Assessment of weaning from mechanical ventilation (MV) is an important process. Rapid shallow breathing index (RSBI) is a standard tool to evaluate a patient's readiness before the spontaneous breathing trial (SBT). Handgrip strength (HGS) is an alternative method for assessment of respiratory muscle strength. Relationship between HGS and RSBI has not been explored. This study aimed to determine the correlation between HGS and RSBI to predict successful extubation in mechanically ventilated patients.  A prospective study was conducted in screened 120 patients requiring MV with tracheal intubation >48 h. HGS was performed at 48 h after intubation, 10 min before and 30 min after SBT, and 1 h after extubation. RSBI was performed at 10 min before SBT. P < 0.001). The equation for predicting RSBI, derived from the linear regression model, was predicted RSBI (breaths/min/L) = 39.285 + (age ∗ 0.138)–(HGS ∗ 0.571).  A total of 93 patients (58% men) were included in the final analysis. Mean age was 71.6 ± 15.2 years. Patients admitted in general medical wards were 84.9%. APACHE II score was 13.5 ± 4.7. Most patients were intubated from pneumonia (39.8%). Weaning failure was 6.5%. The main result shows that HGS was negatively correlated with RSBI (regression coefficient −0.571, TCTR20180323004. HGS had significantly negative correlation with RSBI for assessment of weaning from MV. A prospective study of the HGS cutoff value is needed to investigate the difference between patients who succeeded and those who failed extubation. This trial is registered with  Most patients require mechanical ventilation (MV) with intubation due to respiratory failure. Assessment of their readiness for ventilator weaning is a crucial approach to reduce complications and mortality .The use of rapid shallow breathing index (RSBI) has increased widely due to its simple method and avoidance of calculation of complex pulmonary mechanics . It is tWe hypothesized that HGS indicates respiratory muscle strength and may be correlated with RSBI, an established predictor of successful extubation. This study aimed to evaluate the correlation between HGS and RSBI for weaning from MV.A prospective study was conducted between January 2018 and January 2020 in general medical wards and a medical intensive care unit (ICU) at Thammasat University Hospital, Thailand. Patients requiring MV with tracheal tube intubation for at least 48 h and aged 18 years or older were recruited. All recruits were able to cooperate fully, able to do the HGS test, and were expected to pass the SBT upon extubation from MV. Exclusion criteria were death before weaning from MV, less than 48 h of MV, transfer to other hospitals, self-extubation, accidental extubation, reintubation before enrollment, undergoing tracheostomy, inability to perform the HGS test, and treatment with vasopressor/inotropic drugs. The 34 patients successfully recruited for this study were also among the patients who participated at the same time in our previously published study .2O pressure support with 5 cm H2O-positive end-expiratory pressure. When patients successfully passed the SBT, the physician in charge, in collaboration with the attending medical staff initiated the weaning process. If a patient failed the initial SBT, MV was reinstituted, and the physician reviewed the possible reversible causes of the weaning failure, e.g., bronchospasm, secretion obstruction, pulmonary edema, congestive heart failure, psychoneurological factors, electrolyte imbalances, malnutrition, and anemia. SBT was repeated the following day if the patient then appeared ready for weaning. A patient was rated as successfully weaned when he or she was extubated and breathing spontaneously without any invasive or noninvasive ventilatory support for ≥48 h. Concordantly, weaning failure was defined as either the failure of SBT or the need for reintubation within 48 h following extubation [The weaning process was under the supervision of the attending physicians. However, all members of the team who participated in the weaning process underwent prestudy training on weaning according to the standards of the European and American Respiratory/Intensive Care Societies . Weaningtubation .Participants were classified into 3 weaning groups: 1) simple weaning: successful weaning and extubation on the first attempt without difficulty, (2) difficult weaning: failure of initial weaning and the need for up to three SBTs for as many as 7 days from the first SBT to achieve successful weaning, and (3) prolonged weaning: failure of 3 or more weaning attempts or the need for longer than 7 days of weaning after the first SBT. [ simple wDemographics and baseline characteristics including acute physiology and chronic health evaluation (APACHE) II scores were collected for all participants. During SBT, data on MV weaning were collected including vital signs, RSBI, type of SBT, time of SBT success, and time of extubation. HGS was tested at 48 h after intubation and 10 min and 30 min after SBT. In addition, this measurement was performed at 1 h after extubation. RSBI and HGS test were both performed at 10 min before SBT. The physician in charge did not know HGS results.Ethic approval was obtained from the Human Ethics Committee of Thammasat University (IRB No. MTU-EC-IM-0-198/60), in compliance with Declaration of Helsinki, The Belmont Report, CIOMS Guidelines, and The International Practice (ICH-GCP). All methods were performed in accordance with these guidelines and regulations. All participants provided written informed consent.2O positive end-expiratory pressure and no pressure support.Primary outcome was the association between HGS and RSBI, adjusted for age, sex, and APACHE II score. Secondary outcomes were the difference in HGS between weaning success and weaning failure groups and the differences in HGS between simple, difficult, or prolonged weaning groups. Maximal grip strength from 3 efforts for each hand was recorded using a specialized dynamometer , The measurements were made at rest with the hand unsupported, with the elbow at 90° flexion, and with the wrist in neutral position. RSBI was calculated by patient's respiratory rate divided by tidal volume (breaths/min/L), which were obtained while the patient was undergoing MV with 5 cm Ht-test was used to compare continuous variables between two groups. ANOVA was used for comparison of 3 weaning groups. To determine the set of variables associated with RSBI, we used the linear regression model with the RSBI set as dependent variable. All independent variables  were entered into the regression model simultaneously. We report the regression coefficients, their 95% confidence interval, and corresponding p values. Variables with p value <0.05 were considered statistically associated with RSBI. Using the regression coefficients and the intercept, predicted RSBI for a patient could be calculated from the following equation, where V represents the covariate, β represents the regression coefficient, and i represents the number of variables:The sample size was calculated on the basis of our hypothesis expecting the correlation coefficient between HGS and RSBI of 0.5. Thus, 38 patients would provide a power of 90% and a two-sided alpha level of 5%. Categorical data were shown as number (%). Continuous data were shown as mean ± standard deviation. Student' Statistical analyses were performed using SPSS version 20.0 software .One hundred twenty mechanically ventilated patients were recruited, and 93 of these were included in the final analysis . There w∗ 0.138)–(HGS ∗ 0.571), with regression coefficient −0.571, P < 0.001  = 39.285 + , and RSBI was 39.3 ± 9.7, 43.6 ± 6.7, and 39.0 ± 9.2 breaths/min/L, respectively (P=0.318).At 10 min before SBT in simple, difficult, and prolonged weaning groups, maximum HGS was 16.3 ± 7.2, 13.7 ± 4.7, and 15.1 ± 0.4 kg, respectively (P=0.005). We observed a 75.9% sensitivity and 83.3% specificity for the 12.7 kg HGS cutoff. The best RSBI cutoff value at 10 min before SBT was 43.5 breaths/min/L, with area under the ROC curve of 0.459 , 33.3% sensitivity, and 66.6% specificity.For predicting successful extubation, the best HGS cutoff value at 10 min before SBT was 12.7 kg with area under the receiver operating characteristic (ROC) curve of 0.842  which is more accurate than CROP index , minute ventilation, or maximal inspiratory pressure . HoweverMohamed-Hussein AAR et al., in a study comparing HGS at weaning to intensive care outcomes in 34 patients with chronic obstructive pulmonary disease (COPD), found that HGS may be a good predictor for extubation outcomes as well as MV duration and ICU mortality .P=0.008), but no association was found between HGS and extubation failure [A previous study of Cottereau G et al. demonstrated that there were statistically significant differences in HGS between simple, difficult, and prolonged weaning groups (20 kg, 12 kg, and 6 kg, respectively,  failure . In contThis study has a few limitations. The first is that this study cannot demonstrate the cut point of HGS for extubation failure. The second limitation is limited postextubation monitoring in general wards as discussed above. The last is that this study included only patients from medical wards and thus may not apply to surgical patients. A future study may find the cut point of HGS of extubation success in order to implement it to a ventilator liberation protocol.HGS had significantly negative correlation with RSBI for assessment of the weaning process in mechanically ventilated patients. A prospective study of the HGS cutoff value is needed to investigate the difference between patients who succeeded and those who failed extubation.


HIV infection causes pathological changes in the natural killer (NK) cell compartment that can be only partially restored by antiretroviral therapy (ART). We investigated NK cells phenotype and function in children with perinatally acquired HIV (PHIV) and long‐term viral control (five years) due to effective ART in a multicentre cross‐sectional European study . The impact of age at ART start and viral reservoir was also evaluated.Peripheral blood mononuclear cells (PBMCs) from 40 PHIV who started ART within two years of life (early treated patients (ET), ≤6 months; late treated patients (LT), > 6 months), with at least five years of HIV‐1 suppression (<40 HIV copies/mL), were collected between November 2017 and August 2018. NK phenotype and function were analysed by flow cytometry and transcriptional profile of PBMCs by RNA‐Seq. HIV‐1 DNA was measured by real‐time polymerase chain reaction  to explore the association between NK cell parameters and HIV reservoir modulated by age at ART start.neg NK cells was found in LT compared with ET. We further found in LT a positive correlation of CD56neg NK cells with HIV‐1 DNA. LT also displayed increased expression of the NKG2D and NKp46 activating receptors and perforin compared with ET. Moreover, CD107a+ and IFN‐γ+ frequencies in non‐stimulated NK were associated with HIV‐1 DNA in LT patients. Finally, RNA‐Seq analysis showed in LT an up‐regulation of genes related to NK‐activating pathways and susceptibility to apoptosis compared with ET.A significantly higher frequency of CD56We show that early initiation of ART during infancy preserves the NK compartment and is associated with lower HIV‐1 reservoir. Such condition persists over adolescence due to long‐term viral control achieved through effective ART. On the other hand, early initiation of ART preserves adaptive immune function , decreasbright (CD3‐CD56brightCD16−/low) able to release relevant amounts of antiviral cytokines; CD56dim (CD3‐CD56dimCD16+), which are highly cytolytic; CD56neg (CD3‐CD56negCD16+), functionally anergic, expanded in infectious diseases such as HIV [ex vivo responses that inversely correlated with HIV‐1 reservoir size [NK can be classified in three major subsets based on their differential expression of CD56 and CD16: CD56h as HIV . Severalh as HIV , 16, 17.oir size , 19, 20.oir size . Other soir size , 23. TheNonetheless, according to current knowledge, ART does not fully reconstitute NK phenotype and function and exact characteristics of residual NK subset abnormalities in treated PHIV require further investigation. In this study, we investigated the NK compartment in a homogenous group of PHIV who initiated ART within the first 24 months of life and maintained continuous viral suppression since the first viral control for at least five years [22.1This study was approved by the local institutional ethics committees of Bambino Gesù Children's Hospital , University of Padua , University Hospital 12 de Octubre and Hospital Gregorio Marañón , St Mary's University , Great Ormond Street Hospital , Brighton and Sussex University Hospitals . Study participants or their legal guardians gave written informed consent in accordance with the Declaration of Helsinki.2.2Forty PHIV, who started ART within the first two years of life, were recruited cross ‐sectionally in CARMA (Child and Adolescent Reservoir Measurements on early suppressive ART). This protocol is part of the existing EPIICAL consortium  . Inclusi2.3Peripheral blood mononuclear cells (PBMCs) were isolated from EDTA‐anticoagulated blood using Ficoll and centralized at the University of Rome “Tor Vergata.” To assess viability, PBMCs were stained with the LIVE/DEAD fixable NEAR‐IR dead cell stain kit . Further, they were surface stained before undergoing permeabilization for intracellular staining using FOXP3 Fix/Perm Buffer Set (Biolegend); all antibodies are listed in Table 2.4+ NK degranulating as previously described [A flow cytometry‐based degranulation assay was performed to measure the level of anti‐HIV‐1 cytotoxicity. We used patients' PBMCs as effectors (E) and K562 cells as target (T) cells at an E:T ratio of 10:1 and measured the frequency of CD107aescribed .2.5IFN‐γ accumulation in NK was analysed by flow cytometry after 20 hours of PBMC stimulation with or without cytokines , as described previously. , where P2.66 PBMCs.Total HIV‐1 DNA was isolated from PBMCs and quantified by real‐time quantitative reverse transcription PCR (qRT‐PCR) as previously described . All mea2.7GSE168658.Total RNA was extracted (QIAGEN RNeasy Plus Mini Kit) from cryopreserved PBMC. All samples generated high quality RNA . Total stranded RNA libraries were prepared using Roche Kapa RNA Hyper with HMR ribodepletion according to manufacturer's standard protocol. 20 ng RNA was used as input with 14 cycles of library amplification. Libraries were balanced and sequencing on a NovaSeq 6000 S1 200 cycle V1 flow cell to generate 50M PE100 reads per sample. The raw demultiplexed FASTQ paired‐end read files were trimmed of adapters and filtered using the program skewer to remove any with an average Phred quality score of <30 or a length of <36 bp. Aligned reads were counted and assigned to gene meta‐features using the program featureCounts as part of the Subread package. Count files were assessed for quality control, normalized and analysed using an in‐house pipeline using the LIMMA‐trend method for differential gene expression testing and the gene set variation analysis (GSVA) library for GSVA as previously described , 29  as interaction term were selected as confounders by stepwise AIC variable selection. Statistical analyses were performed using GraphPad Prism 6.0  and R Bioconductor. To visualize interactions, sjPlot R package was used. The threshold for statistical significance was set to p < 0.05. The R package “enrichR” v2.1 was used to perform gene ontology and pathway enrichment analysis in Reactome 2016, WikiPathways 2019 (Human), BioCarta2016 and KEGG2019 (Human) databases [Intergroup comparisons were performed using Mann–Whitney U test for non‐normal variables or unpaired t‐test for normal variables. Normality was assessed using Shapiro–Wilk test. Paired comparisons were made using nonparametric Wilcoxon's test or parametric tatabases , 31. NK‐33.1+ T‐cells and plasma HIV‐1 RNA load at ART initiation, time to viral suppression and male/female ratio. At the time of NK analyses, ET and LT did not differ significantly for median age, ART duration, %CD4+ T cells, HIV‐1 RNA, CMV+ serology .Age at ART initiation was used to stratify the cohort. According to previous data , 7, 8, 93.2neg  associated with a lower proportion of CD56bright cells  in LT compared with ET  and an increase in the proportion of NKp46+ NK  and %perforin+   to fully mature  and, eventually, to memory‐like   Figure . No diff) Figure . Moreove) Figure  versus n3.3p = 0.001 and p = 0.0074 respectively)  in LT. The analysis of NK IFN‐γ response shows that both groups significantly responded to cytokine stimulation (p = 0.0015 and p = 0.0079 respectively), but no differences were found between the groups . We also applied regression to the PBMC transcriptional dataset using the proportion of total NK in order to correlate a gene expression signature with total NK levels, identifying 1,167 DEGs between LT and ET. Among these, 660 genes were UP, whereas 507 genes were DW (p < 0.05). To understand the potential biological roles of these DEGs, we performed pathway enrichment analyses. The top 10 significantly enriched pathways (UP and DW) are shown in Figure To explore the impact of timing of ART initiation on gene expression, we performed regression analysis using RNA‐Seq on PBMCs from 23 patients of the CARMA cohort (16 ET and 7 LT). Overall, we found 388 differentially expressed genes (DEGs) between LT and ET. We found 179 up‐regulated (UP) and 209 down‐regulated (DW) genes in LT vs. ET . BIRC2 protein inhibits apoptosis through the binding to TRAF1 and TRAF that are TNFR‐associated factors. When it is DW, as in the case of our LT, the apoptosis is not inhibited, and cells go through programmed death.4bright, CD56dim and CD56neg subsets was different according to the time of ART initiation, with LT showing a higher proportion of CD56neg cells and a lower proportion of CD56bright cells when compared with ET. The CD56neg subset typically expands in chronic HIV‐1 infection regardless of antiviral treatment and consists of a highly dysfunctional NK subset with impaired effector functions, produces a low amount of anti‐viral cytokines/chemokines and releases regulatory cytokines that suppress IFN‐γ release by CD8+ T‐cells, contributing to the poor immune control over HIV‐1 and to disease progression [neg cell expansion results from the loss of CD56 expression by persistently stimulated NK in prolonged immune activation and inflammatory processes established in chronic HIV‐1 infection [neg cell expansion, but data following ART initiation were missing [neg NK, whereas ET display a normal NK subset distribution that is comparable to that of healthy individuals (data not shown). To the best of our knowledge, this study on PHIV is the first providing evidence that timing of ART initiation, more than a prolonged viral suppression, has an impact on the long‐term recovery of a physiological NK compartment, which is crucial for the innate immune defence against HIV‐1 as well as other pathogens or tumours [This study seeks to provide a description of the NK population in PHIV and it adds on the previous literature showing an impact of timing of ART initiation on NK subset distribution in PHIV despite long‐term viral control due to effective ART. We found that the distribution of NK into the major CD56gression , 36, 37.nfection . Few stunfection , 40. One missing . Here we tumours .in utero exposure to inflammatory stimuli [Additionally, our results showed that delayed ART start is associated with significantly higher expression of NKG2D, perforin and NKp46, that may reflect an increased activation status of NK in LT compared with ET. It is well‐established that HIV‐1 infection induces chronic immune activation and systemic inflammation that may in part persist despite successful ART‐mediated viral suppression as measured by an enhancement of microbial translocation at mucosal membranes, inflammatory cytokine production and activation of immune cells, including NK . Increas stimuli . Therefo stimuli . Similar stimuli , 45.neg cells in LT, the NK cytotoxicity against K562 target cells was comparable in ET and LT. This apparent incongruence may be related to the fact that K562 cells are predominantly recognized and killed by NK via their NKG2D receptor, whose expression is enhanced on LT NK, possibly counterbalancing the presence of anergic CD56neg cells. NK of both groups had also comparable IFN‐γ responses following cytokine stimulation, thus showing similar overall functionality, at least on the basis of generic ex vivo tests.Notwithstanding the increased proportion of dysfunctional CD56neg subset, of NK expressing NKp46 and, in unstimulated conditions, CD107a and IFN‐γ, were significantly inversely associated with the HIV‐1 DNA reservoir burden in ET, whereas a direct correlation was found in LT. These NK features, except for %CD107a+ and %IFN‐γ+ non‐stimulated NK, were significantly higher in LT, who also had higher HIV‐1 DNA as compared with ET. In addition, regression analyses on RNA‐seq of NK markers revealed as LT present a higher susceptibility to apoptosis, cytotoxic function and activation compared with ET. Therefore, these results suggest that a later ART start, previously described to be related to an increased immune activation of the T‐cell compartment [+, perforinhigh and basally activated NK. Also, the multivariable regression analysis showed that frequencies of total and CD56dim NK were directly associated with HIV‐1 DNA in ET but inversely in LT, supporting a preservation of the functional NK compartment if ART is started early, before virus‐induced deregulation of NK has occurred [+ NK and HIV‐1 reservoir, with a positive and inverse association in ET and LT respectively. Previous studies have shown that DNAM‐1, together with NKG2D and NKp46, mediates in vitro NK recognition and killing of HIV‐1‐infected T cells [+ NK could be found in some cohorts of HIV‐1‐positive patients [Multivariable regression analyses highlighted some major differences between ET and LT in the relationship of some NK features with the HIV‐1 reservoir. Specifically, the frequency of the dysfunctional CD56occurred . Finally T cells  and thatpatients , 49. HowOur study has some limitations. First, this is a cross‐sectional analysis of reservoir, thus subjected to causality. The reduced number could represent a weakness, which likely diminishes the prediction capacity of all the models. However, this reflects the clinical reality: a small number of children started ART in infancy, achieve and consistently maintain viral control for many years. Nonetheless, this paper relies on a well‐characterized homogenous cohort of PHIV, in which we could demonstrate that starting ART within the first six months of age has long‐lasting beneficial effects on the NK compartment. This mainly consists of a reduced immune activation and a better functionality of the NK compartment.5Our data suggest that early ART preserves the NK compartment. Further work is needed to define the extent to which timing of ART start affects the potential capacity of NK against latent HIV‐1 in PHIV and if a booster strategy can be used to achieve a better suppression of viral reservoir in these patients through NK pathways.The authors have declared no conflict of interest.P.P., N.C., M.D. and S.Z. designed the study; M.D., S.Z., K.G. and S.R. performed the experiments; M.D., S.Z., S.D.R., M.K., B.R., A.R., S.L.W. and G.R.P. analysed the data; M.D., S.Z. and P.P drafted the manuscript. A.T., P.R.C., E.N., C.F., A.D.R. and C.G. provided patients’ samples. P.P, P.R., S.P., E.N. and P.R.C. supervised the study. All the authors read and approved the manuscript.http://penta‐id.org/), funded through an independent grant by ViiV Healthcare UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was also supported by grants of the Italian Ministry of Health to M.D.  and the National Institute of Health (NIH) to P.P. (5U01AI135941‐04).This study was supported by PENTA‐ID Foundation (Mark Cotton, Shaun Barnabas, Nigel Klein, Thanyawee Puthanakit, Louise Kuhn, Andrew Yates, Avy Violari, Kennedy Otwombe, Paula Vaz, Maria Grazia Lain, Tacilta Nampossa, Denise Naniche, Sheila Fernandez‐Luis, Elisa Lopez, Philip Goulder, Holly Peay, Rajendra Pahwa, Katherine Luzuriaga, Ofer Levy, Kinga Smolen, Moira Spyer, Vincent Calvez, Anne‐Geneviève Marcelin, Maria Angeles Munoz, Annalisa Dalzini, Deborah Persaud, Nicolas Chomont, Mathias Lichterfeld, Silvia Faggion, Daniel Gomez Pena, Andrea Oletto, Alessandra Nardone, Paola Zangari, Carla Paganin,William James, Inger Lindfors, Shrabon Samiur Hassan, Francesca Mazzetto, Hellen Akisinku, Musakanya Chingandu, Francesca Rocchi, Ilaria Pepponi, Rob J De Boer, Juliane Schröter, Viviana Giannuzzi, Andrew Yates, Sinead Morris.Table S1. List of antibodies.Click here for additional data file.


Structural brain MRI measures are frequently examined in both healthy and clinical groups, so an understanding of how these measures vary over time is desirable.To test the stability of structural brain MRI measures over time.In all, 112 healthy volunteers across four sites.Retrospective analysis of prospectively acquired data.3 T, magnetization prepared – rapid gradient echo, and single‐shell diffusion sequence.Diffusion, cortical thickness, and volume data from the sensorimotor network were assessed for stability over time across 3 years. Two sites used a Siemens MRI scanner, two sites a Philips scanner.The stability of structural measures across timepoints was assessed using intraclass correlation coefficients (ICC) for absolute agreement, cutoff ≥0.80, indicating high reliability. Mixed‐factorial analysis of variance (ANOVA) was used to examine between‐site and between‐scanner type differences in individuals over time.All cortical thickness and gray matter volume measures in the sensorimotor network, plus all diffusivity measures  for primary and premotor cortices, primary somatosensory thalamic connections, and the cortico‐spinal tract met ICC. The majority of measures differed significantly between scanners, with a trend for sites using Siemens scanners to produce larger values for connectivity, cortical thickness, and volume measures than sites using Philips scanners.Levels of reliability over time for all tested structural MRI measures were generally high, indicating that any differences between measurements over time likely reflect underlying biological differences rather than inherent methodological variability.4.1. BRAIN MORPHOLOGY and anatomical connectivity are central to the in vivo characterization of biological mechanisms underlying neurodegeneration. Reliably measuring both structure and connectivity can provide information regarding the nature of disease‐related changes that occur across the course of disease progression. This can improve our understanding of the way in which pathology impacts not only structure, but also brain activity and behavior, while such changes can also act as valuable markers for both the timing and efficacy of therapeutic treatment at an exploratory level.In the field of neuroimaging, higher‐resolution structural magnetic resonance imaging (MRI) is used to examine brain macrostructure including volume, cortical thickness, and surface area, while diffusion‐weighted MRI is one method that interrogates the microstructural properties of white matter.Despite the clear utility of both structural and diffusion‐weighted MRI, it is also important to consider that, as with any imaging technique, both noise and variations can be introduced at almost any stage. Rigorous protocol standardization and staff training notwithstanding, potential sources of variation include local magnetic field inhomogeneities, experimenter bias, inherent issues within analysis packages, and, importantly, differences between the individuals being scanned. All of these causes of variability are unrelated to the hypotheses that are being tested, but need to be considered when interpreting data. Multisite studies are becoming more frequent and they customarily include several different scanner types and models, in addition to variation in site personnel. Hence, a sound understanding of the nature and subsequent prevention of intersite differences is increasingly important.Promisingly, there is some evidence to suggest that both standard scalar diffusion tensor imaging (DTI) metrics including diffusivity and fractional anisotropy  and volumetric measures are reproducible across sites and scanner types despite predictably greater between‐site variability.Here we have similarly retrospectively investigated the reliability of structural and diffusion MRI‐based metrics of volume, cortical thickness, and anatomical connectivity focusing on the sensorimotor network in a similar cohort of healthy individuals from TrackOn‐HD.Healthy control participants were recruited into the TrackOn‐HD study at four study sites .1‐weighted image volumes were acquired using a 3D magnetization prepared rapid gradient echo (MPRAGE) acquisition sequence with the following imaging parameters: relaxation time (TR) =2200 msec (Siemens [S]) / 7.7 msec (Philips [P]), echo time (TE) = 2.2 msec (S) / 3.5 msec (P), flip angle (FA) = 10o(S)/8o(P), field of view (FOV) = 28 cm (S) / 24 cm (P), matrix size 256 × 256(S)/224 × 224(P), 208(S)/164(P) sagittal slices with a slice thickness of 1.0 mm with no gap. Diffusion‐weighted images were collected with 42 unique gradient directions (b = 1000 sec/mm2) on both scanner types plus eight images with no diffusion weighting (b = 0 sec/mm2) (S) and one image with no diffusion weighting (b = 0 second/mm2) (P). Acquisition parameters were TE = 88 msec, TR = 13,000 msec, and voxel size 2 × 2 × 2 mm (S); TE = 56 msec and TR = 11 sec, and voxel size 1.96 × 1.96 × 2.75 mm (P).Data acquisition across sites was standardized as previously described.1 scans underwent visual QC upon data collection  to check for incorrect parameters in the metadata and image artifacts such as motion artifacts. Scans were then bias‐corrected to correct for inhomogeneity within the images using the N3 algorithm.Thttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki.0 and T1 structural images were skull‐stripped using the Brain Extraction Tool (BET) and manually corrected for instances of mis‐segmentation, whereby extraneous tissue had not on occasion been removed. To improve quality of the extracted structural image, we combined and dilated a thresholded segmented image with an eroded brain‐extracted T1 mask, which was then applied to the original brain‐extracted T1 image. The new T1 image was then registered to the B0 image using FMRIB's Linear Image Registration Tool. Diffusion tensors were fit using dtifit and crossing fibers modeled using Bedpostx.Diffusion data were preprocessed using standard FSL (FMRIB Software Library) pipelines average two‐way random‐effects intraclass correlation coefficient for absolute agreement (ICC), hereafter written as ICC. For the two‐way random‐effects ICC, participants and observations were treated as random effects .To assess the reliability of our MRI measures over time, we calculated the To establish systematic sources of variation in our data, we also conducted 3 × 2 repeated measures analysis of variance with a within‐participant factor of time and a between‐participant factor of scanner type. For these tests, we applied a Bonferroni correction for multiple comparisons to each effect in the model . Mauchly's test was used to check for violations in sphericity. If violations were found, a Greenhouse–Geisser correction was applied (denoted by adjusted degrees of freedom in the tables below). Within each scanner type, we also conducted pairwise comparisons comparing the different study sites using the same scanner to each other. All analyses were conducted using SPSS v. 23.0 . All descriptive statistics are reported as mean (SD) unless otherwise indicated.All measures of white matter diffusivity  in connections between the M1 motor cortex region, the premotor cortex, or the S1 somatosensory cortex and the thalamus as well as the cortico‐spinal tract met the ICC2,k) cutoff ≥0.80, indicating high reliability . However, this interaction was superseded by a significant Scanner × Time interaction (P < 0.001). Scanner × Time interactions were also found for axial diffusivity and mean diffusivity between PMC and thalamus (P < 0.001). (Details of these interactions are shown in P's < 0.001), BA4a , and BA6 . There were also statistically significant Scanner × Time interactions for BA4 and BA6 , AD and MD measures in the PMC tract (P < 0.001), and for AD and MD measures in the S1 tract (P < 0.001).The majority of diffusivity measures Table  differedP's < 0.001). The only exceptions were BA4a and BA4p in the left  and the right hemispheres . For all other cortical thickness measurements, values were higher bilaterally for BA1, BA2, BA3a, BA3b, and BA6 for Siemens compared to Philips scanners.Cortical thickness Table  also difP's < 0.001). Left hemisphere BA1 showed a significant main‐effect of Scanner (P < 0.001), whereas right hemisphere BA1 did not (P = 0.023). In these regions, volume measures on Siemens scanners were generally higher than on Philips scanners (P < 0.05?). Other regions showed a similar pattern, but the differences were not statistically significant following a correction for multiple comparisons.For cortical volume measurements Table , values P's < 0.001). For the cortical thickness measures, there were no statistically significant differences between sites with the same scanner type (P ≥ 0.05). For cortical volume measures, the only statistically significant differences were in left BA2, in which the Paris site had significantly higher average volume than the London site (P < 0.001).Within the connectivity measures, there were no statistically significant differences between the Leiden and Vancouver (Philips scanner) sites; however, there were significant differences between London and Paris (Siemens scanner) sites for AD and/or MD measures of all tracts ) and the estimated variation in “true” values captured by a single timepoint ). This is consistent with previous studies that have tested the reliability of Freesurfer‐derived cortical thickness measures in healthy people, with reproducibility across a number of visits ranging from just 2 to up to 10.n/group M1‐Thalamus AD, then the observed ICC  was 0.92 participants, per group would be required to achieve 80% statistical power because the error in this measure effectively reduces the effect‐size from an idealized δ = 0.75 to d = 0.72. The ICC  reflects the average ratio of true variance to total variance captured by any single measurement. That is, if the ratio of the variance in “true” scores (T) to observed scores (X) is r2tx = var(T)/var(X) = 0.25 in the population, ICC  will approximate 0.25 (large samples) regardless of the number of observations made. As such, ICC  reflects the average amount of variance in true scores that is captured by a single measurement. Presenting ICC  as a complement to ICC is important because ICC is sensitive to the number of measurements, whereas ICC  is not and, unless experimenters are making a fixed number of repeated assessments, ICC  is most relevant to trial designs with a single pre‐ and posttest assessment.Understanding the magnitude of variation from different sources is useful for researchers planning multisite and/or longitudinal studies. Reliability of a given measurement has important implications for statistical power, and the number of participants needed to achieve a desired level of statistical power is a function of the level of significance, the desired power, and the underlying effect size. However, this effect size assumes no measurement error  and very few constructs are measured that precisely.PMC‐Thalamus RD, then the observed ICC  was 0.68. This would mean that 43 participants are required per group, because the measurement error reduces the idealized δ = 0.75 to d = 0.62. Thus, the n/group required to achieve statistical power varies markedly within these connectivity measures, because the reliability of a measure at a given timepoint ) varied so substantially. Naturally, as the effect‐size increases  the n/group required to achieve 80% power decreases. However, these data show that despite generally good reliability across these neuroimaging measures, the differences in reliability still have negative consequences for statistical power depending on the outcome.Alternatively, if the primary outcome measure was volume decreased by 3%, and BA4a/BA6 volume decreased by 2% on average. While these effect‐sizes are relatively small, the high reliability  provided adequate statistical power to detect change. Cortical thickness, however, did not show statistically significant changes over time, suggesting that the magnitude of change may be smaller than that of cortical volume. It is also likely that Freesurfer measurements of cortical thickness are less reliable, given that small errors in segmentation can significantly inflate thickness values for particular regions where volumetric measures are more robust to this type of error. This could potentially impact detection of the subtle changes in thickness that may occur over a 2‐year period in healthy controls.We also examined the amount of change in our measures over 2 years. Cortical volume measures tended to show reliable decreases over time, consistent with previous research for healthy adults in this age range.Our data were collected on two different types of MRI scanners and despite good within‐participant reliability, there was an effect of scanner type for most measures. There appeared to be a consistent trend for sites using Siemens scanners to produce larger values for connectivity, cortical thickness, and cortical volume measures than sites using Philips scanners. For volumetric and cortical thickness measures, differences between Philips and Siemens scanners are unsurprising, as FreeSurfer was developed primarily on Siemens and GE scanners with the application on Philips data tested later in development.These potential differences are especially relevant when comparing results between studies or planning multisite trials, as researchers need to account for between‐site differences when pooling data sources . In addition, these were relatively underpowered tests compared to those of between‐scanner differences. It is clear, therefore, that despite phantom scanning, rigorous multiscanner quality control and standardization of training, there were still significant differences between scanners (for many measures) and between sites for a particular scanner (mostly for diffusivity measures). This reinforces the importance of thorough training and streamlining of scanning protocols and using analyses that can account for between‐site differences in large multicenter studies and clinical trials using MRI scanning as endpoint measures.It is important to note that these between‐site (or between‐scanner) comparisons are not measures of “interrater” reliability, because different participants were measured at the different study sites. However, these measures are still informative because it is important to understand systematic between‐participant and within‐participant variability in our data.Quantifying sources of between‐ and within‐subject variability in older adults over a long timescale has important implications for clinical neuroscience. Large observational studies, for instance in Huntington's disease or Friedreich's ataxia, suggest that longitudinal studies with a long timescale  are important in understanding disease‐related brain alterations.A. Coleman, J. Decolongon, M. Fan, T. Koren ; C. Jauffret, D. Justo, S. Lehericy, K. Nigaud, R. Valabrègue ; S. Klöppel, E. Scheller, L. Minkova , A. Schoonderbeek, E. P 't Hart ; C. Berna, M. Desikan, R. Ghosh, D. Hensman Moss, E. Johnson, P. McColgan, G. Owen, M. Papoutsi, A. Razi, J. Read, ; D. Craufurd ; R. Reilmann, N. Weber ; H. Johnson, J.D. Long, J. Mills ; J. Stout, I. Labuschagne ; G.B. Landwehrmeyer, I. Mayer .This work was funded by the CHDI Foundation and the Wellcome Trust (G.R.). S.J.T. is partly supported by the UK Dementia Research Institute that receives its funding from DRI Ltd., funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. Some of this work was also undertaken at UCLH/UCL, who acknowledge support from the Department of Health's NIHR Biomedical Research Centre.K.L. is supported by the Canadian Institutes of Health Research (PTJ 153330) and the Auburn University Internal Grants Program (170138). S.G., R.S., G.R., and S.T. receive support from a Wellcome Trust Collaborative Award (200181/Z/15/Z). All authors declare that they have no conflicts of interest.Appendix S1. Supplementary Material.Click here for additional data file.


Deteriorating soil health, diminishing soil organic carbon (SOC), development of subsurface hard compact layer and declining system productivity are barriers to achieving sustainable production in the traditional rice–wheat cropping system (TA) in the eastern Indo‐Gangetic Plain of India. Conservation agriculture (CA), which favours minimum soil disturbance, crop residue retention and crop diversification could be a viable alternative to the TA to address most of those major problems. With that in mind, a long‐term experiment is being implemented at ICAR‐RCER, Patna, Bihar, India, with four treatments: (a) TA, (b) full CA (fCA) and (c and d) partial CA (pCA1 and pCA2), differing in crop establishment methods, cropping system and crop residue management in a randomized complete block design. Measurement of soil health parameters was carried out in the 11th year of the experiment. The results revealed a beneficial effect of CA and 46 and 40% increase in SOC concentration and stock, respectively, under fCA over TA in the 0–7.5‐cm soil layer. The effect of partial CA (pCA1 and pCA2) was variable, but an increasing trend was always observed under pCA compared to TA. There was an enrichment in SOC content of aggregates under CA irrespective of size class; however, no relation was found between SOC content and aggregate diameter. The contribution of macroaggregates to SOC stock was larger (36–66%) under CA in the 0–7.5‐cm soil layer. Adoption of CA improved the macroaggregate content, MWD and GMD of aggregates, and aggregation ratio. Soil macropore content was greater under fCA, whereas other parameters were similar among treatments. The impact of CA was mostly limited to 0–7.5 cm soil layer and a maximum up to 15 cm soil depth while evaluation until 60 cm soil depth was realized. The yield of rice in CA was comparable to or higher than in TA, whereas the system rice equivalent yield was always higher (38–53%) under CA than under the conventional practices. Therefore, a CA‐based cropping system must be encouraged, to increase SOC status, improve aggregation stability and, consequently, sustain or increase system productivity, in order to achieve food and nutritional security in the eastern Indo‐Gangetic Plain of India.Effects of long‐term conservation agriculture (CA) on soil C, aggregation and yield were evaluated.CA improved SOC concentration and stock by 46 and 40%, as well as macroaggregate SOC stock by 36–66%.Macro‐aggregation and mean weight diameter improved in CA but was mostly limited to a shallow soil depth.CA can be promoted for sustainability of a rice–wheat system due to higher productivity (38–53%). Moreover, an increased soil C pool not only augments productivity but also offers yield sustainability Lal, . SequestAlthough no‐tillage (NT) has been suggested extensively for the sustainability of soil health and a better environment, its impact on soil organic C (SOC) is diverse, both temporally and spatially. Many researchers have reported a positive impact of NT on SOC  effects of CA on soil aggregation and SOC in the eastern Indo‐Gangetic Plain is available, research information is lacking on the long‐term  impacts of CA on soil aggregate size distribution (dry sieving) and associated C in the subsoil layers . Moreover, most of the previous studies focused on soil properties and much less importance was given to system productivity and profitability. We hypothesized that the conversion of the traditional rice–wheat cropping system to diversified conservation agriculture improves soil physical health, amasses more SOC and increases the productivity and profitability of the system. Thus, the objectives were to assess the aggregate size distribution and associated OC, quantify SOC accumulation and evaluate the productivity under different tillage, residue management and cropping systems.22.1−1 at the start of the experiment  – Research Complex for Eastern Region (RCER), Patna, Bihar, India   (CTW) – fallow – puddled transplanted rice (Oryza sativa) (PTR) , (b) NT wheat – NT greengram (Vigna radiata) – unpuddled machine transplanted rice (pCA1), (c) NT wheat – NT greengram – NT direct seeded rice (fCA) and (d) NT mustard (Brassica juncea) – NT maize (Zea mays) – NT direct seeded rice (pCA2). The details of the treatments are given in Table 2 each.The experiment was comprised of four treatments: (a) conventionally tilled wheat  NT 2.3Soil samples were collected in 2019 after harvesting of the rice crop . Soils were collected from eight randomly selected points in each plot using a soil auger, from 0–7.5, 7.5–15, 15–30, 30–45 and 45–60‐cm soil depths. Soils collected from each depth were aggregated to get a sample for each plot, which was used for aggregate analysis and SOC determination. Similarly, two soil cores were collected from each plot. One core was used for bulk density determination, whereas the other one was used in a hanging water column for pore‐size determination. The bulk samples were air‐dried, ground, passed through a 2‐mm sieve and stored for further physico‐chemical analysis.2.3.1For determining the bulk density, core samples having a diameter and height of 5.3 and 5 cm, respectively, were collected with a core sampler. The soil cores were then dried at 100°C in a hot air oven until constant weight. The dry weight of the sample was divided by the volume of the core to get the soil bulk density.2.3.2Undisturbed composite samples were collected from the field in triplicate and used for aggregate analysis. After air‐drying, the samples were passed through a 4‐mm sieve and retained over a 2‐mm sieve to remove larger and smaller aggregates, respectively. A Yoder apparatus was used for wet sieving of aggregates Yoder, . A nest (a) Macro‐, micro‐ and water‐stable aggregates: In general, 0.25 mm is taken as the boundary between macro‐ and microaggregates. Aggregates retained over 2, 0.5 and 0.25 mm were summed to get the macroaggregates, whereas aggregates of 0.12 and 0.053 mm gave microaggregates. Water‐stable aggregates were obtained by adding macro‐ and microaggregates.(b) Mean weight diameter and geometric mean diameter: MWD and GMD were calculated by the following formulae:(c) Aggregate ratio (AR): The aggregate ratio of soil was computed as:(d) Fractal dimension (D): The fractal dimension of particle size distribution, which is used as an index of soil erodibility, was calculated by the following formula  as the Y‐axis and Ln(Ri/Rmax) as the X‐axis, and D is then calculated. Where M(r < Ri) is the cumulative percentage of particles of ith size r less than Ri, MT is the total percentage, Ri is the particle radius (mm) of the ith size class and Rmax is the radius of the largest size class.According to Equation 1, 3‐D is the slope of the regression line Ln(M(r < Ri)/M2.3.3Undisturbed soil cores collected for bulk density were used for pore size distribution. Core samples were saturated by capillary intake of water and moisture content was determined to get the total porosity (TotP). The saturated soil cores were then placed over a hanging water column and a suction equivalent to a 60‐cm water column was applied. The volume of water extracted at this suction was equivalent to drained pore and was taken as macropore (MacP). Then the MacP was subtracted from total porosity to get microporosity (MicP). For determining the field capacity (FC) and permanent wilting point (PWP) of the water content, saturated samples placed over a porous plate were kept in a pressure chamber and 33 and 1,500 kPa pressure were applied for determination of the FC and PWP of the water content, respectively. After cessation of water movement, samples were removed from the pressure chamber, weighed and dried at 100°C for moisture content determination. The gravimetric moisture content was then multiplied by respective soil bulk density to get volumetric water content. The available water capacity (AWC) was calculated by subtracting the PWP water content from FC.2.3.4Soil organic carbon concentration of bulk soil, as well as aggregates, was analysed by dichromate oxidation in the presence of sulphuric acid, followed by titration with ferrous ammonium sulphate using a diphenylamine indicator  were also calculated:Crops were harvested at maturity and grain yields at appropriate moisture contents were reported. For calculation of the system rice equivalent yield (SREY), the following equation was used. Among the treatments, number of crops taken per year varied and therefore to observe the effect of tillage and residue management, MSP is the minimum support price in Indian Rupees (INR), which is fixed by the Government of India from time to time.where For calculation of system profitability, all input costs for a particular crop year (winter‐summer‐rainy season) were summed to get the total cost of cultivation. Similarly, total income was obtained by multiplying grain/economic yield by the MSP of the respective crops. The cost of cultivation was then subtracted from total income to get net income.2.5http://stat.iasri.res.in/sscnarsportal). Means were subjected to a significant difference at p < 0.05 by Tukey's honest significant difference (HSD) test. The MS Excel was used for basic calculation, interpretation and preparation of figures.Data were subjected to analysis of variance following a randomized block design by using the Statistical Analysis System SAS,  availabl33.1−3) and the lowest (1.49 g cm−3) BDs were noted in pCA2 and fCA, respectively. In the 15–30‐cm soil layer, TA recorded significantly higher BD  than fCA and pCA2. All other layers revealed no difference in BD among the different treatments.The effect of different treatments on soil BD was absent up to a 15‐cm soil depth  than TA and pCA2 but was at a par with pCA1. However, no significant effect of treatment on SOC was noted beyond 15‐cm soil depth. SOC stock (both on a volume and mass basis) varied considerably due to different management practices  recorded 31–46% greater SOC than the conventional system (TA)  MacA content than TA (Table p < 0.05) and 7.5–15‐cm (27.3–55.3%) soil layers. The largest and the smallest MWDs were registered for fCA and TA, respectively, and the order was fCA > pCA2 > pCA1 > TA in the surface soil layer. In the following layer, fCA resulted in a larger  MWD of aggregates than TA and pCA2. A similar trend was noted for GMD. The fractal dimension of particle size distribution, which is used as an index of soil erodibility, was similar among different treatments in the surface layer, but a slight variation was noted in the second layer and pCA2 resulted in a lower FD value than TA. None of the aggregate fractions or indices exhibited any difference beyond 15‐cm soil depth.In the 0–7.5‐cm soil layer, CA‐based treatments  resulted in 13.6–26.6% larger (3.42 = 0.004) on ASOC, whereas soil depth played a pivotal role in ASOC content . The aggregate C stock was calculated and prominent differences were observed among treatments  macroaggregate SOC stock than TA. The trend was similar up to 30‐cm soil depth but the magnitude decreased and only fCA had a larger (30.7–32.9%) MacA SOC stock than the conventional treatment. The impact of different treatments on MicA SOC stock was absent except for the 0–7.5‐cm soil layer, where fCA2 resulted in a largerstock  than fCA1. Irrespective of treatments, the MAC SOC stock was two to four times greater than the MicA SOC stock.For larger macroaggregates (>2 mm), the highest SOC content was noted in fCA and it was 45.2% larger than in TA in the 0–7.5‐cm soil layer Figure . In the 3.5p < 0.05) than in TA in the 0–7.5‐cm soil layer (Table The largest amount of MacP was noted in fCA and it was greater by 26.7% (er Table . In the 3.6p < 0.05) than the rest of the treatments. To bring uniformity in crops grown in a year, rice equivalent yields (REY) for two crops (rainy and winter season crops) were calculated. The REY (two crops) was again similar or higher in fCA and pCA compared to TA; however, pCA2 resulted in a similar yield of TA but was significantly lower than the other two treatments. The SREY was always higher  in treatments where partial or full CA was adopted in comparison to TA. The total cost of cultivation (COC) varied between US$ 1584 (TA) and US$ 1963 (pCA1) among different treatments  in BD under CA, whereas Mondal et al.  and suggests the stratification of SOC in the soil profile. Non‐disturbance of soil under CA allows the accumulation of organic matter in the surface soil layer . A higher proliferation of a grassy weed (Brachiaria spp.), which acts as an alternate host of mealybug, was the main reason for mealybug infestation  were similar or higher under CA in comparison to TA, except for pCA2, which produced significantly lower yield than the other treatments. Better soil aggregation and increased SOC status might have improved nutrient availability . Both SOC concentration and stock improved considerably under a CA‐based cropping system and thus established the role of CA in maintaining better soil health. Organic C was enriched in each aggregate class and no dependency of SOC was observed for aggregate diameter. The maximum contribution of macroaggregates was to SOC stock. The rice yield was similar or higher in the CA‐based cropping system (except for pCA2), whereas SREY was always higher under CA. The effects of rice–wheat cropping systems on soil C are restricted to surface soil layers. Thus, the no‐tillage system and the maintenance of crop residue on the soil surface were effective, even concentrated in a tiny soil layer, in enhancing rice yield and profitability. The traditional rice–wheat systems followed in the Indo‐Gangetic Plain were not suitable for decreasing the soil bulk density (which is very large due to puddling operations) and improving water retention in relation to available soil water. Therefore, some alternative soil management practices should be adopted to improve these soil traits. Therefore, the CA‐based cropping systems can be promoted in the eastern Indo‐Gangetic Plain for sustaining crop productivity with better soil health. The effect of CA on pore‐size distribution was inconclusive and further studies should be undertaken for better understanding of the role of long‐term CA in the soil pore network.Surajit Mondal: Conceptualization; data curation; investigation; validation; writing‐original draft. Janki Mishra: Conceptualization; project administration; writing‐review & editing. Shis Poonia: Project administration; resources. Rakesh Kumar: Methodology; writing‐original draft. Rachana Dubey: Visualization. Santosh Kumar: Writing‐review & editing. Mausam Verma: Data curation. Koteswara Rao Karnena: Data curation; validation. Akram Ahmed: Validation. Sharad Kumar Dwivedi: Visualization; writing‐review & editing. Bhagwati Bhatt: Project administration; writing‐review & editing. R.K. Malik: Conceptualization; project administration. Virender Kumar: Project administration. Andrew McDonald: Project administration.None.Figure S1. Location of the experimental site, and monthly four years (2015–16 to 2018–19) average of rainfall, pan evaporation, minimum and maximum temperature during the crop growing seasons. Click here for additional data file.


Ctsk) gene on the hematopoietic system in vitro and in vivo. We found that cultures with lineage− SCA1+ KIT+ (LSK) cells on Ctsk deficient stromal cells display reduced colony formation and proliferation, with increased differentiation, giving rise to repopulating cells with reduced ability to repopulate the donor LSKs and T cell compartments in the bone marrow (BM). Subsequent in vivo experiments showed impairment of lymphocyte numbers, but, gross effects on early hematopoiesis or myelopoiesis were not found. Most consistently in in vivo experimental settings, we found a significant reduction of (donor) T cell numbers in the BM. Lymphocyte deregulation is also found in transplantation experiments, which revealed that Ctsk is required for optimal regeneration of small populations of T cells, particularly in the BM, but also of thymic B cells. Interestingly, cell nonautonomous Ctsk regulates both B and T cell numbers, but T cell numbers in the BM require an additional autonomous Ctsk‐dependent process. Thus, we show that Ctsk is required for the maintenance of hematopoietic stem cells in vitro, but in vivo, Ctsk deficiency most strongly affects lymphocyte homeostasis, particularly of T cells in the BM.In this study, we investigated the influence of the loss of cathepsin K ( This manuscript describes how stromal cathepsin K regulates stem cells in vitro and in vivo. Transplantation experiments show that the main effect of the loss of the cathepsin K gene in vivo is a deregulated lymphocyte homeostasis, particularly the T lymphocyte subsets in the bone marrow compartment, in both autonomous and nonautonomous manners. We have previously described that Ctsk is preferentially expressed by stromal cell lines, which maintain repopulating HSC activity in vitro.Sfrp1Wnt5a,Ctsk is overrepresented in HSC‐maintaining stromal cell lines, we hypothesized that loss of Ctsk may affect HSCs and their repopulating activity in culture. To study this hypothesis, we investigate the effects of knockdown of Ctsk in stromal cells in co‐cultures with HSCs. In addition, we explore the physiological role of Ctsk in WT and Ctsk−/− mice under steady‐state conditions and in transplantation experiments.The role of 22.1Ctsk knockout mice (Ctsk−/−) were produced as previously described.Ctsk gene introduces a premature translation stop codon into the open reading frame of the Ctsk gene.Ctsk−/− mice were bred for at least six generations on C57BL/6.J (B6) background. Polymerase chain reaction was performed on genomic tail DNA to validate the genotype. Age‐ and sex‐matched B6 (CD45.2) control mice were obtained from Harlan Laboratories. In extrinsic transplantations, C57BL/6.Pep3b.Ptpcr (CD45.1) mice obtained from Taconic served as donors and recipients in intrinsic transplantations. 129S2/SvPasCrl  were obtained from Charles River Laboratories. In vitro experiments were conducted with (129xCD45.1) F1 (129xCD45.1) mice.The animals were kept according to the Federation of Laboratory Animal Science Associations and institutional guidelines. All animal experiments were approved by the Government of Upper Bavaria.2.2Ctsk gene knockdowns in stromal cells. In brief, shRNAmir contructs against Ctsk, expressed on the pLKO.1 backbone (Open Biosystems–Thermo Fisher Scientific) were transfected into Phoenix E packaging cell line. Collected virus particles were used for the infection of UG26‐1B6 cells followed by selection with 5 µg/ml puromycin. Two such cell lines are used in this study: shCtsk2 and shCtsk4. As a control, empty vector backbone pLKO.1 was used.UG26‐1B6 stromal cells were cultured on 0.1% gelatin coated wells as described.Ctsk gene was validated using Western blot and quantitative PCR. For Western blots, total stromal proteins were separated on SDS‐polyacrylamide gels and transferred to BioTrace polyvinylidene difluoride membranes. After transfer, unspecific antibody binding was blocked, and the membrane was incubated with polyclonal anti‐CTSK or antiactin antibodies  BM cells were plated on stromal cells and cultured in long–term culture medium  supplemented with hydrocortisone in a 1:1000 ratio, penicillin/streptomycin and GlutaMAX  in a 1:100 ratio for two to four weeks. Each week, half of the supernatant was replaced with fresh medium. After coculture, cells were either used for colony forming unit assays or for in vivo intravenous transplantation assay.For cocultures, UG26‐1B6 derivatives sh2.44 Lin− BM cells were seeded into methylcellulose on two 3.5 cm dishes. After 10 days of culture at 37°C, 5% CO2, and more than 95% humidity, the colonies formed were counted under the microscope.The colony forming assay was performed with growth factor‐supplemented methylcellulose . A total of 1 × 102.5− cells, magnetic selection was performed with a lineage cell depletion kit obtained from Mitenyi Biotec. Further purification was performed via cell sorting (below).BM cells were isolated from femora and tibiae of 8‐ to 12‐week‐old mice. The ends of the bones were cut open and the marrow was flushed out and resuspended in HF2+ buffer  using a blunt needle, then filtered and centrifuged. To obtain Lin2.6Ctsk stromal cells.Serum‐free conditioned medium (CM) was essentially generated as described− CD48− CD150+ Lin− SCA1+ KIT+ (CD34− SLAM) cells from the BM were sorted after lineage‐depletion of 8‐ to 12‐week‐old B6 control mice and deposited by FACS sorting as single cells in round‐bottomed 96‐well plates, one cell per well. Each well was filled with 100 µl of 0.22 µm filtered CM, supplemented with 100 ng/ml mSCF and 20 ng/ml interleukin (IL)‐11 (both from R&D Systems). For each condition, one 96‐well plate was used containing 60 replicates per experiment. The outer 36 wells were filled with 200 µl of distilled water to prevent the culture from drying out. The clone size was counted each day under the light microscope.Single cell cultures were performed as previously published.We previously described that this assay assesses separate control of HSC viability, proliferation, and self‐renewal.2.7− cells from 129xCD45.1 (CD45.1xCD45.2) mice and transplanted into lethally irradiated (9 Gy) 129xB6 (CD45.2) recipients. For transplantation of primary cells, 2.5 × 105 freshly isolated BM donor cells from 8‐ to 12‐week‐old mice were injected into 8‐ to 12‐week‐old, lethally irradiated (8.5 Gy) recipients. In “extrinsic transplantations” , CD45.1 WT donor cells were transplanted into either CD45.2 WT or Ctsk−/− recipients. In “intrinsic transplantations” settings , CD45.2 WT or Ctsk−/− BM cells were transplanted into WT CD45.1 recipients.Transplantation experiments for repopulating capacity were performed as previously described.During the first 5 weeks after transplantation, the recipient mice received antibiotic treatment. Sixteen weeks after transplantation, the mice were sacrificed and their peripheral blood (PB), BM, spleen (SPL), and thymus (THY) were analyzed for engraftment of donor cells using the congenic system (CD45.1/CD45.2). Positive engraftment was defined as more than 1% positive myeloid and more than 1% lymphoid donor engraftment.2.8Staining of cell surface antigens was performed with the antibodies described in Table Total cell numbers were calculated by multiplying the subpopulation fraction with the total cell number (determined by trypan blue staining and counting on a Neubauer cytometer) of the spleen and thymus. The total BM cell number was estimated by assuming that two femora and two tibiae represent 20% of the total BM.In this study, we used the flow cytometers EPICS XL . As control cells, we transduced stromal cells with pLKO.1 empty vector backbone. The Ctsk messenger RNA (mRNA) expression was reduced by 70% in shCtsk4 and down to 90% in shCtsk2 as compared with pLKO.1 controls  was reduced, when stromal Ctsk was downregulated  and IL‐11  cells was reduced in cultures with both shCtsk CMs . In addition, the more differentiated myeloid progenitors (MPs) frequency was increased 1.2‐ to 2.3‐fold after culturing in CM from both shCtsks; as with the LSKs, these findings did not reach significance  compared to pLKO.1 CM . Similarly, donor engraftment in the BM of recipient mice was also unchanged. Interestingly, the only mature population affected after transplantation of Lin− cells cultured on shCtsk stroma were donor T (CD3ε+) cells, which were reduced in the small T lymphocytic BM compartment of recipient mice  mice, which do not express CTSK protein in the BM  cells  and also the number of the small population of thymic B cells  were both significantly elevated  cells in four long bones (femurs and tibia) of the BM  mice or their WT littermates (Figure Ctsk stromal cells were evaluated (Figure − LSKs and MPs in the BM of WT and Ctsk−/− recipient mice appeared to be unaffected (Figure Ctsk−/− recipient mice as compared with WT littermate recipients. In particular, we found a significant reduction of CD45R+ B cells in the BM (5.4 ± 1.0 × 106 for WT recipients compared to 2.8 ± 1.0 × 106 for Ctsk−/− recipient mice, p = .036, Figure + cell repopulation was strongly decreased in Ctsk−/− recipient mice (9.5 ± 1.7 × 106 for WT recipients compared to 3.0 ± 0.8 × 105 for Ctsk−/− recipient mice, p < .003, Figure Ctsk in regeneration of the BM and SPL microenvironments in vivo. Furthermore, despite reductions in BM and SPL, the number of thymic CD45R+ B cells was increased in Ctsk−/− recipient mice (11.4 ± 1.5 × 106 for WT recipients compared to 27.3 ± 6.6 × 106 for Ctsk−/− recipient mice, p < .035, Figure Ctsk deletion affects optimal repopulation of small lymphoid subpopulations.In the next series of experiments, we studied whether the knockout of s Figure . These es Figure . In cont−/− CD45. mice or d Figure . When evd Figure , we found Figure . HoweverCtsk, we transplanted HSCs from Ctsk−/− mice into lethally irradiated WT recipient mice (Figure Ctsk‐deficient BM cells poorly repopulated the periphery of recipient mice (50.2 ± 6.8% for WT donors compared to 28.1 ± 6.8% for Ctsk−/− donor mice, p < .003, Figure Ctsk−/− cells, suggesting that myelopoiesis was unaffected (Figure Ctsk−/− environment (Figure 4 for WT donors compared to 11.9 ± 2.8 × 104 for Ctsk−/− donor mice, p < .016, Figure To determine whether the lymphocyte deregulation was caused by cell‐extrinsic or cell‐intrinsic loss of e Figure . Interesd Figure . With red Figure , we fount Figure , a reduc4Ctsk in the maintenance of HSCs in vitro and its importance for the regulation of steady‐state hematopoiesis and the number of regenerating lymphocytes in transplantation experiments in vivo. Our experiments show that Ctsk expression is required for the maintenance and proliferation of HSCs in vitro and was particularly important for the maintenance of cells regenerating the small fraction of BM T cells. Despite these clear indications of the importance of Ctsk for maintaining HSCs in culture, we found that Ctsk loss in vivo did not significantly affect HSC maintenance and myelopoiesis in vivo. Remarkably, our experiments indicate that Ctsk regulates the regeneration of B and T cell numbers in multiple organs. Importantly, while B cell repopulation mainly depends on cell‐extrinsic Ctsk expression, the CD3ε+‐cell compartment in the BM is consistently affected both by cell autonomous and nonautomonous Ctsk deficiency.We explored the role of stromal Ctsk in stromal cells reduces their ability to maintain the number and function of HSCs in culture, resulting in reduced production of granulocytic progenitors, lower HSC cell doubling times as well as reduced capacity of maintenance of their ability to repopulate MP and LSK compartments. Considering that CTSK is a cysteine protease, this result suggests that degradation of CTSK substrates is required for maintaining HSCs in culture. One CTSK substrate is collagen. We previously found that collagen‐mediated activation is upregulated in HSCs exposed to stromal factors.Ctsk depletion on the survival‐promoting activity of stromal CM in vitro, suggesting that the specific HSC survival promoting collagen is not affected by Ctsk, or that collagen may be substituted by other stromal factors to maintain HSCs in vitro. Other Ctsk substrates were shown to be SCF, osteopontin, and the chemokine CXCL12,Ctsk affects both HSC release by regulating the activity of osteoclasts, as well as endothelial transmigration.Ctsk substrates for HSC maintenance on stromal cells in vitro by secretome analysis, and how these are involved in HSC BM and in HSC trafficking.We found that knockdown of Ctsk deletion on HSC proliferation and recovery of immature cells in vitro, the deletion of Ctsk only mildly affected early hematopoietic and more mature myeloid cells in the in vivo experimental systems. We originally identified Ctsk as being overexpressed in stromal cells supporting HSCs in vitro.Ctsk was unique amongst the cathepsins, other cathepsins, such as Ctsa, Ctsb, and Ctsz were also expressed but did not distinguish HSC‐supportive and ‐non‐supportive stromal cells.Ctsk in these stromal cells may explain the effects off shCtsk knockdown in these cells in vitro. In vivo, Ctsk is most highly expressed by mesenchymal sub‐fractions of the hematopoietic niche in the BM.Ctsk deletion may be more difficult to assess, as other cells of the niche, such as osteoclasts and endothelial cells also express Ctsk, as well as cathepsins E and S. Considering that osteoclastsIn contrast to the clear effects of + cell numbers in the THY increase significantly with a particular increase in CD4+ cells. The mechanism here is unclear but could be caused by decreased T cell selection or reduced cell death. In contrast, under stress conditions, such as regeneration of donor T cell numbers in Ctsk−/− mice transplanted with HSCs, the number of T cells is reduced in the THY, suggesting a differential requirement for Ctsk expression under steady‐state conditions and situations of regenerative stress. In this respect, it is interesting to note, that the specific inhibition of cathepsin S, which is coded adjacent to the Ctsk gene on mouse Chromosome 3, prevents invariant chain processing and peptide loading required for antigen presentation.Ctsk activity could, in analogy, lead to dysfunctional presentation of antigens in the context of MHC Class II or CD1d molecules and defective Th2 lymphocyte responses+ regulatory T cells from Ctsk−/− mice were much more potent in suppressing T effector cellsIn our in vivo experiments, we found a more consistent imbalance in the numbers and distribution of T cells in four different experimental settings in different hematopoietic tissues, particularly the BM. Importantly, we describe deregulation under both steady‐state and transplantation conditions. We found both up‐ and downregulation of T cell numbers under different conditions. Most consistently, we observed a reduction of the number of T cells in the BM in all experimental models explored. The picture is not so clear in other tissues. For instance, under steady‐state conditions, CD3ε+ cells in the BM, which is found under steady‐state conditions as well as in transplantation of cultured cells and in both transplantation of WT into Ctsk−/− recipients and vice versa. The regulatory role of the small marrow population of Ctsk‐sensitive CD3ε+ cells is unclear at the moment. This small cell population in the BM might play an important role in the regulation of hematopoiesis. Indeed, it has been reported that the population of FoxP3+ Tregs accounting for 0.15% of total BM, which is slightly less than the number of HSCs,Ctsk−/− mice,+ Tregs, and how these cells localize and regulate myelo‐ and lymphopoiesis in Ctsk‐deficiency.The most consistent finding of our study is the reduction of CD3ε+ B cells in some settings. Under steady‐state conditions, these cells increase in the periphery, and they also increase of 2.7‐fold in the THY. As is the case of the T cell compartment in the BM, B cells represent only a minor population in the THY. Like T cells in the THY, thymic B cells are increased in steady‐state, but they show decreased repopulation in the THY after transplantation. Whereas the reduction of thymic B cells could be caused by impaired regeneration of the thymic microenvironment in the absence of Ctsk, the reason for the increase of the thymic B cells under steady‐state conditions remains to be established. This small thymic B cell population has been shown to play a pivotal role in the negative selection of T cellsCtsk, since the total number of B cells combined from the numbers of BM, SPL, and THY B cells is also decreased (Figure Ctsk−/− mice, the decline in CD45R+ cells in the BM and THY was the result of decreased differentiation into B cells.In addition to imbalances in T cells, we also find in some settings an imbalance in the number of CD45Rd Figure . It is tCtsk is expressed by stromal cells of the hematopoietic niche. Here we describe that Ctsk plays an important role in the maintenance of CD34− SLAM cells and their myeloid differentiation in vitro. In addition, stromal Ctsk deficiency decreases the number and function of cells able to regenerate the LSK compartment after transplantation in vivo. Despite these effects of Ctsk deficiency, in vitro defects in early hematopoiesis and myelopoiesis are only mild in Ctsk−/− mice, suggesting a redundancy for the requirement of Ctsk in vivo which is not observed in vitro. The main effects of Ctsk deletion in vivo reveals a recurrent reduction in T lymphocytes in the BM as well as fluctuations of both B and T cell numbers compared to WT animals or recipients in different organs at steady‐state and in transplantation experiments. Importantly, in transplantation experiments, both autonomous and cell nonautonomous regulation by Ctsk deficiency can be observed. Thus, our study shows that intact activity of Ctsk is required for in vitro HSC maintenance in stromal co‐cultures. In contrast, in vivo, myelopoiesis is only mildly affected by Ctsk deficiency, but Ctsk maintains B and T cell numbers in different hematopoietic organs, where increases or decreases in cell numbers most probably depend on the effects of stress on the microenvironment. The most consistently observed role of Ctsk is to nonredundantly regulate T cell numbers in the BM.In conclusion, Renate Hausinger, Marianne Hackl, Ana Jardon‐Alvarez, Miriam Kehr, Sandra Romero Marquez, Franziska Hettler, Christian Kehr, Sandra Grziwok, and Rouzanna Istvanffy performed experiments. Rouzanna Istvanffy and Robert A.J. Oostendorp designed experiments. Renate Hausinger, Marianne Hackl, Ana Jardon‐Alvarez, Miriam Kehr, Sandra Romero Marquez, and Franziska Hettler acquired, and analyzed data. Christian Peschel provided critical infrastructure and reagents. Renate Hausinger, Christina Schreck, Rouzanna Istvanffy, and Robert A.J. Oostendorp interpreted data. Renate Hausinger, Christina Schreck, Rouzanna Istvanffy, and Robert A.J. Oostendorp drafted the manuscript.Supporting information.Click here for additional data file.


TM and EllaTM immunoassays to assess serum neurofilament‐light chain levels in 203 multiple sclerosis patients from the OFSEP HD study. There was a strong correlation  between both platforms. The EllaTM instrument overestimated values by 17%, but as the data were linear (p = 0.57), it was possible to apply a correction factor to EllaTM results. As for SimoaTM, serum neurofilament‐light chain levels measured by EllaTM were correlated with age and EDSS and were significantly higher in active multiple sclerosis, suggesting that these assays are equivalent and can be used in routine clinical practice.We compared Simoa Neurofilaments (Nf) are major components of the neuronal cytoskeleton, consisting predominantly of three subunits: Nf‐light (NfL), Nf‐medium and Nf‐heavy chains.TM Ella (EllaTM) microfluidic platform (ProteinSimple). The EllaTM instrument allows rapid and ultra‐sensitive measurement of biomarkers.Several ultrasensitive immunoassay technologies are available for quantification of serum NfL. The current reference method is the Single Molecular Array TM platform with Ella™ instrument in MS patients and healthy controls (HCs). Correlations of the serum NfL measures were performed to evaluate whether EllaTM had good clinical performance in reflecting age, EDSS and disease activity, and could be routinely used to monitor MS patients in clinical practice.The objective of this study was to compare the NfL values obtained using the SimoaAnonymized serum samples were taken from 203 of the 1800 anticipated patients ≥15 years old with MS according to the revised McDonald diagnosis criteria included in the OFSEP "High Definition" cohort (NCT03981003), and from 30 HCs. Ethics approvals were obtained, and all patients and controls participated voluntarily in the study and provided written informed consent  on SimoaTM HD‐1 analyzer and Simple PlexTM NfL Assay  on EllaTM instrument, according to the manufacturers’ instructions. Ella™ was calibrated using the in‐cartridge factory standard curve and Simoa™ using the provided standards. All samples were measured in simplicate, on the same day, after a single thaw, with a 1:2 dilution for EllaTM and 1:4 for SimoaTM. In each run, the HC, one control patient with active relapsing remitting MS (RRMS), and one high and one low concentration control sample provided with the kits were assayed. The lower limit of quantification is 0.241 pg/ml for SimoaTM and 2.70 pg/ml for EllaTM.Serum NfL concentrations were prospectively determined in parallel with the SimoaTM) or internal triplicate (EllaTM). Repeatability tests were performed with samples at high (RRMS patient) and low (HC) concentrations by repeated measures for SimoaTM (30 times each) and for EllaTM . Intra‐assay CV was calculated from the standard deviation of the average concentrations divided by the overall mean of the average concentrations.The intra‐assay coefficients of variation (CV) of manufacturer‐provided controls were automatically calculated in duplicate (Simoavs. progressive MS).Median NfL values obtained by each platform were compared using the Wilcoxon–Mann–Whitney test. Spearman correlation coefficients were calculated to assess the association between concentrations obtained by each platform, presented with 95% confidence interval (95%CI). The Bland–Altman methodStatistical analyses were performed on Prism 8.3.0.538 (GraphPad). A p‐value <0.05 was considered statistically significant.Anonymized data will be shared by request from any qualified investigator.TM technology was 2.12% [1.53‐2.70] vs 3.78% [2.93‐4.63] on SimoaTM platform. The mean [min‐max] inter‐assay CV of the three runs was 12.93% [7.59‐18.27] on EllaTM and 5.54% [5.08‐6.00] on SimoaTM. In MS patients, median serum NfL levels [interquartile range] measured by EllaTM were higher than by SimoaTM   and one sample at high concentration (RRMS patient) and showed similar CVs with both platforms  Figure . Serum N) Figure  and in H) Figure .TM showed a 17.6% “overestimation” compared with SimoaTM. Overall, 95% of observations were within the limit of agreement  and the intercept was 2.917 pg/ml . The 95% CI of intercept and slope values differ from zero and one, respectively, indicating a method agreement and allowing application of a correction coefficient.p = 0.57), suitable for concluding on method agreement  has been associated with differences in NfL measure and could explain the differences in absolute values obtained by both assays.TM were globally lower vs EllaTM, as confirmed by the Bland–Altman plot. The “spike recovery” reported in the data sheet of the two assays is 68% for SimoaTM NfL kit and 108% for Simple PlexTM NfL, suggesting that SimoaTM could underestimate the values of NfL by 17% compared to EllaTM due to a greater effect of the serum matrix than in the Simple PlexTM method. Passing–Bablok allowed the bias to be evaluated over the entire measurement range and the linear test shows that the data are linear (p = 0.57). Thus, it is possible to apply a correction factor 2.917. Therefore, EllaTM technology, with the advantage of small footprint and a robust and cheaper platform, represents a reliable substitute for SimoaTM to measure serum NfL.The main finding of this study is the demonstration of a concordance between NfL levels measured using both platforms, even at low levels in the HC group. This is potentially the result of using the same anti‐NfL antibody and of heterophilic blockers limiting potential cross‐reaction between anti‐NfL antibody and antibodies in the serum for both platforms. However, we observed significant differences in absolute biomarker concentrations between these two instruments. Using different calibrators  on MS sponsored by Rennes University Hospital; Sandra Vukusic: grants, personal fees and non‐financial support from Biogen, grants and personal fees from Geneuro, grants, personal fees and non‐financial support from Genzyme, grants and personal fees from Medday, grants, personal fees and non‐financial support from Merck‐Serono, grants, personal fees and non‐financial support from Novartis, grants, personal fees and non‐financial support from Roche, grants, personal fees and nonfinancial support from Sanofi, personal fees from Teva; Christine Lebrun‐Frénay: fees for consulting or lectures from Novartis, Genzyme, Roche; Jérôme De Sèze: consulting and lecturing fees, travel grants and unconditional research support from Biogen, Genzyme, Novartis, Roche, Sanofi Aventis and Teva Pharma; David Axel Laplaud: served on scientific advisory boards for Roche, Sanofi, Novartis, MedDay, Merck and Biogen, received conference travel support and/or speaker honoraria from Novartis, Biogen, Roche, Sanofi, Celgene and Merck and received research support from Fondation ARSEP and Agence Nationale de la Recherche; Olivier Gout: nothing to disclose; Aurélie Ruet: consultancy fees, speaker fees, research grants , or honoraria approved by the institutions from Novartis, Biogen Idec, Genzyme, Medday, Roche, Teva and Merck; Thibaud Moreau: fees as scientific adviser from Biogen, Medday, Novartis, Genzyme, Sanofi; Olivier Casez: funding for travel and honoraria from Biogen, Merck Serono, Novartis, Sanofi‐Genzyme and Roche; Pierre Clavelou: consulting and lecturing fees, travel grants and unconditional research support from Actelion, Biogen, Genzyme, Novartis, Medday, Merck Serono, Roche, and Teva Pharma; Eric Berger: honoraria and consulting fees from Novartis, Sanofi Aventis, Biogen, Genzyme, Roche and Teva Pharma; Hélène Zephir: consulting or lectures, and invitations for national and international congresses from Biogen, Merck, Teva, Sanofi‐Genzyme, Novartis and Bayer, as well as research support from Teva and Roche, and academic research grants from Académie de Médecine, LFSEP, FHU Imminent and ARSEP Foundation; Guillaume Brocard: nothing to disclose; Romain Casey: nothing to disclose; Christine Lombard: nothing to disclose; Sophie Trouillet‐Assant: nothing to disclose; Eric Thouvenot : consulting and lecturing fees, travel grants or unconditional research support from the following pharmaceutical companies: Actelion, Biogen, Celgene, Genzyme, Merck Serono, Novartis, Roche, Teva pharma.The study was funded by CHU de Nimes and has also been supported by a grant provided by the French State and handled by the "Agence Nationale de la Recherche," within the framework of the "Investments for the Future" programme, under the reference ANR‐10‐COHO‐002 Observatoire Français de la Sclérose en plaques (OFSEP).Supplementary Figure S1. Comparison of SimoaTM and EllaTM platforms at low serum NfL levels. A: Repeatability tests of both platforms using samples from one HC and from one RRMS patient tested 30 times. For SimoaTM, average NfL concentrations were 6.55 pg/ml and 14.22 pg/ml and CVs were 11.3% and 8.1%, respectively. For EllaTM, average serum NfL concentrations were 8.60 pg/ml and 38.38 pg/ml and CVs were 12.8% and 8.9%, respectively, as indicated on the graph. B: Spearman correlation (r) between NfL concentration values obtained by the EllaTM compared to the SimoaTM instruments in a cohort of 29 HCs .Click here for additional data file.Supplementary Material and Methods. Origin of serum samples.Click here for additional data file.