Background: Although psychological distress is known to be a risk factor for death, there are relatively few data on the impact of psychological distress on incident functional disability in older adults. The aim of this study was to examine the impact of psychological distress on incident functional disability in older adults. Methods: We conducted a cohort study of 12,365 disability-free individuals aged ≥65 years who live in Ohsaki City, Japan. In 2006, the level of psychological distress was assessed using the K6 (range: 0–24 points). Data on 10-year functional disability were retrieved from the public Long-term Care Insurance database. The multivariate-adjusted hazard ratios (HRs) and population attributable fractions (PAFs) according to the K6 groups  were estimated. Results: Among 94,636 person-years, incident functional disability occurred in 4533 persons (36.7%). Significantly higher risk was observed in higher K6 score groups. The multiple-adjusted HRs (95% CIs) of incident functional disability were 1.14 (1.06–1.22) for 5–9 points, 1.28 (1.15–1.43) for 10–12 points, and 1.62 (1.44–1.84) for ≥13 points, in comparison with <5 points (p-trend < 0.001). The PAFs in each of the K6 score groups were 3.0% for 5–9 points, 1.7% for 10–12 points, and 2.6% for ≥13 points. Conclusions: Even when mild to moderate, psychological distress had a considerable impact on incident functional disability in this cohort. Prevention of functional disability among the older people is a significant healthcare issue . The risPsychological distress would be a more practical concept in the context of comprehensive mental disorder when considering public health strategies . PsycholTo verify this hypothesis, a prospective study to investigate the impact would be needed, because a cross-sectional study would generally be limited by reverse causality. To our knowledge, however, no prospective study has investigated this issue especially with regard to the impact of several levels of psychological distress.The aim of the present study was to examine the impact of psychological distress on incident functional disability in older adults, focusing particularly on population attributable fractions (PAFs) calculated for several levels of psychological distress.The design of the Ohsaki Cohort 2006 Study has been reported in our design paper . In brieThe baseline survey was conducted between 1 and 15 December 2006, and the follow-up survey between 16 December 2006 and 30 November 2016. A questionnaire was distributed by the heads of individual administrative districts, and then collected by mail. In this survey, 23,091 persons who provided valid responses formed the study cohort. We excluded 6333 persons who did not provide written consent for review of their Long-term Care Insurance (LTCI) information, 1979 persons who had already been certified as having a disability by the LTCI at the time of the baseline survey, five persons who had died or moved away during the period of the baseline survey, and 2409 persons who left blank responses on the K6. Thus, 12,365 persons were analyzed for the purpose of this study.During the 10-year study follow-up period, only 173 persons were lost to follow-up because of emigration from the study area; the follow-up rate was 98.6%. From the resulting 94,636 person-years, the number of persons who developed incident functional disability was 4533 (36.7%).The exposure was psychological distress, as assessed by the K6 . The JapHistory of disease was collected by the question which asked whether participants have history about each disease .Motor function was assessed by the Kihon Checklist including five binary questions . We classified individuals with scores of <3 as having better motor function according to the validation study .Smoking and alcohol drinking were assessed using questions that required a response of never, former, or current.Self-reported weight and height were used to calculate BMI as weight (kg) divided by the square of height (m).The degree of social support was assessed by asking the following five binary questions : (1) Do Incident functional disability was defined by disability certification of the LTCI system. The LTCI is a mandatory social insurance to assist daily activities of the disabled elderly ,30,31. EWe obtained a dataset that included information on LTCI certification, death, or emigration from Ohsaki City based on an agreement with Ohsaki City Government related to Epidemiologic Research and Privacy Protection.We considered the return of completed questionnaires to imply consent to participate in the study involving the baseline survey data and subsequent follow-up of death and emigration. We also confirmed information regarding LTCI certification status after obtaining written consent along with the questionnaires returned from the subjects at the time of the baseline survey. The Ethics Committee of Tohoku University Graduate School of Medicine  reviewed and approved the study protocol.We counted the person-years of follow-up for each subject from 16 December 2006 until the date of incident functional disability, date of emigration from Ohsaki City, date of death, or the end of the study period (30 November 2016), whichever occurred first.2; <18.5, 18.5–24.9, ≥25.0 or missing), and social support .We used the Cox proportional hazard model to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident functional disability according to the K6 score groups. Dummy variables were created for the K6 score groups, and the group with the lowest K6 score was used as a reference category. Multivariate models were adjusted for the following variables. Model 1 was adjusted for sex and age . Furthermore, Model 2 was adjusted for history of disease  , motor function score , smoking , alcohol drinking , body mass index  employing IBM SPSS Statistics ver. 24 . We generated 10 different missing datasets.To consider the influence of excluding 2409 persons who left blank responses on the K6, we also checked the multiple-adjusted HRs (95% CIs) using analysis by the multiple imputation method ; and (2) PAF+1 assuming that the K6 score for every subject would have improved by one level , except for subjects with the lowest K6 score (<5 points). All PAFs and their 95% CIs were estimated using the “punafcc package” of Stata/MP 14  [We also calculated the PAFs for incident functional disability in the various K6 score groups. Two PAFs were estimated for the K6 score groups: (1) PAFTX, USA) . Adjustmp < 0.05 were accepted as significant.All data were analyzed using SAS version 9.4  except for the PAFs and the multiple imputation method. All statistical tests described here were two-sided, and differences at The mean age (SD) of subjects at the baseline was 73.6 (5.9) years, and 46.2% of them were men. The proportions of the various K6 score groups were 65.2% for <5, 23.6% for 5–9, 6.5% for 10–12, and 4.8% for ≥13.p-trend < 0.001).p-interaction = 0.651; This inverse association did not differ significantly between the sexes .We also obtained similar results even when the multiple imputation method was applied; the multiple-adjusted HRs (95% CIs) for incident functional disability were 1.14 (1.06–1.22) for 5–9 points, 1.23 (1.08–1.39) for 10–12 points, and 1.51 (1.34–1.70) for ≥13 points, in comparison with <5 points (ALL (PAF of K6 score ≥5) was 7.2% (95% CIs: 4.9–9.4%). The PAFs for each of the K6 score groups were 3.0% (95% CIs: 1.3–4.7%) for 5–9 points, 1.7% (95% CIs: 0.9–2.6%) for 10–12 points, and 2.6% (95% CIs: 1.8–3.3%) for ≥13 points.The PAF+1 was 5.4% (95% CIs: 3.9–6.8%).The PAFp-interaction = 0.425).We also conducted stratified analysis by age subgroup . A dose–p-interaction = 0.425).To confirm whether there was a relationship between the K6 score and incident functional disability, irrespective of motor function, we also conducted stratified analysis . A dose–p-interaction = 0.986).Additionally, we also conducted stratified analysis by social support subgroup , and obsIn this cohort study, we observed a significant dose–response relationship between the score on a psychological distress scale (K6) and incident functional disability, and a significantly higher risk even for a mild level of psychological distress (K6 score ≥5). The lower risk of incident functional disability was consistent even after stratifying for motor function at the baseline.The previous study of elderly disaster survivors also observed a significant dose–response relationship between the K6  and incident functional disability [ALL = 7.2%). However, the degree of impact was smaller than that in a previous cross-sectional study (39.3%) [In the present study, any level of psychological distress (K6 score ≥5) had considerable impact on incident functional disability (PAF (39.3%) . BecauseInterestingly, the PAF for mild psychological distress (K6 score 5–9: 3.0%) was higher than that for severe psychological distress (K6 score ≥13: 2.6%). When psychological distress was summed as mild to moderate (K6 score 5–12: 4.7%), the PAF was approximately twice that for severe psychological distress (K6 score ≥13: 2.6%). This finding suggests that mild to moderate psychological distress should also be considered in any public health strategy for comprehensive prevention of functional disability.Although the HR for severe psychological distress was highest among all the psychological distress levels, the prevalence of severe psychological distress was only one-fifth that of mild psychological distress. This relative difference in prevalence has also been observed in Japanese national statistics and world statistics ,36. TherOur study had several strengths: (1) it was a large cohort study involving 12,365 persons; and (2) it had a follow-up rate of almost 100% (98.6%).On the other hand, it also had some limitations. First, psychological distress was measured only once; we did not consider any changes in the participants’ psychological status. Second, we did not consider the causes of incident functional disability. Third, the target population was limited to residents in a rural Japanese community. Additional studies conducted in other settings will be needed to confirm the external validity of our findings. Fourth, not all potential confounding factors were considered. For example, we did not consider sensory function, living arrangement, or income.Psychological distress, as measured by the K6, was associated with a significantly high risk of incident functional disability in Japanese elderly. Even a mild to moderate level of psychological distress had a considerable impact on incident functional disability.


Background: Many participants experience clinically significant fluctuations in weight before beginning a behavioral weight loss program. Pre-treatment weight gain, often referred to as the “last supper” effect, may limit total weight loss from the time of the pre-treatment screening visit and could be an indicator that a participant will respond poorly to behavioral intervention.Methods: Data were from the weight loss phase of a two-phase weight loss maintenance trial, in which 178 participants with obesity  were provided with a 14 week lifestyle intervention that included a meal replacement diet. Participants were categorized as having gained >1.15%, remained weight stable, or lost >1.15% of initial weight between the pre-treatment screening visit and the first treatment session (48.7 ± 29.4 days). We first examined whether the weight change groups differed in baseline eating characteristics  using one-way ANCOVAs. Linear mixed models were then used to compare weight change groups on total weight loss from the screening visit to week 14 and in-treatment weight loss from weeks 1 to 14.Results: Nearly half of the sample (48.9%) gained >1.15% of initial weight during the pre-treatment period (+2.5 ± 1.2%); 41.0% remained weight stable (+0.2 ± 0.6%); and 10.1% lost >1.15% of initial weight (-2.2 ± 0.9%). There were no significant differences between the groups in baseline eating characteristics. As measured from the screening weight, the weight-gain group had a lower total loss of 6.8%, compared to 7.8% in the weight stable group (p = 0.02) and 9.0% in the weight-loss group (p = 0.003). The weight-gain group lost more weight in the first 4 weeks of treatment, but in-treatment losses did not differ among the groups at week 14.Conclusion: Pre-treatment weight gain was not an indicator of a poor response to a behavioral weight loss intervention and was associated with greater weight loss early in treatment. However, weight gain during the pre-treatment period may limit the total weight loss that participants achieve from the time that they first enroll in a weight loss program. Before joining a behavioral weight loss program, potential participants typically attend one or more pre-treatment screening visits in which staff assess their eligibility, obtain informed consent, and measure their pre-treatment characteristics e.g., . In manyTwo studies have investigated whether pre-treatment weight change predicts later weight loss during behavioral treatment programs. In a second study, Data from the weight loss phase of a two-phase study in which all participants were initially enrolled in the same 14 weeks, group lifestyle intervention  providedAdults with obesity were recruited to participate in a two-phase study that consisted of a 14 week non-randomized group lifestyle intervention, followed by a 52 week randomized controlled trial (RCT) that assessed weight loss maintenance with lorcaserin versus placebo, both combined with behavioral weight loss maintenance counseling. The study’s design, methods, and inclusion/exclusion criteria , as well2 and ≤55 kg/m2 (or ≥30 kg/m2 with an obesity-related comorbidity), and had no serious medical or psychological conditions . This study was carried out in accordance with the requirements of the Institutional Review Board at the University of Pennsylvania, which approved the study protocol. All participants gave written informed consent in accordance with the Declaration of Helsinki.Eligible participants were aged 21–65 years, had a body mass index (BMI) ≥33 kg/mParticipants were recruited in three cohorts that were screened from January 16 to April 1, 2015, June 3 to August 29, 2015, and October 12, 2015 to January 21, 2016. Interested individuals were pre-screened by telephone to assess preliminary eligibility and interest in the study. Those who appeared eligible then completed an in-person screening visit that included: a behavioral evaluation conducted by a psychologist; obtaining informed consent; and assessing medical eligibility . IndividParticipants were provided with 14 weekly, 90-minute group lifestyle modification sessions (10–15 participants per group). The goal of this intervention was to help participants lose at least 5% of initial weight in order to qualify for the weight loss maintenance RCT. Participants were prescribed a 1,000–1,200 kcal per day meal-replacement diet that included four servings of a liquid shake , a prepackaged entrée , 1–2 servings of fruit, and a salad or vegetable serving. The use of shakes was terminated gradually between weeks 12 and 14. Participants were also instructed to gradually increase their physical activity and to self-monitor their food intake , weight, and daily activity.Body weight was measured at the screening visit and at all treatment visits using a digital scale (Tanita BWB-800). Participants were dressed in light clothing, without shoes. As in previous studies , percentBaseline questionnaires that assessed emotional eating, self-regulation (cognitive restraint and uncontrolled eating), and craving frequency were used to compare the baseline characteristics of the pre-treatment weight change groups. The Eating Inventory EI;  is a compost hoc tests were used to evaluate between-group differences. We then compared the baseline eating traits  of the pre-treatment weight change groups using one-way ANCOVAs (controlling for baseline differences).Pre-treatment weight change groups were first compared on demographic characteristics using chi square tests and one-way ANOVAs, and Tukey’s We used linear mixed models with residual maximum likelihood to determine whether the groups differed in their weight change during treatment  or in their total weight change from the screening visit to week 14. Unconditional models were used to determine the appropriate model shape  and variance-covariance structure based on model fit criteria  . Estimatab path) was computed to measure the change in the relationship between pre-treatment weight change and weight loss in treatment when controlling for early weight loss (c path – c′ path). This mediation effect was estimated by first regressing early weight change on pre-treatment weight change (a path), and subsequently regressing in-treatment weight loss on early weight change while controlling for pre-treatment weight change (b path)  and weight loss in treatment  was mediated by early weight change . The indirect effect ((b path) . Bootstr(b path)  with 5,0N = 178) had a mean (±SD) age of 44.2 ± 11.2 years; 87.6% were female and 71.3% were black (21.9% white). Their mean BMI at the screening visit was 40.5 ± 6.0 kg/m2. Participants waited an average of 48.7 ± 29.4 days (range 8–159 days) between the screening visit and first treatment session. The average participant gained 1.1 ± 1.8% of initial weight (1.2 ± 2.1 kg) during this time. Only 10.1% of participants (n = 18) lost >1.15% of initial weight between the screening and first treatment session, with average losses among this group of -2.2 ± 0.9% (range -4.6 to -1.2%). Weight remained within 1.15% of the screening weight for 41.0% of the sample . Nearly half of the sample  gained >1.15% of initial weight, with average gains of 2.5 ± 1.2% (range: +1.2 to +6.6%).Participants (n = 55). Of the 35 participants recruited prior to January 1, 77.1% (n = 27) gained weight before beginning treatment, compared to 42.0% of the remainder of the sample (n = 60 of 143). Of the participants recruited before the holidays, 17.1% (n = 6) remained weight stable and 5.7% lost weight (n = 2), compared to 46.9% (n = 67 of 143) and 11.2% (n = 16 of 143) of participants not recruited during that period. After controlling for days between the screening and first treatment visit, individuals recruited before the holidays had 3.6 times greater odds of gaining weight than those recruited at other times .We observed that weight gain related to the winter holidays could have affected the pre-treatment weight change of individuals in our third participant cohort . Participants who remained weight stable waited for fewer days between the screening and first treatment session (39.4 ± 24.6 days) than those who gained weight . The difference between weight-loss and weight stable participants (53.6 ± 27.8 days) was not statistically significant (p = 0.14). The weight-loss group had a higher mean BMI at screening than the weight-gain group . The weight stable group (40.9 ± 5.3 kg/m2) did not differ significantly from either the weight-loss (p = 0.18) or weight-gain group (p = 0.20) in screening visit BMI. We controlled for BMI at screening and the number of days between the screening and first treatment visit as covariates in all subsequent analyses.There were no significant differences between the pre-treatment weight change groups in any demographic characteristic (Table 1). Neither session attendance nor attrition (defined as dropout prior to week 12) differed significantly by pre-treatment weight change category. Screening BMI and days between the screening and first treatment session did not significantly predict any of the behavioral outcomes.There were no significant differences between the weight change groups in any baseline eating characteristic  or who lost weight (p < 0.001), with losses of 4.7, 3.7, and 2.9%, respectively, during that period. However, relative to their screening weight, the weight-gain group still had lower total losses at week 5 of only 2.2%, compared to 3.2% for the weight stable group (p = 0.002) and 4.6% for the weight-loss group (p < 0.001). The weight-loss group also differed significantly from the weight stable group . From weeks 5 to 14, the groups did not differ in rate of weight loss (p = 0.94), losing an additional 4.6% (SE = 0.3), 4.6% (SE = 0.3), and 4.3% (SE = 0.7), respectively. The weight-gain group therefore continued to have lower total losses relative to their screening weight of 6.8% (95% CI: 5.8–7.6%), compared to 7.8% for the stable group , and 9.0% for the loss group .Table 2). Early weight loss from weeks 1 to 5 mediated the relationship between pre-treatment weight change and weight loss during treatment . Prior to including the mediator variable (early weight loss) in the analysis, the relationship between pre-treatment weight change and weight loss in treatment from weeks 1 to 14 was negative and not statistically significant . After adding early weight loss to the analysis, greater pre-treatment weight loss was directly associated with greater weight loss in treatment from weeks 1 to 14 . However, the indirect effect of pre-treatment weight change on in-treatment weight loss was negative . Greater pre-treatment weight loss predicted smaller early weight loss from weeks 1 to 5 , and the amount of early weight loss was positively associated with in-treatment loss from weeks 1 to 14 .The pre-treatment weight change groups did not differ significantly in weight loss between weeks 1 and 14 , with average gains of 2.5% among this group. This “last supper effect” was associated with early losses within the first 4 weeks of treatment that were 1.0% larger than those of individuals who had remained weight stable, and 1.8% larger than participants who had lost weight prior to beginning the treatment. This differential initial rate of weight loss could represent a regression to the mean . However, the magnitude of the early loss was not large enough to fully reverse the pre-treatment weight gain. The weight-gain group had lower total losses relative to their screening weights after 14 weeks of treatment, losing 1.0% less than the weight stable group and 2.2% less than the weight-loss group. The minority of the sample who lost a clinically meaningful amount of weight before beginning treatment did not differ significantly from weight stable participants in either early or total weight loss in treatment.The association between pre-treatment weight change and weight change in treatment was mediated by early weight loss during the first 4 weeks of treatment. The bivariate relationship between pre-treatment weight change and total weight loss form weeks 1 to 14 was negative and not statistically significant. However, a suppression effect was present . The dirTable 3 we present the primary outcomes of the present study alongside those of previous studies by 2 compared to 35.6 and 35.2 kg/m2) and was more racially diverse (71.3% black compared to 27.4% black and 34.2% non-white).In The results of these three studies consistently support the conclusion that the “last supper effect” is associated with lower total weight loss between the screening visit and the end of treatment. Because greater weight loss is associated with larger improvements in cardiometabolic risk factors , it is pp = 0.13). As described above, a non-uniform distribution of the weight change groups among different treatment conditions could have affected these studies’ results, particularly for The results of these three behavioral studies parallel the more extensively researched link between pre-treatment weight change and post-surgical weight loss in bariatric surgery. Analyses that have statistically combined study results  suggest Previous studies have hypothesized that individual patient characteristics might affect pre-treatment weight stability. The present study contributes to the growing literature on the effect of pre-treatment weight changes on weight loss during obesity interventions. A significant strength of this study was the evaluation of the effect of pre-treatment weight change without the potential confounding effects of treatment assignment. However, the relatively small sample size may have limited our ability to detect differences between pre-treatment weight change groups, particularly in light of the small percentage of the sample who lost weight before beginning treatment. Additionally, only 14 weeks of treatment were provided during the behavioral weight loss phase of this study, so we were not able to evaluate the effects of pre-treatment weight change on longer-term weight loss outcomes.Because this study was based on a secondary analysis of existing data, we may not have captured important differences among the pre-treatment weight change groups. For example, the timing of completion of the eating behavior questionnaires may have affected our ability to detect differences among the groups. It would be useful to measure participants’ eating behaviors at both screening and week 1 of treatment to best determine the relationship between these behaviors and pre-treatment weight change. We also do not know if the groups would have differed in weight stability outside of the pre-treatment period. Due to the nature of obtaining pre-treatment weight change data during recruitment for behavioral weight loss programs, no study has been able to evaluate whether participants’ weight change fluctuations are a product of anticipating participation in a weight loss program (the “last supper effect” and self-initiated weight loss), or would have occurred regardless of whether the individual had decided to lose weight. A study in which participants are randomized to either begin a weight loss program or to not attempt weight loss would be needed to address this question.The present study found further evidence that many individuals experience clinically meaningful weight changes during the period between the initial screening visit and start of a behavioral weight loss program. Weight gain during this period resulted in larger early weight losses, but smaller total losses from the time of the screening visit. Individuals interested in participating in a behavioral weight loss program should be cautioned that weight gain while waiting to start treatment can detract from the overall benefit of their participation. However, pre-treatment weight gain did not affect total weight loss during the treatment, suggesting that participants who gain weight after their screening visit will still respond appropriately to the intervention and should not be excluded from clinical trials.The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher for the purpose of replicating the analyses reported in the present manuscript. The datasets for this manuscript are not publicly available because the research team has not yet completed initial data analyses. Requests to access the datasets should be directed to Thomas A. Wadden, Ph.D. (wadden@pennmedicine.upenn.edu).TW was responsible for the conception and design of the parent randomized controlled trial and co-wrote the study protocol with NasA. JT and RP provided behavioral weight loss treatment, and RB, NasA, NajA, and AC provided medical monitoring to patients during the randomized trial. JT organized the database, performed the statistical analysis, and wrote the first draft of the present manuscript. All authors contributed to manuscript revision, read and approved the submitted version.JT and NajA disclose serving as consultants for Novo Nordisk. TW reports serving on advisory boards for Novo Nordisk and Weight Watchers and has received grant support on behalf of the University of Pennsylvania from Eisai Inc. and Novo Nordisk. TW and AC have received grant support on behalf of the University of Pennsylvania from Shire Pharmaceuticals. RB has received consulting fees from Eisai Inc. RP discloses serving as a consultant for Weight Watchers. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Four offshore island sites located at the Matsu Islands were selected to conduct both regular and intensive sampling of marine PM2.5. Water-soluble ionic species, metallic elements, and carbonaceous contents were then analyzed to characterize the chemical characteristics of marine PM2.5. In order to identify the potential sources and their contributions to marine PM2.5, chemical mass balance (CMB) receptor model was employed along with the backward trajectory simulation to resolve the source apportionment of marine PM2.5 and to explore their transport routes in different seasons. The results showed that high PM2.5 concentrations were commonly observed during the northeastern monsoon periods. Additionally, marine PM2.5 concentration decreased from the west to the east with the highest PM2.5 at the Nankang Island and the lowest PM2.5 at the Donyin Island in all seasons, indicating an obvious concentration gradient of PM2.5 transported from the continental areas to the offshore islands. In terms of chemical characteristics of PM2.5, the most abundant water-soluble ions of PM2.5 were secondary inorganic aerosols  which accounted for 55–81% of water-soluble ions and 29–52% of marine PM2.5. The neutralization ratios of PM2.5 were always less than unity, indicating that NH4+ cannot solely neutralize nss-SO42+ and NO3− in marine PM2.5 at the Matsu Islands. Although crustal elements  dominated the metallic content of marine PM2.5, trace anthropogenic metals  increased significantly during the northeastern monsoon periods, particularly in winter. Organic carbons (OCs) were always higher than elemental carbons (ECs), and the mass ratios of OC and EC were generally higher than 2.2 in all seasons, implying that PM2.5 was likely to be aged particles. During the poor air quality periods, major air mass transport routes were the northern transport and the anti-cyclonic circulation routes. Source apportionment results indicated that fugitive soil dusts and secondary aerosols were the major sources of marine PM2.5 at the Matsu Islands, while, in winter, biomass burning contributed up to 15% of marine PM2.5. This study revealed that cross-boundary transport accounted for 66~84% of PM2.5 at the Matsu Islands, suggesting that marine PM2.5 at the Matsu Islands has been highly influenced by anthropogenic emissions from neighboring Fuzhou City as well as long-range transport from Northeast Asia.This study aims to investigate the spatiotemporal variation, chemical composition, and source apportionment of marine fine particles (PM Chemical composition  and size distribution (0.4–0.9 μm) of PM2.5 play a crucial role in atmospheric visibility degradation  during the prevalence of northeastern monsoons commonly occurs in the seasons of winter and spring. However, there are very few PM2.5 sampling campaigns that have been conducted at offshore islands such as the Matsu Islands all over the world. The chemical characteristics and source apportionment of marine PM2.5 as well as the influences of regional transport on marine PM2.5 have not been thoroughly investigated so far.Previous atmospheric PM2.5 at the Matsu Islands. The source identification and apportionment of marine PM2.5 were further determined by a CMB receptor model and backward trajectory simulation. The percentages of cross-boundary transport (CBT) contributed to marine PM2.5 at the Matsu Islands in different seasons were further estimated.Accordingly, this study aims to investigate the spatiotemporal variation and chemical characteristics of marine PM2.5 was simultaneously sampled at four offshore island sites at the Matsu Islands including Nankang (NK) , Beigang (BG) , Donyin (DY) , and Chiukung (CK)  townships  was applied to collect marine PM2.5 at each sampling site. Both regular and intensive samplings of marine PM2.5 were conducted from July 2013 to May 2014. Regular sampling was conducted to simultaneously collect 24-h PM2.5  at four sampling sites once every 8 days excluding the rainy days. It means that PM2.5 samples would not be collected at all four sampling sites if it rained at any sampling site due to the limitation of ferry transportation to the four island sites as well as the shortage of manpower to rearrange the complementary sampling of PM2.5 in the following few days. Intensive sampling was undertaken to simultaneously collect 24-h PM2.5 at four island sites for five consecutive days on November 18–22, 2013. Intensive sampling mainly focused on increasing the temporal frequency of PM2.5 sampling during a typical PM2.5 episode occurred at the Matsu Islands. As a result, a total of 39 PM2.5 samples were collected at each island site in the Matsu Islands, including 34 regular samples and 5 intensive samples  bottle and pouring the distilled de-ionized water (D.I. H2O) into each bottle for dissolving ions in an ultrasonic vibration process for at least 60 min. An ion chromatography  was used to analyze the concentration of major anions  and cations  of marine PM2.5.Posterior to field sampling of marine PM3/HClO3 = 3:7) at 150–200 °C for at least 2 h, and then diluted to 25 ml with distilled de-ionized water (D.I. H2O) for metallic element analysis. The metallic elements of PM2.5 including Cr, Mn, Fe, Ni, Zn, Cd, Pb, Mg, K, Ca, Ti, Al, and As were then analyzed with an inductively coupled plasma-atomic emission spectrometer (ICP-AES) .Another quarter of the quartz fiber filter was initially digested in a 20-mL mixed acid solution  were measured with an elemental analyzer (EA) . Before weighing, all quartz fiber filters were baked at 900 °C for 1.5 h to remove carbon impurities from the filters. The preheating procedure would minimize the background carbon in the quartz fiber filter matrix, which might cause interference with the analytical results, leading to a possible overestimation of the carbonaceous content of PM2.5. The elemental analyzer was operated in the procedure of oxidation and reduction at 1020 and 500 °C, respectively, with continuous heating for 15 min. Among these two filters, one eighth of the filter was heated in advance using nitrogen gas at 340–345 °C for at least 30 min to expel the organic carbon (OC) fraction from the filters, after which the amount of elemental carbon (EC) was determined. Another eighth of the quartz fiber filter was analyzed without heating to determine the total carbon (TC) of PM2.5. As a result, the amount of OC was then estimated by subtracting EC from TC.By cutting one quarter of the filter into two identical parts, two eights of the filters were further used to measure the carbonaceous content of marine PM2.5 sampling and chemical analysis were further conducted in this study. Prior to conducting PM2.5 sampling, the flow rate of each PM2.5 sampler was calibrated with a film flowmeter . Quartz fiber filters were then carefully handled and placed on the PM2.5 samplers to prevent potential interferences during the sampling procedure. After sampling, aluminum foil was used to fold the sampled quartz fiber filters, which were then temporarily stored at 4 °C and transported back to the Air Pollution Laboratory in the Institute of Environmental Engineering at National Sun Yat-sen University for further chemical analysis.The quality assurance and quality control (QA/QC) for both PM2.5 sampling, while reagent and filter blanks were applied for chemical analysis. The field blanks were exposed in the field when the quartz fiber filters were placed and removed from the PQ-200 samplers. Background contamination was routinely monitored by using operational blanks (unexposed filters), which were proceeded simultaneously with field samples. In this study, the field blanks were extremely low, normally below or close to the method detection limit. Therefore, the background contamination/interference was insignificant and can thus be ignored. The determination coefficient (R2) of the calibration curve for each chemical species analyzed is required to be higher than 0.995. At least 10% of the samples were analyzed by spiking with a known amount of metallic and ionic species to determine their recovery percentages.The sampling and analytical procedure was similar to that described in various previous studies  were employed, and these ratios are defined as follows  , χ2 (≤ 4.0), T statistics (≥ 2.0), and mass percentages accounting for 80–120%. The source profiles used in this study have been reported by USEPA and Southern California Air Quality Study and research data obtained from the investigation on the chemical composition of PM2.5 and the source profiles of primary sources in Taiwan  in all seasons. Moreover, PM2.5 concentrations decreased from the north to the south in fall and winter, while an opposite trend was observed in spring and summer. In particular, PM2.5 concentrations observed at DY were always the lowest while compared to other three sampling sites in the Matsu Islands. It is worth noting that the spatial distribution of marine PM2.5 was, however, inconsistent with the seasonal prevailing wind direction, suggesting that marine PM2.5 at the Matsu Islands might not be solely influenced by long-range transport, but also by other factors, such as local anthropogenic emissions from neighboring Fuzhou City, which has been pointed out in our previous findings at the Kinmen Islands  is a commonly used track model to plot both backward and forward trajectories of an air parcel moved to a specific site at various heights aboveground over a period of time. In this study, the backward trajectories were plotted for 120 h in four typical routes as illustrated in Fig. 2.5 concentrations of the northern routes mainly during the northeastern monsoon periods were mostly higher than those of the southern routes. Accordingly, the concentrations of marine PM2.5 were highly influenced by meteorological conditions, particularly the prevalent winds. We also found that the concentrations of marine PM2.5 in spring, fall, and winter were much higher than those in summer.In addition, to plot the contours of PM2.5 sampled at the Matsu Islands is illustrated in Fig. 2.5 were SO42−, NO3−, and NH4+, indicating that the probable chemical component of PM2.5 was secondary inorganic aerosols (SIAs) composed of ammonium sulfate ((NH4)2SO4) and ammonium nitrate (NH4NO3), which concurred with previous studies  of marine PM42−] + [NO3−] by [NH4+] or [NH4+] + [Na+]. Neutralization ratios (NRs), defined as the equivalent ratio of [NH4+]/(nss-[SO42−] + [NO3−]) in PM2.5, were generally lower than unity, indicating that PM2.5 were mainly acidic particles and NH4+ cannot solely neutralize both nss-SO42− and NO3−. Furthermore, as shown in Fig. 3−]/nss-[SO42−]) in all seasons ranged from 0.21 to 0.84, suggesting that these acidic fine particles were mainly attributed from sulfur-rich fuel burning sources, i.e., heavy oil burning from industrial boilers and coal burning from coal-fired power plants. As for the NRs of PM10 at the Matsu Islands, they were slightly lower than those at the inland sites although their differences were insignificant . These results showed that PM2.5 sampled at the Matsu Islands were highly influenced by cross-boundary transport rather than local emissions. Furthermore, the SOR of PM2.5 was higher than the NOR of PM2.5, indicating that secondary sulfate containing particles formed by SO2 contributed more than secondary nitrate containing particles formed by NO2 in marine PM2.5 sampled at the Matsu Islands.Sulfur and nitrogen oxidation ratios  were further applied to determine the degrees of atmospheric chemical transformation of SO2.5 at the Matsu islands were primarily from natural or anthropogenic processes, the enrichment factor (EF) of each metallic element was determined and applied as the first step in evaluating the influences of crustal sources on the components of atmospheric PM2.5  appeared mainly during the northeastern monsoon periods , showing that the northeastern monsoons could blow fine particles (PM2.5) containing trace metals from the upwind source regions to the Matsu Islands. Al, Fe, and Ca are the main metallic elements in the earth’s crustal particles which are probably emitted from wind-blown dust, paved and unpaved roads, cement plants, and so on. However, anthropogenic metals could be transported toward the Matsu Islands through cross-boundary transport during the periods of northeastern monsoons.To further identify whether the metallic elements in marine PM2.5 sampled at the Matsu Islands. It showed that the seasonal average concentrations of total carbons were ordered as winter > spring > fall > summer. Overall, it was observed that the mass concentrations of TC in PM2.5 in winter and spring were higher than those in fall and summer. Additionally, the concentrations of OC in PM2.5 were always higher than those of EC in four seasons at all sampling sites. These findings were in accordance with previous literature, reporting that elemental carbon (EC) originates primarily from direct emissions from combustion sources, while organic carbon (OC) is mainly emitted from either primary anthropogenic sources or secondary organic aerosols that are chemically formed in the atmosphere  during the sampling periods. As far as the seasonal variation, the mass ratios of OC/EC in winter and spring were commonly higher than those in fall and summer.Finally, Fig. 2.5 at the Matsu Islands was further resolved by using a receptor model based on chemical mass balance  . Oceanic spray  and vehicular exhausts contributed about 8% of PM2.5, respectively. Additionally, the contribution of agricultural debris burning  could rise up significantly to as high as 9–13% of PM2.5 in fall, winter, and spring, reflecting that the open burning of agricultural debris was frequently occurred at the Matsu Islands in these three seasons. It is also worth noting that, during the northeastern monsoon periods, air parcels were mainly transported northerly toward the Matsu Islands due to the dominant high-pressure anti-cyclonic circulation system centering in the Mongolian Plateau. Air parcels blown from northern and northeastern China could migrate downwards along the eastern coastline of China, passing through major metropolitan areas and industrial complexes in the Yangtze River Delta (YRD) and several rapidly developing regions such as Shandong, Jiangsu, and Zhejiang Provinces in the coastal regions of southeastern China.The types of potential sources and their contributions to marine PM2.5, respectively. Moreover, during the intensive sampling periods, secondary sulfate and nitrate, fugitive dusts, sea salts, waste incinerators, petrochemical plants, biomass burning, organic carbon, and vehicular exhausts contributed 26.4%, 31.5%, 8.3%, 7.1%, 5.3%, 5.0%, 4.9%, and 3.56% of PM2.5, respectively. It shows that the contribution percentages of secondary sulfate and nitrate, fugitive dusts, sea salts, waste incinerators, and petrochemical plants during the intensive sampling periods were higher than those during the regular sampling periods.During the regular sampling periods, secondary sulfate and nitrate, fugitive dusts, biomass burning, vehicular exhausts, sea salts, petrochemical plants, iron works, and waste incinerators contributed 20.2%, 29.9%, 8.9%, 8.2%, 5.1%, 3.9%, 3.2%, and 2.2% of marine PM2.5 at the Matsu Islands. The cross-boundary transport of PM2.5 was defined as the PM2.5 emitted from the upwind sources and migrated toward the Matsu Islands. During the sampling periods of this particular study, the lowest PM2.5 concentration of 8.70 μg/m3 measured in an annual basis can be treated as the background level of PM2.5 at the Matsu Islands by assuming that the variation of mixing layer height and meteorological conditions were somewhat negligible. The deduction of the background PM2.5 concentration from the field measured PM2.5 concentrations can be assumed as the contributions of CBT to PM2.5 transported toward the Matsu Islands.This study further estimated the contribution percentages of cross-boundary transport (CBT) for marine PM2.5 transported toward the Matsu Islands in different seasons. It showed that secondary sulfate and nitrate aerosols were mainly attributed from the dramatic increase of cross-boundary transport. Similar trends were also observed for industrial processes, soil dusts, and carbonaceous materials. The overall contribution percentages of cross-boundary transport to PM2.5 at the Matsu Islands in summer, fall, winter, and spring were estimated as 66.3%, 77.0%, 83.9%, and 81.5%, respectively, which were higher than those at the Penghu Islands (36.4–76.8%), the Dongsha Islands (26.5–82.5%), and the Taiwan Island (54.7%)  Hung . The res2.5 at the Matsu Islands showed that high PM2.5 concentrations were mostly observed in winter and spring, particularly during the northeastern monsoon periods, while low PM2.5 concentrations commonly occurred in summer. Although the spatial distribution of PM2.5 concentration seemed to vary with seasons, the highest PM2.5 concentrations were always found at the Nankang Island (NK). It showed very likely that high PM2.5 concentration was mainly attributed to cross-boundary transport of PM2.5 emitted from anthropogenic sources from either neighboring Fuzhou City or upwind northern regions. Additionally, marine PM2.5 sampled at the Matsu Islands was characterized as aged sulfur-rich acidic particles. Secondary inorganic aerosols  were the most abundant water-soluble ionic species of PM2.5, indicating that the dominant inorganic components of PM2.5 were ammonium sulfate and ammonium nitrate. Crustal elements contributed the major portion of metallic content in PM2.5 in all seasons, while trace metals appeared apparently during the northeastern monsoon periods, showing that the northeastern monsoons could migrate atmospheric fine particles containing trace metals from the upwind anthropogenic sources to the Matsu Islands. The mass ratios of OC/EC always higher than 2.2 suggested that secondary organic aerosols (SOAs) could be formed during the transport procedure. A further study employing receptor modeling found four major sources of marine PM2.5 at the Matsu Islands, namely soil dusts, secondary sulfate, secondary nitrate, and biomass burning. As a whole, this study revealed that cross-boundary transport (CBT) has tremendous impact on the presence of marine PM2.5 at the Matsu Islands. More than four fifths of PM2.5 at the Matsu Islands was resulted from the cross-boundary transport in the seasons of winter and spring, particularly during the northeastern monsoon periods.Both regular and intensive samplings of PM


In cases of aortic valve disease, prosthetic valves have been increasingly usedfor valve replacement, however, there are inherent problems with prostheses, andtheir quality in the so-called Third World countries is lower in comparison tonew-generation models, which leads to shorter durability. Recently,transcatheter aortic valve replacement has been explored as a less invasiveoption for patients with high-risk surgical profile.In this scenario, aortic valve neocuspidization (AVNeo) has emerged as anotheroption, which can be applied to a wide spectrum of aortic valve diseases.Despite the promising results, this procedure is not widely spread among cardiacsurgeons yet. Spurred on by the last publications, we went on to write anoverview of the current practice of state-of-the-art AVNeo and its results. Since this procedure is not widely spread among cardiacsurgeons, but it has been gaining some ground, we aimed to assess the currentpractice of state-of-the-art aortic valve neocuspidization (AVNeo) and itsresults.Ozaki et al.The search strategy using MEDLINE, from 1950 to June 2019, and the PubMedinterface was:  AND .Nine hundred and thirty-nine papers were found using the reported search onPubMed. From these, 11 papers were identified, which provided the best evidenceto the topic. published their first case series with 88 patientsfrom April 2007 to August 2009. They retrospectively reviewed these 88 cases andevaluated short-term and mid-term results. No operations converted to prostheticvalve replacement and no anticoagulation was administered postoperatively,except when the patients had atrial fibrillation. The preoperative mean gradientthrough the aortic valve was 81.6±31.1 mmHg and it decreased to19.0±9.1 mmHg one week after surgery and to 12.9±5.8mmHg one yearlater. The degree of aortic regurgitation (AR) was always less than mildpostoperatively. The first author describes that the preparation of theautologous pericardium was initiated by cleansing fat and other redundanttissues on the outer surface of the pericardium with the harmonic scalpel. Then,an excised pericardium (with a size of at least 7 x 8 cm) is treated with 0.6%glutaraldehyde solution for 10 minutes. The treated pericardium is rinsed forsix minutes, three times, using physiological saline solution . There were seven in-hospital mortalities resulting from a noncardiaccause. Postoperative echocardiography revealed good results with low peakpressure gradients after surgery . There were 16 in-hospital deaths.Fifteen patients needed reoperation . See the results in In their last publication, in 2018 operated on a total of 30 patients by means of theOzaki technique between September 2015 and May 2017. The patients’ mean ±standard deviation (SD) age was 66.83±10.55 years, and 66.67% (n=20) ofthem were male. A combined aortic pathology of stenosis and regurgitationoccurred in 11 patients (36.67%). Seven patients (23.33%) had pure aorticstenosis and 12 patients (40%) had regurgitation only. One patient (3.33%) hadactive endocarditis. Between postoperative day 30 and the three-month follow-up,one more patient died from aspiration pneumonia. No patient required reoperationor experienced an additional thromboembolic event within the first three months.One patient developed moderate aortic valve regurgitation postoperatively. Thiswas found to be due to valvular endocarditis; the patient underwent reoperationfive months after the initial operation and a biological valve was implanted.After three months, none of the patients had evidence of aortic valve stenosis.Moderate aortic valve regurgitation was seen in one patient (3.57%). Mild AR wasseen in three patients (10.71%), whereas no or only trace AR was seen in themajority of patients . The mean transvalvular pressure gradientwas 6.57±3.53 mmHg (n=22); and peak gradient was 13.51±8.88mmHg.Reuthebuch et al.performed AVNeo for aortic stenosis in 57 patients from December 2010 to June2017. Their mean age was 77.5±8.8 years. Preoperative echocardiographyrevealed an average peak pressure gradient of 89±32.9 mmHg that decreasedto 22±10.7 mmHg one week after the procedure and to 19.2±9.7 mmHg20 months after the procedure. There were no conversions to AVR. There were twononcardiac-related deaths. Two patients underwent reoperation owing to IE andrecurrent AR. The mean follow-up period was 30.4±20.8 months. Freedomfrom reoperation rates were 98.1% and 95.3% at 12 and 81 months of follow-up,respectively.Iida et al.operated on nine patients with severe aortic valve diseases by means of an upperministernotomy. The pericardium was harvested endoscopically, and then aministernotomy was performed and the Ozaki procedure was accomplished in asimilar manner to the conventional technique. No in-hospital or 30-day mortalitywas observed in their series, and no conversion to full sternotomy was required.Transthoracic echocardiography on discharge showed five competent valves andthree valves with trivial regurgitation.Nguyen et al.carried out a prospective single-centre study including 52 consecutive patientswho underwent AVNeo between September 2015 and March 2017 using autologouspericardium in 16 patients. Most patients presented with aortic stenosis orendocarditis. The mean age was 60±14 years. Early outcomes included onestroke, two patients needing short-term dialysis, and one death. Duringfollow-up (mean 11.2±4.8 months), trace AR was observed in four patients;the mean pressure gradient was 6.8±2.9 mmHg. Three patients died later(of noncardiac reasons) and five patients needed reoperation due toendocarditis.Mourad et al.performed AVNeo for various aortic valve pathologies in 147 patients fromDecember 2010 to October 2017. Of these patients, the aortic annulus dimensionswere measured in 25 patients who underwent AVNeo for aortic valve disease asfollow-up examination and they were compared with those measured in 15 patientswho had normal aortic valves. No significant differences in the aortic annulusdimensions were observed between the patients who had undergone AVNeo and thosewho had normal aortic valves. The authors concluded that the movement of theaortic annulus after AVNeo is comparable with that of the aortic annulus of anormal aortic valve, and thus AVNeo can be regarded as a more physiologicaloperation in that it maintains the characteristics of the aortic valve similarto those of a normal aortic valve .Iida et al.operated on 77 patients undergoing AVNeo following the Osaki procedure betweenOctober 2016 and August 2018. Mean age was 54.9±16.5 years, and aorticstenosis was present in 84.4% and insufficiency in 15.6% of the patients. At1.76-year follow-up, freedom from reoperation was 97.4%. Two patients (2.6%)presented with moderate to severe aortic insufficiency after the procedure. Bothreceived a prosthetic AVR during the same hospital stay. At discharge, meanpressure gradient was 9.3±4.2 mmHg, which decreased to a mean aorticgradient of 1.6±3.4 mmHg at six to 12 months. The authors concluded thatAVNeo following the Ozaki procedure revealed excellent early hemodynamic resultsin terms of effective orifice area, pressure gradients, and prosthesis-patientmismatch.Krane et al.-14. The ideal material for leaflet extensionremains controversial and one of them is also the autologous pericardiumadvocated by Kwak et al., who evaluated the 20-year clinical outcomes of theleaflet extension technique for AR caused by rheumatic valvular disease. Wewould like to highlight that this is not the same technique as Ozaki’s, sincethe leaflets are not resected, but the fact that the autologous pericardium isalso employed might well give us an idea of the durability of this material.Among the various repair techniques for aortic valve diseases, the leafletextension technique for AR has been used in cases of severe cusp retraction thatcannot be corrected by means of other techniques, there were no cases of operative mortality, butpostoperative complications occurred in five patients. Overall survival at 10and 20 years was 93.5% and 87.1%, respectively. Freedom from reoperation at 10and 20 years was 96.7% and 66.6%, respectively. Kwak et al. concluded thatlong-term results of the leaflet extension technique showed acceptabledurability.In that studyCurrent literature suggests that AVNeo with Ozaki procedure seems to be apromising technique and good alternative to AVR with prostheses. Long-termresults with a longer follow-up of 10 to 15 years are to be published yet. Theauthors who published their first experiences with this technique achievedcomparable clinical outcomes to conventional therapies with good hemodynamicsand a warfarin-free condition. Nevertheless, clinical trials are stillnecessary.


The duration and severity of neutropenia directly correlate with the incidence of life-threatening infections. This study aimed to evaluate the clinical characteristics and associated factors of mortality in febrile neutropenia patients.This retrospective cross sectional study was conducted on all febrile neutropenia patients who were admitted to oncology department of two educational hospitals, Tehran, Iran, from 2011 to 2016. Available patients’ data regarding baseline characteristics, treatment, and outcome were collected and analyzed using SPSS 21.3. The absolute neutrophil count (ANC) of all patients was <500 cells/mm3. The most frequent sources of malignancy in studied patients were gastrointestinal (35.9%), breast (22.4%), and sarcoma (15.7%), respectively. The mean time interval between initiation of treatment in ED and increase of ANC to > 500 cells/mm3 was 2.45 ± 2.1 (1 – 16) days. 186 (52.1%) subjects reached ANC>500 cells/ mm3 after 2-5 days of hospitalization. The rate of hospital mortality was 5.3% (338 (94.7%) survived). The correlation between gender (p = 0.11), temperature (p = 0.123), number of ED visits (p = 0.765), presenting clinical manifestation (p = 0.201), source of malignancy (p= 0.328), presence of metastasis (p = 0.69), positive urine culture (p = 0.45), positive blood culture (p = 0.62), time from last chemotherapy (p = 0.677), and time to reach ANC>500 cells/mm3 (p = 0.739) with mortality was not significant. 357 patients with the mean age of 50.9±17.7 years were studied . Mean white blood cell count of the studied patients was 715.1 ± 270.4 (100 – 1400) cells/mmBased on the findings of the present study, the rate of hospital mortality in patients with febrile neutropenia was 5.3%. Older age and lower white blood cell count were among the significant associated factors of mortality in this series.  The occurrence of fever in patients with cancer and reduced immune status is one of their subsequent problems. The lowest normal limit for circulating neutrophils is 1500 cells/mm3. The duration and severity of neutropenia directly correlate with the incidence of infections including those that are life-threatening. The incidence of infection finds a significant increase at the critical level of less than 500 cells/mm3 . Neutropenia can be acquired or inherited. Its acquired type is common and is associated with different malignant conditions, radiotherapy and chemotherapy, and also idiosyncratic response to drugs such as phenothiazines, sulfonamides, penicillins, cephalosporins, and vancomycin . NeutropToday, fever in patients with a reduced level of immunity has become a major problem in hospitals. Due to the medical advances in the use of immunosuppressive drugs for the transplantation of various organs, the number of patients with fever and neutropenia has increased significantly . Those with persistent fever may be at risk for complications and need rapid medical treatment with antibiotics. Individuals with ANC > 500 cells/μl do not require treatment with Granulocyte - colony stimulating factor (G-CSF), but do require close monitoring for inflammation , 8. The mortality rate of febrile neutropenia has been reported from 5.4% to 15% in different studies -11. PatiWe are faced with an increasing number of febrile neutropenia patients in our emergency departments and this study aimed to evaluate the clinical characteristics and associated factors of mortality in these patients.Study design and setting This retrospective cross sectional study was conducted on all febrile neutropenia patients who were admitted to oncology departments of Imam Hossein and Shohadaye Tajrish Hospitals, Tehran, Iran, from 2011 to 2016. Available patients’ data regarding baseline characteristics, treatment, and outcome were collected and analyzed using SPSS 21. The study group adhered to the principles of medical ethics introduced by the Ministry of Health and the Declaration of Helsinki and legislation in the medical ethics committee of Shahid Beheshti University of Medical Sciences. In addition, the ethics committee of Shahid Beheshti University of Medical Sciences approved the protocol of the study (Ethics code: IR.SBMU.MSP.REC.1395.141). ParticipantsData of all ≥ 15 years old febrile neutropenia patients who were admitted to the oncology departments of motioned hospitals were collected using census sampling method. Subjects with incomplete medical records were excluded.Data gatheringData were recorded using a predesigned checklist containing demographic variables , malignancy type, clinical presentation, time from last chemotherapy, type of received antibiotics, blood culture, urinary culture, cell blood count, neutrophil count, presenting temperature, number of ED visits, as well as outcome . A trained third year emergency medicine resident was responsible for data gathering under direct supervision of an emergency medicine specialist.DefinitionsOne time oral temperature of 38.3º C (101º F) or a persistent oral temperature > 38.0º C (100.4º F) for > 1 hour was defined as fever and ANC ≤ 500 cell/mm3 was defined as neutropenia in this study.Statistical analysis Data were analyzed using SPSS version 21 and P<0.05 was considered significant. Findings were presented as mean ± standard deviation or frequency and percentage. T-test was used for comparing quantitative variables and chi-square or Fisher’s exact test for qualitative ones.Baseline characteristics of febrile neutropenia patients3. The ANC of all patients was <500 cell/mm3. 357 patients with the mean age of 50.9±17.7 (16 – 88) years were studied . Mean temperature at the time of presentation to ED was 38.7 ± 0.4 (38.1 – 40) Celsius. Mean white blood cell count of the studied patients was 715.1 ± 270.4 (100 – 1400) cell/mmOutcome3 was 2.45 ± 2.1 (1 – 16) days. 186 (52.1%) subjects reached ANC>500 cells/ mm3 after 2-5 days of hospitalization. The rate of hospital mortality was 5.3% (338 (94.7%) survived). The mean time interval between initiation of treatment in ED and ANC increasing to > 500 cells/mmBased on the findings of the present study, the rate of hospital mortality in patients with febrile neutropenia was 5.3%. Older age and lower white blood cell count were among the significant associated factors of mortality in this series. There was not any significant relationship between mortality and gender, temperature, number of ED visit, clinical manifestation, source of malignancy, presence of metastasis, positive urine culture, positive blood culture, time from last chemotherapy, or time to reach ANC>500 cell/mm3.The mean age of patients in this study was 50.9 years and about 60% of cases were female. This finding is in line with Amini et al. study, which reported the mean age and gender distribution of neutropenia fever patients as 46 years and 52%, respectively  and alsoThe most common sources of malignancy were gastrointestinal and breast, respectively; and diarrhea and vomiting were among the most frequent symptoms of patients in this series. In the study by Lakshmaiah et al. the most common sources of malignancy were lymphoma, leukemia, and germ cell tumors . In HossWe observed that almost 50% of individuals reached ANC>500 cells/mm3 after 2-5 days of treatment. Consistent with our finding, Cortés el al. revealed that the ANC of 56% of cases had improved and reached more than 500 cells/mm3 after four days . AnotherIn this study, we observed that antibiotic type did not significantly influence patient outcome. Malik et al. found that use of the combination of ceftazidime and amikacin did not have any advantages over ceftriaxone monotherapy . These fIn our study, 11.2% and 3.9% of patients had positive blood and urine cultures, respectively. In many studies, bacteremia was reported in about 100% of patients , 27. In In a study on mortality of cancer patients with febrile neutropenia, Gray et al. concluded that adjusted risk of mortality increased 15% in cases experiencing febrile neutropenia . Chen etPatient characteristics, type of malignancy, comorbidities, and infectious complications were among the most important associated factors of mortality in adult cancer patients with febrile neutropenia in one study . In the It is clear that the risk factors of mortality in febrile neutropenia are different among studies. This could be due to different economic, social, health, and case mix of these studies. It seems that each country or even each hospital should try to explore their own contributing factors of mortality and plan to reduce its rate via controlling the modifiable factors. LimitationIt should be noted that in the current study due to lack of access to patients, it was not possible to find other infectious sites.Based on findings of the present study, the rate of hospital mortality in patients with febrile neutropenia was 5.3%. Older age and lower white blood cell count were among the significant associated factors of mortality in this series.


Bifidobacterium genus (19.2 ± 14.5 vs. 6.2 ± 6.6 (%), p = 0.03), fasting glucagon-like peptide 1 (GLP-1) (13.6 ± 2.0 vs. 10.5 ± 2.9 (pg/mL), p = 0.03), and breath hydrogen (23.4 ± 9.9 vs. 8.2 ± 5.5 (ppm), p = 0.02) were significantly higher than those of after the rice period. Plasma SCFAs also tended to be higher after the bread period. White bread contains more dietary fiber than refined short grain rice. These findings suggest that indigestible carbohydrate intake from short grain rice as a staple food may be smaller than that of white bread.The purpose of this study was to examine the influence of two kinds of major Japanese staple foods, white rice and white bread, on gut microbiota against the background in which participants eat common side dishes. Seven healthy subjects completed the dietary intervention with two 1-week test periods with a 1-week wash-out period in cross-over design (UMIN registration UMIN000023142). White bread or white rice and 21 frozen prepared side dishes were consumed during the test periods. At baseline and at the end of each period, fasting blood samples, breath samples, and fecal samples were collected. For fecal samples, 16S rRNA gene sequencing was used to analyze the gut microbiota. After the bread period, the abundance of fecal  Rice is a traditional staple food of the Japanese diet, but per capita rice consumption in Japan has decreased during the past 50 years ,2. MeanwThe dietary pattern of eating rice as a staple food includes lower intake of fat and saturated fat and higher intake of dietary fiber compared with eating wheat flour products as staple foods . A previApproximately 10% of the carbohydrates ingested resist pancreatic amylase and escape digestion in the small intestine and remain a main substrate for fermentation in the colon . During In our everyday meals, side dishes are different from meal to meal, but staple foods are consumed repeatedly. We hypothesized that dietary intake of indigestible carbohydrates derived from staple foods would have effects on host metabolism via the composition of the gut microbiota. In the current pilot study, we focus on the difference of staple foods and their influence on gut microbiota composition and glucose and lipid metabolism in a two-period crossover design using a commercially available package of side dishes.Healthy volunteers from our research department  were recruited for this study. Inclusion criteria were the following: (1) those who were not currently taking any medication; (2) those who had no abnormality in physical checkup in the past year. Subjects who had a fever, diarrhea or upper respiratory inflammation during the research period were excluded from analysis. The protocol (UMIN registration UMIN000023142) was approved by Kyoto University Graduate School and Faculty of Medicine, Ethics Committee. The study was conducted at Kyoto University Hospital according to the principles of the Declaration of Helsinki. All subjects gave written informed consent.The study was a randomized, crossover trial. Following a 1-week run-in period, the subjects were randomized in a 1:1 fashion to one of two intervention sequences: A bread period with supplied side dishes for 1 week followed by a rice period with supplied side dishes for 1 week or a rice period with supplied side dishes for 1 week followed by a bread period with supplied side dishes for 1 week . A 1-weeIn the run-in and washout periods, the subjects were instructed to avoid eating probiotics, yogurt, oligosaccharides and cultured milk drink. During the test periods, the subjects consumed nothing other than staple food (white bread or white rice) and the supplied side dishes. White bread and white rice on the market were prepared by each subject. The supplied side dishes were a package of the frozen prepared 21 sets of side dishes . The subjects chose one set of side dishes for each meal in the order they liked during the first 6 days, but on the last day, three sets of side dishes were fixed in both periods. The subjects recorded the amount of bread or rice they ate in the first test period, and they ate the equivalent energy of rice or bread in the second test period. Nutritional content of bread and rice was calculated based on the Food Composition Database published by the Ministry of Education, Culture, Sports, Science and Technology, Japan , and nutThe fecal samples were collected by subjects at home. The subjects were instructed to put fecal samples in the tubes and put them into boxes with dry ice (−78 °C) immediately after collection, and to bring the boxes to the laboratory. The collected fecal samples were stored at −80 °C until analysis.16S rRNA gene sequencing analyses of microbial community structure in fecal samples was conducted using a MiSeq  at TechnoSuruga Laboratory Co., Ltd.  according to the method previously described . In briehttp://code.google.com/p/ea-utils/). The method of quality filtering of sequences was as follows: Only reads that had quality value (QV) scores of ≥20 for more than 99% of the sequence were extracted for further analyses.Each multiplexed library pool was spiked with 12.5% phiX control to improve base calling during sequencing, as recommended by Illumina for the pooling of two libraries . SequencMetagenome@KIN software  was used to perform homology searching with the determined 16S rDNA sequences, against the TechnoSuruga Lab Microbial Identification Databese DB-BA10.0  which contains only bacteria with standing in the taxonomic nomenclature ,18. BactThe blood samples were drawn after an overnight fast (12 h). At all points, blood samples for measurement of plasma glucose were collected into tubes containing sodium fluoride (NaF) and Ethylenediamine tetraacetic acid (EDTA); blood samples for serum insulin, serum-free fatty acids (FFA) and serum triglyceride (TG) were collected into tubes containing blood coagulation accelerant; and blood samples for incretin were collected into tubes containing dipeptidyl peptidase-4 (DPP-4) inhibitor . These blood samples were centrifuged , and the collected plasma and serum samples were stored at −80 °C until analysis. Blood samples for short-chain fatty acids (SCFA) were collected into ice-cooled tubes containing EDTA, and were immediately centrifuged . The collected plasma samples were frozen instantly in liquid nitrogen and were stored at −80 °C until analysis.Plasma glucose was measured by ultraviolet absorption spectrophotometry at SRL, Inc., Tokyo, Japan. Serum insulin was determined using chemiluminescent enzyme immune assay at SRL, Japan. Serum FFA and serum triglyceride were determined using enzymatic colorimetric kits and glycerol-3-phosphate oxidase method, respectively, at SRL, Japan. Total glucagon-like peptide 1 (GLP-1) was measured by human total GLP-1 (ver. 2) assay kit ; total glucose-dependent insulinotropic polypeptide (GIP) was measured by human GIP  ELISA . Plasma SCFA was measured by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) at LSI Medience Corporation, Tokyo, Japan.Endtidal breath samples were collected into aluminum bags at the same occasion as blood sampling in order to measure breath hydrogen, which is an indicator of colonic fermentation ,19,20,21The sample size calculation was based on a standardized effect size of 2.5 (breath hydrogen) estimated from a previous study . A samplt test. Two-tailed p < 0.05 was considered statistically significant. Statistical analyses were performed with JMP version 13 .All data are expressed as mean with standard deviation. Comparisons between samples at the end of bread periods and those at the end of rice periods were performed using paired 2) 21.0 ± 1.5 (range 18.6–23.1)) were analyzed. Plasma glucose and serum insulin of all subjects were within normal limits  (2) 20.4 ± 1.3) were analyzed for plasma SCFA, breath hydrogen and intestinal microbiota.Ten healthy volunteers participated in this study. Three subjects had a fever or diarrhea in the test period and were excluded from the analysis. Seven healthy subjects ) age 36.7 ± 4.0 years (range 31–42) and body mass index (BMI) (kg/mctively) . Five ofThe provided sets of frozen side dishes were composed of three side dishes . Main dishes were made with meat, fish or egg, and two small side dishes were mainly made with vegetables. Further information on the typical Japanese side dishes used in this study can be seen in An average of 40,754 reads were obtained for each sequencing reaction. The minimum and maximum number of sequencing reads were 35,791 and 46,687, respectively. The abundance was a percentage of each number of read in all sequencing reads.Bacteroidetes, Firmicutes and Actinobacteria were the major three phyla, and dietary interventions did not make any significant difference in the abundance of Bacteroidetes and Firmicutes. (Actinobacteria was significantly higher after the bread period compared with that after the rice period (18.0 ± 9.8 vs. 7.9 ± 5.1 (%), p = 0.02). Class-level analyses revealed that the abundance of Actinobacteria was significantly higher after the bread period compared with that after the rice period (18.0 ± 9.8 vs. 7.9 ± 2.3 (%), p = 0.02). No significant difference was observed in other classes. The abundance of Bifidobacteriales at order-level (14.7 ± 9.9 vs. 5.4 ± 5.5 (%), p = 0.02), and the abundance of Bifidobacteriaceae at family-level (14.7 ± 9.9 vs. 5.4 ± 5.5 (%), p = 0.02) were significantly higher after the bread period than those after the rice period. No significant difference was observed in other orders and families. Genus-level analyses determined that the abundance of Bifidobacterium was significantly higher after the bread period than after the rice period (19.2 ± 14.5 vs. 6.2 ± 6.6 (%), p = 0.03) , p < 0.01). The abundance of Blautia faecis was significantly higher after the rice period compared with that after the bread period (0.6 ± 0.4 vs. 1.0 ± 0.7 (%), p = 0.046; the abundance at the baseline = 0.8 ± 0.4 (%)). No significant difference was observed in other species.micutes. . However = 0.03) . Althouge period . No signp = 0.03) , p = 0.12, respectively). Breath hydrogen was significantly higher after the bread period than after the rice period (23.4 ± 9.9 vs. 8.2 ± 5.5 (ppm), p = 0.02).The plasma GLP-1 level after the bread period was significantly higher than that after the rice period (13.6 ± 2.0 vs. 10.5 ± 2.9 (pg/mL),  = 0.03) . GlucoseBifidobacterium, fasting plasma GLP-1 level, and breath hydrogen were significantly higher than those of after the rice period.Dietary fiber and resistant starch are prebiotics, which act as a fermentation substrate within the colon and stimulate preferential growth and activity of specific microbial species  and confer health benefits on the host [Bifidobacterium and higher excretion of hydrogen. At species level, the abundance of Blautia faecis which belongs to the genus Blautia was significantly higher after the rice period compared with that after the bread period. A previous study reported that the composition of the Japanese gut microbiome showed more abundance in the genus Bifidobacterium and Blautia compared with those of Western and other Asian people [Blautia faecis. In the current study, we examined the influence of staple foods on gut microbiota against the background in which subjects consumed fixed sets of side dishes. Many previous studies on the effect of dietary intervention on gut microbiota were conducted by using foods with a large difference in dietary fiber content ,12,20,21the host ,28. The the host . In thisn people . HoweverGLP-1 is an incretin secreted by intestinal endocrine L cells located mainly in the ileum and colon . SCFAs pjaponica rice cultivars (short grain rice), which contain lesser resistant starch than long grain rice cultivars, are popular [One limitation of this study is that resistant starch content of bread and rice was not measured. Murphy et al. reported in her review article that the mean value of resistant starch content of white bread and white rice was almost the same , but in  popular ,38. In tBifidobacterium, plasma GLP-1, and breath hydrogen were generally higher than those after the rice period. It is possible that the intake of indigestible carbohydrate during the washout period was larger than that during the rice period. The mean amount of dietary fiber consumed in the rice period was calculated to be about 14.4 (g/day), which was comparable to the average amount of the dietary fiber intake of Japanese people, 14.2 (g/day) [Another limitation of this study is that food records of the subjects’ usual diet before the test periods were not obtained. The baseline values of fecal  (g/day) .Given that the Japanese diet is almost always composed of one staple food and side dishes , the choBifidobacterium, fasting GLP-1, and breath hydrogen compared with 7 days intake of white rice.Against the background of people eating common side dishes, 7 days intake of white bread induced significantly higher abundance of fecal


This study tests the accuracy of the Injury Severity Score (ISS), New Injury Severity Score (NISS), Revised Trauma Score (RTS) and Trauma and Injury Severity Score (TRISS) in prediction of mortality in cases of geriatric trauma.Prospective observational studyThis was a prospective observational study on two hundred elderly trauma patients who were admitted to JSS Hospital, Mysuru over a consecutive period of 18 months between December 2016 to May 2018. On the day of admission, data were collected from each patient to compute the ISS, NISS, RTS, and TRISS.p <0.0001).Mean age of patients was 66.35 years. Most common mechanism of injury was road traffic accident (94.0%) with mortality of 17.0%. The predictive accuracies of the ISS, NISS, RTS and the TRISS were compared using receiver operator characteristic (ROC) curves for the prediction of mortality. Best cutoff points for predicting mortality in elderly trauma patient using TRISS system was a score of 91.6 , similarly cutoff point under the NISS was score of 17; for ISS best cutoff point was at 15 and for RTS it was 7.108. There were statistical differences among ISS, NISS, RTS and TRISS in terms of area under the ROC curve :73-77.Javali RH, Krishnamoorthy  Trauma is one of the leading causes of mortality and morbidity worldwide1. Injuries in the elderly are rapidly becoming a major public health concern2.6.An appropriate scoring system to predict the mortality and morbidity in elderly is necessary because the elderly population has continued to grow and will continue to represent an increasing proportion of patients in our trauma bays. Additionally, elderly trauma patients have an increased morbidity and mortality for a given severity of injury, a decreased 5-year survival compared to their uninjured counterparts, as well as an increase in intensive care unit (ICU) stays and longer hospital length of stay7.Nonetheless, aggressive treatment for elderly trauma patients is paramount, as patients who survive such events often return to independent living8.Trauma scores were introduced more than 30 years ago for assigning numerical values to anatomical lesions and physiological changes after an injury. Physiological scores describe changes due to a trauma and translated by changes in vital signs and consciousness. Anatomical scores describe all the injuries recorded by clinical examination, imaging, surgery or autopsy. If physiological scores are used at first contact with the patient (for triage) and then repeated to monitor patient progress, anatomical scores are used after the diagnosis is complete, generally after patient discharge or postmortem. They are used to stratify trauma patients and to measure lesion severity. Scores that include both anatomical and physiological criteria (mixed scores) are useful for patient prognosisAbbreviated injury scale (AIS)Injury severity score (ISS)New injury severity score (NISS)Organ injury scale (OIS)Anatomic profileInternational Classification of Diseases (ICD-9) Injury Severity Score (ICISS)Revised trauma scoreGlasgow coma scoreAPACHE scoring Trauma and injury severity scores (TRISS)A severity characterization of trauma (ASCOT)9.The injury severity score (ISS) is an anatomical scoring system that provides an overall score for patients with multiple injuries. Each injury is assigned an abbreviated injury scale (AIS) score and is allocated to one of six body regions. The highest AIS score in each body region is used. The three most severely injured body regions have their score squared and added together to produce the ISS scoreThe ISS score takes values from 0 to 75. If an injury is assigned an AIS of 6 (Unsurvivable injury), the ISS score is automatically assigned to 75. The ISS score is virtually the only anatomical scoring system in use and correlates linearly with mortality, morbidity, hospital stay and other measures of severity .Major trauma is considered when ISS> 15.etal. categorised and validated the ISS as follows10:Bolorunduro <9 = Mild9 – 15 =Moderate16–24 = Severe>/=25 = Profound11.The most important drawback of the ISS is that it only considers one injury in each body region. This leads to injuries being overlooked and to less severe injuries occurring in other body regions being included in the calculation over more serious ones in the same body regionet al. in 1997 to counter this problem12. The NISS is simply the sum of squares of the three most severe injuries, regardless of body region injured. Therefore, the NISS will be equal to or higher than the ISS , was designed by Osler  the ISS .13.The revised trauma score is a physiological scoring system, with high interrater reliability and demonstrated accuracy in predicting death. It is scored from the first set of data obtained on the patient and consists of glasgow coma scale, systolic blood pressure and respiratory rate 13.RTS = 0.9368 GCS + 0.7326 SBP + 0.2908 RRRTS score coding is shown in TRISS determines the probability of survival (Ps) of a patient from the ISS and RTS using the following formulae:-b)Ps= 1/  + b2 (ISS) + b3 (Age Index)The coefficients b0 - b3 are derived from multiple regression analysis of the Major Trauma Outcome Study (MTOS) database. Age Index is 0 if the patient is below 54 years of age or 1 if 55 years and over. b0 - b3 are coefficients which are different for blunt and penetrating trauma. If the patient is less aged than 15 years, the blunt coefficients are used regardless of mechanism .14.The TRISS calculator determines the probability of survival from the ISS, RTS and patient's age. ISS and RTS scores can be given independently or calculated from their base parametersTRISS uses combination of both anatomic and physiologic scoring systems and gives a more accurate probability of survival.Our study was a prospective observational study carried out over a period of 18 months (December 2016 - June 2018) on elderly victims of trauma presenting to the Department of Emergency Medicine. Patients were selected after taking into account the inclusion and exclusion criteria .Age more than 60 years.History and clinical evidence of trauma.Patients discharged against medical advice.Patients/ Attendants of patients who were not willing to participate in the study.15. Descriptive statistics were expressed as mean ± one standard deviation unless otherwise stated. A receiver operating characteristic (ROC) curve depicts the relation between true-positive and false-positive for each scoring system. This method compares scores without fixing arbitrary cutoff points. The area under ROC curve was calculated and evaluated. AUROC represents that a randomly chosen diseased subject is more correctly rated or ranked than a randomly chosen non-diseased subject16.The sensitivity, specificity and correct prediction of outcome for each cutoff point were calculated for ISS, NISS, RTS and TRISS. The best cutoff point in each scoring system is determined when the point yields the best specificity and sensitivity in the two-by-two Table. The best Youden index also determines the best cutoff point. The Youden index was used to compare the proportion of cases correctly classified. The higher the Youden index, the more accurate is the prediction  at the cutoff point17.A value of 0.5 under the ROC curve indicates that the variable performs no better than chance and a value of 1.0 indicates perfect discrimination. A larger area under the ROC curve represents more reliability and good discrimination of the scoring systemTwo hundred trauma patients aged more than 60 years were included in our study with a mean age of 66.35 ± 6.865 years. Of the total, 74% were men (n= 148) and 26% were women (n=52). Most common mechanism of trauma was road traffic accident (94%), followed by fall (5.5%). All cases were blunt trauma. 166 patients (83%) recovered after treatment in hospital and 34 patients (17%) died .Among those who were discharged, mean± SD of ISS, NISS, RTS and TRISS scores were 7.52±5.03, 8.80±6.19, 7.60±0.48 and 95.49±4.41 respectively. Among those who were expired, mean ± SD of ISS, NISS, RTS and TRISS scores were 20.85 ±5.00, 27.65 ±7.49, 5.43±1.29 and 58.48 ±25.58 respectively .In our study mean trauma scores of ISS and NISS were significantly higher in expired subjects than in Living subjects, RTS and TRISS were significantly lower in expired subjects than in Living subjects.The predictive accuracies of the ISS, NISS, RTS and the TRISS were compared using receiver operator characteristic (ROC) curves for the prediction of mortality. Best cutoff points for predicting mortality in elderly trauma patients in ISS, NISS, RTS and TRISS systems were 15, 17, 7.108, 91.6 with sensitivity of 91%, 91%, 97%, 97% and specificity of 89%, 93%, 80%, 87%, respectively.The area under ROC curve was 0.963, 0.970, 0.947, 0.972 in the ISS, NISS, RTS and TRISS respectively. The Youden index had best cut-off points at 0.80, 0.84, 0.77 and 0.85 for ISS, NISS, RTS and TRISS respectively .19. Mean age of our patients was 66.35 years with age range of 60 to 95 years. This is lesser than other studies21. The present study indicated that the most common mechanisms leading to trauma in the elderly were motor vehicle accidents, followed by falls. A study by Aydin et al. also revealed that most common mechanism leading to trauma was motor vehicle accident22. Most frequently injured organ was extremity followed by head and neck. Aydin et al. study also reported that the most common injury sites were head and extremities22. In this study, 17% of elderly trauma patients died. Yousefzadeh-Chabok etal. study reported mortality rate of 13.9%23.Most of elderly trauma patients in the present study were men. Some studies have shown similar resultsIn the present study, mean plus standard deviation of ISS for non-survivors was 20.85±5.00 and 7.52±5.03 for survivors; mean plus standard deviation of NISS for non-survivors was 27.65±7.49 and 8.80±6.19 for survivors, mean plus standard deviation of RTS for non-survivors was 5.43±1.29 and 7.60±0.48 for survivors. Mean plus standard deviation of TRISS for non-survivors was 58.48±25.58 and was 95.49± 4.41 for survivors.et al., mean plus standard deviation of ISS for non-survivors was 24.37 ± 12.85 and 5.78 ± 6.71 for survivors;; mean plus standard deviation of NISS for former was 27.62 ± 12.85 and 6.92 ± 8.13, mean plus standard deviation of RTS for former was 5.62 ± 1.31, and 7.75 ± 0.46 for latter group. Mean plus standard deviation of TRISS for non-survivors was 72.80 ± 19.35 and was 98.34±6.58 for survivors24.In a study by Orhon et al., mean plus standard deviation of ISS for non-survivors was 15.95±10.46 and 7.31±6.22 for survivors; mean plus standard deviation of RTS for former was 5.65±1.82, and 7.79±0.27 for latter group. Mean plus standard deviation of TRISS for non-survivors was 1.04±1.49 and was 3.49±0.6 for survivors23.In a study by Yousefzadeh-Chabok Our results indicated that ISS and NISS value for survivors is significantly lower than for non-survivors. RTS and TRISS value for survivors were higher than non-survivors. This difference was statistically significant.In the present study, area under ROC curve using ISS, NISS, RTS, and TRISS for predicting death was 0.963, 0.970, 0.947 and 0.972 respectively; all of these scores were statistically significant in terms of mortality prediction.et al. study, area under ROC curve using ISS, NISS and TRISS for predicting death was 0.907, 0.914 and 0.934, respectively22. In Yousefzadeh-Chabok et al. study, area under ROC curve using ISS, RTS and TRISS for predicting death was 0.76, 0.87 and 0.94, respectively23.In Aydin et al. in Canada published in 2007, it was reported that scoring systems including TRISS had a good ability to predict the prognosis of patients with trauma25. In a study conducted in India, Hariharan et al. concluded that using TRISS system to predict morbidity and mortality after fall in the elderly can play an important role in treatment planning26.In a study conducted by Mitchell According to logistic regression model used in our study, NISS and TRISS were strong predictors of mortality in elderly trauma patients.The sample size is relatively small.This is a single centre studyOur study findings suggest that utility and applicability of injury severity scoring systems in elderly trauma patients using ISS, NISS, RTS, and TRISS scores can better help the emergency physicians in predicting the prognosis. However, TRISS has maximum prediction in outcome when compared with the other scores.