This suggests that in previous protocols allergen sensitisation was still ongoing during challenge and an increased period between the two was required for the generation of a full response. This modification restores the gap between sensitisation and challenge to the duration used PD0332991 datasheet in this laboratory’s original sensitisation protocol (Lewis et al.,

1996) which had decreased with previous modifications (Smith & Broadley, 2007). Notwithstanding the reduced time between final sensitisation dose and challenge when increasing to 3 sensitisations, there was still a loss of allergic responses with protocol 1 compared to previous studies. The addition of a 3rd sensitisation injection on day 7 resulted in a further shortening of the sensitisation period to 8 days. 8 days between the final allergen sensitisation and challenge may not be enough time to produce a full immunological response, except when the sensitisation conditions are increased to a certain extent, as seen in guinea-pigs sensitised with an increased adjuvant

concentration. The late asthmatic response is associated with an influx of a range of inflammatory cells including eosinophils and T lymphocytes (Nabe et al., 2005). Eosinophilia is correlated with the magnitude of the LAR, both being significantly JAK inhibitor increased in humans and animal models following allergen challenge (Dente et al., 2008, Evans et al., 2012 and Gauvreau et al., 1999). Additionally, corticosteroids which reduce eosinophil and lymphocyte numbers also decrease the LAR (Kawayama et al., 2008 and Leigh et al., 2002). The present study demonstrated that increases in both eosinophils and lymphocytes coincided with the development of

a LAR, supporting a link between these parameters. Although we examined cellular influx at also 24 h after Ova challenge and not at the peak of the LAR, our previous studies with earlier versions of this model have shown significant increases in neutrophils, macrophages and eosinophils at the time of the LAR (Danahay et al., 1999 and Toward and Broadley, 2004). However, not all results from this study support this relationship; eosinophils were also increased in protocols 1–4, which did not demonstrate a LAR. Studies in humans have also demonstrated similar results. Blocking OX40, a co-stimulator receptor important in generating allergic responses significantly attenuated eosinophilia with no effect on the LAR (Gauvreau et al., 2014). Additionally, older studies have demonstrated that anti-IL-5 therapy reduced eosinophilia but not AHR and the LAR in humans (Leckie et al., 2000). Overall, the role of eosinophils in the LAR remains uncertain. The investigation of factors such as the activation status of eosinophils may be more revealing than cell number.

The

MDS estimates the proportionate mortality due to diarrhea in <5 year children to be 13.2%. Thus the under-5 diarrheal mortality rate in India is 8.04 per 1000 live births or an annual mortality of 160.80 per 100,000 children. see more In the IRSSN, 1405 (39%) of 3580 children hospitalized with diarrhea during this period tested positive for rotavirus. Using WHO CHERG approach [20] of applying rotavirus proportion in hospitalized diarrhea to mortality data, the <5 rotavirus diarrhea mortality rate is 2.89/1000 live births or an annual rate of 58 per 100,000 children. Applying these rates of mortality to the 2011 birth cohort of India, estimated at 27,098,000 children, we estimate 78,583 deaths occur each year due to rotavirus with 59,336 of these deaths occurring in the first two years of life. Based on the 2241 child years of follow up in five birth cohorts, with 108 diarrheal hospitalizations including 32 rotavirus diarrheal hospitalizations, the rotavirus hospitalization

rate was 1427 per 100,000 children <2 years. The IRSSN data identified 88.2% of all <5 rotavirus diarrheal hospitalization occurs in children <2 years of age [12] providing a corrected estimate of 643 hospitalizations per 100,000 children <5 years age or 872,000 hospitalizations annually in India (Table 2). Unpublished data from a large phase III clinical trial, where 1500 children in Vellore were followed up for the first two years life and healthcare provided for without cost to participants, provide a ratio of 3.75 rotavirus outpatient

visits for every rotavirus hospitalization. The number of rotavirus diarrheal episodes GSK126 clinical trial and requiring outpatient visit is thus estimated annually in India at 3,270,000. The < 5 year rotavirus gastroenteritis rate in the four cohorts where rotavirus testing was performed was 8394 episodes per 100,000 children. Extrapolating this rate to India’s < 5 population 11.37 million episodes of rotavirus diarrhea occur each year. The vaccine efficacy (VE) of Rotavac® against severe hospitalized rotavirus gastroenteritis was 53.6% and that against rotavirus gastroenteritis of any severity was 34%. The 4 month to 5 year risk of rotavirus related death, hospitalization and outpatient visit were 251, 2714, and 9891 per 100,000 children. Introduction of Rotavac® in the National Immunization Program at current immunization coverage would result in 26,985 fewer deaths, 291,756 fewer hospitalizations and 686,277 fewer outpatient visits each year in India assuming no indirect effects for the vaccine (Table 3). The NNV to prevent one rotavirus related death was 743 children, while vaccinating 69 children would prevent a rotavirus hospitalization. Similarly, for every 29 children vaccinated one rotavirus outpatient visit can be averted. The median total direct cost (medical and non-medical) associated with rotavirus hospitalization was calculated at Rs. 8417 at a tertiary care hospital, Rs. 6969 at a secondary level hospital and Rs.

2D). The adjuvant activity of the cleavage product NSP4(112–175) was tested using KLH. Similar to full-length NSP4, either 10 μg or 20 μg of the cleavage product NSP4(112–175) enhanced KLH-specific serum IgG (5-fold) and fecal IgA (30-fold) (Fig. 3A and B) to levels higher than those observed in mice that received KLH alone (p < 0.05, Mann–Whitney U). As both doses induced equivalent antibody titers we chose the lowest dose to perform the subsequent experiments. These data indicate that the adjuvant domain of this protein is located in the C-terminus of NSP4 and that 10 μg of the cleavage product is optimal to elicit this effect. To test whether NSP4 from other rotavirus strains besides

the simian SA11 Cl3 NSP4 can also function as adjuvants, we tested the adjuvant activity of NSP4 from Anti-cancer Compound Library in vivo both a virulent and tissue culture-attenuated pair of porcine

rotavirus strains, OSU-v and OSU-a, respectively. As shown in Fig. 4, both OSU-a (GMT = 14,703) and OSU-v (GMT = 14,703) NSP4 induced an enhanced (8-fold increase) TT-specific serum, but not fecal, antibody response compared to the group receiving TT antigen alone. In addition, the levels of antibody induced by OSU-a and OSU-v NSP4 were similar to that induced by SA11 Cl3 NSP4. We next determined if NSP4, localized within a VLP, retained adjuvant activity. NSP4(112–175) was genetically fused to the inner core protein VP2 and when co-expressed with VP6 in insect cells VLPs (NSP4-2/6) were produced which were morphologically

indistinct from 2/6 VLP (Fig. 5A). Significantly increased (12-fold) TT-specific serum antibody was induced Adriamycin in the group of mice that received NSP4-2/6 intranasally with TT (GMT = 1838) compared to the TT alone group (GMT = 159) (Fig. 5B). In addition, despite the inability of the soluble NSP4 to enhance humoral response against TT, NSP4 internalized within 2/6-VLPs elicited significantly increased fecal IgA levels (p ≤ 0.05) compared to the co-administered antigen ( Fig. 5C). This adjuvant effect was due to the presence of NSP4 since 2/6 VLPs given with TT did not increase antigen-specific antibody responses and the level of antibody was comparable to the group receiving second TT alone In this study we demonstrated the mucosal adjuvant activity of rotavirus nonstructural protein NSP4 using model antigens. Full-length NSP4 from the SA11 rotavirus strain as well as a cleavage product NSP4 (112–175) were able to function as intranasal adjuvants and enhanced both serum and mucosal antibody responses specific to the co-administered antigen. In addition, an attenuated NSP4 from an avirulent porcine OSU-a rotavirus as well as NSP4 delivered inside a rotavirus VLP can efficiently enhance antigen-specific antibody responses. The adjuvant property of NSP4 varied depending upon the co-administered antigen suggesting that the outcome of adjuvanticity is affected by the nature of the antigen tested.

31% at 1000 μg/ml, followed by a moderate inhibition percentage and 43.41% at 500 μg/ml respectively. Hydrogen peroxide itself is not reactive, as it can sometimes be toxic to cell because it may give rise to OH radical in the cells. Addition of hydrogen peroxide to cells in culture can lead to transition metal ion dependent OH radicals mediated DNA damage. Scavenging of hydrogen peroxide by our crude endophytic extract

may be attributed to their phenolic nature, which can donate electrons to H2O2, thus find more neutralizing it to water.21 Nitric oxide scavenging activity of EEA is listed Table 4. In case of nitric oxide scavenging activity, EEA showed high activity 69.24% at 1000 μg/ml followed by a moderate activity 35.40% at 400 μg/ml. BHT and Ascorbic acid were used as the positive control. Nitric oxide is a diffusible free radical, which plays many roles as an effector molecule including neuronal signaling, and regulation of cell mediated toxicity. Nitric oxide (NO) is generated in different cell types by at least three

isoforms of NO synthase (NOS). Neuronal NOS (nNOS) and endothelial NOS (eNOS) are constitutively expressed and their enzymatic activity is Ca2+/calmodulin-dependent.22 Suppression of NO released may be partially attributed to direct NO scavenging, as the extract decreased the amount of nitrite generated from the decomposition of sodium nitroprusside in vitro. Based on the results obtained from the in vitro α-glucosidase inhibition, EEA was found Panobinostat research buy to show high activity. Hence in vivo studies were carried out using EEA on lowering maltose and sucrose levels in the blood. At 30 min after maltose load, the normal control Rolziracetam animals had shown an increase in plasma glucose level; whereas the EEA treated as well as the Acarbose treated animals had not shown any significant rise in plasma glucose level. As shown in Table 5 incubation of the EEA at different concentrations with intestinal alpha glucosidase enzyme caused an increased

activity with 83.33% inhibition when incubated at 1000 μg/ml concentration. However, the inhibitory effect was equally comparable to that of the acarbose, which is well known alpha glucosidase inhibitor. With the interesting result obtained using EEA, further in vivo study of α-glucosidase inhibition was carried out. The study reveals that there is no significant rise in the plasma glucose level. At 30 min after administration of maltose and sucrose orally, the normal control animals had shown an increase in plasma glucose level 109.79 mg/dl at 120 min; whereas the EEA treated as well as the Acarbose treated animals had not shown any significant rise in plasma glucose level. At 60 min after sucrose load, the control animals had shown an increase in plasma glucose level 118.81 mg/dl whereas the EEA treated as well as the Acarbose treated animals had not shown any rise in plasma glucose level Tables 6 and 7.

Rotaviruses, of the family Reoviridae, are triple-layered particles (TLPs) ISRIB consisting

of the outer capsid, inner capsid and core. The rotavirus genome consists of 11 dsRNA segments which code for the six structural (VP1-VP4, VP6, VP7) and five non-structural (NSP1-NSP5) proteins. The outer capsid proteins, VP7 and VP4, serve as viral attachment proteins and neutralization antigens [3]. VP4 is activated by proteolytic cleavage into two fragments—VP8* and VP5*. VP8* forms a globular attachment domain at the tip of the VP5* stalk [4]. A binary system classifies group A rotaviruses into 27 G and 37 P types [5] and [6], a classification initially based on neutralization specificities of VP7 (Glycoprotein) and VP4 (Protease sensitive protein). Globally, G1P[8], G2P[4], G3P[8], G4P[8] and G9P[8] genotype combinations of rotavirus strains are the most common cause of human infections [7]. Of these, G1P[8] strains are most predominant (37.7%) [7]. These strains exhibit diversity in the form of 11 G1 and 4 P[8] subgenotypic lineages

[8] and [9]. According to a multi-centre hospital-based study carried out in India from 2005 to 2009, G1P[8] strains were highly prevalent [10]. Two rotavirus vaccines, Rotarix and RotaTeq, are currently licensed in Venetoclax mouse many countries including India. Rotarix is a monovalent vaccine containing the attenuated human G1P[8] rotavirus strain 89-12. RotaTeq is a pentavalent vaccine containing five human-bovine reassortant rotavirus strains, each representing one human genotype—WI79-9 (G1), SC2-9 (G2), WI78-9 (G3), MycoClean Mycoplasma Removal Kit BrB-9 (G4) and WI79-4 (P[8]). Studies from different countries have revealed that the G1 and P[8] subgenotypic lineages included in these vaccines, prevalent at the time of vaccine development (1980s), are not predominant today [8], [9], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22] and [23]. Earlier, we have reported

identification of different lineages within VP7 gene of G1 rotaviruses circulating in Pune, western India [24]. The study did not include analysis of the corresponding P[8] lineages of VP4 genes and the rotavirus vaccine strains, 89-12 (Rotarix G1P[8]), WI79-9 (RotaTeq G1) and WI79-4 (RotaTeq P[8]) were not compared due to the unavailability of their sequence data at the time. The aim of the present study was to assess the diversity of G1P[8] rotavirus strains circulating among children with diarrhoea in Pune during the two time periods, 1992–1993 and 2006–2008, and compare sequences with the G1 and P[8] components of vaccines. A surveillance program for rotavirus disease and strains was carried out in children (<5 years), hospitalized for diarrhoea in Pune city during 1990s and 2000s [10] and [25] (Table 1). The G1P[8] rotavirus strains identified during the years 1992 (n = 8), 1993 (n = 11), 2006 (n = 21), 2007 (n = 29) and 2008 (n = 13) were selected in the present study for further characterization.

There are plausible mechanisms related to mechanical and immunological changes that may render women more vulnerable to respiratory infections during pregnancy [4] and [5]. The European Centre for Disease Prevention and Control (ECDC) has concluded that vaccination of pregnant women could reduce the number of influenza-related hospitalizations and deaths in this group and potentially the burden of influenza in children younger than six months [6]. The WHO SAGE committee has referred to “compelling evidence of substantial risk of severe disease in

this group…” [7], and WHO has subsequently recommended pregnant women as the highest priority group for vaccination against seasonal influenza. However, a recent systematic review [8] concluded that pregnancy as a risk factor for seasonal influenza, as opposed to pandemic influenza including A(H1N1)pdm09, is not sufficiently studied. Furthermore, check details ECDC has concluded that European studies of the disease

burden of seasonal influenza in pregnant women are needed [6]. Whereas an increased risk of influenza-associated GDC-0068 nmr deaths for pregnant women has been documented during pandemics [9], [10], [11], [12] and [13], deaths in pregnant women due to inter-pandemic influenza have only been described in occasional case reports [14], [15] and [16], suggesting that this outcome is unusual. Moreover, the evidence of an increased risk of severe disease for healthy pregnant women due to seasonal, inter-pandemic influenza mainly consists of observational studies of health Parvulin service utilization in USA and Canada [17] and [18]. Albeit healthcare utilization often being applied as an indicator of disease severity, it should be interpreted

with caution since healthcare utilization may be context dependent. For example, despite similar symptoms and severity, there may be differences in healthcare seeking behaviour, access to healthcare or medical recommendations. Furthermore, the relative risk does not inform on burden of hospitalization, and a sufficient absolute risk is needed to motivate vaccination. Hospitalization rates of 15 and 25 per 10,000 pregnant women or third trimester women have been found in Canada and USA, respectively [17] and [18], and in a study set in the UK the rate was estimated to 13 per 10,000 pregnant women [19]. Since these rates may be context dependent and estimates in a European setting are sparse, it was deemed that a national estimate for Sweden was necessary for policy purposes. Therefore we conducted a study of hospitalizations due to seasonal, inter-pandemic influenza or respiratory infection attributable to inter-pandemic influenza among pregnant women in Sweden and assessed the number needed to vaccinate (NNV) to prevent one such hospitalization. We conducted a retrospective, register-based study of inter-pandemic seasons, using ICD-10 codes that indicate influenza hospitalizations.

Biomechanical factors support the osteophyte development.29 One of the mechanisms of articular cartilage damage is stiffness of subchondral bone, if the bone becomes stiffer; it may be less able to absorb impact loads, which may in turn lead to increased stresses in the cartilage.28 Softening of articular cartilage in the patella, frequently described as chondropathy or chondromalacia of the patella, causes to erosion of the cartilage.30 Although chondromalacia of the patella is a common phenomenon, its aetiology is unclear; in addition to several functional and morphological changes in OA, studies has shown different inflammatory mediators, CCI 779 proteinases, Cell proliferation,

biochemical parameters in development of disease.31 Chondrocytes are the only cells in cartilage responsible for synthesis and breakdown of matrix which regulated by cytokines

and growth factors, under arthritis condition their balance may be disturbed.32 Cytokines which have an impact on articular cartilage metabolism are classified in three groups including, catabolic (IL1α, IL1β, TNF α), regulatory and enzyme inhibitory (IL-6, Il-8, IL-4, IL-10, IFNγ) and anabolic (Growth factors, IGF, COMPs, TGF β).33 It is generally accepted that IL-1 is the key cytokine at early and late stages of OA; the interleukin-1 (IL-1) family includes two agonists, check details IL-1α and IL-1β, are produced by two different genes34 and a specific receptor antagonist, IL-1Rα.35 Interleukin-l is a multifunctional pro inflammatory cytokine that affects most cell types and results in several effects including lymphokine production, cartilage breakdown, interfering with the activity of growth factors such as insulin-like growth factor, or decreasing the synthesis of key matrix components such as aggregan and proliferation

of fibroblast have a crucial role in arthritis disease.35 and 36 The presence of activated macrophages will release the IL which has a role in destruction of cartilage.37 NF- kβ (nuclear factor kappa-light-chain-enhancer of activated B cells) is nearly one of the key regulatory mechanisms involved in regulating and controlling expression of cytokines are critical in immune function, inflammation.38 It is known that stimulus of NF-kβ leads to expression of TNFα and IL1β.39 and 40 The TNF superfamily is a group of cytokines with important functions in immunity and inflammation, among these, TNF α is effective proinflammatory cytokine that plays an important role in inflammation, and matrix degradation by stimulating proteolytic enzyme secretion from chondrocytes and synovial fibroblasts.41 TNF induces fever initially by increasing prostaglandin E2synthesis in the hypothalamus and subsequently production of IL-1and IL6.

281, p < 0.001. Following the addition of belief composites (behavioural beliefs; normative beliefs; control beliefs) and attendance for first MMR, chi-squared improved only slightly, χ2(7) = 100.615, p < 0.001. There was, however, no reliable improvement with the addition of these four variables, χ2(4) = 6.335, p > 0.05. The

three direct predictors of intention selleck chemicals llc accounted for 48.0–64.4% of the variance in intention, with 82.7% of LMI and 85.7% of MI parents successfully predicted. Overall, 84.0% of predictions were accurate. With the inclusion of the three belief composites and attendance for the first MMR, the model accounted for 50.3–67.4% of the variance in intention, with 84.0% LMI and 85.7% of MI parents successfully predicted. Overall, 84.7% of predictions were accurate. Table 7 shows

the contribution of the seven individual predictors to the final model. Using the criterion of p ≤ 0.007, only attitude and perceived control reliably predicted parents’ intentions to take their child for the second dose of MMR, with attitude being the most important predictor. An increase in attitude of one point AZD9291 in vivo was associated with an increase in the likelihood of a parent taking their child for MMR by a factor of 6.84. An increase in perceived control of one point increased intention by a factor of 3.90. Thus, stronger intentions to immunise were associated with having more positive attitudes towards vaccination and having greater perceptions of behavioural control. Subjective norm exerted no influence on intention. Following the removal of four outliers, 104 cases were analysed. Using the criteria outlined in Section 3.6.2, a others sample size of 106 was recommended to test the overall fit of the model. Thus, a sample of 104 was adequate. Using a criterion of p ≤ 0.007 (Bonferroni correction for seven predictors), there was a good model fit based on the three direct predictors of intention (attitude; subjective norm; perceived behavioural control), χ2(3) = 60.534, p < 0.001. Following

the addition of belief composites (behavioural beliefs; normative beliefs; control beliefs) and number of children, chi-squared improved: χ2(7) = 76.506, p < 0.001. This time, there was a reliable improvement with the addition of these four variables, χ2(4) = 15.972, p = 0.003. The three direct predictors accounted for 44.1–58.9% of the variance, with 73.5% of LMI and 85.5% of MI parents successfully predicted. Overall, 79.8% of predictions were accurate. Belief composites and number of children in the family accounted for a further 18.6% of the variance in intention (between 52.1–69.5%). With the addition of these predictors, 81.6% of LMI and 85.5% of MI parents were successfully predicted, with 83.7% of predictions accurate overall. Table 7 shows the contribution of the individual predictors to the model. Using the criterion of p ≤ 0.

4) by following literature method.12 The homogenate was centrifuged at 14,000 rpm for 15 min. The supernatants (1 mL) were incubated with different concentration of compounds (10–500 μM) in the presence of 10 μM FeSO4 and 0.1 mM ascorbic acid at 37 °C for 1 h. The reaction was terminated by the addition of ABT 888 1.0 mL of trichloroacetic acid (TCA; 28%) and 1.5 mL of thiobarbituric acid (TBA; 1%). The solution was heated at 100 °C for 15 min, cooled to room temperature,

and centrifuged at 2500 rpm for 15 min, and the color of the MDA–TBA complex in the supernatant was read at 532 nm using a spectrophotometer. Butylated hydroxy anisole was used as a positive control. The inhibition ratio (%) was calculated using the following formula: Inhibitionratio(%)=(A−A1)/A×100, where A is the absorbance of the control and A1 is the absorbance of the test sample. Anti-lipoxygenase activity was studied using linoleic acid as substrate and lipoxidase enzyme.13 Test samples with varying concentration was dissolved in 0.25 mL of 2 M borate buffer pH 9.0 and added 0.25 mL of lipoxidase enzyme Selleck BGB324 solution (20,000 U/mL) and incubated for 5 min at 25 °C. After which, 1.0 mL of linoleic acid solution (0.6 mM) was added, mixed well and absorbance was measured at 234 nM. Indomethacin was used as reference standard. The percent inhibition was calculated from the following equation,

Inhibitionratio(%)=(A−A1)/A×100, where A is the absorbance of the control and A1 is the absorbance of the test sample. A dose response curve was plotted to determine the IC50 values. All

tests and analyses were run I triplicates and averaged. The structures of the newly synthesized indole based scaffolds Parvulin having pyrazole ring were confirmed by spectroscopic studies (IR, 1H NMR, 13C NMR, mass spectroscopic data) and elemental analysis. All the synthesized compounds (7a–n) were subjected for in vitro antioxidant activity evaluation. All the compounds showed moderate to high antioxidant activity compared with the standards (ascorbic acid and BHA). 50% inhibitory concentrations (IC50) were calculated and are depicted in Table 2. In all the antioxidant assays performed the results obtained were in the similar trend. Compounds 7d and 7b showed a very good antioxidant activity among the series that may be due to the electron donating nature of –OH and –OCH3 and also introduction of electron withdrawing groups such as Cl, NO2 in compounds i.e., 7g, 7f, 7m and 7n has led to the lower antioxidant potential when compared with the standards. For further assessment of biological significance, the compounds were preliminarily evaluated in vitro for their ability to inhibit soybean lipoxygenase by taking indomethacin as standard. Perusal of IC50 values shows that the compound 7c is the most active, within the set followed by 7b ( Table 2).

Nevertheless, it is being presented in this paper as it is applicable to analyzing any similar sigmoidal curve relationship in Excel, which is almost universally used. Furthermore, although the template provided here will work satisfactorily in the majority of cases, savvy users may modify the formulas and VBA code to suit their particular circumstances more precisely. However, the results provided by the Excel template are restricted to the regression line and the estimates of c and d of Eq.  (1), and do not permit the response of the flies to the anesthetics to be classified into sensitive, normal or resistant types — one of the major goals of the laboratory. The stand-alone

GUI-based this website Windows program HEPB does the same analyses as above, but in addition it constructs a prediction band at a user-defined confidence level and then determines the cut-off values from those prediction band limits that help to objectively distinguish among sensitive, normal and resistant phenotypes. These values also enable GSI-IX in vitro researchers to determine rapidly and objectively if experimental values are statistically different from their control ranges in their assays. As far as we are aware, HEPB is the only program that does

the four-parameter logistic regression, constructs the prediction band for the data, and provides objective, empirically determined cut-off values to distinguish among response phenotypes. Furthermore, it optionally generates 500 simulated values of the response variable within the range of the observed dose variable. This can be useful particularly when the sample size is limited and the user is unable to visualize the dose–response behavior in the data. While it might seem redundant to provide these two different avenues for performing this analysis,

much we believe that each program fills a niche within the laboratory. Most users will find the Excel template straightforward and will be comfortable with its interface. Additionally, it can interface with other Microsoft Office software, like Access, to store data in a laboratory database, if needed. There are other sources that also involve the use of Solver to fit non-linear equations (Harris, 1998). In addition, there are instructions available in several websites on the internet. However, none of these sources provide a template such as the one presented here that not only makes it easy for the uninformed user (who merely needs to enter the data in the template) but more importantly, has been programmed to auto-check for the goodness of fit and redo the analysis with sets of alternative starting values for c and d in Eq.  (1) until the goodness of fit criterion is met. It has been tested with a number of datasets that span a wide range of relationships between the dose and response and sample size ( Fig. 9), and has performed remarkably well ( Table 1).