In vitro glycation experiments further revealed that MJ and its components inhibited each stage of protein glycation as well as the generation of intermediate chemicals. Furthermore, CA (IC50: 3.10) and SA (IC50: 3.0 µg/mL) inhibited aldose reductase effectively in a non-competitive manner, respectively. The binding affinity of these substances towards the enzymes examined has been proposed by molecular docking and molecular dynamics simulation studies, which may explain their inhibitory activities. The found potential of MJ in antihyperglycaemic activity via inhibition of α-glucosidase and in antidiabetic action via inhibition of the polyol pathway and protein glycation is more likely to be related to the presence of the phenolic compounds, according to our findings.For the first time, α-glucosidase, α-amylase, aldose reductase, and glycation at multiple stages inhibitory assays were used to explore the antidiabetic potential of whole unripe jackfruit . Two polyphenols (phenolic acids) with strong antihyperglycaemic activity were isolated from the methanol extract of whole jackfruit flour (MJ) using activity-guided repeated fractionation on a silica gel column chromatography. The bioactive compounds isolated were identified as 3--2-propenoic acid (caffeic acid: CA) and 4-Hydroxy-3,5-dimethoxybenzoic acid (syringic acid: SA) after various physicochemical and spectroscopic investigations. CA (IC Type 2 diabetes mellitus (T2DM) is a metabolic condition marked by persistently high blood glucose levels (hyperglycaemia). The International Diabetes Federation anticipated that this condition would impact over 783 million individuals by 2045 . HyperglArtocarpus heterophyllus L. (Jackfruit) belongs to the mulberry family (Moraceae). Although it is native to India, it can also be found in Malaysia, Sri Lanka, India, Indonesia, the Philippines, Burma, Pakistan, Burma, and China. It is a high-yielding crop that bears fruit all year, with the best months being June and December ) [max and Km value. Secondary plot  at various doses (IC (1/[S]) . For CA ary plot  was usedSaccharomyces cerevisiae α-glucosidase MAL-32 (UniProt ID: P38158) obtained from UniProt was used to construct a homology model using SWISS-MODEL. The model was built with the help of X-ray crystal structure of S. cerevisiae isomaltase (PDB ID: 3AXH), showing 72% identical and 84% similar sequence at a resolution of 1.8 Å [Protein and ligand preparation was performed according to the previous study conducted by Patil et al. 2021a) . The proof 1.8 Å . In ordeof 1.8 Å  to under21a . Theof 1.8 Å .The X-ray crystal protein structures of α-amylase (PDB ID: 1DHK) and human aldose reductase (HAR) (PDB ID: 1IEI) were retrieved from RCSB PDB database. For the protein preparation for docking simulation, AutoDock Tools 1.5.6 software was used . The bin® Core™ i7-11700 x64-based CPU, NVIDIA GeForce RTX 3060 Ti GPU (8 GB GDDR6), and 32 GB (16 × 2) DDR4 RAM. The CHARMM36 force field was used to assign all of the protein–ligand complexes, and the CGenFF server was used to acquire the ligand topology [+ and Cl− counter ions, the entire system was neutralised. A total of 9 protein–ligand complexes were prepared which included the following—α-glucosidase-acarbose: 9472 residues, α-glucosidase-caffeic acid: 9430 residues, α-glucosidase-syringic acid: 9430 residues, α-amylase-caffeic acid: 7601 residues, α-amylase-syringic acid: 7601 residues, α-amylase-acarbose: 7643 residues, HAR-caffeic acid: 5078 resides, HAR- syringic acid: 5078 resides, and HAR-quercetin: 5089 residues. Along with these, 3 protein backbone atoms (bare protein) were also prepared for simulation. Using the steepest descent and conjugate gradient approaches, the energy of the resulting systems was reduced. It was then followed by a brief (1000 ps) equilibration in the NVT ensemble and then an NPT ensemble (1000 ps). At 310 K temperature and 1 bar pressure, all simulations took 100 ns. All the simulations were performed in triplicates. Using the XMGRACE software, a trajectory analysis of root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible-surface-area (SASA) parameters was performed, with the findings shown in graphical representation [For the molecular dynamics simulation, the docked conformations of protein and respective ligands with most negative binding affinity were selected, and the simulation was performed according to the study conducted by Patil et al. (2021b) . For thetopology . The pdbentation . FurtherThe chemical structures of the experimental compounds were submitted to the ADMETlab 2.0 server in SMILES format for druglikeness and pharmacokinetic analyses. Lipinski’s rule of five was applied to the druglikeness assessment. In pharmacokinetic studies, characteristics such as MDCK permeability, Caco-2 cell permeability, volume distribution (VD), BBB (blood–brain barrier), cytochrome P (CYP) inhibition, clearance (CL), human Ether-à-go-go-related Gene (hERG), and the AMES carcinogenicity test were taken into consideration .The outcomes of the molecular dynamics simulation were used to calculate the binding free energies of the protein–ligand complexes using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) technique, according to the previous study conducted by the authors Patil et al. (2021b) . It is ap’ values were 0.05 or lower. GraphPad PRISM was used to calculate the IC50/EC50 values (version 4.03).All the analyses were carried out in triplets. The data were provided as a mean with a standard deviation. To compare the treatment groups to the control group, one-way analysis of variance (ANOVA) was employed, followed by Duncan’s multiple range test using SPSS Software . The results were considered statistically significant if the ‘1H NMR, 13C NMR and MS). The following are the details of their structures: caffeic acid was recovered as a pale yellowish semi-solid substance from Fr. II (71–79). m.p. 223.5 °C. UV (methanol): λmax 327 nm. IR (KBr): 1622 (Aryl-substituted C=C), 1730 (−COOH), 3510–3600 cm−1 (OH). 1H NMR (DMSO): δ 4.5 , 6.3 , 6.5 , 6.8 , 10.9 . 13C NMR (DMSO): δ 113.5, 115, 117, 120, 127, 145, 147, 149, 171. MS: m/z 181 (M + 1). Analytical calculated data for C9H8O4 (180.04): C, 60.00; H, 4.48. Found: C, 60.04; H, 4.45%.In the present study, we established the antihyperglycaemic potential of methanol extract of jackfruit flour. In this regard, the isolation of caffeic acid and syringic acid was achieved after repeated chromatographic separations using silica gel column chromatography . The commax 217 nm. IR (KBr): 1745 (−COOH), 3550–3600 cm−1 (OH). 1H NMR (DMSO): δ 3.5 , 4.3 , 7.2 , 7.3 , 10.6 . 13C NMR (DMSO): δ 56 (2), 108 (2), 124, 138, 152 (2) 168. MS: m/z 199 (M + 1). Analytical calculated data for C9H10O5 (198.05): C, 54.55; H, 5.09. Found: C, 54.57; H, 5.05%.Syringic acid was obtained from Fr. II (97–101) as a yellowish semi-solid substance. m.p. 205.5 °C. UV (methanol): λCompounds (CA and SA) were identified as caffeic acid and syringic acid based on the above-mentioned results and a comparison with NMR and MS data in the literature ,26,27. T50 values for MJ, CA, and SA were found to be 10.0, 8.0, and 7.50 µg/mL, respectively. On the other hand, acarbose (therapeutic drug) exhibited an IC50 value of 11.0 µg/mL, indicating that MJ, CA, and SA exhibited greater inhibitory potential in this experiment. With respect to IC50 values, it is clear that CA and SA inhibited yeast α-glucosidase strongly and were significantly (p ≤ 0.05) greater than acarbose and MJ  exhibited effective inhibitory potential in vitro against α-glucosidase. The ICe and MJ . The ord50: 25.25 µg/mL) was shown to have the strongest inhibitory activity when compared to CA (IC50: 26.90 µg/mL), whereas MJ had the lowest inhibitory activity (IC50: 26.90 µg/mL). Overall, MJ, CA, and SA had a lesser (p ≤ 0.05) α-amylase inhibitory effect  than the positive control acarbose (IC50: 28.0 µg/mL). Information activity of every fraction has been given in the Similar experiments were conducted on MJ and its derivatives against α-amylase, another major carbohydrate hydrolysing enzyme. The 50 percent inhibition of α-amylase by MJ and its active components is shown in Because of their substantial inhibitory impact, CA and SA were chosen for additional kinetic inhibition experiments against α-glucosidase and α-amylase. Lineweaver Burk plots showed that the crossing point for different concentrations of CA A,C and S50 value of 3.75 µg/mL, MJ inhibited aldose reductase (50: 3.10 µg/mL) isolated from MJ was found to be a strong inhibitor; however, it showed  a weaker inhibitory effect than SA (IC50: 3.0 µg/mL). MJ and its components were found to be potent inhibitors (p ≤ 0.05) than quercetin (IC50: 4.10 µg/mL).EF and its isolated compounds were identified to have a stronger inhibitory effect on the aldose reductase enzyme than quercetin, a phenolic inhibitor. With an ICeductase . CA  in p ≤ 0.05) than the standard (positive control). CA and SA were more effective than crude extract and fractions in all of the tests employed in this investigation, with activity ascending in the sequence SA > SA > EaFr. > MJ > BHA. The results showed that the extract, ethyl acetate fraction, CA and SA had high antioxidant properties and were significantly higher (p ≤ 0.05) than the positive control .With an extraction yield of 143.07 g/kg, the methanol extract of green jackfruit flour was found to contain a significant flavonoid (601.05 mg QE/g) and phenolic (252.07 mg GAE/g) content. The findings indicated that the total flavonoids and phenols were concentrated in the MJ and that the compounds separated from these active extracts had high TFC and TPC .Flavonoids, sterols, tannins, alkaloids, coumarins, and phenolic substances were found in the early phytochemical analysis of MJ. HPLC analysis was also performed to obtain a detailed polyphenolic composition of MJ, and the results are shown in The inhibitory effects of various dosages of MJ, CA, SA, and aminoguanidine  on early glycation products (fructosamine), intermediate (protein carbonyls), and late-stage glycation moieties (AGEs) after 3 weeks of incubation are shown in p ≤ 0.05) in comparison to MJ and its constituents. Similarly, when fructose was added to HSA, the amount of protein carbonyl formation increased as compared to HSA alone. When compared to HSA incubated with fructose, the reduction of protein carbonyl production by MJ and its components was significantly stronger (p ≤ 0.05) than aminoguanidine.The quantity of fructosamine and protein carbonyl content in the various incubated test samples  with aminoguanidine at 1, 2, and 3 weeks is given in In the present study, the potential of MJ and its components on thiol group modification was also explored. Treatment with MJ and its isolated compounds exhibited effective denaturation protection, resulting in a significant increase in the thiol levels. The modelled structure of α-glucosidase was validated using PROCHECK tool to understand the stereo-chemical quality of the modelled protein structure, which evaluate the backbone of phi-psi (Φ–Ψ) dihedral angles. The Ramachandran plot analysis showed that 88.1% of the residues were present in the favoured region, 11.1% of the residues were in the additional allowed region, 0.6% of the residues were in the generously allowed region, and 0.2% of the residues were included in the disallowed region. Results from the plot indicated that the proposed model is reliable. Further, to evaluate the statistics of non-bounded interaction between atoms ERRAT tool was used, which shows that the overall quality of the model, such as its reliability and stability, is 94.912. To evaluate the quality of the protein structure, PROVE tool was used, which evaluates the Z-score root-mean-square deviation (Z-score rms) to measure the average magnitude of irregularities. The scored atom value was found to be 3.0% of the standard deviation, which is considered away from the mean of that atom type. The total number of buried outlier protein values is 77. Results from the protein validation are given in In addition, the physiochemical properties were calculated using Expasy-ProtParam server tool. The protein had 101,724 numbers of amino acids, with an estimated molecular weight of 12,290,969.97 Da and a theoretical pI of 6.63. The instability index was calculated was 37.91, which showed that the protein was stable. The hydrophilicity was calculated by the grand average of the hydrophilicity (GRAVY) of −0.044 and the aliphatic index as 42.79.The compounds identified in HPLC were subjected to molecular docking simulation and were virtually screened for the best compound using the parameters such as binding affinity, the total number of non-bonding interactions, and the total number of hydrogen bonds. The summary of virtual screening has been given in In the case of α-amylase, caffeic acid was able to bind to the key residues of the binding pocket located between loop one and loop two. It was bound to the catalytic residue ASP 197 with a hydrogen bond (2.37 Å) and one electrostatic π-cation bond (4.80 Å). It was also bound to another catalytic residue ASP 300 with an electrostatic π-anion bond (4.95 Å). However, it could not form any bond with the third catalytic residue, GLU 233. Furthermore, there were other hydrogen bonds with GLN 63 (2.75 Å), TYR 62 (2.68 Å), and a π-π hydrophobic bond with TYR 62 (4.69 Å). With these six non-bonding interactions and three hydrogen bonds, the binding affinity of caffeic was found to be −8.1 kcal/mol. In comparison with caffeic acid, syringic acid was predicted with extensive binding interactions and robust affinity. It formed a total of 11 non-bonding interactions with three hydrogen bonds. It bound all the three catalytic residues of the binding pocket through hydrogen bonds with GLU 233 (2.77 Å), ASP 300 (3.51 Å), and electrostatic π-cation bond with ASP 197 (4.30 Å). Apart from these, it bonded with TYR 62 (2.61 Å) with a hydrogen bond. Hydrophobic bonds dominated the interaction of syringic acid with α-amylase. It formed a π-π bond with TYR 62 (5.29 Å), three alkyl bonds with TRP 58 (4.87 Å), ALA 198 (5.38 Å), and HIS 201 (4.74 Å), and three π-alkyl bonds with LEU 162 (4.06 Å), ALA 198 (4.19 Å), and HIS 299 (4.41 Å). With these interactions, the binding affinity of syringic acid was found to be −12.5 kcal/mol. In the case of acarbose, the binding interaction and affinity were found to be inferior compared to that of caffeic acid and syringic acid. The total number of binding interactions were found to be 2, while both of them were hydrogen bonds with key residues GLU 233 (2.87 Å) and ASP 197 (3.40 Å). In addition, three unfavourable bonds with ASP 300 (2.82 Å), ASN 298 (2.49 Å), and ARG 195 (2.50 Å). The binding affinity of acarbose with α-amylase was found to be −6.2 kcal/mol. The binding interactions of experimental compounds with α-amylase have been given in Upon binding with HAR, caffeic acid occupied the same binding site of the co-crystallized ligand zenarestat, which is located near the NADPH binding site. However, caffeic acid was able to form only four non-bonding interactions, with two hydrogen bonds. It was bound to only three key residues, namely LEU 300 (3.63 Å) with a π-σ bond, TRP 111 (5.15 Å) with a π-π bond, and TYR 309 (2.07 Å) with a hydrogen bond. It was also bound to CYS 80 (3.41 Å) with another hydrogen bond. The binding affinity of caffeic acid was found to be −7.4 kcal/mol. In the case of syringic acid, the binding was to five of the six key residues, including LEU 300 (4.43 Å) with a π-alkyl bond, HIS 110 (2.76 Å and 3.04 Å) with two hydrogen bonds, TYR 48 with a hydrogen bond (2.11 Å) and π-alkyl bond (5.48 Å), CYS 298 (3.72 Å) with a hydrogen bond, and TRP 111 (5.15 Å) with an alkyl bond. The other non-bonding interactions included TRP 219 (5.08 Å), VAL 47 (3.72 Å), TRP 20 (4.38 Å), with π-alkyl bonds, TRP 20 (4.50 Å) with a π-π bond, TRP 20 (2.60 Å) with a hydrogen bond. Due to the extensive interaction with residues through hydrophobic bonds, the binding affinity of syringic acid was found to be −12.9 kcal/mol. In the case of quercetin, the compound was bound to only three of the six key residues. It was bound to TRP 111 with hydrophobic π-π bonds (5.74 Å and 5.22 Å), LEU 300 with one π-alkyl bond (5.13) and one π-σ bond (3.63 Å), CYS 298 with a hydrogen bond (2.47 Å). The other residues were TRP 20 (5.03 Å) and VAL 47 (4.38 Å) with π-alkyl bonds. It also formed two unfavourable bonds with TYR 309 (2.42 Å) and CYS 298 (2.39 Å). In total, quercetin formed seven non-bonding interactions with one hydrogen bond, with a binding affinity of −10.3 kcal/mol. The visualization of binding interactions of the experimental molecules with HAR has been depicted in Several parameters such as the protein–ligand complex RMSD, RMSF, Rg, SASA, ligand RMSD, and ligand-hydrogen bonds were studied during molecular dynamics simulation to assess the complex’s overall stability. The RMSD plot of the protein–ligand combination displays the ligand’s stability inside the binding pocket over the course of a 100 ns simulation. The RMSF of a protein–ligand complex, on the other hand, is used to compute the average deviation of a particle  over time from a reference site. As a result, RMSF focuses on the protein structural regions that are the most/least different from the mean. In addition, the radius of gyration (Rg) shows the structural compactness of the molecules by calculating the root-mean-square distances with respect to the central axis of rotation. The area around the hydrophobic core created between protein–ligand complexes were shown in SASA plots for all protein–ligand complexes. During the simulation, the greatest number of H-bonds remained consistent with the molecular docking, and only a few bonds were concurrently broken and rebuilt. Therefore, ligand-hydrogen bonds also play a crucial role in dynamic trajectory analysis.2. Yet, the protein model was found with the SASA value of ~350 nm2. In addition, all three ligands: caffeic acid, syringic acid, and acarbose, were able to form three hydrogen bonds. In the case of α-glucosidase, the RMSD plot depicts that the protein backbone atoms became stable after 20 ns. However, all the protein–ligand complexes became stable after 15 ns protein–syringic acid complex and protein backbone atoms were equilibrated at 0.4 nm, while protein–acarbose and protein–caffeic acid complexes equilibrated at 0.25–0.30 nm. In Rg, the protein backbone atoms equilibrated at 3.1 nm. All the protein–ligand complexes were found to be equilibrated at 2.4 nm. In the case of RMSF, the protein model showed more fluctuations at C-terminal, N-terminal, and loop regions. The α-glucosidase protein molecule extends up to ~1000 residues. The protein–ligand complexes, however, are extended up to only ~600 residues. Residues from 600–900 showed minimal fluctuations that depict the stability of the protein backbone atoms. However, the C-terminal region of the α-glucosidase protein shows the highest fluctuation (~0.85 nm) at the residues 900–1000. However, in comparison with the other protein–ligand complexes, protein–syringic acid was found with lesser fluctuations. Similar to Rg, SASA of all the protein–ligand complexes were found to be similar, within the range of 225–250 nm2. In the case of the ligand-hydrogen bonds, syringic acid formed more hydrogen bonds (n = 7). During the dynamics simulation of α-amylase, the RMSD plot depicts that the protein model equilibrated at ~0.3 nm, with protein–syringic acid being equilibrated at 0.25–0.30 nm. However, both protein–acarbose and protein–caffeic acid complexes were equilibrated at 0.35–0.40 nm. In the case of Rg, all the protein–ligand complexes except caffeic acid were equilibrated at 2.3 nm, whereas the latter reached equilibration at 2.25 nm. In the RMSF analysis, protein–syringic acid complex was found with minimal fluctuations in comparison with the other complexes. The protein model showed maximal fluctuation only at the loop region at 350 residues. Apart from this, caffeic acid was found with maximal fluctuation at 240 residues at the other loop regions. In the case of SASA, all the protein–ligand complexes showed a similar pattern of equilibration, where the SASA value of all the molecules, including protein, was found to be ranging between 180–190 nm2. The ligand hydrogen bonds of syringic acid were also found to be the highest of all the ligands simulated (n = 5). The trajectory analysis of HAR complexed with different experimental molecules shows that RMSD of the protein backbone atoms, protein–syringic acid, protein–quercetin complexes were equilibrated at ~0.35–0.40 nm. However, the protein–caffeic acid complex was found with 0.25 nm of RMSD. In the case of Rg, the protein backbone atoms and protein–syringic complex were found to be equilibrated at 1.850–1.875 nm. Yet, the other protein–ligand complexes were found with the Rg values of ~1.875–1.900 nm. During the RMSF plot analysis, it was found that the protein–caffeic acid has the highest fluctuations at the loop regions (100–150 residues) and C-terminal region. In comparison with the other complexes, protein–syringic acid complex was found with minimal fluctuations. However, in SASA analysis, all the plots, including protein backbone atoms, were found with a similar pattern of values, ranging between 130–140 nmDuring the druglikeness evaluation, caffeic acid, syringic acid, aminoguanidine, and quercetin obeyed Lipinski’s rule of five. In the case of absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, which is also known as pharmacokinetic analysis, only syringic acid was found with optimal Caco-2 cell permeability. In addition, quercetin was not able to clear the AMES mutagenicity test, thus revealing its probable carcinogenic effect. Therefore, druglikeness and pharmacokinetics reveal that syringic acid is the best potential drug candidate among the selected experimental compounds. Details of the druglikeness and pharmacokinetics assay have been given in Binding free energy calculations indicate that all the protein–ligand complexes are majorly formed Van der Waal’s energy. This was followed by binding energy, electrostatic energy, and SASA energy. In comparison with the other compounds, syringic acid showed a higher binding free energy when complexed with the protein molecules. In this context, results from binding free energy calculations show that syringic acid is a stable inhibitor of all the target proteins, in accordance with the results from molecular dynamics simulation. Details of the binding free energy calculations have been depicted in To optimize the frequency, progression, and severity of diabetic complications, successful hyperglycemia intervention is important. Using a single therapeutic strategy, on the other hand, has not been successful in preventing all of the adverse implications of high blood glucose levels. As a result, α-glucosidase inhibitors, aldose reductase inhibitors, antiglycation medicines, and antioxidants may be a potential option for reducing the adverse effects of glucose. Numerous studies have shown that crude plant extract, as well as bioactive combinations derived from it, can help lower blood glucose levels . In owinIn the conversion of dietary carbohydrates to glucose, the enzymes α-amylase and α-glucosidase are involved. Inhibition of these enzymes is being used to treat diabetic individuals since it delays carbohydrate breakdown and, as a result, slows down the glucose absorption in the intestine . In compCaffeic acid is renowned for its antioxidant, anti-inflammatory and anticarcinogenic properties , while SAldose reductase is involved in the polyol pathway as the first rate-limiting enzyme responsible for the conversion of glucose to sorbitol. In normoglycemic conditions, the glycolytic pathway metabolises the majority of glucose; however, in hyperglycemic conditions, the aldose reductase-associated polyol pathway increases dramatically, resulting in sorbitol buildup in the cells due to its low membrane permeability . SynthetExcess glucose in the bloodstream causes the glycation of different proteins, rendering them inactive. Fructose has recently been discovered in glycation, either directly or through activating the polyol pathway to promote fructose production from glucose. According to studies, fructose participates in glycation at a faster rate than glucose, implying greater damage. The nucleophilic addition reaction between the free amino group of proteins and the carbonyl group of the reducing sugar initiates the creation of a Schiff’s base, which results in irreversible Amadori products such as fructosamine. The fructosamines in Amadori products are transformed to a variety of carbonyl compounds in the second stage of glycation, including glyoxal, methylglyoxal, and deoxyglucosones. Carbonyl proteins are formed as a result of this process, which results in the loss of protein thiols, which are promising protein oxidation markers . As the In this regard, our study was designed to assess if MJ, CA and SA could prevent each stage of glycation and the production of AGE. Under in vitro conditions, the first stage of protein glycation was achieved by exposing HSA to high fructose concentrations, which was expected to glycate HSA. As expected, in our study, the fructosamine levels were higher than the non-glycated HSA levels, and MJ, CA, and SA treatment greatly decreased this, outperforming the usual inhibitor aminoguanidine. Furthermore, the second stage of protein glycation was revealed by the increased protein carbonyl groups formed after HSA was exposed to a high fructose load and the correspondingly low levels of protein thiols. Treatment of MJ and compounds reduced this to about 75–85 percent, indicating that it has a protective role. Finally, the ability of the compounds to generate fluorescent products was used to assess AGE formation, with the results indicating a significant reduction in AGE formation. As a result, the possible function of MJ and the compounds in preventing each stage of protein glycation is affirmed, implying that they could be useful in controlling various complications linked with it. The polyphenolic compounds found in the whole jackfruit extract may be responsible for its anti-glycation effect . These fAminoguanidine, a well-known AGE inhibitor, works by inhibiting the formation of carbonyl intermediates. Other inhibitors, which act as metal chelating agents or antioxidants, are also implicated. However, the long-term consequences of these compounds include a hepatotoxic effect, necessitating the development of safer alternatives. Furthermore, these molecules capture all undesirable free radicals in vivo. In order to avoid these disadvantages, it is beneficial to boost antioxidant defences in order to avoid protein glycation . VariousMolecular docking simulation is performed to know the interaction of ligands with the target proteins at a molecular level. It determines the extent of ligand interaction which in turn shows the protein inhibition/activation. In this study, we have selected two phenolic acids from jackfruit methanol extract for targeting the inhibition of α-glucosidase, α-amylase, and HAR. Results from the docking simulation reveal that caffeic acid and syringic acid had the highest binding affinity with all the target proteins. During the docking simulation of α-glucosidase, all the experimental compounds were bound deep inside the binding pocket. The docking was accurate, according to the previous study conducted by Patil et al. (2021a) . AlthougThe binding interaction analysis of the compounds with α-amylase revealed that syringic acid is bound to all the three catalytic residues of the protein , which are present in the hydrophobic binding pocket located between loop one and loop two. Conversely, both caffeic acid and acarbose were not able to bind to all three catalytic residues. Binding to these catalytic residues would effectively reduce the activity of α-amylase, as indicated in recent studies ,53. TherAnalysis of binding interactions from HAR indicates that all the ligands occupy the exact binding site as the co-crystallized ligand zenarestat, which is present in the vicinity of the NADPH binding site. HAR catalyzes the NADPH-dependent conversion of glucose to sorbitol, the first step in the polyol pathway of glucose metabolism . Thus, iMolecular dynamics simulation is performed to assess the overall stability of the protein–ligand complex kept in s defined environment for a definite amount of time. Simulating experimental molecules with their target proteins has given variable results in this study. In case of α-glucosidase, all the plots describe syringic acid as the most stable compound. The protein–syringic acid complex was never predicted with abnormal fluctuations in any of the plots . However, plots of protein–caffeic acid and protein–acarbose complexes were not as concurrent as the protein–syringic acid complex with the protein backbone atoms. The same trend was observed in the simulation of all four target proteins. The concurrent plots of protein–ligand complex with the protein backbone atoms indicate stronger binding affinity during the simulation study ,57. In tDruglikeness and pharmacokinetics analysis is performed to evaluate the bioavailability of the potential drug candidates. None of the above-mentioned studies has performed this assay to evaluate the bioavailability of their reported lead compounds. Yet, in our study, syringic acid was reported with zero risk of violating the druglikeness and pharmacokinetic parameters. It had passed the druglikeness test by obeying Lipinski’s rule of five, which is dependent on the physico-chemical parameters of the compound . In addiThis study is the first to investigate the antihyperglycaemic potential of the whole unripe jackfruit, as well as the identification of its polyphenol constituents. Moreover, MJ exerts a significant antihyperglycaemic effect by inhibiting carbohydrate hydrolysing enzymes, as well as reducing diabetes-related comorbidities by suppressing aldose reductase and AGE-related pathways. The beneficial effects of MJ accord with the positive effects of the isolated CA and SA, implying that they could be used as antidiabetic drugs. Our findings clearly establish MJ and its components for its antioxidant activities, implying a relationship with their corresponding antidiabetic effects. Furthermore, the identification of significant amounts of caffeic acid (0.52%) and syringic acid (0.58%) in MJ gave evidence for a strong chemical basis for MJ-associated antidiabetic capabilities. The hallmark outcome of this study is the identification of syringic acid as a lone potential inhibitor of all the selected targets of diabetes mellitus, which have their roles in different stages of vicious metabolic disorder. The phytocompound has surpassed all the other experimental compounds in the in silico investigations to become a common inhibitor of all the target proteins. In this context, we show that our study has identified a single, novel multi-target inhibitor for different proteins that act as targets in the stages of diabetes mellitus by playing a crucial role in the decadence of the metabolic state.


Memory for odors is believed to be longer-lasting than memory for visual stimuli, as is evidenced by flat forgetting curves. However, performance on memory tasks is typically weaker in olfaction than vision. Studies of odor memory that use forced-choice methods confound responses that are a result of a trace memory and responses that can be obtained through process of elimination. Moreover, odor memory is typically measured with common stimuli, which are more familiar and responses may be confounded by verbal memory, and measure memory in intentional learning conditions, which are ecologically questionable. Here we demonstrate the value of using tests of memory in which hit rate and correct rejection rate are evaluated separately  and uncommon stimuli are used. This study compared memory for common and uncommon odors and pictures that were learned either intentionally (Exp. 1) or incidentally (Exp. 2) and tested with either a forced-choice or a one-stimulus-at-a-time (“monadic”) recognition task after delays of 15 min, 48 h or 1 week. As expected, memory declined with delay in most conditions, but depended upon the particular measure of memory and was better for pictures than odors and for common than uncommon stimuli. For common odors, hit rates decreased with delay but correct rejection rates remained constant with delay. For common pictures, we found the opposite result, constant hit rates and decreased correct rejection rates. Our results support the ‘misfit theory of conscious olfactory perception’, which highlights the importance of the detection of novelty in olfactory memory and suggests that olfactory memory should be studied using more ecologically valid methods. Remembrance of Things Past, Marcel Proust described the smell of a madeleine  evoking a strong autobiographical childhood memory and most people are familiar with this experience. Interestingly, it has been reported that odors are more effective than visual cues at evoking autobiographical memories, e.g., [Memory for odors has long been believed to be robust and long-lasting . The lons, e.g., ,6 and ths, e.g., ,8. The Ps, e.g.,  seems toAnother form of long-term olfactory memory is odor recognition memory, e.g., ,11,12,13Notwithstanding the flat forgetting curves, classic data in the odor memory literature do not support the notion that odor memory is better than visual memory in an absolute sense. For example, the data from Lawless  see Figb demonstOdor recognition memory is likely be affected by a variety of parameters , such asNot denying that top-down effects can be important in human olfactory processing, see , in the An additional parameter that has received less attention in the literature on odor recognition memory involves the use of intentional and incidental learning paradigms. In many studies of odor recognition memory participants know that they are part of a memory study and can try to use focused encoding strategies to aid in their memory of stimuli. This is quite an artificial scenario compared to the way people typically learn about their sensory world, which is largely incidentally and thus the use of incidental learning paradigms are preferable in tests of odor memory .In the current study, we re-examine odor and visual recognition memory over long delays (up to 1 week) with common and uncommon odors and pictures and under conditions of both incidental and intentional learning. This study was motivated by the question: Does odor memory rely on the same mechanisms as visual memory? Before addressing this question, we first outline a set of methodological and theoretical considerations.Tests of sensory memory, such as memory for odors or pictures, differ from tests of cognitive processes, such as verbal memory. It is difficult, if not impossible, to test recall of a sensory experience since people cannot conjure up and report a sensory experience in that modality, whereas it is possible to recall, and trivial to report the memory of a word, as such. One could argue that for visual and perhaps auditory stimuli , memory can be tapped by the recall of very simple stimuli, such as by drawing a picture or reproducing a sound. This does not seem to be the case for the “near senses” . In this paper, we distinguish between “far” and “near” senses. For near senses the epithelium is in direct contact with the material properties of the stimulus  whereas this is not the case for “far” senses . Recalling an odor may be particularly difficult because the ability to conjure up an “odor image” is less common in people than their ability to conjure up an image of a picture ,44. Thusn-alternative forced choice paradigm, a problem in interpretation of performance is that a correct response could indicate the recognition of the target odor ; however, it is also possible that the target odor was selected because the participant recognized that the other odors, hereafter referred to as foils, were not presented previously, see [In an sly, see . In otheThere are, at least, two ways to address this issue methodologically. First, one could give participants the option of saying that “neither” of the presented stimuli were the target in a 2-AFC task. A “neither” response stops a participant from being forced to make a false positive. A false positive would be a response made when the participant has detected that the foil is a novel odor, and thus selects the other odor as the target. A second alternative method of testing odor memory is a so-called “monadic” version of the test phase, which is effectively a “yes/no” task, see . In suchThe mechanisms involved in olfactory memory may be different from those in other memory systems . AccordiIn this paper we address the question: Does olfactory memory rely on different mechanisms than visual memory? In particular, we ask whether “novelty detection” is an important mechanism in long-term olfactory memory? We address this by examining memory for common and uncommon pictures and odors for up to one week in both intentional and incidental learning conditions, by giving participants the option of a “neither” response in a forced-choice task and by examining performance in a “monadic” test paradigm. We discuss the results of our experiments in the context of the “Misfit Theory of Spontaneous Conscious Odor Perception” MITSCOP, , which sVisual and olfactory memory depend differentially on ‘novelty’ detection as will be demonstrated with differential hit and correct rejection rates and patterns of responses with delay in the monadic “yes/no” task.Olfactory memory suffers less forgetting than visual memory. We expect to find main effects of delay for pictures but not for odors.Memory of uncommon stimuli decays similarly to memory of common stimuli, as demonstrated with no interactions between the variables of delay and “commonness”.The research hypotheses were:This condition was designed to ask whether novelty detection is important in olfactory memory by using a two-alternative forced choice task with the option of a “neither” response and by examining performance in a “monadic” test paradigm. Memory was tested within a typical intentional learning paradigm over delays between 15 min and 1 week.One hundred and sixty-five young adults  and 116 females (19.55 years) all between 18 and 23 years of age) participated in this experiment and each participated in only one delay condition. Most participants were non-smokers . Participants were recruited from individual classes (with instructor permission), via email, through word of mouth, and via an online college bulletin board. Participants were compensated with a $10 gift card upon completion of the memory task.Appendix AThe common pictures  and the uncommon, free-form pictures see  taken frParticipants were tested in small groups (1–7 people) in a well-ventilated classroom or lab setting. The experimenter told the participants that this was a memory test and that they should make every effort to remember the stimuli. The memory test occurred after a delay of 15-min, 48-h, or 1-week during which time there was no control over what participants did.In Phase I, participants were presented with a set of 20 odors and 20 pictures  and rated the familiarity of each stimulus, using a Likert scale (1–9). Participants opened each jar, one at a time, sniffed the contents, privately wrote a familiarity rating, recapped the jar and handed it to the person to their right. They then opened a card and viewed a picture, rated the familiarity, closed the card and passed it to the right. This continued until all participants had smelled and rated each jar and each picture. The experimenter controlled the speed of testing, which was about one jar or picture every 10–15 s, thus there were about 30 s between odor exposure. Stimulus presentation order was not identical for each person. Familiarity ratings were used to encourage participants to attend to the stimuli in the learning phase of this experiment, and confirmed that common stimuli were rated as more familiar than uncommon ones. They confirmed that common and uncommon stimuli were rated as such. Mean familiarity ratings were as follows: common odors (6.15), uncommon odors (4.34), common pictures (8.43) and uncommon pictures (3.85).In Phase II (a recognition memory test), participants reconvened in the same group after either 15 min, 48 h or 1 week, in the same room and were presented with 40 odors and 40 pictures. Odor memory was tested using one of two methods: (1) two-alternative forced-choice task with option of a “neither” response: Participants received stimuli in pairs  and indicated which odor or picture in the pair had been presented in Phase I. The experimenter kept the trials going at a swift pace, so participants were limited in the amount of time they could sample the stimuli. “Neither” was a response option, which participants could use to indicate that they did not think either stimuli had been presented previously. Unbeknownst to the participants, “neither” was never the correct response as one of the stimuli had always been presented. Pairs of odors and pictures were either common or uncommon and never mixed across category. The order of pairs of stimuli was randomized so that the target was presented equally often as the first or second stimulus. (2) Monadic task: Participants received stimuli one at a time and their task was to indicate whether or not the stimulus had been presented in Phase 1. This was a between-subjects design, so participants either participated in a 2-AFC or monadic task, not both.This experiment received approval from the Carthage College Institutional Review Board and was conducted in accordance with the ethical principles regarding human experimentation, as outlined in the Declaration of Helsinki. All participants gave written informed consent.We had no predictions about the difference in absolute level of performance of pictures and odors (these could have been made more or less similar by manipulating the stimuli), thus we ran separate analyses for pictures and odors for this and all analyses.F = 1.43; p = 0.25, η2 = 0.04) between commonness and delay. There was a main effect of commonness  = 155.63, p < 0.001, η2 = 0.67), but not of delay  = 1.73, p = 0.19, η2 = 0.04). Thus, performance was much higher for common than for uncommon pictures, but performance did not decline significantly with delay.A 2 (commonness) × 3 (delay) mixed ANOVA on percent correct for the picture stimuli revealed no significant interaction  = 2.23; p = 0.11, η2 = 0.06) between commonness and delay. However, there were main effects of both commonness  = 20.13, p < 0.01, η2 = 0.21), and delay  = 6.18, p < 0.01, η2 = 0.14). Thus, performance was much higher for common than for uncommon odors and performance declined with delay. Tukey post hocs for multiple comparisons indicated significant differences between the 15-min and 48-h condition (p = 0.02) and between the 15-min and 1-week (p < 0.01), but not for the difference between 48 h and 1-week (p = 0.92).A 2 (commonness) × 3 (delay) mixed ANOVA of percent correct for the odor stimuli also revealed no significant interaction  They could have chosen the foil, which could be interpreted as meaning that any potential template does not have high fidelity, or (2) they could have chosen to report that neither of the odors in the test phase was the target. In this case, the foil was correctly evaluated as novel and the target was, incorrectly, evaluated as novel.We ask two questions about the “neither” responses in the current data. First, we explored how many “neither” responses were made overall for each stimulus type. For common and uncommon pictures, the rates of “neither” responses were 12.81% and 21.26%, respectively. For common and uncommon odors, the rates of “neither” responses were 32.27% and 13.92%, respectively. Only ~6% of participants never used the option of “neither”. Without the option “neither”, such as is in a typical 2AFC task, contributions to apparently correct target detections can arise in the following ways: (1) By relying on a memory trace of the target (a true ‘recognition’ of the target); (2) by detecting that the foil is a novel target and thereby deduce that the other test stimulus is the target; and (3) by guessing correctly in cases where both stimuli appear to be ‘novel’, i.e., that neither of the stimuli are recognized as having been smelled before, participants will have to make a random choice, thereby inflating apparent correct target detection with half of these guesses. For common odors in the present experiment this contribution was about 16% (averaged over delay). The percent correct responses for common odors shown in Second, we ask whether “neither” responses make up more than half of all incorrect responses. Such responses indicate that participants perceived both the target odor and the foil as novel. t(24) = 5.39, p < 0.01, d = 1.08) and the mean at 1 week was 63.35% (t(26) = 2.24, p = 0.02, d = 0.43).In the case of common odors, single-sample t-tests demonstrated that at the 48-h and 1-week delays, participants were more likely to choose “neither” than chance (50%). The mean at 48 h was 80.45% (t(26) = 10.04, p < 0.01, d = 1.93) at 48 h 26.88% < 50% (t(24) = 5.21, p < 0.01, d = 1.04) and at 1 week, 25.35% < 50% (t(26) = 6.77, p < 0.01, d = 1.32)In the case of uncommon odors, the rates of “neither” responses were significantly lower than chance. At 15 min, 14.67% < 50%  = 2.83; p < 0.01, η2 = 0.17). There was a main effect of commonness  = 341.48, p < 0.01, η2 = 0.80), and of delay  = 6.41, p < 0.01, η2 = 0.13). Univariate analyses conducted for the common and uncommon pictures separately indicated that the effect of delay was significant for the common stimuli only  = 14.2, p < 0.01, η2 = 0.26). Tukey post hocs for multiple comparisons indicated that the difference between the 15-min condition and the 48-h (p < 0.01) and 1-week (p < 0.01) conditions were significant, but the difference between 48 h and 1-week conditions were not (p = 0.15).A 2 (commonness) × 3 (delay) mixed ANOVA of d’ for the picture stimuli revealed a significant interaction  = 4.21; p < 0.02, η2 = 0.09). There was a main effect of commonness  = 36.89, p < 0.01, η2 = 0.31), but the effect of delay was not significant  = 0.86, p = 0.43, η2 = 0.02). Univariate analyses conducted for the common and uncommon odors separately indicated that the effect of delay was significant for the common stimuli only  = 4.02, p = 0.02, η2 = 0.09). Tukey post hocs for multiple comparisons indicated that the only significant difference was between the 15-min and 1-week conditions (p = 0.019).A 2 (commonness) × 3 (delay) mixed ANOVA of d’ for the odor stimuli revealed a significant interaction  and the extent to which they are relying on novelty detection (correct rejection). F = 1.61; p = 0.21, η2 = 0.04). There was a significant main effect of commonness  = 161.59, p < 0.01, η2 = 0.66), but not of delay  = 1.09, p = 0.34, η2 = 0.03). Thus, hit rates were significantly higher for common than uncommon pictures, but did not decline significantly over one week.A 2 (commonness) × 3 (delay) mixed ANOVA of hit rates for the picture stimuli revealed no significant interaction  = 3.30; p = 0.04, η2 = 0.07) between commonness and delay. The main effects were both significant—delay  = 6.90, p < 0.01, η2 = 0.14) and commonness  = 43.6, p < 0.01, η2 = 0.35). Univariate analyses conducted for the common and uncommon odors separately indicated that the effect of delay was significant for the common stimuli only  = 7.18, p < 0.01, η2 = 0.15). Tukey post hocs for multiple comparisons indicated significant differences between the 15-min and 48-h condition (p = 0.04) and between the 15-min and 1-week (p < 0.01), but not for the difference between 48 h and 1-week (p = 0.40). The higher hit rates for uncommon odors are reflected in their bias scores (see below).By contrast, a 2 (commonness) × 3 (delay) mixed ANOVA of hit rates for the odor stimuli revealed a significant interaction  = 1.38, p = 0.26, η2 = 0.03). There was a main effect of stimulus type  = 99.4, p < 0.01, η2 = 0.55) as memory was better for pictures than for odors, and a main effect of delay  = 9.93, p < 0.01, η2 = 0.19). Tukey post hocs for multiple comparisons indicated significant differences between the 15-min and 48-h condition (p = 0.01) and between the 15-min and 1-week (p < 0.01), but not for the difference between 48 h and 1-week (p = 0.26).Although we recognize the limitations of direct comparisons between performance on our odor and picture tasks, in order to address the question as to the difference between visual and olfactory memory, have selected a few cases where we thought comparisons were meaningful. For example, we conducted a 2 (stimulus type) × 3 (delay) mixed ANOVA of the hit rates for common pictures and odors. In the case of hit rates, we found no interaction between our variables  = 2.64; p = 0.08, η2 = 0.06). There was a main of commonness  = 71.90, p < 0.01, η2 = 0.46), and of delay  = 5.07, p < 0.01, η2 = 0.11). Thus, correct rejection rates were significantly higher for common than for uncommon pictures and decreased significantly after one week. Tukey post hocs for multiple comparisons indicated a significant difference between the 15-min and 1-week delay conditions (p = 0.01) and a narrowly significant difference between the 15-min and 48-h conditions (p = 0.05), but no significant difference between the 48 h and 1-week conditions (p = 0.78).A 2 (commonness) × 3 (delay) mixed ANOVA of CR rates for the picture stimuli revealed no significant interaction  = 2.07; p = 0.13, η2 = 0.05) between commonness and delay. There was a main effect of commonness  = 240.3, p < 0.01, η2 = 0.74), and a trend towards an effect of delay  = 2.53, p = 0.09, η2 = 0.06). Thus, CR rates were significantly higher for common than for uncommon odors and did not decrease with delay.A 2 (commonness) × 3 (delay) mixed ANOVA of CR rates for the odor stimuli revealed no significant interaction  = 7.25, p < 0.01 η2 = 0.15). There was a main effect of stimulus type  = 12.48, p < 0.01, η2 = 0.13) as memory was better for pictures than for odors, and a marginally significant main effect of delay  = 3.15, p = 0.05, η2 = 0.07). Univariate analyses conducted for picture and odor conditions separately indicated that the effect of delay was significant for the pictures only  = 9.25, p < 0.01, η2 = 0.18). Tukey post hocs for multiple comparisons indicated significant differences between the 15-min and 48-h condition (p = 0.02) and between the 15-min and 1-week (p < 0.01), but not for the difference between 48 h and 1-week (p = 0.27). Thus, the pattern of memory performance was that CR rates decreased as a function of delay for pictures but not for odors.Again, to address the question as to the difference between visual and olfactory memory, we conducted a 2 (stimulus type) × 3 (delay) mixed ANOVA of the CR rates for common pictures and odors. In the case of CR rates, we found a significant interaction between variables  = 1.06; p = 0.35, η2 = 0.03). There was a main effect of commonness  = 4.65, p = 0.03, η2 = 0.05), but not of delay  = 0.88; p = 0.42, η2 = 0.02).A 2 (commonness) × 3 (delay) mixed ANOVA of the criterion for the picture stimuli revealed no significant interaction  = 1.99; p = 0.14, η2 = 0.05). There was a main effect of commonness  = 162.71, p < 0.01, η2 = 0.66), and a significant effect of delay  = 6.16, p < 0.01, η2 = 0.13).A 2 (commonness) × 3 (delay) mixed ANOVA of the criterion for the odor stimuli revealed no interaction  and also produce remarkably low correct rejection rates. With delay, correct rejection rates for odors appears to remain constant or rise slightly.One hundred and sixteen adults volunteered for this study. One hundred and fifteen completed the study—46 males (21.33 years) and 70 females (19.82 years), mostly between 18 and 22 years of age. There were three older females  and one 54-year-old male participant). Participants were recruited via an email listserv and an online bulletin service of the college at three separate times. Data from one participant with self-reported anosmia were excluded from analysis. Most participants were non-smokers . Those with interfering allergy or cold symptoms were rescheduled for a later date. Participants were instructed not to consume anything but water an hour before the testing session. Small gifts  were given as compensation.Testing was conducted as in the intentional learning condition except that participants were not told that they were in a memory experiment, but rather that there was a second phase of the experiment without specifying what the task would involve. Phase II was the same recognition memory test and participants were either tested with the forced-choice or monadic version of the task after a delay of either 15 min, 48 h or 1 week.F = 0.62; p = 0.54, η2 = 0.02) between commonness and delay. There was a main effect of commonness  = 125.98, p < 0.01, η2 = 0.71), and of delay  = 3.84, p = 0.03, η2 = 0.13). Thus, performance was significantly higher for common than for uncommon pictures and performance declined with delay. Tukey post hocs for multiple comparisons indicated no significant difference between the 15-min and 48-h condition (p = 0.96), nor between 48 h and 1-week (p = 0.07) but there were significant differences between the 15-min and 1-week (p = 0.03) delay conditions.A 2 (commonness) × 3 (delay) mixed ANOVA on percent correct for the picture stimuli revealed no significant interaction  = 1.31, p = 0.33, η2 = 0.04) between commonness and delay. There was a main effect of commonness  = 10.61, p < 0.01, η2 = 0.17), and a trend for delay  = 2.80, p = 0.07, η2 = 0.10). Thus, performance was higher for common than for uncommon odors, but performance did not decline significantly with delay.A 2 (commonness) × 3 (delay) mixed ANOVA on percent correct for the odor stimuli revealed no significant interaction  = 1.15, p = 0.13, d = 0.26) and at 1 week, it was 62.02% (t(13) = 1.70, p = 0.06, d = 0.45).The pattern of “neither” responses is similar to the results of the intentional learning condition, but there was only a trend for the “neither” responses for common odors to be greater than chance at the longer delays. Only ~3.5% of participants never used the option of “neither”. For common odors, single-sample t-tests demonstrated that at the 48-h and 1-week delays, there was a trend towards participants choosing the “neither” response more often than chance. At 48 h, the mean percent of “neither” responses was 57.62% (t(21) = 4.55, p < 0.01, d = 0.97), at 48 h 18.38% < 50% (t(19) = 5.43, p < 0.01, d = 1.25) and at 1 week, 30.01% < 50% (t(13) = 3.22, p < 0.01, d = 0.86).In the case of uncommon odors, the relatively low rates of “neither” responses were significantly lower than chance. At 15 min, 25.41% < 50%  = 3.74; p = 0.03, η2 = 0.12) between commonness and delay. There was a main effect of commonness  = 300.0, p < 0.01, η2 = 0.84), indicating that d’ was higher for common than for uncommon pictures. There was also a main effect of delay  = 9.79, p < 0.01, η2 = 0.26). Univariate analyses conducted for the common and uncommon pictures separately indicated that the effect of delay was significant for the common stimuli only  = 4.04, p < 0.01, η2 = 0.30). Tukey post hocs for multiple comparisons indicated significant differences between the 15-min and 48-h condition (p < 0.01) and between the 15-min and 1-week (p < 0.01) but not for the difference between 48 h and 1-week (p = 1.0).A 2 (commonness) × 3 (delay) mixed ANOVA on d’ for the picture stimuli revealed a significant interaction  = 2.26; p = 0.11, η2 = 0.07) between commonness and delay. There was a main effect of commonness  = 34.37, p < 0.01, η2 = 0.38) and of delay  = 3.50, p = 0.04, η2 = 0.11). Thus, sensitivity was higher for common than for uncommon odors and sensitivity decreased with delay. Tukey post hocs for multiple comparisons indicated significant differences only between the 15-min and 1-week condition (p = 0.03)A 2 (commonness) × 3 (delay) mixed ANOVA on d’ for the odor stimuli revealed no significant interaction  = 0.35, p = 0.71, η2 = 0.01) between commonness and delay. There was a main effect of commonness  = 28.12, p < 0.01, η2 = 0.33), and a trend towards an effect of delay  = 2.69, p = 0.08, η2 = 0.09). Thus, performance was higher for common than for uncommon pictures and performance declined, but not significantly, with delay.A 2 (commonness) × 3 (delay) mixed ANOVA on hit rate for the picture stimuli revealed no significant interaction  = 2.15; p = 0.13, η2 = 0.07) between commonness and delay. There was a main effect of commonness  = 35.98, p < 0.01, η2 = 0.38), and a main effect of delay  = 21.9, p < 0.01, η2 = 0.44). Thus, hit rate was higher for uncommon than for common odors and hit rate declined significantly with delay for both conditions. Tukey post hocs for multiple comparisons indicated a significant difference between the 15-min and 48-h condition (p < 0.01), and between the 15-min and 1-week (p < 0.01), but not between 48 h and 1-week (p = 0.06).A 2 (commonness) × 3 (delay) mixed ANOVA on hit rate for the odor stimuli revealed no significant interaction  = 6.85, p < 0.01 η2 = 0.19). There was a main effect of stimulus type  = 77.68, p < 0.01, η2 = 0.57) as memory was better for pictures than for odors, and a significant main effect of delay  = 12.56, p < 0.01, η2 = 0.31). Univariate analyses conducted for picture and odor conditions separately indicated that the effect of delay was significant for the odors  = 15.34, p < 0.01, η2 = 0.35). Tukey post hocs for multiple comparisons indicated significant differences between the 15-min and 48-h condition (p < 0.01) and between the 15-min and 1-week (p < 0.01), but the difference between 48 h and 1-week (p = 0.15) was not significant. Thus, the pattern of memory performance was that hit rates decreased as a function of delay for odors but not for pictures.We conducted a 2 (stimulus type) × 3 (delay) mixed ANOVA of the hit rates for common pictures and odors. We found a significant interaction between variables  = 1.25, p = 0.30, η2 = 0.04) between commonness and delay. There was a main effect of commonness  = 111.76, p < 0.01, η2 = 0.66) and there was a trend towards an effect of delay  = 2.59, p = 0.08, η2 = 0.08). Thus, the CR rate was higher for common than for uncommon pictures and there was a trend towards a decrease in correction rate with delay for both conditions.A 2 (commonness) × 3 (delay) mixed ANOVA on correct rejections for the picture stimuli revealed no significant interaction  = 0.42; p = 0.66, η2 = 0.01) between commonness and delay. There was a main effect of commonness  = 199.43, p < 0.01, η2 = 0.78). The effect of delay  = 3.15, p = 0.05, η2 = 0.10) narrowly missed significance. Thus, CR rate was higher for common than for uncommon odors and there was a trend towards performance increasing with delay for both odor conditions.A 2 (commonness) × 3 (delay) mixed ANOVA on correct rejections for the odor stimuli revealed no significant interaction  = 9.28, p < 0.01 η2 = 0.25). There was a main effect of stimulus type  = 18.57, p < 0.01, η2 = 0.25) as memory was better for pictures than for odors, but no main effect of delay  = 0.26, p = 0.77, η2 = 0.01). Univariate analyses conducted for picture and odor conditions separately indicated that the effect of delay was significant for the pictures only  = 8.99, p < 0.01, η2 = 0.24). Tukey post hocs for multiple comparisons indicated significant differences between the 15-min and 48-h condition (p < 0.01) and between the 15-min and 1-week (p < 0.01), but not for the difference between 48 h and 1-week (p = 0.97). Thus, the pattern of memory performance was that CR rates decreased as a function of delay for pictures but not for odors.We conducted a 2 (stimulus type) × 3 (delay) mixed ANOVA of the correct rejections for common pictures and odors. We found a significant interaction between variables  = 0.18; p = 0.84, η2 = 0.01) between commonness and delay. There was a main effect of commonness  = 9.67, p < 0.01, η2 = 0.15), and no main effect of delay  = 0.26, p = 0.77, η2 = 0.01). Thus, bias was closer to “neutral” for common pictures and more “liberal” for uncommon pictures. Bias did not change with delay.A 2 (commonness) × 3 (delay) mixed ANOVA on bias for the picture stimuli revealed no significant interaction  = 0.59; p = 0.56, η2 = 0.02) between commonness and delay. There was a main effect of commonness  = 131.51, p < 0.01, η2 = 0.70) and of delay  = 11.48, p < 0.01, η2 = 0.29). Thus, bias was significantly different for common odors  than uncommon odors  and bias increased with delay (becoming more “conservative”) with delay for common odors and more “neutral” with delay for uncommon odors.A 2 (commonness) × 3 (delay) mixed ANOVA on bias for the odor stimuli revealed no significant interaction  or as sensitivity in a “monadic” task) decreased with delay for both odors and pictures. We do not find evidence for flat forgetting curves in olfaction as reported previously ,18,23, aThe lack of a flat forgetting curve for common odors is in accord with results of Cornell Kärnekull and colleagues . In a moAs discussed below (and previously in the paper), the main reason for the flat forgetting curves often observed in odor memory studies might be an artifact of using forced-choice methods to quantify memory or forgetting.Some studies of odor memory employ forced-choice methods. There is a fundamental problem with forced-choice methods in memory studies, as described in the Introduction, which is that a correct answer can be arrived at in one of two ways. It can be the result of comparing the test stimuli with an internal representation of the target . Template-matching may be the primary mechanism in visual memory and is the standard interpretation of a correct response. On the other hand, a correct response can also be arrived at with no recollection of the target, if the participant can identify the foil as a novel stimulus. If the foil is detected as novel, the participant could deduce that the other stimulus, which the participant cannot match to any internal representation, is actually the target. Thus, template-matching may not be the only mechanism involved in making a correct response.Confounding two possible memory mechanisms is not the only problem with forced choice methods in the context of memory. In an odor memory experiment using 2AFC methods, participants can still feel that they have not smelled either of the odors before. That is, both odors in the test could seem to the participant to be novel. The appropriate response in such a situation is to respond that neither of the odors have been smelled before. We, therefore, added the option of responding ‘neither’ in our forced-choice task and found that participants chose ‘neither’ in about 30% of all trials for common odors in both the incidental and intentional learning conditions. This suggests that in a typical 2AFC memory task with common odors, on about 30% of trials participants would be of the opinion that neither of the two odors presented had been smelled before. However, since an answer is required, participants would have to guess because both stimuli are evaluated to have about the same degree of novelty. This means that percent correct may be inflated by about 15% in a standard paradigm. The problems with using 2AFC methods are not unique to studies of memory for odors.Overall memory for odors in the current forced-choice task was apparently low (55–60% correct), but since the task included “neither” as a response option, chance was 33.33%. Even considering that performance could have been ~15%  higher had this been a typical 2AFC task, performance was lower than the ~85% correct performance levels reported by Lawless  for delaThe most serious problem with forced-choice methods in memory studies is that they confound two different memory mechanisms: matching a test stimulus to a memory trace vs. novelty detection. This problem is avoided when using monadic methods as discussed earlier in the paper.In monadic testing, one stimulus at a time is presented in the test phase and the participant is required to decide whether the stimulus was encountered in the learning phase. For the intentional learning condition, For both modalities, in intentional conditions, where it is revealed to participants that they will be tested on their memory for the stimuli with which they are presented in the learning phase, verbalization and imagery might help participants to encode stimuli into memory. Comparing results from the intentional learning condition, as depicted in Comparing percent correct in the forced-choice task  and d’ iThe plots of hit rates  show thaBoth hit rates and correct rejection rates for common pictures were larger than hit rates and correct rejection rates for uncommon pictures. Contrary to this, hit rates were lower for common odors than for uncommon odors, whereas correct rejection rates were higher for common odors than for uncommon odors . These rIn agreement with previous studies of odor memory ,18,19,56Reliable short term or working memories are crucial for speech comprehension and for reading. Long term visual memory of spatial layout is necessary for wayfinding as well as for object and face recognition, without which most of social and societal activity would not be possible. There are, thus, good behavioral reasons for accurate, high capacity visual and auditory memories.Measuring odor memory by presenting odors in a bottle to participants treats odors as if they were objects to be perceived on their own. Low percent correct and small d’ in long term odor memory tests suggest that odors are not there to be remembered as ‘separate entities’ or ‘odor objects’. Except for a few professionals, such as cooks and perfumers, being able to remember odors per se, does not seem to be an ecologically important capability. On the other hand, there is no doubt that olfactory memory, referring to memories relying on olfactory input, is ecologically important. Examples include autobiographical memories induced by odors, context dependent memories and olfactory memory to help us feel at home and to detect when something is out of place ,47. ThesEven though the present study treated odors as separate entities, we believe the results contain hints about differences between visual and olfactory memory. We found different rates of decay of hit and correct rejection rates for common pictures and common odors and different hit and correct rejection rates for common and uncommon stimuli. These results suggest that long term odor memory is strongly dependent on ‘novelty detection’ and less on a memory trace of odors encountered in the learning phase. This is very different from the case of memory of pictures where participants have a robust internal representation of pictures.We agree with Engen and Ross  and otheAs with any study, ours has some limitations. First, although we tested all available participants at our college location, some of our conditions had more participants than others. On the other hand, standard errors were quite low and the results of the two experiments were so similar that it seems that our results were quite robust. Second, it is true that baseline levels of performance were not the same for all conditions. We believe that this is a function of differences between memory for odors and pictures (people remember pictures better than odors). It is possible that a different set of stimuli could have generated more similar baseline levels of performance, but we do not believe that would explain away the differences we observed between memory for odors and pictures.


Commiphora leptophloeos is widely used in folk medicine without any scientific basis. Considering this, the aim of this study was to evaluate the chemical profile and the antioxidant activity of C. leptophloeos leaf extracts using in vitro and in vivo assays. Six extracts were obtained from fresh leaves using a serial extraction (nonpolar to polar solvents). These extracts were first evaluated with the presence of phytochemical compounds using the methods thin layer chromatography (TLC), ultrahigh performance liquid chromatography (UHPLC-DAD), and high performance liquid chromatography, both with diode array detection (HPLC-DAD). Based on the compounds identified, it was used some bioinformatics tools in order to identify possible pathway and gene targets. After that, the antioxidant capacity from these extracts was analysed by in vitro assays and in vivo assays using Caenorhabditis elegans model. Phytochemical analyses showed the presence of polyphenols, such as rutin, vitexin, and quercetin diglycosides in all extracts, especially in ethanol extract (EE) and methanol extract (EM). Bioinformatics analysis showed these polyphenols linked to antioxidant pathways. Furthermore, EE and EM displayed a high antioxidant capacity in DPPH and superoxide radical scavenging assays. They also had no effect on cell viability for 3T3 nontumour cell. However, for B16-F10 tumour cell lines, these extracts had toxicity effect. In vivo assays using C. elegans N2 showed that EE was not toxic, and it did not affect its viability nor its development. Besides, EE increased worm survival under oxidative stress and reduced intracellular reactive oxygen species (ROS) levels by 50%. Thus, C. leptophloeos EE displayed an important in vitro and in vivo antioxidant capacity. The EE extract has polyphenols, suggesting that these compounds may be responsible for a myriad of biological activities having this potential to be used in various biotechnological applications. Medicine plants have a millenary practice in some cultures . It is kMany age-related and chronic illnesses like diabetes, cardiovascular disease, Parkinson's disease, Alzheimer's disease, inflammatory diseases, and cancer have been associated with oxidative stress due to an imbalance between reactive oxygen species (ROS) production and degradation, consequently increasing the ROS levels , 7. PlanCommiphora leptophloeos (Mart.) J. B. Gillett. (Burseraceae) is a native species from Caatinga, an exclusive Brazilian biome characterised by low water availability, high temperatures, and high light. Pereira et al.  × 100, where A0 is the absorbance of the control (DPPH) and A1 is the absorbance of the samples.μg/mL) were mixed to 3 mL reaction mixture (50 mM phosphate buffer (pH 7.8), 13 mM methionine, 2 mM riboflavin, 100 mM EDTA, and 75 mM NBT) and exposed to a fluorescent lamp for 10 min. Thereafter, absorption was measured at 560 nm—EDTA was used as a control and distilled water as the blank. Results were expressed as percent of radical scavenging:This assay was based on the method by Dasgupta and De  inside aPercentage of radical scavenging = ([Acontrol − Asample]/[Acontrol − Ablank]) × 100.Acontrol: absorbance of the control; Asample: absorbance of the sample; Ablank: absorbance of the blank.ATCC CRL-6475, Manassas, VA, USA) was evaluated against ethanol extract (EE). The EE was chosen due to the results obtained with in vitro antioxidant assays. Cells were grown in Dulbecco's modified Eagle's medium (DMEM)  in 75 cm2 flasks, supplemented with fetal bovine serum (FBS) . Cells were seeded into 96-well plates at a density of 5 × 103 cells/well and allowed to attach overnight in 200 μL of medium with 10% FBS  at 37°C, in 5% CO2. After 24 h, the EE was added in three different concentrations: 50, 100, and 250 μg/mL for a period of 24 h at 37°C and 5% CO2. Thereafter, the medium and extract were removed and 100 μL of fresh medium and 10 μL of 12 mM MTT -2,5-diphenyltetrazolium bromide)  dissolved in PBS were added. The plate was incubated for 4 h at 37°C in 5% CO2. In order to solubilise the reduced MTT product, 100 μL of absolute ethyl alcohol was added to each well and mixed [Cell viability of the murine fibroblast cell line NIH/3T3  and B16-F10 cell lines (Caenorhabditis elegans strains used in this study were the N2 (wild-type) and transgenic line CF1553 (muIs84[pAD76(sod-3::GFP) + rol-6(su1006)]. Caenorhabditis elegans were cultivated in Nematode Growth Medium (NGM) with Escherichia coli OP50 and maintained at 20°C [μm filter (Merck), and subsequently, it was added to the NGM at a final concentration of either 1 mg/mL or 10 mg/mL. Worms were incubated with E. coli OP50 for 48 h.The  at 20°C . SynchroCaenorhabditis elegans eggs were incubated in plates containing NGM and E. coli OP50 in the absence or presence of EE at 100, 250, 500, and 1,000 μg/mL. Approximately 50 eggs per plate were added to a total of five plates per treatment. After the incubation period, the number of hatched eggs was analysed using the following formula:The effects of EE on egg hatching was determined. E. coli OP50 in the absence or presence of EE at 100, 250, 500, and 1000 μg/mL. These plates were kept at 20°C for 48 h. Approximately 25 animals were used per treatment, and each experiment was performed in triplicate. The animals were photographed, and their body length was measured using ImageJ software, version 1.8.The other aspect analysed was the body length. The L1 synchronised worms were kept at NGM medium plus E. coli OP50. These worms were treated with EE at concentrations of 0, 100, 500, and 1000 μg/mL for 48 h. Thereafter, worms were washed thrice with M9 buffer and incubated with 500 μL M9 buffer. Afterward, approximately 40 animals were transferred to 96-well plates containing 2 mM H2DCF-DA , to measure ROS levels. Samples were read using a multilabel microplate reader GloMax®-Multi Detection System , with excitation at 490 nm and emission at 510-570 nm, and the mean values were calculated. Readings were done intermittently every 30 min during 4 h. This assay was performed in triplicate.Wild-type animals synchronised at L1 were kept on NGM plates with μg/mL for 48 h. Thereafter, approximately 50 animals were transferred to 24-well plates with NGM containing 10 mM t-BOOH (tert-butyl hydroperoxide). Survival was monitored every three hours until all animals were considered dead (no movement). Each group was made up of 10 animals, and each assay was analysed five times [Synchronised wild-type animal at L4 were treated with EE at concentrations of 0, 100, 500, and 1000 ve times .μg/mL for 48 h. Thereafter, 25 worms were transferred to microscopic slides with 1% agarose and covered. These worms were photographed using an Olympus BX51 fluorescence microscope using a 10-fold eye magnification and 365 nm excitation filter. The GFP fluorescence intensity was analysed using ImageJ Software version 1.8 (https://imagej.net).The transgenic line CF1553 worms were treated with EE at 0, 100, 500, and 1000 P < 0.05).The results were expressed as the mean ± standard deviations. Each assay was repeated 2–3 times and was performed in triplicate or quintuplet. Statistical analysis was performed using GraphPad Prism 6.0 (2014), and the results were obtained using one-way analysis of variance (ANOVA) followed by Tukey's post hoc analysis (C. leptophloeos leaves: hexane (EH), chloroform (EC), ethanol (EE), methanol (EM), and water , and the sixth extract was prepared only with water-EA. The total phenolic content of compounds obtained ranged from 5.44 ± 0.4 mg/g to 126.41 ± 1.8 mg/g. The EH had 10.18 ± 0.4 mg/g, while EC had 42.47 ± 0.2 mg/g, EAR had 24.27 ± 0.6 mg/g, and EA had 5.44 ± 0.4 mg/g. The highest values were obtained from the EM (126.41 ± 1.8 mg/g) and EE (105.67 ± 1.7 mg/g).In this study, a serial approach using solvents from different polarity (nonpolar to polar) was done in order to obtain five extracts from The presence of terpenes was observed in TLC analysis for the EH. The presence of terpenes, phenolic acids, and flavonoids was detected in the EC; terpenes, phenolic compounds, flavonoids, saponins, and tannins were observed in both the EE and EM. Phenolic compounds were detected for EAR and not observed for EA. Moreover, the fingerprinting analysis of extracts via UHPLC-DAD allowed the detection of several compounds similar to phenols at higher concentrations mainly in EE and EM according to UV spectra see . In greeBased on the biological results obtained with EE, then it was analysed using HPLC-DAD at 360 nm. The presence of vitexin—peak 1 ; peak 2), and peak 2 suggested the presence of quercetin diglycosides  see .C. leptophloeos leaf extract: rutin, vitexin, and quercetin diglycosides. It may be observed that these molecules may be associated to pathways that were related to anti-inflammatory, cardiovascular disease, neurodegenerative disease, cancer, and others. It was observed more than 56 targets   see . Moreove/mL) see . The EE μg/mL up to 45% at 500 μg/mL extract concentration.Since EE presented the highest antioxidant activity amongst all extracts, its effect on NIH/3T3 cell viability was analysed see . As expeC. elegans was used to analyse the EE effect on animal development as well as its antioxidant capacity using this in vivo model. The EE concentration used were 0, 100, 250, 500, and 1000 μg/mL. The 1000 μg/mL concentration was used due to the C. elegans cuticle. The effect of EE on the development C. elegans was evaluated, and any effects on egg hatching at any of the concentrations analysed were not observed . Moreover, during oxidative stress, excessive production of ROS may promote lesions on DNA as well as promote cell damage. The compounds present in extracts reduced or neutralised ROS effects; thus, they are important for redox homeostasis [μM of rutin reduced ROS levels by 15% in C. elegans. The data presented here also showed an effect on life span after EE treatment in animals that have been associated with the antioxidant capacity.Furthermore, eostasis , 32. Kameostasis  showed tsod-3::GFP expression (reporter gene) was observed in animals treated with EE. This reduction may be associated with the ROS scavenging effect of EE. Similar results were also observed when animals were treated with quercetin or Anacardium occidentale extracts [C. elegans from oxidative stress by assisting in ROS reduction, consequently helping to maintain cellular homeostasis [In this study, a reduction of extracts , 42. Theeostasis , 42–44.C. leptophloeos leaf extracts, especially for ethanol extract (EE). Our data showed the presence of phenolic compounds , and no cytotoxic effects were observed on cell viability and C. elegans. Furthermore, potential antioxidant activity was observed using bioinformatics, in vitro and in vivo assays. The intermediate metabolites observed in ethanol extract (EE) may potentially reduce ROS effects and consequently maintain redox homeostasis. Thus, Commiphora leptophloeos is a promising plant species for future studies on biological activity as oxidative stress is involved in different chronic diseases.The results presented here showed the antioxidant capacity of


Phragmites australis wetlands to different land use types. Coastal reclamation enhanced total soil microbial biomass and various species  following the conversion of P. australis wetland to aquaculture pond, wheat, and oilseed rape fields. In contrast, it greatly decreased total soil microbial biomass and various species following the conversion of P. australis wetland to town construction land. Coastal reclamation reduced fungal:bacterial PLFA, monounsaturated:branched PLFA ratios, whereas increasing gram-positive:gram-negative PLFA ratio following the conversion of P. australis wetland to other land use types. Our study suggested that coastal reclamation shifted soil microbial communities by altering microbial biomass and community composition. These changes were driven primarily by variations in soil nutrient substrates and physiochemical properties. Changes in soil microbial communities following coastal reclamation impacted the decomposition and accumulation of soil carbon and nitrogen, with potential modification of carbon and nitrogen sinks in the ecosystems, with potential feedbacks in response to climate change.Coastal reclamation seriously disturbs coastal wetland ecosystems, while its influences on soil microbial communities remain unclear. In this study, we examined the impacts of coastal reclamation on soil microbial communities based on phospholipid fatty acids (PLFA) analysis following the conversion of  In contrast, various anthropogenic activities, particularly coastal reclamation, are altering coastal wetland ecosystems on a global scale3. Many Euro-American countries, for instance, the United States and Spain, and Asian countries, such as South Korea and Japan4, have reported intensive coastal reclamation operations. The coastal wetlands of China have been dramatically reclaimed for the development of agriculture, aquaculture3, urbanization, and industrialization in an attempt to alleviate the conflict between a growing population and limited land resources5. The total reclaimed coastal areas in China, from 1950–2008, was approximately 13,380 km26, which accounted for approximately half of its overall coastal wetlands. According to the land-use plan of China, 5780 km2 of coastal wetlands will be reclaimed from 2010–2020. However, this immensely decreased number of coastal wetland areas following reclamation has been accompanied by multiple negative effects on coastal ecosystems, including a significant reduction in coastal habitats and biodiversity, and disrupted ecosystem structure, processes and function, and has a far-reaching effect on their ecological services6.Coastal wetlands are the transitional zone between terrestrial and marine ecosystems, which provide critical ecosystem services7. Hence, coastal wetlands play a significant role in the global C cycle. Coastal reclamation can modify the morphologies, hydrodynamics, and sediment transport of coastlines8, toward the further alteration of the physicochemical properties of soils6. Ultimately, these changes alter soil organic carbon and nitrogen  sequestration in coastal wetlands9. Although the response of SOC and SON sequestration to coastal reclamation have been widely documented11, there remains no definitive consensus. For example, Ding et al.10 revealed that SOC and SON stocks were rapidly sequestered within an initial 50 years following the reclamation of coastal wetlands, and then increased slowly within the reclaimed paddy soil of China's Yangtze River Delta. However, a previous study documented that the sequestration of soil C decreased, while C emissions accelerated, following the conversion of coastal wetlands to farmlands and other land uses12. These inconsistent results might have been attributed to variable land use patterns, reclamation intensity, and field management practices6.Coastal wetlands are recognized as one of the vital components of ‘blue carbon (C)’ sinks, as the result of high primary productivity and low decomposition rates of soil organic matter (SOM)13. Soil microbial communities are driven by topography, vegetation, soil nutrient substrates14, as well as physiochemical properties16. Soil pH is considered to be one of the vital drivers that shifts the composition of microbial communities15. Soil salinity has been observed to exert an inhibitory effect on most microbial populations17, where high salinity reduces the osmotic potential of the soil, which further impacts microbial composition and functionality16. Soil moisture has been reported to affect microbial abundance/population structures by influencing soil aeration conditions18. Yuan et al.19 demonstrated that high soil aeration stimulated microbial biomass and shifted the composition of microbial communities in paddy fields. High soil aeration can increase the fungi to bacteria ratio in farmlands. Previous investigations revealed that soil nutrient levels changed greatly, soil pH and salinity decreased considerably, and soil aeration was enhanced following the conversion of coastal wetlands to farmlands via diking, ditch drainage, and fresh water irrigation20. Changes in nutrient levels and physiochemical properties strongly drive variations in the microbial biomass and community compositions of soils16. The alterations in soil microbial biomass and community composition, particularly the fungi to bacteria ratio, in turn, impact the turnover and sequestration of SOC and SON. Therefore, an accurate assessment of the effects of coastal reclamation on soil microbial biomass and community compositions is critical to better understand the influencing mechanisms of coastal reclamation on SOC and SON accumulation and decomposition in coastal wetlands.Soil microbes have vital roles in the regulation and control of soil C and nitrogen (N) cycling4. Currently, a large portion of the natural coastal wetlands in Jiangsu have been reclaimed by embanking , and subsequent conversion to aquaculture ponds, farmlands, and town construction lands , SON and soil physiochemical properties, which in turn affects SOC and SON decomposition and accumulation following the conversion of coastal wetland to aquaculture pond, farmland, and town construction land. To test this, we examined the microbial biomass and community composition of the soils through phospholipid fatty acids (PLFA) analysis, and a chloroform fumigation-extraction method. We analyzed SOC, WSOC, SON, soil moisture, salinity, pH, and bulk density (BD) in reclaimed coastal aquaculture pond, wheat and oilseed rape fields, and town construction land by comparing them with an adjacent natural Phragmites australis wetland. The objectives of this study were to: (1) evaluate whether the responses of soil microbial biomass and community composition to coastal reclamation varied between different land use types; (2) identify the most important driving factors for causing shifts in soil microbial biomass and community composition following coastal reclamation.Jiangsu province contains the most abundant coastal wetland resources in Eastern China, and is undergoing intense coastal reclamationnds Fig. 4. NumeroP. australis wetland  higher than that of the wheat field, oilseed rape field, and town construction land (Table P < 0.05) higher than those in the P. australis wetland  concentration was highest in the aquaculture pond, followed by that of the wheat and oilseed rape fields, relative to the and Fig. a. The aqand Fig. b. The loand Fig. a,b. The and Fig. c. The P.pes Fig. c.Figure P. australis wetland, wheat and oilseed rape fields, and town construction land  bacterial, actinomycete, and branched PLFA were highest in the aquaculture pond followed by the wheat and oilseed rape fields, the P. australis wetland, and town construction land  bacterial PLFA content in the aquaculture pond was significantly (P < 0.05) higher than that in P. australis wetland, wheat and oilseed rape fields, and town construction land  higher than that in oilseed rape field and town construction land  higher than that in P. australis wetland and town construction land  lower than that in oilseed rape field  higher than that in town construction land  PLFA ratio was observed in the pes Fig. d. The soeld Fig. d. The grely Fig. g. The grand Fig. g. The P.eld Fig. f. The baand Fig. c.P < 0.05) related to SOC , salinity , WSOC   inputting into the sediment of the aquaculture pond, and ultimately promoted sediment organic C and N sequestration in the aquaculture pond  exhibited a lower decomposition rate25.Coastal reclamation enhanced the total microbial biomass  Figs.  and 3a, ds Figs. . Whereasnd Figs. , 3 and 420, which are the primary limiting factors for agricultural production in coastal zones26. Grybos et al.27 reported that high soil pH can lead to insufficient nutrients for crop growth owing to promoting the immobilization of manganese, iron, and zinc in soils. Krishnamoorthy et al.28 documented that high soil salinity severely restricted plant growing, which caused physiological drought to plants, cell toxicity, and nutrient imbalance for crops. Currently, fresh water irrigation has been regarded as a very effective measure to dealkali and desalinate the soil to accommodate the growth of crops following the reclamation of coastal wetlands21. In this study, we found that SOC and SON levels greatly increased following conversion of P. australis salt marsh into wheat and rapeseed fields , as well as various microbial community composition  following the conversion of P. australis wetland to aquaculture pond, wheat, and oilseed rape fields were primarily attributed to the higher level of soil nutrient substrates, and decreased soil salinity and pH which lifted the restriction of high salinity and alkalinity on the growth of soil microbial communities in aquaculture pond, wheat, and oilseed rape fields  for P. australis community, and assist P. australis community to adapt oligotrophic and extreme environment with multiple stresses  increased soil bacterial PLFA following the conversion of P. australis wetland to aquaculture pond and wheat field  as an energy source, with slow growing rates49. Conversely, gram− bacteria favor soils with easily degradable organic substances  as carbon sources42, which are viewed as copiotrophic r-strategists48. However, our the Pearson's correlation analysis showed that the gram+:gram− PLFA ratio was positively correlated with SOC, WSOC, and SON  and physiochemical properties  of the soil. Coastal reclamation greatly altered the F:B PLFA, gram+:gram– PLFA, and monounsaturated:branched PLFA ratios. These changes in microbial community structures were involved in regulation of SOC and SON decomposition and accumulation. This study offers new insights toward a better understanding of the consequences of coastal reclamation to ecosystem processes and functions, as well as the further elucidation of variations in, and drivers of, soil microbial communities.In conclusion, this study emphasized the shifts in soil microbial biomass and community composition in Spartina alterniflora, Suaeda salsa, Imperata cylindrica, and P. australis communities  and oilseed rape (Brassica campestris L.) fields are widely distributed along the middle Jiangsu coast. P. australis wetlands are the easiest to reclaim to other land use types due to their growing further inland, as they are farthest from the sea, relative to S. alterniflora, S. salsa, and I. cylindrica salt marshes , aquaculture pond, wheat field, oilseed rape field, and town construction land , where prior to determination, the soil samples were added to 1 M HCl to eliminate inorganic C and N. The determination of WSOC proceeded according to the technique described by Yang et al.32.Each root-sampling block was put through a 0.15 mm sieve and repeatedly flushed with water; the roots remaining in the sieve were then collected57. Fresh soil samples  were fumigated for 48 h with ethanol-free chloroform at 25 °C in the dark. Additional aliquots of fresh soil were employed as unfumigated controls. Both the fumigated and unfumigated samples were then extracted with 100 mL of 0.5 M K2SO4 by agitating for 30 min at 200 rpm using a reciprocal shaker, after which the K2SO4 extracts were passed through 0.45 μm filters. Soil extractable organic C and total N by K2SO4 extracts was quantified with a Liqui TOCII analyzer and the Kjeldahl method, respectively. MBC and MBN were calculated according to the equation: MBC = Ec/0.38, MBN = En/0.54, where Ec and En are organic C and total nitrogen (TN) extracted from fumigated soil, subtracted organic C, and TN extracted from unfumigated soil, respectively.The soil MBC and MBN were measured via chloroform fumigation-extraction58. The PLFA was determined in accordance with the procedure previously described by Bossio and Scow58 and Yang et al.32. Briefly, 8 g of a dry weight-equivalent of the soil subsamples was extracted in 23 mL of a chloroform: methanol: phosphate buffer mixture . The extraction was decanted into a separatory funnel and added to 12 mL of CHCl3 and 12 mL of phosphate buffer following centrifugation. The separatory funnel was shaken for 2 min., and the extracts were layered overnight. The CHCl3 layer was collected and dried under N2 at 32 °C, whereas the lipids were re-dissolved in chloroform and fractionated on a 0.5-g silica gel solid-phase extraction column . Neutral and glycol lipids were eluted by 5 mL of CHCl3 and 10 mL of acetone. Polar lipids were collected via 5 mL of methanol, dried under N2 at 32 °C, and then subjected to a mild-alkali methanolysis to recover the PLFA as methyl esters. The samples were re-dissolved in 200 mL of hexane solvent containing nonadecanoic acid methyl ester (19:0) as an internal standard. The samples were analyzed using a Hewlett-Packard 6890 Gas Chromatograph equipped with an Ultra 2-methylpolysiloxane column with N2 as the carrier gas, and H2 and air to support the flame. A 2-μL injection of the above dilution with a 1:50 split was employed at 250 °C for the injector and 300 °C for the detector. The oven temperature was ramped from 170 °C to 300 °C at 5 °C/min–1 and was maintained for 12 min.. The peaks were identified using bacterial fatty acid standards and MIDI peak identification software . The quantities (ng g–1 dry soil) of the PLFA in each sample were analyzed using an internal standard . The quantities of the PLFA in each sample were expressed as ng PLFA g–1 dry soil and were used to estimate the microbial biomass. The bacterial biomass was indicated by the biomarkers i14:0, i15:0, a15:0, 15:0, i16:0, i17:0, a17:0, 17:0, cy17:0, 14:1ω5c, 15:1ω6c, 16:1ω7c, and 18:1ω7c59. Indicators of gram+ bacteria included i13:0, i14:0, i15:0, a15:0, i16:0, a16:0, i17:0, and gram– bacteria included 14:1ω5c, 15:1ω6c, 16:1ω7, 16:1ω9c, 17:1ω8c, 18:1ω7c, 12:0 2OH, 15:0 3OH, 16:1 2OH, cy17:0, cy19:0 ω8c, and 18:1ω7c 11-methyl60. The fungal biomass was quantified by the sum of the PLFA 18:1ω9c, 18:2ω6,9c, and 20:1ω9c60. The AMF biomass was assessed by the PLFA 16:1ω5c60. The 10me 16:0 and 10me 17:0 biomarkers were used as representatives of the Actinomycete biomass54. The monounsaturated PLFA was quantified by the sum of 14:1ω5c, 15:1ω6c, 16:1ω5c, 16:1ω7c, 16:1ω9c, 17:1ω8c, 18:1ω7c, 18:1ω9c, and 20:1ω9c56. The sum of i13:0, i14:0, i15:0, a15:0, i16:0, a16:0, i17:0, a17:0, 10me 16:0, 10me 17:0, 12:0 2OH, 15:0 3OH, and16:1 2OH were used as indicators of branched PLFA58. The SSC PLFA was indicated by the biomarkers 12:0, 13:0, 14:0, 15:0, 16:0, 17:0, 18:0, and 20:058. The total PLFA of soil microbial communities was calculated by the sum of the fungal PLFA, gram+ bacterial PLFA, gram– bacterial PLFA, AMF PLFA, actinomycete PLFA, SSC PLFA, and 20:4ω6,9,12,15c. The F:B PLFA, gram+:gram– PLFA, and monounsaturated:branched PLFA ratios were calculated from the above PLFAs. Bacterial stress indexes, indicating the microbial physiological status under environmental stresses, were typically represented by cy17:0:16:1ω7c9.The PLFA analysis was used to determine soil microbial biomass and community compositionhttps://www.ibm.com/products/spssstatistics?lnk=STW_US_STESCH_P1_BLK&lnk2=trial_SPSSstat&lot=1&pexp=def&psrc=none&mhsrc=ibmse arch_a&mhq = spss). Pearson's correlation analysis was used to evaluate the relationship between the C and N fractions of the soil, and microbial biomass with soil physiochemical properties. Linear regression analysis was performed to determine the relationship between soil C and N, and the soil microbial biomass with plant biomass between the P. australis wetland, wheat field, and oilseed rape fields. The relationships between the soil microbial communities  and soil properties were conducted using RDA with CANOCO software . The statistical significance of the RDA was tested using the Monte Carlo permutation test . The map in the Fig. http://desktop.arcgis.com/zh-cn/desktop/).One-way analysis of variance (ANOVA) was employed to analyze the impacts of coastal reclamation on soil and plant characteristics, SOC, SON, microbial biomass, and various types of PLFA using SPSS statistical software (Version 24.0, URL link: