The pCO2, SAL, and SST values from the LDEOv2009 climatology (Tak

The pCO2, SAL, and SST values from the LDEOv2009 climatology (Takahashi et al., 2010), and calculated TA values described below were GSK-3 assay used with CO2SYS (Van Heuven et al., 2009) to estimate TCO2 and Ωar. The carbonic acid dissociation constants of Millero et al. (2006) were used for the calculation as the estimated errors for these thermodynamic constants are considered to be smaller compared to other published constants (Millero et al., 2006). For the calculation of Ωar (Eq. (1)), the concentration of dissolved calcium ions in μmol kg− 1 is estimated from salinity using [Ca2 +] = 2.934 × 10− 4SAL

(Culkin and Cox, 1976). The concentration of carbonate ion [CO32 −] is a function of TA and TCO2 at a given SST and SAL, and K⁎sp

is a function of SST and SAL. This section describes the updated TA–SAL relationship, and then focuses on the estimation, variability and distribution selleck chemical of TA, TCO2, and Ωar for the Pacific study area. The seasonal variability of pCO2, SST and SAL has been documented for the region in previous studies (Bingham et al., 2010, Johnson et al., 2002 and Takahashi et al., 2009). For the Pacific Ocean study region, the GLODAP and CLIVAR TA and SAL data ( were used to derive the following relationship: equation(2) TAcalc=(2300.0±0.2)+(66.3±0.4)×(SAL−35).TAcalc=2300.0±0.2+66.3±0.4×SAL−35. The standard error of the fit is ± 6 μmol kg− 1 and the coefficient of determination R2 = 0.98. An SST term in (2) reduced the residuals by only 0.1%, and was not included in the equation. The TA–SAL relationship is compared to earlier relationships (Chen and Pytkowicz, 1979, Christian et al., 2008 and Lee et al., 2006) in Fig. 3. The TA residuals in Fig. 3 are the difference between the measured TA values for surface samples from the GLODAP and CLIVAR/CO2 section data and the derived TA values. The variance of the residuals is indicated by the slope of the lines. The range of the residuals should be small and the variance constant (i.e. little Telomerase or no gradient) if the relationship is a good predictor of TA. Comparison of

the measured and calculated TA from (2) shows the residuals range from − 20 to 20 μmol kg− 1. Most of the surface samples used to calculate the TA–SAL relationship are from nutrient-poor, oligotrophic waters in the Pacific study area. The effect of dissolved nitrate on TA (Brewer and Goldman, 1976) was estimated using surface (< 10 dbar) data from the CLIVAR/CO2 Pacific Ocean sections P06 and P21. These cruise data were collected after 2008 and were not used to calculate the relationship in Eq. (2). The TA residuals average − 5.8 ± 3.9 μmol kg− 1 (n = 117) for leg 1 of P06 along 30°S, and − 3.2 ± 6.0 μmol kg− 1 (n = 8) for leg 2 of P21 between 17°S and 25°S. The surface nitrate concentrations for the TA samples used were less than 5 μmol kg− 1.

This is the first report on identification and characterization o

This is the first report on identification and characterization of an isoamylase gene from the rye genome. Hexaploid spring wheat (Triticum aestivum L.) cv. Chinese Spring and diploid spring rye (Secale cereale L.) cv. Rogo

were grown under controlled environmental conditions (24 °C day, 20 °C night with a 16 h photoperiod of 240 μmol m− 2 s− 1) in the same growth cabinet. Various plant materials (stem, leaf, root, seed) were sampled, flash frozen in liquid nitrogen, and stored at − 80 °C until used. Genomic DNA was extracted from young leaf tissue at Zadoks growth Stage 22 [20] using a DNeasy Plant Mini Kit (Cat. No. 69104, Qiagen Inc., click here Mississauga, ON, Canada). Total RNA was isolated from immature seeds (12 days post anthesis, DPA) according to a phenol/SDS protocol [21]. RNA was further purified using the RNeasy Plant Min Kit (Cat. No. 74904, Qiagen Inc., Mississauga, ON, Canada). Primers for cloning the rye isoamylase gene were designed according to the conserved regions of Aegilops tauschii isoamylase gene sequence (GenBank accession no. AF548379) [22], wheat iso1 mRNA sequence (GenBank accession no. AJ301647) [23] and barley isoamylase mRNA sequence (GenBank accession no. AF490375) [14]. Ten pairs of primers were designed to amplify the overlapping genomic DNA sequences that correspond I-BET-762 in vivo to the rye isoamylase gene. Furthermore, three pairs of primers

were developed to amplify the overlapping cDNA sequences. Typically, 25 μL of PCR mixture contained 20 pmol primers, 30 ng of genomic DNA or 5 μg of cDNA, 1 × buffer, 1 × Q-solution and 1.25 U of Qiagen HotStar HiFidelity Polymerase (Cat. No. 202605, Qiagen Inc., Mississauga, ON, Canada). Reverse transcription (RT)-PCR was performed using total RNA as the template with Superscript III Reverse Transcriptase (Cat. No. 18080-093, Invitrogen, Burlington, ON, Canada). Primer sequences and PCR conditions are listed in Table 1. Amplified isoamylase DNA fragments were cloned into the PCR4-TOPO vector (Cat. No. K4575-02, Invitrogen, Burlington, ON, Canada) and at least three independent clones for

each fragment were sequenced in both directions by the DNA Sequencing Service Centre, University of Calgary (Calgary, Glutathione peroxidase Canada). Rye isoamylase sequences and the corresponding protein were blasted with the NCBI BLASTN tool ( and aligned with previously reported isoamylase sequences using DNAMAN software v5.0 (Lynnon Biosoft, U.S.A.). The putative encoding regions of transit peptides and mature proteins of isoamylase genes from different plant genomes were predicted using the ChloroP 1.1 server ( Total RNAs were isolated from rye leaves, stems, roots and rye seeds at different developmental stages (9, 15, 24 and 33 DPA) with an RNA Extraction Kit (Cat No. 74904, Qiagen Inc., Mississauga, ON, Canada).

Another two QTL explaining 43% of phenotype variation were detect

Another two QTL explaining 43% of phenotype variation were detected on chromosomes 1 and 4 in a different cross [111]. The QTL on chromosome 1 was common to both crosses. In rice and maize, Al tolerance seemed to be quantitatively inherited and QTL analysis showed that multiple loci/genes may control the trait. Nguyen et al. [112] detected 10 QTL for Al tolerance in rice using a double haploid population. They also identified three QTL using recombinant inbred lines

derived from a cross between one cultivar and one wild species [113]. In maize, five QTL were see more identified on chromosomes 2, 6 and 8, accounting for 60% of the phenotype variation [114]. Two QTL responding to Al tolerance in maize were mapped on the short arms of chromosomes 6 and 10 in a different study [115]. Considerable effort was made in searching for genes involved in Al tolerance in barley; one gene along with additional minor gene effects were detected [52] and [116]. Major QTL, Alp [117], Pht [118], Alt [119] and Alp3 [120] on chromosome 4H, were reported, but it is unknown whether these QTL/genes are the same or allelic [52]. Minor QTL for aluminum tolerance were identified on 2H, 3H and 4H in the Oregon Wolfe Barley (OWB) mapping population [100] and [121]. The reason that different QTL were detected in the different populations may be the heterogeneity between different parents [122].

More information is required to validate all QTL pheromone for Al tolerance in cereals. Association mapping is based on associations between molecular markers and traits that can be attributed to the strength of linkage disequilibrium in large populations without crossing [123]. It differs from bi-parental QTL mapping that evaluates only two alleles. Association mapping can evaluate numerous alleles simultaneously and is useful for studying the inheritance of complex traits controlled by multiple QTL [124]. Using association mapping, six genes in different metabolic pathways were significantly associated with response to Al stress in maize [125]. In triticale, several molecular markers had strong associations with phenotypic data from 232 advanced breeding lines

and the marker wPt-3564 on chromosome 3R was validated by various approaches [126]. Using multiple molecular approaches, several genes responding to Al tolerance in plants were identified. These genes mainly belong to the MATE (multidrug and toxic compound extrusion) and ALMT (aluminum-activated malate transporters) families. MATE genes encode transporters excreting a broad range of metabolites and xenobiotics in eukaryotes and prokaryotes [127] and ALMT family members encode vacuolar malate channels [128]. In wheat, Al tolerance is mainly controlled by two genes. TaALMT1 which encodes a malate transporter on chromosome 4D is constitutively expressed on root apices [129]. TaMATE1 reportedly responds to Al stress based on citrate efflux [59].

The adjusted EPCO (plant uptake compensation factor) value of 0 3

The adjusted EPCO (plant uptake compensation factor) value of 0.38 indicated that most water used by vegetation would be

from the upper soil profile because of a relatively higher groundwater table, sufficient soil moisture, and limited transpiration. The ESCO value of 0.69 also indicated that more water was being extracted from the upper level to compensate for the evaporative demand. A good calibration is most likely a combined effect from all selected parameter coefficients. However, the sensitivity of individual parameters varies. Because of snow and diverse elevations, the temperature and precipitation lapse rates were found to be important in simulating the hydrological processes in the Brahmaputra basin. The optimized temperature lapse rate was −5.5 °C per 1-km rise in elevation, which was found in agreement with temperature lapse rate between −5 °C to −7 °C per 1-km elevational rise used in other studies (Baral 3MA et al., 2014 and Thayyen et al., 2005). Precipitation in the Himalayan region

clearly varies with elevation (Bookhagen and Burbank, 2006), although the precipitation elevation relationship is not always linear (Immerzeel et al., 2014). Precipitation was observed to increase at a rate of 150 mm per 1-km rise in elevation in the valleys with elevations between 1396 and 2492 m; Precipitation then decreased at a rate of 240 mm per 1-km rise in elevation between the elevation range of 3539–3875 m, and then increased again at a rate of LDK378 manufacturer 60 mm per 1-km rise in elevation between 3981 and 5100 m (Baral et al., 2014). It was also reported that precipitation decreased with an increase in elevation in very high elevation regions in the Himalayas (Immerzeel et al., 2014). However, SWAT incorporates the PLAPS variable to account for the precipitation lapse rate as a global variable and does not allow incorporation of PLAPS values by elevation bands; therefore, the SUFI2 optimized precipitation lapse rate of 172.25 was used as a universal value for all elevation bands. This Liothyronine Sodium limitation can be considered a weakness of the SWAT

model. The low 8.26 value of GWQMN helped increased the baseflow, while the value of 0.01 for GW_REVAP facilitated the increase in baseflow by decreasing the water transfer from the shallow aquifer to the root zone, which was necessary to simulate flow during the low flow seasons. The observed and simulated estimates of the hydrological components for the 16-year baseline period are provided in Table 4. The average annual total observed precipitation was 1849 mm. The annual average simulated streamflow at Bahadurabad gauge station was 22,875 m3 s−1, which was slightly larger than the average observed streamflow (22,345 m3 s−1) for the same period (Table 3). The average daily observed minimum and maximum temperature was 3.2 °C and 14.2 °C, respectively. The average annual total water yield from the baseline simulation was 1279 mm.

The model has shown excellent performance in different applicatio

The model has shown excellent performance in different applications, from basin-scale estimates of the upwelling features in the entire Baltic Sea and mean circulation and water age of the Gulf of Finland (Andrejev et al. 2004a,b) down to the small-scale reproduction of surface buoy drift (Gästgifvars et al. 2006). The quality of the simulation of the hydrophysical fields is analysed in detail within the framework of a model intercomparison for the Gulf of Finland (Myrberg et al. 2010). The model resolution for the Gulf of Finland was originally restricted to 1 nm in order to match the available bathymetric information

for the entire Baltic Sea (Seifert Protein Tyrosine Kinase inhibitor et al. 2001) but has been recently increased

to 0.25–0.5 nm. A detailed description of the features and approximations of the latest high-resolution version of the model is presented in Andrejev et al. (2010). GSK2118436 concentration In order to ensure comparability of the results with earlier studies (Andrejev et al. 2004a,b, Soomere et al. 2010), we used the simulation period of 1 May 1987–31 December 1991. The OAAS model was run for the Gulf of Finland to the west of longitude 23° 27′E (Figure 2) at three different horizontal resolutions – 0.5, 1 and 2 nm – but with an otherwise identical vertical resolution (1 m) and forcing and boundary conditions. The impact of the rest of the Baltic Sea is accounted for in the form of the relevant boundary conditions along this longitude, optionally with the sponge layer approach (see Andrejev et al. 2010 for details). The

boundary conditions (salinity, temperature and sea-level elevation) were extracted at 6 hour intervals from simulations performed with the Rossby Centre coupled ice-ocean model (RCO, Meier et al. 2003). The RCO model is based on the Bryan-Cox-Semptner primitive equation ocean model with a free surface but contains several parameterizations with a special importance for the Baltic Sea, such as a two-equation turbulence closure scheme, open boundary conditions and a sea-ice model. It was run with a horizontal resolution of 2 nm that is usually sufficient for eddy-resolving runs Ponatinib order in the Baltic Proper (Lehmann 1995). The initial sea water temperature and salinity fields for all the OAAS model resolutions were constructed by an interpolation of the RCO data. The modelling in the Gulf of Finland started from the resting water masses and with the sea level equal to the barometric equilibrium. Owing to the realistic initial data and high-quality boundary information, the modelled fields are plausible from the very beginning of calculations and the final spin-up of the model takes ca 1–2 weeks for the surface layer dynamics.

Similarly, following short-term or low levels of sedimentation, s

Similarly, following short-term or low levels of sedimentation, structural (i.e. polyp re-colonization) (Wesseling et al., 1999) and functional (i.e. photosynthetic activity) (Philipp and Fabricius, 2003) recovery within days to weeks has been demonstrated for some, but not all, coral species. Coral growth recovered within weeks following short-term enrichment of N, and of find more N and P combined, but not of P (Ferrier-Pages et al., 2000). It is unlikely for such swift recovery to occur following restoration of more natural freshwater, sediment

and nutrient fluxes, given that coral ecosystem processes would have been chronically impacted for years to decades, if not centuries. The well-known case of Kane’ohe Bay, Hawaii, is the only example demonstrating partial reversal of coral reef degradation following a reduction in terrestrial nutrient fluxes. Following sewage diversion in 1978, turbidity, nutrients and chlorophyll a concentrations, as well as macroalgae biomass, declined within months ( Laws and Allen, 1996 and Smith et al., 1981). In the next few decades, coral cover more than doubled and subsequently

stabilized, however, further recovery may at least be partly constrained by nutrient sources other than sewage outfalls, by modified freshwater and sediment fluxes resulting from historical and recent changes in the Bay and its catchments ( Hunter and Evans, 1995), and by additional impacts of introduced macroalgae

( Conklin and Smith, 2005). To reverse coral reef degradation, AZD6244 order it is critical to define the different ecosystem states of a coral reef system, and understand the ecological processes that drive the change from one state to another. This relates to the concept of resilience, i.e. the capacity of an ecosystem to absorb perturbations before it shifts to an alternative state with different species composition, structure, processes and functions (Folke et al., 2004). For coral reefs, multiple alternative states can exist and have been documented for coral reefs, generally dominated by organisms other than reef-building coral (Gardner et al., 2003, Hughes et al., 2010 and Mumby et al., 2007). Chronic environmental pressures such as changes in terrestrial fluxes of freshwater, sediment, and nutrients (De’ath and Fabricius, BCKDHB 2010, Dubinsky and Stambler, 1996 and Fabricius, 2011) reduce resilience by decreasing the threshold at which the coral-dominated state shifts into a different state. A return to the more desirable coral-reef dominated state by reducing chronic drivers of change such as land-based pollution may be difficult to achieve due to the inherent stability of the degraded state, known as hysteresis (Mumby and Steneck, 2011). We identified multiple examples in the global literature where reductions of land-based pollution to coastal ecosystems have been achieved (Table 2).

Currently, second-line chemotherapy is the standard of care for p

Currently, second-line chemotherapy is the standard of care for platinum-pretreated NSCLC even though its efficiency is poor [1], [2] and [5]. Docetaxel and pemetrexed are currently the standard second-line chemotherapy agents for NSCLC.

Treatment with pemetrexed generally results in clinically equivalent efficacy outcomes with docetaxel in the second-line treatment of patients with advanced NSCLC [1]. However, pemetrexed and docetaxel only produced overall response rates (ORRs) of 9.1% and 8.8% with a median survival time of 8.3 and 7.9 months, respectively, RAD001 in vivo in platinum-pretreated NSCLC [1]. The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) erlotinib and gefitinib have also been used as standard second-line agents in treating NSCLC. Sensitivity to EGFR TKIs is dependent on the activation of the EGFR pathway or the presence of EGFR-interacting proteins [5]. Studies showed that no significant differences in efficacy were noted between patients treated with TKIs and those treated with docetaxel or pemetrexed in platinum-pretreated NSCLC [5], [6] and [7]. Therapeutic inhibition of EGFR with TKIs has resulted in favorable response rates only in 11.14% to 15.25% of platinum-pretreated NSCLC, mostly because the EGFR mutation or gene amplification rate is only 16.6% in NSCLC [5] and [6]. In addition, median survival

of selleck chemicals llc 7.6 months for gefitinib in platinum-pretreated NSCLC and 5.3 months for erlotinib in platinum-resistant NSCLC indicate the desperate need for novel approaches to treat the patient population [5], [7], [8] and [9]. We previously found that 5% ethanol-cisplatin injected intratumorally could eradicate cisplatin-resistant lung tumors and extend survival by improved killing of lung CSCs in mice [10]. We believe that 5% ethanol improves the efficacy of CSC killing by inhibiting breast cancer resistance protein (BRCP/ABCG2) SB-3CT drug transporter function and by improving the penetration of cisplatin into the tumor cells [10]. On the basis of our model organism studies, it is possible that computed tomography (CT)–guided percutaneous fine-needle 5% ethanol-cisplatin intratumoral

injection (CT-PFNECII) might also regress platinum-pretreated or even platinum-resistant tumors in patients with NSCLC by killing chemoresistant cancer stem cells and cancer cells. Furthermore, it is possible that the residual unkilled but damaged tumor cells after 5% ethanol-cisplatin treatment might be more fragile and sensitive to systemic second-line chemotherapy agents. Thus, combination of CT-PFNECII with systemic second-line chemotherapy might provide a new way to improve survival of this patient population. This study is aimed to investigate the efficacy and safety of CT-PFNECII combined with second-line chemotherapy in patients with platinum-pretreated stage IV NSCLC. The study protocol was approved by the Institutional Review Boards of the No.

Although the ratio of kLung→Lym to k1 showed a dose-dependent inc

Although the ratio of kLung→Lym to k1 showed a dose-dependent increase (0.4% at 0.375 mg/kg to 5% at 6.0 mg/kg), most clearance

from lung could occur via other routes, such as the bronchial mucociliary escalator. In the previous compartmental models for pulmonary clearance, compartments 1 and 2 were considered to be the alveolar surface and the interstitium, Talazoparib in vivo respectively, and the clearance pathways from compartment 1 and 2 were considered to be the bronchial mucociliary escalator via the bronchi, and translocation to lung-associated lymph nodes via the interstitium, respectively ( Stöber, 1999 and Kuempel et al., 2001). In the present study, however, it was suggested that clearances both by the bronchial mucociliary escalator via the bronchi after macrophage phagocytosis and translocation to the thoracic lymph nodes should be described as clearance from compartment 1. Therefore, it is better to consider compartment 2 as a lung compartment where particle accumulate, rather than as Ceritinib manufacturer an intermediate compartment for slow particle clearance. Compartment 2 might correspond to macrophages which have phagocytosed TiO2 nanoparticles and have subsequently been

sequestered within the interstitium. Measured pulmonary burden can be well modeled effectively using the classical 2-compartment model in the present study. Nintedanib mw The advantage of the classical model in the present study over the previous physiologically based models is that it eliminates the arbitrariness and uncertainty in deciding the clearance mechanism and compartment meanings because the clearance mechanism and compartment meanings do not have to be predicted in advance. On the other

hand, the disadvantage of the current model is that the meaning of the compartments is assumed only on the basis of circumstantial evidence. In addition, fitting of the results could be unclear if there is only a small amount of data. In the results of 2-compartment model fitting, the k1 (0.014–0.030/day, equivalent half-life: 23–48 days) was higher than the k12 (0.0025–0.018/day, equivalent half-life: 39–280 days), and the k2 (0–0.0093/day, equivalent half-life: 75–>840 days) ( Table 1B). The rate constants for clearance from compartment 1, k1, and translocation from compartments 1 to 2, k12, were lower at doses of 1.5–6.0 mg/kg than at doses of 0.375 and 0.75 mg/kg. The rate constants for clearance from compartment 2, k2, (or transfer rate constants from compartment 2 to 1, k21) were much lower at doses of 1.5–6.0 mg/kg than at doses of 0.375 and 0.75 mg/kg. One of possible mechanism that could explain these dose-dependencies would be follows.

The swab was then rotated through

180° on its long axis t

The swab was then rotated through

180° on its long axis to ensure good mucosal contact and withdrawn. Swabs were inoculated into 1.5 ml skim milk-tryptone-glucose-glycerin broth (STGG) and frozen.21 After storage and thawing, 50 μl of broth was subsequently inoculated onto sheep blood agar containing 5 μg/ml gentamicin. S. pneumoniae was identified by alpha hemolysis, colony morphology, bile salt solubility and optochin sensitivity. 22 The proportions and absolute numbers of B and T cells were estimated in EDTA whole blood samples by flow cytometry using the following antibodies: fluorescein isothiocyanate (FITC)-labeled anti-CD19 & anti-CD21; phycoerythrin (PE)-labeled anti-CD8, anti-CD27 & anti-IgD; peridinin chlorophyll protein (PerCP)-labeled CD3 & anti-CD19; allophycocyanin (APC)-labeled selleck chemical anti-CD4, anti-CD10 & anti-CD27. All antibodies used in flow cytometry assays were obtained from BD Biosciences Ltd, with the exception of anti-CD21 (Beckman Coulter). B-cell subtypes

were characterized using surface markers described by Moir and colleagues.18 and 23 Whole blood was Selleckchem AZD2281 incubated with respective antibodies for 20 min at room temperature in the dark. The red blood cells were lysed for 30 min using 1x lysis solution (BD). The white blood cells were then pelleted by centrifugation (450 g, 30 min, 25 °C), washed in phosphate buffered saline (PBS) supplemented with 0.5% bovine serum albumin (Sigma) and fixed with 2% paraformaldehyde (Sigma) before acquisition on a flow cytometer. At least 100,000 events were acquired within

the lymphocyte gate using CellQuest Pro software on a four-color flow cytometer (BD FACSCalibur, BD Biosciences) or the Summit software version 4.3 on a CyAn ADP (Beckman Coulter). Lymphocytes were gated using forward and side scatter characteristics. Results were analyzed using FlowJo software version 7.2.2 CYTH4 (Tree Star Inc., San Carlos, CA). Polyclonal stimulation was used to induce differentiation of memory B cells into antibody secreting cells (ASC) in vitro. 24 Pneumococcal specific ASC were then enumerated using an ELISPOT assay. Briefly, peripheral blood mononuclear cells (PBMC) were isolated by density gradient centrifugation using Lymphoprep (Axis Shield plc), resuspended in complete RPMI medium (RPMI-1640 supplemented with 10 mM HEPES, 100 U/ml Penicillin, 0.1 mg/ml streptomycin and 2 mM l-glutamine) containing 10% fetal calf serum, plated at 1 × 106 cells/ml in 2 ml volumes per well in 24-well plates (Appleton woods). Freshly isolated PBMC were cultured for 6 days at 37 °C in the presence of a combination of 1/100,000 standardized pansorbin cells (heat-killed, formalin-fixed Staphylococcus aureus, Cowan 1 strain; SAC), 1 μg/ml phosphothiolated CpG oligodeoxynucleotide 2006 (CpG DNA) and 1/1000 pokeweed mitogen extract (PWM). Cells were then harvested and plated at 4 × 105 cells/well on 96-well multiscreen plates (Millipore) pre-coated with a pneumococcal protein antigen (1.

Multivariable logistic regression analyses were performed to iden

Multivariable logistic regression analyses were performed to identify covariates that may influence the exposure-response relationship for infliximab in UC patients receiving selleck inhibitor 5 mg/kg or 10 mg/kg during induction and maintenance treatment. The final logistic regression model for induction treatment through

week 8 showed that higher serum infliximab concentration at week 8, higher body weight, and female sex were associated independently with clinical response at week 8. Similar analyses conducted through week 30 of maintenance treatment showed that a higher infliximab concentration at week 30 and absence of corticosteroid therapy at baseline were associated independently with a greater probability of maintaining clinical response at week 30 (Supplementary Table 2). To identify optimal infliximab concentration target thresholds associated with clinical improvement in UC patients, ROC curves were generated for efficacy end points during both induction and maintenance treatment periods. The ROC curves for the end point of clinical response in patients who received the 5-mg/kg or 10-mg/kg infliximab dose regimen are shown in

Figure 4 for induction and maintenance treatment. Although the magnitude of the area under the ROC curves (AUC) was moderate for the induction analysis (0.63; 95% confidence interval [CI], 0.59–0.68) (ie, week-8 concentration (CW8) compared with clinical response at GDC-0068 week 8), it was significantly greater than the null value of 0.5

(P < .0001). Furthermore, the most AUC under the ROC curve for the preinfusion concentration at week 6 (CPW6) (0.62; 95% CI, 0.57–0.66) was not significantly different from that using CW8 (P = .553). In contrast, the preinfusion infliximab concentration at week 2 (CPW2) was a poor predictor of clinical response at week 8 (AUC, 0.51). With respect to the maintenance ROC curve analysis, the AUC was 0.71 (95% CI, 0.66–0.76) for the week-30 preinfusion concentration (CPW30) vs clinical response at the week-30 ROC curve and 0.75 (95% CI, 0.68–0.82) for the week-54 preinfusion concentration (CPW54) vs clinical response at the week-54 ROC curve. The AUC from the ROC curve of the serum infliximab preinfusion concentration at week 14 (CPW14) (0.68; 95% CI, 0.63–0.72) was comparable with that of the CPW30 for the clinical response at week 30 (P = .087) but was not equivalent to that from the CPW54 ROC curve for the week-54 clinical response end point (P = .041). In addition, the AUC from the CPW30 ROC curve was comparable with that from the CPW54 ROC curve (P = .746). The ROC analysis identified different target thresholds depending on the time point of the PK sampling or the efficacy assessment (Table 3). For clinical response at week 8, the threshold infliximab concentration of 41 μg/mL at week 8 was associated with a sensitivity, specificity, and positive predictive value (PPV) of 63%, 62%, and 80%, respectively.