The high narrow sense heritability of carcass traits suggests that the underlying additive genetic potential of an individual should be strongly correlated with both animal carcass quality and quantity, and therefore, by extension, carcass value. Therefore, the objective of the present study was to detect genomic regions associated with three carcass traits, namely carcass weight, conformation and fat cover, using imputed whole genome sequence in 28,470 dairy and beef sires from six breeds with a total of 2,199,926 phenotyped progeny.MSTN in carcass performance was re-affirmed with the segregating Q204X mutation explaining 1.21, 1.11 and 5.95% of the genetic variance in carcass weight, fat and conformation, respectively in the Charolais population. In addition, a genomic region on BTA6 encompassing the NCAPG/LCORL locus, which is a known candidate locus associated with body size, was associated with carcass weight in Angus, Charolais and Limousin. Novel candidate genes identified included ZFAT in Angus, and SLC40A1 and the olfactory gene cluster on BTA15 in Charolais. Although the majority of associations were breed specific, associations that operated across breeds included SORCS1 on BTA26, MCTP2 on BTA21 and ARL15 on BTA20; these are of particular interest due to their potential informativeness in across-breed genomic evaluations. Genomic regions affecting all three carcass traits were identified in each of the breeds, although these were mainly concentrated on BTA2 and BTA6, surrounding MSTN and NCAPG/LCORL, respectively. This suggests that although major genes may be associated with all three carcass traits, the majority of genes containing significant variants  may be trait specific associations of small effect.Major genes previously associated with carcass performance were identified, as well as several putative novel candidate genes that likely operate both within and across breeds. The role of MSTN and NCAPG/LCORL, the majority of variance is attributed to the additive (and possibly multiplicative) effect of many polymorphisms of small effect.Although plausible novel candidate genes were identified, the proportion of variance explained by these candidates was minimal thus reaffirming that while carcass performance may be affected by major genes in the form of The online version of this article (10.1186/s12864-019-6071-9) contains supplementary material, which is available to authorized users. Profit in cattle production systems is a function of both revenue and costs of production. In beef cattle finishing systems, carcass value is the main revenue source which is dictated by both quantity  and quality . The eventual carcass phenotype realised (as with any phenotype) is a function of both the underlying genetic potential of the animal and the environment the animal has been exposed to. The high narrow sense heritability of carcass traits in cattle –3 suggesBos Taurus autosomes (BTA) 6 and 14, flanking the LCORL and PLAG1 genes, respectively, have been putatively associated with carcass weight in both dairy and beef cattle breeds [Despite the vast quantity of phenotypic cattle carcass data available, studies have identified relatively few quantitative trait loci (QTL) associated with carcass performance. Genomic regions on e breeds –10. To oe breeds . Associae breeds ; cattle e breeds ).The objective of the present study was to detect genomic regions associated with three carcass traits, namely carcass weight, conformation and fat cover, using imputed whole genome sequence data in 28,470 dairy and beef sires with phenotyped progeny; carcasses were assessed for conformation and fat score based on video image analyses. Detected genomic regions associated with each of the three carcass traits was compared within and across breeds. In addition, genomic regions associated with all three carcass traits were also identified.n = 2366), Charolais , Hereford (HE = 1216), Holstein-Friesian , Limousin  and Simmental . De-regressed estimated breeding values for three carcass traits were analysed including carcass weight, carcass fat and carcass conformation and genomic regions associated with all three traits were also examined. Strong regions of known association were detected for all three carcass traits in the more numerous breed populations and several putative novel candidate genes were also suggested.Genotypes of 41,389,526 sequence SNPs were imputed for 28,470 sires from six cattle breeds with a combined 2,199,926 progeny, and used to identify genomic regions associated with carcass-related metrics within and across breeds. The breeds represented included Angus  with carcass weight within the AA, CH, HF and LM populations  and carcass conformation (unadjusted p = 1.60 × 10− 76), explaining 1.21 and 5.95% of the genetic variation, respectively. However, the strongest association with carcass fat in the CH breed, rs41638272, was located 107 kb further upstream on BTA2. The QTLs containing each of these SNPs overlapped on BTA2 from 1.851 to 8.394 Mb and encompassed a total of 40 genes including MSTN. This genomic region on BTA2 was also significantly associated with each of the carcass traits in the LM population. The SNP, rs43285042 on BTA2, had the strongest SNP association within this QTL for each of the carcass traits in the LM but the allele substitution effect direction was opposite for carcass fat relative to both carcass weight and conformation. The proportion of genetic variance explained by rs43285042 in the LM population ranged from 1.34% for carcass weight to 4.35% for carcass conformation. Although BTA2 was strongly associated with each of the carcass traits in the LM population, it was BTA6 that contained the strongest associations with carcass weight in the LM population. In fact, 84.69% of the 2849 significant SNPs associated with carcass weight in the LM population were located on BTA6. These significant SNPs were primarily distributed across two QTL on BTA6; the first was from 32.210 to 33.884 Mb encompassing five uncharacterised genes and the second was further downstream from 37.463 to 42.711 Mb surrounding 22 genes including the LCORL/NCAPG locus. The latter QTL also overlapped significant associations detected in the AA and CH populations for carcass weight. Within the HF population, only QTL associated with carcass weight were identified. The strongest association in the HF was a downstream variant of PLAG1 (unadjusted p = 4.54 × 10− 8). In comparison to the other breeds, the positive alleles identified with the HF population were almost fixed in the majority of the QTLs; the most significant SNP within 16 of the 20 QTLs associated with carcass weight had a positive allele frequency > 0.99.The strongest SNP association detected within a breed was often the strongest association across two or all of the carcass traits. For example, in the CH population, the same SNP, rs799943285 an intergenic variant on BTA2, was the SNP with the strongest association with carcass weight  score of zero and was located in exon 2 in the transmembrane helix region where a leucine amino acid becomes replaced with a phenyalanine amino acid. The allele frequency of the positive allele (A), which was predicted to be deleterious according to the SIFT scoring system, was 2.07% within the AA population; a similar allele frequency was detected in the HF population (4.09%) whereas the A allele was only marginally segregating in the remaining beef breeds (< 1%). Of the 10 missense variants significantly associated with carcass weight in the CH population, all were located on BTA15 but only one, rs210125929 in the olfactory receptor OR5AK2, was deemed to be deleterious on protein function (SIFT score 0.01). The remaining 9 significant missense variants were located in either ENSBTAG00000014309 or ENSBTAG00000039331, both of which are olfactory receptor orthologues. Similar to carcass weight, variants within olfactory genes on BTA15 were also significantly associated with carcass fat in the CH population; the missense SNP, rs446111343, located in ENSBTAG00000038539 was strongly associated with carcass fat (unadjusted p = 2.36 × 10− 6) and had a SIFT score of 0.01. The sole significant missense variant identified in the HF population located within FBX032 was also deemed to be deleterious to protein function (SIFT score 0), although the frequency of the SIFT predicted deleterious allele (T) was low (0.27%) within the population.Genome annotation revealed multiple missense variants were significantly associated with all three carcass traits. A total of 3 significant missense variants were associated with carcass weight in the AA population, representing a 1.85 fold enrichment of missense variants among the significant variants as compared to what would be expected by chance  by undertaking a multi-breed genome-wide association across all 28,470 sires with breed fitted as a fixed effect. In the window-based analyses, the majority of the 10 kb windows harbouring a significant SNP  were unique to a single breed and only a small proportion of overlap was evident in more than one breed; no window was significant in all six breeds for any of the traits  identifying overlapping 10 kb windows that contained at least one SNP with an unadjusted MSTN gene. Two genomic regions on BTA6, the first from 0.45 to 0.53 Mb and the second at 90.92 Mb surrounding the pseudogene ENSBTAG00000032764 and MTHFD2L, respectively, and one region on BTA20 spanning from 24.95 to 29.97 Mb overlapping ARL15 were identified from the remaining shared windows between the CH and LM populations. Overlap across four breeds  was identified on BTA6, where 7 windows spanning from 38.67 to 39.02 Mb encompassing the NCAPG and LCORL genes were significantly associated with carcass weight. Relative to carcass weight or carcass conformation, proportionally more significant windows were unique to each breed for carcass fat; on average, 92.48% of windows detected across all breeds harbouring significant SNPs for carcass fat were unique, compared to 89.57 and 87.41% for carcass weight and conformation, respectively. Across all breeds, the HF population had the greatest percentage of unique significant windows across all traits; on average across traits, 97.71% of all significant windows identified in the HF population were only significant in the HF population, whereas the LM population had the lowest percentage of unique significant windows; 77.37% of significant windows identified in the LM population were unique to the LM population.The CH and LM populations had the greatest number of overlapping windows Fig.  with 316MSTN gene on BTA2  in ARL15. The lone SNP on BTA4 (rs137332278) strongly associated with both carcass weight and conformation was also a novel association and was located in the novel gene ENSBTAG00000031548, which is a member of the solute carrier family 23 vitamin C transporters. Similarly, a novel QTL on BTA21 spanning from 12.577 to 13.433 Mb encompassing the MCTP2 gene was identified to be associated with carcass fat. Variants within ARL15 and MCTP2 were moderately segregating within each of the breeds suggesting scope for selection exists, although rs137332278 on BTA4 was found to be fixed for the positive G allele in AA, CH and LM populations.Several strong associations were detected in the multi-breed analysis for each of the carcass traits, although the strongest association for each trait was a QTL encompassing the TA2 Fig. . The NCAp-value < 10− 4 were compared within breed across the three carcass traits. The degree of overlap across traits differed per breed, ranging from four (Holstein-Friesian) significant windows to 382 (Charolais) significant windows associated with carcass weight, fat and conformation , no genes were identified within the windows on BTA3 and 17, and GRB2 and HSPA12A were located within the windows on BTA19 and 26, respectively. Similarly to the LM population, 86.06% of the significant windows between carcass fat and conformation in the CH population were located on BTA2 from 0.00 Mb to 10.09 Mb; the remainder were located further downstream on BTA2 (from 21.68 to 25.68 Mb) as well as BTA17 (from 50.43 to 50.44 Mb and 66.47 to 66.49 Mb), BTA21 (from 47.98 to 47.99 Mb) and BTA28 (from 14.34 to 14.35 Mb).A greater percentage of overlap was detected between carcass fat and conformation in the CH and LM populations, while in the remaining breeds a higher percentage of overlap was detected between carcass weight and conformation. A total of five genomic regions were identified on BTA2, 3, 17, 19, and 26 from significant windows shared just between carcass fat and conformation within the LM population; 85.71% of these 98 shared windows Fig.  were locp-value ≤10− 4 for carcass weight and conformation had an opposite SNP effect direction on carcass fat   and contained a total of 9 genes.KEGG pathway analysis was completed within each breed for each trait and only pathways with an unadjusted p-value ≤0.05 are described in Additional file The ability to accurately identify when an animal may reach the desired carcass weight, subcutaneous fat level, and carcass conformation is desirable both from an economical and precision management perspective. Substantial genetic variability in the growth trajectories of young cattle has been previously reported by Englishby et al. , and sugMSTN were associated with all three carcass traits in the CH and LM populations and with carcass conformation in the AA population. The Q204X mutation (rs110344317), a stop-gain disruption within the myostatin gene, although not the strongest association within each of the QTLs, did explain up to 6% of the genetic variance in each of the carcass traits in the CH population . Allais et al. [MSTN variant which has also been shown to have a moderate muscle hypertrophy effect is the F94L mutation [p = 5.99 × 10− 10) and conformation (unadjusted p = 9.61 × 10− 13) in the CH population in the present study; this was again reflected in the mean EBVs of homozygous carriers (n = 150) which had a greater mean EBV for carcass conformation  and reduced fat  than non-carriers . The 150 homozygous carriers of the F94L mutation did not carry the Q204X mutation. Although the F94L mutation has been previously associated with increased carcass performance in the LM breed, this mutation was only significantly associated with carcass performance in the CH population in the present study due to poor imputation of the F94L SNP in the LM population (minimac r2 = 0.04). Similar to the Q204 mutation, the F94L mutation was only marginally segregating in the remaining beef breeds; this however still suggests that the targeted selection of the F94L and Q204X mutations for improved carcass performance through exploitation of the muscling hypertrophy phenotype within these breeds is feasible. Nevertheless, it is important to note that animals with the hypertrophy phenotype, on average, experience increased calving difficulty and reduced fertility [The role of myostatin on carcass merit for carcass traits has long since been established –18. In ts et al.  previousmutation . AlthougNCAPG-LCORL locus were associated with carcass weight in multiple breeds in the current study, corroborating results elsewhere in cattle studies [NCAPG-LCORL locus had a MAF > 0.002 in each of the AA, CH and LM populations and only two (rs109570900 and rs110251642) within NCAPG were of moderate significance in the AA population  while none were significant in the CH and LM populations; therefore it was not possible to infer the causative gene or mutation. While the strongest associations within the AA, CH, and LM populations were upstream of the LCORL gene, it is most likely that these associations are within enhancer regions of the NCAPG/LCORL complex and it is the expression quantity of the NCAPG/LCORL complex that is influencing carcass weight rather than a disruptive loss-of-function mutation.The strong known correlation between carcass weight and body size  implies  studies –24. Iden studies . In the p-value < 10− 4) with carcass weight in the HF population. Complete concordance however was not expected since the genetic correlation between height and carcass weight is just 0.69 (S.E. ± 0.06) [CCND2 on BTA5, has also been previously associated with height and insulin secretion in humans [PLAG1, a gene that has been well documented to be associated with stature in both humans [PLAG1, suggesting that carcass weight may be influenced by the expression quantity of PLAG1, a hypothesis also supported by Karim et al. [To further elucidate the genomic overlap between carcass weight and stature, we examined the 163 SNPs variants that were recently identified by Bouwman et al. , to expl ± 0.06) . The intn humans , whereash humans  and catth humans –29. Indem et al. .ZFAT gene on BTA14. ZFAT, which was the closest gene to the strongest associated SNP, has been previously associated with stature in both humans [ZFAT as a likely breed-specific candidate gene for carcass weight within the AA population. Further work is needed on understanding exactly how this zinc finger is involved in carcass weight but its role in the development of the hematopoieic system [ZFAT were moderately significant  and only two missense variants within ZFAT were segregating (rs483021047 and rs526028162), neither of which were significant in the AA population. As the strongest associations were located upstream of ZFAT, it is plausible that perhaps a regulatory region altering the expression of the zinc finger may be what is impacting its association with carcass weight. In addition, the gene TMPRSS11A was also identified as an AA-breed specific candidate association. Although, the role of TMPRSS11A, a transmembrane serine protease, in carcass weight is unclear, it was previously identified within a QTL associated with marbling score in Korean cattle [Within the AA population, a strong association for carcass weight was detected within 160 kb from the h humans , 31 and h humans , 33, andh humans , but mosh humans . The puth humans  and was c system  may be cc system , 38. In n cattle . The ideMSTN gene has been shown to associated with fat deposition [SLC40A1 which makes the protein ferroportin and is involved in iron absorption [SLC40A1 exhibited the second strongest association for carcass fat in the CH population  and further evidence was provided when a genomic window 16 kb upstream of SLC40A1 was one of the four overlapping windows identified between the AA, CH and LM populations for carcass fat . Increased body fatness in humans has been associated with increased expression of SLC40A1 [Although the position , it is asorption . The int SLC40A1  whereas  SLC40A1 .ENSBTAG00000035988, whose human ortholog is OR8K3, contained six significant downstream variants that were associated with both carcass weight and fat in the CH population and with carcass fat in the multi-breed analyses. This suggests that perhaps the expression quantities of ENSBTAG00000035988 is influencing carcass performance; the allele frequencies of the positive alleles in the downstream variants ranged from 0.27 to 0.56 within each of the six breeds analysed suggesting targeted selection is indeed feasible. Another candidate gene identified located further upstream on BTA15 associated with carcass performance was PRDM11. A QTL containing PRDM11 was associated with carcass weight in the multi-breed analysis and with both carcass weight and fat in the CH population. Although its role in carcass performance is unknown, a family member PRDM16 has been previously documented as a “master regulator” of brown adipocyte differentiation and has been associated with improved metabolic phenotypes in mice [Olfactory receptors and the olfactory transduction pathway have been previously associated with feed intake in both cattle , 45 and  in mice .SORCS1, ARL15 and MTCP2. SORCS1, which was associated with carcass weight in the present study, has been previously associated with obesity induced type 2 diabetes mellitus [SORCS1 and SORCS3 in knockout mice resulted in greater food intake, decreased locomotor activity, and increased adiposity [SORCS1, rs4210220 an intronic variant, explained 0.35% of the genetic variance in the multi-breed analyses in the present study and was moderately segregating within each breed; the allele frequency of the positive allele ranged from 0.12 in HF to 0.47 in CH. ARL15, which was associated with carcass conformation in the present study, and is expressed in insulin responsive tissues such as adipose tissue and skeletal muscle, has been previously documented to regulate circulating levels of adiponectin in humans [MTCP2 on BTA21 which was associated with carcass fat in the present study, has also been previously associated with body fat and abdominal fat in humans [Additional novel candidate genes identified from the multi-breed carcass analyses included mellitus , as wellmellitus . Loss ofdiposity . The mosn humans . Reducedn humans . Lastly n humans  and withn humans .MSTN and NCAPG/LCORL, the majority of variance is attributed to the additive (and possibly multiplicative) effect of many polymorphisms of small effect. In addition, the enrichment in the present study for non-coding variants  may be trait specific associations of small effect. However, it is important to note that the present study may not have been sufficiently powered to detect pleiotropic genes of larger effect.As genes that operate in the same pathway might be expected to show the same pattern of effects , it was MSTN and NCAPG/LCORL on carcass performance were re-affirmed in the present study, the proportion of variance accounted for these major genes was minimal and rather it is the effect of many breed specific polymorphisms of small effect that are attributing to the genetic variation underlying carcass weight, fat and conformation. Indeed, only a few significant genomic regions were common across the large breed populations investigated in the present study and those identified were mainly centred around the MSTN and NCAPG/LCORL major gene complexes. Nevertheless, some candidate genes such as SORCS1, MCTP2 and ARL15 that exhibited associations with carcass merit in multiple breeds were identified, indicating that a portion of the genomic variation attributed to carcass merit is common across breeds which may have implications for across-breed genomic evaluations. Lastly, only a small proportion of significant genomic regions were shared across all three carcass traits emphasising that the majority of significant variants (unadjusted p < 10− 4) identified in the present study were not only breed specific but also trait specific.Our results reveal that the genetic architecture of carcass performance is highly polygenic across all six breeds. Although the role of major genes such as The data used in the present study originated from a pre-existing database managed by the Irish Cattle Breeding Federation (ICBF). Therefore, it was not necessary to obtain animal care and use committee approval in advance of conducting this study.Cattle carcass weight in Ireland is measured, on average, 2 h after slaughter following the removal of the head, legs, thoracic and abdominal organs, internal fats, and hide. Carcass conformation and carcass fat grade are scored on the 15-point EUROP classification system from a video image analysis of each carcass; a carcass conformation score of 1 and a carcass fat score of 1 represents a poorly conformed carcass with little fat cover while and a carcass conformation score of 15 and a carcass fat score of 15 represents an excellently conformed carcass with considerable fat cover [Estimated breeding values (EBVs) for carcass weight, carcass conformation and carcass fat and their associated reliabilities were obtained from the ICBF database from the December 2017 national genetic evaluation for all dairy and beef bulls. In Ireland, genetic evaluations for carcass traits are estimated using a multi-trait, multi-breed mixed model. Heritability estimates used in the national genetic evaluations were 40% for carcass weight, 35% for carcass conformation and 32% for carcass fat. Carcass phenotypes on 6,360,190 animals were included in the genetic evaluation with an associated pedigree file of 14,785,918 animals.Of the animals with EBVs, only purebred (i.e. ≥87.5% of a single breed) genotyped sires with ≥5 carcass weight progeny records for any of the following breeds were retained for analysis; AA, CH, HE, HF, LM and SI. The effective record contribution (ERC) of each sire, taking into consideration what animals were genotyped, was estimated using the Harris and Johnston  method an = 717; 54,001 SNPs), Illumina High Density , or the custom Irish Dairy and Beef (IDB) V1 , IDBV2  or IDBV3  genotype panels. Prior to imputation to whole genome sequence (WGS), each of the 638,662 genotyped animals had a call rate ≥ 90% and only autosomal SNPs, SNPs with a known chromosome and position, and SNPs with a call rate ≥ 90% were retained within each panel.All 28,470 sires with carcass phenotypes were imputed to whole genome sequence as part of a larger dataset of 638,662 genotyped animals from multiple breeds. Each of the 28,470 sires included in the present study were genotyped on a variety of genotyping panels including the Illumina Bovine SNP50 . All imputed sequence SNPs, scored as 0, 1 or 2, were included individually as a fixed effect covariate in the model one at a time. The equation for the whole genome association analysis is detailed below;μ is the mean term, b is fixed effect of the candidate SNP to be tested for association, x is the vector of imputed genotypes, g ~ N and as such the p-values from the multi-breed analyses were adjusted accordingly. The Benjamini and Hochberg method assuming a false discovery rate of 5% was used to correct for multiple testing; the number of tests assumed was equal to the number of SNPs across the entire genome and differed slightly per breed from 15,409,084 to 18,863,675. The proportion of the genetic variance in each carcass trait attributable to individual SNPs was calculated as 2pqa2/σ2, where p was the major allele frequency, q was the minor allele frequency, a was the estimated allele substitution effect and σ2 was the genetic variance for the phenotype under investigation.where http://ensemble.org) and NCBI map viewer (http://www.ncbi.nlm.nih.gov/mapview) on the bovine UMD 3.1. Candidate genes were chosen from QTL based on previous literature and their biological function. If no gene resided in the QTL region, genes within 250 kb of the start and end position of the QTL, were considered as putative candidate genes. Previously reported cattle QTL were obtained from the animal QTLdb .Within each of the analyses, QTL regions were defined as all regions where a minimum of three SNPs were significantly associated with the trait of interest following adjustment for multiple testing using the Benjamini and Hochberg approach with a false discovery rate of 5%, and that resided within 500 kb of each other. Genes within and overlapping each QTL were identified using Ensembl  v.6.8. P-values were calculated by EASE (an adoption of the Fisher Exact test to measure the gene-enrichment in annotation terms) and Benjamini-Hochberg was used to correct for multiple testing.To identify over-represented pathways associated with carcass performance within each breed, all genes containing SNPs with a non-adjusted p-value < 10Additional file 1:Quantitative trait loci (QTL) associated with carcass weight. All QTL significantly associated with carcass weight within six breeds after adjustment for Benjamini and Hochberg multiple testing. (DOCX 37 kb)Additional file 2:Quantitative trait loci (QTL) associated with carcass fat. All QTL significantly associated with carcass fat within six breeds after adjustment for Benjamini and Hochberg multiple testing. (DOCX 27 kb)Additional file 3:Quantitative trait loci (QTL) associated with carcass conformation. All QTL significantly associated with carcass conformation within six breeds after adjustment for Benjamini and Hochberg multiple testing. (DOCX 29 kb)Additional file 4:p value <0.05) associated with carcass weight, fat and conformation in six breeds. All KEGG pathways associated with carcass weight, fat and conformation identified from using all genes containing a SNP with an unadjusted p-value of < 10− 4 from each of the respective association analyses in each breed. (DOCX 24 kb)Significant pathways (


The multiple disease domains affected in psoriatic arthritis (PsA) may make composite endpoints appropriate for assessing changes in disease activity over time. Tofacitinib is an oral Janus kinase inhibitor for the treatment of PsA. Data from two phase 3 studies of patients with PsA were used to evaluate the effect of tofacitinib on composite endpoints.Oral Psoriatic Arthritis triaL (OPAL) Broaden was a 12-month study of tumor necrosis factor inhibitor (TNFi)-naïve patients with an inadequate response to at least one conventional synthetic disease-modifying anti-rheumatic drug; OPAL Beyond was a 6-month study of patients with inadequate response to TNFi. Patients with active PsA received tofacitinib 5 or 10 mg doses twice daily (BID), adalimumab 40 mg subcutaneous injection once every 2 weeks (OPAL Broaden only), or placebo advancing at month 3 to tofacitinib 5 or 10 mg BID. The disease-specific composites were Psoriatic Arthritis Disease Activity Score (PASDAS), Disease Activity Index for Reactive Arthritis/Psoriatic Arthritis (DAPSA), and Composite Psoriatic Disease Activity Index (CPDAI). Change from baseline in composite endpoints was also assessed for minimal disease activity (MDA) responders versus non-responders.P ≤0.05) were seen with both doses of tofacitinib versus placebo for all endpoints except CPDAI for tofacitinib 5 mg BID in OPAL Broaden. Effect sizes generally increased from 3 to 6 months. Mean changes from baseline were greater in MDA responders than MDA non-responders for all composite endpoints across all time points and treatments.Overall, 422 patients from OPAL Broaden and 394 patients from OPAL Beyond were treated. The mean changes from baseline to month 3 for tofacitinib 5 mg BID, tofacitinib 10 mg BID  were OPAL Broaden: PASDAS, −2.0 , −2.4 ; DAPSA, −20.2 , −24.4 ; and CPDAI, −2.9 , −4.2 ; OPAL Beyond: PASDAS, −1.9 , −2.1 ; DAPSA, −22.5 , −21.0 ; and CPDAI, −3.3 , −3.4 . Greater changes from baseline to month 3  contains supplementary material, which is available to authorized users. Psoriatic arthritis (PsA) is a chronic, systemic, immune-mediated inflammatory disease with multiple disease manifestations, including peripheral arthritis, enthesitis, dactylitis, spondylitis, and psoriatic skin and nail disease –3. OwingA number of composite endpoints have been developed for PsA in order to assess multiple aspects of disease activity and identify patients who have achieved treatment targets of remission or minimal disease activity (MDA). Available instruments incorporate different types of assessments, including clinical , laboratory , and patient-reported outcome (PRO)  endpoints. Although there is no clear agreement on a standardized composite assessment approach that provides the optimal combination of individual variables , agreemeTofacitinib is an oral inhibitor of the Janus kinase (JAK) family for the treatment of PsA. Tofacitinib preferentially inhibits signaling via JAK3 or JAK1 (or both) with functional selectivity over JAK2 . The effDetails of patient populations and study designs for both OPAL Broaden  and OPAL Beyond  have been published in detail ); SJC 66 joints) and TJC (68 joints); Leeds Enthesitis Index (LEI) score; tender dactylitic digit score; physical component summary (PCS) score of the 36-item short-form survey version 2 ; and CRP (in milligrams per liter)  comprised all patients who were randomly assigned to the study and received at least one dose of study medication. Changes from baseline analyses were based on a repeated measures model, without imputation for missing values in the FAS, with the fixed effects of treatment, visit, treatment-by-visit interaction, geographic location, and baseline value; an unstructured covariance matrix was used. For results up to month 3, patients randomly assigned to the two placebo sequences were combined into a single placebo group. The repeated measures model included data from all visits up to month 3 for the treatment groups of tofacitinib 5 mg BID, tofacitinib 10 mg BID, adalimumab 40 mg SC Q2W (OPAL Broaden only), and placebo. For results beyond month 3 to the end of study, the two placebo sequences were analyzed separately. The calculation of effect sizes and standardized response means for treatment groups of tofacitinib 5 mg BID, tofacitinib 10 mg BID, and adalimumab (OPAL Broaden only) at months 3, 6, and 12 (OPAL Broaden only at month 12) was based on patients with greater than or equal to 3% baseline psoriasis body surface area (BSA) in the FAS in order to permit comparison based on the same set of patients, with no missing values for any of the three disease-specific composite endpoints at baseline or months 3, 6, and 12 (OPAL Broaden only at month 12).The effect size for a given composite endpoint at a time point was defined as (mean at baseline – mean at time point)/(standard deviation [SD] at baseline). The standardized response mean for a given composite endpoint at a time point was defined as (mean at baseline – mean at time point)/(SD of change from baseline at time point). Effect size and standardized response mean are unitless measures and are adjusted for the endpoints’ variability, which allows comparisons to be made. For both effect sizes and standardized response means, levels of responsiveness have been proposed as small (≥0.20 to <0.5), moderate (≥0.50 to <0.8), and large (≥0.80), respectively , 14.In order to investigate the relative strength of the composite endpoints in predicting MDA response at a given time point, multiple logistic regression was used to model MDA response as a dependent variable and the mean changes from baseline of the three disease-specific composite endpoints at the same time point as predictors. The estimated slope coefficient from this regression model is the change in log-odds of MDA response resulting from a 1-unit increase in change from baseline of the composite endpoint. It represents the strength of association between the composite endpoint and MDA response and is standardized  to adjust for the variability of the composite endpoint to permit comparison of their associations with MDA response. In order to compare the correlations of the three disease-specific composite endpoints with MDA response, another standardized measure related to STB above, called logistic pseudo partial correlation , was also calculated . A valueThe PASDAS response rate was calculated at months 3, 6, and 12 (OPAL Broaden only for month 12) as the percentage of patients who had a good response  . Non-resP values are reported for comparisons with placebo in repeated measures model analyses and for testing slope coefficients in multiple logistic regression analyses without adjustment for multiplicity. The significance level was set at two-sided, less than or equal to 0.05.The derivation of the composite endpoints was pre-specified in the original study protocols and statistical analysis plans; except for analysis using a repeated measures model, all analyses were performed post hoc. The FAS comprised 422 patients from OPAL Broaden and 394 patients from OPAL Beyond  at baseline was 4.38 to 4.67 across studies and treatment groups . The effect size for the composite endpoints was highest for PASDAS across all treatment groups at months 3, 6, and 12 in OPAL Broaden and months 3 and 6 in OPAL Beyond and was lowest for DAPSA across treatments, studies, and time points, with the exception of the adalimumab group in OPAL Broaden at month 3, in which the effect size for both DAPSA and CPDAI was 1.05  in the GRACE study ; mean CPThe DAS28–3(CRP) was included for comparative purposes only. Baseline DAS28–3(CRP) scores were somewhat higher than the mean DAS28–3(CRP) score of 3.96 observed for patients changing treatment (a surrogate for high disease activity) in the GRACE study ; howeverAll reported effect size and standardized response mean values were greater than 0.80, the value generally taken to indicate a large treatment effect or response . The larThe definition of MDA using the criteria applied in this analysis and in previous tofacitinib publications , 10 has The differing findings with respect to tofacitinib treatment for the three disease-specific composite endpoints considered in this analysis could have resulted from the different composition of the endpoints evaluated. The PASDAS and CPDAI both include assessment of the skin manifestations of PsA  and the severity of enthesitis and dactylitis as well as TJC and SJC. DAPSA, however, is focused on TJC and SJC, with no consideration of skin disease, enthesitis, or dactylitis and an arthritis-focused global score. The PASDAS and CPDAI also both incorporate PROs; the PASDAS incorporates the PCS score of the SF-36v2 acute, and the CPDAI the DLQI and ASQoL. In this analysis, the PASDAS appeared to be the most sensitive to improvements in the signs and symptoms of PsA related to treatment with tofacitinib and adalimumab; the effect size observed with the PASDAS was higher than for any other endpoint at all time points in both studies. The ability of the PASDAS to detect change in these two studies might reflect the components of the measure; skin manifestations, enthesitis, dactylitis, and PROs all appeared to be sensitive to treatment-related changes in OPAL Broaden and OPAL Beyond, although the adoption of a hierarchical testing scheme for key secondary endpoints precluded demonstration of significance for all measures and time points , 10. TheIt is clear from these analyses that PASDAS has superior performance in this context and it has already been reported that the consensus view is that PASDAS should be the outcome measure of choice in PsA clinical trials . The DAPP values reported for comparison with placebo should be considered nominal.This analysis had a number of limitations. The OPAL Broaden and OPAL Beyond studies were not designed for evaluation of the composite endpoints’ longitudinal validity and sensitivity to change. In addition, for the calculation of effect size and standardized response mean, only patients with greater than or equal to 3% psoriasis BSA affected at baseline were included, with no missing values of the composite endpoints across multiple visits. Consequently, patient numbers were relatively low in some cases; CPDAI data were available for only 63% and 52% of patients receiving tofacitinib 10 mg BID in OPAL Broaden at month 12 and OPAL Beyond at month 6, respectively, and effect size and standardized response mean were calculated in only 47–64% of patients. Also, there was no adjustment for multiplicity; therefore, the Overall, while the merits of specific composite measures for assessing PsA status are under discussion , this evAdditional file 1:Table S1. Least squares (LS) mean change from baseline in composite endpoint scores . (DOCX 44 kb)Additional file 2:Table S2. Change from baseline in composite endpoint scores by minimal disease activity (MDA) response status across studies. (DOCX 39 kb)


Our laboratory has the fundamental responsibility to study cancer stem cells (CSC) in various models of human and animal neoplasms. However, the major impediments that spike our accomplishment are the lack of universal biomarkers and cellular heterogeneity. To cope with these restrictions, we have tried to apply the concept of single cell analysis, which has hitherto been recommended throughout the world as an imperative solution pack for resolving such dilemmas. Accordingly, our first step was to utilize a predesigned spiral microchannel fabricated by our laboratory to perform size-based single cell separation using mast cell tumor (MCT) cells as a model. However, the impact of hydrodynamic shear stresses (HSS) on mechanical cell injury and viability in a spiral microchannel has not been fully investigated so far. Intuitively, our computational fluid dynamics (CFD) simulation has strongly revealed the formations of fluid shear stress (FSS) and extensional fluid stress (EFS) in the sorting system. The panel of biomedical assays has also disclosed cell degeneration and necrosis in the model. Therefore, we have herein reported the combinatorically detrimental effect of FSS and EFS on the viability of MCT cells after sorting in our spiral microchannel, with discussion on the possibly pathogenic mechanisms of HSS-induced cell injury in the study model. A neoplasm is composed of heterogeneous cell subpopulations ,2 in whiTheoretically, neoplastic cells in a given neoplasm are not biophysically identical, particularly their diverged sizes. Fortunately, many studies have potentially suggested that the sizes of putative cancer stem cells in a given cancer may be smaller than 10 μm (average at 5–7 μm); meanwhile, the major constituent cells—terminally differentiated cancer cells—are frequently larger than 10 μm ,7,8. NotA blessing in disguise, microfluidics has recently been developed and introduced throughout the world as an attractive means for label-free single cell separation . This inEven though the active processes are precisely controllable and somewhat sensitive, their applications are frequently unfavorable because of their low-throughput performance and the external force field application and multiplex auxiliary system required. These might result in an increased complexity of device fabrication. Moreover, the residential time of the sorted cells in the microchannel is usually prolonged. Thence, they can consistently accumulate more stresses from the external fields ,16. On tFor passive microfluidic cell separation, an inertial cell focusing on secondary flow fields, in particular Dean Vortices, is the most usable methodology. A plethora of scientific evidence has demonstrated the successfulness of its application worldwide. For instance, Kuntaegowdanahalli and colleagues employed an Archimedean spiral microchannel for sorting 15 μm SH-SY5Y-neuroblastoma cells from 7 μm C6-rat-glioma cells , as well−1 [In addition, our preliminary studies have substantially demonstrated the efficacy of our predesigned Archimedean spiral microchannel in the separation of multiple-sized polystyrene microspheres, intact canine blood cells and feline leukemic cells at the optimal flow rate of 1 mL·min−1 ,20, and −1 .Although most of the studies would indicate the powerful performance of a spiral microchannel in inertial microfluidic cell separation, they have almost always emphasized the effects of curvilinear geometry and the physics of fluid flow on focusing resolution instead of the underlying dreadful factors , especiaBecause of the lack of cell walls, all mammalian cells under a passive microfluidic platform are prone to be mechanically damaged after experiencing hydrodynamic shear stresses (HSS) ,27,28. H−1 [For a spiral microchannel, several studies have suggested that the viability of a sorted cell is greater than 80% ,30. Howe−1 .Fundamentally, canine cutaneous mast cell tumors (MCT) are a member of canine round cell tumors. It implies that their morphological feature is uniquely round and close to the ideal cells or polystyrene microbeads commonly engaged for testing the theoretical functionality of a spiral microchannel. However, cellular pleomorphism may occur in the high-grade ,33. TherBesides, our laboratory has currently been endeavoring to establish a label-free passive microfluidic platform which is capable of tracking and isolating putative CSCs depending on their sizes in various human and animal study models. Furthermore, based on our supposition, the platform must (1) be high-throughput and highly sensitive ,34,35,  will have been awakened [D = 3πμUDeanap or 5.4 × 10−4πμDe1.63ap; where μ, UDean, ap, and De are the viscosity of the fluid media, the Dean velocity (UDean = 1.8 × 10−4 De1.63), the diameter of each microparticle, and the Dean Number, respectively.Fundamentally, inertial focusing under microfluidic platforms utilizes the intrinsic hydrodynamic effect of one or more fluid sheaths flowing around microparticulates, including cells, for aligning them into two-dimensional (2D) bands or three-dimensional (3D) single streamlines ,36. For awakened . They fuL) generated from the inner walls. This brings about the backward migration of the cells to the microchannel centre. Basically, the inertial lift forces are equated with the following parameters: FL = ρG2CLap4; where ρ, G, and CL are the fluid density, shear rate, and lift coefficient, respectively [D and FL are equipoising. It causes the cells to entrain into a focusing streamline. Since two counterbalanced forces and cell diameters handle the stream focusing, larger cells will reach their stabilized locations closer to the inner walls than smaller cells [p) and the microchannel height (H), expressed by ω = ap/H [When the cells have been moving closer to the inner walls, they are counteracted by inertial repulsion forces , was poured on the mold and was allowed to cool down. The PDMS replica was peeled off and bounded on a 5 cm × 7.5 cm glass slide with oxygen plasma. The inlets and the outlets of the microdevice were drilled using a metal punch and bonded to silicone tubes glued by liquid PDMS.TM, Osaka, Japan), a 20G3/4 intravenous catheter (NiproTM), a 20 cm silicone tube , and a 21G1 metal connector. Both syringes were simultaneously empowered by a two-port programmable automatic syringe pump . The optimal flow rate used in this study was at 1 mL·min−1 based upon the previous studies [The instrumentation of the microdevice initiated with the connection of the inlets to the feeding conduits: one for cell suspension and the other for the fluid buffer. Each feeding conduit was systematically composed of four separated portions: a 3 mL plastic syringe with a lure tip , under the finite element method. The predilection parameters of the fluid were selected from the software; meanwhile, the equations for the simulation were manually determined as following; FSS = μ(∂v/∂z + ∂w/∂y) and EFS = 3μ(∂u/∂x). The computational fluid dynamics (CFD) were performed at the first loop of the spiral microchannel. The simulations were grid-dependent. The spiral loop was modeled with a 5 × 106 controlling meshwork. Moreover, the fluid medium was supposed to be incompressible Newtonian fluid with a batch of physicochemical properties of density at 1000 kg·m−3 and a dynamic viscosity of 1.003 × 10−3 kg·m−1·s−1 at 20 °C. A symmetrically non-slipped boundary condition was used for establishing the flow condition. The flow rate was 1 mL·min−1, and the characteristic of the flow was uniformity. The simulations were performed at the final loop of the spiral microchannel. The magnitudes of FSS and EFS were calculated by the software at the outlet and at the curvatures of the spiral microchannel, respectively [In the study, we systematically operated computerized simulations to predict the formations of two main hydrodynamic stress components: fluid shear stress (FSS) and extensional flow stress (EFS). The simulation was performed using a modeling software, COMSOL Multiphysicsectively .To reduce the bias on cell vulnerability, freshly single MCT cells were noninvasively isolated and harvested from MCT masses removed by operative interventions at the Faculty of Veterinary Science, Chulalongkorn University, during 2015 to early 2016. Stepwise, one gram of each lean MCT tissue was chopped into tiny cubes (approximately 2 mm × 2 mm × 2 mm). Each diminutive tissue specimen was further collected in a 2 mL centrifuge tube and was trypsinized (200 μL of 0.025% Trypsin and 0.01% EDTA in 500 μL PBS) at 38 °C for 20 min. The reaction was then terminated with 1 mL of 2% fetal calf serum (FCS). Undigested tissue remnant was filtered out via a 30 μm cell strainer. The cell flow-through of each was pooled together into a 15 mL centrifuge tube and was centrifuged at 885 RCF  for 5 min. The cell pellet was then washed with phosphate-buffered saline (PBS) and was centrifuged at 885 relative centrifugal force (RCF) for 5 min at room temperature twice. Finally, single MCT cells were re-suspended in PBS. In addition, one-third of each mass was sent to the Department of Pathology, Faculty of Veterinary Science, Chulalongkorn University, for routine histopathology.Based on our preliminary study, the result has revealed the distributive trend of MCT cells after sorting as shown in MCT cells from each outlet were then obtained and used for systemic viability assays. The appraisal criteria were composed of (1) cell morphology, (2) cell membrane integrity, and (3) cell functionality as previously described in the literature . It is nMCT cells were re-suspended with 1 mL PBS. Further, 35 μL of the cell suspension was smeared onto a clean glass slide. The specimen was stabilized with a cover slip. Cell configuration was visualized under plain light microscopy at the high power field (40×), and data acquisition was performed with the following interpretative benchmarks.Morphometrically, the cell morphology index (CMI), the ratio of the width and the length of MCT cells, was measured. This evaluative parameter principally indicates the ability of non-degenerated MCT cells to cope with mechanical induction factors, including hydrodynamic shear stresses, by allowing them to be elongated and recoiled. Ideally, the CMI is equal to one in normal MCT cells; meanwhile, the index is zero (unmeasured) when MCT cells are necrotized despite cell wreckage. In the case of cell deformation (benign degeneration), the index would fall into the range in between zero and one.To verify membrane disintegration, we performed scanning electron microscopy , a measurement of DNA content leakage, and a trypan blue exclusion assay under their standard protocols to test membrane discontinuity.4) in 0.1 M CAC buffer (pH = 7.3) for 1 h. The cells were then serially dehydrated by graded ethanol , a series of three mixtures of ethanol-iso-amyl acetate at the ratio of 3:1, 1:1, and 1:3, and pure iso-amyl acetate. Several drops of cell suspension were transferred onto 10mm aluminum membrane dishes and allowed to settle for 2 min. Finally, the cells were dried in CO2 in a critical point dryer, and the specimen dishes were attached to aluminum stubs by carbon–propanol glue and coated with gold in a sputtering device. The MCT cells were examined with a Scanning Electron Microscope  at the acceleration voltage of 15 kV [Scanning electron microscopy (SEM) was performed under a recommended protocol previously used for the study of chicken leukocytes. Briefly, single MCT cells were washed twice and re-suspended in 1 mL Hank’s balanced salt solution (HBSS). They were fixed in 1.2% glutaraldehyde in 0.1 M sodium cacodylate (CAC) buffer (pH 7.3) for 1 h and washed twice in 0.16 M CAC buffer (pH = 7.3). They were further post-fixed in 1% osmium tetroxide (OsOof 15 kV . The pos260) of DNA was measured by a spectrophotometer at the wavelength of 260 nm (A260). The DNA concentration was calculated back by the following equation: dsDNA concentration = 50 μg/mL × OD260 × dilution factor.Leaky DNA owing to cell rupture or an increment increase in membrane permeability was measured with standard spectrophotometry. Briefly, MCT cell suspension from each outlet was harvested after sorting and centrifuged  at 394 RCF for 5 min. The bias of protein effect on optical density (OD) was reduced by purifying DNA in the supernatant using a commercial DNA extraction kit . Stepwise, the supernatant was transferred onto a silica membrane-bounded collection tube and centrifuged at 10,000 RCF for 1 min. DNA on the silica membrane was washed with 500 μL TD-2 washing solution and centrifuged at 10,000 RCF for 1 min twice. Ultimately, DNA was eluted by incubating with 50 μL TD-3 eluting solution and centrifuging at 10,000 RCF for 1 min. The purified DNA solution was then diluted with deionized (DI) water at the ratio of 1:100. The optical density  in 200 μL PBS at room temperature for 5 min in a dark chamber. The cells were twice washed with PBS and centrifuged at 885 RCF for 10 min.For cell permeability, a trypan blue exclusion assay was utilized. Stepwise, 300 μL MCT cell suspension was aliquoted to a new 2 mL collection tube. The cells were incubated by 500 μL of 0.4% Trypan blue  cationic probe . Briefly, 200 μL of MCT cells in PBS was incubated with 1 mL of 10 μM TMRM in PBS at 25 °C for 30 min. Excessive dyes were flushed out from the suspension by PBS. The fluorescent signals were acquired under fluorescent microscopy with a tetramethylrhodamine (TRITC) filter. Basically, active mitochondria in healthy MCT cells usually stain with TMRM; meanwhile, only cortical mitochondria are positive for TMRM in case of degenerated and necrotic MCT cells.Under standard photoresist-soft-lithography, we had successfully fabricated our predesigned Archimedean spiral microchannel (ASM) with polydimethylsiloxane (PDMS). The geometrical configuration of the microdevice consisted of a five-turned curvilinear rectangle microchannel with a constant interspace between two adjacent loops of 500 μm. At the cross-section, the dimension of the microchannel was 500 μm in width and 130 μm in height. The instrumentation is schematically illustrated in Principally, unsorted MCT cells were non-degenerated cells. Histopathological sections disclosed that all neoplastic masses in this study are canine cutaneous mast cell tumors a. By tryCytologically, non-degenerated MCT cells are round cells b; howeveHowever, some MCT cells may contain binuclei or even polynuclei as well. The nucleus-to-cytoplasm (N/C) ratio varies from 1:1 to 2:1. Individually, the sizes are often varied from 6 to 15 μm; however, aberrantly non-degenerative MCT cells may be larger than 15 μm ,46. WithStep by step, our CFD distinctly exhibited the rising up of fluid shear stress (FSS) and extensional fluid stress (EFS) in our predesigned spiral microchannel. At the selected flow rate, the computational simulation exhibited the generation of FSS at the upper and the lower perimeters of the microchannel a. Meanwh4 cells/mL. Obviously, the cells were morphologically round and solely separated. The cellular contour was normal in range as previously described  patently asserted membrane discontinuity and cell morphology. Basically, viably non-degenerated MCT cells were obviously round. Their cell membranes were completely integrated and covered by integral proteins on their external surfaces a,b. In cSpectrophotometrically, the measurement of leaky DNA in supernatant clearly implied cell membrane disintegration and cell wreckage. Before cell sorting, the mean concentration of leaky DNA was 5200 ± 928.72 μg/mL and its coefficient of variation (CV) was 17.85. However, it was approximately twofold after sorting: 12,470 ± 2080.41 μg/mL with the CV of 16.68 .m) were stabilized by proton transfers between their matrices and intermembrane spaces in association with succinate and ADP [Ultimately, with the lipophilic cation TMRM fluorophores, the viability probed by the functionality of the sorted MCT was assessed. Distinctly, the fluorescent signals were positive throughout the cytoplasm in viably unsorted MCT cells, indicating that most mitochondria were actively functioning a. Accord and ADP ,48,49. T and ADP . The unsIndubitably, live cells always provide valuable information that aids our understanding of how cells maintain their homeostasis after receiving external and internal stimuli. Under the disease process, all abnormality initiates from an alteration or a dysfunction of cells and/or subcellular components. Therefore, any information on the intricate biology of diseasing cells is indispensable. The loss of viable cells incurs the wasting of critical information on the biological mechanisms underlying diseases. As well as being at the heart of our microfluidic sorting system, the viability of MCT cells is therefore the crucial parameter for determining the system’s efficacy. To encourage this presumption, we had utilized a panel of biomedical methods for assessing the viability of the sorted MCT cells systematically.Fundamentally, mammalian cells are liable to: (1) degenerate or necrotize under high-grade hydrodynamic shear stresses according to the lack of cell walls ; and (2)For instance, the high-level fluid shear stress in a bioreactor can cause the sloughing of cultivated cells from their bases resulting in cell degeneration and necrosis . In addiOur postulation in this study was that both FSS and EFS would take place in our ASM sorting system. Intuitively, our CFD simulations substantially indicated the formation of FSS at the upper and lower boundaries of the spiral microchannel. Meanwhile, EFS was simultaneously observed at the lesser curvature of the microchannel. With respect to the study consequence, the hemocytometry enumeration ratified the loss of MCT cells in the system. The result unveiled that the MCT cell concentration after sorting sharply dwindled as compared to that before sorting. Approximately forty percent of the cells vanished in the spiral microchannel itself. Taken together, the combined effect of EFS and FSS might originate in the spiral microchannel and affect the viability of the sorted MCT cells.The pathogenesis of hydrodynamic shear stress-induced cell damage in passive microfluidics is currently unclear. However, to the best of our knowledge, fluid shear stress (FSS) and extensional flow stress (EFS) are the critically influential parameters involved in HSS cell injuries. Ordinarily, mammalian cells are physiologically able to adapt themselves to cope with many mechanical injuries, including hydrodynamic shear stresses, by altering their intracellular components, such as cytoskeletal proteins, and/or the expressions of metabolic and survival genes. Meanwhile, under several pathological conditions, such as neoplasia, diseasing cells may lose these adaptive abilities and become ready to degenerate or necrotize.Owing to this study, this consequence could explain some of the pathogenesis of HSS-induced cell injury. The first consideration is the competence of hydrodynamic shear stresses to induce the reorganization or disarrangement of cytoskeletons suddenly. This is a common cause of cellular deformity. Nevertheless, some deformed cells can recover spontaneously if they have not been across the point of no return ,57. In a−3 –10 × 10−3 N/m), and the membrane tension (Tm), a relative force applied to extend and deform the cell membranes of intact mammalian cells, is also small [Probably, the secondary pathologic explanation is that of the disintegration of plasma membranes. Basically, cell membranes are composed of phospholipid bilayer domains. The continuity of cell membranes is maintained by the inherited hydrophobic bonds, referred as Van der Waals forces, of nearby long-chain fatty acids in the phospholipids, hydrogen bonds, and electrostatic bonds originating in the charged amino acid residues of transmembrane proteins. Biophysically, the shear modulus of plasma membranes is low as a result of bilayer fluidity  explicitly unveiled lipid raft disintegration leading to membrane perforation in sorted MCT cells. Furthermore, the devastation of basic membrane components, including high-molecular-weight (HMW) integral protein, was an outstanding feature as well. Membrane perforation inevitably predisposed to cells to an increased permeant of water and trypan blue influx, followed by cell swelling, metabolic disturbances, and cytoplasmic protein staining with trypan blue. Furthermore, the efflux of macromolecules, such as DNA, in spite of the membrane defect was also observed. Hence, leaky DNA was a crucial investigative parameter. Normally, it relates to not only membrane perforation but also cell wreckage. As in our MCT case, the leaky DNA was increased by approximately two-fold when compared to the positive control.The other influential factors that should be considered are the magnitude and the exposure time of both hydrodynamic shear stresses . For exaNevertheless, in one other study, colorectal adenocarcinoma cells (COLO 205) had been injected out of a syringe through the needle, and they were dead in the system owing to the tremendously high FSS of 75–600 Pa. Undoubtedly, at these FSS scales, the cells could not endure the shear stresses and then underwent cell necrosis ,61. BasiLast but not least, a cell species itself also involves hydrodynamic cell death. Fundamentally, the type of cell is the first determinant of cellular resistance to FSS and EFS at different levels. In our preliminary study, the results have suggested that canine leukocytes were much less vulnerable than MCT cells under the same sorting procedure (unpublished data). The survival rate of canine leukocytes was much higher when compared to MCT cells . One of the probable explanations is the differences in the extracellular and intracellular components of the structural macromolecules or the competence of physiological adaptations between these two cell species.Physiologically, most canine leukocytes usually circulate throughout the body via the circulatory system. Therefore, they always experience the mechanical effects of hemodynamic shear stress along their circulative trajectories. Pathologically, the primary function of leukocytes is to respond to the protective mechanism of the body referred to as inflammation. By this process, they usually deployed into the injured sites via a biological process called transmigration . They haHowever, in case of MCT cells, they are cutaneous cancer cells and are not motile. In addition, MCT cells usually release physiological chemomediators, especially vasoactive amines, from their cytoplasmic granules that are akin to normal mast cells. Hence, they do not need to fortify their structures similarly to canine leukocytes. Accordingly, MCT cells are less durable to high-grade hydrodynamic stresses than canine leukocytes, although they also experience low-intensity interstitial fluid shears in their microenvironments.We have herein reported the tenor of hydrodynamic cell injury in an MCT model after exposure two major hydrodynamic shear stresses (HSS): fluid shear stress (FSS) and extensional shear stress (EFS) that had formed in our predesigned spiral microchannel. Based on the study, CFD clearly verified the originations of FSS and EFS in our microdevice. Furthermore, the panel of systematic biomedical assays substantially indicated the decrement of cell viability and the increment of cell degeneration, necrosis, and/or debris of MCT cells after sorting. A hemocytometry enumeration confirmed a decreased MCT cell concentration as well.The first possible pathomechanism underlying HSS-induced cell injury in our MCT model might be the sudden disorganization of cytoskeletons causing the morphological change in degenerated and necrotized cells as observed by light microscopy and CMI measurement. The second was the plasma membrane disintegration evaluated with SEM. Moreover, it led to reductions in cell susceptibility to the trypan blue exclusion assay and the leakage of DNA. TMRM also suggested the abating of mitochondrial functionality in both degenerative and necrotic MCT cells. Moreover, the other influential factor would be the type of cell itself. Therefore, hydrodynamic shear stresses in a spiral microchannel would have a noxious impact on the viability of the sorted cells, at least in our MCT model.To the best of our knowledge, this study might be a pioneer work that provides preliminary information on the combinatorically detrimental effect of FSS and EFS on the viability of neoplastic cells after size-based cell sorting with a spiral microchannel. The pros of this study are a provision of a caveat on the next geometrical design of a curvilinear microchannel, which could mitigate the deadly effect of both de novo hydrodynamic shear stresses on sorted cells. In addition, the other biologically influential factors of neoplastic cells that might be involved in HSS-induced cell injuries, such as a variety of cell types and cell stages as well as HSS generation in delivery (feeding) systems, also require further investigations to complete our comprehension of hydrodynamic cell injury under inertial microfluidic cell sorting before the method will be in use in the upcoming years.


S. will respond to climate change and sea level rise. Here, we investigated interactions among variables and identified the impacts of climate change, sea level rise, and their interactions on the distribution of Spartina alterniflora.Global change seriously threatens the salt marsh ecosystem, while it remains unclear how Northern Chinese coast and Southern Chinese coast.Spartina alterniflora Loisel.S. alterniflora. Integrating the Venn's four‐set diagram, we built ecological niche models under current and three future scenarios to identify pure, shared, and coupling effects of climate change and sea level rise on the distribution of S. alterniflora.With global sensitivity analysis, we determined interactions among variables and their relative importance to the distribution of S. alterniflora on the Chinese coast, and interactions among variables of the northern coast were much greater than that of the southern coast. Habitats change was mainly caused by pure effects of climate change, except habitats reduction on the southern coast. Pure effects of sea level rise were low, but it can influence habitats change through shared and coupling effects from complex interactions with climate change. Interactions of climate change and sea level rise can drive habitats change, and the changed habitats caused by shared and coupling effects were mainly distributed the areas near the landward side.Mean diurnal range (Bio02) and Elevation were the two most critical variables controlling the distribution of S. alterniflora will provide scientific references for assessing the risk of similar coastal species.Our research suggests paying attention to interactions among variables when calculating the relative importance of explanatory variables. Identifying pure, shared, and coupling effects of climate change and sea level rise for the distribution of  The presence records were resampled in ArcGIS 10.2 to ensure that there is only one observation within the 1° by 1° cell to avoid spatial autocorrelation and reduce sampling bias  . Furthermore, we obtained the global mean sea level when the sea level rises by 1 meter from CReSIS (https://www.cresis.ku.edu/content/research/maps). Seven soil variables  were derived from the Harmonized World Soil Database (HWSD) version 1.2.1 with a spatial resolution of 1 km.We selected three factor types including climate, topography, and soil as environmental variables. A total of 19 bioclimatic variables were obtained from Worldclim  and total sensitivity indices (TS) using the terms in the Fourier decomposition of the model output. The FS measures the main effect contribution of each variable to the total output variance, and the TS accounts for the total contribution including main effects and interactions effects  is the study of how the uncertainty in the output of a model can be apportioned to different sources of uncertainty in the model input . The probability distribution functions were generated for all variables and significance levels were set at the 1% level. All fitted results passed the chi‐square test (p < 0.005) (Supporting information Table N=(2M∗Wmax+1)∗m model simulations, where M is the interference factor (M = 4), Wmax is the largest among the set of Wi frequencies (Wmax=416), m is the number of input factors (m = 15), and N is the total number of parameter sets and model executions. A total of 49,935 input parameter sets were generated using probability distribution functions on different regions with Simlab software version 2.2. These parameter sets were run in MaxEnt and then used for global sensitivity analysis in Simlab.In order to identify interactions among variables and their relative importance for the distribution of 2.5Feature types combination (FC) and regularization multiplier (RM) are two important parameters that affect model complexity  metric, a typical threshold‐independent measure, was utilized as a measure of model accuracy , under which climatic variables changed but remaining variables kept constant, obtained habitats when only considering climate change. The scenario of sea level rise (SLR), under which elevation changed but remaining variables kept constant, obtained habitats when only considering sea level rise. The scenario of combining climate change and sea level rise (CCS), under which climatic and soil variables were changed, obtained habitats when considering both climate change and sea level rise. We modeled the current potential distribution of S. alterniflora along the northern and southern Chinese coast and projected them into future under three scenarios of CLC, SLR, and CCS. Given the uncertainty of the MaxEnt output  and future scenarios  as respectively. The Venn's four‐set diagram, showing all possible logical relations between a finite collection of different sets  and three kinds of future habitats   and unchanged habitats(Hunchanged):To explore the impacts of climate change, sea level rise, and their interactions on the distribution of ) Figure . Based oS. alterniflora, it was spilt into eight parts in Equation c and j, they belonged to the habitats when just considering sea level rise, so they were assumed as increased and decreased habitats caused by pure effects of sea level rise (pH_slr). Similarly, n and h are increased and decreased habitats caused by pure effects of climate change (pH_clc). e and k are increased and decreased habitats shared by HCLC and HSLR, and we assumed e and k are caused by shared effects due to interactions of climate change and sea level rise (pH_ics). For b and i, they did not belong to habitats when considering only climate change or sea level rise, so, we assumed them as increased and decreased habitats caused by coupling effects due to interactions of climate change and sea level rise (cH_ics).For the changed habitats of Hchanged-), the decreased habitats caused by shared effects (Hs_ics-), pure effects of climate change (Hp_clc-), pure effects of sea level rise (Hp_slr-), and coupling effects (Hc_ics-) can be expressed as the following:Specifically, the decreased suitable habitats (Hchanged+), the increased habitats caused by shared effects (Hs_ics+), pure effects of climate change (Hp_clc+), pure effects of sea level rise (Hp_slr+), and coupling effects (Hc_ics+) can be expressed as the following:Similarly, the increased suitable habitats  were greater than 0.7 in Figure 3.2FS) and total sensitivity indices (TS) than the other variables, which meant they were the two most important variables for the distribution of S. alterniflora. The sum of TS arrived at 1.6994, which was three times than that of FS(0.5096). It indicated that there were strong interactions among variables. The FS of soil electrical conductivity (Tece) and soil drainage class (Drainage) ranked higher than their TS, while the FS of Soil pH (Tph) ranked lower than their TS, indicating interactions reduced the importance of Tece and drainage, while enhanced the importance of Tph. Meanwhile, some variables  had great differences between FS and TS, especially for Bio02 (0.3663) and Elevation (0.2383), which meant that they had strong interactions with the others.As shown in Table FS or TS, Elevation was by far the most important for the distribution of S. alterniflora with the values being 0.8561 and 0.9294, respectively, which were much greater than those of the second most important Bio02 with FS and TS being only 0.0658 and 0.1423, respectively. The FS of Bio14, Bio19, Tgravel, Tph, Tsand, and Tclass were 0, while the TS of them became non‐zero values. Moreover, the sum of FS is 0.9289 and the sum of TS was 1.3310. It indicated that there were still interactions among variables, but much weaker than that on the northern Chinese coast. Although the FS and TS of Elevation were much higher than Bio02, the difference was as great as that of Bio02, which indicated that both of them had distinguished interactions, especially for Bio02.As seen from the Table 3.3S. alterniflora increased by 30,934 km2, while decreased by 9,628 km2. 87.41% of habitats increment can be interpreted as being caused by pure effects of climate change, while only 0.53% by pure effects of sea level rise, 3.47% by shared effects, and 8.60% by coupling effects. For habitats reduction, the proportion caused by pure effects of climate change, pure effects of sea level rise, shared effects, and coupling effects accounted for 95.23%, 0.36%, 2.49%, and 1.91%, respectively. Pure effects of climate change mainly explained habitats change, while pure effects of sea level rise were quite low for whether habitats increment or reduction. Shared and coupling effects could explain 12.07% of habitats increment and only 4.4% of habitats reduction, which meant that the increased habitats were more deeply affected by interactions than the decreased habitats.As shown in TableS. alterniflora increased by 69,417 km2, while decreased by 4,580 km2. It indicated that habitats will greatly increase in the future. 78.86% of habitats increment could be interpreted as being caused by pure effects of climate change, 1.25% by pure effects of sea level rise, 0.99% and 18.90% by shared and coupling effects, respectively. For the decrease habitats, the proportion caused by pure effects of climate change, pure effects of sea level rise, and shared effects accounted for 38.38%, 57.16%, and 4.45%, respectively. Coupling effects of interactions had no impacts on habitat reduction. Different from the northern coast, pure effects of climate change mainly explained habitats increment, while habitats reduction were explained by pure effects of sea level rise. For habitats increment, coupling effects were much greater than shared effects with the sum of shared and coupling effects of 19.89%, while shared effects were greater than coupling effects with the sum of only 4.45% for habitats reduction. The interactive way was different, which mainly appeared as coupling effects in habitats increment and shared effects in habitats reduction.As shown in Table 3.4Along the northern Chinese coast Figure a, the de4S. alterniflora on the northern coast, whether main or total effects. The finding was consistent with previous studies  to determine the variable importance and reduction (shared effects) on the southern coast. So, if interactions were ignored, the influences of climate change and sea level rise may be underestimated  is a superior technology for modeling the potential distribution of species, it has several limitations including its uncertainty and transferability (Phillips et al., Haibo Gong formed the original idea and wrote the original manuscript; Huiyu Liu offered valuable comments and was responsible for the manuscript revisions; FuSheng Jiao created figures and tables; Zhenshan Lin and Xiaojuan Xu analyzed the data. Click here for additional data file.


Fusarium (82%), Penicillium (63%), and Aspergillus species (33%) compared to other genera. Fusarium occurred in 90% and 74% of small scale and commercial samples, respectively, while Penicillium occurred in small scale and commercial samples at an incidence rate of 64% and 62%, respectively. However, among the species, Fusarium verticilloides have the highest incidence of 70% and 76% in commercial and small‐scale maize, respectively, while Penicillium digitatum has 56% total incidence. Aspergillus fumigatus (27%) were also the most dominant of these genera. Fungal genera isolated included Alternaria and Cladosporium although occurring at a lower incidence level of 30%, 32% and 16%, 20%, respectively, in small‐scale and commercial samples. The results emphasize that farmers and consumers should be alerted to the danger of fungal contamination in maize.One‐hundred maize samples were analyzed for fungal contamination using conventional and molecular methods. The percentage incidence of different genera isolated revealed the predominance of  They interact with plants, animals, and humans in order to ensure their growth and survival. These interactions, although in some cases are beneficial to the host, in most of the cases they are harmful interactions, causing diseases in plants, animals, and humans, as well as causing spoilage of food and feed Basappa, . Among tZea mays L.) is one of the major cereals in the world and is the third highest produced grain after wheat and rice. Maize does not only serve as human food and as a feed for livestock but also an important commodity in international trade  region is used to identify different fungal species  and commercial farmers (silos) in the North‐west Province from April to August 2013. A total of 1 kg of each of the samples was collected in sterile plastic bags, carefully labeled and conveyed to the laboratory. Each of the samples was thoroughly mixed and milled using a sterile high speed commercial blender and packaged in sealed sterile plastic bags to avoid contamination. Samples were stored prior to analysis in the freezer.2.2−6 of the original concentration. One milliliter of each dilution was spread on agar plates and incubated at 30°C for 7 days. Two different media were used, Potato Dextrose Agar (PDA) and Malt Extract Agar (MEA) (Merck) and both supplemented with 80 mg/L each of Chloramphenicol and Streptomycin to suppress bacteria growth. After incubation, the fungal colonies were counted using a colony counter and the number of colonies per gram of sample was calculated and expressed in colony‐forming units per gram (cfu/g).Fungal culture was done under aseptic conditions using dilution plating technique as described by Rabie, Lubben, Marais, and Jansen van Vuuren  with theSubculturing of the fungal colonies was done on PDA and MEA using sterile wire loop, plates were sealed with parafilm and further incubated at 30°C for 7 days. Small portions of the pure colonies were harvested and placed on microscopic slides containing a drop of lactophenol cotton blue solution, covered with a cover slip and viewed microscopically. Macro and microscopic identification of the fungal species isolated from the samples was done according to their morphological characteristics between the 5th and 7th day of incubation using the identification keys as described by Pitt, Hocking and Klich  according to the manufacturer's instructions. The fungal isolates were grown on PDA plates and 7‐day‐old cultures were used for DNA extraction. The mycelia were harvested by scraping the agar surface and up to about 200 mg were suspended in 750 μl of lysis solution contained in a 1.5 ml ZR Bashing Bead™ lysis tube. The eluted purified DNA was stored at −20°C until further analysis.2.3.2The ITS region of the fungal DNA was amplified by PCR using the primer set: FF2; 5′‐GGT TCT ATT TTG TTG GTT TCT A‐3′ (forward) and FR1; 5′‐CTC TCA ATC TGT CAA TCC TTA TT‐3′ (reverse). The primer set: FF2 and FR1 was designed by Zhou et al.,  and prod2.3.3® 3700XL automated DNA Sequencer  at the Inqaba Biotechnological Industries (Pty) Ltd, Pretoria, South Africa.PCR products were purified to remove excess primer using shrimp alkaline phosphatase and E. coli exonucleous 1. The purified products were sequenced in both directions (forward and reverse) with the primer set FF2 and FR1. Sequencing of the amplified ITS region was accomplished using the ABI PRISM2.3.4The resulting ITS region chromatograms of forward and reverse sequences of fungal DNA obtained in this study were cleaned, combined, analyzed, and edited using Chromas Lite version 2.4 software (Technelysium Pty Ltd 2012). Nucleotide sequences were analyzed and edited using the BioEdit software Hall,  to form 33.1Fusarium (82%), Penicillium (63%), and Aspergillus species (33%) compared to other genera. Fusarium occurred in 90% and 74% of small scale and commercial samples, respectively, while Penicillium occurred in small scale and commercial samples at an incidence rate of 64% and 62%, respectively. Out of the Aspergillus species, 36% and 30% occurred, respectively, in small scale and commercial samples.The investigation showed that all the samples were contaminated with at least one fungal species, whereas co‐contamination with different fungi occurred in most of the samples. The percentage incidence of different genera isolated revealed the predominance of Fusarium verticilloides have the highest incidence of 70% and 76% in commercial and small‐scale maize, respectively, while Penicillium digitatum has 56% total incidence. Aspergillus fumigatus (27%) were also the most dominant of these genera. Fungal genera isolated included Alternaria and Cladosporium although occurring at a lower incidence level of 30%, 32% and 16%, 20%, respectively, in small‐scale and commercial samples.However, among the species, 4Fusarium, Penicillium, and Aspergillus in the maize samples. The findings are in line with other studies done in South Africa , Penicillium (63%), and Aspergillus species (33%) compared to other genera  were also the most dominant of these genera. This agrees with previous reports that F. verticilloides is the dominant fungal species in South African maize  harvesting of crops in rural areas and the lack or poor storage facilities characterized by poor ventilation, high temperatures and humidity  that are slowly but surely significantly affecting the quality of grains produced. This might also explain the presence of these fungi in the analyzed maize. A number of agricultural entities could be affected by these climatic factors, including soil quality, crop yields, and the biological environment of crops such as the abundance of pests and plant pathogens  are associated with maize and previous studies have shown that at least 190 toxigenic fungi are associated with food and feed commodities in South Africa (Rabie & Marais, So, judging from the high incidence of fungal colonization observed in these grains, it is evident that consumers might be at risk of chronic mycotoxin exposure since these fungal species are known to produce a range of toxins. Hence, the presence of fungal contaminants in the samples should warrant intervention strategies to minimize their occurrence. It is therefore recommended that continuous monitoring for fungal infection, improved agronomic practices and postharvest handling of maize are necessary to minimize consequences to consumers’ health. Furthermore, disease‐management practices, such as planting of locally adapted maize varieties, reducing pest damage, early harvesting and discarding mouldy kernels should be adopted, with farmer education in rural areas which can help to reduce fungal contamination.The authors declare no conflict of interest.