Comprehensive data are needed to monitor antibiotic prescribing and inform stewardship. We aimed to evaluate the current antibiotic prescribing patterns, including treatment switching and prolongation, in the paediatric primary care setting in Italy. This database study assessed antibiotic prescriptions retrieved from Pedianet, a paediatric primary care database, from 1 January 2012 to 31 December 2018. Descriptive analyses were stratified by diagnosis class, calendar year, and children’s age. Generalized linear Poisson regression was used to assess variation in the prescriptions. In total, 505,927 antibiotic prescriptions were included. From 2012 to 2018, the number of antibiotics per child decreased significantly by 4% yearly from 0.79 in 2012 to 0.62 in 2018. Amoxicillin prescriptions decreased with increasing children’s age, while macrolides and third-generation cephalosporins had the opposite trend. Prescriptions were associated with a diagnosis of upper respiratory infection in 23% of cases, followed by pharyngitis (21%), bronchitis and bronchiolitis (12%), and acute otitis media (12%). Eight percent of treatment episodes were prolonged or switched class, mostly represented by co-amoxiclav, macrolides, and third-generation cephalosporins. Our findings report an overall decrease in antibiotic prescriptions, but pre-schoolers are still receiving more than one antibiotic yearly, and broad-spectrum antibiotics prescription rates remain the highest. Italy is one of the countries with the highest rate of antibiotic prescribing for children in Europe ,2,3, as Studies have demonstrated that around 20 to 50% of antibiotic prescriptions are unnecessary or inappropriate ,7,8, witThis unnecessary exposure to antibiotics may subsequently increase the risk of serious drug side effects and costs . It has In order to understand the scope and the burden of antibiotic prescriptions in Italy, comprehensive data are needed. Despite several studies having been carried out in hospitals ,13,14,15Information captured in population datasets is valuable, as it will enable us to monitor antibiotic prescribing patterns. It will also assist in implementing antibiotic stewardship and informing policymakers.The primary aim of our study was to comprehensively explore the current antibiotic prescription patterns in the paediatric primary care setting in Italy over the years, by child’s age and by diagnosis. Secondly, we described the treatment switch and prolongation patterns between different antibiotic classes.In total, 157,915 children , for a total of 717,202 person-years of follow-up, were included. Overall, the median age was 7 years and most of the children resided in the north of Italy . The median number of children managed by each FP was 1144 (IQR: 640). In total, 505,927, prescriptions were considered. Descriptive data are presented in p = 0.004) from 0.80 prescriptions per child in a year (95% CI: 0.78–0.81) in 2012 to 0.64 (95% CI: 0.63–0.65) in 2018, with an annual 4% reduction   .The highest AIs were reported for children aged from 1 to 4 years, with values around 1.2; on average, for every year of increase in the patient’s age, the AI was reduced by 13% , from a maximum of 36% in 2012 for children <1 year of age to a minimum of 14% in 2014 for children aged 13 years for amoxicillin and from a maximum of 9% in 2012 for children aged 3 years to a minimum of 1% in 2018 for children aged 13 years for second-generation cephalosporins .An increasing trend with increasing age was noticed for co-amoxiclav, third-generation cephalosporins, and macrolides . Co-amoxiclav rates varied from a minimum of 28% in 2012 for children age <1 year to a maximum of 37% in 2017 for children aged 12 years; for second-generation cephalosporins, the rates varied from a minimum of 9% in 2018 in infants to a maximum of 18% in 2017 in 12-year-old children. In 2014, the macrolide rates were the highest with respect to the different calendar years in children aged 3–13 years .Males were at a higher risk of receiving co-amoxiclav and macrolides , and children residing in the centre and the south of Italy were at a higher risk of receiving co-amoxiclav and cephalosporins compared with children in the northern regions, who were at a higher risk of receiving amoxicillin .Of a total of 497,186 visits associated with an antibiotic prescription, 3595 visits were excluded because they had more than one diagnosis associated with the prescription.n = 113,518), followed by diagnoses of pharyngitis , bronchitis and bronchiolitis , and acute otitis media . In 84,006 prescriptions (16%), no diagnosis was associated with the antibiotic (Respiratory tract infections accounted for around 75% of the antibiotic prescriptions. Antibiotics were associated with a diagnosis of upper respiratory tract infection (URTI) in 23% of cases (tibiotic .Co-amoxiclav prescription rates varied from 51% for soft tissue infections (STI) to 24% for bronchitis and bronchiolitis. The amoxicillin prescription rate was 32% for pharyngitis, 30% for AOM, and 20% for URTI. Twenty-seven percent of prescriptions for urinary tract infections (UTIs) were for third-generation cephalosporins and J01XX class antibiotics  .The prescription rates for the different diagnosis classes stratified by calendar year and years of age are presented in Overall, 485,976 had just one diagnosis associated with one antibiotic prescription and 312 were excluded because the follow-up was shorter than 14 days. The 485,664 visits were grouped by 448,058 treatment episodes, and 1268 were excluded because they were associated with antibiotic prophylaxis.In total, 35,460 treatment switches or prolongations were included. Treatment episodes with at least one switch were 17,044, of which 453 had two or more switches. Treatment episodes with at least one prolonged antibiotic numbered 19,516. No difference in the diagnosis prevalence compared with overall prescriptions was noted .n = 5750 times, 31% of overall switches), especially for macrolides  and third-generation cephalosporins . The second most frequent class of antibiotics that was switched was macrolides , followed by amoxicillin . . .The antibiotics that were prolonged the most were co-amoxiclav , followed by third-generation cephalosporins (N = 3890) and macrolides (N = 3900), which each accounted for 10% of the overall treatment switches or prolongation .When stratified by diagnosis, the most frequent switch was for the combination of macrolides and co-amoxiclav for URTI, which accounted for 15% of the total switches for URTI.Overall, 3358 antibiotics were switched before the fourth day (early switch), while the rest were classified as late switches. Co-amoxiclav was switched with a third-generation cephalosporin in 123 cases of pharyngitis, which was the most common early switch overall. The antibiotic class that was switched the most after the fourth day was the macrolides, which were substituted with co-amoxiclav in 577 episodes of URTI .Overall, the mean day of treatment switch was the eighth (SD: 4.0), with variations based on the antibiotic class accounting for the switch and the diagnosis . In totaTo our knowledge, this is the first longitudinal study to comprehensively assess antibiotic prescribing patterns in paediatric primary care in Italy. Over the period 2012–2018, we observed a decrease in total antibiotic prescriptions. Male children and those residing in central or southern regions were at a higher risk of receiving an antibiotic. In the overall population, co-amoxiclav and amoxicillin prescription rates increased over the years, with co-amoxiclav increasing, and also with an increase in the child’s age, and amoxicillin decreasing with an increase in the child’s age. Children residing in northern Italy were at a higher risk of receiving amoxicillin than those in the central and southern regions. Co-amoxiclav was the most prescribed antibiotic for respiratory infections , accounting for half of the prescriptions. The prescribing patterns remained similar, with minimal variations between years of age over time. Overall, 8% of antibiotic treatment episodes were prolonged or switched in class, especially among co-amoxiclav, macrolides, and third-generation cephalosporins.Assessing the drivers for the overprescribing of broad-spectrum antibiotics in the community is crucial, especially nowadays, since antibiotic resistance is dangerously rising globally. Indeed, many ecological studies showed a population-level association between antibiotic use and resistance . GoossenIn line with previous studies ,5, ItaliSimilar to our previous study , amoxiciMost of the switches occurred on the eighth day after the initial antibiotic prescription. This indicates that treatment failure could be the cause of switching antibiotics, since if an adverse event occurs, the switch would likely be in the first days after the prescription. On the other hand, adverse events such as diarrhoea following high clavulanate dosages might appear around the fifth day after the treatment initiation, since the hypothesized changes in the microflora could take some time ,45. OtheSeveral limitations need to be addressed in our study. First, even though the study was based on individual-level patient data, we were not able to retrieve a diagnosis in 16% of cases. However, we believe that missing diagnoses in a few patients will not change the overall prescribing patterns in our study. Second, due to the nature of the infectious disease, some children had multiple treatment episodes, which could lead to close monitoring by their FPs. Thus, a 14-day screening period from the initial episode and any subsequent similar diagnosis treatment during the period was considered as a follow-up visit for the same episode. It might be argued that in some cases , the healing could be slower; however, we believe that this is an exception and not the rule. Moreover, by defining an episode, we were able to reduce the over-representativeness of complex patients. Third, since family paediatricians are the primary referrals for health-related matters as well as the gatekeepers for specialist referrals in the context of the NHS, we believe that loss to follow-up, in the case of treatment switching or prolongation, is rare. Indeed, most of the diagnoses considered consisted of uncomplicated community infections that are treated at the primary care level, where access to healthcare is free and granted by the NHS. Moreover, caregivers are discouraged from requiring further specialistic evaluations for uncomplicated community infections due to the long waiting lists and increased costs related to a specialist’s evaluation. However, we acknowledge this as a limitation. Fourth, most of the oral paediatric formulations are liquid formulations, and the dosage is based on the child’s body weight; thus, older children might require two packages to finish the antibiotic treatment course instead of one. According to our definition, if the second prescription was on a follow-up visit of the same treatment episode, it was reported as treatment prolongation. Fifth, data on allergies and vaccination status were not assessed. A change in these factors might have been the cause of the high broad-spectrum prescription rates or switches in therapy. Sixth, we were unable to determine the reasons for switching antibiotics in our current study. It is warranted to explore the reasons for switching antibiotics further.The Pedianet database was used as the source of the study. Pedianet is a national population database that contains anonymous patient-level data of more than 500,000 children since 2004, corresponding to around 4% of the annual paediatric population, who received healthcare from 161 family paediatricians (FPs) in Italy who were part of the Pedianet network.®)  in their professional practice. About 4500 family paediatricians use Junior Bit® in Italy and they are all potential candidates to be part of the Pedianet network [The network links FPs distributed throughout several Italian regions designated by the Italian NHS, including Friuli-Venezia Giulia, Liguria, Lombardia, Piemonte, Veneto, Lazio, Marche, Toscana, Abruzzo, Campania, Sardegna, and Sicilia, and who use the same software .Antibiotic index (AI): the number of antibiotic prescriptions per person-year.Treatment episode: all prescriptions occurring within 14 days of the first antibiotic prescription.Switch: treatment episode with a second prescription different in class from the first Early switch: the first switch occurring within 1–3 days of the first prescription Late switch: the first switch occurring within 4–14 days of the first prescription Treatment prolongation: treatment episode with a second prescription.Day of switching: difference in days between the date of the second prescription and the date of the first prescription in a switch.The following definitions were applied:The person-years of children included in the study were accumulated from either January 2012 or when the patients started receiving care. All patients were followed up until the last date of their care or the end of the study period in December 2018.The primary outcomes considered were: (i) antibiotic index, (ii) antibiotic prescriptions rat, (iii) time to switch/prolongation, (iv) early switch rate, (v) late switch rate, and (vi) prolongation rate. Switch and treatment prolongation were only assessed for prescriptions associated with one diagnosis amongst patients who had an antibiotic prescribed for a reason other than prophylaxis and for children with at least 14 days of follow-up after the prescription index date.Antibiotics were classified according to ATC codes as amoxicillin (J01CA04), co-amoxiclav (J01CR02), macrolides (J01FAx), third-generation cephalosporins (J01DCx), second-generation cephalosporins (J01DDx), J01XX (J01XXx), thiamphenicol (J01BA02), lincosamides (J01FFx), other aminoglycosides (J01Gx), and others (J01x). If two or more prescriptions with the same specific 7-digit ATC code were reported on the same visits for the same diagnosis, just one was considered for the purposes of the study.All diagnoses linked to the same visit (max = 3) were classified according to the diagnostic categories for common outpatient infections. For determination of the years of age, the date of birth was approximated to 1 January and the year of age was calculated as the difference between 1 January and the date of birth for all the calendar years considered.If two or more prescriptions occurred the same day, they were excluded from the analysis of Outcomes (iii), (iv), (v), and (vi) because the order of drug events could not be determined. Early and late switches were assessed for only the first switch in the treatment episode.The analysis had a mainly descriptive nature. Stratification was performed according to the diagnosis class, calendar year, and year of age . Categorical variables were expressed using numbers and percentages, and continuous variables were expressed as means and standard deviations (SD) or medians with interquartile ranges (IQR). Categorical variables were compared with a chi-squared test in a contingency table (r × c). Continuous variables were compared by a non-parametric Kruskal–Wallis rank sum test.To estimate the annual changes in antibiotic prescriptions, the Mann–Kendall test was performed to determine whether the time series had a monotonic upward or downward trend and, if significant, the generalized linear Poisson regression was fitted. The Poisson model was chosen over linear regression because of the use of count data for antibiotic prescriptions (non-negative integers) and the high number of zero values in the dependent variables. The dependent variable was a count of antibiotic prescriptions, while calendar year, sex, age , and geographical residency of the patient according to the ISTAT area were the covariates considered . The logp < 0.05 was considered statistically significant. The analysis was performed using R statistical software–v. 3.6.2  [A two-sided Austria) . Graphs Due to the retrospective nature of the study and the use of anonymized data, no ethical consent was required to conduct the study. The study protocol and access to the data were approved by the Internal Scientific Committee of So.Se.Pe. Srl, the legal owner of Pedianet.Our findings report an overall decrease in antibiotic prescriptions over the years, but pre-schoolers are still receiving more than one antibiotic yearly. Moreover, most of the prescribed antibiotic can be classified as broad-spectrum antibiotics  and little to no variation was noted over the years. Estimating the drivers of antibiotic prescriptions is essential to define the area of intervention for antibiotic stewardship in primary care, either to reduce prescriptions for infection that have mainly a viral cause, or to improve treatment selection . However


Background: Pulmonary arterial hypertension (PH) is a common complication in patients with obstructive hypertrophic cardiomyopathy (OHCM). The risk factor of PH in patients with OHCM has not been fully elucidated, and even atrial fibrillation (AF) was considered a risk factor of PH. Thus, our study aimed to investigate risk factors of PH and the relationship between PH and AF in patients with OHCM.Methods: We retrospectively enrolled 483 consecutive patients diagnosed with OHCM at Fuwai Hospital  from January 2015 to December 2017. Clinical and echocardiographic parameters were compared between patients with and without PH.Results: Eighty-two (17.0%) patients were diagnosed with PH in this study. Compared to patients without PH, those with PH were significantly older, had a lower body mass index (BMI), were more likely to be female and more symptomatic [New York Heart Association Class 3 or 4 symptoms], and had a higher AF prevalence. A multivariate analysis indicated that AF was an independent risk factor of PH . Moreover, PH was independently associated with a higher AF incidence after adjusting for age and left atrial diameter .Conclusion: AF was independently associated with PH in patients with OHCM. Further, PH was significantly associated with an increased risk of AF, which suggested that AF could aggravate PH and that PH may promote AF processes, forming a vicious circle. Pulmonary arterial hypertension (PH), characterized by a progressive increase in pulmonary vascular resistance, leading to right ventricular dysfunction, is a common complication in patients with hypertrophic cardiomyopathy (HCM) , 2. PatiN = 82) and patients without PH (N = 401) groups based on the pulmonary artery systolic pressure (PASP) at initial evaluation. Clinical and echocardiographic parameters were compared between the two groups.A total of 483 consecutive patients diagnosed with OHCM at Ward 7 of cardiovascular surgery of the Fuwai Hospital  from January 2015 to December 2017 were enrolled in this study. All patients enrolled in this study were admitted to our hospital for surgical treatment, and no outpatients were included because their complete medical records cannot be obtained. We retrospectively analyzed patient echocardiographic data and divided patients into patients with PH  and then use an equation of mean PAP = 2/3rd of PADP + 1/3rd of PASP to calculate PASP. In this study, 79 patients diagnosed with PH through TR jet velocity and 3 patients through peak PR velocity. The RAP  was estimated in the subcostal view based on the inferior vena cava size and collapsibility during inspiration at rest . Complete M-mode, two-dimensional, and Doppler studies were performed on patients in the left lateral decubitus or supine position, using standard parasternal, apical, and subcostal approaches. Diameters of cardiac chambers were presented as the maximum value of the anteroposterior diameter in cardiac cycles. Maximum left ventricular (LV) wall thickness was the greatest dimension measured at any site within the LV chamber at end diastole. The LVOT gradient was scanned with a continuous-wave Doppler ultrasound to measure the maximal outflow velocity and was estimated using the simplified Bernoulli equation. PASP was estimated from the tricuspid regurgitant jet velocity using the modified Bernoulli equation and adding to right atrial pressure (RAP) when patients' tricuspid regurgitation jet can be detected. Moreover, we measured mean pulmonary arterial pressure (PAP) through peak pulmonary regurgitation (PR) velocity among those who failed to measure PASP through the tricuspid regurgitation jet. Furthermore, mean PAP was calculated using the following formula: mean PAP = 4(peak PR velocity) at rest , 13. Mor at rest .t-test, and continuous variables were compared using the Mann-Whitney U test continuous variables; the χ2 test or Fisher's exact test was used to compare nominal variables, as appropriate. A least absolute shrinkage and selection operator (LASSO) logistic regression analysis was performed to select variables associated with AF to be included in a multivariate logistic regression model , R version 3.6.0, and GraphPad Prism version 8.0  were used for calculation and illustration, respectively.Results are presented as mean ± standard deviations or as number (percentage), as appropriate. Independent variables were compared using Student's on model . All p-vp < 0.001), had a lower body mass index (BMI) , were more likely to be female  and more symptomatic [New York Heart Association Class 3 or 4 symptoms] , and had a higher AF prevalence . Moreover, values of echocardiographic parameters such as left atrial diameter, LVOT gradient, and the proportion of moderate or severe mitral and tricuspid regurgitations were significantly higher in patients with PH than those without PH.A total of 82 (17.0%) patients were diagnosed with PH at initial echocardiographic evaluation. The clinical characteristics of patients with and without PH are presented in p = 0.006) and pulmonary aterial diameter  increased. However, we did not find a direct association between PASP and the rest LVOT gradient  and age . When we divided patients with PH into two groups based on PASP , AF prevalence was higher in patients with PH than in patients without PH; however, AF prevalence was the same in group I and group II . Furthermore, the multivariate analysis revealed that, in addition to age, left atrial diameter, and rest LVOT gradient, PH  was an independent risk factor of AF (Univariate analysis of the differences between patients with and without AF was presented in Risk factors associated with PH and AF selected from the LASSO logistic regression analysis are present in The main findings of this study are that, first, PH prevalence in this study population is ~17.0%. Second, in addition to female sex, BMI, left atrial diameter, moderate or severe mitral and tricuspid regurgitation, AF was found to be an independent risk factor of PH. Third, PH was independently associated with a higher incidence of AF, even though there was no relationship between PASP and AF prevalence.Studies concerning PH prevalence in patients with HCM were limited. PH prevalence in this single-center cohort study was 17.0%, and PH frequency was similar to that noted in Musumeci's study, which had a PH incidence of 11.4% at initial evaluation . In a hiThere were several factors that may contribute to the development of PH in patients with HCM. Consistent with previous studies, our findings confirmed that PH has a strong female predominance, which suggested that sex-related differences in the production and metabolism of hormones may affect the pulmonary vasculature , 15, 16.Previous studies reported a higher incidence of supraventricular arrhythmia, AF, and stroke events in patients with PH than those without PH , 23, 24.Pulmonary hypertension is a common complication in patients with HCM and is usually associated with worse survival. A large population study reported a 40% risk of death at 10 years after the initial evaluation of OHCM in patients with PH who were treated medically; this rate is two-fold higher than in OHCM patients without PH . ConversOur study was a single-center, retrospective, observational study with some inherent limitations. Due to the relatively small sample size of patients diagnosed with PH, our study findings should be prudently extrapolated to other centers, and further studies with a larger population are needed to confirm our results. Moreover, because of the retrospective nature of data collection, some factors, such as right atrial diameter, main pulmonary arterial diameter, level of right atrial fibrosis, and the electric remolding of the right atrium could not be evaluated in this study. This limited our ability to illustrate the role of PH in AF development directly. Moreover, PH assessment was based on echocardiography, not right heart catheterization, which is the gold standard for evaluating pulmonary pressure.In conclusion, in addition to female sex, BMI, left atrial diameter, moderate or severe mitral and tricuspid regurgitation, AF was found to be independently associated with PH in patients with OHCM. Further, PH was independently associated with an increased risk of AF, which suggested that AF could aggravate PH and that PH may promote AF processes, forming a vicious circle.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Ethics Committee of Fuwai Hospital. The patients/participants provided their written informed consent to participate in this study.CN and SYW contributed to the design of the study. CN, CZ, and MX contributed to the data collection and analysis, while all authors contributed to data interpretation. CN drafted the manuscript and ZL, QY, YM, and RW contributed significantly to the preparation. All authors critically revised the manuscript and gave final approval and agreed to be accountable for all aspects of the work, to ensure its integrity and accuracy.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cerebral ischemia induces oxidative brain injury via increased oxidative stress. Epigallocatechin gallate (EGCG) exerts anti-oxidant, anti-inflammatory, and metal chelation effects through its active polyphenol constituent. This study investigates whether EGCG protection against cerebral ischemia-induced brain cortex injury occurs through modulating lipid peroxidation, antioxidant activity, the essential elements of selenium (Se), zinc (Zn), magnesium (Mg), copper (Cu), iron (Fe), and copper (Cu), Zn/Cu ratio, and the hazardous metal lead (Pb). Experimentally, assessment of the ligation group was performed by occlusion of the right common carotid artery and the right middle cerebral artery for 1 h. The prevention group was intraperitoneally injected with EGCG (50 mg/kg) once daily for 10 days before cerebral ischemia. The brain cortex tissues were homogenized and the supernatants were harvested for biochemical analysis. Results indicated that cerebral ischemia markedly decreased SOD, CAT, Mg, Zn, Se, and Zn/Cu ratio and increased malondialdehyde (MDA), Fe, Cu, and Pb in the ischemic brain cortex. Notably, pretreating rats with EGCG before ischemic injury significantly reversed these biochemical results. Our findings suggest that the neuroprotection of EGCG in the ischemic brain cortex during cerebral ischemia involves attenuating oxidative injury. Notably, this neuroprotective mechanism is associated with regulating lipid peroxidation, antioxidant activity, essential elements, Zn/Cu ratio, and hazardous metal Pb. Epigallocatechin gallate (EGCG) is a natural compound that mainly exists in fruits and plants to protect against environmental stress . The ratCerebral ischemia results from the occlusion of cerebral arteries, blocking blood flow to the affected brain tissues. Cerebral ischemia-generated reactive oxygen species (ROS) can result in elevated oxidative stress and further brain injury. Recent studies have revealed that the biochemical changes resulting in brain cell death after cerebral ischemia occur in an orderly series of steps: ionic perturbation, oxidative stress, and inflammation ,2,7,8. TSuperoxide dismutase (SOD) and catalase (CAT) are important antioxidants that are pivotal for protecting the brain from oxidative attack ,8. DetoxMaintaining appropriate brain concentrations of several essential elements is required for normal brain functions . SignifiDespite numerous studies exploring the mechanism underlying EGCG neuroprotection, whether this mechanism involves the regulation of brain concentrations of essential elements and hazardous metal is still unknown and is the aim of this present study.In total, forty male Sprague-Dawley rats weighing from 250 to 300 g were enrolled in this present work. The experimental animals were purchased from BioLASCO, Taipei, Taiwan. In order to stabilize the physiological conditions of animals, rats were housed in stainless-steel mesh cages under controlled conditions. The relative humidity was controlled in the range of 50 ± 20% and the temperature was controlled at 22 ± 2 °C and maintained at 12-h light-dark cycle for 7 days. Rats were randomly assigned into four groups of 10 each as below: control , ligation  and the right middle cerebral artery (MCA) for 60 min on day 10th), EGCG  at a dosage of 50 mg/Kg once daily for consecutive 10 days), and prevention . The cerebral ischemic operation was performed at the rat age of eight-week that the right CCA was exposed and carefully isolated from the vago-sympathetic trunks followed by loosely encircled for further ligation. After performing a midline incision, the skull bone was craniectomized to expose the right MCA. Meanwhile, an 8–0 suture  was used to position so as to encircle the MCA for further ligation. For CCA ligation, a midline neck incision was made and the right CCA was exposed and carefully separated from the vago-sympathetic trunks. The right CCA was loosely encircled with a 4-O suture for later occlusion. At the end of ligation surgery, the right brain cortex tissues were immediately collected and homogenized, followed by harvesting of the supernatants for further biochemical analysis. Experimentally, all performed protocols throughout this animal experiment were approved in advance by the Institutional Animal Care and Use Committee  of Central Taiwan University of Science and Technology in Taiwan.3PO4 and 800 μL of the KCl solution and vortexed carefully. In addition, the standard solution of 1,1,3,3-tetra ethoxy propane was applied to react with the thiobarbituric acid (TBA) substance followed by boiling for 60 min. Experimentally, 4 mL of the butanol was carefully added into the solution and mixed for 5 min followed by harvesting the supernatant for MDA analysis. Basically, the detective principle is to measure the intensity of the generated pink color via the reaction of MDA with TBA substance. The MDA level in the right brain cortex homogenates was measured by spectrophotometry at the wavelength of 532 nm .The malondialdehyde (MDA) level was measured to evaluate ROS-mediated lipid peroxidation in the brain cortex. In this work, 0.2 g of the obtained right brain cortex tissues were homogenized with the volume of 5 mL of ice KCl (154 mM) solution via Teflon pestles homogenizers followed by centrifuge at 4 °C for 10 min at the speed of 650 g, and the supernatant was immediately harvested. For the MDA analysis, 200 μL of the supernatant was mixed with 3 mL of the H2•−). The generated superoxide radical interacts with the tetrazolium salt, and the antioxidant activity of SOD was analyzed by spectrophotometry . The enzyme activity of SOD was expressed in terms of U per mg of protein concentration. For the analysis of CAT activity, the commercial kit was purchased from Cayman Chemical Company . For the analytical procedure, the methanol was reacted with hydrogen peroxide under the catalyzation of the CAT enzyme to produce the product of formaldehyde. The generated formaldehyde interacted with the chromogen of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole formaldehyde, and the CAT activity was detected by spectrophotometry . The enzyme activity was expressed in terms of U per mg of protein concentration.In this study, a total of 0.2 g of the right brain cortex tissue was harvested for measuring the antioxidant activity of SOD and CAT. Briefly, the SOD activity was analyzed according to the detective procedures for the Cayman’s superoxide dismutase assay kit purchased from Cayman Chemical Company, Ann Arbor, MI, USA. The analytical principle is that the xanthine oxidase reacts with the hypoxanthine so as to produce the superoxide radical  was used in this study. The standard solution of each element and hazardous metal was dissolved in the concentration of 0.1 mol/L nitric acid solution purchased from Merck, Darmstadt, Germany. The SavantAA Z graphite furnace atomic absorption spectrophotometer with longitudinal Zeeman effect background correction and PAL4000 auto-sampler system was used in this study .In this present study, the commercial BioChain protein assay kit, purchased from San Francisco, CA, USA, was used to analyze the protein concentration. The analytical method of the protein concentration assay kit was improved by the Coomassie Blue G method. In brief, the analytical principle of this method is that the reagent was reacted with the protein to produce a blue color complex. The color intensity of the generated blue color complex is paralleled with the protein concentration. Finally, the protein level was assayed by spectrophotometry  at the wavelength of 595 nm experimentally.p-value was less than 0.05, the statistical differences were considered significant in this present study. a: p < 0.05, vs. control; b: p < 0.05, vs. ligation.All the obtained values from the experimental analysis were expressed as mean ± S.D. The obtained data were analyzed by the statistical method of Kruskal–Wallis one-way analysis of variance (ANOVA). Basically, if the experimental values exhibit significant differences among groups, Fisher’s least significant difference (FLSD) was applied to compare each group. If the p < 0.05) in the ligation group as compared to the control group. Conversely, pretreatment of rats with EGCG before cerebral ischemic insult obviously (p < 0.05) attenuated the MDA concentration in the prevention subject as compared to the ligation group.In comparison with the control group, the SOD activity was significantly decreased in the ligation group . PretreaExperimentally, the CAT value was significantly diminished in the prevention subject as compared to the ligation group . HoweverIn the ligation group compared to the control group, a significant decrease of the brain cortex Mg level was found . PretreaThe value of essential element Zn was significantly diminished in the ligation subject as compared to the control group . InteresThe Fe level in the ligation group was markedly higher as compared to the control group . Pretreap < 0.05) in the artery ligation group as compared to the control group. Specifically, pretreating rats with EGCG before cerebral ischemic injury obviously (p < 0.05) attenuated the Pb concentration in the prevention group as compared to the ligation subject.Our present findings highlight that cerebral ischemia not only results in brain tissue decreases in Mg, Zn, Se, and the Zn/Cu ratio and antioxidant activity of SOD and CAT, but also increases concentrations of MDA, Fe, Cu, and Pb. Notably, pretreating rats with EGCG before ischemic insult significantly reversed these effects. EGCG is known for its wide array of beneficial properties to humans and animals in terms of anti-oxidation and anti-inflammation . Thus, mCerebral ischemia is characterized by the occlusion of cerebral artery caused by thrombi or embolism to interrupt blood flow into the brain ,18. IschDespite the numerous and continuous achievements over the years in exploring the mechanisms of EGCG underlying neuroprotection, it has not yet been elucidated so far whether the neuroprotective mechanism of EGCG is associated with the modulations of essential elements and hazardous metal during cerebral ischemic injury. A recent study reveals that cerebral ischemia-induced brain cell death is implicated with three dominant mechanisms and suggests that ionic perturbation is the first, followed by oxidative stress, and inflammation is the last one . In factMg is known as the most abundant intracellular cation and is involved in a wide range of biological functions in all living organisms . Mg actsEssential element Zn is important in numerous physiological functions in humans, including anti-oxidation, anti-inflammation, brain development, and wound healing ,19. Our It has been evidenced that essential trace element Se exerts multiple beneficial effects, including ROS scavenging, anti-inflammation, anti-oxidation, and increasing the antioxidant capacity . ConversRecent studies have revealed that phenolic hydroxyl groups of EGCG exert a metal chelating effect ,32,33. ACu is essential for the brain and is mainly involved in the biological functions in the synthesis of neuropeptides, catecholamine, and cytochrome oxidase ,38. SimiA recent study revealed that the Zn/Cu ratio is a useful biomarker for assessing oxidative stress, nutritional status, and inflammation . A studyAccumulating evidence indicates that the mechanism underlying Pb toxicity involves elevated oxidative stress due to ROS production and antioxidant capacity depletion ,43. In tEGCG confers neuroprotection through multiple mechanisms. This present study is the first to highlight that EGCG modulates the levels of essential elements and hazardous metals in the brain cortex during cerebral ischemic injury. Altogether, our findings demonstrate that the neuroprotective effect of EGCG involves the attenuation of ROS-mediated lipid peroxidation and the Fenton reaction-associated elements Fe and Cu, increasing the antioxidant activity of SOD and CAT, the concentrations of the natural antioxidant elements Mg, Zn, and Se, and the Zn/Cu ratio. More critically, EGCG exerts its chelating property to significantly chelate Fe, Cu, and Pb, thereby obviously ameliorating Fe-, Cu-, and Pb-induced ROS generation, lipid peroxidation, and further oxidative brain injury. The limitation of this present study is the lack of cerebral blood flow measurement to precisely confirm the status of ischemic surgery. In addition, the detailed mechanism by which EGCG increases antioxidant-related elements of Mg, Zn, and Se will require further elucidation.


Long noncoding RNA (lncRNA) is reported to be upregulated in many tumors. Although the expression of lncRNA in oral squamous cell carcinoma has been assessed, the association between lncRNA expression and prognosis or clinicopathological feature still remains controversial. Therefore, we conducted a meta-analysis to verify whether lncRNA expression was related to prognosis or clinicopathological features in patients with oral squamous cell carcinoma.We searched Embase, PubMed, Web of Science, Cochrane library, Chinese National Knowledge Infrastructure, and Wanfang databases from inception to February 2021. The language included Chinese and English. The published literature on lncRNA expression and prognosis or clinicopathological characteristics of patients with oral squamous cell carcinoma was statistically analyzed. The combination of hazard ratios (HRs), odds ratios (OR), and 95% confidence intervals (95% CIs) were applied to evaluate the effects of lncRNA on the prognosis and clinicopathological features of oral squamous cell carcinoma.This study could provide a comprehensive review of the available evidence of lncRNA on the prognosis and clinicopathological features of oral squamous cell carcinoma.The conclusion of our study will provide the updated evidence to judge the lncRNA on the prognosis and clinicopathological features of oral squamous cell carcinoma. Clinically, surgical treatment, radiotherapy, and chemotherapy are the main treatment methods. However, due to its high degree of malignancy and local invasion and lymph node metastasis, up to 60% of patients with oral squamous cell carcinoma are in the late stage of clinical progress. As a result, the survival rate of patients is low, and the life quality of patients is seriously affected. In the past 30 years, the overall survival (OS) rate of patients with oral squamous cell carcinoma reached about 50%, without any significant improvement. Early diagnosis and treatment of oral squamous cell carcinoma are the key steps to control the disease and improve the survival rate. Therefore, it is clinically important find new markers for diagnosis, prognosis, and treatment of oral squamous cell carcinoma.Oral cancer is one of the common oral malignant tumors, accounting for more than 90%, while oral squamous cell carcinoma occupies over 80% of head and neck malignant tumors, often occurring in tongue, gingiva, buccal mucosa, and other parts of body.–11 In recent years, it has been obvious that lncRNA plays an important role in the occurrence and development of tumors. In oral squamous cell carcinoma, the abnormal expression of lncRNA is also closely related to its occurrence and progression, and may be applied as a biomarker or therapeutic target for the diagnosis and prognosis of oral squamous cell carcinoma.–16With the length of more than 200bplong-stranded noncoding RNA (lncRNA) lacks the potential of protein coding and is a RNA transcription product. It is involved in DNA replication, RNA transcription, protein translation, cell development, and differentiation, and is an important regulator of cell biology.,17–23 However, there still exists the lack of effective evidence-based medicine to prove it. Therefore, this study conducted a systematic evaluation and meta-analysis of the included articles to further explore the relationship between lncRNA and clinicopathological features and the prognosis of patients with oral squamous cell carcinoma.Many studies have revealed that lncRNA is closely associated with the clinicopathological features, survival, and biological behavior of tumor cells in patients with oral squamous cell carcinoma.22.1 this protocol is drafted.This protocol has been registered at Open Science Framework and its registration number is DOI 10.17605/OSF.IO/Z5Q3K. Any significant amendments of this protocol will be recorded in the Open Science Framework before the review is completed. According to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols (PRISMA-P) statement,2.2The study would be included in this meta-analysis if it meets the following criteria: (1) Patients were diagnosed with oral squamous cell carcinoma; (2) lncRNA expression level was detected; (3) Patients were divided into two groups based on the lncRNA expression level; (4) Efficient data were provided; (5) Full-text was available; (6) The research data were complete, and literatures on hazard ratio (HR), 95% confidence interval (CI), and observation indexes can be extracted. The following studies would be excluded from this meta-analysis: duplicated publications or patients, reviews, case reports, letters, comments, animal experiments, cell experiments, or studies without efficient data.2.3Embase, PubMed, Web of Science, Cochrane library, Chinese National Knowledge Infrastructure, and Wanfang databases were comprehensively searched up to February 2021. The language was Chinese and English. We also checked the references of retrieved articles to avoid missing relative studies. The combined method of MeSH Term and free words would be adopted for literature retrieval. A search strategy of PubMed is summarized in Table 2.42.4.1All reviewers received evidence-based training and adhered to the summarized process. The 2 reviewers independently screened the literature based on the title, abstract, and key words of literatures, and excluded the irrelevant literatures. The rest of literatures were further confirmed by 2 researchers after reading the full text. The excluded research and the reasons for the exclusion were recorded. The differences between the 2 reviewers were resolved through consensus or a third independent reviewers. The process of the selection is illustrated in Figure 2.4.2Two authors extracted the data and assessed the quality of included studies independently. Any disagreement during this process was resolved through group discussion.According to the inclusion and exclusion criteria, the literature was screened and the relevant data were extracted. The data extracted from the literature include the first author, publication year, number of patients and region, lncRNA name, cut-off value of lncRNA expression level, detection methods, prognostic indicators such as OS and DFS, and clinicopathological data, including age, sex, tumor differentiation, tumor diameter, depth of tumor invasion, lymph node metastasis, distant metastasis, TNM stage.2.4.3 Disagreement will be reported and resolved by a third reviewer. Meanwhile, score < 6 is considered low quality, and ≥6 is classified as high quality.The quality of all the included studies will be evaluated by 2 reviewers independently based on the Newcastle--Ottawa scale (NOS) that is used to evaluate the quality of observational studies.2.4.4OS and disease-free survival (DFS) would be taken as prognostic outcomes. The results would be expressed as HRs with 95% CIs.2.4.5If there are insufficient or missing data in the literature, we would contact the author via email. If the data is not available, we would only analyze the currently available data and discuss its potential impacts.2.4.6I2 statistic. I2 ≤ 50% or P value for heterogeneity >.10 proved that there was no obvious heterogeneity among studies. As a result, a fixed-effect model should be utilized. If not, a random-effect model should be applied. The association was considered to be significant when P < .05.All analysis was conducted with Review Manager 5.3  and Stata 12.0  for Windows. For OS and DFS, HR and corresponding 95% CI were applied as the summary measures. For clinicopathological parameters, odds ratio (OR) and corresponding 95% CI were applied. Besides, inter-study heterogeneity was assessed by carrying out Chi-squared test and 2.4.7 Funnel plots, Begg test, and Egger test were performed to evaluate the publication bias.If sufficient studies are included (at least 10), we will test the reporting bias in the meta-analysis by using an inverted funnel plot.2.4.8We will conduct a subgroup analysis based on the detection method of lncRNA expression, race, and the source of survival data.2.4.9If sufficient studies are available, we will perform sensitivity analyses to confirm the robustness of the primary results. The meta-analysis will be respectively processed by excluding studies with small sample size, and low methodological quality.2.4.10The content of this article does not involve moral approval or ethical review and would be presented in print or at relevant conferences.3–29 lncRNA can be used as a regulator of gene expression through gene modification, transcription, and post-transcriptional processing. Many studies have revealed that lncRNA can participate in the proliferation, apoptosis, angiogenesis, metastasis, and invasion of oral squamous cell carcinoma through various pathways and molecular mechanisms.–32 In the past few years, a large number of studies have illustrated that the upregulation and downregulation of lncRNA are involved in the development and progression of oral squamous cell carcinoma.,34 Therefore, the expression of lncRNA may be related to the prognosis of oral squamous cell carcinoma, while controversial results have been discovered. Here, we conducted this meta-analysis for the first time to further summarize the prognostic value of lncRNA expression in oral squamous cell carcinoma.A large number of studies at home and abroad have confirmed that lncRNA is involved in the pathological process of the occurrence and development of a variety of tumors at different levels.There are several key points in our research. First of all, as far as we know, this is the first meta-analysis to explore the prognostic value of lncRNA expression in oral squamous cell carcinoma. Second, this study comprehensively analyzed the prognostic and pathological parameters, which further confirmed the prognostic role of lncRNA expression in oral squamous cell carcinoma. Third, our research strictly follows the rules of PRISMA, so the method is normative. Nevertheless, our research has no limitations. There are few studies on the meta-analysis, and a relatively small sample size may reduce the reliability of the results. Furthermore, there is a great difference in the critical value among the included studies, which may limit the clinical application of this conclusion. Most importantly, although there are no restrictions on the country in the process of literature selection, most of the studies are carried out in China. Consequently, this conclusion may be difficult to extend to other countries. Therefore, more high-quality and extensive researches should be carried out to clarify this issue.Nevertheless, our study will provide evidence to support the prognostic role of lncRNA expression in oral squamous cell carcinoma and provide relevant strategies for the accurate treatment of tumors.Data curation: Qingjie Lin and Yong Zhang.Formal analysis: Yong Zhang.Methodology: Yong Zhang.Project administration: Yong Zhang.Supervision: Yanguo Liu.Validation: Yanguo Liu.Visualization and software: Yanguo Liu.Writing – original draft: Qingjie Lin and Xin Xu.Writing – review & editing: Qingjie Lin and Xin Xu.Conceptualization: Xin Xu, Qingjie Lin.Data curation: Qingjie Lin, Yong Zhang.Funding acquisition: Xin Xu.Investigation: Yong Zhang.Methodology: Yong Zhang.Project administration: Xin Xu.Resources: Yong Zhang.Supervision: Yanguo Liu.Validation: Yanguo Liu.Visualization: Yanguo Liu.Writing – original draft: Xin Xu, Qingjie Lin.Writing – review & editing: Xin Xu, Qingjie Lin.


Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver metabolic disease worldwide. Up to 70%–80% of patients with NAFLD were obese, especially abdominal obesity. Many indicators of abdominal obesity have been reported, including waist circumference (WC), visceral obesity index (VAI), lipid accumulation (LAP), and Chinese VAI (CVAI). However, few studies investigated the associations between these indices with NAFLD. This present study aims to explore the associations between abdominal obesity indices with NAFLD. A total of 7,238 participants were involved in the cross-sectional study, and 1,584 participants were included in the longitudinal study from Jidong communities. NAFLD was assessed by abdominal ultrasonography. The trajectory of WC, VAI, LAP, and CVAI during 2013–2016 was identified by a group-based trajectory model. The logistic regression and Cox proportional hazards models analyzed the correlations and causality between abdominal obesity indices with NAFLD. In this study, the prevalence and incidence of NAFLD are approximately 44% and 26%, respectively. In the cross-sectional study, WC, VAI, LAP, and CVAI are associated with NAFLD. After adjustment for potential confounders, the moderate-rising and high-rising groups of CVAI had the highest risk of NAFLD in longitudinal analysis (hazard ratio (HR): 3.903, 95%CI: 2.434–6.259; HR: 5.694 95%CI: 3.098–10.464, respectively). Receiving operating characteristic curves show that CVAI has the best diagnostic value for NAFLD (area under the curve (AUC) = 0.868). CVAI is independently associated with the risk of NAFLD and may also have an important value to the diagnosis of NAFLD. Obesity is a significant public health issue associated with increased morbidity and disease burden worldwide. In 2016, over 1.9 billion people worldwide were overweight, and over 650 million were obese .The national prevalence of NAFLD was estimated to be 29.2% in China, which has imposed substantial health and economic burden on patients and society . NAFLD iIn recent years, the incidence of NAFLD has been increasing worldwide, which is driven by the obesity epidemic . NotablyThe development of imaging technologies such as CT and MRI has made it possible to explore the field of adipose tissue distribution . The proThis large-scale community-based study contained cross-sectional and longitudinal data in North China. We aimed to investigate the relationships between abdominal obesity indices, including WC, VAI, LAP, and CVAI, with the prevalence of NAFLD. Our findings may provide important implications for the early detection primary prevention of NAFLD.This study was designed to investigate the association between abdominal obesity indices with NAFLD among Chinese adults. The participants in this study were recruited from Jidong communities in Tangshan City . From 2013 to 2014, 9,078 participants received examinations, and participants were excluded due to lack of information about NAFLD (n = 1021), excess alcohol intake (one drink or more daily among women and two drinks or more daily among men), the presence of the hepatitis B surface antigen (HBsAg) (n = 235), and missing the data of WC, TG, BMI, or HDL (n = 584). In total, 7,238 participants were involved in the cross-sectional study. In the longitudinal study, by matching ID numbers from the datasets of 2013–2014, 2015, and 2016, the information of 2,058 participants without NAFLD was acquired. A total of 1,584 participants were included in the final longitudinal study, and 474 participants were excluded for missing the data of NAFLD or WC, TG, BMI, or HDL, or excess alcohol intake, or the presence of the HBsAg from 2015 to 2016 Figure 1The study was conducted in accordance with the guiding principles of the Declaration of Helsinki and approved by the Medical Ethics Committee, Staff Hospital, Jidong Oilfield Branch, China National Petroleum Corporation . Written informed consent was obtained from all participants.After exclusion of participants with excessive drinking and other liver diseases, participants with two or more of the following abnormal characteristics were considered to have NAFLD: 1) diffusely increased echogenicity of the liver relative to the kidney; 2) ultrasound beam attenuation; and 3) poor visualization of intrahepatic structures. NAFLD was diagnosed in accordance with the Asia‐Pacific Working Party on NAFLD and the Chinese Association for the Study of Liver Disease criteria , 22. TheWC was measured twice by the technician at the level of the umbilicus, and the average value was used to determine the WC of each participant. Fundamental indicators calculated the VAI, LAP, and CVAI, and the specific calculation process as follows:All participants completed a standardized questionnaire, clinical examinations, and laboratory tests. The questionnaire covered demographic and lifestyle information collected by the researchers who had received professional training. According to self-reported information, the information on smoking habits (current smoker or not) and drinking habits  was obtained. Education level was categorized as “illiteracy or primary school”, “middle school”, or “college and above”. Participants’ average monthly earnings were classified as “≤¥3,000”, “¥3,001–5,000”, or “>¥5,000”.2. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were taken twice in a sitting position and averaged. Blood samples were collected using venipuncture after an overnight fast. Fasting plasma glucose (FPG), TG, total cholesterol (TC), HDL, and low-density lipoprotein (LDL) were performed with a Hitachi 747 auto-analyzer . Hypertension was defined as SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg or hypertension history or currently taking medication for hypertension. Diabetes was defined by any one of the following: FPG level ≥7.0 mmol/L, use of diabetes medication, or diabetes history.Clinical examinations were performed by experienced nurses and included measurements of height, weight, WC, and blood pressure. BMI was calculated by weight (kg)/height (m)p-value (two-sided) <0.05. The normality distribution of continuous variables was assessed by the Kolmogorov–Smirnov test. Continuous variables were expressed as the median with interquartile range , and categorical variables were presented as frequencies and percentages in cross-sectional or longitudinal studies. Continuous and categorical variables were compared using the Kruskal–Wallis H tests and chi-square tests, respectively. Multicollinearity was assessed by the variance inflation factor (VIF). Logistic regression analysis was used to analyze the relationship between WC, VAI, LAP, and CVAI with NAFLD by calculating the odds ratios (ORs) and 95%CI in a cross-sectional study.Statistical analyses were performed using SAS, version 9.4 , and the significance level was set as http://www.andrew.cmu.edu/user/bjones/index.htm), and because it was shown to be superior for identifying underlying longitudinal trajectories . The focus was on this trajectory model because it is easy to achieve using “Proc Traj,” a free downloadable add-on package to base SAS (ectories . In the p < 0.05).We categorized the study population based on two observed discrete trajectories of WC, VAI, and LAP from 2013 to 2016, labeled as low-rising  and high-rising . Participants of the longitudinal study were clustered into three groups according to the trajectory of changing patterns in CVAI over time. According to the initial value, all the change trajectories are upward trends and are divided into low-rising, medium-rising, and high-rising. In the low-rising group, the CVAI remained with a range of 17.6–24.2. The CVAI remained at a moderate level in the moderate-rising group and rose slightly (64.5 to 72.9). The initial level of CVAI was as high as 110.2, and CVAI increased to 122.4 after follow-up, which was defined as the high-rising group Figure 3The high-rising CVAI pattern experienced the highest future risk of developing NAFLD among all abdominal obesity indices patterns Figure 4p < 0.05)  . The dose–response relationship between abdominal obesity indices and NAFLD risk was analyzed using the restricted cubic spline regression model  of WC, VAI, LAP, and CVAI for NAFLD was 0.812, 0.767, 0.836, and 0.847  and cholestatic disease, the participants with these medications were not excluded, which is one of the limitations of our study. In addition, the current study did not evaluate the visceral fat function parameters and did not directly measure the visceral fat function area but only referred to the description of the abdominal obesity index in other studies. Therefore, we could not further verify the relationship between CVAI and VAT. Our study did not consider some potential impact of unmeasured confounders on the main results, such as changes in diet, medication, or comorbidity; thus, our results should be interpreted with caution. The key strength of our research includes cross-sectional and longitudinal data collected from a community with standardized measurement. To the best of our knowledge, it is the first study to detect the various associations of obesity phenotype indices with NAFLD and predict the power of abdominal obesity indices.In conclusion, the present study indicated that CVAI was the strongest and independent risk factor for NAFLD among the abdominal obesity indices. Compared to WC, VAI, and LAP, CVAI demonstrates the higher diagnosis power for NAFLD based on the ROC curve, especially in women. Overall, the CVAI may be a practical and straightforward approach for predicting NAFLD, and individuals with high CVAI should receive additional screening and preventive interventions for NAFLD.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Medical Ethics Committee, Staff Hospital, Jidong Oilfield Branch, China National Petroleum Corporation . The patients/participants provided their written informed consent to participate in this study.All authors equally contributed to the present study.This project was supported by the Doctoral supervisor matching funds (grant no. 6010220052), the Research Development Fund of The Second Hospital of Shandong University (grant no. 11681808), and Jinan clinical medical science and technology innovation plan (grant no. 202019194).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In recent years, food safety incidents have been frequently reported. Food or raw materials themselves contain substances that may endanger human health and are called toxic and harmful substances in food, which can be divided into endogenous, exogenous toxic, and harmful substances and biological toxins. Therefore, realizing the rapid, efficient, and nondestructive testing of toxic and harmful substances in food is of great significance to ensure food safety and improve the ability of food safety supervision. Among the nondestructive detection methods, infrared spectroscopy technology has become a powerful solution for detecting toxic and harmful substances in food with its high efficiency, speed, easy operation, and low costs, while requiring less sample size and is nondestructive, and has been widely used in many fields. In this review, the concept and principle of IR spectroscopy in food are briefly introduced, including NIR and FTIR. Then, the main progress and contribution of IR spectroscopy are summarized, including the model’s establishment, technical application, and spectral optimization in grain, fruits, vegetables, and beverages. Moreover, the limitations and development prospects of detection are discussed. It is anticipated that infrared spectroscopy technology, in combination with other advanced technologies, will be widely used in the whole food safety field. With the rapid development of the food industry, China’s food safety level is constantly improving. However, it still faces many problems, such as illegal food additives and the abuse of antibiotics. The quality and safety of food involve everyone’s health and safety. Such problems not only exist in individual countries but also present as a global problem. For instance, “melamine” and the “Sudan red duck egg” event, among others, have brought fatal consequences ,2. AccorFor different kinds of toxic and harmful substances, the commonly used detection methods are gas chromatography (GC), high-performance liquid chromatography (HPLC), gas chromatography–mass spectrometry combined technology (GC-MS), liquid chromatography–mass spectrometry (LC-MS), enzyme-linked immunosorbent assay (ELISA), etc. ,14,15. M−1), mid-infrared region (4000–400 cm−1), and far-infrared region (400–10 cm−1) [Infrared spectroscopy is an analytical method for using intermolecular vibrations to identify molecular structures. When an infrared light source with a continuous wavelength radiates the measured object, the infrared light at a specific frequency is absorbed by a specific molecule corresponding to the characteristic bond in the sample molecule that selectively absorbs light from the infrared region of the electromagnetic spectrum, causing molecular vibration. The absorption specificity corresponds to the characteristic chemical bonds in the sample molecule. Generally, the infrared spectral interval is divided into three regions, namely, the near-infrared region  .Fourier transform infrared spectrometer is the mainstream instrument used for infrared spectroscopy analysis. There are four main Fourier transform infrared detection modes: transmission, attenuated total reflection (ATR), mirror reflection, and diffuse reflection mode. Users can choose appropriate detection methods according to different detection needs and physical states of the sample. Solid samples can be detected by transmission spectroscopy, attenuation full reflection spectroscopy, etc. Liquid samples can be detected by transmission spectroscopy of an infrared liquid pool or reflection spectrum of attenuated full reflection accessories.−1. The near-infrared spectra mainly absorb C-C, O-H, C-H, N-H, S-H, etc. [Near-infrared spectroscopy (NIR) is an instrument detecting molecular vibration spectra ranging from 12,800 to 4000 cm-H, etc. . The dou-H, etc. ,19. Mode-H, etc. ,21.In the qualitative analysis, the NIR spectrum is related to the chemical composition and content of the material itself, and the same chemical composition. The material structure and content of the sample determine the attribute characteristics of the sample. The NIR spectrum is used as a variable to establish the correspondence between the sample genus and the NIR spectrum, which is then applied to the NIR spectrum to calculate and obtain the genus or characteristics of the sample . ChangesIn the process of spectral acquisition using infrared spectroscopy, in order to improve the instrument’s signal-to-noise ratio, it is necessary to eliminate the interference of mixed stray light, noise, and other factors, separate overlapping peaks, and then extract useful information. The spectral data is usually combined with chemometrics. Spectrum combined with the mode recognition method can quickly and accurately identify the authenticity of the objects to be measured and improve prediction accuracy. Using multiple correction techniques, such as multiple linear regression (MLR), principal component regression (PCR), and partial least squares (PLS), can improve the accuracy and reproducibility of analysis tests and reduce background interference.Valas et al.  used theChemometrics is a discipline that combines theories and methods from mathematics, statistics, computer science, and multiple disciplines to maximize useful chemical information that can be obtained from measurement data by optimizing the process of chemical measurements. Given that IR spectroscopy mainly reflects the chemical composition of the substance to be measured as a whole, standardized experiments must be strictly controlled in order to obtain high-quality and reproducible spectral data during specimen selection, sample preparation, and spectral acquisition. For liquid samples, transmission, and horizontal attenuation total reflection methods are mostly used for measurement. For solid and powder samples, diffuse reflection methods are mostly used for measurement. In order to obtain high-quality IR spectra and build more accurate models, chemometric methods are frequently used.Spectral preprocessing is the processing or transformation of spectral data to reduce or even eliminate the interference of non-target factors in the spectrum and to improve spectral resolution and sensitivity. Commonly used chemometric methods are mainly smoothing, derivatives, multiple scattering correction, etc. After dividing the processed spectral data into sample sets, principal component analysis is used to extract the main characteristic components of the data. Qualitative model building is divided into supervised and unsupervised modeling. The supervised modeling is used to divide the data set first, start building the model after spectral processing, and evaluate the model with PLS-DA. The unsupervised modeling differs in that, after spectral processing, classification, and modeling are performed with PCA. For the prediction of quantitative models, the partial least squares method is mostly used. For the built prediction model, model testing is also needed to verify its prediction accuracy and sometimes model transfer is required.During the production and processing of food or raw materials, contamination may be caused by various external causes and illegal human factors. The detection of such substances by infrared spectroscopy is of great importance.PR) for aminopyralid was 0.93, the root mean square error of prediction (RMSEP) was 3.22 mg kg−1, and the relative prediction error (RPD) was 2.28. The prediction correlation coefficient (PR) for azoxystrobin was 0.91. Lu et al. [PR) of chlorpyrifos samples was 1 and the root mean square error of prediction (RMSEP) was 0.03 mg kg−1. The average spiked recoveries ranged from 98.95–102.26% and the relative prediction errors ranged from 0.88–9.97% when the chlorpyrifos concentration was greater than 5 mg kg−1. The prediction correlation coefficient (PR) of chlorpyrifos samples was close to 1, the prediction mean-spiked recoveries ranged from 99.10% to 100.66%, and the relative prediction errors ranged from 0.39% to 5.01% when the concentration of carbendazim was greater than 1 mg kg−1, and the two samples had good consistency and reproducibility.Arzu Yazici et al.  establisu et al.  used visPR) of the optimal MSC-CCM-RBF model was 0.9999, the root mean square error of prediction (RMSEP) was 0.0765, and the relative prediction error (RPD) was 65.0909. Che et al. [−1 were prepared using a stepwise dilution method in the concentration range of 0–300 mg kg−1. The abnormal samples were identified and rejected by combining the Marxian distance method and leave-one-out cross-validation, and the radial basis function model could better predict the concentration of azodicarbonyl in flour by selecting the characteristic band through correlation and using the preprocessing method of the first-order derivatives and SNV. The method yielded prediction correlation coefficients and the root mean square error of prediction (RMSEP) of 0.99996 and 0.5467, which were within 0.01 for each sample, and LOD and LOQ of 3.2 and 9.7 mg kg−1, respectively. Li et al. [PR2 of 0.864 and a root mean square error of prediction (RMSEP) of 0.291. The results showed that infrared spectrometry was feasible for the detection of Pb–Cr green in green tea. Kurrey et al. [−1, the limits of detection (LODs) were 0.032 ng mL−1, and the recoveries were 83.1%-102.3%. The method is simple, sensitive and suitable for high-throughput analysis of food samples. Gu et al. [RMSEP) of 6.08 and a limit of quantification of 15 mg kg−1. The method took 5 min to determine formaldehyde, while the HPLC method took 1.5 h to determine formaldehyde.Liu et al.  investige et al.  used Visi et al.  used Fouy et al.  used difu et al.  studied The analysis of the above studies shows that infrared spectroscopy is a good technique for the nondestructive detection of pesticide residues in fruits and vegetables, illegal additives in flour and green tea, and antibiotics in eggs. However, differences in the manufacturers of fruits, vegetables, and eggs from different origins and illegal additives used in the experiments may lead to the differentiation of the final models. Therefore, the establishment of a set of standardized and perfect data models for detecting illegal food additives, pesticides, and antibiotics could well improve the universality of the models and broaden the application of this technology.Toxins produced by microorganisms can be classified into bacterial toxins, actinomycete toxins, and toxins produced by fungi. Microbial toxins are an important factor in endangering human health. Therefore, it is very necessary to realize rapid detection of microbial toxins for people’s life and health and to ensure food safety.R2 of the quantitative model was 0.9705, the root mean square error of prediction (RMSEP) was 3.567 μg kg−1, the relative prediction error (RPD) was 5.98, the accuracy of the ranking method was 90.32%, and a food safety classification system based on 0–110 μg kg−1 of trichothecene A in stored maize was established. Tao et al. [Aflatoxin is a strong carcinogen, of which heterotoxin A is a precursor of aflatoxin B1 (AFB1) and can be used as an indicator of the presence of AFB1. Zheng et al.  developeo et al.  used visR of 0.936–0.973, RPD of 2.5–4.0) and mid-infrared spectral model  both had good prediction accuracy. The main contaminant in peanut mold is aflatoxin, a toxin that poses a serious risk to life and health. Fumonisins (FBs) and zearalenone (ZEN) are naturally occurring mycotoxins in cereals and grains, both of which endanger human health. Tyska et al. [R), coefficient of determination (R2), and relative prediction error (RPD) of FBs were 0.809, 0.899 and 3.33, respectively. The correlation coefficient (R), coefficient of determination (R2), and relative prediction error (RPD) for ZEN were 0.991, 0.984, and 2.71, respectively. This method has proven to be a reliable alternative for the analysis of such toxins. Lin et al. [Fusarium niger contaminated raisins and demonstrated that Fusarium niger contaminated raisins showed an increased absorbance in the characteristic bands at 1733 cm−1, 1736 cm−1, and 1708 cm−1, demonstrating that the FTIR spectroscopy technique is capable of determining fumonisins and other mycotoxins in agricultural products. Annalisa et al. [−1 DON. The overall recognition rate was 87–91% for FT-NIR and 86–87% for FT-MIR spectral ranges. Murat et al. [Bacillus cereus and Bacillus thuringiensis. The spectral data were preprocessed, and the neural network model was constructed using the deep learning toolbox of MATLAB R2018b for feature selection. The model has a 100% recognition rate for the training set and a 99.5% overall recognition rate for the test set. This method can be used for the identification of Bacillus cereus in food as well as in soil samples.Shen et al.  investiga et al.  studied n et al.  proposedn et al.  used NIRn et al.  used thea et al.  used Fout et al.  developeThe NIR spectra of microbial cell walls, cell membranes, and internal biomolecules are highly specific. Therefore, the use of NIR spectroscopy allows for the rapid identification and classification of different microorganisms. The method is simple to operate and is fast and nondestructive. Infrared spectroscopy has good application prospects for the detection of microbial contamination in food due to mold, bacteria, etc.RMSEP) was 0.11 mg g−1, the coefficient of determination (pR2) was 0.92, and the relative prediction error (RPD) was 2.82. The intake of trans fatty acid (TFA) is directly related to human health. TFA can cause a variety of diseases ,48. It i2 > 0.9) was obtained between the volatile content and the IR spectra. Near-infrared spectroscopy (NIRS) gave better results than ATR-FTIR. The prediction error of ATR-FTIR (18%) is higher than that of NIRS (9%). Therefore, near-infrared spectroscopy makes it possible to develop online detection for the production of infant milk powder.The formation of acrylamides has had a great impact on the potato processing industry. Smeesters et al.  used broIn summary, it is feasible to use infrared spectroscopy for the detection of pesticides, veterinary drugs, antibiotic residues, illegal additives, mycotoxins, and other toxic and hazardous substances in food. However, due to the complicated variety of such toxic and hazardous substances, infrared spectroscopy still requires a lot of research on food safety detection. (1)The infrared spectral fingerprint library of toxic and hazardous substances in the food industry has not yet been established. Thus, a complete, comprehensive, and standardized database of infrared spectral analysis models of toxic and hazardous substances in food is to be established for sharing resources and satisfying the needs for real-time online detection and analysis of such substances. Through standardized means, we can promote the development of infrared spectroscopy detection methods for toxic and hazardous substances in food and thus lay a good foundation for the application of infrared spectroscopy technology in food and agriculture.(2)At present, most of the research on the application of infrared spectroscopy technology in the detection of toxic and hazardous substances in food at home and abroad is basic research done under laboratory conditions, which makes it difficult to meet the needs of the domestic food safety field. Therefore, the development of small and portable infrared spectrometers to meet detection requirements is another important development direction for infrared spectroscopy technology in the field of food safety. At the same time, this would assist in solving the technical bottleneck in instrument manufacturing, as well as for innovative training of instrument manufacturing personnel, and should be increased. Only in this way can we produce domestic infrared spectrometers that are better suited for the detection of toxic and hazardous substances in food.(3)The development of chemometric methods. When conducting infrared spectroscopy on samples to be tested, both in terms of spectral preprocessing and establishing quantitative and qualitative analysis models, chemometric methods are needed. Understanding the current algorithms used in the detection of toxic and hazardous substances in food by infrared spectroscopy can help develop new algorithms and broaden the application of this technology in the field of food safety.(4)The combination of infrared spectroscopy with advanced technologies, such as hyperspectral imaging, can yield richer feature information, including image information of external features and spectral information of internal features of food and agricultural products. This helps to more efficiently identify and quantitatively detect toxic and harmful substances in them qualitatively. As many of the food and agricultural products contain more moisture, the absorption peaks of water are stronger and may cover the absorption peaks of other substances. Therefore, infrared spectra are susceptible to the influence of moisture absorption peaks. The method of attenuated total reflection surface-enhanced infrared spectroscopy (ATR-SEIRAS) can effectively reduce the interference of solvent water on the signal to be measured ,56, whicIn recent years, infrared spectroscopy has been intensively researched in the field of detection of toxic and hazardous substances in food. This paper reviews the latest progress in applying this technology for the detection of toxic and hazardous substances in food. Infrared spectroscopy has the advantages of rapid, high accuracy, and non-destructive in-line identification. It is expected to be an alternative or complementary method to existing food safety detection methods when combined with suitable chemometric methods. In addition, the combination of hyperspectral, infrared spectral imaging, surface-enhanced infrared spectroscopy, and other techniques can broaden the detection range and enhance the detection capability. Infrared spectroscopy, a nondestructive testing technique, has become the preferred testing method for more and more food products. At this stage, infrared spectroscopy has made great achievements in food safety detection, but there are still some difficult problems that need to be focused on with research at a later stage. The types of toxic and harmful substances in food are complex and difficult to detect. Novel substances will also be encountered in the future for infrared spectroscopy detection, which puts forward higher requirements on the performance of the instrument. High sensitivity, high accuracy, and good reproducibility are all key to achieving successful IR detection. Therefore, future research efforts should focus more on the following points:With the continuous development of related theories and technologies, infrared spectroscopy will have more extensive applications in food safety and even biomedical fields.


Prevotella, Porphyromonas, Ezakiella, and Peptoniphilus species, with a high diversity of microbiota. The vulvovaginitis-associated microbiota were dominated by Streptococcus, Prevotella, Haemophilus, and Granulicatella, with lower diversity than that in healthy girls. Furthermore, the compositions of the vaginal and intestinal microbiomes were completely different. ANOSIM, MRPP, Adonis, and AMOVA were used to analyze the beta diversity, and the results showed that there were significant differences in the microbial communities among the three groups. Lactobacillus deficiency and high bacterial diversity were characteristics of the vaginal microbiome in healthy prepubertal girls; this is inconsistent with that in reproductive-age women. The vulvovaginitis-associated vaginal microbiota differed dramatically from normal microbiota, and the main causative agents were not fecal in origin.The present study focused on the characteristics of the vaginal microbiomes in prepubertal girls with and without vulvovaginitis. We collected 24 vaginal samples and 16 fecal samples from 10 girls aged 3–9 years with vulvovaginitis and 16 healthy girls of the same age. The samples were divided into three groups: fecal swabs from healthy controls (HF), vaginal swabs from healthy controls (HVS), and vaginal swabs from girls with vulvovaginitis (VVS). Sequencing of the V3–V4 region of the 16S rDNA gene was performed with the NovaSeq PE250 platform to reveal the vaginal microbial community structure in healthy prepubertal girls and vulvovaginitis-associated microbiota. The intestinal microbiomes of healthy children were also analyzed for comparison. This study revealed that the healthy vaginal tract in prepubertal girls was dominated by  Escherichia coli, Enterococcus faecalis, coagulase-negative Staphylococcus, α-hemolytic Streptococcus, and group A β-hemolytic Streptococcus accounted for 66% of all isolated microbes. In another study, Sikanić-Dugić N et al. reported that among 115 prepubertal girls with vulvovaginitis, causative agents were isolated from vaginal cultures in 38 (33%) cases, of which 21 indicated group A β-hemolytic Streptococcus, 5 indicated Haemophilus influenzae, 3 indicated Escherichia coli, 2 indicated Enterococcus spp., and one each indicated Staphylococcus aureus, Proteus mirabilis, and Streptococcus pneumoniae [Lactobacilli [Vulvovaginitis, characterized by inflammation of the vulvar and vaginal areas, is the most common gynecological disease in prepubertal girls and frequently causes great anxiety in children and their parents . Anatomieumoniae . The maiIn the present study, we focused on two questions: (i) What are the characteristics of the vaginal microbiome in healthy prepubertal girls? (ii) What are the differences in vaginal microbiome between healthy prepubertal girls and prepubertal girls with vulvovaginitis?The study was performed at the outpatient clinic of the National Center for Children’s Health between February 2020 and April 2020. The study protocol was approved by the Institutional Review Board of the National Center for Children’s Health. Parents of all the study subjects provided written consent for participation in the study and for publication of the study results.Ten prepubertal girls (aged 3–9) with vulvovaginitis and 16 healthy girls of the same age were enrolled in the study. The clinical signs of vulvovaginitis were genital redness, vaginal discharge, itch, soreness, rash, irritation, and dysuria. Girls included in the control group had no signs of vulvovaginitis and no history of sexual abuse. All the subjects were prepubescent without secondary sexual characteristics.Exclusion criteria were patients with diseases other than vulvovaginitis or a history of sexual abuse.All vaginal specimens were collected using sterile swabs by the same trained pediatric and adolescent gynecologist from the lower third of the vagina. All samples were placed on dry ice immediately after collection and transferred to a − 80 °C freezer within 30 min, where they were stored without any additives until analysis.All microbial DNA was extracted from each specimen using the CTAB method, and DNA samples were stored at − 80 °C. Using diluted genomic DNA as a template, specific primers with barcodes and high-efficiency enzymes were used to perform PCR to ensure the efficiency and accuracy of amplification. The PCR primers were 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′). The library was constructed with the TruSeq® DNA PCR-Free Sample Preparation Kit and quantified by Qubit and Q-PCR. After the library was qualified, NovaSeq6000 was used for sequencing.According to the barcode sequence and primer sequence for PCR amplification, each sample dataset was differentiated from the offline dataset. After the barcode and primer sequences were removed, the reads of each sample were spliced with Flash, and the splicing sequence was considered the original tag data; the raw tags obtained by splicing were meticulously filtered to obtain high-quality tag data (clean tags). Uparse software was used to cluster all the effective tags from all samples. By default, the sequences were clustered into operational taxonomic units (OTUs) with 97% consistency, and representative OTU sequences were selected. According to the algorithm principle, the OTU sequence with the highest frequency was selected as the representative OTU sequence. The OTU sequences were annotated to obtain taxonomic information. QIIME software was used to calculate the observed OTUs and the Chao1, Shannon, Simpson, and abundance-based coverage estimator (ACE) indices. The dilution curve, rank abundance curve, and species accumulation curve were drawn by R software, and the differences among alpha diversity index groups were analyzed by R software. QIIME software was used to calculate the UniFrac distance and construct the UPGMA sample clustering tree. Principal component analysis (PCA), principal coordinate analysis (PCoA), and nonmetric multidimensional scaling (NMDS) were performed with R software. R software was used to analyze differences in the beta diversity indices among groups. Linear discriminant analysis effect size (LEfSe) software was used for the LEfSe analysis, and the filter value of the LDA score was 4 by default.A total of 26 prepubertal girls aged 3–9, including 10 girls with vulvovaginitis and 16 healthy controls, were enrolled in this study. The 26 subjects contributed 24 vaginal swabs and 16 fecal swabs for analysis. The samples were divided into three groups: fecal swabs from healthy controls (HF), vaginal swabs from healthy controls (HVS), and vaginal swabs from girls with vulvovaginitis (VVS). The 40 swab specimens produced a total number of 2,652,630 reads, averaging 63,158 ± 3142 reads per sample. The mean read length was 416 bp. For the effective tags of all samples, OTU clustering was carried out with 97% consistency, and a total of 1789 OTUs were obtained.Venn diagrams show the common and unique OTUs between the HVS and HF groups and between the HVS and VVS groups. A total of 1343 and 912 OTUs were detected in the HVS and VVS groups, respectively, compared with 603 OTUs in the HF group. Venn analysis identified 363 common OTUs and 980 and 240 unique OTUs in the HVS and HF groups, respectively, as well as 693 common OTUs and 650 and 219 unique OTUs in the HVS and VVS groups, respectively Fig. .Fig. 1BaFirmicutes, Bacteroidetes, unidentified bacteria, and Proteobacteria. The VVS group had a greater abundance of Actinobacteria, Fusobacteria, and Proteobacteria and a lower abundance of Bacteroidetes than the HVS group (P < 0.05)  were enriched (P < 0.05), while others  were depleted in the VVS group (P < 0.05)  Fig. . The gut05) Fig. . The domLactobacillus deficient; this result is different from that in reproductive-age women. Vulvovaginitis-associated genera were Granulicatella, Streptococcus, Gardnerella, and so on, which were not of fecal origin.These observations suggest that the vaginal microbiome of healthy prepubertal girls is The Simpson index and Shannon index are commonly used to characterize species diversity in a community. They indicate both the richness and evenness of the species present. Neither the Shannon diversity index nor the Simpson diversity index differed between the HF and HVS groups. However, both the Shannon index and Simpson index in the VVS group were significantly lower than those in the HF and HVS groups, indicating that the VVS group had less diversity Fig. .Fig. 2AlThe Chao1 richness estimator and ACE were used to estimate the number of OTUs (community richness) in the different samples. The larger the Chao1 and ACE indices are, the higher the richness of the community is. Compared with those in the HF group, the Chao1 index and ACE index in the HVS group increased significantly, but there was no significant difference between these indices in the VVS and HVS groups Fig. .A rarefaction curve is often constructed to describe the diversity of the sample species and to indicate whether enough observations have been collected to obtain a good alpha diversity measurement. In Fig. The rank abundance curve visually depicts both species richness and species evenness. In Fig. Measures of alpha diversity indicated that the diversity of the vaginal microbiome in healthy prepubertal girls was high; vulvovaginitis-associated dysbiosis is generally characterized by loss of diversity and the presence of pathogens with high levels of abundance.Beta diversity measures the variation in species diversity from one environment to another. Figure p values < 0.05 for all taxa listed as enriched) , and the HF group was enriched for Actinobacteria (HF vs. HVS). At the genus level, the HVS group was enriched for Prevotella, Porphyromonas, Ezakiella, Peptoniphilus, Campylobacter, Anaerococcus, Negativicoccus, and Dialister (HVS vs. HF). In contrast, the dominant taxa in the VVS group samples were more likely to be pathogenic, e.g., Streptococcus and Haemophilus (VVS vs. HVS).LEfSe was used to predict biomarker taxa in each group (LEfSe ed) Fig. . The claAlthough the clinical symptoms and signs associated with vulvovaginitis in prepubertal girls are relatively easily measured, it is difficult to achieve consistent detection to verify the cause of vulvovaginitis . It has Lactobacillus genus, which is able to produce lactic acid that consequently reduces vaginal pH and thus facilitates an acidic environment in the vagina [Prevotella, Porphyromonas, Ezakiella, Peptoniphilus, and Fusobacterium. Prevotella, not Lactobacillus, was the predominant vaginal species in healthy prepubertal girls, although there is evidence that several species of Prevotella found in the vagina are associated with bacterial vaginosis (BV) [Granulicatella, Streptococcus, and Haemophilus, which have been found to be closely related to vaginal infection, was enriched in the VVS group [Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria [The first step was identifying the compositions of bacterial communities. According to the literature, the vaginal microbiota in asymptomatic reproductive-age women can be categorized into five major bacterial community groups with distinct diversity and species compositions. Most vaginal communities are dominated by the e vagina . In our sis (BV) . CompareVS group , 12. It bacteria .Lactobacillus. BV and high-risk sexual behavior are associated with increased diversity in the vaginal microbiome and a lack of Lactobacillus [Alpha diversity, representing the microbial diversity within each sample, was analyzed based on the OTU richness, the Shannon index, and the inverse Simpson index. In women of reproductive age, vaginal health includes vaginal microbiota with low diversity that is predominated by bacillus . Unlike Prevotella, Porphyromonas, Ezakiella, and Peptoniphilus, with a high microbial diversity, which is inconsistent with the normal vaginal community structure of the majority of reproductive-age women. The vaginal microbiota associated with vulvovaginitis was less diverse, with an increased abundance of Granulicatella, Streptococcus, and Haemophilus. Furthermore, bacteria of fecal origin may not be the pathogen responsible for vulvovaginitis in prepubertal girls.In this study, we found that the vaginal microbiome of healthy prepubertal girls was dominated by