The percentage of patients with below-reference sperm volume (<1.5 mL), cell count (<15 × 106/mL), motility (A + B) (<32%) and morphology (<4%) was higher in the low TT group but not statistically different compared to the normal TT group. Multivariable regression analysis revealed that low TT and free T levels had no significant effect on the aforementioned semen parameters . Despite our robust methodological approach, the presence of biases related to retrospective design cannot be excluded. Our findings highlighted the lack of association between low TT levels and semen parameter alterations in male partners of infertile couples undergoing IVF, with a total sperm count greater than 5 million. However, it is important to emphasize that more patients in the low TT group had subnormal semen parameters, albeit the difference was not statistically significant. Larger, prospective studies are warranted in order to validate these findings, as well as to investigate the existence of a TT threshold below which semen parameters might be negatively affected.Low serum testosterone is found in approximately 15% of subfertile men. Although testosterone is essential in spermatogenesis, it is unclear whether low testosterone levels may have a negative impact on the semen parameters of men belonging to infertile couples with a total sperm count greater than 5 million. Furthermore, it is debatable whether the initial evaluation of the subfertile male should include an endocrine assessment. This was a retrospective, single-center cohort study conducted at a tertiary fertility clinic. Male partners of infertile couples undergoing in vitro fertilization (IVF), with a total sperm count greater than 5 million, were included. All men provided morning blood samples, and none had been on exogenous testosterone or other relevant medications. Low total testosterone (TT) was defined as <264 ng/dL. Free T was calculated using TT and sex hormone-binding globulin (SHBG) levels (nmol/L) by a constant albumin concentration of 43 g/L. In total, 853 patients were included: 116 had low TT (<264 ng/dL) and 737 had normal TT (≥264 ng/dL). Semen volume, sperm cell count, progressive (A + B) motility and morphology (≥4% strict Kruger) were lower in the low TT group but not significantly different between low and normal TT groups (3.2 ± 1.79 vs. 3.23 ± 1.64,  Male factor infertility accounts for approximately 40–50% of all infertility cases , with stLow blood serum testosterone (T) has been observed in approximately 15% of infertile men . SertoliHowever, although evidence exists suggesting that T plays a crucial role in testicular function  and a miIn this scenario, the aim of our study was to investigate the effect of low TT blood serum levels on semen parameters in the male partner of couples with a sperm count greater than 5 million and undergoing in vitro fertilization (IVF) treatment.This was a retrospective, single-center cohort study including men with a total sperm count greater than 5 million among all consecutive infertile couples attending the Centre for Reproductive Medicine, Universitair Ziekenhuis Brussel, Belgium, from January 2009 to December 2017. Infertility was defined as the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse as reported by the World Health Organization (WHO) . The stuThe study population consisted of 853 male partners of all consecutive couples with primary infertility undergoing IVF and reporting a baseline semen analysis with a total sperm count greater than 5 million and at least 5 million motile sperm . Men werSemen collection took place after the blood sample. Sperm was collected in the laboratory by masturbation; 2–5 days of sexual abstinence were required. Only results from fresh ejaculates were used for the analysis, while frozen sperm samples were not considered. After 30 min to 1 h of liquefaction at 37 °C, semen analysis started with a macroscopic examination of the view, odor, viscosity, volume and pH. All semen samples were microscopically analyzed for sperm concentration, motility and morphology according to the criteria reported by the WHO in 2010 . Sperm cSerum sex hormone-binding globulin (SHBG) and TT were measured with the Elecsys Testosteron II and Elecsys SHBG assay on a Cobas 6000 immunoanalyzer . The same assay was used for all patients.p-value < 0.05 was considered as statistically significant.Continuous data were presented as median (interquartile range)/mean ± standard deviation (SD) and categorical data were described in a number of ways, including the numerator and denominator, and percentages. Continuous variables were analyzed using the independent T-test or Mann–Whitney U test depending on the normality of the distribution. Normality was examined by the use of the Shapiro–Wilk test. Categorical variables were analyzed by Pearson’s chi-squared test or Fisher’s exact test, as appropriate. The association of TT status and free T with semen parameters , after adjusting for potential confounders, was examined by multivariate linear regression. All covariates  were simultaneously entered into the multivariate linear regression model. The assumptions for the final model were successfully tested. All statistical tests used a two-tailed α of 0.05. Analyses were performed using STATA 13.0. A p < 0.001; 213.37 ± 40.4 vs. 472.58 ± 141.02, p < 0.001, 33.8 ± 8.9 vs. 35 ± 14.7, p < 0.001, respectively). Conversely, age, blood serum follicle-stimulating hormone (FSH) and smoking status did not differ significantly between the two groups.In total, 853 male patients with a total sperm count greater than 5 million were included. Among them, 116 (13.6%) had low TT (<264 ng/dL) and 737 (86.4%) had normal TT (≥264 ng/dL). Patient baseline characteristics according to the blood serum TT levels are summarized in p = 0.34; 76.82 ± 83.18 vs. 67.55 ± 57.70, p = 0.7; 54.89 ± 19.45 vs. 56.25 ± 19.03, p = 0.6, respectively). Similarly, semen volume and morphology did not differ significantly . The percentage of patients with below-reference volume (<1.5 mL), sperm cell count (<15 × 106/mL), motility (A + B) <32% and/or morphology (<4%) was higher in the low TT group, although the difference when compared to the normal TT group was not significant    . Similarctively) . Results6/mL), motility (A + B) <32% and/or morphology (<4%) was higher in the low TT group than in the normal TT group, the difference was not statistically significant.The results of our large retrospective study demonstrated that low TT (<264 ng/dL) was not associated with subnormal sperm parameters in infertile couples with a total sperm count greater than 5 million. Furthermore, despite the fact that the percentage of patients with below-reference sperm volume (<1.5 mL), sperm cell count  in the male partner were not associated with low sperm parameters, except for poor sperm morphology . However4 ng/dL iThe strength of our study relies on its design, including a large sample size of infertile couples, with the male partner having a total sperm count greater than 5 million, performing the semen analysis in the same laboratory according to WHO 2010 guidelines. Moreover, the natural diurnal variation in blood serum testosterone levels was avoided due to the collection of patients’ blood samples during the early morning (before 10 a.m.). Despite the robust design of our study, limitations do exist and should be taken into consideration when interpreting the results. The retrospective nature of our study is associated with an inherent risk of bias. Therefore, although a significant effort has been made to eliminate all known sources of systematic error through multivariable analysis, nonapparent sources of bias might still exist. Pregnancy rates were not available for several couples. Moreover, data for the testosterone-to-estrogen ratio (T:E) were not available, although it has been reported to be an important parameter for the male fertility evaluation . FinallyThe evaluation of other semen biomarkers such as the DNA fragmentation index or oxidative stress was not performed.In conclusion, low male TT blood serum levels do not seem to have an impact on semen quality. However, it is important to emphasize that more patients in the low TT group had below-reference semen parameters. In the future, larger, prospective studies are warranted in order to validate these findings, as well as to investigate the existence of a TT threshold below which semen parameters are negatively affected. The next step would be to evaluate whether correcting T levels could have an impact on semen parameters and pregnancy rates and eventually examine the gene expression level of the sperm by using RNA sequencing.


Helianthus annuus) orients eastward after its anthesis , from which point it no longer tracks the Sun. Although several hypothetical explanations have been proposed for the ecological functions of this east facing, none have been tested. Here we propose an atmospheric-optical explanation. Using (i) astronomical data of the celestial motion of the Sun, (ii) meteorological data of diurnal cloudiness for Boone County located in the region from which domesticated sunflowers originate, (iii) time-dependent elevation angle of mature sunflower heads, and (iv) absorption spectra of the inflorescence and the back of heads, we computed the light energy absorbed separately by the inflorescence and the back between anthesis and senescence. We found that the inflorescences facing east absorb the maximum radiation, being advantageous for seed production and maturation, furthermore west facing would be more advantageous than south facing. The reason for these is that afternoons are cloudier than mornings in the cultivation areas of sunflowers. Since the photosynthesizing green back of mature heads absorbs maximal energy when the inflorescence faces west, maximizing the energy absorbed by the back cannot explain the east facing of inflorescences. The same results were obtained for central Italy and Hungary, where mornings are also less cloudy than afternoons. In contrast, in south Sweden, where mornings are cloudier than afternoons, west-facing mature inflorescences would absorb the maximum light energy. We suggest that the domesticated Helianthus annuus developed an easterly final orientation of its mature inflorescence, because it evolved in a region with cloudier afternoons.The mature inflorescence of sunflowers ( Helianthus annuus) continuously change their orientation so that they always face approximately normal to the incoming solar radiation throughout the day1. At sunset the immature head faces nearly west, while at night it reorients and turns nearly east well before sunrise3. Sunflower anthesis  begins approximately on the 60th day after sowing, when the inflorescence begins to open, immature ray flowers become visible, flowers are not heliotropic anymore, and usually get locked facing east/northeast4. Leaf expansion also ceases at anthesis, when the sunflower begins investing its resources in seed production in its flowering head. Solar tracking of sunflower inflorescences slows down and stops by anthesis due to a putative molecular signal and/or structural change5. The head contributes more than 25% of the whole-plant light absorption at maturity6. After anthesis, the fruiting head bends gradually more and more downwards due to its increasing weight7, and the heliotropism of sunflower leaves continues with a dampened amplitude2.The leaves and the immature non-flowering head of common sunflowers , Xanthium strumarium (Asteraceae), and diverse arctic and alpine species, for example13.Besides the domesticated and wild 14. This explanation, however, is directionally non-selective and holds true for any azimuth angle.Non-skyward orientation may be adaptive, because the narrower perch could reduce seed depredation by birds15. However, a fixed westerly orientation has the same advantage.The easterly direction of the flowering heads may have the advantage of reducing the heat load at noon4. This idea has not been tested experimentally.The eastward orientation permits greater reception of solar radiation in the early morning, which may speed up drying of morning dew and thus could reduce the opportunity for fungal attack16. In field experiments Atamian et al.17 showed that the surface temperature of sunflower blooms contributes to, but does not solely determine, the differential attractiveness of east- and west-facing flowers to pollinators.The eastward orientation may promote sunflower attractiveness to pollinators through increased morning interception of solar radiation, coincident with the daily timing of anther emergence and pollen presentation14. Keeping cooler floret temperatures may boost yield or fitness by preventing reductions in pollen viability and fertilization (pollen sterility increases at temperatures > 30 °C) or by improving grain filling during seed development and set19. However, in afternoons the air temperature, rather than the solar irradiance, is usually higher than in mornings20.Eastward orientation could reduce heat load especially during afternoon periods of high irradiance18. The surface temperature of eastward oriented sunflower heads is 3–8 °C lower than that of heads artificially facing skyward at midday16. However, due to symmetry, the same is also true for westward oriented sunflower heads.Higher head temperatures result in more rapid seed maturation and reduced grain fillingThere are several non-mutually exclusive hypothetical explanations for the ecological functions of the final eastward orientation of the inflorescence of mature sunflower heads:25, the ecological functions of the eastward orientation of mature heads are still unresolved. In this work we propose an atmospheric-optical explanation for the final easterly orientation of sunflower inflorescences. According to our hypothesis, the east facing of the flowering head maximizes the solar and sky radiation absorbed by the inflorescence, which is advantageous for seed production and maturation. Using numerical calculations with astronomical (solar movement in the sky), meteorological  and physiological (absorption spectra and time-dependent elevation angle of sunflower heads) input data, we show that in the region from which domesticated sunflowers originate  sunflower inflorescences facing east, rather than west or south, absorb the maximum amount of radiation, because afternoons are usually cloudier than mornings. This complementary explanation does not invalidate the earlier hypotheses emphasizing the ecological significance of eastward orientation of mature sunflower inflorescences.Although many physiological details of sunflower heliotropism and nocturnal reorientation have been revealedOne of the most important input data for our calculations were the solar paths across the sky in Boone County  in July, August, and September Fig. . The sunt) in July, August, and September in Boone County  versus time is the following:t is given in day. In the first 3 weeks after anthesis the average θn decreases steeply from 10°, reaches its minimum at − 75° on the 42nd day, and finally increases slightly to − 72°. The steady decrease of θn is mainly due to the gradual increase of the head’s weight. Although the final small increase of θn is statistically non-significant because of the high standard deviations, it is a real biological effect: during necrosis of the curved end of the stem at the head, the stem’s upper side dries quicker than its lower side (and thus the upper stem length decreases faster than the lower one), because the former receives more direct sunlight than the latter.Figure A(λ) ≤ 1 of young (2 weeks after anthesis) and old (4 weeks after anthesis) inflorescence and back of the sunflower head measured in the field under overcast conditions. For both young and old inflorescences, light absorption was weaker in the red and near infrared (NIR) spectral ranges than in the blue, violet, and near ultraviolet (NUV) ranges  spanning the NUV, violet, and blue ranges, and a narrower secondary maximum around 680 nm.Figure A ≈ 1) in the NUV and violet, and a narrower secondary maximum around 680 nm.The shape of the absorption spectra for the back of sunflower heads Fig. B is simietotal per unit area absorbed by the inflorescence and the back of a mature sunflower head between anthesis (1 July) and senescence (7 September) as a function of the azimuth angle αn of the head’s normal vector. Table einflorescence and eback as well as esum = einflorescence + eback. The total energy absorbed by the back is 4–5 times larger than that absorbed by the inflorescence. According to Fig. max1 =  − 94°, which is almost equal to the east direction (αeast =  − 90°). The function e(αn) of the inflorescence has a secondary maximum at αmax2 =  + 92° being approximately the same as the west direction (αwest =  + 90°). The minimum of e(αn) is at αmin =  + 1° coinciding practically with the south direction (αsouth = 0°). An inflorescence facing east absorbs emax1/emax2 = 1.1 and emax1/emin = 1.5 times more energy than that facing west and south  of the back has a secondary maximum at αmax2 =  − 125° being a northeast direction. The minimum of e(αn) is at αmin =  − 12°, which is close to south. The back of a head whose inflorescence faces northwest absorbs emax1/emax2 = 1.04 and emax1/emin = 1.22 times more energy than that of a head with inflorescence facing northeast and south , if the maximization of energy absorbed by the back were the goal.According to Fig. einflorescence(αn) is at αn =  − 92°  and αn =  − 91° (Hungary), while the first maximum of eback(αn) is at αn =  + 125° ; furthermore, an east-facing sunflower inflorescence absorbs 5%  or 7% (Hungary) and 57% (Hungary) or 75%  more energy than that facing west and south, respectively. The reason for this similarity is that in Italy and Hungary the afternoons are also cloudier than the mornings, as in Bone County  > σafternoon(t) . The secondary maximum of einflorescence(αn) is at αmax2 =  − 88° being practically the same as east (αeast =  − 90°). The minimum of einflorescence(αn) is at αmin =  + 1°, which is practically south (αsouth = 0°). An inflorescence facing west absorbs 5% and 34% more energy than that facing east and south, respectively, while an inflorescence facing east instead of south absorbs 28% more energy.In contrast, in south Sweden mornings are cloudier than afternoons, that is σ(t) Fig. D. The comax1 =  − 125°. The function eback(αn) has a secondary maximum at αmax2 =  + 125° and a minimum at αmin =  + 5°. The back of a head with inflorescence facing northeast absorbs emax1/emax2 = 1.02 and emax1/emin = 1.41 times more energy than that of a head with inflorescence facing northwest and south  < σafternoon(t) in the area of the origin of cultivated sunflowers.Our final conclusion is that the east facing of mature sunflower heads has the advantage of maximizing the total light energy absorbed by the inflorescence, if afternoons are cloudier than mornings as is the case in Boone County . The excess energy is mainly due to the NUV, violet, blue and red spectral ranges in which the absorption of the inflorescence is large Fig. A. This e28. The existing six explanations for the east facing of mature sunflower heads mentioned in the Introduction are all hypothetical and untested. In order to reveal a potential new explanation of the ecological function of the permanent east facing of sunflower heads, in this work we computed the total light interception by the head after anthesis when solar tracking is stopped. We found that the energetically ideal azimuth direction for a mature sunflower inflorescence to receive maximal solar and sky radiation is east  since their domestication they could not have adapted to the local cloudiness conditions. The genetic coding is the reason, we believe, for the observation that the eastward orientation of mature sunflower heads is independent of the specifics of regional cloudiness.Because different regions have different diurnal cycle (and asymmetry) of cloudiness, the question arises: Why do mature sunflower heads orient always eastward regardless of geographic location? The answer might be due to the fact that domesticated sunflowers originate from eastern North American decreases steeply from 10° at anthesis to a minimum of − 75° on the 42nd day after anthesis . This can explain the east orientation of the mature heads of sunflowers originating from the region of Boone County, where afternoons are indeed cloudier than mornings in the growing season.However, we found that if afternoons are cloudier than mornings, then the total energy absorbed by the inflorescence is maximal at azimuth angle α14, a non-skyward orientation of the inflorescence of mature sunflower heads could reduce seed depredation by birds. This hypothesis is supported by our qualitative observation that birds clinging onto the edge of sunflower heads with higher elevation angles can easier pick the seeds than on more downward-facing heads. Birds usually land on the uppermost edge of the head, because they can pick the uppermost seeds the easiest  of east facing of sunflower heads:4. Another advantage of this surplus energy may be a stronger attraction of morning-active pollinators to east-facing inflorescences as hypothesized by Lamprecht16 and Atamian et al.17. Hence, our atmospheric-optical explanation for the merit of east facing of sunflower inflorescences is consistent with these two hypotheses.The 10–50% surplus energy absorbed by an east-facing sunflower inflorescence compared to an inflorescence facing west-south obtained in this work may be advantageous for seed development and maturation. Furthermore, this surplus energy could accelerate the evaporation of morning dew condensed on the inflorescence, leading to a reduced chance of fungal attack as hypothesized by Lang and Beggn <  + 40°, Fig. n and s of the head and the sun rays  sunflower head facing east or west at smaller γ, thus resulting in larger received solar energy. Under persistently clear skies the ideal east and west azimuth angles of sunflower heads would be mirror-symmetrical to south. However, this symmetry is broken if the frequency of clouds is larger in the afternoon than in the morning, as is usually the case in the region where sunflowers were domesticated  Sunflower heads absorb solar radiation to develop their seeds only for 1–2 months in the summer, while solar panels operate during the whole year. Per our explanation, one of the reasons for the east facing of sunflower heads is that easterly oriented inflorescences absorb maximal radiation because afternoons tend to be cloudier than mornings due to the well-known diurnal cycle of continental convection, which peaks in late afternoon. The strong asymmetry in daily cloudiness during the warmest and hence convectively most active summer months is lessened if averaged for the whole year. (ii) Depending on latitude, the normal vector of fixed solar panels has a relatively large positive elevation angle , for which south facing maximizes the received sky radiation40° Fig.  east azis(t) from the horizon and the solar azimuth angle αs(t) from south  and the planetary theory VSOP 87 (Variations Séculaires des Orbites Planètaires) of Bretagnon and Francou30. This method is valid for the 1950–2050 period with an accuracy of 0.01°. Using this algorithm, we calculated the geocentric ecliptical, then the geocentric equatorial, and finally the geocentric horizontal coordinates of the Sun, resulting in the values of θs(t) and αs(t).For our numerical calculations, the solar elevation angle θ31. The geographic coverage is global with a native spatial resolution of 0.25° × 0.25° ≈ 27 km × 27 km. Climatological mean values of TCC were determined by averaging for each hour of each calendar day of every year in the vegetative period of sunflowers. Since TCC is a dimensionless relative parameter in the range 0–1 , the hourly climatological means are equivalent to the time-dependent probability 0 ≤ σ(t) ≤ 1 of cloudy situation. We determined the diurnal cloud probability function σ(t) in July, August and September in Boone County  time series of high temporal resolution (1 h) were evaluated for the period 01.01.2009–31.12.2018 from the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecastsn of the normal vector of the mature head of the same 100 sunflowers as a function of time t, approximately weakly from 6 July to 11 September 2020. The studied sunflowers were individuals in a given row of the plantation.In a sunflower plantation at Budaörs (near Budapest), we measured the elevation angle θA(λ) of young (2 weeks after anthesis) and old (4 weeks after anthesis) inflorescence and back of mature sunflower heads were measured in the field with an Ocean Optics STS-VIS spectrometer  in July 2020. Measurements were performed under total overcast conditions to ensure isotropic diffuse skylight illumination. At first, the reflection spectrum of the inflorescence/back was determined as follows: a spectrum was measured by directing the spectrometer’s head on the target at a distance of 5 cm, then another spectrum was registered by pointing the spectrometer to the overcast sky. In the laboratory these two spectra were divided by each other. Finally, assuming that all non-reflected light was absorbed, the absorption spectrum A(λ) = 1 − R(λ) was obtained by subtracting the reflection spectrum R(λ) from 1. Absorption spectra were measured for 3 sunflowers and then averaged.The absorption spectra x–y-z reference frame of Fig. x and y point to west and south, axis z points vertically upward, the elevation angle − 90° ≤ θn ≤  + 90° is measured from the horizontal , and the azimuth angle αn is measured clockwise from axis y (south). In our model, after sunflower anthesis angle αn is constant, because the head does not follow the motion of the Sun in the sky, and angle θn(t) decreases monotonously with time t  as measured in the field , the region from which the domesticated sunflower originates26. In the case of αn =  − 90°, 0° and 90°, the inflorescence faces east, south, and west, respectively. Northeast, southeast, southwest and northwest facing inflorescences are defined as − 180° < αn <  − 90°, − 90° < αn < 0°, 0° < αn < 90° and 90° < αn < 180°, respectively.In the eld Fig. . The uniles Fig. A. When θD is the diffuse fraction of global radiation. From  ≤ 1 clouds dominate which frequently occlude the Sun. (ii) In sunny situation with probability 1 − σ(t) direct sunlight dominates, because the Sun is occluded by clouds only rarely. The total light energy absorbed by a sunflower inflorescence from dawn to dusk on the i-th day is the sum of the energy of direct solar radiation There are two different meteorological situations: (i) In cloudy situation with time-dependent probability 0 ≤ σ is the absorption spectrum of the inflorescence, and the factor (θn + π/2)/π describes the proportion of the sky hemisphere from which an inflorescence with elevation angle θn receives diffuse skylight  of young (2 weeks after anthesis) and old (4 weeks after anthesis) sunflower inflorescences. In our computations Ainflor(λ) was set to the absorption spectrum of young inflorescences in the first 3 weeks after anthesis, and then it was set to that of old inflorescences. In the spectral range λmin = 400 nm ≤ λ ≤ λmax = 700 nm, under cloudy conditions the average direct radiation is approximately 20% of the direct solar radiation  and the 0.2 factor, we obtain:Isun is the spectral distribution of irradiance of direct sunlight  surface, while the irradiance ISun of sunlight in Fig. Isun is multiplied by sinθs in (9).Under cloudy conditions the diffuse energy component is:entclass1pt{minima36 version 3.7 with a solar constant of 1362.12 W/m2 and the 1976 US Standard Atmosphere37, we have computed the ground-level direct-normal spectral solar irradiance Isun as described by Egri et al.38. ISun is the energy of solar radiation per unit time, per unit area perpendicular to the radiation and per unit wavelength interval. Figure ISun of direct sunlight for 91 different solar elevation angles θs.Using MODTRAN (MODerate resolution TRANsmittance code)D under cloudy and sunny conditions measured by Liu et al.39 over northern China. Since we did not find similar publicly available measurements for other regions of the Earth, we use these data further on in this work. From   D under Idirect is the direct irradiance illuminating the sunflower [Joule/(second·nanometer·meter2)], then the elementary direct light energy dE absorbed by the inflorescence in a time interval dt and in a wavelength bin dλ is:n (n = 1) and s (s = 1), the cosine of which can be expressed as:If The inflorescence can absorb direct sunlight only if the following condition is satisfied: − 90° < γ <  + 90°, 0 < cosγ < 1, that ist), the direct solar energy per unit area received by the inflorescence is:s is again necessary for the reason mentioned when (9) was derived. Figure Dsunny under sunny conditions39. Using (16), under sunny conditions with probability 1 − σ(t), the diffuse light energy per unit area received by the inflorescence is:n + π/2)/π is again the proportion of the sky hemisphere from which the inflorescence receives diffuse skylight , under sunny conditions with probability 1 – σ The normal vector nback of the back is opposite to the normal vector n of the inflorescence  The back is illuminated by diffuse light originating from the sky region which is the complementary part of the sky-dome illuminating diffusely the inflorescence. This can be taken into consideration by factor 1 − [θn(t) + π/2]/π = [π-2θn(t)]/(2π), being the proportion of the sky from which the back of a sunflower head with elevation angle θn(t) receives diffuse skylight  of the back of sunflower heads is different from the absorption spectrum Ainflor(λ) of the inflorescence  are:The back side of sunflower heads is green (later yellowish green) due to its chlorophyll content, because it takes an important role in photosynthesisnce Fig. B: nback ght Fig. B. (iii) esun,i,back expressed by (22) increases only when the back absorbs direct sunlight, which happens only, if the following condition is satisfied:The component t) < 0, the negative sign of cosγ(t) in (Since in this case cosγ(sγ(t) in  is necesAback(λ) of young (2 weeks after anthesis) and old (4 weeks after anthesis) backs of sunflower heads. In our computations Aback(λ) was set to the absorption spectrum of young backs in the first 3 weeks after anthesis, and then it was set to that of old backs.Figure For our studies no permission, licence or approval was necessary.


We measured three novel CSF p‐tau biomarkers, phosphorylated at threonine‐181 and threonine‐217 with an N‐terminal partner antibody and at threonine‐231 with a mid‐region partner antibody. These were compared with an automated mid‐region p‐tau181 assay (Elecsys) as the gold standard p‐tau measure. We demonstrate that these novel p‐tau biomarkers increase more prominently in preclinical Alzheimer, when only subtle changes of amyloid‐β (Aβ) pathology are detected, and can accurately differentiate Aβ‐positive from Aβ‐negative cognitively unimpaired individuals. Moreover, we show that the novel plasma N‐terminal p‐tau181 biomarker is mildly but significantly increased in the preclinical stage. Our results support the idea that early changes in neuronal tau metabolism in preclinical Alzheimer, likely in response to Aβ exposure, can be detected with these novel p‐tau assays.In Alzheimer’s disease (AD), tau phosphorylation in the brain and its subsequent release into cerebrospinal fluid (CSF) and blood is a dynamic process that changes during disease evolution. The main aim of our study was to characterize the pattern of changes in phosphorylated tau (p‐tau) in the preclinical stage of the Alzheimer’s  This study investigated novel CSF and plasma p‐tau biomarkers in the preclinical stage of the Alzheimer’s continuum and compared them with the widely used CSF Mid‐ptau181. Cerebrospinal fluid (CSF) phosphorylated tau (p‐tau) is one of the core biomarkers for Alzheimer’s disease (AD). Most p‐tau assays target the mid‐region (Mid) fragment of the protein and the phosphorylation at threonine‐181 (Mid‐p‐tau181). Recently, new assays have been developed, targeting different phosphorylation sites  or different fragments [N‐terminal (N) vs. mid‐region tau]. Moreover, new p‐tau assays in blood have shown high accuracy to detect AD.continuum, when in Aβ pathology is emerging. In this study, we aimed at characterizing the pattern of changes in p‐tau in the preclinical stage of the Alzheimer’s continuum. We performed a head‐to‐head comparison of the following p‐tau biomarkers: (a) CSF Mid‐p‐tau181 (used as a reference biomarker), (b) CSF N‐p‐tau181, (c) CSF N‐p‐tau217, (d) CSF Mid‐p‐tau231 and (e) plasma N‐p‐tau181.However, it is less known whether these novel p‐tau biomarkers change early in the asymptomatic stages of the Alzheimer’s continuum, probably in response to subtle Aβ pathology. Furthermore, plasma N‐p‐tau181 also significantly increases at this stage, although that increase is milder.We found that the novel p‐tau biomarkers CSF and plasma N‐p‐tau181, CSF N‐p‐tau217 and CSF Mid‐p‐tau231 increase early in the preclinical stage of the Alzheimer’s These results suggest that there are early changes in tau metabolism in preclinical Alzheimer, which can be detected with these novel p‐tau assays. Therefore, they support the idea of therapeutically targeting tau very early in the disease, as soon as Aβ pathology arises.et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, Cerebrospinal fluid (CSF) phosphorylated tau (p‐tau) is, together with CSF amyloid‐β 42 (Aβ42) and CSF total tau (t‐tau), a core biomarker for Alzheimer’s disease (AD). Overwhelming evidence indicates that CSF p‐tau is increased in patients with AD  compared to controls  is aberrantly hyperphosphorylated and several phosphorylated sites have been identified  and compared them with the well‐established Elecsys® Mid‐p‐tau181 assay flutemetamol Aβ PET.This study was performed with the first consecutive 381 participants of the ALFA+ cohort, which is a nested longitudinal study of the ALFA (for ALzheimer’s and FAmilies) study  if CSF Aβ42/40 ratio < 0.071, and tau pathology‐positive (T+) if CSF Mid‐p‐tau181 > 24 pg/ml. These cut‐offs were previously derived using a two‐Gaussian mixture modelling  and immediately frozen at −80°C.Blood samples were obtained the same day of the lumbar puncture and, therefore, in fasting conditions. Whole blood was drawn with a 20 g or 21 g needle gauge into a 10‐ml ethylenediaminetetraacetic acid (EDTA) tubes . Tubes were gently inverted 5–10 times and centrifuged at 2,000 For both CSF and plasma, the time between collection and freezing was less than 30 min and the samples were kept at room temperature during their processing. The time of the storage of the samples used in the present study ranged from 6 to 38 months. Samples were shipped on dry ice from Barcelona to the Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal (Sweden), where all the measurements were performed.et al, ® NF‐light Kit cat. no. 103186) on a HD‐X analyser following the manufacturer’s instructions . Samples were run in singlicates and using the same batch of reagents. Each assay plate included internal quality control samples with high and low plasma NfL concentrations analysed in duplicate both in the beginning and end of the plate. Quality control samples had a mean intra‐assay and inter‐assay coefficient of variation of less than 5 and 7%, respectively.CSF Mid‐p‐tau181 and t‐tau were measured using the electrochemiluminescence immunoassays Elecsys phospho‐tau(181P) CSF and total‐tau CSF, respectively, on a fully automated cobas e 601 instrument . CSF Aβ42, Aβ40 and NfL were measured with the NeuroToolKit  on a cobas e 411 or e 601 instrument. Measurements of CSF Mid‐p‐tau181, t‐tau, Aβ42, Aβ40 and NfL were previously reported . The specificity of the assays for tau forms that contain the indicated analytes was verified by mass spectrometry as described before  following previously published protocols (Karikari 224KVAVVR(pT)PPSSAK224‐C as a KLH‐coupled antigen. Candidate hybridomas were selected on brain extracts of AD and control brain tissue. The final cloned and purified monoclonal antibody, ADx253, was characterized on synthetic peptides spanning the region T217 till S241 for its affinity, its phospho‐specificity and its preferred selectivity in which P232 was replaced by a Pip, to simulate cis‐selectivity of ADx253 . New monoclonal mouse antibodies were generated using a 17‐mer synthetic peptide, phosphorylated on hTau corresponding Thr231, spanning the tau region KAmong the 381 samples, the following measurements were not available: 4 CSF N‐p‐tau181, 6 CSF N‐p‐tau217, 1 CSF Mid‐p‐tau231 and 11 plasma Mid‐p‐tau181. All measurements were performed at the Clinical Neurochemistry Laboratory, University of Gothenburg, Mölndal, Sweden, by laboratory technicians and scientists blinded to participants’ clinical information.18F]flutemetamol PET scans (referred throughout the text as Aβ PET) were available for 86.9% of the participants. Imaging acquisition and preprocessing protocols have been described previously  using the transformation previously calibrated  pipeline (Klunk The ALFA+ study (ALFA‐FPM‐0311) was approved by the Independent Ethics Committee “Parc de Salut Mar”, Barcelona, and registered at Clinicaltrials.gov (Identifier: NCT02485730). All participants signed the study’s informed consent form that had also been approved by the Independent Ethics Committee “Parc de Salut Mar”, Barcelona. The experiments conformed to the principles set out in the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont Report.10‐transformed: CSF Mid‐p‐tau181, N‐p‐tau181, N‐p‐tau217, Mid‐p‐tau231, t‐tau and NfL, and plasma N‐p‐tau181 and NfL. The CSF Aβ42/40 ratio followed a normal distribution and was not transformed. We excluded from the main analyses the following extreme values (as defined by the Tukey criteria): 2 CSF N‐p‐tau181, 1 CSF Mid‐p‐tau231 and 1 plasma NfL. Including them in the analyses did not change the results.We tested for normality of the distribution for each biomarker using the Kolmogorov–Smirnov test and visual inspection of histograms. The following biomarkers did not follow a normal distribution and were logt‐test was used to compare continuous variables between Aβ status groups and Pearson’s chi‐square to compare categorical variables. The association between each biomarker and age was assessed with a linear regression adjusting for the effect of sex. These analyses were further conducted stratifying by Aβ group and, additionally, including in the linear regression a “Age × Aβ status” interaction term. We also tested the effect of sex in each biomarker in a one‐way ANCOVA adjusted by age.A post hoc pairwise comparisons were conducted if there were more than two categories. Changes of p‐tau biomarkers as a function of CSF Aβ42/40 or Aβ PET CL were assessed with a linear regression adjusting for the effect of age and sex. These analyses were further conducted stratifying by Aβ group and, additionally, including in the linear regression a “Aβ biomarker × Aβ status” interaction term.The comparisons of the p‐tau biomarkers between Aβ status groups  were performed with a one‐way ANCOVA adjusted for the effect of age and sex. Bonferroni‐corrected et al, P‐values applying a false discovery rate (FDR) multiple comparison correction following the Benjamini–Hochberg procedure.We tested the accuracy of p‐tau biomarkers to discriminate between A+ and A− groups with a receiver operating characteristic (ROC) analysis. We computed areas under the curve (AUC), the 95% confidence intervals and tested whether they were significantly different from the null hypothesis that the AUC equals 0.50, which corresponds to a random test. We compared the AUCs of two ROC curves using the DeLong test . Figures were built using R and Matlab (v2018b).All tests were 2‐tailed, with a significance level of α = 0.05. Statistical analyses were performed in SPSS IBM 20.0 and R software , which is a part of the GU Ventures Incubator Program. KB has served as a consultant or at advisory boards for Abcam, Axon, Biogen, Lilly, MagQu, Novartis and Roche Diagnostics, and is a co‐founder of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures‐based platform company at the University of Gothenburg. JLM has served/serves as a consultant or at advisory boards for the following for‐profit companies, or has given lectures in symposia sponsored by the following for‐profit companies: Roche Diagnostics, Genentech, Novartis, Lundbeck, Oryzon, Biogen, Lilly, Janssen, Green Valley, MSD, Eisai, Alector, BioCross, GE Healthcare, ProMIS Neurosciences. The remaining authors declare that they have no conflict of interest.https://www.barcelonabeta.org/AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Review Process FileClick here for additional data file.


Three-D head geometrical models of eight healthy subjects and 11 hydrocephalus patients were built using their CINE phase-contrast MRI data and used for computer simulations under three different inlet/outlet boundary conditions (BCs). The maximum cerebrospinal fluid (CSF) pressure and the ventricular system volume were more effective and accurate than the other parameters in evaluating the patients’ conditions. In constant CSF pressure, the computational patient models were 18.5% more sensitive to CSF volume changes in the ventricular system under BC “C”. Pulsatile CSF flow rate diagrams were used for inlet and outlet BCs of BC “C”. BC “C” was suggested to evaluate the intracranial compliance of the hydrocephalus patients. The results suggested using the computational fluid dynamic (CFD) method and the fully coupled fluid-structure interaction (FSI) method for the CSF dynamic analysis in patients with external and internal hydrocephalus, respectively. Seifollah Gholampour et al. develop a computational model to examine the flow of cerebrospinal fluid (CSF) in hydrocephalus patients and healthy controls, and simulate how different biophysical parameters can influence CSF dynamics in the brain. Ultimately, their results could be used to better examine the CSF dynamics in a healthy or hydrocephalus brain, without the need for invasive procedures. There are diseases like hydrocephalus syndrome, in which the intracranial pressure (ICP) increases due to the same cause that has led to the disease3. Hydrocephalus is caused by abnormal accumulation  of CSF within the brain3. Hydrocephalus is still considered clinically complex since it has unknown pathophysiology aspects and various management methods4. Thus, more comprehensive studies are required to gain more in-depth insight into the nature of the disease.Cerebrospinal fluid (CSF) acts as a medium for transporting nutrients and neuroendocrine substances and removing toxic metabolites. It also preserves the brain’s chemical environment6. They are also insufficiently accurate in measuring biomechanical loadings and local flow patterns7. Thus, many studies have been carried out using computerized biomechanical simulation. The hydrodynamic simulation of temporal and spatial CSF flow-distribution patterns may raise the awareness of hydrocephalus complexities and their practical clinical value. However, the most challenging part of biomechanical simulations is boundary conditions (BCs)9. Results calculated with solution methods such as fluid-structure interaction (FSI) are highly sensitive to BCs11. Generally, the study of BCs in hydrocephalus modeling includes evaluating changes in both essential BCs  and natural BCs . This study mainly aimed to evaluate the effects of natural BC changes on the computational biomechanics of hydrocephalus.Experimental methods have limitations in measuring parameters affecting the disease12. The finite-element method (FEM) with CSF pressure as BC was used in that study. The ventricular system and brain tissue have many geometrical complexities, affecting problem-solving conditions considerably. Hence, the 2D simulation cannot fulfill the requirements regarding the geometrical complexities. Wirth et al. and Cheng et al. analyzed the biomechanics of hydrocephalus using 3D FEM modeling14. Although 3D studies better show the geometrical complexities than 2D studies, the FEM solution can analyze solid models. However, it cannot simulate the CSF flow circulation in the head15. It should also be mentioned that the first 3D FEM models in the literature were simplified13, which did not reflect the real geometrical complexities of the head. Therefore, to remedy this shortcoming, some previous studies concentrated on the computational fluid dynamic (CFD) simulation of the CSF flow with various inlet/outlet BCs.In the past, various computational methods and BCs were used to analyze hydrocephalus Table . Taylor 17. Hadzri et al. modeled the third and fourth ventricles of a healthy subject and a hydrocephalus patient with the pulsatile CSF flow rate and constant pressure as the inlet and outlet BCs, respectively18. Fin et al. simulated the CSF flow in the CA of a healthy subject and used pulsatile velocity as the inlet BC19. Howden et al. modeled CSF dynamics in a healthy subject with pulsatile flow rate as the inlet BC and constant pressure in the outlet of ventricles as the outlet BC20. Kurtcuoglu et al. set zero pressure as the outlet BC to assess CSF hydrodynamics in healthy subjects and hydrocephalus patients21. In other studies, Kurtcuoglu et al. considered the effect of brain motion by specifying the explicit boundary grid motion in the third ventricle and CA23. Gupta et al. modeled the subject-specific CSF flow and defined the pulsatile velocity field and zero pressure as the inlet and outlet BCs25. Farnoush et al. studied the effect of endoscopic third ventriculostomy on treating a hydrocephalus patient using the biomechanical simulation of CSF hydrodynamics in the third and fourth ventricles26. They used pulsatile velocity and zero pressure as the inlet and outlet BCs, respectively. However, the pure CFD method fails to reflect the complexities of the fluid–solid boundary in the computer simulation of hydrocephalus27. The brain’s inner and outer surfaces, which are in contact with CSF, deform due to volume changes of the brain and ventricles, resulting from the disease. However, the CFD method fails to consider deformable boundaries. Hence, some other studies were dedicated to FSI simulation.Jacobson et al. analyzed the fluid flow in the cerebral aqueduct (CA) of a hydrocephalus patient with constant and sinusoidal pressures, and velocity functions as inlet BC, and zero pressure as the outlet BC32. They defined the CSF flow as the inlet BC and different constant pressures in the healthy subjects and patients’ sagittal sinus as the outlet BC. In other studies, Gholampour et al. examined the correlation between CSF hydrodynamic changes and hydrocephalus patients’ clinical symptoms before and after shunting with the flow rate as BCs34. In this study, the effect of different combinations of BCs was quantitatively evaluated on changes in hydrodynamic parameters of the CSF flow to find the most accurate BCs for simulating hydrocephalus patients.Linninger et al., Sweetman et al., and Gholampour et al. performed some studies by the FSI simulation of the CSF flow and brain tissue in healthy subjects and hydrocephalus patientsResults of FSI simulations were calculated using ADINA software in five cardiac cycles. As there was no apparent difference between the data of 4th and 5th cycles, the last cycle results are reported. The CSF flow dynamics in the healthy subjects and patients (after contracting disease) were calculated and compared under three BCs.35 such as hydrocephalus. Hence, in the first section, the CSF flow-rate diagram was evaluated at CA. The difference between the positive areas above the x axis and the negative areas under the x axis in the CSF flow-rate diagram reflects the CSF stroke volume, which is the volume of CSF passing into the aqueduct in diastole and systole phases36. The aqueductal CSF stroke volume is useful for assessing hydrocephalus patients’ treatment process and is used as a shunt-responder predictor in patients treated with shunting37. This parameter is also an index for assessing the degree of irreversibility of neuronal damages due to hydrocephalus disease during the treatment process5. Hence, the aqueductal CSF stroke volume was investigated in this study due to its significance. The difference in the mean values of the maximum aqueductal CSF stroke volume between BCs “A”, “B”, and “C” was about 0.9% in the group of patients and about 0.3% in the group of healthy subjects  is a nondimensional parameter for evaluating the laminarity or turbulence of flow38. The Reynolds number is the ratio of inertial forces to viscous forces39. The results showed that the mean value of the maximum Reynolds numbers of the CSF flow in the patients and healthy subjects was 388.6 ± 6.2 and 332.3 ± 5.1 under BC “A”, 374.3 ± 5.9 and 323.3 ± 5.0 under BC “B”, and 372.4 ± 5.8 and 321.8 ± 5.0 under BC “C”, respectively. The results also demonstrated that the difference of the mean values of the maximum Reynolds number between BCs “A”, “B”, and “C” was <4.4% in the group of patients and <3.3% in the group of healthy subjects. It is noteworthy that CV for the maximum Reynolds number under all the BCs was <4.6% in the group of patients and less than 4.5% in the group of healthy subjects. Based on the results of ANOVA and Student’s t tests and the values of SE, SD, and CV, the data dispersion of the maximum Reynolds number was in an acceptable range in both groups. According to the results, the mean value of the maximum Reynolds numbers in patients under BCs “A”, “B”, and “C” was respectively 16.9%, 15.8%, and 15.7% higher than that in the healthy subjects.In the second section, the Reynolds number was calculated. The Reynolds number  of the cardiac cycle is compared between all the patients under BC “B” in Fig. The CSF pressure diagrams in the subarachnoid space (SAS) and CA of patient No. 7 under BCs “A”, “B”, and “C” are compared in Fig. cle Fig. . This re33, and CSF pressure in the upper convexity of the brain in SAS and CA is called CSF pressure in SAS and CSF pressure in CA, respectively. Based on the pulsatility of pressure and to achieve numerical values reported in this paper, first, the CSF pressure diagram based on the cardiac cycle was calculated for the healthy subjects and patients at each location, such as the upper convexity of the brain in SAS and CA  between the mean values of maximum pressure under three BCs. The CVs of the maximum CSF pressure in SAS for BCs “A”, “B”, and “C” were 9.6%, 9.5%, and 4.7%, respectively. The corresponding data for the maximum CSF pressure in CA were 9.8%, 9.6%, and 4.6%, respectively. As observed, the CVs of the maximum CSF pressure under BCs “A” and “B” were more than twice the CV of the maximum CSF pressure under BC “C”.Finally, the results showed that the mean values of the maximum CSF pressure in SAS in the group of patients under BCs “A”, “B”, and “C” were 5.5, 5.1, and 5.1 times the similar values in the group of healthy subjects, respectively. The corresponding data for the maximum CSF pressure in CA were 5.7, 5.3, and 5.2, respectively  of them. The reason was that CA had the smallest cross-sectional area in the CSF circulation pathway, and thus, CSF fluid had the most significant velocity in this section based on the law of continuity in fluid mechanics. The computational results provided the velocity data in mesh nodes, whose locations matched the pixel locations in CINE PC-MRI CSF velocity measurement. The CSF velocity diagrams calculated by FSI simulation and the experimentally measured diagram based on the cardiac cycle under BC “B” in the CA of patient No. 7 are shown in Fig. Maximum differences between the maximum CSF velocity in CA measured with CINE PC-MRI and the maximum CSF velocity calculated using FSI simulation under BCs “A”, “B”, and “C” for all healthy subjects and patients were less than 4.8%, 3.6%, and 2.3%, respectively Fig. . Althoug41. CSF pressure in SAS was not measured in healthy subjects due to the invasiveness of ICP monitoring and the subjects’ health. Moreover, CSF pressure in SAS was not measured in patient No. 11 because of his special conditions and limitations. The P values in the Shapiro–Wilk test for all CSF pressure datasets were >0.66, which confirmed the normality of all CSF pressure data of ICP monitoring and FSI simulation. The P values in all Student’s t tests were >0.65. It should be noted that the P values of variance equality in all Student’s t tests were >0.59.Second, the SAS’s CSF pressure values were experimentally measured in 10 of the 11 patients by continuous monitoring of ICP using a reliable sensor that introduced 1–2 cm through a minimal opening and a small burr holeThe results showed that the difference between the experimental and computer-simulated values of the maximum CSF pressure in SAS in patient No. 1–10 was <4.6% under BC “C” Table . However43. Therefore, investigating the geometric and volumetric changes of the cranium is of great importance44. Furthermore, specialists make their primary decisions based on apparent volume changes in the head substructures diagnosed by visual examination of MRI images. For this reason, the volumes of various head parts in the groups of patients and healthy subjects were compared in this section. It should be noted that these volumes were calculated after 3D modeling of the geometry of the head substructures in CATIA software before analysis. Therefore, these data are independent of the applied BCs “A”, “B”, and “C”. Tables t tests, the dispersion of the volume data in head substructures of healthy subjects and patients was acceptable.Hydrocephalus is caused by an imbalance of production and reabsorption in CSF that involves ventriculomegaly and CSF accumulation in the brain’s extraventricular spaceThe mean value of the ventricular system volume in the patients was 13.9 times the corresponding volume in the healthy subjects Tables  and 3. TIn this research, the inlet/outlet BCs were defined in terms of the CSF pressure and CSF flow rate based on the previous studies and in vivo examinations. However, in general, one can use other definitions for flow rate or pressure and consider them for the inlet/outlet BCs. Therefore, any discussions in this section are purely for the specific inlet/outlet BCs defined in this research.This part is dedicated to analyzing and comparing the effects of changing the inlet/outlet BCs on CSF flow dynamics. The results showed that the difference between the maximum aqueductal CSF stroke volumes under the three BCs was <0.9%. The assessment of the maximum Reynolds number value of the CSF flow under the three BCs revealed that the CSF flow remained in the laminar phase after the occurrence of hydrocephalus disease and that the difference between the maximum Reynolds numbers calculated under BCs “A”, “B”, and “C” was <4.4%. The difference between the maximum CSF velocities calculated under BCs “A”, “B”, and “C” was insignificant (<3.3%) Fig. . The resP < 0.0046), 0.66 (P < 0.0041), and 0.81 (P < 0.0011), respectively. PCC in BCs “A” and “B” was, respectively, 42.0% and 18.5% lower than that in BC “C”.Comparison of the results of all the parameters calculated under all the three inlet/outlet BCs showed that one of the highest differences between the data of the patients and healthy subjects was related to the maximum CSF pressure (5.1–5.7 times) and the ventricular system volume (13.9 times). The investigation of the CVs of the maximum pressure data and the ventricular system volume also showed a small dispersion in these two parameters. Thus, these two parameters can be considered more effective and accurate for analyzing conditions of hydrocephalus patients. The Pearson correlation coefficient (PCC) was used in this study to assess the correlation between the maximum CSF pressure in SAS and the ventricular system volume (two effective and accurate indices) under the three inlet/outlet BCs. As observed in Fig. Moreover, the weakest correlation between the maximum CSF pressure in SAS and the ventricular system volume occurred under BC “A”. Accordingly, in constant pressure changes, the biomechanical model was less sensitive to the ventricular system volume changes under BC “A” compared to the other inlet/outlet BCs.V/∆P) slope during hydrocephalus patients’ treatment process indicates changes in these patients’ ICC during the healing process46. It is noteworthy that ICC is a highly valuable index for assessing hydrocephalus patients47.The ventricular system volume, as mentioned above, was not related to our inlet/outlet BCs and was measured in CATIA before analysis. Therefore, only the difference between maximum CSF pressure under the three BCs led to a difference in PCCs. There was also a significant nonconformity between the maximum CSF pressure in SAS, which is obtained experimentally from ICP monitoring, and the FSI simulation results of CSF pressure in SAS under BCs “A” and “B”. These nonconformities in BCs “A” and “B” were 36.2% and 14.1%, respectively Table . Thus, t48. As mentioned before, the ventricular system volume and CSF pressure in SAS can reveal ICC, a valuable index for analyzing hydrocephalus patients’ conditions during the treatment process. However, on the one hand, experimental methods for measuring CSF pressure like ICP monitoring or lumbar puncture, which are also invasive or minimally invasive techniques, were not recommended for measuring CSF pressure in all types of hydrocephalus46. On the other hand, it is not always possible to measure CSF pressure at all locations, such as CA or optic nerve sheath, using these experimental techniques5. The computer simulation-based analysis in this study can calculate the velocity, flow rate, stroke volume, and CSF pressure at all cross sections of the head noninvasively.Knowing the exact amount of ICP can help understand the biomechanical properties of brain tissue49. Therefore, in this section, the effect of different solution methods with various fluid–solid interfaces was examined and compared on the analysis of CSF dynamics in internal and external hydrocephalus.The impact of BCs is not limited to the inlet/outlet BCs, and the interface between fluid and solid models is also a useful parameter of BCs in a variety of solution methods like CFD and FSI52. The FEM solution can only be used for biomechanical simulation of solid models53 like a skull. In these studies, the interaction between the CSF interfaces and the internal and external brain tissue walls was not considered. Therefore, the results calculated from separate brain tissue analyses without considering the CSF effect are not comprehensive for evaluating hydrocephalus patients. In FEM studies, the CSF effect is often replaced with a constant distributed compressive load12. However, the fluid flow is pulsatile and highly complex, and thus, replacing it with a distributed compressive load cannot fulfill the requirements for flow analysis. Therefore, the FEM solution cannot be used for CSF flow analysis in hydrocephalus patients. Thus, this solution method was not examined in this study.The effect of CSF flow was ignored in previous FEM studies49. The question arising now is whether it is necessary to use the FSI method, while the difference between these two methods is merely based on the deformability of BC at the interface between fluid and solid, and the deformation of these boundaries is negligible in SAS.The results showed that the smallest volume change in the head occurred in SAS. The SAS volume change was <2.3% and could be neglected. The main difference in using the CFD and FSI methods to solve engineering problems is whether the fluid–solid boundary is deformableFor this reason, only the CSF flow in SAS of all the patients was analyzed in this step using the CFD method. The results above revealed BC “C” to be more accurate for evaluating CSF, and hence, the CSF pressure values calculated in SAS using the CFD method were compared with those calculated using the FSI method. The no-slip BCs governing Eq.  were assThus, the CFD method can be used to calculate CSF pressure in SAS for external hydrocephalus, such as Chiari malformation and pseudotumor cerebri, which occurs due to a cause outside the ventricular system. In contrast, in internal hydrocephalus diseases, such as aqueductal stenosis, mesencephalic tumor, and aqueductal web, it is necessary to solve the FSI problem to calculate CSF pressure in SAS due to significant volume changes of the ventricular system (the ventricular system volume increased 13.9 times after the disease occurrence) and the significant deformation of the fluid–solid boundary.49 and treat33 hydrocephalus patients.As one of the most significant parameters in the evaluation of hydrocephalus patients, measuring the maximum CSF pressure is not possible using current experimental noninvasive methods. The present noninvasive biomechanical simulations are useful for accurately measuring the CSF hydrodynamic parameters, such as CSF pressure changes, using the suggested BC and solving method, which help diagnose54. On the other hand, in Lefever et al.’s study, a noncommunicating hydrocephalus patient’s brain tissue was simulated with a biphasic model55. Therefore, it is suggested that future studies evaluate and compare the impact of single-phase or biphasic brain tissue models49, and also, the effect of fusing the cerebellum with the cerebrum under the BCs examined in this study.Brain tissue was considered a homogeneous material in the form of a single-phase model in this study, and the results showed that considering this model for brain tissue was accurate enough to analyze the patients’ conditions. Dutta-Roy et al. showed no significant difference in the ventricular system volume calculation in normal pressure hydrocephalus patients using a biphasic and single-phase model57. This notion led to the use of the porous media theory for brain biomechanics58. Later, concepts beyond the passive sponge concept were proposed for brain tissue58. Many studies, such as the present one, used Darcy’s law for the biomechanical description of brain tissue, based on the study by Cheng et al.14. However, Tavner et al. indicated that Darcy’s law might be inadequate for describing the brain tissue constitutive behavior59. There exist many contradictions and conflicts in using hyperelastic models60 or models based on Darcy’s law62 for modeling the brain during the biomechanical simulations of the hydrocephalus. Hence, it is suggested to develop advanced theoretical models for a more accurate description of the brain’s constitutive behavior during the biomechanical simulation of hydrocephalus patients in future studies.Classical studies have shown that the brain acts like a sponge63. They also found that a significant part of this problem with hydrocephalus was the disrupted input flow to microvessels in the brain. Regarding the approach and goals of the present simulation study, this phenomenon could not make a proper assessment. Hence, future studies are suggested to consider this effect in the inlet BC and assess changes in cardiac pulsation absorbance in the simulation results using a microscopic approach to utilize this parameter as complementary metrics in the evaluation of hydrocephalus patients. Future studies are also recommended to evaluate the relationship between BCs and symptom severities in larger sample sizes.Park et al. revealed that the cardiac pulsation absorbance went down significantly after chronic hydrocephalus in animal subjectsThe 3D biomechanical simulation of the ventricular system, SAS, and brain tissue was performed in 11 hydrocephalus patients and eight healthy subjects. Moreover, the dynamic interaction of the CSF flow was investigated under three different inlet/outlet BCs. The results of examining the parameters of the aqueductal stroke volume, the Reynolds number, CSF velocity, and pressure and evaluating the volumes of the head substructures showed that the maximum CSF pressure and the ventricular system volume were the more effective and accurate indices for evaluating conditions of hydrocephalus patients. The results also showed that among the three BCs examined in this study, BC “C”, considering the pulsatile CSF flow-rate diagram as the inlet and outlet, was more accurate for analyzing the maximum CSF pressure in SAS. The volume changes in SAS after the disease occurrence were small, and the analysis of the CSF flow in SAS showed that the CSF pressure values calculated in SAS using the CFD and FSI solution methods agreed with each other and had only an insignificant difference. The results also showed the necessity of using the FSI method only when the disease’s cause was inside the ventricular system.66, and this has a significant effect on biomechanical simulation results. Consequently, there were many limitations in selecting healthy subjects and patients in the present study. Accordingly, 11 patients and eight healthy subjects were recruited from 42 hydrocephalus patients and 23 healthy subjects. The patients  and healthy subjects  had an age range of 53–74 and 48–69 years, respectively. Moreover, the body mass index range of the patients and healthy subjects was 23.8–29.4 and 25.0–30.2, respectively. CINE PC-MRI was performed on the head in all the healthy subjects and patients . Furthermore, this paper did not include any studies on animals. All the patients and healthy subjects provided verbal informed consent before undergoing any study-specific procedures.2 area surrounding the pixel. More details on the imaging protocol are explained by Kapsalaki et al.67. Scanning was performed using a 3 Tesla MRI system , with an acquisition time of 45 min.A velocity-encoding value (VENC) of 100 cm/s was chosen to measure the CSF flow. Further parameters used in the measurement included repetition time = 18 ms, flip angle = 23°, echo time = 8.3 ms, field of view = 23 cm, slice thickness = 3 mm, and matrix size = 256 × 198. The pixel velocity in CSF areas was corrected by subtracting the average velocity of solid brain tissue in a nearly 29 × 29-mmDICOM files obtained from MRI of each healthy subject and patient were transferred to Mimics software v13.1 to prepare the points cloud that the points in the cloud were voxel centers. Delineation of boundaries between CSF spaces and brain tissue was also performed using Mimics software. The point clouds of the head substructures  were produced for each healthy subject and patient and transferred to CATIA v5.R21 for 3D geometrical modeling. After creating 3D geometrical models of the head of the healthy subjects and patients separately Fig. , the mod67. Methods based on medical imaging have had many improvements recently; however, they cannot be applied for CSF pressure measurement. CSF pressure is measured with invasive methods such as ICP monitoring. However, these invasive methods cannot measure CSF pressure at all CSF circulation locations in the head6. Thus, it is required to use computerized simulation models for the quantitative assessment of hydrocephalus. In the present study, the CSF velocity data measured at the CA of all the healthy subjects and patients with the help of CINE PC-MRI were compared with the similar velocities calculated by biomechanical simulations, and the blood-flow rate data measured at the basilar artery of all the healthy subjects and patients with the help of CINE PC-MRI were used for inlet/outlet BCs.CINE PC-MRI was used as a measurement tool for the in vivo investigation of the CSF flow inside CNS68. An iterative solution was employed to solve the coupling of fluid and structure models68. Variables in this solution were fully coupled and defined at the element center. The velocity–pressure coupling was handled iteratively. The equations of the fluid and structure domains were solved individually using the counterpart equation’s latest results in the coupled system. The iteration process continued until the acceptable convergence of the results was achieved. It should be noted that the CSF pressure values obtained with ADINA software were calculated in Gauss points69.Simulations were performed using a fully coupled FSI (two-way FSI with strong coupling) model, in which arbitrary Lagrangian–Eulerian (ALE) equations were used for the simultaneous analysis of fluid (CSF) and structural (brain tissue) models. In an FSI problem, the computational grid of the fluid model deforms. Hence, in the ALE formulation, velocity relative to mesh velocity is used in the momentum equation’s convective term. It means that velocity at the faces of a moving and deforming control volume is the relative velocity concerning the faces of the control volume 75:W, P, µ are the moving mesh velocity vector, CSF pressure, body force per unit volume, and CSF dynamic viscosity, respectively, uF is the CSF velocity vector, ρF is the CSF density, and (uF-W) in ALE is the relative fluid velocity concerning the moving coordinate velocity. Equation .It is noteworthy that boundaries between brain tissue, the ventricular system, and the skull are highly complex and significant in Eqs.  and 2),,76. The 3 and 0.001 kg m−1 s−1, respectively30. Furthermore, CSF was defined as a Newtonian fluid.The density and dynamic viscosity of CSF were assumed as those of water at 37 °C, i.e., 998.2 kg/m82. Linninger et al. and Sweetman et al. assumed poroelastic parenchyma in their computer simulations31. In other studies, viscoelastic and hyperviscoelastic models have been used for brain tissue simulation84. Among numerous assumptions used in previous studies, the assumption of poro-viscoelastic material for brain tissue in biomechanical simulation by Cheng et al. had a better agreement with previous experimental studies14. Hence, the same assumption was made for brain tissue in the present study. The solid model equation, the law of Darcy for the flow of a fluid through a porous medium, and the stress field’s equilibrium conditions were used to derive general equations according to Cheng et al.14. The relaxation modulus was another parameter of the poro-viscoelastic model14. The Prony series was used to express the constitutive relation of the viscoelastic solid phase14. The equation of the time-dependent shear modulus of relaxation is as follows:τk indicate the Prony series’s input parameters dominating the relaxation response and Go represents the instantaneous shear modulus.In previous biomechanical simulations, various constitutive models were considered for brain tissue. However, it has been demonstrated that brain tissue shows time-dependent stress–strain behavior12. Other studies have used values of 350.0 and 420.6 Pa for elastic modulus86. There was a good agreement between the subsequent experimental studies and the study of Taylor et al. performed on hydrocephalus patients. Therefore, the elastic modulus of brain tissue used the value of 584.4 Pa in this study. There is also a large variation in the brain tissue Poisson’s ratio used in different studies. Some studies have considered the value of 0.45–0.5 for the Poisson’s ratio65. In this study, however, the Poisson’s ratio was assumed 0.35 based on Dutta-Roy et al.’s and Cheng et al.’s studies due to the better agreement with the subsequent experimental studies86. Further, the void ratio and permeability values were defined as 0.2 and 4.08 × 10−12 M4/N s, respectively54. The input parameters of the Prony series were also selected according to studies by Dutta-Roy et al. and Cheng et al.54: τ1 =  3.1 (s), τ2 = 27 (s), τ3 = 410 (s).Taylor et al. suggested the value of 584.4 Pa for the elastic modulus of hydrocephalus patients’ brain tissue26. According to the study by Marsden et al., this assumption is not sufficient for solving the problem using the FSI method10. The conclusion of the previous BCs is summarized in Table As mentioned before, pressure and velocity or the CSF flow rate was defined as inlet/outlet BCs in previous studies. Zero pressure was considered as the outlet BC in many previous CFD simulations17. Baseline pressure in the sagittal sinus as the outlet pressure function was 500 and 2700 Pa in the healthy subjects and patients, respectively31.BC “A”: A sinusoidal pressure function was used at the inlet and outlet BCs to mimic CSF’s normal oscillation. In BC “A”, the inlet pressure function was defined as 80 + 160 sin(2πt) Pa, according to the study by Jacobson et al.32. In this study, the CSF pulsatile flow rate profile was used as the inlet BC in BC “B”, and the outlet BC was defined similarly to that in BC “A”. The inlet BC was calculated with the superposition of a constant value of 0.35 ml/min87 and the normalized pulsatile profile of the blood flow rate in the basilar artery, which was measured with the CINE PC-MRI for all the healthy subjects and hydrocephalus patients.BC “B”: The pulsatile blood flow profile was always considered in the literature as the inlet BC, and the pressure value was imposed at the outlet BC89, at extracranial lymphatic pathways90, and in additional intraparenchymal routes49. After CSF is produced and passed through the lateral, 3rd and 4th ventricles, it flows through a series of apertures into SAS, which surrounds brain tissue between the pia mater and the arachnoid mater1. In SAS, part of the fluid enters the sagittal sinus91, and the rest drains through the central canal into the spinal cord92. Of course, the drainage of CSF also occurs through other parts like small granulation tissues of arachnoids. As in previous computer simulations of hydrocephalus, the primary inlet flow was considered in lateral ventricles, whereas the significant outlet flow was considered in the sagittal sinus and the spinal cord95. These locations were merely defined in BC “C” as locations to apply the inlet and outlet BCs. In BC “C”, the inlet and outlet flow diagrams were pulsatile CSF flow-rate diagrams  using IBM SPSS Version 20 software. Generally, the mere investigation of mean, SD, and SE is not sufficient to compare some statistical datasets, and the comparison of data dispersion is also necessary. CV as the standard deviation ratio to the data mean value is a useful statistical index to compare the data dispersion between different data series99 to compare the CSF pressure, the aqueductal CSF stroke volume, the Reynolds number, the CSF velocity, and the volume between healthy subjects and patients under the three BCs “A”, “B”, and “C”. The homogeneity of the variance test showed that all the variances were equal. Hence, Tukey’s post hoc test was used for pairwise comparison after ANOVA when comparing the data under the three BCs. Moreover, the normally distributed data led to using Student’s t test after ANOVA to compare both groups of CSF velocities obtained from computer simulation and CINE PC-MRI, and two groups of CSF pressures obtained from computer simulation and ICP monitoring for assessing data validation. Student’s t test with equal variance was also used to compare the CSF pressure results of CFD and FSI simulations. The test statistics for ANOVA and Student’s t test were T and F, respectively.The results of the Shapiro–Wilk test showed that the datasets had a normal distribution. Parametric ANOVA multiple comparison was used100. Hence, after ensuring the normal distribution of the data, PCC was used to assess the correlation between the maximum CSF pressure and the ventricular system volume under the three BCs. The data were described as mean ± SE, and the P value of 0.05 was considered statistically significant.The PCC, a number between −1 and +1, is an index for evaluating the correlation between two phenomenaFurther information on research design is available in the Peer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Finally, we performed Kaplan-Meier plots for overall survival by using the log-rank test to evaluate the clinical relevance of neutrophils and their subsets. In our study, neutrophils in tumor tissues were significantly higher than those in nontumor tissues and were positively associated with tumor progression but negatively correlated with GC patient survival. Most intratumoral neutrophils showed an activated CD54+ phenotype and expressed high-level immunosuppressive molecule B7-H4. Tumor tissue culture supernatants from GC patients induced neutrophils to express CD54 and B7-H4 in both time-dependent and dose-dependent manners. Locally enriched CD54+ neutrophils and B7-H4+ neutrophils positively correlated with increased granulocyte-macrophage colony-stimulating factor (GM-CSF) detection ex vivo, and in vitro GM-CSF induced the expression of CD54 and B7-H4 on neutrophils in a time-dependent and dose-dependent manner. Moreover, GC tumor-derived GM-CSF activated neutrophils and induced neutrophil B7-H4 expression via Janus kinase (JAK)-signal transducer and activator of transcription 3 (STAT3) signaling pathway activation. Furthermore, higher intratumoral B7-H4+ neutrophil percentage/number was found in GC patients with advanced tumor node metastasis stage and reduced overall survival following surgery. Our results illuminate a novel regulating mechanism of B7-H4 expression on tumor-activated neutrophils in GC, suggesting that functional inhibition of these novel GM-CSF-B7-H4 pathways may be a suitable therapeutic strategy to treat the immune tolerance feature of GC.Neutrophils are prominent components of gastric cancer (GC) tumors and exhibit distinct phenotypes in GC environment. However, the phenotype, regulation, and clinical relevance of neutrophils in human GC are presently unknown. Here, immunohistochemistry, real-time PCR, and flow cytometry analyses were performed to examine levels and phenotype of neutrophils in samples from 41 patients with GC, and also isolated, stimulated, and/or cultured neutrophils for  Gastric cancer (GC), with 5-year survival of less than 40%, is one of the leading causes of tumor death in many less-developed countries . Althoug+ neutrophil percentage and higher intratumoral B7-H4+ neutrophil number are found in GC patients with advanced tumor node metastasis (TNM) stage and reduced overall survival following surgery.Herein, we show that neutrophils are highly enriched within the GC environment and that their enrichment positively associated with GC tumor progression but negatively correlated with GC patient survival. Moreover, we demonstrate that GC tumor-derived granulocyte-macrophage colony-stimulating factor (GM-CSF) efficiently activates neutrophils and induces B7-H4 expression on neutrophils by activating the JAK-STAT3 signaling pathways. Furthermore, higher intratumoral B7-H4Helicobacter pylori (H. pylori) infection was determined by a serology test for specific anti-H. pylori antibodies. Antibodies and other reagents are listed in Supplementary Table Fresh gastric tumor, peritumoral, and nontumor  tissues and autologous peripheral blood were obtained from GC patients who underwent surgical resection at the Southwest Hospital of the Third Military Medical University and Qijiang Hospital of the First Affiliated Hospital of Chongqing Medical University. None of these patients had received chemotherapy or radiotherapy before surgery. Patients with infectious diseases, autoimmune disease, or multiprimary cancers were excluded. The clinical stages of tumors were determined according to the TNM classification system of the International Union Against Cancer (8th edition). μm sections. For immunohistochemical staining, the sections were incubated with rabbit anti-human myeloperoxidase (MPO) and then were stained by horseradish peroxidase (HRP) anti-rabbit immunoglobulin G (IgG) followed by diaminobenzidine. All the sections were finally counterstained with hematoxylin and examined using a microscope . All sections were analyzed independently by two experienced pathologists who did not have access to the clinical data of patients. Five fields were observed in each section, and the cells with uniform brown granules were counted at 200x magnification in each case using average values.According to our previously established methods , paraforμm cell strainer (BD Labware). Cell viability, as determined by trypan blue exclusion staining, was typically >90%.According to our previously established methods , fresh tAccording to our previously established methods , peripheAccording to our previously established methods , tumor tμg/ml), or 50% autologous NTCS with human recombinant (hr) GM-CSF (100 ng/ml) for 12 h, or were stimulated with TTCS  or hr GM-CSF  for 12 h, or were stimulated with 50% TTCS or hr GM-CSF (100 ng/ml) for 3, 6, or 12 h. In some cases, blocking antibody for GM-CSF receptor (GM-CSFR) (20 μg/ml) was added into neutrophil suspensions and incubated for 2 h before stimulation. After stimulation, the cells were harvested for flow cytometric analysis and western blot. For the signaling pathway inhibition experiments, these neutrophils were pretreated with AG490 (a JAK inhibitor), FLLL32 (an STAT3 inhibitor), BAY 11-7082 (an IκBα inhibitor), SP600125 (a JNK inhibitor), SB203580 (a MAPK inhibitor), U0126 (an MEK-1 and MEK-2 inhibitor), Wortmannin (a PI3K inhibitor), or GSK-3β inhibitor  for 1 h; then, the cells were stimulated with 50% TTCS or hr GM-CSF (100 ng/ml) for 12 h and harvested as above. Since the inhibitor was dissolved in DMSO, parallel cell groups were treated with DMSO (5 μl) or culture media as controls.Neutrophils from healthy donors were stimulated with 50% TTCS or 50% autologous NTCS, or 50% TTCS with a neutralizing antibody against human GM-CSF , strictly following the manufacturer's protocol. Microarray experiments were performed at the Genminix Informatics (China) with the microarray service certified by Affymetrix.t-test was generally used to analyze the differences between two groups, but when the variances differed, the Mann–Whitney U test was used. For multigroup data analysis, an ANOVA analysis was used. Correlations between parameters were assessed using the Pearson correlation analysis and linear regression analysis as appropriate. Overall survival was defined as the interval between surgery and death. The known tumor-unrelated deaths  were excluded from the death record for this study. Cumulative survival time was calculated by the Kaplan-Meier method, and survival was measured in months; the log-rank test was applied to compare between 2 groups. SPSS Statistical Software (version 13.0) was used for all statistical analyses. All data were analyzed using 2-tailed tests, and P < 0.05 was considered statistically significant.Results are expressed as the mean ± SEM. The Student + neutrophils were accumulated in tumors compared to nontumor tissues , a marker for neutrophils that has been reported in many human cancers and diseases –27, with tissues . Taken tWe next analyzed the immunophenotyping of these enriched intratumoral neutrophils. First, we found that peripheral neutrophils from GC patients expressed little neutrophil activation marker CD54 Figures . NotablyFurthermore, we next hypothesized that GC environments contribute to the activated and highly B7-H4-expressing phenotype of neutrophils. Consistent with our hypothesis, we stimulated neutrophils with NTCS or TTCS from autologous tumor or nontumor gastric tissues, and found that, compared to NTCS-conditioned neutrophils, TTCS-conditioned neutrophils significantly upregulated CD54 and B7-H4 expression . We alsoβ, IL-33, TNF-α, M-CSF, G-CSF, IL-1β, GM-CSF, IL-17A, IL-10, IL-6, IL-17F, IL-23, IL-4, IL-21, and IL-12. We found that only GM-CSF remarkably upregulated the expression of B7-H4 on neutrophils  versus low (<22.9% median level) B7-H4+ neutrophil percentage level, the 25-month survival rate was significantly lower for those within the higher B7-H4+ neutrophil percentage group (+ neutrophil number (+ neutrophil was correlated with increased tumor size and advanced tumor stage (Supplementary Figure + neutrophils are associated with tumor progression and poor survival for GC patients.Finally, we studied whether increased B7-H4early GC , suggestge group . Similarl number . In keep+-activated and highly B7-H4-expressing phenotype significantly increase with tumor progression. Although increased neutrophils have already been described in patients with many types of tumors including GC [In this study, we have shown that, within the GC environment, neutrophils with CD54uding GC , to our Neutrophils have been found to be enriched in tumors; however, very little is currently known about the phenotype of tumor-infiltrating neutrophils, as well as its regulation and clinical relevance. In human GC, it has been reported that increased neutrophils in peripheral blood  and tumo+ tumor macrophages exert suppressive functions in human ovarian carcinoma [It has been known that immune suppression exhibits a hallmark of cancer . It is sarcinoma . Our res+ or B7-H4+ neutrophils within the GC environment. Importantly, we further identify GM-CSF as a novel proinflammatory factor to induce B7-H4 on neutrophils in GC and show that GC tumor-derived GM-CSF effectively activates the JAK-STAT3 pathway to induce this B7-H4 expression on tumor-infiltrating neutrophils.B7-H4 upregulation occurs during inflammation in the tumor environment. It has been shown that IL-6 effectively induces the upregulation of B7-H4 on human macrophages in ovarian carcinoma . Our resCell stimulation by cytokines or growth factors induces JAK activation, resulting STAT3 phosphorylation, and phosphorylated STAT3 directly mediate signaling from the cell membrane to the nucleus . Many cy+ neutrophils in GC. Specifically, we have shown that increased frequencies and numbers of intratumoral B7-H4+ neutrophils predict lower rates of GC patient survival. As the relationship between B7-H4 expression and tumor vasculature in renal cell carcinoma and their associations with advanced cancer progression and poor survival [+ neutrophil frequencies or numbers might prove useful clinical markers for GC.Importantly, our findings also shed light on the clinical relevance of B7-H4survival , intratuin vitro and ex vivo data, we identify a novel pathway involving the activation of neutrophils and the induction of B7-H4 expression on neutrophils within GC. First, within the GC environment, GM-CSF with proinflammatory feature is produced. Second, released GM-CSF has effects on intratumoral neutrophils by activating the JAK-STAT3 signaling pathway. Third, the GM-CSF-activated JAK-STAT3 signaling pathway mediates intratumoral neutrophil activation with increasing CD54 expression, a process that is accompanied by the induction of B7-H4 expression on these neutrophils. In this way, neutrophils appear to acquire CD54+B7-H4+ phenotype via the GM-CSF-JAK-STAT3 pathway, which is consistent with our observations that advanced tumor staging and poor patient prognosis is associated with a significant increase of B7-H4+ neutrophils in GC tumors. In the future, therapeutics is aimed at interfering these tumor-infiltrating neutrophils and their regulatory pathway may be developed to provide novel strategies for GC treatment.Collectively, based on our Our results illuminate a novel regulating mechanism of B7-H4 expression on tumor-activated neutrophils in GC, suggesting that functional inhibition of these novel GM-CSF-B7-H4 pathways may be a suitable therapeutic strategy to treat the immune tolerance feature of GC.