COVID-19 has altered health sector capacity in low-income and middle-income countries (LMICs). Cost data to inform evidence-based priority setting are urgently needed. Consequently, in this paper, we calculate the full economic health sector costs of COVID-19 clinical management in 79 LMICs under different epidemiological scenarios.We used country-specific epidemiological projections from a dynamic transmission model to determine number of cases, hospitalisations and deaths over 1 year under four mitigation scenarios. We defined the health sector response for three base LMICs through guidelines and expert opinion. We calculated costs through local resource use and price data and extrapolated costs across 79 LMICs. Lastly, we compared cost estimates against gross domestic product (GDP) and total annual health expenditure in 76 LMICs.COVID-19 clinical management costs vary greatly by country, ranging between <0.1%–12% of GDP and 0.4%–223% of total annual health expenditure (excluding out-of-pocket payments). Without mitigation policies, COVID-19 clinical management costs per capita range from US$43.39 to US$75.57; in 22 of 76 LMICs, these costs would surpass total annual health expenditure. In a scenario of stringent social distancing, costs per capita fall to US$1.10–US$1.32.We present the first dataset of COVID-19 clinical management costs across LMICs. These costs can be used to inform decision-making on priority setting. Our results show that COVID-19 clinical management costs in LMICs are substantial, even in scenarios of moderate social distancing. Low-income countries are particularly vulnerable and some will struggle to cope with almost any epidemiological scenario. The choices facing LMICs are likely to remain stark and emergency financial support will be needed. The COVID-19 pandemic has ravaged every corner of the world and resource needs of clinical management are estimated to be high: published global annual estimates range from US$130 billion to US$1 trillion and monthly estimates for low-income and middle-income countries (LMICs) range from US$33 to US$61 billion.While useful for budgeting purposes, currently available cost estimates of clinical management largely report incremental financial costs based on normative guidelines which are not adequate for priority setting.We present the first country-specific set of full economic ‘real-world’ costs of COVID-19 clinical management for 79 LMICs which are urgently needed for cost-effectiveness analyses of vaccines, as well as to inform the redesign of essential services packages and to feed into discussions on the balance between broader macroeconomic costs of mitigation strategies and the costs to the health system.Average COVID-19 costs vary greatly by country and scenario of social distancing, making up between <0.1%–12% of gross domestic product and 0.4%–223% of total annual health expenditure (excluding out-of-pocket payments).Without mitigation policies, average COVID-19 clinical management costs per capita range from US$43.39 to US$75.57, surpassing total annual health expenditure in 22 out of 76 LMICs; in a scenario of moderate social distancing, these drop to US$38.52—US$58.08 and in one of stringent social distancing to US$1.10–US$1.32.Health sector costs of COVID-19 are substantial in LMCIs, even when assuming lower-cost critical care options.High levels of social distancing by the general population throughout the year would greatly reduce costs to the health system; as social distancing is relaxed, emergency financial support will be needed.COVID-19 was declared a public health emergency of international concern by the World Health Organization (WHO) in January 2020.A limited number of studies have explored country-specific unit costs and total costs of COVID-19 clinical management in high-income settings14–16While the abovementioned global studies are key to mobilising resources for COVID-19, they either present incremental financial costs, exclude broader health systems costs or assume a normative approach to resource use unlikely to be followed in LMICs faced with severe resource constraints. Full economic costs of a broader COVID-19 health system response, including ‘real-world’ plausible estimates of service delivery in LMICs under different mitigation scenarios, are urgently needed to inform the priority-setting process. These costs will be required to understand the cost-effectiveness of novel COVID-19 curative and preventive interventions, including those focused on vaccination, as well as to define the extent to which essential services can be maintained during the pandemic. Further, full economic cost data are needed to inform policy choices seeking to balance the broader macroeconomic costs of mitigation strategies against the costs to the health system from the disease. Lastly, country-specific resource estimates are needed to highlight the gaps between currently available financial resources and those which would be required for adequate care and treatment of COVID-19 in LMICs. Such estimates may further contribute to country-specific resource mobilisation efforts.This paper presents the first estimates of full economic costs of the COVID-19 response to health systems in LMICs taking a ‘real-world’ approach under different pandemic mitigation scenarios over a 12-month period.We used country-specific epidemiological projections from a dynamic transmission model which estimated total numbers of cases, days of hospitalisations and deaths under different mitigation scenarios. We defined the health sector response for three different LMICs in detail , using a combination of guidelines and expert opinion. We used local resource use and price data from a range of primary and secondary data sources. We then extrapolated costs across LMICs at similar income levels. Lastly, we compared cost estimates against country-specific measures of gross domestic product (GDP) and health expenditure. Greater details on our methods can be found in the 10.1136/bmjgh-2021-005759.supp1Supplementary datahttps://cmmid.github.io/topics/COVID-19/LMIC-projection-reports.html). For each country, the model produces different projections of the number of clinical cases, number of required days in hospital for severe cases  and critical cases (intensive care unit (ICU)) and deaths for 57 distinct mitigation scenarios that may occur over a 1-year period.17Our estimates of COVID-19 cases for different scenarios come from the CovidM epidemiological model, produced by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene & Tropical Medicine, which projects the health impact of COVID-19 for 92 LMICs : (a) emergency response mechanisms at the national level; (b) risk communication and community engagement; (c) case finding, contact tracing and management; (d) surveillance; (e) public health measures (hygiene education); (f) screening and diagnosis  and (g) case management.To estimate the average unit costs for each activity, we used an ingredients-based costing approach.We conducted our costing based on global guidelines. However, we adapted the level of COVID-19-specific resource needs to take into account feasibility in LMIC contexts by a combination of reviewing COVID-19 resource planning tools and budgets and scoping literature searches for primary data on clinical care practices in LMICs. Clinical management resource use estimates were adapted based on informal consultations on low-cost critical care options, including an estimation of oxygen therapy needs see . While wTo generate costs for other LMICs, we extrapolated our detailed unit cost estimates for Ethiopia, Pakistan and South Africa to LICs, lower-MICs and upper-MICs, respectively, based on country-specific epidemiological and health systems data and standard approaches to adjusting prices. In effect, the one constant element between countries is the model of care, with all other aspects of costs adjusted using national-level data in each of the 79 countries.Each cost input in the ingredients costing was classified as a tradeable good, non-tradeable good or staff cost.Non-tradeable goods cannot be easily traded in international markets and generally need to be consumed in the country where they have been produced . To convert these, we multiplied the proportion of the unit cost that was defined as non-tradeable (in 2019 US$) by the ratio between the 2019 GDP per capita  of the second country and the 2019 GDP per capita (adjusted for PPP) of the base country. Data on GDP per capita (adjusted for PPP) were found in the World Bank database.26To convert staff costs from a base country to a second country, we used conversion rates from a regression analysis on wages of health workers for 193 countries to predict wages by country income category relative to GDP per capita. We estimated the number of working hours for nurses, doctors and other medical staff and applied GDP per capita multipliers in order to value their time.27We calculated unit costs per activity for a total of 129 LMICs, as well as a mean unit cost per activity per country income category  weighted by population.Unit costs per activity were multiplied by the number of activities expected in each country, in some cases driven by the epidemiological estimates  and in others by fixed time and geographical area . Since scenario 1 models an unmitigated epidemic only clinical management costs were included. See While an effort was made to ensure that the resource use costed is feasible in LMICs, our total cost estimates assume that every patient with severe or critical disease will be hospitalised regardless of existing hospital bed capacity. In other words, we estimate total resource needs regardless of current country-specific non-financial constraints.Total country-level costs were estimated for 79 LMICs where we had epidemiological estimates. The 79 countries have a combined population of more than 3.98 billion people, which accounts for 60% of the total population of LMICs.Estimates of annual cost per capita of each scenario in each country were estimated by dividing total costs by the population of each country. These were compared against: (1) country-specific GDP per capita, (2) national health spending  per capita, (3) national health spending (including OOP expenditure) per capita and (4) government health expenditure per capita in 76 LMICs where relevant data were available,Data on the percentage of symptomatic cases tested with PCR were unavailable and so our assumed base case estimate (10%) was considered highly uncertain. Consequently, we performed a deterministic sensitivity analysis by increasing this value between 20% and 100%.While there was no patient involvement in our research, we consulted several actors involved in policy-making in LMICs to ensure our work was useful in national-level decision-making.Country-specific unit costs can be found in 10.1136/bmjgh-2021-005759.supp2Supplementary dataMean costs per capita per country income group weighted by population are presented in In all scenarios, the largest cost drivers were screening and diagnosis and case management. Costs of screening and diagnosis were particularly substantial for LICs, accounting for 51.62%–59.47% of total costs, and less substantial for upper-MICs, accounting for 20.10%–26.45% of total costs. Conversely, the costs of case management were particularly substantial for upper-MICs , and less so for LICs (37.75%–48.38%). Most of the costs of case management are related to hospital-based critical care (>76% across country income groups and scenarios). Case finding, contact tracing and surveillance and public health measures in contrast made up less than 4% of the total response costs (in scenarios 2–4), see The maps in COVID-19 costs per capita as a percentage of GDP per capita are highest in scenario 1 (unmitigated epidemic) and scenario 4  across all countries, ranging from 1.43% in Eswatini to 11.85% in Burundi (both scenario 4). They were consistently lowest in scenario 3 : between 0.03% in Angola, Bolivia and Ghana and 0.83% in Zimbabwe.Likewise, COVID-19 costs as a percentage of health expenditure were highest in all countries in scenarios 1 and 4: 23.35%–216.36% and 23.42%–222.34% of total national health spending excluding OOP payments, respectively; 14.68%–171.29% and 15.68%–183.51% of total national health spending including OOP payments, respectively, and 35.15%–1344.28% and 38.24%–1451.34% of government health spending, respectively. Lowest proportions were observed in scenario 3: 0.38%–18.96% of total national health spending excluding OOP payments, 0.26%–15.64% of total national health spending including OOP payments and 0.68%–40.78% of government health spending.We found that estimates were highly sensitive to our assumptions on the number of symptomatic cases tested for scenarios 2–4 see . IncreasWe provide the first set of country-specific full economic cost estimates of COVID-19 management in LMICs, from a health sector perspective. The countries included in our study account for 60% of the total population of LMICs.We find that the costs to the health sector of responding to COVID-19 are substantial in LMICs, even when assuming lower-cost critical care options. High levels of social distancing by the general population throughout the year (scenario 3) would greatly reduce costs compared with a policy of allowing the pandemic to proceed unmitigated (scenario 1), but also, importantly, compared with scenarios leading to moderate levels of social distancing (scenarios 2 and 4).Our findings suggest that the total number of cases is highest in a policy scenario of no mitigation (scenario 1). However, the highest total costs vary by country between scenario 1 and scenario 4 (30-day lockdown followed by minor reductions in contacts). While scenario 4 has a lower number of total cases, costs are comparable to those in scenario 1 because scenario 1 only accounts for clinical management costs and excludes the costs of any mitigation strategies, including testing beyond severe and critical cases in hospital. These results should not be taken as an endorsement that it is preferable, from a financial perspective, to have no mitigation strategy over a strategy of limited social distancing. A no mitigation strategy may result in slightly lower costs of COVID-19 management in some settings but would have substantial knock-on effects on costs and outcomes for other health interventions not quantified in our study.We compared total COVID-19 costs per country across the four epidemiological scenarios against GDP per capita and three metrics of national-level health expenditure per capita: total national health expenditure with and without OOP payments and government health expenditure. We found that while some countries are likely able to absorb the costs (particularly in scenario 3), even moderate levels of social distancing would lead to high levels of the health spending being directed towards COVID-19 in nearly all countries. In scenario 2, for example, 74 out of 76 countries would need to direct more than 20% of their total national health expenditure (excluding OOP costs) to COVID-19.COVID-19-related costs would exceed total health spending in several countries, although this varies by expenditure metric examined and epidemiological scenario. COVID-19 costs could exceed total expenditure in between 8 and 11 countries out of 76 when compared against total national health expenditure including OOP. This figure increases to 18–23 countries when compared against total national health expenditure excluding OOP and to 52–54 countries when compared against government health expenditure only. This highlights that in many countries, OOP expenditure and non-government sources of health expenditure, such as donor funding, may play a critical role in covering the costs of the COVID-19 pandemic. As the global macroeconomic situation deteriorates due to the pandemic and overseas development aid is reduced, international agencies and donor nations need to be aware of the potentially catastrophic consequences of reducing funding towards health services in LMICs and the consequent impact on OOP expenditure.No country is expected to exceed total health expenditure under scenario 3. However, in eight countries, COVID-19 costs are projected to exceed spending across all three health expenditures metrics for scenarios 1, 2 and 4: Burundi, Central African Republic, Democratic Republic of Congo, Ethiopia, The Gambia, Madagascar, Mozambique and Tanzania. These countries, all LICs located in sub-Saharan Africa, are therefore particularly vulnerable and will likely require considerable financial support.Further research is required to better understand the effect that shocks of this magnitude have on the health system and, particularly, on essential services. It is crucial to understand which services are most vulnerable to being displaced and the levels of funding required to ensure their continued provision, as well as identifying which non-urgent services can be temporarily delayed without causing lasting impact.We carried out a costing from the perspective of the health sector focusing exclusively on COVID-19. We have not quantified the health impacts or costs of deferring other key health sector activities. Further, while high levels of social distancing would lead to better COVID-19-related health outcomes and lower health sector costs, they may also imply economic losses in other sectors as well as social and other non-economic consequences. Decision-makers should consider all these factors when debating COVID-19 mitigation policies.et al,et al included a larger population in their analysis . While the scope of our costing was narrow and focused largely on clinical activities, Tan-Torres Edejer et al also included non-clinical interventions . Lastly, Tan-Torres Edejer et al assumed higher remuneration of staff during the pandemic by including hazard pay as per international guidelines, whereas we assumed staff salaries would remain constant with prepandemic salaries.Our unit cost estimates per day of hospitalisation for severe and critical cases are broadly in line with those published in the literature for LMICs.The two activities with highest proportion of costs in our analysis were screening and diagnosis and case management. Screening and diagnosis costs accounted for a particularly high percentage of total costs in LICs (over 50% of in some scenarios). We assumed all testing across all settings would be PCR based, which led to relatively high unit costs. As the pandemic evolves, we expect less resource intensive diagnostic technologies with adequate accuracy to replace PCR, leading to lower overall costs. Our total costs were highly sensitive to variations in testing scale-up: a 10% increase in the proportion of symptomatic cases tested led to increases of up to 18% in total costs. Country-specific data on numbers of people tested are needed to better calibrate our cost model.Case management costs were high across settings, particularly in upper-MICs. Healthcare staff salaries made up a large proportion of the costs per day in hospital, particularly for critical cases; higher unit costs in upper-MICs are a product of comparatively higher staff salaries in these settings. Our cost model already assumes a conservative staff-to-patient ratio. We do not expect costs per day of hospitalisation to drop considerably unless this ratio is further reduced. With the advancement of new therapeutics, costs per day of hospitalisation may increase; however, the costs per case treated may decrease if new therapeutics allow for faster patient recovery and reduce length of hospital stay required.We aimed to calculate overall resource needs, so our total costs assume that all severe and critical patients will be hospitalised. However, this is currently unlikely in certain settings, particularly in LICs, as not all those who need care will be able to access it; ICU capacity remains extremely limited in many settings.We did not account for country-specific short-run health system constraints. Accurately quantifying resources constraints, particularly those related to critical care at a global level, is difficult as many components are required . It is important for policy-makers to measure and consider these constraints in a country-specific manner when making allocative decisions between different health needs, acknowledging that some resources  cannot normally be relaxed at scale in the short term, even with additional funding.While we did not factor access to care in our calculations, we did estimate resource use levels that were considered feasible in LMICs: we assumed, for example, that only one-third of critical cases would receive mechanical ventilation and the other two-thirds would receive other methods of oxygen supplementation. However, such respiratory support is complex and many LMICs may struggle to provide it even in lower quantities. What constitutes a ‘feasible strategies of service delivery’ will inevitably vary between LMICs, but our estimates suggest that, in many settings, even service delivery that is comparatively less resource intensive would lead to very high health sector costs.Other essential (and less costly) critical care options may be more realistic in certain settings.Our methods are subject to several important limitations. First, we rely on epidemiological projections modelled at the start of the epidemic. However, recent data from several settings, notably sub-Saharan Africa, suggest lower numbers of cases and fewer deaths than projected by models see .2 The exFurther, the case numbers that our estimates rely on cover a range of possible epidemic dynamics; one given scenario is unlikely to match the real case numbers currently being observed across all LMICs. Differences between reported and modelled estimates may also be explained by the fact that mitigation policies in place through the pandemic have varied over time in most countries,Second, ‘real-world’ costs are ideally estimated by collecting extensive primary cost data on actual service delivery. We have not been able to do this for COVID-19 and therefore relied on data collected for other purposes and on expert opinion from LMICs to make key assumptions on how services may be delivered. We only used three countries estimates to extrapolate to other settings.The length of hospital stays necessary for severe and critical cases used in the epidemiological models were based on evidence from early in the pandemic. As more data become available on length of stay and treatment options improve, epidemiological and costing models should be revisited. Service uptake and health-seeking behaviour may also differ by setting and should be considered in further work.Third, our work focuses on a narrow set of health sector COVID-19 interventions. We do not include the costs of protecting healthcare workers delivering other essential services outside the COVID-19 response  or COVID-19-related costs beyond the health sector , which may be considerable but were beyond the scope of our analysis.Despite these limitations, our work provides several critical qualitative recommendations for those working in COVID-19 policy-making. First, it is imperative that global agencies and funders continue to ensure sufficient targeted resources are available for LMICs to respond as the pandemic evolves, with most LMICs expected to shift substantial amounts of funding to COVID-19, even with policies of moderate social distancing in place. While much of the focus is on the macroeconomic impact and mortality impact of COVID-19, the fiscal impact on the health sector is likely to be substantial. LICs are particularly vulnerable and some will struggle to cope with almost any COVID-19 scenario. When thinking through mitigation strategies, decision-makers should consider the macroeconomic implications alongside associated potential reductions in healthcare-related costs, including patient costs.Second, in thinking through resource needs, it is important for countries to re-evaluate interventions and adapt response measures in ways that are context appropriate, affordable and sustainable, particularly in relation to high-cost activities, namely screening and testing and hospital-based care. This could include intervention delivery re-design and adaptations such as integration of care, leveraging of community health workers and home-based care, better targeting of interventions such as testing, and lower cost diagnostic approaches and critical care, among other ideas.Finally, while our results reflect the myriad decisions about care, protection and patient experience that are required to plan resource use, there is little discussion or data on what is feasible in LMICs. This is a task that cannot be met using a global perspective but needs country-specific inputs to reflect the health system characteristics of each country. We therefore also call for urgent support to encourage interaction of economists, planners, service managers and epidemiological modellers to inform COVID-19 policy at the country level across LMICs.We present the first dataset of COVID-19 clinical management costs across LMICs. These data can be used for cost-effectiveness analyses of prevention strategies, notably vaccines, and can assist policy-makers understand trade-offs between essential services as well as inform discussions on the balance between broader macroeconomic costs of mitigation strategies and health sector costs. We find that COVID-19 clinical management costs are substantial in LMICs, even in scenarios of moderate social distancing and assuming lower-cost critical care options. LICs are particularly vulnerable and some will struggle to cope with almost any COVID-19 scenario. As social distancing is relaxed, emergency financial support will be needed. The choices facing LMICs are likely to remain stark.


Retinal pigment epithelium (RPE) replacement therapy is evolving as a feasible approach to treat age-related macular degeneration (AMD). In many preclinical studies, RPE cells are transplanted as a cell suspension into immunosuppressed animal eyes and transplant effects have been monitored only short-term. We investigated the long-term effects of human Induced pluripotent stem-cell-derived RPE (iPSC-RPE) transplants in an immunodeficient Royal College of Surgeons (RCS) rat model, in which RPE dysfunction led to photoreceptor degeneration. iPSC-RPE cultured as a polarized monolayer on a nanoengineered ultrathin parylene C scaffold was transplanted into the subretinal space of 28-day-old immunodeficient RCS rat pups and evaluated after 1, 4, and 11 months. Assessment at early time points showed good iPSC-RPE survival. The transplants remained as a monolayer, expressed RPE-specific markers, performed phagocytic function, and contributed to vision preservation. At 11-months post-implantation, RPE survival was observed in only 50% of the eyes that were concomitant with vision preservation. Loss of RPE monolayer characteristics at the 11-month time point was associated with peri-membrane fibrosis, immune reaction through the activation of macrophages (CD 68 expression), and the transition of cell fate . The overall study outcome supports the therapeutic potential of RPE grafts despite the loss of some transplant benefits during long-term observations. Age-related macular degeneration (AMD), one of the most common causes of blindness in the developed world, is a degenerative disease of the retina often leading to progressive vision loss. Geographic atrophy, the advanced form of AMD, is characterized by dysfunction of retinal pigmented epithelium cells (RPEs) followed by degeneration of overlying photoreceptors leading to the loss of central vision. At present, no proven clinical treatments exist for the preservation or replacement of vulnerable RPE cells; however, RPE cell transplantation is perhaps the most obvious therapeutic option and has garnered significant interest. In the early stages of AMD, although the RPE cells are dysfunctional, surviving photoreceptors and the inner retina that transmit visual signals to the brain remain functional, rendering a realistic possibility that replacing the degenerating RPE with functional young RPE will restore vision.Potential sources of healthy RPEs are pluripotent cells derived from embryonic ,2,3,4 orThe autologous induced pluripotent stem-cell-derived RPE (iPSC-RPE) transplantation is considered more advantageous as the chance of graft rejection issues can be minimized. Recent research focuses on the generation of iPSC lines from adult cell sources, such as skin fibroblasts or peripheral blood mononuclear cells ,6,7,8,9.Existing evidence indicates that the delivery of cells as a suspension may not consistently develop into a monolayer of RPE and that their long-term survival rate will be low compared to RPE cells transplanted as a monolayer . In manyAlthough transplantation of iPSC-RPE cells to replace the diseased RPE has been tested by several groups through preclinical studies and there are preliminary reports of ongoing preclinical studies, no major attempt has been made to perform an in-depth analysis of the long-term viability and fate of the transplanted RPE. Based on previous reports from our group , the proIn preclinical studies of RPE transplantation, one of the major factors that influence the long-term benefits is the immune reaction and associated xenograft rejection. Sharma et al.  showed 72 on Matrigel and grown in mTeSR1 in a 10% CO2/5% O2 incubator for 5 days. Afterward, they were transferred to a 5% CO2/20% O2 incubator and cultured for 5 additional days. At this point, the culture medium was switched to a differentiation medium (DM) [2, and grown in RPEM. The passage 3 cells were used for transplantation experiments. These cells are extensively characterized for clinical applications in Dr. Bharti’s lab [iPSC-RPE  generated from iPSCs, reprogrammed from healthy adult fibroblasts, was obtained from Dr. Kapil Bharti, Unit on Ocular and Stem Cell Translational Research, National Eye Institute, NIH, Bethesda, USA . Brieflyium (DM) . After 1ium (DM) . Differeti’s lab ,30,31.Ultrathin parylene membranes (0.3 μm thickness supported on a 6.0 μm-thick mesh frame) made from parylene C were specially designed for implantation on rat retinas (1.0 × 0.4 mm) ,17 and uiPSC-RPE cells grown on Matrigel-coated parylene were stained for RPE specific markers zonula occludens protein 1 (ZO-1), and RPE65, based on established protocols . StainedThe Royal College of Surgeons (RCS) rat is an established model of retinal degeneration, which has been mainly used for studying photoreceptor rescue with treatment at the age of 3–4 weeks. These rats develop a fully functional visual system, which degenerates secondarily due to their dysfunctional RPE (MertK mutation), resulting in the loss of most photoreceptors at the age of 3 months. Immunodeficient RCS rats were produced from a cross between female homozygous RCS (RCS-p+/RCS-p+) and male athymic nude rats  as described previously . All ratAnimals underwent surgery at postnatal day (P) 28. Anesthesia was induced by intraperitoneal injection of ketamine (37.5 mg/kg) and xylazine (5 mg/kg). Only the left eyes were used for transplantation surgeries. All surgeries were performed by the same surgeon. Topical anesthesia was administered with a 0.5% proparacaine hydrochloride ophthalmic solution . Pupils were dilated using ophthalmic solutions of 2.5% phenylephrine hydrochloride and 0.5% tropicamide . Once the conjunctiva is removed, a scleral incision was performed in the temporal superior quadrant followed by an anterior chamber paracentesis to reduce intraocular pressure. A small incision (approximately 0.8–1.0 mm) was cut transsclerally at the temporal equator of the eye until the choroid was exposed with the help of a 32-gauge needle, and a 5 μL balanced salt solution  was injected to create a local retinal detachment. The implant held by forceps was introduced through the sub scleral space into the subretinal bleb. Clinical assessment as well as retinal imaging by optical coherence tomography (OCT) using a Spectralis HRA + OCT device  were performed to confirm the placement of the implant. The rats were then allowed to recover from anesthesia in a thermal care incubator. Animals with surgical complications such as excessive bleeding, perforation of the retina, and implant delivery into the vitreous were immediately excluded from the study. Based on OCT images, 15 animals were selected for short-term experiments (1-month and 4-month study group) and 15 animals were selected for the 11-month study group.Cohorts of rats were euthanized by intracardiac injection of euthasol  at 1, 4-, and 11-months post-surgery, and eyes were processed for histology. Contralateral eyes were considered as controls. Whole eyes were fixed in Davidson’s solution overnight, and the cornea and lens were removed. Finally, the eye cups were embedded in paraffin and processed for sectioning (5-μm sections). Groups of consecutive slides were stained with hematoxylin and eosin (HE) for light microscopy. The HE-stained slides were scanned and photographed using an Aperio Scanscope CS  microscope. Histological sections of cell-seeded membranes were evaluated to assess iPSC-RPE survival. The surgical placement was considered acceptable if more than 70% of the implant was located inside the subretinal area. Transplant survival was confirmed only if iPSC-RPE were observed in at least three consecutive sections based on light microscopy and immunostaining evaluations. Cell migration or dead cell aggregation was considered when pigmented cells or cell clumps were seen adjacent to the substrate and confirmed by immunohistochemistry. If no human/RPE marker was found, the specimen was considered as non-surviving RPE clumps. The outer nuclear layer (ONL) integrity was evaluated for photoreceptor preservation in the transplanted area. Cellular reaction around the implants, observed by light microscopy, was assessed for the presence of macrophages or the expression of glial cells. Adjacent sections of the implanted eye were processed for immunohistochemical analysis using the following antibodies as needed: Human-specific cell surface marker (anti-TRA-1-85), a marker of differentiated RPE cells (anti-RPE65), a macrophage marker (anti-CD68), an astrocyte/Müller cell marker (anti-GFAP), mesenchymal markers (α Smooth muscle actin and Vimentin), photoreceptor phagocytosis marker (Rhodopsin), and RPE binding protein (RBP1).Details of the antibodies used are included in Electrophysiological mapping of the superior colliculus (SC) was performed at approximately 11-months post-surgery based on an established protocol followed in our laboratory ,17,28. Bhttp://mdds.nyc/okn-stripes-visualization, accessed on 12 April 2021). A clear plexiglass holder was used to restrain the rat and keep its head continuously exposed to the tablet screen. A micro camera attached to the top of the rat holder recorded the headtracking responses during clockwise (1 min) and anticlockwise (1 min) stripe rotations. Visual acuity was tested by changing the stripe width at decrements of 0.5. Video recordings were evaluated to compute the head-tracking scores by two separate investigators who were both masked to the experimental condition. The OKN responses at various spatial frequencies were assessed based on the presence or absence of clear head-tracking and based on the duration of head-tracking.Optokinetic (OKN) testing was performed at 4 months and 11 months post transplantation using a previously described protocol . To recot-test was used for analyzing the OKN data. The remaining data were analyzed using Student’s t-test or by the Analysis of Variance (ANOVA) followed by the appropriate post hoc test. For all comparisons, the significance level was determined at p < 0.05.Statistical comparisons were made using GraphPad Prism software . The Paired iPSC-RPE cells a,b cultuAfter the transplantation surgery, OCT imaging was performed to screen the animals for proper implant placement a–c. AnimEvaluation of histology images from serial sections at 11 months post-implantation showed the presence of transplanted RPE in seven eyes (7/15). Out of these seven eyes, four eyes retained an intact RPE monolayer structure. Immunostaining showed rhodopsin-containing phagosomes in the transplanted RPE. This was more prominent in eyes in which better preservation of the iPSC-RPE monolayer structure was observed c. In theONL preservation was not observed in the eyes in which iPSC-RPE survival was absent. The presence of fibrosis was noticed in the majority of the above eyes a,b. A suThe expression of CD68 and GFAP was used to analyze inflammatory and glial reactions to donor tissues. GFAP was strongly expressed in the ganglion cell layer, the inner nuclear layer, and the choroid area, but was absent in the transplant area c. CD68 piPSC-RPE-implanted immunodeficient RCS rats were subjected to SC luminance threshold mapping . ElectroVisually evoked activities in these rats were observed only in a small SC area corresponding to the implant placement in the eye. Visual activities were robust (higher light sensitivity) in two of the above rats b whereasBased on OKN data, visual improvement in the iPSC-RPE-implanted eyes was observed at 4 months post-transplantation . HoweverOngoing multicentral clinical studies have proven that stem-cell-derived RPE transplantation is a practical option to restore failing vision in retinal dystrophies ,13,19,24The majority of the previous iPSC-RPE transplantation studies were conducted in immunosuppressed animal models, and assessments were made for a short duration only, which may not be sufficient to extrapolate into long-term implications ,34,35,36Previous studies have shown that the survival and integration of transplanted ESC-RPEs in the pathologic environment of a diseased retina is challenging due to it being prone to attack by macrophages . CD 68 eGlial fibrillary acid protein (GFAP) expression, which is known to occur in response to retinal injuries , can be In some of our transplanted eyes, the iPSC-RPEs developed into cell clumps on the surface of the parylene membrane d. PrevioThe cell clump formation observed in about 20% of the implanted eyes (long-term study group) might have occurred even after transplantation due to cell migration. Cell migration is mainly attributed to the loss of RPE tight junctions . This caThe RCS retina is widely known for its acute reactions to surgical interventions. Surgical trauma in rat eyes of a severe nature as a result of their small size can lead to increased tissue reactions in the implanted area. In support of this, mild inflammation and peri-membrane fibrosis were visible around the majority of the implants, in which the RPE monolayer was lost. It may be noted that, although the immunodeficient RCS rat is T-cell deficient, they possess bone-marrow-dependent B cells and natural killer (NK) cells. All of the above factors can contribute to the loss of transplanted iPSC-RPE cells.In the long-term study group (11-month post-implantation), the visual functional preservation (based on SC electrophysiology) was correlated to the survival of the transplanted iPSC-RPE. However, no considerable OKN visual activities were observed in these rats at this time point. Previously, in hESC-RPE-implanted immunosuppressed RCS rats, a progressive loss of OKN responses has been reported . ImproveIn conclusion, the present study demonstrated the survival and functionality of iPSC-RPE transplanted as a polarized monolayer on a non-degradable substrate containing similarities to an artificial Bruch’s membrane. The transplant benefits are higher during the earlier post-implantation period. Progressive deterioration of the transplant benefits observed in this study was correlated with the loss of transplanted iPSC-RPEs. The immune reactions and subretinal fibrosis can be considered the major causes of the loss of transplanted iPSC-RPE. From a clinical perspective, many of these adverse effects can be less severe in humans due to the differences in the eye architecture, surgical procedures, and the nature of the disease microenvironment. Moreover, in human eyes, easy application of target-specific and effective immune suppressants can help to reduce potential immunological reactions.


Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2.This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository.Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas.The study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020.Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support.Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 2152 (99.9%) had a discharge disposition at the end of the study period. Of these, 329 (15.3%) required invasive mechanical ventilation and 305 (14.2%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 80.7 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race  and Hispanic ethnicity  were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups.This observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines. Severe acute respiratory syndrome virus (SARS-CoV-2) has infected over 110 million people with nearly 2.5 million deaths worldwide [Prior studies have identified many risk factors for SARS-CoV-2 infections and complications . Older aDrawing from a highly curated real-world data set, we describe a diverse cohort from a catchment area that represents the diversity of the nation located in an early epicenter of the US outbreak. We extend the current literature with a detailed assessment of the characteristics of patients tested, and the clinical courses and outcomes of those testing positive, and among those admitted with SARS-CoV-2. We sought to identify risk factors for admission among those with SARS-CoV-2 and in-hospital mortality among discharged patients. We also characterize the patterns of treatment to provide the context to guide interpretation of these results.This was an observational, retrospective study of patients who were tested for SARS-CoV-2 within the Yale New Haven Health (YNHH) system, located within one of the US epicenters of Covid-19. The healthcare system is comprised of a mix of pediatric, suburban community, urban community, and urban academic inpatient facilities at five sites with a total of 2,681 licensed beds and 124,668 inpatient discharges in 2018  data repThe study cohort consists of all adult patients (≥18 years old) at YNHH who had an order for SARS-CoV-2 RT-PCR testing and a test result documented within the medical record between March 1, 2020 and April 30, 2020 . SARS-CoPatients admitted more than 24 hours prior to testing were excluded from the admissions group to reduce the likelihood of including hospital acquired infections. Data and outcomes were limited to those collected between March 1, 2020 and April 30, 2020. An extract of our local OMOP data repository from September 13, 2020 was used to allow for final discharge disposition and vendor-provided transformations of the clinical data warehouse to complete. For patients with multiple admissions in the study period, only data from the first admission was used. Race and ethnicity were extracted from the demographics section of the EHR and mapped to the OMOP common data model . For demWe extracted primary outcomes of admission and discharge disposition along with secondary outcomes of supplemental oxygen use and mechanical respiratory support. The maximum respiratory requirement during admission was used. Covid-19 related admissions were identified by extracting data from each patient’s first inpatient admission that had a visit start time within a window 14 days following or 24 hours before a positive SARS-CoV-2 test was ordered for a patient. For patients with a transfer to another facility (n = 44), the outcome from the first visit was used. Visit-related data and in-hospital mortality were directly extracted from our OMOP data repository. Supplemental oxygen requirements were computed based on presence of clinical documentation in flowsheets or vitals measurements and were mapped to one of four categorical variables: low-flow oxygen, high-flow oxygen, noninvasive mechanical ventilation, and/or invasive mechanical ventilation. Outcomes were limited to patients who were discharged and were therefore not extracted for patients who were still admitted at the end of the study period. Digitally extracted outcomes were validated for 30 patients via medical record review by a clinician. All ages were calculated relative to the time of SARS-CoV-2 test order.To document clinical treatment pathways, we extracted medication administration records of all admitted patients for their initial visit. Medications related to Covid-19 treatment based on institutional guidelines were grouped by calendar day of first administration. All forms of corticosteroids were mapped to a single drug class rather than their individual active ingredients. The order of medication initiation defined the separate treatment regimens and final treatment pathway. Treatment pathway visualizations were created with the JavaScript library Data Driven Documents  .The tables of demographic data and outcome data were built using the R (version 3.5.1) package tableone. Logistic regressions were performed using the core R function glm. Model 1 was among those testing positive to identify risk factors associated with admission. Candidate variables included the features described in Elixhauser comorbidity  analysisAge-adjusted in-hospital mortality was calculated with direct standardization  based onThe number of patients positive for SARS-CoV-2 increased rapidly beginning in March 2020 . A totalOf the patients tested for SARS-CoV-2, a majority  were female . The mosAge was similarly distributed between the SARS-CoV-2 tested and positive populations. Of those who tested positive, the median age was 52.3 years (IQR 38.3–64.8). Patients with a positive test were more frequently female  with uncomplicated hypertension , obesity , and chronic pulmonary disease  as the most common comorbidities. Patients with a positive test were most frequently reported as White , followed by Other race  and Black . Those who were reported as Hispanic ethnicity accounted for 28.1% (n = 2245) of SARS-CoV-2 positive patients.The median age of SARS-CoV-2 positive patients admitted to the hospital was 66.2 years (IQR 53.7–79.9) and a majority were male  as shown in In multivariable analyses, older age was significantly associated with risk of admission . Age ≥85Of the patients admitted for COVID-19 the majority  had a known disposition and therefore had complete outcomes available at the time of data extraction . The medThe majority of patients with respiratory outcomes did not require invasive ventilation . For these patients, male (49.8%) and female (50.2%) sex were similar in frequency with most frequently self-reported races of White , Black , and Other race . The most prevalent comorbidities included uncomplicated hypertension , fluid & electrolyte disorders , and cardiac arrhythmia . Invasive ventilatory support was required for 15.3% (n = 329) of patients with respiratory outcomes. The majority of those who required invasive ventilatory support were male  with self-reported race of White , Black , and Other race . The most prevalent comorbidities included uncomplicated hypertension , diabetes without complication , and fluid & electrolyte disorders .In-hospital mortality was 14.2% (n = 305) of patients with a discharge disposition and these patients had a median length of stay of 7.9 days (IQR 3.5–15.1). The majority of patients who experienced in-hospital mortality were male  and mortality increased with age ; the medAs seen with admission, regression analysis demonstrated that increased age had the highest risk for in-hospital mortality , with thOf patients with known outcomes, 1895 (88.1%) received medications for Covid-19 treatment while admitted. We assessed treatment pathways for 13 Covid-19 related medications. Patients were treated with 188 different possible medication regimen permutations with 50 unique combinations . The mosIn one of the largest real-world analyses of risk factors associated with Covid-19 infection and disease severity, we identified age as the primary risk factor associated with both admission and in-hospital mortality in those infected with SARS-CoV-2. Black race and Hispanic ethnicity were associated with increased risk of admission in our cohort and had increased disease and mortality burden, but age-adjusted in-hospital mortality was similar among all reported races and ethnicities. Comorbidities had much less impact on risk for either admission or in-hospital mortality in our study.Our work extends the literature in several important ways. Firstly, we followed a single large cohort to identify risks associated with infection and severe disease from the time of testing through discharge. Secondly, we provide further evidence that age and male sex are significantly associated risk factors for both admission and in-hospital mortality. Thirdly, we found that comorbidities, while common in those with SARS-CoV-2, were not strongly associated with either admission or in-hospital mortality based on multivariable analysis. Fourthly, we found race and ethnicity to be associated with infection and admission in this cohort, but with in-hospital mortality that was similar among these groups in our discharged population. Finally, we identified consistent use of medications within our admitted population, but with many possible treatment pathways for any individual patient and with frequent use of investigational therapies for Covid-19. Further investigation is needed to characterize potential benefits or risks associated with the various treatment pathways that have been available over the course of the pandemic.Our data confirm findings in other studies that show age as a primary risk factor for admission and in-hospital mortality in adult patients and that male sex is also highly associated with these outcomes , 23–26. Many studies have shown that Covid-19 has disproportionately affected minority populations across the US –32. WithOur data reflect the prominence of comorbidities in those with SARS-CoV-2 infection. While comorbidities were common, some of the most commonly reported risks for severe disease , 34 wereThe risks and outcomes reported here should be assessed in the context of the treatment protocols used during this period of the epidemic. Treatment standards based on early recommendations led to a majority of patients receiving disease related therapy, often with investigational treatments. Of patients who received a Covid-19 targeted therapy, 94.7% received hydroxychloroquine, 51.0% received tocilizumab, and 28.8% received azithromycin. The use of Covid-19 directed treatments was consistent among races and ethnicities in our cohort. But despite an early push to use promising medications from in vitro studies, such as hydroxychloroquine and azithromycin, evidence now demonstrates that neither is likely beneficial for admitted patients. As such, the treatment context of future studies should be similarly assessed to determine whether changes in treatment pathways impact the reported risks and outcomes in those with Covid-19.Our analysis leveraged real-world data derived from the EHR to assess all patients tested for SARS-CoV-2 within our health system. We implemented computed phenotypes to identify cases and clinically relevant outcomes, with a subset manually reviewed for accuracy. Our findings add to a growing base of evidence related to Covid-19 risk factors and outcomes. However, as an observational study based on real-world data, this study also has several limitations. First, while standardized testing protocols were in place, testing was often limited to symptomatic individuals or those with known exposure risks, thus potentially biasing our cohort to those who were symptomatic and sought care. The study was also limited to a single health system, but one that consists of a mixture of academic, urban, and suburban care facilities with a diverse patient population. In addition, while our health system implemented standardized treatment protocols, patients received therapies that were investigational for Covid-19 at the time of the study and use of these medications may not be similar at all institutions, especially as Covid-19 treatment protocols rapidly evolve as new evidence is obtained. Another limitation is that features associated with risk of admission may not correlate to risk of disease severity, as the decision to admit can be impacted based on discharge options or perceived clinical risks by healthcare providers. Finally, due to the timeline of the current outbreak, this study was limited to the initial admission and only assessed in-hospital mortality. Therefore, additional studies are needed to assess the impact of disease on patients not admitted to the hospital and the long-term effects of SARS-CoV-2 infection.There is an ongoing need to rapidly generate and communicate evidence, while also being cautious that only high-quality data are used to inform policy and develop clinical recommendations. While waiting for larger, more comprehensive case control and population-scale studies to define COVID-19 specific risks, prevalence, treatment, and outcomes, providers and public health officials need the best available evidence for clinical use. The data presented here provide findings from a large cohort that was followed from testing through discharge, identified increased age and male sex as the strongest risk factors for admission and in-hospital mortality, and found that in-hospital mortality was similar in racial and ethnic groups within our health system. Ongoing studies that further elucidate the risk of comorbidities, particularly given rapidly evolving treatment guidelines, remain needed as the Covid-19 pandemic continues to grow.The early COVID-19 experience at YNHH demonstrated that increasing age and male sex are the risks most strongly associated with admission and in-hospital mortality in those with SARS-CoV-2 infection. Minority racial and ethnic groups had increased risk of admission and higher disease burden, including mortality. But, for discharged patients, in-hospital mortality rates were similar in all racial and ethnic groups. While comorbidities were frequently observed in patients with SARS-CoV-2, few were associated with admission or in-hospital mortality in our cohort. Despite the limitations, this dataset from a multi-hospital health system with a diverse patient population presents valuable information related to risk factors for SARS-CoV-2 infection and short-term outcomes.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableThose categories with less than 4 patients were reported as ≤3.(DOCX)Click here for additional data file.


Early out-of-bed mobilization may improve acute post-intracerebral hemorrhage (ICH) outcomes, but hemodynamic instability may be a concern. Some recent studies have showed that an increase in mean systolic blood pressure (SBP) and high blood pressure variability (BPV), high standard deviation of SBP, may lead to negative ICH outcomes. Therefore, we investigated the impact of an early mobilization (EM) protocol on mean SBP and BPV during the acute phase.The study was an assessor-blinded, randomized controlled non-inferiority study. The participants were in An Early Mobilization for Acute Cerebral Hemorrhage trial and were randomly assigned to undergo EM or a standard early rehabilitation (SER) protocol within 24 to 72 hour after ICH onset at the stroke center. The EM and SER groups each had 30 patients. 24-measurement SBP were recorded on days 2 and 3 after onset, and SBP were recorded three times daily and during rehabilitation on days 4 through 7. The two groups’ mean SBP and BPV under three different time frames  were calculated and compared.P < .001). There were no significant differences in mean SBP and BPV during the acute and late acute phase between the two groups for the three analyses  (P > .05).At baseline, the two groups’ results were similar, with the exception being that the mean time to first out-of-bed mobilization after symptom onset was 51.60 hours (SD 14.15) and 135.02 hours (SD 33.05) for the EM group and SER group, respectively (It is safe to implement the EM protocol within 24 to 72 hour of onset for mild-moderate ICH patients during the acute phase. This may increase systolic blood pressure (SBP) and disrupt the blood-brain barrier, resulting in vasogenic perihematomal edema during the acute phase. Therefore, clinicians often struggle to conduct early out-of-bed mobilization programs due to the perceived lack of evidence, as well as the fear of hemodynamic instability and occurrence of re-bleeding events in a stroke center.Early mobilization (EM) refers to out-of-bed mobilization within 72 hour of admittance to an intensive care unit, and it is important to restoring function based on neuroplasticity mechanisms after stroke.–13 but also that blood pressure variability (BPV) may independently influence hemorrhage growth, recurrence, edema occurrence, and outcomes at two to seven days.,14,15 Cerebral blood flow is regulated during rapid SBP changes and BPV indicates as static autoregulation that occurs within minutes to hours in response to changes in SBP.,17 Therefore, disrupted cerebral autoregulation mandates strict control of SBP after ICH through anti-hypertensive treatments during the acute phase. However, depending on its intensity and type, out-of-bed mobilization could result in rising noradrenaline and adrenaline plasma concentrations that increase SBP or affect BPV stability. There is rare evidence from randomized trials regarding the stability of mean SBP and BPV when implementing an EM protocol at two to seven days after ICH onset.Recent findings not only suggest that having high SBP after acute ICH is significantly associated with hematoma expansion and unfavorable outcomes, The aim of this study was to examine whether an early out-of-bed mobilizaton protocol could be implemented in a stroke center without causing significant changes in mean SBP and inter-individual BPV during the acute phase.Therefore, we assessed the clinical significance of the mean SBP and BPV within seven days after onset in patients with ICH who participated in the An Early Mobilization for Acute Cerebral Hemorrhage (AEMACH) trial at a stroke center.22.1 The randomized controlled AEMACH trial was conducted to examine the usefulness of initiating an early mobilization protocol within 24–72 h after onset of spontaneous ICH at the stroke center of the largest medical center in Taiwan. The intervention time and daily session duration were similar to those of standard care and a standard early rehabilitation (SER) approach focused on restoring the motor domain of functional independence measures,22 by three months after stroke. The study protocol was approved by the Institutional Review Board of the National Taiwan University Hospital (201706073RINB) and registered at ClinicalTrials.gov . All of the participants provided written informed consent before being subjected to randomization. All research procedures were conducted in accordance with the ethical standards of the Declaration of Helsinki.This study was a prospective, assessor-blinded, and randomized controlled non-inferiority trial with 2 parallel groups and the collected data was subjected to a secondary analysis that used data from the AEMACH trial.2.2The inclusion criteria in the study were as follows: having had a first primary ICH occurrence with unilateral hemiparesis/hemiplegia that was confirmed via computed tomography; not having failed the set physiological safety limits for mobilization within 72 hour of stroke onset  before intervention); a National Institutes of Health Stroke Scale score of <20 at admission; complete pre-stroke independence in activities of daily living; and being aged 20 to 80 years. However, patients were excluded if they had ICH due to trauma, surgery, a hemorrhagic transformation from stroke, or an underlying mass; other medical conditions preventing mobilization; or a rapid early deterioration of symptoms within 24 h of stroke onset.2.3 The protocol applied to the EM group was functional and involved sitting unsupported on the edge of the bed by rolling, sitting up for a minimum of 15 minutes, and then standing with/without hand support for approximately 3 to 5 minutes, whenever possible. During their stay at the stroke center, the participants in the SER group focused on performing training activities in bed, which included joint range-of-motion, bridging, straight leg-raising, and stretching exercises; facilitation techniques; and rolling and sitting for bed training (sitting in bed). In the SER group, out-of-bed mobilization was started as soon as possible after intensive care unit discharge and was adjusted and implemented according to each participant's ability and progress. Both groups received care five days a week, with one 30-minutre session being conducted daily until discharge from acute stroke care. The mean time to mobilization after symptom onset (defined as the time elapsed from admission to out-of-bed unsupported sitting) was 51.60 hours for the EM group and 135.02 hours for the SER group.The EM and SER groups underwent different rehabilitation protocols.2.4The SBP data were collected within seven days after ICH onset. On day 2 and day 3 after ICH, SBP was recorded hourly (with an automated electronic device worn around the nonparetic arm) at 23 time points by a nurse at the stroke center. Another BP reading was also taken during rehabilitation, after which an ER group participant would spend 5 minutes sitting at the edge of a bed under the supervision of a physical therapist, while an SER group participant would spend 5 minutes performing an in-bed activity. For the next four to seven days after ICH, SBP levels were recorded by a stroke center or general ward nurse three times daily  with an automated electronic device. SBP levels on days 4 through 7 after ICH onset were also recorded during rehabilitation, which was followed by 5 minutes of intervention for both groups every day. Data relating to the incidence of hypotension (SBP<100 mm Hg) and serious intervention-related adverse events during rehabilitation were recorded.2.5,23 It is calculated using the standard deviation of SBP.In addition to mean SBP, the other primary outcome in the study was BPV during the acute phase. Commonly used in related research, BPV is directly related to mean SBP, easy to measure, and significant for clinical practice.BPV and mean SBP were analyzed under the three different time frames as follows: day 2 after onset during the acute phase, day 3 after onset during the acute phase, and days 4 through 7 after onset during the late acute phase. Three different analyses were used to assess how the phase that the patients were in (acute or late acute phase) after ICH onset could potentially affect their outcomes. Therefore, the first and second analyses were performed for the acute phase, during which the SER and EM protocols were started at the stroke center. Mean SBP and BPV (standard deviation of the SBP) were calculated using 24 blood pressure measurements taken on days 2 and 3. The third analysis was performed for the late acute phase (days 4 through 7) during which the patients were discharged from the stroke center and transferred to a general ward. The mean SBP and BPV for the late acute phase were estimated using the SBP measurements obtained on days 4 through 7 (or until discharge).2.6 Therefore, for the calculations, a 10% non-inferiority margin, 90% power, and 2.5% one-sided level of significance were assumed. Accordingly, 99% of the participants in the SER group, and 90% of those in the EM group were assumed to have controlled BPV levels in the two parallel-sample designed trail. Each group must have approximately 30 participants in order to detect a non-inferior difference in BPV stability between the two groups.Based on the results of the ASCOT-BPLA trial, an appropriate margin of non-inferiority for this trial is a decrease in the variability of systolic blood pressure from 10 to 8%, which should be regarded as clinically significant as it is associated with a reduction in stroke incidence.2.7 These 60 participants were enrolled at the stroke center of the National Taiwan University Hospital ≤24 h after ICH onset for intensive antihypertensive treatment, which consisted mainly of intravenous nicardipine infusion based on Joint National Committee guideline recommendations. The 60 participants received a BP-lowering treatment with the primary aim of achieving an SBP of 130 to 140 mmHg within 1 hour of starting treatment and maintaining this SBP level for at least 7 days, which is recommended by the NICE guidelines released in 2019. The patients initially received intravenous agents but subsequently switched to oral agents due to drug availability. The objective was to maintain their SBP levels until they were transferred to the general ward.Of the 89 patients who were screened, 60 were subsequently included and randomized at a ratio of 1:1 into the EM group and the SER group.2.8U test was used to compare the two groups’ BPV parameters with non-normal distribution. A two-sided P <.05 was considered to be significant.The results were analyzed using the software SPSS . Descriptive statistics were expressed as mean (standard deviation) and median (interquartile range) values for continuous and ordinal variables, respectively, while number (n) and percentage (%) were used for categorical variables. Descriptive statistics pertaining to relevant patient characteristics were also compiled and reported. All continuous variables were first assessed for normality using the Shapiro–Wilk test. The independent t-test was used to compare the mean SBP between groups. The Mann–Whitney 3 at admission, with the exception being the mean time to first mobilization after onset. Although the percentage of participants with dyslipidemia (a baseline stroke risk factor) in the SER group and the EM group participants was 36.7% and 20%, respectively, no significant difference was observed.A flow diagram is presented in Figure At the early acute phase, the BPV on day 2 was 0.58 mm Hg higher in the SER group and 0.73 mm Hg higher in the EM group on day 3, but these differences were not significant  and hematoma volume, this finding from our study may be also related to the presence of participants with mild-moderate clinical severity and low hematoma volumes. In addition, mobilization could adversely affect intracranial pressure in the early post-ICH period, but the inclusion criteria in the study had excluded patients with hydrocephalus or raised intracranial pressure. The exclusion of patients with raised intracranial pressure may partly explain why mean SBP and BPV were not significantly different between the two groups. Accordingly, the results cannot be generalized to patients with more severe ICH or those with high intracranial pressure. Moreover, cerebral autoregulation impairment may be more problematic in stroke patients with comorbid diabetes, as observed in type II diabetes studies. In the AEMACH trial, the rate of comorbid diabetes was relatively low; 33.3% of the SER group participants and 20% of the EM group participants had diabetes. In other words, it may be necessary to establish appropriate screening criteria for safe early mobilization interventions.In addition, early studies that utilized radiotracers have suggested that static cerebral autoregulation was preserved in intraparenchymal hemorrhage. Third, patients with mild-to-moderate ICH stroke should be included to determine the applicability of the results to the overall population of ICH patients. Fourth, our study was based on a small sample from a single hospital. Further studies are needed to investigate intracerebral or cerebral perfusion pressure variations during early mobilization in patients with acute ICH, or moderate-severe ICH patients could also be included.One important clinical implication of this study is that the early mobilization protocol's implementation after acute ICH onset may be feasible during the early rehabilitation process. One of the study's strengths was that it controlled for the type and frequency of early mobilization activity, as well as the frequency and duration of mobilization. However, the study has several limitations. First, data on cerebral blood flow during mobilization or dynamic cerebral autoregulation, which were derived from transient and sudden rapid changes in SBP, were not assessed. Second, the type of antihypertensive agent that is used can also affect BPV. Visit-to-visit variability in SBP was reportedly greater with β-blockers than with calcium-channel blockers (nicardipine/perdipine) in treated hypertensive patients.5Our study showed that it is safe to implement the early mobilization protocol within 24 to 72 hours after ICH onset. Therefore, we recommend that stroke centers implement the early out-of-bed early mobilization for mild-moderate ICH patients without raised intracranial pressure.The authors wish to thank the staff at the stroke center of National Taiwan University Hospital for supporting this study.Hsiao-Ching Yen performed the literature search, data extraction and drafted of the manuscript; Hsiao-Ching Yen, Chiung-Hua Cheng and Guan-Shuo Pan collected the data; Jiann-Shing Jeng and Wen-Shiang Chen gave lots of suggestions; Hsiao-Ching Yen and Jiann-Shing Jeng designed the study. All authors approved the final manuscript.Conceptualization: Hsiao-Ching Yen, Jiann-Shing Jeng, Wen-Shiang Chen.Data curation: Hsiao-Ching Yen, Chiung-Hua Cheng, Guan-Shuo Pan.Formal analysis: Hsiao-Ching Yen, Guan-Shuo Pan.Investigation: Hsiao-Ching Yen, Jiann-Shing Jeng, Chiung-Hua Cheng, Guan-Shuo Pan.Methodology: Hsiao-Ching Yen, Chiung-Hua Cheng.Project administration: Hsiao-Ching Yen, Jiann-Shing Jeng, Wen-Shiang Chen.Resources: Jiann-Shing Jeng, Chiung-Hua Cheng.Supervision: Jiann-Shing Jeng, Chiung-Hua Cheng, Wen-Shiang Chen.Writing – original draft: Hsiao-Ching Yen.Writing – review & editing: Hsiao-Ching Yen, Jiann-Shing Jeng, Chiung-Hua Cheng, Guan-Shuo Pan.


Prevotella and Veillonella, which, of note, are species that produce LPS. The oral microbiome of patients with long COVID was similar to that of patients with chronic fatigue syndrome. Altogether, our findings suggest an association with the oral microbiome and long COVID, revealing the possibility that dysfunction of the oral microbiome may have contributed to this draining disease.In the COVID-19 pandemic, caused by SARS-CoV-2, many individuals experience prolonged symptoms, termed long-lasting COVID-19 symptoms (long COVID). Long COVID is thought to be linked to immune dysregulation due to harmful inflammation, with the exact causes being unknown. Given the role of the microbiome in mediating inflammation, we aimed to examine the relationship between the oral microbiome and the duration of long COVID symptoms. Tongue swabs were collected from patients presenting with COVID-19 symptoms. Confirmed infections were followed until resolution of all symptoms. Bacterial composition was determined by metagenomic sequencing. We used random forest modeling to identify microbiota and clinical covariates that are associated with long COVID symptoms. Of the patients followed, 63% developed ongoing symptomatic COVID-19 and 37% went on to long COVID. Patients with prolonged symptoms had significantly higher abundances of microbiota that induced inflammation, such as members of the genera  The oral cavity holds the second largest microbial community in the human body, after the gut, with over 1000 species of commensal bacteria residing therein . There iSARS-CoV-2 is responsible for the current COVID-19 pandemic. This pandemic began in early 2020 and has caused over half a million deaths in the United States alone . BuildinMost hospitalized patients have persistent, long-lasting symptoms that can take weeks to resolve  and negaGiven the emerging associations between the human microbiome and SARS-CoV-2 infection and the unknown driver for patients with COVID-19 suffering from long-lasting symptoms, we sought to explore if oral microbiome dysbiosis associates with ongoing symptoms among patients with COVID-19 after hospitalization. Accordingly, we enrolled a cohort of patients with COVID-19 who tested positive for SARS-CoV-2 infection by PCR from an emergency department in the United States, collected oral swabs early in the disease course, and followed them for 4-week and 10-week symptom resolution outcomes. We analyzed oral microbiome composition by shotgun metagenomic sequencing. Our findings uniquely describe how dysbiosis of the oral microbiome may have played a pivotal role in lengthening symptom duration, leading to the long COVID syndrome.From a prospective sampling of 164 patients presenting with COVID-19 symptoms over a 9-month period, 84 (51.2%) tested positive by PCR for SARS-CoV-2. Of these patients, 27 were successfully contacted for follow-up at both 4 weeks and 10 weeks . The aveThe average length of symptom duration was 45.8 days (SD 30.4), with 14 patients (51.9%) experiencing continuation of symptoms after 4 weeks from disease onset, and 10 patients (37.0%) experiencing symptoms longer than 10 weeks. The symptoms that lasted the longest were respiratory in nature (81.5% cough or short of breath) followed by fatigue (55.6%), gastrointestinal symptoms (14.8%), confusion or “brain fog” (22.2%), and ageusia or anosmia (14.8%). Brain fog is a symptom more recently linked to long COVID and characterized by a lack of clear memory or an ability to focus , 24. Thehttps://doi.org/10.1172/jci.insight.152346DS1). PERMANOVA analysis on samples classified according to COVID-19 symptoms duration was not statistically significant (P < 0.05). We then applied random forest classification . Microbial species abundances were determined by running Metaphlan3 . We estiVeillonella dispar and Veillonella infantium) as well as 2 other species associated with ongoing symptomatic COVID-19 belong to the genus Veillonella. Members of this genus are gram-negative anaerobic coccus that can cause infection in humans  belong to the genus Actinomyces. Actinomyces cause actinomycosis, a rare infectious disease in which bacteria can spread to the respiratory tract causing inflammation  microbial associations that are known to promote inflammation via LPS production or other mechanisms, (b) reduction of antiinflammatory metabolic pathways, (c) pathobionts known to cause pulmonary infections, and (d) microbiota previously shown to have associations with COVID-19. Thus, our work begins to shed light on the hypothesis that the oral microbiome composition may influence the duration of COVID-19 symptoms.Prevotella and Veillonella were distinctive in the oral microbiota of patients with COVID-19  as per the manufacture’s protocol.Oropharyngeal samples were collected using OMNIgene•ORAL collection kits . Briefly, the posterior oropharynx was swabbed for 30 seconds and then the swab was inserted into a tube with a DNA/RNA stabilization buffer. Samples were heated to 65RS-CoV-2  and storhttps://bitbucket.org/biobakery/kneaddata). As in our previous work  and sequenced on a NextSeq500 Sequencing System as 2 × 150 nucleotide paired-end reads. Shotgun metagenomic reads were first trimmed and quality filtered to remove sequencing adapters and host contamination using Trimmomatic  and Bowtous work , 97, metous work  and HUMAous work , respectPCR was performed using the ViiA 7 Real-Time PCR System (Applied Biosystems) and the GoTaq Probe 1-Step RT-qPCR System . The primer-probe set N1  designed by the CDC were obtained from Integrated DNA Technologies (10006713) and used at concentrations of 500 nM and 125 nM, respectively . Eluted https://www.ncbi.nlm.nih.gov/bioproject/) and are available for download via accession PRJNA735193 under the title “Oral Microbiome associated with Coronavirus disease 2019 (COVID-19).”Data relating to the metagenomic sequencing that support the findings of this study have been uploaded to the NCBI BioProject . Because most of the signal from the unsupervised analysis was accounted by interindividual variability, we then decided to run supervised machine learning models. We built a RFC pipeline to predict either ongoing symptomatic COVID-19 or long COVID from a given data subset. One sample failed the sequencing run, and thus 26 samples were included in our modeling. The first step of our pipeline used the feature selection algorithm Boruta on 5-fold cross-validated data to estimate model performance . The perThis prospective cohort study was approved by the IRB at the University of Massachusetts Medical School. Written informed consent was received from all study participants prior to inclusion in the study.JPH, BAM, AM, and EB conceived and led the study. JPH, EB, and PD supervised the conduct of the study and data collection. PD, MCS, SM, and OA managed the clinical data, including quality control. LC OA, and MMS handled the sample collection and storage. DVW managed sample extraction and sequencing and performed metagenomic profiling. ALZ and VB provided statistical advice on study design and analyzed the data. JPH and EB wrote the manuscript with input from all authors. JPH composed the first draft of the majority of the manuscript and was responsible for incorporation of all authors edits. Accordingly, JPH was assigned the first author slot.


M. smegmatis was specifically expressed in astrocytes, it reduced intracellular lactate pools as well as lactate release upon stimulation. D-lactate dehydrogenase derived from L. bulgaricus diverts pyruvate towards D-lactate production and release by astrocytes, which may affect signalling properties of lactate in the brain. Together with lactate oxidase, which we have previously described, this set of transgenic tools can be employed to better understand astrocytic lactate release and its role in the regulation of neuronal activity in different behavioural contexts.Astrocytes support and modulate neuronal activity through the release of L-lactate. The suggested roles of astrocytic lactate in the brain encompass an expanding range of vital functions, including central control of respiration and cardiovascular performance, learning, memory, executive behaviour and regulation of mood. Studying the effects of astrocytic lactate requires tools that limit the release of lactate selectively from astrocytes. Here, we report the validation in vitro of novel molecular constructs derived from enzymes originally found in bacteria, that when expressed in astrocytes, interfere with lactate handling. When lactate 2-monooxygenase derived from  The glycolysis product L-lactate (lactate) in the brain has been receiving increasing attention as a growing number of studies suggest its roles in the modulation of neuronal circuit function as diverse as memory, decision making, response to environmental novelty, depression, central arousal, or autonomic regulation ,4,5,6,7.Astrocytes can be considered the main source of brain extracellular lactate since they are endowed with the machinery to import glucose from the periphery, store energy in the form of glycogen, and dynamically shuttle energy substrates throughout the astrocytic network and to the extracellular space . VariousIn spite of considerable progress and interest in the subject, many questions remain open. As an intermediate product in equilibrium-based enzymatic processes that exits its cells of origin in a gradient-driven but also partially regulated manner, the flux of lactate through the cellular brain compartments in vivo has proven difficult to match up quantitively with its specific actions on cellular targets and brain circuits. We, therefore, aimed to develop a set of molecular tools to support studies into the roles of astrocyte-derived lactate in the brain. The rationale was to harness the activity of bacterial enzymes that metabolise lactate, and to express them in astrocytes. Our aim was to decrease intra-astrocytic lactate pools that can be released to the extracellular space, constitutively or in response to local signals, and consequently to reduce lactate action. We recently reported that viral vector-mediated expression of bacterial lactate oxidase (LOx) in hippocampal astrocytes modulates behaviour in the context of environmental novelty in mice . Here weLMO, like LOx, irreversibly oxidises lactate into pyruvate at the expense of flavin cofactor reduction, which in turn is re-oxidised by molecular oxygen in a 2-step reaction . In LOx,locus coeruleus, DL can act as lactate antagonist [DLDH catalyses the reversible stereospecific NAD-dependent conversion of pyruvate into D-lactate DL; . This watagonist .Here we demonstrate the enzymatic activity of viral vector-based expression of LMO and DLDH in rat astrocytes in vitro and suggest that the use of these constructs may facilitate future studies into the functional roles of lactate in the CNS.Mycobacterium smegmatis (GenBank: AIU22323.1) and the DLDH (E.C. 1.1.1.28) sequence from Lactobacillus delbrueckii subsp. bulgaricus (GenBank: WP013438907) were optimised for mammalian codon usage and synthesised by Invitrogen . Expression cassettes were cloned to be driven by either CMV, EF1-α or the astrocyte-selective transcriptionally-enhanced sGFAP promoter [The LMO (E.C. 1.13.12.4) coding sequence from promoter . In addipromoter . For expTo enable assessment of changes in intracellular lactate or pyruvate, AVVs for CMV-driven expression of the FRET-based reporters for lactate and pyruvate, Laconic and Pyronic, respectively, were used ,18,19.Wistar rat pups were sacrificed in accordance with Schedule 1 of the UK Home Office (Scientific Procedures) Act (1986) and as approved by the University of Bristol ethics committees. Primary dissociated astrocytes and organotypic slice cultures were prepared as previously described ,20, and For estimation of cell viability with the Trypan Blue exclusion assay, astrocytes were trypsinised, harvested, and stained with trypan blue 0.4% . The percentage of blue non-viable cells in the suspension was determined in a hemocytometer. For the XTT cell viability assay , the formazan formation reaction was performed for 2 h. The relative metabolic viability of the cells was derived from the sample’s absorbance at 450 nm using a microplate reader .Primary cultured astrocytes seeded on glass coverslips or brainstem organotypic slices excised from the culture membrane were fixed for 15 min in ice-cold 4% paraformaldehyde (PFA) in phosphate buffer . After rinsing in PBS, the samples were incubated for 1 h at room temperature in a solution of 0.3% Triton X-100  and 10% serum. Samples were incubated overnight at 4 °C with anti-RFP antibody , rinsed in PBS and incubated for 1 h at room temperature with Alexa Fluor 594 antibody .g and frozen at −80 °C until use. Protein extracts were prepared for SDS-PAGE with sample buffer  and reducing agent , then heated for 5 min to 95 °C. Following protein quantification using Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific), equal amounts of protein were run on 10% acrylamide gel , then transferred to a nitrocellulose membrane  using Trans-Blot Cell  and visualised with Ponceau staining (Sigma-Aldrich). Membranes were blocked with 2% of ECL blocking reagent in PBS-0.1% Tween 20 (PBS-T) for 1 h at room temperature, before being incubated overnight at 4 °C with the primary antibodies: LDHA , LDHB , or cleaved caspase 3 antibody . The same membranes were later incubated with Actin antibody . Following three 5 min washing steps in PBS-T, membranes were incubated for 1 h at room temperature with secondary HRP-linked antibodies: Swine anti-rabbit IgG HRP  or goat anti-mouse IgG HRP . Enhanced Chemiluminescence EZ ECL kit  was used for immunodetection. Images were taken with G:BOX  operated with GeneSys 1.6.9 software. The quantification of immunoblots was performed with Image-J software. Total levels of protein were calculated as the ratio of the LDHA or LDHB with actin from the same membrane. After 72 h of AVV transduction or 3 h of staurosporine (5 µM) treatment, respectively, astrocytes were washed twice with cold PBS, treated with RIPA buffer containing protease and phosphatase inhibitors (Sigma-Aldrich) and harvested. After lysis, cells debris was centrifuged at 12,000× 2HPO4 0.34, KH2PO4 0.44, CaCl2 1.6, MgSO4 0.8, NaHCO3 4.2, HEPES 10, Glucose 5.5 or 2; pH 7.4; 32.5 ± 1 °C). Laconic or Pyronic were excited with the 458 nm line of an Argon ion laser and the emission of mTFP and Venus was detected at wavelength 465–500 nm and 515–595 nm, respectively. Images were taken every 3 s. Regions of interest were selected and the FRET ratio (mTFP/Venus) was calculated and normalized to the baseline. Primary cultured astrocytes on glass coverslips expressing Laconic or Pyronic and either LMO or DLDH with tdTomato were transferred to a recording chamber on an upright confocal microscope (Leica SP1 or SP5) and superfused with HEPES-buffered solution . Lactate and DL concentrations were calibrated to a standard curve.Lactate or DL levels in conditioned media from dissociated cells expressing LMO or DLDH were determined with fluorometric assays . HEK293 cells or astrocytes were exposed to serum-free culture media for 2 or 6 h, respectively. Samples used for quantification of DL underwent deproteination using a 10 kDa spin filter to deplete any potential activity of endogenous LDH enzyme. Samples were then processed according to the manufacturer’s instructions and fluorescence intensity was measured in a microplate reader  were used for real-time recording of extracellular lactate dynamics in organotypic brainstem slices. Slices were transferred to a recording chamber continuously superfused with HBS (see above). The lactate electrode was placed in contact with the surface of the slice and allowed to stabilise for at least 30 min. The null electrode was positioned on the contralateral side of the slice and its readout was subtracted from the current generated by the lactate sensor.The potential at the electrode surface was controlled by a potentiostat (Duo-Stat ME200+) and the signal was acquired and analysed using a 1401 interface and Spike 2 software . Known amounts of lactate were applied to the chamber at the beginning and at the end of each recording to calibrate the electrodes.t-tests, paired or unpaired, as indicated. Data from three or more groups were compared using ANOVA followed by post hoc Tukey’s test or non-parametric Wilcoxon Mann–Whitney U test, as indicated. The bars in the scatter plot graphs represent the mean and the error bars the standard deviation. Statistical tests were performed using GraphPad Prism software. Differences with p < 0.05 between the experimental groups were considered significant.Comparison between two experimental groups was made using two-tailed The consequences of interfering with lactate metabolism or outcomes from the presence of new enzymatic reactions in astrocytes are hard to predict. Therefore, in order to identify any cytotoxic effects of the novel tools and to optimise the viral vector titres, primary cultured astrocytes were transduced with AVV and cell viability was assessed using XTT and Trypan Blue exclusion assays. AVV-sGFAP-DLDH-IRES-tdTomato and the control vector AVV-sGFAP-EGFP did not compromise cell viability in any MOI test . TransduThe enzymatic activity of DLDH cells was assessed through analysis of DL content in conditioned media collected from DLDH-expressing cell cultures. HEK293 cells transfected with pTYF-EF1α-DLDH-IRES-EGFP showed a 25.8-fold rise in secreted DL  trans-acceleration by bath application of a high pyruvate concentration (10 mM), a well-established approach to deplete cells of lactate in vitro and in vivo ,14,19. BBath application of pyruvate evoked a substantial drop in the intracellular concentration of lactate in cultured astrocytes as reported by the FRET sensor Laconic a. ExtracNext, we examined the effects of LMO and DLDH on intracellular pyruvate processing in cultured astrocytes using the FRET sensors Pyronic and Laconic. In order to exclude constitutive lactate release, we blocked MCT1 with AR-C155858. Both lactate and pyruvate built up to a plateau, however, the increase in pyruvate levels were still well within the working range of Pyronic. We, therefore, focused on the rate of pyruvate accumulation which was significantly decreased in astrocytes expressing LMO or DLDH, and comparable to the effect of LOx c.Studies of lactate-mediated metabolic and signalling interactions between astrocytes and neurons have lately been held back by the lack of experimental tools to chronically and selectively manipulate the lactate release from astrocytes. To address this issue, we developed viral vector-based molecular tools that take advantage of the superior capacity of bacteria to metabolise lactate.Mycobacterium smegmatis and the DLDH from Lactobacillus delbrueckii subsp. bulgaricus based on their better documented enzymatic activities [Both LMO and DLDH are active in many microorganisms where they are involved in glycolysis and fermentation. We selected the LMO sequence from tivities ,22. WhenA range of factors, such as non-specific side effects of AVV transduction, potential trafficking problems associated with the expression of foreign gene products, interference with glycolytic pathways, as well as by-products of additional enzymatic processes, can all potentially lead to harmful consequences for cells. However, we found that within a normal in vitro working range of AVV MOIs, the expression of LMO and DLDH led to functional enzymatic activity without causing either measurable detriment or signs of apoptotic processes in astrocytes. This was consistent with the expression of the bacterial enzyme LOx which we reported earlier, even though LOx enzymatic activity results in hydrogen peroxide as a by-product, whereas LMO produces acetate, which is expected to be efficiently metabolised further by astrocytes . This would apply back-pressure on the LDH reaction until an equilibrium between pyruvate and lactate was re-established, consistent with the plateau in the recorded traces. Since the increased lactate levels under these conditions were difficult to quantify accurately, we evaluated the rate of pyruvate accumulation as an indirect measure of lactate build-up when constitutive lactate release was inhibited. The rates of pyruvate accumulation were significantly decreased in LMO-, DLDH- and also LOx-expressing astrocytes and, considering that LDH expression itself remained unchanged, these results are consistent with LMO, LOx, and DLDH activity as an additional lactate sink in the cell.Direct recording of DL production in astrocytes was limited as we had no access to specific biosensors. Laconic is insensitive to DL . We wereIn unstimulated conditions, in dissociated as well as organotypic slice cultures, lactate release was not significantly affected by DLDH activity in astrocytes. Transient disruption of the lactate/pyruvate/DL equilibrium during trans-acceleration, however, indicated that by diverting pyruvate towards DL synthesis, DLDH had an impact on the maintenance of the steady-state lactate pool.Endogenous DL production in the brain may occur through the glyoxalase pathway, so the brain must be adequately equipped to process this metabolite. Toxic levels as associated with short bowel syndrome or in the context of diabetic complications cause DL encephalopathy and can interfere severely with brain function . InteresDL shares various molecular targets, such as transporters and receptors, with lactate. For example, MCT1 pumps DL, albeit with a lower affinity and at a slower rate than lactate, or DL activates the lactate receptor HCA1 but with a lower potency than lactate ,25,26. TWith low levels of DL production by astrocytic DLDH expression, we would, therefore, not expect any significant actions of DL via the above targets. We have, however, previously described DL as a potent antagonist which inhibited lactate-induced stimulation of noradrenaline release from locus coeruleus neurons via an unidentified receptor-mediated mechanism . For thiIn this study, in vitro tests showed that LMO and DLDH are enzymatically active when expressed in mammalian astrocytes and are ready for studies requiring modulation of lactate, production, release and actions in the brain.