Zoysia spp. Willd.) is an important warm-season, perennial turfgrass that tolerates adversity, wear, trampling and extensive management. However, whole photosynthetic characteristics reaction of the zoysiagrass to shade stress have not been described because our knowledge in this area is very limited. In this study, 85% shade treatment was applied to nineteen zoysiagrass genotypes, and morphological observations and extensive determinations on plant heights, photosynthetic pigments, fluorescence dynamic curves among other parameters were made. The results showed that vegetal and photosynthetic responses of zoysiagrass were affected by shade treatment to varying degrees. Further analysis based on the principal component, subordinate function analysis and clustering methodology revealed that different shading response strategies were adopted by zoysia under shade surroundings. They were divided into four categories. The strongest shade-avoidant response strategy was adopted by ‘ZG48’ and ‘WZG59’, which had the largest comprehensive evaluation (D) values, and the stabilized shade-tolerant response was taken on by ‘ZG-3’ and ‘ZG64’, which had the lowest D values. Other varieties applied a medium strategy but with a certain tendency. These findings provide new insights into different shading response tactics of turfgrass: shade avoidance and shade tolerance response, which could be selected for further elucidation of the molecular mechanism of plant adaptation to shade environments.Reduction of ambient solar radiation is an important external challenge for plants, which affects photosynthesis and morphogenesis in agroforestry or gardening. As bottomed sessile organisms, turfgrasses have a set of sophisticated photosynthetic strategies to survive and deal with this abiotic stress. Zoysiagrass ( Arabidopsis, its disadvantage is that it is not a naturally existing understory and cannot be authentically considered a shade-tolerant plant (Zoysia spp. Willd.) is a warm-season perennial turfgrass with strong resistance to heat, drought and abrasion caused by traffic and soil compaction and relative resistance to shade and salinity compared to other turfgrasses  is transferred from the reaction center chlorophyll, P680, to the primary quinone acceptor of PSII, QA. Simultaneously, absorbed light energy is lost from PSII as chlorophyll fluorescence or heat energy  could be used to drive the primary photochemical reaction, in which an electron , subordinate function analysis and clustering methodology. These analyses subsequently laid a foundation for the molecular response mechanisms.−2 s−1 of photosynthetically active radiation on average. The temperature and humidity were obtained with a digital hygrometer thermometer. Photosynthetically active radiation values were measured by in-situ measurement approach using a hand-held illumination photometer at 11 a.m. on a clear day for three biological repetitions. The grasses were irrigated with 1/2-strength Hoagland’s solution and clipped once a week until growth was consistent after 30 days.The study was carried out on nineteen zoysiagrass accessions including ‘ZG-3’, ‘Wuhao-1’, ‘WZG99’, ‘ZG63’, ‘Manila’, ‘ZG31’, ‘Nanling’, ‘ZG45’, ‘WZG55’, ‘WZG59’, ‘ZG66’, ‘ZG65’, ‘ZG67’, ‘WZGF8’, ‘WZG91’, ‘WZG97’, ‘ZG64’, ‘WZG85’ and ‘ZG48’  were applied using a polyethylene black shade cloth by constructing a shade structure surrounding the established plots in the greenhouse. Each pipe represents one replication, cessions . All meaGrowths of zoysiagrass under shade treatment and natural light were observed and compared. Plant height was measured from the substrate surface of each tube to the top in four directions using a ruler. The average value of the three tubes was used as the plant height of the zoysiagrass.a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chls) and carotenoid (Caro), were calculated by following formulas with three biological repetitions. Photosynthetic pigment contents were calculated according to the methods described by −2 s−1 red light conditions to generate the OJIP transients and chlorophyll fluorescence kinetics. The lowest fluorescence when exposed to light was defined as the O point, while the highest fluorescence was defined as the P point. The rapid chlorophyll fluorescence induction kinetic curve referred to the fluorescence changes from the O point to the P point, which was mainly related to the initial photochemical reaction of PSII. Based on the theory of energy fluxes in biofilms, the OJIP tests can further translate the primary data into other biophysical parameters and be used to calculate a series of indices according to A pulse-amplitude modulation (PAM) fluorometer  was used to measure changes in chlorophyll fluorescence. First, the top 2nd–3rd leaves of shaded and unshaded zoysiagrass were dark-adapted for 20 min to close all the PSII reaction centers and acquire the maximal fluorescence intensity. Then, they were exposed to 3,000 mmol photons mj = Ej/ ΣEj , where Ej indicates the jth eigenvalues. Subordinate function values were reckoned with Uj = (Yj - Ymin)/(Ymax - Ymin) , where Yj indicates the score of the jth principal component index, Ymin indicates the minimum score of the corresponding principal component index, and Ymax indicates the maximum score of the corresponding principal component index. Comprehensive evaluation values (D) of different zoysiagrass accessions were computed as D= Σ(Uj × Wj) , which indicates the shade response degree exposed to 85% shading stress  and ** (P < 0.01), indicate significant differences obtained through IBM SPSS software by the independent samples T-test, when indicators in shade were compared with those in light, respectively.The results are presented as the mean values of at least three independent biological replicates. The calculated averages were used to create bar charts with Origin Pro 9. Error bars in figures represent standard deviations calculated from triplicates. The asterisks, * , compared to their control, morphological differences were observed among nineteen zoysiagrass . Some zoferences .a, Chl b, and Caro in leaves, and the contents of individual photosynthetic pigments differed. Shade significantly augmented pigment contents to capture more light energy in ‘Wuhao-1’, ‘WZG99’, ‘Manila’, ‘ZG31’, ‘Nanling’, ‘ZG45’, ‘WZG55’, ‘WZG59’, ‘ZG66’, ‘ZG65’, ‘ZG67’, ‘WZGF8’, ‘WZG91’, ‘WZG97’, ‘WZG85’, and ‘ZG48’ while there were nearly no changes in ‘ZG-3’ and ‘ZG64’. In addition, the pigment contents in ‘ZG63’ decreased markedly , specific energy fluxes per reaction center , and performance indices   were dramatically increased in ‘ZG-3’, ‘WZG99’, ‘ZG31’, ‘WZG55’, ‘WZG59’, ‘ZG64’, and ‘ZG48’ the electron transport (ETo/RC) was notably boosted in ‘Manila’, ‘WZG55’, ‘ZG66’, ‘ZG67’, ‘WZG91’, ‘WZG97’ and ‘ZG64’ and the energy dissipated (DIo/RC) was conspicuous in ‘ZG-3’, ‘ZG31’, ‘WZG55’, ‘WZG59’, ‘ZG65’, ‘ZG67’, and ‘ZG48’ (ABS) and unit cross-sectional area (PICS), which are more sensitive to changes in the photosynthetic apparatus, were enhanced in ‘Manila’, ‘ZG31’, ‘Nanling’, ‘WZG55’, ‘WZG59’, ‘ZG66’, ‘ZG65’, ‘ZG67’, ‘WZGF8’, ‘WZG91’, ‘WZG97’, and ‘ZG48’  increased significantly to elevate photosynthetic efficiency and capacity whose indicators in light were correspondingly low, but most of them did not reach the levels above. In conclusion, a large number of indicators were obtained, but their photosynthetic strategies could not be well determined.Chlorophyll fluorescence kinetic parameters could reflect plentiful photosynthesis information, such as absorption, capture, transfer and distribution of light energy, activity of the reaction center, photosynthetic efficiency and performance. We obtained a large number of PSII photosynthetic parameters under shaded and sunlight conditions, including fluorescence parameters derived from the extracted data , yield or flux ratio parameters  . After sPItotal) . The ratferences . Additiod ‘ZG48’ –5F. In ad ‘ZG48’ –5H. We oThe units and ranges of different indices were disparate, causing confusion in the comparative analysis. To avoid the influence of different dimensions, the shading response coefficients of each test index were calculated . From thTo reduce the overlap and make up for the deficiency of index evaluation, a PCA was implemented to integrate these eighteen shading response coefficients . The KaiφPo, ψEo, φEo, PIABS, PICS and PItotal, mainly reflecting the changes in light energy yields, flux ratio and photosynthetic efficiency. Principal Factor 2 comprised Chl a, Chl b and Caro, explained by photosynthetic pigment factors. Principal Factor 3 mainly included ABS/RC, TRo/RC and ETo/RC, explaining specific energy fluxes per reaction center. Principal component 4 represented plant height, Fo and Fm, depending on the growth, and the initial and maximum fluorescence. Principal component 5 was included by Chl a/Chl b and Chls/Caro, explained by the composition of chlorophyll and carotenoid.Furthermore, the eigenvector coefficients of the five principal components were obtained by the original component matrix divided by SQRT (E), respectively. Expressions for Y1 ∼Y5  were obtained in The membership function values of each test zoysiagrass, U1 ∼U5, were evaluated according to the formula . Based oUsing a statistical index of squared Euclidean distance, nineteen shade trial zoysiagrass were clustered into four categories by D values . RelativCynodon dactylon and Zoysia spp. are both widely used warm-season grasses; between them, Zoysia spp. had better shade tolerance  increased markedly, the majority of the captured light energy used for reduction QA− (TRo/RC) added significantly, and the energy used for electron transport (ETo/RC) increased slightly, but a small amount of energy was dissipated (DIo/RC), suggesting that they were very patient in shading surroundings. Some ones, for example, ‘ZG48’, ‘WZG59’, and ‘WZG97’ had relatively low indices under light, which increased significantly after shading, promoting their photosynthetic maximum quantum yields, efficiencies and capacities, but most of them still did not reach the level mentioned above. The light energy absorbed by the PSII unit reaction center decreased significantly, the energy used for QA− reduction was observably reduced, the electron transfer energy increased dramatically, and the energy dissipation lessened significantly.In addition, plants might optimize the light capture efficiencies and reactions of the photosynthetic apparatus to accommodate the adjustment and decrease in the quality and quantity under low light solar radiation conditions. It is very important to perform photosynthesis measurements to inspect their photosynthetic physiological adaptation. Even though it does not represent the entire photosynthetic chain, chlorophyll fluorescence could be used to estimate the efficiency of the preliminary steps of photosynthesis, including light energy harvesting, absorption, transmission, and dissipation associated with PSII . Thus, iIn brief, the photosynthetic system parameters of zoysia were affected by shade treatment to varying degrees, and the role of each index in the shade response might be inconsistent. It is one-sided and confusing to judge shade avoidance or shade tolerance resources by multiple indicators accurately. Therefore, more statistical methods were applied to integrate the index information to comprehensively evaluate the shading response of each accession. PCA has the advantages of eliminating the correlation among indicators, avoiding information duplication and simplifying data analysis . The memIn this study, nineteen zoysia grasses were treated with 85% artificial shading, and a large number of growth and photosynthetic characteristic indices under shaded and full sun environments were obtained. Then, they were divided into four categories based on comprehensive evaluations by PCA, membership function analysis and D-value cluster analysis. Category I, ‘WZG59’, and ‘ZG48’ adopted a strong shade-avoidance strategy by significantly increasing plant height, photosynthetic pigment contents, fluorescence kinetic curves and photochemical parameter responses to shading surroundings. In category II, ‘WZGF8’, ‘WZG91’, ‘WZG55’, ‘WZG85’, ‘Nanling’, ‘ZG65’, ‘ZG31’, ‘ZG66’, ‘ZG67’, and ‘WZG97’ the moderate shade avoidance strategy was taken, and the shade avoidance indices were medium. Category III, ‘ZG63’, ‘ZG45’, ‘Wuhao-1’, ‘WZG99’, and ‘Manila’, tended to choose a degree of shade-tolerant strategy. The index values of category IV, ‘ZG-3’ and ‘ZG64’ were at very high levels and did not change basically after shading, with very strong photosynthetic physiological endurance to be employed in shade tolerance. According to these methods, we deeply understood the shade avoidance and shade tolerance response of zoysiagrass. Additionally, the evaluation system can be used to rapidly evaluate and predict the shade response of grass.10.7717/peerj.14274/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.14274/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.14274/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.14274/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.14274/supp-5Supplemental Information 5Click here for additional data file.


The DPPH and •OH radicals scavenging activity of fruit methanolic extracts was estimated using EPR spectroscopy. All cultivars are almost uniformly effective in the scavenging of •OH radical, while ‘Tea’, ‘Premy’, and ‘Joly’ were marked as highly potent cultivars (over 70%) in terms of DPPH-antiradical activity. Specific peroxidase activities were the highest in ‘Garda’, ‘Federica’, and ‘Rumba’ . ‘Laetitia’, ‘Joly’, ‘Arianna’, ‘Tea’, and ‘Mila’ cultivars were distinguished from others as the richest concerning almost all flavonoids and phenolic acids, including some other parameters of bioactivity. These cultivars could be recommended to consumers as functional fruit foods.Phenolic compounds of 25 newly introduced strawberry cultivars were profiled using spectrophotometry, electron paramagnetic resonance (EPR) spectroscopy, and high-performance liquid chromatography-mass spectrometry. Total phenolic and anthocyanin content , as well as vitamin C, and concentrations of individual phenolic compounds in fruits were evaluated to identify the most promising cultivars according to their phenolic profile. The highest values of TPC, TACY, and vitamin C were recorded in ‘Premy’ (1.53 mg eq GA g Today, consumer awareness of the impact of fruit intake on overall health and well-being is constantly on the rise. Therefore, the consumption of berry fruits, especially strawberries, has been promoted as valuable due to their overall health benefit properties .Fragaria × ananassa Duch.) is a good source of essential vitamins and minerals [Strawberry  in the hydroxyl cycle, a highly reactive hydroxyl radical (•OH) is formed [In protection against oxidative damage, various oxidase enzymes can also play a significant role. Their increased activity in fresh strawberries can cause significant quality deterioration, including loss of color and texture and the formation of unwanted brown pigments . Peroxids formed . Togethes formed . Our research aim was to quantify and characterize phenolic compounds in 25 strawberry cultivars and correlate them with their antioxidant capacity. Thus, phenolic components may be used as a selection criterion for defining the quality of strawberry fruit with well-demonstrated potential health benefits. The intercultivar variation of phenolic profiles could be employed for the promotion of promising cultivars with improved quality traits that fulfill consumer preferences, contributing to the ‘educated consumer concept’.The concentration and composition of phenolic compounds strongly affect the sensory-organoleptic properties and nutritional values of strawberry fruit, thus contributing to their possible health benefits. Therefore, the increase in consumption of high-quality strawberry fruit should be associated with both high consumer acceptance, due to its sensory attributes, and the presence of bioactive compounds.Previous studies reported variation in phenolic content among strawberry cultivars focusing on polyphenol-rich cultivars that may thus be an important source of health-promoting compounds in the human diet ,18,28. BThe results of the total phenolic content (TPC) of 25 strawberry cultivars are given in −1 FW. Cultivars ‘Premy’, ‘Aprika’, ‘Rumba’, ‘Vivaldi’, and ‘Sibilla’ were dominant in TPC , while ‘Irma’, ‘Alba’, ‘Roxana’, and ‘Jeny’ had the lowest TPC . A previous study [TPC of strawberry extracts varied from 0.46 to 1.53 mg eq GA gus study  showed t−1 FW, followed by ‘Arianna’ and ‘Vivaldi’ . These results are comparable to the content of anthocyanins determined by other authors in various strawberry cultivars [−1 FW). Pelayo et al. [Strawberry fruits are characterized by the high content and moderate diversity of anthocyanins . In the ultivars ,33. In co et al.  reported−1 FW), while ‘Sibilla’ reached its lowest point (32.27 mg 100 g−1 FW). Previously published average values of vitamin C in strawberry fruits [A comparison of homogeneous subsets of tested strawberry cultivars demonstrated that the maximum value of vitamin C content was recorded in ‘Laetitia’  are produced during the normal growth and metabolism of plants . At low It was observed that anthocyanins have ROS-scavenging capacities up to four times stronger than analogs of vitamin E and C, due to their high reactivity as proton and electron donors, ability to stabilize and delocalize unpaired electrons, and capacity to chelate transition metal ions . In thisAs shown in In DPPH is a free radical commonly used in EPR studies to measure the ability of plant-born anthocyanins to eliminate highly reactive species. The antioxidative potential of strawberry extracts is expressed as a decrease in DPPH radical concentration B. This i−1 prot, respectively). The pattern of specific POD activity did not follow that of the total protein content . In the scarce literature data regarding enzyme activities in strawberry fruits, specific POD activities were presented in different units , so they are not comparable with our results [−1 prot) [−1 prot) [Plants have developed effective protection mechanisms against reactive oxygen species . The ant content B. Regard results ,52. In a results , which, −1 prot)  or signi−1 prot) .One of the most characteristic classes of phenolic compounds that are widely distributed in the plant kingdom are flavonoids. They are subdivided into different subclasses depending on which carbon of the C ring the B ring is attached to and the saturation and oxidation status of the C ring . FlavonoThe basic flavonoid structure is aglycone, but they also occur as glycosides, and glycosidic linkage is usually located at positions 3 or 7 . OccurreFour phenolic compounds subgroups, anthocyanins, flavonols, flavan-3-ols, and hydroxycinnamic acids, which are considered as the main secondary metabolites in strawberry, were separated, identified, and quantified by HPLC in our study . The glycosylation pattern of tested cultivars showed that 92.23% of anthocyanins were presented as basic glycosides, while 7.77% appeared in acylated form . A diffeAcquired flavonoid glycosides profiles of all tested cultivars indicated various sugar moieties: glucosides, acetylglucosides, rutinosides, malonylglucosides, glucuronides, and coumaroylglucoside. In total, 87.79% of anthocyanins were presented as glucosides, 7.34% as malonylglucosides, 4.44% as rutinosides, and only 0.43% as acetylglucosides. In derivatives of phenolics, the glycosylation pattern was different, where 53.50% of them occurred as coumaroylglucoside, 26.08% as glucuronides, 15.14% as glucosides, 3.71% as deoxyhexoside, and 1.56% as acetylglucosides.O-glucoside (P-3-G) was predominant in all cultivars, making up almost 90% of the total anthocyanin content. The highest values were detected in ‘Joly’, ‘Asia’, ’Quicky’, and ‘Arianna’ . Similarly, in the study of Crecente-Campo et al. [−1 of FW, representing 91.5% of the total anthocyanins detected in strawberry fruit. Among five detected anthocyanin compounds, pelargonidin-3-o et al. , P-3-G wO-malonylglucoside was the second most abundant anthocyanin in all cultivars, with an extremely high concentration in ‘Joly’ (156.58 µg eq P-3-G g−1 FW), while its concentration in all remaining cultivars was in the range from 1.60 to 54.89 µg eq P-3-G g−1 FW. Since the acylation of pelargonidin occurs mainly in achenes [O-rutinoside (P-3-R) and cyanidin-3-O-glucoside (C-3-G) have also been detected in strawberries with concentration ranges from 0.10 to 31.10 µg eq P-3-G g−1 FW and 1.03 to 9.50 µg g−1 FW, respectively, and their maximal values were recorded in ‘Laetitia’ and ‘Albion’ in the order already mentioned. Similar levels of P-3-R (13-55 µg g−1 FW) and C-3-G (9.8 µg g−1 FW) were reported for conventionally cultivated strawberries [Pelargonidin-3- achenes , it insp−1 FW), while in ‘Clery’ the lowest content was detected (2.59 µg eq Querc g−1 FW). In kaempferol compounds, different sugar moieties were represented: acetylglucosides, glucosides, glucuronides, and coumaroylglucosides. Cultivar ‘Lofty’ was predominant concerning kaempferol-3-acetylglucoside (20.38 µg eq Kaempf g−1 FW), followed by ‘Federica’, ‘Quicky’, ‘Tea’, and ‘Mila’ , while the remaining cultivars exhibited significantly lower values. Compared to other cultivars, ‘Mila’ was super-dominant in terms of kaempferol-3-glucoside, kaempferol-3-glucuronide, and kaempferol-3-coumaroylglucoside , followed by ‘Arianna’  and ‘Tea’ . The average mean values of most cultivars are in line with previously published content of kaempferol-3-glucoside (10–70 µg g−1 FW) and kaempferol-3-coumaroylglucoside (10–40 µg g−1 FW) in eastern strawberry [Flavonols are the most common and largest subgroup of flavonoids in fruits. They have a hydroxyl group in position 3 of the C ring , which mFlavan-3-ols, represented by catechins, are compounds in which the hydroxyl group is always bound to position 3 of the C ring . Catechi−1 FW), along with ‘Laetitia’ (66.56 µg g−1 FW). However, the HPLC profile of the most tested cultivars demonstrated lower values corresponding to those of 44.7 µg g−1 of the fresh edible weight of strawberry fruit reported in the literature [In our study , the higterature .p-coumaric acid, belongs to this important phenolic acid group. Previously, p-coumaric acid was identified as the major hydroxycinnamic acid in the ripe stage of four strawberry cultivars in the concentration range of 4.2–20.7 µg g−1 FW [p-coumaric acid were the highest in ‘Vivaldi’ and ‘Rumba’ cultivars with values of 11.37 µg g−1 FW, while in the remaining cultivars the concentrations were significantly lower (−1 FW), followed by ‘Laetitia’ (252.72 µg eq CA g−1 FW), while almost 10 times smaller quantities were detected in ‘Federica’ (35.76 µg eq CA g−1 FW). Since the different hydroxycinnamic acids can be present in various derivatives among different cultivars [Hydroxycinnamic acid derivatives are an important class of polyphenolic compounds originating from the Mevalonate–Shikimate biosynthesis pathways in plants, also serving as precursor molecules for flavonoids and anthocyanins . A simplg g−1 FW . In our ly lower , which i−1 FW, while the highest concentrations of both ellagic acid and ellagic acid deoxyhexoside were detected in ‘Laetitia’ (30.95 µg g−1 FW) and ‘Joly’ (34.23 µg eq EA g−1 FW), respectively  was estimated for the sum of the flavonoids presented in 2 = 0.2115) and phenolic acids (r2 = 0.0138). Thus, flavonoids seemed to be the major antioxidant foundation of strawberry fruit. However, when all of the analyzed cultivars were taken in to the account, no support for such a correlation was found among sums of all tested groups of phenolic compounds and DPPH quenching capacity. The right answer stands in the synergistic interaction of different amounts of different antioxidants present in each cultivar defining overall antioxidative potential. The aforementioned conclusion about the complexity of antioxidant interplay inside the fruit can never be grasped without comprehensive research of a number of various cultivars such as that which we presented.From presented data, it is obvious that the majority of new cultivars are rich in bioactive phenolic compounds. Additional evaluation was to point out the most important group of analyzed compounds that stood out as a major contributor to antioxidant properties. As expected, the answer was not straightforward; the correlation of the sum of the compounds presented in each Table  with theA total of 25 newly introduced strawberry cultivars, of which 22 are June-bearing cultivars  and 3 are ever-bearing types , were used in our experiment. All cultivars were grown in a plantation in Serbia located in the municipality of Šid . This region is characterized by a temperate continental climate, with a mean annual air temperature of 10.7 °C and a mean annual precipitation of 650 mm. The soil, a fine sandy loam, had a pH of 6.8 and medium to high levels of all nutrients. Cold-stored plants of tested strawberry cultivars were planted on raised double beds covered with black polyethylene foil in July 2020. Plant spacing was 30 cm × 30 cm. Drip irrigation with two laterals per raised bed and emitters at a 10 cm distance were applied. Fertigation was performed at a frequency in accordance with crop requirements previously reported by Tomić et al. .Fully ripe fruit samples were collected in three repetitions per 20 fruits (60 fruits per cultivar) during the second harvest in the first year after planting (2021). Following the harvest, fruits were stored for a short time at −20 °C until chemical analysis to minimize the effect of postharvest factors.w/v), while extraction of anthocyanins was performed in an extraction solution containing methanol/water/hydrochloric acid at a ratio of 70:30:5 by volume. After centrifugation at 13,000× g for 10 min at 4 °C, supernatants were used for further analyses.Before chemical analysis, the whole fruits were carefully thawed, measured for exact weight, and homogenized using mortar and pestle. Phenolic compounds were extracted in 80% methanol at a ratio of 1:3 (w/v) polyvinylpyrrolidone (PVP) and 0.1% Triton X 100. After centrifugation at 13,000× g for 10 min at 4 °C, supernatants were used for spectrophotometric determination of peroxidase activities. Three extracts were prepared for each sample analyzed.Extracts for enzyme activity determination were prepared as follows: 1 g of homogenized fruit tissue were extracted in 2 mL 0.05 M sodium–phosphate buffer pH 7, containing 4% .Spectrophotometric determination of total phenolic content (TPC), total anthocyanin content (TACY), and protein concentration was performed on a Multiskan−1) [−1 FW). Determination of TPC in extracts was carried out with the use of the Folin−Ciocalteu spectrophotometric procedure using gallic acid (GA) as a standard for the calibration curve (0−340 μg of GA ml−1) . PrepareO-glucoside  equivalents per 100 g of fresh weight (mg of P-3-G equiv 100 g−1 FW).A modified pH differential absorbance method was used to determine TACY . For theThe Bradford method was used for the determination of protein concentration in samples after reading the absorbance at 595 nm, with bovine serum albumin (BSA) as a standard . −1 cm−1) from guaiacol at 470 nm in the presence of H2O2 [v/v) guaiacol in 0.05 M sodium phosphate buffer pH 6.0 and 0.01 M H2O2. The total POD activity was divided by the protein concentration (in mg ml−1), and specific activity values were quoted as units per mg of proteins (U mg−1 prot).Spectrophotometric determination of peroxidases (POD) activity was performed on a Shimadzu spectrophotometer . POD activity was measured by monitoring the formation of tetraguaiacol (e = 25.5 mM of H2O2 . The rea−1 FW).Vitamin C was quantified with the reflectometer set  as formerly described by Pantelidis et al. . Results2 gas flow of 500 l\h. The data acquisition and spectral evaluation for peak confirmation were carried out by Waters Empower 2 Software .Analysis of individual phenolic compounds was performed by a reversed-phase HPLC-MS system consisted of 1525 binary pumps, thermostat, and 717+ autosampler connected to the Waters 2996 diode array and EMD 1000 Single quadrupole detector with ESI probe . Symmetry C-18 RP column (150 mm × 4.6 mm) packed with 5 µm particle diameter  connected to an appropriate guard column was used for separation. Binary gradient of mobile phases A (0.1% formic acid) and B (acetonitrile) were used at a flow of 1 ml per min, with the following gradient profile: in the first 20 min from 10 to 20% B; next 10 min of linear rise up to 40% B, followed by 15 min reverse to initial 10% B and additional 5 min of equilibration time. A postcolumn flow splitter  with a 5/1 split ratio was used to obtain the optimal mobile phase inflow for the ESI probe. A DAD detector was set at 254, 315, and 510 nm for the detection of various classes of phenolic metabolites. For LC-MS analysis of anthocyanins, the positive ESI scan mode was used with the following parameters: capillary voltage 3.5 kV, cone voltage +35 V, and extractor and radio frequency (RF) lens voltages were 3.0 and 0.2 V, respectively. The negative ESI scan mode was used for other phenolic compounds with all parameters similar except for capillary and cone voltage: 3 kV and −30 V, respectively. When necessary for the quantification of metabolites, chromatograms were recorded in SIR mode. Source and desolvation temperatures were 120 °C and 360 °C respectively, with an N2O2 , and 1 µL of spin trap DEPMPO . To initiate the reaction, 1 µL of FeSO4  was added to the reaction solution that was immediately transferred into the gas-permeable Teflon tube and placed inside the resonator cavity. Recordings were made using the following experimental settings: center field 3500 G, microwave power 10 mW, microwave frequency 9.85 GHz, modulation frequency 100 kHz, modulation amplitude 1 G.To investigate the hydroxyl radicals (•OH) scavenging activity of strawberry extracts, EPR spectroscopy, in combination with the spin-trapping technique, was used following the procedure previously described ,73. In bThe same settings were used for recording EPR spectra of DPPH free radical degradation kinetics. The interaction of freshly prepared DPPH solution with strawberry-derived antioxidant compounds was studied by measuring the intensity of the DPPH EPR signal. Then, 28 µL of MeOH was mixed with 1 µL of 3.2 mM DPPH, and 1 µL of the strawberry extract was added before transferring the mixture into a 1 mm diameter Teflon tube and recording the EPR spectra . In bothFor the 2D EPR imaging, strawberry fruit was incubated in the redox-sensitive, membrane-permeable aminoxyl spin-probe , as described in Dragišić Maksimović et al. . After 2−1, microwave power 10 mW, modulation frequency 30 kHz, modulation amplitude 1 G.To perform L-band 2D EPR imaging measurements, the whole fruit was inserted into an ER540R36 resonator and measured using the following parameters: gradient strength 15 G cmStatistical analysis was performed using SPSS . To examine significant differences in mean values of analyzed parameters between strawberry cultivars, analysis of variance (ANOVA) was used, while the Duncan test provided post hoc analysis. The level of statistical significance was set at 0.05.In addition to yield, fruit chemical composition becomes very important as selection criteria toward health-promoting characteristics that can attribute to marketing prevalence as well as a consumer health benefit. This comparative study revealed that various strawberry cultivars differ in their phenolic profiles, so it is very important to select cultivars that are the most suitable for both commercial production and human health. Regardless of a large number of cultivars and monitored parameters that were included in this study, several cultivars stood out in terms of the phenolic profile together with other bioactive components: ‘Laetitia’, ‘Joly’, ‘Arianna’, ‘Tea’, and ‘Mila´. Intercultivar variations in phenolic profiles among them should be considered in breeding programs aimed at selecting promising cultivars with improved antioxidant capacity and nutraceutical properties. Increasing awareness of healthy nutrition in modern life places scientific studies of phytochemicals and their disease-preventing properties at the forefront, which is accompanied by higher consumer demands for ‘super fruits’. It opens up a new approach to personalized nutrition in relation to phenolic compounds naturally occurring in strawberries.


Figures, Supplementary Material, and within the article text , as described below.In the original article, there were errors relating to the There were errors in some of the figures:In . The corrected Figure 5 appears below.In In There were errors in the legends for “Figure 6(B): Expression of 24 immune checkpoint genes in high- and low-risk groups.Figure 6(C): Boxplots showing immune score, stromal score, and ESTIMATE score in high- and low-risk groups.https://ljzwhdx.shinyapps.io/FRLPdynanomo/) integrating FRLP risk score, tumor stage, and age for predicting time-independent survival probabilities in TCGA. (A) Input area for users to select stage (stages I–IV) or age (>65 or ≤65) as well as input the risk score and the follow-up time (futime). (B) Survival plots, showing patients’ survival probabilities at different time points. (C) Predicted survival probabilities with a 95% confidence interval (CI), which could be obtained after inputting patient information in the input area. For example, when selecting “>65” for age, “stage III” for stage, and entering “3” for risk score, then a patient’s 1-, 3-, and 5-year survival probabilities with a 95% CI are displayed using a black line, a blue line, and a red line, respectively. (D) Numerical summary showing the exact values of survival probabilities with a 95% CI.”Supplementary Figure 5: The interface of online dynamic nomogram (There were also errors in the in-text citation of Results, subsection: “The Correlation of the FRLP Risk Score Model and Tumor Microenvironment,” Paragraph 1. The sentence previously stated: “The results demonstrated that the stromal score and ESTIMATE score were higher in the high-risk group, while there was no significant difference in the immune score between the two groups (A correction has been made to the o groups . High-rio groups .” The co“The results demonstrated that the stromal score and ESTIMATE score were higher in the high-risk group, while there was no significant difference in the immune score between the two groups . High-riResults, sub-section: “The Correlation of the FRLP Risk Score Model and Immunotherapeutic Sensitivity,” Paragraph 1.A correction has also been made to the The sentence previously stated: “Of all the 24 selected ICGs, CD28, CD86, FGL2, HAVCR2, PDCD1LG2, TNFSF4, and TNFSF18 were highly expressed in the high-risk group .” The co“Of all the 24 selected ICGs, CD28, CD86, FGL2, HAVCR2, PDCD1LG2, TNFSF4, and TNFSF18 were highly expressed in the high-risk group .”Discussion, Paragraph 4.There was an error in the original article text, as there were seven differentially expressed ICGs instead of six. A correction has been made to the The sentence previously stated: “In the present study, we observed a heightened expression of six ICGs in the high-risk group […], which indicated that high-risk STAD patients may not actually benefit from ICB treatment though highly expressed six ICGs.” The corrected sentence appears below:“In the present study, we observed a heightened expression of seven ICGs in the high-risk group […], which indicated that high-risk STAD patients may not actually benefit from ICB treatment though highly expressed seven ICGs.”Finally, some minor typographical errors in the article text have been corrected.The comma was placed at the wrong position.Materials and Methods, “Risk Score Model Construction,” Paragraph 1. This sentence previously stated: “Using R package “glmnet,”.” The corrected sentence appears below:A correction has been made to “Using R package “glmnet”,”.In the original published article, there was an error in the main text: an excessive dash was wrongly typed.Materials and Methods, “Tumor Microenvironment Analysis,” Paragraph. This sentence previously stated: “TIMER, xCell, quanTIseq, MCP-counter, EPIC, CIBERSORT-ABS, and -CIBERSORT.” The corrected sentence appears below:A correction has been made to “TIMER, xCell, quanTIseq, MCP-counter, EPIC, CIBERSORT-ABS, and CIBERSORT.”In the original article, there was an error in the main text, and the period was placed in the wrong position.Results, “The Correlation of the FRLP Risk Score Model and Tumor Microenvironment,” Paragraph 1. This sentence previously stated: “and ESTIMATE scores of two risk groups by the R package “ESTIMATE.”.” The corrected sentence appears below:A correction has been made to “and ESTIMATE scores of two risk groups by the R package “ESTIMATE”.”.In the original article, there was an error in the main text and a letter “s” when typing the word “patients.”Results, “Construction of a Nomogram Based on GS Model,” Paragraph 1. This sentence previously stated: “Considering the inconvenience of using GS score directly in predicting patient’ prognosis,”. The corrected sentence appears below:A correction has been made to “Considering the inconvenience of using GS score directly in predicting patients’ prognosis,”.The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated.


Age-related macular degeneration is a visual disorder caused by abnormalities in a part of the eye’s retina and is a leading source of blindness. The correct detection, precise location, classification, and diagnosis of choroidal neovascularization (CNV) may be challenging if the lesion is small or if Optical Coherence Tomography (OCT) images are degraded by projection and motion. This paper aims to develop an automated quantification and classification system for CNV in neovascular age-related macular degeneration using OCT angiography images. OCT angiography is a non-invasive imaging tool that visualizes retinal and choroidal physiological and pathological vascularization. The presented system is based on new retinal layers in the OCT image-specific macular diseases feature extractor, including Multi-Size Kernels ξcho-Weighted Median Patterns (MSKξMP). Computer simulations show that the proposed method: (i) outperforms current state-of-the-art methods, including deep learning techniques; and (ii) achieves an overall accuracy of 99% using ten-fold cross-validation on the Duke University dataset and over 96% on the noisy Noor Eye Hospital dataset. In addition, MSKξMP performs well in binary eye disease classifications and is more accurate than recent works in image texture descriptors. Age-related macular degeneration is a visual disorder caused by abnormalities in a part of the eye’s retina and is a leading source of visual impairment Ref. . TherefoRecent research in academia and industry has allowed artificial intelligence, Machine Learning, and Deep Learning (DL) to develop computerized algorithms to classify retinal disorders. These retinal disorders include diabetic macular edema (DME), aged macular degeneration (AMD), and Choroidal Neovascularization (CNV), see u = v + vη, where  are pixel position, v is the clean image, u is the noisy image, η is a Gaussian noise distribution with zero-mean and some variance σ2. It is well known that smoothing filters can reduce speckle noise. These techniques convolve an image with various neighbor sizes, 3 × 3, 5 × 5, etc., using a weighted average or selecting a median value, i.e., a non-weighted median filter. However, selecting a median value without any weights from a high noise-density image is not practical because a noise pixel may be selected instead of an image pixel. Therefore, we propose a new texture descriptor, MSKξMP, which echoes or repeats pixel values in a kernel to encode OCT images while avoiding a high level of speckle noises. This paper makes the following contributions:-(i)Avoid speckles noises;(ii)Perform eye disease classifications the Choroidal Neovascularization and Aged Macular Degeneration;(iii)Perform highly accurate eye disease classifications between Diabetic Macular Edema, AMD, and Normal eyes.Develops a new, simple, and highly accurate local texture descriptor algorithm, Multi-Size Kernels ξcho-Weighted Median Patterns (MSKξMP), to-Offers a Unique Singular Value Decomposition and Neighborhood Component Analysis based weighted feature selection method for establishing the optimal accuracy using SVM and Random Forest classification techniques.-Presents computer simulation results that show: (i) 99.78% accuracy on the Duke Dataset, 96.63% and 88.51% on the noisy Noor OCT Volume datasets; (ii) good eye disease diagnosis and recognition outcomes compared to the recent texture descriptors.The correct detection, precise location, classification, and diagnosis of choroidal neovascularization (CNV) may also be challenging if the lesion is small or if OCT images are degraded by speckle noises. Speckle noises in OCT images must be addressed to achieve good classification performance. Speckle noise, called multiplicative noise, is a granular noise texture. It degrades image quality as a consequence of interference among wavefronts in coherent imaging systems, such as radar, laser imaging, medical ultrasound, and optical coherence tomography. The speckle noise is signal-dependent and governed by the Fisher-Tippett distribution. Mathematically, Speckle noise Refs. ,11,12,13This paper has the following structure: the next section contains a Literature Review that offers an overview of methods that give detailed descriptions of the MSKξMP algorithm; the computer simulations section gives numerous experiments showing the performance of MSKξMP; finally, the conclusion and future work are presented in the last section.In recent years, deep learning networks can perform vision recognition in various applications such as self-driving cars, natural language and image processing, and medical diagnosis . Ref.  proposesThe multipath convolution layers extract critical global structures with a large filter kernel and use a sigmoid function as the classifier. Ref.  presenteSeveral hand-crafted feature techniques show good classification performance on ocular disorder classifications in OCT images. Ref. ’s algoriThe current works using hand-crafted features fall short compared to the deep learning technique in achieving extremely high classification accuracies. However, deep-learning models usually require long training times and heavy computational hardware such as GPUs. They are also data-hungry and complex. A simple, aggressive machine-learning approach is proposed here to overcome these shortcomings. This technique contains a smaller number of weights, making them easier to implement, requires less training time, and does not require specialized hardware Ref. . TexturaOur algorithm has the following steps. (i)Preprocessing: performs image segmentation using thresholding, then flattens and aligns the retina layers;(ii)Generating Hand-Crafted Features: generating hand-crafted features using Multi-Size Kernels Echo-Weighted Median Patterns (MSKξMP);(iii)Feature Weights Selection and Classification: best features are selected using Singular Value Decomposition, and neighborhood component analysis (NCA) is classified using Gaussian and Polynomial Kernels Support Machine Vector, Random Forest, Naïve Bayes, Adaboost, and RUS Boost.2 fitness function. In most cases, the 3rd-order polynomial is selected to flatten the retinal layers.OCT scanner misalignment between the eye and sensor during the acquisition of the retinal images cause white areas in the image. Therefore, preprocessing Ref.  starts wThis section presents new, so-called Multi-Size Kernels ξcho-Weighted Median Patterns (MSKξMP), hand-crafted features. This is an extension of the Local Binary Patterns (LBP) feature extractor  and is uCI is the center pixel, iI are the surrounding pixels, f(i) is equal to 2i and R is a region defined by the kernel size. The kernel size is usually selected to be 3 × 3; however, sizes such as 5 × 5 and 7 × 7 are also known to be used. s(iI − CI) = {1, iI − CI ≥ T; 0, iI − CI < T} where T is selected to be zero. The generalized LBP operates on all the pixels in an image using a specific kernel size. Each of these kernels is placed over a pixel, CI, and is compared to its surrounding neighboring pixels iI using Equation (1). If a neighbor pixel is greater than or equal to the center pixel, then s(·) is assigned 1; if a neighbor pixel is less than the center pixel, then s(·) is assigned 0. A binary sequence is obtained, and each sequence is assigned to the appropriate decimal weight, 2i, which is then converted into a decimal value. The decimal weights, 2i, are then summed and encoded to structural information, generating an LBP image. The LBP is simple, fast, easy, and robust to illumination changes. However, it suffers when speckle noises are present.In this proposal, we start with the generalized form of the LBP given by the following:CI in (1) with the median value of all the pixels within the region R: CI = medianI = median(R) = median, where N is the number of pixels in region R. However, the current median LBP does not go far enough to prevent speckle noises from falling into the texture-based calculation. For example, The median LBP Ref.  was propmedianI. However, this median value is calculated differently than Ref. [nRm×, where n’s and m’s have the following values: 3 × 3, 5 × 5, 7 × 7, 3 × 5, 5 × 7, and 3 × 7. Numerous kernel sizes are used because each kernel captures a variation of texture within the OCT; one kernel can miss a specific feature that another kernel size picks up. A larger kernel like the 7 × 7 can capture regional details that a 3 × 3 kernel can miss and vice versa.MSKξMP is a textural feature descriptor similar to median LBP. MSKξMP encodes the textural pattern information by comparing surrounding pixels to the median pixel han Ref. . Its surnRm×. The first is to calculate medianI, the second is to find each im’s found in nRm×, and the third step is to calculate each nmedianLBPm× using parameters obtained from steps one and two. The nmedianLBPm× is defined by the following equation:CI is the current pixel being encoded and is the center pixel of the kernel. The combination of all nmedianLBPm× are the MSKξMP. The calculation of medianI is motivated by the weighted median filter in Ref. [echoes or repeats pixel values a predetermined number of times. medianI = 75, which is not a noise pixel. These weights can be determined by centering a two-dimensional Gaussian function onto CI, defined by the following:xσ and yσ are standard deviations in the x and y directions, respectively. When a square kernel is utilized, the standard deviations should be the same, xσ = yσ, and when a rectangular kernel is utilized, the standard deviations could be different, xσ ≠ yσ. The selection of xσ and yσ is flexible, as long as the center pixel has the most weight and the surrounding weights are not too small compared to the center. Please see min), to ensure its minimum value equals 1. We then took the ceiling of each element in the normalized Gaussian Function to obtain integer values . The Echo formula can be represented by the following:ξ represents the echo weights of our kernels. The standard deviations utilized in the 3 × 3 weight kernel, shown in xσ = yσ = 1.2. The values of the standard deviations should increase with increasing kernel size. The weight of the center pixel should always have the highest repetition because this gives a non-noise pixel a better chance of being selected as the median.The MSKξMP is calculated in three steps for each kernel size,  in Ref. , which emedianI can be written in terms the pixel intensities in nRm×:ε0, ε1, … Nε−1 are elements in the matrix ξ and iI, i = 0, 1, …, N − 1 are the pixel values in nRm×. I0 is duplicated ε0 number of times.Finally, im values. These median values are selected within sub-regions of nRm× and are color-coded in red, yellow, and blue, see top of im’s within a 5 × 5 kernel is illustrated in the following. East (yellow): m1 = median = 67.5, northeast (red): m2 = median = 106; north (blue): m3 = median = 106; northwest (Red): m4 = median = 106; West (yellow): m5 = median = 106; southwest (red): m6 = median = 70; south (blue): m7 = median = 153; and southeast (red): m8 = median = 140.5.The second step is to determine im’s are calculated, we moved to the third step, where we compared im’s to medianI found in the first step using (2). The comparison encoded the im’s to binaries which were then multiplied by their respective decimal weights, and the sum was calculated to obtain medianLBP5 × 5(CI). The rest of the nmedianLBPm×’s are obtained similarly. nmedianLBPm× image displays different textural information, which is beneficial in distinguishing the different types of ocular disorders.Once all the nmedianLBPm× image to extract their features. One histogram was generated from the entire nmedianLBPm× image, which represented the global features. The other three histograms were generated from local overlapping regions and labeled with different color brackets, as seen in nmedianLBPm× are concatenated to form the MSKξMP feature vector for each OCT image.Four histograms were generated for each 2.Three datasets were used to test the performance of MSKξMP. The first dataset, designated as Dataset 1, was taken from Duke University, Harvard University, and the University of Michigan Ref. . This daThe third dataset, Dataset 3, was also collected by the Heidelberg SD-OCT imaging system at Noor Eye Hospital (NEH) and was obtained from the Mendeley database website Ref. . It contiF that are linear functions of those in the original features, while successively maximizing variance and maintaining uncorrelatability. This allows the representation of a class of features in the following form:iλ eigenvalues are sorted according to the size as 1λ ≥ 2λ ≥ … ≥ pλ ≥ 0, iF, i = 1, 2, … p, features, F1 is the best feature and the 2nd, 3rd, …, kth (k ≥ 4) are other principal components.Principal Component Analysis (PCA) is one of the oldest and most widely used statistical techniques that reduces feature dimensionalities. PCA improves interpretability while preserving variability, which minimizes informational loss. This means finding new variables X = TUΣV represents the SVD of an input matrix X, where U represents the reflection of X, the diagonal of Σ represents the variabilities, σ1, …, ησ, up to the η-th feature, TV is the ‘eigen vectors’ of X. X will be subtracted by its mean of each column of meanX, to obtain mX = X − meanX. This subtraction is commonly used for normalizing PCA data. We then calculated PCAX, which is defined by the following:M is selected based on the percentage of variability up to η, η ≥ M, N is the number of total observations in the dataset and the dimensions of PCAX is N rows by M columns.The PCA used in this paper was based on Singular Value Decomposition (SVD), which was used to select our best features. ω, of length of η representing the weight importance of each feature. Each element in ω is assigned to each column of matrix PCAX.Our feature selection also included feature weighting using neighborhood component analysis (NCA) Ref.  to refinPCAX through iterations. The values of the weights in ω were used for removing features from PCAX at iteration. The weights can be written as the following:lM, is the number of features remaining after some features have been removed at lth iteration. This idea was taken from Ref. [l), were recorded in each lth iteration at the kth classifier. a was defined by the following:kC was one of the kth classifiers listed above. The maximum accuracy was found using the following:lth at kth classifier. Dataset 1, 2, and 3 were trained in this fashion. The η was selected to be 100 for Dataset 1, and 70 for Dataset 2 and 3. We then searched for sets of optimal features by reducing the number of columns of rom Ref.  “Drop Ouost Ref. , and Naïost Ref.  and suppost Ref. , Random ost Ref.  and Adabost Ref. . τω(l) vTp and Tn are the true positive and true negative and Fp and Fn are the false positive and false negative, respectively.To evaluate and determine the performance of the proposed feature extraction approach, the accuracy, sensitivity, specificity, precision, and F1-score were compared with the results of HOG Ref. , BACNN RCI. Therefore, kernel sizes up to n × 9 were omitted from the result section. As using more kernels with varying sizes would have diminishing returns and performance improvements would be minimal. Using six kernels is the right balance between computation times and performances of our MSKξMP.For datasets 1 and 2, four different classification schemes were tested using MSKξMP, AMD vs. DME vs. Normal, AMD vs. DME, DME vs. Normal, Normal vs. AMD, and all classes. Since dataset 1 was slightly imbalanced, AMD (723 images), DME (1101), and has Normal (1407), using accuracy as the only measurement would not be enough to detail the efficacies of the MSKξMP. Using measurements such as recall, specificity, precision, and F1-score was more effective. For dataset 3, we also tested using four classification schemes: Normal vs. CNV, CNV vs. Drusen, Normal vs. Drusen, and all classes, Normal vs. CNV vs. Drusen. Our experiments included kernel sizes up to 3 × 9 and 5 × 9, however, these window sizes were too large, and important features detected may not be within the local areas of the pixel, The highlights of our experiments on datasets 1 and 2 are shown in The main takeaway from the results in dataset 3 is that our MSKξMP was able to achieve 89.3% in accuracy using SVM polynomial kernel with specificity of 94.37%. This means high confidences in the true negative diagnoses. The other classifiers performed similarly to dataset 1 and dataset 2. Normal vs. CNV achieved the highest accuracy at 98.53% due to the significant differences between their visual features. It can be observed that MSKξMP was able to achieve 93.69% accuracy with the CNV vs. Drusen classification scheme. This is a good score because while CNV images contain drusen particles that may cause some confusion with AMD images, our feature extractor was able to differentiate between these two classes reliably.For our ablation study, we tested the feature extraction capabilities of each kernel size and weights shown in  on datasComparing dataset 1 with recent state of the art shows our MSKξMP is able to achieve slightly higher accuracies, as shown in This work presents a new image textural descriptor, MSKξMP, for differentiating between ocular diseases such as AMD, Drusen, DME, and CNV in OCT images. The presented method can be used to encode textural patterns at local and regional scales and to improve edges in various directions while avoiding speckle noises. The computer simulations show that our measurements outperformed (a) the current state-of-the-art deep learning techniques and vision transformer; and (b) the FPN-EfficientNetB0 in dataset 3 and is comparable to the FPN-ResNet50 network. Some of our binary classifications from dataset 1 also performed well, achieving perfect accuracy, or close to it. MSKξMP had high specificity, suggesting high confidence in their true negatives. Future work should be focused on constructing 3D retinal images, such as 3D OCT images, by extracting volume and depth information to determine the spread of the disease. The 3D structures should be used for multi-view classification.


SD: 2·3) from the Health ABC, NuAge, LASA and Newcastle 85+ cohorts were pooled. Baseline protein intake was assessed with food frequency questionnaires and 24-h recalls and categorized into < 0·8, 0·8–<1·0, 1·0–<1·2 and ≥ 1·2 g/kg adjusted body weight (aBW)/d. The prospective association between protein intake, its interaction with PA, and grip strength (sex- and cohort-specific) was determined using joint models . Grip strength declined on average by 0·018 SD  every year. No associations were found between protein intake, measured at baseline, and grip strength, measured prospectively, or rate of decline of grip strength in models adjusted for sociodemographic, anthropometric, lifestyle and health variables . There also was no evidence of an interaction between protein intake and PA. We failed to find evidence in this study to support the hypothesis that higher protein intake, alone or in combination with higher PA, slowed the rate of grip strength decline in older adults.Higher dietary protein, alone or in combination with physical activity (PA), may slow the loss of age-related muscle strength in older adults. We investigated the longitudinal relationship between protein intake and grip strength, and the interaction between protein intake and PA, using four longitudinal ageing cohorts. Individual participant data from 5584 older adults  followed for up to 8·5 years (mean: 4·9 years,  Dietary protein, in excess of the current recommended dietary allowance, 0·8 g/kg body weight (BW)/d according to the European Food Safety Authority and the Institute of Medicine, has been proposed to slow the decline of age-related muscle strength in older adults. However, the relation between protein intake and grip strength, often used as measure of overall muscle strength, is inconsistent across the literature, and very limited for strength decline over time. Prospective observational studies showed that higher protein intake was associated with higher grip strength–10, while some have not,12. Higher protein intake has also been associated with a slower rate of decline in grip strength over time,8,10, while others have not found similar results,11,12.Older adults gradually lose muscle mass, muscle strength and physical function with age which increases the risk of falls, frailty, disability and all-cause mortality,13. Since both protein intake and PA stimulate muscle protein synthesis, combining both approaches may better protect muscle mass and muscle strength than each alone,13. However, few observational studies have addressed this issue and evidence is inconclusive,14. In many instances, lack of statistical power in individual studies does not allow for testing the interaction between protein intake and PA, or for achieving robust estimates. We overcome this limitation by pooling individual participant data from multiple ageing cohorts. In this study, we hypothesise that higher protein intake slows down the rate of muscle strength decline in older adults in a dose-dependent manner and has a synergistic effect with PA. In order to test our hypothesis, we investigated the prospective relationship between protein intake, and its interaction with PA, and (decline in) grip strength in four longitudinal cohorts in the PROMISS consortium.Several expert groups have proposed that the beneficial effect of higher protein intake on muscle strength may work in synergy with physical activity (PA)–19. Briefly, Health ABC is a longitudinal cohort study that included 3075 well-functioning community-dwelling Black and White males and females aged 70–79 years at baseline living in the USA. Participants were recruited from Medicare-eligible residents in the metropolitan areas of Memphis, Tennessee, and Pittsburgh, Pennsylvania, between April 1997 and June 1998 and followed annually (clinic visit) or every 6 months (telephone interview) for 16 years. NuAge is a longitudinal cohort that recruited 1793 generally healthy community-dwelling males and females aged 67–84 years living in Montreal and Sherbrooke areas  in 2003–2005 and followed them annually (clinic visit) or every 6 months (telephone interview) for 3 years. LASA is an ongoing nationally representative longitudinal study of older males and females aged ≥ 55 years residing in the Netherlands. The study started in 1992/93 (n 3107), and participants were followed every 3 years until 2018/2019 . Two additional cohorts were recruited from the same sampling frames at 10  and 20 years  after the baseline. The Newcastle 85+ Study is a longitudinal population-based study that approached all people turning 85 years in 2006/2007 (born in 1921) in Newcastle and North Tyneside, UK. At baseline, there were 845 very old males and females who agreed to a health assessment and a review of their GP records, who were re-examined after 18, 36 and 60 months. We used year 2 (baseline), 4, 6, 8 and 10 from Health ABC; T1 (baseline), T2, T3 and T4 from NuAge; wave 3B, the Nutrition and Food-related Behavior sub study (baseline) and wave I from LASA; and phase 1 (baseline), 2, 3 and 4 from the Newcastle 85+ .As part of the PROMISS consortium, four longitudinal prospective observational studies of community-dwelling older adults aged > 55 years were included: (a) The Health, Aging and Body Composition Study  from the USA, (b) The Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge) from Canada, now the NuAge Database and Biobank (database received on May 2019), (c) The Longitudinal Aging Study Amsterdam (LASA) from the Netherlands and (d) The Newcastle 85+ Study from the UK. These studies are described in detail elsewheren 44), had very poor cognitive status  and no proxy for dietary assessment (n 18), had missing dietary intake data (n 800), had very high reported energy intake, that is, > 3500 kcal/d for women or > 4000 kcal/d for men (n 52), had no data on BMI (n 58) or missing grip strength (n 141). The analytic sample at baseline comprised 5584 community-dwelling participants .We excluded participants who were institutionalised  on two weekdays and one weekend day in T1, in LASA by a self-administered 238-item FFQ reflecting the preceding 4 weeks and collected from fall to spring in the ‘Nutrition and Food-related Behavior Study 2014–2015’ sub study,22, and in Newcastle 85+ by two 24-h recalls on two non-consecutive weekdays at least 1 week apart in phase 1,24. In all studies, energy and protein intake were calculated by using country-specific food composition databases. In Newcastle 85+ and NuAge, individual intakes of protein and energy were averaged within the two or three recall days, respectively.For all studies, data on dietary intake were available at baseline (referred to as wave 1 in our study). Dietary intake was assessed in Health ABC by a 108-item interviewer-administered FFQ reflecting the preceding 12 months in year 22, BW was adjusted to be within the desired BMI range and calculated as previously described,26. By calculating adjusted BW (aBW), we attempted to control for the deficit and excess protein intake needs in underweight and overweight people, respectively. Protein intake was expressed per kg of aBW/d (measured at baseline as well), categorised into < 0·8, 0·8–< 1·0, 1·0–< 1·2 and ≥ 1·2 g/kg BW/d and g/kg aBW/d, and used as exposure. These cut-offs were based on expert recommendations for optimal protein intake,13, or currently used recommended dietary allowances for protein  countries and 1·2 by the European Nordic countries), or on previously published studies on protein intake in older adults,26,29–33. All variables are described in Supplementary Table 1.Energy intake was transformed into cohort-specific z-scores. For participants with a BMI outside the desirable range for older adults of 22–27 kg/m. Participants indicated whether they had performed exercise in the past 7 d and for how long they spent in each activity. A metabolic equivalent (MET) value in kcal per week per kilogram of BW was determined for each activity and total PA calculated as MET values for each activity × BW. In NuAge, PA was measured using the validated self-reported Physical Activity Scale for the Elderly (PASE) which asked about the frequency, duration and intensity of activities during the past 7 d. The total PA score was calculated as time spent on each activity (in hours per week) × item weights and then summed. PA in LASA was measured with a validated questionnaire that estimates the frequency and duration and intensity of specific activities in the previous 14 d. MET scores were assigned to each activity based on published MET scores lists. The frequency × duration × MET was calculated for each activity, summed over and then divided by 14 d. In Newcastle 85+, a validated purposely designed PA questionnaire included questions on how frequently participants engaged in mildly, moderately and highly energetic activities. The resulting total PA score was calculated as 3 × highly energetic activities + 2 × moderately energetic activities+mildly energetic activities. PA at baseline was transformed into cohort-specific tertiles  and used to categorise PA for subsequent waves. Lower, medium and higher PA in Health ABC was considered as 0–3·27, 3·28–14·20 and ≥ 14·20 kcal/BW/week, respectively; in NuAge as a PASE score of 0–71·3, 71·4–115·4 and ≥ 115·5, respectively; in Newcastle 85+ as a specialised questionnaire score of 0–2, 3–6 and ≥ 7, respectively; and in LASA as 0–32, 32·1–59 and ≥ 59 MET h/week, respectively.In Health ABC, PA was measured by a specifically designed questionnaire as described previously. In Health ABC, grip strength was measured twice on each hand with an isometric dynamometer (JAMAR). Participants who underwent recent hand surgery or had severe hand pain were excluded. In NuAge, grip strength was measured three times on each hand with a pneumatic dynamometer (Martin Vigorimeter) and expressed in KPa. In LASA, grip strength was measured twice on each hand with a hydraulic dynamometer (Model JAMAR 5030J1). The dynamometer was adjusted for hand size. In Newcastle 85+, grip strength was measured twice  on each hand with an isometric dynamometer . In NuAge and LASA, participants were in a sitting position with arms alongside the body with elbows at 90°, whereas in Health ABC participants were sitting but with arms rested on the table and elbows at 90°. In Newcastle 85+, participants were standing with arms alongside the body and elbows at 90°. In all cohorts, the mean grip strength value of the maximum measurement of each hand was used for analysis. Grip strength measured by dynamometer and measured by vigorimeter shows a high correlation. For descriptive purposes, we converted grip strength measured by vigorimeter (in KPa) into kg using a factor of 0·46 as has been previously used. Furthermore, since methods to assess grip strength were different between cohorts, grip strength at baseline was transformed into sex- and cohort-specific z-scores, and the mean and sd were used to create z-scores for the other waves (z = (x–µ)/σ) where x stands for raw score, µ for population mean and σ for sd.Grip strength was used as an objective measure of upper-body and general muscle strength and was used as our outcomeAscertainment of vital status differed between cohorts and ranged from a review of hospital records and obituaries to linkage with the National Mortality Registry. In Health ABC, survival time was calculated as the time between age at year 2 (1998–1999) and age of death (censored at 30 September 2014); in NuAge, survival time was calculated as the time from age at T1 (2003–2005) to age of death (censored at 3 May 2010); in LASA, survival time was calculated as the time from age at wave 3B (2012–2013) to death (censored at 22 July 2018); and in Newcastle 85+, survival time was calculated as the time from age at phase 1 (2006–2007) to age of death (censored at 16 January 2018).1.Other sociodemographic, anthropometric, lifestyle and health variables used are described in Supplementary Table 1. All harmonised variables were merged to create one dataset. Normality was assessed by Q–Q plots: normally and non-normally distributed variables are presented as means and standard deviations, and medians and interquartile ranges, respectively, and categorical data as percentages and frequency. To determine the association between protein intake and grip strength, we fitted a hierarchical linear mixed effects models with the lme4 package (version 1.1-20) and Cox proportional hazards for time-to-event data  with the survival package (version 2.43-3). Briefly, exposure, outcome, confounders and effect modifiers were selected based on their theoretical and clinical relevance, group imbalance (protein intake categories) and their position in directed acyclic graphs. Models with these variables were then fitted, removed and refitted until the best possible and parsimonious model converged. Clustering by cohort was accounted for by the inclusion of a random term for study membership. If data are missing at random, it is accounted for in linear mixed models, but in a longitudinal cohort with a mean age of 75 years at baseline, attrition is high and failure to account for mortality (data not missing at random) would likely result in biased estimates. These outcomes  are typically analysed separately, but joint models analyse the two outcomes together with shared parameters in a single likelihood function (maximum likelihood estimation). We therefore fitted joint models with the JoineRmeta package (version 0.1.2) in R v3.6.3.Data cleaning, quality control and harmonisation were performed separately for each cohort prior to merging. In order to be harmonised, grip strength was transformed into sex- and cohort-specific z-scores, energy intake into cohort-specific z-scores, and PA and cognition into cohort-specific tertiles. Harmonisation of other sociodemographic, anthropometric, lifestyle and health variables is described in Supplementary Table Separate models for the association between protein intake and grip strength were fitted. Model l included id (random effect), time since baseline (random effect) and study membership (time-independent and random effect), protein intake (time-independent) and its interaction with time, age (time-dependent), sex (time-independent), height (time-independent) and education (time-independent); model 2 was adjusted for the previous variables plus smoking (time-independent), energy (time-independent) and alcohol intake (time-independent); model 3 was further adjusted for multimorbidity (time-dependent) and cognitive status (time-dependent); and model 4 was further adjusted for PA . Models 1–3 were also stratified by PA level. Apart from id, study membership and time since baseline, all terms in the models are fixed effects.βs and 95 % CI (determined by refitting the models to 150 bootstrap samples).As a sensitivity analysis, model 4 (fully adjusted) was re-run with 0·8–< 1·0 g/kg aBW/d of protein intake as referent, or with protein in g/kg BW/d (hence with non-adjusted BW), or with percentage of total energy from protein (%), or with protein intake per MJ of energy, or further adjusted for BMI (time-dependent), or weight (time-dependent), or inputting the missing values for multimorbidity and cognitive status (if this was available at an adjacent observation of the same participant), excluding each of the cohorts from the analysis, or using cohort-specific z-scores of grip strength (not sex-specific). Point estimates and CI were used to assess statistical and clinical significance. Results are presented as 2).More women and more older adults with a lower cognitive status had missing data on grip strength. All other health and sociodemographic characteristics were similar between those with and those without grip strength (online Supplementary Table sd: 2·3 years), and maximum survival time until event or censoring was 16·2 years . Most participants were from the Health ABC study (45·6 %), followed by NuAge (30·8 %), Newcastle 85+ (12·8 %) and LASA (10·8 %) (1). At baseline, 27 (n 1530), 23 (n 1304), 21 (n 1195) and 28 % (n 1555) of the participants had a protein intake < 0·8, 0·8–< 1·0, 1·0–< 1·2 and ≥ 1·2 g/kg aBW/d, respectively. Most of the participants with protein intake < 0·8 g/kg aBW/d were from the Health ABC study (65·2 %), while most participants with protein intake ≥ 1·2 g/kg aBW/d were from NuAge (37·6 %). Participants with higher protein were more often alcohol drinkers, had higher energy intake and had generally higher PA. For example, 29·3 % of those with protein intake < 0·8 g/kg aBW/d and 39·1 % of those with protein intake ≥ 1·2 g/kg aBW/d had a high level of PA. Age, body height and cognitive status were statistically different between protein intake categories but not clinically significant (2). Health and sociodemographic characteristics by wave of follow-up, cohort and protein intake categories, and protein intake categories and PA are shown in Supplementary Tables 3–5. Grip strength was lowest in Newcastle 85+  kg) and highest in LASA  kg) at baseline (online Supplementary Table 3). Grip strength decreased from 28·0  at baseline to 25·0  kg at wave 5 (last follow-up time point), which meant a decrease of 0·45 (0·94) sds over the follow-up (online Supplementary Table 4).The analytic sample consisted of 5584 men (47·4 %) and women (52·6 %) with a median age of 75·0 (interquartile range: 71·6–79·0) years at baseline. Maximum follow-up time was 8·5 years  every year since baseline. We found no associations between protein intake (< 0·8 (referent), 0·8–< 1·0, 1·0–< 1·2 and ≥ 1·2 g/kg aBW/d and g/kg) and grip strength (sex- and cohort-specific z-score) and rate of decline of grip strength after adjustment for sex, age, education and height (model 1). The results were similar in more complex models (models 2–4) further adjusted for smoking, energy and alcohol intake, cognition, multimorbidity and PA   .6), or further adjusted for BMI (time-dependent), or weight (time-dependent), or inputting the missing values for multimorbidity and cognitive status (if these were available at an adjacent observation of the same participant), or excluding each of the cohorts from the analysis , or using cohort-specific z-scores of grip strength (not sex-specific), but none of these substantially changed the results. For example, participants with protein intake < 0·8, 1·0–< 1·2 and ≥ 1·2 g/kg aBW/d had similar rates of grip strength decline than those with 0·8–< 1·0 g/kg aBW/d . However, it is worth noting that, although not significant, the sensitivity analysis excluding HABC from the fully adjusted model resulted in the change of direction of the estimates for protein intake (not rate of decline) .Final models were re-run with 0·8–< 1·0 g/kg aBW/d of protein intake as referent, or with protein in g/kg BW/d (hence with non-adjusted BW), or with percentage of total energy from protein (%), or with protein intake per MJ of energy . We also stratified the fully adjusted models by PA  and found no strong evidence of protein intake being associated with grip strength in any PA category .We found no clear interaction between protein intake and PA  and grip strength and rate of decline of grip strength over a maximum follow-up of 8·5 years in community-dwelling older adults. Following from this, we also did not find evidence of an interaction between protein intake and PA in this pooled analysis of individual participant data from four longitudinal ageing cohorts. nor with rate of decline in grip strength in Newcastle 85+ over 5 years. However, higher protein intake was associated cross-sectionally with higher knee extensor strength in NuAge (but not grip strength) at the last data collection and with higher grip strength in Newcastle 85+. Further, energy-adjusted protein intake was associated with muscle strength score  in NuAge as well. In Health ABC, no analysis of protein intake and grip strength has been conducted, but higher protein intake was associated with reduced lean mass and appendicular lean mass decline over 3 years,49, although, not with the change in mid-thigh muscle cross-sectional area and appendicular lean mass over 5 and 6 years, respectively,50. We did not observe any association between protein intake and grip strength in this pooled individual data from the Health ABC, NuAge, LASA and Newcastle 85+. Prospective observational studies (cohorts not included in our study) on protein intake and grip strength in older adults are inconsistent with most finding a protective effect,8,10, while others did not. There are a few important differences that may explain the different results, namely that Beasley et al. and Mclean et al. only recruited women, that Beasley et al. used protein intake adjusted for energy intake with the residual method, that Mclean et al calibrated the FFQ for doubly labelled water and 24-h urinary nitrogen, and that Isanejad et al used 3-d food records. Our analysis adjusted for energy intake or used protein intake expressed by % of total energy or by 1 MJ of energy, but it is possible that residual confounding remained.In previous analyses of one of the individual cohorts of the pooled analysis, NuAge, higher protein intake was not associated with the 3-year change in grip strength or knee extensor strength. This discrepancy with our previous findings may be because higher protein intake may be more relevant to physical function than to handgrip strength alone. Using a different measure of muscle strength than grip strength, such as knee extensor strength, or a combination of measures reflecting overall muscle strength might have yielded different results. In fact, the muscles are required to perform a grip strength test, but a small proportion of the overall muscle mass and a significant part of the decline in grip strength with ageing appear to relate to neuromuscular activation rather than contractile volume.Previously, we showed that participants  with protein intake ≥ 0·8 g/kg aBW/d had slower decline in walking speed and were less likely to report incident mobility limitations, and in a dose-dependent mannersd per year. Original scores are from different distributions (so caution interpreting the back-transformation is needed), but using the mean (28·9 kg) and sd (10·3 kg) at wave 1, grip strength decline would be equivalent to a decline of 0·19 kg/ year or 0·9 kg (3 %) over 4·9 years. This is slightly lower than younger participants  from the English Longitudinal Study of Ageing who lost on average 0·03 sd of grip strength every year which equated to a reduction of 1·6 kg (6 %) in women and 2·3 kg (5 %) in men over 9 years. Beasley et al. found that older women from the Women’s Health Initiative lost, on average, 3·8 % of the baseline grip strength over 7 years, and Mclean et al found that older adults in the Framingham Offspring Cohort lost, on average, 1·6 % of the baseline grip strength over almost 6 years. It is possible that a more pronounced decline in grip strength in our study or longer follow-up would have been necessary to observe an association with protein intake. In fact, the minimal clinically important difference for grip strength is somewhere between 5·0 and 6·5 kg which is considerably higher than the mean grip strength decline in our study.In the fully adjusted models, grip strength declined on average by 0·018 Future analysis of secondary data should consider strata of grip strength decline and/or extending the follow-up time during study design.. However, those with the high red meat dietary pattern had worse grip strength (but not worse decline) than those with a low meat dietary pattern. Although our analyses adjusted for several confounders, namely energy intake, several other dietary factors may affect muscle strength and the cumulative and synergistic effect of the complex mixture of foods may offer an alternative explanation for the null findings in this study.Previously in the Newcastle 85+, three dietary pattens were derived: a high red meat, a low meat  and a high butter dietary pattern. Very old adults with a high red meat dietary pattern had the highest protein intake (non-adjusted) and highest % of energy from protein of the three dietary patterns. Muscle protein synthesis may be further stimulated if protein intake occurs in closer temporal proximity to exercise and especially if it involves resistant training. However, in this pooled analysis, we could not determine when protein intake or PA occurred with an acceptable degree of precision and accurately distinguish between exercise types. Furthermore, although PA was transformed into cohort-specific z-scores prior to analyses, it is possible that this transformation was not enough to deal with all residual differences. For example, PA was estimated in NuAge for the previous 7 d, while in LASA this was asked for the previous 14 d. A major strength of this study is that we harmonised data from four large ageing cohorts and performed an individual participant pooled analysis, which allowed us to significantly increase our sample size and test for interactions that we could not test in individual cohorts through stratification by PA level. For example, there were 103 participants in LASA with a protein intake of 0·8–1·0 g/kg aBW/d at baseline, and of those, 31, 32 and 40 had lower, medium and higher PA, respectively. A model that tested the interaction between protein intake and PA in a single cohort like LASA would have resulted in considerably more unprecise estimates than those we report. The use of an objective measure of muscle strength (grip strength), the large range of covariates adjusted for, and the use of joint modelling to account for non-random attrition and study membership are other major strengths of this study. One important, yet common limitation, is that protein intake was measured at baseline only and assumed to be stable or have declined proportionally over time. If that assumption does not hold, non-differential misclassification of protein intake during follow-up might have occurred and may have biased the results towards the null. Misreporting is a common limitation for self-reported methods, especially underreporting in dietary intake. Although protein-rich foods are not usually underreported, it is possible that protein intake was misclassified and biased the association with grip strength towards the null. Additionally, although protein intake was categorised as part of the harmonisation process, dietary intake was assessed by FFQ in Health ABC and LASA, and with multiple 24-h recalls in NuAge and Newcastle 85+. These two methods may give slightly different estimates, resulting is misclassification. We also did not distinguish between animal and vegetable protein which may have yielded a different result than total protein. In fact, Mclean et al. found that older adults with higher total or animal protein intake had lower declines in grip strength but failed to see the same for vegetable protein intake. Body composition is a major driver of grip strength and as such, protein was expressed per kg of BW, analyses were adjusted for height, and sensitivity analyses further adjusted for weight or BMI. However, measures such as appendicular lean mass or fat-free mass were not available for all cohorts and may have been important effect modifiers.We did not find a clear indication for effect modification by PA or a synergistic effect of protein and PA. A 2018 systematic review and meta-analysis of randomised controlled trials also failed to find a synergistic effect of protein supplementation and resistance exercise on muscle strength in non-frail community-dwelling older adultsWe found no convincing evidence in this study that protein intake (measured at baseline and expressed as g/kg aBW/d) was associated with grip strength over time in community-dwelling older adults, or that there was an interaction between protein intake and PA. It is possible that a higher grip strength decline or longer follow-up was needed to observe an association.


As the incidence of gastric cancer (GC) increases sharply in adults aged over 40 years, screening of this high-risk population is important. This study aimed to explore knowledge level of GC related risk factors and symptoms, and to identify influencing factors associated with intention toward GC screening among people aged 40 years old and above in China.A cross-sectional, web-based survey was conducted among people aged 40 years old and above between October 2021 and March 2022 in Southeastern China. The participants' knowledge was assessed by a series of questions about risk factors  and warning symptoms .A total of 2547 complete responses were received. The mean age was 47.72 (±7.20) years and near 60% were male. Respondents had a moderate level of knowledge about risk factors and warning symptoms of GC. The total mean knowledge score was 23.9 (±9.8) out of a possible score of 38. Majority (80%) of respondents reported intention to be screened for GC in the next 5 years. The most influential predictors of screening intention were income level , perceived benefits , perceived severity , ever took GC screening , perceived poor overall health , and perceived barriers . Other significant factors were ever diagnosed with chronic gastric diseases, total knowledge score, and cues-to-action. The major reasons for not willing to take screening were “endoscopy is uncomfortable” (29.6%), “worry about screening results” (23.6%), and “have no symptoms” (21.3%).High-risk population aged 40 years and above expressed high intention to receive GC screening. Intervention to improve health promotion and reduce the barriers to uptake of GC screening among high-risk populations in China is warranted. Gastric cancer (GC) remains an important cancer worldwide and is responsible for over one million new cases in 2020 and an estimated 769,000 deaths . EasternEarly detection of GC has great potential to improve survival and reduce disease mortality. Endoscopic screening for GC in moderate to high risk populations was found to be cost-effective , and it Helicobacter pylori and Epstein–Barr virus also play a role in gastric carcinogenesis  . AccordiWe commenced a cross-sectional, web-based anonymous survey using an online questionnaire during October 2021 and March 2022. Convenience sampling was conducted to recruit subjects for this study. The research team used WeChat  to advertise and circulate the survey link to their network members. Network members were requested to distribute the survey invitation to all their contacts that satisfy the inclusion criteria. The inclusion criteria were that (1) aged 40 years and above; (2) living in Fuzhou, Putian, Quanzhou, Xiamen, and Zhangzhou city of Fujian province; (3) having no history of cancer. Upon completing the survey, each respondent providing a valid questionnaire was awarded an incentive of 5 Chinese Yuan . In an attempt to reach a more comprehensive recipient coverage, we also encouraged participants to disseminate the survey link to all their contacts with a thank you note at the end. The participants were informed that their participation was voluntary, and consent was implied through their completion of the questionnaire. The reason for selecting these five cities was due to they are the major cities with the highest incidence of GC in Fujian Province. In total, the accumulated population of these five cities accounts for 73.43% of the total population in Fujian province .The questionnaire was self-developed and pilot tested. Local experts of both epidemiologists and clinicians validated the content of the questionnaire. The survey consisted of four sections, which mainly assessed (1) demographic and general health; (2) knowledge about GC-related risk factors and warning symptoms; (3) history of treatment-seeking, and (4) attitudes and intention toward GC screening.Ever diagnosed with chronic gastric diseases ”, perceived overall health, smoking, alcohol drinking, health insurance, and if they ever took GC screening.The first section of the questionnaire assessed participants' demographic characteristics such as age, gender, height, weight, highest education level, marital status, current residing location , current residing city, occupational types, and monthly average income. Participants were also asked if they ever knew any first-degree relatives, or any friends, neighbors, or colleagues who have been diagnosed with GC. For general health status, participants were asked if they “The participants' knowledge was assessed by a series of questions about risk factors  and warning symptoms . The response options were “true,” “false,” or “don't know.” A correct response was given a score of one, and an incorrect or “don't know” response was scored zero. The total possible knowledge scores ranged from 0 to 38, with higher scores representing higher levels of knowledge. The median score was used to divide participants into high or low knowledge groups.Helicobacter Pylori in their lifetime, and (3) their own perceived risk of having GC. Perceived severity assessed participants' perception of harm of GC. Questions evaluating perceived benefits queried participants their views about the benefit of GC screening in early diagnosis and treatment of GC, and prognosis. Perceived barriers to conduct GC screening explored participants' concerns/hesitations when thinking of having screening. Cues-to-action questioned participants about motivation to conduct screening. The response options were “strongly agree,” “agree,” “disagree,” and “strongly disagree.”Health beliefs about GC screening was measured using the constructs from the Health Belief Model (HBM) . The queA four-point scale was also used for questions about participants' intention to take GC screening in the next 5 years, namely “certainly yes,” “probably yes,” “probably no,” and “certainly no.” The domain reason for not being willing to take screening was also queried. Respondents were also requested to report their preferences of screening method by selecting one of the following options: “endoscopy,” “blood test,” “fecal examination,” and “none of them.”N = [2. The prevalence rate was 15% (π) based on the GC screening rate reported in the previous study  in the univariate regression analysis were entered into multivariable logistic regression analyses using a simultaneous forced-entry method. Odds ratio (OR), 95% confidence interval (95% CI) and p-values were calculated for each independent variable. The model fit of multivariable logistic regression analysis was assessed using the Hosmer-Lemeshow goodness-of-fit test , version 26.0.The reliability of the knowledge score was evaluated by assessing the internal consistency of the items representing the knowledge scores. Multivariable logistic regression was used to determine the factors influencing screening intention. All factors found to be statistically significant .Table 3. The mean age of study participants was 47.72 years (±7.20). A large proportion of participants were aged 40–50 years (78.2%). Near half of the participants lived in urban (56.9%) and had monthly average income > 5,000 RMB (750 USD) (48.0%). The highest education level is distributed nearly even in secondary school and below (33.3%), high school/technical school (35.1%), and university and above (31.6%). Only 18.8% of participants reported first-degree relatives had GC, while 40.6% were aware of their friends, neighbor, or colleagues had ever been diagnosed with GC. A total of 40.0% of participants reported a history of chronic gastric disease and 42.6% ever took GC screening.Between October 2021 and March 2022, a total of 2,547 completed responses were received. The most highly recognized risk factors were “irregular diet habit” (75.0%), and “alcohol drinking” (74.9%), followed by “history of digestive disease” (72.6%), “consumption of pickled food” (71.6%), “consumption of smoked food” (71.2%), “aged 40 years and above” (70.9%), and “stomach ulcer” (70.9%). The least recognized risk factor was “male” (46.3%). In particular, only 56.7% of male respondents (data not shown) were aware of this inherent risk. Meanwhile, majority of participants wrongly regarded “too short/long sleeping time” (55.0%) as a risk factor of GC. The most highly recognized warning symptoms were “gastrointestinal bleeding” (77.1%), followed by “chronic gastritis can't be cured for a long time” (72.4%), “upper abdominal pain” (72.0%) and “recurrent nausea and vomiting” (71.5%). The least recognized warning symptoms were “early satiety” (57.4%) and “hypoferric anemia” (49.5%), while 70.2% of respondents wrongly considered “gastric perforation” (70.2%) as a warning symptom.n = 2,038) of participants reported “certainly yes/probably yes” and 20.0% (n = 509) reported “certainly no/probably no” regarding their intention to screen in the next 5 years , the three most common reasons, in descending order, were “endoscopy is uncomfortable” (29.6%), “worried about screening results” (23.6%), and “no symptoms” (21.3%). Other reasons included “no time” (8.3%), “don't know the benefits of screening” (6.9%), “screening cost is too high” (5.5%), and “believe that gastric cancer cannot be cured even detected by screening” (3.4%).The domain reasons for not willing to take screening in the next 5 years are shown in To our knowledge, the current study is the first investigation aimed to explore the knowledge level, attitudes to GC screening in high-risk populations in China. In general, the study participants reported a moderate level of knowledge in GC risk factors and warning symptoms. Majority of participants intended to take GC screening in the next 5 years. Significant factors influencing intention to screen were income level, previous history of GC screening or chronic gastric diseases, perceived overall health, total knowledge score, and HBM components . “Endoscopy is uncomfortable,” “worry about screening results,” and “no symptoms” were the domain reasons for not willing to take screening.Adequate knowledge about risk factors and warning symptoms of GC play an important role in cancer screening and early diagnosis. Poor knowledge about GC has been considered a barrier of GC screening . Result via opportunistic screening and individual own self is responsible for the related medical cost  . Undoubtcal cost . Japan acal cost . A Koreacal cost . Our stucal cost . EstabliHelicobacter pylori (a group I carcinogen) has been confirmed to have an important role in gastric carcinogenesis , severity (afraid of getting GC), perceived benefit (GC screening is effective in saving life), perceived barriers (is uncomfortable), and cues-to-action (only take screening when it is free of charge). The finding of HBM could be utilized as a theoretical fundamental to design future health promotion program. In particular, discomfort related to endoscopy has been regarded as the most important reason for not being willing to take screening. Meanwhile, the majority of respondents without previous experience with endoscopic screening prioritized blood test for their future screening plan. These results implied that many people fear physical discomfort from the invasive endoscopy procedure. Although China government launched endoscopic screening program since 2005 in more than 110 high-risk areas throughout the country, the compliance rate (33.5%) was found to be low . The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Claudins are a tetraspan membrane protein multigene family that plays a structural and functional role in constructing tight junctions. Claudins perform crucial roles in maintaining cell polarity in epithelial and endothelial cell sheets and controlling paracellular permeability. In the last two decades, increasing evidence indicates that claudin proteins play a major role in controlling paracellular permeability and signaling inside cells. Several types of claudins are dysregulated in various cancers. Depending on where the tumor originated, claudin overexpression or underexpression has been shown to regulate cell proliferation, cell growth, metabolism, metastasis and cell stemness. Epithelial-to-mesenchymal transition is one of the most important functions of claudin proteins in disease progression. However, the exact molecular mechanisms and signaling pathways that explain why claudin proteins are so important to tumorigenesis and progression have not been determined. In addition, claudins are currently being investigated as possible diagnostic and treatment targets. Here, we discuss how claudin-related signaling pathways affect tumorigenesis, tumor progression, and treatment sensitivity. Here, the first loop regulates paracellular charge selectivity, and the second loop of CLDN3 and CLDN4 is the receptor for a bacterial toxin (Claudins (CLDNs) are a group of indispensable membrane tight junction (TJ) proteins that range in size from 17 to 27 kDa and include up to 27 members in mammals that exhibit a high degree of sequence homology  and estrogen receptor β (ERβ)  , 134. In β (ERβ) . The res β (ERβ) , 136. CL β (ERβ) . CLDN6 b β (ERβ) . Under h β (ERβ) .in vitro apoptosis, proliferation, invasiveness, or migration  family, junctional adhesion molecules (JAMs), and membrane lipids to play a central role in the structure and function of tight junctions. In MDCK II cells, Claudin-1/2/3/4/7 quintuple-KO (claudin quintuple-KO) cells lack TJ chains, but the adjacent plasma membranes remain tightly attached to each other, showing only disruption of the permeability barrier to ions and small molecules. However, when JAM-A was further removed from claudin quintuple-KO MDCK II cells, the intercellular space was enlarged, and the macromolecular permeability barrier was disrupted .These phAll authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.