Patulin (PAT) is a mycotoxin, with several acute, chronic, and cellular level toxic effects, produced by various fungi. A limit for PAT in food of has been set by authorities to guarantee food safety. Research on PAT in tea has been very limited although tea is the second largest beverage in the world. In this paper, HPLC−DAD and GC−MS methods for analysis of PAT in different tea products, such as non-fermented (green tea), partially fermented , completely fermented (black tea), and post-fermented (dark tea and Pu-erh tea) teas were developed. The methods showed good selectivity with regard to tea pigments and 5-hydroxymethylfurfural (5-HMF) and a recovery of 90–102% for PAT at a 10–100 ppb spiking level. Limit of detection (LOD) and limit of quantification (LOQ) in tea were 1.5 ng/g and 5.0 ng/g for HPLC−UV, and 0.25 ng/g and 0.83 ng/g for GC−MS. HPLC was simpler and more robust, while GC−MS showed higher sensitivity and selectivity. GC−MS was used to validate the HPLC−UV method and prove its accuracy. The PAT content of 219 Chinese tea samples was investigated. Most tea samples contained less than 10 ng/g, ten more than 10 ng/g and two more than 50 ng/g. The results imply that tea products in China are safe with regard to their PAT content. Even an extreme daily consumption of 25 g of the tea with the highest PAT content (124 ng/g), translates to an intake of only 3 μg/person/day, which is still an order of magnitude below the maximum allowed daily intake of 30 µg for an adult. Mycotoxins are naturally occurring, toxic chemicals produced by fungi. Foods and fodder infected by such fungi pose serious risks for human and animal health. Many mycotoxins have been identified, e.g., aflatoxins, ochratoxin A, fumonisins, patulin, moniliformin, sterigmatocystin, tricothecenes and zearalenone, and their presence in foods and fodder is undesirable . PatulinFor safety reasons, the FAO/WHO Joint Expert Committee on Food Additives and Contaminants (JECFA) implemented a provisional maximum tolerable daily intake (PMTDI) of 0.4 μg/kg body weight/day for PAT, which equals 30 µg/day for a 75 kg adult. The maximum acceptable level of PAT in apple juice is set at 50 μg/L for adults and at 10 μg/L in apple-based foods for infants and babies .In addition to apples and apple-based products, PAT has also been found in other fruit commodities at too high levels, e.g., dried figs and dried longans in China  . UnfortuCamellia sinensis, is the second most frequently consumed beverage in the world [Tea, produced from the leaves of he world ,12. Averhe world . Tea prohe world . As micrhe world ,16,17. Ahe world . A numbehe world ,19. Penicillium oxalicum during the fermentative process of Pu-erh tea, a PAT level of 12.6 μg/kg was found in one tea sample [Therefore, more attention is being paid to the occurrence of those mycotoxins in tea products and concomitant safety issues ,21,22. Ha sample . In a sta sample . The aut13C5–7 patulin, 25 μg/mL × 1.2 mL, 6500.00 CNY = $1000) for patulin [A prerequisite for the determination of PAT in tea is an accurate validated analytical method. Different chromatography-based methods such as GC−MS, HPLC with UV detection at 276 nm, and HPLC-tandem mass spectrometry (HPLC−MS/MS), have been employed ,34,35,36 patulin . Thus, L patulin . When HP patulin ,31,32,34Major matrix components in tea, such as the polyphenolic water-soluble pigments thearubigin and theaflavin and fat-soluble ones such as chlorophyll and carotenoids are different from those in fruits. As such matrix components may interfere with the determination of PAT and removal of pigments is required, e.g., by sample pretreatment methods such as LLE or SPE. Thus, to be able to analyze PAT in tea samples, first existing analytical sample clean-up and chromatographic methods for PAT in fruits have to be tested and, if needed, adapted for tea samples.In this paper, both the analytical goal, i.e., development of an efficient method for PAT analysis in tea, and the food safety goal, i.e., is the occurrence of PAT in different tea samples an issue? are addressed. For this purpose, HPLC−UV and GC−MS combined with trimethylsilylation derivatization were compared for their suitability to determine PAT in tea products and for cross validation. Also, attention was given to a simple sample pretreatment method in combination with HPLC analysis. Next, 219 commercial tea products with different stages of fermentation and ageing were analyzed for their PAT content to arrive at a food safety conclusion regarding the role of PAT in Chinese teas. To our knowledge, never before such an exhaustive targeted chemical analysis of the mycotoxin patulin in seven different types of teas with multiple samples per type by two complementary analytical methods was undertaken. Results are presented herein.Although the average tea consumption is a modest 1.55 g/person/day in China, this will vary strongly from person to person. As heavy tea drinkers are most at risk, a safety factor of 20 was used corresponding with a consumption of 30 g tea per person per day. This amounts to about 4 L of strong tea (7.5 g/L) per person per day. In turn, this translates—with a maximum safe dose of 30 µg PAT/person/day—that any tea should not contain more than 1 µg PAT per g (1 ppm). To be able to detect such concentrations accurately without the risk of false-positives, an analytical safety factor of 20 was set in this study. In other words, if the targeted analytical method for PAT in tea is able to accurately and precisely determine 50 ng PAT per g of tea (50 ppb), concentrations potentially deleterious for health (>1000 ppb) will not pose a problem from an analytical point of view. For the sample pre-treatment of PAT in fruit matrixes, mostly liquid-liquid extraction (LLE), and matrix dispersion SPE (dSPE) have been used ,29,31,32TM T3 column was used for the separation. An optimized particle pore size and C18 surface concentration of stationary phase enable reliable operation with 100% aqueous mobile phases needed for retaining highly polar analytes. 5-HMF (10.2 min) and PAT (13.9 min) are well separated on this column , the retention was not affected. The column temperature significantly affected the retention of PAT on the column. The retention time was 13.9, 13.7, 13.0, 12.5 and 10.5 min at 20, 24, 28, 35, and 40 °C, respectively. Therefore, the column temperature must be controlled to obtained reproducible separations.To obtain retention of PAT as well as high sensitivity, 95% water was used in the mobile phase in combination with a relatively large injection volume of 100 μL. The sample solution must have a low organic solvent content as otherwise PAT is not retained on the column. 2O; (2) extraction of tea with ACN, addition of PSA to adsorb pigments, drying of supernatant, redissolving in H2O; (3) extraction of tea aqueous solution with ethyl acetate, and alkaline clean-up [2O, filtration. In all cases, final extracts were analyzed by HPLC−UV. For the removal of pigments, method (1) worked best, the sample solutions were colorless. After method (4), sample solutions were pale yellow-green. Method (2) and (3) gave strongly colored samples and largely failed to remove pigments. The recoveries of PAT of the four methods are listed in Having established the HPLC−UV conditions, next the sample preparation step was investigated. In short, the four selected methods were as follows  ultAll seven different types of tea were analyzed as such, and after a 50 ng/g spike with PAT, to determine the suitability of the HPLC−UV approach. The chromatograms of different blank tea samples and the same tea samples spiked with 50 ng/g PAT are shown in The results demonstrate that method (4) provides sufficient selectivity for the analysis of PAT in teas at the low concentration of 50 ng/g. Non-fermented green tea and partially fermented Oolong, yellow and white teas clearly contain fewer matrix components than the completely fermented black tea and dark tea but the PAT peak of tea spiked at only 50 ng/g is still well observable in the latter two teas. To show what a HPLC−UV profile of a potentially dangerous concentration of PAT looks like, in To validate the HPLC−UV method, a second orthogonal analytical method, i.e., GC−MS, was used. GC−MS has higher selectivity due to its higher separation power and the ability to focus on a specific mass. Additionally, many higher MW matrix components that might interfere with PAT quantitation by HPLC−UV, will not elute from a GC even after silylation. For PAT analysis by GC−MS, both off-line TMS derivatization  and on-lThe results show that there is no significant difference in derivatization efficiency between both methods when the injection-port temperature is 280 °C. The RSD of on-line derivatization was twice smaller than that of off-line derivatization. Thus, on-line derivatization was chosen in this study as it saves both time and effort besides being more precise.13C5–7 patulin [13C5–7 patulin is too expensive at $ 40,000/mg for routine use, and HCB might occur as organochlorine pesticide residue in tea, 3-NBA was selected as I.S. 3-NBA has never been detected in tea and is thus suitable to serve as an internal standard for GC−MS analysis of patulin in tea samples. m/z 183 for PAT and m/z 194 for 3-NBA. The same ions were used in earlier GC−MS studies [For the quantitative analysis of PAT by GC−MS, different internal standards such as  patulin , hexachl patulin , and 3-n patulin  have bee studies ,39.Sample pretreatment method (4) described in The calibration curves for HPLC−UV and GC−MS are shown in The mean relative recovery of PAT was 89.7–102.6% in GC−MS and 95.2–102.5% in HPLC−UV, respectively. Relative standard deviations were 3.7–11.1% in case of GC−MS and 2.6–5.8% for HPLC−UV. So, the accuracy and precision of both methods meet the requirements for PAT analysis in tea samples.2 = 0.998) indicating that both—very different—methods provide a reliable outcome . Duvivier et al. [The 10 samples with more than 10 ng PAT per g tea , includer et al.  have shoIn conclusion, it can be stated that a large survey of different types of tea by two orthogonal targeted chemical-analytical methods showed that the mycotoxin patulin occurs in only a limited number of tea samples, and then always in low concentrations. In other words, even the consumption of many liters of strong tea does not lead to the ingestion of dangerous amounts of patulin. All 219 investigated Chinese teas were found to be safe with regard to their patulin content.Acetonitrile , ethyl acetate  and other solvents were purchased from Merck . Deionized water was obtained by using a Milli-QTM Direct ultrapure water system . N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) containing trimethylchlorosilane (TMCS) (99:1) was purchased from EKEAR . Primary secondary amine (PSA) sorbent and graphitized carbon black (GCB) were purchased from Macklin  and PAT (≥98%) and 3-NBA (≥98%), 5-HMF (≥98%) standards were purchased from Pribolab Biotech Co. .All 219 tea products were purchased in local markets and online e-shops. The tea products were divided into four categories: non-fermented, partially fermented, completely fermented and post-fermented. The sample information details are listed in 2O + ACN . PAT standard series were 5.00, 10.0, 25.0, 50.0, 100, 125 and 250 ng/mL in H2O + ACN  prepared by diluting the working solution.5.0 mg of patulin standard was dissolved in 10.00 mL of ACN and stored at –40 °C as the stock solution (500 μg/mL). The working solutions (5.00 μg/mL) were prepared by diluting the stock solution with H2 stream at 30 °C in a 2 mL tube. The residue was then extracted twice with 0.50 mL of H2O . The extract was filtered through a 0.2 μm membrane  and then analyzed by HPLC.1.00 g of tea powder was extracted three times with 2 mL of ACN in an acoustic wave bath for 10 min. After centrifugation  of each extract, the three extracts were combined in a 10 mL glass tube. The extract was dried in stages under a NTM T3 column  . The column temperature was set at 20 °C. The injection volume was set at 100 μL and the flow rate at 0.8 mL/min. Solvent A was H2O and solvent B was acetonitrile (ACN). Gradient elution program was as follows: 0–13 min 5% B, 13–15 min 100% B, 15–20 min 5% B.HPLC was performed using an HPLC series Nexera X2 equipped with a SPD-M20A DAD set at 276 nm and CTO 20A column oven . Chromatographic separation was carried out with an Atlantis(1)2 stream, the residue was redissolved in 1.00 mL of H2O, and the solution was analyzed by HPLC after filtration through a 0.22 μm membrane.1.00 g of tea sample was extracted by 2.0 mL of ACN for three times, 100 mg of GCB was added to the pooled ACN extracts to adsorb pigments, after centrifugation the ACN supernatant was dried by a N(2)2 stream, the residue was redissolved in 1.00 mL of H2O, and the solution was analyzed by HPLC after filtration through a 0.22 μm membrane.1.00 g of tea sample was extracted by 2.0 mL of ACN for three times, 100 mg of PSA was added to the pooled ACN extracts to adsorb pigments, after centrifugation the ACN supernatant was dried by a N(3)1.00 g of tea sample was extracted by 10.0 mL of water for three times. After centrifugation  of each extract, the three extracts were combined in a separatory funnel. The aqueous solution was extracted three times, each time with 15.0 mL of ethyl acetate. The combined organic solution was washed with 10.0 mL of 1.5% solution of sodium bicarbonate in water, and the aqueous phase was extracted with an additional portion of ethyl acetate (10.0 mL). The organic phases were combined, dried over anhydrous sodium sulfate, and evaporated to dryness in a stream of nitrogen. The residue was dissolved in 1.00 mL of water . The sol(4)1.00 g of tea sample was pretreated according to the procedure described in Four different pretreatment methods were tested using different kinds of tea spiked with PAT standard solution at a 50 ng/g level (10.0 μL of 5.0 μg/mL PAT was added to 1.00 g of tea powder): 5.0 mg of patulin standard was dissolved in 10.0 mL of ACN and stored at –40 °C as the stock solution (500 μg/mL). The working solution (5.00 μg/mL) was prepared by diluting the stock solution. 5.0 mg of 3-NBA standard was dissolved in 10.0 mL of ACN and stored at –40 °C as the I.S. stock solution (500 μg/mL). The I.S. working solution (5.0 μg/mL) was prepared by diluting the stock solution. PAT standard series were 5.00, 10.0, 25.0, 50.0, 100, 150, 200 and 250 ng/mL in ACN and prepared by diluting the working solution. 200 μL of the standard series solution were transferred to autosampler vials by means of a 250 μL syringe. 2.0 μL of I.S. working solution and 25.0 μL of BSTFA (containing 1% TMCS) were added to the vial. After thorough mixing, 1.0 μL of the solution was injected into the GC−MS by the autosampler. The silylation derivatization was completed inside the injector of the GC. 2 stream at 30 °C in a 2 mL autosampler vial in stages. The residue was then dissolved in 200 μL of ACN. 2.0 μL of I.S. working solution and 25.0 μL of BSTFA (containing 1% TMCS) were added to the solution. After thorough mixing, 1.0 μL of the solution was injected into the GC−MS by the autosampler.1.00 g of tea powder was extracted by ACN in an acoustic wave bath for 10 min. The extraction was repeated three times, each time with 2.0 mL of ACN. The extracts were combined in a 10 mL glass tube. The extract was dried under a N2. The derivatization was carried out after evaporation: first, 200 μL of ACN was added to the dry residue and vortexed for 1 min. Then 100 μL of the solution was transferred to a brown silanized magnetic screw-cap vial and 25.0 μL of BSTFA (containing 1% TMCS) were added. Each mixture was vortexed for 10 s, and heated at 50 °C for 40 min on a dry heating block. After cooling, 1.0 μL of liquid sample was injected into the GC−MS instrument for analysis.On-line derivatization was used for comparison. For all of the tea products, sample preparation was as follows: (1) tea samples were pulverized by a grinder to tea powder; (2) 5.00 g of tea sample powder was weighed and added to a 50 mL fluoroethyleneproylene (FEP) centrifugation tube; (3) 10.0 mL of ACN was added to the tube; (4) the powder was ultrasonically extracted for 30 min; (5) vortex mixing for 30 s; (6) 2.00 mL of solution was filtered through a 0.22 μm membrane and transferred to a 5 mL polypropylene (PP) tube; (7) the tea extract solution was evaporated to dryness under a stream of N®-5 capillary column (30 m × 0.25 mm ID × 0.25 μm film thickness)  was employed. Carrier gas was helium at a constant column flow of 1.0 mL/min. Split ratio was 1:1 and injector temperature was set at 300 °C. The column oven temperature program was as follows: 100 °C held for 1 min, increased to 180 °C at 10 °C/min, then increased to 280 °C at 30 °C/min, and finally held at 280 °C for 12.67 min. The MS interface temperature was set at 280 °C. The EI source temperature was set at 250 °C and ionization occurred at 70 eV. Quantification was carried out in SIM mode, with the following ions to monitor: m/z 183 (quantitative ion), m/z 226  and m/z 136  for PAT and m/z 194 (quantitative ion), m/z 164 , and m/z 210  for 3-NBA. A Mass spectrometric library  was used for identifying the analyte and internal standard.GC−MS analyses were carried out on a GC 2010 Plus coupled with a QP 2010 Ultra mass spectrometer and an AOC-20i autosampler . An RtxSpecificity, linearity, Accuracy and Precision of the HPLC-DAD method were assessed with different tea samples (Non-Fermented (Green Tea), Partially Fermented (Oolong Tea), Completely Fermented (Black Tea), and Post-Fermented (Dark Tea)) Spiked at 10, 50 and 100 ng/g. Uncontaminated (Blank) Dried Tea Powder was Split Into 1.00 g Portions, which Were Spiked with 2.0 μL, 10 μL, and 20 μL of PAT Working Solution Patulin Solution (5.00 μg/mL in ACN) to Give Final Levels. The Samples Were Stored Overnight in the Dark at 4 °C Prior to ExtractionLinearity, accuracy and precision were assessed by carrying out five intra-day injections at each of the spiked concentrations. The standard curve was constructed by plotting peak areas versus concentrations and was analyzed using linear regression.The Specificity, Linearity, Accuracy and Precision Were Determined with Tea Samples Spiked at 2.0, 10 and 20 ng/g  Including the Same Samples as Used for HPLC-DAD. Linearity, Accuracy and Precision Were Assessed by Carrying Out Five Intra-Day Injections at Each of the Spiked Concentrations. The Standard Curve Was Constructed by Plotting Ratios of PAT Peak Areas and I.S. Peak Areas Versus Concentrations and Was Analyzed Using Linear Regressionm/z 183 in GC−MS for PAT. Black tea spiked at 50 ng/g was pretreated according to the procedure described in The LOD was calculated at an S/N ratio of 3.3 at 276 nm in HPLC−UV and at


To explore the associations of body mass index (BMI) and mortality among people with normal fasting glucose (NFG), impaired fasting glucose (IFG), and type 2 diabetes mellitus (T2DM) in an elderly Chinese population. A retrospective cohort study was conducted that included 59,874 elderly people who were aged 60 and older at baseline. Data for the study came from a health check-up program in China between 2011 and 2019. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using multivariable Cox proportional hazard models of BMI categories by glycemic status.2, 24 ≤ BMI < 28 kg/m2, and BMI ≥ 28 kg/m2 compared to 18.5 ≤ BMI < 24 kg/m2: HR (95% CI): 1.33 (1.18 to 1.49), 0.88 (0.83 to 0.93), and 0.90 (0.82 to 0.98), respectively, for NFG; 0.89 (0.55 to 1.46), 0.84 (0.71 to 0.99), and 0.88 (0.70 to 1.11), respectively, for IFG; and 1.42 (0.88 to 2.29), 0.75 (0.64 to 0.89), and 0.76 (0.62 to 0.93), respectively, for T2DM. There were curvilinear-shaped associations between BMI and mortality in the NFG and T2DM groups  and no significantly association between BMI and all-cause mortality was observed in the IFG group .During the median of 5.96 years of follow-up, 7928 participants died . In adjusted Cox models, risk of mortality showed a decreasing trend with BMI < 18.5 kg/mHigh BMI compared to normal BMI was associated with decreased mortality, especially in the old populations with NFG and T2DM. Future studies are needed to explain the obesity paradox in elderly patients with T2DM. An analysis of the Global Burden of Disease Study in 195 countries and territories observed that the number of global deaths and disability-adjusted life years (DALYs) attributable to high body mass index (BMI) substantially increased between 1990 and 2017. Successful population-wide initiatives targeting high BMI may mitigate the burden of a wide range of diseases [In the general population, the association between obesity and increased risk of cardiovascular disease (CVD) has been well established , 4. Howe2) and mortality among participants with and without diabetes [. A pooled analysis of 5 longitudinal cohort studies found that adults (age ≥ 65 years) who were normal weight  at the time of incident diabetes had higher mortality than adults who were overweight or obese (BMI ≥ 25 kg/m2)  [Some studies have found that overweight or obese diabetic patients do not have a lower mortality rate at diagnosis than normal-weight diabetic patients or obesity paradox patients . For insdiabetes . Some stdiabetes , 11. A pto 3.20) .In summary, the results of such studies are not always consistent. The association between BMI and the risk of all-cause mortality has been studied in both patients with T2DM and elderly people, while similar studies are lacking in elderly patients with T2DM. Meanwhile, there may be different associations between BMI and mortality in elderly individuals with different glycemic statuses, which needs to be examined. Therefore, we examined the relationships between BMI and all-cause mortality at three fasting glucose levels in an elderly population. The hypothesis is that high BMI compared to normal BMI is associated with decreased risk of all-cause mortality at three fasting glucose levels among elderly people.n = 50,385); (2) missing information on BMI (n = 170); (3) age of under 60 years ; and (4) missing information on covariates (n = 7184). Ultimately, 59,874 participants were included in the analysis. A flowchart of the study participant selection process is shown in Fig. To monitor risk factors for all-cause death in elderly people and improve the health of urban and rural residents, an annual health check-up project has been carried out since 2010 in Xin zheng, Henan Province for the city’s elderly population aged 60 years and above . We usedThis was a retrospective population-based cohort study approved by the Ethics Committee of Zhengzhou University in China, and informed consent was obtained from each participant before data collection. Study procedures were performed in accordance with the Declaration of Helsinki ethical principles for medical research involving human subjects.2), normal weight (18.5 ≤ BMI < 24 kg/m2), overweight (24 ≤ BMI < 28 kg/m2), and obese (BMI ≥ 28 kg/m2) [Height and weight were measured twice by trained nurses following rigorous protocols, and the average of each value was used for analysis. Body height of participants was measured without shoes using a stadiometer, and body weight was measured with participants in light clothing and without shoes using electronic scales . BMI was8 kg/m2) .After fasting for at least 8 h, blood samples were collected from the participants. Fasting plasma glucose (FPG), total cholesterol (TC), and triglyceride (TG), were detected with an automatic biochemical analyzer  . ParticiOther relevant data from the study included study participants’ demographic characteristics, lifestyle behaviors and clinical data. Demographic variables included age, sex (men/women), comorbidities (coronary heart disease (CHD), chronic obstructive pulmonary disease (COPD), cancer and stroke) and medical history of T2DM. Diagnosis of diseases was conducted according to the International Classification of Diseases. CHD included ICD-10 codes I20–I25, stroke included ICD-10 codes I60–I69, cancer included ICD-10 codes C00-C97, and COPD included ICD-10 code J44 . Stroke The study cohort was observed until the end of 2019. The outcome of interest in this study was all-cause mortality. Death certificates were collected from the annual standard health check-up data with a digital linkage to the hospital dataset for admissions. Mortality data were obtained from the National Causes of Death Register.Participants’ baseline sociodemographic characteristics, clinical characteristics, and anthropometric measurements based on the three groups were presented. Descriptive data are presented as numbers (percentages) and quantitative variables are presented as medians (interquartile ranges (IQRs)) with median (IQR). Categorical data were analyzed using the chi-squared test. Continuous data were compared using the Kruskal–Wallis test.2) as the reference group, we constructed two Cox regression models: Model 1 was unadjusted, and Model 2 was adjusted for age, sex, smoking, alcohol consumption, physical activity, history of CHD, COPD, cancer and stroke, SBP and TC at baseline, with the proportional hazard assumption being satisfied. Linear trend tests were conducted by entering the median value of each category of BMI as a continuous variable in the models.First, we calculated the 9-year all-cause mortality by both glycemic categories and BMI categories. Then, we used Cox proportional hazard models to calculate the adjusted HRs with 95% CIs for the four BMI categories and the all-cause mortality for the three glycemic categories. With normal weight  deaths within the first 2 years of follow-up; (2) ever smokers; and (3) participants who self-reported cancer, stroke, coronary heart disease and/or COPD at baseline.P value < 0.05 was considered to be statistically significant.Restricted cubic splines were performed in R × 64 4.0.4 , and the forest plot was performed by Microsoft Excel 2010. The other analyses were performed by SPSS software, V.21.0 . A two-sided At baseline, the median (IQR) age was 65 (62–72) years. Of the 59,874 participants, 49,057 81.9%) had NFG, 5898 (9.9%) had IFG and 4919 (8.2%) had T2DM  were 79.46, 38.19, 24.88, and 25.04, respectively, for those with T2DM; 36.15, 28.37, 19.07, and 17.79, respectively, for those with IFG; and 48.38, 28.46, 19.82, and 18.48, respectively, for those with NFG (Table The median follow-up time was 5.96 years (IQR: 3.50–7.49). The all-cause mortality in the underweight and normal weight ranges was significantly higher in patients with diabetes than in the NFG or IFG groups Table . Among dP for trend < 0.001). Furthermore, in the multivariable-adjusted model, with BMI < 18.5 kg/m2, 24 ≤ BMI < 28 kg/m2, BMI ≥ 28 kg/m2 compared with 18.5 ≤ BMI < 24 kg/m2, the risk values of mortality were HRs: 1.33 (95% CI 1.18 to 1.49), 0.88 (0.83 to 0.93), and 0.90 (0.82 to 0.98), respectively, for NFG; 0.89 (0.55 to 1.46), 0.84 (0.71 to 0.99), and 0.88 (0.70 to 1.11), respectively, for IFG; and 1.42 (0.88 to 2.29), 0.75 (0.64 to 0.89), and 0.76 (0.62 to 0.93), respectively, for T2DM  increase in BMI for T2DM, the risk of all-cause mortality was reduced by 14% .We used restricted cubic spline analyses to examine the association between BMI on a continuous scale and all‐cause mortality after adjustment for age, sex, smoking, alcohol consumption, physical activity, history of CHD, COPD, cancer and stroke, SBP and TC at baseline, as shown in Fig. The analysis of all-cause mortality for BMI per SD change stratified by age and sex is shown in Fig. 2 (the reference range), low BMI significantly increased the risk of mortality among the NFG group  and/or obesity (≥ 28 kg/m2), whereas underweight (< 18.5 kg/m2) had the highest risks. In addition, we found a higher absolute mortality rate among males, but the relative magnitude of the effect on mortality risk was similar among both females and males. These findings indicated that there may be different associations between BMI and mortality among Chinese people with different glycemic statuses.In this study of 59,874 participants from the elderly population, 10,817 (18.1%) had hyperglycemia (IFG and T2DM), highlighting a high prevalence of hyperglycemia in China’s county-level urban elderly population. There were curvilinear-shaped associations between BMI and mortality in the NFG and T2DM groups and no significant association in the IFG group. For people with NFG, lower BMI increased the risk of mortality. For people with NFG and patients with T2DM, compared with 18.5 ≤ BMI < 24 kg/mHui Liu et al. observed that for patients with T2DM aged ≥ 60 years, the overweight and obesity groups were both protective against all-cause mortality compared with the normal weight group [Jihong Ma et al. [Our findings that overweight and obesity were protective factors for all-cause mortality in older adults with T2DM are consistent with previous studies on older adults , 21, 22.ht group . Similar2), but not obese (≥ 27.5 kg/m2), was associated with a reduced risk of death among T2DM patients aged 65 or older. However, the follow-up time was relatively short, with a median follow-up period of 2.9 years [2, the risk of all-cause mortality displayed a U-shaped increase with age of 65 years or older and was not a source of heterogeneity among studies [Information on the association between BMI and mortality in older Asian diabetic patients  is .9 years . A dose– studies . However studies . However studies . The reaThis study found that males had a higher absolute mortality rate than females in the entire older population, which was consistent with previous findings , 26. HowThere are several other possible explanations for the obesity paradox. BMI is only a rough measure of obesity, and a low BMI may reflect sarcopenia (loss of muscle mass), which in turn may reflect factors such as catabolic status, malnutrition, comorbidities or reduced physical activity –30. Low 2, as well as the median of 5.96 years of follow-up, with these attributes, our study was an accurate representation of the older population. The BMI cutoff points used in our study were also the most suitable for the Chinese population, allowing for evaluation of the BMI–mortality association by diabetes status and multivariate analyses to deal with potential confounders, including age, sex, smoking, alcohol consumption, physical activity, history of CHD, COPD, cancer and stroke, SBP and TC at baseline. The results remained unchanged in the sensitivity analysis. Furthermore, we established nonlinear relationships between BMI and risk for all-cause mortality via the natural spline function test among the NFG and T2DM groups, and inverse J curves were identified. Therefore, the findings of this study were credible. The current study has some limitations. First, prevalent diabetes and newly diagnosed diabetes may influence the BMI–mortality association because diabetes treatment often leads to weight loss, or because long-standing diabetes may lead to complications and death. However, data on the duration of diabetes are lacking. Second, the limited investigation time, with a median follow-up of 5.96 years, may not be sufficient for all end points of interest to occur. Lastly, we had no information on socioeconomic and nutritional status, medical history of falls, or social support, which might be particularly relevant to these older adults. More studies investigating all-cause mortality among older Chinese people with different BMI levels by glycemic status is needed to verify these results and enhance the accuracy of our study.The main strengths of this prospective cohort study include the large sample size  composed of participants aged 60 and over who had comorbidities , or BMI < 18.5 kg/mIn this 9‐year cohort study of older Chinese adults, curvilinear associations between BMI and all‐cause mortality were demonstrated for glycemic status in the NFG and T2DM groups, and no significantly association was demonstrated in the IFG group. For Chinese patients with diabetes, both overweight and obesity significantly decreased the risk of mortality compared with normal weight, while underweight increased the risk of mortality, being not significantly. Future studies that aim to determine the relationship between BMI and all-cause mortality in older adults need to be cognizant of the effects of fasting glucose levels.


Vibrio cholerae is the etiological agent of the illness cholera. However, there are non-O1/non-O139 V. cholerae (NOVC) strains that generally lack the toxin gene (ctx) and colonization factors that cause cholera. These NOVC strains are autochthonous members of estuarine environments and a significant cause of seafood-borne gastroenteritis in the United States. The objective of this study was to identify environmental parameters that correlate with NOVC prevalence in oysters, water, and sediment at three ecologically diverse locations in Mobile Bay, AL, including Dog River (DR), Fowl River (FR), and Cedar Point (CP). Oyster, water, and sediment samples were collected twice a month when conditions were favorable for NOVC growth and once a month when they were not. A most probable number (MPN)/real-time PCR assay was used to determine NOVC abundances. Environmental parameters were measured during sampling to determine their relationship, if any, with NOVC at each site. NOVC abundances in oysters at DR, FR, and CP were 0.87, 0.87, and −0.13 log MPN/g, respectively. In water, the median NOVC levels at DR, FR, and CP were 1.18, −0.13, and −0.82 log MPN/mL, and in sediment, the levels were 1.48, 1.87, and −0.03 log MPN/g, respectively. Correlations of NOVC abundances in oyster, water, and sediment samples with environmental parameters were largely site specific. For example, the levels of NOVC in oysters at DR had a positive correlation with temperature but a negative correlation with dissolved oxygen (DO) and nutrient concentrations, NO2−, NO3−, dissolved inorganic nitrogen (DIN), total dissolved nitrogen (TDN), and dissolved inorganic phosphorus (DIP). At FR, however, the levels of NOVC in oysters displayed only a negative correlation with NO2−. When grouping NOVC abundances by temperature, the main driving factor for prevalence, additional correlations with salinity, total cell counts, dissolved organic nitrogen (DON), and dissolved organic carbon (DOC) became evident regardless of the site.IMPORTANCE NOVC can cause gastrointestinal illness in humans, which typically occurs after the consumption of raw or undercooked seafood. Incidence rates of NOVC gastroenteritis have increased during the past decade. In this study, NOVC was enumerated from oysters, sediment, and water collected at three sites in Mobile Bay, with environmental parameters measured concurrently over the course of a year, to identify potential environmental drivers of NOVC abundances. The data from this study, from an area lacking in V. cholerae research, provide a useful baseline for risk analysis of V. cholerae infections. Defining correlations between NOVC and environmental attributes at different sites and temperatures within a dynamic system such as Mobile Bay provides valuable data to better understand the occurrence and proliferation of V. cholerae in the environment. Vibrio cholerae is a pathogenic bacterium that causes the disease cholera by producing cholera toxin (CT) when colonizing the intestines of infected individuals. CT is responsible for the profuse, rice-water diarrhea characteristic of a cholera infection. Thin filamentous appendages on the surface of V. cholerae known as toxin-coregulated pili (TCP) facilitate colonization of the small intestine. Both major virulence factors are encoded on mobile genetic elements integrated into the chromosome of toxigenic V. cholerae O1 and O139 strains  (e (NOVC) .rtx (encoding repeat toxins), toxR (toxin regulatory gene), hly (encoding hemolysins), and omp (outer membrane proteins specific for V. cholerae) genes and a type III secretion system (T3SS)  , 5. The m (T3SS) , 7.V. cholerae strains that are observed when using next-generation sequencing techniques  among the Dog River (DR) (0.87 log most probable number [MPN]/g), Fowl River (FR) (0.87 log MPN/g), and Cedar Point (CP) (−0.13 log MPN/g) sites . The median level of V. cholerae in water at DR (1.18 log MPN/mL) was higher than that at CP (−0.82 log MPN/mL) (P < 0.05), while V. cholerae abundances in water at FR (−0.131 log MPN/mL) were not significantly different from those at DR or CP. In sediment, the abundances of V. cholerae at DR and FR were 1.48 and 1.87 log MPN/g, respectively (P < 0.05).The median abundances of g) sites . In wateg) sites , howeverectively , both ofP = 0.02) and between CP and DR (P = 0.002). NO2− and chlorophyll a (Chl a) were significantly different between CP and DR (P = 0.02 and P = 0.04), and total dissolved nitrogen (TDN) and particulate carbon (PC) were significantly different between CP and FR (P = 0.04 and P = 0.03).The mean environmental parameters  exhibiteV. cholerae levels ranged from −1.82 to 5.46 log MPN/g in oysters, −2.64 to 2.38 log MPN/mL in water, and 1.82 to 3.97 log MPN/g in sediment , and TDN. V. cholerae levels in water at DR were similarly correlated with temperature and DO, which ranged from 3.38 to 11.02 mg/L, and with V. cholerae levels in oysters (2− and NO3− ranged from below the limit of detection (LOD) to 0.8 μM and from below the LOD to 13.76 μM, respectively, while the concentration of DIN ranged from 0.36 to 16.96 μM, and that of TDN ranged from 22.6 to 55.6 μM. Temperatures at DR ranged from 10°C to 32°C.At DR, sediment  to C. Th oysters . The conV. cholerae levels in oysters, water, and sediment ranged from −1.82 to 3.63 log MPN/g, −2.13 to 1.96 log MPN/mL, and −1.13 to 3.97 log MPN/g, respectively , and the levels of V. cholerae in sediment were positively correlated with DO (2− concentration ranged from below the limit of detection (<0.02 μM)   to 0.8 μV. cholerae levels in oysters, water, and sediment ranged from −1.82 to 2.85 log MPN/g, −3.44 to 0.97 log MPN/mL, and −1.82 to 3.97 log MPN/g, respectively . The abundances of V. cholerae in water (P = 0.08) (P = 0.80)   and sedi = 0.80)  did not V. cholerae abundances in oysters (which ranged from −1.82 to 3.63 log MPN/g) and abundances in water (−1.82 to 2.38 log MPN/mL) and sediment (−1.82 to 3.97 log MPN/g)   . V. cholen (DON) . V. cholV. cholerae levels in water, sediment, and oysters during periods of cold water temperatures (9.6°C to 19.0°C) were −3.44 to 1.63 log MPN/mL, −1.82 to 3.97 log MPN/g, and −1.82 to 2.87 log MPN/g, respectively  were complex  and were found only in water and sediment samples during the warm season in two of the lagoons when all three were within 40 km of each other    and was Dissolved oxygen (DO), salinity, and surface water temperature were determined during each collection in the field with a YSI handheld meter . pH and turbidity were measured in the laboratory using a Mettler Toledo  EL20 pH meter and a La Motte  2020 turbidimeter, respectively.V. cholerae in oyster, water, and sediment samples. To obtain a representative estimate of the abundance of V. cholerae in the oyster community, 12 oysters from each site were opened aseptically and homogenized using a sterile blender. For oysters and sediment, 90 mL of alkaline peptone water (APW) /real-time PCR assay was used to determine the abundances of er (APW)  was inocer (APW)  through l Manual .V. cholerae , targeting the ompW gene   Invitrogen Express supermix (2×), forward and reverse V. cholerae primers (0.25 μM each), a V. cholerae probe (0.1 μM), IAC forward and reverse primers (0.2 μM each), an IAC probe (0.1 μM), ROX reference dye (0.004 μM), an IAC template , and a V. cholerae template (2 μL of the boiled enrichment). Amplification was conducted with an initial denaturation step (95°C) for 2 min and 40 cycles of denaturation (95°C for 1 min), annealing (60°C for 1 min), and elongation (72°C for 1 min). Default analysis settings were used, except that the threshold was set at 0.015 . Detection of V. cholerae by real-time PCR was used qualitatively to determine positive/negative tubes in the MPN series.A 1-mL aliquot of the MPN overnight enrichment from each tube showing turbid growth was transferred to a microcentrifuge tube and heated at 100°C for 10 min. Sample aliquots were immediately cooled on ice or frozen at temperatures of below −15°C. If frozen, samples were thawed completely before use. Sample tubes were centrifuged at 8,000 relative centrifugal force (RCF) for 60 s, and the resulting supernatant was used in a real-time PCR assay to specifically detect ity, CA)  with an 2−, NO3−, and NH4+) were measured in water samples from each site on the Skalar San++ autoanalyzer, using glutamic acid as a standard  (micromolar) and dissolved inorganic nitrogen  (micromolar) were measured from 10 mL of filtered water sampled at each site. Samples were placed into muffled scintillation vials with 0.2 mL of 0.17 M MgSO4 and evaporated to dryness at 95°C. The residue was baked at 450°C to decompose organic phosphorus compounds. The dissolved organic phosphorous (DOP) concentration (micromolar) was calculated as the difference between TDP and PO43−. Particulate organic phosphorous (POP) (micromolar) was measured from 100-mL filtered water samples. Filters were rinsed twice with 2-mL aliquots of 0.17 M Na2SO4, placed into muffled scintillation vials with 2 mL 0.017 M MgSO4, dried at 90°C, and stored in a drying oven at 40°C. All phosphorus-based analyses were performed on the Shimadzu UV-160 spectrophotometer at 885 nm, using potassium phosphate as a standard  (micromolar) and phosphate  concentrations (per microgram), fluorescence was measured for 100-mL water samples. Each sample was filtered through a 25-mm Whatman glass fiber filter , which was folded in half and submerged in 10 mL acetone (90%). The samples were then frozen for 24 h. The samples were then shaken vigorously and centrifuged for 10 min at 2,205 RCF. The fluorescence of the samples was measured using a Turner Designs TD-700 fluorometer calibrated with a chlorophyll a solid standard (For the determination of chlorophyll standard .−1), incubated for 2 min, and filtered through a black 0.2-μm-pore-size polycarbonate filter with a cellulose-nitrate backing filter. The filter was washed with 2 mL of 18.2-MΩ water, removing the unbound DAPI, and then dried in the dark. Filters were mounted onto a slide and frozen at −20°C until they were ready to be examined by epifluorescence microscopy  is the volume of the sample stained/total volume of the sample available.A standard 4′,6-diamidino-2-phenylindole (DAPI) protocol was used to determine total cell counts in water and sediment. Water (4.9 mL) from each site was mixed with 0.1 mL glutaraldehyde and held at 2°C to 4°C for 15 min. A 0.2-μm polycarbonate filter was placed over a backing filter on a Millipore  model 1225 sampling manifold vacuum filtration cell harvester. Two milliliters of the sample and 50 μL of DAPI were placed onto the vacuum and allowed to stand for 2 min. The sample and stain were drawn through the filter, which was then removed from the vacuum and allowed to dry before affixing to a slide. Labeling conditions vary by cell type, and the protocol was modified for this application . To detecroscopy , 53. TheV. cholerae was done using standard MPN tables based on real-time PCR results  with a Tukey post hoc test was used to determine significant differences among V. cholerae abundances in oysters, sediment, and water among sites. As the environment data were nonnormal, differences in environmental parameters among sites were evaluated using Kruskal-Wallis ANOVA with Tukey’s post hoc test. Significant differences between the abundances of V. cholerae during cold- and warm-water periods were analyzed using a Mann-Whitney-Wilcoxon test. Pearson correlation analysis was used to determine the relationship between V. cholerae abundances and environmental parameters when tested by site and seasonality. A significance level of a P value of <0.05 was set for all analyses for correlations between V. cholerae abundances and environmental parameters at different sites and temperatures. All analyses were conducted using SigmaPlot 12.5 .Data were analyzed by location and warm or cold seasonal conditions. Warm conditions were defined as any sampling period when water temperatures were ≥20°C, while cold conditions were defined as periods with temperatures of <20°C. Enumeration of  results . Half th


Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable.We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains.The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu.For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder. A total 157 patients with dementia were included, and 156 normal elderly people were assessed by matching cognition of age, gender, and educational level. This study was approved by the Ethics Committee of Shanghai Tongji Hospital. All subjects were informed with a signed consent.n = 80), cerebral small vessel disease (n = 46), frontotemporal dementia (n = 2), Parkinson's disease with dementia (n = 2), and mixed dementia (n = 27). Various types of dementia use the following diagnostic criteria: (1) Cerebral small vessel disease‐related reference VASCOG (2014) , 4th edition of the American Society of Psychiatry Diagnostic, and Statistical Manual of Mental Disorders. The causes of dementia included AD  was used to determine the overall cognitive function. MMSE: illiterate (0) ≤ 17 points, primary school (6) ≤ 20 points, junior high school and above (9) ≤ 24 points, reference to criterion, 1990  was used to represent the memory function in the cognitive domain, and the immediate memory  was considered to be impaired in the single cognitive domain (> 1SD). Verbal fluency test (VFT) is used to represent language function, and VFT single cognitive domain is impaired (> 1SD). Using Shape Trail Test A (STT A) to represent executive function, STT, a single cognitive domain, was impaired (> 1SD). Rey‐Osterrieth complex graphics test was impaired (> 1SD).12, thyroid function, or syphilis antibody positive, and HIV antibody positive; (7) cannot cooperate with neuropsychological tests.Exclusion criteria: (1) with a history of symptomatic stroke or head MR found cerebral infarction lesions, diameter greater than 20 mm; (2) have other central nervous system diseases such as infection, epilepsy, and demyelination history; (3) psychiatric history of schizophrenia and severe depression; (4) severe somatic diseases ; (5) alcoholic or drug addiction; (6) obvious abnormal folic acid, vitamin B2.2The general information of all subjects, including gender, age, and education level were collected. Assessment of cognitive function and daily living ability: all patients completed a set of neuropsychological tests, including the overall cognitive function assessment: MMSE. The subcognitive domain assessment included: Hopkins Verbal Learning Test (HVLT) immediate memory, 5‐min delay in memory (DeM 5 min), 20‐min delay in memory (DeM 20 min), Logical Memory‐W Story (LM), VFT, Boston Naming (BNA), STT A, and Shape Trail Test B (STT B), and Rey‐Osterrich complex graphics test copy and memory.2.2.1The Siemens Verio 3.0 T superconducting magnetic resonance and standard orthogonal head coil were used. The patient was in supine position, and the head was fixed in the head bracket as far as possible. The surrounding space was filled with sponge, and the orthogonal head coil was used. AC‐PC was used as the baseline, with a thickness of 5 mm, interval of 1 mm, and FOV of 230 mm × 230 mm for scanning. The axial, sagittal, and hippocampal oblique coronal T1 weighted sequences, axial T2 weighted sequences, axial plane fluid attenuation inversion recovery (FLAIR) sequences, and T2* sequences were obtained, respectively. The parameters were as follows: TI weighted sequence: repeat time (TR) 1530 ms, echo time (TE) 9 ms, inversion time (TI) 559 ms. T2 weighted sequence: TR 4210 ms, TE 96 ms, and TI 1800 ms. FLAIR sequence scan parameters: TI 1800 ms: TR 5000 ms, and TE 94 ms. Reverse time (TI) 1800 ms. T2* sequence scan parameters: TR 55 ms, TE 45 ms, and TI 1800 ms.2.2.2Global cortical atrophy (GCA)    number of lacunar lesions ≥ 1; (2) the number of cerebral microbleeding (CMB) ≥ 1; (3) medium and severe degree of enlarged perivascular space (EPVS) in basal ganglia (> 10); (4) White matter hyperintensity (WMH): Periventricular Fazekas score ≥ 3 or deep score 2–3 points for 1 point. The above items scored 1 point each, and the sum of each item was the total CSVD score, 0–4 points.2.2.3p < .01.All the grade indexes and linear measurement indexes of brain atrophy were completed by two neurologists with systematic training and ensuring that intra‐class correlation coefficient (ICC) > 0.8, 2.3All statistical analyses were completed based on software R 3. 6. 3 version. Demographic data were calculated as means ± SD or quartile IQR. Statistical analyses were used the t‐test or Wilcoxon rank sum test. Spearman or Pearson correlation coefficient was used to analyze the correlation between brain atrophy index and cognitive function. The brain atrophy data and demographic data  of all subjects were divided into 4/5 training set and 1/5 test set through the calculation of machine learning by R caret. The fivefold cross‐validation training models were used, and the following five machine learning algorithms were used: random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and regularized logistic regression (LR)  and positively correlated with the genu of corpus callosum (p < .01). The positive correlation coefficient between executive function and temporal horn ratio was the largest , and the negative correlation coefficient between executive function and the genu of corpus callosum was the largest . The detailed results are shown in Figure From the graph, the general cognitive function and subcognitive domain were correlated with multiple brain atrophy indexes. The score of MMSE, immediate memory, word fluency, and visualspatial function negatively correlated with temporal horn ratio p < .0 and posi3.3By analyzing the characteristics of brain atrophy and clinical features of patients, XGBoost showed the best specificity (0.839) and AUC 0.784) specificity (0.839) and AUC (0.784) on the test set, and the sensitivity was 0.645. GBM showed the optimal sensitivity (0.839), specificity (0.613) and AUC (0.767) on the test set. The detailed results are shown in Table  specific3.4When predicting the overall cognitive impairment, the SVM model showed the optimal sensitivity 0.783), specificity (0.529) and AUC (0.661) on the test set. GBM showed the best specificity (0.941), sensitivity (0.348) and AUC (0.668). The RF model showed the best AUC 0.686, the sensitivity was 0.413, and the specificity was 0.941. The detailed results are shown in Table , specifiWhen predicting single cognitive domain‐immediate memory impairment, SVM model has the best sensitivity 0.684), specificity (0.667), and AUC (0.669) on the test set. The test set of random forests showed the optimal specificity (0.917), sensitivity of 0.421, and AUC of 0.684. GBM showed the best AUC (0.766) on the test set, the sensitivity was 0.658, AUC was 0.875, and the details are shown in Table , specifiThe XGBoost model has the best sensitivity (0.719), specificity (0.700), and AUC (0.766) when predicting single cognitive domain word fluency (vegetable). The RF model showed the best specificity (0.933), sensitivity was 0.562, AUC was 0.765, and the details are shown in Table When predicting a single cognitive domain‐executive function, SVM model has the best sensitivity of 0.791, specificity of 0.579, and AUC of 0.710. The LR model showed the best specificity was 0.895, the sensitivity was 0.605, and the AUC was 0.742; the details are shown in Table When predicting single cognitive domain visuospatial dysfunction, GBM model showed the best sensitivity (0.681), specificity 0.733), and AUC (0.691). RF and XGBoost model showed the best specificity (1.000), RF sensitivity (0.383), and AUC (0.667). The details are shown in Table 33, and A3.5Construction of nomogram to predict the overall cognitive impairment: Based on the important feature distribution of the random forest model, education, THD/MTIW, cella media index, and SCW/MTIW are selected to construct the screening model. The AUC of the nomogram was 0.809, the sensitivity was 0.814, and the specificity was 0.668. The details are shown in Figure Construction of nomogram for predicting single cognitive‐immediate memory impairment: Based on the important feature distribution of GBM, education, THD/MTIW, SCW/MTIW, and the DBCP are selected to construct the model. The AUC of the nomogram was 0.780, the sensitivity was 0.726, and the specificity was 0.716. The details are shown in Figure Construction of a nomogram for predicting single cognitive‐word fluency disorder: Based on the important feature distribution of XGBoost, education, THD/MTIW, cella media index, SCW/MTIW, and corpus callosum body thickness are selected to construct the model. The AUC of the nomogram was 0.774, the sensitivity was 0.664, and the specificity was 0.806. The details are shown in Figure Construction of a nomogram for predicting single cognitive‐executive dysfunction. Based on the important feature distribution of LR, education, THD/MTIW, cella media index, SCW/MTIW, and the thickness of the corpus callosum genu are selected to construct the model. The AUC of the nomogram was 0.761, the sensitivity was 0.698, and the specificity was 0.726. The details are shown in Figure Construction of the nomogram for predicting single cognitive‐visualspatial disorder: Based on the important feature distribution of LR, education, THD/MTIW cella media index, SCW/MTIW, and GCA hippocampal atrophy are selected to construct the model. The AUC of the line map was 0.770, the sensitivity was 0.844, and the specificity was 0.603, as shown in Figure Construct a nomogram for predicting dementia: Based on the XGBoost model, the top four brain atrophy indexes are selected: THD/MTIW, cella media index, SCW/MTIW, and genu thickness of corpus callosum. Combined with education, the prediction was established. The AUC of dementia was 0.798, the sensitivity was 0.790, and the specificity was 0.692. The details are shown in Figure 4It is urgent to establish a convenient screening tool for dementia, and brain atrophy is closely related to human brain structure and advanced cognitive function, and it is widely used as an important factor in dementia screening model. Previous studies mostly established prediction models based on high‐resolution structural images of the whole brain or magnetic resonance data of diffusion tensor imaging of 3DT1  for 12 years of follow‐up by using visual scale assessment of brain atrophy , lacunar, and WMH to establish a model. The study found that cortical atrophy was relative to cognitive decline  This study is a single‐center study, which still needs larger multicenter data to expand the sample size, so as to train more optimized models. (2) The dementia screening model established in this study had good predictive effect in internal validation and could continue external validation in follow‐up cohort or other cohorts. (3) This study established a two‐class prediction model based on normal cognition and dementia. We need to further collect data to establish a three‐class prediction model of normal cognition, MCI, and dementia. (4) The brain atrophy evaluation index selected in this study was a linear measurement index. Although it was practical, more objective brain atrophy evaluation indexes based on clinical MRI needed to be developed.5For nonstroke elderly people, the machine learning model based on clinical brain MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder. XGBoost model has the best effect in predicting dementia, temporal horn ratio, corpus callosum genu thickness, cella media index, suprasellar cistern ratio, and education are important predictors of dementia.Database Project of Tongji Hospital of Tongji University (Grant No. TJ (DB)2102); Clinical Research Project of Tongji Hospital of Tongji University (Grant No. ITJ(ZD)2002); Project of the National Social Science Foundation of China (NSSFC) (No. 19CYY018); Shanghai Hospital Development Center Foundation (Grant No. SHDC12021110)The study desgined by Wei Zhang, Xiaoran Zheng, Ningxin Dong and Yunxia Li. Material preparation, data collection and analysis performed by Wei Zhang, Xiaoran Zheng, Renren Li, Meng Liu, and Weixin Xiao. Statistical analysis was performed by Wei Zhang and Feiyang Xu. The first draft of the manuscript written by Wei Zhang and Lihe Huang and critically revised by all authors. Feiyang Xu provided technical assistance with the machine learning aspects of the project. The authors thank him for his support. All authors read and approved the final version of the manuscript.The authors declare no conflict of interest.https://publons.com/publon/10.1002/brb3.2726.The peer review history for this article is available at


Dryopteris fragrans (L.) Schott, had antifungal activity against several dermatophytes. This study was aimed at exploring the antifungal mechanism of PB against Trichophyton rubrum (T. rubrum). The effectiveness of PB in inhibiting T. rubrum growth was detected by time-kill kinetics study and fungal biomass determination. Studies on the mechanism of action were investigated using scanning electron microscopy (SEM), transmission electron microscopy (TEM), sorbitol and ergosterol assay, nucleotide leakage measurement, and UPLC-based test and enzyme-linked immunosorbent assay. Fungicidal activity of PB was concentration- and time-dependent at 2 × MIC (MIC: 20 μg/mL) after 36 h. The total biomass of T. rubrum was reduced by 64.17%, 77.65%, and 84.71% in the presence of PB at 0.5 × MIC, 1 × MIC, and 2 × MIC, respectively. SEM analysis showed that PB changed mycelial morphology, such as shrinking, twisting, collapsing, and even flattening. TEM images of treated cells exhibited abnormal distributions of polysaccharide particles, plasmolysis, and cytoplasmic content degradation accompanied by plasmalemma disruption. There were no changes in the MIC of PB in the presence of sorbitol. However, the MIC values of PB were increased by 4-fold with exogenous ergosterol. At 4 h and 8 h, PB increased nucleotide leakage. Besides, ergosterol content in T. rubrum membrane treated with PB at 0.5 × MIC, 1 × MIC, and 2 × MIC was decreased by 9.58%, 15.31%, and 76.24%, respectively. There was a dose-dependent decrease in the squalene epoxidase (SE) activity. And the reduction in the sterol 14α-demethylase P450 (CYP51) activity was achieved after PB treatments at 1 × MIC and 2 × MIC. These results suggest that PB displays nonspecific action on the cell wall. The membrane damaging effects of PB were attributed to binding with ergosterol to increase membrane permeability and interfering ergosterol biosynthesis involved with the reduction of SE and CYP51 activities. Further study is needed to develop PB as a natural antifungal candidate for clinical use.Isoflavaspidic acid PB (PB), a phloroglucinol derivative extracted from aerial parts of  Trichophyton, Epidermophyton, and Microsporum [Trichophyton rubrum is the main etiological agent, accounting for about 70% of dermatophytosis [Superficial mycoses have affected about 20-25% of the worldwide population with incidence, in particular those involving dermatophyte infection, having increased in the recent decades , 2. Dermphytosis .Currently, controlling dermatophytosis primarily relies on several classes of commercial antidermatophytic drugs, including polyenes (amphotericin B), azoles , allylamines (terbinafine), and echinocandins  . DespiteDryopteris fragrans (L.) Schott (D. fragrans), a deciduous perennial herb, belongs to family Dryopteridaceae and broadly distributes in Asia-temperate, Europe and North America [ America , 13. Inf America . In orde America .D. fragrans. A series of phytochemical studies on this plant led to the identifications of phloroglucinols, terpenes, flavonoids, phenylpropanoids, etc. It has been reported that 95% ethanol extract was the most effective fraction of D. fragrans, which possessed fungistatic and fungicidal activities on T. rubrum [Microsporum canis (M. canis) was found to be sensitive to the effective fraction of D. fragrans both in vivo and in vitro [D. fragrans.Over the past few years, numerous scholars have focused on the screening of antifungal active ingredients from the aerial parts of . rubrum . Similarin vitro , 18. As in vitro , HPLC-ESD. fragrans, had a broad spectrum of antifungal activity, with minimal inhibitory concentration values for a total of 25 dermatophyte strains between 20 and 40 μg/mL [ERG6 and ERG11 [T. rubrum is still poorly explored. In this study, time–kill kinetics study, fungal biomass determination, scanning and transmission electron microscopy, sorbitol protection assay, ergosterol binding assay, intracellular leakage measurement, and ergosterol quantification assay, as well as the activities of squalene epoxidase and sterol 14α-demethylase, were performed to investigate the mode of action of PB against T. rubrum in vitro.Isoflavaspidic acid PB (PB), a phloroglucinol derivative extracted from aerial parts of 40 μg/mL . Our prend ERG11 . HoweverT. rubrum (CMCC(F)T1d) and Candida parapsilosis (ATCC 22019) were obtained from the Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China. The strains were maintained at -70°C on Sabouraud Broth  supplemented with 30% glycerol. Before conducting the experiments, these strains were reactivated and subcultured on the Sabouraud dextrose agar medium  at 28°C until suitable conidiation. The SDA medium contained dextrose 20 g, agar 15 g, and peptone 10 g in 1.0 L of sterile water.Two standard strains including T. rubrum (CMCC(F)T1d) was grown on SDA at 28°C for 7 days. Fungal colonies were suspended in approximately 5 mL of phosphate buffer saline  by gently rubbing them with the tip of a Pasteur pipette. The densities of these suspensions were adjusted to obtain the inoculum of 1 × 106~5 × 106 CFU/mL and confirmed by counting the microorganisms in a Neubauer chamber.The inoculum suspension was prepared in accordance with the previous studies . T. rubrTerbinafine , amphotericin B , and miconazole  were used as positive controls. They were obtained from Sigma-Aldrich Co. LLC. . The medicines were dissolved in dimethyl sulfoxide  to obtain an initial concentration of 6.4, 2, and 2 mg/mL, respectively, and kept at -20°C until use. The final concentration of DMSO did not exceed 0.5% in the assays, suggesting that it did not to interfere with fungal growth , 25.D. fragrans sample taken in August 2017 in Wu-Da-Lian-Chi, Heilongjiang province, China . The plant's voucher specimen (registration number: XLMJ-201708) was deposited in the School of Traditional Chinese Medicine, Guangdong Pharmaceutical University.Prof. De-Lian Zhang  identified the D. fragrans and the subsequent PB isolation (purity > 95%) was carried out as previously described [D. fragrans were extracted by 50% ethanol three times under refluxing. The crude extract was preliminarily isolated by six gradients of silica gel column chromatography with a mobile phase of petroleum-acetone . Six fractions (Frs. A→F) were prepared in column chromatography. Frs. C were further applied to gel Sephadex LH-20 column (60-100 mesh), eluted with chloroform-methanol (1 : 1), and purified by preparative HPLC to obtain the final products. The molecular formula of PB was analyzed by ESI-MS analysis, and its chemical structure .Ethanolic extract of aerial parts of escribed . The airtructure  was confT. rubrum (CMCC(F)T1d) were investigated by broth microdilution method. For the MIC determination, the stock suspensions were diluted 1 : 100 in RPMI-1640 medium to obtain the final inoculum of 2 × 104~5 × 104 CFU/mL. Serial twofold dilutions were incorporated into these wells to yield final concentrations ranging from 0.6250 to 320 μg/mL for PB and between 0.0625 and 32 μg/mL for the AMB-treated group, 0.0039 and 2 μg/mL for the TBF-treated group, and 0.0156 and 8 μg/mL for the MCZ-treated group. Subsequently, 100 μL of corresponding fungal suspensions were added to the 96-well microplates. Negative control (no medicines) was used in this study to verify conidia viability and susceptibility to DMSO (0.5%). Finally, the microplates were sealed aseptically and incubated at 35°C. A reading was made to evaluate MIC values after the appropriate incubation time. MIC was defined as the lowest drug concentration that was able to visually inhibit 100% fungal growth. Candida parapsilosis (ATCC 22019) was utilized as a quality control (QC) strain to confirm accuracy and duplicability of our tests .Based on Clinical and Laboratory Standards Institute (CLSI) document (M38-A2), minimal inhibitory concentration (MIC) and minimal fungicidal concentration (MFC) values of PB against For the MFC determination, aliquots of inoculum were taken away from the wells of no visible growth to petri dish containing SDA medium. The microplates were incubated at 28°C for 14 days. The MFC was defined as the lowest concentration resulting in no growth on subculture . The assT. rubrum, time-kill kinetics was determined as previously described [4~5 × 104 CFU/mL in RPMI-1640 medium containing PB at 0.5 × MIC, 1 × MIC, and 2 × MIC along with positive control (TBF). Control growth without drugs was tested similarly. The suspensions were incubated at 30°C with continuous agitation (150 rpm). At different time intervals , samples were removed from the tubes and diluted with RPMI-1640 medium. Then, aliquots of 10-fold dilution were spread uniformly onto the surface of SDA culture medium. After the appropriate time of incubation, colonies were analyzed through the plate colony counting technique. Time-kill curve took shape by plating log10 CFU/mL against time (h). Fungicidal activity was considered as reduction in growth more than 99.9% or >3log10 in CFU/mL from the initial inoculum [To evaluate the dynamic fungicidal effect of PB on escribed , with miinoculum .T. rubrum (CMCC(F)T1d) under different environments was assessed using a previous method [4~5 × 104 CFU/mL was incubated with PB at the concentration of 0.5 × MIC, 1 × MIC, and 2 × MIC. Correspondingly, positive controls were made with TBF at the concentration of 1 × MIC. Drug-free RPMI-1640 medium with the strain was used as growth control. These resulting mixtures were incubated at 30°C for 7 days and transferred into previously weighted Eppendorf tubes. After centrifugation at 14000 × g for 20 min, pellet was collected and dried at 60°C for 3~6 h to reach a constant weight. The total biomass of mycelium was determined in triplicate using an analytic balance.For determining the effect of PB on mycelial growth, the total biomass of s method , with slT. rubrum (CMCC(F)T1d), SEM observation was performed in accordance with previous research [T. rubrum cells were fixed at 4°C with 2.5% (v/v) glutaraldehyde in PBS  for 4 h. The fixed samples were washed with the same buffer twice each for 10 min and dehydrated through a graded ethanol series  for 15 min at room temperature. Finally, samples were subjected to critical point drying in liquid carbon dioxide , coated with gold using ion sputtering device , and examined under a scanning electron microscope .In order to identify the changes in the apparent structure of research . After eT. rubrum (CMCC(F)T1d), TEM observation was carried out as previously described [Due to the changes observed in the internal structure of escribed . In brieT. rubrum (CMCC(F)T1d) were measured by using the broth microdilution method in 96-well microplates. The plates were incubated at 35°C, and the results of analysis made after 4 days. This assay was carried out in triplicate to calculate the geometric mean values.To assess if the test product destroys the cell wall, sorbitol protection assay was performed using medium with and without sorbitol (control) as previously described . The sorT. rubrum (CMCC(F)T1d) was determined by broth microdilution techniques, in the absence and presence of ergosterol  added to culture medium at a concentration of 400 μg/mL. Since AMB has been known to affect ergosterol in the cytoplasmic membrane, it was used as a positive control. The plates were incubated at 35°C and counted after 4 days. Each result was expressed as geometric mean of triplicates.In order to determine whether the mechanism of PB was involved in binding to ergosterol in the fungal membrane, ergosterol binding assay was detected based on the previous method , with slT. rubrum (CMCC(F)T1d) was measured based on the method described previously [4~5 × 104 CFU/mL. Subsequently, aliquots of the fungal inoculum were exposed to PB  and TBF (1 × MIC), respectively. Growth control involved the equal amount of sterile distilled water replacing the drugs. Cultures were incubated at 35°C under agitation in an incubator shaker . At different time intervals (4 h and 8 h), fungal cells were passed through 0.22 μm filters and collected for absorbance analysis at 260 nm with a UV-visible spectrophotometer . Moreover, SDS (2%) was regarded as lysing agent, which produces 100% nucleotide leakage. Rate of nucleotide leakage was computed by comparing the test values with the lysing agent values. Results presented are the means of values from three independent assays.The leakage of nucleotides from eviously , 34. FunT. rubrum mature biofilm. Initially, fungal suspensions with 100 mL of RPMI-1640 medium containing PB  and TBF (1 × MIC) were prepared at a cell density of 2 × 104~5 × 104 CFU/mL. Following incubation in the shaking incubator at 35°C for 24 h, fungal cells were harvested by centrifugation at 2000 × g for 5 min and the upper suspensions were discarded. The wet weight of cell pellets was detected after washing twice with sterile distilled water. Each sample was added the lysing agent (25% potassium hydroxide ethanol solution) and mixed well. The sample was placed in a water bath at 80°C for 2 h, cooled to room temperature, and decanted into a separatory funnel. Then, sterols were extracted by the mixture of 2 mL sterile distilled water and 3 mL petroleum ether. The organic layer was moved to sterile tubes and evaporated to dryness in a water bath at 80°C. Finally, these dry products were dissolved in 5 mL methanol and obtained by filtrating through 0.22 μm filters.Ergosterol extraction and quantitation were performed by an UPLC-based test. This method was adapted from Lin et al.  in our gμm). The mobile phase was methanol at a flow rate of 0.5 mL·min−1. Aliquots of 10 μL were directly injected into HPLC for determination. The detection wavelength was set at 281 nm, calculating ergosterol content from the standard calibration curve using ergosterol as standard. The results were expressed as Corresponding prepared samples were separated and analyzed at 35°C by using a Venusil MP C18 analytical column . The samples were suspended in extracting solution, sonicated on an ice bath, then frozen, and thawed twice more to extract SE and CYP51 enzymes. The supernatant was removed by centrifugation at 5000 × g for 5 min at 4°C. SE activity was determined strictly by a commercial kit assay kit , and CYP51 activity was detected using a commercial kit assay kit . The OD values at 450 nm were read using a microplate reader  within 15 min.The effects of PB against SE and CYP51 activities were investigated by ELISA according to published protocols . Fungal X ± SD). Levels of statistical significance were set at ∗p < 0.05 and ∗∗p < 0.01.IBM SPSS Statistics 21.0 software and GraphPad Prism version 8.0.2 were used to analyze data. Analysis of variance using one-way ANOVA was performed to test the difference between treated and untreated samples. Values were presented as mean ± standard deviation  was within the range specified in the CLSI reference documents (0.5~4 μg/mL). Besides, both MIC value and MFC value of MCZ for T. rubrum (CMCC(F)T1d) were 0.25 μg/mL.MIC and MFC were used to determine the susceptibilities of T. rubrum (CMCC(F)T1d) is shown in 10 in the CFU/mL versus the initial inoculum). However, a decrease in the population of T. rubrum reached 100% at 2 × MIC after 36 h treatment. Changes between fungistatic and fungicidal activity further revealed that PB had a concentration-dependent antifungal activity. In addition, TBF at 2 × MIC also exerted fungicidal activity up to 24 h.In order to understand the rate of antidermatophyte activity, PB and TBF (positive control) were selected for time-killing kinetics assay. The time-killing curve of PB against T. rubrum were determined by measuring the dry mycelia mass . Additionally, the decreased rate of total biomass exposed to TBF at 1 × MIC was 54.12%. A dose-dependent inhibition of total biomass was noted in T. rubrum cells after PB treatment (p < 0.01). The total biomass was reduced by 64.17%, 77.65%, and 84.71% in the presence of PB at 0.5 × MIC, 1 × MIC, and 2 × MIC, respectively.The effects of PB on the total biomass of lia mass . The resT. rubrum was observed by SEM. As illustrated in T. rubrum showed the normal morphogenesis with smooth surfaces, plump hyphae, uniform cytoplasm, and good growth. The treatment of PB at 0.5 × MIC caused shrinkage of fungal filaments in T. rubrum . Fungal growth was observed in control.T. rubrum, the nucleotide leakage was tested. The results are shown in p < 0.01). At different time intervals (4 h and 8 h), 0.5 × MIC, 1 × MIC, and 2 × MIC of PB increased the amount of nucleotide leakage with a concentration-dependent effect (p < 0.01).To examine the membrane damaging extent of PB against T. rubrum cell membrane treated with PB at different concentrations was evaluated using an ultraperformance liquid chromatography (UPLC) method. The linear relationship between peak height (y) and ergosterol concentration (x) (4-40 μg/mL) was good using the equation y = 6287x − 361.06, r = 0.9997. As seen in T. rubrum. Among them, the ergosterol content was decreased by 9.58%, 15.31%, and 76.24% in the presence of PB at 0.5 × MIC, 1 × MIC, and 2 × MIC, respectively. And the decreased rate of ergosterol content in 1 × MIC of TBF was 41.21%.The ergosterol content of T. rubrum membrane (p < 0.01). Additionally, PB repressed the activity of SE enzyme in T. rubrum in a dose-dependent manner (p < 0.01).The effect of PB on SE activity was investigated by enzyme-linked immunosorbent assay (ELISA) using TBF as positive control. As illustrated in p < 0.01) on CYP51 activity. Similarly, PB at 1 × MIC and 2 × MIC also caused an obvious reduction in the CYP51 activity (p < 0.01). The CYP51 activity in T. rubrum cells treated with PB at 0.5 × MIC was smaller than that of the control group but has no statistical significance (p > 0.05).The effect of PB on CYP51 activity was investigated by ELISA using MCZ as positive control. The results are shown in T. rubrum is an anthropophilic specie that can get carbon, nitrogen, phosphorus, and sulfur from the host's nutritional macromolecules [olecules . Since iolecules .D. fragrans is a traditional medicine fern used to treat dermatophytosis. Previous investigations have shown that the antifungal activity of this genus of plants is closely related to the amount of phloroglucinol [D. fragrans [μg/mL) [For decades, natural products and their phytochemicals have provided the basis for the vast majority of anti-infective therapies in clinical use, which are potential valuable source of new agents. glucinol , 37. PB fragrans  and possfragrans . In susc [μg/mL) . TherefoT. rubrum was submitted to the experimental approach for time-kill kinetics in order to characterize a dynamic connection between concentration and antidermatophyte activity over time. This assay has valuable therapeutic significance because it not only analyzes the duration of fungistatic action but also measures the fungicidal speed [T. rubrum at 2 × MIC after 36 h of incubation. TBF as a positive control showed a similar effect by 24 h. These results implied that PB could inhibit the growth of T. rubrum, which resembled those of Liu et al. [T. rubrum after treatment with ethanol extracts of D. fragrans and TBF.al speed . In thisu et al. , with reT. rubrum could be evidenced by our findings that PB treatment led to the reduction of mycelial biomass production. However, its possible antifungal mechanisms have not been elucidated and need to be further explored.In the pathogenesis of dermatophytosis, filamentous hyphae were produced during the infection process, which exacerbated the damage and penetrated into the deeper keratinized tissues . Thus, bT. rubrum. The hypha morphology of T. rubrum in the control group was normal and homogeneous with plump fungus and smooth surfaces. After being exposed to PB at 0.5 × MIC, the hyphae shrank and formed a rough surface, whereas those treated with PB at relatively higher concentrations (1 × MIC and 2 × MIC) were distorted, collapsed, and even flattened. According to the report [T. rubrum hyphae. This is consistent with our results of T. rubrum treated with TBF at 1 × MIC. SEM observation revealed that PB induced aberrant morphologies, which are essential for growth inhibition, cell viability, and virulence reduction.It has been reported that there is a significant correlation between morphological abnormalities and growth inhibition . SEM imae report , TBF couT. rubrum treated with PB [T. rubrum treated with PB showed abnormal distributions of polysaccharide particles, plasmolysis, degradation of organelles, and even excessive vacuolization accompanied by disintegration of cytoplasmic membrane. Our findings are in accordance with the previous investigations demonstrated below. The increase in hypha cell membrane permeability had been reported to give possible rise to plasmolysis [T. rubrum was damaged by PB as a target.TEM was regarded as an auxiliary technique to observe changes in the internal morphology of  with PB . The SEMsmolysis . As discsmolysis , polysacThe existing antifungal classes are mostly focused on the cell wall and membrane, in which the cell wall confers cell morphology against external osmotic shocks . If drugT. rubrum.The sorbitol protection assay was performed to investigate whether PB acted on the fungal cell wall. This method used sorbitol as an osmotic protectant to stabilize fungal protoplasts . Cell waThe cell membrane is a dynamic structure consisting of a lipid bilayer with enzymes and transport proteins embedded . Since cAMB, a polyene antifungal drug, binds to the ergosterol in the fungal cell membrane and forms stable pores, producing membrane degradation and increased permeability. Small molecular substances and ions flow out of the cytoplasm, resulting in the death of fungi . ErgosteT. rubrum was subjected to the method for nucleotide leakage. MOPS buffer is not capable of being degraded by cells and keeps T. rubrum in the stationary phase. Compounds with uracil, mainly nucleotides, release to the SDA medium from T. rubrum cells and exhibit the strongest absorption at 260 nm [T. rubrum in a concentration-dependent manner after 4 or 8 h of incubation. Similar results were recorded in the studies concerning the effects of magnoflorine and AMB on the cellular material leakage of T. rubrum [T. rubrum.In order to assess the degree of damage to cell membrane, t 260 nm . In this. rubrum . It suggT. rubrum membrane after the treatment of PB. We speculated that disruption of ergosterol biosynthetic pathways might explain the mechanism of PB on fungal growth inhibition.Cell membrane of fungi is enriched with diverse lipids, including glycerophospholipids, sphingophospholipids, and sterols . ErgosteT. rubrum treated with PB, preventing growth in a way similar to TBF.Ergosterol biosynthesis relates to a complex metabolic process. SE and CYP51 are among the key enzymes in the ergosterol biosynthesis pathway . For mosα-methyl group [Other major classes of antifungal agents take effects at later steps in the ergosterol biosynthesis pathway. For instance, MCZ acts by the inhibition of CYP51. This enzyme rests on activation of cytochrome P450, of which the active site is an iron protoporphyrin unit . During yl group . Therefoyl group . From thM. canis [Methylphloroglucinol derivatives had been reported to reduce ergosterol content by inhibiting the activities of SE and CYP51 in M. canis . This reD. fragrans could inhibit the growth of T. rubrum and induce the morphological alternations. The active target of PB was not the cell wall, but cytoplasmic membrane. PB primarily exerted antifungal activities not only by binding to ergosterol in cell membrane but also by lowering the ergosterol biosynthesis. Operation of these two modes of action altogether by PB could be very beneficial to combating infections caused by T. rubrum. However, the toxicity of PB plays a significant role in the clinical application. In order to develop a potential treatment of dermatophytosis for PB, more attention has been focused on the therapeutic benefits and safety on animal models.In summary, our study demonstrated that PB derived from aerial parts of