Then, support vector regression (SVR) and kernel principal component analysis (KPCA), multiple linear regression, and partial least squares regression were employed to develop the regression models for quantitative analysis of chlormequat chloride residue with spectra around the characteristic peaks at 666, 713, and 853 cm−1. As for the residue in wheat, the predicted recovery of established optimal model was in the range of 94.7% to 104.6%, and the standard deviation was about 0.007 mg/L to 0.066 mg/L. The results demonstrated that SERS, SVR, and KPCA can provide the accurate and quantitative determination for chlormequat chloride residue in wheat.A simple and sensitive method for detection of chlormequat chloride residue in wheat was developed using surface-enhanced Raman spectroscopy (SERS) coupled with chemometric methods on a portable Raman spectrometer. Pretreatment of wheat samples was performed using a two-step extraction procedure. Effective and uniform active substrate (gold nanorods) was prepared and mixed with the sample extraction solution for SERS measurement. The limit of detection for chlormequat chloride in wheat extracting solutions and wheat samples was 0.25 mg/L and 0.25  Plant growth regulator can increase crop production, improve quality, and enhance stress resistance , 2 throuChlormequat chloride detection methods as gas  or liquiSpectroscopic methods are promising tools for detection of farm chemical residue because they are simple, rapid, specific, and partially or completely automatic. The commonly used spectroscopic methods include near-infrared spectroscopy (NIR) , 14, FouThe objective of this study is to develop a simple and sensitive SERS method for quantitative determination of chlormequat chloride in wheat coupled with some chemometric methods on a portable Raman spectrometer, in which pretreatment of wheat samples is performed using a two-step extraction procedure. To the best of our knowledge, this paper is the first to report detection chlormequat chloride in grains using SERS technique.Wheat samples were purchased from Hefei Zhougudui market. Chlormequat chloride powder (99.6%) was obtained from Beijing Puxi Technology Co., Ltd. Anhydrous methanol was acquired from Sinopharm Chemical Reagent Co., Ltd. Cetyltrimethylammonium bromide (CTAB), hydrogen tetrachloroaurate, trisodium citrate, L-ascorbic acid, sodium borohydride, and silver nitrite were purchased from Aladdin Industrial Corporation.The pretreatment method for wheat was developed based on the extraction method in gas chromatography (GC). Wheat was first grinded using a pulverizer  and filtered through 10-mesh sieves. Wheat powder of 5.00g was added with 15 mL of methanol in 50 mL centrifuge tube and then vibrated for 10 min. The mixture was centrifuged at 4000 rpm for 3 min, and the supernatant was moved to the concentrated bottle. Wheat residue was extracted using 10 mL of methanol again, and the supernatant was also moved to the concentrated bottle. The supernatant was evaporated to dry on a Rotavapor  and redissolved in 5 ml of methanol.μg/g. The contaminated samples were extracted using the above pretreatment method.Wheat extracting solutions containing different chlormequat chloride were then prepared. The obtained extraction solution was used to dissolve chlormequat chloride powder for getting the solution of 20, 10, 5, 2.5, 1, 0.5, and 0.25 mg/L. Additionally, to simulate actual residue, wheat powder was spiked with chlormequat chloride to yield final residue at 10, 5, 2.5, 1, 0.5, and 0.25 μL of GNRs colloid was dropped on silicon chip. After the droplet became dry, 2 μL of testing solution was dropped on the GNRs film. When the solvent was evaporated to dry, spectra were collected on a portable Raman spectrometer  equipped with a 785 nm laser of 150 mW. The measurement was performed with 3 scans and exposure time of 5 s, and the spectral resolution was 2 cm−1 in the Raman shift range of 600 cm−1 to 1800 cm−1. The spectra of 10 from five different spots on each sample were collected as the representative spectra. Five samples were measured for chlormequat chloride residue of each concentration.The synthesis of gold nanorods (GNRs) was performed using a seed-mediated growth method previously developed by El-Sayed . GNRs soAbsorption spectra of the GNRs were recorded on an ultraviolet-visible (UV–Vis) spectrometer . Morphologies of the GNRs were surveyed using the scanning electron microscope (SEM) image on a JSM 7500F microscope . As shown in σ) on feature extraction was discussed.The obtained spectra were first baseline-corrected using asymmetric least squares method to eliminate baseline and linear slope effects . Subsequσ), the optimal values of C and σ are obtained by traversing their empirical values [To examine accurate and quantitative determination of analyte further, MLR, PLSR, and SVR were used to develop the regression models. MLR is a regression algorithm that is very efficient in building calibration models when the number of samples is more than that of variables. PLSR is one of the most robust and reliable tools in the development of a multivariate calibration model. Based on the linear algorithm, PLSR is often applied to predict a set of dependent variables from a large set of independent variables. PLSR decomposes the spectral array and concentration array with considering their relationships. Corresponding calculation relationships are strengthened for the better correction model. SVR is a variation of support vector machine with introduction of insensitive loss function. Despite finite sample, SVR still possesses excellent robustness and high sensitivity through balancing complexity and learning ability of model. Meanwhile, with the aid of kernel function, SVR can project data into the high-dimensional space for obtaining higher analysis accuracy. Moreover, RBF was also selected as the kernel function of SVR. Considering the performance of obtained regression models highly depends on penalty coefficient (C) in loss function and width of kernel function , respectively. Furthermore, the peak at 853 cm−1 was assigned to C-N symmetric stretching vibration.The characteristic peaks reflect the information of molecular vibration and rotation, and these peaks are the basis for analysis and detection of substance using Raman or SERS technique. To determine the characteristic peaks of chlormequat chloride, pure chlormequat chloride powder was placed on the silicon wafer, and then Raman spectra were obtained through direct laser irradiation on it. The main Raman peaks of chlormequat chloride at 666, 713, 765, 853, and 1447 cm−1 were for SERS of GNRs, which were attributed to CTAB residue. Furthermore, the two bands influenced the appearance of peaks at 765 and 1447 cm−1 of chlormequat chloride. Conversely, the other peaks at 666, 713, and 853 cm−1 were not affected by the substrate and complex media. Therefore, the peaks at 666, 713, and 853 cm−1 were the key features for detection of chlormequat chloride residue in wheat extraction solution using SERS.However, the characteristic bands of SERS of molecule in complex media may have changes, which is mainly due to influence of Raman active substrate and background signals of complex media. Then, SERS spectra of GNRs, 100 mg/L of chlormequat chloride in methanol, and 20 mg/L of chlormequat chloride in wheat extraction solution were measured and shown in −1 was still obvious, but the peaks at 666 and 853 cm−1 were just dimly visible. The phenomenon indicated that SERS technique with GNRs can detect the limit of detection for 0.25 mg/L of chlormequat chloride in wheat extraction solution.Then, SERS spectra of wheat extraction solution with 20, 10, 5, 2.5, 1, 0.5, or 0.25 mg/L of chlormequat chloride were measured with the uniform GNRs . As seen−1 were selected for the subsequent analysis. Then, KPCA with RBF was adopted to extract the principal feature of processed spectra. Feature extraction was highly dependent on σ in RBF, and the effects of different σ were discussed. σ of 1000, 5000, 8000, and 10000. When σ was 1000, the corresponding score of wheat extraction with 20, 10, and 5 mg/L chlormequat chloride overlapped one another, which suggests bad subsequent results. However, for other larger values, the scatter of each category was separated well. Afterward, the obtained first two principal component scores were employed to develop the regression models for quantitative determination, and the model performance was evaluated with RMSECV (σ of 1000 was worse than others and consistent with the above assumption. Too large σ (10000) also caused the quantitative analysis results to yield poor results [σ of 8000 was used for quantitative determination of chlormequat chloride, and the predicted error of the optimal model is shown in Intelligent analysis of spectra using chemometric methods can automatically obtain the information of substances without intervention of professionals, and this process is of significance for simple and rapid detection. SERS spectra are of high dimension and carry useless information for target analyte. The appropriate variable selection and feature extraction can improve the analysis results. Considering the fingerprint properties of SERS, the spectra around characteristic peaks were selected for the intelligent analysis, and the interference can be avoided from the irrelevant information in spectra of other ranges. In particular, for SERS of chlormequat chloride in wheat extraction, the spectra of 653–683, 705–728, and 847–872 cmh RMSECV . As can  results . AccordiIn addition, an unbiased estimation for generalization of the model was conducted with an independent testing set. The independent testing set was spectra of wheat extraction with 15, 8, 4, and 2 mg/L obtained through remeasurement, and the representative spectra were shown in μg/g. The contaminated samples were extracted using the two-step procedure, and the obtained extraction solution was directly used for SERS measurement. For the residue of each concentration, 50 spectra were collected from five samples, respectively. The representative spectra are shown in −1 were still obvious and feasible for quantification of analyte. However, a small difference in spectral intensity can be observed, which depended on the extraction efficiency of pretreatment methods for residue in wheat samples.To simulate actual residue, wheat powder was spiked with chlormequat chloride to yield final residue at 10, 5, 2.5, 1, 0.5, and 0.25 μg/g was far below maximum residue limit of chlormequat chloride in wheat (5 μg/g). These results also demonstrated SERS and SVR with KPCA can realize accurate quantification of residue with good repeatability and high sensitivity. Furthermore, the portable Raman spectrometer made the quantitative determination easy and efficient to be performed. In the future, the presented method can be applied for the detection of various farm chemicals in other grains.Afterward, all the spectra were processed using KPCA, and the first two principal component scores were used to predict the sample concentration combining with the established model. The experiment results are shown in In this work, a method for detection of chlormequat chloride in wheat was developed using SERS and chemometric methods on a portable Raman spectrometer. The extraction of residue in wheat was performed using a two-step procedure originated from GC detection. As for the spiked wheat samples, the optimal predicted recovery was in the range of 94.7 % to 104.6 %, and standard deviation was from 0.007 mg/L to 0.066 mg/L. These results indicated that the present method is an effective and feasible approach for determination of chlormequat chloride residue in wheat. Meanwhile, with aid of a portable Raman spectrometer, the present method could be executed onsite, which is suitable for rapid residue analysis in grains. However, spectral variation induced by instability of substrate and differences in sample pretreatment should be avoided and resolved prior to application of SERS. In conclusion, SERS with chemometric methods is a potentially powerful approach for detecting chlormequat chloride or other toxic residues in grains which can greatly help improve the safety and quality of agricultural products.


To validate the caries risk profiles in 5- and 12-year-old school-going children and to single out main contributing factor, if any, using cariogram over a period of 1 year.A cariogram model was used to create caries risk profiles on 499 children aged 5 and 12 years ±6 months. They were divided into 2 groups. The group I and group II consisted of 250 and 249 children respectively. Re-examination was done after 1 year and caries increment was recorded. The caries risk profiles generated by the cariogram software were compared with caries increment.Streptococcus mutans count, fluoride, salivary flow rate, and buffer capacity are significantly associated with actual chance to avoid caries.Percentage of subject having caries increment in groups I and II in high-, medium-, and low-risk group after 1 year was 66.2, 39.5, and 13%, and 48.5, 27.3, and 13.9% respectively. The mean caries increment after 1 year in groups I and II in high-, medium-, and low-risk patients was 0.96, 0.49, and 0.13, and 0.7, 0.36, and 0.11 respectively. Linear regression analysis showed dental caries, diet content, diet frequency, plaque index, The risk of developing new carious lesions consistently reduced from high-risk category to low-risk category, reflecting the cariogram ability in accurately estimating future caries. Hence, cariogram can be said to be a useful tool for caries prediction. Initial dental caries came out to be the strongest predictor of future caries.How to cite this article: Garg A, Madan M, Dua P, Saini S, Mangla R, Singhal P, Dupper A. Validating the Usage of Car-iogram in 5- and 12-year-old School-going Children in Paonta Sahib, Himachal Pradesh, India: A 12-month Prospective Study. Int J Clin Pediatr Dent 2018;11(2):110-115. Everyone has heard the famous quote by Benjamin Franklin, “An ounce of prevention is worth a pound of cure.” This statement certainly echoes the sentiments of the dentistry profession, which has proudly been one of the most proactive of all health care professions in the area of disease prevention.1 A key factor for planning any preventive program is to accurately access a person’s risk of developing a disease.2For developing appropriate preventive approaches, anticipating utilization patterns and planning effectively for organization and financing of dental resources, the knowledge of oral health status and treatment needs of populations with different characteristics is important.3Dental caries is a disease of the mineralized dental tissue with a multi-factorial etiology related to the interactions over time between tooth substance, microorganisms, and dietary carbohydrates producing plaque.4 It is prudent to prevent dental caries by applying suitable preventive procedures to avoid complications. In developing countries where there is scarcity of resources, the high-risk individuals should be carefully segregated. The idea of caries risk assessment is to identify persons most likely to develop caries and to provide them proper preventive and inter-ceptive treatment.5Dental caries and its sequel often involve pain and affect esthetics. Treatment of dental caries is costly in terms of both time and money.5To assess the susceptibility of caries, Professor D. Bratthall developed the concept and formula for the car-iogram. The “cariogram” is an interactive PC program for caries risk evaluation. It is a graphical picture illustrating in an interactive way the individual’s/patients’ risk for developing new caries in the future, simultaneously expressing to what extent different etiological factors of caries affect the caries risk for that particular patient. It shows a pie diagram that illustrates a possible overall caries risk scenario in the form of five sectors of following colors: green, dark blue, red, light blue, and yellow indicating the different groups of factors related to dental caries. The green sector shows an estimation of the “Actual chance to avoid new cavities.” The dark blue sector “Diet” is based on a combination of diet contents and diet frequency. The red sector “Bacteria” is based on a combination of amount of plaque and mutans streptococci. The light blue sector “Susceptibility” is based on a combination of fluoride program, saliva secretion, and saliva buffer capacity. The yellow sector “Circumstances” is based on a combination of past caries experience and related diseases.5The cariogram does not specify a particular number of cavities that will or will not occur in the future.6 Hence, our study was planned to validate the caries risk profiles in both deciduous and permanent dentition using cariogram in India.In the year 1997, it was estimated that 22.7% of Indian population is aged 5 to 14 years. This being a high proportion of the population, the prevalence of dental disease among this age group needs to be assessed.The present study was conducted at the schools of Paonta Sahib, District Sirmour . Ethical clearance and necessary permissions were obtained from the authorities concerned prior to the start of the study.A total of 520 school-going children aged 5 years ± 6 months (deciduous dentition) and 12 years ± 6 months (permanent dentition) were randomly selected and divided into two groups: Group I: Deciduous dentition (n = 260) Group II: Permanent dentition (n = 260)Children aged 5 years ± 6 months with fully erupted deciduous dentition and 12 years ± 6 month-old children with fully erupted permanent dentition who were studying in schools located at Paonta Sahib and were permanent residents of Paonta Sahib were included in the study. Children on regular use of chlorhexidine gluconate etc., over the last 3 months or antibiotics for the last 1 month and those having mixed dentition were excluded from the study.S. mutans—estimation of levels of S. mutans in saliva, salivary secretion rate, and salivary buffer capacity. An optional factor for scoring, i.e., clinical judgment, is also available which gives an opportunity for the examiner to express his/her clinical feelings, if it differs from the program in-built estimation. By default, its value is set to “1.”Caries risk profiles were assessed by the cariogram by putting the parameters in PC software in weighted scores using following criteria: related general disease, diet content, diet frequency, fluoride exposure, caries scores at baseline, plaque scores, Lactobacillus and S. mutans counts. For S. mutans, mitis salivarius-bacitracin agar  was used and for Lactobacillus, de Man, Rogosa and Sharpe agar  was used.A set of questions were asked from the patient or patient’s parents/attendants regarding medical history, method of cleaning of teeth, and brushing frequency. Also, history of use of fluorides including the topical fluoride application and toothpaste used was noted and used for scoring. A 24-hour diet recall was done for all the 520 children selected for the study for both the groups. The diet recall was taken by personal interview of the child/ parents by the investigator following the recollection of intake of anything eaten or drunk in the last 24 hours in a backwardly preceding fashion. After the interview, caries prevalence, decayed, missing, and filled teeth (DMFT) index, and decayed, missing, and filled surfaces (DMFS) index were recorded using the World Health Organization (WHO) standard criteria for oral health surveys. Oral hygiene was estimated by employing plaque index. Paraffin-stimulated whole saliva was collected from all the children to measure the (1) saliva secretion rate (expressed as mL/min), (2) buffering capacity of saliva, and (3) These risk factors for dental caries were evaluated using cariogram for both the age groups at baseline. All the children were cariographed to know their caries risk profile i.e., high-risk group, medium-risk group, and low-risk group, which was further kept for record and analysis. At baseline, no intervention or preventive interception was done intentionally. However, children were free to get any knowledge or treatment on their own.After a period of 1 year, only 499 children were available, i.e., 5 years (n = 250) and 12 years (n = 249), who were considered for statistical analysis.All the children were reexamined by the same examiner in order to avoid interexaminer variation and the caries status was recorded using DMFT/dmft by WHO criteria. The difference between the caries status at baseline and after one year was assessed and statistical analysis was done.The results obtained after 1 year were compared with predicted results of cariogram to check the validity of cariogram.The nine factors of the cariogram were compared with actual chance to avoid caries by linear regression analysis. Mean and standard deviation of dental caries increment were calculated after 1-year follow-up according to age and caries risk profile of the cariogram.There were total 520 children at the start of the study, divided into two groups of 5- and 12-year-olds (260 each). Out of 520 children, 499 children were available for follow-up after 1 year (21 dropouts) and were considered and examined for the study. The groups are: group I: 5-year-old children—250; group II: 12-year-old children—249.S. mutans, fluoride, salivary flow rate, and buffer capacity were significantly associated with actual chance to avoid caries. Linear regression analysis of 5- and 12-year-old children  and 2 shThe percentages of subjects having caries increment in group I, i.e., 5-year-old children in high-, medium-, and low-risk groups after 1 year were 66.2, 39.5, and 13% respectively, whereas in 12-year-old children, the percentages of subjects having caries increment were 48.5, 27.3, and 13.9% respectively . CariogrMean caries increment  after 1 7 Thus, our study aimed to evaluate the individual caries risk profile of children aged 5 and 12 years. The age groups of 5 and 12 years were chosen in the study, as they are WHO global monitoring ages for dental caries.The most important challenge in pediatric dentistry is to maintain healthy teeth, and reliable caries risk assessment is essential for early oral health promotion and disease management.8 also showed that the highest-risk group developed almost 10 times more caries compared with the best group (increment 2.58 vs 0.27). Similar relationship is seen in other studies.9109 also showed highest percentage of individuals (83.2%) developing new caries lesion in the category of very high-risk children and lowest percentage of individuals (10.8%) to develop new caries lesion in the category of very low-risk children. Petersson et al11 also showed in his study that those children with increased risk compared with baseline developed significantly more caries than those with unchanged category.According to S. mutans, fluoride, salivary flow rate, and buffer capacity were significantly associated with actual chance to avoid caries.Linear regression analysis of 5- and 12-year-old children  and 2 sh12 In our study  and secondly, the diet frequency, i.e., the number of meals consumed per day by the child including snacks. In our study, a highly significant correlation was found between the diet content and actual chance to avoid caries  and statistically significant difference was found between the plaque index score and actual chance to avoid caries ,4 Petersson et al,11 and Peker et al.27It is generally accepted that as found  and 2 be35 In the present study (36 and Animireddy et al37 also gave similar results in their study.Saliva plays an important role in oral health, as it maintains the integrity of the oral hard and soft tissues, protects the oral tissue against immunologic bacterial, fungal, and viral infections.nt study  and 2, s36 A clear inverse relationship between salivary buffer capacity and caries susceptibility has been clearly demonstrated.36 In our study (36 and Animireddy et al.37The buffer capacity of a sample of saliva was measured as the “amount of acid needed to lower the pH of saliva through a fixed pH interval” as is described in Federation Dentaire Internationale technical report no. 31.ur study  and 2, a38Related general disease and medical health of each child were taken into consideration, as it has been reported that oral health may be related to some systemic disease.Dental caries has a high prevalence rate and in a developing country with limited resources, the value of prevention based on caries risk assessment cannot be overemphasized. For a practicing dentist in an industrialized country, cariogram can be used in the daily clinical routine, as it is affordable, user-friendly, and easy to understand. Thus, cariogram can be an effective tool for motivating the patient and also serve as a support for clinical decision making, when selecting preventive strategies for the patient.


Saccharomyces cerevisiae strains, isolated from white wine, red wine, and beer, were monitored by a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS), a direct injection analytical technique for detecting volatile organic compounds with high sensitivity (VOCs). Two ethanol-related peaks (m/z 65.059 and 75.080) described qualitative differences in fermentative performances. The release of compounds associated to the peaks at m/z 89.059, m/z 103.075, and m/z 117.093, tentatively identified as acetoin and esters, are coherent with claimed flavor properties of the investigated strains. We propose these mass peaks and their related fragments as biomarkers to optimize the aromatic performances of commercial preparations and for the rapid massive screening of yeast collections.In light of the increasing attention towards “green” solutions to improve food quality, the use of aromatic-enhancing microorganisms offers the advantage to be a natural and sustainable solution that did not negatively influence the list of ingredients. In this study, we characterize, for the first time, volatile organic compounds (VOCs) associated with aromatic bakery yeasts. Three commercial bakery starter cultures, respectively formulated with three  Saccharomyces cerevisiae, responsible with its primary metabolic activity (transformation of fermentable carbohydrates to ethanol and carbon dioxide) of the alcoholic fermentation. Associated with the secondary metabolism of this organism is the release in the dough of a heterogeneous arena of volatile molecules  which may affect bread sensory quality  of ethyl propionate [m/z 89.059. In this case fragmentation (m/z 89.059 represents the protonated molecular ion [MH+] of acetoin (23), a compound that, due to the buttery character, might be linked with the buttery notes claimed for the A18 bakery starter culture.Considering the characteristics reported for the A18 strains, other than general increasing trends respecting to the C strain similar to those highlighted for A17 yeast preparation, we found two peaks markedly higher with respect to the other performant aromatic strain. In the first case, we remained in the field of esters as the variation involved the opionate , a compoentation  informatThese preliminary data did not rule out the involvement of VOCs belonging to other molecular classes in the definition of aromatic properties claimed for these commercial preparations.With concern of fragmentation patterns and methodological aspect, it is important to underline that, while PTR approach is usually characterized by a “soft” ionization conditions that minimize fragmentation phenomena, the presence of ethanol in the matrix negative influences these occurrences, reducing the analytical potential of the techniques. In previous works on commercial bakery starter cultures ,9, we suEven though flavor perception is a consequence of the interaction of complex physicochemical, biochemical, physiological, psychological, and neurobiological phenomena ,37,38, tMicrobial resources impact at different levels on the quality of cereal-base products ,45,46,47S. cerevisiae cells in the matrix. In fact, it has been lately proved that the initial level of yeast starter culture can influence the release of the majority of the volatile associated with the alcohol in the dough [Considering the experimental design, we assured a common starting point in terms of CFU of yeasts per gram of dough to avoid differences addressable to the different amount of he dough .m/z 65.059 and 75.080 as target VOCs to assess the progress of alcoholic fermentation in the dough. From one side, we reported findings suitable to describe the fermentative performances claimed by the manufacturer. On the other hand, even if we obtained similar trends for the analyzed mass peaks, they provided different descriptions of the intensity of the same phenomena, highlighting the need for further analysis before proposing this approach for quantitative analysis.We proposed the monitoring of the two ethanol-related peaks S. cerevisiae, metabolic pathways responsible for volatile compound production are often part of the secondary cellular metabolism [In the case of the dough prepared with the A16, the disposition of the points in the PCA  reflectstabolism , hence, et al. [2 production, separated 19 moderate fermentative strains and five strains with a slow fermentative character, during the screening of 24 S. cerevisiae strains with dissimilar genetic backgrounds and belonging to different industrial applications .More generally, the diversity in fermentative behavior is consistent with evidence reported by Aslankoohi et al.  that, stm/z 89.060, m/z 103.075, and m/z 117.093 intensities as a function of the average of the two ethanol-related peaks m/z 65.059 and 75.080)  .S. cerevisiae in the same “nutritive arena”, we stress the evidence of a strain-dependent features influencing esters production in yeast, findings in accordance with a recent “volatome” study that compares indigenous and commercial strains [S. cerevisiae [The application of PTR-MS in the rapid characterization of VOCs released by commercial aromatic bakery yeasts during dough fermentation indicates mass peaks as interesting markers and suggest a possible role of acetoin and esters. Esters are formed by chemical or lipase-catalyzed enzymatic condensation of organic acids and alcohols and by other several biochemical routes in microbes ,50. In e strains . Interesrevisiae , given trevisiae   were used to obtain different dough samples, prepared according to the procedure described in the manufacturer specifications. The preparation was carried out using a bread homemaker machine . The dough samples were supplemented with one of the following commercial starter cultures preparations: C , A16 , A17 , and A18 . The dough was divided into 3.0 g pieces . During preparation, the dough was kept at 4 °C in order to slow the fermentation process. Then, the samples were transferred at 30 °C for the whole duration of the experiment (approximately 16 h). Un-inoculated dough had been used as a blank. Five replicates of each dough sample were analyzed  and the entire experiment was repeated three times in different days. To confirm the absence of residual VOCs, an empty vial was introduced in the oven during baking and later used as a blank control.Microstructures of the preparation were observed by scanning electron microscopy .One gram of the dough was suspended in 9 mL of sterile physiological saline . Serial decimal dilutions were prepared in sterile physiological saline and 0.1 mL samples of appropriate dilutions were spread on sterile YPG agar plates . The plates were incubated at 26 °C for 48 h before counting the yeast colony forming units (CFU).−17 V·cm2). The inlet line consisted of a PEEK capillary tube  heated at 110 °C. The inlet flow was set at 120 sccm and 40 sccm for dough and bread measurements, respectively.In order to measure the headspace of the dough, a commercial PTR-ToF-MS 8000 apparatus from Ionicon Analytik GmbH , was used in its standard configuration (V mode). The ionization conditions in the drift tube were the following: 110 °C, 2.30 mbar, 550 V. This led to an E/N ratio of about 140 Townsend  and in-house developed scripts written in the R programming language .


Preeclampsia has been suggested to increase the risk of end-stage kidney disease (ESKD); however, most studies were unable to adjust for potential confounders including pre-existing comorbidities such as renal disease and cardiovascular disease (CVD). We aimed to examine the association between preeclampsia and the risk of ESKD in healthy women, while taking into account pre-existing comorbidity and potential confounders.p < 0.001), which remained in the extensively adjusted  models. Women who had preterm preeclampsia  and women who had preeclampsia in 2 pregnancies  had the highest risk of ESKD compared with women with no preeclampsia. Considering this was an observational cohort study, and although we accounted for several potential confounders, residual confounding cannot be ruled out.Using data from the Swedish Medical Birth Register (MBR), women who had singleton live births in Sweden between 1982 and 2012, including those who had preeclampsia, were identified. Women with a diagnosis of chronic kidney disease (CKD), CVD, hypertension, or diabetes prior to the first pregnancy were excluded. The outcome was a diagnosis of ESKD, identified from the Swedish Renal Registry (SRR) from January 1, 1991, onwards along with the specified cause of renal disease. We conducted Cox proportional hazards regression analysis to examine the association between preeclampsia and ESKD adjusting for several potential confounders: maternal age, body mass index (BMI), education, native country, and smoking. This analysis accounts for differential follow-up among women because women had different lengths of follow-up time. We performed subgroup analyses according to preterm preeclampsia, small for gestational age (SGA), and women who had 2 pregnancies with preeclampsia in both. The cohort consisted of 1,366,441 healthy women who had 2,665,320 singleton live births in Sweden between 1982 and 2012. At the first pregnancy, women’s mean (SD) age and BMI were 27.8 (5.13) and 23.4 (4.03), respectively, 15.2% were smokers, and 80.7% were native Swedish. The overall median (interquartile range [IQR]) follow-up was 7.4 years (3.2–17.4) and 16.4 years (10.3–22.0) among women with ESKD diagnosis. During the study period, 67,273 (4.9%) women having 74,648  singleton live births had preeclampsia, and 410 women developed ESKD with an incidence rate of 1.85 per 100,000 person-years. There was an association between preeclampsia and ESKD in the unadjusted analysis  among women who had preeclampsia compared with women who had no preeclampsia.Only few studies reported such as association between preeclampsia and the risk of developing end-stage kidney disease (ESKD).Some of these studies did not adjust for key potential confounders and may have lacked high-quality data.N = 1,366,441 healthy women who had 2,665,320 singleton live births).We performed this study to examine the association between preeclampsia and ESKD using a large cohort from the Swedish national registers .This association was independent of several sociodemographic factors such as maternal age and education and prepregnancy comorbidity such as renal disease and CVD.These results highlight the importance of preeclampsia as a risk marker for developing ESKD.These findings need to be investigated further to see whether preventive strategies would reduce the risk of ESKD. The prevalence of chronic kidney disease (CKD) is estimated at 10% to 12% of the global population . WorldwiPreeclampsia—defined as hypertension and the coexistence of one or more of the following new-onset conditions: proteinuria, maternal organ dysfunction , or evidence of uteroplacental dysfunction indicated by fetal growth restriction ,12—complwww.snronline.se), the present population-based cohort study aimed to examine the association between preeclampsia and the risk of ESKD in healthy women without CKD, diabetes, or CVD before pregnancy.Whether the association between preeclampsia and the risk of ESKD is confounded by prepregnancy CKD, diabetes, and CVD is unknown, with mawww.snronline.se). Data from these registers were linked using the personal identification number given to all Swedish residents ) follow-up was 7.4 years (3.2–17.4) and 16.4 years (10.3–22.0) among women with ESKD diagnosis, i.e., the longer the follow-up, the more likely a woman was to develop ESKD. Women who had preeclampsia were older on average and had higher BMI. Among women with no preeclampsia in the first pregnancy, 14.2% were overweight, and 4.9% were obese compared with 20.1% and 11.8% overweight and obese, respectively, among women with preeclampsia. Women who had preeclampsia were more likely to be native Swedish (80.3% versus 86.3%) and nonsmokers (77.5% versus 81.4%). Details are summarised in p < 0.001), which remained largely unchanged in the adjusted model . The associations between each potential confounder included in this model and the risk of ESKD are presented in p < 0.001; The incidence rate of ESKD per 100,000 person-years was 1.85 (95% CI 1.66–2.05) among women with no preeclampsia and 12.35 (95% CI 9.61–15.88) among women who had preeclampsia. There was an association between preeclampsia and the risk of end-stage renal disease (ESRD) in the crude analysis , whereas women who had preeclampsia in 1 pregnancy had a more than 4-fold increased risk of ESKD , whereas the association between preterm preeclampsia and the risk of ESKD was almost 9-fold . The association between preeclampsia without SGA and ESKD was almost 5-fold , whereas the HR was slightly larger for preeclampsia and SGA .Women who had 2 pregnancies complicated with preeclampsia had more than 7-fold increased risk of ESKD .There was no statistically significant interaction between preeclampsia and maternal age, BMI, or smoking at the first pregnancy . Of the p < 0.001]) due to interstitial nephritis was 10-fold , whereas it was 3-fold for glomerulonephritis  and other causes ; therefore, the population attributable fraction associated with preeclampsia was 0.14. Thus, in our study, exposure to preeclampsia accounted for 14% of all ESKD in the studied population.The results of the association between preeclampsia and ESKD among women who had CKD, CVD, hypertension, or diabetes before the first pregnancy  and amonThis large, nationwide cohort study of healthy women followed up to 30 years after their first pregnancy suggests that women who have preeclampsia are at almost 5-fold increased risk to develop ESKD. The association is independent of a number of key potential confounders, including pre-existing CKD, CVD, diabetes, and hypertension. The highest risk of ESKD was observed among women with preterm preeclampsia, preeclampsia and SGA in the same pregnancy, and women who had preeclampsia in 2 pregnancies. The highest risk was associated with development of diabetic nephropathy. The population attributable fraction suggested that preeclampsia was responsible for 14% of all ESKD cases in parous women, although this estimate is based on the assumption that the association between preeclampsia and ESKD is causal. Restricting the analysis to women who had their first delivery from 1991 onwards resulted in a higher HR. Our results suggested that the risk of ESKD was increased in relation to preeclampsia in the first 5 years and the first 10-year follow-up , which mThe present findings are consistent with a population-based study from Norway, which included about 570,000 women compared with our 1.36 million . HoweverTwo other studies using insurance claims data on approximately 240,000 and 940,000 women, respectively, both from Taiwan, reported an increased risk of ESKD in relation to hypertensive disorders in pregnancy, including gestational hypertension and preeclampsia ,25. The The precise pathophysiological mechanisms linking preeclampsia and ESKD are not clear. CKD and preeclampsia have common risk factors , indicating a shared aetiology most likely through the widespread endothelial dysfunction that has been shown to be present in both preeclampsia and ESKD . SimilarPrevious studies have shown that women with a history of preeclampsia have an increased incidence of microalbuminuria, a marker for renal disease  and predChanges in renal haemodynamics postpartum in women with preeclampsia have also been suggested to contribute to an increased risk of ESKD. However, the few studies that assessed renal function and haemodynamics after preeclamptic pregnancy  suggesteOur study has several strengths. First, it is the largest study on preeclampsia and ESKD to date using one of the best available epidemiological data sources worldwide. The MBR contains data on more than 99% of all births in Sweden, which rules out selection bias due to nonparticipation. Furthermore, the data were collected prospectively, and the preeclampsia diagnoses were ascertained by obstetricians and registered during follow-up in the MBR, which minimised the risk of misclassification. Linkage to the national and validated SRR confirmed the ESKD diagnosis directly and reduced the risk of misclassification of outcome commonly seen when using only administrative databases. We were able to identify the primary renal diagnosis made by the nephrologist. Our access to data on maternal comorbidity before the first pregnancy and during the reproductive period is a significant strength, which allowed us to address the potential confounding of CKD, diabetes, hypertension, and CVD by excluding them from the main analysis. In addition, due to the prospectively collected information in the MBR, we were able to adjust for key sociodemographic variables, including age, BMI, smoking, education, and native country. The size of the cohort allowed us to perform several subgroup and sensitivity analyses.The study has some limitations that should be considered when interpreting the results. Although we excluded women who had a recorded prepregnancy diagnosis of a major related comorbidity, we cannot rule out that some women may have had undiagnosed or unknown disease leading to residual confounding. Although the study is population based, the results are not generalisable to women who had stillbirth or twins because these were not included in the cohort. However, the percentage of women who may have had stillbirth or twins is very small. Also, we did not have data on miscarriages during the study period; however, this is unlikely to influence the reported results because these women would not have had preeclampsia. We cannot rule out potential confounding by environmental factors related to diet and physical activity and genetic factors, which we did not have access to. Finally, data on BMI were missing in 23% of the population; however, this was similar between exposed and unexposed groups, and maternal characteristics did not appear to confound the association between preeclampsia and ESKD.We report that healthy women with preeclampsia are at a 5-fold increased risk of ESKD compared with parous women with no preeclampsia. This shows that preeclampsia is a sex-specific, independent risk factor for the subsequent development of ESKD. However, it should be noted that the overall ESKD risk remains small. Whether screening and/or preventative strategies will reduce the risk of ESKD in women with adverse pregnancy outcomes is worthy of further investigation.S1 STROBE Checklist(DOC)Click here for additional data file.S1 TextCI, confidence interval; ESKD, end-stage kidney disease; HR, hazard ratio.(DOCX)Click here for additional data file.S2 TextESKD, end-stage kidney disease.(DOCX)Click here for additional data file.S3 TextESKD, end-stage kidney disease.(DOCX)Click here for additional data file.S4 TextESKD, end-stage kidney disease.(DOCX)Click here for additional data file.S1 TableICD, international classification of diseases.(XLSX)Click here for additional data file.S2 TableESKD, end-stage kidney disease; HR, hazard ratio.(XLSX)Click here for additional data file.S3 TableESKD, end-stage kidney disease; HR, hazard ratio.(XLSX)Click here for additional data file.S4 TableESKD, end-stage kidney disease; HR, hazard ratio.(XLSX)Click here for additional data file.S5 TableAll HRs are based on comparing women who had preeclampsia in at least 1 pregnancy with women who never had preeclampsia during the follow-up period. ESKD, end-stage kidney disease; HR, hazard ratio.(XLSX)Click here for additional data file.


This study proposes fabric composites with improved static and dynamic puncture via increasing a friction force to restrain the slide of filaments as well as the compression and abrasion between the fibers and the puncture probe. The the bi-layered shell layers of composite fabrics are composed of aramid staple fibers and nylon staple fibers and a layer of low-melting-point polyester (LPET). The nonwoven layer consisting of recycled aramid and nylon staple fibers provides a shear effect to dissipate part of the puncture energy. Reinforcing interlayers include a woven fabric and PET filaments that are circularly aggregated between the surface layers, providing isotropic filament reinforcement and strengthening the resistance against the tip of the puncture probe. The reinforcing filaments may slide after the employment of needle punching, and to compensate for this disadvantage, the LPET layers are used to thermal bond the composite fabrics and the total thickness is controlled at 2 mm. The thermally bonded fabric composites are evaluated in terms of puncture resistance, thereby examining the effects of fabric structure and thermal bonding. According to the test results, the optimal composite structure is the sample N/L/W/F/L/N, which was reinforced by the LPET adhesive layer and irregularly aligned filaments. The sample which used the LPET adhesive layer had a positive influence on static puncture resistance and dynamic puncture resistance, preventing the slide of filaments, but the poor interfacial combination only contributed to limited reinforcement. Owing to the increasing awareness of the importance of self-protection, protective materials are being efficiently developed, including barks, hides, leather, metal, and composites. The progressing technology gives rise to diverse industries and subsequently demands for the preparation of protective materials that require large amounts of advanced materials have risen ,2,3,4, aIn light of the material types and purposes, puncture resistant materials can be divided into flexible, semi-flexible, rigid, and liquid stab resistant materials. Ceramic and metal plates are commonly used rigid stab resistant materials. They have excellent stab resistance but are heavyweight, which restricts the mobility of users. By contrast, flexible or semi-flexible stab resistant materials, such as high-density polyethylene, aramid mixture fabric, and specially made composites , have goTo sum up, achieving good puncture resistance is mainly based on sufficient fiber density, high friction among fibers, a profound aggregation of fibers where the spike strikes, and firmly secured fibers. Hence, regardless of the type of stab resistant material, friction is one of the major factors influencing its stab resistance. This study aims to develop a flexible sandwich-structured puncture resistant fabric composites which can be used in the protective clothing field and geotextiles field. One of the materials we used in this study was the waste selvages of aramid plain woven fabrics (K129 and K29). Aramid fiber can usually be used in a wide variety of applications due to its light weight, high performance, and toughness. It is used in a variety of protective clothing and equipment to make it safer and more durable. Although aramid waste selvages can only be recycled into staple fibers, nonwoven fabrics can still take advantage of their original characteristics while reducing costs.On the other hand, based on the fabric structure, knitted/woven fabrics have greater stab resistance against knives while nonwoven fabrics have puncture resistance against spikes ,19,20,21Besides this, we used the nonwoven fabric process in this whole study. This process technology has the advantages of being fast, low cost, and having a high output, and can fully mix two or more kinds of fiber materials and composite multi-layer fabrics to make the flexible sandwich-structured puncture resistant fabric composites we designed.2. PET continue filaments  have a fineness of 500 D.Recycled aramid staple fibers  are obtained from aramid woven fabrics and have a fineness of 1.2 D and a length of 50–65 mm. High strength nylon 6 staple fibers  have a fineness of 6.0 D, a length of 64 mm, single fiber strength of 10 g/d, and an elongation of 24.7%. Two-component low-melting-point polyester (LPET) staple fibers  have a fineness of 4.0 D and a length of 51 mm. The melting temperatures of the sheath and the core are 110 °C and 265 °C, respectively. Basalt woven fabrics  are composed of basalt fiber bundles at both warp and weft directions with a fineness of 2970 D and an areal density of 328 g/m2; the thickness is about 1 mm, the tearing strength force is 299.53 N at the cross machine direction (CD) and 291.75 N at the machine direction (MD), the static puncture strength is 55.90 N, and the dynamic puncture strength is 35.52 N.The commercially available geotextile is supplied by Hsinnjy Ltd. Co. , and the main material is PET fiber, which is a nonwoven fabric formed by multiple needle rolling and heat treatment. Its characteristics are based on the relevant test standards of this study. The basis weight is 272.46 g/m2) and pure LPET fabrics . Waste aramid selvages are processed by opening  to have staple fibers, after which they and nylon staple fibers are needle punched to form the nonwoven layers . Pure LPET fabrics are composed of LPET fibers, serving as the adhesion layer to stabilize the composite structure. All of the laminates are needle punched  and thermally treated at 130 °C at a speed of 0.2 m/min , forming the filamentor and woven-reinforced fabric composites. The first and second sub-figures in The symmetrically sandwich-structured composite fabrics are composed of double-layered surfaces and a reinforcing interlayer of a woven fabric and/or irregularly aligned filaments. As shown As specified in ASTM D5035-11 (2015) , fabric The static puncture resistance of samples was measured at a puncture rate of 508 mm/min using a universal strength testing machine  as specified in ASTM F1342 . SamplesThe dynamic puncture resistance of samples was measured by a drop-weight impact machine  that was equipped with a data collector  according to the energy level of E1-1 24 J) in the NIJ Standard-0115.00 [ J in theThis study aims to develop sandwich-structured puncture resistant fabric composites, and the optimal N/L/W/F/L/N has good puncture resistance at level 3. The proposed fabric composites are characterized in terms of tearing strength, static puncture resistance, and dynamic puncture resistance. The test results indicate that thermally treated N/L/W/F/L/N has a 63.3% greater tearing strength at CD orientation, 63.9% greater static puncture resistance, and 32.5% greater bursting strength than N/L/L/N.Compared to other studies, using the LPET adhesive layer had a positive influence, preventing the slide of the filaments, but the poor interfacial combination only contributes limited reinforcement. Hence, improving the interfacial affinity between laminates is suggested to be conducted in future studies.


A plant-based diet is an effective strategy in the treatment of obesity. In this 16-week randomized clinical trial, we tested the effect of a plant-based diet on body composition and insulin resistance. As a part of this trial, we investigated the role of plant protein on these outcomes.n = 75) were randomized to follow a plant-based (n = 38) or a control diet (n = 37). Dual X-ray Absorptiometry assessed body composition, Homeostasis Model Assessment (HOMA-IR) assessed insulin resistance, and a linear regression model was used to test the relationship between protein intake, body composition, and insulin resistance.Overweight participants (p < 0.001), fat mass , and HOMA-IR . The decrease in fat mass was associated with an increased intake of plant protein and decreased intake of animal protein . In particular, decreased % leucine intake was associated with a decrease in fat mass , in both unadjusted and adjusted models for changes in BMI and energy intake. In addition, decreased % histidine intake was associated with a decrease in insulin resistance , also independent of changes in BMI and energy intake.The plant-based vegan diet proved to be superior to the control diet in improving body weight, fat mass, and insulin resistance markers. Only the vegan group showed significant reductions in body weight (treatment effect −6.5 [95% CI −8.9 to −4.1] kg; Gxt, These findings provide evidence that plant protein, as a part of a plant-based diet, and the resulting limitation of leucine and histidine intake are associated with improvements in body composition and reductions in both body weight and insulin resistance. Certain dietary habits, such as high intakes of sodium and processed meat products and low intakes of fruits and vegetables, are associated with 45.5% of cardio-metabolic deaths in the United States3. Fortunately, research has shown a plant-based vegan diet to be beneficial in improving nutrient intake4, decreasing all-cause mortality, and decreasing risk of obesity, type 2 diabetes, and coronary heart disease5.Suboptimal nutrition is a major cause of obesity, chronic disease, and premature death across the nation and worldwide6, people sometimes question the ability to reach protein requirements on a plant-based vegan diet. A sufficient protein intake is necessary to supply nitrogen and amino acids to our cells to ensure the growth and maintenance of the protein pool in our bodies7. However, a diet based entirely on plants provides all essential amino acids and an adequate quantity of overall protein, even without the use of special food combinations6. Further, the consumption of exclusively plant proteins has been associated with reduction of the concentrations of blood lipids11, obesity12, and cardiovascular disease15.A plant-based vegan diet excludes all animal products and is centered around grains, legumes, vegetables, and fruits. While adequate in macro and micronutrients14, which may play a role in body composition and insulin resistance12. A high intake of branched chain amino acids  can increase insulin resistance16. In addition, dietary restriction of sulfur containing amino acids (methionine and cysteine), is associated with a reduction in body weight, adiposity and metabolic changes in both adipose and liver tissues, which enhance insulin sensitivity and energy expenditure17. Plant protein low in sulfur also reduces blood lipids, homocysteine, and blood pressure19. Furthermore, low protein diets are also associated with increased life span, especially if the consumed protein is plant derived20.The specific composition of dietary protein has been shown to influence the balance of glucagon and insulin activity21, we explore the effects of plant protein, as part of a plant-based diet, on weight control, body composition, and insulin resistance in overweight individuals.In this secondary analysis of data from a 16-week randomized clinical trial2, were enrolled. Exclusion criteria were history of diabetes, smoking, alcohol or drug abuse, pregnancy or lactation, and current use of a vegan diet. The study protocol was approved by the Chesapeake Institutional Review Board on October 12, 2016. All participants signed a written informed consent. Registration on ClinicalTrials.gov was initiated on October 20, 2016 (Identifier: NCT02939638).The study was conducted between October 2016 and June 2017, using a single-center, randomized, open parallel design. Otherwise healthy overweight or obese adult men and women, with a body-mass index between 28 and 40 kg/mParticipants were randomly assigned in a 1:1 ratio to a vegan or a control group based on a computer-generated randomization protocol. The randomization protocol was not be accessible beforehand. The particpants were not blinded to their group assignment. They were examined at baseline and 16 weeks. The vegan group was asked to follow a low-fat vegan diet consisting of vegetables, grains, legumes, and fruits. They were instructed to avoid animal products and added oils. Daily fat intake was 20–30 g. No meals were provided. Participants in the control group were asked to maintain their current diets, which included animal products, for the duration of the study. Laboratory measurements and statistical analyses were made by staff members blind to group assignment.22. The study participants were instructed not to change their physical activity, and to continue their chronic medications, except as modified by their personal physicians. Physical activity was assessed by the International Physical Activity Questionnaire23.To monitor adherence, a 3-day dietary record was completed by each participant at baseline and 16 weeks. Dietary intake data were collected and analyzed by a registered dietician, using Nutrition Data System for Research version 2016, developed by the Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN24. Self-reported dietary intake of animal and plant protein was analyzed. Amino acid intakes were assessed and used as predictors of changes in body composition and insulin resistance.All measurements were performed at baseline and 16 weeks on an outpatient basis, after a 10–12 h overnight water-only fast. Height was measured using a standiometer. Weight was measured using a periodically calibrated scale accurate to 0.1 kg. Body composition was measured using a DXA scan . Insulin resistance was evaluated using HOMA-IR (Homeostasis Model Assessment)t-tests were calculated to test whether the change from baseline to 16 weeks was significantly different from zero. Pearson correlations were calculated for the relationship between changes in reported protein and amino acid intake on one side, and body composition and insulin resistance on the other. Values were first unadjusted and then adjusted for changes in BMI and energy intake. Regression analyses assessed the effect size of changes in animal and plant protein and of changes in amino acid intake on body composition and insulin resistance.We based a calculation of the sample size on an alpha of 0.05 and 0.80 beta to detect between-group differences in outome variables: a clinically relevant 10% difference in insulin resitance (HOMA IR) and a 5% difference in BMI. We required 54 participants to complete the intervention they were randomised to. The intention-to-treat analysis included all participants. A repeated measure ANOVA model, that included factors group, subject, and time, was used to test the between-group differences throughout the 16 week study. Interaction between group and time (Gxt) was calculated for each variable. We tested the data for normal distribution. Within each diet group, paired comparison n = 72) of whom completed the study  of participants were women. Additional baseline characteristics can be found in  Table The total randomized sample size was 75 participants, 96% . Every 1 gram reduction in animal protein intake was associated with a reduction of 0.040 kg in fat mass. Overall, the average reduction of 36.2 g of animal protein consumption in the vegan group was associated with a reduction in fat mass of 1.45 kg . Every 1gram increase in plant protein was associated with a reduction in fat mass of 0.046 kg. Overall, the average increase of plant protein of 19.2 g in the vegan group was associated with a reduction in fat mass by 0.88 kg . This was shown in the vegan group in which a 0.3% reduction in leucine intake was associated with a 0.82 kg .We observed a positive correlation between changes in leucine consumption as a percent of total protein and changes in fat mass. Lowering % leucine intake was associated with a decrease in fat mass . A 0.2% reduction in histidine intake in the vegan group was associated with a reduction in HOMA-IR by 0.79 (p = 0.003) . In addition, changes in % intake of the following amino acids were also positively associated with changes in HOMA: threonine , leucine , lysine , methionine , and tyrosine . These correlations were no longer significant after adjustment for changes in BMI and energy intake.Lowering % histidine intake was associated with a decrease in HOMA (03) Fig. . This asThis study demonstrated that the quality and quantity of dietary protein from a plant-based vegan diet are associated with improvements in body composition, body weight, and insulin resistance in overweight individuals. A decreased intake of animal protein and an increased intake of plant protein were associated with a decrease in fat mass, by 1.45 and 0.88 kg respectively. Exchanging plant protein for animal protein explains more than half of the reduction in fat mass in the vegan group (2.33 out of 4.3 kg). A large portion of fat mass reduction may be explained by the amino acid composition of plant protein, specifically by decreased leucine intake, which was associated with a decrease in fat mass by 0.82 kg, independent of changes in BMI and energy intake. Additionally, decreased histidine intake was associated with a decrease in insulin resistance, also independent of changes in BMI and energy intake. Finally, decreased intakes of threonine, leucine, lysine, methionine, and tyrosine were each associated with a decrease in insulin resistance. However, these associations were mainly driven by weight loss.26. Plant-based diets have also been shown to decrease the risk of developing diabetes in additional prospective studies27. The specific role of plant protein in weight regulation and metabolic health is of particular interest. In a study focusing specifically on the association between protein sources and body weight regulation using data from the European Prospective Investigation into Cancer and Nutrition study, increases in body weight were positively correlated with an increased intake of animal protein, especially in women28. Similarly, in a 2011 observational study, increases in animal protein consumption were found to be positively correlated with increases in BMI, while increases in plant protein intake were negatively associated with changes in BMI29.Multiple randomized controlled studies have established the effectiveness of plant-based diets for weight loss12. Specifically, a higher intake of essential amino acids can stimulate secretion of insulin and up-regulate insulin like growth factor 1 (IGF-1)12. Essential amino acids are found in greater abundance in animal protein, compared to plant protein. In contrast, a higher intake of non-essential amino acids is associated with down-regulation of insulin secretion and increased glucagon secretion, resulting in stimulation of gluconeogenesis, hepatic lipid oxidation, lipolysis and reduction in both IGF-1 and cholesterol synthesis. Hepatic lipid oxidation promotes appetite control and lowers the respiratory quotient, which may play a role in body weight reduction, and may further be supported by the thermogenic effect of glucagon. Human adipocyte express IGF-1 receptors, thus down-regulation of IGF-1 activity can also promote leanness12. Non-essential amino acids in plant protein promote higher net glucagon activity than an omnivorous diet, promoting weight loss and reduction of LDL-cholesterol12.Dietary protein triggers release of both insulin and glucagon16.A 2018 prospective study that included more than 1,200 adults, who were followed-up for a mean of 2.3 years, showed that higher intake of branched chain amino acids (BCAA), especially leucine, can increase insulin resistance. Participants in the highest tertile for leucine intake had a 75% higher risk of developing insulin resistance compared with people in the lowest tertile 31. High serum BCAA levels activate the mammalian target of rapamycin complex 1 (mTORC1) signaling pathway, leading to inhibition of glucose transport in muscle and fat tissues16. Animal protein from meat and dairy products contains a high percent of leucine. Therefore, these foods may stimulate the mTORC1 pathway, thus contributing to insulin resistance, and obesity32.Increased serum concentrations of BCAA have been associated with increased risk of type 2 diabetes and underlying metabolic abnormalities34. In our study, the vegan group consumed less than 75% of the control group’s daily grams per day of BCAA. Our data also show that reduced dietary intake of leucine, in particular, was associated with decreased fat mass and reduced insulin resistance.Randomized controlled trials have shown that reduced dietary intake of BCAA promote weight loss, reduce adiposity, and improve glycemic control and metabolic health17. Dietary restriction of methionine and cysteine without caloric restriction has been associated with reductions in body weight, adiposity, blood levels of insulin, IGF-1, and glucose17, as well as reductions in cardiovascular risk factors including blood lipids, homocysteine, and blood pressure19. Our results suggest that reduced intake of methionine through a plant-based diet may correlate with a decrease in both body weight and insulin resistance.Additionally, our results suggest that a decreased intake of histidine, leucine, threonine, lysine, methionine, and tyrosine were all associated with a decrease in HOMA, with histidine being the only one having a significant association independent on changes in BMI and energy intake. The vegan group reduced both its absolute and relative intake of all six of these amino acids. The significant decrease in the consumption of sulfur-containing amino acids, i.e. cysteine and methionine, in the vegan group, is of particular interest. Several studies have shown that diets restricting sulfur-containing amino acids have shown beneficial effects in the prevention of chronic diseases, including type 2 diabetes, cancer, and cardiovascular disease20. Our data suggest that both the decreased intake of animal protein and the amino acid composition of the plant-based diet are associated with decreased body fat and reduced insulin resistance.Higher animal protein consumption has been associated with increased risk of metabolic disease and mortality. A 2015 study using data from NHANES II reported the link between protein intake and mortality in men and women. Subjects in the high-protein group  had a 73-fold increase in risk of diabetes mortality and a 74% increase in relative risk of all-cause mortality35. In the current study, all participants in the vegan group exceeded the recommended daily intake of protein and of each individual amino acid. While animal protein is higher in essential amino acids, containing significant amounts of leucine, histidine, threonine, methionine and lysine, consumption of plant protein, which is higher in non-essential amino acids, offers clear metabolic benefits. People following a plant based diet still consume more than 100% of the recommended dietary intake of essential amino acids. The main plant sources of these amino acids are legumes, grains, and vegetables. For example, 2 servings of oatmeal made from 100 g of oats contain 102% of recommended daily intake of tyrosine36.The United States Department of Agriculture recommends a minimum of 46 g of protein per day for women and 56 g per day for menUtilizing a randomized control trial allowed us to analyze the relationship between dietary protein and specific amino acids with changes in body weight and insulin resistance. This 16 week study provided an ample amount of time for participants to adapt to the diet. The low attrition rate suggests that a plant-based diet is sustainable and can be incorporated into diverse lifestyles. A limitation to this study that must be considered is the dietary assessment method. Three day dietary records at baseline and week 16 were used which likely have some degree of error due to inaccurate and therefore misrepresentative reporting. To minimize this error, participants were taught how to give detailed reports. Random periodic phone calls were also used to evaluate and monitor participants’ food records. This study could not definitively prove a causal relationship between protein intake and metabolic outcomes. Such a conclusion would require a specifically-designed randomized clinical trial. However, our study suggests overall benefits of a plant-based diet.The quantity and quality of dietary protein, as part of a plant-based diet, are associated with improvements in body weight, body composition, and insulin resistance in overweight individuals. A greater consumption of plant protein, in replacement of animal protein, resulted in decreased fat mass. More specifically, decreased leucine intake was associated with a decrease in fat mass, independent of changes in BMI. In addition, decreased intakes of histidine, threonine, leucine, lysine, methionine, and tyrosine were each associated with a decrease in insulin resistance. For histidine, this association remained significant after adjustment for changes in BMI and energy intake. Our study highlights the need for additional research to explore the mechanisms explaining the beneficial role of plant protein and specific amino acids in regulating body weight, body composition, and insulin resistance.Supplemental Figure 1


Nondaily smoking is an increasingly prevalent smoking pattern that poses substantial health risks.We tested the feasibility of using a smartphone app with positive psychology exercises to support smoking cessation in nondaily smokers.Smiling Instead of Smoking (SiS) app for 3 weeks while undergoing a quit attempt. The app assigned daily happiness exercises, provided smoking cessation tools, and made smoking cessation information available. Participants answered surveys at baseline and 2, 6, 12, and 24 weeks after their chosen quit day and participated in structured user feedback sessions 2 weeks after their chosen quit day.In this prospective, single-group pilot study, nondaily smokers (n=30) used version 1 of the P=.001; external cues: b=15.8, 95% CI 5.4 to 26.1, P=.004), urge to smoke decreased , and perceptions of smoking became less positive . Self-reported abstinence rates were 40% (12/30) and 53% (16/30) of participants 2 and 24 weeks post quit, respectively, with 30% (9/30) biochemically validated as abstinent 2 weeks post quit.App usage during the prescribed 3 weeks of use was high, with an average 84% (25.2/30) of participants using the app on any given day. App use was largely driven by completing happiness exercises  of participants per day), which participants continued to complete even after the end of the prescribed period. At the end of prescribed use, 90% (27/30) of participants reported that the app had helped them during their quit attempt, primarily by reminding them to stay on track  and boosting their confidence to quit  and belief that quitting was worthwhile . Happiness exercises were rated more favorably than user-initiated smoking cessation tools, and 80% (24/30) of participants proactively expressed in interviews that they liked them. App functionality to engage social support was not well received. Functionality to deal with risky times was rated useful but was rarely used. Within-person changes from baseline to the end of prescribed use were observed for several theorized mechanisms of behavior change, all in the expected direction: confidence increased (on a 0-100 scale, internal cues: b=16.7, 95% CI 7.2 to 26.3, A smartphone app using happiness exercises to aid smoking cessation was well received by nondaily smokers. Given the high nonadherence and dropout rates for technology-delivered interventions reported in the literature, the high engagement with positive psychology exercises is noteworthy. Observed within-person changes and abstinence rates are promising and warrant further development of this app. Cigarette smoking continues to be the leading cause of preventable disease and death in the United States, accounting for more than 480,000 deaths every year . AlthougOne promising emerging treatment option for providing such support is the use of smartphone apps. Smokers are motivated to quit smoking. Recent estimates suggest that 69% of all smokers want to quit; 52% made a quit attempt in the past year, but only 6% successfully quit . Proven Despite considerable interest in smoking cessation smartphone apps, evidence-based apps remain few and far between. Existing apps in the iPhone and Android stores generally fall short of adhering to clinical practice guidelines for smoking cessation ,19,20 anTo address this need, we developed a smoking cessation app for nondaily smokers. Nondaily smokers are even less likely than daily smokers to seek or receive treatment . To engaSmiling Instead of Smoking (SiS) app. In this study, 30 nondaily smokers interested in quitting smoking were asked to use the app (SiS1) for 3 weeks. We evaluated the feasibility and acceptability of the app on 3 dimensions: actual app usage patterns, direct feedback via survey and structured user feedback, and by testing if theorized within-person changes were taking place.This paper presents the findings of the first in a series of 3 studies designed to pilot-test and further develop the On the basis of previous research, and as detailed in our previous paper detailing the design of this app , we hypoParticipants were adult nondaily smokers, who were interested in using a smartphone app to help them quit smoking . Study recruitment information was displayed on Craigslist, Smokefree.gov, a study recruitment website at Massachusetts General Hospital, websites of local universities, and an ad placed in a public transportation newspaper. Potential participants were included in the study if they were older than 18 years; smoked at least weekly, but no more than 25 out of the past 30 days; had a current quit intention; owned an Android smartphone (version 1 of the app was only created for the Android); spoke English; and were willing to come in for 2 in-person study visits. The study was approved by the Partners Healthcare Institutional Review Board. All participants provided informed consent.Participants were phone-screened and asked to complete a screening test. To pass, participants had to (1) complete an Web-based baseline survey, and correctly (100%) respond to 5 randomly placed check-questions to verify that respondents were truly reading survey items; participants received US $10 versus US $35 for surveys with incorrectly versus correctly answered check-items, respectively, (2) provide contact information for 2 collaterals who would be able to assist research staff in locating participants for follow-ups, if necessary, and (3) provide their social security number to enable remuneration by check. Participants were notified by phone if they passed the screening test, and during this phone call, they were asked to set a quit day. An enrollment visit was then scheduled to occur 1 week before the chosen quit day at the research lab. During visit 1 (enrollment), participants were guided through downloading, installing, and using the app. After 3 weeks, participants returned for visit 2  for a structured user feedback session and the 2-week follow-up survey. Participants who reported abstinence provided a saliva sample for biochemical verification. Thereafter, participants completed follow-up surveys online 6, 12, and 24 weeks after their initially chosen quit day. All surveys were administered via the research electronic data capture system, REDCap, a secure, Web-based application designed to support data capture for research studies .In total, we phone-screened 157 individuals, 58% (91/157) of whom signed online consent and took the baseline survey; 15% (24/157) decided against the study during the phone screen, and 27% (42/157) were ineligible, largely because of being daily smokers (n=25) or not owning an Android smartphone (n=12). After signing online consent, 33 individuals failed the check-items in the survey, and 27 changed their mind about participating in the study. A total of 31 individuals came in for visit 1; 1 person was unable to install the app because of too little phone storage space and decided against the study at that point. The remaining 30 comprise the sample reported in this paper. Remuneration was US $40 per survey and US $75 per in-person visit. Participants who missed a survey were contacted for subsequent surveys, unless participants actively withdrew from the study.3 Good Things (participants enter text describing 3 good things that happened to them that day), Savoring (participants enter text describing 2 experiences they savored), and Experiencing Kindness (participants describe an act of kindness they performed and one they witnessed). The app logged all entries and allowed participants to browse through their log of happy moments.Participants received version 1 of the SiS app, as described elsewhere . In brieParticipants were also prompted to complete 3 app-delivered behavioral support sessions, scheduled to be completed 1 week before the chosen quit day, on the quit day, and 1 week after the quit day. Session content was based on recommended clinical guidelines , in thatThe app passively time stamped interactions with the app, from which we calculated the percentage of participants who used each function on a given day.not easy at all, 1=somewhat easy to use, 2=easy to use, and 3=very easy to use; useful: 0=not at all useful, 1=somewhat useful, 2=usef ul, and 3=very useful). Participants also indicated if the app helped them in their quit attempt (yes/no) and in which ways the app helped them .In the 2-week survey, participants were asked to rate the ease-of-use and usefulness of each component of the app (10 items each) on a 4-point Likert scale , so that scores can be interpreted directly on the scale participants used to rate them. Baseline Cronbach alphas observed in this study are reported below.very slightly or not at all, 5=extremely) [not at all happy, 100=extremely happy) to indicate how happy they were feeling right before completing the survey. Overall satisfaction with life and happiness were assessed with the Satisfaction with Life Scale  [In general, I consider myself…,1=not a very happy person, 7=a very happy person) [The Positive and Negative Affect Schedule   uses mooy agree)  and the  person) .not at all confident, 100=extremely confident) [The Smoking Self-Efficacy Questionnaire   assessedstrongly disagree and 7=strongly agree) [I have an urge for a cigarette).The brief Questionnaire of Smoking Urges   assessedGiven this feeling, please list all the things you would like to do right now. This instruction is followed by 20 blank lines that began with the following: I would like to… To score, the number of items completed is counted, resulting in a score from 0 to 20, with larger scores indicating a larger thought-action repertoire. In this study, participants were asked to name the strongest emotion you feel when thinking about your quit attempt.To measure the breadth of participant’s thought-action repertoire, we used the Twenty Statements Test . In thisstrongly disagree, 5=strongly agree) [smoking is ruining my health), psychoactive benefits of smoking , and pleasure of smoking . The Decisional Balance Inventory for Smoking  [Smoking cigarettes relieves tension) and cons . Participants also used single-item sliders  to rate their own defined pros and cons .The Attitudes Towards Smoking Scale-18 items (1=y agree)  assessedportant)  assessedstrongly disagree, 5=strongly agree) [not motivated at all, 100=extremely motivated) to rate their motivation .The Commitment to Quitting Smoking Scale   capturesWhat is your smoking status? The response options were I smoke daily, I smoke nondaily, and I do not smoke at all. If participants reported not smoking, they were asked Have you been abstinent for 7 days? (yes/no). If yes, they were asked Have you been abstinent for 30 days? (yes/no). For the primary endpoint , abstinence self-reports were biochemically verified using saliva cotinine (<15 ng/mL) [In each survey, participants were asked: 5 ng/mL) ,48.To describe feasibility, acceptability, and outcome, we calculated descriptive statistics. For user feedback sessions, content analyses were performed on the responses to the 22 questions by 2 independent coders. Major themes were identified. Coding differences were reviewed with the study team to resolve discrepancies.To test if nondaily smokers using SiS experienced changes over time on constructs theorized to underlie smoking cessation, we used the online survey data (n=30) and fit one repeated measures mixed effects model per construct of interest, where the construct was the dependent variable and time  was the predictor. Per protocol, the primary endpoint of interest was treatment end . Correlations over time were modeled with an unstructured covariance matrix. Given the explorative nature of this study, we did not correct for multiple testing.The participants’ age ranged from 23 to 63 years, and the mean age was 45 years (SD 14.1). Most participants were male , Social Support , Personal Reasons , and Benefits .App usage during the prescribed 21 days of use was high . On averIn addition, frequently used were the behavioral sessions, which were intended to be accessed once per week but could be accessed multiple times, and making cigarette reports in the cigarette log. Within a weekly context , on averAfter the end of treatment, some participants continued to use the app. Excluding week 4, during which participants may still have been completing visit 2, 40% (12/30) of participants completed happiness exercises during any given week from week 5 to 8.Scheduling Your Quit Day and Your Reasons for Quitting  and in giving confidence  were also important aspects. The app was less successful in helping participants seek social support  and dealing with risky situations  disliking the functionality for enlisting the help of social support. Several participants  reacted negatively to the suggestion that supportive people in their lives could reward them for reaching smoking cessation milestones. Rather than involving people in their lives in their smoking cessation process, participants suggested adding links to or information about support groups. If involving people in their lives, participants suggested that it would be better to do so without a focus on smoking cessation .During structured user feedback sessions, 80% (24/30) of participants expressed that they liked the happiness exercises  and feltunctions , with maWhen asked about their personal bottomline recommendation for the app, more than half of the participants  did not want to cut anything from it. At the same time, most participants  felt that things could be added to the app, where the most frequent recommendations were to add functionality to encourage more frequent interaction with the app , reinforce the pros of quitting , add a component to interact with other app users , add gamification to the app , and/or a relational agent .Within-person changes from baseline  to treatment end  were observed for several theorized mechanisms of behavior change, all in the expected direction . Namely,pleasure of smoking, as measured via the ATS, which did not reach statistical significance at the 6- or 12-week assessment , but were statistically significant 24 weeks after the quit day . At the same time, however, other trends emerged that were less supportive of smoking cessation. Namely, the importance of the pros of quitting, as measured via a single item, was significantly lower 6, 12, and 24 weeks after the quit day by 10 to 14 points on a 0 to 100 scale  than that at baseline. Similarly, motivation decreased and was significantly lower than baseline 6 months after the quit day, by about 14 points (P=.03). The thought-action repertoire, as measured by the TST, also decreased after treatment end . Similarly, positive affect was lowered, with a significantly lower past week PANAS score 12 weeks post quit  and a significantly lower momentary happiness score 6 weeks post quit .After the end of treatment, these effects were sustained through study end , with the exception of decreases in At the end of prescribed app use, 40% (12/30) of participants reported having been abstinent for the past 7 days. Salivary cotinine values for 3 participants, however, were greater than 15 ng/mL without having reported current use of other nicotine products . Thus, the biochemically verified point-prevalence abstinence rate was 30% (9/30). Following the end of prescribed app use, and assuming that survey nonresponders were not abstinent, 7-day point-prevalence abstinence was self-reported by 53% (16/29), 48% (14/29), and 55% (14/29) of participants 6, 12, and 24 weeks after the quit day, respectively; 30-day point-prevalence abstinence was self-reported by 41% (12/29), 45% (13/29), and 48% (14/29) of participants 6, 12, and 24 weeks after the quit day, respectively.SiS, we evaluated the feasibility and acceptability of the app. To provide a comprehensive test, we used 3 types of data sources: passively recorded app usage, standardized survey self-reports, and face-to-face feedback sessions with participants after they had used the app.In this first pilot test of version 1 of our smoking cessation app On the whole, the gathered data support the notion that leveraging positive psychology tools, or more precisely, happiness exercises, may be well received by nondaily smokers and feasible, at least over the short term. A very high percentage of participants completed the daily exercises throughout the prescribed period of app use, and after completing them for 3 weeks, participants rated the exercises as easy to use and useful. Moreover, in structured user feedback sessions, participants pointed out that they enjoyed doing the happiness exercises, and many continued to complete them long after the formal treatment period was over. As was our goal, by the end of prescribed app use, positive affect appears to have been maintained at precessation levels; at least, we did not observe any statistically significant declines by treatment end, where, of course, our ability to detect such differences was limited because of our small sample size. Once prescribed app use had ended, there was some evidence of a decline in the positive affect.In designing the app, we had hypothesized that maintained positive affect would favorably affect known mechanisms of smoking cessation . In lineClickotine (n=416) reporting being 30 days abstinent at 2-month follow-up [Also promising were self-reported quit rates. To date, mobile health  approaches to smoking cessation have only been tested in daily smokers. Here, abstinence rates have ranged from 26% to 36%, with 26% of daily smokers using the app ollow-up ; 36% of ollow-up ; and an ollow-up -57. AbstThese promising abstinence rates notwithstanding, our data also suggested that it may be useful to consider implementing more app-initiated, varied, and frequent interactions with the app, and to do so over a longer period. This theme emerged from several types of data. First, participants directly told us that they desired more app-initiated, frequent, varied interactions with the app. Second, once app support was withdrawn, some less-than-favorable within-person changes emerged, where there was evidence of sporadically lowered positive affect, decreased motivation, and diminished thought-action repertoire. Although these decreases did not seem to impact smoking cessation in this small pilot sample of engaged and motivated study participants, they may translate to poorer outcomes in smokers who use the app on their own without research staff interactions. This is especially of concern given that participants indicated in surveys that a key reason the app helped them was because it reminded them to stay on track. As an app can only serve as a reminder when users are actively engaging with it, we think it may be important to continuously engage users with the app. This feedback is in line with emerging findings on adherence to technology-delivered interventions. Dropout and nonadherence are often high in technologically delivered interventions , which mThe happiness exercises seemed to engage persons well on a day-to-day basis and indeed, were the primary driver of interactions with the app . NeverthPersonal Reasons functionality only sporadically, possibly because this functionality did not offer novelty over time. Future studies should address how to improve user engagement with more varied content about the pros of quitting smoking over a longer period.Another theme for improvement that emerged was an increased focus on the pros of quitting smoking. Participants recommended that we add more content on the pros of quitting smoking to future versions of the app. This recommendation was supported by the quantitative data on within-person changes, where the importance of the pros of quitting diminished after treatment end, when app support was withdrawn. SiS version 1 included functionality that asked participants to list their personal reasons for quitting smoking during the first behavioral counseling session, a list that could be updated by users subsequently. This function was, surprisingly, the highest user-rated function of the app . Users oFurthermore, a noteworthy aspect was the negative appraisal of the social support functionality in the SiS app. We had included app functionality that encouraged app users to reach out to important people in their lives to support them in their quit attempts because enlisting social support has been recommended for smoking cessation support . In retrFirst, as a first pilot study of an evidence-based smoking cessation app that leverages positive psychology exercises to engage and support nondaily smokers in their quit attempts, this study is subject to several important limitations, with the chief limitation among them being the small sample size and the lack of a randomized control group, which preclude conclusions about the potential efficacy of the app. Observed effects are likely influenced by a self-selection process, where participants particularly interested in using a smoking cessation app, and thus potentially particularly likely to benefit from such an app, enrolled in the study. Second, a surprisingly small percentage of women participated in the study. In a similar study with identical eligibility and screening procedures, with the exception of no requirement to attend in-person visits , we recrThis first feasibility test of a smoking cessation app for nondaily smokers shows that an app that leverages positive psychology exercises to engage and support users is well received by nondaily smokers, who used the app on a near daily basis for the prescribed 3 weeks. Users of the app appeared to make important smoking cessation progress, showed within-person changes on several theorized mechanisms of change, and proactively expressed liking the positive psychology approach of the app. Further development of this line of work is warranted.