In this study, several questions were answered: 1) What is the dynamics of both carbon components

in the Baltic Sea? 2) Do the dynamics and concentrations of both carbon pools differ in different regions of the southern Baltic Sea? 3) What factors influence POC and DOC concentrations? Selleckchem XL184 The highest fluctuations of DOC and POC occurred in the growing period (spring/summer) in the surface water layer. Concentrations changed rapidly during a year. This is attributed to DOC and POC concentrations strongly depending on recurrent intensive phytoplankton blooms (Dunalska et al., 2012 and Gustafsson et al., 2013). The most characteristic feature of both DOC and POC concentrations in the Baltic are distinct seasonal fluctuations. Best developed

in the surface water layer, they are caused by phytoplankton activity in the growing period that exceeds microbiological degradation/mineralisation. Surprisingly enough, seasonal dynamics is evident in both the subsurface (above the halocline) and the sub-halocline water layers. This can be attributed to particulate organic matter sinking (POC source) and biodegradation (DOC source) (Amann et al. 2012). As phytoplankton activity ceases in late autumn, the supply of fresh, Selleckchem RG7420 labile DOC and POC stops as well, and constant DOC concentrations (biochemically stable DOC) and residual POC are observed from then on until the resumption of biological activity in April of the following year. The importance of

phytoplankton in developing pools of DOC and POC in Baltic seawater is best indicated by the high correlation coefficients (R = 0.8) of the linear dependences DOC = f (pH) and POC = f (Chl a) (R = 0.9) ( Table 5). The abundance of dissolved organic substances in seawater depends on the POC concentration, water temperature and the intensity of photosynthesis. The last-mentioned process is responsible for CO2 depletion in seawater, which governs the seawater pH (Omstedt et al. 2014). The chlorophyll a concentration, used in Succinyl-CoA this study as a measure of living phytoplankton biomass ( Wasmund and Uhlig, 2003 and Granskog et al., 2005), demonstrated that phytoplankton must be the main source of POC in Baltic seawater. Hence, the natural variability of DOC and POC concentrations and its large fluctuations can be attributed to the main processes, namely, phytoplankton and zooplankton activities, bacterial decomposition and mineralisation of organic matter, and the contribution of fresh (river run-off) and highly saline (North Sea inflows) water masses. We can therefore conclude that organic matter in Baltic seawater, and most likely in seawater in general, consists of two fractions – labile and stable – with respect to biochemical degradation and mineralisation.

An alternative approach to the development of a CMV vaccine has been to utilise DNA vaccination to induce host responses to CMV gB and phosphoprotein 65 (pp65 is another viral target). Recent studies have shown that injection of combinations of plasmids, formulated with an adjuvant, can induce vaccine-specific immune responses, and can

prime for effective memory responses. The hallmark of herpes simplex virus types 1 and 2 (HSV-1 and HSV-2) is their ability to establish and maintain latent infection in sensory ganglion neurons. Periodic reactivation of the latent infection results in recurrent infections. Both HSV-1 Sorafenib in vitro and HSV-2 can cause myriad diseases but the greatest public health problem is genital herpes. Genital HSV-2 infection increases the risk of HIV acquisition and transmission, and control of genital herpes has been predicted to

significantly impact the HIV epidemic. Given the complex natural history of HSV infections, vaccines could have a variety of possible risks and benefits (Table 6.10). An effective HSV selleck compound vaccine has been sought for more than 80 years. Recently, an HSV-2 glycoprotein D (gD2) candidate vaccine containing the AS04 adjuvant (see Chapter 4 – Vaccine adjuvants), was tested in three large, double-blind, Phase III controlled trials. The first two studies recruited volunteers with a partner with genital herpes disease and found the candidate vaccine was 73% effective against genital herpes disease in women seronegative for both HSV-1 and HSV-2 ( Stanberry et al., 2002). Trends towards protection against infection were also observed, but were not statistically significant. The candidate vaccine was not effective in HSV-1 seropositive women; or in men, regardless of their HSV seropositivity status. These were the first studies to report a significant difference in vaccine efficacy between men and women. This Thalidomide finding could have important implications for other vaccines targeting sexually transmitted diseases. The basis for this difference could relate to differences in how men and women respond to novel adjuvants or may reflect differences in the acquisition and natural history of

genital herpes in men and women. A third Phase III efficacy trial of the gD2 candidate vaccine in HSV-1 and HSV-2 negative women who thought themselves possibly at risk of acquiring genital herpes (a different risk population than in the original two trials) has been completed and is being analysed. An initial assessment of the results of the third trial showed that the vaccine had an acceptable safety profile but the primary trial endpoint, prevention of genital herpes disease, was not met ( NIAID, 2010). Although the development of the vaccine has been stopped, further analyses and comparison of the trials may guide researchers as they continue seeking vaccines to control HSV infections. As discussed in Chapter 2 – Vaccine immunology, some pathogens have complex life cycles.

, 2011). Westerhausen et al. (2011) demonstrated that men show higher FA and lower diffusion strength compared to women in the genu and truncus of the corpus callosum. Interestingly, the diffusion parameters correlate with regional callosal size (exception: anterior genu subregions). The absolute size of the corpus callosum was found to be larger

in men. As a larger corpus callosum might provide less noisy DTI measures, this may lead to an overall higher sensitivity in the analysis of intelligence-related differences in this structure in men. No significant AD differences between intelligence Crizotinib cell line groups or women and men were observed in our study. As the axial diffusivity represents the diffusivity along the primary diffusion direction whereas the radial diffusivity indicates the diffusivity orthogonal to the primary diffusion direction (calculated by averaging the second and third eigenvalues of the diffusion tensor), it was hypothesized that axial

selleck diffusivity is an indirect indicator of the integrity of axons. Differences in FA and AD without differences in RD could be shown in studies investigating corpus callosotomy, optic neuritis, and axonal injury (Concha et al., 2006, Naismith et al., 2009, Song et al., 2003 and Thuen et al., 2009). Thus, lowered FA driven by decreased AD is considered a marker of acute and primary axonal damage. Since our sample comprised healthy subjects who reported no medical or psychological disorders, we expected no differences in axial diffusivity related to intelligence. Bennett, Madden, Vaidya, Howard, and Howard (2010) suggested that this result pattern (lowered FA driven by decreased AD) may be also a consequence of disrupted macrostructural reorganization of the fibers, such as less coherent fiber alignment. In this study, intelligence was associated with higher FA in the corpus callosum

and lower radial diffusivity in Mdm2 antagonist men. The FA differences between lower and higher intelligent men were previously reported by Navas-Sánchez et al. (2014). In a similar vein, in the voxel-wise analysis male adolescents showed significant correlations between IQ and FA, mainly in the corpus callosum (genu, body and splenium). Interestingly, our findings are not in line with previous findings by Tang et al. (2010) or Wang et al. (2012). Tang et al. reported lower FA in the forceps major in highly intelligent males and higher FA in this region in highly intelligent females. The discrepant findings could in part be the result of the different analysis methods. While Tang et al. (2010) used a “multiple region brute-force” fiber tracking method before FA maps were analyzed using a region of interest approach, we analyzed whole-brain DTI scans without a priori hypotheses using TBSS calculating maps of FA, RD, and AD. Wang et al. (2012) did the same analyses as we did with the only difference that their sample comprised adolescents.

Our further studies will be addressed to interactions of gallates with lipid membranes. The authors declare that there are no conflicts of interest. This research was supported by grants and fellowships from CNPq (Conselho Nacional de Desenvolvimento Científico

e Tecnológico), CAPES (Coordenação de Pessoal de Nível Superior) and FAPESC (Fundação de Apoio à Pesquisa Científica e Tecnológica de Santa Catarina). This work is part of the thesis of Clarissa Amorim Silva de Cordova who is PhD student in Pharmacy at the Universidade Federal de Santa Catarina. “
“Microtubules are a valuable target for cancer chemotherapy due to their crucial role in vital cellular click here functions of tumor cells. Tubulin inhibitors act by binding to some site on the tubulin dimers. These sites can be classified into three major categories based on their respective tubulin-binding Buparlisib domains, which include the “vinca alkaloid” domain, the “colchicine” domain, and the “paclitaxel” domain (Mollinedo and Gajate, 2003). Several clinical agents are able to interact with microtubules, promoting either microtubule disruption, such

as combretastatin, vinca alkaloids, and colchicine, or stabilization, such as paclitaxel and epothilone B. Although these compounds exert opposite effects on microtubules, both types of antitubulin agents share the common property of suppressing microtubule dynamics. Racecadotril This interference with microtubule dynamics results in metaphase arrest in dividing cells (Mollinedo and Gajate, 2003 and Jordan and Wilson, 1998). Phenstatins are a novel family of tubulin polymerization inhibitors. The first compound, phenstatin, was first synthesized by Pettit during research directed at the study of the structure–activity relationship of combretastatins, where it

was an unexpected product of the oxidation of combretastatin A-4 (CA-4) (Pettit et al., 1998, Cushman et al., 1992, Liou et al., 2002 and Liou et al., 2004). Phenstatin showed strong cytotoxicity and antitubulin activity similar to that of CA-4, and it has been extensively researched (Alvarez et al., 2008, Alvarez et al., 2009 and Alvarez et al., 2010). Therefore, a large number of phenstatin derivatives have been reported. (4-Methoxyphenyl)(3,4,5-trimethoxyphenyl)methanone (PHT) (Fig. 1) is a phenstatin analog. PHT is a known tubulin inhibitor that has potent cytotoxic activity (Frank and Tarbell, 1948, Liou et al., 2002, Alvarez et al., 2009 and Barbosa et al., 2009). Recently, Magalhães et al. (2011) showed that PHT displays antitumor effects in vitro and in vivo, without substantial systemic toxicity. On the other hand, the genotoxic/mutagenic activities of the compounds belonging to the phenstatin family had remained unexplored.

Takahashi et al. (2009) used an interpolation

scheme based on assumed advective transport. When we sub-sample the model to match the point measurement locations and months observed, and construct a model representation of data corresponding in time and space to the data, we see that the areas of high sources along 60°S are considerably reduced in intensity and extent (Fig. 11). The localized high source region from longitudes 20°E to 75°E nearly disappears. Now, the reduction Ivacaftor mouse and disappearance does not mean that the model agrees with data. We note that there is some evidence of outgassing in the data in this region, such as the portion just north and slightly west of the Ross Sea, and in the central Atlantic sector. However, the residual disagreement between the sub-sampled model and data points to model issues. The outgassing in the model, and to a lesser extent the data, is intensified in austral autumn and winter. This corresponds with high pCO2 (data not shown), resulting from convection of deep DIC and low ocean temperatures. The model is not capable of sequestering carbon uptake and sinking by biological processes in austral summer deep enough to avoid return to the surface in local winter. We note that other models exhibit outgassing along this BIBF 1120 supplier 60°S band as well (e.g.,

Doney et al., 2009), but they are admittedly less intense and less widespread than seen here. A similar explanation helps explain the discrepancies between the model and data in the South Atlantic. Poor sampling produces

a distorted view of the model-data comparison in the interpolated representations. In the sub-sampled model, the correspondence is improved (Fig. 11), although there are mismatches Parvulin along two north–south lines toward the eastern portion of the basin. In fact, the basin mean model-data flux difference here falls from −1.17 mol m−2 y−1 in the full interpolated data and model to −0.18 mol m−2 y−1 in the sub-sampled representation. Data sampling issues also contribute to the discrepancies in the South Pacific. Here the basin mean model-data flux bias is −0.45 mol m−2 y−1 for the interpolated comparison (Fig. 5). When the sampling biases are removed the difference is nearly half at 0.27 mol m−2 y−1. Model-data biases in the North Atlantic and Pacific are more complicated. Some of the difference is due to data sampling, as the LDEO data are missing in the northern Labrador Sea and the Sea of Okhotsk. But otherwise data sampling in these two basins is relatively complete spatially and temporally. The near-coastal source regions in the model near the US/Canada borders are in contrast to the data and suggest model formulation issues. Since the discrepancies appear in all the reanalysis versions (although variable), they are apparently not due to differences in forcing.

For the sample size calculations, we expected that the diagnostic performances of the different methods were similar. As a consequence, we designed our study as an equivalence study of alternative methods. Also, because the objective of each method was to identify tumor cells in samples obtained from the same patient, we tried to estimate differences in sensitivity and specificity between methods by comparisons within each patient. We assumed that when a selleck chemical method had a sensitivity of 80% and a specificity of 80% to identify tumor cells, the 2 methods would be considered equivalent if they could be performed within 20%

of one another (range of equivalence of 0.80). Also, because about 75% of patients

were expected to have a final diagnosis of malignancy, the calculated sample size was 77, with a power of 90% and a 2-sided significance level of 5%. Data were analyzed by using SPSS 18.0 for Windows (SPSS Inc, Chicago, Ill). A total of 85 patients were eligible during the study period. Two patients were excluded due to refusal. Another 2 were omitted from the analysis because the intended procedures could not be completed because of poor cooperation. Therefore, the final analyses were performed selleck products for a total of 324 punctures from 81 consecutive patients. Baseline characteristics and the final diagnosis are summarized in Table 1. One patient whose result of EUS-FNA was atypical cells was found to have

chronic pancreatitis after surgery. Of 4 cases with negative cytopathology results, 1 patient was diagnosed with pancreatic endocrine tumor and Thymidine kinase another with metastatic renal cell carcinoma after surgery. The other 2 patients were finally diagnosed as having pancreatic cancer during follow-up. There were no procedure-related adverse events except for 2 patients who developed mild acute pancreatitis and improved with conservative treatment. The number of diagnostic samples (118 [72.8%] of 162 vs 95 (58.6%) of 162; P = .001), cellularity (OR 2.12; 95% CI, 1.37-3.30; P < .001), and bloodiness (OR 1.46; CI, 1.28-1.68; P < .001) were higher in S+ than in S- ( Table 2). No air-drying artifact was observed in either group. Also, S+ was superior to S- in terms of accuracy (85.2% vs 75.9%; P = .004) and sensitivity (82.4% vs 72.1%; P = .005), although specificity was similar (95.8% vs 100%; P = .999). Bloodiness was greater in RS than in AF (OR 1.16; CI, 1.03-1.30; P = .017), although the number of diagnostic samples (108 [66.7%] of 162 vs 105 [64.8%] of 162; P = .608), cellularity (OR 0.99; CI, 0.86-1.14; P = .870), and air-drying artifact (none for both; P = .999) were not different ( Table 3). There were no differences in accuracy (79.6% vs 81.5%; P = .582), sensitivity (75.7% vs 78.8%; P = .455), and specificity (100% vs 95.8%; P = .999) between RS and AF.

). Standard demographic information such as age, sex, race and ethnicity provides basic information about the study population. The additional demographic characteristics listed in Table 1 have all been found to be important in CFS studies. Some, such as body mass index (BMI), socioeconomic status, insurance, living arrangements, may be associated with risk for illness (Friedberg and Jason, 1998; Jason et al., 2003). Other variables, such as mode of onset and duration of illness are important to a subgroup of patients with CFS. In particular, acute versus gradual onset have been consistently

noted to be important in stratifying disease. However these terms do not have accepted definitions, so it is essential that investigators specify what approach was used to make the distinction. The specific questions or methods used to determine mode of onset should be cited (if previously SP600125 research buy published) BTK inhibitor order or be provided in supplementary

material. Duration of illness is an important characteristic, as increasing time from onset increases the potential for secondary co-morbidities to develop (Friedberg et al., 2000). Factors that exacerbate or trigger illness are of interest, although not necessary for all studies. One might also ask about the episodic nature of the illness and the perceived periodicity of symptoms and periods of relative remission. If the information is provided, the method of collection (i.e. specific questions, approach to summary) should be provided. Whenever information is collected via questions or questionnaires, the method of administering these should be provided; for example given by interviewer selleck kinase inhibitor over telephone

or in person, self-administered written or on-line. Questionnaire should be provided as supplementary material along with scoring method, or if fully described in publications, the citation given. In the case of published instruments, any change in format or scoring should be noted. The case definition used to enroll patients should be specified (see footnote 1). In addition, the method used to apply the case definition should be indicated. Parts of case definition are often gathered through symptom inventories. Symptoms probed should include post-exertional malaise, unrefreshing sleep, impaired memory or concentration, muscle pain, multi-joint pain, headaches, tender cervical or axillary lymph node, and sore throat. Additional symptoms may be in neurologic, autonomic, neuroendocrine, immune areas. Examples of symptom inventories used in CFS studies include the DePaul Symptom Inventory and the CDC Symptom Inventory. Until there are specific diagnostic markers for CFS, the diagnosis remains one of exclusion. While patients with exclusionary conditions, i.e.

Louis, MO, USA). Secondary antibodies (α-mouse

IgG and α-rabbit IgG) conjugated to peroxidase were obtained commercially from Boehringer Mannheim (Mannheim, Germany). Adult honey bees (workers, drones, and queens) were collected from an A. mellifera colony (Africanized hybrids) at the experimental garden of the Federal University of Uberlandia (Uberlândia, MG, Brazil). To distinguish between nurse and forager worker honey bees, physical features, i.e., coat condition and damage to wings were considered, as well as the development of the hypopharyngeal gland observed at the time of brain dissections. Pre-pupal honey bee larvaes were collected from A. mellifera colonies (Africanized hybrids) and maintained at the experimental apiary of the University of São Paulo (Ribeirão Preto, SP, Brazil). Rabbits and rats used in the assay described in Fig. 1 were provided http://www.selleckchem.com/products/abt-199.html by the University’s Animal Facility and were used under the supervision of the Animal Experiments Review Board at our University. Honey bees were anesthetized on ice and dissected. Larval ganglia and adult brains were removed, frozen in liquid nitrogen, and stored in microtubes at −80 °C. The tissue samples (1 worker/queen or ∼30 worker/drone bee brains, or 2 rabbit/rat learn more brains) were homogenized with a hand blender in cold homogenization buffer (40 mM Hepes, pH 7.7, 10 mM EDTA, 2 mM EGTA, 5 mM ATP, 2 mM

DTT, 1 mM benzamidine, 0.1 mM aprotinin and 0.5 mM PMSF). Supernatants were obtained by centrifugation at 40,000g for

40 min at 4 °C. When necessary, protein extracts were concentrated by precipitation with 10% trichloroacetic acid for 15 min on ice, which was followed by centrifugation at 12,000g for 10 min at 4 °C. The precipitates were then solubilized in a small volume of SDS–PAGE sample buffer (100 mM Tris–HCl, pH 8.0, and 25% glycerol). The optical and antennal lobes, mushroom bodies and central region from thirty honey bee brains were dissected, homogenized and centrifuged as described above. Total protein concentrations ( Bradford, 1976) were determined to allow comparison SDS–PAGE and Western blot analyses, as described below. Total protein samples (20 μg) were applied to 5–22% polyacrylamide gradient gels under denaturing conditions (Laemmli and Favre, 1973). Cyclooxygenase (COX) The molecular weight markers were purchased from Sigma–Aldrich (St. Louis, MO, USA), and the gels were stained with Coomassie brilliant blue. For immunoblotting, proteins were transferred to nitrocellulose membranes in Tris–glycine buffer as described by (Towbin et al., 1979). The blots were incubated with 5% dried milk in Tris-buffered saline (TBS-T) (50 mM Tris–HCl, pH 8.0, 150 mM NaCl, 0.05% Tween 20) and probed with primary antibodies diluted to 0.2 μg/mL in TBS-T and a peroxidase-conjugated anti-rabbit IgG secondary antibody.

It should find protocol be noted that such spectra are particularly useful for the radiometric remote sensing of the sea surface (see, for example, Heron et al. 2006). Another representation of the high frequency spectra was put forward by Hwang & Wang (2001), who for the equilibrium and saturation parts of the wave number spectra assumed that equation(9) S1(ω)={2bgu*ω−4forωp<ω≤ωiBg2ω−5forωi<ω<ωu,where ωi   = 6ωp  , and the friction velocity u  * is given by ( Massel 2007) equation(10) u*=CzU10,where equation(11) Cz≈(0.8+0.065 U10)×10−3.Cz≈(0.8+0.065 U10)×10−3.The upper limit of the frequency ωu   above which wave components

are suppressed by a slick is ωu=gku=2πg/0.3~14.33 rad s−1. The impact of the low-frequency part of the spectrum on surface wave slopes is generally MEK inhibitor small, but for simplicity we will apply here the JONSWAP and Pierson-Moskowitz spectra (Hasselmann et al. 1973, Massel 1996), when the high frequency part of the spectra attenuates according to the function ≈ ω−5. Thus, we have: equation(12) S(ω)=αg2ω−5exp[−54(ωωp)−4]γδ1,in which γ = 3.3; equation(13) δ1=exp[−(ω−ωp)22σ02ωp2], equation(14) σ0={0.07forωωp<10.09forωωp≥1.The coefficient α and peak frequency ωp are defined by the non-dimensional fetch as equation(15)

α=0.076(gXU102)−0.22, equation(16) ωp=7πgU10(gXU2)−0.33.When the peak enhancement factor γ = 1, the JONSWAP spectrum reduces to the Pierson-Moskowitz spectrum. In the Pierson-Moskowitz and JONSWAP spectra, negligible energy is contained in the frequency band 0 < ω^=ω/ωp < 0.5. Hence, we set the lower limit at ω^l=0.5. The upper limit ω^u, which is not necessarily equal to ∞, requires more attention as its influence on spectral moments, especially

on higher moments, is substantial. In particular, for moment mn   we have equation(17) mn=αg2ωpn−4∫ω^lω^uω^n−5exp(−54ω^−4) γrdω^,ω^=ωωp.Let us now assume that ω^l=0, ω^u=∞, and γ   = 1 in the Pierson-Moskowitz spectrum. Hence, the moment mn   many becomes ( Massel 2007) equation(18) mn=αg2ωpn−4∫0∞ω^n−5exp(−54ω^−4)dω^=βg2ωpn−44(54)n−44Γ(4−n4),where Γ(x  ) is the gamma function ( Abramowitz & Stegun 1975). Equation (18) indicates that the fourth moment m  4, for example, becomes infinite as Γ(0) = ∞. The only way to calculate this moment for practical applications is to impose some threshold frequency ω^u≠∞. In oceanological and engineering practice it has usually been assumed that ω^u=6. Waves with frequency ω = 6ωp can still be considered gravity waves, as the viscous effects are negligible. Therefore, using eq. (17), the moment m4 for the JONSWAP and Pierson-Moskowitz spectra becomes ( Massel 2007) equation(19) m4=0.076 a4g2(gXU2)−0.02,where X is the wind fetch, V10 is the wind speed at the standard height of 10 m above sea level. The coefficient a4 for the JONSWAP spectrum is a4 = 1.

Unfortunately, both of these studies were mainly discovery efforts to establish a reliable and reproducible workflow for the analysis of carrier protein-bound peptides and have yet to validate their putative OvCa markers in independent cohorts. The identification of autoantibody signatures in serum has also been investigated for OvCa biomarker discovery. OvCa is often characterized by the complex network of inflammatory cytokines present in Navitoclax clinical trial the microenvironment and the involvement of immune-related cells such as tumour-associated macrophages. As such, populations of anti-tumour antibodies may be present and

detection of said immunological responses to tumorigenesis may help to detect early stage disease. In a laying hen model of human

OvCa, Barua et al. identified 11 proteins as immunoreactive ovarian antigens through LC MS [52]. Although this was the first study to identify immunoreactive ovarian antigens by serum anti-tumour antibodies, the authors recognized the fact that the ovarian antigens could Afatinib research buy not discriminate laying hens with non-malignant ovarian conditions from those with OvCa. Philip et al. investigated the immunoproteome of OvCa and healthy control sera, as well as that of the conditioned media of the OVCAR3 and SKOV3-A2 cell lines [53]. Overall, 8 autoantibody-reactive autoantigens were identified that were present in all five cancer serum composites and in both cell lines: A-kinase anchor protein 9, eukaryotic translation initiation factor 4, midasin, RAD50, talin 1, vinculin, vimentin, and centrosome-associated protein 350. Furthermore, the authors identified a subset of the MS-generated autoantigens that were implicated in both

humoral (B-cell) and cell-mediated (T-cell) immunity. However, the suggested novel autoantibody biomarkers for OvCa diagnosis were not validated in an independent cohort. Future studies will thus need to address how well such putative autoantibody-based markers perform in independent, blinded validation. A final approach that has been gaining popularity is MALDI MS imaging of cancer tissues to identify markers that may be shed into the extracellular space. In this technique, tissues are directly subjected to ionization and mass analysis to generate an array of mass spectra for all positions across the tissue specimen. Etofibrate As a result, the protein content of specific regions of interest can be determined, as well as the spatial distribution of specific proteins across the tissue [54]. El Ayed et al. was able to identify the reg-alpha fragment of the 11S proteasome activator complex as a putative biomarker through correlative analyses between MALDI MS imaging and immunohistochemical analysis with an anti-reg-alpha C-terminal antibody [55]. Expression of this protein was validated using Western blot and PCR on the SKOV-3 OvCa cell line. However, the authors did not validate overexpression of the marker in clinical samples. Liu et al.