Understanding the biological functions of human milk oligosaccharides (HMOs) in shaping gastrointestinal (GI) tract microbiota during infancy is of great interest. A link between HMOs in maternal milk and infant fecal microbiota composition is examined and the role of microbiota in degrading HMOs within the GI tract of healthy, breastfed, 1‐month‐old infants is investigated.N‐fucopentaose I and 2′‐fucosyllactose. Overall, Bifidobacterium, Bacteroides, Escherichia–Shigella, and Parabacteroides are predominating genera. Three different patterns in infant fecal microbiota structure are detected. GI degradation of HMOs is strongly associated with fecal microbiota composition, and there is a link between utilization of specific HMOs and relative abundance of various phylotypes .Maternal breast milk and infant feces are from the KOALA Birth Cohort. HMOs are quantified in milk and infant fecal samples using liquid chromatography‐mass spectrometry. Fecal microbiota composition is characterized using Illumina HiSeq 16S rRNA gene amplicon sequencing. The composition is associated with gender, delivery mode, and milk HMOs: Lacto‐Bifidobacterium, Bacteroides, and Lactobacillus.HMOs in maternal milk are among the important factors shaping GI tract microbiota in 1‐month‐old breastfed infants. An infant's ability to metabolize different HMOs strongly correlates with fecal microbiota composition and specifically with phylotypes within genera  Genetic and various environmental factors and life events further shape the microbial communities, making them specific to each body site and to each individual. These microbial ecosystems acquired and developed in early life play an important role in well‐being and health, both during infancy and beyond.Bacteroides among others, while indirectly limiting growth of other bacteria. This prebiotic effect has been demonstrated for selected bacterial species, both in vitroIn breastfed infants, breast milk is the sole source of nourishment during the first few months of life. Breast milk is a complex biofluid that contains high concentrations of lactose, lipids, proteins, and milk glycans, the latter being present as either glycoproteins or free human milk oligosaccharides (HMOs).N‐acetyl‐glucosamine, which are further decorated with fucose and/or sialic acid. The fucosylation of HMOs depends on the presence of specific glycosyltransferases, including the α1‐2‐fucosyltransferase FUT2 and the α1‐3/4‐fucosyltransferase FUT3, in lactating women. Milk of secretor women having active Se gene locus that encodes the FUT2 has high amounts of α1‐2‐fucosylated HMOs, while milk of Lewis‐positive women with active Le gene locus that encodes the FUT3 is abundant in α1‐4‐fucosylated HMO. In contrast, milk of non‐secretor or Lewis‐negative women lack α1‐2‐ or α1‐4‐fucosylated HMO, respectively.Maternal genotype  determines the HMO composition in breast milk, and the concentrations of different HMOs vary between individuals and across lactation stages.In the light of growing evidence supporting the role of the early colonization of GI tract microbial ecosystem in health, understanding the biological function of the different HMOs is of great interest. Previous studies focused mainly on in vitro fermentation of HMOs by fecal bacterial inoculum, or by fecal isolates.22.1N = 2834), and the exclusion criteria included prematurity (birth before 37 weeks of gestation), twins, congenital abnormalities related to growth, and administration of antimicrobial agents before feces collection. Only infants who were exclusively breastfed, and for whom both the fecal and the corresponding maternal breast milk samples were available were included in the analyses (n = 121). All infants were born in the years 2002–2003, healthy, full term, at home or hospital via either vaginal delivery or C‐section. Three infants were reported by the parents as sick on the sample collection day, but none of the infants received antibiotics during the first month of life (−1 peptone water in 20 v/v% glycerol) at 1 g of feces in 9 mL of solution. Breast milk samples were collected into sterile tubes  in the morning on the same day as the fecal samples, refrigerated (±4 °C), transported on ice, and processed in the lab on the same day. Breast milk samples were centrifuged  and the lipid and aqueous fraction were separated and stored in plastic vials  at −80 °C in the European Biobank, Maastricht. The remaining debris was not used to avoid contamination with cell fragments.The milk and fecal samples used in this study originated from the KOALA Birth Cohort . The design, selection criteria, and feces collection procedure have been described elsewhere, and the study was approved by the Ethics Committee of the University Hospital of Maastricht.2.2The total amount of HMOs ingested daily by each infant depended on the HMO concentration in breast milk and the total amount of breast milk consumed per day. The daily intake was estimated based on the literature data on infants of similar age (approximately 1 month postpartum) who were breastfed without feeding problems and consumed amounts as regulated according to their needs.2.3Total DNA was extracted from the stool samples, as previously described,2.4m NaOH and B) 1 m NaOAc in 0.1 m NaOH. With a flow rate of 0.3 mL min−1, the gradient elution profile was as follows: 0–10 min, 0–10% B; 10–10.1 min, 10–100% B; 10.1–15 min, 100% B; 15–15.1 min, 100–0% B; 15.1–30 min, 0% B. The elution was monitored by a pulsed amperometric detector (Dionex ICS‐5000 ED). Data processing from HPAEC was done by using Chromeleon 7.1 software (Dionex). The HMOs concentrations were measured in micrograms per milliliters of milk or micrograms per gram of feces.HMOs were isolated and purified from milk and infant feces using solid phase extraction.2.5https://www.arb-silva.de/).The 16S rRNA gene sequencing data were analyzed using the NG‐Tax analysis pipeline using default settings.Infants were classified into three distinct microbial cluster types based on genus level microbial abundance data using Dirichlet Multinomial Mixture (DMM) modeling.p‐values were corrected for multiple comparisons using FDR. The Chi‐square test was used to assess the significance of the association between infant's gender and infant's microbial cluster type, and between mother's secretor status  and infant's microbial cluster type.Redundancy analysis (RDA) was done in Canoco5HMO degradation (consumption) in the infant GI tract was estimated based on profiles in breast milk and corresponding infant feces. Based on the utilization of the 17 individual HMOs, infants were assigned to consumption categories: “Complete,” “Non‐specific,” and “Specific” . The Chi‐square test was used to assess the significance of the association between the assigned consumption category for each HMO and the microbial DMM cluster type of each infant.Based on the extent to which each individual HMO was consumed , infants were divided into tertiles  for consumption levels of each individual HMO. If a given HMO was not detected in milk, the consumption score was not included in the analysis, and if the amount in feces exceeded the amount detected in milk, the infant was assigned to the “low” category for that HMO. Associations between fecal microbiota composition and the assignment of each infant to a “low,” “medium,” or “high” consumption category for each HMO were investigated with RDA analysis in Canoco5, with significance assessed using a permutation test.2.6https://dataverse.nl/; 10411/CEGPGR) upon request to Prof. C. Thijs of the KOALA Study Management Committee at Department of Epidemiology, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands; e‐mail: c.thijs@maastrichtuniversity.nl, tel: +31(0)43 3882374.KOALA data sets cannot be made publicly available due to data confidentiality and the potential to identify individual study participants from the data. Data are available to the research community through the Dataverse repository , and the total amounts are summarized in 3.2Bifidobacterium, Bacteroides, Escherichia–Shigella, and Parabacteroides, with a mean relative abundance of 32% (0–91.5%), 21.3% (0–76.7%), 11.8% (0–57.8%), and 6.7% (0–64%), respectively. Genera with the highest number of contributing OTUs included Bacteroides (64 OTUs), Bifidobacterium (38 OTUs), Parabacteroides (31 OTUs), Lactobacillus (24 OTUs), and Streptococcus (20 OTUs). Eighty two OTUs of the total 531 OTUs were found in at least 6 or more infants of the 121 infants studied (Bifidobacterium (Cluster C), or a high relative abundance of both Bifidobacterium and Bacteroides (Cluster B).Illumina HiSeq sequencing of the V4 region of bacterial 16S rRNA genes yielded 14 474 685 high‐quality reads that passed the quality check and could be assigned to 531 OTUs from 113 genera. In case an OTU could not be classified to a given genus level, it was assigned to the next available taxonomic rank. The predominating genera in this infant cohort were 3.3Figure p < 0.05), and milk 2′FL concentration was borderline significant (p = 0.06). PLS regression also showed a significant association between 2′FL (and LNFPI) concentrations in milk with infant microbiota . We used Chi‐square analysis to test if the mother's secretor status (yes/no), which is known to affect the ability to synthesize (α1‐2)‐linked fucose, and thus, the amount of some major neutral HMOs in milk , had an effect on infant microbiota profiles, as characterized by different DMM cluster types, but the association was not significant (p = 0.08)  between the frequency of different consumption patterns and each DMM microbial cluster. Forty percent of infants who were classified in the mixed microbial cluster A also showed a non‐specific HMO consumption pattern , while the association of “Specific neutral” and “Specific other” was not significant. In addition, “Complete” consumption correlated with high relative abundance of bifidobacteria, including the two highly abundant 8 Figure a. FurtheFigure Figure p < 0.05) differences were detected between clusters with respect to consumption of 2′FL, LNFPIII, LNFPII, DFL, and 6′SL. For the aforementioned HMOs, the highest proportion of infants with the lowest ability to break down these HMOs was found in cluster A , LSTb (FDR = 0.06), LSTc, and 3′SL (FDR = 0.07) was associated with differences in the fecal microbiota composition Figure . For allFigure Bifidobacterium 418 and Lactobacillus 744 (FDR < 0.05). In addition, “high” DFL consumption was associated with significantly higher relative abundance of Bifidobacterium OTUs 406, 643, 423, and 597. Similarly, infants who showed “high” consumption of LNT and LNnT, LNFPIII, LNFPII, and LNH had a significantly higher relative abundance of Bifidobacterium OTUs 418, 406, 643, 423, and 597. Relative abundance of Bifidobacterium 418 was higher in infants who were efficient degraders of LNnH and LNDFHI, and Bifidobacterium 416 was associated with degradation of LNFPII, and Bifidobacterium 614 with LNFPII and LNH. We could not detect statistically significant differences (with FDR <0.05) in the relative abundance of taxa between infants characterized as “high” and “low” degraders of LNFPI, LNFPV, 3′SL, LSTa, LSTb, and LSTc.Kruskal–Wallis analysis was used to compare microbiota composition at the OTU level between infants who were classified as either “high” or “low” consumers for each HMO measured in this study . The observed patterns in the abundance of these taxa between the “low” and “medium” consumption classes mimicked those seen between “low” and “high” consumers for these HMOs . Infants classified as “low” and “medium” consumers showed significant differences (FDR <0.05) in relative abundance of 4Fecal microbiota composition of healthy, 1‐month‐old breastfed infants was characterized in this study using 16S rRNA gene sequencing and revealed high inter‐individual variability in the fecal microbiota composition. Microbial patterns could be observed in the sequencing data, and the DMM analysis revealed that all infants could in fact be classified into three categories based on their fecal microbial profiles. Even though the high variability in infant fecal microbiota composition had been reported,Several factors in early life may affect the dynamics of the developing infant GI microbiota. Our results showed that at about 4 weeks of age, mode of delivery and gender could explain the observed variation in the microbiota composition. The effect of mode of delivery has been indicated in infants of similar age in another study.4.1n = 16), which further indicated that LNFPI was positively associated with Bacteroides and Bifidobacterium, and 2′FL with Bacteroides.Bacteroides and Bifidobacterium. In addition, none of the other HMOs showed significant association with microbiota composition, but based on the vector positions , commonly found in the infant GI tract. Our analysis showed that Bifidobacterium 614 was associated with high in vivo consumption of various HMOs, specifically 2′FL, DFL, LNDFHI, LNFPII, LNFPIII, LNH, and LNT and LNnT . The presence of Bifidobacterium 418 correlated strongly (FDR <0.05) with high consumption levels of 2′FL, LNT and LNnT, LNFPIII, LNFPII, LNH and its isomer LNnH, LNDFHI, and DFL, and with LNFPI, LSTa, LSTb, and LSTc (p < 0.05). B. bifidum has been shown to be an efficient HMO degrader in in vitro fermentation studies able to secrete glycosidases to degrade HMOs extracellularly, also making it possible for other species/subspecies to access the HMO degradation byproducts and metabolites during cross‐feeding.B. bifidum DSM 20456 could efficiently degrade LNT, 2′FL, LNnT, LNFPI, LNFPII, LNFPIII, and LNDFHI, though the rate at which it was degrading these HMOs varied.Bifidobacteria are the main group of microorganisms in the lower GI tract of healthy infants, and also the main consumers of HMOs.Streptococcus and Staphylococcus OTUs showed an increase in relative abundance specifically in relation to high consumption of the fucosylated HMOs—DFL, LNFPII, and LNFPIII. Although, in vitro studies showed that Streptococcus and Staphylococcus cannot effectively metabolize HMOs,Staphylococcus by activating growth‐promoting signaling and without being actively metabolized by this strain.Streptococcus and Staphylococcus cross‐feeding on HMO metabolites might also play a role, though to date there are no studies documenting it.Bacteroides and Parabacteroides  are among the first dominant bacterial groups, next to bifidobacteria, established in the infant GI tract.Bacteroides can degrade a broad range of simple and complex sugars, oligosaccharides, and polysaccharides, including HMOs, mucus glycans, and plant‐derived polysaccharides.Bacteroides spp. can grow on milk glycans as a sole carbon source; however, bifidobacteria might be better adapted to utilize a wider range of HMO structures, including simple HMO structures, as it has been shown for B. infantis and LNnT.Bifidobacterium, such as those classified in the DMM cluster C tend to have lower levels of Bacteroides. On the other hand, Bacteroides has been shown to efficiently degrade mucus glycans, and because of the similarity of HMO structures and mucus glycans, some Bacteroides species could also effectively degrade specific HMOs by activating the mucus‐degrading pathway.Bifidobacterium which might be less adapted for HMO utilization. Our analysis indicated that infants who were efficient degraders of the sialylated (acidic) HMOs  and classified into “Specific acidic” consumption category were also often assigned to the Bacteroides dominated DMM cluster B , but the distribution of specific bifidobacterial OTUs within these two clusters was not identical (data not shown). Until now, few in vitro studies demonstrated that closely related species or strains might exhibit different metabolic activities and be involved in a range of complementary trophic interactions. Future studies should strive to identify the species or strains that are present in the infant gut and to build understanding on the interactions between these species. In the future, a better understanding on how the bacterial assemblages form in vivo and the identification of the key species and their roles in driving the colonization, as well as their effects on the host, could be translated into practical applications within infant nutrition and health.5Bifidobacterium and to a lesser extent within the genera Bacteroides and Lactobacillus.GI tract microbiota composition in 1‐month‐old breastfed infants is shaped by multiple factors, including HMOs. We observed a direct link between 2′FL and LNFP I in breast milk and microbial community composition in this cohort, but it is likely that the infant microbiota is shaped through the combined effect of all HMOs and other bioactive components in breast milk. We showed that breast milk HMO degradation patterns differed among infants belonging to different microbial cluster types. Degradation of specific HMOs could be correlated with an increase in relative abundance of various phylotypes (OTUs) within the genus The authors declare no conflict of interest.Supporting InformationClick here for additional data file.


A 67-year-old woman with a history of end-stage renal disease on hemodialysis received a therapeutic dose (150 mg daily) of flecainide for three weeks. She was admitted to the Emergency Department for malaise and dizziness, and the electrocardiogram revealed ventricular tachycardia treated by amiodarone. Hemodynamic condition remained stable, and the toxicity of flecainide was initially not suspected until she developed within 8 hours a cardiogenic shock requiring vasopressors. The patient then received sodium bicarbonate (300 mmol) and dobutamine but experienced cardiac arrest two hours later. The administration of intravenous fat emulsion (IFE) was associated with return of spontaneous circulation, but there was a relapse of cardiovascular shock at the end of IFE infusion. The patient was placed on extracorporeal cardiac life support (ECLS), continuous hemofiltration, and hemoadsorption using the CytoSorb® cartridge. Serial determinations of serum flecainide concentration were obtained during the course of hemoadsorption, with a terminal half-life of 3.7 h during the first four hours and a global plasma clearance of 40.3 ml/min over the first 22 hours. The weaning of ECLS was possible on day 7. Intravenous fat emulsion infusion was followed by a significant increase in serum flecainide concentration. In addition, while conventional techniques of extrarenal epuration usually appear as poorly effective for flecainide removal, a mean plasma clearance of 40.3 ml/min was observed using the hemoadsorption technique based on CytoSorb® cartridge. However, the impact on the clinical course was probably extremely modest in comparison with ECLS. Flecainide is a Vaughan Williams class 1C antiarrhythmic drug influencing the fast inward sodium channel during the phase 0 of the action potential. Overdoses with flecainide have been associated with a high mortality rate (22.5%) in comparison with other class 1C molecules. Toxicity is less easily recognized in case of chronic therapy. Ventricular arrhythmias and decreased cardiac contractility in relationship with conduction disturbances are the main complications of flecainide overdose [μg od, pantoprazole 40 mg od, calcitriol 0.25 mg od, and cinacalcet 30 mg od. Three weeks after hospital discharge, the patient suddenly felt dizzy and was referred at night to the Emergency Department. A 12 lead electrocardiogram showed an extremely broad QRS complex tachycardia at a rate of 115 bpm and without P waves. The negative concordance of the QRS complexes in the precordial leads, as well as the extreme right QRS axis deviation, was consistent with the diagnosis of ventricular tachycardia. The i.v. administration of 300 mg amiodarone resulted in a decrease of heart rate to 78 bpm with a blood pressure of 122/76 mmHg. A second ECG showed a regular rhythm without P waves and with broad QRS complexes (189 ms) and with diffuse repolarization abnormalities. Relevant laboratory investigations were arterial pH 7.44, bicarbonate 22 mmol/l, lactate 1.9 mmol/l, serum creatinine 9.57 mg/dl, urea 99 mg/dl, and hsTnI 40 ng/l. Liver function tests were normal. Blood was also drawn for toxicological investigations, but the results were not immediately available. The patient was admitted in the Intensive Care Unit (ICU) for further monitoring of blood pressure and heart rhythm. Systolic blood pressure remained initially above 100 mmHg, heart rate around 75/min, and arterial pH above 7.45. Eight hours after hospital admission, blood pressure progressively decreased and norepinephrine was started. The lactate level started to increase at the same time (7.7 mmol/l). The ICU physician on duty reconsidered the initial diagnosis of ventricular tachycardia as a sign of flecainide toxicity. Transthoracic echocardiography showed a marked interventricular dyssynchrony with severely decreased systolic function of both ventricles. Left ventricular ejection fraction was calculated using the biplane Simpson method at 36%. Cardiac output was calculated at 1920 ml/min. Administration of sodium bicarbonate (300 mmol), calcium gluconate (4 g), and dobutamine was initiated. Over the next 3 hours, the norepinephrine requirements rapidly increased up to 1.5 mcg/kg/min. Lactate levels increased up to 10 mmol/l, and neurological status worsened. The patient was intubated, and mechanical ventilation was started but cardiac arrest  occurred. Cardiopulmonary resuscitation (CPR) was immediately performed with 1 mg epinephrine administration every 3 minutes. CPR was briefly interrupted every 2 minutes for rhythm analysis but asystole persisted. Thirteen minutes after the initiation of CPR, intravenous fat emulsion  (IFE) bolus (1.5 ml/kg) was given over 1 minute and was followed one minute later by return of spontaneous circulation. Continuous infusion of IFE was continued at a rate of 0.25 ml/kg/min immediately after bolus administration for a total of 450 ml administered. At the same time, hemodynamics dramatically improved with complete weaning of norepinephrine requirements but without perceptible shortening of the QRS complexes. Five minutes after the end of IFE administration, blood pressure again dropped and norepinephrine was restarted with increasing doses up to 1.5 mcg/kg/min. Initiation of an extracorporeal cardiac life support (ECLS) and transfer to a tertiary center were then decided. In the meantime, profound hypotension occurred (46/28 mmHg). A second bolus of IFE (1.5 ml/kg) was administered, followed by continuous infusion at a rate of 0.25 ml/kg/min. At the time of venoarterial cannulation for ECLS two hours later by the mobile team of the tertiary hospital, norepinephrine and epinephrine requirements peaked at 2.6 mcg/kg/min and 0.5 mcg/kg/min, respectively. The patient had received an additional dose of 200 mmol of sodium bicarbonate. The lactate level was 19.9 mmol/l. IFE administration was discontinued immediately after cannulation. A total volume of 20% IFE administration was 1500 ml (33 ml/body weight kg).A 67-year-old woman (46 kg weight) underwent an aortic valve repair with aortic root replacement for severe dilation of the ascending aorta. In the postoperative period, she developed atrial fibrillation and was then prescribed flecainide two days before hospital discharge. Her other relevant medical history included ESRD requiring chronic hemodialysis, hypertension, and hypothyroidism. Her daily treatment included flecainide 150 mg od, losartan 100 mg od, acenocoumarol, levothyroxine 75 After transfer to the tertiary center, continuous venovenous hemodiafiltration (CVVHDF) and additional hemoadsorption using CytoSorb® cartridge were started with a blood flow of 200 milliliters per minute. The detailed toxicokinetics of flecainide in relationship with the epuration technique are presented in Figures μg/kg/min and QRS duration on ECG was reduced to 98 msec . After 7 days, echocardiography showed a normal biventricular systolic function and ECLS could be weaned and removed. The patient maintained normal liver function tests over the ICU stay. Further clinical course was marked by pulmonary and abdominal infections, slow respiratory weaning, and gastrointestinal bleeding. The patient could be discharged to the ward on day 34, with an excellent neurological outcome and recovery of left ventricular function, and had a 6-month uneventful follow-up.Within 72 hours, norepinephrine requirement dropped to less than 0.2 ∗1, ∗2, ∗3, ∗4, ∗5, ∗6, ∗7, ∗8, ∗9, ∗10, ∗11, ∗15, ∗17, ∗29, ∗35, ∗41, duplication, and deletion. The patient genotype was confirmed to be CYP2D6∗1/∗4.Genetic testing for CYP2D6 polymorphism was obtained. The investigated alleles were CYP2D6 VD (4.9 L/kg)) and a high Log P (octanol/water) coefficient (3.8). This would predict a favorable response to IFE therapy in case of overdose with refractory shock [Flecainide has similar characteristics to other medications for which IFE therapy has been successfully utilized. Flecainide has a large volume of distribution  genotype are considered as extensive metabolizers. Our patient was genotyped CYP2D6∗1/∗4  and should be classified as an intermediate metabolizer. Impaired CYP2D6 activity reduces flecainide clearance by 21% in intermediate metabolizers [Approximately 30% of an oral dose of flecainide is excreted in urine as unchanged drug, and in patients with renal impairment, the total clearance of this drug might fall by approximately 40% . Our pat2. It mainly absorbs hydrophobic low and middle molecular substances in a concentration-dependent manner. In vitro data suggest that among cardiovascular drugs, digoxin, amlodipine, and verapamil could be effectively removed, while no data were available for flecainide [Due to its pharmacological properties, flecainide is not effectively removable by different extrarenal epuration techniques , and this has been verified by isolated case reports , 6. Flececainide . The totecainide –10. The ecainide . The infThe limitations of this single observation have to be acknowledged. First, sodium bicarbonate should have been administered earlier, as soon as flecainide toxicity is suspected, and the total amount of IFE here exceeded the recommended dose. As IFE was not the sole medication used during resuscitation, the apparent clinical improvement could not be ascribed to IFE therapy only. Second, the respective role of each intervention  for the hemodynamic improvement remains difficult to establish, but ECLS was undoubtedly the most saving technique. Third, the effectiveness of the CytoSorb® cartridge for flecainide removal cannot be fully appreciated by the calculations of blood extraction as the amount of drug coated on the cartridge cannot be determined.


This study characterised oxygen uptake efficiency (OUE) in children with mild-to-moderate cystic fibrosis (CF). Specifically, it investigated (1) the utility of OUE parameters as potential submaximal surrogates of peak oxygen uptake (GET) and respiratory compensation point (OUERCP). Pearson’s correlation coefficients, independent t tests and factorial ANOVAs assessed differences between groups and the use of OUE measures as surrogates for Cardiopulmonary exercise test (CPET) data were collated from 72 children , with OUE assessed as its highest 90-s average , the gas exchange threshold (OUEp < 0.05) reduction in allometrically scaled p < 0.05) correlations between measurements of OUE and allometrically scaled r = 0.49–0.52) and CON (r = 0.46–0.52). Furthermore, measures of OUE were significantly (p < 0.05) correlated with pulmonary function (FEV1%predicted) in CF (r = 0.38–0.46), but not CON (r = −0.20–0.14). OUEP was able to differentiate between different aerobic fitness tertiles in CON but not CF.A significant (OUE parameters were reduced in CF, but were not a suitable surrogate for  It is well established that a high level of aerobic fitness, typically characterised by peak oxygen uptake  —the ratio between oxygen uptake  plateau , which typically occurs prior to, or at, the ventilatory threshold (VT)  were included in this study. Participant characteristics are presented in Table This study was a retrospective analysis of existing data, and, therefore, did not require additional ethics approval. Ethics approval for original data collected was approved by the South West NHS Research Ethics Committee and the University of Exeter Sport and Health Sciences Ethics Committee. Fully informed written consent and assent were obtained from parents/guardians and paediatric participants, respectively.Stature was measured to the nearest 0.001 m using a stadiometer  and body mass to the nearest 0.01 kg using digital scales . Body mass index (BMI) was subsequently calculated, and body surface area (BSA) was estimated using the Haycock equation  and forced vital capacity (FVC) expressed relative to normative reference values from the Global Lung Function Initiative  by 35  and All participants undertook a CPET to volitional exhaustion on an electronically braked cycle ergometer  to determine maximal and submaximal measures of aerobic fitness. Breath-by-breath changes in pulmonary gas exchange and ventilation were measured, and subsequently averaged to 10-s time intervals. Of the 72 participants within the study, 33 children  undertook a previously described supramaximal verification bout to determine a ‘true’ r et al. —the dispr et al. ], and thr et al. ]. \documGET) and RCP (OUERCP). The OUEP was taken as the highest 90 s OUE values were calculated in line with previous work . Allometric scaling was utilised to remove the influence of body mass from , as well as, the traditional clinical marker of disease severity, FEV1 (expressed as a percentage of predicted). Independent sample t tests established mean differences in anthropometric, pulmonary function and CPET parameters between groups. Factorial analyses of variance (ANOVAs) were used to establish interaction effects between disease status and aerobic fitness tertiles  Abdi . Statistor) Abdi .GET and OUERCP were identified in 35/36 (97%) of children and adolescents in the CF group. In the CON group, OUEGET was detected in all participants , and OUERCP was detected in 34/36 (94%) of participants. The profiles of OUEP, OUEGET and OUERCP during a CPET for a representative individual with CF are shown in Fig. All OUE outcomes were detected in 68/72 participants (94%). Both OUEs 36/36, 00%, and GET, OUERCP and OUEP, with CON being significantly higher than CF. No significant difference was observed between CF and CON groups for p < 0.05) greater Differences between groups were observed for pulmonary function, absolute GET, OUEmentclass2pt{minimGET were significantly and positively correlated with absolute RCP was not correlated with absolute GET, but not OUERCP, were correlated with allometrically scaled OUEP and OUEentclass1pt{minima−0.86.min−1) and CON (110.1 ± 16.3 vs. 86.0 ± 7.3 vs. 62.0 ± 12.5 mL kg−0.86.min−1) groups . However, when comparisons were made between groups, a significant difference in allometrically scaled p < 0.001, ES = 1.47) and middle  aerobic fitness tertiles, not for the lowest .When the data were split by tertiles according to allometrically scaled p < 0.001) and fitness tertile (p < 0.001), but no significant fitness tertile by group interactions were evident .When assessing OUEP by fitness tertile and disease group, significant main effects were seen for group , but not between high and middle  nor middle and low . Further pairwise comparisons revealed differences within the CON group, as the tertile with the highest aerobic fitness had a significantly higher OUEP than the middle  and lowest  tertiles. No significant difference was evident between the middle and lowest tertiles with regard to OUEP for CON  for both parameters. Further significance (p < 0.05) between groups was identified when split by fitness tertile. A main effect for fitness tertile was present for OUEGET (p < 0.001), but not OUERCP (p = 0.08). Main interaction effects between group and aerobic fitness were not present for either OUEGET (p = 0.34) or OUERCP . For CON, differences were found between high and low , and high and middle fitness tertiles . For OUERCP, there were no differences between tertiles within groups . In addition, pairwise comparisons revealed significant differences (p < 0.05) between groups at each tertile for both OUEGET and OUERCP  in CF , but not CON .OUEP and OUEON Table . Allomet1) in the CF group. An example is shown in Fig. GET) and FEV1 (%predicted) within the CF cohort, showing that OUE is associated with traditional clinical markers of disease severity.In this study, whilst all OUE parameters were significantly reduced in children and adolescents with CF in the current study, results show that OUE does not provide a viable surrogate for GET and OUERCP). The OUEP occurs at a submaximal point near the VT  and OUE at the VT  to those of the CON group in the current study  between the OUEP and absolute −1 respectively) relative to the current CON group (69.2 ± 33.5 L min−1), which may, therefore, bias the relationship between Of the limited research to have characterised the OUEP in youth, a large cross-sectional study of 214 healthy Dutch children identified similar mean values for OUEP  is not able to act as a surrogate measure of aerobic fitness, alternative submaximal factors must be considered. Ventilatory drive (1 > 40%predicted). However, defining severity on FEV1 alone does not account for the nutritional measures, number of exacerbations, inflammatory markers and infection statuses that also contribute towards a patient profile and definition of severity. Consequently, these results may not be applicable to those with lower lung function, a cohort for whom FEV1 has a greater influence upon p value of 0.11, yet an ES of 0.78, thus indicating an effect is likely present but cannot be statistically confirmed. We have utilised the Sidak correction factor in this study as opposed to the more conservative Bonferroni in an attempt to alleviate the potential for Type 2 errors, yet statistical significance was not found in some comparisons and a statistical error might still have occurred. Larger clinical sample sizes would be advantageous but are not always feasible in young people who are sick.A number of limitations associated with the present study are worthy of comment. Primarily, this study is focused in children and adolescents with mild-to-moderate CF (FEVirs Fig. . In addiirs Fig.  within CThe current study is the first to comprehensively characterise parameters of OUE in children and adolescents with mild-to-moderate CF, and assess the utility of such parameters as submaximal surrogates for


To recognize risk factors and build up and validate a simple risk model predicting 8‐year cancer events after nonalcoholic fatty liver disease (NAFLD).This was a retrospective cohort study. Patients with NAFLD (n = 5561) were randomly divided into groups: training (n = 1254), test (n = 627), evaluation (n = 627), and validation (n = 3053). Risk factors were recognized by statistical method named as a Cox model with Markov chain Monte Carlo (MCMC) simulation. This prediction score was established based on the training group and was further validated based on the testing and evaluation group from January 1, 2007 to December 31, 2009 and another 3053 independent cases from January 1, 2010 to February 13, 2014.The main outcomes were NAFLD‐related cancer events, including those of the liver, breast, esophagus, stomach, pancreas, prostate and colon, within 8 years after hospitalization for NAFLD diagnosis. Seven risk factors (age (every 5 years),LDL, smoking, BMI, diabetes, OSAS, and aspartate aminotransferase (every 5 units)) were identified as independent indicators of cancer events. This risk model contained a predictive range of 0.4%‐37.7%, 0.3%‐39.6%, and 0.4%‐39.3% in the training, test, evaluation group, respectively, with a range 0.4%‐30.4% for validation groups. In the training group, 12.6%, 76.9%, and 10.5% of patients, which corresponded to the low ‐, moderate ‐, and high‐risk groups, had probabilities of, <0.01, <0.1, and 0.23 for 8‐year events.Seven risk factors were recognized and a simple risk model were developed and validated to predict the risk of cancer events after NAFLD based on 8 years. This simple risk score system may recognize high‐risk patients and reduce cancer incidence. Seven risk factors including age (every 5 years), low‐density lipoprotein‐cholesterol, smoking, body mass index, diabetes, obstructive sleep apnea syndrome, and aspartate aminotransferase (every 5 units) were independent indicators of 8‐year cancer events in patients with NAFLD. A simple risk model and a score system were developed to predict the risk of cancer events after NAFLD based on 8‐year follow‐up of 1254 NAFLD patients. The risk model was well validated in the test samples (n = 627) and evaluation samples (n = 627) with 8‐year follow‐up. Importantly, the risk model performed well in another independent validation samples (n = 3053). This risk model provides a basis for clinicians to better understand patients' risk of long‐term cancer events after NAFLD. BMIbody mass indexHDLHigh‐density lipoproteinLDLLow‐density lipoproteinMCMCMarkov chain monte carloMSmetabolic syndromeNAFLNonalcoholic fatty liverNAFLDNonalcoholic fatty liver diseaseNASHnonalcoholic steatohepatitisOSASobstructive sleep apnea syndromeRDIrespiratory disturbance indexROC curvesreceiver operating characteristic curvesROSreactive oxygen species1Nonalcoholic fatty liver disease (NAFLD) has a high prevalence and increasing morbidity in China and other countries.Although the risk factors for NAFLD‐relevant hepatocellular carcinoma Accordingly, our study developed and evaluated a simple risk model by identifying significant clinical risk factors to predict NAFLD‐related cancers on the basis of 5561 patients with NAFLD in the First Affiliated Hospital of Zhengzhou University, the largest tertiary medical hospital in China, which has approximately 10 000 beds, 15 000 inpatients/ day and 20 000 outpatients/ day. Patients with NAFLD diagnosed between 1/1/2007 and 2/13/2014 were included and followed until cancer diagnosis, death, or through 12/31/2017. In this cohort, all the samples were extracted from medical records about patients with NAFLD and were well designed to further stratify risk after NAFLD diagnosis. The main endpoint of the study was to predict the presence or absence of cancers by a combination of simple and clinically relevant variables in the elderly Chinese population.22.1A total of 5561 patients with NAFLD in this study were first confirmed by ultrasound. The radiological features of these patients included fatty liver and increased or heterogeneous echogenicity. They visited the First Affiliated Hospital of Zhengzhou University between January 1, 2007 and February 13, 2014 more than two times and were followed until cancer diagnosis, death or through December 31, 2017. Medical records were extracted individually by three doctors; consistency was 97% for main data elements. Based on 8‐year follow‐up of NAFLD patients, information on all types of cancer events was collected from medical records, including liver, breast, esophagus, stomach, pancreas, prostate, and colon cancers.Patients with liver disease of other etiologies were appropriately excluded, including autoimmune or viral hepatitis, alcohol‐induced, or drug‐induced liver disease and cholestatic or genetic liver disease. These other liver diseases were excluded applying specific clinical, biochemical, radiographic, and/or histological criteria. All patients had a negative history of ethanol abuse, as indicated by a weekly ethanol consumption of ≤140 g in women and ≤210 g in men. A history of alcohol consumption was specifically investigated from medical records. Patients with clinical or imaging evidence of decompensated cirrhosis were specifically excluded from this study because they most likely had cirrhotic‐stage NAFLD.The cohort from 2007 to 2009 included 2508 patients. These samples were randomly divided into three independent groups named as training group(50% [1254patients]), test group(25% [627patients]), and evaluation group(25% [627 patients]). The training group was used to identify risk factors of cancer events for NAFLD patients with 8‐year follow‐up. The test group and evaluation group were used for validation. The other independent cohort from 2010 to 2014 enrolled a total of 3053 unique patients for further validation analysis. This study was agreed by the Institutional Review Board of the First Affiliated Hospital of Zhengzhou University ), medical history , lifestyle factors , laboratory evaluation including routine liver biochemistry , complete blood count, total cholesterol, HDL cholesterol, LDL cholesterol, and total triglycerides.The candidate risk factors contained clinical and laboratory data were easily and dependable collected within hospitalization for NAFLD as well as were selected by their clinical meaning, supported document. Not only detailed medical history but also entire physical examination were abstracted from patients. Initial factors included patient demographics /height ; diabetes mellitus (fasting glucose ≥126 mg/dL or treatment with antidiabetic drugs); and obstructive sleep apnea syndrome (based on the respiratory disturbance index (RDI) ≥5 obstructive events/h of sleep, for which patients were diagnosed with obstructive sleep apnea syndrome).2.3In this risk model, the outcome was 8‐year cancer events, a binary variable recognized as the occurrence of cancers, including those of the liver, breast, esophagus, stomach, pancreas, prostate, and colon, within 8 years of diagnosis for NAFLD. Messages on the outcome were received and acknowledged by medical records.33.1In the training group, we fitted the statistical MCMC simulation and computed a posterior probability for the whole risk factors.We test this predictive model performance by the following statistics method: Harrell's c‐statistic to evaluate the total accuracy of prediction,Additionally, this predictive model performance was evaluated and compared in the test, evaluation, and validation group, respectively.3.2For the convenient application of elected risk factors and this predictive model, we developed a easily applied score system for every patient with NAFLD on the basis of the regression coefficients assessed from the predictive model with the training group. The scores for every risk factor were counted by grading the coefficient of risk factor by the sum of all coefficients in this model, multiplying by 100, meanwhile rounding to the nearest integer. Then, the risk score was calculated by summing points of patients.44.1A total of 5561  patients were enrolled. The mean age was 69.4 years (standard deviation [SD] 8.1), and 49.4% were female. The common comorbidities were diabetes mellitus (27.2%), obstructive sleep apnea syndrome (19.2%), hypertension (27.8%), and dyslipidemia (70.5%). There were not significant differences in basic characteristics of patients across the training, test, and evaluation group. However, the common comorbidities in validation groups were higher than in the other three groups (Table 4.2The rates of 8‐year cancer events were 10.1% (95% [CI] 8.5‐11.9), 10.2% (95% [CI] 7.9‐12.8), and 10.2% (95% [CI] 7.9‐12.8) for the training, test, and evaluation group, respectively. Overall, the nonfatal cancer event rate was 10.2%. The median [interquartile range (IQR)] number of days from NAFLD diagnosis to a cancer event was 2533 (2179‐2923) days; prostate and liver occurred earlier than other primary areas of cancer  to 25 (LDL) Table . The str4.5p value was 0.21 . The average observed cancer outcome extended from 0.4% to 30.4% in the predicted decile Figure . The Hos1 Figure .For the validation group, the mean (SD) of the risk score was 2.90 (SD 0.91) Figure . In the 5In this large cohort study, we found that seven risk factors, including age (every 5 years), low‐density lipoprotein‐cholesterol, smoking, body mass index, diabetes, obstructive sleep apnea syndrome, and aspartate aminotransferase (every 5 units), were independent indicators of 8‐year cancer events in patients with NAFLD. This simple risk model and the score system were developed and validated to predict 8‐year cancers NAFLD diagnosis. Importantly, the risk model performed well in another independent validation NAFLD patients. These factors were selected on the basis of data selected from medical records and ease of collection and ready availability at the time of discharge as well as long‐term follow‐up. Furthermore, statistical algorithms in this study is robust. Not only this predictive model but also this score system help clinicians recognize patients with NAFLD at increased risk of 8‐year cancers and assist patients understand their risk of cancers. The capability to recognize patients with the highest risk of cancers after NAFLD diagnosis may provide targeted, higher‐quality, and intensive healthcare after discharge.Our study, based on information selected from medical records and continuing for 8 years of follow‐up, presents a large cohort study of risk factors that predict the spread of outcomes for senior citizens with NAFLD in the central plains of China. Furthermore, the patients represented in these data always visit the same hospital many times to acquire the comprehensive and professional treatment and healthcare in this general teaching and urban hospital. Importantly, evidence shows that most of the patients developing NAFLD present at least one of the traits of metabolic syndrome (MS).The MCMC algorithm was used to evaluate the strength of the association between the risk factors and the outcome. On the basis of data from 2007 to 2009, we developed and evaluated this simple noninvasive predictive risk model. In contrast to other studies, our study had better predictive accuracy based on another independent cohort of patients with NAFLD from January 1, 2010 to February 13, 2014, which was used to revalidate this scoring system.Our risk factors were recognized on the basis of a large cohort that always visited and followed up in the same hospital for many times. Data for an effective risk factor ought to be stabilized by clinical illustration, conveniently collected, widely received during hospitalization and at discharge. In this study, these seven risk factors found fitted the whole criteria, meanwhile the majority of them have been recognized in many studies.All cases with NAFLD were diagnosed by abdominal ultrasound of hepatic steatosis, which provides less accuracy than diagnosis by liver biopsy. However, ultrasonographic detection has been widely used in other studies to verify fatty liver.In conclusion, this simple risk model had a robust predictive scope and could provide a basis for clinicians to better understand patients' risk of long‐term cancer events after NAFLD. It assists clinicians make better‐targeted, evidence‐based decisions for postdischarge NAFLD management.All authors declare no conflict of interest.QK, ZY, and GY designed the study. ZW, ZR, and RS collected data; GY and SH analyzed data; ZW wrote the manuscript; ZR revised the manuscript. All authors reviewed and approved the manuscript.This study was approved by the First Affiliated Hospital of Zhengzhou University (2017‐XY‐002). The study was performed in accordance with the Helsinki Declaration and Rules of Good Clinical Practice. All participants signed written informed consents after the study protocol was fully explained.No individual data are reported in this article. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Cultured human bone marrow stromal  stem cells (hBM‐MSCs) are heterogenous cell populations exhibiting variable biological properties. Quantitative high‐content imaging technology allows identification of morphological markers at a single cell resolution that are determinant for cellular functions. We determined the morphological characteristics of cultured primary hBM‐MSCs and examined their predictive value for hBM‐MSC functionality. BM‐MSCs were isolated from 56 donors and characterized for their proliferative and differentiation potential. We correlated these data with cellular and nuclear morphological features determined by Operetta; a high‐content imaging system. Cell area, cell geometry, and nucleus geometry of cultured hBM‐MSCs exhibited significant correlation with expression of hBM‐MSC membrane markers: ALP, CD146, and CD271. Proliferation capacity correlated negatively with cell and nucleus area and positively with cytoskeleton texture features. In addition, in vitro differentiation to osteoblasts as well as in vivo heterotopic bone formation was associated with decreased ratio of nucleus width to length. Multivariable analysis applying a stability selection procedure identified nuclear geometry and texture as predictors for hBM‐MSCs differentiation potential to osteoblasts or adipocytes. Our data demonstrate that by employing a limited number of cell morphological characteristics, it is possible to predict the functional phenotype of cultured hBM‐MSCs and thus can be used as a screening test for “quality” of hBM‐MSCs prior their use in clinical protocols.  Clinical trials employing cultured human bone marrow mesenchymal stem cells (hBM‐MSCs) demonstrate the presence of a large inter‐donor variability in biological functions and clinical efficacy. By applying high‐content imaging methodology to cultured BM‐MSCs obtained from a large cohort of donors, morphological features including cell area and nucleus geometry as predictors for cell differentiation and proliferation were identified. These parameters can serve as selection criteria for BM‐MSC populations to be used in clinical trials of enhancing bone regeneration.1Human bone marrow‐derived stromal  stem cells (hBM‐MSCs) are non‐hematopoietic, self‐renewing, plastic adherent cells with the ability to differentiate into several mesodermal lineages including osteoblasts (OB) and adipocytes (AD).The reported clinical outcome of hBM‐MSC‐based therapies for treating non‐healed bone fractures or bone defects has revealed inconsistent results with respect to the efficacy of in vivo bone formation, which may be explained by variations in the phenotype of the transplanted cells.Comparing the outcome of clinical trials for bone regeneration that has used hBM‐MSCs is often hampered by the lack of “common” cellular biomarkers that define the “quality” of transplanted cells. Identification of biomarkers that facilitate the selection of clinically relevant hBM‐MSC populations is needed in order to improve the outcome and consistency of hBM‐MSC‐based therapies. Traditionally, hBM‐MSCs have been defined by a limited number of CD markers, for example, CD44, CD73, CD90.Changes of cell morphology have been observed during cell proliferationThe availability of quantitative high‐content imaging technologies at a single‐cell resolution has allowed studies on the role of cell morphology as a predictor of cell differentiation potency.22.1Bone marrow aspirates were collected from lower extremities of 56 adult donors: 26 males (age 18–81 years) and 30 females (26–97 years), undergoing surgeries at the Department of Orthopedics and Traumatology, Odense University Hospital. The samples of bone marrow were considered as “waste material” from routine operations, and, therefore, the material collection was without extra risk for patients. All subjects received oral and written information and signed a consent. The project was approved by Scientific Ethics Committee of Southern Denmark (project ID: S‐20160084).2.22 incubator). After a week, when the first adherent cells were visible, media was switched to MEM media supplemented with 10% FBS, 1% P/S, 1% GlutaMAX, 1% sodium pyruvate, and 1% nonessential amino acids (S‐MEM growing medium). When the cells reached around 80% of confluence, they were trypsinized and used for analysis and for further cell expansion.Bone marrow (5–10 mL) was collected into EDTA‐coated vacutainers. hBM‐MSCs were cultured from mononuclear cell population following gradient centrifugation using lymphoprep of the bone marrow, through plastic adherence as described previously.2.3The study workflow is illustrated in Figure 2.4cellsday6/Ncellsday1]).Short‐term proliferation was performed in cultured hBM‐MSCs at first passage. The cells were seeded in a 6‐well plate  in triplicates and cultured under standard conditions. At days 1, 3, 6, 9, 12, and 15, the cells were trypsinized and counted in a hemocytometer. The proliferation ability of the cells from each donor was quantified as the area under the curve (AUC) and expressed as arbitrary units (AU). In addition, we calculated population doubling time (PDT) in hours between days 1 and 6 using the following formula PDT = 120 hours×log(2)/ for 10 minutes with rotation in room temperature (RT). After that, the cells were washed with PBS for several minutes to remove nonspecific binding. Alizarin red staining was quantified as red intensity using ImageJ software and expressed as AU.After 14 days, the osteoblastic differentiation was assessed by the ability of the cells to form mineralized matrix visualized by alizarin red staining. Briefly, the cells were washed with PBS and fixed with 70% ice‐cold ethanol in −20°C for 1 hour. Subsequently, hBM‐MSCs were washed with H3.1.23 and 1 mM MgCl2, pH 9.6 for 20 minutes at 37°C. The reaction was stopped by adding 3 M NaOH. Absorbance was measured at 405 nm and corrected per cell number. The cell number was determined by CellTiter‐Blue reagent (cell viability reagent) for 1 hour at 37°C. The fluorescent intensity of the reagent was measured in FLUOstar Omega plate reader (560ex/590em).The cells were washed, fixed with 3.7% formaldehyde‐90% ethanol, and incubated with p‐nitrophenyl phosphate (1 mg/mL) in 50 mM NaHCO3.22 in a 4‐well plate for 24 hours. At near full confluence, the media were replaced with adipocytic induction media containing DMEM supplemented with 10% FBS, 1% P/S, 5% horse serum, 1 μM BRL 49653, 3 μg/mL insulin, 100 nM dexamethasone, and 225 μM IBMX. Media were changed every 2–3 days for 2 weeks.Human BM‐MSCs at the first passage were plated at a density of 30,000 cells/cm3.2.12O). Images of the differentiated cells were captured using Olympus optical microscope (×10 magnification objective) and quantified by lipid droplets area (average of six images per sample) using ImageJ software and expressed as AU.Adipocyte differentiation efficiency was determined by lipid droplets area as visualized by Oil Red O staining. The cells were fixed with 4% paraformaldehyde (PFA) for 10 minutes at RT, washed with 3% isopropanol, and incubated with filtrated Oil Red O solution  and percentage of lipid droplets area (adipocytic differentiation).3.32+ and Mg2+) with 2% FBS. The cells were incubated with primary fluorophore‐conjugated antibodies as follows: CD44‐PE, CD73‐PE, CD90‐PE, CD105‐PE, CD146‐PE, CD271‐FITC, ALPL‐APC, for 25 minutes in 4°C. After the incubation, cells were washed twice to remove unspecific antibody binding and analyzed using BD LSR II Flow Cytometer with BD FACSDiva software and data were analyzed with Kaluza Flow Cytometry Analysis Software Version 1.3 (Beckman Coulter).Human BM‐MSCs were trypsinized and washed with PBS  in S‐MEM media. After 48 hours, the cells were washed with PBS and fixed with 4% PFA for 10 minutes at RT. The cells were permeabilized with buffer (0.3 M Tris‐glycine and 0.25% Triton‐X in PBS) for 15 minutes (RT), incubated in blocking buffer  for 1 hour at RT, followed by incubation with antibody for α‐tubulin (dilution 1:500). The cells were washed and incubated with AlexaFluor 488 (dilution 1:1000) for 1 hour followed by incubation with Phalloidin (35 μM for 2 hours at RT). Cell nuclei were visualized using DAPI.3.4.2Spot, Edge, Ridge, Saddle, Valley Hole, Bright, Dark) at 1px scale were determined to measure the differences in cytoskeletal and nuclear structure. Each morphological parameter was quantified using an average of at least 1,000 cells per donor sample. The list and the description of all measured and quantified cell morphology and texture features are defined in Table Images of the hBM‐MSCs cultured from each individual donor (15 different areas per well in 9 wells) were acquired using the Operetta high‐content screening system . Cell and nucleus morphology parameters including area, roundness, width, length, and ratio of width to length were analyzed using Harmony 3.1 software by employing a predefined protocol based on the building blocks method, which includes nuclear and cellular segmentation, as shown in Figure 3.53 (kind gift from Leo Pharma), p‐nitrophenyl phosphate , Alizarin Red , Oil Red O , horse serum , rosiglitazone , insulin , 3‐isobutyl‐1‐methylxanthine , anti‐CD44 , anti‐CD73 , anti‐CD90 , anti‐CD105 , anti‐CD146 , anti‐CD271 , alkaline phosphatase antibody , CellTiter‐Blue cells viability assay reagent , Phalloidin , mouse anti‐human α‐tubulin , DAPI , Alexa Fluor 488 .Lymphoprep , Minimum Essential Media , Dulbelcco's Modified Eagle Medium , FBS , GlutaMAX , Non‐Essential Amino Acids , Trypsin–EDTA , β‐glycerophosphate , Dexamethasone , Vitamin C , Vitamin D3.63.6.1rs = Spearman correlation coefficient). For the correlation analysis, outliers were identified and removed using the ROUT method, which detects outliers from nonlinear regression, based on the maximum false discovery rate Q = 1%. The number of independent donors (n) in each correlation analysis is described in the Results section and in each figure. Differences between groups were tested by unpaired two‐tailed Student's t‐test. All analyses were performed using GraphPad Prism 7.1 software. Statistical significance was considered when P < .05.Statistical analyses of the correlation between variables were performed using the Spearman two‐tailed correlation test , which denotes the predictive power of the model employing new data set. For determination of the individual prediction value of the variables, the estimated AUC for the receiver operator characteristic was calculated.A stable multivariable linear regression model was created for predicting the osteogenic and log transformed adipogenic differentiation outcome and performed for the whole data set of 56 donors. Due to the large number of potential morphological predictors, we applied the variable selection procedure introduced by Meinshausen.44.1Our initial analysis of cell morphology    , CD146+ , and CD271+ .) Figure A. CD271 4.3rs = −0.51, P < .0001, n = 53 and rs = −0.45, P = .0008, n = 53, respectively). The AUC of cell proliferation curves was generally used as a good metric to summarize the growth curve.rs = −0.73, P < .0001, n = 53, Figure rs = 0.56, P < .0001, n = 53) and cell geometry  which can be seen in Figure We examined the correlation between morphology of hBM‐MSCs  and cellular functions. Mean cell area Figure A‐C and cP = .78 and P = .48, respectively, Figure P = .36 and P = .29, Figure In contrast to cell proliferation findings, adipocyte differentiation capacity (estimated by the lipid droplets area of mature AD) or OB differentiation  did not correlate with cell area    Figure B. We did4.5rs = −0.30, P = .027, n = 53) and positive correlation with osteoblastic differentiation potency     and high AD group . The photomicrographs in Figure As illustrated in Figure 4.6To identify the morphological predictors for hBM‐MSC differentiation potential, we performed a multivariable analysis that included all measured morphological and texture features of cells and nuclei. Using a stability selection procedure, we identified parameters with explanatory power of the primary outcome of osteoblastic Figure A and adiFor adipocyte differentiation, the nuclear “Hole” texture pattern was selected as a highly predictive variable (θ > 0.75) with negative influence on adipocytic differentiation potential. However, “Hole” texture pattern, did not reach statistical significance in bivariant analysis, Figure . MoreoveThe relative performance of the model was measured by AIC that determines the predictive power of the model when applying it to a new data set. We performed the analysis comparing various models  for osteogenic and adipogenic outcomes including different combination of covariates. For osteogenic outcome, models were as follow: m0 , nucleus geometry (m1) and combination of identified top four valuables including nucleus geometry, cell geometry (α‐tubulin), nucleus length, and cell texture Spot (m2). Similarly, for adipogenic outcome, we included: m0 (intercept only), nucleus texture Hole (m1), and top four identified variables: nucleus texture Hole, nucleus geometry, cell geometry (actin), and cell texture Spot (m2). Our data demonstrated Table  that by 5Cultured hBM‐MSCs represent a heterogenous population of cells with respect to their in vivo differentiation potential which may lead to inconsistent and variable results when being used in clinical trials.We observed as expected, that there exist large intra‐ and inter‐individual variations in the morphological characteristics of cultured hBM‐MSCs. The inter individual variations allowed us to identify predictive factors that are associated with biological functions relevant for their use the clinic. Similar to our findings, a number of studies of cultured hBM‐MSCs reported the presence of inter individual cellular heterogeneity with respect to their differentiation capacity that was correlated with donors characteristicsCanonical hBM‐MSCs are defined by expression of a number of cell surface markersCell proliferation capacity is an important prerequisite for regenerative therapy protocols as large numbers of cells are needed. Also, highly proliferative hBM‐MSCs exhibit better engraftment in preclinical mice models.Osteoblast differentiation is the default differentiation pathway of cultured hBM‐MSCsSeveral previous studies have correlated cell morphology of the whole cultured cell population with the progression of stem cell differentiationThe association of cellular shape and functional outcome may be explained by changes in actin dynamics. We have previously demonstrated that changes in cell shape induced by alternations in the actin cytoskeleton structure determine differentiation outcome of hBM‐MSCs with cellular changes associated with actin de‐polymerization led to enhanced adipocytic differentiation, whereas inhibition of actin de‐polymerization increased osteoblast formation of hBM‐MSCs.Multivariable analysis identified nucleus geometry as the most stable predictor factor of the differentiation capacity of the cells. The cell nucleus has been proposed to function as a mechano‐sensor,Our study has some limitations. We used in vitro differentiation assay as a surrogate marker for the in vivo bone‐forming capacity. Several markers of in vitro differentiation to OB are not predictive for bone forming capacity of the cells.Routine clinical use of hBM‐MSCs in therapy requires development of easy and noninvasive assays for determining “cell quality.” We have previously demonstrated that Raman spectroscopy can be used to confirm the normal, nontransformed phenotype of hBM‐MSCs prior to clinical transplantation.H.S. declared consultant/advisory role with Arthrex and ownership interest in Johnson & Johnson. The remaining authors declared no potential conflicts of interestJ.M.K.: conception and design, collection and assembly of data, data analysis and interpretation, manuscript writing; H.S.: provision of study material; U.H.: data analysis and interpretation; J.B.H.: data analysis and interpretation; M.K.: conception and design, data analysis and interpretation, manuscript writing, final approval of manuscript.Figure S1 Scheme of the experimental design. Human primary bone marrow stromal stem cells (hBM‐ MSCs) were examined for (1) osteoblastic or adipocytic differentiation, cell proliferation, cell membrane marker expression and morphological analysis employing high‐content imaging . The cells were stained with DAPI and antibodies for cytoskeletal proteins (actin and tubulin) (2). Images were analyzed based on nuclear and cellular segmentation (3). Border cells were deselected from further analysis (4). Cellular and nuclear features were analyzed (15 areas in 9 wells per sample) and quantified (5) and subsequently correlated with results obtained from functional assays.Click here for additional data file.Figure S2 Data showing that PDT correlates with AUC analysis and significantly predicts cell morphology. The values of population doubling time (PDT) (A) of BM‐MSCs as expected significantly and negatively correlated with AUC values. PDT of hBM‐MSCs correlated positively with (B) cell area and with (C) cell geometry.Click here for additional data file.Figure S3 Relationship between cell morphology and hBM‐MSC functions. Cell area of hBM‐MSCs based on F‐actin staining is significantly correlated with (A) proliferative capacity but not with mature (B) adipocyte formation or (C) osteoblast formation. Cell geometry expressed as width to length ratio exhibited significant negative correlation with cell proliferation ability, but did not correlate with mature (E) adipocyte formation or (F) osteoblast formation.Click here for additional data file.Figure S4 Relationship between nucleus texture and adipogenic differentiation of hBM‐MSCs. The nucleus texture was determined following DAPI staining. The Hole pattern of nuclear texture exhibited a negative tendency with the adipogenic differentiation potential of BM‐MSCs.Click here for additional data file.Table S1: Supplementary informationClick here for additional data file.Table S2: Supplementary informationClick here for additional data file.


Visual field asymmetries in the encoding of groups of faces have rarely beeninvestigated. Here, eye movements (percentage of dwell time [pDT] and number offixations [nFix]) were recorded during the encoding of three groups of fourfaces tagged with cheating, cooperative, or neutral behaviours. Faces in eachgroup were placed in the top left, top right, bottom left, or bottom rightquadrants. Face recall was equally high in the three behavioural groups.Conversely, pDT and nFix were higher for faces in the upper hemifields. Most ofthe first saccades were made to the top left visual quadrant, which alsocommanded a higher pDT and nFix than the other quadrants. The findings arerelevant to the understanding of visual field asymmetries in the processing ofmultiple faces, a common social scenario, and may be linked to reading habits inconjunction (or not) with cultural and environmental cues. Face-to-face communication is at the core of human social life and it relies stronglyon our ability to recognize the faces of people we have interacted with, not only todevelop stable future associations and exchanges  but alsoFew studies examined face recognition with multiple faces at once and even fewerinvestigated tagging faces with different behaviours, a common behaviour in socialgatherings and relevant information in eye witnesses’ reports. The findings fromearlier studies showed a range of face recognition biases in social scenarios, frombiases towards cooperators cf.,  or free-Recognition biases have also been associated with the location of faces in the visualfield . FurtherAn upper visual hemifield advantage in face processing and a lower hemifieldadvantage in perceptual motion accuracy have been consistently reported in paststudies using assorted experimental paradigms . Such VFThe origin of VFAs in face recall is not known. VFAs related to face processing maybe modulated by environmental cues , cultural cues  or cognitive cues . The advantage of the tophemifield might be linked to prior visual knowledge of the environment and one’speri-personal space, since most natural and artificial light comes from above ourheads. Such knowledge could be used to disambiguate scenes and lead to processingbiases towards upper, left-lit stimuli . VisuospDespite the wide range of studies on eye movements and memory for faces, to date nostudy has investigated the eye movements of neurotypical adults during multiple faceencoding, a frequent situation in social scenarios. Hence, this study examined ifthere were perceptual VFAs in eye movements during the encoding of multiple faces,as suggested by the different recognition accuracy for faces presented at differentlocations in the visual field . Here, tM = 22.76, standard deviation[SD] = 4.32). From the initial 42 participants, one wasexcluded due to problems during the calibration of the eye tracker and anotherparticipant was excluded from the nFix analysis due to outlier values. Abouttwo-third participants were university students and one third were members ofthe public living or working near the university premises. They were recruitedvia opportunity sampling and on a voluntary basis.There were 40 participants (8 men and 33 women) with age ranging from 19 to 38years . The 12 faces to be memorized were divided inthree groups tagged as cheaters, cooperators or neutrals . The face recognitiontest was written using EyeLink’s integrated software. The faces on each of thebehavioural groups were randomly allocated to one of four visual quadrantsaround a centred fixation point: TL, TR, BL and BR. The remaining 12 faces wereused in the subsequent recall test. A pilot study showed the faces used here didnot differ significantly from each other in a face recognition test  each face during a trial and expressed aspercentages. The EyeLink Data Viewer software was then used to calculate themean pDT, the first saccade to an area of interest and the mean nFix to eacharea of interest.The observer’s left or right eye movements were recorded using a video-based eyetracker with a spatial resolution of 0.1° . For the scan path analysis, regions were defined using Data Viewersoftware supplied by SR Research. The eye tracker was calibrated with the12-point procedure. Eye movements were sampled at a rate of 1000 Hz and eyeposition was sampled automatically (500 times/second). Eye gaze data wereanalysed in two stages. Saccades were identified using the default settings ofEyeLink 1000’s automatic parser. An eye movement was classified as saccade if ithad an instantaneous velocity of greater than 30°/second, or an accelerationgreater than 8,000°/secondAfter being briefed about the study and signing a consent form, participants weretested in a quiet room and sat in a height-adjustable chair to prevent anyrotation about the vertical axis. Participants were tested individually andaccording to the following core experimental protocol: (a) encoding of 12 faceswith correspondent behaviours, (b) distracter task during memory consolidation(10 multiplications) and (c) ‘yes-no’ face recognition test .The 12 faces in the encoding phase were divided in three behavioural groups withfour faces each: cheaters, cooperators and neutrals. The social scenario wasbased on a fictitious character (‘John’) able to lend his friends £2,500,000.Some friends borrowed the money and paid it back with interest after a year(cooperators), some borrowed it but never paid it back (cheaters) and some neverborrowed any money from John . The faces were counterbalanced andrandomly presented in the different quadrants and groups acrossparticipants.Yes; 2 = No). A cycle of 24 faces (12memorized and 12 new) was presented in randomized order to each participant. Thewhole procedure lasted 10 to 15 minutes. For further details, see The simple recall phase started with a face at the centre of a screen and thequestion ‘Have you seen this face before?’ Half of the faces had been memorizedin the encoding phase, and half of the faces were new . Participants answered by pressing a key was used to refer to effect size . The following ‘rules of thumb’ were used to evaluate effect size: .01small or modest, .06 medium or moderate and .14 large, but such cut-off valuesshould not be taken as precise boundaries .The datasets were assessed for normality and means were accepted as having anormal distribution if the kurtosis fell in the range of ± 2.0. The analysis ofvariance (ANOVA) had the descriptors tagged to faces  and the visual hemifields (top vs. bottom and left vs. right) as theindependent variables and the pDT and nFix as dependent variables.Greenhouse–Geisser adjustments to the degrees of freedom were performed whensphericity could not be assumed (Mauchly’s sphericity test). Bonferroniadjustments were used in all pairwise comparisons. The partial eta-squared(pηN = 41) had attended to the faces during the encodingphase. The accuracy to faces tagged with three behavioural conditions washigh and statistically similar, F = .38,p = .684, pɳ2 = .01. The mean accuracy andSD were as follows: Cheaters 85% ± 2,Cooperators 86% ± 2 and Neutrals89% ± 2. The relatively high accuracy levels suggest that attentionalresources were deployed efficiently during the preceding face encodingphase.An easier and shorter version of the face recall test used in an earlierstudy  was usedd prime ord′) confirmed that the mean perceptual sensitivity tothe tagged faces was indeed similar in the three conditions,F = .37, p = .69,pɳ2 = .01 .A response sensitivity analysis  = .31, p = .73,pɳ2 = .01, which is in line with the recall accuracy for thosefaces. The mean pDT ( ± SD) for each behavioural conditionwere Cheaters (23% ± 5), Cooperators(23% ± 3) and Neutrals (23% ± 5).The overall time spent by the participants looking at the faces of cheaters,cooperators and neutrals was statistically similar,F = 1.04, p = .40,pɳ2 < .03.A 4 (quadrants) × 3 (tagged behaviours) ANOVA showed no significantinteraction between dwell time and the behaviours tagged to the faces ineach quadrant, F = .66, p = .52,pɳ2 < .02. A similar 2 (top × bottom hemifield) × 3 (taggedbehaviours) ANOVA revealed no significant interactions between pDT andbehaviours across the top and bottom hemifields,F = .62, p = . 54,pɳ2 = .02.The presence of left or right visual hemifield asymmetries was tested with a2 (left × right hemifield) × 3 (tagged behaviours) ANOVA. Although pDT inthe left hemifield tended to be higher than in the right hemifield, thedifference was not significant across behaviours,Most of the first saccades were made to the TL quadrant (78%). Only a few of thefirst saccades were directed to the TR (10%), the BL (8%) or the BR (2%)quadrants or to the fixation point (2%) at the centre of the screen.Since there were no reliable differences in eye movements to the faces taggedwith cheating, cooperative or neutral behaviours, those datasets were mergedaccording to the visual quadrants in which the faces were displayed. Thefindings with the aggregated dataset are presented later.F = 21.95, p < .001,pɳ2 = .36. Pairwise comparisons revealed thatparticipants looked longer at faces in the TL quadrant than in the TR(p = .03), BL (p < .001) or BR(p < .001) quadrants. The pDT in the BL and BRquadrants was similar (ps = 1)  . Figure F = 26.29, p < .001,pɳ2 = .40. The nFix for faces in the TL quadrant washigher than in the TR, BL and BR quadrants(p < .001). As observed with pDT, the nFix in the BLand BR quadrants was also statistically similar(ps = 1)  .F = 1.88, p = .18,pɳ2 = .05, even though the pDT in the left hemifield wasslightly higher than in the right hemifield.There was no difference in the mean pDT between the right and lefthemifields, F = 4.34,p < .04, pɳ2 = .10  = 57.43,p < .001, pɳ2 = .60  = 46.95,p < .001, pɳ2 = .55 .The upper left quadrant pDT and nFix advantage during face encoding is supported byneurophysiological studies. For example, Strong evidence supporting the current findings comes also from an assorted array ofbehavioural studies. VFAs have been associated with a multitude of different factors, from visuospatialattention and brain lateralization of face processing, to reading habits  and prioThe role played by the reading of the behavioural tags required before the encodingof each group of faces has not been directly addressed in this study, and a seriesof studies are under way to investigate the role of language in VFAs in eyemovements. Nonetheless, many studies showed an advantage of the upper hemifield inthe recognition of letters and words . Taking Informed consent to execute genetic analysis and to publish Videos have been obtainedby the patient’s guardian.


In children suffering from severe lower airway illnesses, respiratory virus detection has given good prognostic information, but such reports in the elderly are scarce. Therefore, our aim was to study whether the detection of nasopharyngeal viral pathogens and conventional inflammatory markers in the frail elderly correlate to the presence, signs and symptoms or prognosis of radiographically-verified pneumonia.N = 382) were prospectively studied as a cohort. Standard clinical questionnaire was filled by the study physician. Laboratory analyses included PCR diagnostics of nasopharyngeal swab samples for 14 respiratory viruses, C-reactive protein (CRP) and white blood cell count (WBC). Chest radiographs were systematically analysed by a study radiologist. The length of hospital stay, hospital revisit and death at ward were used as clinical endpoints.Consecutive episodes of hospital care of patients 65 years and older with respiratory symptoms (9/L (P = .006) and a CRP value over 80 mg/l (P < .05). A virus was detected in 30% of pneumonia episodes and in 40% of non-pneumonia episodes, but this difference was not significant (P = 0.09). The presence of a respiratory virus was associated with fewer revisits to the hospital (P < .05), whereas a CRP value over 100 mg/l was associated with death during hospital stay (P < .05). Respiratory virus detections did not correlate to WBC or CRP values, signs and symptoms or prognosis of radiographically-verified pneumonia episodes.Median age of the patients was 83 years (range 76–90). Pneumonia was diagnosed in 112/382 (29%) of the studied episodes. One or more respiratory viruses were detected in 141/382 (37%) episodes and in 34/112 (30%) episodes also diagnosed with pneumonia. Pneumonia was associated with a WBC over 15 × 10Among the elderly with respiratory symptoms, respiratory virus detection was not associated with an increased risk of pneumonia or with a more severe clinical course of the illness. CRP and WBC remain important indicators of pneumonia, and according to our findings, pneumonia should be treated as a bacterial disease regardless of the virus findings. Our data does not support routine virus diagnostics for the elderly patients with pneumonia outside the epidemic seasons.The online version of this article (10.1186/s12877-019-1125-z) contains supplementary material, which is available to authorized users. The number of over 80-year-old patients with weaning immune system is rising rapidly in Western societies, but the clinical significance of respiratory virus infections among this group remains unclear. The burden of pneumonia among the elderly is high as it includes significant morbidity, mortality and costs around the world . In the As polymerase chain reaction (PCR) and rapid antigen detection tests are increasingly available for respiratory virus detection , currentAmong the elderly, the all-cause mortality rate associated with respiratory viral infection increases with age and is approximately 6–7% among the over 85-year-old subjects , 13. In All of the aforementioned findings support a clinically meaningful role of virus diagnosis among the frail elderly. Thus, the aim of this study was to investigate how viral pathogens detected in the nasopharynx and conventional inflammatory markers (WBC and CRP) correlate to signs, symptoms and prognosis of pneumonia among the age group of 65 years and over. We hypothesized that virus detection could give clinically relevant information in addition to conventional inflammatory markers and chest radiograph findings in treating frail elderly patients.This prospective follow-up study investigated the association between virus detection and predefined clinical outcomes in elderly hospitalized patients. STROBE criteria were respected.The study was carried out in geriatric wards of the Turku City Hospital between July 2007 and April 2009 as part of a previously introduced study [Patients were considered to have respiratory symptoms if they had coryza, cough, sore throat, hoarseness or nasal stuffiness. In pneumonia episodes, the need for oxygen was considered as a sign of dyspnea. At study entry patients or their trustees were interviewed using a standardized questionnaire  by study physicians within 24 h of admission. The swabs were then stored in dry tubes in a refrigerator for a maximum of 24 h before transportation to the laboratory where they were stored at − 80 °C. The swab samples were analysed at the Department of Virology, University of Turku, Turku, Finland by a multiplex reverse-transcriptase (RT-)PCR test  for adenovirus, coronavirus NL63 and OC43, human bocavirus, human metapneumovirus (MPV), influenza A and B, and PIV1–3, and by using an ‘in-house’ RT-PCR test for RSV, RV - including rhinovirus type C - and enteroviruses (EVs) . Based oBlood samples for CRP and WBC analysis were routinely collected from all the patients as part of hospital treatment. The serum samples were stored at − 80 °C and analyzed by the hospital laboratory. The highest values measured during the hospital stay were used in statistical analysis.t-test, χ2 test and Fischer exact test (when counts < 5) were used when appropriate. Logistic regression with full model was used to analyse the association between clinical outcomes and virus etiology, pneumonia, chronic illnesses , age, gender and laboratory findings . Statistical significance was established at the level of P < .05. For statistics SAS Enterprise Guide 4.3  was used.In basic statistics, two sample P > .08, data not shown).A total of 921 episodes of hospital care were screened . The weight of the patients seemed to be lower in the group diagnosed with pneumonia (P = 0.05), whereas heart dysrhythmia seemed to be more common among patients not diagnosed with pneumonia (P = .03). In connection with the study episodes without pneumonia, in 16% the patient had a history of a stroke or transient ischemic attack (TIA) and in 16% the patient smoked, compared to 9.2 and 25% of the study episodes diagnosed with pneumonia, respectively. These differences were, however, not statistically significant . Otherwise cardiovascular, respiratory and other diseases were equally common in the study episodes diagnosed with pneumonia and the study episodes not diagnosed with pneumonia .Mean age of the patients in the study was 82.9 (sd 7.2) years Table . A diagnP = 0.09). Virus detection was also found not to be associated with dyspnea (P > .5), nor were there virus-specific differences found between study episodes diagnosed with pneumonia and with dyspnea, and study episodes diagnosed with pneumonia but without dyspnea (P > .1).A respiratory virus was detected in 141/382 (37%) nasopharyngeal swabs of the study episodes . As a categorical variable, a CRP value over 100 mg/l, or even over 80 mg/l, was associated with a pneumonia finding in the chest radiograph .When comparing study episodes diagnosed with pneumonia and with one or more respiratory viruses, and study episodes diagnosed with pneumonia but without respiratory viruses, no differences between WBC or CRP values were found . Also in connection with study episodes diagnosed with pneumonia, there was no association between the above mentioned clinical outcomes and laboratory findings (WBC over 15 × 109/L/l or CRP over 100 mg/l) (P > .2). In study episodes diagnosed with pneumonia and with dyspnea, death at ward was seen in 15% of the study episodes, whereas in connection to study episodes diagnosed with pneumonia but without dyspnea, the same was true in 5.3% of the study episodes, although this difference was not statistically significant (P = 0.07). However, study episodes diagnosed with pneumonia and with dyspnea lasted longer than study episodes diagnosed with pneumonia but without dyspnea (P = .02).In study episodes diagnosed with pneumonia, the presence of a respiratory virus was neither associated with clinical outcomes  nor with WBC values over 15 × 10P < .05, Table P = .04. Table No difference in the number of deaths at ward was seen between study episodes diagnosed with pneumonia and study episodes not diagnosed with pneumonia. A negative association was found between hospital revisit and virus detection; a revisit was less probable when a virus was present than when a virus was not present; 43 (31%) revisits occurred among the virus-positive study episodes and 93 (39%) revisits among the virus-negative study episodes