The principal factors influencing the composition of the vaginal microbiota in pregnancy are individual identity and gestational age at sampling. Other factors are maternal age, parity, obesity, and self-reported Cannabis use. The general pattern across gestation is for the vaginal microbiota to remain or transition to a state of Lactobacillus dominance. This pattern can be modified by maternal parity and obesity. Regardless, network analyses reveal dynamic associations among specific bacterial taxa within the vaginal ecosystem, which shift throughout the course of pregnancy. This study provides a robust foundational understanding of the vaginal microbiota in pregnancy and sets the stage for further investigation of this microbiota in obstetrical disease.The composition of the vaginal microbiota is heavily influenced by pregnancy and may factor into pregnancy complications, including spontaneous preterm birth. However, results among studies have been inconsistent due, in part, to variation in sample sizes and ethnicity. Thus, an association between the vaginal microbiota and preterm labor continues to be debated. Yet, before assessing associations between the composition of the vaginal microbiota and preterm labor, a robust and in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required. Here, we report a large longitudinal study (IMPORTANCE There is debate regarding links between the vaginal microbiota and pregnancy complications, especially spontaneous preterm birth. Inconsistencies in results among studies are likely due to differences in sample sizes and cohort ethnicity. Ethnicity is a complicating factor because, although all bacterial taxa commonly inhabiting the vagina are present among all ethnicities, the frequencies of these taxa vary among ethnicities. Therefore, an in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required prior to evaluating associations between the vaginal microbiota and obstetrical disease. This initial investigation is a large longitudinal study of the vaginal microbiota throughout gestation resulting in a term delivery in a predominantly African-American cohort, a population that experiences disproportionally negative maternal-fetal health outcomes. It establishes the magnitude of associations between maternal characteristics, such as age, parity, body mass index, and self-reported Cannabis use, on the vaginal microbiota in pregnancy. Lactobacillus spp. , and the other CST (CST IV) is typically not dominated by any one bacterium but rather is comprised of a diverse array of microorganisms , and especially CST I, which is dominated by L. crispatus, are associated with optimal vaginal health , GardneL. iners . Importal health –19 and f health ––29. By chealth ––, 30–35, lth ––30–, 36–38, Lactobacillus-dominated CSTs . The distribution of the gestational ages at which the 1,862 vaginal fluid samples were collected from these patients is depicted in +6 weeks of gestation.The demographic characteristics of the 474 patients with term delivery included in this study (the largest cohort sampled to date) are presented in 2 = 58% to 61%; structure, R2 = 65% to 68%) and by the patient-specific variation with gestational age . Only a modest proportion of the variance in the composition and structure of the vaginal microbiota was explained by maternal characteristics, such as age (0.2% to 1.4%), parity (0.3% to 1.9%), and self-reported Cannabis use (0.3% to 1.8%). Overall, these findings illustrate the large influence that patient  identity has on the composition and structure of the vaginal microbiota, which is consistent with general observations of microbiotas at other body sites, such as the oral cavity, gut, and skin  models). This finding is consistent with previous reports of alpha diversity in cohorts of primarily African-Americans (Lactobacillus spp.). Indeed, it has been shown that the relative abundance of Lactobacillus in the vaginal microbiota is highest among African-American women with high free glycogen levels  and Shannon and Simpson  indices. Both richness  and C anmericans , 48, 58.mericans , 48, 85.mericans . The decmericans . These an levels .Lactobacillus spp. (L. crispatus (I), L. gasseri (II), L. iners (III), or L. jensenii (V), and three more diverse CSTs (CST IV) comprised of L. iners, Gardnerella sp., and Megasphaera sp., with Ca. Lachnocurva vaginae, Atopobium vaginae, and Bifidobacterium sp. being relatively abundant in CST IV-A, -B, and -C, respectively. These CSTs are consistent with previous investigations of the vaginal microbiota in smaller cohorts  for which samples were available from each of four discrete time points across gestation, there was a pronounced shift in CST composition with advancing gestational age; specifically, there was an increase in CSTs I and III at the end of pregnancy, derived primarily from patients with an initial CST IV-A or IV-B  and negatively correlated with ASVs that are typical members of vaginal CST IV  modeling to analyze the relationships between gestational age and maternal characteristics with the relative abundances of individual bacterial taxa denoted as amplicon sequence variants (ASVs) . Increased gestational age was positively correlated with 33 exclusively Cannabis use, ethnicity, and race were included as covariates. Seventy-five ASVs were positively or negatively associated with gestational age   to identonal age . This paoutcomes , 26, 28,outcomes , 28, 38.estation . Ca. Lacas BVAB1 , is an eas BVAB1 , 103, anCa. Lachnocurva vaginae). The fundamental difference between LME and ANCOM is that ANCOM is inferring about abundances, whereas LME is inferring about relative abundances. Specifically, LME evaluates whether the abundance of a particular taxon in a unit volume of an ecosystem, relative to all other taxa, has changed between two ecosystems. On the other hand, ANCOM evaluates whether the abundance of a particular taxon, in a unit volume of an ecosystem, has changed between two ecosystems. This difference in inference between LME and ANCOM explains the differing results obtained by using these two approaches. Regardless, LME and ANCOM identified ecologically plausible variation in microbiota membership across gestational age, and a large proportion of the bacterial ASVs changing in composition and abundance across pregnancy were discovered with both approaches.Overall, the results were largely congruent between the LME models and ANCOM analyses, although there were some notable differences , while lower maternal parity was exclusively correlated with 7 Lactobacillus ASVs, of which 6 were classified as L. crispatus (q < 0.1) (Table S1). This finding is consistent with a recent report of increased vaginal microbiota diversity with higher parity during subsequent gestations . Nevertheless, differences in CST membership based on parity, while adjusting for gestational age, were found , with pastations . The ecoL. iners, while only 4 ASVs, classified as L. crispatus, were positively correlated with maternal age (Table S1). While these correlations contrast with a previous report . Nevertheless, differences in CST membership based on maternal age, while adjusting for gestational age, were found , with his report , the dif2, on alpha diversity of the vaginal microbiota . Nevertheless, Cannabis use was associated with an increase in CST III (L. iners (16 ASVs) and a decrease in those classified as L. crispatus (6 ASVs) (Table S1). L. iners is hypothesized to represent a transient vaginal microbiota state that is common following perturbations  between pairs of bacterial taxa during pregnancy. In this analysis, each genus-level taxon  was represented by one ASV retained based on the strongest association with gestational age. A subset of these significant correlations (involving the most relatively abundant ASVs) is shown in Atopobium (ASV10) and Gardnerella (ASV3) (r = 0.74), Eggerthellaceae (ASV39) and Parvimonas (ASV21) (r = 0.83), Dialister (ASV25) and Eggerthellaceae (ASV39) (r = 0.83), and Sneathia (ASV11) and Parvimonas (ASV 21) (r = 0.74) were among the most highly correlated pairs of bacterial taxa in pregnancy (Table S3). These data suggest potential synergistic relationships among these typical members of CST IV in pregnancy and potentially beyond.Table S3 in the supplemental material shows some of the strongest associations  that included a mixture of L. crispatus, L. jensenii, and L. gasseri and a secondary group (ASVs 1 and 6) comprised exclusively of L. iners , Atopobium (ASV 10), and Megasphaera (ASV 4) throughout gestation. By contrast, L. iners ASVs had very few associations with other ASVs, either positive or negative  and—to a lesser extent—hydrogen peroxide, each of which can create hostile conditions for other bacteria , Atopobium (ASV 10), and Megasphaera, exhibited strong negative associations with Lactobacillus ASVs , had only weak negative associations with Lactobacillus ASVs , representing CST IV-C  and were consistently positively associated throughout gestation in the network analyses , which were in a separate cluster throughout most of gestation ; they may represent unique Gardnerella strains. This finding is important because Gardnerella is associated with bacterial vaginosis . The increase in positive edge percentage was a combination of a decrease in negative Lactobacillus-CST IV-B associations and an increase in the strength of positive associations among CST IV-C-associated ASVs. These gestational changes observed in positive edge percentage were associated with a significant decrease in closeness  for Gardnerella ASVs 3, 8, and 9 since many of their strong negative Lactobacillus spp. associations were lost or diminished as pregnancy progressed  significantly decreased and positive edge percentage  significantly increased  as pregnogressed  to D. CoLactobacillus species with members of other genera, but also, through longitudinal collection of vaginal samples, to identify shifts in network connectivity as gestation progressed. Furthermore, different ASVs attributed to the same genus  demonstrated distinct ecological dynamics, suggesting that strain-level variation is a driver of community phenotypes commonly denoted as CSTs. Therefore, this study highlights the need for strain-level investigations utilizing metagenomic data to further characterize these shifts in vaginal microbiota ecology throughout gestation and to determine their underlying causes and consequences.Collectively, these network analyses demonstrate the complex interactions between members of the vaginal microbiota. We were able not only to confirm classical associations of The composition of the vaginal microbiota is broadly consistent across populations of reproductive-age women worldwide –5; yet, –2–65–Lactobacillus species with advancing gestation. Such typical changes can be partially mitigated by maternal parity and obesity. Importantly, Lactobacillus species, especially L. crispatus, are generally perceived to promote the vaginal and reproductive health of women , and the Detroit Medical Center . The collection and use of human materials for research purposes were approved by the Institutional Review Board of the National Institute of Child Health and Human Development and Wayne State University [no. 110605MP2F(RCR)]. All participating women provided written informed consent prior to sample collection.Vaginal fluid samples were obtained at the Perinatology Research Branch, an intramural program of the We conducted a retrospective longitudinal cohort study to characterize variation in the vaginal microbiota across gestation in pregnancies ending in normal term delivery. A normal pregnancy was defined as a woman with no obstetrical syndromes , fetal anomalies, or neonates with birth weights outside the 10th to 90th percentiles; who agreed to participate in this study and provided written signed informed consent; and who delivered at term (38 to 42 weeks of gestation). Three or four samples of vaginal fluid were collected longitudinally across pregnancy from each woman under direct visualization from the posterior vaginal fornix using a Dacron swab . Vaginal swabs were stored at −80°C until DNA extraction.n = 1,862) alongside nontemplate negative controls, addressing any potential background DNA contamination (n = 73). All vaginal swabs were randomized across extraction runs. Extractions were conducted using a MagAttract PowerMicrobiome DNA/RNA EP extraction kit , with minor modifications to the manufacturer’s protocols. Briefly, swabs were transferred to clean, labeled Corning cryovials  and immersed in 750 μL solution MBL preheated to 60°C. Swabs were then vortexed for 10 min. A provided empty PowerBead plate was then centrifuged for 1 min at 4,400 × g, and vaginal swab lysates were added to corresponding wells of the PowerBead plate. Plates containing lysates were centrifuged for 1 min at 4,400 × g. The plates were then loaded onto a TissueLyser II plate shaker (QIAGEN), firmly secured, and shaken at 17 Hz for 20 min. Plates were then removed from the shaker and immediately centrifuged at 4,400 × g for 6 min. The supernatant was then carefully transferred 185 μL at a time to a provided collection plate. Following the transfer, 150 μL of solution IRS were added to each well, and plates were incubated at 4°C for 10 min. Plates were centrifuged for 15 min at 4,400 × g, and the supernatant were transferred to a new collection plate. The plate was centrifuged for 2 min at 4,400 × g, and 850 μL of the supernatants were transferred to a clean collection plate. The collection plate was loaded onto an epMotion 5075 liquid handler  for further processing following the default onboard protocols. The above procedure yielded between 0.13 and 550 ng/μL purified DNA from the vaginal swabs as measured by a Qubit 3.0 fluorimeter and Qubit double-stranded DNA (dsDNA) assay kit , following the manufacturer’s protocol. The purified DNA was transferred to the provided 96-well microplates and stored at −20°C.Genomic DNA was extracted from vaginal swabs (https://rtsf.natsci.msu.edu/) using the dual-indexing sequencing strategy developed by Kozich et al. (GTGCCAGCMGCCGCGGTAA-3′) and the reverse primer was 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Each PCR contained 0.5 μM of each primer, 1.0 μL template DNA, 7.5 μL of 2× DreamTaq hot start PCR master mix , and nuclease-free water to produce a final volume of 15 μL. Reactions were performed using the following conditions: 95°C for 3 min; followed by 30 cycles of 95°C for 45 s, 50°C for 60 s, and 72°C for 90 s; and an additional elongation at 72°C for 10 min.The V4 region of the 16S rRNA gene was amplified from vaginal swab DNA extracts and sequenced at the Michigan State University Research Technology Support Facility  with minor modifications, as described previously  defined by 100% sequence similarity using DADA2 version 1.12  in R vereviously . These mEscherichia, Pelomonas, Pseudomonas, and Micrococcaceae, were identified as contaminants and were removed.The R package decontam version 1.6.0  was usedhttps://github.com/ravel-lab/speciateIT) and the classify.seqs command in mothur (For assigning community state types (CSTs) to the bacterial community profiles, ASVs were first taxonomically classified using the V4_trimmed_noEuks_nr_Complete.fa reference library supplied with the speciateIT classifier code (n mothur  with a bn mothur .(i) Analysis of vaginal microbiota composition and structure. To determine the percentage of variance explained (R2) in the composition (Jaccard index) or structure (Bray-Curtis index) of the vaginal microbiota, permutational multivariate analysis of variance (PERMANOVA) analyses  while accounting for gestational age were obtained in a separate analysis in which PERMANOVA analysis was performed on bootstrap samples of subjects. For each subject, only one random longitudinal observation was selected, hence generating cross-sectional data sets in which observations were independent, and hence, PERMANOVA could be applied. Empirical 95% confidence intervals of R2 statistics were obtained using 1,000 bootstrap iterations.analyses  were peranalyses . The con(ii) Changes in alpha diversity with gestational age and maternal characteristics. The relative abundance for each amplicon sequence variant (ASV) was determined as the ratio of the count of each ASV divided by the total number of ASV counts in each sample. Starting with the relative abundance data, for each sample, we calculated the Shannon and Simpson diversity indices using the “diversity” function in the vegan package and the Chao1 diversity index using the “chao1” function in the fossil package. Each measure of diversity was then correlated with the continuous variable gestational age at sampling using LME models implemented in the lme4 package in R. In these models, a random intercept and a random slope with gestational age were allowed for each subject to account for the repeated and potentially correlated observations from the same subject. Gestational age values were centered at 8 weeks and then scaled by 4 to facilitate the interpretation of random intercepts and convergence of model fitting algorithms. The complexity of the gestational age dependence was assessed by comparing the model fit between a linear and quadratic trend using a likelihood ratio test for LME models. The same test was used to determine the need for subject-specific gestational age slopes. To further inspect nonlinear trends in alpha diversity as a function of gestational age, generalized additive models (GAM) for repeated observations were also fit based on spline transformation of gestational age. Such models were available from the mgcv package in R. The effects of maternal characteristics  were assessed by including them as covariates in LME models. A P value of <0.05 was used to infer significance in these analyses.(iii) Changes in vaginal community state types (CSTs) with gestational age and maternal characteristics. The log-odds of membership in a given community state type (CST) were modeled using binomial LME models using the glmer function in R. Fixed effects in these models included gestational age at sampling  and maternal characteristics. Of note, due to the sparse responses in these models (membership to a given CST), it was not feasible to test whether there were subject-specific departures in the CST membership probability trends versus gestational age , and yet, subject-specific shifts in membership probabilities were allowed via random intercepts in the mixed effects models.(iv) Changes in the relative abundance of individual amplicon sequence variants (ASVs) with gestational age and maternal characteristics. The analysis of the relative abundance of each amplicon sequence variant (ASV) in association with gestational age at sampling was performed using LME models based on ASV count data while assuming a negative binomial distribution of the counts. Such models were implemented in the glmmTMB package in R and included an offset term of the total number of reads per sample so that changes in relative abundance with gestational age were being estimated as opposed to differences in absolute counts. These models included gestational age and maternal characteristics as fixed effects and random intercept and random gestational age slope for each subject. All analyses involved control of the false discovery rate at a 10% level (q < 0.1).Cannabis use, ethnicity, and race as covariates, while allowing a random intercept and a random slope for each subject. For a given taxon, the output W statistic represents the number of ALR-transformed models where the taxon is differentially abundant with regard to the main fixed effect, after adjusting for multiple testing correction for the number of ALR models corresponding to each taxon. The larger the value of W, the more likely the taxon is differentially abundant between compared sample groups.Additionally, we implemented analysis of composition of microbiomes (ANCOM)  for furt(v) Bacterial taxa with the most highly associated relative abundances during normal pregnancy. To assess the association between pairs of ASVs, we modeled their log-transformed relative abundance data using LME models. In these models, one of the two ASVs was treated as a response variable while the other was treated as an explanatory variable. A random effect was allowed for each subject. Naive Spearman correlation coefficients were also calculated for each pair. The significance of correlations was based on an adjusted P value of <0.05.(vi) Associations of ASVs across term pregnancy through network analysis. The R packages NetCoMi 1.0.2  as BioProject


Patients suffering from severe acute respiratory distress syndrome (ARDS) are usually treated with mechanical ventilation. Extracorporeal membrane oxygenation (ECMO) has traditionally been considered a life-saving therapy and was reserved as a last resort when other treatment options were exhausted. However, this report outlines our successful initial experience with early implementation of awake venovenous extracorporeal membrane oxygenation (VV-ECMO) in a case of pneumocystis jirovecii pneumonia complicated by severe acute respiratory distress syndrome (ARDS), offering a promising new approach for recovery.We present a case report of the effective application of awake VV-ECMO in a 29 years-old man with severe ARDS caused by pneumocystis jirovecii pneumonia. The patient initially received antibiotic treatment and non-invasive ventilation (NIV) for respiratory distress, but these interventions failed to improve the worsening dyspnea that occurred in the patient. Following the combined antifungal therapy, high-flow nasal cannula (HFNC) oxygen therapy, and VV-ECMO for a duration of 7 days, the patient’s symptoms improved, showing relief.Awake VV-ECMO proved to be an effective treatment for critically ill patients with ARDS, avoiding the need for invasive mechanical ventilation. However, increased clinical evidence is needed to verify whether awake ECMO could be widely used in severe ARDS caused by other diseases or conditions. These patients often require immunosuppressive medications to prevent rejection of the transplanted organ or graft-versus-host disease, which can weaken their immune system and make them more susceptible to opportunistic infections such as PJP . As a lu2.2) of 99%, a blood pressure of 120/82 mmHg, and lung auscultation revealed diminished sounds with a few moist crackles heard. Other physical examinations were negative. Blood tests indicated a white blood cell count of 3.75 × 109/L, a neutrophilic granulocyte percentage (NE%) of 77.6%, a procalcitonin (PCT) of 21.8 ng/mL, and a C-reactive protein (CRP) of 22 mg/L. The results of 1-3-β-D-glucan and G-lipopolysaccharide tests were negative, and the absolute counts of helper T lymphocytes and killer T lymphocytes were 220/μL and 261/μL. Upon admission, a chest computed tomography (CT) scan indicated minimal bilateral pulmonary exudates. According to the aforementioned findings, the preliminary diagnosis indicated that the patient had community-acquired pneumonia. Empiric therapy with cefuroxime was initiated upon admission for prophylactic purposes. Nevertheless, the patient’s pyrexia remained refractory, and there was an escalation of bilateral moist rales on the third day. Therefore, for further insights into the etiology, bronchoalveolar lavage fluid (BALF) samples were obtained through fiberoptic bronchoscopy, which revealed yellow foam-like exudates in the lungs that were positive for Gomori methenamine silver staining and displayed a small quantity of pneumocystis jirovecii encapsulation. Thus, the patient was diagnosed with PJP. The immunosuppressive agent and cefuroxime were discontinued, and caspofungin was introduced in conjunction with sulfamethoxazole (SMZ) for anti-infective purposes. Additionally, 40 mg of methylprednisolone was administered to mitigate the inflammatory response. After treatment, the patient continued to experience recurrent hyperpyrexia and dyspnea, accompanied by deterioration in vital signs. The patient’s temperature was recorded at 39.4°C, heart rate was 101 beats per minute, and respiratory rate was 32 breaths per minute. Then, despite the utilization of high-flow nasal cannula (HFNC) with an oxygenation flow rate of 50 L/min and FiO2 of 70%, as well as non-invasive positive pressure ventilation (NIPPV) with an IPAP of 18 cmH2O, an EPAP of 8 cmH2O, a frequency of 20 breaths/min, and FiO2 of 90% on alternate occasions, the pO2/FiO2 (P/F) ratio of arterial blood gas (ABG) was 100 mmHg. The P/F value satisfied the diagnostic criteria for severe ARDS. The patient experienced an exacerbation of dyspnea, and a reexamination of chest CT revealed an aggravation of lesions in both lungs . Arterial blood gas analysis showed severe hypoxemia: pH: 7.41, PaO2: 51.3 mmHg, PaCO2: 29.7 mmHg, and P/F 86 mmHg. The patient was expected to require mechanical ventilation for an extended period, which significantly increased the risk of mechanical complications. Meanwhile, given the patient’s desire for autonomy to manage his own behavior during dyspnea, without resorting to endotracheal intubation, coupled with the urgent need for oxygenation, the early employment of awake VV-ECMO represented the optimal course of action. With the patient’s consent, the VV-ECMO was connected to him. A 15F cannula inserted through the right internal jugular vein and a 23F catheter through the right femoral vein were placed for VV-ECMO under local anesthesia. ECMO parameters  were continued to be given with HFNC . On the basis of SMZ treatment, meropenem (1 g q8h) combined with vancomycin (1 g q12h) was given for antimicrobial therapy, dexmedetomidine (from 0.3 μg/kg/h to 0.5 μg/kg/h) was used for shallow sedation, and remifentanil (from 0.03 μg/kg/min to 0.05 μg/kg/min) for analgesia with the purpose to smooth the dyspnea. We maintained the RASS score from −1 to 0 of shallow sedation and controlled the spontaneous breathing rate at 8–15 breaths/min for lung protection. To prevent the formation of blood clots, we employed continuous infusion of heparin and frequently monitored the patient’s activated partial thromboplastin time (APTT) with the aim of maintaining a target APTT value between 60 and 80 s. Additionally, we administered enteral nutrition through a nasogastric tube to ensure a daily caloric intake of 2,100 kcal for the patient. On the 17th day, respiratory physiotherapy was initiated, which included daily use of the Portex Acapella Breathing Trainer was made at 6250 Shier Ring and sitting at the bedside with the intention of exercising respiratory muscles, preventing ICU-acquired weakness, and promoting pulmonary recruitment. On the 21st day, the manifestation of respiratory distress exhibited complete alleviation, the body temperature gradually regressed to the normal range, and the parameters obtained from blood gas analysis  of VV-ECMO was 21%. Subsequently, we gradually reduced the sweep gas flow rate by 1 L/min from 5.0 L/min to our goal of 1 L/min over 4 h. Since the patient was able to tolerate the low sweep gas flow rate of VV-ECMO, we turned off the sweep gas for 4 h. Then, the arterial blood gas results obtained during the off-sweep gas trial demonstrated a PaO2 of 125 mmHg and pH of 7.41 without any excessive work of breathing based on the patient’s condition. Following an 8 h spontaneous breathing test mentioned above, the patient’s P/F was 277 mmHg, leading to the withdrawal of ECMO support. The patient was finally discharged from the ICU, and the LOS in ICU was only for 7 days without any adverse and unanticipated complications.A 29 years-old man was hospitalized in May 2023, presenting with a 3 days history of fever and dyspnea. The patient had previously been diagnosed with primary biliary cirrhosis (PBC) 9 years ago and had been on a long-term use of ursodeoxycholic acid (UDCA). He denied any related family history. On 6 November 2021, the patient was admitted to the hepatology department presenting symptoms of jaundice and lower limb edema. After admission, the patient was diagnosed with acute-on-chronic liver failure (ACLF) and underwent liver transplantation surgery after 1 year. The patient adhered to the prescribed regimen of tacrolimus and mycophenolate mofetil after liver transplantation to prevent transplant rejection. The physical examination of the patient revealed a temperature of 38.4°C, a heart rate of 84 beats/min, a respiratory rate of 20 breaths/min, an oxygen saturation   . Based o= 0.011) . On the = 0.011) , 19. Sec= 0.011) . The pulBy carefully managing these aspects, the primary goal of lung protection was successfully achieved by effectively reducing the frequency of spontaneous breathing and mitigating the potential adverse effects associated with excessive ventilation volume caused by driving pressure. Ultimately, the patient with PJP complicated with severe ARDS secondary to immunodeficiency after liver transplantation was treated with early awake ECMO successfully for only 7 days. Importantly, this remarkable outcome was achieved without any untoward complications, such as the development of ICU-acquired weakness, catheter-related bloodstream infection, or the onset of septic shock, further highlighting the efficacy and safety of this novel treatment modality.Undoubtedly, certain limitations were observed in this particular case. The criteria for selecting the patient limit the ability to establish the safety and efficacy of awake ECMO in elderly individuals, those with multiple comorbidities, poor compliance, or impaired consciousness. Furthermore, the unsuccessful application of awake ECMO in a severe case of H7N9 avian influenza indicates that awake and non-invasive mechanical ventilation may not always be suitable for managing severe ARDS . Consequ4.In this case, we present the effective application of awake VV-ECMO in the treatment of critically ill PJP patients, avoiding invasive mechanical ventilation. However, with the limitation outlined above, increased clinical evidence is needed to verify whether awake ECMO could be widely used in severe ARDS caused by other diseases or conditions.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving humans were approved by the Ethics Committee of Shenzhen Third People’s Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. Written informed consent was obtained from the patient for the publication of this case report.QW: Data curation, Formal analysis, Project administration, Resources, Supervision, Visualization, Writing – original draft. FC: Investigation, Supervision, Writing – review & editing. JH: Investigation, Project administration, Resources, Supervision, Visualization, Writing – review & editing. GW: Review & editing.


In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery. The advent of high-dimensional single-cell technologies has transformed our ability to study the complex array of cell types, states, and behaviors comprising the immune system , 2. SingThe high dimensionality of these data has necessitated the development and application of tools that can parse these data in a semi-automated way. This includes identifying cell populations via clustering algorithms –11, dimeHere, we present the CYtometry CLustering Optimization aNd Evaluation (Cyclone) (github.com/UCSF-DSCOLAB/cyclone/) pipeline for the analysis of a wide range of cytometry data, including but not limited to CyTOF, fluorescence-based cytometry, and multiplexed immunofluorescence (IF). Cyclone clusters data using FlowSOM —selectedTo create a functional and accessible workflow for cytometry datasets, we were interested in building a pipeline optimized to meet criteria that address challenges in analyses of this nature  scale to a large number of cells and parameters while maintaining reasonable runtimes and memory usage and 2) recapitulate populations identified via expert manual gating. We therefore evaluated a subset of available clustering algorithms, focusing on popular algorithms shown to perform well in the literature , 24. The+ T-cell subsets including memory or naïve are annotated as CD4+ T) . However, the unsupervised nature of the clustering facilitated the identification of cell subsets not included in our manual gating, such as CD4−CD8− T cells and intermediate monocytes  and permits maximum interoperability with other R-based normalization, debarcoding, and batch correction packages, early investigations explored annotations from both R and python packages; thus, we benchmarked FlowSOM and PARC cluster annotations against manual gating , a withi CD4+ T) , Fowlkes CD4+ T) , and mut CD4+ T)   response . Liver l+) and membrane markers  and labeled each cell-type annotation based on rational gating parameters  . After DBI evaluation, we chose a grid size of 2 × 4, which had the lowest DBI value (+ and CD8+ T cells (Clusters 3 and 8), mononuclear phagocytes (MNPs) , and one cluster with low expression for all markers (Cluster 1) (+ and CD163+ and low HLA-DR expression. Since the combination of the Cyclone and DeepCell segmentation outputs provides cluster identities (Cyclone) and x,y coordinates (DeepCell) for each cell within the same data frame, we leveraged this to evaluate where these XCR1+CD163+ cells were located within the tissue. When overlaying Cluster 7 onto the image of CRC1, cells depicted as Cluster 7 (green arrows), we were able to confirm that they had both XCR1+ and CD163+ expression  and kidney (KID1) tumor samples subjected to Cyclone  consisting of a tumor marker (EPCAM), T-cell markers , and myeloid markers   segmenta+CD163+ cells is possible, the thresholding strategy of comparing independent markers with strict cutoffs likely misses out on what Cyclone identifies when leveraging the entire set of markers to identify cell types  that provided accurate identification of cell populations in clustering as compared to manual gating and 2) that was reasonably scalable to large datasets. In our evaluation, we found that FlowSOM could handle datasets of up to 50 million cells, while also identifying both our coarse and fine manually gated cell populations at a high level of accuracy Figure 1Cyclone is readily generalized to a variety of cytometry datasets beyond CyTOF data, demonstrating its versatility as a pipeline for high-dimensional datasets that can be extensible and adaptable as technologies continue to evolve. We demonstrate the application of Cyclone to spectral flow cytometry data, in this case in the setting of a mouse model of viral infection, as well as immunofluorescence imaging data of the tumor microenvironment. Excitingly, Cyclone has been further applied to a CO-Detection by indEXing (CODEX) , 33 data+XCR1+ cell subset that was enriched in the stroma region. It is now well accepted that XCR1 expression defines the classical dendritic cells of type 1 (cDC1) across a wide range of organisms  chronic infection and cancer and have been associated with a cycling T-cell stage, which precedes exhaustion program upon chronic stimulation  “over-clustering” the data  or 2) “subclustering” the data . We also noted decreased accuracy clusters defined by markers with a continuum of expression such as CD45RA ; however, because the placement of this manual gate on a continuum is somewhat arbitrary, modest discrepancy between the manually defined abundance and cluster abundance seems of low importance as long as each is applied consistently across samples of interest. In addition, while we found these algorithms to be robust to downsampling, we have found that FlowSOM and CLARA could not accommodate a larger CyTOF data set of ~90 million cells, regardless of resource dedication. Further optimization of those algorithms is needed to be able to accommodate increasingly large cytometry datasets. Finally, while we have invested in interoperability and extensive documentation and vignettes for ease of use, Cyclone could be even more accessible to a non-coding user base with the development of a graphical user interface (GUI) such as an R-shiny application (https://shiny.rstudio.com) or as a workflow in web-based tools such as the University of California San Francisco (UCSF) Data Library , the Chan Zuckerberg Initiative’s CELLxGENE (https://github.com/chanzuckerberg/cellxgene), or CellEngine (https://github.com/primitybio/cellengine-python-toolkit), which could be the subject of future efforts. In sum, Cyclone takes the next step forward in the optimization and democratization of cytometry-based analysis tools to further power biological discovery.This work has several limitations, which can be explored or developed in future work. The set of clustering algorithms we compared for selection was restricted to four popular algorithms that are prevalent in cytometry analysis and have been elsewhere benchmarked , rather Blood samples from patients were obtained under institutional review board (IRB) #11-07994 protocol approved by the UCSF IRB, and #2012.059-2 (SCoo) protocol approved by Sutter Health IRB the IRB of record for the study. Written informed consent was obtained from all patients. De-identified healthy donor sample was obtained from Vitalant Research Institute . Blood was collected into sterile EDTA vacutainer tubes  and processed within 24 hours of collection.7 cells per vial in cell freezing media (10% dimethyl sulfoxide (DMSO) in fetal bovine serum (FBS)) and cryopreserved.PBMCs were isolated using Ficoll-Paque Plus  density gradient centrifugation; after isolation, cells were aliquoted in 0.5 × 103 to between 0.1 mg/mL and 0.3 mg/mL, and stored long-term at 4°C. Each antibody clone was titrated to optimal staining concentrations using unstimulated or anti-CD3/CD28 stimulated PBMC samples. All mass cytometry antibodies and concentrations used for analysis can be found in 6 cells/sample were stained for 1 min with 25 mM of cisplatin (Sigma-Aldrich) in phosphate-buffered saline (PBS) plus EDTA, before undergoing quenching 1:1 with PBS/EDTA/bovine serum albumin (BSA) to determine viability. Staining was performed on a shaker (90 rpm). For staining, cells were first resuspended in cell staining media (CSM)  with 5 μL of Human TruStain FcX™ block  for 5 min at room temperature to block Fc receptors, followed by staining with CXCR5 antibody in CSM (3 μg/mL) for 30 min at 4°C. Cells were washed, fixed with Fix I Buffer from The Cell-ID™ 20-Plex Pd Barcoding Kit following the manufacturer’s instructions , and barcoded by mass-tag labeling with distinct combinations of stable Pd isotopes diluted in Maxpar Barcode Perm Buffer  as described previously  and run on a Fluidigm CyTOF2 Helios mass Cytometer within 1 week of staining.For acquisition, cells were washed and resuspended at 1×10https://github.com/ParkerICI/premessa) to normalize data and deconvolute individual samples. From the individual sample files, normalization beads were excluded based on Ce140 and Eu153 signals. Single-cell events were identified based on Ir191 DNA signal measured against event length, and CD45− Pt195+ dead cells were excluded . Therefore, we decided to compare the runtime and memory usage of the selected clustering tools. Different parameters affect the runtime and memory usage in different tools. We observed the parameters affecting the number of clusters were the ones that controlled algorithm runtime. To perform a fair comparison between the tools, we identified these parameters affecting cluster counts and used values that produced a similar number of clusters across all four tools. We used k = 24 in CLARA, xdim = 6 and ydim = 6 in FlowSOM, k = 25 in PhenoGraph, and resolution = 1.3 in PARC. These parameters resulted in 30–37 clusters called by each tool. We measured the time for clustering and the memory usage on CentOS nodes of a high-performance computer cluster.In order to begin a Cyclone run, metadata files “file_metadata.csv”, “marker_metadata.csv”, and “config.yml” must be generated. These files are unique to your dataset, but Cyclone requires certain metadata to locate the FCS files and associate metadata with them. In the file metadata csv, column “file_name” records the name of each FCS file, “donor_id” denotes sample origin, “pool_id” denotes batch identification (if any), and “control_sample” is a Boolean indicating whether the sample is a control or not. The file metadata csv is created via any scripting language based on the FCS files present. To create the “marker_metadata.csv”, we provide “cyclone/make_marker_metadata_csv.R”, which will read in an FCS file, and create a file with the following columns: channel_name, marker_name, used_for_UMAP, used_for_clustering, and used_for_scaffold. It may be advantageous to use a marker for clustering, but not use that marker for UMAP calculation. Thus, the Boolean values for each marker in the “used_for_*” columns provided granular controls for using markers for UMAP, clustering, and SCAFFoLD analysis. The final file “config.yml” controls how Cyclone will be run. In brief, it contains the absolute path of FCS files and save location for pipeline outputs, location of metadata files, and parameters associated with data processing , UMAP , and clustering  to tune node connectivity. Cyclone is able to cluster with both FlowSOM and CLARA, and the choice is specified in “config.yml”. CLARA editable parameters are k (default 20), metric (default euclidean), and samples (default 50). The FlowSOM editable parameters are xdim (default 6) and ydim (default 6), which define the grid (6 × 6). If meta_clustering with FlowSOM is TRUE, (default FALSE), the “nClus” of FlowSOM (i.e. the number of meta-clusters) can be defined using “k” (default 3).At the start of the Cyclone run, the pipeline references the metadata.csv files to read in FCS data files and creates a raw expression matrix. After the arcsinh transformation of count values, a transformed expression matrix is created using values specified in config.yml. Cyclone also creates a “cell_metadata” object, which associates each cell/event with file and marker metadata. Next, Cyclone uses the uwot package with default parameters to calculate UMAP on the transformed matrix with the “used_for_UMAP” column of the markers’ metadata file. Each cell/event UMAP dimensions are assigned to the cell_metadata object. Next, clustering is performed using either FlowSOM or CLARA. In FlowSOM, a default series of grids (cyclone/grid_sizes.csv) are specified, and different resolutions and clustering parameters are calculated and evaluated with the DBI. After cluster optimization, the Cyclone pipeline exits to await user evaluation of the Cluster VS DBI plot . Fine annotations further parse cell types into subtypes according to their phenotype . We then calculated similarities between cluster annotations of single cells and the single-cell annotation based on a third immunology expert gating of the CyTOF data (ground truth annotation). We performed this comparison for subsets of data multiple times and calculated averages. Specifically, we subsampled cluster annotation and manual gating annotation of randomly selected 10,000 cells, calculated accuracy, and adjusted the Rand index, Fowlkes–Mallows index, and mutual information. We repeated this process 10 times with different random seeds and calculated the mean across the iterations for each similarity metric. We performed this analysis for FlowSOM  were used to create a SingleCellExperiment object  in R con−/− mice were previously described  proteincoding region under the constitutive transcriptional control of the mouse albumin promoter. Young  or adult (8 to 12 weeks old) HBVEnvRag−/− mice were given 108 syngeneic splenocytes pooled from adult (8 to 12 weeks) WT mice in 0.5 mL of phosphate-buffered saline via tail vein injection. Mice were maintained at the Laboratory Animal Resource Center (LARC) facility at UCSF where health was monitored daily by the LARC staff. Experimental procedures were performed in accordance with Institutional Animal Care and Use Committee (IACUC)-approved protocols, and all efforts were made to minimize animal suffering.Wild-type (WT) C57BL/6 mice were purchased from Jackson Laboratory . HBVEnvRagescribed . Brieflynstitute ] backcroMice were anesthetized in chambers with 1.5% oxygen and 3% isoflurane. Samples for the T cell-focused panel were isolated from the liver after perfusion and digestion. Briefly, mice were perfused via the inferior vena cava using digestion media . Livers were forced through a 70μm filter using a syringe plunger, and debris was removed by centrifugation (30 g for 3 min). Supernatants were collected and centrifuged for 10 min at 650 g. Cells were isolated from the Percoll interface using a 60%:40% Percoll gradient. Samples for the myeloid-focused panel were isolated from the liver after 6 min of perfusion via the inferior vena cava using digestion media as above. Livers were chopped and further digested with liberase and DNase I (Roche Diagnostics)  for 30 min at 37°C in a shaking water bath. Livers were forced through a 70μm filter, and debris was removed by centrifugation (30 g for 3 min). Supernatants were collected and centrifuged for 10 min at 650 g. Cells were isolated from the interface of a 60%:40% Percoll gradient.Cells were prepared as above. Samples stained with the T cell-focused panel were first stained with custom HBV-specific tetramers developed by the National Institutes of Health (NIH) Tetramer Core Facility at Emory University. Cells were stained first with MHC Class II Tetramer for 1 hour at 37°C protected from light. Next, these cells were then stained with MHC Class I Tetramer for 1 hour at 4°C protected from light. For both panels, cells were then stained with Live/Dead Fixable Blue (Thermo Fisher Scientific) according to the manufacturer’s instructions. Next, surface markers on cells were stained according to standard protocols with anti-mouse antibodies detailed in + events in FlowJo  with a 5-laser setup at the UCSF Flow CoLab. Following sample collection, spectral signatures were checked to ensure reliable unmixing, and channel spillover was adjusted in SpectroFlo (Cytek). Prior to Cyclone and manual gating analyses, FCS files were gated on leukocyte size/granularity, singlets, live, and CD45Cyclone pipeline was run as described above, specifying an arcsinh transformation cofactor of 6000, with all other values kept as default . Samples were obtained after surgical excision with biopsies taken by pathology assistants to confirm the presence of tumor cells. Freshly resected samples were placed in ice-cold PBS or Leibovitz’s L-15 medium in a 50-mL conical tube, immediately transported to the laboratory for sample labeling, and formalin fixed for imaging analysis. Clinical data on three samples were denoted as follows: CRC1 = IPICRC072, CRC2 = IPICRC057, and KID1 = IPIKID090.A 7-plex immunofluorescent panel was created using the Ventana BenchMark Ultra (Roche Diagnostics) automated staining platform. All reagents were from Discovery  and used according to the manufacturer’s instructions, except as noted. Heat-Induced Epitope Retrieval (HIER) was performed with the Cell Conditioning 1 (CC1) solution (cat. 950-124) for 64 min at 97°C. The primary antibodies used were CD3 , CD4 , CD8 , CD163 , HLA-DR , XCR1 , and EpCAM . The tissue was counterstained with DAPI (Akoya cat. FP1490) for nucleus localization. The staining was conducted in two cycles: the first cycle had CD3, CD4, CD8, CD163, HLA-DR, and XCR1; the second cycle had EpCAM. Both cycles had DAPI. The slide was scanned using a whole slide scanner after each staining cycle. Finally, the images from both cycles were registered to achieve the 7-plex image shown in Cell segmentation was performed by utilizing ark-analysis (v0.2.9) DeepCell  softwareAfter determining an optimal grid size, the Cyclone output heatmap showing arcsinh transformed and scaled marker expression data were used to annotate clusters and scaled by row. Log2 fold change of each cluster in stroma versus tumor was determined by calculating the frequency of stromal over frequency of tumor in each cluster and transformed by a log of 2. Contour plots comparing Cyclone versus manual gating were generated in python (version 3.8.12) by plotting arcsinh transformed cell ROIs annotated as XCR1 and CD163.http://flowrepository.org/id/FR-FCM-Z6LS). The cyclone pipeline and package are available at github.com/UCSF-DSCOLAB/cyclone/. The documentation on running the cyclone is available at https://github.com/UCSF-DSCOLAB/cyclone/blob/main/vignettes/Running.md and the documentation on follow up analyses is available at https://github.com/UCSF-DSCOLAB/cyclone/blob/main/vignettes/FollowUp.html.The FCS files for the CyTOF experiments used in this study are deposited and publicly available at FlowRepository (The studies involving humans were approved by UCSF and Sutter Health Institutional Review Boards. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. All animal experiments done in this study were approved by the UCSF Institutional Animal Care and Use Committee. The study was conducted in accordance with the local legislation and institutional requirements.RP and RJ contributed equally to this manuscript. RP, RJ, KI, NDC, TC, NWC, BS, DB, LL-J, JJ, JP, and LA, generated and analyzed data and contributed to the manuscript by providing figures, tables, and important intellectual contributions. RP, RJ, AC, and GF had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the analyses. RP, RJ, KI, DB, NDC, AC, and GF wrote and edited the manuscript. RP, RJ, BS, DB, AR, and LL-J performed computational analyses of the data. MN and NWC were part of the UCSF Immunoprofiler team who performed tissue preparation and staining for the immunofluorescence performed on the different human tumor specimens. RP, RJ, BS, DB, and AR developed the Cyclone package and wrote the tutorial. AE and KI performed computational analysis of the multiplexed immunofluorescence data on human tumors. MK, SC, JB, AC, and GF were actively involved in the direction of projects and provided financial resources for the obtainment of the data. AC and GF co-led this project. All authors edited and critically revised the manuscript for important intellectual content and gave final approval for the version to be published.


Simulation is an increasingly used novel method for the education of medical professionals. This study aimed to systematically review the efficacy of high-fidelity (HF) simulation compared with low-fidelity (LF) simulation or no simulation in advanced life support (ALS) training.A comprehensive search of the PubMed, Chinese Biomedicine Database, Embase, CENTRAL, ISI, and China Knowledge Resource Integrated Database was performed to identify randomized controlled trials (RCTs) that evaluated the use of HF simulation in ALS training. Quality assessment was based on the Cochrane Handbook for Systematic Reviews of Interventions version 5.0.1. The primary outcome was the improvement of knowledge and skill performance. The secondary outcomes included the participants’ confidence and satisfaction at the course conclusion, skill performance at one year, skill performance in actual resuscitation, and patient outcomes. Data were synthesized using the RevMan 5.4 software.0.0003, I2 = 70%] and skill performance  for HF simulation when compared with LF simulation and traditional training. The subgroup analysis revealed a greater benefit in knowledge with HF simulation compared with traditional training at the course conclusion . Studies measuring knowledge at three months, skill performance at one year, teamwork behaviors, participants’ satisfaction and confidence demonstrated no significant benefit for HF simulation.Altogether, 25 RCTs with a total of 1,987 trainees were included in the meta-analysis. In the intervention group, 998 participants used HF manikins, whereas 989 participants received LF simulation-based or traditional training . Pooled data from the RCTs demonstrated a benefit in improvement of knowledge [standardized mean difference (SMD) = 0.38; 95% confidence interval (CI): 0.18–0.59, P = Learners using HF simulation more significantly benefited from the ALS training in terms of knowledge and skill performance at the course conclusion. However, further research is necessary to enhance long-term retention of knowledge and skill in actual resuscitation and patient’s outcomes.The online version contains supplementary material available at 10.1186/s12909-023-04654-x. Cardiovascular disease (CVD) accounts for 30% of all-cause mortality. On average, over 17.5 million people die of CVD annually worldwide, with approximately one death every 10 s . SimulatLearners can assess physical findings and make clinical decisions regarding the simulated patients . This maThis systematic review and meta-analysis was performed according to the Cochrane Reviewers’ Handbook and presented according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The protocol for this systematic review and meta-analysis is available at PROSPERO (CRD42022333898).Randomized controlled trials (RCTs) were identified from PubMed, ISI (Web of Science: Science Citation Index Expanded), Cochrane Library , China Knowledge Resource Integrated Database, World Health Organization Global Index Medicus, and Chinese Biomedicine Database from their inception dates to April 31, 2022. The search keywords and MESH terms were (“simulation” OR “patient simulation” OR “mannequin” OR “manikin”) AND  AND (“education” OR “training” OR “teaching”). References from the RCTs were browsed, and the corresponding authors were consulted for any further information that they have not reported publicly. Ongoing RCTs were reviewed using clinical trial registers. The complete terms and strategies for identifying these articles are listed in the supplementary document , teamwork behaviors and patient outcomes.The studies identified from the electronic searches were evaluated independently by 2 researchers (W.K. and Q.Z.) using a study eligibility form based on the inclusion criteria. Relevant studies were initially screened using titles and abstracts. Potential articles for inclusion were independently assessed by the two reviewers (W.K. and R.S.Q.), and any dissentions were resolved by a third reviewer (W.M.X.). The methodological quality of the included studies was assessed based on the Cochrane Reviewers’ Handbook . Each stI2 statistic was used to quantify heterogeneity, and forest plots were generated and counterchecked by two reviewers (S.Q.R. and W.M.X.). If I2 < 25%, the pooled outcomes were considered to have low statistical heterogeneity; if I2 > 75%, the pooled outcomes were considered to have high statistical heterogeneity. We also performed sensitivity analysis by the sequential removal of trials for each outcome. Publication bias was assessed by the funnel plots when no less than 10 trials were included in the meta-analysis. If data which were reported instead of mean and standard deviation , we would transform it by the methods created by Hozo et al. and Higgins and Green. Data were synthesized using the RevMan 5.4 software. The research protocol, outcomes, and relevant items in this systematic review are reported following the PRISMA Statement .  and traditional training . (Supplement 2 = 0%) and three- and four-year medical/nurse students  were benefit from HF simulation. However, fist-year medical/nurse students had no benefit from HF simulation . (Supplement P < 0.001). However, no significant difference was observed between the HF and control groups (P = 0.28).Fifteen studies , 40, 42  studies , 20, 24 2 = 92%). .. . (Supplement P ≤ 0.001).A total of twenty-one RCTs , 36–42 mP = 0.08, I2 = 77%)    Fig. . Similar%) Figs. .The risk of bias assessment of the included RCTs is summarized in Fig. The results of this systematic review and meta-analysis indicated that participants benefited from improving knowledge and skill performance at the course conclusion with HF simulation. The subgroup analysis showed a greater benefit in knowledge with HF simulation compared with traditional training at the course conclusion. However, the use of HF simulation in comparison to LF simulation and traditional training showed no benefit for knowledge at 3 months, teamwork, and participant’s confidence. Higher means of satisfaction were observed in the HF group.To the best of our knowledge, this is the first systematic review and meta-analysis of RCTs to identify the efficacy of HF simulation in ALS training. The benefits of HF simulation for improving skill performance and satisfaction in this study are consistent with the results of previous studies , 6, 43. The retention of ALS knowledge and skills is widely recognized as a significant factor in actual resuscitation. Only two RCTs in this study , 40 haveTeamwork is the key to resuscitation in a real clinical setting, and it could be improved through briefing and debriefing , 49. DurWhile the goal of HF simulation-based training is to enhance knowledge and skills, cost-effectiveness is also a key point for educational intervention. If the cost of HF simulation-based training is prohibitively high, it could not be a viable option. Previous studies have analyzed the cost-effectiveness of simulation-based training programs on a learner basis . IsaranuThis study had some limitations that need to be considered. First, of the included RCTs, no studies measured skill performance during actual resuscitation or patient outcomes. Furthermore, high heterogeneity was observed in a meta-analysis of knowledge and skill performance. Hence, the studies might have had different training settings, outcome measurements, and types of participants. Third, no uniform standard assessment of confidence and satisfaction was implemented. The included RCTs used various questionnaires, which may have led to unobjective and incomparable results. Finally, only few of the included studies met the high-quality standards of evidence-based medicine because the participants were not blinded to the level of simulation. We would suggest future work commit more resources to optimize instructional design, instructor and debriefing training.Learners using HF simulation benefited from knowledge and skill performance in ALS at the course conclusion than those using LF simulation or traditional training. A high-quality multicenter RCT is needed to enhance retention of knowledge and skills in actual resuscitation and patient outcomes in the future.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6


The New Nordic Diet (NND) has been shown to promote weight loss and lower blood pressure amongst obese people. This study investigates blood plasma metabolite and lipoprotein biomarkers differentiating subjects who followed Average Danish Diet (ADD) or NND. The study also evaluates how the individual response to the diet is reflected in the metabolic differences between NND subjects who lost or maintained their pre-intervention weight.Centrally obese Danes (BMI >25) followed NND (90 subjects) or ADD (56 subjects) for 6 months. Fasting blood plasma samples, collected at three time-points during the intervention, were screened for metabolites and lipoproteins (LPs) using proton nuclear magnetic resonance spectroscopy. In total, 154 metabolites and 65 lipoproteins were analysed.The NND showed a relatively small but significant effect on the plasma metabolome and lipoprotein profiles, with explained variations ranging from 0.6% for lipoproteins to 4.8% for metabolites. A total of 38 metabolites and 11 lipoproteins were found to be affected by the NND. The primary biomarkers differentiating the two diets were found to be HDL-1 cholesterol, apolipoprotein A1, phospholipids, and ketone bodies . The increased levels of ketone bodies detected in the NND group inversely associated with the decrease in diastolic blood pressure of the NND subjects. The study also showed that body weight loss among the NND subjects was weakly associated with plasma levels of citrate.The main plasma metabolites associated with NND were acetate, methanol and 3-hydroxybutyrate. The metabolic changes associated with the NND-driven weight loss are mostly pronounced in energy and lipid metabolism. Human blood plasma contains hundreds of molecules related to metabolism (metabolites) as well as a diversity of lipoproteins, small particles involved in the transport of fats and cholesterol in the aqueous blood streams. Despite a strong push towards homeostasis, the levels of metabolites and lipoproteins in blood change with time depending on multiple factors such as diet, age, health, etc. The importance of diet on human health is well known, but the underlying molecular mechanisms remain elusive. For this reason, blood plasma has been increasingly employed in metabolomics studies to better understand the impact of diet on human metabolism and health .https://clinicaltrials.gov/ct2/show/NCT01195610)  project was launched  . During 1H NMR spectroscopy. The major advantages of NMR are that it is unbiased and inherently quantitative toward a broad range of metabolite classes present in human blood plasma . The number of recruited subjects was 181 , between 20 and 66 years old (average: 42 years old). Moreover, the participants had one or more of the following characteristics: impaired fasting glucose level >5.6 mmol/L, plasma triglyceride concentrations ≥1.7 mmol/L, HDL-cholesterol concentrations ≤1.03 mmol/L for men and ≤1.29 mmol/L for women, and systolic/diastolic blood pressure >130/85 mm Hg. Exclusion criteria included: diagnosed diabetes (both type 1 and 2), total cholesterol ≥9 mmol/L, triglyceride concentration ≥3 mmol/L, familial hypercholesterolemia, food allergies contrasting with the intervention, pregnancy, or lactation. In addition, subjects who lost >2 kg in the preceding 2 months were excluded.2), and whether they were enrolled as individuals or couples. The two diets differed in the composition of 15 food groups that were specific for NND, and in macronutrients, including total intake of proteins, carbohydrates, and fats. NND was characterized by a higher fish, whole grain, fruit, and vegetable consumption and by lower intakes of terrestrial meat, compared to ADD. A detailed description of the two diets is given in a previous article , BMI  and by the Danish Data Protection Agency (2007-54-0269).−1 trimethylsilylpropionate (TSP) in 2 mL Eppendorf tubes , and then transferred to 5 mm NMR tubes  (1H NMR) spectra were acquired from all plasma samples using both the Carr–Purcell Meiboom–Gill (CPMG) experiment and the NOESY-presat pulse sequences from Bruker’s library. The former suppresses, apart from water resonance, also resonances from large molecules , and can thus better facilitate the identification of small molecules. The latter, instead, only suppresses water resonance, providing a more unbiased quantitative picture of the sample composition. All experiments were performed at 310 K with a fixed receiver gain (RG) of 40.3. A total of 64 scans were acquired and the measured free induction decays (FID) were collected into 128k data points. The automation program controlling sample measurements included the acquisition routines for locking, automatic tuning and matching, shimming, pulse calibration, and optimized pre-saturation power for each sample, as well as automatic data processing including Fourier transformation (FT) of FID, with a Lorentzian line-broadening of 0.3 Hz before FT, phasing, and baseline correction.Plasma samples were thawed on ice for 60 min. A 300 μL plasma aliquot was then mixed with 300 μL phosphate buffer (pH 7.4), containing 0.8 mg mLGermany) . NMR ana1H NMR spectra were converted to a metabolite concentration table using the SigMa software  and XT2  were further corrected for baseline  as follows: ΔXT1 = XT1–XT0 and ΔXT2 = XT2–XT0. This procedure yielded a total of six datasets, ΔXT1_NOESY,ΔXT1_CPMG,ΔXT1_LP,ΔXT2_NOESY,ΔXT2_CPMG, and ΔXT2_LP to be scrutinized for evaluating metabolic differences related to the diet, NND vs. ADD, and metabolic changes related to weight loss of the NND subjects. The weight loss factor was defined as described before (weight losers (n = 62) with at least 6% weight loss according to their body weight at inclusion and weight maintainers (n = 52) who lost ≤2% of their pre-intervention body weight. ADD subjects showed very limited weight change, and thus were excluded from this investigation (weight losers (>6%) and consistent weight maintainers (<2%) both at T1 and T2 were limited to only 22 and 19 subjects, respectively  were firstly resolved into metabolite tables and LP tables for the different time points as described above. The datasets representing d before , by straXT1 T1; 1 weeks anectively . AdditioXT1 T1; 1 weeks annperm. = 1,000) was applied to quantify variations explained by the study design factors, diet (NND vs. ADD) and weight loss (NND weight losers vs. NND weight maintainers). Partial least squares-discriminant analysis (PLS-DA)   was used(PLS-DA)  was empl(PLS-DA) . One-wayThe averages and standard deviations of the main parameters, relevant to this study, of the individuals following the two diets, NND and ADD, are presented in 1H NMR spectrum of human blood plasma is shown in 1H resonances of methyl and methylene groups corresponding to fatty acids and lipoproteins (1.4–0.6 ppm). Overall, the most abundant plasma metabolites included cholesterol-C18 , lactic acid , alanine , valine  and glucose . Using Signature Mapping (SigMa) software the 1H NMR data was converted into a metabolite table. The resulting table consisted of 154 variables: 65 were SS of known plasma metabolites, 33 SUS and 56 BINS  showed a significant effect of diet with explained variations ranging from 0.6 to 4.8% (ΔXT2_NOESY) showed the largest fraction (4.8%) of the variation explained by diet (p < 0.0001). Further investigation of the effect of the diet was performed using PLS-DA which allowed to find the pattern of metabolites discriminating NDD and ADD subjects using above mentioned datasets. Classification performances of the PLS-DA models developed on the six datasets after variable selection are summarized in T1_NOESY dataset  and included 75 variables to be important markers of the diet, the second best was developed on ΔXT2_NOESY dataset . The consistently selected 59 variables between these two models included 22 plasma metabolites  failed to classify the two groups at T0 , with 3.2% and 3.4% of the variation in the data being associated with the weight loss, respectively. Interestingly, a significant weight loss effect was also observed in the ΔXT1_LP_NND_BW dataset . The effect of BW loss was further investigated by PLS-DA (ΔXT2_NOESY_NND_BW (p < 0.05 with FDR correction) performed to test BW effect . One-way ANOVA was applied to evaluate differences in individual metabolite or lipoprotein levels between the two groups, low 1/3 versus high 1/3 tertiles. It was found that several metabolites, were inversely associated with the increase in DBP from baseline to T1 (increased by up to 53%), including acetoacetic acid, acetone and succinate, and T2 (increased up to 31%), including the former metabolites and another ketone body (3-hydroxybutyric acid), in the NND group . Increasing blood plasma level of citrate was also associated with weight loss in another study  Danish Data Protection Agency (2007-54-0269). The patients/participants provided their written informed consent to participate in this study.LD, AA, and SE: conception and design. AT, BK, and MR: development of methodology. SE: data acquisition. AT, BK, and SE: data analysis. AT, BK, MR, LD, TL, AA, and SE: interpretation of results and writing. All authors contributed to the article and approved the submitted version.www.clinicaltrials.gov as NCT01195610 . The method for calculating the lipoprotein distributions was supported by the COUNTERSTRIKE project Danish Strategic Research Council/Innovation Foundation Denmark (grant number 4105-00015B). The contribution from LD was funded by the PRIMA grant from the Novo-Nordisk Foundation .The study was conducted as part of the OPUS project, which is supported by a grant from the Nordea Foundation, Denmark. OPUS is an acronym of the Danish title of the project “Optimal wellbeing, development and health for Danish children through a healthy New Nordic Diet”. The trial was registered at The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fnut.2023.1198531/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.


As the climate and environmental crises unfold, eco-anxiety, defined as anxiety about the crises’ devastating consequences for life on earth, affects mental health worldwide. Despite its importance, research on eco-anxiety is currently limited by a lack of validated assessment instruments available in different languages. Recently, Hogg and colleagues proposed a multidimensional approach to assess eco-anxiety. Here, we aim to translate the original English Hogg Eco-Anxiety Scale (HEAS) into German and to assess its reliability and validity in a German sample.Following the TRAPD  approach, we translated the original English scale into German. In total, 486 participants completed the German HEAS. We used Bayesian confirmatory factor analysis (CFA) to assess whether the four-factorial model of the original English version could be replicated in the German sample. Furthermore, associations with a variety of emotional reactions towards the climate crisis, general depression, anxiety, and stress were investigated.The German HEAS was internally consistent  and the Bayesian CFA showed that model fit was best for the four-factorial model, comparable to the factorial structure of the original English scale . Weak to moderate associations were found with negative emotional reactions towards the climate crisis and with general depression, anxiety, and stress.Our results support the original four-factorial model of the scale and indicate that the German HEAS is a reliable and valid scale to assess eco-anxiety in German speaking populations. Their validation studies  yielded The global adverse effects of the climate and environmental crises on mental health highlight the importance of making reliable and valid scales of eco-anxiety available in different languages to facilitate cross-country research. Since no validated eco-anxiety scale is currently available in German, the aim of this study was to translate the original English HEAS  into Ger2.We used a two-step approach in this study. In a first step, we translated the original English HEAS into German using standardized guidance . In a se2.1.We translated the original English HEAS  into Ger2.2.M [SD] = 29.43 [10.63], median: 26 years, range: 18–73). To reach an adequate sample size for our planned analyses, we used three different recruitment approaches: 158 participants (students) were recruited via online advertisements and mailing-lists at 40 German universities (sample 1). One hundred and ninety-six participants (students and university staff) were recruited at Freie Universität Berlin via flyers, posters, and emails (sample 2) and 132 participants (students) at University of Potsdam via an online recruitment system for university students and online advertisement (sample 3). Using a link/QR code on the study invitation, participants accessed and completed an online survey implemented in the survey software Unipark .We tested the German HEAS with 486 participants  in Germany . This permits a more nuanced understanding of the relationships between variables, resulting in a more comprehensive representation of the underlying constructs  to examine the factorial structure of the German HEAS. Bayesian CFA, unlike conventional CFA, offers certain benefits: Bayesian CFA employs probabilistic methods to effectively estimate parameters even when sample sizes are modest, mitigating issues associated with statistical power and enhancing the robustness of findings. Another notable feature of Bayesian CFA is its capacity to incorporate existing knowledge or beliefs into the analysis through “informed priors.” This means that researchers can introduce relevant information about parameter values before analyzing the data. Such nstructs . These aWe chose Bayesian CFA to incorporate information from prior work including data about the factorial structure and item loadings reported by plus . To establish stable calculations, we initiated a preliminary phase of 50,000 iterations  without recording the results, followed by a fixed number of 50,000 iterations with recorded outcomes (postburn-in iterations). The The reporting of the analyses follows the recommendations by plus and assumed that they followed a normal distribution. As Hogg and colleagues did not provide variances for the factor loadings, we conducted sensitivity analyses with different variance priors  to test the robustness of our findings. We relied on the Mplus default prior settings for error variances of items and the latent factor covariance matrix.When implementing the informative priors for the main loadings of items on their respective factors, i.e., using the estimates from Table 5 in χ2/df ≤ 2, Comparative Fit Index , rumination (M [SD] = 0.60 [0.67]), and anxiety about personal impact (M [SD] = 1.20 [0.70]) showed good internal consistency . The internal consistency of the subscale behavioral symptoms (M [SD] = 0.33 [0.50]) was acceptable .The HEAS subscales affective symptoms . The model fit was acceptable based on a PP value of χ2 value: χ2/df = 2.45 (144.47/59), p ≤ 0.001, CFI = 0.983, TLI = 0.977, RMSEA = 0.055 , and SRMR = 0.024.Model fit indices from conventional frequentist CFA support the conclusion of good model fit with exception for the The intercorrelations of the latent factors were high. Affective symptoms correlated with rumination (0.79), behavioral symptoms (0.74), and anxiety about personal impact (0.65). Further correlations were: rumination with behavioral symptoms (0.55) and anxiety about personal impact (0.61); behavioral symptoms with anxiety about personal impact (0.43).7 = 0.16, l9 = 0.19, and l13 = 0.11. Thus, prior assumptions about factor loadings slightly affected the analyses but the conclusions regarding the number and structure of factors remained unchanged.To examine the effects of informed priors for factor loadings on model estimates, we compared the results of the four-factorial model with informed priors with the model parameters that resulted based on diffuse priors for factor loadings with normal distribution. Differences in main factor loadings and factor covariances were, according to 3.3.n = 158, M[SD] = 4.97 [4.41]), anxiety (3.01 [3.67]), and stress subscales (6.32 [4.32]) were below clinical thresholds. A multiple regression model investigating unique associations of each HEAS subscale and the DASS-21 anxiety subscale showed that the affective symptoms subscale  and the behavioral symptoms subscale  were significantly associated. Bivariate correlations between all HEAS subscales and all DASS-21 subscales are reported in Scores of the DASS-21 depression (3.4.p < 0.001), “desperate” , and “hurt”  were significantly related when controlling for all other emotions. See When entering all 18 emotional reactions towards the climate crisis into the multiple linear regression model to test unique relationships with the HEAS affective symptoms subscale, only the emotions “anxious” . Regression results and weak to moderate associations between the HEAS subscales and the DASS-21 subscales indicate that eco-anxiety is distinct from general depression, anxiety, and stress, but shares a significant proportion of variance. Taken together, the study suggests that the German HEAS is a reliable and valid scale to measure eco-anxiety.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving humans were approved by local ethics committee of Freie Universität Berlin, Berlin, Germany. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.SH, FS, GR, FP, JK, and ME contributed to conception and design of the study. SH, MS-H, and FP translated the HEAS scale. MT, MS-H, FS, CP, and JK coordinated the study administration and data acquisition. SH, MS-H, and MB performed the statistical analyses. SH and MB wrote the first draft of the manuscript. GR, FP, and ME wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


SGLT2i are now recommended in a wide spectrum of indications including type 2 diabetes (T2DM), heart failure, and chronic kidney disease. This medication class is now available in combination with metformin, which is still a fundamental treatment in patients with T2DM. Despite excellent proven safety profile for both drugs, the expanding use of these agents in clinical practice may lead to an increased incidence of rare side effects, like metformin-associated lactic acidosis (MALA) and euglycemic diabetic ketoacidosis (EDKA), which can be life-threatening. A 58-year-old woman with T2DM and severe heart failure treated by metformin and empagliflozin developed progressive EDKA triggered by fasting that was also complicated by severe acute renal failure and MALA. She was successfully treated with intermittent hemodialysis. This case report highlights the importance of the recognition of rare, but very serious adverse effects due to combined metformin and SGLT2i therapy. According to current guidelines, metformin and sodium-glucose cotransporter-2 inhibitors (SGLT2i) are considered first-line oral antidiabetic therapies in patients with type 2 diabetes (T2DM) and confirmed or at high risk of cardiovascular disease . Moreove2]. Her medication included the following: aspirin 100 mg od, clopidogrel 75 mg od, atorvastatin 40 mg od, ezetimibe 10 mg od, metoprolol 100 mg od, sacubitril/valsartan 50 mg bid, spironolactone 25 mg od, empagliflozin 10 mg od, metformin 500 mg bid, and long-acting insulin 6 units od.A 58-year-old female patient known for T2DM was admitted to our institution for breathlessness and confusion. Four months before, she had been diagnosed with an ST-elevation myocardial infarction due to an occluded circumflex artery treated by angioplasty and stenting. Chronic occlusion of the left anterior descending artery was not revascularized due to the absence of myocardial ischemia or viability on functional imaging. During follow-up, because of advanced heart failure with New York Heart Association functional class III and LVEF 25% despite maximum tolerated heart failure medical therapy, she underwent implantable cardioverter-defibrillator implantation and was listed for heart transplant. At that moment, T2DM was well controlled [glycated hemoglobin (HbA1c] 6.9%) and an impaired kidney function, which was stable compared with previously [estimated glomerular filtration rate (eGFR) 48 ml/min/1.73 mA few days before the admission to our hospital, the patient wanted to celebrate her birthday with her husband and drove 250 km by car to one the most touristic places in Switzerland, Interlaken. Unfortunately, all restaurants and hotels were closed because of the coronavirus disease 2019 pandemic and the couple finally had to return to Geneva late the same day without having been able to eat or drink. At night, the patient started to feel progressive symptoms of nausea, vomiting, diarrhea, anorexia, and abdominal pain. She could hardly eat or drink but continued her medications until hospital admission 4 days later.Upon admission, the patient was confused, with Glasgow Coma Scale 11/15, and in respiratory distress. Vital signs were the following: blood pressure 134/85 mmHg; heart rate 90 bpm; respiratory rate 40/min; oxygen saturation 97% on room air; temperature 36.0 °C. There was no sign of congestion. Blood chemistry showed the following: sodium 135 mmol/l, potassium 6.4 mmol/l, creatinine 801 μmol/l, glucose 9 mmol/l. Arterial blood gases are shown in Table Metformin has been the first-line therapy for patients with T2DM for decades, mainly because of its high efficacy in glycemic control (via multiple molecular targets) with a concomitant low risk of hypoglycemia . Benign − <18 mmol/l) and ketosis [SGLT2i represent a new class of antidiabetic therapy improving glycemic control by enhancing urinary excretion of glucose . The mos ketosis .Symptoms of lactic acidosis and ketoacidosis are similar and include nausea, vomiting, malaise, abdominal pain, and, in more severe cases, altered consciousness, Kussmaul breathing, and clinical signs of shock . Our maiVery few cases in the literature describe the combination of MALA and EDKA in patients treated with metformin and SGLT2i. We found one case described in a patient taking canagliflozin and metformin who had gastroenteritis, and another in a patient treated by empagliflozin and metformin who suffered from acute renal failure due to high doses of celecoxib, and both were successfully treated with continuous renal replacement therapy (CRRT) and parenteral insulin/glucose infusion ,10.Initial management for MALA is based on administration of intravenous fluid and supportive care. There is no specific treatment, and sodium bicarbonate alone is generally not sufficient to correct acidosis ; however2 [From a prevention perspective, patients with severe renal failure remain at high risk of MALA during treatment with metformin, therefore its prescription should be avoided when eGFR is below 45 ml/min/1.73 m2 . Regardi2 . Sick da2 . Once ED2 . There iThis case illustrates the potentially serious risks associated with metformin and SGLT2i. Because of their association in one unique pill in addition to the growing evidence around the benefits of SGLTi, these drugs are likely to be more and more prescribed, and thus, clinicians need to be aware of MALA and EDKA in order to counsel their patients better, diagnose these conditions early when they occur, by measuring ketones and lactates in the presence of metabolic acidosis, and provide adequate and rapid management in order to minimize complications. Finally, we suggest caution when combining these drugs in patients with potential risk factors or triggers for MALA/EDKA.There are no conflicts of interest.