This baseline study evaluated the ecological risk associated with the concentration of six common Phthalate esters (PAEs) in sediment samples collected from the U-Tapao canal in Southern Thailand. Deterministic approaches consisting of standard sediment quality guidelines (SQGs) and Risk quotient (RQ) were used to evaluate the potential ecological risk of individuals and a mixture of Phthalate esters (PAEs) detected in sediment samples. Of the 6 PAEs measured, only three, including di-n-butyl phthalate (DBP), di-2-ethyl hexyl phthalate (DEHP) and di-isononyl phthalate (DiNP), were identified and quantified. The total concentration of the 3 PAEs congeners found in the sediment samples ranged from 190 to 2010 ng/g dw. The results from the SQGs and RQ were not consistent with each other. The SQGs results for individual PAEs showed that DEHP and DBP found in sediment was estimated to cause moderate risk on benthic organisms, DiNP was not estimated due to lack of SQGs data. However, the RQ method indicated a low risk of DEHP and DBP on algae, crustacean and fish, whereas DiNP poses no risk on crustacean. Furthermore, based on the result obtained in this study, the consensus SQGs for mixture effects prove to be a more protective tool than the RQ concentration addition approach in predicting mixture effects. Despite inevitable uncertainties, the integration of several screening approaches of ecological risk assessment (ERA) can help get a more inclusive and credible result of the first tier of individuals and a mixture of these pollutants. Phthalate esters (PAEs) are essential industrial chemicals widely used in a diversity of products. PAEs are mainly used as plasticizers to improve the softness, processability, flexibility and durability of polyvinyl chloride (PVCs) products, polyvinyl acetate and polyurethane resins . PAEs arSediment ecosystems are influenced by integrating physical, chemical and biological processes, enhancing sediment capacity to maintain a functioning, active and varied aquatic population . Neverth3/day, which have negatively impacted the ecological integrity of the canal [The U-Tapao canal, the mainstream freshwater resource of a significant watershed, is located in a tropical region in Songkla Province, Southern Thailand. The canal originates from Bantad mountain and flows through Hatyai city before emptying into Songkhla Lake. Like other water bodies, the canal is frequently exposed to both point and non-point environmental pollution sources due to rapid economic development, urbanization and changes in land-use patterns in the area surrounding the water body. The primary water quality degradation sources of the freshwater are wastewater effluents discharge from rubber, plastics, parawood and seafood processing industries at the rate of 41,000 mhe canal ,19. Besihe canal . NeverthEcological risk assessment (ERA) aims to evaluate the likelihood and the extent of adverse effects due to exposure to pollutants or other stressors . ERA conTherefore, the primary objective of this study was to evaluate the toxicity induced in aquatic organisms by individuals and a mixture of PAEs congeners, namely DBP, DEHP and DiNP, in sediment samples collected from the U-Tapao canal by using the deterministic approaches, including SQGs and RQ. The importance of these pollutant contaminations will be measured in terms of their possible effects on the benthic community and sensitive trophic biota, including algae, crustacean and fish, determining which areas of the canal will be of particular concern, thus needing further investigation. Furthermore, the comparative ERA of the individuals and the mixture of PAEs in the freshwater ecosystem located in a tropical region can contribute to the definition of freshwater sediment quality guidelines in the Water Framework Directive (WFD).3 in dry and rainy seasons. Two monsoons strongly influence the river’s tropical monsoon climate; the northeast and southwest monsoon, with average rainfall estimated to range from 1600 mm to 2400 mm annually. The temperature within and around the riverine ecosystem varies between 24 °C and 32 °C all through the year.The study area and sampling sites are indicated in In this present study, seventeen sampling sites of sediments were selected along the canal, from the upstream to downstream. Sampling points were identified by using a global positioning system (GPS). On-site sampling of all the surface sediment was performed from August 2018 to March 2019 at 17 locations in the U-Tapao canal. These locations were classified into two different groups viz: urban and rural areas. Sampling sites in urban areas include ST1, ST2, ST 3, ST4, ST6, ST7, ST9, ST10, ST12 and ST13. Sites located in the vicinity of the rural area were ST5, ST8, ST11, ST14, ST15, ST16 and ST17. Sediment samples were collected by using a grab sampler and transferred onto pretreated wide-mouthed brown glass bottles. The bottles were immediately submerged in crushed ice and transported to the laboratory. The samples were freeze-dried for 72 h, ground to pass through a 0.5 mm sieve, thoroughly homogenized, dried, and kept in a deep freezer at −22 °C. All sediment samples were analyzed within three days.Solvents used for this work include high performance liquid chromatography (HPLC) grades of hexane, methanol, acetone, ultrapure water and dichloromethane . Phthalate standards included di-n-butyl phthalate (DBP), benzyl butyl phthalate (BBP), di-2-ethyl hexyl phthalate (DEHP), di-n-octyl Phthalate (DnOP), di-isononyl phthalate (DiNP), diisodecyl phthalate (DIDP) . Internal standard solutions, including phenanthrene-d10 and chrysene-d12 and standard surrogate solutions, 2-fluorobiphenyl and 4-terphenyl-d14, were obtained SUPELCO Inc. .v/v) and 0.2 mL of 10 mg/L mixture of standard surrogate solutions (2-Fluorobiphenyl and 4-Terphenyl-d14). A procedural blank not containing the sediments were also prepared by using a similar procedure; 1:1 (v/v) acetone/n-hexane was used to prepare a check standard mixture. The spiked sample was made by mixing a standard mixture with a sediment sample. All samples were vortexed for 1 min and ultrasonicated for 20 min. The samples were further centrifuged at 3000 revolutions per minute (rpm) for 10 min. After centrifugation, the organic layer containing the extracted PAEs was siphoned out and placed in tubes using a Pasteur pipette and the process was repeated twice with 10 mL of a 1:1 (v/v) acetone/n-hexane. The extracts were pooled together. Desulphurization was achieved by adding activated copper to the extract. The extract was further dried over anhydrous sodium sulfate, concentrated to 0.8 mL using a gentle stream of nitrogen, added to 0.2 mL of 5 mg/L internal standard  mixture solutions and analyzed using gas chromatography (GC) with the mass selective detector (MSD).The freeze-dried sediment samples collected from the U-Tapao canal were pretreated based on a method developed  with slim/z 35–500 for qualitative analysis. Acquisition for quantitative analysis was carried out in the single-ion monitoring mode along with target ions, as indicated in All samples were evaluated using a gas chromatograph/mass spectrometer (GC–MS), Agilent model 6890N GC–5973 MSD , functional electron influence as well as a selective ion monitoring mode with an HP-5 MS (30 m × 0.25 mm × 0.25 mm). Chromatographic separation was performed by using the fused-silica capillary column. Pure helium gas (99.9999%) was used as the carrier gas and was maintained at a constant flow rate of 1 mL/min. The temperature program column oven was set to 30 °C for 1 min, raised to 280 °C at 15 °C maintained for 1 min, then increased up to 310 °C and held for 4 min. Each extract volume of 2.0 µL was injected into the GC–MS system in non-pulsed and splitless mode with an injector temperature of 290 °C. MS was operated in full-scan mode from All sampling equipment comprises of glass or stainless steel. Amber glass bottles were thoroughly washed with laboratory-grade detergent, cleaned twice with an HPLC grade of acetone, hexane and dichloromethane and then heated in a muffler oven at 400 °C for at least ten hours. After baking, the bottles were pre-rinsed three times with acetone, hexane and dichloromethane, then covered with clean aluminum foil. Before their usage, aluminum foils were also rinsed in acetone and hexane and heated in a hot oven at 350 °C for ten hours. Stainless steel sampling utensils such as spoons, flat trays and buckets were washed and wrapped with aluminum foil before sampling. The sediment grab sampler and glass water samplers were cleaned with lab-grade detergent and then passed three times with an HPLC grade of acetone, n-hexane and dichloromethane, respectively. Mortars and pestles were cleaned using the same procedure as that for glassware but were baked at the 150 °C for ten hours. Besides, to ensure that the results obtained in this work are reliable, various techniques were employed, including the development of calibration curve, usage of procedural blank, the establishment of the lower limit of detection (LOD) and Limit of quantification, assessment of the precision; and the calculation of recovery percentage. The instrument was calibrated daily by preparing a calibration curve at five different concentrations. All procedural blanks’ values were less than the detection limits. For the various PAEs congeners, limits of detection (LOD) and limit of quantification (LOQ) for individual PAEs were assessed based on a signal-to-noise ratio of 3 and 10 times. Recovery efficiencies for the surrogate standards are between 81 (2-Fluorobiphenyl), 93 (4-Terphenyl-d14) and the average recovery efficiencies for the spiked samples are between 81 and 105%. The percent recovery, precision (% RSD), LOD and LOQ of the six targeted PAEs are shown in Pollutants in sediments can adversely affect benthic organisms via direct toxicity . There aeco) and the environmental risk limits (ERLs). Since the values of MPCs, SRCeco and ERLs have been set on a 10% OM-normalized basis, the original DEHP concentrations and DBP levels were divided by the OM content (%) and multiplied by 10 to compare it with the SQG  [PAEs were evaluated by using Equations (1)–(3) below:In this present study, empirical SQGs were used in evaluating the ecological risk of PAEs to benthic organisms, including the effect approaches such as threshold effect level (TEL), the probable effect level (PEL), new sediment quality guideline quotient (NSQGQs) ,29. Alsond ERLs) ,31. The NSQGQ estimates both PELs and TELs. iC represents the concentration of pollutants in sediments, TEL is the threshold effect level of contaminants and PEL is the probable effect level.C is the concentration of the pollutant in sediment in dry weight, PEC is SQG concentration of chemicals in dry weight. The mixture effects of measured PAEs by using consensus SQGs were performed by using Equation (3) below:PEC-Qi was estimated.mix) of PAEs was calculated based on the summation of individual RQ values of PAEs congeners detected in sediments, as indicated in Equation (5).The Ecological risk assessments of PAEs were also carried out by using the risk quotient method (RQ) following the European Commission’s Technical Guidance Document  and prevhttp://cfpub.epa.gov/ecotox/). The Equation S1, based on the mechanistic or EqP approach, was used for calculating PNEC values in the sediment and listed in The criteria used to assess the ecological risk assessment were RQ > 1 (the log Kow of PAE congener between 3 and 5) showed high risk, whereas RQ > 10 (log Kow > 5) suggested high risk . The US 5 g of sediment sample was used to determine the organic matter levels in each subsample of sediments collected from the U-Tapao canal. The samples were oven-dried at 105 °C for 8 h to obtain a constant weight. After drying, the samples were baked in the furnace at 550 °C for 5 h and the OM level was obtained by measuring the weight loss .Statistical analysis was performed with SPSS version 20.0 .The statistical summary of PAEs measured in sediment samples collected from the U-Tapao canal is shown in p < 0.05 and 0.484) between the levels of DEHP and OM, suggesting that the spatial distribution of DEHP in this study was not only controlled by sediment OM but also depended on other factors, such as transport, mixing and sedimentation mechanisms and source compositions, which is consistent with other studies [p < 0.01) to ΣPAEs concentration in sediments of the U-Tapao canal. Spearman’s correlation coefficient was 0.675, 0.759 and 0.854 between ΣPAEs and DBP, ΣPAEs and DEHP and ΣPAEs and DiNP, respectively. This suggests that DBP, DEHP and DiNP are representative of ΣPAEs levels in sediments of the U-Tapao canal. However, the most substantial relationship (p < 0.01 and 0.854) was observed between ∑PAEs and DiNP, indicating that this congener can be a more suitable marker pollutant for detecting ∑PAEs in sediment samples. Moreover, these imply that these contaminants might have the same sources. Owing to the physical and chemical properties of PAEs, namely low-water solubility, high organic carbon–water partition coefficients (Koc) and hydrophobicity, PAEs tend to adsorb onto suspended particles and eventually accumulate in the sediment as particles settle out of solution [In this research, the Spearman correlation was used to assess the relationship between pH, OM, DBP, DEHP, DiNP and total PAEs concentrations in sediment samples of the U-Tapao canal. As shown in  studies ,11. Howe studies . Besides studies ,11,37. Fsolution .As shown in The effect characterization of PAEs in sediment samples was based on several SQGs values reported by international and national agencies and in the literature.Based on the DEHP empirical SQGs value documented by Reference , DEHP TESeveral derived empirical SQGs values reported for pollutants exhibit a high variability; thus, some authors developed the consensus-based approach to harmonize the existing values . This apAlternate methods such as the equilibrium partitioning theory by Reference , have beOverall, the SQGs results showed moderate sediment toxicity to benthic organisms in the freshwater canal ecosystem. Sediments are very useful to demersal bivalves as they play a dual role as habitats and a source of food through direct ingestion and indirect exposure . Contamisediment of individual PAEs for these three groups, are presented in sediment) in Furthermore, to understand the environmental risk of PAEs to the major trophic organisms, including primary producers, primary and secondary consumers, inhabiting U-Tapao canal, surrogate species of algae, crustaceans and fish were selected and treated separately in the calculation of the risk quotient (RQ) for detected PAEs . The toxPAEs value can be used to predict the toxicity of a mixture of contaminants in sediment. Mean PEC-QPAEs enables risk assessors to estimate the probability that contaminated sediments will be toxic to sediment-dwelling organisms [In an aquatic ecosystem containing mixtures of PAEs at specific concentrations, the concentrations of some individual PAEs are so low that their specific adverse biological effects may not be apparent if applied individually . Howeverrganisms ,34. Base−1 and 3.9 × 10−1 for RQmean and RQmax on algae and the lowest RQmix values were 7.0 × 10−2 and 1.8 × 10−1 on crustacean. However, the RQmix values were approximately 5 and 2 times higher than the individual risk of DBP and DEHP, respectively, for Algae. For crustaceans, the cumulative RQ value was two times higher than the individual risk of DBP and DEHP and 68 times higher than DiNP. For fish, the total RQ value was six and 1-fold more elevated than the individual risk of DEHP and DBP, respectively.As shown in Nevertheless, overall results based on the RQ approach indicate that the mixture effects of DEHP and DBP in the sediments would cause a low or no adverse impact on algae and fish and a crustacean. However, the sediment can be an extended/archived source of anthropogenic compounds in the aquatic environment . Since PBecause the SQGs values used in this study were not derived from this region, the degree of variability in the use of SQGs may be attributed to regional differences in the geochemistry of sediments and the relative bioavailability of sediment-associated toxicants , should The comparison with the different SQGs as shown in p < 0.01), indicating that NSQGQs could be an acceptable method to evaluate the sediment toxicity of DEHP. Compared with NSQGQs, the risks of DEHP for some sediment samples were underestimated using the technique of individual PEC-Q. For example, 29% of the canal sediment samples were identified as no effects based on the individual mean PEC-Q for DEHP, while these samples were suffering from moderate biological effects according to NSQGQs. Thus, NSQGQs established in this study could better evaluate the ecotoxicological risks of DEHP.As shown in In contrast, the consensus TEC approach seems less conservative and protective for the U-Tapao canal because only five samples (29%) showed DEHP levels higher than TEC invariance to 100% obtained for MPC, ERLs and TEL, respectively . The difPAEs was used to assess the toxicity induced by the mixture of the two PAEs congeners (DBP and DEHP) with TEC and PEC values indicating that sediments from the nine sites  might be inducing sediment toxicity to aquatic organisms in the canal. Besides, ST13, which is located in the Hat Yai city, exhibited the highest potential toxicity to benthic organisms and relatively high sediment toxicity occurring in sampling sites ST4, ST2, ST1 and ST3, located close to high anthropogenic areas. On the contrary, the sites ST14 and ST15, located near aquaculture ponds, showed a negligible toxic effect of the target chemicals due to the impact of surface water dilution and ST16 and ST17, found in the less anthropogenic area of the U-Tapao canal.The mean PEC-QPAEs higher than 0.25, which indicates a 20% probability of toxicity in these sites. This may be attributed to the discharge of domestic effluent, industrial wastes, urban runoff, the outflow from agricultural areas, atmospheric deposition and plastic products’ indiscriminate disposal. Thus, an ecological change is evolving in this canal due to frequent discharges of untreated domestic and industrial effluents.In the ecosystem with intense agricultural and aquaculture activities and many industrial and municipal sources of chemical inputs, such as the U-Tapao canal, it is difficult to pinpoint a clear source-occurrence relationship. Moreover, the sediment resuspension from tidal movements in the canal may promote transport, further masking source-concentration matches, as was indicated by the contamination revealed at site ST 12 and 14, which was lower than that observed at the close sampling sites ST11 and 15, respectively. Again, the canal’s hydrological characteristics, such as macro-tidal effects and seasonal fluctuations in flow-rates due to profound impacts of the Southwest Monsoon , can influence the distribution of these compounds in sediments ,19,54.PAEs higher than 0.25, indicating 20% of sediment toxicity, while the close sampling sites ST 17 showed a low mean PEC-Q value of 0.13, which corroborated the insignificant effect on the sediment-dwelling organisms. The threshold level was exceeded at ST 16 due to the relatively high DEHP levels, which also exceed TEL, ERLs and MPC values in 100% and 29% for TEC.The sampling site (ST16) was the only site located in the rural area of the canal with a mean PEC—QPAEs was estimated at 0.10, indicating the negligible toxic effects of the PAEs mixture in sediment. This situation represents a typical example in which SQGs contrasts with a simple comparison between chemical data.Notwithstanding that ST 12 showed MPC, ERLs and TEL threshold value violations, the calculated mean PEC-QERA’s limitations are expected irrespective of the risk assessment approach used, whether deterministic  or probabilistic (quantitative). The uncertainties in ERA are associated with an irregularity in ecosystem stressors, exposure data, species effect data, type of model and knowledge gap ,56. For Empirical SQGs are obtainable for a few numbers of PAEs congeners. For instance, the only PAEs congener with TEL and PEL value is DEHP, ERLs values are available for DEHP and DBP, whereas TEC and PEC values are available for DBP, DMP, DEHP and DnOP. Overall, empirical SQGs are available for 4 PAEs congeners, representing a minute fraction of the total PAEs extensively used globally. Underestimation of the ecological risk of PAEs in sediment may be attributed to the unavailability of SQGs data for several other PAEs. Empirical SQGs predict thresholds at which a toxicity response of benthic animals is probable.Nevertheless, SQGs, in general, do not provide a perspective on the risk and possible adverse outcomes associated with toxic concentration levels, which necessitates a separate ERA. Concentration ranges between upper and lower empirical SQG values are generally significant. The higher the variation between these values, the more the extrapolative value and sensitivity of the SQGs are compromised. For instance, the PEL value for DEHP is approximately 15 times higher than the TEL value. Besides, TELs are derived from the lower 15th percentile of effects data and the 50th percentile of no effects data, whereas PELs are derived from the geometric mean of the 50th percentile of the 85th percentile of the no effects data.Equilibrium partitioning (EqP) SQG, including ERL, MPC and SRCeco, predicts the concentrations of PAEs based on equilibrium conditions. Besides, functional relationships do not model contaminants’ release trapped in pores of sediments where they can be sorbed on interstitial walls . The EqPThe RQ approach cannot account for the spatial-temporal variability of exposure levels and the probability and magnitude of ecological effects. To address these limitations of this method, a probabilistic approach has been recommended and used by many researchers for a further assessment. The actual amount of Pollutants known will vary depending on the application method, configuration, and equipment calibration and specific field conditions. Unrealistically high RQs are most prevalent in the preliminary assessment level of ERA. Unrealistic high ERA results may occur due to the inconsistent application of uncertainty factors and the continued overlooking of the biological definitions of RQs, as being more of a multiplicative of a benchmark by risk assessors when conducting screening-level risk assessments .Besides, the lack of unification of deterministic approaches, including SQGs and RQ methods, may block the valid comparison between calculated risk values in the preliminary assessment of risk by different risk assessors. The risk level might differ significantly if separate database or risk assessment models are employed. To solve this problem, integrated approaches should be developed and used by researchers and public agencies to evaluate preliminary ecological risk.ERA aims to support decision-making aiming to minimize the risk level in risk management effectively . The inhThis work evaluated the level of six PAEs, including DBP, BBP, DEHP, DnOP, DiNP and DIDP in the U-Tapao canal. Several deterministic approaches were used to analyze the potential ecological risk of individual PAEs and their mixture in the canal ecosystem. Deterministic risk assessment is a useful tool for the initial characterization of hazardous pollutants’ risks to ecosystems. The results revealed that SQGs and RQ, were not consistent with each other in the first tiered framework. The ecological risk assessment using SQGs suggested that DEHP and DBP levels in sediments posed a moderate risk on benthic organisms and the RQ method showed low or no threats on different trophic levels, including algae, crustacean and fish in the canal. However, amongst the target pollutants, DEHP toxicity in sediments seems to be the most critical issue. The combined risk caused by all the PAEs is significantly higher than any individual PAEs due to joint action. This baseline study would be helpful to the management and controlling of PAEs contamination in the aquatic environment. Moreover, to our knowledge, this is the first study to demonstrate a comparative screening ERA of individuals and a mixture of PAEs in an aquatic ecosystem. Furthermore, this work would serve as baseline data to support regulatory decision-making and for strategic pollutant mitigation measures in the canal ecosystem.


To analyze the association of television food advertisements with eating habits in Brazilian adolescents. The sample was composed of 1,011 adolescents, aged from 10–17 years. The influence of television food advertisements on eating habits, as well as food consumption and socioeconomic variables were assessed through questionnaires. A binary logistic regression was performed to assess the magnitude of the associations, adjusted for gender, age, socioeconomic status, and parental schooling.versus 3.39, SD = 2.1) and vegetables . Adolescents that consume food while watching TV had higher weekly consumption of fried foods , sweets , soft drinks , and snacks . For 73,8% of the sample, food advertisements induce product consumerism, most commonly sweets and fast foods. Buying or asking to buy food after seeing it on the television was associated with fried foods , sweets , and snacks . Of the sample, 83.3% (n = 843) reported food consumption while watching TV. Adolescents who do not consume food while watching TV had a higher weekly consumption of fruits (3.98, SD = 2.0  Food advertisements were associated with greater consumption of fried foods, sweets, and snacks in adolescents, even after adjusting for confounding factors. However, a considerable portion of the worldwide population presents overweight or obesity, closely related to the public health problem of inappropriate nutrition2.Healthy habits in childhood tend to remain during adult life and contribute to prevent comorbidities like obesity and cardiovascular diseases3 the most frequently reported reasons to justify the association between nutrition and obesity are excessive consumption of energy-dense foods4. Unhealthy food intake is linked to impulsivity5 and the observed positive correlation of frequency of advertised foods on television and its consumption suggests an influence of television advertisements exposure on food choices6. The lack of exposure to healthy foods and the temptation to consume unhealthy foods act as barriers to healthy choices by adolescents, independent of race, gender and age7.Although obesity has a multifactorial cause-genetic factors, environmental influences and lifestyle behavior8, which may lead to physical growth and intellectual activity impairment, as well as higher risk of morbidity and mortality9, being a public health issue even in those not obese.Unhealthy eating habits has been related to iron and vitamins A and C deficiencies10. Moreover, the consumption of high-energy foods increases in the presence of activities based on screens11. Beliefs about the consumption of unhealthy foods and health risks tend to vary according to TV exposure: more time spent watching television is associated with lower negative and higher positive beliefs about the consequences of unhealthy food consumption12. Another study observed that eating snacks while watching television was a predictor of high energy food consumption13.A meta-analysis with over 25,000 children observed that high consumption of sugary drinks has been linked to weight gain in childrenTherefore, this study aimed to analyze the association of TV food advertisements with the desire to consume food, such as the weekly consumption of high-energy foods, in adolescents, even after controlling for confounding factors.14.According to the Department of Education, the city of Presidente Prudente-SP has approximately 37,000 students regularly enrolled in public and private systems of which 27,860 are enrolled in elementary school and 9,105 in high school. Approximately 20% of these are private schools. Presidente Prudente is located in the southeast region of Brazil, with a population of 207,610 inhabitants and a high human development index (HDI = 0.806)The sample of this study comprised students aged from 10 to 17 years, all regularly enrolled in schools of the city educational system (public and private). To contemplate students in the different regions , one school was chosen from each region in a randomized process. All classes of the selected schools were evaluated. If a region lacked the minimum sample required by this research, a second school was randomly selected and evaluated.The inclusion criteria were: I) adolescents aged 10 to 17 years; II) enrolled in elementary or middle levels of the public or private educational network; and III) who returned an informed consent form, signed by one of their parents or guardians. This study was approved by the Ethics and Research Committee of the Universidade Estadual de São Paulo (Unesp).15. As the study was performed by conglomerates, a correction of the design effect of 1.5 was applied. Coupled with possible sample losses of 20% and considering a tolerable maximum error of five percentage points, the total minimum required for the investigation was 576 subjects.Sample processing considered a prevalence of use of screen time of 70%, based on a previous studyUse of television and advertising relationship with eating habitsFood consumption by adolescents when watching television was self-reported and assessed by the following questions:1-a) Do you ingest food when you’re watching TV? 1-Yes; 2-No.1-b) If “Yes,” what is your favorite food to eat when watching TV?1-Fruits; 2-Vegetables; 3-Dairy; 4-Fried food; 5-Sweets; 6-Soft drinks; 7-Snacks.The influence of television advertisements on food consumption was assessed by the following questions:2-a) When you are watching TV, do the food advertisements make you want to eat? 1-Yes; 2-No2-b) If “Yes,” what kind of food do they make you want to eat?1-Fruits; 2-Vegetables; 3-Dairy; 4-Fried food; 5-Sweets; 6-Soft drinks; 7-Snacks.For the anthropometric assessment, the adolescents were barefoot and wearing light clothes. Body mass was obtained by a digital scale , with a precision of 0.1 kg. Height was obtained using a portable stadiometer  with a maximum extension of two meters and precision of 0.1 centimeter.16. We analyzed the consumption of fruits, vegetables, sweets, soft drinks, fried foods, and dairy . The classification for high weekly food consumption considered consumption of five or more times a week of each food.Weekly food consumption was rated by weekly frequency intake of different types of food17. This instrument classify the economic condition into classes A1, A2, B1, B2, C1, C2, D, and E. After the subjects’ classification, the sample was stratified between high economic class , middle economic class (B2 and C1), and low economic class .Economic condition was determined by the “Criteria for Classification of Brazil”Sample characterization was presented as mean and standard deviation for the consumption or non-consumption of food during the time watching television. The odds ratio (OR), estimated through binary logistic regression, assessed the association between the dependent variable  and the independent variable (frequency of weekly food consumption). The adjusted model considered the variables gender, age, socioeconomic condition, and the mother’s educational. The significance level was set at 5% and the Confidence Interval at 95% (95%CI). The statistical package used was SPSS software, version 15.0.Of the 1,011 adolescents assessed in the study, 83.3% reported food consumption while watching television. When asked their favorite food to consume, 33.2% of the sample reported eating sweets, 10.5% soft drinks, 11.1% snacks, and 6.0% fast foods. Among healthy food consumption, 10.1% of the adolescents reported fruits as their favorite food, while only 6.7% and 5.7% preferred dairy or vegetable, respectively (p ≤ 0.001).We observed the following prevalence of food patterns in adolescents: I. 16.5% of adolescents reported no food consumption when watching TV; II. 60.9% of adolescents reported unhealthy food consumption  when watching TV; and III. 22.6% of adolescents reported healthy food consumption  when watching TV . Around 20% of the sample reported food consumption five days a week while watching TV and 55% on both days of the weekend.A total of 73.9% of adolescents answered that food advertisements left them wishing to eat the advertised foods and 26.1% answered negatively . The Both the crude and adjusted analysis of the association between the wish to eat food after seeing it on TV advertisements and the kinds of foods, showed significant associations for the greater consumption of fried foods, sweets, soft drinks, and snacks . AdolescThis study observed that adolescents reported consuming energy-dense foods when watching television, most commonly fried foods, sweets, soft drinks, and snacks. The most desired foods to eat after seeing in TV advertisements were sweets and fast foods. Adolescents who reported wishing to eat the food after watching advertisements had higher chances of consuming fried foods, sweets and snacks-36%, 69% and 57%, respectively.18, while watching TV for at least 2 hours a day leads to high energetic drink and salty snacks consumption19; findings corroborate those of the present study. A study warned about the observed changes in the eating habits of young Brazilians, such as increases in the intake of meat, milk, and dairy products which are rich in fats; elevation of the already excessive consumption of refined sugar and soda; and the reduction in fruits and vegetables consumption20. Increased caloric intake is associated with the habit of watching television, due to the high intake of foods that are dense in energy and low in nutrients21.Previous studies found that fruits and vegetables intake has an inverse association with watching TVtheory of cultivation as the likely cause of TV advertisement influencing young people’s food preferences22: advertising reduces the likelihood of individuals recognizing unhealthy behaviors, which remain unchanged23.A study suggested the 24, inducing them to ignore satiety, and increasing their propensity for food intake25.The present study revealed that the association between the habit of watching TV and unhealthy food consumption in school-aged is a reality in the country. A possible factor justifying those results is the ability of TV advertisements to stimulate food consumption in this population, even if they are not hungry26. The high cost of healthier diets can also contribute to obesity among low-income groups, since the limitation of financial resources constitute an important reason why people do not eat healthy27. European studies support this statement: in the United Kingdom fruits and vegetables consumption were related to diet higher costs28; in Denmark, low-fat diets for children were associated with higher costs29. Thus, family eating habits influences healthy or unhealthy eating behavior in adolescents30.Environmental factors can also direct choices around specific products of low nutritional values and high levels of fats and sugars, while fruits and vegetables lack the same emphatical advertisements31. Consumption of unhealthy foods among adolescents was associated with mothers with more than 9 years of schooling32. Socioeconomic status was the only predictor associated with daily meal patterns in adolescents33. Concerning gender, girls reported eating vegetables and fruits more frequently than boys34.This study advances in literature with the analysis between food requests by young people and television advertisements adjusted for possible confounding factors, like gender, age, socioeconomic condition, and the parents education level. Thus, adolescents with low socioeconomic level show low consumption of many nutritive foods like fruits, vegetables and dairy foods35. This study also lacked the assessment of types and frequency of TV food advertisements, which could help infer whether specific types of food were more frequent or more efficient in their message.The cross-sectional design, precluding cause-effect inferences, and the self-reported assessment of the variables limited this study. Food advertisements in other screen devices  were not assessed in the present study, since the screen time in these devices has already been associated to unhealthy eating habits in adolescentsAn important aspect of the study was the randomly selected sample in a developing country and the analyses controlled by confounding factors, minimizing possible biases. It also considered not only the association between screen time and weekly food consumption, but verified which kinds of food in advertisements may be associated with greater consumption. Future studies should examine food advertisements inserted between episodes of TV programs, which may contribute to better understand the intake of energy-dense foods in adolescents during television time. Another important question is whether adolescents from families with healthy habits are less susceptible to advertisements.In conclusion, we observed a strong association between television commercials and consumption of energy-dense food. Health promotion actions should target reducing screen time and investing in advertisements for healthier foods, or at least reducing advertisements of foods rich in fat and sugar, from the early ages, since this problem affects younger populations.


Bombyx eri, 36.6% in Periostracum cicadae (cicada sloughs), and 26.2% in Chyrysomya megacephala. Therefore, according to the data reported by previous researchers, demonstrating comparable yield values to those of crustacean chitin and the great interest in insects as alternative sources, efforts towards comprehensive knowledge in this field are relevant.Chitin, being the second most abundant biopolymer after cellulose, has been gaining popularity since its initial discovery by Braconot in 1811. However, fundamental knowledge and literature on chitin and its derivatives from insects are difficult to obtain. The most common and sought-after sources of chitin are shellfish  and other aquatic invertebrates. The amount of shellfish available is obviously restricted by the amount of food waste that is allowed; hence, it is a limited resource. Therefore, insects are the best choices since, out of 1.3 million species in the world, 900,000 are insects, making them the most abundant species in the world. In this review, a total of 82 samples from shellfish—crustaceans and mollusks (n = 46), insects (n = 23), and others (n = 13)—have been collected and studied for their chemical extraction of chitin and its derivatives. The aim of this paper is to review the extraction method of chitin and chitosan for a comparison of the optimal demineralization and deproteinization processes, with a consideration of insects as alternative sources of chitin. The methods employed in this review are based on comprehensive bibliographic research. Based on previous data, the chitin and chitosan contents of insects in past studies favorably compare and compete with those of commercial chitin and chitosan—for example, 45% in  Chitin and its derivatives represent a well-reviewed biopolymer with many beneficial applications. The preparations for chitin and its derivatives as a biomaterial vary according to process conditions and potential applications. However, their main sources are crustaceans, and research on alternative sources is still developing. In this article, we comprehensively review the preparation of chitin and its derivatives, especially chitosan, via chemical extraction, for a comparison of the optimal demineralization and deproteinization processes with a consideration of insects as alternative sources. Chitin is the basic structure and a major constituent of the cell wall of many fungi, insect exoskeletons, and crustacean shells. It largely exists in waste from the processing of marine food products, such as shrimp, prawn, crab, lobster, crayfish, squid, cuttlefish, and barnacles . Chitin 10–1012 tons annually [Chitin is a biopolymer and is the most abundant biopolymer after cellulose, with a production of approximately 10annually ,6,7. Chiannually ,6.Chitin isolates from crustaceans, other aquatic invertebrates, and arthropods, especially those with a hard exterior, usually come in the α-form. This is because the chains are aligned in an anti-parallel structure, thus making the structure more stable owing to stronger and rigid hydrogen bonding. Meanwhile, β-form chains are arranged in a parallel fashion, and most chitin sources with this β-form are obtained from mollusks, such as squid pens . The γ-f3–CO) for the molecule to be soluble in most diluted acids. Chitosan is a fiber-like substance and a homopolymer of β-(1→4)-linked N-acetyl-D-glucosamine. The actual difference between chitin and chitosan is the acetyl content of the polymer. Chitosan, which has a free amino group, is the most useful derivative of chitin [2) group in the C-2 position of chitosan instead of the hydroxyl (-OH) group found in cellulose. However, unlike plant fiber, chitosan possesses positive ionic charges that give it the ability to chemically bind with negatively charged fats, lipids, cholesterol, metal ions, proteins, and macromolecules. In this respect, chitin and chitosan have attained increasing commercial interest as suitable materials due to their excellent properties, including their biocompatibility, biodegradability, adsorption, and abilities to form films and chelate metal ions [Chitosan is derived from chitin by removing a sufficient number of acetyl groups , as in the shells of crustaceans and mollusks, chitin produces a much stronger composite. This composite material is much harder and stiffer than pure chitin and is tougher and less brittle than pure CaCO3. Another difference between pure and composite forms can be seen by comparing the flexible body wall of a caterpillar (mainly chitin) to the stiff, light elytron of a beetle (containing a large proportion of sclerotin) [In its pure, unmodified form, chitin is translucent, pliable, resilient, and quite tough. In most arthropods, however, it is often modified, occurring largely as a component of composite materials, such as in sclerotin, a tanned proteinaceous matrix, which forms much of the exoskeleton of insects. Combined with calcium carbonate (CaCOlerotin) ,15,16,18In addition, chitin and chitosan have the ability to bond with different links, forming materials such as fibers, hydrogels, beads, sponges, and membranes. Chitosan has been used in several fields, such as agriculture, food protection , biomediEven though chitin and chitosan have been called our “last biomass resource” and are expected to lead to a new functional polymer, their utilization is scarce, and they have hardly been explored. Though a variety of interesting biological activities have been reported throughout the years, practical application has lagged. One of the main reasons for this is that these biological activities are not specific to chitosan; such activities are also found in other materials. The second reason is the issue of cost, since chitosan is relatively expensive (20–30 US dollars per kg). If a specific biological activity was found to be unique to chitosan materials, practical utilization would be encouraged despite the cost, especially for biomedical use. Chitin and chitosan are structurally similar to heparin, chondroitin sulfate, and hyaluronic acid, which are all biologically important mucopolysaccharides in all mammals. These mucopolysaccharides are anionic polymers owing to substituent carboxyl and sulfuryl groups. On the other hand, chitosan is almost the only cationic polysaccharide in nature, and it is nontoxic and biodegradable in the human body . This spChitin is gaining popularity, especially because many beneficial traits are being discovered for fertilizers ,36,37, fThere are numerous alternative methods of extraction for chitin, where their sole purpose is to remove impurities and foreign organic matter, including protein and minerals, because it is naturally formed in the structure of the exoskeleton ,55. Howe3), sulfuric acid (H2SO3), acetic acid (CH3COOH), and formic acid (HCOOH), with HCl being the most commonly used reagent at a concentration of 0.25–4 M at 21,100 °C, from 15 min to 48 h. The solid-to-solvent ratio is employed in the range of 1:9–1:50 (w/v). Although other effective reagents have been reported, alkaline treatment for deproteinization is a commonly used method of protein extraction. [2CO3), potassium carbonate (K2CO3), calcium hydroxide (CaOH2), and sodium sulfate (Na2SO4), at various concentrations in aqueous solutions, are applied, with NaOH as a commonly used base. The alkaline concentration for deproteinization can be seen to range from 0.025 to 4 M at 25–150 °C, from 20 min to 96 h, where the solid-to-solvent ratio ranges from 1:5 to 1:100 (w/v). In addition, a new trend has recently emerged for assisting with the process of extraction. Aside from chemical and biological treatments, physical treatment has also gained popularity. The basic treatment still involves acids and bases as the main media, but is assisted by ultrafast microwave [For demineralization, also known as decalcification, hydrochloric acid (HCl) is the most commonly used acid by far. Although it may be the cause of the detrimental effects on the intrinsic properties of the purified chitin, it remains the most used decalcifying agent for both the laboratory and industrial-scale production of chitin. It is widely used at various concentrations. Hence, chemical extraction is the most convenient and effective method when it is optimized, as optimization of the extraction can minimize the degradation of chitin and reduce the impurity levels to a satisfactory level for specific applications ,57. The raction. ,12. Anotraction. ,59,60. Hraction. . These draction. ,62. In araction. . For proicrowave  treatmenicrowave .4), with or without hydrogen peroxide (H2O2), sodium hypochlorite (NaClO), phosphorus pentoxide (P2O5), sulfur dioxide (SO2), and sodium carbonate (Na2CO3). In addition, solvents are often used in pigment removal. Examples of solvents used include acetone, chloroform, ethyl acetate, and ethanol. However, when pigment recovery is important for economical accounts, for instance, carotenoids, solvent extraction is used to recover pigments [Decolorization is performed using several methods: alcohol washing, chloroform and alcohol solutions, phosphorus pentoxide solutions, potassium permanganate, a mixture of oxalic acid and sulfuric acid solutions, and sodium hypochlorite solutions. Decolorization or color bleaching is an optional step, though almost no researcher skips this step due to certain factors. Exoskeletons or crustaceans naturally have pigments that give each characteristic a different color. Decolorization can occur, or the pigment can be destroyed, using oxidants such as potassium permanganate , the lower the resulting molecular weight of chitin ought to be. The same conditions are applied to decalcification via acid treatment . Therefo3), and involved the use of a strong acid [In 1970, Peniston and Johnson patented a method for treating an aqueous medium with chitosan and other derivatives of chitin to remove impurities . Taking ong acid . In 1980ong acid . For theong acid ,78,79,80ong acid , and eveong acid ,82. The ong acid .In 2CO3 for 20 h. The residue was washed with distilled water and dried on P2O5 in a vacuum. The result was a 15%–20% yield of chitin [4) [Zhang et al. isolated chitin from beetle larva cuticles and silkworm pupa exuviae using chemical treatment with 1 N HCl at 100 °C for 20 min for the removal of inorganic minerals, mostly catechols. For removing protein, they used 1 N NaOH at 80 °C for 36 or 24 h and refluxed it with 0.4% NaB. eri and yielded 45% of high-purity chitin with only one extraction step [Silkworm yielded an average value of 15%–20%. The isolated treatment involved demineralization  and deproteinization steps  . Less chion step . This hiBombus terrestris); the process arose from the biotechnological production of bumblebees used for vegetable and fruit pollination [Pandalus borealus shells (shrimp). In their report, insect chitin was prepared using dead bumblebees treated with 1 M HCl and 1 M NaOH. The bumblebee chitin was compared with the shrimp chitin. The comparison was based on their characteristics studied via elemental analysis, Fourier-transform infrared (FTIR) spectroscopy, solid-state 13C cross-polarization magic-angle-spinning nuclear magnetic resonance (13CP/MAS-NMR) spectroscopy, and confocal microscopy. It was found that the chitin yield from the bumblebee was lower than that from shrimp. On the other hand, bumblebee chitin and chitosan exhibited a finer texture. Chitin extracted from both sources (bumblebee and shrimp) showed identical spectra. However, since solid-state 13C NMR spectroscopy was sensitive to changes in the local structure, the result for bumblebee chitin had a 5% lower degree of acetylation and was characterized by a fine membrane texture. In conclusion, the extraction method described allowed the end-product to be isolated with a high chemical purity [Matjan et al. described the isolation process of chitin, as well as its characterization, from bumblebees , X-ray diffraction (XRD), scanning electron microscope (SEM), and elemental analysis. The chitin extracted from adult H. parallela is thus suitable for chitosan production. The large numbers of H. parallela adults captured for the control of this pest in fields every year provide an abundant source of chitin. In addition, it was concluded that attempts to domesticate the beetle for a stable supply have a high potential for relieving the impact on ecological systems in the near future [Another alternative source of chitin was proposed by Liu et al.: adult y beetle . The yier future .13CP/MAS-NMR, and SEM [In a report by Song et al., blowfly larvae were isolated and studied as an alternative source of chitosan. The physical properties of blowfly larvae chitosan were evaluated via approaches similar to those of previous researchers, with methods including color-change identification, molecular weight determination, elemental analysis, FTIR,  and SEM . This wa and SEM . The res and SEM .Leptinotarsa decemlineata) via the same chemical extraction process. Both adults and larvae were compared for chitin isolation, and the results showed that the structure of chitin for both was in an alpha form, as in other insects [Kaya et al. studied both adult and larval potato beetle  and compared them with crustaceans (Oniscus asellus) based on their chitin isolation characterization. The same procedure was followed for chitin isolations for both species. First, HCl was used to remove minerals in the organisms, and the protein structure was then removed using NaOH. Chitin obtained from these two species was characterized physicochemically. The physicochemical properties of chitins isolated from the insects and crustaceans were compared. The chitin content for the dry weights of M. melolontha and O. asellus were recorded as 13%–14% and 6%–7%, respectively. The results of FTIR, TGA, and XRD analysis were found to be more or less similar. The surface morphologies of chitins were examined via environmental scanning electron microscopy and nanofibers, and pore structures were observed. While the chitin nanofibers of O. asellus were adherent to each other, the nanofibers of M. melolontha were non-adherent. On the other hand, the number of pores was much higher in the chitin from M. melolontha than in the chitin from O. asellus. Looking at the elemental analysis results, the M. melolontha chitin was found to be purer than O. asellus chitin. For this reason, M. melolontha has more potential as an alternative source of chitin [In the same year, Kaya et al. studied cockchafer beetles  had the highest yield of chitin (12.2%), followed by shrimp (P. monodon), beetles (C. rugosa), and honeybee (A. mellifera), with a yield of 10%, 5%, and 2.5%, respectively. It was found that the degrees of deacetylation of chitosan obtained from locust, beetle, and honeybee cuticles were comparable (95%–98%) and higher than that of shrimp chitosan (75%). The water-binding capacity (WBC) and fat-binding capacity (FBC) of the chitosan isolated from locusts, honeybees, and beetles were comparable or lower than those of shrimp chitosan. Shrimp chitosan and honeybee chitosan had the highest ash content. The X-ray powder diffraction (XRD) showed that the chitosan isolated from locusts had the highest crystallinity. SEM analysis indicated a dense nanofiber surface structure in the shrimp, locust, and beetle chitosan and a hard, rough surface in the honeybee chitosan. For these reasons, chitin and chitosan extracted from insects are comparable to those from crustaceans [Marei et al. used chitin isolated from four different sources: staceans .Gryllus bimaculatus). The treatment was associated with the following chemical extraction with a strong acid and alkali [w/v) and 50% NaOH (w/w) solutions were used to further disintegrate protein associated with the main structure. The average yield of chitin and chitosan was 2.42% and 1.65% on a fresh weight basis, and 10.91% and 7.50% on a dry weight basis, respectively. The results indicate that adult exoskeletons of G. bimaculatus could be used as a source of chitin and chitosan. The reports suggest that chitin and chitosan are beneficial as functional additives in industrial animal feeds [Kim et al. studied the extraction of chitin and chitosan from all specimens of Type I and II two-spotted field crickets  were used without concern for the parts of the body or the sex of the insects. The chitin and chitosan obtained from the house crickets were subjected to physicochemical analysis (moisture and ash content), including elemental analysis, and were compared to commercial shrimps [Ibitoye et al. yielded chitin and chitosan via chemical extraction from house crickets for amounts ranging between 4.3% and 7.1% and between 2.4% and 5.8%, respectively . House c shrimps . The aut shrimps .A recent study by Shin et al. was conducted on mealworm beetles and rhinoceros beetles. Chitin yields from different stages of the life cycle of the beetles  showed values between 3.9% and 14.2%. These values can be considered low compared to the crustaceans’ chitin yield. However, it was found that the chitin and chitosan from insects were nontoxic and possessed antimicrobial traits. Therefore, they are suitable as alternative material for massive production and application for commercialization .Bombyx eri [Bombus terrestris (bumblebees) [Periostracum Cicadae (cicada sloughs) [Holotrichia parallela (dark black chafer beetles) [Chyrysomya megacephala  [Leptinotarsa decemlineata) [Leptinotarsa decemlineata) [Melolontha melolontha (cockhafer beetles) [Schistocerca gregaria (desert locusts) [Apis mellifera (western honeybees) [Calosoma rugosa (beetles) [Gryllus bimaculatus (crickets) [Brachytrupes portentosus (house crickets) [Tenebrio molitor, Zophobas morio) [Allomyrina dichotoma) [Based on data collected by previous researchers , the chimbyx eri , 15%–20%mbyx eri , 6.89% iblebees) , 36.6% isloughs) , 15% in beetles) , 26.2% ie flies) , 20% in beetles) , 12.2% ilocusts) , 2.5% inneybees) , 5% in Cbeetles) , 2.35% irickets) , 4.5%–7.rickets) , 3.9%–8.s morio) , and 10.chotoma) .Shellfish are widely cultivated around the world as important food sources, and their shells are often thrown away as industrial food waste. Hence, attention towards utilizing their waste has been increasing . In this3, pigments, and lipids. Hence, to obtain chitin in its purest form, treatments for removing foreign materials associated with its structure need to be performed [Chitin is closely associated with proteins, inorganic material that, in shellfish, consists of CaCOerformed . Table 2Parameters for the chemical treatment of shellfish chitin extraction include the type of reagent, the concentration of reagent, the temperature, the solution-to-solid ratio, and the total duration of the process. However, there are other reports regarding the usage of inorganic or organic acids, such as hydrochloric acid, nitric acid, sulfuric acid, acetic acid, and formic acid. However, seeing the data collected, hydrochloric acid is the most commonly used reagent in the demineralization process. In protein treatment, reagents such as potassium hydroxide, sodium carbonate, potassium carbonate, calcium hydroxide, and sodium sulfate are applied at various concentrations in aqueous solutions. Sodium hydroxide exceeds all other reagents as the most commonly used base.2O2 and C6H8O7. The concentration varies from 4 to 0.25 M. While up to 100 °C of heat is applied in this step, the most common temperature used is room temperature for 10 min or even 2 days. To remove the protein associated with chitin from shrimp, prawns, crabs, lobsters, and other aquatic invertebrates, most researchers prefer to use NaOH, as When looking at 3 and the lowest chitin yield (5.4%) [The highest chitin yield can be seen from squid pen, which displays a yield of 49%. Lower amounts of minerals associated with chitin exhibited a higher chitin yield. Based on a report by Abdou et al., the highest chitin yield (cuttlefish pens) had the lowest amount of minerals. The sample was treated with 1 M NaOH for protein removal at different concentrations and for an extended period of 24 h, in addition to autoclaving, in order to increase the reaction efficiency. For treating calcium carbonate, a strong acid was used at a 1 M concentration at room temperature . This wad (5.4%) , even whw/v) within 15 min [Solenocera melantho) shell waste were studied. The optimal deproteinization condition occurred at 75 °C, for 2.5 N NaOH. In order to have constant fluidity, a minimal solution-to-solid ratio of 5 mL/g was applied during deproteinization. Deproteinization exhibited two-stage first-order reaction kinetics. The maximum deproteinization rate constant approached 0.1 min−1 when the protein content was decreased from 16% to slightly above 7%. After the first 30 min, the deproteinization rate constant decreased by up to two orders of magnitude. The optimal demineralization condition was around 1.7 N HCl, with an acid solution-to-solid ratio of 9 mL/g at ambient temperature. Demineralization could be described as a pseudo-first-order reaction. The demineralization rate constant ranged from 0.00020 to 0.017 min−1 [The extraction parameters for shrimp shell chitin were optimized in 2003. A study on the kinetics for both demineralization and deproteinization, including the role of temperature in the extraction process, was also carried out to optimize the extraction methods . The autn 15 min . This wa17 min−1 .3 associated with the structure of chitin. Hence, due to the high amount of mineral content, the chitin yields from these samples were the lowest [Compared to other crustaceans, mollusks, and other aquatic invertebrates, the sea snail chitin yield is rather average, presenting a value of 21.65%. Meanwhile, crabs, woodlice, and barnacles showed the lowest chitin yield, only ranging from 3.1% to 17% ,102,103.e lowest ,99. As aAs with the other samples from crustaceans, mollusks, and other aquatic invertebrates, as well as from insects, the chemical media used for isolation were a strong acid (HCl) and base (NaOH). Demineralization involved a 0.5–4 M concentration of acid at a temperature ranging from ambient to as high as 100 °C. For protein removal, the concentration of NaOH utilized was between 0.1 and 2 M for up to 3 days, at temperatures varying from room temperature to 140 °C.Fomes fomentarius (fungi) and various mushroom species , as well as its characteristics [Plumatella repens, of the Bryozoa phylum, were first conducted in 2015 by Kaya et al. [Palomena prasina) of the Arthropoda phylum and from Fomes fomentarius of the Fungi kingdom. It was observed that bryozoan chitin is in the same form as it is in the arthropod and fungal chitins. The chitin content in the dry weight of the bryozoan, fungal, and insect species was observed to be 13.3%, 2.4%, and 10.8%, respectively. The insect chitin exhibited the highest thermal stability, followed by that of the bryozoan and subsequently the fungal chitins. Surface morphologies revealed that the insect and bryozoan chitins were composed of nanofiber and pore structures, whereas the fungal chitin had no pores or fibers. The crystallinity of the insect chitin was higher than that of the bryozoan and fungal chitins. These superior characteristics of chitin from insects further support that it can compete as an alternative source of chitin in industrial aspects [The first ever reported chitin was obtained from fungi. However, since its first discovery, research development on the isolation and characterization of chitin from fungi has been scarce. Kaya et al. and Ifuku et al. have contributed additional data on the isolation of chitin from eristics ,97. In aa et al. . The chi aspects .M. melolontha) and potato beetles (L. decemlineata), Kaya et al. also produced a report on two spider species (Geolycosa vultuosa and Hogna radiata). It was claimed that, despite being a huge group of more than 44,000 species, data on the chitin structure from spiders are insufficient. In their study, it was reported that chitin isolated from these two spider species (G. vultuosa and H. radiata) represented 8%–8.5% and 6.5%–7%, respectively [G. vultuosa has two different pore structures. One type of pore is rarely sequenced and its size ranges between 190 and 240 nm, while the second type is tightly sequenced, with pore sizes ranging from 11 to 32 nm. A new chitin surface morphology has been determined in G. vultuosa, and no one has reported a chitin structure with two different pore morphologies. The chitin extracted from H. radiata has common surface characteristics, and the nanofiber structure is in the range of 10–17 nm, with pore sizes ranging from 195 to 260 nm. The degree of acetylation values of the chitin of both G. vultuosa and H. radiata were found to be 97% and 99%, respectively [In addition, besides cockchafer beetles (ectively . Environectively .http://scholar.google.com), and via the databases PubMed (http://www.ncbi.nlm.gov/pubmed), Scopus (http://www.scopus.com), Sciencedirect (http://www.sciendiect.com), Elsevier (http://www.elsevier.com), MDPI (http://www.mdpi.com), Researchgate (htpps://www.researchgate.net), the American Association for Cancer Research (cancerres.aacrJ.s.org), Emeraldinsight , and Google Books (https://books.google.com). This review aims to systematically study chitin and chitosan extraction methods via chemical degradation processes previously reported around the globe to compare final yields from difference sources. The compilation of references in this review includes books, book chapters, journal articles, online articles, reports, conference proceedings, patents, documents from websites, and other web sources. References cover a time range from the 1990s to the year 2019. Since chitin was introduced in 1811, it is important to include records from these years as references. A summary of the demineralization and deproteinization processes of chitin and chitosan from other sources is tabulated to allow for a clear comparison of samples from various sources, according to their clusters.A comprehensive bibliographic study was conducted to provide an in-depth insight into the purification methods of chitin, mainly involving demineralization and deproteinization. The authors carried out a literature review by means of the scientific engine Google Scholar (The chitin and chitosan contents of insects in the studies examined in this review favorably compare and compete with those of commercial chitin and chitosan. The characteristics of chitin and its derivatives from insects are similar to those of commercial chitin from crustaceans and other aquatic invertebrates. It is also non-toxic and very safe to use. In addition, because of their large numbers and the ease of breeding, insects provide an abundant resource for larger scale chitin production. However, the mechanism and applications for chitin and chitosan from insects are still limited. To elucidate this insufficient information, further studies are needed. Therefore, according to the data reported by previous researchers, showing comparable yield values to those of crustacean chitin, and owing to the great interest in insects as an alternative source of chitin, efforts towards comprehensive knowledge in this field are of merit.


Canopy morphology at different PDs was measured with destructive samplings when maize canopy was fully expanded. The relationship of changes of organ morphology in relation to increased plant density was analyzed based on 2016 data. The ADEL-Maize was first calibrated for the hybrid at 4.5 plants m–2 and then revised by introducing relationships identified from 2016 data, followed by independent validation with 2018 field data. A heatmap visualization was shown to clearly illustrate the effects of increased plant density on final morphology of laminae, sheaths, and internodes. The logarithmic + linear equations were found to fit changes for the organ size versus increased plant density for phytomers excluding ear position or linear equations for the phytomer at ear position based on 2016 field data. The revision was then further tested independently by having achieved satisfactory agreements between the simulations and observations in canopy size under different PDs with 2018 field data. In conclusion, this study has characterized the relationship between canopy morphology and increased interplant competition for use in the ADEL-Maize and realized the simulations of final size of laminae, sheaths, and internodes, as affected by increased plant density, laying a foundation to test an ideotype for maize withstanding high interplant competition.Increased plant density markedly affects canopy morphophysiological activities and crop productivity. This study aims to model maize canopy final morphology under increased interplant competition by revising a functional–structural plant model, i.e., ADEL-Maize. A 2-year field experiment was conducted at Mengcheng, Anhui Province, China, in 2016 and 2018. A randomized complete block design of five plant densities (PDs), i.e., 4.5, 6, 7.5, 9, and 15 plants m Greater crop productivity and grain quality are required to ensure current and future food security due to continuous expansion of a large population and climate change . IncreasThe light distribution within crop canopies is dependent upon the features of plant canopy architecture, such as leaf number, leaf size, shape, curvature, and leaf inclination and azimuth, which is highly spatiotemporally variable. Crop architecture with erect leaves at the upper layer of the canopy has been bred, known as a compact canopy type, which allows irradiance to penetrate the canopy, reaching the bottom leaves for a greater entire canopy photosynthetic capacity . HoweverHowever, most of the research has focused on modeling of individual plants , and it –2; (ii) establish response functions of canopy production to increased plant population densities; and (iii) revise and validate the ADEL-Maize in simulating canopy morphology in response to increased PDs.Within the above context, the objectives of the present study are to (i) calibrate the ADEL-Maize for a local hybrid at 4.5 plants m–2 . A maize cultivar, i.e., Zhengdan 958 (referred to as ZD958), planted widely across China was sown in east–west-oriented rows. Each plot was 9 m long and 5 m wide (eight rows). The row space was 0.6 m at each PD, and the plant space was 0.38 m (PD4.5), 0.28 m (PD6), 0.22 m (PD7.5), 0.19 m (PD9), and 0.11 m (PD15). The population from 4 to 7.5 plants m–2 is commonly used by local farmers for this genotype. The five adjacent reference plants in a row representing the average growth status at the middle of each plot were tagged with plastic labels for leaves 5 and 10 to identify the phytomer rank in guiding destructive samplings. In order to maintain non-limiting conditions for water and nutrients, soil samples were taken a few days prior to sowing to determine the nutritional content, based on which base nutrients were used at a rate of 150 kg ha–1 for N, 112.5 kg ha–1 for P2O5, and 112.5 kg ha–1 for K2O; an initial irrigation was applied at 900 cm3 ha–1, and the subsequent irrigation depended on fully irrigated from sowing to completion of canopy production. Maize seeds were manually sown to achieve a designated density on June 12, 2016, and June 10, 2018. The weeds in the field were removed manually, and the insect pests were rigorously controlled by appropriate pesticide.A 2-year experiment was carried out at Mengcheng Agricultural Sci-Tech Park, Anhui province, China  with a lime concretion black soil type in 2016 and 2018. The data in 2016 were reported in Measurement of canopy morphology from each replicate, with destructive sampling of plants, was carried out when the canopy was fully expanded. The detailed approach has been described , 2016. TLamina length and width, sheath length and width, and internode length and diameter are affected by increased PD . We atte–2, was provided as the base value of canopy size. The total leaf number was 18 in both seasons. The lower rank leaves at phytomers 1–5 cannot be considered due to early dropping off.The final canopy size, i.e., lamina length and width, sheath length and width, and internode length and diameter, at different phytomers for ZD958 at the lowest PD, i.e., 4.5 plants mThe module describing the relationships between canopy sizes, i.e., lamina length (LL), lamina maximum width (LW), sheath length (SL), sheath maximum width (SW), internode length (IL), and internode diameter (ID) at different density levels relative to PD4.5 and increased PD relative to 4.5 was added to the ADEL-Maize model, and the 2018 dataset was used to independently assess the performance of the revisions made to ADEL-Maize using data from the 2016 experiment. The revised model was used to predict the final length of leaf and internode for each phytomer.OBSi) and the simulation values (SIMi) is the normalized root mean square error (NRSME)  to compare the simulation and observation. As there are n sites in the comparisons, the index computed from the observed values ( (NRSME) . The sim (NRSME) .SIMi and OBSi represent simulated and observed values, respectively, and where Once validated, a visualization of integrative effects of the maize canopy under PD4.5, PD6, PD7.5, PD9, and PD15, respectively, in 2018 was presented visually to show the impacts of increased PD.–2 were listed in the second column after phytomer rank  = Ao∗In(PD) + Bo∗PP + Co for phytomers 6–11 and phytomers 13–18 . For phytomer 12, the relationship was found to be nicely fitted by linear equations as Y = Do∗PD + Eo .The relationship between increased rates of organ size at different densities and increased PD relative to PD4.5 in 2016 is shown . The relo, Bo, and Co in o, Eo in 2 is generally high (greater than 80%) except for IL fitting, indicating that the fitting with logarithmic + linear relationships was reasonable in describing the changes of organ size versus increased PD and phytomer position. These equations were introduced into ADEL-Maize, and the parameters were retrieved in the simulations.The parameters for A–2 but was not affected further by increased interplant competition. The leaf angle in other positions was not significantly affected by increased PD. Thus, leaf angle in the positions near the ear for the compact type was relatively stable across PDs. The leaf angle detail was used in ADEL-Maize calibration for the experimental hybrid.The angles between leaf insertion point and vertical stem for three representative leaves under different PDs in 2016 are presented . The leaThe canopy morphology at full expansion for phytomers from 6 to 18 under various PDs in 2018 was simulated with the revised ADEL-Maize in an L-studio platform. The observation data for canopy size at the same time were used to test the simulation under different PDs. The comparison between simulation and observation of canopy morphology including lamina length, maximum width, sheath length, sheath maximum width, internode length and diameter, as well as leaf area (LA), and leaf inserting height (LIH), exampled as PD4.5, PD7.5, and PD15, is shown in In addition, the calculated value of RMSE in determining the difference between simulation and observation under different PDs in 2018, exampled as PD4.5, PD7.5, and PD15, is shown . RMSE vaTherefore, this study suggested that the revised ADEL-Maize to simulate canopy final morphology during flowering in relation to increased PD was acceptable.The canopy morphology composed of lamina, sheath, and internode size at different PDs in 2018 was visualized, exampled as PD4.5, PD7.5, and PD15 using the revised ADEL-Maize in an L-studio platform. The effects of increased PD on canopy morphology were visually illustrated as in the images .–2 as the reference, widely adopted for local summer cropping in the south Huang-Huai-Hai region, one of the major maize-producing areas in China, and increased the strength of interplant competition to moderate and even severe level for the rest of the PDs. The complex effects of increased PD on canopy morphology composed of individual organ size can be retrieved by the heatmap  calibrated the model parameters for the local hybrid, and (ii) inserted the equations identified into the model. The revisions were also successfully validated by achieving satisfactory agreement between simulations and observation from an independent dataset. The visualization of canopy production at different PDs has been completed by visually showing the effect as a whole. To allow the revised model to be used in designing an ideotype to maximize canopy photosynthesis to overcome ear tip abortion due to lack of carbohydrate supply, it is necessary to further incorporate a model of irradiance distribution within the canopy and a biochemical model of CAll datasets generated for this study are included in the article/LHe did the experiments and modeling analysis and drafted the manuscript. WS contributed the modeling analysis and draft. XC and LHa contributed to modeling analysis. JL and YM contributed to designing the experiments. YS conceived this study and finalized 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.


The gut microbiome and microbiome-gut-brain (MGB) axis have been receiving increasing attention for their role in the regulation of mental behavior and possible biological basis of psychiatric disorders. With the advance of next-generation sequencing technology, characterization of the gut microbiota in schizophrenia (SZ) patients can provide rich clues for the diagnosis and prevention of SZ.In this study, we compared the differences in the fecal microbiota between 82 SZ patients and 80 demographically matched normal controls (NCs) by 16S rRNA sequencing and analyzed the correlations between altered gut microbiota and symptom severity.F =3.337, p < 0.001, uncorrected). At the phylum level, relatively more Actinobacteria and less Firmicutes  were found in the SZ group. At the genus level, the relative abundances of Collinsella, Lactobacillus, Succinivibrio, Mogibacterium, Corynebacterium, undefined Ruminococcus and undefined Eubacterium were significantly increased, whereas the abundances of Adlercreutzia, Anaerostipes, Ruminococcus and Faecalibacterium were decreased in the SZ group compared to the NC group . We performed PICRUSt analysis and found that several metabolic pathways differed significantly between the two groups, including the Polyketide sugar unit biosynthesis, Valine, Leucine and Isoleucine biosynthesis, Pantothenate and CoA biosynthesis, C5-Branched dibasic acid metabolism, Phenylpropanoid biosynthesis, Ascorbate and aldarate metabolism, Nucleotide metabolism and Propanoate metabolism pathways . Among the SZ group, the abundance of Succinivibrio was positively correlated with the total Positive and Negative Syndrome Scale (PANSS) scores  as well as the general PANSS scores ; Corynebacterium was negatively related to the negative scores of PANSS .The alpha diversity showed no significant differences between the NC and SZ groups, but the beta diversity revealed significant community-level separation in microbiome composition between the two groups (pseudo-Succinvibrio and Corynebacterium were associated with the severity of symptoms for the first time, which may provide some new biomarkers for the diagnosis of SZ.Our findings provided evidence of altered gut microbial composition in SZ group. In addition, we found that  Schizophrenia (SZ) is a complex, chronic psychiatric disorder with a heterogeneous genetic and neurobiological background . TreatmeRecently, a few articles have focused on the role of the MGB axis in SZ. Epidemiological studies have shown that prenatal microbial infections appear to increase the risk of SZ in offspring . AdditioChanges in the gut microbiota and its metabolites may cause neuronal damage, apoptosis and abnormal brain development, leading to SZ . An incrProteobacteria was significantly increased in SZs; the genera Succinivibrio, Megasphaera, Collinsella, Clostridium, Klebsiella and Methanobrevibacter were significantly higher, whereas Blautia, Coprococcus and Roseburia were decreased compared to NCs, and receiver operating characteristic curve analysis demonstrated that 12 microbiota could be used to distinguish SZs from NCs  the small sample size of these studies; (2) various factors, such as region, diet, environment, etc.  subThe objective of this study was to characterize the gut microbiome in SZs and preliminary analyze the correlation between the altered gut microbiota and the severity of symptoms. We excluded individuals with any chronic disease that may affect the stability of the gut microbiota, including intestinal inflammation, Constipation, diarrhea and diabetes; expanded the sample size; and controlled the drug use of the NCs to eliminate possible bias. We hypothesized that (1) gut microbial composition might differ between the SZ and NC groups and (2) the altered gut microbiota in SZs might significantly correlate with symptom severity.A total of 162 subjects were collected in this study from September 2017 to February 2019, including 82 SZs and 80 NCs. The SZs were recruited from Guangzhou Huiai Hospital and were diagnosed by trained and experienced clinical psychiatrists according to the structured clinical interview according to the Diagnostic and Statistical Manual of Mental Disorder-IV-Text Revision (DSM-IV-TR) (SCID) criteria ; The psy2; (6) absence of any major gastrointestinal tract surgery within 5 years; and (7) absence of any head surgery and no mental disorders.Normal controls were recruited in Guangzhou and surrounding areas through multiple methods, including recruitment flyers in the community, internet ads and word-of-mouth. The age, sex and nationality of all NCs were matched with the SZs. The inclusion criteria of NCs were as follows: (1) the Han nationality, no special religious beliefs; (2) 18–65 years; (3) absence of antibiotic intake for the last 3 months and with no diarrhea at present; (4) absence of any chronic disease that may affect the stability of gut microbiota; (5) BMI 18–30 kg/mAll participants signed the information consent form, indicating their agreement. The sample collection and the protocol of analysis were approved by Guangzhou Brain Hospital. A questionnaire was conducted among all subjects to collect general information, including age, sex, height, weight, years of education, history of taking medicine, and history of smoking and drinking.Fresh fecal samples were obtained from participants, and all of the samples were stored at −80 °C until DNA extraction. A total of 200 mg of each fecal sample was used for DNA extraction.® DNA Isolation Kit 12,888–100 protocol. Prior to sequencing, the DNA was stored in Tris-EDTA buffer solution at −80 °C. To enable amplification of the V4 region of the 16S rRNA gene and add barcode sequences, unique fusion primers were designed based on the universal primer set 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACNVGGGTWTCTAAT-3′) along with barcode sequences. PCR mixtures in 50 μL reaction volumes contained 1 μL of each forward and reverse primer (10 μM), 1 μL of Easy Pfu DNA Polymerase (2.5 U/μL), 4 μL of dNTPs (2.5 mM), 1 μL of template DNA, 1 μL of double distilled water, and 5 μL of 10 × EasyPfu Buffer. Thermal cycling consisted of an initial denaturation step at 95 °C for 5 min, followed by 30 cycles of denaturation at 94 °C for 30 s, annealing at 60 °C for 30 s, and extension at 72 °C for 40 s, with a final extension step at 72 °C for 4 min. Amplicons from each sample were run on an agarose gel. The expected band size for 515f–806r is approximately 300–350 bp. Amplicons were quantified with Quant-iT PicoGreen dsDNA Assay Kit  following the manufacturer’s instructions. According to the manufacturer’s instructions, the amplicon libraries for high-throughput sequencing on the Illumina MiSeq platform were combined in equal amounts and subsequently quantified (KAPA Library Quantification Kit KK4824).Community DNA was extracted under the manual of the MOBIO PowerSoilH test in QIIME2. The beta-diversity indicates differences in taxa composition between groups, which were calculated using Bray-Curtis dissimilarity. Principal coordinate analysis (PCoA) based on the Bray-Curtis distances matrix was used for visualizing sample relationships, and PERMANOVA with 999 permutations was used to assess the statistical significance of beta-diversity distances between groups. Output matrices were ordinated and visualized using the vegan package from R  effect size (LEfSe) was used to identify different markers, an alpha = 0.01 was used in the factorial Kruskal–Wallis test among groups, and the log value for the LDA score was set to >2. To determine the association between differential abundance at the genus level and clinical characteristics, we further calculated the residuals of relative abundance of those taxa with significant group differences, controlling for age, sex and years of education, by the ‘vglm’ function in the VGAM package . To obtain insight into the possible functional pathways that differ between SZs and NCs, we used PICRUSt  or sex . The years of education  and BMI  of the SZ group were lower than those of the NC group. The ratio of tobacco using was higher in the SZ group than in the NC group , while alcohol intake was lower . Comparing high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and glucose in serum, the SZ group showed lower values of HDL-C , LDL-C  and glucose  compared to the NC group. In addition, the SZ group showed lower values of total cholesterol (TC)  and triglyceride (TG) .A total of 82 SZs and 80 NCs were recruited according to the inclusion criteria. Demographic and clinical characteristics of the groups are presented in n = 162), ranging from 15,449 to 95,651. After quality filtering and removal of the chimeric sequences, we obtained 6,817,960 high quality reads for further analysis of bacterial composition, ranging from 13,581 to 90,203 and with a mean of 42,086.2 reads. After clustering all the high-quality reads, a total of 2,031 features were obtained, and the frequency per feature ranged from 2 to 533,200, with a mean of 3,356.9.We obtained 7,456,515 raw sequences from all subjects . PCoA of Bray-Curtis distances showed that the SZ and NC groups formed distinct clusters .Then, alpha-diversity and beta-diversity calculations were performed. The results showed no significant difference in all alpha-diversity indices between the two groups . Analysiclusters . AdditioFirmicutes, Bacteroidetes, Actinobacteria, Proteobacteria and Verrucomicrobia. When comparing the relative abundances of the phyla of the two groups, Actinobacteria was significantly higher in the SZ group than in the NC group , whereas Firmicutes was lower   .Faecalibacterium, followed by Megamonas, Prevotella, Ruminococcus and Blautia , Collinsella , undefined Eubacterium , Lactobacillus , Succinivibrio , Mogibacterium  and Corynebacterium  were significantly higher in the SZ group. However, Adlercreutzia , Anaerostipes , Ruminococcus  and Faecalibacterium  were higher in the NC group.At the genus level, the most abundant genus in the SZs was  Blautia . The bacp < 0.01, uncorrected, LDA score > 2) . We also observed that the NC group was differentially enriched with the genera Anaerostipes, Faecalibacterium, Adlercreutzia, Butyricimonas , whereas the SZ group was enriched with Lactobacillus, Mogibacterium, Bulleidia, Eubacterium, Succinivibrio, Corynebacterium, Collinsella and Prevotella   . At the ore > 2) .p < 0.05, FDR corrected). We found that varieties of biosynthesis and metabolism pathways were enriched in the NC group, such as Polyketide sugar unit biosynthesis, Valine, Leucine and Isoleucine biosynthesis, Pantothenate and CoA biosynthesis, C5-Branched dibasic acid metabolism and Phenylpropanoid biosynthesis. While Ascorbate and aldarate metabolism, Nucleotide metabolism and Propanoate metabolism pathways were enriched in the SZ group.We performed PICRUSt analysis to predict the genetic potentials of the fecal microbiota metagenome based on 16S rRNA sequences. PICRUSt predicted metagenome content to Level 3 KOs and identified 328 functional pathways belonging to different Level 1 KOs, including 19 Cellular Processes, 28 Environmental Information Processing, 28 Genetic Information Processing, 40 Human Diseases, 146 Metabolism, 40 Organismal Systems and 40 Unclassified pathways . We idenSuccinivibrio . While increased negative symptoms were negatively associated with the abundance of the genus Corynebacterium . The results of the relationship between the 11 genera and the severity of symptoms are shown in We analyzed the relationship between 11 genera altered in the SZs and the PANSS scores. Greater severity of SZ symptoms was positively correlated with the abundance of the genus Succinivibrio was more abundant in SZs and correlated positively with the severity of symptoms. In contrast, Corynebacterium was more highly represented in SZs and negatively associated with the severity of negative symptoms, which may suggest that a greater abundance of Corynebacterium in SZs could remit the symptoms of blunted affect, poverty of speech and loss of drive. Thus, we speculated that an altered gut microbiome profile contributes to the pathogenesis and remission of SZ. Interestingly, Succinivibrio was detected in the SZs but not in the NCs, which may further suggest that Succinivibrio plays an important role in the development of SZ. In addition, we found that Lactobacillus was significantly higher in SZs. Lactobacillus was significantly increased in first episode SZ and was positively correlated with the severity of symptoms. However, the correlation between Lactobacillus and symptom severity was not significant in this study. One possible reason was the drug use of the subjects included in this study.To the best of our knowledge, this study is the first to indicate that altered gut microbiota is significantly correlated with symptom severity in SZs from South China. Consistent with previous studies, our results demonstrate that the SZs showed altered gut microbiome composition, including two phyla and 11 genera . Importap = 3.36 × 10−8, uncorrected). An expanding body of evidence supports the notion that microbes can metabolise drugs and vice versa drugs can modify the gut microbiota composition. Bacteroidetes/Firmicutes ratio was significantly lowered in chronic and short-term RIS users. Bacteroidia, and increased abundances of Erysipelotrichia, Actinobacteria and Gammaproteobacteria in female mice treated with OLZ. However, Second-generation antipsychotics (SGAs) have been used successfully for the treatment of SZ , risperiLactobacillus in SZs can stimulate TNF production. Based on this, it is speculated that the increased Lactobacillus may induce changes in inflammatory factors and induce SZ. Amino acids and derivatives participate in the biosynthesis and downstream effects of numerous neurotransmitters . Red and blue represent NC group and SZ group, respectively.Click here for additional data file.