In the present work, polyacrylonitrile (PAN) nanofibers reinforced dental composites were investigated to achieve the improved interfacial adhesion between the PAN nanofiber and resin matrix using surface modification of nanofibers. PAN nanofibers mat were prepared by electrospinning and then, surface treated with the activated bisphenol A glycidyl methacrylate (Bis-GMA)/triethyleneglycol dimethacrylate (TEGDMA) (50/50 mass ratio) dental resin followed by photo-curing. Also, the treated nanofibers mat was milled into a powder to achieve the uniform distribution of nanofibers in the matrix resin. The reinforced dental composite were prepared by mixing the various mass fraction of the powder (0.5–15 wt%) with the Bis-GMA/TEGDMA dental monomers. The effect of weight ratio of surface-modified nanofibers to blend resin on the chemical structure, morphology, compression and flexural properties, color and polymerization shrinkage of dental composites was evaluated. The results showed that using surface-treated nanofibers with content of 5 wt% enhanced the compression strength, flexural strength, flexural modulus and work of rupture of the resultant dental composite by factors of 23%, 7%, 80%, and 145%, respectively, comparing to the unreinforced neat resin. Also, the polymerization shrinkage reduces by 37%. These significant improved properties of the dental composite could be due to the semi-interpenetration network formation between surface-modified nanofibers and resin matrix and well distribution of nanofibers in the dental resin. Further increasing the nanofiber content led to poor mechanical properties of obtained dental composites. The results also, revealed that the color of resin composite could be whiter using modified PAN nanofibers as the filler. In recent years, dental composites have been attracted great attention for restorative dentistry. Having fascinating appearance, better clinical handling, and less unfavorable side effects, these composites rapidly replaced the traditional materials such as amalgam, particularly in the restoration of incisor teeth –3.The blend of BIS-GMA and TEGDEMA monomers has been widely used as the most popular dental materials –6. MechaThe interfacial adhesion between filler and matrix profoundly affected the mechanical properties of the dental composite –16. In tLiu et al. modified the surface of hydroxyapatite whisker with poly Bisphenol A glycidyl methacrylate (Bis-GMA) to achieve enhanced adhesion between the whisker and matrix. Using the modified whisker as filler increased the bending strength and reduced the contraction of the dental composite compared to the unmodified whisker. Poly(Bis-GMA) on the surface of whisker acted as a layer by which the interfacial compatibility and hence, the interaction between modified whisker and matrix enhances. Consequently, the modified whisker was uniformly distributed within the resin and the polymerization shrinkage of obtained composites was considerably decreased. Totally, graft polymerization on the filler surface was a promising technique to prepare dental composites , 20.2 remarkably increased the degree of resin conversion. In additiona, The combination of PGSHW:S-SiO2 with mass ratio of 2:4 enhanced the bending strength, bending modulus, compression strength and work of rupture of the composite by 39.1%, 61.1%, 50.1%, and 85.9%, respectively, compared to the composite containing PGSHW [The reinforcement of triethyleneglycol dimethacrylate (TEGDMA)/GMA dental resin with poly(Bis-GMA)-grafted silanized hydroxyapatite whisker (PGSHW) has been reported by Liu et al. They found that S-SiOng PGSHW , 21.The effect of fiber surface modification of the dental composite reinforced with ultra-high molecular weight polyethylene fiber was evaluated by Bahramian et al. Their results revealed that corona and silane surface modification of fibers increased the surface roughness of fibers leading to the better interfacial adhesion between the fibers and matrix resin. Therefore, flexural properties and fracture toughness of the reinforced composite with surface-modified fibers significantly improved .Yi et al. found that the tight interfacial adhesion between the glass fiber and Bis-GMA/TEGDMA dental resin was achieved by surface coating the glass fiber with poly(methyl methacrylate) (PMMA) and polydopamine (PDA) especially in the PDA/PMMA coated fibers. Subsequently, the flexural strength and modulus of the resulting reinforced dental composite increased .In recent years, many works have been done to reinforce dental resins with electrospun nanofibers such as nylon 6, PAN, and nanoscale glass fibers. The nanometric-sized diameter of fibers provides a high specific surface area which improves the interfacial area between the fibers and dental resin leading to improved mechanical properties of nanofiber reinforced dental composites –26. MoreConsidering the great contribution of nanofiber surface properties and enhancing interfacial adhesion between fibers and matrix resin to the mechanical properties of dental composites, this work is aimed at developing a new Bis-GMA based dental composite reinforced with surface treated PAN nanofibers mat with the activated Bis-GMA/TEGDMA dental resin followed by photo-curing. As the reinforcement of the dental resin with electrospun nanofibers mat or felt via layer by layer impregnation with dental resin is not applicable method for the dentistry, the treated nanofibers mat was milled into a powder to achieve the uniform distribution of nanofibers in the matrix resin. The reinforced dental composite finally was prepared by mixing the various mass fraction of powder with the Bis-GMA/TEGDMA dental monomers. The effect of weight ratio of surface-treated nanofibers to blend resin on the dental composite properties was evaluated via chemical structure, morphology observation, compression and flexural properties, color and polymerization shrinkage of composites.Polyacrylonitrile (PAN) powder (Mw = 150000 g/mol) and N,N-dimethylformamide (DMF) solvent were purchased from Merck CO. for production of the electrospun nanofibers. Bis-GMA and TEGDMA monomers, camphorquinone as photo-initiator, and 2-(dimethylamino) ethyl methacrylate (DMAEMA) as co-initiator were also supplied from Merck CO.The solution of 13 wt% PAN dissolved in DMF was prepared. The electrospinning of PAN nanofibers was carried out by a custom-made electrospinning apparatus used a high voltage power supply and a syringe as feeder system. The solution was fed to the syringe pump at 8.5 ml/min flow rate. The nanofibers were collected on the aluminum foil sheet at the collector drum rate of 5 rpm. The applied voltage was kept at 9.6 kV, and the distance between the needle tip and the collector was 16 cm.A coating mixture of Bis-GMA/TEGDMA (50/50 weight ratio), 0.5 wt% QC and 1 wt% DMAEMA were prepared. The PAN nanofibers mat was removed from the aluminum foil and immersed into the coating mixture at 2 wt% of mixture and then light-cured by LED lamp. The treated PAN nanofibers mat was cryogenically fractured in liquid nitrogen and milled into a powder with particle size range of 500–600 μm which used as the filler of reinforced resin composite samples.Dental resin mixtures were prepared by mixing the Bis-GMA and TEGDMA monomers with QC as photo-initiator DMAEMA as co-initiator at room temperature. The composition of resin mixtures are described in Table The morphology of PAN electrospun nanofibers mat, ground modified nanofibers as the filler of dental resin composites and the fracture surface of the composite samples after three-point bending test were evaluated by scanning electron microscopy .A spectrometer  was used to evaluate the chemical groups of the samples.F is the maximum applied load (N), the L is the spam length (16 mm), b is the width of the test sample (mm) and h is the thickness of the test sample (mm) and m is the slope of the linear portion of load-deflection curve.The flexural properties of resin composites were examined in a three-point bending test according to the ISO4049 standard using a Hounsfield universal test machine with a 16 mm span and crosshead speed of 0.5 mm/min. The sizes of composite specimens were 2 × 2 × 25 mm for this test and light-cured by four exposures of 40 s by LED lamp. Flexural strength (FS) and flexural modulus (FM) were calculated from Eqs.  prepared for the compressive test.The compressive strength of modified nanofibers reinforced composites was determined according to the ISO9917-1 standard by a Hounsfield universal test machine with speed of 0.5 mm/min. The cylindrical sample test  were prepared and photo-cured in the different light-curing of 5, 10, 20, 40, and 60 s.The color change of the dental composite samples during the photo-curing examined by the evaluation of their whiteness and yellowness indexes. The whiteness and yellowness indexes were measured by a reflectance spectrophotometer (Tex Flash). The disc shape test samples  dental resin was carried out to improve the dental composite performance. The results show the incorporation of the 0.5–5 wt% surface-modified PAN nanofibers into the neat resin increases its compressive strength and flexural properties. In fact, the nanofibers surface modification used to increase the adhesion of PAN nanofibers to resin leading to further improvement of mechanical properties of the PAN nanofibers reinforced composites. This can be attributed to the formation of semi-IPN between the coated polymer on the surface of PAN nanofibers and Bis-GMA/TEGDMA matric resin in light-cured composites . The coaHowever, by further increasing the content of surface-modified nanofibers to values of 10 and 15 wt%, not only no improvement in the mechanical properties of the resin composite can be observed, but also a decrease in the mechanical properties to values less than that of the neat resin has occurred. In fact, at reinforced nanofiber weight ratios >10%, poor dispersion of filler might have occurred which reduces the interfacial adhesion between nanofiber and matrix resin leading to the uneven stress transfer from matrix to the nanofibers as a filler and hence, decreased compressive and flexural properties are resulted .The high roughness of the fractured surface of composite indicates its high resistance to the applied force. Energy dissipation during the fracture process could also be increased due to nanofiber crack bridging and deflection by nanofibers. Surface modification of nanofibers by coating with Bis-GMA/TEGDMA resin enhances the interfacial adhesion between nanofiber as a filler and matrix resin via the IPN structure formation leading to better stress transfer. Therefore, the resistance of the reinforced resin composites to fracture is increased , 30.The interaction of the filler and matrix resin plays the pivotal role in the polymerization shrinkage of dental composites. It is expected that the polymerization shrinkage could be decreased by increasing the filler/resin interface , 32. TheDental composites reinforced with surface-treated polyacrylonitrile (PAN) nanofibers were investigated. Surface modification of the produced nanofibers mat was carried out by coating the nanofibers mat with the activated Bis-GMA/(TEGDMA) dental resin followed by photo-curing. Also, the treated nanofibers mat was milled into a powder to achieve the uniform distribution of nanofibers in the matrix resin.The electrospun nanofibers showed an average diameter of 387 nm, which increased to 675 nm after surface treatment. Surface treatment increased the adhesion of PAN nanofibers to resin consequently, enhanced the mechanical properties due to the interpenetration network formation between surface-modified nanofibers and matrix. Also, the well distribution of the PAN nanofibers in the dental resin led to further improvement of mechanical properties of the PAN nanofibers reinforced composites. Effect of weight ratio of surface-modified nanofibers to blend resin on the chemical structure, morphology observation, compression and flexural properties, color, and polymerization shrinkage of reinforced composites were investigated. The result showed that the incorporation of the 0.5–5 wt% surface-modified PAN nanofibers into the neat resin increased its compressive strength and flexural properties. The compression strength, flexural strength, flexural modulus and work of rupture of 5 wt% nanofibers reinforced composite enhanced by factors of 23, 7, 80, and 145%, respectively, comparing to the unreinforced neat resin. Further increasing the nanofiber content of composite led to poor mechanical properties of composite. Also, the polymerization shrinkage reduces by 37% and the color of resin composite could be whiter using modified PAN nanofibers as the filler.


Streptomyces clavuligerus is a known producer of such compounds; however, much of its metabolic potential remains unknown, as many associated biosynthetic gene clusters are silent or expressed at low levels. The overexpression of ribosome recycling factor (frr) and ribosome engineering (induced rpsL mutations) in other Streptomyces spp. has been reported to increase the production of known specialized metabolites. Therefore, we used an overexpression strategy in combination with untargeted metabolomics, molecular networking, and in silico analysis to annotate 28 metabolites in the current study, which have not been reported previously in S. clavuligerus. Many of the newly described metabolites are commonly found in plants, further alluding to the ability of S. clavuligerus to produce such compounds under specific conditions. In addition, the manipulation of frr and rpsL led to different metabolite production profiles in most cases. Known and putative gene clusters associated with the production of the observed compounds are also discussed. This work suggests that the combination of traditional strain engineering and recently developed metabolomics technologies together can provide rapid and cost-effective strategies to further speed up the discovery of novel natural products.Bacterial specialized metabolites are of immense importance because of their medicinal, industrial, and agricultural applications.  Streptomyces are Gram-positive filamentous bacteria that undergo sporulation and comprise the largest genus of the phylum Actinobacteria . Th. ThS. clnt study . In addiStreptomyces griseoflavus subsp. thuringiensis , tS. clavuligerus /type I polyketide synthase (PKS), NADP or FAD-dependent oxidoreductase and sterol desaturase , all of uligerus B and canuligerus  67], wh, whS. clther SMs  and exemther PTM . The prouligerus B. FurtheStreptomyces species –) ionization modes, using the recommended parameters  and experimental (by LC-MS/MS) fragmentation spectra was conducted using MetFrag [Network annotation propagation (NAP) ,82 in GNrameters . The GNP MetFrag  was alsofrr, rpsL, and rpsL variants on specialized metabolism in S. clavuligerus using untargeted metabolomics, GNPS-based molecular networking, and in silico metabolite annotation. We putatively annotated 28 metabolites from eight different classes, which have not been previously reported in S. clavuligerus. Other studies have shown that the manipulation of frr and rpsL increases the production of known SMs in some Streptomyces spp. [rpsL-K88E mutation has been reported to promote the expression of frr through an unknown mechanism [frr or rpsL-variant overexpression in S. clavuligerus, suggesting that the two mechanisms can sometimes function independently. Overexpression of either frr or rpsL-K88E is known to enhance protein synthesis in other Streptomyces spp. [rpsL variants are still unclear but are thought to involve a somewhat separate mechanism as compared to rpsL-K88E [frr or rpsL-variant overexpression in S. clavuligerus somehow leads to altered SM production profiles. Furthermore, we demonstrate that the synthesis of putative plant-associated metabolites, including triterpenoids, triterpenoid glycosides, and flavonoid glycosides, can be promoted under laboratory conditions in S. clavuligerus through ribosome manipulation. Other putative SMs such as streptovirudins, PTMs, and bafilomycin J are also reported for the first time in S. clavuligerus. In addition, we matched S. clavuligerus BGCs with some of the predicted SMs, and propose biosynthetic routes based on similar systems. The combined application of GNPS and NAP not only predicted the discussed structures but also pointed to novel SMs such as one (m/z 843.423) from the molecular network of streptovirudins. It should be noted that further investigations are warranted to elucidate some of the described findings. Overall, our results suggest that industrial microorganisms such as S. clavuligerus harbor the potential to produce additional metabolites under laboratory conditions and that the current approach employing molecular networking and in silico annotation can be used to hasten their discovery.This is the first study to examine the effect of overexpressing ces spp. . In addiechanism , suggestces spp. ,24, and ces spp. . The reapsL-K88E . Therefo


A new ultrasound-assisted enzymatic extraction (UAEE) method of starch from kiwifruit was established and optimized using response surface methodology (RSM). Under optimal conditions , the kiwi starch (KS) yield was about 4.25%, and the starch content of KS was 873.23 mg/g. Compared to other extraction methods, UAEE can obtain KS with high yield and purity with a shorter extraction time and less solvent and enzyme. The extracted KS has a low gelatinization enthalpy (8.02 J/g) and a high peak viscosity (7933 cP), with obvious particle properties and low adhesion. In addition, KS is rich in polyphenols, has strong antioxidant activity, and has higher contents of amylose starch (30.74%) and resistant starch (60.18%). This study established a novel and highly efficient method for KS extraction and suggest several possible applications for KS in the food industry. Generally, fruit-derived starches not only have the basic functional properties of certain traditional starches (such as corn starch and potato starch) but also have some unique nutrients and functional properties, such as a unique fruit flavor, rich in polyphenols, strong antioxidant activity, etc. However, at present, information about KS is scarce, and the extraction method is only limited to the traditional alkali extraction method, which is time-consuming, more impurities, and the purity is not ideal 22.1Huayou kiwifruit was purchased from a local orchard in Mei County, Shanxi Province, China. Porcine pancreatic α-amylase (23 U/mg); pancreatic lipase (57 U/mg); invertase (≥300 U/mg); glucosidase (8 × USP); gallic acid; catechin 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox); 1,1-diphenyl-2-picrylhydrazyl (DPPH); and Folin–Ciocalteu reagent were all purchased from Sigma-Aldrich Co. Ltd. . Pectinase (500 U/mg); cellulase (50 U/mg), papain (800 U/mg); 2,4,6-tripyridine-s-triazine (TPTZ); and potassium bromide (Spectrography) were purchased from Yuanye Biotechnology Co., Ltd. . The starch content test kit and glucose test kit (glucose oxidase method) were purchased from Nanjing Jiancheng Bioengineering Institute. Other reagents were analytical pure and were purchased from Xinfang Chemical Reagent Co., Ltd. .2.2g for 10 min, the supernatant was poured out, and the precipitate was dissolved in 85% ethanol to wash off the glucose in the precipitation. The slurry was centrifuged twice for 10 min each. The precipitate, after alcohol washing, was re-dissolved in water and centrifuged again at 11,520 × g for 10 min; the supernatant was discarded; and a precipitate was obtained. Pave the precipitates, dry them in the oven at 30℃, grind them, and sieve them for 100 mesh to obtain KS. The flow chart of extraction was as Figure S1:Six batches of kiwis were picked during commercial harvest period, when their hardness were 90–100 N and the content of soluble solids were 6.5–8.0 Brix°. Kiwi fruit were washed, peeled, and removed the seeds. Adding ice water at a 20% mass of the fruit (FW) mass and used a high-speed blender to beat the pulp. Add a certain amount of pectinase, cellulase, and papain; adjusted the extraction pH of the system with 2% NaOH solution; and set the appropriate ultrasonic conditions. After enzymolysis, the mixed slurry was centrifuged at 11,520 × 2.3Take 100.0 g of kiwifruit homogenate (FW), and add the prepared hydrolase solution in a ratio of 5 times the weight of the fruit pulp. The hydrolase solution preparation method is shown in Figure S2. Adjust the extraction pH of the enzymatic hydrolysate to 5.0 with 2% NaOH and set the ultrasound power to 200 W, the extraction temperature to 50 ℃, and the ultrasound time to 60 min. Then, proceed to extract KS according to the steps in 2.4Take 100.0 g of kiwifruit homogenate (FW) and add the prepared hydrolase solution with a certain liquid–solid ratio. For the preparation of the hydrolase solution, the pectinase-to-cellulase-to-papain ratio was 1:2:1 g/kg. Adjust the extraction pH of the enzymatic hydrolysate with 2% NaOH. The single factor test design and specific parameters are listed in Table S1. Then follow the step in 2.5Through the single factor experiment, four factors were selected to optimize, including liquid/solid ratio, extraction pH, ultrasound power, and extraction temperature. The starch content of the final extract was chosen as the response value. The test factors and level design are shown in 2.62.6.1Fix a small amount of starch powder (DW) on the metal sample table with conductive double-sided tape and observed it under a SEM , at an accelerating voltage of 5.0 kV. A small amount of 2% (w/v) KS milk was placed on the sample stage of a PLM  for observation and photography.2.6.2PSD was determined according to the method of Ding et al. 2.6.3XRD was performed according to the method of Wang et al. 2.6.4−1 and the spectral range was 4000–400 cm−1. The starch sample (DW) was mixed with potassium bromide and then pressed into tablets, where the ratio of sample to potassium bromide was 1:100. The number of replicates per samples was three, background corrections were performed and normalisation of spectra was performed.FTIR was performed according to the method of Wang et al. 2.72.7.11.000 g KS (DW) was accurately weighed to prepare 1.0% starch milk. The pH of the starch milk was measured using a PHS-3E pH meter . The light transmittance of starch paste was determined following the method of Zhang et al. 2.7.20, on a dry basis) of KS into a plastic centrifuge tube, added 50 mL of distilled water, and heated it in a water bath at 85 ℃ for 30 min. Then, immediately cool the centrifuge tube to room temperature in an ice bath and centrifuged it at 3000 × g for 30 min. The supernatant was carefully poured out and dried to constant weight (W1) at 110 ℃, and the precipitate (Ws) was accurately weighed. The WSI and SP of KS were calculated according to the following formulas:The WSI and SP were determined according to the method of Wang et al. 2.7.3The DSC was performed with a slightly modified method of Zhang et al. 2.7.4The pasting properties of starch were measured using a Rapid Visco-Analyzer . The standard 1 program of the instrument was used for the measurement; except for the pre-shearing process (960 rpm), the blade rotation speed was 160 rpm. The temperature of the starch paste reached 50 ℃ in 1 min, was heated to 95 ℃ in 4 min and 42 s, maintained at 95 ℃ for 2 min and 30 s, then cooled to 50 ℃ in 11 min, and maintained for 2 min; at 13 min, the process was terminated. After that, the starch was gelatinous. Time to peak viscosity (Ptime), pasting temperature (PT), peak viscosity (PV), hot paste viscosity (HPV), cold paste viscosity (CPV), breakdown value (BD), and setback value (SB) were automatically analyzed by the analyzer’s software 2.7.5−1) and decreasing (1000 to 0.1 s−1) with shear rate. After shear scanning analysis, the variation in the storage modulus (G'), the loss modulus (G''), and the loos tangent (tan δ = G''/G') with angular frequency from low (0.1 Hz) to high (20 Hz) frequency were measured simultaneously at 25 ℃, and the strain remained unchanged The gelatinized starch paste obtained in 20% starch milk (w/v) was prepared for temperature scanning. In the linear viscoelastic region of the sample , the temperature was heated at a rate of 5 ℃/min. The changes in G', G“ and tan δ with temperature during the process from low temperature (25 ℃) to high temperature (90 ℃) and back to low temperature (25 ℃) were measured 2.7.6The gel properties were determined following a slightly modified method of Li and Zhu 2.82.8.1Starch content in KS was tested using a starch content test kit (A148-1–1 Starch). The content of amylose was determined following the method of Lan et al. 2.8.2The antioxidant capacity of the KS was determined by the DPPH and FRAP methods based on previous reports 2.9g/min for 5 min, and the supernatant was removed to determine the glucose content.In vitro digestion was determined following the method reported by Li and Zhu 2.10p < 0.05). RStudio-1.1.463  was used for the correlation test. Excel 2016  and Origin 9.1  were used for other image rendering. All the experiments were performed at least in triplicate. The data are expressed as the mean ± standard deviation (SD).The experimental results of the response surface design were analyzed using Design-Expert 8.0.6.1 software . One-way analysis of variance (ANOVA) and Duncan’s multivariate test were performed in SPSS 23  (33.1p > 0.05). In conclusion, combination 3, the pectinase/cellulase/papain ratio of 1:2:1 was selected as the best combination for extracting KS.The effect of enzyme type and combinations on the KS content is shown in Figure S2. The synergism of pectinase, cellulase, and papain in the starch extraction was better than the combination of pectinase and cellulase. The extraction effect of the pectinase-to-cellulase-to-papain ratio at 2:1:1 or 1:2:1 was significantly better than that of the 1:1:2 pectinase-to-cellulase-to-papain ratio. The reason for this finding may be that kiwifruit is rich in pectin, cellulose, and protein. The structure of the kiwifruit cell wall could not be fully destroyed when certain enzymes were lacking, and it was difficult to effectively enzymatically decompose related components. Different types of compound enzymes in the appropriate proportion may stimulate enzyme synergy, which can damage the cell wall, increase the permeability of the cell membrane, and effectively improve the effect of enzymatic hydrolysis 3.23.2.1The effect of different liquid/solid ratios  on starch content is shown in Figure S3A. When the liquid/solid ratio was 1 mL/g, the starch content of KS was lower (653.41 ± 10.19 mg/g); with the increase in the liquid/solid ratio, the starch content increased sharply and reached a maximum (795.22 ± 1.83 mg/g) at 5 mL/g. Then, it gradually decreased with the increase in liquid/solid ratio, which agrees with the findings of Zhang et al. 3.2.2The effect of different extraction pH values  on starch content is shown in Figure S3B. The KS content first increased and then decreased with the increase in pH, potentially because the optimal pH values of the three enzymes used in this experiment are different 3.2.3The effect of different ultrasound power  on starch content is shown in Figure S3C. With the increase in ultrasound power, the starch content of KS first increased and then decreased, reaching a maximum when the ultrasound power was 300 W. In the early stages, with the increase in ultrasound power, the fluctuation of the sound field was enough to produce violent vibration and cavitation collapse to prompt the rapid swelling of the kiwifruit cell wall. Higher ultrasound power ensured a sufficient cavitation range, the overall mass transfer resistance was effectively reduced, and the extracted KS content increased 3.2.4The effect of different extraction temperatures  on starch content is shown in Figure S3D. The starch content reached a maximum at 50 ℃. This might be because, at a low temperature, increases in temperature lead to the intensification of molecular motion, thus accelerating the dissolution rate of small molecular compounds in kiwifruit cells. The appropriate temperature allows complex enzymes fully to play their role in enzymolysis 3.2.5The effect of different ultrasound time  on starch content is shown in Figure S3E. Within a certain time range (30–60 min), the starch content gradually increased with ultrasound time, but did not change significantly between 60 and 120 min, and decreased significantly between 120 and 150 min. The reason for this finding might be that when the ultrasound time is too long, the strong ultrasonic cavitation and thermal effects degrade or denature the starch in kiwifruit , which leads to a decrease in starch content 3.3According to the design principle of the BBD test and the results of the single-factor test, four factors were selected: liquid/solid ratio, extraction pH, ultrasound power, and extraction temperature. The extraction process of KS was optimized based on the single-factor test using a four-factor, three-level response surface analysis method. The results of the single-factor tests showed that the starch content of each single-factor group reached the highest when the solid/liquid ratio was 1:5, the extraction pH was 5.0, the ultrasound power was 300 W, and the extraction temperature was 50 ℃. The RSM experimental results of ultrasonic-assisted enzymatic hydrolysis to extract KS are shown in Using Design-Expert 8.0.6.1 and the test data in where Y is the predicted value of starch content; and A, B, C, and D are the coding value of the independent variables as described above.p < 0.01), and the lack of fit was not significant (p > 0.05), indicating that the model was appropriate. The coefficient of determination (R2) was 0.9529, indicating that the model explained about 95.3% of the response value changes. The correlation coefficient of the model (R) was 0.9764, indicating that its fitting degree was good and the experimental error was small. So, the KS extraction process could be analyzed and predicted.The results of the variance analysis of the response surface model and the significance test of the model coefficients are shown in p < 0.01); the interaction between the factors and the primary term had both significant and insignificant effects. According to the F value, the main effect sequence of the four influencing factors could be judged as follows: extraction pH > extraction temperature > liquid/solid ratio > ultrasound power.From Figure S4 depicts a 3D response surface diagram providing a graphical interpretation of the regression equation and intuitively reflecting the influence of various factors on KS content. Figure S4A-F shows the relationship between the extraction variables and the KS content obtained by UAEE. Liquid/solid ratio, extraction pH, and extraction temperature had significant effects on KS content. With the increase in these three variables within a certain range, starch content increased significantly, and when these variables exceeded a certain value, they showed a stable trend or gradually decreased. In addition, it could be found that ultrasound power has little effect on KS content in UAEE, which is consistent with the conclusion in 3.4After optimization by RSM, the obtained optimal UAEE conditions for KS content were: solid/liquid ratio of 1:6.68, extraction pH of 5.23, ultrasound power of 292.43 W, and extraction temperature of 51.66 °C. Under these conditions, the theoretical yield of KS is 885.63 mg/g. To further verify the results, the above conditions were used for verification experiments. Considering the convenience of actual operation, the extraction conditions were optimized as follows: solid/liquid ratio of 1:6.68, extraction pH of 5.23, ultrasound power of 300 W, and extraction temperature of 52 °C. From the results of three repeated experiments was that the KS content was 873.23 ± 8.39 mg/g, and the relative error between the measured value and the theoretical prediction was only 0.84%; the yield of KS was about 4.25%. Therefore, the optimized UAEE process parameters based on RSM are accurate and reliable, and have application value.3.5The KS sample extracted by UAEE is shown in The XRD of KS particles E showed −1 (R1047/1022) 1047/1022 of KS was 1.18, similar to that sweet potato starch (1.05) FTIR spectroscopy is commonly used to identify functional groups of substances, and to characterize the short-range ordered structure of starch 3.63.6.1The pH of KS extracted by UAEE is 5.32 , which iThe study of the properties of starch–water systems in the processing of starch-based foods is important, as the WSI and SP of starch reflect the interaction between starch and water. 3.6.2The onset temperature (To), peak temperature (Tp), conclusion temperature (Tc), and enthalpy change (ΔH) were recorded by DSC, which were 63.71 ℃, 67.90 ℃, 74.15 ℃, and 8.02 J/g, respectively . Compare3.6.3Malaysia 1 jackfruit starch , indicating that it was relatively easier to cook. Studies showed that the PT depends on the amylose content and the size of starch particles By studying the pasting properties of KS, its cooking performance and food utilization characteristics were predicted. After heating about 10% (w/w) starch milk according to the set procedure, the pasting properties of KS were obtained by RVA, as shown in 3.6.42) is above 0.95 (Table S2). The difference in flow behavior of KS paste during the two shear processes indicated that the starch gel network undergoes irreversible changes under the action of shear force KS paste can be regarded as a function of shear rate under shear stress A. It is The dynamic rheology test was mainly used to determine the viscoelasticity of KS. Its main parameters are the storage modulus , the loss modulus , and the loos tangent . As shown in 3.6.5In TPA mode, the first pressure was applied to the sample to deform it to correspond to the first bite of human chewing, and then the second pressure was applied to determine the gel texture properties of starch by simulating human chewing movements. The results of TPA measurement are shown in 3.7The purity of the KS extracted by the UAEE process is high. As shown in The kinds and contents of elements in KS were detected. Except for Cu, the other eight elements were detected in KS . Among tIn addition to the nutrients contained in traditional starch, KS also retains some unique nutritional and functional characteristics of kiwifruit. For example, KS contains extremely high amounts of phenolic substances (2543.52 μg GAE/g). These phenolic compounds might be tightly bound to starch molecules, existing in a tightly bound state and surviving under high-intensity ultrasound. Zhang et al. 3.8Starch is a staple food in people’s daily lives, and its digestion rate seriously affects the postprandial blood glucose level. According to the digestion behavior of starch under the action of enzymes, starch can be divided into three types: rapidly digestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS) The digestion process of KS is shown in Figure S5A. However, KS did not reach the hydrolysis equilibrium within 300 min of the experiment. So, the digestion kinetics could not be analyzed. In subsequent experiments, if the digestive properties need to be further quantified, a longer digestion time may be needed to fully digest KS.4KS with high yield (4.25%) and purity (87.32%) was developed by the ultrasonic-assisted enzymatic extraction of KS by RSM for the first time. The starch particles obtained by this method are irregular in shape, with many edges and corners, showing a B-type crystal structure, with low relative crystallinity (48.62%) and high short-range structure order degree. The PV, BD, and SB of KS are all high, the elastic characteristics and particle properties are more obvious, and the gelatinization enthalpy is lower. Compared to other traditional starches, KS is rich in calcium (973.23 mg/kg), phenolic substances (2543.52 μg GAE/g), and KS (60.18%), exhibits strong antioxidant capacity and digestive resistance. The role of functional food formula of KS will be further researched.Jiaqi Wang: Conceptualization, Methodology, Software, Investigation, Data curation, Writing – original draft. Tian Lan: Data curation, Writing – original draft. Yushan Lei: Visualization, Investigation, Supervision. Jiangtao Suo: Writing – review & editing. Qinyu Zhao: Software, Validation. Haoli Wang: Software, Validation. Jing Lei: Writing – review & editing. Xiangyu Sun: Writing – review & editing. Tingting Ma: Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Nucleic acid approaches are highly adaptable and could be tremendously beneficial for personalized strategies to combat AMR pathogens.The coronavirus disease 2019 (COVID-19) pandemic is a challenge for ongoing efforts to combat antimicrobial-resistant (AMR) bacterial infections. As we learn more about COVID-19 disease and drug stewardship evolves, there is likely to be a lasting impact of increased use of antimicrobial agents and antibiotics, as well as a lack of consistent access to health care across many populations. Sexually transmitted infections have been underreported during the pandemic and are often caused by some of the most drug-resistant pathogens. In their recent article in  Antimicrobial resistance (AMR) is one of the most serious threats to public health. There are approximately 700,000 deaths/year globally due to AMR, and this number is estimated to balloon to 10 million AMR deaths/year by the year 2050 unless urgent measures are taken to combat the spread of drug-resistant organisms. Global strategies to combat AMR and prevent the emergence of new drug-resistant organisms are becoming further complicated in the era of coronavirus disease 2019 (COVID-19).Enterobacterales spp., MDR Pseudomonas aeruginosa, and Acinetobacter baumannii have been reported  protein as a preventative strategy against Neisseria gonorrhoeae infection.The COVID-19 pandemic has had an impact on diagnosis and reporting of sexually transmitted infections (STIs), with potentially serious implications for development of AMR infections. The National Coalition of STD Directors reported on the disruption of COVID-19 to intervention services for fections . Multiplfections  and supefections . In theih et al.  describeOvercoming infections with AMR bacteria has been an ongoing challenge since shortly after the introduction of antibiotics. While there are several classes of broad-spectrum antibiotics for treating infections with Gram-positive and Gram-negative bacteria, many pathogens are rapidly evolving or acquiring resistance to first-line treatments and will respond only to last-resort antibiotics. There are significant efforts to discover new antibiotic classes and develop derivatives and multidrug combinations. Recent high-throughput small molecule screens for compounds that target bacterium-specific pathways are promising , 13; howClostridium difficile toxin B (bezlotoxumab) in conjunction with the standard of care. MAbs against other bacterial pathogens in human phase trials include Staphylococcus aureus alpha hemolysin (Aridis and AstraZeneca) and Pseudomonas aeruginosa  are increasingly attractive biologics against AMR pathogens. The FDA has approved MAbs targeting inhalational anthrax (raxibacumab and obiltoxamab) and anti-learance . Howeverlearance .mBio, Parzych et al. directly injected mice with plasmid DNA encoding the immunoglobulin heavy and light chains of MAb 2C7_WT and Fc variants designed to activate complement via IgG hexamerization (2C7_E345K and 2C7_E430G) . Additionally, they show long-term protection against a difficult-to-treat pathogen. As the incidence of gonorrhea and other STIs increase, approaches such as DMAb delivery and other gene-delivered approaches could become key to reducing the emergence of AMR and preventing new infections in at-risk populations. In vivo delivery of biologics with increased potency such as 2c7_E430G DMAb as described by Parzych et al. could have an important impact on the course of AMR infection.In their recent article in 7_E430G) . The DNACollectively, the medical field is moving toward different aspects of personalized medicine to help tackle challenges like cancer and genetic diseases. Although infectious diseases are a part of daily life, their impact is often underappreciated, and AMR strategies have yet to embrace more personalized approaches . It is c


Functional genomics applied in clinical disease diagnosis and prognosis allow the achievement of the progress in all aspects of biology in health and disease. This Special Issue of IJMS on the topic of “Functional Genomics in Health and Disease” has practical importance, trying to encompass the full biology of the coding and non-coding genes.In this Issue, Maestri presents a Long-Read Sequencing Approach for Direct Haplotype Phasing in Clinical Settings. This has great translational significance, as analysis can be performed for multiple samples. These predictive biomarkers can be used for patient selection for treatment groups in clinical settings .Vyse and colleagues cover the important topic of Trans-Ancestral Fine-Mapping and Epigenetic Annotation used as tool to Delineate Functionally Relevant Risk Alleles in Systemic Lupus Erythematosus .Dudea-Simon and colleagues report the involvement of certain coding genes in cervical cancer, using an integrative analysis, and the diagnostic and prognostic potential of key core genes for this disease .Biswas and colleagues review the role of a highly expressed miRNA pattern, and discuss the data related to its potential interaction of miRNAs with key signaling pathways in infected monocyte-derived macrophages and peripheral mononuclear blood, allowing the identification of unique biomarkers that can differentiate HIV-1 and HIV-2 infection based on an altered miRNA pattern .Zayed present a novel bioinformatics method for the assessment of the molecular genetic susceptibility pattern in asthma. This paper led to the identification of novel genetic risk factors responsible for the development of moderate-to-severe asthma, serving as a platform for better treating asthma .Potashkin et al. present bioinformatics analysis, which was focused on the key genes and molecular pathways that may trigger dementia. This study identified phosphodiesterase 4D-Interacting Protein as key factor for the frontal cortex dementia switch gene .Chitoiu and colleagues discuss the roles of extracellular vesicle biology, a particularly important topic in light of the recent success of the multi-omics data .The review by Jurj et al. gives an overview of the genome wide association studies, mainly in triple negative breast cancer, providing significant and new knowledge for both research and clinical studies .Thus, the aim of this Special Issue is to answer to many of our readers’ questions regarding this topic, and we also hope that will open new avenues of research, providing important discoveries in this ongoing revolution.


Enterobacterales is a global health threat. Capacity for individual-level surveillance remains limited in many countries, whilst population-level surveillance approaches could inform empiric antibiotic treatment guidelines.Antimicrobial resistance (AMR) in Enterobacterales clinical isolates using metagenomic (Illumina) profiling of pooled DNA extracts from human faecal samples was developed and tested. Taxonomic and AMR gene profiles were used to derive taxonomy-adjusted population-level AMR metrics. Bayesian modelling, and model comparison based on cross-validation, were used to evaluate the capacity of each metric to predict the number of resistant Enterobacterales invasive infections at a population-level, using available bloodstream/cerebrospinal fluid infection data.In this exploratory study, a novel approach to population-level prediction of AMR in Enterobacterales were common colonisers and pathogens, and faecal taxonomic/AMR gene distributions and proportions of antimicrobial-resistant Enterobacterales infections differed by setting. A model including terms reflecting the metagenomic abundance of the commonest clinical Enterobacterales species, and of AMR genes known to either increase the minimum inhibitory concentration (MIC) or confer clinically-relevant resistance, had a higher predictive performance in determining population-level resistance in clinical Enterobacterales isolates compared to models considering only AMR gene information, only taxonomic information, or an intercept-only baseline model : -330,-116]; model considering only AMR gene information = -186 ; model considering only taxonomic information = -151 ).Population metagenomes comprised samples from 177, 157, and 156 individuals in Kenya, the UK, and Cambodia, respectively, collected between September 2014 and April 2016. Clinical data from independent populations included 910, 3356 and 197 bacterial isolates from blood/cerebrospinal fluid infections in Kenya, the UK and Cambodia, respectively (samples collected between January 2010 and May 2017). Whilst our findings are exploratory and require validation, intermittent metagenomics of pooled samples could represent an effective approach for AMR surveillance and to predict population-level AMR in clinical isolates, complementary to ongoing development of laboratory infrastructures processing individual samples. Enterobacterales is a significant health threat, and individual-level surveillance remains difficult to implement systematically. Non-targeted, metagenomic approaches enabling prediction of population-level AMR from pooled minimally invasive clinical samples, could be of potential major public health benefit by: (i) enabling surveillance of AMR at the population-level; (ii) informing empiric treatment guidelines; and (iii) monitoring the impact of interventions. From a PubMed search using the terms “metagenomic”, “population”, “colonisation” and “infection” (01/Jan/2006–02/Dec/2020), 76 abstracts were evaluated and one full text reviewed; no studies identified used pooled colonisation samples from a population subset to infer phenotypic resistance in clinical isolates obtained from the same setting.Antimicrobial resistance (AMR) in Enterobacterales infections. We studied three geographic settings with different Enterobacterales AMR prevalence  and three different age groups . Taxonomy-adjusted AMR metrics combining taxonomic and AMR metagenomic profiles from pools, showed high out-of-sample prediction performance when considered in a Bayesian generalized linear model for their ability to predict the population-level prevalence of AMR in clinical isolates, compared to other metrics or no metrics.This exploratory study described a metagenomics approach using pooled faecal/rectal samples from population subsets  to predict setting-specific resistance in clinical Whilst further validation of this exploratory novel approach is needed, it shows potential to rapidly overcome a lack of quality AMR surveillance data and inform empiric treatment guidelines, particularly in LMICs where surveillance infrastructures remain sparse. It could be evaluated for other priority bacteria and different colonisation samples. Surveillance based on population colonisation metagenomics and taxonomy-adjusted AMR metrics should be further evaluated for its potential public health benefit in combatting AMR and supporting antibiotic stewardship.Alt-text: Unlabelled box1Antimicrobial resistance (AMR) is a global health emergency Staphylococcus aureusEnterobacterales, Colonisation with specific species and/or drug-resistant organisms, such as nasal colonisation with Enterobacterales AMR prevalence and infection rates, namely Cambodia, Kenya and the United Kingdom (UK), creating three pooled population metagenomes. We developed a set of Bayesian generalised linear models - each using different combinations of taxonomy and AMR metagenomic metrics derived from these metagenomes - to predict AMR prevalence at a population-level in clinical Enterobacterales isolates in each setting. Bayesian model comparison was then used to determine the value of metagenomic taxonomy-adjusted AMR metrics for out-of-sample predictive accuracy in determining the prevalence of AMR in clinical Enterobacterales isolates .The concept of a metagenomic taxonomy-adjusted AMR metric or “resistance potential” has been described previously ,11 as th22.1Faecal material collected after 2014 at pre-admission clinics or on admission to hospital from three age-groups and settings was studied, namely: children 1–59 months in Kilifi, Kenya; newborns in Siem Reap, Cambodia; and adults ≥18 years in London, UK (appendix p2). Rectal swabs and faecal samples have both been used as approaches for intestinal metagenomics ,13, and For each study site, microbiology metadata for blood/cerebrospinal fluid samples (as most robustly representative of truly invasive infections) collected within 0–72 h of admission to hospital (i.e. community-associated) from 01/Jan/2010–31/May/2017 were collated. Each site has a microbiology laboratory participating in external quality assurance schemes and accreditation processes (appendix p2). Samples were processed using standard operating procedures in accordance with international guidelines (appendix p2). Collated metadata included bacterial species and antibiotic susceptibility test (AST) results, specimen type, and basic patient details to validate aggregate-level stratification by age. Infection metadata were collated for individuals < 90 days of age in Cambodia, ≤ 60 months of age in Kenya and ≥ 18 years of age in the UK (appendix pp2-3).2.2DNA was extracted from each sample using the MoBio PowerSoil® DNA isolation kit , as per the manufacturer's instructions with optimisation steps to achieve sufficient DNA yields for sequencing  of data per sample; appendix pp4-8).2.3DNA extracts were stored at −20 °C prior to pooling and sequencing. For each study setting, we created a “population pool”, which consisted of the pooling of equimolar concentrations of all extracts from that setting with ≥1 ng DNA/μl. To validate our pooling approach , we also created one smaller pool for each setting, a “30-sample pool”, which consisted of equimolar concentrations of 30 randomly selected extracts with ≥300 ng DNA/34μl. An aliquot from each extract included in the 30-sample pools was also sequenced individually for the validation . An aliquot from all extracts sequenced individually and included in the 30-sample pools was also included in the population pools.2.4n = 90 individual metagenomes, n = 3 30-sample pools, n = 3 population pools]; appendix p9).Sequencing of all faecal sample DNA extracts  was performed using the HiSeq 4000 Illumina platform, generating 150 bp paired-end reads  specific lateral coverage is the proportion of the AMR gene covered by sequences mapping exclusively to the gene; (3) AMR gene length is the length of the gene the sequence is mapped to; and (4) average read length is average length of reads that mapped to the AMR gene.Taxonomic abundance of bacterial species and AMR genes at individual and pooled sample levels was determined using a published bioinformatics pipeline, ResPipe DEF, included only AMR determinants with the CARD “Confers_Resistance_to_Antibiotic” relationship ontology term, whereby the gene is known to confer or contribute to clinically relevant resistance to a specific antibiotic ALL, contained corrected counts of all AMR determinants with clear experimental evidence of increasing the MIC, including those associated with clinically relevant resistance (as for AMRDEF), but also those without the definitive “Confers_Resistance_to_Antibiotic” relationship ontology term. In this study we have used “antimicrobial resistance (AMR) gene” to define any relevant marker of resistance, including genes that confer resistance by mutation (but can have a susceptible wild type), and genes that confer resistance through presence/absence.The CARD database classifies each reference AMR gene by its association with phenotypic resistance. To be in CARD, an AMR gene must be described in a peer-reviewed scientific publication, have its DNA sequence available in GenBank, and include experimental evidence of elevated minimum inhibitory concentration (MIC) over controls. 2.6CGCR) aim to summarise information about relative abundance of genes conferring resistance for a given antibiotic. These are given through the sum of corrected gene counts (CGCs) of AMR gene variants associated with resistance to a given antibiotic, CGCjj (R), divided by the total CGC of all AMR genes in the pool. We considered two possibilities. First, CGC_DEFR considered only AMR determinants known to definitively confer clinically relevant resistance . Second, CGC_ALLR was calculated for AMR determinants with clear experimental evidence of increasing the MIC (using AMRALL). Taxonomic metrics (TaxR) aim to represent relative taxonomic abundance, and were given through the estimated abundance of a clinically relevant bacterial grouping divided by the total estimated abundance of bacterial taxa in the pool. Three bacterial groupings were evaluated: (i) the entire Enterobacterales order (Tax_ER); (ii) species in the Enterobacteriaceae family only (Tax_eR); and (iii) the grouping of the four most common and clinically relevant bacterial genera/species within the Enterobacteriaceae family across study sites ). Our prediction models included at most one taxonomic and one resistance metric, and evaluated six resistance and taxonomy metric combinations in total. We refer to these six combinations as taxonomy-adjusted AMR metrics.We developed AMR metrics and taxonomy metrics from pooled metagenomes (i.e. “population pools”) to predict the number of resistant isolates causing infection in each setting. Resistance metrics  for the analyses. We let i denote the setting , and j the antibiotic being evaluated. We assumed that counts of resistant isolates follow a binomial distribution. Our model then predicts the count of resistance  amongst tested Enterobacterales isolates  using a probability of resistance .For each of the six taxonomy-adjusted AMR metrics, we fitted a Bayesian generalized linear model to the infection data and applied Bayesian model comparison based on out-of-sample prediction accuracy. This allowed us to assess the potential of each metric to predict antibiotic resistance amongst clinical invasive j) and the effect parameters . In addition, we restricted the effect of AMR gene abundance to be positive, reflecting our view that only a positive association of AMR genes and clinical resistance is biologically reasonable. Each model was fit using Stan software We included only those antibiotics that had existing antibiotic susceptibility test (AST) data in ≥2 of three settings; missing observations were excluded from the likelihood evaluation. Due to the limited number of infection isolates with AST results , we chose standard weakly informative priors for the intercept (αCGCR) but without taxonomy metrics (TaxR), one with only taxonomy metrics (TaxR), and a baseline (null) model with only intercepts (keeping only αj). This was to assess the value of considering only taxonomy or only AMR metrics (i.e. whichever TaxR or CGCR metric performed best in the models considering combinations), against the value of combining these into a taxonomy-adjusted AMR metric for predicting clinical resistance. The within sample fit of the best model  and of the null model, was assessed using logarithmic scoring of the posterior predictions against the observed counts of resistant isolates. This type of scoring assigns low scores to models with highly diffuse (uncertain) predictive distributions, and also to narrow but wrongly placed distributions, and is rooted in information theory and the definition of entropy The best taxonomy-adjusted AMR metric was selected using Bayesian leave-one-out cross validation 2.8Ethical approval for faecal/rectal samples was already in place . Population pools in Kenya, the UK and Cambodia therefore comprised 177, 157, and 156 pooled faecal sample extracts . The tot3.2Enterobacterales were the most abundant bacterial taxa identified in the UK (75.7%) and Cambodia (69.7%) but not Kenyan  population metagenomes, consistent with previous Kenya data Enterobacterales, >95% abundance was attributable to the Enterobacteriaceae family, across settings  (1A). The most abundant Enterobacterales genera/species were E. coli and K. pneumoniae, followed by Enterobacter spp. . The abundance of E. coli was >20-fold higher than that of K. pneumoniae in UK  and Kenyan  metagenomes, reflecting the fact that in general, E. coli is thought to be a more dominant gastrointestinal coloniser than K. pneumoniae. In contrast both species had similar abundance in the Cambodian metagenome , consistent with previous high rates of K. pneumoniae colonisation identified on culture in the neonatal group studied Enterobacter spp. abundance was also higher in Cambodia (4.5%) compared to the UK (1.6%) or Kenya (0.2%). Other Enterobacterales genera represented <2% of bacterial abundance across settings (appendix p12).3.3Enterobacterales accounted for approximately a third across settings  (2A). Similar to the population-pool metagenomic data, most of these involved Enterobacteriaceae , and specifically E. coli and K. pneumoniae, with higher proportions of E. coli infections in the UK  and Kenya , versus Cambodia  (2B). Enterobacter spp. was the next most common Enterobacterales genus causing infection across settings , with other Enterobacterales genera accounting for <2% of the total invasive infections (appendix p13). A notable exception was Salmonella spp., accounting for 9.9% of the total infections in Kenya , consistent with data supporting the high rates of non-typhoidal salmonellosis here Amongst 197, 910 and 3356 bacterial isolates cultured from blood/cerebrospinal fluid infections in Cambodia, Kenya and the UK, respectively, infections by 3.4DEF) and to increase the MIC for 163 antimicrobials (AMRALL). A specific evaluation of AMR gene richness, on rarefied data (appendix p9), demonstrated that the number of AMR genes in population pools and individual samples differed by geographical setting, being highest in Cambodia (appendix p18).We identified 863 different AMR genes across all samples/pools, including those known to confer clinically-relevant resistance for 113 antimicrobials   data in ≥2 study settings , and the abundance of AMR genes increasing the MIC or conferring clinically relevant resistance (CGC_ALLR). The model considering both Tax_e4R and CGC_ALLR as predictors outperformed other models in terms of out-of-sample predictions (CGC_ALLR), or only taxonomic information (Tax_e4R) only marginally improved predictions relative to a baseline (null) model without any metagenomic information, whilst models with combined CGCR and TaxR showed substantially improved performance . Thus, we expect our best model considering both Tax_e4R and CGC_ALL,R to make substantially improved predictions compared to models with only one metric or no metagenomic metrics.The best taxonomy-adjusted AMR metric  used the taxonomic metric dictions . Models Tax_e4R and CGC_ALLR) had a score of −133  and the null model a score of −298 . As a means to visualise the predictions of our best model, predictions were made for the 16 antibiotics with AST data in ≥2 settings for Enterobacterales isolates causing infection and then plotted against the observed resistant counts , with a single centralised infrastructure  undertaking metagenomic sequencing, analysis and prediction of population-level AMR in clinical Enterobacterales isolates. Although this could be done in parallel with the local development of microbiology laboratory networks, our approach would not be dependant on consistent sampling of individuals with infection. Our findings are supported by other studies successfully using sewage for global AMR surveillance and prediction of AMR in clinical isolates [In this exploratory study we suggest that metagenomic analysis of pooled extracts from individual faecal samples could be effective at predicting resistance in invasive isolates –10, but blaCTX-M-33 versus blaCTX-M-63) alongside being able to aggregate by gene family. This is especially important as genes that differ by only single nucleotides/amino acids can have distinct phenotypic spectra. Although further validation of sample sizing and pooling strategies are needed, population pools comprising rectal swabs with as little as ≥1 ng/ul DNA/sample appeared to be sufficient to demonstrate predictive value in this proof-of-principle study.Based on pool size and sequencing depth (50–55 Gbp/pool), we avoided the need for potentially more expensive and labour-intensive individual indexing of DNA extracts in a pool, or the issues associated with targeted sequencing based on predefined AMR gene panels. Our strategy also enabled us to include samples with low DNA yields which may otherwise have failed library preparation; exclusion of these samples could potentially introduce bias. Uniquely, our bioinformatics pipeline incorporates the capacity to identify both specific AMR gene variants  collected from neonates after hospitalisation, potentially compromising the analysis designed to reflect community-associated profiles . This includes all commands and parameters run for with TrimGalore, Kraken2, Bracken, BBPMAP and ResPipe (the bioinformatics pipeline). The curated analysis datasets  can be found at https://data.mendeley.com/datasets/sxn6sw4r57/1  along with the R code used to produce these and the code to run the Bayesian analysis.The raw sequence data reported in this study have been deposited in the European Nucleotide Archive under accession number PRJEB34871. The code to extract CARD data, including relationship ontology terms that were required to generate the final datasets, plus any required input files, are available from the ResPipe GitLab repository (Bill & Melinda Gates Foundation (grant agreement OPP1160974) and was sponsored by University of Oxford. The study was also supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, and NIHR Health Protection Research Unit in Healthcare-associated Infections and Antimicrobial Resistance . Kenyan samples were collected in a study funded by the MRC/DfID/Wellcome Joint Clinical Trials scheme: MR/M007367/1.The study was funded by This work was first conceived by O.T.A., with support from N.S. and B.S.C.; O.T.A, N.S., B.S.C., R.N. and H.S.G. designed the study. K.C., J.W., O.T.A. and N.S. developed and validated modified DNA extraction protocols for this study. K.C., J.W. and R.B. conducted or facilitated most of the pre-sample-pooling laboratory work.  S.L. designed the methods and provided technical guidance for sample pooling and sequencing and conducted the sequencing work. J.A.B., J.D.E., P.T. and R.B. facilitated the collation and transfer of samples and data from participant settings. They also provided technical support for clinical and microbiology study procedures and for the development of context-appropriate standard operating procedures. N.S., A.S.W., T.E.P., D.W.C. and B.S.C. provided support and guidance for all technical aspects of the study  and contributed to the revision of study outputs. T.N. contributed to the mining, standardisation and analysis of infection metadata from each setting. H.S.G. conducted the bioinformatics work, designed the methods for corrected gene counts and extracted the data from CARD. J.S. provided the computing support for the study. O.T.A., H.S.G conducted mining, linkage and visualisation of study data. R.N. conducted the validation and Bayesian analyses and B.S.C. contributed to revision of these methods. O.T.A, N.S., R.N. and H.S.G. produced the first manuscript draft. All authors contributed significantly to the iterative review of the draft. O.T.A, N.S., R.N., H.S.G., J.A.B., P.T. and R.B verified the underlying data for this article.The authors declare no competing interests.


New challenges are significantly raising for making decisions in real-world complicated environments, such as processing large-scale data, recognizing complex relationships, investigating hidden mechanisms, forecasting future trends, and developing models with robustness and adaptability. Bibliometrics, as an effective tool, has provided systematic solutions for analyzing bibliometric data , and thus supporting research policy and strategic management. Further, the rapid development of information technologies, particularly, artificial intelligence and data science techniques, brings evolutionary changes to traditional bibliometrics, as well as its broad applications. For example, large-scale data analytics facilitates citation networks and textual segments to identify emerging topics and discover latent connections among multiple bibliometric sources , and netThis topic aims to explore the ways of fully facilitating the power of advanced analytics to enhance its capability in decision support for research policy and strategic management in scalable, uncertain and complicated environments in the real world. Targeting to broad issues in science, technology and innovation (ST&I) studies, this topic emphasizes the development and applications of advanced analytic approaches that provide transparent, explainable, and evaluable evidence for decision making. The topic also aims to focus on roles of analytics in decision making processes. Despite recent advancement of analytical technologies, there are still unfulfilled gaps among data, evidence, decision support and decision making. We also seek effective organizational methods and protocols to promote adaptation of advanced analytics. This cutting-edge topic is to spearhead a cross-disciplinary direction crossing disciplines such as information science, computer science, and broad business disciplines. We expect the topic will create fundamental theories, conceptual methodologies, and practical toolkits for advancing studies and practices in research policy and strategic management.This topic collects four manuscripts, touching diverse aspects of advanced analytics, modeling, and their applications for research policy and strategic management.Aljohani and Cristea developed a novel solution by incorporating certain state-of-the-art deep learning models and architectures for text segmentation and analytics. Such demographic characteristics specifically focus on employment status and gender information.Aiming to improve the performance of profiling the demographic characteristics of learners in the platform of Massive Open Online Course (MOOCs), Hook and Porter facilitated the Dimensions database in the Google BigQuery Cloud environment and proposed a visualization solution for mapping institutions and their productivity in a geographical map.While arguing the accessibility of traditional bibliometric data sources as well as nonspecific and outdated “global” measures, Wu et al. proposed a solution exploiting a set of intelligent bibliometric models, such as scientific evolutionary pathways for tracking topic evolution and bio-entity network analytics for discovering gene-disease associations.With a specific interest in profiling COVID-19-related genetic research, Mejia et al.). In this study, the authors used several advanced bibliometric tools, such as citation network analytics, scientific evolutionary pathways, and hierarchical topic trees, to profile the keen interests of the bibliometric community and how such interests changes over time in the past decades.As a conclusion for this special topic, the topic editors and their colleagues particularly investigated topics and their evolutionary pathways in bibliometric research (With the four submissions in this collection, we observed that using, refining, and developing advanced analytic models, incorporating with state-of-the-art artificial intelligence and data science techniques, has become a rising trend in bibliometrics, and such a trend further adapts to actual needs from the community of research policy and strategic management, who are facing challenging issues on decision support in changing environments. Given that, we believe this special topic may feed readers with both novel methodological solutions and ingenious practical applications.


Even though exenatide is known to be chemically and physically unstable at pH 7.5, there lacks a systematic evaluation of the impact of pH and excipients on the peptide solution stability. In this study, we established analytical methods to measure the chemical and physical degradation of the peptide in solution. Exenatide remained relatively stable at pH 4.5 when incubated at 37 °C. At pH 5.5–6.5, degradation was driven by oxidation, while driven by deamidation at pH 7.5–8.5. Significant aggregation of exenatide at pH 7.5 and 8.5 was detected by size exclusion chromatography and dynamic light scattering. Each pH value greater than 4.5 exhibited unique profiles corresponding to a loss of α-helical content and an increase in unordered structures. The addition of sugars, including mannitol, sorbitol and sucrose, conferred small protective effects against peptide aggregation when incubating at pH 7.5 and 37 °C, as measured by size-exclusion chromatography and dynamic light scattering. The results of this study will be useful for investigators developing generic exenatide products, peptide analogs and novel exenatide drug delivery systems.Exenatide, a glucagon-like peptide-1 receptor agonist, is the active pharmaceutical ingredient in Byetta Teva Pharmaceuticals reached an agreement with AstraZeneca allowing them to manufacture and commercialize a generic version of Byetta® as of October 2017 but has yet to reach the market due to its pending ANDA FDA approval [® [Exenatide, also known as exendin-4, is a 39-amino acid glucagon-like peptide-1 (GLP-1) receptor agonist that acts as an incretin mimetic for the treatment of type II diabetes mellitus ,3,4,5. Enjection ,7. In 20approval ,11. Asidroval [® ,13,14. Sroval [® ,15,16,17® [A recently published FDA guidance on ANDA submissions for generic synthetic peptides highlights the importance of characterizing product-related impurities that may affect their safety, immunogenicity and effectiveness . Five pe® .Thus, understanding the chemical and physical stability of exenatide is critical to the development of potential generic exenatide drug products and novel extended-release formulations. Identifying the peptide’s potential mechanisms of degradation allows for the optimization of formulation and selection of appropriate manufacturing conditions to avoid the formation of product impurities ,21. At p® Pharmaceuticals . All other materials were purchased from commercial suppliers.Exenatide powder was generously provided by Amnealw/v mannitol, sorbitol or sucrose) were dissolved in 30 mM HEPES buffer (pH 7.5). These solutions were then used to reconstitute exenatide powder. Excipient samples were compared against exenatide reconstituted in 30 mM acetate buffer (pH 4.5) containing 4.3% (w/v) mannitol, the negative control. Samples were incubated at 37 °C for 30 min to induce conformational equilibrium. Reconstituted exenatide samples at various pH and excipient conditions were subjected to 4 weeks of incubation at 37 °C, removed from the incubator at 0, 1, 2 and 4 weeks and immediately analyzed. No significant changes in pH were observed over the incubation.Exenatide solutions were prepared at a concentration of 0.5 mg/mL in various 30 mM buffer solutions according to their respective buffer capacities, including sodium acetate (pH 4.5), sodium citrate (pH 5.5), sodium phosphate (pH 6.5), HEPES (pH 7.5) and HEPBS (pH 8.5). To investigate the impact of excipients, salt (154 mM NaCl) or sugars (4.3% 2O (0.1% TFA) at a flow rate of 1 mL/min, with a gradient ramping from 30 to 55% ACN (0.1% TFA) over the course of 25 min. The column temperature was held at 40 °C. Exenatide samples were detected from UV absorbance chromatograms that were extracted at 280 nm.Exenatide and its chemical degradation impurities were separated by hydrophobicity by reverse-phase liquid chromatography on a C4-Pack column  with a Waters 2595 Alliance System  on interfaced to a 2995 Photodiode Array Detector . Samples were filtered and injected at a concentration of 0.125 mg/mL at a volume of 50 µL and delivered using a mobile phase of acetonitrile (ACN) (0.1% TFA)/H2O (0.05% TFA) at a flow rate of 0.3 mL/min with a gradient ramping from 30 to 55% ACN (0.05% TFA) over the course of 25 min. The column temperature was also held at 40 °C. The MS parameters included a capillary voltage between 1.2 and 2.0 kV, a ToF-MS range of 300–3200 m/z, a drying-gas temperature of 325 °C, a drying-gas flow rate of 12 L/min, a nebulizer pressure of 45 psi, and a fragmentor voltage of 225 V. The total ion chromatogram (TIC) was detected at 280 nm, with impurity peak mass extractions generated by Qualitative Analysis Mass Hunter Software version b0705 .Exenatide chemical degradation impurities were separated by reverse-phase liquid chromatography on the same C4-Pack column and analyzed by a dual electrospray ionization equipped Agilent 6520 Accurate-Mass Q-ToF . Samples were filtered and injected at a concentration of 0.125 mg/mL at an injection volume of 20 µL and delivered using a mobile phase of ACN (0.05% TFA)/HRs) were correlated with their MWs, imported onto a semi-log plot with the generated equation, that were used to estimate the MW of exenatide monomer and aggregate peaks (not shown). Exenatide samples were detected from UV absorbance chromatograms that were extracted at 280 nm.Exenatide physical degradation impurities were separated by molecular weight using size exclusion chromatography on a Superdex Increase 75 10/300 GL column  with a Waters 2707 autosampler  connected to a 1525 binary HPLC pump , interfaced with a 2489 UV/Vis detector . Samples were filtered and injected at a concentration of 0.125 mg/mL at a volume of 50 µL and isocratically delivered with a pH 7.4 PBS mobile phase at a flow rate of 1 mL/min over the course of 25 min. Molecular weights of exenatide peaks were determined from a standard calibration, generated from the injection of Uracil (MW 114 Da), Aprotinin (MW 6.5 kD), Cytochrome C (MW 14.5 kD), Carbonic Anhydrase (MW 19.5 kD) and BSA (MW 65 kD). Maximum peak height retention times for each standard . A total of 100 µL aliquots of undiluted exenatide (0.5 mg/mL) were placed into low-volume TruView cuvettes  at an undiluted concentration of 0.5 mg/mL and analyzed. Attenuation and accumulation numbers were automatically optimized for each sample by the instrument prior to analysis. Particle size distributions were reported by volume and combined into 0.3–10, 10–100, 100–500, 500–1000 and 1000+ nm size ranges to match our instrument’s limit of detection specification ranges .Intrinsic fluorescence was used to determine the exenatide’s tertiary structure. Undiluted exenatide samples were placed into a QS 1.5 mm quartz cuvette  and analyzed at a concentration of 0.5 mg/mL on a SpectraMax M3  plate/cuvette reader. The fluorescence was measured using a wavelength emission range of 280–450 nm and an excitation wavelength of 270 nm while obtaining fluorescence at 6 flashes per reading.Circular dichroism was used to determine exenatide’s secondary structure. Exenatide samples were analyzed at a concentration of 0.125 mg/mL on a J-815 . Instrument sample temperature was held at 37 °C using a Peltier attachment. Circular dichroism spectra were averaged from 5 collected scans over a wavelength range of 245 to 195 nm, with an accumulation rate of 20 nm/min and a data integration time (DIT) of 4 s. Percent contributions from α-helix, β-sheet and unordered secondary structures were quantified using a third-party Spectra Manager Suite (JASCO) add-in, CDPro analysis. In this software, the CONTIN method was chosen, using soluble-membrane protein 56 (SMP 56) as the reference protein for secondary structural analysis.Liquid chromatography-mass spectrometry (QToF) was implemented to analyze the formation of exenatide chemical impurities. A short-term study was performed by incubating exenatide, reconstituted at pH 8.5, at an elevated temperature of 60 °C for 24 h. ® is commercially formulated in an acetate buffer solution at pH 4.5. On the other hand, rapid, pH-dependent degradation of exenatide was observed at pH 6.5, 7.5 and 8.5. We also observed a loss of total AUCs, indicating some precipitation occurring at pH 7.5 and 8.5 after 2 and 4 weeks, decreasing to 71.9 ± 10.1% and 70.9 ± 9.8% relative to day 0, respectively  were indicators of unfolding through increased solvent exposure of Trp A–C. ExenCircular dichroism is useful in combination with intrinsic fluorescence, providing further information on changes of underlying secondary structural content, including α-helical, β-sheet and unordered content. Exenatide samples were qualitatively measured by circular dichroism spectra D–F and t® formulation).Sugar excipients are generally known to offer protection against aggregation during freeze/thaw cycles and long-term storage, while other excipients, such as salt, may induce aggregation ,26. The Chemical degradation profiles, in terms of parent peak A and oxip < 0.05 for all solutions), indicating some minor protective abilities of these excipients against physical degradation. Physical degradation of exenatide at pH 7.5 in the presence of various excipients was mainly driven by aggregation.Physical degradation profiles, in terms of monomers D and aggFurther characterization of peptide aggregates was performed using dynamic light scattering. Our data showed that exenatide was stable for the negative control, with particles remaining in the 0.3–10 nm size range over the incubation. At pH 7.5, mannitol, sorbitol and sucrose exhibited stabilizing effects, where 100% of particles were retained in the 0.3–10 nm size range after 4 weeks of incubation . On the Subsequent structural analysis was performed by intrinsic fluorescence and circular dichroism. All pH 7.5 formulations experienced a large red shift of maximum emission wavelength and increase in maximum fluorescence intensity after 4 weeks, indicative of Trp-cage unfolding A,D. CD PThe goals of this study were to identify and characterize exenatide’s chemical and physical degradation using accelerated stability testing to elucidate exenatide’s potential underlying degradation mechanisms. To do so, we developed and implemented several orthogonal analytical techniques to investigate the impact of pH and the addition of excipients at elevated pH  over the course of long-term incubation at 37 °C. Chemical impurities were investigated using reverse-phase liquid chromatography and identified by liquid chromatography-mass spectrometry (QToF). Physical impurities were analyzed by size-exclusion chromatography, with a subsequent examination of particle size distributions by dynamic light scattering. Tertiary and secondary structures were analyzed by intrinsic fluorescence and circular dichroism. Overall, our study provides a valuable body of information regarding exenatide degradation that can be potentially useful for the approval of generic exenatide versions and the development of novel sustained-release formulations.Over the course of incubation, exenatide appeared to undergo a rapid, pH-dependent degradation at pH 6.5 and above while remaining relatively stable at lower pH. Analogously, endogenous GLP-1 has also shown degradation when pH was slightly increased, though pH was adjusted on a smaller scale . While tOur experiments investigating the impact of different excipients on exenatide’s stability at pH 7.5 revealed the limited ability of excipients to stabilize exenatide as well as their varied effects on exenatide’s degradation profiles. Of the excipients tested, only mannitol, sorbitol and sucrose had minor protective effects against physical degradation, where they slightly prevented monomer loss. Additionally, these excipients appeared to prevent particle size-shifting and even appeared to prevent oligomer formation relative to the formulation without excipient and with salt. Even so, these excipients were unable to prevent chemical degradation. The main impurities observed were aggregation and deamidation for all formulations after 4 weeks of incubation. While we determined that exenatide’s degradation kinetics were pH-dependent, we have not fully defined the mechanism of peptide aggregation. Currently, we propose that degradation occurs through a combination of chemical and physical degradation . At high


GSE57495 dataset and International Cancer Genome Consortium dataset. Four molecular subtypes and 4249 differentially expressed genes (DEGs) were identified. The DEGs obtained by Weighted Gene Coexpression Network Construction analysis were intersected with 4249 DEGs to obtain a total of 1340 DEGs. The final prognosis model included CA8, CEP55, GNB3 and SGSM2, and these had a significant effect on overall survival. The area under the curve at 1, 3 and 5 years was 0.72, 0.79 and 0.87, respectively. These same results were obtained using the validation cohort. Survival analysis data showed that the model could stratify the prognosis of patients with different clinical characteristics, and the model has clinical independence. Functional analysis indicated that the model is associated with multiple cancer‐related pathways. Compared with published models, our model has a higher C‐index and greater risk value. In summary, this four‐gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator.Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. Such abnormal lipid metabolism provides energy for rapid proliferation, and certain genes related to lipid metabolism encode important components of tumor signaling pathways. In this study, we analyzed pancreatic cancer datasets from The Cancer Genome Atlas and searched for prognostic genes related to lipid metabolism in the Molecular Signature Database. A risk score model was built and verified using the  Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. In this study, we analyzed pancreatic cancer datasets from The Cancer Genome Atlas and searched for prognostic genes related to lipid metabolism in the Molecular Signature Database. Compared with published models, our model has a higher C‐index and greater risk value. This four‐gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator. AUCarea under the receiver operating characteristic curveCIconfidence intervalDEGdifferentially expressed geneFDRfalse discovery rateGEOGene Expression OmnibusGOGene OntologyGSVAGene Set Variation AnalysisHRhazard ratioICGCInternational Cancer Genome ConsortiumKEGGKyoto Encyclopedia of Genes and GenomesKMKaplan–MeierLassoleast absolute shrinkage and selection operatorNMFnon‐negative matrix clustering algorithmOSoverall survivalRNA‐seqRNA sequencingROCreceiver operating characteristicssGSEAsingle‐sample gene set enrichment analysisTCGAThe Cancer Genome AtlasTIMERTumor Immune Estimation ResourceWGCNAWeighted Gene Coexpression Network ConstructionAs a malignancy of the digestive system, pancreatic cancer is one of the most aggressive malignancies in the world. In recent years, pancreatic cancer morbidity and mortality rates have steadily increased, with an overall 5‐year survival rate of about 8% for patients . Despiteet al. , and C2 had the best prognosis . The significant pathways enriched by KEGG are related to insulin secretion and dopaminergic synapse pathways , and TNM stage presented significant margin  were significantly associated with survival , 7‐gene signature (Cheng), 5‐gene signature (Raman) and 9‐gene signature (Wu), were selected and compared with our four‐gene model. To make the models comparable, we calculated the risk scores of each pancreatic cancer sample in TCGA using the same method. Samples were divided into the risk‐H and risk‐L groups according to the median risk score, and the KM prognosis curve showed that there were significant differences in OS prognosis of samples from the four models in the risk‐H and risk‐L groups  KM curve between C1 and C2 molecular subtypes. (B) KM curve between C1 and C3 molecular subtypes. (C) KM curve between C1 and C4 molecular subtypes. (D) KM curve between C2 and C3 molecular subtypes. (E) KM curve between C2 and C4 molecular subtypes. (F) KM curve between C3 and C4 molecular subtypes.Fig. S2. Comparison of clinical characteristics of molecular subtypes. (A) Sample distribution of T stages in four subtypes. (B) Sample distribution of N stages in four subtypes. (C) Sample distribution of M stages in four subtypes. (D) Sample distribution of TNM stages in four subtypes. (E) Sample distribution of tumor stages in four subtypes. (F) Sample distribution of sex stages in four subtypes. (G) Sample distribution of age stages in four subtypes.Fig. S3. Comparison of immune characteristics in molecular subtypes. (A) B cell score between molecular subtypes. (B) CD4 cell score between molecular subtypes. (C) CD8 cell score between molecular subtypes. (D) Neutrophil cell score between molecular subtypes. (E) Macrophage cell score between molecular subtypes. (F) Dendritic cell score between molecular subtypes. (G) Stromal score between molecular subtypes. (H) Est_Immune score between molecular subtypes. (I) ESTIMATE score between molecular subtypes.Fig. S4. Prognostic ability of four‐gene signature.  ROC curve and KM curve of RiskScore in GSE28735 dataset.  ROC curve and KM curve of RiskScore in GSE62452 dataset.  ROC curve and KM curve of RiskScore in GSE85916 dataset.Click here for additional data file.