This article performed a retrospective cohort study to estimate the annual incidence rates of diabetes and to assess the utility of HbA1c as a predictor for progression to diabetes in Chinese community adults aged 40 years or older.  In all, 2778 nondiabetic subjects (including 1901 women) underwent HbA1c testing and oral glucose tolerance test (OGTT) measurements at baseline and after 3 years. Diabetes and prediabetes were defined using the WHO criteria. The HbA1c cutoff points were evaluated to predict the future risks of diabetes. Relative risk (RR) was calculated using the chi-square test. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive efficiency of fasting plasma glucose (FPG), 2 hr postprandial plasma glucose (2hPG), and HbA1c for progression to diabetes. A superior cutoff point was defined as the point on the ROC curve with a larger Youden index. p < 0.001). Compared to subjects with HbA1c values ≤ 5.6%, the RRs of progression to diabetes in subjects whose HbA1c ranged from 5.7 to 5.8%, 5.9 to 6.2%, 6.3 to 6.4%, and ≥6.5% were 1.165, 2.582, 5.732, and 16.619, respectively. However, the RRs for subjects with HbA1c ranging from 5.7 to 5.8% and those with HbA1c ≤ 5.6% did not differ significantly (p = 0.615). The AUCs for predicting diabetes after 3 years by FPG, 2hPG, and HbA1c were 0.752 , 0.710 , and 0.756 , respectively. The HbA1c cutoff point of 5.9% (sensitivity of 0.771 and specificity of 0.580) may better identify individuals at high risk of progression to diabetes than the 5.7% value (sensitivity of 0.862 and specificity of 0.371) due to the former's larger Youden index of 0.351, which exceeded the indices for FPG and 2hPG.  Overall, 7.5% (210/2778) of the subjects progressed to diabetes, yielding an annual 2.5% diabetes incidence rate. Additionally, 4.5% (100/2227) of the subjects with normal glucose tolerance (NGT) and 19.6% (110/561) of the subjects with prediabetes progressed to diabetes, and the relative risk of progression to diabetes was 5.188 times higher in subjects with prediabetes than in subjects with NGT ( The use of HbA1c values ≥ 5.9% may provide greater accuracy in evaluating the risk of progression to diabetes and identify individuals with prediabetes with greater reliability among Chinese adults. According to a recent survey, an estimated 148.2 million Chinese adults have prediabetes [An HbA1c value of ≥6.5% has been adopted for the diagnosis of diabetes according to the clinical practice recommendations from the American Diabetes Association (ADA), the European Association for the Study of Diabetes (EASD), and the International Diabetes Federation (IDF) –9, and tOur previous cross-sectional study proposed an HbA1c cutoff point of 6.3% to diagnose diabetes and 5.9% to diagnose prediabetes in Chinese adults . Howevern = 1073), were newly diagnosed with diabetes at baseline (n = 462), had liver dysfunction (n = 94), had chronic kidney disease (n = 68), had cancer (n = 27), had undergone gastrointestinal surgery (n = 2), splenectomy (n = 1), or glucocorticoid treatment (n = 7), or had incomplete survey data (n = 266). Ultimately, 2778 nondiabetic subjects (including 1901 women) at baseline were eligible for the analysis. The institutional review board at the Department of Endocrinology and Metabolic Disease, Ruijin Hospital, Shanghai Jiaotong University School of Medicine approved the study protocol. All subjects gave informed consent.The present study was one component of the baseline and 3-year follow-up surveys conducted for the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study , 12. In 2). WC was measured from the midpoint between the lower borders of the rib cage and the anterior superior iliac spine. Three consecutive BP measurements were obtained at 1 min intervals using the right arm, and the mean of the 3 measurements was used for analysis.All investigators who participated in both surveys received extensive training related to the study questionnaire and outcome measures before the investigation. A standard questionnaire applied through face-to-face interviews was used to obtain data on demographic characteristics and lifestyle. The anthropometric data collected included height, weight, waist circumference (WC), and blood pressure (BP). Body mass index (BMI) was calculated as weight (kg) divided by height squared . All clinical determinations were measured according to the manufacturers' instructions.Diabetes and prediabetes were defined using the WHO criteria in this study, rather than ADA guidelines. According to the 1999 World Health Organization diagnostic criteria , newly dp < 0.05 were considered statistically significant.Statistical analyses were performed using SPSS 22.0 . Continuous variables with normal distributions were expressed as the mean ± SD, and categorical variables were expressed as the number (proportion). Relative risk (RR) was calculated using the chi-square test. The area under the receiver operating characteristic (ROC) curve (AUC) was determined to evaluate the predictive value of progression to diabetes after 3 years using the baseline blood glucose and HbA1c values. The sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were determined according to the diagnostic testing methodology. The point with the larger Youden index, equal to sensitivity + specificity − 1, was defined as the superior cutoff point. Two-sided values for After a median of 3 years, 2778 nondiabetic subjects participated in the 2015 follow-up survey, including 2227 subjects with normal glucose tolerance (NGT) and 561 subjects with IGR. Overall, 7.5% (210/2778) of the subjects progressed to diabetes, which included 4.5% (100/2227) of the subjects with NGT and 19.6% (110/561) of the subjects with IGR. Additionally, 22.8% (508/2227) of the NGT subjects converted to IGR. p < 0.001). Subjects with combined IGT showed the highest propensity for diabetes. Of greater concern, the incidence rate of diabetes was also estimated by the level of HbA1c at baseline. The RR of developing diabetes in those whose HbA1c values ranged from 5.7 to 5.8% did not differ significantly from that in subjects with HbA1c values ≤ 5.6% (p = 0.615). However, in the other three groups whose HbA1c values ranged from 5.9 to 6.2%, 6.3 to 6.4%, and ≥6.5%, the RRs of developing diabetes were significantly higher than those in individuals with HbA1c values ≤ 5.6% , that is, 2.582, 5.732, and 16.619, respectively. Overall, the annual incidence of diabetes for nondiabetic subjects was 2.5%.According to The present study harmonized a baseline survey in 2012 and a 3-year follow-up completed in 2015 among Chinese community adults aged 40–90 years. Of the 2778 nondiabetic subjects at baseline, 7.5% progressed to diabetes, which yielded an annual diabetes incidence rate of 2.5%. The 3-year cumulative incidence rates of diabetes were clearly higher in our study than those in another cross-sectional 3-year follow-up study conducted in China  (7.5% veAlthough HbA1c, FPG, and 2hPG values have been recommended as screening tests for prediabetes, the optimal predictor of diabetes development has not been determined thus far, particularly for different races. Additionally, some controversy exists regarding the criteria used to define prediabetes. Currently, the most popular prediabetes diagnostic criteria in China are those of the WHO . ClearlyRecent longitudinal studies using HbA1c to predict future diabetes risks have yielded various results –21. For Interestingly, we found no significantly higher risk of developing diabetes in nondiabetic subjects whose HbA1c values ranged from 5.7 to 5.8% compared to subjects whose HbA1c values were ≤5.6%, which did not support the ADA-recommended HbA1c value of 5.7% for prediabetes screening in Chinese individuals. Moreover, the ROC curve in the present study showed that an HbA1c value ≥ 5.9% had a higher predictive value of progression to diabetes than an HbA1c value ≥ 5.7%. These present study findings confirmed an HbA1c value of 5.9% as a superior cutoff point for prediabetes. Additionally, we observed that both the FPG concentration of 6.1–6.9 mmol/L and 2hPG concentration of 7.8–11.0 mmol/L had high specificity and low sensitivity for predicting future diabetes risks. In contrast, HbA1c showed high sensitivity and low specificity whether the cutoff point was 5.7% or 5.9%. Therefore, HbA1c and blood glucose used together may efficiently identify subjects who are likely to progress to diabetes and thereby permit early intervention. Although this conclusion is supported by published findings , 23, 24,Of course, our study had some limitations. First, the possibility of residual confounding cannot be completely eliminated because of the epidemiological nature of our investigation. Moreover, although the HbA1c assay was performed at the same laboratory using standardized methods, we did not examine other blood cell parameters, which might have excluded some conditions that possibly affected the HbA1c results, such as hemoglobinopathies and anemia .In the present study, we aimed to evaluate an indicator to identify subjects at high risk of developing diabetes rather than to detect individuals with prediabetes using different criteria. In this regard, HbA1c performed better than FPG and 2hPG, and an HbA1c cutoff point of 5.9% had a higher predictive value for such risk than the 5.7% value recommended by the ADA. Therefore, we conclude that HbA1c values ≥ 5.9% may be used to more accurately evaluate risk for progression to diabetes among Chinese adults and may identify subjects with prediabetes more reliably.


P < 0.001) and increased with age. After an average of 8 years of annual MDA, the prevalence of self-reported side effects was less than 10% in this population.A region-wide population-based post–mass drug administration (MDA) coverage survey was conducted 3 weeks following the 2016 trachoma MDA in Amhara, Ethiopia. The prevalence of self-reported side effects was assessed among those who self-reported receiving azithromycin. A total of 16,773 individuals from 5,129 households reported taking azithromycin during the 2016 MDA in Amhara. The regional prevalence of any self-reported side effect was 9.6% (95% CI: 8.3–11.2%) and ranged from 3.9% to 12.4% among the 10 zones. The most common reported side effects were abdominal pain (53.1%), nausea (21.7%), vomiting (12.8%), and diarrhea (12.5%). Side-effect prevalence among female members was higher than in male members (11.6% versus 7.6%;  Each year in eligible districts, all consenting community members were offered either a single oral dose of azithromycin (1 g for adults and 20 mg/kg for children approximated by height) or a course of tetracycline eye ointment (TEO) according to national guidelines. Tetracycline eye ointment is provided to self-reporting pregnant women in their first trimester, infants aged < 6 months, persons with serious illness, those who previously experienced adverse drug reaction to azithromycin, and to participants who decline oral azithromycin.4 A total of 32 clusters (villages) were selected in each zone. Informed consent was obtained from the head of participating households or from another adult household member. Each household member was presented with a medication sample and asked about taking the drug. The distributions of trachoma medications were not integrated with other medications during the MDA. A secondary outcome of this survey was to estimate the prevalence of self-reported side effects among those who reported taking azithromycin. Parents or caretakers responded for children aged < 10 years or for those who could not respond for themselves.In 2016, a region-wide population-based coverage survey was conducted approximately 3 weeks following the trachoma MDA in Amhara. As detailed previously, a multistage cluster randomized sampling design was implemented to allow for zonal coverage estimates for each of the 10 zones in Amhara.Zonal prevalences of self-reported side effects were estimated using survey weights that accounted for selection probability. CIs were calculated using Taylor linearization, and survey procedures in Stata, version 13.1 , were used to account for clustering. To assess clustering of side effects, we calculated the intraclass correlation coefficient (ICC) with a random-effects logistic regression model without the inclusion of independent variables.4 Among those who reported taking azithromycin, the regional prevalence of any self-reported side effects among all age groups was 9.6% (95% CI: 8.3–11.2%) and 4.7% (95% CI: 3.6–6.2%) among children aged 1–9 years. A greater amount of clustering was observed at the household level  than at the village level . Zonal-level side effects ranged from 3.9% (95% CI: 2.5–6.2%) in Oromia to 12.4% (95% CI: 9.4–16.1%) in South Wollo  reported taking the MDA medications .th Wollo .P < 0.001). This difference was age dependent whereby there was little difference among children aged 1–9 years , whereas the difference in prevalence in the child-bearing age groups was clearer as CIs did not overlap, 14.7% (95% CI: 11.4–18.6%) in female members versus 6.0% (95% CI: 4.1–8.7%) in male members for the age group 20–29 years; and 14.8% (95% CI: 11.8–18.4%) in female members versus 8.2% (95% CI: 5.8–11.7%) in male members for the age group 30–39 years , listed only one side effect and 24.0% (95% CI: 18.5–30.5%) listed two or more side effects. The most commonly reported side effects were abdominal pain (53.1%), nausea (21.7%), vomiting (12.8%), and diarrhea (12.5%) . In the 10 These data provide further evidence that azithromycin remains a safe, well-accepted medication for use in trachoma control programs, particularly among the key demographic of young children.In this region-wide population-based survey of nearly 17,000 individuals, the prevalence of self-reported side effects was 9.6% among all age groups and 4.7% among children aged 1–9 years demonstrating that after numerous rounds of MDA in Amhara, reported medication side effects were low compared with a previous report from the same region.18 Direct comparison with these studies is difficult because of the differences in study settings or target populations. In one study conducted in 12 communities in a trachoma-endemic district of Amhara, adverse event surveillance indicated a prevalence of 7.0% in children aged 1–9 years and 18.7% in persons aged ≥ 10 years.10 Our survey was conducted to better understand the side effects among a sample relevant for large trachoma control programs. Several zones had a prevalence of side effects > 10%, and thus increased follow-up and health education may be warranted before a fear of side effects becomes a barrier to high coverage. The most commonly reported side effects within our survey were similar to those reported in different trachoma-specific settings.14 Although these side effects are not ideal, it has been noted that some beneficiaries take these side effects as a sign that the medications are working.14Previous reports of side effects of azithromycin for different diseases, including trachoma, varied across studies ranging from 0% to 41.0%.4 whereas prevalence of side effects in the same age group was lower than that in older age groups. These findings are promising as these children are the primary focus of the Trachoma Control Program. The fact that these data come from communities which have received numerous rounds of MDA also suggests that despite the longevity of the program, the medication is accepted and tolerated. As noted previously, lower side effects among children may be because of reporting bias if survey respondents, normally parents or heads of household, underreported or underestimated symptoms in young children.10 A recent report, however, has demonstrated that beyond its effect on reducing chlamydial infection, MDA with azithromycin has been shown to reduce childhood mortality, particularly in the youngest children.19 The minor side effects noted among this population may be acceptable given these positive benefits of azithromycin.Within Amhara, the prevalence of self-reported MDA coverage was high in children aged 1–9 years,10 The higher rates of side effects reported by women in this survey may be related to the reporting of health symptoms associated with pregnancy. Our data suggested that the difference between female members and male members was age dependent, with the greatest differences observed in the child-bearing age groups, particularly those aged 20–29 years and 30–39 years. Women beyond the first trimester of their pregnancies may have taken azithromycin during this campaign and may have reported pregnancy-associated health symptoms at the time of this survey. Unfortunately, it was not possible to identify pregnant participants within this survey.The overall prevalence of self-reported side effects was significantly higher among female members than male members in Amhara. This relationship was suggestive, but not statistically significant in an earlier report from the region.This study was conducted in a random sample of households representing all age groups following community-wide MDA, thus providing important data for the Amhara Trachoma Control Program. Limitations of this survey include the fact that it was hard to verify self-reported side effects, especially in young children where response from caretakers was relied on. Furthermore, general illness was not assessed in MDA non-participants, which would have helped contextualize the magnitude of side effects among participants.19In summary, a < 10% prevalence of side effects reported from communities which have received numerous MDAs in Amhara is encouraging because trachoma MDA programs may take longer to reach their goals than the guidelines suggest and because MDAs with azithromycin may at some point be considered a childhood mortality prevention intervention.


Podoviridae family. It produces large plaques (3.5 ± 0.2 mm) with halo zones (10.5 ± 0.5 mm), suggesting it has the ability to depolymerize exopolysaccharides and biofilms.Phage ZPAH7, isolated from a sediment sample from a fish farm, is a novel lytic phage belonging to the  Podoviridae family. It produces large plaques (3.5 ± 0.2 mm) with halo zones (10.5 ± 0.5 mm), suggesting it has the ability to depolymerize exopolysaccharides and biofilms. It has a 30,791-bp genome encoding 29 putative coding sequences.Phage ZPAH7, isolated from a sediment sample from a fish farm, is a novel lytic phage belonging to the  Aeromonas hydrophila is one of the key causative agents for fish mortality, resulting in substantial financial loss. No effective method has been proposed for the control of A. hydrophila infection in aquaculture, except for the application of additional antibiotics  and filtered again using 0.22-µm filters . Then, phage activity in the supernatant was detected with the spot assay. This phage was a novel lytic phage with large plaques (3.5 ± 0.2 mm) compared with those of other phages (0.38 to 1.6 mm)   and BWA Podoviridae family, in the order of Caudovirales. BLAST analysis indicated that the genome sequence of phage ZPAH7 was related to that of Aeromonas phage phiAS7 (GenBank accession number JN651747), with a coverage of 91% . No data are available on the size of the plaques of phiAS7 and whether halo formation was observed, and thus, it is unknown whether this phage has the ability to degrade exopolysaccharides. The gene encoding the tail fiber protein of ZPAH7 was considerably different from that of phiAS7, sharing only 18% amino acid identity, as determined by BLASTP comparative analysis. This divergence in sequence may indicate that the host range of the two phages is different. The annotation results from the Rapid Annotations using Subsystem Technology (RAST) server (http://www.mgc.ac.cn/VFs/main.htm), and no antibiotic resistance genes were found using the Antibiotic Resistance Gene Database  server  predicteDatabase . The aboPRJNA517350. The complete genome sequence has been deposited in GenBank under the accession number MH992513.The raw sequences are available in the NCBI SRA database under BioProject number


In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years.Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM Biomass burning products, generally referred to as “smoke”, can reduce visibility and have adverse health effects. Epidemiological studies have shown associations between short-term PM2.5 exposure from fires and health endpoints, such as mortality, respiratory effects, and cardiovascular effects [2.5 and other pollutants. Prescribed burning, another type of biomass burning, is a land management practice used to improve native vegetation and wildlife habitat, control insects and disease, and reduce wildfire risk [2.5 in the U.S., with an estimated 14% of total primary PM2.5 emissions coming from prescribed burning, while wildfires account for 16% [The World Health Organization’s International Agency for Research on Cancer classifies outdoor air pollution as carcinogenic to humans . Particu effects ,5,6,7,8.ire risk . In the  for 16% . Land ma2.5 concentrations. In the southeastern U.S., prescribed burning contributes about 30% of PM2.5 emissions, while only 3% of PM2.5 emissions come from wildfires. Georgia actively uses prescribed burning for land management, with an annual statewide total burned area of over one million acres [2.5 emissions come from prescribed burning in Georgia [2.5 exposures and acute health effects, such as respiratory disease [2.5 concentrations attributed primarily to prescribed forest burning. Krall et al. [2.5. A better understanding of the contributions of prescribed burning to human health is important, especially for the people who are directly affected by prescribed burning.Rappold et al.  found thon acres , one of  Georgia . People  disease ,14,15,162.5 in Georgia during the first four months (January–April are the most active burning months in Georgia) of 2015–2018. We then used the Community Multiscale Air Quality (CMAQ) model [2.5 concentrations. Based on the data fusion method of Friberg et al. [2.5 exposure fields for use in health impact estimations. Finally, we employed a general health impact function to estimate the impacts of prescribed burning on asthma-related ER visits in Georgia during the first four months of 2015–2018.Although the air quality and health impacts of wildland fires have been studied in the past ,8,11, thQ) model  to compu2.5 concentrations for the first four months of 2015–2018 were simulated using the CMAQ  model in a domain at 4 km spatial resolution over Georgia and surrounding states. These simulated PM2.5 concentrations were then fused with daily total PM2.5 observations at ambient monitors ).i and j are the horizontal column and row indices for the vertical column, t is the day, B is the burn impact from CMAQ–DDM (in µg/m3), C is the total PM2.5 concentrations from CMAQ–DDM (in µg/m3), DFC is the total PM2.5 concentration after applying the data fusion method (in µg/m3), and AdjustedB is the observation-adjusted burn impact (in µg/m3).Fire emissions estimates were developed using the BlueSky framework  with locmonitors a using tnto CMAQ  was used2.5 and respiratory disease-related ER visits [We used a log-linear relationship between air pollutant concentration change and health outcome incidence to quantify the health impact from prescribed burning as follows:ollution . Here, wR visits . We used2.5. We extracted the population from the Environmental Benefits Mapping and Analysis Program, Community Edition (BenMAP-CE) [2.5, the spatial and temporal variations would be similar for other health impacts that can be formulated with Equation (2).We used a health effect estimate of al study . ΔPM is nMAP-CE) , and allnMAP-CE) . While t2.5 concentrations over the four-year period from 2015 to 2018 during the prescribed burning season showed that the CMAQ simulations of total PM2.5 concentrations were biased low compared to the observations  > 0.4) from Emery et al. [R2 of 2016 did not meet the criterion. All years’ normalized mean errors (NMEs) met the criterion (<50%) and goal (<35%)  Estimates meet the criteria if model performance is in the top 2/3 of all past applications; (2) estimates meet goals if performance is better than 2/3 of past model applications.Comparisons of daily total PM to 0.30 . Based oy et al. , only thl (<35%) . The criR2 values between 0.74 and 0.85. Data fusion normalized root mean square errors (NRMSEs) decreased by 60% compared to CMAQ NRMSEs. NMEs also decreased and were close to zero . The reason we chose 30% here was because about 30% of PM2.5 emissions came from prescribed burning in Georgia according to the 2014 NEI. Hence, the days with a ratio larger than 30% received a larger than average prescribed burning impact and may be seen as high fire impact days. The red dots in 2.5 concentrations due to fire impacts. Future epidemiological studies could focus on such days to find a relationship between short-term high-level PM2.5 exposures due to fire and health impacts over a series of single-day lags. On the other hand, the blue dots are the days when the prescribed fire was still a major source of total PM2.5. However, due to the low observed PM2.5 concentrations, while concerns cannot be raised about air quality on those days, health impacts from smoke may be non-negligible. Finally, the green dots are the high PM2.5 days, when other sources are more to blame than prescribed fire smoke. Nearly 13% of PM2.5 observations in 2018 had burn impacts larger than 30%, which was somewhat larger than previous years  Slopes that were less than 0.5 before ranged from 0.73 to 0.80 after data fusion, while (2) R2 values previously between 0.13 and 0.30 later ranged from 0.74 to 0.85. Even with 10% data withholding, errors were significantly reduced: (1) The normalized root mean square error decreased from approximately 60% to 40%, while (2) the normalized mean error dropped from approximately 25% to less than 5%.Data fusion of observed PM2.5 exposure fields. The method consists of scaling the data-fused PM2.5 fields with the ratio of DDM-calculated fire impact to CMAQ-computed PM2.5 concentration. Using observation-adjusted fire impact could help to identify days and areas with major prescribed fire impact on local air quality, even if the observations are low, and also distinguish those days from the other days when the fire impact is low. Such days and areas should be investigated further in epidemiological studies to find the relationship between health effects and prescribed burning.In this paper, we have described a new method for deriving observation-adjusted prescribed fire impact from data-fused PM2.5 concentrations, and were represented by the asthma-related ER visits. Variations in prescribed burning activity during the first four months of 2015–2018 in Georgia led to temporal and spatial differences of health impacts. This points to the importance of distinguishing seasons and areas when studying prescribed fire and its health impacts. April 2018 had approximately 65% more burned areas and an 80% larger estimated total health impact than Aprils in previous years. This was likely due to an extension of the burn season in response to an increased demand for burning throughout Georgia.Health impacts were formulated with a log-linear relationship between the health outcome and the contribution of prescribed fire to PM2.5 levels in the state. However, the largest health impacts, in terms of absolute number of asthma-related ER visits, with an average of about 66 people in the burn months during the reporting years, were found in the Atlanta MSA due to the much larger population exposed to moderate levels of prescribed burning emissions. In the less populated county of Dougherty in southeastern Georgia, the number of asthma-related ER visits was limited to 4, although the prescribed fire impact on PM2.5 levels was highest there.Southern Georgia had the greatest prescribed burning activity and consequently the highest PM


Acute Coronary Syndrome (ACS) is one of the leading causes of death worldwide and studies have shown higher mortality rates and premature death in South Asian countries. The occurrence and effect of risk factors differ by type ofACS.Epidemiological studies in the Sri Lankan population are limited.This is a cross sectional descriptive study conducted at the Teaching Hospital Peradeniya, Sri Lanka among patients presenting with ACS. Data was collected by an interviewer administered structured questionnaire and epidemiological patterns and risk factors were analyzed.P value of 0.001. This study showed higher mean age of 62.2 ± 11.4 years amongst unstable angina (UA) patients and 61.9 ± 14.5 years amongst non ST elevation myocardial infarction (NSTEMI) patients compared to 59.2 ± 11.2 years for ST elevation myocardial infarction (STEMI) patients with no significant statistical difference (P = 0.246). Approximately 55.8% STEMI patients, 39.8% UA and 35.5% NSTEMI patients were smokers indicating a significant association between smoking and STEMI (P = 0.017). Nearly 54.5% STEMI, 35.4% UA and 32.7% NSTEMI patients consumed alcohol and there was a very strong association between alcohol consumption and STEMI (P = 0.006). Almost 51.8% NSTEMI patients, 47.8% UA patients and 29.9% STEMI patients had hypertension(HT) (P = 0.008) indicating significant association of HT with UA and NSTEMI. About 33.6% UA patients and 30.0% NSTEMI patients had DM whilst only 22.1% of STEMI patients had DM of no significance (p = 0.225). Around 15.0% patients with UA, 25.5% with NSTEMI and 11.7% with STEMI had dyslipidemia (P = 0.032). There was a very strong association between a past history of ACS or stable angina with NSTEMI and UA (P = 0.001).The sample of 300 patients had a mean age of 61.3+/− 12.6 and male sex showed higher association with all three type of ACS compared to female with a Smoking and alcohol abuse are significantly associated with STEMI.Patients with NSTEMI or Unstable Angina had higher rates of hypertension and were more likely to have a history of ACS or stable angina than STEMI patients. Patients with NSTEMI were more likely than patients with STEMI or UA to have dyslipidemia. Ischaemic heart disease (IHD)is the most common form of heart disease and the single most important cause of premature death worldwidedespite major breakthroughs in management . AccordiAccording to the Annual health bulletinof Sri Lanka in2013 increasing trend was seen in hospitalizations per 100,000 population due to ischaemic heart diseases; 494.9 in 2012 and 506.1 in 2013. Ischemic heart disease isranked as the leading cause of hospital deaths in Sri Lanka since 1995 having great clinical and financial impact. It accounted for 29.1 deaths per 100,000 population in 2013, including 14.7% hospital deaths with a case fatality rate of 5.76 . Hypertension was diagnosed with systolic blood pressure ≥ 140 mmHg and/ or diastolic blood pressure ≥ 90 mmHg and/or being on antihypertensive treatment. Family history of ACS was defined as a first degree male relative withIHD which developed below 55 years of age or a first degree female relative with IHD which developed below 65 years of age. In addition, the family history of other risk factors like ischaemic stroke or transient ischaemic attack, hypertension, dyslipidemia and DM were assessed. Other modifiable risk factors like smoking, alcohol consumption and social stressors were also assessed. The clinical presentation of the patients was assessed on admission and they were followed up daily during their hospital stay while assessing their management and outcomes.Teaching Hospital Peradeniya is a tertiary care hospital situated in the Central Province of Sri Lanka. It consists of 800 bedsincluding Professorial Teaching units in Medicine, Surgery, Pediatrics and Obstetrics and Gynaecology. Patients with ACS will either present directly to the medical ward or to the Preliminary Care Unit where initial emergency managementis doneincluding administration of Streptokinase. Patients will subsequently be transferred and treated in the High Dependency Care Unit during the initial days and once stabletransferred to the medical ward. An intensive care unit facility is available within the hospital in case of emergency. Patients who require further specialized cardiac management including percutaneous coronary intervention are transferred to the closest specialized coronary care unit at the Teaching Hospital Kandy which can be reached within ten minutes by ambulance.Data collection commenced following approval from the Institutional Ethical Review Committee (IERC) of the Faculty of Medicine, University of Peradeniya. A pilot study was conducted on 30 individuals prior to data collection to finalize the questionnaire. Participants selected for the study were interviewed after informed written consent was obtained. Data was collected by an interviewer administered structured questionnaire  was used to compare differences in age between three ACS groups. As the P value was non-significant, no post hoc tests (Bonferroni test) were used. Probability of less than 0.05 was used to ascertain statistical significance.The data wascoded and entered into an excel data sheet and was analyzed using SPSS, version 20.0 . Aunivariate analysis was conducted initially and for selected variables a bivariate analysis was conductedsubsequently. The p = 0.014). The male: female ratio was approximately 2:1. The majority of the patients were Sinhalese in ethnicity (84.0%)  males (mean age 60.1+/− 12.2) and 101(33.7%) females with a mean age of 63.8+/− 12.9   (Table Out of 300 diagnosed acute coronary syndrome patients 113(37.7%) had unstable angina, 110(36.7%) NSTEMI and 7725.7%) had STEMI. Of the total NSTEMI patients a majority of 64.5%(71) were male, whereas a more significant majority of 83.1%(64) of the STEMI patients were male. A further 56.6% (64) of unstable angina patients were male. Thusthe male sex showed a higher association with all three type of ACS with a 5.7% had p = 0.807)   with UA compared to the other two types.Out of 300 patients 127(42.3%) were regular smokers and the mean number of cigarettes per day was 3.85+/− 6.5 and the mean duration of smoking was 9.6+/− 13.8 years. 55.8% STEMI patients were smokers whereas only 39.8% UA and 35.5% NSTEMI patients were smokers indicating a significant association between smoking being a risk factor for STEMI compared to NSTEMI and UA (P = 0.008) indicating a significant association between HT and UA and a more significant association with NSTEMI. Around 33.6% UA patients and 30.0% NSTEMI patients had DM whilst only 22.1% of STEMI patients had DM (p = 0.225) without reaching any significant association between DM and type of ACS. Nearly 15.0% patients with UA, 25.5% with NSTEMI and 11.7% with STEMI had dyslipidemia and showed a slightly significant association between dyslipidemia and NSTEMI (P = 0.032). Almost 48.2% patients with NSTEMI, 46.0% patients with UA and 23.4% patients with STEMI had a past history of ACS or stable angina. This indicates a very significant association between a past history of ACS or stable angina with NSTEMI and UA (P = 0.001) compared to STEMI (Table Among 300 patients 88 (29.3%) had DM, 134 (44.7%) had HT, 123 (41.0%) had a prior history of acute coronary syndrome or stable angina and 54 (18.0%) had dyslipidemia. 51.8% NSTEMI patients, 47.8% UA patients and 29.9% STEMI patients had HT (P = 0.358) or stroke (P = 0.787) was not significantly associated with the type of ACS. Approximately 35.5% NSTEMI patients, 23.4% STEMI patients and 29.2% UA patients had a family history of DM whilst 35.5% NSTEMI patients, 23.4% STEMI patients and 31.9% UA patients had a family history of HT.Therewas no significant statisticalassociation between family history of DM or HT with the type of ACS (P = 0.202 and 0.207 respectively). 24.5% NSTEMI patients, 22.1% STEMI patients and 18.6% UA patients had a family history of dyslipidemia (P = 0.555) indicating no significant association with the type of ACS(Table Out of 300 patients 99 (33.0%) had a family history of ACS, 90 (30.0%) had a family history of diabetes mellitus, 93 31.0%) had a family history of hypertension, 37(12.3%) had a family history of strokes and 65 (21.7%) had a family history of dyslipidemia. About 37.3% of NSTEMI, 32.7% UA and 27.3% of STEMI patient had a family history of ACS whereas 14.3% STEMI, 12.4% UA and 10.9% of NSTEMI patients had a family history of stroke. The presence of family history of ACS (.0% had aOn admission the majority of 299 patients (99.7%) used chewable or dissolved aspirin as an anti-platelet agent. Out of 300, 278(92.7%) patients recovered, 20(6.7%) were transferred to a coronary care unit for further management and 2(0.7%) died after initial management. Mean hospital stay was 4.2+/− 1.3 days and all patients were given primary health education on discharge.Epidemiological studiesin South Asian countries have led to important conclusions regarding the prevalence, the type of presentation and the treatment of ACS in the region , 8, 13. Rajapakse et al.in 2010 reported 33.6% ACS in Sri Lanka to be STEMI [Medagama et al reported 32.8% ACS to be STEMI [The ACCESS group of investigators reported 46% ACS in developing countries to be STEMI and 54% to be NSTEMI/UA . Rajapakbe STEMI , while ibe STEMI , while o100 /apolipoprotein A-I ratio and higher waist to hip ratio [Sharma et al alsoshowed that ACS occurred a decade earlier in South Asians compared to the Western population [Medagama et al in 2012 [South Asians show a higher rate of MI at a younger age (mean age 53 years) compared to those in other countries (mean age 58.8 years). This is very likely due to South Asians having more risk factors at ages < 60 years when stratified by age, including higher apolipoprotein Bip ratio . Sharma pulation . However in 2012 . Therefop = 0.014) compared to males (60 years). Studies have shown that the onset of cardiovascular disease in women occurs seven to ten years after that for males [Our study showed that the mean age for females (64 years) was significantly higher  [P = 0.246).Our study showed that the highest proportion of 27.3% NTEMI patients and 32.7% UA patients were between 60 and 69 years of age and the highest percentage of 35.1% STEMI patients were aged between 50 and 59 years with no significant statistical difference in age distribution with the type of ACS.s of age . Sharma = 0.103) . SimilarP = 0.001).However males did not show a preponderance for any type of ACS.In our studynearly a two third (66.3%) of patients with ACS were maleand themale gender was strongly associated with all three type of ACS compared to the female gender. A study done in Nepal also showed astrong association of ACS with male(75.7%) . This map < 0.0001), whereas alcohol consumption had a weaker association (p = 0.03). Multivariate analysis showed the two strongest risk factors to be current smoking and increased apolipoprotein B100 /apolipoprotein A-I ratio, followed by a history of DM, HT and psychosocial factors. Rare novel risk factors are high inflammatory markers and homocysteine levels [The INTERHEART study describes nine risk factors which account for > 95% of acute MI. Eight risk factors including dyslipidemia, smoking, hypertension, DM, abdominal obesity, psychosocial factors, lack of consumption of fruits, vegetables and of regular physical activity were significantly associated with MI  patients being hypertensive. According to the INTERHEART study the prevalence of hypertension in the South Asian cohort was 31.1% which is much lower than our study. However, a study done in India  reportedh NSTEMI . Sympathh NSTEMI . These cp = 0.017) and alcohol consumption (p = 0.006) to be more significantly associated STEMI than for NSTEMI and UA. Similarly, Medagama et al and Sharma et al alsoshowed smoking and alcohol abuse to be more common in STEMI patients [Smoking is an established risk factor for IHD and nearly 30% of all deaths from ACS were found to be attributed to smoking . Alcoholpatients , 17. Curpatients , 27.Medagamaet al [Nearly 41% of patients had a prior history of ACS or stable angina in contrast to a study in India reporting only 20.7% having a prior history of ACS . This coamaet al . A studyamaet al .Sharma et al (showed DM to be significantly associated with NSTEMI) [Donahoe et al showed diabetics with STEMI to have a higher one year mortality, and the one year mortality for non-diabetics with STEMI to be similar to that of diabetics with NSTEMI/UA [Diabetes is another important risk factor which was found in 29.3% of the study population compared to a study in India reporting 35.5% having DM . Interes NSTEMI) . HyperglSTEMI/UA .Among the strongest predictors of in-hospital deaths in STEMI patients were found to be untreated dyslipidemia, as total cholesterol and LDL levels undergo significant alterations post-MI, with a change of 7 and 10% in STEMI and 5 and 6% in UA respectively . Our stuThe presence of family history of ACS, DM, HT, Dyslipidemia or stroke did not show statistically significant association with the type of ACS.Many studies have been done on risk factors for ACS in the West according to which most management and prevention protocols have been carried out. This study as in some other studies in the South Asian population shows that the epidemiology and risk factors for ACS differs from that of the Western world. Thus lifestyle modifications for prevention and management of ACS should be altered and implemented according to Sri Lanka. The effect and occurrence of certain risk factors differ according to type of ACS. STEMI is known to have a higher mortality rate than NSTEMI/UA . FortunaThe occurrence and effect of risk factors differ by type of ACS. Modifiable risk factors like smoking and alcohol abuse are significantly associated with STEMI. Patients with HT and a past history of ACS or stable angina are more likely to develop NSTEMI/UAthan STEMI whereas patients with dyslipidemia are more likely to develop NSTEMI than STEMI/ UA. Patients with DM do not have a preponderance for any type of ACS. More studies are required to understand epidemiology, presentation and risk factors of ACS particularly in regional levels as they can differ from one region to the other. This understanding would help to implement preventive measures including lifestyle modification and drug treatment optimizing risk factors.Additional file 1: The English Version of the Questionnaire used for data collection.


Autism spectrum disorder (ASD) is a lifelong disorder. In the UK, risperidone is the only psychotropic medication approved for the management of the behavioural symptoms that may accompany autism. This is a population-based study aimed to provide an evaluation of the changing trend in the incidence and prevalence of ASD and to analyse the pattern of psychotropic medication prescribing in the UK. 20,194 patients with ASD were identified. The prevalence increased 3.3-fold from 0.109 per 100 persons in 2009 to 0.355 per 100 persons in 2016. Approximately one-third of the identified cohort was prescribed at least one psychotropic medication. Although the medications approved to manage the symptoms of ASD are limited, the prescribing of such medications is increasing.The online version of this article (10.1007/s10803-019-04291-8) contains supplementary material, which is available to authorized users. Autism spectrum disorder (ASD) is a persistent neurodevelopmental condition characterised by social communication impairment and stereotyped, repetitive patterns of behaviour APA . A previPsychotropic medications, such as antipsychotics, antidepressants, antiepileptic drugs and stimulants, have been used for ASD patients with associated comorbid conditions  for this study. THIN is a primary care database of anonymised general practice records, covering a period from the early 1990s to the present day, of more than 11 million individuals, including 3.7 million active patients, which covers approximately 6% of the UK population  which was established to review research using the THIN database (Ref: 18THIN010).All individuals in the THIN database from all age groups who had a diagnosis record of ASD in the study period between 1st of January 2009 and 31st of December 2016 were identified.For each patient, the latest of either the date of the patient’s registration at the general practice or the date that the general practice began using a computerised clinical management system was identified as the patient’s start date. Only patients who had an observation period of at least 12 months available from their start date were included. The date of the first recorded diagnosis of ASD following the patient’s start date was identified as the patient’s index date. Age at first recorded diagnosis was defined as the time between the birth year and the year of the diagnosis. Figure For each patient, the records were screened from the start date to identify the incident cases throughout the study period (2009–2016). ASD-incident patients were defined as (1) those who had a first diagnosis of ASD following the 1st-year screening period and (2) those with an ASD diagnosis aged < 2 years during the 12-month screening period .Fombonne et al. have validated the ASD Read codes list ; this coAdditionally, we set out to ascertain medications prescribed to people with ASD and neuropsychiatric comorbidities by using related treatment/diagnostic Read codes.Based on the NICE guideline on the management of ASD in individuals under 19 years of age   of the same year multiplied by 100.Prevalent cases of ASD were defined as all patients having an ASD diagnosis. Similar to the annual incidence, the annual prevalence of ASD per 100 was calculated by dividing the number of prevalent cases in each year by the total number of the THIN MYP of the same year multiplied by 100.Both annual incidence and prevalence were stratified by gender and age groups: children at age 2 years or younger, 3–5-year-olds (young children), 6–12-year-olds (children), 13–17-year-olds (adolescents), 18–24-year-olds (young adults), 25–64-year-olds (adults) and ≥ 65-year-olds (elderly). It should be noted that operational definitions have been used for both incidence and prevalence; the true figures would be dependent on complete, accurate diagnosis—see discussion.The annual proportion of ASD patients treated with psychotropic drugs was calculated by dividing the number of treated patients in each class in a year by the ASD prevalent cases in the same year multiplied by 100. A secondary analysis was performed by excluding patients with only one psychotropic drug prescription.The Kaplan–Meier survival analysis was used to estimate the average discontinuation rate of psychotropic drugs in this cohort. The percentage of the ASD cohort having other neuropsychiatric diagnoses was calculated by dividing the number of patients having a record of each diagnosis over the total ASD cohort multiplied by 100. Analyses were performed using the Statistical Analysis System (SAS) version 9.4.Over the study period, there were 20,194 patients with at least one recorded diagnosis of ASD, 78% of them were male. The mean age of the first recorded diagnosis among females was 14.03 (SD 11.9) years and 11.5 (SD 10.7) years among males. Further details of the study cohort are provided in Table Over the study period, the prevalence of ASD increased 3.3-fold from 0.1095 per 100 persons (95% CI 0.1094–0.1096) in 2009 to 0.3555 per 100 persons (95% CI 0.3553–0.3557) in 2016. Generally, the prevalence of ASD was higher among males than females. In 2016, the ASD prevalence was 0.1576 per 100 persons (95% CI 0.1574–0.1577) and 0.5576 per 100 persons (95% CI 0.5573–0.5580) in females and males, respectively. The prevalence of ASD was highest in individuals in the age groups of 6–12 years (children) and 13–18 years (adolescents). In 2016, the prevalence of ASD was 1.6092 (95% CI 1.6077–1.6107) per 100 persons in children and 1.5694 per 100 persons (95% CI 1.5678–1.5710) in adolescents. Figure The incidence of ASD rose 2.9-fold during the period 2009–2016: 0.0226 per 100 persons (95% CI 0.0226–0.0227) to 0.0647 per 100 persons (95% CI 0.0646–0.0648). The incidence rate increased 5.1-fold among females and 2.5-fold among males. During the study period, the incidence of ASD was the highest in 2016 among young children aged from 3 to 5, at 0.4505 per 100 persons (95% CI 0.4493–0.4517). The annual ASD incidence is shown in Fig. p < 0.0001. Among the males, the three most commonly prescribed psychotropic drugs were hypnotics , antidepressants (24.6%) and stimulants (20.5%). Whereas in the females, the three most commonly prescribed psychotropic drugs were antidepressants (38.8%), hypnotics (24.1%) and antipsychotics (12.5%).Within the total cohort, 6529 patients (33.4%) received at least one psychotropic prescription; overall, 270,391 psychotropic prescriptions were issued. The prescribing rate was significantly greater among females  compared to males , The highest numbers of prescriptions issued throughout the study period were for methylphenidate , followed by melatonin  and risperidone . Valproic acid was the most frequently prescribed antiepileptic drug . The most commonly prescribed antidepressants were fluoxetine  and sertraline .The percentage of patients prescribed both antidepressant and hypnotic drugs approximately doubled over the period from 2009 to 2016: from 6.2% (95% CI 5.6–6.8) to 11.5% (95% CI 10.9–12.1) and from 5.5% (95% CI 5.0–6.2) to 11.2% (95% CI 10.6–11.8), respectively. For the remaining psychotropic drug classes, the percentage of patients prescribed these medications remained relatively steady over the study period. A secondary analysis conducted by excluding patients with only one prescription for each drug class resulted in similar findings to those of the primary analysis. Figure Kaplan–Meier survival curves demonstrated that approximately one-third of those prescribed antipsychotic drugs, 403 patients of 1254 (32.1%), were on continuous antipsychotic therapy for more than 1 year, and 6.1% (77 patients) continued for up to 5 years. Furthermore, 29.9% (190 of 634) remained on risperidone therapy for more than 1 year, and 31.5% of those prescribed aripiprazole remained on the treatment for more than 1 year. For antidepressants, almost a quarter of the patients  continued on therapy for more than a year, while 2.9% of them remained on antidepressants for more than 5 years. 25% of patients prescribed fluoxetine remained on the treatment for more than 1 year. More than half of the patients prescribed antiepileptics continued on therapy for 1 year, and 12.5% of them continued for more than 5 years. The majority of ASD patients prescribed anxiolytic medication (94%) had stopped the treatment after 1 year. For patients treated with hypnotics and stimulants, 21.5% and 32% continued treatment for up to 1 year, respectively. The detailed survival analysis for psychotropic drugs identified in this study is illustrated in Fig. Of the total ASD cohort, 57.3% of the patients had at least one neuropsychiatric comorbidity. The most commonly prescribed psychotropic drug classes in these patients were antidepressants 14.3%) and hypnotics (10.5%). The three most common neuropsychiatric diagnoses accompanying ASD were behavioural/conduct disorders at 30.7% (95% CI 30.1–31.3), anxiety at 15.2% (95% CI 14.7–15.7) and ADHD at 14.3% (95% CI 13.8–14.8). The percentage of diagnoses was higher in males than females for ADHD (15.4% and 10.3%) and behavioural/conduct disorders (31.5% and 27.7%). However, the proportion of patients with anxiety was greater among females, at 23.2% compared to 13.1% in males. Almost twice as many females (18.1%) than males (9.1%) were diagnosed with depression. For the remaining neuropsychiatric diagnoses, the proportions were similar for both males and females. Table 4.3% and Among ASD patients on antipsychotics, more than half (53.3%) were diagnosed with behavioural/conduct disorders and 34.9% as having anxiety, whereas 8.1% were diagnosed with no additional neuropsychiatric comorbidities identified. Among the patients with ASD but no comorbid neuropsychiatric diagnoses, 87.6% of them were not on any psychotropic therapy; of the remaining 12.4%, 7.7% were prescribed hypnotics and 2% were on antidepressants. In those who had neuropsychiatric comorbidities, 52.8% were not on psychotropic therapy, although almost 60% of these patients had behavioural/conduct disorders.This study has extended the previous findings of research in this area by (1) providing the most recent and comprehensive description of ASD in the UK, (2) analysing a broader cohort, including patients with ASD of all age groups, and (3) using survival analysis to examine the duration of treatment for different psychotropic drug classes over the study period.The incidence and prevalence of ASD have increased markedly over recent years. This probably reflects the current broader diagnostic criteria for ASD and increasing awareness of the condition. In our study, the incidence and prevalence of ASD increased 2.9-fold and 3.3-fold, respectively, from 2009 to 2016. During the study period, the greatest prevalence of ASD was seen in children (6–12-year-olds), followed by adolescents (13–18-year-olds) and then young children (3–5-year-olds). Similar trends were observed in the study by Murray et al. which covered the period from 1992 to 2008 . HoweverAlthough in our study there were no restrictions on age, the findings regarding psychotropic drug prescribing were consistent with the findings of Murray et al. , in whicn = 191) on risperidone and 32% (n = 112) on aripiprazole . Because of this, it is not possible to determine whether recorded drugs were being prescribed to treat neuropsychiatric comorbidities of ASD. Last, information on patient compliance and adherence to the prescribed medication cannot be obtained from the database; hence, we are not certain if the patients prescribed any of the drugs were taking them correctly, if at all.Reported ASD prevalence has been increasing over time; in our study population, it reached 1.6 per 100 children in 2016. One-third of the identified cases with ASD were on psychotropic medication. 12.4% of the treated cohort were prescribed antipsychotic drugs, of which 50.7% was risperidone and 49.3% was other antipsychotic medication, the latter having been “off-label”. This suggests that the guidance on psychotropic prescribing in children with ASD needs to be reviewed. We recommend further studies to obtain better data on the efficacy, tolerability and safety of psychotropic medication in patients with ASD, particularly children.Supplementary material 1 (PDF 41 kb)Below is the link to the electronic supplementary material.


The main aims of this study were to examine the incidence and in-hospital outcomes of mechanical and bioprosthetic surgical mitral valve replacement (SMVR) among patients with and without T2DM.We performed a retrospective study using the Spanish National Hospital Discharge Database from 2001 to 2015. We included patients with SMVR codified in their discharge report. We grouped admissions by diabetes status. Propensity score matching (PSM) was used to compare outcomes of isolated SMVR.We identified 42,937 patients (16.41% with T2DM). Incidence rates of mechanical and bioprosthetic SMVR were higher among T2DM patients than among non-T2DM patients. In both groups of patients, the use of bioprosthetic SMVR increased over time. The use of mechanical valves remained stable among T2DM patients. In T2DM and non-T2DM patients with mechanical SMVR, in hospital mortality (IHM) and MACCE decreased significantly (p < 0.001) from 2001 to 2015. T2DM patients had an overall 11.37% IHM, compared with 10.76% among non-T2DM patients (p = 0.176). Regarding MACCE figures were 14.72% vs. 14.22% (p = 0.320) after mechanical SMVR. Total crude IHM were 14.29% for T2DM patients and 15.13% for those without T2DM with bioprosthetic SMVR (p = 0.165) and 18.22 vs. 19.64%, for a MACCE (p = 0.185). Using PSM we found that the IHM and the MACCE of isolated SMVR did not differ significantly between patients with or without T2DM beside the type of valve replacement. Among T2DM patients, those who received bioprosthetic valves had higher IHM  and a higher rate of MACCE  than T2DM patients with mechanical SMVR.In Spain from 2001 to 2015, the incidence rates of hospitalization to undergo mechanical or bioprosthetic SMVR were higher among the population suffering T2DM than among the non-T2DM population. In both groups of patients the use of bioprosthetic SMVR increased over time and the use of mechanical valves remained stable in T2DM. T2DM patients have IHM and MACCE after mechanical and bioprosthetic SMVR which are not significantly different to those found among non-diabetic patients. Among T2DM patients, the crude IHM was significantly higher in those who received a bioprosthetic SMVR than those with mechanical SMVR.The online version of this article (10.1186/s12933-019-0866-5) contains supplementary material, which is available to authorized users. Surgical mitral valve replacement (SMVR) is the recommended treatment for irreparable valve pathologies, and mechanical or bioprosthetic valves have been the mainstream options. However, these preferences may change in the future with the emergence of transcatheter mitral valve replacement .SMVR is more likely to be performed in patients with several comorbidities, including diabetes, , 3 and tPrevious studies have found that those suffering from diabetes have a higher long-term mortality after SMVR than those not suffering from diabetes . For othThe effect of diabetes on perioperative major adverse cardiovascular and cerebrovascular events (MACCE) and IHM after SMVR is not as well defined. Therefore, the aim of the present study was to examine nationwide trends, characteristics and in-hospital outcomes in mechanical and bioprosthetic SMVR among patients with or without T2DM. To accomplish this aim, we used Spanish National Hospital Discharge Database (SNHDD) data from 2001 to 2015. Also, we aimed to identify which factors are associated with suffering from MACCE among patients with or without T2DM after SMVR according to implanted valve type.The SNHDD covers over 95% of all hospital admissions in Spain. This database includes up to 14 discharge diagnoses and up to 20 procedures performed during the hospitalization . The ICDFor this retrospective study, we used data from 2001 to 2015.We selected all admissions of patients aged ≥ 40 years with a code for SMVR (ICD-9-CM codes: 35.23 and 35.24).We defined T2DM patients as those with ICD-9-CM codes 250.x0 and 250.x2 as the primary or as a secondary diagnosis. Those without these codes were classified as non-T2DM patients. We excluded patients with a diagnosis of type 1 diabetes mellitus (ICD-9-CM codes 250.x1 and 250.x3).Clinical characteristics included information on the overall comorbidities at the time of discharge, which was assessed by calculating the Charlson comorbidity index (CCI) . CalculaAmong the procedures, we specifically analyzed if coronary artery bypass graft (CABG), surgical aortic valve replacement (SAVR), surgical procedures on pulmonary and/or tricuspid valves, intra-aortic balloon counter-pulsation, pacemaker implantation and blood transfusions were performed during the hospitalization for SMVR.We also collected information on the etiology of the mitral valve disease and the presentation.The ICD-9-CM codes for the diagnoses identified and the procedures conducted during the hospitalization for SMVR used in our investigation are shown in Additional file Weight loss” is defined as an “abnormal weight loss over the last months in the opinion of the treating physician”. There is no specific BMI to define “Weight loss”.According to the SNHDD coding procedures “We evaluated the mean length of hospital stay (LOHS) and the IHM. IHM was defined by the proportion of patients who died during admission for each year of study.The main end points of our investigation were trends in incidence rates of hospitalizations for SMVR and the incidence of MACCE after SMVR. MACCE include IHM, acute myocardial infarction and ischemic stroke, as described by Newman et al.  using ICThe analyses were stratified according to the type of valve used  and for each group of patients (T2DM patients and non-T2DM patients). We considered three time periods, which included 5 consecutive years each .The incidence rates of admission for SMVR among T2DM patients and non-T2DM patients per 100,000 inhabitants were estimated using the methods described in previous studies [2 test and ANOVA, as appropriate.A descriptive statistical analysis was performed for categorical and continuous variables. Variables are expressed as proportions and as means with standard deviations. Bivariable analysis was performed using the χTo identify variables associated with MACCE, we constructed six multivariable logistic regression models, one for each type of SMVR  and for each group of patients . The variables included in the models were those with significant results in the bivariable analysis and those considered relevant in other investigations. Estimates were reported as odds ratios (ORs) with their 95% CIs.To avoid the confounding effect of CABG and other concomitant heart valve surgery on our main study outcome variables (MACCE and IHM) we conducted a sensitivity analysis. To do so we excluded those T2DM who had undergone a CABG or any other concomitant heart valve surgery (isolated SMVR) and used a propensity score matching (PSM) to select a matched non-T2DM for each T2DM patient. PSM was done using multivariable logistic regression for mechanical and bioprosthetic SMVR patients separately. The variables included to estimate the PSM were year of surgery, age and all the comorbidities analyzed . Statistical significance was set at p < 0.05 (2-tailed).The study maintained data confidentiality at all times. Given the anonymous and mandatory nature of the database, it was not necessary to obtain informed consent or approval by an ethics committee in accordance with Spanish legislation.In our study, we identified a total of 42,937 hospitalizations of patients aged 40 years or older who underwent SMVR in Spain (2001–2015). We identified 35,904 (83.62%) hospitalized patients who underwent mechanical SMVR and 7033 (16.37%) who underwent bioprosthetic SMVR.The proportion of T2DM patients receiving a mechanical valve decreased from 88.2% in 2001–2005 to 73.6% in 2011–2015 (p < 0.001). Equivalent figures for non-T2DM patients were from 90.5 to 77.7% (p < 0.001).The incidence of mechanical SMVR among T2DM patients remained stable from 2001 to 2015, with values oscillating between 14.26 and 16.45 cases per 100,000 individuals suffering from T2DM. However, among non-T2DM patients, the incidence decreased significantly from 12.5 cases per 100,000 in 2001 to 9.18 cases per 100,000 in 2015 (p < 0.001) .In patients who underwent mechanical SMVR, there was a significant female predominance (65.31% for T2DM and 61.06% for non-T2DM patients). Overall, patients with T2DM were older  than patients without diabetes  and were more likely to undergo simultaneous CABG (16.93% vs. 10.02%) and pacemaker implantation (5.34% vs. 4.07%) . The proportion of female patients decreased significantly in both groups of patients, from 69.94% in 2001–2005 to 60.94% in 2011–2015 among T2DM patients and from 62.05% in 2001–2005 to 59.67% in 2011–2015 among non-T2DM patients.We found a significant increase in the frequency of SAVR, other valve procedures on pulmonary or tricuspid valves, and pacemaker implantation in patients with and without T2DM over time. However, blood transfusion decreased in both group of patients during the study period Table .The mean LOHS for patients with and without diabetes undergoing mechanical SMVR was 24.61 and 22.76 days, respectively, in the period from 2001 to 2005, decreasing to 20.32 and 20.38 days, respectively, in 2013–2015 . The overall mean LOHS was significantly higher in patients with T2DM (22.75 vs. 21.95 days).In T2DM patients with mechanical SMVR, IHM and MACCE decreased significantly  over time from 13.48 and 17.74%, respectively, in 2001–2005 to 8.85% and 12.18%, respectively, in 2011–2015. Among patients without T2DM, IHM and MACCE also decreased significantly over time (p < 0.001). No differences were found between the two groups of patients for the overall IHM and MACCE  proportion of non-T2DM patients (9.88%). The most common clinical presentation for both groups is mitral stenosis with insufficiency coded in around 16% of patients , pulmonary hypertension (30.78%) and coronary artery disease (26.63%)  . Significant increases, from 11.8 to 26.4% and from 9.5 to 22.3%, were observed for T2DM patients and for non-T2DM patients, respectively .Additional file The Poisson regression models yielded an adjusted IRR for patients with T2DM who underwent bioprosthetic valve replacement of 1.92 (95% CI 1.82–2.05). Therefore, compared to patients without diabetes, the incidence of bioprosthetic SMVR hospitalizations was almost twice as high among T2DM patients.The mean age increased significantly (p < 0.001) in both group of patients (in T2DM patients from 72.45 years in 2001–2005 to 74.29 in 2011–2015 and in non-T2DM patients from 72.14 to 74.42 years). We found a significant increase in the frequency of other valve procedures on pulmonary or tricuspid valves and blood transfusion in patients with and without T2DM patients over time Table .Table 3IThe use of SAVR increased significantly in non-T2DM patients . In T2DM patients, we found a reduction in the use of intra-aortic balloon counter-pulsation over time . Over the entire period, T2DM patients were more likely to receive concomitant CABG than non-T2DM patients   . 18.22%, of T2DM and 19.64%, of non-T2DM suffered a MACCE over the study period (p = 0.185).In non-T2DM patients, IHM and MACCE decreased significantly over time (p < 0.001). However, no significant changes in frequency were observed in IHM  and in MACCE  in T2DM patients who received a bioprosthetic valve from 2001 to 2015 , coronary artery disease 37.14%) and pulmonary hypertension (30.42%) were the most common comorbidities for T2DM patients who underwent bioprosthetic valve replacement. Overall, compared to non-T2DM patients, T2DM patients had higher rates of coronary artery disease (37.12% vs. 26.7%), obesity (12.05% vs. 4.98%) and renal disease (12.66% vs. 9.25%) .For patients with T2DM the prevalence of isolated mitral stenosis (p < 0.001) and mitral stenosis with insufficiency (p < 0.001) is higher among those who underwent a mechanical than a bioprosthetic valve replacement.T2DM patients who received bioprosthetic versus mechanical valves had more comorbidities  and required more concomitant CABG , intra-aortic balloon counter-pulsation , pacemaker implantation  and blood transfusion .Among T2DM patients, those who received bioprosthetic valves had higher IHM  and a higher rate of MACCE  than T2DM patients with mechanical SMVR .Shown in Additional file In the past 15 years, there has been an increase in the number of T2DM patients who underwent SMVR in Spain. In our investigation, the prevalence of T2DM among patients who underwent SMVR increased from 13.4 to 16.06% over the study period. Myllykangas et al.  describeWe found that, from 2001 to 2015, the incidence rates of hospitalization to undergo a SMVR were higher among the Spanish population suffering T2DM than the incidence rates for this procedure among the non-T2DM population. These higher incidence rates of hospitalizations were observed for either mechanical or bioprosthetic SMVR. This finding could be due to several factors, such as advanced age and a high index of comorbidities, leading to an increased risk of hospitalization for patients with T2DM and SMVR . In addiAmong T2DM patients, we found an increase in the rate of implanted bioprosthetic valves from 2001 to 2015 meanwhile the trend in the use of mechanical valves remained stable. These findings are similar to those reported by the Society of Thoracic Surgeons Adult Cardiac Surgery Database, where the election of mechanical valves has decreased from 68 to 37% during the last decade . This trIn our opinion factors such as the greater use of bioprosthetic SMVR, increased number of patients who undergo mitral valve plasty/repair (as will be commented in the limitations section) and lastly, technological advances, like the transcatheter mitral valve procedure (TMVP), that have provided new alternatives to surgery in selected patients, could explain the stable trend in the use of mechanical valves from 2001 to 2015 among T2DM patients in our country.As in patients who underwent SAVR, age in SMVR plays an important role in valve type selection for patients with T2DM , 18, witIHM following mitral valve replacement is high . We founThe IHM after SMVR found in our investigation is high when compared to studies from other countries , 15, 19.Our results showed that T2DM patients have rates of mortality and MACCE after mechanical and bioprosthetic SMVR which are not significantly different to those found among non-diabetic patients. This conclusion was confirmed after sensitivity analysis using PSM. One explanation may be that patients who undergo SMVR in the current era have high-risk features, including a significant prevalence of comorbid conditions   and not In our study, SMVR in T2DM patients with a mechanical valve, compared to those with a bioprosthetic valve, was associated with lower IHM and MACCE. In the general population, the choice of biologic or mechanical prosthesis does not significantly affect long-term patient survival after mitral valve replacement . HoweverOlder age and more comorbidities are factors that increased MACCE in patients with T2DM regardless of the type of valve implanted. These findings are similar to those reported in other studies  and in tDiabetes is a predictor of long-term mortality for patients undergoing SMVR  and in tWe found that factors associated with a lower risk of dying or suffering MACCE after SMVR included atrial fibrillation, pacemaker implantation, pulmonary hypertension and obesity. The protective effect of atrial fibrillation and pacemaker implantation has been previously found in T2DM patients undergoing surgical aortic valve replacement (SAVR) and atrial fibrillation among patients suffering COPD with SAVR , 25. In Regarding obesity, several but not all, authors have reported the existence of the “obesity paradox” after cardiac surgery, finding that overweight and obese patients had lower mortality and adverse perioperative outcomes compared with normal weight, underweight, and morbidly obese patients –29.There are some points that should be taken into consideration when interpreting the results of the present study.In our investigation we analyzed the use of SMVR but not of those patients who underwent mitral valve plasty/repair that is nowadays a common operation in many centers. Therefore the influence of the use of valve plasty/repair in our results is not evaluated. As can be seen in Additional file We used the SNHDD, an administrative database rather than a clinical database . TherefoUnfortunately in the SNHDD among 35,151 patients who received mechanical SMVR only 11,038 (30.74%) had information regarding the etiology of the mitral valve disease and presentation . For those with bioprosthetic SMVR this proportion was even lower 17.86% (1256/7033). The lack of this information makes impossible to analyze these relevant variables and constitutes an important limitation of our investigation.Despite these limitations, the quality and validity of our dataset has been assessed and has been found to be useful for health research . FurtherIn Spain from 2001 to 2015, the incidence rates of hospitalization to undergo mechanical or bioprosthetic SMVR were higher among the population suffering T2DM than among the non-T2DM population. In both groups of patients the use of bioprosthetic SMVR increased over time and the use of mechanical valves remained stable in T2DM. T2DM patients have IHM and MACCE after mechanical and bioprosthetic SMVR which are not significantly different to those found among non-diabetic patients. Among T2DM patients, the crude IHM was significantly higher in those who received a bioprosthetic SMVR than those with mechanical SMVR.Additional file 1: Table S1. Diagnosis and procedures analyzed with their corresponding ICD-9-CM codes.Additional file 2: Figure S1. Incidence of mechanical mitral valve replacement among type 2 diabetes and non-type 2 diabetes patients in Spain 2001–2015.Additional file 3: Figure S2. Incidence of bioprosthetic mitral valve replacement among type 2 diabetes and non-type 2 diabetes patients in Spain 2001–2015.Additional file 4: Table S2. Distribution according to study variables of propensity score–matched T2DM and non-T2DM patients who underwent a mechanical surgical mitral valve replacement.Additional file 5: Table S3. Distribution according to study variables of propensity score–matched T2DM and non-T2DM patients who underwent a bioprosthetic surgical mitral valve replacement.Additional file 6: Table S4. Mitral valve plasty/repair hospitalizations in Spain from 2001 to 2015.


To evaluate the association of body mass index (BMI) and albumin withpulmonary function in cystic fibrosis (CF) pediatric subjects. This is a cross-sectional study with clinically stable CF’s subjects.Clinical (pulmonary function) and nutritional evaluation  were performed. Univariate analysis was performed using simplelinear correlations. Regression analysis was performed using an exit levelof p<0.05.1%. An albumin increase of 0.1 mg was associated with2.7% increase in predicted FEV1%, and one year increase in age wasassociated with reduction in 1.2% of predicted FEV1%. Seventy-eight CF’s subjects (mean age 12.8±3.8 years) with mean albumin4.2±0.4 mg/dL, predicted forced expiratory volume in 1 second (FEV1%)80.8±22.6 and BMI median percentile 51.2 (1.3-97.7). In the multipleregression models, albumin, age and BMI percentile were associated withpulmonary function. Subjects with lower than 25 BMI percentile had 12.2%lower FEV BMI percentile, albumin and age were independently associated with predictedFEV1% in a tertiary referral hospital. Forced expiratory volume in 1second  percentiles at the 50th percentile andhigher. A previous study demonstrated that weigh-for-age ≥10th percentile at agewere associated with higher survival at 18 years.Association of lung function with nutritional status has long been recognized.Several studies have shown that weight gain leads to an improved pulmonary function,whereas weight loss can accelerate pulmonary function decline.Pseudomonas aeruginosa in respiratory tract hasbeen previously reported to be associated with faster decline in lung function in CFsubjects. Studies showed a significantly association of chronic Pseudomonasaeruginosa infection with lower FEV1%.,,,Presence of chronic The pathophysiology of CF is one of increased inflammation-related oxidative stress,particularly during exacerbations.In a previous study, Khatri et al.The objective of this study was to evaluate the association of BMI and albumin withpulmonary function in CF pediatric subjects.This was a cross-sectional study. Subjects were consecutively recruited from thepediatric CF outpatient clinic at the Hospital de Clínicas de Porto Alegre, RioGrande do Sul, Brazil. Subjects were eligible if they had a confirmed CF diagnosisby at least two abnormal sweat chloride test results. Subjects experiencing apulmonary exacerbation in the last two weeks were excluded. The study was approvedby the Human Research Ethics Committee (Protocol no. 09-429). Demographic, clinical,nutritional and biochemical data were obtained from patient medical records.Staphylococcus aureus,methicillin-resistant Staphylococcus aureus - MRSA,Pseudomonas aeruginosa, mucoid Pseudomonasaeruginosa and Burkholderia cepacia complex), plasmaalbumin levels, and FEV1% predicted were obtained from subject charts,and all the variables were acquired at the moment of the patient check-ups.All participants underwent a clinical and nutritional evaluation. Clinical anddemographic data such as age, age at diagnosis, pancreatic insufficiency (denoted byuse of enzyme replacement therapy), genetic mutation analysis, presence or absenceof bacterial colonization , using the Zapletal table for the expected values. Spirometrywas always performed by the same examiner and its quality was checked by theattending physician by analyzing the curves. Spirometry was done according to theguidelines for Pulmonary Function testing 2002.1% was chosen for analysis, since it is the most widely usedparameter in the literature to quantify the obstructive ventilatory damagecharacteristic of CF.A flow-volume curve was performed in the Master Screen JaegerNutrition data (weight and height measurement) were analyzed as percentiles accordingto the World Health Organization (WHO) equations .The cut-off point established to assess the relationship between nutritional statusand lung function was the 25th percentile, according to the study of Konstan etal.1% and albumin, according to previous data from Simon et al.Sample size was calculated considering r=0.42 in the association betweenFEV1% as the main outcome, usinganthropometric, biochemical and clinical data as independent variables. Univariateanalysis was performed using simple linear correlations. Where a significantrelationship was identified (p<0.10), a stepwise multiple linear regression wasdone using FEV1% as the dependent variable and significantly associatedanthropometric and biochemical data as the independent variables, controlling forage and sex. Regression analysis was performed using the exit level of p<0.05.All analyses were done using Statistical Package for the Social Sciences (SPSS) forWindows version 18.0 .Descriptive statistics were used to describe subject characteristics. We firstconstructed regression models for FEV1% predicted was80.8±22.6, the BMI median percentile was 51.2 (1.3-97.7). Additional clinical dataand other characteristics are provided in 1% as the main outcome are shown in Seventy-eight CF pediatric subjects participated in the study; 50% were female. Themean age of participants was 12.8±3.8 years old, the median of age at diagnosis was2.1 years old. Mean albumin was 4.2±0.4 mg/dL, and FEV1%. An albumin increase of 0.1 mg was associated with 2.7%increase in FEV1% predicted, and one year increase in age was associatedwith reduction in 1.2% of FEV1% predicted.In the multiple regression models, albumin, age and BMI percentile were associatedwith pulmonary function. Subjects with lower than 25 BMI percentile had 12.2% lowerFEV1% has a direct association with BMI lowerthan 25th percentile, with age, and with albumin levels, underscoring the hypothesisthat good nutritional status and albumin levels are important in CF. The regressionmodel was able to explain approximately 40% of FEV1% variability.This study demonstrates that FEV1% with BMI percentile is well recognized.,,,1%. Stallings et al.1%status below 90% predicted in subjects aged 6 to 12 years old, and below 80% insubjects aged 13 to 20. In our previous study, we observed that subjects with a BMIbelow the 10th percentile had 25.58% lower FEV1%.Association of FEV1%predicted.In CF, lung function decreases with time, and it is thought that even a small declineof 1-2% per year is deleterious in the life expectancy of the subjects.1% predicted, even having most of the subjectsa normal albumin level. In our previous study, we found that plasma albumin levelslower than or equal to 4.1 mg/dL predicted 18.6% fall in FEV1%.1% of 60% with good sensitivity, specificity andaccuracy. Therefore, we hypothesized that albumin is an indicator of inflammatoryprocess, and for this reason it is related with poor pulmonary function.1%predicted among asthmatic subjects .The present study data demonstrate that an albumin increase of 0.1 mg was associatedwith 2.72% increase in FEVPseudomonas aeruginosa infection wasnot significantly associated with decreased lung function. These results could beexplained by chronic Pseudomonas aeruginosa infection and age beingcovariates and, for this reason, loosing statistical power. However, otherstudies,Pseudomonas aeruginosaand lower lung function.In the current study, chronic One of the limitations of our study was the cross-sectional design, so findings donot necessary reflect causality.1% predicted in a tertiary referralhospital. The results emphasize the relevance of evaluate the association betweenalbumin levels and lung function in CF.In conclusion, in pediatric CF’s subjects BMI percentile, albumin and age wereindependently associated with FEV


In marine snow, DMSP concentrations were up to four times higher than in the ambient water and three times higher in marine snow in summer than in winter. The average dmdA:recA gene ratio over the sampling period was 0.40 ± 0.24 in marine snow and 0.48 ± 0.21 in the ambient water. However, at the subclade level, differences in the demethylating bacterial community of marine snow and the ambient water were apparent. Seasonal patterns of potentially demethylating bacteria were best visible at the oligotype level. In the ambient water, the SAR116 and the OM60/NOR5 clade were composed of oligotypes that correlated to high DMSP concentrations, while oligotypes of the Rhodospirillales correlated to low DMSP concentrations. Our results revealed a pronounced seasonal variability and spatial heterogeneity in DMSP concentrations and the associated demethylating bacterial community.The extent of DMSP demethylation has been hypothesized to depend on DMSP availability and bacterial sulfur demand, which might lead to niche differentiation of the demethylating bacterial community. In this study, we determined DMSP concentrations in marine snow and the ambient water over a seasonal cycle and linked DMSP concentrations to the abundance of bacteria harbouring the demethylation  DMSP acet al., et al., In this study, we analysed the spatial and temporal distribution patterns of the demethylating bacterial community and determined DMSP concentrations in marine snow (MS) and the ambient water (AW) over a seasonal cycle. We expected substantially higher DMSP concentrations in MS than in the AW as MS originates mainly from transparent exopolymeric particles (TEPs) released by phytoplankton into the ambient water where they randomly collide  due to a diatom bloom (8.7 × 108 cells m−2) and high abundance of coccolithophores (1.6 × 108 cells m−2)  and 19 July (1.4 × 108 cells m−2). Silicoflagellates were the least abundant group  were present during the seasonal cycle  than in AW (33.62 ± 12.57 nmol L−1) with the monthly averaged enrichment factor (EF) ranging from 2 to 4  concentrations averaged over all samples were significantly higher in marine snow (MS)  contribution to the total demethylating bacterial community  for the dmdA subclades A/2, B/3, B/4, D/3 and E/2 were observed in both, the AW and MS. Clade A primers target Rhodospirillales and Roseobacter. Roseobacter was present only in MS and accounting for 0.1% of the OTUs there, hence subclade A/2 was mostly represented by Rhodospirillales. The SAR11 clade (with members representative for dmdA subclade D/3) amounted in total to 4.6% in MS and 10.6% in AW, Rhodospirillales (with members representing the subclade A/2) contributed 3.5% to MS and 6.2% to AW, the SAR116 clade (with members representative for subclades B/3 and B/4) constituted 3.4% to MS and 5.7% to AW, and the OM60 clade (with members representative for subclade E/2) contributed 2.5% in MS and 3.3% in AW  and over the seasons, with only few exceptions, the composition of the demethylating bacterial community varied considerably between MS and AW Fig. A and B. yle Fig. A and B. AW Table . This is, et al.  indicatidmdA subclade level for most subclades   was less obvious using oligotyping. This stresses the finding of the particle‐association lifestyle as a deeply rooted trait in the phylogenetic tree  are shown in brown, SAR11 oligotypes (subclade B/4) in turquois and OM60 oligotypes (subclade E/2) are shown in purple. Correlation coefficient (r) and p values are indicated, correlation plots with p values ≤0.05 are in bold.Click here for additional data file.Figure S3: Box‐plot of leucine incorporation into heterotrophic microbes  in marine snow (grey) and ambient water (white).Click here for additional data file.Figure S4: Box‐plot of prokaryotic abundance  in marine snow (grey) and ambient water (white).Click here for additional data file.Figure S5: Map of sampling area in the northern Adriatic Sea. The diamond marks the location of station RV001 where MS, AW and phytoplankton samples were collected seasonally.Click here for additional data file.Table S1: Excel fileClick here for additional data file.Table S2: Primer pairs used for amplification of recA, 16S rRNA gene and five specific dmdA subclades. Annealing temperatures for PCR and qPCR, and efficiency of the qPCR are indicated.Table S3: Characteristics of the NGS dataset. Singletons and reads that appeared in the negative control were subtracted from the presented values. Percent of reads indicates the percentage of reads remaining after quality filtering as compared to the original reads in each sample. The asterisk indicates that the sample was removed from the analysis due to low number or percentage of reads after quality filtering, as indicated in the Methods section.Table S4: Diversity indices of the NGS dataset.Click here for additional data file.Video S1: Supplementary video.Click here for additional data file.