Rattus rattus and Mus musculus. In terms of trap success, we found that our live‐trap regime was more effective than snap‐traps. While all 3 control strategies appeared to reduce in‐house rodent activity in the short term, we found no evidence of a longer‐term effect, with in‐house rodent abundance in treated sites comparable to non‐treatment sites by the following month. Endemic flea, Synopsyllus fonquerniei, is a key plague vector usually found on rats living outdoors. Although we found no evidence that its abundance inside houses increased following control, this may have been due to a lack of power caused by significant variation in S. fonquerniei abundance. The presence of S. fonquerniei in houses was more likely when S. fonquerniei abundance on outdoor rats was higher, which in turn correlated with high rat abundance. Our results emphasize that control strategies need to consider this connectivity between in‐house rat–flea populations and the outdoor populations, and any potential consequences for plague transmission.Rodents represent a serious threat to food security and public health. The extent to which rodent control can mitigate the risk from rodent‐borne disease depends on both the effectiveness of control in reducing rodent abundance and the impact on disease epidemiology. Focusing on a plague‐endemic region of Madagascar, this study compared the effectiveness of 3 methods: live‐traps, snap‐traps, and rodenticides. Control interventions were implemented inside houses between May and October 2019. Tracking tiles monitored rodent abundance. Rodent fleas, the vector involved in plague transmission, were collected. Rodent populations consisted of  Rattus rattus, the main reservoir of plague in Madagascar. Control strategies need to consider the connectivity between in‐house and outdoor rat–flea populations and any potential consequences for plague transmission.Focusing on a plague‐endemic region of Madagascar, this study compared the effectiveness of 3 methods: live‐traps, kill‐traps, and Kartman bait station containing anticoagulant rodenticide. Live‐trap regime was more effective than kill‐traps. Wire‐mesh BTS trap catch a higher number of  Rodents pose a serious threat to agricultural productivity and public health. Across the globe, pre‐ and post‐harvest losses to rodent pests contribute to malnutrition and global food insecurity , control programs are designed around the target species’ population dynamics as well as the local ecological and sociocultural setting  in badgers  van Loghem, 1944, the causative agent, via the bite of infected rodent fleas. In rural areas of the Central Highlands, the black rat Rattus rattus  is the main reservoir while 2 flea vectors have been implicated: Xenopsylla cheopis  the cosmopolitan tropical rat flea, and Synopsyllus fonquerniei  an endemic flea which is restricted to altitudes >800 m   Klein . It is aX. cheopis (the species mostly found on rodents in houses) and the presence of S. fonquerniei ; and (v) assess how in‐house rodent and flea populations are impacted by the abundance of rodents and S. fonquerniei in the immediate surrounding areas.With a view to informing how rodent control could contribute to future plague prevention strategies in Madagascar, we conducted a study in villages within the plague‐endemic area to test the efficacy of 3 different types of control of in‐house rodents: live‐traps, snap‐traps, and KBS containing rodenticide and insecticide. To minimize any risks associated with the lethal control of rodents in the absence of prior flea control, the study was carried out during the low plague season (May‐September) in an area without any reported plague cases during the preceding 5 years. The specific objectives of the project were to (i) compare the effectiveness of live‐traps and snap‐traps for capturing rodents in houses; (ii) evaluate the effect of treatments on in‐house rodent abundance in the short‐term (over 5 consecutive nights) and long‐term (over 5 consecutive months); (iii) assess evidence of increased reproduction in in‐house rodent populations subjected to control; (iv) assess the impact of treatments on fleas inside houses, including the abundance of The study was conducted in the commune of Miantso , in the district of Ankazobe, a plague endemic region of Madagascar. The location is shown in Fig. Twelve (12) villages were selected based on their size (between 9 and 22 houses) and accessibility. Most families practiced subsistence agriculture, and houses were traditionally constructed with mud walls and grass‐thatched roofs. For security, domestic animals and food crops are often kept within the household. The field study was carried out between May and October 2019, during the dry‐cold season which is associated with low prevalence of plague. To ensure consistency between sites, control treatment methods were implemented monthly by the project team. In each village, we obtained verbal consent from the local leader and each household head to carry out activities. Mean household participation was 88.1%, ranging from 60% to 100%.This experiment tested the effectiveness of 3 different types of rodent control treatments: live‐traps, snap‐traps, and rodenticide delivered in KBS Fig. . Three vIn villages within the live‐trap treatment, each household was provided with 2 wire‐mesh BTS traps (30L × 10W × 10H cm) and one aluminum Sherman trap (23L × 7.5W × 9H cm). In the snap‐trap group of villages, each household received 3 snap‐traps . Traps were baited with peanut butter. In the villages within the KBS treatment, each household received 2 KBS (40L × 10W × 10H cm) containing 150 g of insecticide (Fenitrothion 2%) powder  and anticoagulant rodenticide (chlorophacinone 0.05%) bait block , a lethal dose of which will kill a rat 4‐days post‐ingestion. Traps and KBS were set at fixed locations inside houses, in areas thought to be used by rats but out of reach of children and non‐target species. Control treatments were left in place for 5 consecutive nights, every 4 weeks, between May and September 2019 . Three villages within the non‐treatment group did not receive any rodent control.Captured animals were collected every morning and traps were reset at the same place. In households using snap‐traps, residents were permitted, but not required, to immediately remove rodent carcasses to avoid the risk of dispersal of rodent fleas. They were provided with zip‐loc bags and gloves to handle and store dead rats.In October 2019, 6 months after initiating the control trial and one month after the last treatment cycle, we conducted a post‐treatment trapping session in all 12 villages to evaluate the long‐term impact of the three treatments on rodent abundance. In each house, one BTS and one Sherman trap were installed for 3 nights and were baited with peanut butter. Over the same 3 nights, to assess rodent abundance in the surrounding areas, we installed 40 BTS traps outside of houses in each of the 12 villages. These outdoor traps were positioned within a 3–80 m radius of houses, both within the village periphery and in surrounding agricultural areas, in areas thought to be used by rats  and at intervals of ≥ 20 m.http://eur‐lex.europa.eu/Lex‐UriServ/LexUriServ.do?uri = OJ:L:2010:276:0033:0079:EN:PDF) and the guidelines of the American Society of Mammologists for the use of wild animals in research and education , and were used to provide a proxy measure of rodent abundance both inside and outside houses in all twelve villages. White ceramic tiles (20 × 20 cm) were painted with a mixture of 32.0% blue chalk powder, 64.0% white spirit, and 4.0% motor oil  and after (TASHHpost) each control treatment session. For outdoor areas, each tile obtained a score (TASEXT) out of 25 immediately before each control treatment session, and during the post‐treatment session.The following morning, a 20 × 20 cm acetate sheet divided into a 5 × 5 grid was placed over each tile, and the number of squares containing rodent marks was counted to assign each tile an activity score out of 25 Fig. . The proY. pestis infection in captured small mammals was performed using a molecular test followed by culture if positive. DNA from spleen sample was extracted using QIAamp DNA kit according to the manufacturer's instruction. PCR screening was done according to the new diagnosis algorithm of the Central Laboratory for Plague based on real time and conventional PCR  as previously described . Each household was provided with one light trap , which was positioned in the center of the main living room. The candle was lit at dusk and extinguished the following morning. Fleas are attracted to the light and fall into the soapy water Kilonzo . These wet al. P‐value threshold of P <0.05. For ZIP or ZINB models, selection was conducted separately for the zero and count parts of the model. Full models and the best‐selected models for each analysis are given in Table Analysis was conducted in R  per tile was used as the response variable (TASEXTempty). We ran GLMs with village as a fixed effect, as well as GLMMs with treatment as a fixed effect and village as a random effect. A ZINB model provided the best fit to the in‐house rodent abundance index data (TASHHpre) and a NB model fitted the outdoor rodent abundance index data (TASEXTempty)  Table .pre and TASEXT as proxies for rodent abundance , and a variable to describe temporal variation. The full model also included interactions between trap regime and TASHHpre, and between trap regime and the variable describing temporal variation. In species‐specific analyses we included the interaction of trap regime and the presence of the other rodent species (PRRR or PRMM) in the house to test for evidence of inter‐specific competition for traps. Prior to final selection of the full model, we used AIC to select the best representation of temporal variation, comparing 3 different ways of modeling the temporal variation: VISITcat, where visit number was a 5‐level categorical variable; SEAS, a 3‐level categorical variable with visit 1, visits 2/3 and visits 4/5 as levels, and VISITcont which allowed for a linear change over time. Models included house nested within village as random effects, and the natural log of the number of available traps as an offset. Corrected trap nights were calculated as the number of traps containing rodents or not sprung plus half the number of traps which were sprung or had bait removed but which had not caught a rodent , we used GLMMs to model capture rates using data from the 6 villages and all 5 treatment sessions. Analyses were limited to households with evidence of rodent infestation (houses with tile activity score >0 and/or where rodent captures were ≥1). Three analyses were conducted: all rodent captures, rat‐only captures, and mouse‐only captures. NB models provided the best fit to all 3 datasets . Gestation rates were based on females in age categories 2, 3, and 4.X. cheopis abundance could have been directly influenced by any effect of treatment on in‐house rodent abundance or, in the case of KBS treatment, by the use of insecticide, while S. fonquerniei abundance could have been influenced by any impact of treatment on the movement of outdoor rodents into houses. A Mann–Whitney U‐test was used to compare the S. fonquerniei index of rats caught outside at villages where S. fonquerniei were and were not collected from rats inside houses. For each village, the S. fonquerniei index of rats caught outside was calculated as the total number of S. fonquerniei divided by the number of rats caught.To examine the impact of treatment on rodent flea abundance, we used the data from rodents caught in houses during the post‐treatment capture session. The flea burden of rats was compared across treatment groups using a Kruskal Wallis test. We assessed the 2 flea species separately as empty from May–October using a GLMM containing village, a variable to describe temporal variation  and their interaction. A NB model provided the best fit to the data  = 26.7, P = 0.003) and in TASEXTempty (χ2(11) = 161.6, P < 0.005) prior to the start of treatment. However, we detected no systematic variation between treatment groups in the probability of in‐house rodent infestation (χ2(3) = 5.7, P = 0.13), the abundance index in infested houses (χ2(6) = 6.0, P = 0.11), or outdoor rodent abundance (χ2(3) = 4.0, P = 0.26).There was variation between villages in the probability of in‐house rodent infestation  from an effort of 589 trap nights, 105 in BTS (9 M. musculus and 96 R. rattus) from an effort of 1159 trap nights, and 114 in snap‐traps  from an effort of 2008 trap nights. A full breakdown of small mammal captures is provided in Table R. rattus and 6 and 29 g for M. musculus  = 0.01, P = 0.919; Sherman χ2(1) = 0.5, P = 0.479; Snap χ2(1) = 1.6, P = 0.209).During the treatment period, the 3 trap models used in the 2 trapping regimes captured 393 small mammals, belonging to 3 species: 170 in Sherman (162 pre) indicated higher abundance (All rodents: χ2(1) = 7.73, P = 0.005, Fig. 2(1) = 3.0, P = 0.08, Fig. pre χ2(1) = 7.6, P = 0.006, Fig. 2(1) = 5.0, P = 0.03, Fig. 2(1) = 5.3, P = 0.02, Fig. 2(1) = 7.2, P = 0.007, Fig. 2(1) = 6.2, P = 0.01, Fig. 2 (1) = 4.2, P = 0.04, Fig. 2(1) = 5.5, P = 0.02, Fig. As expected, traps caught more animals when the house tracking tiles index (TASHHpre = 0), the median change in in‐house tile activity score immediately after treatment  was negative in households within the live‐trap (−2.00 [IQR −8.67 − 3.67]), snap‐trap (−3.5 [IQR −10.8 − 4.75]), and KBS groups (−4.5 [IQR −11.00 − 0.00]). As shown on Fig. P = 0.05; Snap‐trap: W = 1215, P = 0.05; KBS: W = 1851, P < 0.001), indicating a short‐term effect of treatment on in‐house rodent activity. Conversely, in households within the non‐treatment group, there was no difference in the median tile activity score pre‐ and post‐treatment .Excluding households with no evidence of rodent activity prior to treatment (i.e. excluding households where TASHH IQR −8.6 − 3.67),M. musculus (n = 105), followed by R. rattus (n = 43). One S. murinus was also captured. A further breakdown of post‐treatment small mammal captures is provided in Table During the post‐treatment trapping sessions at the 12 study sites, 149 rodents were caught inside houses over 835 trap nights. The majority of captures were 2(3) = 0.53, P = 0.9), rats only (χ2(3) = 1.96, P = 0.58), or mice only (χ2(3) = 0.63, P = 0.9). Capture rates did vary between villages for all rodents combined (χ2(11) = 22.52, P = 0.02) and mice only (χ2(11) = 22.7, P = 0.02).Based on post‐treatment capture data, we did not find evidence that any of the treatments resulted in a decrease in the abundance of rodents in houses when compared to the non‐treatment group. We found no effect of treatment group on capture rate for all rodents combined (χpre. Although in occupied houses the abundance index varied significantly between treatment groups (χ2(3) = 11.41, P = 0.01), there was no evidence that treatment villages exhibited a different seasonal change in abundance compared to non‐treatment villages as the interaction between treatment and SEAS was not included in the final model (Table pre and TASEXT (χ2(1) = 7.69, P = 0.005), indicating that the probability of in‐house rodent infestation increased when outdoor rodent abundance was higher. In terms of temporal variation, we found that rodents were recorded in a greater proportion of houses in villages during seasons 2 and 3 (χ2(3) = 16.37, P < 0.005) compared to season 1, but numbers per house tended to be fewer (χ2(3) = 7.88, P = 0.02; Table In the analysis to test for a treatment effect on in‐house rodent abundance over the course of the 5 treatment sessions, the variable SEAS provided the best representation of temporal variation in TASHHel Table . This moP = 0.14); while the gestation rate in in‐house rats was 50% (n = 6) in non‐treated sites and 17% in treatment sites, but again this difference was non‐significant (Fisher test: P = 0.27).There is no suggestion that in‐house rats in treated sites showed increased reproduction in the aftermath of this experiment. Indeed, if anything, there was a suggestion that reproduction was lower in these sites during the post‐treatment trapping session. The in‐house rat population in treated sites appeared to have a lower proportion of very young (class 1) and old rats (class 4), and a higher proportion of class 2 and 3 rats than non‐treatment sites Fig. , althougn = 923) were tested negative on qPCR/conventional PCR; however, among 794 serum samples tested, 2 were positive on anti F1 IgG antibodies (sera from R. rattus caught outdoor in October 2019).All spleen . Instead, we found that house rats carrying S. fonquerniei were found in sites where S. fonquerniei index of rats caught outside was higher . The median S. fonquerniei burden of rats caught outside was 3.4 at villages where S. fonquerniei were also collected from in‐house rats, compared to 0.55 at villages where they were not.Fifteen (15) X. cheopis were collected from R. rattus in houses during the post‐treatment trapping session, although a further 5 were collected from 5 R. rattus collected outside houses at 3 of the 12 sites. Due to the low numbers of X. cheopis on in‐house rats, no statistical analysis was conducted.Only 6 S. fonquerniei and 83 X. cheopis were collected from R. rattus in houses. As expected, the majority (97.0%) of these came from live‐trap sites. There was clear difference in flea abundance between sites. For example, one live capture site (Ambohimandroso) yielded 98.0% of S. fonquerniei collected from rats, but only 7 X. cheopis. In contrast, 85.5% of X. cheopis were collected from a second live capture site (Andranonomby), from which no S. fonquerniei were collected. The third live capture site had a single S. fonquerniei and a single X. cheopis collected. In addition to the variation between sites in the abundance of these 2 species, a strong effect of house was evident; a single house in site Ambohimandroso provided 62.5% of the infested house rats and 84.3% of the total number of S. fonquerniei for that site.During the treatment sessions, a further 52 S. fonquerniei and one X. cheopis), whereas the vast majority of sampled fleas were the human flea, Pulex irritans (93.8%), as well as Ctenocephalides felis (6.0%).A total of 2608 free fleas were collected with light traps between May and October 2019. Only 5 of these were rodent fleas (4 2(55) = 304.96, P < 0.005).Analyses of outdoor rodent abundance revealed significant spatial and temporal variation. TASEXT between May and October showed high levels of rodent activity in the areas surrounding houses, with all sites having at least 1 month where >50% of tiles had rodent tracks in all 25 squares. Change in TASEXT over the 6 months varied between sites, with some showing consistently high activity scores throughout, while others showed some month‐to‐month variation (e.g. ABM), a general increase over time (e.g. TSM), or a decrease over time (e.g. TSR)  = 66.11, P < 0.005).During the post‐treatment trapping session in all sites, outdoor traps caught 380 S. fonquerniei were collected from the 380 R. rattus. There was substantial variation between villages in the S. fonquerniei flea burden for outdoor rats, ranging from 0.18 to 4.11. The data was best fit by a ZINB model (Table S. fonquerniei collected from infested rats and TASEXT during the post‐treatment session (χ 2(1) = 4.8, P = 0.03).During the post‐treatment session, 688 R. rattus populations compared to sites with no control. In fact, there was a suggestion of lower gestation rates and fewer old and very young individuals at treated sites compared to non‐treated sites. Thus, our results are most consistent with treatments being generally effective at removing the resident breeding individuals, but compensatory immigration tends to replace these individuals within the following 4 weeks. Although we found no evidence that in‐house rodents in treatment sites were more likely to be carrying S. fonquerniei  at the end of the experiment, our power to detect such an effect was impacted by significant spatial variation between villages in the outdoor abundance of S. fonquerniei, which was the main driver of S. fonquerniei presence inside houses. In turn, the abundance of S. fonquerniei on outdoor rats was linked to high outdoor rodent abundance, while the probability of rodent infestation in houses was also higher when outdoor rodent abundance was high. Thus, our findings emphasize the connectivity between in‐house and outdoor rat and flea populations at our study site. Further studies should seek to confirm whether similar trends are observed at other sites, particularly in areas of plague risk.Fundamental to future plague prevention programs in Madagascar is the control of rodents and their fleas. Our study provides important information to inform the development of future strategies. We found evidence that our live‐trapping regime was more effective than lethal snap‐traps at removing in‐house rodents. However, when implemented over only 5 days per month, controls were insufficient to impact in‐house rodent abundance between months. After 5 months of intermittent control, we found no evidence of increased reproduction among in‐house R. rattus and M. musculus. This may be attributed to the use of multiple trap types at live‐trap sites; while BTS traps catch a higher number of R. rattus, Sherman traps will catch M. musculus and smaller R. rattus. Conversely, when using snap‐traps, the lower capture rate of R. rattus when mice were present indicated a likely issue of trap saturation, which was not found at live‐trap sites. The capture rate of mice was likewise lower at snap‐trap sites when rats were present, but the relatively low capture rate of rats suggests an impact of rat presence on mouse behavior  and southern Africa (e.g. Swaziland), where R. rattus is largely restricted to houses and peri‐domestic settings , although not significant, point to compensatory immigration rather than compensatory reproduction being primarily responsible for the recovery of in‐house rat populations. Although more studies are needed in this system, this would be consistent with many previous studies of rodent populations  of S. fonquerniei collected from in‐house rodents, the risk is also likely to fall predominantly in some households. Similarly, we also found that in‐house rat abundance varied greatly between houses within the same village, while rodent abundance in the immediate vicinity of villages (within approximately 80 m) was highly variable between villages. However, as our study was restricted to one season (May–October 2019), we were unable to confirm whether variation in rodent and flea abundances between houses and between villages are consistent year‐on‐year.Plague transmission in rat populations is thought to be initiated by the seasonal increase in ak Klein  and a peet al. X. cheopis  at sites during the experiment period was not sufficient to draw robust conclusions on the impact of control on this flea. The human flea, P. irritans, which has been suggested to play a role in the human‐to‐human transmission of Y. pestis during outbreaks  analysesTable S3 Summary of Generalized Linear Mixed Model (GLMM) analyses of tracking tile, rodent capture, and flea index dataTable S4a Breakdown of small mammal and flea capture data, April‐September 2019Table S4b Breakdown of small mammal and flea capture data from post‐treatment trapping session, October 2019Table S5 Results of GLMM analyses of variables predicting in‐house rodent, rat and mouse captures in Miantso, Ankazobe District, Madagascar, 2019Table S6 Results of GLMM analysis of variables predicting in‐house tile activity scores (TASHH) between April and September 2019, in Miantso, Ankazobe District, MadagascarFigure S1 Median, and upper and lower quartiles, of outdoor tile activity score (TASEXT) per village, during each treatment session (visits 1–5) and the post‐treatment session (visit 6)Click here for additional data file.


The patients with prolonged mechanical ventilation (PMV) have the risk of ineffective coughing and infection due to diaphragm weakness. This study aimed to explore the effect of abdominal weight training (AWT) intervention with/without cough machine (CM) assistance on lung function, respiratory muscle strength and cough ability in these patients.n = 12), AWT + CM group (n = 14) and control group (n = 14). Change of maximum inspiratory pressure (MIP), Maximum expiratory pressure (MEP) and peak cough flow (PCF) between 1 day before and 2 weeks after the intervention were compared among these three groups.Forty patients with PMV were randomly assigned to three groups: AWT group (p < 0.05) and AWT + CM group  compared with control group  was significantly improved. MEP before and after intervention in AWT group  and AWT + CM group  compared with control group  was significantly improved. PCF before and after intervention in AWT group in AWT group  and AWT + CM group  compared to control group  showed significant improvements. AWT + CM group had significantly greater improvements than control group in MIP and peak cough flow than control group .MIP before and after intervention in AWT group . The respiratory muscles are composed of the diaphragm, the internal and external intercostal muscles, and the abdominal muscles. The diaphragm plays a key role in the process of breathing. Ventilator support can lead to diaphragmatic weakness, and animal and human biopsies have shown that short-term mechanical ventilation can lead to the early stages of diaphragmatic fiber atrophy. Levine found that in human biopsy specimens, after 18 and 69 h of mechanical ventilation, the cross-sectional areas of slow and fast muscles were reduced by 57% and 53%, respectively, resulting in significant atrophy of diaphragm muscle fibers. This may be related to increased diaphragm muscle proteolysis during periods of inactivity . Animal Pulmonary rehabilitation exercises can be divided into general exercise training  to increase muscle strength and endurance and improve physical function, and respiratory muscle training. Lung recovery exercises for long-term ventilator-assisted patients are mostly based on respiratory muscle training, including respiratory muscle resistance training, threshold pressure training, and load training on the diaphragm and respiratory muscles . AbdominThis study was a randomized controlled trial with a study period of August 21, 2019 to August 13, 2020. Subjects were recruited at Kaohsiung Chang Gung Memorial Hospital, a medical center in southern Taiwan, and the study site was in a subacute respiratory care center. Subjects were accepted after evaluation to determine they met the requirements and after informed consent was obtained. The study was reviewed and approved by the hospital's Institutional Review Board (IRB: 201900885B0A3) prior to subject enrollment. This study aimed to explore the intervention of abdominal weight training (AWT) with/ without cough machine (CM) on lung function, respiratory muscle strength and cough ability in the patients with prolonged mechanical ventilation (PMV).Subject enrollment criteria: 1) Invasive ventilator users, (2) Hemodynamically stable, (3) Intubated endotracheal tube or tracheotomy tube, (4) Clearly conscious and cooperative, (5) Vital capacity (VC) < 10 ml/kg; Exclusion criteria: (1) Unconscious or unwilling to sign the informed consent form, (2) No spontaneous breathing, (3) Active bleeding with unstable hemodynamics, (4) Acute infection symptoms, (5) Abdominal distension, digestion problems (including nausea and vomiting), (6) Severe heart failure (ejection fraction ≤ 30%), (7) Unhealed wounds in the chest and abdomen, (8) Bullous emphysema, (9) Sensitive pneumothorax or mediastinal pneumothorax, (10) Recent history of traumatic stress, (11) Acute head and neck injury (unless the injury site is immobilized), etc. According to the order of enrollment, the participants were randomized into AWT + CM group, AWT group and control group in sequences as showed in flow diagram  is maintained for 30 min; the starting weight is 1–2 kg, and the previous day's weight is maintained each day as well as adding 0.5 kg daily. Cough machine training is based on the cough assist machine Comfort Cough II (CC20), in which the inhalation and exhalation times are adjusted to 1–3 s, and the positive and negative pressure of the lower pressure 10–15 cmHThe primary outcome included the change of maximum inspiratory pressure (MIP), maximum expiratory pressure (MEP), and vital capacity change between 1 day before and 2 weeks after the intervention. The secondary outcome included the outcome of weaning from the ventilator and disease severity after the intervention among three groups.The basic information of subjects, the days of intubation, the total number of days of ventilator use, and the number of days of ventilator use in the subacute respiratory care center were collected. Respiratory function was monitored using an nSpire Haloscale Standard spirometric meter and pressure meter , which measure the parameters related to lung volume, including vital capacity (VC), tidal volume (TV), rapid shallow breathing index (RSBI), peak expiratory flow (PEF), and peak cough flow (PCF). An intensive care physiological monitoring system (Philips IntelliVue MX600), continuously measured heartbeat, respiration, blood pressure, pulse blood oxygen concentration, etc.Basic descriptive statistics are presented as the mean and standard deviation. Because the distribution of the statistical population is unknown and the sample size is small, a nonparametric analysis method was used. The pretest data between groups were tested using the Kruskal–Wallis test. The Wilcoxon test was used to compare and analyze the differences between the groups before and after the test. The changes in the volume parameters, pulmonary function parameters and disease severity were tested using the Kruskal–Wallis test between groups, and then the Mann–Whitney test was used for post-hoc independent sample analysis between each group (two groups).f = 0.25, error value α = 0.05. The before and after measurements of the three groups are calculated using the G*Power software, sample size error α = 0.05, effect size and test power were analyzed and calculated post-hoc. The SPSS 20 software was used for statistical analysis, and p ≤ 0.05 was used as the threshold for statistical significance.The number of samples accepted is estimated to have an explanatory power = 80%, moderate correlation effect size There were 130 patients in the subacute respiratory care center during the study period, and 45 patients were eligible for admission. Among them, the participation of 5 patients (12%) was terminated, including 2 patients with acute infection and 1 patient with abdominal pain and discomfort in the abdominal weight group; 1 patient with neuromuscular disease in the AWT + CM group who was transferred to the intensive care unit; and 1 patient in the control group who went into shock , and the AWT + CM group improved by 27% (p = 0.023). The AWT group improved its RSBI by 15% (p = 0.034) and TV by 22% (p = 0.012). The AWT + CM group improved its RSBI by 18% (p = 0.055) and TV by 14% (p = 0.167). There were significant differences in both groups that received training, but there was no significant difference in the control group. Respiratory muscles: The maximum inspiratory pressure (MIP) of the AWT group improved by 18% (p = 0.011), and the AWT + CM group improved by 21% (p = 0.011); There was a 36% (p = 0.033) improvement in the maximum expiratory pressure (MEP) in the AWT group and a 55% improvement in the AWT + CM group (p = 0.001). There were significant differences in both groups that received training, but there was no significant difference in the control group. PCF in the AWT group in terms of cough efficacy: 105.83 ± 16.21 increased to 114.17 ± 15.20, an improvement of 8% (p = 0.011); PCF in the AWT + CM group: 108.57 ± 18.85 increased to 131.79 ± 38.96, an improvement of 21% (p = 0.001). There was no significant difference in the control group. Compared to the control group, the difference between the pre- and post-measured values for cough efficacy in the AWT + CM group showed significant differences in PCF (p = 0.030) and MEP (p = 0.035) , which represented a significant difference . There were no significant differences in the number of ventilator days, the success rate of ventilator weaning, and re-intubation rates. This may be due to the old ages and pulmonary and respiratory diseases of the subjects.Prolonged mechanical ventilation (PMV) accounts for 15% of the subjects undergoing the ventilator weaning process, and advanced age and underlying lung disease are factors that lead to difficult and prolonged weaning . Respira2O, an improvement of 55%). It can improve the efficacy of coughs when combined with cough assist machines.Cough efficacy declines with age, and researchers have compared and contrasted different methods to enhance cough efficacy Table . Vinken This study enrolled only a small number of subjects, which may limit the validity of inferred results. The number of subjects, the duration, and the number of centers should be increased in future studies to collect more comprehensive pulmonary function data for respiratory muscle training, including maximum voluntary ventilation (MVV), forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), peak expiratory flow rate (PEFR) and maximal mid‐expiratory flow (MMEF25-75%), thereby gaining a deeper understanding of the benefits of cough machine assisted abdominal weight training. Training should be offered earlier in the intensive care unit, and follow-ups should be conducted. Discussions should be conducted by combining diseases in the same category. In the treatment offered in clinical care and domestic research, the abdominal weight can vary between 2 and 5 kg, which is maintained between 15 and 30 min, which indicates a lack of scientific rigor and makes it difficult to explore the benefits. Future studies may more deeply examine the application of abdominal pressure.Abdominal weight exercise training improves lung function and respiratory muscle strength, and is easy to perform and easily portable. Cough machine assisted abdominal weight training can effectively improve vital capacity and improve respiratory muscle and cough functions, which can be used as a reference when selecting auxiliary training for respiratory muscles in clinical care.


Although frailty is a common geriatric syndrome in old adults, a simple method to assess the degree of frailty in a person has not yet been established. In this study we have tried to establish the association between calf circumference (CC) and frailty among older Chinese people.We used the data obtained from the 2014 edition of the Chinese Longitudinal Healthy Longevity Survey; 1216 participants aged ≥60 years were included for the study. Body mass index, CC and waist circumference measurement data, and laboratory test results were collected. Frailty status was measured using the frailty index (FI). Participants were then classified into non-frail (FI < 0.25) and frail (FI ≥ 0.25) groups.P < 0.001). Logistic regression analysis revealed that CC  was an independent impact factor associated with frailty. The CC value of 28.5 cm was considered the best cut-off value in women with area under the curve (AUC) was 0.732 (P < 0.001) and 29.5 cm in men with AUC was 0.592 (P = 0.004);We created a simple prediction model for frailty that included age,sex and CC:P = elogit(P) /1 + elogit(P), and AUC is 0.849 (P < 0.001).There were 874 participants (71.9%) in the non-frail group and 342 (28.1%) in the frail group. The CC was significantly different between the two groups (31.54 ± 4.16 versus 28.04 ± 4.53, CC is a convenient and predictable marker of frailty in older adults.The online version contains supplementary material available at 10.1186/s12877-022-03644-w. Frailty is a multidimensional geriatric syndrome that leads to serious health problems across the world. Chinese population is aging at an unprecedented speed. The data of prevalence for frailty among community-dwelling old adults in our country is as follows: approximately 6, 15, and 25% of people are aged 65–74, 75–84 and ≥ 85 years, respectively [The most frequently used instruments to assess frailty in clinical research are the Fried frailty phenotype and frailty index (FI) . The FriCalf circumference (CC) is a simple and convenient measurement method applied both in the community and in hospitals. The Asian Working Group for Sarcopenia 2019 recommends the use of CC as a method to diagnose sarcopenia because of its high sensitivity and specificity in the Asian population –7. AccorIn our research, we wanted to find a method to assess frailty using a simple tool. Therefore, we explored the association between CC and frailty among old people by gathering data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS).n = 28); (2) Missing frailty index data (n = 973), laboratory measurement data (n = 93), anthropometric measurements (n = 174), and demographic data (n = 62). Finally, we retained 1216 participants who were aged ≥60 years and for whom complete data were available  Age < 60 years . We converted the years of education into a binary variable (< 1 year of schooling or ≥ 1 year of schooling). The smoking status, alcohol drinking status, exercise status, and financial status were binary variables (To be answered yes or no). We collected information by using questions such as “Do you smoke at the present time,” “Do you drink alcohol at the present time,” “Do you do exercises regularly at present,” and “Does all of your financial support sufficiently pay your daily costs”.2). The participants were advised to relax their body and a measuring tape was used to measure the CC and waist circumference (WC) of each participant.In our survey, we measured the height (in meters) and weight (in kilograms) of the participants; we calculated body mass index (BMI) by dividing the weight (in kgs) by the square of height  in the local center for disease control and prevention. Serum samples were analyzed for lipid profile , plasma albumin (Alb), vitamin D3 (Vit D3), vitamin B12 (Vit B12), and high-sensitive C-reactive protein (HS-CRP) at the central clinical laboratory at Capital Medical University in Beijing. The neutrophil-lymphocyte ratio (NLR) was calculated as (white blood cells – lymphocyte)/lymphocyte as given in the literature .Frailty status was measured by the FI that was similar to previous CLHLS studies , 15. TheP values were less than 0.05. We provided odds ratio (OR) with 95% confidence interval (CI) as well.The demographic characteristics , anthropometric assessment  and laboratory results  were analyzed for two groups (non-frailty and frailty). We used Student’s t test for continuous variables and Wilcoxon Mann–Whitney test for categorical variables. To explore the risk factors for frailty, multiple logistic regression analysis was performed. We adjusted for demographic characteristic variables (model 1), then adjusted for laboratory measurement variables (model 2), and finally the model was further adjusted for all variables (model 3). In order to predict frailty, receiver operating characteristic (ROC) curve was used to find cut-off points of CC. We established a prediction model for frailty by binary logistic regression. We used the SPSS 26.0  software for statistical analysis. We considered it to be statistically significant if p<0.001), sex (p < 0.001), smoking habit (p < 0.001), alcohol consumption (p < 0.001), exercise (p < 0.001), education (p < 0.001), BMI (p < 0.001), Alb (p < 0.001), Hb (p < 0.001), Vit D3 (p < 0.001), TG (p = 0.014), and HS-CRP (p = 0.002) factors were significantly different between the two groups. However, financial status and values of WC, Vit B12, Cholesterol, HDL-C, and NLR were not significantly different between the two groups.In our study, we enrolled 1216 participants and divided them into two groups. 874 (71.9%) were categorized as non-frail and 342 (28.1%) as frail at baseline. The characteristics of subjects are presented in Table p < 0.001) , exercise , CC , Alb , and Vit D3 levels  were independent risk factors.To explore which factors may affect the frailty status, we used multiple logistic regression analysis and built 3 models Table . The modP < 0.001); and the Youden index was 0.365; the sensitivity was 66.8% and the specificity was 69.7%  and the Youden index was 0.164; the sensitivity was 82.7% and the specificity was 33.7%.  (Supplementary Fig. (95% CI:0.826–0.872, In our study, we observed that CC and frailty are closely correlated in Chinese adults aged over 60 years. In addition, CC is an easy and helpful indicator to predict frailty in community-dwelling old adults. The best cut-off value was 29.5 cm in man and 28.5 cm in women.China is one of the most rapidly aging societies in the world and frailty is a common geriatric syndrome in old adults. We found that the prevalence of frailty were higher in women than in men, which was consistent with previous literature . We usedResearchers have recently started to pay close attention to CC. It is not only a useful predictor of sarcopenia in patients with liver disease , but alsOur study, is the first to suggest that CC may predict frailty in elder community. Frailty is associated with various factors and the causative mechanism is complex and still unclear . Up till2 and waist circumference > 102 cm for men or > 88 cm for women were associated with increased frailty among old people [The Asian Working Group for Sarcopenia  diagnosed people . HoweverThe mechanics of frailty are complex, and seem to be related to inflammatory cytokines, nutrient status, vitamin levels, and other factors , 22. BasOur study was a large cohort study among older communal adults, utilizing face-to-face interviews. However, several limitations associated with the study must be mentioned. First, the study design did not define the standard method for measuring the CC. Second, there was no consensus regarding which leg was to be measured; the result may have been influenced by whether or not the dominant leg was measured. Moreover, all participants were from China. Therefore, we may not be able to extrapolate our findings to the elderly population of other countries. Studies involving more participants from across the world may be needed.In summary, our research confirms the association between CC and frailty syndrome in old adults living in Chinese communities. The CC is an easily measurable and convenient marker of frailty.Additional file 1.


Zeolite can impart antibacterial properties to dental materials in the long-term when incorporated with inorganic cations. However, due to its porosity, it may jeopardize the mechanical integrity of the dental material. The aim of this project was to determine the effect on physical properties when zeolite is added to commercially available Ag-reinforced Glass Ionomer Cement (GIC). Sample groups were prepared according to the percentage of zeolite-clinoptilolite  added to Ag-GIC. Water sorption, solubility, Vickers hardness, and flexural strength were determined. Specifically, 10 × 2 mm circular disks were fabricated for the Vickers hardness, water sorption, and water solubility tests and 25 × 5 × 2 mm bars were created for the flexural strength test. The results from the surface hardness, water sorption, and flexural strength tests suggested that adding 0.5–4% wt of zeolite to Ag-reinforced GIC did not diminish its physical properties. However, the water solubility results showed that higher concentrations (2–4% wt) of zeolite had a statistically significant increase in water solubility compared to the control. Up to 4% wt zeolite can be incorporated into Ag-reinforced GIC without compromising mechanical properties. Incorporation of 0.5–1% wt zeolite to Ag-reinforced GIC will maintain an adequate surface hardness, water sorption, and flexural strength without compromising water solubility. Further research is needed to determine the effects of higher water solubility on clinical efficacy of zeolite modified Ag-GIC. Glass Ionomer Cements (GIC) have been widely used in dentistry as a base and restorative material, especially in primary teeth and for Atraumatic Restorative Treatments (ART) . Due to 4 and AlO4, which is very similar to the composition of GIC. Due to its porosity, zeolite can readily undergo cationic exchange with the surrounding environment [Zeolite is a porous natural earth mineral with a tetrahedral crystalline structure made of SiOironment . Researcironment . Zeoliteironment . This meironment , 11, (2)ironment , 12, andironment , 13. Conironment , 9, 14.When zeolite is combined into Ag-reinforced GIC to enhance its antibacterial properties, it is equally important to consider the impacts on the physical properties as well. Dental materials such as GIC must be able to withstand a variety of everyday forces, from mastication to speech, without compromising their structural integrity and strength. Some of the most common physical properties studied in dental materials include flexural strength, bond strength, compressive strength, setting time, and surface microhardness . HoweverIn this study, various concentrations of zeolite were incorporated into Ag-reinforced GIC to determine its effects on the physical properties of the material. Ag-reinforced GIC has been shown to possess silver ion release properties that may result in an antibacterial effect when used in dental restorations . HoweverPrevious studies have generally combined zeolite at 0.2–5% when testing for antibacterial and physical properties , 17–19. Using the standard flat scoop provided in the Riva Silver kit, three scoops of Riva Silver powder were weighed using an analytical scale with an accuracy of 0.0001 g. An aliquot of the measured powder equivalent to the intended percent by weight of zeolite was removed, and zeolite was added until the original weight was achieved. The powders were transferred to a mixing pad, and gently mixed by hand with a metal spatula until homogenous. Following the instructions from the manufacturer, three drops of the Riva silver liquid was added to the powders to obtain an approximate 7:1 powder-liquid ratio. The liquid and powder were mixed by hand with a metal spatula for 30 s until homogenous throughout.After mixing, the cement was immediately placed in excess into a premade plastic mold that formed 10 mm × 2 mm circular disks. A thin layer of petroleum jelly was first applied on the internal surfaces of the plastic mold to facilitate extraction of the disks. After adding the material, the mold was placed between glass slabs and held down by a 250–300 g weight to ensure the formation of smooth surfaces on the disks. Following the instructions of the manufacturer, the samples were allowed to sit for at least 6 min until the material set. After extracting the disks from the mold, extruded excess was removed using a scalpel. This procedure was repeated so that 12 samples per group were made for the water solubility/sorption tests, and 10 samples per group were made for the Vickers hardness test. The Vickers hardness test disks were wrapped in wet gauze in a sealed container and placed in an incubator at 37 °C for 24 h until use to mimic the conditions of the oral cavity. The water sorption/solubility samples were stored in a dry, empty petri dish until use.Two sets of Vickers hardness measurements were taken for the samples via a Leco Microhardness Tester machine . The first set of measurements were taken prior to the water sorption test, and the second set was taken 1 week into the water sorption test to compare the change in surface hardness after storage in water for one week.Three indentations were made for each of the 10 samples in every test group with a load of 500 g for 10 s dwell time. The indentation lengths were measured immediately after indentations to minimize the possibility of viscoelastic recovery. The Vickers hardness was automatically calculated by the machine and averaged amongst the three values and 10 samples.The water sorption test utilized 12 samples of 10 mm × 2 mm circular disks from each of the five zeolite concentration groups. Using a digital caliper, the diameter and height of each sample were measured to an accuracy of 0.01 mm. The volume of each sample was then calculated using the following formula:1) of each sample was determined on an analytical scale with an accuracy of 0.0001 g. Each sample was placed into a separate well in a 12-well plate and submerged in a deionized water bath to mimic the presence of saliva in the oral cavity. The plates were incubated at a constant 37 °C, which mimicked the temperature of the human body. Every 24 h, the samples were blotted dry completely until there were no visible water droplets on the sample, and their masses were measured on the analytical scale.The initial mass (M2) was reached for each group, the samples were placed in a desiccator containing anhydrous calcium sulfate and cobalt chloride  at room temperature. The calcium sulfate served as a desiccant and the cobalt chloride acted as a visual indicator, which transitioned from blue to pink upon absorbing the water from the Ag-reinforced GIC samples and surrounding air in the desiccator. After every 24 h in the desiccator the samples were weighed on the analytical scale, weighing was repeated daily until a constant mass was achieved (M3).After a constant mass (Msp) and water solubility (Wsl) were calculated using the constant masses determined above using the following formulas [1 = conditioned mass prior to water immersion (g); M2 = sample mass after water immersion (g); M3 = reconditioned sample mass after drying (g)V = calculated sample volume (mm).Water sorption (Wformulas :\documens) of the zeolite-incorporated Ag-reinforced GIC samples were evaluated according to the specifications outlined in the ISO Standard ISO 9917-2 and Bonifacio et al. [The flexural strength , the samples were subjected to a 3-point bending test with 16.65 mm between the supports. A loading force was applied at a constant crosshead speed of 1 mm/min until the sample bar fractured. The load at fracture was used to determine the flexural strength . The flexural strength  was calculated from the equation :\document test (α = 0.05) to test for differences in the surface integrity of the sample groups and the effect of time. The mean values of water sorption, water solubility and flexural strength properties were evaluated using a Kruskal–Wallis test at α = 0.05. Initially one-way ANOVA tests were attempted. However, the equal variance and normality tests were not always passed for the data. Therefore, the authors decided to use the aforementioned tests in place of the ANOVA.The Vickers hardness data sets were further analyzed via t test at α = 0.05, the difference between the means at each time was compared for all five test groups. When comparing the mean hardness values 24 h after fabrication and after one week of water sorption for any of the concentration groups for the control (P = 0.293), 0.5% (P = 0.778), 1% (P = 0.086), 2% (P = 0.153), and 4% (P = 0.419), there was no significant difference. Next, t test at α = 0.05 was used to compare the difference between control vs. 0.5%, 1%, 2%, and 4% at each time. There was no statistically significant difference between the control and the 0.5% (P = 0.105), 1% (P = 0.409), 2% (P = 0.492), and 4% (P = 0.072) groups at 24 h after fabrication. In addition, there was no statistically significant difference between the control and the 0.5% (P = 0.615), 1% (P = 969), 2% (P = 0.923), and 4% (P = 0.078) groups after one week of water sorption.Figure p = 0.302) was found when each experimental group was compared with the control in the water sorption tests. However, the mean water solubility values, which utilized the difference between sample mass prior to immersion and the reconditioned mass after drying, increased as the concentration of zeolite increased. Compared with the control group, the difference in the mean values for 2% zeolite (P = 2.37 × 10−6) and 4% zeolite (P = 3.98 × 10−4) were greater than would be expected by chance, so a statistically significant difference was observed. On the other hand, there is no significant difference with 0.5% (P = 8.47 × 10−2) and 1% (P = 9.88 × 10−3) zeolite compared to the control. The lowest water solubility value (60.21 ± 30.26 μg/mm3) was for the control group (0% zeolite) with increasing values to the highest (122.33 ± 25.56 μg/mm3) for 2% zeolite.Figure P = 0.151), 1% (P = 0.052), 2% (P = 0.274), as well as 4% (P = 0.0712).Figure Vickers hardness is one of the most common physical properties tested to ensure the durability of dental materials in withstanding everyday forces and wear. Specifically, the surface of the material must be able to resist deformation caused by indentation, penetration, scratching, and abrasion . In the There are many factors, such as the presence of moisture, within the oral cavity that may influence the properties of restorative materials. To simulate in vivo conditions, the samples were stored in a 37 °C-water bath (water sorption test) followed by a desiccator (water solubility test). Water sorption and water solubility are commonly tested properties since erosion due to varying levels of moisture may directly affect the degradation of the Ag-reinforced GIC. Specifically, water sorption may change the physical properties of GICs through lamination and degradation, while solubilization may cause a loss of material mass, decrease in mechanical properties, and increase in rate of restoration failure , 27, 28.3 after 7 days in deionized water, and the solubility values ranged from 36.76 to 115.21 μg/mm3 [The Ag-incorporated GIC water sorption and solubility data obtained in this present study were consistent with the previously published values. Specifically, water sorption of GIC ranged from 81.60 to 143.76 μg/mmDespite the prevalence of GIC usage in dentistry, one of the major drawbacks of the material is its relatively low fracture strength and higher occlusal wear rate compared to modern resin composite materials and amalgam . In a liThe flexural test data obtained in this present study were similar to the values published from previous studies, which generally ranged from 14.2–23.1 MPa in a similar time range , 28. ThrWhile most published studies observed the uses of synthetic zeolite, the current study incorporated natural zeolite-clinoptilolite to Ag-reinforced GIC for a variety of reasons. First, natural zeolites have a high affinity towards heavy metal ions in comparison to synthetic zeolites –33. ThisThe results from our surface hardness, water sorption, and flexural strength tests suggest that adding any concentration, from 0.5–4% of zeolite by weight, to Ag-reinforced GIC does not diminish its physical properties. However, the water solubility results showed that higher concentrations (2–4% wt) of zeolite had a statistically significant increase in water solubility compared to the control. Further research should be conducted to determine the extent of the effects that higher water solubility values may have on the physical integrity of the material.Although none of the statistical analyses showed significant changes in Vickers hardness, water solubility, and flexural strength as a result of zeolite percentage, the authors recommend adding 0.5–1% wt zeolite to Ag-incorporated GIC. This is because 0.5–1% wt zeolite showed the least deviation from the control in water solubility while increasing in flexural strength. These results are used in addition with previous literature, which has shown that zeolite improves the antibacterial properties of Ag-GIC from as low as 0.2% , 19. How


Samples were analyzed by conventional culture, matrix‐assisted laser desorption/ionization‐time of flight (MALDI‐TOF), mass spectrometry (MS), confocal laser scanning microscopy (CLSM), and 16S/ITS high throughput sequencing (HTS). A mathematical model simulated the risk of needle contamination during injections. Injection site populations were in 102 cells/cm2 order, with increased viable bacteria and anaerobic bacteria on the skin in persons with diabetes (p = 0.05). Interpersonal variation dominated other factors such as sex or location. A higher prevalence of Staphylococcus aureus on abdominal skin was found in persons with diabetes than control skin (p ≤ 0.05). Most needles and cartridges (95% and 86%) contained no biological signal. The location of the device collection  and use regimen did not affect contamination. CLSM revealed scarcely populated skin microflora scattered in aggregates, diplo, or single cells. Our mathematical model demonstrated that penetrating bacteria colonies during subcutaneous injection is unlikely. These findings clarify the lack of documented skin infections from subcutaneous insulin injections in research. Furthermore, these results can motivate the innovation and development of durable, reusable injection systems with pharmacoeconomic value and a simplified and enhanced user experience for patients.Needle reuse is a common practice and primary cause of customer compliance issues such as pain, bruising, clogging, injection site reactions (ISR), and associated lipodystrophy. This study aimed to characterize skin microflora at injection sites and establish microbial contamination of used pen injectors and needles. The second objective was to evaluate the risk of infections during typical and repeated subcutaneous injections. 50 participants with diabetes and 50 controls (n = 100) were sampled through tape strips and skin swabs on the abdomen and thigh for skin microflora. Used pen injectors and needles were collected after  The global prevalence of diabetes is rapidly increasing with severe individual and societal costs. Currently, subcutaneous injections (SCIs) of insulin remain the most common form of treatment in the industrialized world. Administration with a pen injector represents approximately 60% of insulin users globally, with higher percentages in Europe, Japan, China, and Australia (80–95%), but only about 20% of users in United States and India . AccordiMotivations for needle reuse are multifaceted, including economic costs, needle‐related anxiety, dexterity and vision issues, and convenience . HoweverDiabetes is associated with perturbations in skin homeostasis caused by reduced vascularization, neuropathy, and metabolic alterations . The etiThis study endeavored to establish the overall risk of microbiological contamination of insulin pens during intended and repeated use. To facilitate a deeper understanding, we first characterized skin microbiota biogeography at injection sites for persons with diabetes that might adhere to pen‐injectors during subcutaneous insulin injections. Microbiological samples were acquired through skin tape strips and skin swabs and evaluated through culture, 16S/ITS high throughput sequencing (HTS), and confocal scanning laser microscopy (CSLM) and compared with non‐diabetic controls. Secondly, we explored the rate and nature of microbial contamination of the needle and cartridge through molecular analysis of used devices. Finally, we constructed a mathematical model to simulate device contamination during subcutaneous injections based on the experimentally obtained data.Subjects with diabetes were recruited from Steno Diabetes Center Copenhagen, Gentofte Hospital, and Steno Diabetes Center NordJylland, Aalborg University Hospital, Denmark, and controls were recruited through a Danish volunteer recruitment website (forsoegsperson.dk) and by word‐of‐mouth. Data collection was officially exempted from ethical approval by the National Committee on Health Research Ethics case‐nr.: H‐19070331.Based on previous data, at least 20 subjects per group were required for a power of 0.8 and p < 0.05 statistical significance . Two groTwo groups (group A and B) of 25 participants with type 2 diabetes (n = 50) and corresponding groups of age‐ and sex‐matched (±3 years) control subjects (n = 50) were included in March–June and in November 2020, respectively . Group A consisted of 28 males and 22 females with ages 63.7 ± 11.5 and BMIs 31.0 ± 6.0 for the participants with diabetes ages 63.2 ± 11.08 and BMI 25.0 ± 3.7 of patients without diabetes (controls). Group B consisted of 22 males and 28 females pairwise from  and controls .Participants were sampled in emblematic subcutaneous injection sites either on the right or left side of the periumbilical abdomen and anterior proximal thigh aseptically by the sampling person to avoid contamination.Group A: 7 layers of DS100 D'Squame Disc tapes  were collected from 25 participants with type 2 diabetes (T2 DM) and 25 participants without diabetes (No DM) applying a D‐square pressure instrument (225 g/cm2) (Monaderm) for 15 s. Tape 1 was discarded to remove particles, tape 2 was saved for CLSM, tape 3–6 cultured the same day, and tape 7 saved for 16S/ITS HTS. Three used needles and pen‐injectors were collected from participants with diabetes after home‐use and returned by envelope through local postage.Group B: Four tape strips  were sampled pairwise from another 25 participants with type 2 diabetes (T2 DM) and 25 participants without diabetes (No DM). Tape 1a and 2a were saved for HTS, tape 1b for backup, and tape 2b for CLSM. ESwab™ 480Cs  pre‐moisturized by molecular‐grade water  were rubbed 50 times in 5 × 5 cm and saved for HTS.Group C: One to three used needles were collected from ~30 patients with diabetes (DM) administered by a nurse on disinfected skin and saved for HTS.Under aseptic conditions, needles were cut at base with pliers, while remaining cartridge liquid was removed with a syringe and saved for HTS.2  and anaerobic conditions  at 37°C for 1–3 days. Colonies were quantified in colony forming units (CFU), isolated, and stored at −80°C.Three milliliter 0.9% NaCl were added to tapes 3–6, then degassed and sonicated for 5 min by ultrasonic bath . Samples were plated in dilution series on 5% blood agar, chocolate agar, and fastidious anaerobic agar  and incubated in aerobic, with CORe‐cultivated bacterial isolates were transferred to the MSP 96 target polished steel BC , identified with MTB Compass program and MALDI‐TOF microflex LT/SH (Bruker). Reads with scores >2.0 were accepted as identified on species‐level.Tapes were stained for 15 min with 3 mg DAPI, placed and sealed onto a microscope slide using clear nail polish and examined using an Axio Imager.Z2, LSM710 CLSM  with Plan‐Neoflour and 63×/1.4 plan‐apochromatic oil objectives (Zeiss) and UV light of excitation of 405 nm and emission 410–483 nm. Three representative images per tape were taken using the ZEN black 2010, v. 6.0 (Zeiss) and deconvoluted using Imaris 8 × 64 version 8.1.2 .Sequencing was performed at Clinical Microbiomics A/S, Denmark. RNA was prepared using NucleoSpin® 96 Soil  manual with positive  and negative controls included with each batch of samples. For PCR, universal primers 341‐F and 785‐R ITS3‐F and ITS4‐R targeted the 16S rDNA and ITS. Indices were added with 8 cycles of 55°C annealing program using the Nextera Index Kit V2  and verified on agarose gels. Sequencing was done on an Illumina MiSeq desktop sequencer using the MiSeq Reagent Kit V3 (Illumina) for 2× 300 bp paired‐end sequencing. For bioinformatics analysis, an adjusted DADA2 pipeline was used, primer sequences removed using cutadapt, filtered, and trimmed (dada2::filterAndTrim command) at 3′‐ends on sample‐specific quality. The remaining reads were dereplicated and denoised, forward and reverse reads merged, and pairs without sufficient overlap or mismatch discarded. Default taxonomic using a naïve Bayesian classifier algorithm compared reference databases for ASVs. In silico extracted amplicons were checked against corresponding primers from reference databases , 21, 22.A 2D stochastic mathematical model was constructed to simulate microbial distribution and contamination rates during SCIs. Initially, we tested the assumption of independent, homogenously dispersed colonies with a random, uniform distribution given by:a and b represent boundaries of the area giving a total of 100 mm2 (1 cm2).where However, CLSM images, previous observations, and studies of cutaneous microbiota in wound studies suggest a heterogeneously dispersed growth in aggregates . A modelμ is the mean of the distribution, σ is its standard deviation, and e is Euler's number. Colony size was set to 50 with the standard deviation varying from 0.01 to 0.5 based on microscopic observations and approximations. Finally, research and our CFU determinations suggest that bacteria are dispersed following a Poisson distribution [where ribution . Assuminx is the number of occurrences, and λ is the intensity or the expected occurrence of an event per interval (CFU/cm2).where A = πr2. The bacteria were quantified as the number positioned either on or within the circle circumference or needle lumen, given by:For simplification purposes, the needle was modeled as a cylinder, with the tip shaped as a circle given an area by: x, y) represents the colony center,  the needle center and r the needle radius. The injection was simulated 100 times with 30G (outer diameter (OD) of 0.312 mm) and 32G (OD of 0.235 mm) for each dispersal models with average of 350 CFU/cm2, where bacteria penetrated in each simulation was quantified. This was further challenged by the maximum density found from experimental data of 5200 CFU/cm2. Algorithm implementation and simulations of SCIs were performed in Python3 . The code can be found in where .2 and 4·102 CFU/cm2 were found on the abdomen and thigh by aerobic and CO2 growth conditions, and 2·102 and 4·102 CFU/cm2 under anaerobic growth conditions, for abdomen and thigh, respectively. In healthy skin, 3·102 and 2·102 CFU/cm2 were found by aerobic and CO2 growth, 7·101 and 3·101 CFU/cm2 under anaerobic conditions from both areas. The data followed a Poisson distribution, ranging from 0 to 5200 CFU/cm2  with S. epidermidis, S. hominis, and S. capitis prevailing. Of all identified, 10.2% were gram‐negative . An overview is shown in Fig. The down‐stream analysis was performed on species present in ≥2 participants. The outcome included 24 unique species from 12 different genera. Of these, 63.5% were Firmicutes, 29.1% Actinobacteria, and 7.8% Proteobacteria. Bacteria were generally scarcely populated, heterogeneous distributed in small aggregates or single or diplo scattered cells. They were found in the outermost stratum corneum as seen in Fig. Proteobacteria spp., as found in negative controls.From the first round of sampling (group A), only 67/100 and 46/100 of tape 7 from thigh and abdomen surpassed the 3500‐high quality (HQ) reads threshold for composition analyses for 16S and ITS, respectively. Most tapes were dominated by Staphylococcus spp., Corynebacterium spp., and Micrococcus spp. were the most abundant genera. Generally, intra‐individual taxonomical similarities were found. In swab samples, Haemophilus spp. and Actinomyces spp. had lower but not significant average abundances in participants with diabetes than without diabetes. The abundance of S. aureus showed higher prevalence on abdomen swabs of participants with diabetes (p < =0.05). In addition, S. aureus and S. epidermidis were more abundant in swab of men samples.In Group B, 62/68 (91.2%) of tapes and all swabs surpassed the threshold for 16S analysis. Samples were dominated by Actinobacteriota , Firmicutes , Proteobacteria , and Bacteriodita  for participants with diabetes and without diabetes, respectively. Candida spp., Malassezia spp., Alternaria spp., and Vishniacozyma spp.For ITS, 18/68 tapes and 72/100 swabs surpassed the threshold. Samples were dominated by Ascomycota, Basidiomycota, and Mucoromycota with most abundant genera including A significant effect of sex (p = 0.045) and diabetic status (p = 0.008) was found on swab thigh samples' overall composition, but not on swab or tape abdomen samples. No discernible difference was seen in richness or Shannon diversity index. For diabetic status, no significant taxa were identified.Vishniacozyma spp. and Alternaria were more but not significantly abundant. There were no differentially prevalent taxa between sexes or sampling area. For ITS, sampling area was not significant, though the thigh contained greater abundance of Candida spp. and prevalence of Cladosporium spp.For ITS, beta diversity analyses showed no significant effect of diabetic status on the overall composition. Comparing alpha diversity values showed mildly increased richness values of swab thigh samples from participants without diabetes than with diabetes, but no differences in Shannon index. Similarly, no alpha diversity differences were seen between sexes. In diabetic samples, Staphylococcus spp., followed by Corynebacterium spp., with higher abundances in swabs than tapes. No significant difference was found using ITS.A PERMANOVA test showed significant differences in overall composition between swab and tape samples (p = 0.001), explaining 5% of the overall variation. Regarding alpha diversity, swab samples had higher richness, while there were no significant differences in Shannon index values compared with tapes. The most differentially abundant genus was Oceanobacillus spp. Furthermore, the tested devices' origin and corresponding use regimen (single‐use versus reuse) had no significant effect on contamination rates.The majority (93–97% and 86%) of needles and cartridges exhibited no biological signal. Subsequently, 22 species in 26 genera remained . Genera 2 and homogenous distribution on average penetrated 0.24 ± 0.51 and 0.17 ± 0.38 bacteria with a 30 and 32G needle, respectively. With a clustered dispersal pattern, 0.18 ± 0.49 and 0.11 ± 0.34 were pierced. With the Poisson distribution, 0.19 ± 0.66 and 0.07 ± 0.38 bacteria were displaced. The 32G needle and simulations using a Poisson distribution showed lower average bacteria hit per injection, reflecting the reduced cross‐sectional area of the needle lumen. The simulation repeated with the upper limit (5200 CFU/cm2) can be seen in Fig. Simulations of the subcutaneous injections confirmed the improbability of penetrating a bacterial colony with a needle. As shown in Fig. . A fraction of devices revealed low quantities of microbial residue. Genera previously published as DNA contaminants in laboratory consumables were thus removed to circumvent the documented caveat of sequencing low microbial biomass samples [Oceanobacillus, a halophilic bacteria isolated in multiple environments. Whether this contamination originates from the use or production remains unknown. However, it is generally acknowledged that biological material such as cells and blood can enter the cartridge during injections due to backflow pressure [Generally, used pen‐injector needles and cartridges exhibited strikingly low amounts of biological signal. In fact, most devices contained insufficient signal for reliable microbiome analyses. Similarly, no fungi were detected in most used needles and cartridges samples . The rempressure . Neverth2) and 100 repeated injections, most needles encounter no bacteria. This is especially of note considering the model's broad oversimplification of the injection process, where bacteria were modeled approximately 300–600× larger than reality . This vast exaggeration renders our findings highly conservative and generously biased toward contamination, as the likelihood of penetrating a bacteria would be lower with greater distances between microbial clusters.Our CFU determination and microscopy imaging of injection sites reveal a scarce and heterogeneously scattered microflora. Undeniably, simulations through our mathematical model offers vivid testimony to the high improbability of penetrating a bacterial colony with a needle. Even when the model is challenged at the utmost extremes with the uppermost observed bacterial density  in biopsies from injection sites would be an exciting avenue for future investigations. In addition, our model assumed a 2D colony structure, penetrated by circular needle shafts in even punctures. However, microscopic observation revealed anisotropic colonies of multiple layers, and hypodermic needle tips have grinded angles to facilitate skin penetration through forces such as cutting, lubricant friction, and elastic stiffness. These forces likely influence the trajectory and subsequent dislocation of microorganisms, and thus, the rate of needle contamination. Finally, our mathematical model ignores environmental dynamics between microorganisms, such as competition for nutrients and inhibitory mechanisms. In addition, the geometries of the needle tip and bacterial colonies were simplified to spherical shapes and represented solely on a 2D axis. Future research should focus on modeling colony shapes through other more fitting models such as a Boolean distribution with anisotropy ratios from experimental data and include needle shape parameters for a more accurate depiction.“real‐world” usage of patients' homes. Finally, the mathematical model confirmed the heterogeneous distribution of microorganisms and subsequent high improbability of penetrating a colony during subcutaneous injections.This paper aimed to establish and facilitate a deeper, more comprehensive understanding of the risk of microbial contamination during intended and repeated subcutaneous injections. Participants with diabetes exhibit significantly more viable and anaerobic bacteria, suggesting diabetic status is associated with more favorable growth conditions for skin microflora. Injection site analysis through sampling and microscopy exposed scarce and inconsistently distributed microflora dominated by interpersonal variation. Analysis of used pen‐injectors revealed negligible contamination, whether collected from professional administration in a hospital ward or the Our findings of low‐ and randomly populated injection sites explain why there is a distinct lack of evidence for infections from subcutaneous injections. The microbial contamination of pen‐injectors during intended use and reuse is simply inconsequential. However, the reuse of needles cannot be recommended, as current needle designs' physical dimensions lack robustness for multiple injections without increased risk of tissue damage. Therefore, researchers and industry should attempt to develop mechanically durable needles with self‐cleaning mechanisms to offer simpler, safer, more convenient delivery options for patients.This work was supported by a research grant from the Danish Diabetes Academy, which is funded by the Novo Nordisk Foundation, grant number NNF17S0031406 and the Innovation Fund grant number 9065‐00120B. In addition, the authors would like to thoroughly thank all volunteers, Dr. Dorte Lindqvist Hansen and the Steno Diabetes Center Copenhagen, Steno Diabetes Center Nord Jylland, Christian Andreasen for statistics, Lasse Kvich and Ida Clement Thaarup for general training in the laboratory/microscope, Dinesh Krishnamoorthy for assistance with Python and, finally, the LEO foundation for funding Lene Bay.Advanced Treatment and Technologies in Diabetes (ATTD) 2021.SWM, VP, and HB are currently employed at NN. TB and LB are paid consultants for NN. No other potential conflicts of interest were reported. Some of this data was previously presented at the online conference SWM was involved in literature search and writing, figures and tables, and guarantor. SWM, LB, TB, VP, and HB performed study design. SWM and NF was involved in data collection. SWM and LB performed data analysis. SWM, LB, and TB performed data interpretation. SWM, LB, VP, and TB performed editing and approval of final version of manuscript.Appendix S1. Supplementary Information.Click here for additional data file.


Camellia japonica’s petal by fermenting the decoction with Saccharomyces cerevisiae or brewer’s yeast. pH, brix, specific gravity and alcohol percentage were tested to study the physicochemical properties of the wine. Qualitative tests indicated presence of phenols such as flavonoids, coumarins; protein; glycosides; glycerin; terpenoids; steroids; and fatty acids in the wine. Total phenol content was found high in the decoction and in its fermented form as well. In vitro biological activities such as antioxidant activity, antidiabetic activity and lipid peroxidation inhibition power were assessed in samples. Furthermore, GC-MS analysis helped to detect volatiles present in the unfermented decoction and understand the effect of fermentation on its changing metabolome while column chromatography assisted the separation of solvent-based fractions. Notable outcomes from this study were detection of bioactive compound quinic acid in the decoction and a proposed pathway of its metabolic breakdown after fermentation. Results of this research revealed biochemical and physicochemical acceptability of this wine prepared from an underutilized flower.A novel wine has been developed from  Saccharomyces cerevisiae or brewer’s yeast is used as fermentation starter by the western world, while the Asian scenario is different. Asians rely on their traditional expertise where fermentation of foods and beverages are carried out using various microbial cultures such as old cultures of yeast, filamentous molds, bacteria, symbiotic cultures of bacteria and yeasts etc. In fermentation process, utilization of substrate depends on availability and productivity of cultivated grain crops and fruits of a region. There are a number of such region specific fermented beverages, for example; red wine and white wine from grape producing European countries like Italy, France, Spain and Germany; probiotic fermented tea kombucha from largest tea producer China; wheat based fermented alcohol vodka from Russia; rice based beverages like haria, apong, chhyang, zutho, lugdi etc. from India; traditional alcoholic beverages like sake and shochu of Japan which are prepared fermenting substrates like sticky-rice, millet and satsumaimo (Japanese sweet potato); rice based traditional fermented beverage-Brem from Indonesia; South African alcoholic beverage amarula prepared from African marula (Sclerocarya birrea) fruits; pomegranate wine from Israel; Filipino Tuba (coconut wine); lychee and cherry wines from China etc.Wine brewing is considered as an important part of fermentation science and food technology where cereals, fruits, leaves, edible flowers etc. from various plants are used as raw materials. Wines contain alcohols; fatty acids; terpenoids; polyphenols such as flavonoids, anthocyanins, flavonols, catechins; and other secondary metabolites . Moreover, that research work inspired us to brew a wine from another underutilized edible flower i.e., Camellia japonica which is a plant of the same family as of tea i.e., Theaceae or “tea family”. In the wild, this plant grows as an understory plant on hillsides at altitudes of around 300–1100 m (http://www.efloras.org/florataxon.aspx?flora_id=2&taxon_id=200014034). Its botanical origin is in Eastern Asia (Japan) and also found in the Himalayas, Southern China, and other South-Eastern Asian countries . Regarding metabolomics, different organs of this plant have been studied by various researchers for a long period of time  is limited upto exploring anti-inflammatory properties of it where the flower is underutilized due to lack of appropriate research.Flowers such as, rose, China-rose, dandelion, marigold, lavender, gorse, mahua etc. are also used as potential brewing substrates traditionally or industrially. In the rhododendron growing regions of the Himalayas, guras  is a popular drink among the local inhabitants and tourists. Flower wines are often praised by consumers as well as oenologists for unique flavors and medicinal properties  and the specimen was properly identified as Camellia japonica L. by botanists of Kalimpong College . Freshly bloomed flowers  were added to one liter of sterile distilled water and boiled for 20 min to prepare Camellia japonica petal decoction or CJPD. Decoction was filtered through sterile muslin cloth into an autoclavable jar. Sucrose (30 g) was added as an additional carbon source or nutrition for fermenting yeasts. Then, the broth or decoction was autoclaved and left for cooling at room temperature (25 ± 1 °C). After cooling, pH of the broth was measured, and sterile synthetic white vinegar was added to lower the pH around 5 to create an acidic environment and controlled condition for fermentation. Two grams of dried brewer’s yeast were inoculated as starter in the jar. Mouth of the jar was then covered with sterile polythene where pores were made with a sterile steel needle to facilitate the release of fermentation derived CO2. Following this procedure, triplicates were prepared and left for 15 days in a dark room at 25 ± 1 °C for incubation to produce Camellia japonica petal wine (CJPW). Similarly, a control batch of wine (CW) was prepared without using petal where the petal decoction was replaced with double distilled water. Camellia japonica petal wine or CJPW and control wine or CW (after 15 days of incubation) and freshly prepared decoction or CJPD were taken as samples for further experiments. Moreover, CJPW . All the experiments were carried out on both CJPW and CW to study the changes in physicochemical properties during fermentation. Each result has been expressed as mean ± SD (n = 3).Physicochemical parameters such as, optical density (OD), transmission percentage (%T), pH (for acidity), brix (%Bx) and alcohol by volume (%ABV) etc. were measured in the samples following the protocol of Majumder et al.  to studyhttps://www.biologydiscussion.com/plants/detection-of-alkaloids-plants/57192). Results were noted in a comparative approach to prepare a heatmap. Qualitative test for free fatty acid was done following phenolphthalein test developed by Cox and Pearson  and results were expressed as gallic acid equivalent (mg GAE/100 ml).Total phenolic content (TPC) in samples  were determined by the Folin–Ciocalteu method . Beside this conventional experiment i.e., quantification of antioxidant value, DPPH assay was also applied to study the “DPPH inhibition kinetics” . The redox titration endpoint was determined by the first iodine excess that is complexed with starch, giving a deep blue-violet color. As long as there is any antioxidant or reducing agent, the iodine is reduced to iodide ions which apparently arrests the formation of starch-iodine complex (https://www.canterbury.ac.nz/media/documents/science-outreach/vitaminc_iodine.pdf). Ascorbic acid was used as a calibration standard  to quantify the values of reducing power shown by samples. Results have been expressed as μg AAE/ ml .Antioxidant activity of the samples  were also determined using this titrimetric assay. According to experimental procedure, 1 ml of 1% starch indicator was added in each sample solution (20 ml of sample dissolved in 150 ml of distilled water) which was titrated with 0.005 mol/L iodine solution . Results have been expressed as mg TAE/ml . In this research, goat liver homogenate was used to evaluate lipid peroxidation inhibition activity, so, hepatoprotective efficacy was also determined indirectly through this simple in vitro assay.The in vitro lipid peroxidation inhibition assay was done following Rahman et al. . Value oR2 = 0.9862; y = 0.0119x - 0.0003). Results were expressed as mg MetE/ml .Glucose uptake capacity by yeast cells was determined for this assay  were subjected to GC-MS analysis following the pre-standardized research protocol for tea petal wine developed by Majumder, Saha, et al. CJPW (5 ml) was dried and put in a silica gel  loaded column where a series of nonpolar to polar solvents  were passed to fractionize the sample on the basis of molecular affinity towards solvents of different polarities .After completing 15 days of fermentation and experiments, CJPW was left in fermentation jars upto 3 months for aging. Following the same protocols described above, physicochemical properties  and antioxidant activity (DPPH assay) of the sample CJPW were determined on regular intervals upto 3 months of aging to investigate the alteration of qualities of the prolongedly fermented wine. Specific gravity was determined by calculating the ratio of the specific weight of sample to the specific weight of water at 4 °C . The test for statistical difference was performed using one-way ANOVA . Differences were considered significant at https://www.ebi.ac.uk/chebi/) and PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The data obtained from GC-MS analysis were further studied from available literature to analyze the fermentation metabolomics  to study their impact on flavour and taste imparting properties . List of peak reports were then prepared with the names of the compounds detected. Chemical classification of each compound was based on ChEBI  ontology  and %T was increased  in CJPW and CW respectively. A pH of 5.32 ± 0.28 was recorded for CJPD. On day 0, the pH of CJPW and CW was lowered using vinegar and maintained at 5 to level the acidity of broths before initiation of fermentation process. Furthermore, this pH level of the broths was again decreased after fermentation (3.89 ± 0.19 and 4.19 ± 0.2 for CJPW and CW respectively). Alcohol percentage for CJPW was calculated to be 8.31 ± 0.47% as its Brix was dropped from 19.5 ± 0.2 to 5.9 ± 0.3. In CW, 5.19 ± 0.12% of ABV was measured as Brix was changed to 2.5 ± 0.4 from 12.1 ± 01 after 15 days.Qualitative biochemical tests revealed the presence of phenol, flavonoid, coumarin and tannin in both CJPD and CJPW while CW was found to be devoid of most of the qualities except one fermented product i.e., glycerin which was detected higher in CW compared to CJPW. Terpenoid and steroid were detected in all three samples through respective indication tests. A heatmap Fig.  has beenThe TPC of the decoction or CJPD (100 ml) was quantified to be 52.698 ± 3.66 mg GAE and the value was tested to be decreased (31.775 ± 2.36 mg GAE/100 ml) in the wine sample (CJPW). Demonstrating phenols as usual fermentation metabolites of wine yeasts, CW also exhibited a considerable amount of phenolics (7.18 ± 1.22 mg GAE/100 ml) but it was nowhere near the petal samples. A graphical representation of this result has been given in Fig. CJPW showed highest DPPH scavenging activity (73.16 ± 2.9%) followed by the unfermented decoction or CJPD (53.63 ± 0.87%) while the control one (CW) showed the least activity with 40.25 ± 1.5%. Figure Iodine reducing power of CJPW was calculated to be 181.79 ± 11.41 μg AAE/ ml which was also higher than others Fig. C i.e., uFigure Lipid peroxidation is a series of oxidative degradation reaction occurred with lipid molecules. It is the process in which free radicals collect electrons from the lipids in cell membranes, resulting in oxidative damage. Inhibition of this lipid peroxidation level by 1 ml of CJPD was estimated to be equivalent to 76.06 ± 1.1 mg tocopheryl acetate. For fermented wine sample, the value was slightly lower with 71.8 ± 1.5 mg TAE/ml  Fig. B.Metabolites of CJPD, CJPW and CW detected by GC-MS analysis have been listed in Table Result of this experiment revealed that activity shown by crude wine sample was higher than its fractions. However, fraction obtained in diethyl ether solvent (65.16 ± 2.1%) followed by acetone (54.09 ± 0.82%) and methanol (44.36 ± 2.33%) fractions exhibited a moderate activity Fig. .Fig. 7AnAging of CJPW has introduced some changes in wine’s quality which was studied evaluating selected physicochemical parameters  and antioxidant (DPPH assay) property. Noticeable alterations were observed during aging mostly upto first 30 days of fermentation Fig. . pH of CPhysicochemical properties like OD, %T, pH, specific gravity, %Bx and %ABV are considered important characteristics to judge the rate of fermentation and physicochemical acceptability of any fermented beverage  on aging of CJPW Fig.  were cleResults of qualitative tests and TPC assay revealed high phenolics in CJPD compared to the fermented sample signifying complete breakdown of phenolics during fermentation which was further validated by GC-MS results. A large peak of phenolic compound quinic acid was detected in CJPD while CJPW was rich with its breakdown forms as described below. Fermented product CJPW indicated low fatty acid content and it was further validated by GC-MS analysis as compounds like carbonyl component methylglyoxal, valeraldehyde, acetone derivatives etc. were found in noticeable amount which was possibly formed from lipids upon oxidation (Shibamoto P < 0.05) antioxidant activity (DPPH assay and iodometric assay) compared to other samples as it was further found to be rich in potential antioxidative wine volatiles such as 2-hydroxy-GBL, DDMP  can be useful against diabetes.Camellia japonica leaf ; antimicrobial; and anti-inflammatory properties .Regarding other biological activities, Uzcátegui et al.  reportedhttps://pubchem.ncbi.nlm.nih.gov/compound/78475) and glycerin formal (https://pubchem.ncbi.nlm.nih.gov/compound/21618) respectively. PEA is another yeast metabolite detected 1.73% in control whereas 3.03% in the petal wine. A variety of floral essential oils  contain this compound. This compound is also a major aroma component of Muscat wine and responsible for honey-like aromas (http://www.ymdb.ca/compounds/YMDB01072). Antimicrobial properties of PEA are also reported  and glyceraldehyde (2.88%) are metabolites of the same pathway where glycerin is formed i.e., yeasts glycerol biosynthesis pathway  is one of such compounds that produces caramellic flavour in wine. pGlu has been detected as a methyl ester in CJPW. This esterification actually happens during fermentation that releases a fermented aroma (Pfeiffer & König Camellia japonica flower petals and added sugar during fermentation. The key outcomes of this research were detection of quinic acid in the substrate i.e., Camellia japonica petal decoction and fermentation-led breakdown of the compound in the wine. The breakdown was also validated by metabolomics through a proposed quinic acid degradation pathway. However, further research is needed to discover the enzymes responsible for the degradation. Being a source of quinic acid and other bioactive compounds like DDMP; eucalyptol; alpha-gurjunene; beta-caryophyllene; squalene; alpha-spinasterol; vitamin E etc., this flower can be used by pharmaceuticals or in development of formulations like herbal tea and herbal medicines etc. beside producing fermented beverages.This petal wine was an outcome of metabolic pathways of brewer’s yeast and several other reactions, which converted the metabolites of