrombotic disease states, where thrombin is activated . This woDendritic cells (DCs) are antigen presenting cells and play a critical role in identifying and capturing pathogens in peripheral tissues and subsequently priming T cells in the lymph nodes to initiate adaptive immune responses. Sexual transmission is the main route of HIV-1 dissemination and in the absence of surface lesions, the genital epithelia presents a barrier to viral crossing , 46. HIVA number of cell surface receptors have been implicated in facilitating both the recognition, binding and transmission of the HIV-1 virus; different receptors have been shown to interact with HIV-1 on DCs, such as the C-type lectin DC-SIGN , 52, thein trans of cells that express CD4 and chemokine receptors   . More reT cells)  found SDInterestingly, HIV-1 infected individuals known as HIV controllers (HICs) who are able to control viral replication without anti-retroviral therapy exist, but are rare. DCs from these individuals express higher levels of SDC3 and DC-SIGN and have been shown to be less susceptible to HIV-1 infection than cells from healthy donors. On the contrary, DCs from HICs show an enhanced capacity to capture HIV-1 when compared to cells from healthy donors or HIV-1 patients currently on anti-retroviral treatment with suppressed viral load . High leIn this review, we have explored the diverse roles that the HSPG, SDC3, plays in a range of disease and developmental contexts. Although many phenotypes have been described, there is still a great deal to learn about how SDC3 is actually functioning in these scenarios. SDC1, 2 and 4 have been intrinsically linked with integrins and in particular modulating cell adhesion and migration responses. SDC1 has been shown to modulate the activity of a number of αV integrin heterodimers, as has SDC4 , 61. SimIn many instances, it is interactions with SDC3 HS chains that appear to be driving the biological effects observed, whether it be acting to bind neuropeptides, inflammatory mediators or viral particles. However, this does raise the question as to what is driving this selectivity of the molecules toward SDC3 HS. In many cases, the other syndecans and indeed glypican family members are present in abundance but cannot perform the same functions as SDC3 despite possessing HS chains. One possibility is that sequences specific to SDC3 may influence the type and nature of GAG substitution, and it is this that differentiates SDC3 from other HSPGs. This would not be without precedent since studies have shown that sequences in the SDC2 ectodomain can influence the sulphation pattern of SDC2 HS . AlternaThe studies described above point to important roles for SDC3 in inflammatory disease which require further investigation and hint that this molecule is important not just in the neuronal context but in other tissues. More detailed studies are required in both the SDC3 null mouse, and patient samples, to explore disease models where inflammation and angiogenesis are a feature. Greater mechanistic insight is required to understand how SDC3 exerts its functions. It remains a possibility that modulating SDC3 function could prove useful in the treatment of the pathologies described above, however, the means by which this could be achieved requires elucidating.JW and SA generated the text and performed the literature searches. MB prepared the figures and GD critically appraised and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Atoh8flox/flox;Col2a1-Cre) and a germline-  specific Cre allele. In both strains, Atoh8 deletion leads to a reduced skeletal size of the axial and appendicular bones, but the stages of phenotypic manifestations differ. While we observed obviously shortened bones in Atoh8flox/flox;Col2a1-Cre mice only postnatally, the bones of Atoh8flox/flox;Prx1-Crefemale mice are characterized by a reduced bone length already at prenatal stages. Detailed histological and molecular investigations revealed reduced zones of proliferating and hypertrophic chondrocytes. In addition, Atoh8 deletion identified Atoh8 as a positive regulator of chondrocyte proliferation. As increased Atoh8 expression is found in the region of prehypertrophic chondrocytes where the expression of Ihh, a main regulator of chondrocyte proliferation and differentiation, is induced, we investigated a potential interaction of Atoh8 function and Ihh signaling. By activating Ihh signaling with Purmorphamine we demonstrate that Atoh8 regulates chondrocyte proliferation in parallel or downstream of Ihh signaling while it acts on the onset of hypertrophy upstream of Ihh likely by modulating Ihh expression levels.Atonal homolog 8 (Atoh8) is a transcription factor of the basic helix-loop-helix (bHLH) protein family, which is expressed in the cartilaginous elements of endochondral bones. To analyze its function during chondrogenesis we deleted Atoh8 in mice using a chondrocyte- ( During endochondral ossification, bones are formed by a multistep process, which includes the formation of a cartilage template of the later skeletal element and its subsequent replacement by bone. The cartilage anlagen originate in mesenchymal cells, which condense and differentiate into chondrocytes. These chondrocytes proliferate and express the extracellular matrix protein Collagen type 2 (Col2) . Two subAtonal homolog 8  is a transcription factor of the basic helix-loop-helix (bHLH) protein family .In contrast to other atonal-related proteins, which show a tissue restricted pattern of expression, Atoh8 is broadly expressed in many organs including the brain, kidney and skeletal muscles and regulates proliferation and differentiation of distinct cell types. For instance, overexpression of Atoh8 in retinal explant cultures promotes the differentiation of neuronal progenitors towards the neuronal versus the glial cell lineage . ConsistIn the developing skeleton, Atoh8 has been shown to be expressed in avian growth plates  and in mIn this study, we show that Atoh8 deficiency results in reduced zones of proliferating and hypertrophic chondrocytes and, consequently, in a decreased length of endochondral bones. In addition, we observed a reduced proliferation rate due to Atoh8 depletion suggesting Atoh8 positively regulates chondrocyte proliferation. Moreover, the altered domain of proliferating chondrocytes indicates an additional function as a regulator of hypertrophy. In limb explant cultures, we found Atoh8 to regulate the onset of chondrocyte differentiation upstream of Ihh, while it acts independent or downstream of Ihh in the regulation of proliferation.Col2a1-Cre Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: I Don't Know**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Atoh8 is a broadly expressed transcription factor affecting differentiation in many organs. Its expression in avian growth plate and mouse limb buds prompted the present study of any role in limb development.The investigators have knocked out Atoh8 using both Prx-Cre for germline and Col2a1-Cre for chondrocytes. The stages of phenotype development were different for the two. Zones of proliferative and hypertrophic chondrocytes were reduced for both, but the chondrocyte-specific knockouts had shortened bones postnatally whereas the Prx-Cre mice did so already at birth. Evidence was presented of interaction of Atoh8 with IHH signalling and expression levels.1. The paper is descriptive only , revealing a contributory role for Atoh8 in limb endochondral bone formation but no mechanistic insights. In its technical aspects the work has been carried out to a high standard.2. The insertion of figure legends in the text is unusual. Maybe it is a policy of the journal? It is a distraction, interrupts the flow of description of Results.3. Figure 1 needs a better description. Its details and interpretation would not necessarily be obvious to readers of the journal. The zones of limb buds need to be specified clearly in the figure. It is stated that Atoh8 is expressed in the proliferating and hypertrophic zones but especially in prehypertrophic, but it is not so clear in 1C that Atoh8 is expressed in the hypertrophic zone (Col X). Another interpretation is that Atoh8 is not expressed in hypertrophic but appears to be so in prehypertrophic.4. Fig 1D and E. What do the micromass culture data mean, apart from showing the knockdown of Atoh8?5. Fig 4C and P 13 lines 366 -373. This explanation of Fig 4C is difficult to understand, especially lines 369-371. The authors might explain carefully just exactly how these measurements were made.6. Fig 4C. It is impossible to discern any detail in 4C, to the extent that there seems little point in including it iof better images are not available. . This is particularly so in view of the extremely small . although statistically significant, difference between columnar cells of control and Atoh-deficient .7. Fig 6 and Discussion, lines 501-510. The negative outcome of this experiment is not conclusive. Atoh8 is located primarily in the nucleus and cannot be detected in cytoplasm with Csa treatment. Excluding the possibility of Csa outside the nucleus would require more throrough investigation, rather than concluding as has been done in Discussion lines 501 et seq.8. Fig 7 . The quantitative data in Fig 7 are unconvincing, and in 7C the same comment is made as for Fig 4C (above) – it is impossible to discern any detail in 7C. Furthermore the quantitative data is so weak that it is difficult to accept the conclusions that Atoh 8 acts upstream andin parallel or downstream of Ihh in regulating the onset of hypertrophy and proliferation rate, respectively ( Discussion lines 547-550). Indeed the linkage between Ihh and PTHrP is so strong physiologically that any attempt to reach conclusions about Atoh8 - Ihh relationship in isolation is beset with difficulty.Reviewer #2: The aim of this study is to uncover the role of atonal homolog 8 (Atoh8) transcription factor in endochondral skeletal growth. The authors first studied expression of Atoh8 in developing limbs by whole mount ISH and conventional ISH on tissue sections, and the qPCR analysis of RNA prepared from micromass cultures of control and Atoh8 CKO. The authors then carried out histological and morphometric analyses of various parameters of control (Cre negative litter mates) and conditional Atoh8 deficient mutant samples . Expression analysis revealed that Atoh8 was expressed in cartilage in developing skeleton. In addition, ablation of the gene resulted in marked reduction in collagen 2 expression at least in cultured chondrocytes. In contrast, histological and various morphometric analyses of the mutant mice showed very modest differences compared to those in control mice. Though the authors claim the possible role of Atoh8 in regulation of skeletal growth and interaction with Ihh signaling, the reviewer thinks that the study is inconclusive due to a small size of sample number and insufficient information on the mouse strains. They should also consider functional redundancy among other family members and more carefully interpret the results. Otherwise, their conclusion and discussion potentially mislead the readers.The culturs prepared from E12.5 limb buds contain various cell populations. The results from this culture system should not be interpreted as the phenotype of chondrocytes.To identify and establish very modest differences in size of skeleton and histomorphometric parameter of growth-plate, the authors need to have appropriate control and analyze enough number of specimens. In this study, the authors always used Cre negative littermates as control, but the strains of the parent mice are not given. If the compound mice generated in this study came from more than 2 strains, they need to consider impact of genetic drift on skeletal growth difference. Col2Cre could be an another control mouse. The sample size for analysis of skeleton length is 3-4, which may not be enough to conclude the growth retardation in the mutant mice. It is not provided how many biological different samples were used for histological analysis although the sample size ranges 10-12?. It should be provided how they define the anatomical landmark to orient the limbs and to secure the longest or same tissue level for measurement of various histological parameters.By searching “GenePaint” database, the reviewer found other members of Atoh families are also expressed in developing skeleton. Are there functional redundancy among other family members? Have you checked expression of other Atoh transcription factors? These points are required to reach conclusion on the exclusive role of Atoh 8 in endochondral ossification.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  1 Aug 2019Response to reviewer #1:1. The paper is descriptive only, revealing a contributory role for Atoh8 in limb endochondral bone formation but no mechanistic insights. In its technical aspects the work has been carried out to a high standard.In our publication, we initially describe the bone phenotype of mice with a chondrocyte-specific or a ubiquitous Atoh8 deletion and we show that Atoh8 regulates endochondral bone growth by activating chondrocyte proliferation and inhibiting chondrocyte differentiation. Additionally, functional data from in vivo and in vitro experiments indicates that Atoh8 regulates Ihh expression and in this way modulates the onset of differentiation, while it regulates chondrocyte proliferation either downstream or independent of Ihh. Although the data includes a morphological analysis, it clearly reveals first mechanistic insights into the interaction of Ihh and the transcription factor Atoh8.2. The insertion of figure legends in the text is unusual. Maybe it is a policy of the journal? It is a distraction, interrupts the flow of description of Results.The insertion of figure legends in the text corresponds to the guidelines of PLOS ONE.3. Figure 1 needs a better description. Its details and interpretation would not necessarily be obvious to readers of the journal. The zones of limb buds need to be specified clearly in the figure. It is stated that Atoh8 is expressed in the proliferating and hypertrophic zones but especially in prehypertrophic, but it is not so clear in 1C that Atoh8 is expressed in the hypertrophic zone (Col X). Another interpretation is that Atoh8 is not expressed in hypertrophic but appears to be so in prehypertrophic.For a better visualization of the data summarized in Figure 1, we included a whole-mount in situ hybridization using a Col2 probe  to demarcate the cartilage templates in the limb buds. Moreover, we added a schematic model showing the zones of proliferating, prehypertrophic and hypertrophic chondrocytes as well as the Col2 and Col10 expression domains . Atoh8 can be detected in all three chondrocyte populations with stronger expression in prehypertrophic chondrocytes. The hybridization signal is clearly visible in the high-resolution images of the manuscript . 4. Fig 1D and E. What do the micromass culture data mean, apart from showing the knockdown of Atoh8?As the reviewer states correctly, the verification of the Atoh8 knockdown is one important reason for showing the qPCR data obtained from micromass cultures . In addition, these data show that Atoh8 expression is maintained during the differentiation process in vitro. Although the expression is very low at d14, it increases again at later stages. This is in line with the results of the Atoh8 in situ hybridization revealing Atoh8 expression in proliferating, prehypertrophic and hypertrophic chondrocytes and with the observation that Atoh8 regulates not only the onset, but also the process of hypertrophic differentiation. Furthermore, the decreased Col2 expression in Atoh8-deficient micromass cultures indicates that the reduced nodule formation is at least partly based on the decreased proliferation of Atoh8-deficient chondrocytes as mentioned on p. 15 lines 385-387 (first version).5. Fig 4C and P 13 lines 366 -373. This explanation of Fig 4C is difficult to understand, especially lines 369-371. The authors might explain carefully just exactly how these measurements were made.For a better representation of our data, we added a schematic representation of the analyzed bone showing the zones of proliferating, prehypertrophic and hypertrophic chondrocytes and the Col2, Ihh and Col10 expression domains in Figure 4 . We also included a detailed description of how we performed the measurements in the materials and methods section. 6. Fig 4C. It is impossible to discern any detail in 4C, to the extent that there seems little point in including it if better images are not available. This is particularly so in view of the extremely small. Although statistically significant, difference between columnar cells of control and Atoh-deficient .We agree that it is difficult to discern details in Fig. 5C in the PDF file generated upon submission. However, in the original high-resolution image submitted (tiff file) the labeled cells are clearly visible. As the reviewer states correctly, the differences between mutant and control mice are only small at this developmental stage (around 2.5 %). Nevertheless, a slight, but continuously reduced proliferation rate will ultimately lead to a reduced skeletal size. 7. Fig 6 and Discussion, lines 501-510. The negative outcome of this experiment is not conclusive. Atoh8 is located primarily in the nucleus and cannot be detected in cytoplasm with Csa treatment. Excluding the possibility of Csa outside the nucleus would require more throrough investigation, rather than concluding as has been done in Discussion lines 501 et seq.It has been shown before that the nuclear Atoh8 translocates to the cytoplasm upon CsA treatment in HEK cells . In contrast, the results presented here demonstrate that under the same experimental conditions  the nuclear localization of Atoh8 is not altered by CsA treatment in ATDC5 cells indicating a cell type specific regulation of Atoh8 by Calcineurin. In fact, the potential regulation of Atoh8 by CsA and the corresponding connection to NFAT was one of the reasons we decided to investigate Atoh8 function in chondrocytes. As the reviewer states the result was negative concerning the localization dependency of Atoh8 on Calcineurin. Nevertheless, we thought it is important to publish the cell type specific regulation of Atoh8 localization, while the localization and activity of Calcineurin was not the scope of this study. 8. Fig 7. The quantitative data in Fig 7 are unconvincing, and in 7C the same comment is made as for Fig 4C (above) – it is impossible to discern any detail in 7C. Furthermore the quantitative data is so weak that it is difficult to accept the conclusions that Atoh 8 acts upstream and in parallel or downstream of Ihh in regulating the onset of hypertrophy and proliferation rate, respectively (Discussion lines 547-550). Indeed the linkage between Ihh and PTHrP is so strong physiologically that any attempt to reach conclusions about Atoh8 - Ihh relationship in isolation is beset with difficulty.As discussed for figure 5C, the low quality of Fig. 7C is based on the conversion of a high-resolution tiff file visualizing details to a PDF file. We fully agree that the interaction of Ihh and Pthrp is complex. While Ihh regulates chondrocyte proliferation independent of Pthrp, the onset of hypertrophic differentiation/pool of proliferating chondrocytes is regulated by the upregulation of Pthrp. Importantly, the expression of Pthrp is strongly dependent on Ihh and reduced Ihh signaling or activation by Purmorphamine treatment is followed by the respective alterations in the Pthrp levels . Our hypothesis that Atoh8 differentially interacts with Ihh in regulating chondrocyte differentiation and hypertrophy is based on several lines of evidence: The data in Fig. 7E show that the Ihh expression is significantly decreased in Atoh8 mutants (around 40 % at E14.5 and around 70 % at E16.5) indicating that Atoh8 activates Ihh expression. The reduced Ihh expression is linked to an accelerated onset of hypertrophic differentiation as shown in Fig. 3D and 4E placing Atoh8 upstream of the Ihh/Pthrp system. This is supported by data from the limb explant cultures  demonstrating that activation of the Ihh/PthrP system by Purmorphamine increases the pool of proliferating chondrocytes independent of Atoh8 function. Reduced Ihh levels will also lead to a reduced chondrocyte proliferation rate. However, the data shown in Fig. 7C and D indicate that activation of Ihh signaling does not rescue the proliferation rate downstream of Atoh8 strongly pointing to an Ihh independent or downstream function of Atoh8. A similar relationship has also been observed for FGF signaling which acts upstream of Ihh on the differentiation (inhibits Ihh expression) and independent or downstream on the proliferation rate . In order to clarify the interdependency of Ihh and Pthrp, we changed the discussion in lines 516-528 .Response to reviewer #2:1. The cultures prepared from E12.5 limb buds contain various cell populations. The results from this culture system should not be interpreted as the phenotype of chondrocytes.We agree that micromass cultures can only supplement morphological in vivo data. In this context, we used this culture system to complement the observations made in the Atoh8 mutants . The reduced Col2 expression and the reduced nodule formation in Atoh8-deficient cultures are in line with the reduced chondrocyte proliferation and the defects in initial chondrocyte differentiation we found in Atoh8 mutant mice .2. To identify and establish very modest differences in size of skeleton and histomorphometric parameter of growth-plate, the authors need to have appropriate control and analyze enough number of specimens. In this study, the authors always used Cre negative littermates as control, but the strains of the parent mice are not given. If the compound mice generated in this study came from more than 2 strains, they need to consider impact of genetic drift on skeletal growth difference. Col2Cre could be another control mouse. The sample size for analysis of skeleton length is 3-4, which may not be enough to conclude the growth retardation in the mutant mice. It is not provided how many biological different samples were used for histological analysis although the sample size ranges 10-12? It should be provided how they define the anatomical landmark to orient the limbs and to secure the longest or same tissue level for measurement of various histological parameters.As stated in our discussion Atoh8 deletion results in a modest bone phenotype, which due to the biological variability is difficult to analyze using conventional statistical analyses. Therefore, we implemented a Bayesian probability based statistical analysis, which takes differences between litters and experiments into account and allows to generate meaningful data with relatively low sample sizes and animal numbers. In addition, we added detailed information about the sample numbers (see figure legends), the numbers of sections analyzed and a more detailed description about how the measurements were performed . Both Cre lines have been used in multiple studies and no effect of the Cre allele has been published to our knowledge. To further exclude effects of the Cre driver line, we compared the bone length of mutant, heterozygous and control mice . These data show, that the reduced bone length is based on the Atoh8 deletion and not on the Cre recombinase . Moreover, we analyzed two mouse strains with different Cre recombinases at different stages in our study  and found comparable differences between mutants and controls making it unlikely that the effect observed is due to two different Cre alleles. To exclude strain differences all mouse lines were kept on a C57BL/6J background and mutants were compared to littermates.3. By searching "GenePaint" database, the reviewer found other members of Atoh families are also expressed in developing skeleton. Are there functional redundancies among other family members? Have you checked expression of other Atoh transcription factors? These points are required to reach conclusion on the exclusive role of Atoh 8 in endochondral ossification.As the reviewer mentioned, in situ hybridization data of sagittal E14.5 sections available at “GenePaint” indicate an expression of other Atoh genes in the developing skeleton. Based on this, we performed qPCR analyses to test the expression of Atoh1, Atoh2, Atoh3, Atoh4, Atoh5 and Atoh7 in forelimb skeletal elements of E14.5 control and Atoh8-deficient embryos. We could however not detect expression of any of these Atoh genes in control or Atoh8-deficient mice indicating that there is no functional redundancy or compensation during endochondral ossification. We added these results as Fig. S4 to the supplementary material.AttachmentResponse_to_Reviewers_Schroeder_et_al.docxSubmitted filename: Click here for additional data file. 15 Aug 2019Atoh8 acts as a regulator of chondrocyte proliferation and differentiation in endochondral bonesPONE-D-19-14765R1Dear Dr. Vortkamp,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #2: All comments have been addressed**********2. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Yes**********3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: I Don't Know**********4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #2: Yes**********5. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #2: No**********6. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #2: (No Response)**********what does this mean?). If published, this will include your full peer review and any attached files.7. PLOS authors have the option to publish the peer review history of their article (If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? 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Association between parent’s Body Mass Index (BMI) and their children, has been widely documented. Individual, familiar and structural factors play a role in this relation. We analyzed the association between maternal BMI change during the first year post-partum and their offspring’s growth-trajectories and energy intake in their first five years of life.Compare growth-trajectories and children’s caloric intake according to post-partum mother´s BMI classification.The anthropometric assessment was taken in 935 mother-child pairs along the study period. Mothers were classified into four groups according to their BMI-trajectories in the post-partum. Children’s weight for height z-scores (WHZ) was compared among groups using random-effects regression models. A longitudinal comparison of children’s caloric intake by the maternal group was carried out.At 42 months of age, infants from mothers that remained overweight during the first year post-partum had, on average, 0.61 SD higher WHZ than those from mothers who remained in a recommended BMI group (R-BMI) in the same period. At 60 months of age, children´s prevalence of obesity was almost twice in the maternal overweight group vs R-BMI group (14.2% and 7.3% respectively). Chances for a child of having an over caloric intake were 36.5%  and significantly higher among children from overweight mothers than those from R-BMI mothers. The difference in children’s WHZ trajectory remained significant after adjusting for caloric intake, suggesting that contextual factors play a role in shaping children’s obesity. A concurrent ethnographic study with the study subjects provides suggestions as to what these factors might be, including changes in the food landscape.Children from overweight mothers tended to have a more caloric diet yielding a higher propensity to obesity. Contextual factors such as food landscape might contribute to childhood obesity beyond having an overweight mother. Pregnancy and post-partum is a window of opportunity for interventions to decrease the incidence of children’s overweight. The prevalence of overweight and obesity (OWO) in children has increased worldwide . Betweenimmediate causes are the positive balance of energy mediated by physical activity, genetic and epigenetic factors. However, these immediate causes are influenced by underlying causes such as food access (processed or not), food costs, eating habits within families, and accessibility to environments that promote physical activity. These underlying causes are in turn, influenced by basic causes such as changes in family structure, urbanization and globalization [Obesity is the result of complex factors that interact affecting individuals at different levels in different stages of life. As Rivera et al.,  argue balization .immediate predictors of childhood obesity [underlying and basic causes, such as built environments and food landscapes that promote or inhibit physical activity and healthy diets [Parental obesity is recognized as one of the most significant  obesity ,8 throug obesity –11. Addi obesity . However obesity . In the hy diets .2 [The association between the parent’s BMI and their children has been widely documented ,15. The 2 . Evidenc2 ,20–24, h2 –28, part2 . The maj2 ,31 livinThe objective of this study was to compare growth trajectories and to estimate the prevalence of overweight (>2SD WHZ score) in children in their first five years of life according to mother´s BMI classification along one year post-partum; and also, to conduct a longitudinal comparison of children’s caloric intake according to the above mentioned maternal group-classification. We hypothesized that those children whose mothers became or stayed obese during post-partum have hypercaloric diets and more chances of presenting overweight compared to those children whose mothers stayed in the recommended BMI.Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) project. The sample population pooled participants of four successively birth cohort studies, recruited between 1994 and 2004 (the same period when the rising of overweight in children started) at 6 public maternity hospitals, which serve low to moderate income populations in Mexico City.We studied mother–child pairs from the The four cohort studies had their specific aims, but shared eligibility and exclusion criteria allowing estimation of pooled effects. Anthropometrical measurements and relevant covariates were collected with a standardized technique by the same group of investigators and fieldwork staff.Cohort 1   or five (cohorts 2 and 3) years of life. Women were not eligible to participate in the cohorts if they were planning to change residency from Mexico City within the following five years after recruitment; had a history of infertility, diabetes, or psychosis; daily consumption of alcohol during pregnancy; addiction to illegal drugs; physician’s diagnosis of current multiple or high-risk pregnancy; or did not intend to breastfeed . Eligibility criteria to be included in this study were mothers with at least two anthropometrical measurements in the first year post-partum and children with at least one anthropometric measurement during infancy. Among the 1263 mother-child pairs that fulfilled these criteria, 935 were included in this study based on the availability of information for the analysis.All research protocols were approved by the Research, Ethics and Biosafety committees of the National Institute of Public Health of Mexico and the Internal Review Board of the Harvard School of Public Health, the Brigham and Women’s Hospital, the University of Michigan School of Public Health, and the participating hospitals. All data were fully anonymized before you accessed them and the patients provided informed written consent to have data from their medical records used in research.2) and then classified following WHO guidelines [WHO Anthro macros [We analyzed the intra-family variation of the feeding decisions process through the maternal BMI trajectory given that within most Mexican households, mothers, rather than fathers, are responsible for feeding their children ,40. Mateidelines : Recommeidelines  calibrato macros .Trained personnel administered a semi quantitative food frequency questionnaire (FFQ) to the mothers at each study visit to inquire on children’s diet over the past 3 months, beginning when the child was 12 months old. This instrument was validated in Mexican population using the Willet methodology . CaloricStatus and duration of breastfeeding were determined by questionnaires administered to the mothers in each study visit. Maternal education, measured as years in school, was used as a proxy for socioeconomic status (SES) since ELEMENT did not collect information on SES in Cohort 1. Gestational age (GA) was calculated based on the date of last menstruation and the date of birth of the child, we consider a child as a premature for GA less than 37 weeks. Parity was defined in two group, as primiparous and multiparous. We classified marital status as with/without a partner. Overweight children was defined as >2SD of the WHZ score.We performed a data exploratory analysis to describe the distributional behavior of the main variables as well as to identify the association patterns among them. This included cross-sectional and longitudinal analyses of the information aimed to establish the kind of children growth curve, types of maternal changes in BMI, and relationships between them and relevant covariates.Post-partum BMI pattern (PP-BMIP): 1) mothers who stayed within the recommended BMI(R-BMI); 2) mothers who stayed within OWO categories; 3) mothers who moved from OWO to R-BMI and 4) mothers who moved from the R-BMI to OWO.According to BMI changes along the study period, women were classified in four groups that we called Post-partum BMI pattern; 2) modeling weight for age z-scores trajectories during the study period according to these groups.We used maternal anthropometry in the first year post-partum as an indirect way to assess pregnancy weight as many studies has done before –50, wherrecommended caloric intake and high caloric intake. This was confirmed by the adjustment a 2-normal mixture model [children diet groups along the study period according to Post-partum BMI pattern based on a logistic model with random effects adjusted by sex and child´s age.Cross-sectional analysis of the caloric intake per study visit suggested the existence of two types of infant’s diet overall: re model . We carrPost-partum BMI pattern was assessed using a linear regression model with random effects [by children diet group, birth weight, duration of breastfeeding, maternal age, and maternal education. To account for the rapid increase in prevalence of OWO in Mexico, models were adjusted by year of birth. The quadratic relationships between maternal education and year of birth with the outcome were modeled using a second degree polynomial for each year. All statistical analyses were conducted in STATA 14 .Kernel density estimators for WHZ along every study visit by these maternal groups were obtained showing a symmetric and unimodal distribution in any case. Modeling WHZ along the study period according to  effects  adjustedTo understand the variability that was not explained by the statistical analysis, we used results from an ethnographic study , 45 cond2 (R-MBI) and 478 (51.1%) remained OWO (≥25kg/m2) throughout the study period. Maternal demographic characteristics at time of recruitment by Post-partum BMI pattern are displayed in Post-partum BMI pattern (p = 0.048), it is not meaningful. Women attended on average to three study visits during the first year post-partum and 82.78% attended to all of the visits designed for its corresponding cohort.Mother’s age ranged from 14 to 44 years at one month post-partum with a mean of 26.21(5.4). On average, women attended school 10.39(3.0) years; most of them (90.56%) had a partner and for 26.7% this was the mother´s first child. Among the 935 women included in these analyses, 317 (33.9%) remained within a BMI<25kg/mPost-partum BMI pattern are displayed in Post-partum BMI pattern.Characteristics of the offspring by On average, children attended 5.3 visits, out of the 7 or 8 scheduled according to the specific research protocol, 27.6% attended all visits designed in its corresponding cohort (data not shown in tables). Overweight children percentage tends to be higher among the OWO mothers all along from 18 to 60 moa. The prevalence of obese infants at 60 months in group from OWO mothers was 14.20% vs 7.32% and in the group from R-BMI. The overweight percentage difference goes up to 7.2% at 36 moa between OWO group (11.18%) and recommended BMI group (3.96%).Children from mothers that remained in the R-BMI group had lower WHZ along all study visits, in comparison with those from women that remained in the OWO category . The higs estimators of each mixture. At 12 months of age, mothers reported almost a 700 kcal difference in children’s intake between these two components or groups . This difference was statistically significant and remained or even increased in the following ages, suggesting that the study population is integrated by two subgroups of infants: those with a recommended caloric intake (RCI) and those with a high caloric intake (HCI). The proportion of children in the first category globally and by Post-partum BMI pattern is displayed in the right panel of The children’s caloric intake increased over time. For each age, the distribution of caloric intake behaves as a mixture of distributions with a bimodality; this was confirmed by the adjustment of a 2-normal mixture model. Children’s caloric intake in the OWO group is consistently higher than the corresponding to the R-BMI mothers . Left paPost-partum BMI pattern is shown in Most of the children were in the HCI group at 24 months and later, whereas the opposite happened at earlier ages. The percentage of children in the HCI group was higher at all ages in the group of mothers that remained in the OWO category. Adjusted association between children caloric intake group and After adjusting for year of birth, years in school and breastfeeding, we documented that, independently on their caloric intake group, children from mothers that remained OWO showed and overall increase of 0.35  in WHZ in comparison with children from mothers that remained within the recommended category of BMI . A proteOur concurrent ethnographic study with a subset of the maternal child populations provides context for 38.2% that remains unexplained by our statistical model. Results from the ethnographic study document show that there are environmental factors beyond mothers BMI that yield overweight children. In the last 25 years calorie-dense processed foods have become more plentiful and affordable in Mexico City. Mexico City is more accurately classified as a “food swamp” rather than a “food dessert”.  Fruits aThus, changes in Mexico’s food landscape have made it easier to provide care through cheap processed foods than ever before. While heavier mothers tend to provide more of both kinds of food, all mothers described how they provided more food than in the past. Our ethnographic observations not only documented shifts in the quantity and quality of study participants diet, but also the fact that transformations in the food landscape effect how people eat independent of their age and pregnancy condition. An abundance of cheap processed foods shape diets from gestation, through infancy and the life course.Our results documented that, after adjusting for confounders, children from mothers that remained in the OWO category had 0.3(0.06) overall higher WHZ during the first five years of life with the biggest difference at 4 years of age (0.6SD), stage near to the adiposity rebound . This didirect causes of obesity related with caloric intake. It has been documented the stronger relationship between maternal anthropometry and children’s feeding practices [Post-partum BMI pattern (p = 0.048), we don’t think that education would be related to the way mothers behave in the different groups given that the observed differences are not meaningful. We interpreted maternal anthropometry first as a proxy of the intrauterine environment where over caloric maternal diet is associated to obesity of the offspring [OWO mothers tended to report high caloric diets to their children compared to mothers with R-BMI, addressing the ractices  and thisffspring ,20, consffspring . Second,Nevertheless, we do not mean to imply that mothers are the only influence on children’s weight. Based on Naess , having It has been documented that in the first six months of life, birthweight and breast-feeding were the most important determinants in weight gain . This re.Our results are in accordance with previous findings in Mexican and Mexican-American  populatiThe largest strength of the study is its longitudinal design in both generations, and the complementary strategies of analyses. Complementing statistical analysis from a cohort study with ethnographic observations allowed us to discuss further the environmental and developmental origins of obesity in a comprehensive approach including immediate, underlying and structural factors under Rivera’s framework. However, we also identify some limitations: the study population participating in the ELEMENT cohort studies, is a non-probabilistic sample that was recruited in public hospitals with no population representativeness. In the beginning, the main focus of ELEMENT was towards biological mechanisms where this kind of representativeness is not crucial. However, in this research, we acknowledged that the social environment plays a relevant role, and the socioeconomic homogeneity of the sample may constitute a limitation preventing the extrapolation of the results to a different population where the social environment differs. However, the percentage of the Mexican population that receives health services from the public hospitals is so high (72.58% in 2012 and 83.8% in 2016) ,63 that We neither have quantitative information on children physical activity and on physical environments, but through our ethnographic study, we know that ELEMENT participants live in environments with very limited access to physical activity regularly: The only regular physical activity was walking to school . FinallyAs these studies highlight the importance of early factors that influences the development of obesity in children, future directions of this research should focus on replicating this analysis with a longer-time effect (adolescence or young adulthood) as this is an ongoing cohort. There needs to be further studies utilizing data from birth-cohorts conducted in developing countries to confirm our findings.Maternal obesity during the first year post-partum might reflect an obesogenic environment during gestation provided by an over caloric maternal diet. The obesity condition almost doubled the prevalence of overweight and obesity at five years of age in the offspring with respect to non-obese mothers.S1 Table(DOCX)Click here for additional data file.