The consumption of substances is a common practice among the medical students and their use might reduce educational and clinical performances as well as judgment of the students. This study aims to find out the prevalence of substance use among medical students in BP Koirala Institute of Health Sciences.th September 15th to December 15th 2018 after obtaining ethical approval from the Institutional Review Committee (Ref: 1394/017). The study was conducted among 326 medical and dental students from first to fifth year by using the stratified sampling method. A self-reported questionnaire was developed which included types, frequency, duration, age to first use, and motives for the use of different substances. Data was analyzed using Statistical Package for Social Sciences version 11.5.A descriptive cross-sectional study was carried out among medical students of BP Koirala Institute of Health Sciences from 15The prevalence of substance use among medical students of BP Koirala Institute of Health Sciences was 196 (61.4%) at 95% Confidence Interval (56.05-66.75%). Among substance use, the use of alcohol 190 (59.6%), tobacco 90 (28.2%), and marijuana 38 (11.9%) was more prevalent. One hundred forty four (45.2%) male students used more substances as compared to 52 (16.3%) female students. Fun sake or partying 131 (68.9%) was the main motivation of the students to use substances.The overall substance use among medical students was high compared to other studies. Alcohol was the most common substance misused by the student followed by tobacco and marijuana. Proper counseling and awareness programs about the potential risk of substances are recommended for the betterment of the students. Medical school life is an important transition period of life where students may start to consume alcohol, tobacco, and other substances. The use of these substances may cause several physical, psychological, emotional, and social problems in medical students.2 The importance of studies like this one is more as we lack the cumulative evidence related to substance use.The use of addictive substances might cause psychiatric morbidity, cognitive and functional impairments, and behavioral changes that ultimately affect student health, wealth, and education. Medical students have a constant burden of academic and clinical activities and have easy access to drugs that might influence the students to misuse the substances.The study aims to find out the prevalence of substance use among medical students in BP Koirala Institute of Health Sciences.th to December 15th, 2018. Ethical approval was obtained from the Institutional Review Committee of BPKIHS (IRC/1394/017). Prior to the study, students were informed about the objectives of the study and written consent was obtained. Inclusion criteria constituted all the students from the first year to the final year of both medical and dental disciplines. Students who did not agree to participate in the study or failed to give informed consent were excluded from the study. Confidentiality of the participants was maintained.A descriptive cross-sectional study was conducted at BP Koirala Institute of Health Sciences (BPKIHS), from September 15The sample size was calculated by using the following formula,2 × p × (1-p)/n = Z2 × 0.425 (1-0.425)/(0.05)2   = (1.96)   = 375.52   ≈ 376n = sample sizeZ = 1.96 at 95% Confidence Interval3p = prevalence of substance use in BPKIHS, 42.5%.e = margin of error, 5%Using the formula for finite population correction, s= n/(1+n/N)The corrected sample size of the source population of 853 students was 376/(1+376/853) = 260.97        ≈ 261.where,After adding 20% non-response rate, the final sample size was 314. It was 36.8% of the total population. By using a stratified sampling technique, an equal proportion of male and female (36.8% each) students were selected by using the lottery method from both medical and dental disciplines, separately. However, the study was done among 326 students.4 Data were analyzed using SPSS version 11.5. Descriptive parameters such as frequency, mean, standard deviation, and percent were calculated and presented in table and graph.A self-prepared semi-structured questionnaire was used to conduct this survey, which was finalized after pre-testing on 30 students from different disciplines. The questionnaire mainly focused on the types, frequency, and duration, age to the first start, and motives for the use of different substances. It also included whether they started before or after joining the medical school and their use was increased or decreased after joining the BPKIHS. Those students who have used substances within 30 days were categorized as current substance users, and included in the study.The prevalence of substance use among medical students in BP Koirala Institute of Health Sciences was 196 (61.4%) at 95% C.I. (56.05-66.75%). Alcohol 190 (59.6%) was the most common substance used by the students followed by tobacco 90 (28.2%), marijuana 38 (11.9%) and diazepam 3 (0.9%). Among 196 students, 144 (76.6%) male students used various substances as compared to 52 (26.5%) female students. Hence, the use of alcohol, tobacco and marijuana was more prevalent among male students .The majority of the students belonged to the medical background 217 (68%) while there were 102 (32%) students from dental background. The age of the participants ranged from 17-28 years (mean age of 21.76±1.81) among them majority have belonged to age group 20-25 years 281 (88.1%) .Whisky 106 (55.8%), Wine 86 (55.3%), Beer 69 (36.3%), Cigarettes 76 (84.4%) and Marijuana 38 (92.7%) were the common substances used by the students .4 Similarly, study by Khanal, et al. found 60.3% medical students had used various substances. Among them alcohol (57.6%), tobacco (27.6%), and cannabis (12.8%) were commonly used.2 These findings are well consistent with our survey in which the use of alcohol, tobacco, and marijuana was 59.6%, 28.2%, and 11.9% respectively. These patterns of the use of alcohol and tobacco might reflect the substance use pattern of Nepalese society and is recognized as a social sanction.2 However, the consumption of these substances is largely influenced by socio-demographic factors, education level, peer pressure, independent lifestyle, stressful activities, psychological factors and many more.4The overall prevalence of substance use among the medical and dental students of BPKIHS was 61.4%. A study conducted by Budhathoki, et al. on third-year medical students in Kathmandu valley found the overall prevalence of substance use to be 49.6%.3 Khanal et al. study also depicted that the use of alcohol (70.6%), tobacco (39.2%), cannabis (20.8%) was more in males as compared to females.2 Similarly, Pitanupong, et al. study in Thailand showed 60% males and 48.1% females had drank alcohol.5 This male predominant use of substances was mainly due to our social structure that provides more freedom to male, and the male has a tendency to take more risk.Our study showed the use of alcohol (75%), tobacco (40.9%) and marijuana (18.6%) was more prevalent among male students as compared to females. Shyangwa et al. study in BPKIHS found 54.6% of male students drank alcohol.6 Similarly, Shyangwa et al. found junior residents were the highest (72%) user of alcohol and lowest being undergraduate medical students (31.7%).3It is well reported that the progression of the educational ladder may influence the use of various substances. In our survey, the final year students used more substances as compared to first-year students. Medical students used more substances than dental students. Other studies also shown the students from different disciplines were used substances in various degrees. Panthee et al. found that the use of marijuana was common in pharmacy (13%) and public health students (10.6%) than nursing students (1.3%).2A medical school is a vulnerable place where students may start to misuse various substances. Medical students have a burden full academic and clinical activity, easy access to substances that may catalyze the students to consume substances. In our study, 52.6% alcohol users, 56.6% tobacco users, and 82.9% marijuana users were stated to use these substances after joining the BPKIHS and their use of these substances was increased during the study period. Even, 61.1% of tobacco users consumed tobacco on a daily basis. One to four-time uses of alcohol and marijuana was 53.2% and 48.7% respectively. Khanal et al. also found 17.2% of medical students were stated to use substances after joining the medical school.7Our survey showed that the main motives to use alcohol was fun sake or consumed during partying (68.9%), tobacco was to give pleasure and reduced stress or tiredness (64.4%) and the marijuana was friend encouragement to do it (65.9%). However, other studies showed the experimentation or curiosity followed by giving pleasure, stress relief, peer pressure were the main reasons for the use of substances.This study might help in education, prevention, screening, and policymaking related to the consequences of the substance use for the betterment of the students and the medical school environment. Although this study has several strengths, including the investigation of the motives and patterns of the use of substances, some limitations should be noted. The data of this study were based on the past 30-day’s memory, self-reported and drug-related, which may produce recall and response biases. The samples were medical and dental students of a medical college, so the finding of this study cannot be generalized to all Nepalese populations. A further study needs to be done to explore the amount and other factors that influence the use of substances.The overall substance use among undergraduate students was high. Alcohol was the most common substance misused by the student followed by tobacco and marijuana. Most of the students start their consumption of substances after joining medical school. Partying or fun sake, reduced stress or tiredness and friend encouragement were the motives for the use of the substances. Preventive measures such as proper counseling and awareness programs about the potential risk of substance use are further recommended for the betterment of the students.


Neonatal hypothermia is the reduction in the body temperature of the newborn (less than 36.5°C). It is a global problem in neonates born both at hospitals and homes, but it showed a higher prevalence in developing countries (>90%). Although hypothermia is rarely a direct cause of death, it contributes to a substantial proportion of neonatal mortality globally.  To assess neonatal hypothermia and associated factors among newborns admitted in the NICU of Dessie Referral Hospital. p value < 0.05 was used to identify variables that had a significant association with neonatal hypothermia.  An institution-based cross-sectional study was conducted from March 15 to May 30, 2018. The data was collected from the mother and the chart of the newborn using a semistructured questionnaire. Data were cleaned, coded, and entered in EPI-info version 7.1.2.0 then exported to Statistical Package for Social Sciences (SPSS) version 20 software for analysis. Descriptive statistics were used to summarize the data. Bivariate and multivariate logistic regression and crude and adjusted odds ratio with their 95% confidence interval were computed. Finally,  The proportion of neonatal hypothermia in the study area was 66.8%. Preterm delivery , no skin-to-skin contact within 1 hour of delivery , delivered at night time , and neonates who had resuscitation  showed significant association with neonatal hypothermia.  In this study, the proportion of hypothermia was high. Preterm delivery, no skin-to-skin contact within 1 hour, night-time delivery, and having resuscitation were significantly associated with neonatal hypothermia. Therefore, special attention is needed for the thermal care of preterm neonates and neonates delivered at night time. Furthermore, there should be strict adherence to cost-effective thermal care recommendations like warm resuscitation and skin-to-skin contact. Globally, 2.5 million children died in the first months of life in 2017, which accounts for 47% of all under-five child deaths . A high Neonatal hypothermia is a progressive reduction in the axillary temperature of the newborn (temperature < 36.5°C). It is categorized as mild hypothermia (36°C–36.4°C), moderate hypothermia (32°C–35.9°C), and severe hypothermia (<32°C) . HypotheAlthough neonatal hypothermia is a global problem that occurs in neonates born both at hospitals 32%-85%) and homes 11%-92%), it showed higher prevalence in developing countries (>90%) [2%-85% an2%, it shPhysiological, environmental, and behavioral risk factors predispose the newborn infants for neonatal hypothermia . ThermalAs a result, identifying different factors that had an association with neonatal hypothermia is valuable to design an appropriate intervention strategy to decrease neonatal hypothermia as well as to improve the quality of newborn care. So, this study was aimed to assess neonatal hypothermia and associated factors among newborns admitted in the NICU of Dessie Referral Hospital.A hospital-based cross-sectional study design was conducted from March 15 to May 30, 2018, in the NICU of Dessie Referral Hospital. Dessie town is located in South Wollo Zone of Amhara Regional State that is 401 km away from Addis Ababa, the capital city of Ethiopia, and 480 km from Bahir Dar, the capital city of Amhara Regional State. Dessie Referral Hospital deliver health care services for patients coming from different areas, including the Afar region. In 2017, over five thousand mothers gained antenatal, delivery, and postnatal care services, and there were 1868 neonatal admissions in the NICU.All neonates with their mothers admitted in the NICU of Dessie Referral Hospital were the source populations, and the study populations were randomly selected neonates with their mothers admitted in the NICU of Dessie Referral Hospital from March 15 to May 30, 2018.Neonates whose mother was not present during the study period were excluded.p = 0.64) proportion of neonatal hypothermia taken from the study conducted in Addis Ababa University, with a 95% confidence level and 5% margin of error [The sample size was determined using a single population proportion formula with the assumption of 64%  was used to select the study participants based on their admission and registration number.By considering a 10% nonresponse rate of participants, the final sample size was 210. A systematic random sampling technique  with 95% CI was used as a measure of association, and variables that had a p value less than 0.05 in the multivariate logistic regression were considered as a significantly associated variable.All field questionnaires were checked for completeness, consistency, and accuracy. The data were entered into EPI info (version 7.1.2.0) then exported to SPSS  for data analysis. Descriptive statistics  were used to summarize the data. Bivariate logistic regression was used to assess the association of independent variables with the outcome variable. Variables found to have Hypothermia: an axillary temperature of less than 36.5°C.Mild hypothermia (cold stress): an axillary temperature of 36.0°C-36.4°C.Moderate hypothermia: an axillary temperature of 32.0°C to 35.9°C.Severe hypothermia: an axillary temperature of <32.0°C.Nonhypothermic: an axillary temperature of ≥36.5°C.Neonate: an infant under 28 days of age.Admission temperature: the first temperature obtained from the neonate at admission in the NICU.Inborn: a newborn delivered from the study hospital .Outborn: a newborn delivered other than the study hospital.A total of 202 neonates with their mother admitted in the NICU were included in the study, resulting in a 96.2% response rate. The majority (72.3%) of mothers were aged 20-29 years (mean age and SD = 25.88 ± 4.86 years). One hundred fifty-four (76.2%) and 65 (32.2%) respondents were from urban residents and attained primary education, respectively. Seventy-five (37.1%) were housewives in occupation and one hundred eighteen (58.4%) respondents had monthly income ≥ 2500 ETB (mean and SD = 2944 ± 1731 ETB) .Out of 202 neonates, 111 (55%) were male, and 146 (72.3%) had gestational age ≥ 37 weeks (mean and SD = 37.4 ± 2.5 weeks) at delivery. Most of the neonates 124 (61.4%) had birth weight ≥ 2500 g (mean and SD = 2580.9 g ± 732.8 gram), and 44 (21.8%) neonates received CPR. One hundred sixty-one (79.7%) had skin-to-skin contact immediately after birth, and 175 (86.6%) had late initiation of breastfeeding .The majority of the pregnancies 192 (95%) were a singleton, 116 (57.4%) of mothers were primiparous, and 126 (62.4%) neonates were delivered in SVD. Most of the participants 179 (88.6%) had no obstetric complications, and 118 (58.4%) mothers delivered during night time. Nearly all neonates 199 (98.5%) were admitted in the NICU at a room temperature of ≥25°C (mean and SD = 27.2 ± 1.3°C) .Out of the total neonates, 135 (66.8%) were hypothermic at the time of admission in the NICU, and the mean and SD of axillary temperature were 35.8 ± 1.3°C. From those hypothermic neonates, the majority (53.3%) had moderate degree hypothermia   .p value of <0.2 in the bivariate analysis, were further analyzed using multivariate logistic regression. The result of this analysis showed that preterm delivery, no skin-to-skin contact with their mother immediately after birth, time of delivery, and undergoing CPR procedure were significantly associated variables.Variables, which had a p = 0.03, 95% CI: 1.1-6.2). Neonates who had no skin-to-skin contact with their mother immediately after delivery were 3.1 times more likely to be hypothermic when compared to those who have skin-to-skin contact . Similarly, neonates delivered at night time were 2 times more likely to have hypothermia compared with neonates delivered at day time . Newborns who had resuscitation at birth (CPR) were 2.9 times more likely to be hypothermic when compared with those who had no resuscitation   .In this study, the proportion of hypothermia was 66.8% (95%CI = 60.4%–73.7%). The finding of this study was comparable with studies conducted in Nigeria (62% and 67.6%), Gondar teaching and referral hospital (68.9%), and governmental hospitals in Addis Ababa (64%) , 15, 17.In this study, gestational age was significantly associated with neonatal hypothermia. The odds of neonatal hypothermia were 2.6 times higher in preterm than term neonates. The possible reason for this finding might be preterm neonates have a large surface area-to-body mass, minimal subcutaneous fat stores, poor clinical status, low tone, and limited capacity to generate heat from fat stores . This fiNeonates who had no skin-to-skin contact within 1 hour after delivery had a 3.1 times higher odds of hypothermia compared to those who had skin-to-skin contact. Skin-to-skin contact enables the newborn to achieve and maintain thermal control, better temperature gain, and lesser morbidity , 26. ThiDelivery time was the other variable that showed a significant association with neonatal hypothermia. Neonates delivered at night time were 2 times more likely to develop hypothermia than neonates delivered at day time. These will be due to the temperature difference at night and day time. When there is no provision of added warmth during cold nights, the newborn infant is at risk of becoming hypothermic . AdditioAnother variable that showed significant association in this study was neonatal resuscitation. The odds of hypothermia were 2.9 times higher in neonates having CPR than those who had not. Heat loss is a particular problem at resuscitation. Keeping infants sufficiently warm immediately after birth, especially during resuscitation, is difficult , 29. A sThis study was not conducted in multiple health facilities including private hospitals, and it did not incorporate a qualitative method to address cultural and behavioral factors that will affect neonatal hypothermia. The effect of seasonal variations was not included because of a short data collection period.In this study, the proportion of hypothermia was high (66.8%). Being preterm, no skin-to-skin contact with their mother, night-time delivery, and neonatal resuscitation had a significant association with neonatal hypothermia. Therefore, health care providers should have special attention for the thermal care of preterm neonates and newborns delivered at night time. Additionally, there should be strict adherence to the WHO recommendation of thermal care for newborns including warm resuscitation and skin-to-skin contact immediately after delivery.


The water and UV barrier properties of the composite films showed better performance than those of neat CS film. Results suggested that CNC/CS nanocomposite films can be used as a sustainable packaging material in the food industry.In this study, green composite films based on cellulose nanocrystal/chitosan (CNC/CS) were fabricated by solution casting. FTIR, XRD, SEM, and TEM characterizations were conducted to determine the structure and morphology of the prepared films. The addition of only 4 wt.% CNC in the CS film improved the tensile strength and Young’s modulus by up to 39% and 78%, respectively. Depending on CNC content, the moisture absorption decreased by 34.1–24.2% and the water solubility decreased by 35.7–26.5% for the composite films compared with neat CS film. The water vapor permeation decreased from 3.83 × 10 Petrochemical-based polymeric materials are extensively used in various applications ranging from packaging materials to aircraft components. The broad application of these materials had led to severe environmental challenges due to improper disposal in the environment. In this regard, researchers have focused on biodegradable sustainable packaging materials from scientific and technical sectors ,2,3. HenN-acetylglucosamine) that can participate in several chemical reactions 76], 21.0 MPa obtaarticles .As presented in The EB values of CS0, CS2, CS4, CS6, and CS8 were 21.8% ± 0.5%, 16.8% ± 0.6%, 9.9% ± 0.4%, 9.2% ± 0.5%, and 8.9% ± 0.3%, respectively . The EB As presented in The food industry is one of the highest packaging disposal creators; as such, obtaining biodegradable packages for foodstuff is an important requirement to avoid environmental problems. Therefore, researchers have focused on using sustainable biomaterials for food packaging. The biodegradability of food material is well studied by Yabannavar et al. . The worThe stability of prepared films in water is presented in CNC-reinforced CS-based sustainable biocomposite films with enhanced properties were successfully obtained by mixing CNC and CS through solution casting. The needle-shaped CNC was successfully synthesized from CMC by acid-hydrolysis treatment. The modification of the chemical structure of CNC/CS composite was analyzed by FTIR and XRD studies, revealing the formation of hydrogen bonds between CNC and CS. FESEM and OM images confirmed a dispersion of CNC throughout the CS matrix without remarkable agglomerations. TGA results indicated that chitosan and its composites exhibited almost same thermal stability. In the presence of CNC in the nanocomposites, water and UV barrier properties were also improved. FWS and EMC were reduced by 27% and 32%, respectively, after incorporation of only 4 wt.% CNC. The aim of improving UV absorption with the presence of CNC in composites was achieved, and the composites evidently showed lower transmittance compared with control CS. The TS and YM of CS film was improved with the incorporation of CNC, whereas EB decreased with increasing brittleness of the composite films. Moreover, the composites with CNC loading showed lower biodegradation percentage than the control CS due to the presence of strong interaction through H-bonding between CS and CNC. Therefore, CNC/CS bionanocomposite film could be candidates for potential applications in the food packaging field.


The first HPV vaccines licensed targeted two HPV types responsible for most cervical cancers. A 9-valent vaccine (9vHPV), targeting 5 additional types, was introduced in 2016 and is currently the only HPV vaccine available in the United States. Previous studies demonstrated high rates of HPV infection in Alaska Native (AN) women. We sought to measure prevalence of high risk HPV types in AN women undergoing colposcopy and to determine those preventable by vaccination.For this cross-sectional study, we recruited women who were undergoing colposcopy for clinical indications at Alaska Native Medical Center to obtain cervical brush biopsy samples. Specimens were shipped to Atlanta, Georgia for DNA extraction, HPV detection, and typing using L1 PCR with type-specific hybridization to detect 37 HPV types.p < 0.001) HPV 16 or 18 were detected in 77% (17/22) with CIN 3, 53% (24/45) with CIN 2, and 36% (67/184) with CIN 1. (p < 0.001).Four hundred eighty eight specimens from 489 women were tested. At least one HPV type was found in 458 (94%) specimens. Of 458 participants who were HPV positive, 332 (72%) had two or more types. At least one type targeted by 9vHPV was detected in 95% of participants with CIN 3 (21/22), 82% with CIN 2 (37/45), and 65% with CIN 1 (119/184). (A substantial proportion of AN women attending colposcopy clinic had evidence of HPV 16/18 infection, as well as other high risk types targeted by 9vHPV. At least one 9vHPV type was detected in 62% of the participants overall, and 95% of participants with CIN3. AN women are expected to benefit from vaccination against HPV 16/18, and will have greater benefit from 9vHPV. Information from this study could be used to develop public health strategies to increase vaccine uptake, or to track HPV genotype prevalence over time. Human papilloma virus (HPV) infection is common among young Alaska women and these infections cause many cases of cervical cancer among Alaska Native people –5. In a The worldwide HPV prevalence in cervical carcinomas is 99.7% ; more thIn a 2000 study cervical cancer specimens from 53 Alaska Native people, 32 Greenland Native people and 34 Danish Caucasians from 1980 to 1989 were analyzed by polymerase chain (PCR) for HPV types 16, 18, 31, 33, and 45 [Vaccines against HPV types 16 and 18 have been in use since 2006 in the United States . The quaIn February 2015 ACIP recommended 9 valent HPV vaccine (9vHPV)  as one of three HPV vaccines that can be used for routine vaccination. 9vHPV is a noninfectious, virus-like particle (VLP) vaccine . SimilarStatewide HPV Vaccine for Alaska Natives: Projects and Evaluations (SHAPE) developed this project with the goal of establishing HPV genotype surveillance among a risk population. We sought to obtain a baseline of HPV prevalence that could be used in future evaluations to determine changes in HPV vaccine-type prevalence after vaccine introduction.A multi-agency Alaskan working group was established in 2006 to develop projects to improve HPV vaccine uptake and evaluate the impact on cervical cancers and other health outcomes. The group called The SHAPE group undertook a cross-sectional study to determine HPV genotypes in AN women undergoing colposcopy for abnormal cervical screening results at ANMC in 2009. ANMC, located in Anchorage, Alaska, is a tertiary care center for the Alaska Native Health Consortium which also serves as the primary and secondary health facility for approximately 140,000 Alaska Native and American Indians people.All non-pregnant Alaska Native Health System beneficiaries 18 years old or older undergoing indicated colposcopy at the ANMC from October 1, 2009 – September 30, 2011 were eligible to participate. The colposcopy was indicated based on the American Society for Colposcopy and Cervical Pathology cervical cancer screening consensus algorithms. Exclusion criteria included age less than 18 or pregnancy. A routine urine pregnancy test was done prior to colposcopy. Basic demographic data, tobacco use, and vaccination status were obtained though the electronic medical record at the time of the colposcopy. There was no information collected for women who refused enrollment.After informed consent, a clinician-collected cervical sample was obtained at the time of pelvic examination. After visualization of the cervical os, the sample was taken with a Digene Cervical Sampler brush . The brush was placed in specimen transport medium  and stored in the ANMC colposcopy refrigerator, then transported on ice to the CDC Arctic Investigations Program in Anchorage, Alaska USA and was frozen. Specimens were batched and shipped on dry ice to CDC in Atlanta, Georgia, USA for extraction, HPV detection, and typing using the Roche linear array test as previously described . SpecimeP-values less than 0.05 were considered statistically significant and all tests were 2-sided. HPV types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68 were considered the 13 high risk types [The de-identified data were analyzed with proportions of patients with HPV types compared using a nonparametric test for trend across ordered groups. sk types . Analysin = 4), then the second specimen was used. One additional woman was excluded because she did not have a cervix. Four hundred and eighty eight specimens were included in the final analysis.Five hundred and twenty specimens from 489 women were collected and tested. In the 31 women who were tested twice, the first study specimen was included, unless no biopsy obtained at that time (Self-reported ethnic groups of the sample included: Inuit/Yupik 238 (49%), Aleut 7315%), Athabascan /Tlingit /Haida 149 (31%), and other 28 (5%). The mean age of the participants was 29.7 years. The largest age group was less than 25 years old, 40%, followed by 25–29 years (25%).  had received any HPV vaccine and only twenty one (4.3%) had completed a three dose series at the time of the colposcopy. No significant difference was found in HPV type distribution or diagnostic category between participants who received any vaccine doses and those who received no vaccine. There was no significant difference in HPV type distribution or diagnostic category between tobacco using and non-tobacco using participants.The most common cytology referral diagnosis was atypical squamous cells of undetermined significance 231 (47.3%), followed by low grade squamous intraepithelial lesion , high grade squamous intraepithelial lesion , atypical squamous cells cannot exclude high-grade squamous intraepithelial lesion , and unknown .An ectocervical biopsy was obtained from 438 (90%) women and an endocervical curettage was performed in 402 (82%). There were 42 (8.6%) loop electrosurgical excision procedure (LEEP) specimens. CIN 3 was found in 22 (4.5%) specimens; CIN 2 , CIN 1 , metaplasia , other , no biopsy , and insufficient .One or more of the 37 HPV types were found in 458 (94%) specimens. Of the 458 participants who were HPV positive, 332 (72%) had two or more types. Among those with any HPV detected, the mean number of HPV types per participant was 2.6. Table  Three huOf the 9vHPV high-risk types, type 16 was found in 127 (26%) of all participants; type 31 in 64 (13%); type 58 in 59 (12%); type 52 in 58 (12%); type 45 in 40 (8%); type 18 in 39 (8%); and type 33 in 15 (3%).  AN women. 76 16%) had 2 types, 19 (4%) 3 types, and 4 (0.8%) 4 types. There was a mean of 0.88 9vHPV types per participant. (Table 6% had 2 There was no significant difference in HPV genotype distribution or diagnostic category by vaccination history or by history of tobacco use. Table .p < 0.001). (Table p < 0.001).One or more of the 9vHPV types was detected in 95% with CIN 3 21/22), 82% with CIN 2 (37/45), and 65% with CIN 1 (119/184)  [The distribution of HPV types in this AN group was similar to the pattern reported in American Indian women in the Northern Plains , 16. In Of the 458 AN participants who were HPV positive, 332 (72%) had two or more types, with an mean of 2.6 HPV types per specimen. This is a similar pattern to previous findings in AN women in 1980–1989 in which multiple types were found in 43% of AN women [This study has some important limitations. The participating women were a convenience sample of all patients presenting to colposcopy clinic. However, the clinical staff estimated they enrolled approximately 95% of eligible patients, minimizing selection bias. Thus, these results are likely generalizable to Alaska Native women with abnormal cytology who receive care at Alaska Native Medical Center. The proportion of women who had received any doses of HPV vaccine series was very low and there was no difference in HPV genotype distribution by vaccine status. So, although this group was not entirely vaccine naïve, these data effectively represent the HPV genotypes in a population with abnormal cervical screening prior to use of HPV vaccine.This study provides data which may help to plan future evaluations of HPV genotype prevalence and vaccine impact among this clinical population. For example, 532 participants 18 years or older would be needed to detect a change from a baseline prevalence of HPV 16/18 of 31 to 25%, using an alpha = 0.05, beta = 0.10. One hundred fifty seven participants 18 years or older would be needed to detect a decreased rate to 20% for HPV 16/18 positive. For comparison, assuming that the HPV type changes would be more rapidly detected in participants < 25 years of age, only 272 participants would be necessary to detect a change from a baseline rate of 34% for HPV 16/18 to 25%; whereas 106 participants < 25 years of age would be needed to detect a decrease of HPV 16/18 from the baseline of 34 to 20%. From these calculations, focusing on younger women would improve efficiency and reduce the impact of the study on clinical services. Consideration could also be given to using recruitment strategies other than continuous enrollment, such as the first week of the month.A substantial proportion of AN women attending colposcopy clinic have HPV 16/18, as well as the other high risk types in 9vHPV - 31/33/45/52/59. While any of the 9vHPV types were detected in 62% of the participants overall, this increased to 95% of participants with CIN3. AN women would be expected to benefit from vaccination against HPV 16/18, and would be expected to have greater benefit from 9vHPV. In 2014, the CDC reported that 81.2% AN adolescent females aged 13–17 years received one dose of HPV vaccine, while only 58.3% received all three HPV vaccine doses as recommended by ACIP at that time [


To describe trends in modifiable and non-modifiable unfavorable factors affecting pregnancy outcomes, over time (years 2004–2017), in women with diabetes of childbearing age from an English primary care perspective.We identified women with diabetes aged 16–45 years from the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) network, an English primary care sentinel database. Repeated annual cross-sectional analyses (2004–2017) assessed the prevalence of unfavorable factors for pregnancy, such as obesity, poor glycaemic control, microalbuminuria, hypertension, use of medications for treating diabetes, and associated comorbidities not recommended for pregnancy.We identified 3,218 women (61.5% with Type 2 diabetes) in 2004 and 6,657 (65.0% with Type 2 diabetes) in 2017. The proportion of women with ideal glycaemic control for conception (HbA1c<6.5%) increased over time, in patients with Type 1 diabetes from 9.0% (7.1%–11.0%) to 19.1% (17.2%–21.1%), and in those with Type 2 diabetes from 27.2% (24.6%–29.9%) to 35.4% (33.6%–37.1%). The proportion of women with Type 2 diabetes prescribed medications different from insulin and metformin rose from 22.3% (20.5%–24.2%) to 27.3% (26.0%–28.6%).In 2017, 14.0% (12.6%–15.4%) of women with Type 1 and 30.7% (29.3%–32.0%) with Type 2 diabetes were prescribed angiotensin-modulating antihypertensives or statins. We captured at least one unfavorable factor for pregnancy in 50.9% (48.8%–52.9%) of women with Type 1 diabetes and 70.7% (69.3%–72.0%) of women with Type 2 diabetes. Only one third of women with Type 1 diabetes  and a quarter of those with Type 2 diabetes  were prescribed hormonal contraception. Contraception was prescribed more frequently to women with unfavorable factors for pregnancy compared to those without, however, the difference was significant only for women with Type 1 diabetes.Despite significant improvements in general diabetes care, the majority of women with Type 1 or Type 2 diabetes have unfavorable, although mostly modifiable, factors for the start of pregnancy. Good diabetes care for women of childbearing age should include taking into consideration a possible pregnancy. Effective pre-pregnancy care for women with Type 1 or Type 2 diabetes reduces adverse pregnancy outcomes , have unIn the United Kingdom, the 2015 National Institute for Health and Care Excellence (NICE) guidelines on diabetes and pregnancy set out how pre-pregnancy care should be integrated into routine diabetes care from puberty onward. Conception should ideally take place when HbA1c is below 48 mmol/mol 6.5%) and be discouraged if above 86 mmol/mol (10%). Folic acid at the dose of 5 mg per day should be prescribed before conception. Several medications commonly prescribed for the treatment of diabetes  and of comorbidities  should be discontinued before conception or replaced as soon as pregnancy is confirmed .5% and b. The AmeWe carried out this study to estimate the prevalence over time of factors that may affect pregnancy outcomes in women with Type 1 or Type 2 diabetes.The UK general practice system is a registration-based system, for every citizen who wishes to access the service is registered with a single practice. Medical records have been computerized since the 1990s with electronic transfer of records between practices  and offeThe Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is a long-established general practice sentinel network , 12, whiWe included in our analysis women aged between 16 and 45 years with a diagnosis of either Type 1 or Type 2 diabetes . We exclDiagnosis and type of diabetes were identified using coded data from the CMR system, using a validated algorithm. Key data were recorded using the Read terminology in UK primary care and identified using an established ontological process , 16.2 and obesity was defined as unfavorable factor when BMI ≥ 30.Demographic variables included in the study were age, ethnicity (self-reported), and socio-economic status. Socioeconomic status was derived from the English index of multiple deprivation (IMD) and divided into quintiles, where 1 is the most and 5 the least deprived quintile. We extracted the latest recorded glycated haemoglobin value (HbA1c) in each year, reported as percent of total haemoglobin according to Diabetes Control Complications Trial (DCCT), but also stating the equivalent International Federation of Clinical Chemists (IFCC) units. Glycaemic control was categorized as follows: ideal HbA1c < 6.5% (48 mmol/mol); good [6.5%–7.5% (48–58 mmol/mol)]; moderate . We reported the presence of microalbuminuria as a single measurement of elevated Urine albumin to creatinine ratio (UACR). Evidence of hypertension was defined as code for hypertension or SBP ≥ 140 mmHg or DBP ≥ 90 mmHg, regardless of whether prescribed antihypertensive medication. Smoking status was categorized as current smoker, ex-smoker and never smoked. BMI was reported in kg/mWe analyzed the use of medications for the treatment of diabetes and associated comorbidities, which are not recommended during pregnancy. For diabetes these included: sulfonylureas, DPP4 inhibitors, meglitinide, SGLT-2 inhibitors, GLP-1 receptor agonists, alpha-glucosidase inhibitors (acarbose), and pioglitazone. We looked at the following medications for comorbidities: ACE inhibitors, angiotensin receptor blockers (ARBs) and statins. We analyzed the prescription of contraception including oral combined and progesterone only preparations, injectable, implantable, and intrauterine device, i.e., all contraceptive methods requiring medical prescription.We carried out yearly repeated cross-sectional analyses from 2004 to 2017. Descriptive statistics were used to summarize the clinical features of the study population . We used Pearson’s Chi-square test to compare proportions, comparisons with p values < 0.001 were considered significant.2; HbA1c ≥ 8.5% (69 mmol/mol); presence of microalbuminuria; diagnosis of hypertension; use of statins, ACE inhibitors or ARBs; use of at least one diabetes medication other than metformin and insulin (for Type 2 diabetes patient only) (We computed a cumulative risk score of unfavorable factors should a pregnancy occur. We included in this score each of the followings: BMI ≥ 30 kg/mnt only) .All statistical analyses were performed using R 3.5.1 statistical software.http://www.hra-decisiontools.org.uk/research/) /Medical Research Council (MRC) “Is my study research?” tool (search/) . The stuThere were 316,461 women of childbearing age registered with RCGP RSC practices in 2004 and 465,898 in 2017 (p < 0.001] (Women with Type 1 diabetes were younger than those with Type 2 diabetes across the years under investigation . Furthermore, women living in the most deprived areas (IMD quintile 1) were more likely to have Type 2 than Type 1 diabetes. The majority of women had their smoking status recorded (in 2017 95.5% in Type 1 diabetes and 97.1% Type 2 diabetes), with the prevalence of current smokers reducing over time  Table 1.p < 0.001), while among women with Type 2 diabetes the proportion having a record of microalbuminuria increased from 22.8% (20.9%–24.6%) to 30.1% (28.7%–3.5%) (p < 0.001).Between 2004 and 2017 in women with Type 1 diabetes, mean HbA1c improved from 8.6% to 8.1% [70 mmol/mol (SD 18) to 65 mmol/mol (SD 19)], and for those with Type 2 diabetes from 7.8% to 7.4% [62 mmol/mol (SD 19) to 57 mmol/mol (SD 19)] Table 2.p < 0.001]. The prevalence of women with evidence of hypertension decreased significantly both in women with Type 1 or Type 2 diabetes, being higher in the latter group .Compared to Type 1 diabetes, women with Type 2 diabetes were more likely to be obese. The prevalence of obesity increased over time for all women with diabetes , while that of women with Type 2 diabetes remained unchanged and accounted for approximately two thirds of the cases .We compared proportions of women with unfavorable factors for pregnancy in 2011 and 2017, according to diabetes type Figure 2p < 0.571].Prescribing of contraception was higher among women with Type 1 diabetes than with Type 2 diabetes, with no changes across all years under observation [Type 1 diabetes: 2004: 33.8% (31.2%–36.5%), 2017: 32.2% (30.3%–34.0%), p = 0.323; Type 2 diabetes: 2004: 22.5% (20.7%–24.4%), 2017: 23.1% (21.9%–24.4%), p = 0.001; 2017: 36.4% vs. 28.6%, p < 0.001). Women with Type 2 with at least one unfavorable factor were prescribed contraception more frequently than those without in 2017  and in 2011 , although not significantly  compared to women with Type 1; though we documented a reduction in the prescription of these medications between 2004 and 2017 in both types of diabetes. The high prevalence of obesity, especially among women with type 2 diabetes, is of concern, since obesity itself, independently of diabetes, is associated with negative pregnancy outcomes. The relatively high frequency of microalbuminuria in both patients with either Type 1 or Type 2 diabetes may be, at least in part, accounted for by the fact that we counted as having microalbuminuria patients with a single measurement of elevated UACR, rather than sustained elevated UACR, as recommended by guidelines . As for the high rate of microalbuminuria observed in women with Type 2 diabetes, we may hypothesize that is associated to the higher rate of hypertension and/or to a longer duration of disease than documented because of a delay in diabetes diagnosis.Only one third of the women with Type 1 diabetes and one quarter of those with Type 2 diabetes were prescribed contraception by their GPs. Women with Type 1 diabetes with unfavorable factors were significantly more likely to be prescribed contraception than those without. Although, the same was also observed in 2017 among women with Type 2 diabetes, this was not significantly.The majority of studies looking at the effects of pre-pregnancy care for women with diabetes collected data at their presentation to the antenatal clinic, i.e., when already pregnant. These studies have highlighted a high proportion of unplanned pregnancies for both women with Type 1 diabetes who usually attend secondary care/specialist clinic, and women with Type 2 diabetes, usually seen only in general practice . CompareEducational interventions aimed at healthcare professionals and patients have shown improvements in pregnancy preparation, and were found to be cost-effective in reducing adverse pregnancy outcomes , 23. A rOur results expand the relevance of those findings, describing the patients who would benefit from these interventions with the breath of a 13-year observation of a nationally representative sample of patients  with diabetes attending primary care practices in England.In the general population, unplanned pregnancy is estimated to be around 40% . PreconcThe adoption of 2015 NICE guidelines for diabetes and pregnancy increased the use of contraception in women with more than one unfavorable factors, especially in those with Type 1 diabetes . HoweverThe strength of our study lies in our real-world representative sample of women of reproductive age with diabetes in England. Data were collected from primary care centres and they reflect the reality of diabetes management, thus including also patients with milder forms of Type 2 diabetes who, nevertheless, do carry an increased risk for adverse pregnancy outcomes.Limitations of the study are related to missing data for some variables of interest . Unfortunately, irregularities in the frequency of GP appointments and the limited time allocated to visit, may limit data collection and are beyond our control. However, the imperfect available data that we report conforms to what we would expect based on studies on similar patients in the UK .Contraception may have been underestimated because the CMR system does not capture the use of contraceptives available without prescription.Further research should focus on tailored educational interventions for both patients and healthcare professionals, as well as feedback aimed at improving the quality of care. Early data from MyPracticeDashboard, an innovative feedback platform, reports and compares recording rates of diagnoses and episodes for each practice of the RCGP RSC network . This tyFrom 2004 to 2017, several aspects of diabetes care improved for women with diabetes of childbearing age. However, most of these women have unfavorable factors for an unplanned pregnancy, especially women with Type 2 diabetes where the management involves the use of medications not recommended in the case of pregnancy. Pre-pregnancy care including more active promotion of family-planning, detailed medication review, and weight management should be enhanced in routine diabetes care to achieve safer pregnancy, wherever the care of these women takes place.www.rcgp.org.uk/rsc.The datasets generated for this study can be found in the RCGP RSC repository. RCGP RSC data are available for the analysis following the application form that can be found online at ND and SL conceived the idea for this study. MG contributed to the literature search, led the analysis, and wrote the manuscript. SL, ND, MF, MS, AC, and WH contributed to the literature search, the clinical discussion, and reviewed/edited the final manuscript. MG, MJ, and JVV contributed to the development of the statistical model and analysis. SL is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version.MG’s study at University of Surrey was supported by the European Union through the Erasmus exchange programme. This project had no external funding.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In Table 1 of the above article, the values for `Singleton' and `Multiple birth' in 2003 were inverted. This has now been corrected as follows:2003Type of BirthSingleton 3494 (96.0)Multiple birth 145 (4.0)


Therefore, measuring indoor PM2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM2.5 data is difficult and expensive. Therefore, several indoor PM2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM2.5 concentrations in cold or temperate regions. The factors that influence PM2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM2.5 concentrations in Taiwan  by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM2.5 concentrations, the difference between indoor and outdoor CO2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM2.5 concentrations and suggested that outdoor PM2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM2.5 concentrations.Exposure to indoor particulate matter less than 2.5 µm in diameter (PM In Taiwan, 78 air quality monitoring stations have been established since 1996 to measure outdoor PM2.5 concentrations (https://airtw.epa.gov.tw/ENG/default.aspx), and PM2.5 data are available on Taiwan’s Environmental Protection Administration (EPA) website. However, obtaining indoor PM2.5 data is difficult because of complications that often arise in the long term after installing sampling instruments in crowded indoor areas, particularly in homes. Moreover, sampling instruments are expensive, and samples containing a few homes may not be representative of exposure situations. Therefore, some studies developed prediction models for indoor PM2.5 concentrations. Yuchi and Clark [2.5 levels. Tang and his co-workers used the mass balance method to create a prediction model for indoor PM2.5 levels in homes [2.5 levels in schools [2.5 in homes [2.5 [2.5 concentration is also a concern in tropical and sub-tropical regions [2.5 concentration prediction model to rapidly obtain data is necessary.In countries such as the United States  analyze the factors influencing indoor PM2.5 concentrations. These results can prove beneficial for studying the relationship between PM2.5 exposure and health effects and can help the government in formulating policies for reducing indoor PM2.5 concentrations.Dampness in Buildings and Health (DBH) is a cross-country project that investigates the association between chemical and biological pollutant exposure and childhood allergies, asthma, and reported symptoms. We are part of DBH’s research team and conducted a study from the period 2008 to 2009 in Tainan, Taiwan ,33, whic2) is a city in southwest Taiwan. It is the sixth largest city in Taiwan, with a population of over 1.88 million people. The climate region is split between tropical and subtropical regions. The annual temperature, relative humidity, and rainfall are 24.3 °C, 77.2%, and 1698 mm [The study framework is presented in  1698 mm , respect2.5 data) aged from 3 to 9 years who completed questionnaires regarding the building characteristics of children’s bedrooms, human indoor activities, furniture materials, and disease history in Tainan, Taiwan, from August 2008 to September 2009. The number of study cases was 21, 39, 13, and 20 in the spring, summer, fall, and winter, respectively. The indoor air quality  of the children’s bedrooms was recorded during visits. The samplers also simultaneously collected outdoor air quality, and the hourly outdoor PM2.5 concentrations of each household was also modeled using the Kriging model. We used a simple linear model regression to select variables, and MLR was used to estimate a final model. This study also employed cross-validation to assess the prediction model.In brief, the sample comprised 93 children , human indoor activities , and furniture materials  during visits. All sources of potential predictors are shown in 2.5 instruments were located near the wall of children’s bedrooms, and the instruments’ inlet was directed toward the air in their bedrooms. Another PM2.5 instrument was simultaneously used to collect data of outdoor PM2.5 concentrations on the balconies of children’s bedrooms during the same visits. When outdoor PM2.5 instruments were not available on the balconies, the sampler was attached to the windows of children’s bedrooms and their inlets were used to connect the tube for outdoor sampling. PM2.5 instruments’ inlets were located 1.0 to 1.2 m above floor level for indoor and outdoor sampling. The concentrations of indoor and outdoor PM2.5 were detected using DUST-TRAK aerosol monitors . The instruments automatically recorded data at 5-min intervals for 22–24 h. We then downloaded the data from instruments and analyzed the hourly average PM2.5 concentration. We recorded 1979 measurements for hourly indoor and outdoor PM2.5 concentrations. Before sampling, we compared the two DUST-TRAK aerosol monitors in the same space, and in each measurement, R2 was required to exceed 0.995.During each visit of DBH study, to prevent the occupants’ daily activities, such as walking, from being obstructed, the indoor PM2.5 samples were simultaneously collected through both personal environmental monitoring with an air flow of 10 L/min on 37-mm Teflon filters and DUST-TRAK aerosol monitoring. In total, indoor and outdoor air quality measurements were collected from 15 and 12 study households, respectively (three outdoor samples had missing data), to calibrate the data from the DUST-TRAK aerosol monitors, respectively. The filters were weighed before and after sampling and stored in a room with controlled temperature at 23 ± 3 °C and relative humidity at 40 ± 5% for at least 24 h. The sampling time for each sample was 24 h. The calibration equations were calculated as Equations (1) and (2) for indoor and outdoor PM2.5, respectively, by using a linear regression model.DUST-TRAK aerosol monitoring is a type of light-scattering aerosol measuring device. Previous studies have indicated that the data from the DUST-TRAK aerosol monitoring system could be overestimated when the relative humidity is greater than 60% ,36,37. T2.5 concentration, we collected outdoor PM2.5 concentrations near the study households from Taiwan’s EPA and calculated the outdoor PM2.5 levels of study cases through the Kriging model [p < 0.0001) between outdoor PM2.5 concentrations of the Kriging model and the study households.To reduce the difficulty of collecting variables for a prediction model of indoor PMng model . Pearson2.5 concentrations, we also used two Q-TRAK air quality monitors  to measure indoor and outdoor CO2 and temperatures, respectively. The instruments automatically recorded data at 5-min intervals for 22–24 h. We downloaded the data from instruments and analyzed the hourly average concentration. The sampling strategies were consistent with the descriptions provided in this subsection.To develop a prediction model for predicting indoor PM2.5 concentrations. The process was as follows:2.5 level and all variables. Variables with p > 0.05 were excluded;Step1: A simple regression analysis was used to analyze the association between indoor PMStep 2: A simple linear regression model was used to assess the collinearity between variables. Values with a variance inflation factor >3 were excluded to establish the prediction model;2.5 concentrations. We repeated this process until no more variables could be removed without statistically significant (p > 0.05) changes in the regression model.Step 3: MLR (stepwise) was used to analyze the association between all variables and indoor PMThe MLR was used to select variables to establish a prediction model for indoor PM2) and root mean square error. We used 5-fold cross-validation  to confirm model reliability. This study also used the developed prediction model to calculate the indoor PM2.5 concentrations and further analyzed the corrections between predicted PM2.5 concentrations and measured PM2.5 concentration to investigate the performance of the final prediction model in different seasons and areas. SAS  statistical software was used to analyze all data.Prediction model performance was assessed using the coefficient of determination (RAs presented in 2.5 levels. Average concentrations of indoor and outdoor PM2.5 were 19.5 ± 10.6 (DUST-TRAK) and 38.1 ± 20.6 µg/m3 (Kriging), respectively. The highest  and lowest  levels of indoor PM2.5 occurred during the winter and summer, respectively. The highest  and lowest  levels of outdoor PM2.5 occurred during the winter and summer, respectively. Seasonal distributions of outdoor PM2.5 concentrations were consistent with those of indoor PM2.5. The levels of indoor and outdoor temperature and CO2 are presented in 2 were observed during the winter and spring, respectively. However, the seasonal change was not obvious.2.5 concentrations (Kriging), difference in indoor and outdoor CO2 levels, building types, building floor levels, the frequencies of bed sheet cleaning and replacing, and mosquito burning behavior were associated with the indoor PM2.5 concentrations. The overall R2 was 74%. The equation of indoor PM2.5 concentration prediction model is as followsThe results of the MLR model are presented in 2.5 concentrations. As presented in 2 had a range of 72–78%, with an average of 75%. 2.5 concentrations . We also used Equation (3) to calculate the indoor PM2.5 concentrations and further analyzed the associations between predicted and measured values by season and area (2.5 concentrations (p < 0.05) in different seasons or areas, thus indicating that the prediction model sufficiently estimated hourly average indoor PM2.5 concentrations.and area . The res2.5 concentrations by using MLR model, the results of which suggested that outdoor PM2.5, ventilation, building characteristics, and human activities were key predictors. This prediction model also sufficiently estimated hourly average PM2.5 concentrations in different seasons and areas. Based on the modeling results, we suggest that controlling outdoor PM2.5 emission and certain human activities are beneficial for reducing indoor PM2.5 concentrations. Occupants should also consider the outdoor air quality when they open or close windows or doors to regulate ventilation. This prediction model can be used to assess variations in indoor PM2.5 concentrations across seasons and areas for future epidemiological research and make the control policy.We created a prediction model for hourly average indoor PMThe average age of buildings in Taiwan is 29.6 years, and 54% of buildings are aged 20–40 years . In this2.5 concentrations were the highest in the winter , and the next highest of concentration in spring. This reveals that indoor and outdoor PM2.5 pollution is more serious in the winter and spring than that in the summer and fall in Taiwan. Studies have indicated that dust storms, biomass burning, and long transportation from China are notable contributors to outdoor PM2.5 in the winter or spring [2.5 concentrations were high during dust-storm periods. Moreover, our prediction model indicated that outdoor PM2.5 concentrations was a critical factor affecting indoor PM2.5 concentrations. This explains why high indoor and outdoor PM2.5 concentrations were observed in the spring and winter, and similar seasonal patterns.In this study, both indoor and outdoor PMr spring ,41,42. K2.5 concentrations is a critical parameter in the indoor PM2.5 concentration prediction model. In Taiwan, opening windows is a universal behavior, and indoor PM2.5 concentration is associated with outdoor PM2.5 concentration in residential areas [2.5 is a major variable in indoor PM2.5 prediction models [2.5 concentration plays an important role in predicting indoor PM2.5 concentrations.al areas ,43. Prevn models ,44. Ther2.5 concentrations to predict indoor PM2.5 concentrations [2.5 concentration in studied households for the prediction of indoor PM2.5 concentration is difficult. Studies have directly collected outdoor PM2.5 data from monitoring stations [2.5 concentrations surrounding studied households. Chiang used a Kriging model to estimate outdoor PM2.5 concentration in Taiwan [2.5 concentrations by using the Kriging model, and these data were easily collected online. Moreover, a satisfactory correlation was observed between the outdoor PM2.5 concentrations surrounding studied households determined using the Kriging model and measurements in this study . Therefore, we can directly collect outdoor PM2.5 concentrations from Taiwan’s EPA air quality monitoring stations and estimated the outdoor PM2.5 concentrations of studied households by using the Kriging model. These data from the Kriging model can be inputted in the prediction model for calculating the indoor PM2.5 concentrations.Some studies directly measured outdoor PMtrations ,16; howestations ,15; howen Taiwan . The dat2.5 concentration is negatively associated with the differences of indoor and outdoor CO2 levels in 2.5 concentrations. In Langer and Bekö’s study [2 level. Outdoor air was an important contributor to NO2 in indoor air, due to high ventilation rate increasing the contribution of outdoor NO2 to indoor air. Hänninen and co-workers indicated that the ventilation rate was positively associated with the PM2.5 infiltration rate [2.5 was an important factor influencing indoor PM2.5 concentrations. In this study, the average differences of indoor and outdoor CO2 concentrations were 359, 284, and 256 ppm in buildings with townhouse, single-family, and apartment, respectively. High ventilation rate may increase the contribution of outdoor PM2.5 to indoor air. We determined that apartments contribute more to the indoor PM2.5 concentration than single-family houses and townhouses do. We speculated that the small space of the apartments has a large cumulative effect on the PM2.5 concentration. Our data indicated that the indoor PM2.5 concentrations was higher on the first floors than on other floors. We speculated that emissions from vehicular emission was a critical factor influencing the indoor PM2.5 concentration [2.5. Taken together, building characteristics were important influence factors and occupants should consider the outdoor air quality when they regulate the ventilation situation.Our data indicate that indoor PM’s study , they foion rate . Our modntration . Therefo2.5 concentrations. One study investigated the variations in particulate matter concentrations for different human activities indoors and found that a folded blanket also contributed particulate matter due to resuspension [spension . We specspension ,48. Mosq2.5 concentration [2.5 concentrations are not associated with smoking and incense stick burning. We speculate that the burning time of cigarettes and incense sticks was shorter and the effects of cigarette and incense stick burning was weaker on indoor PM2.5 concentrations. Moreover, this study lacked detailed records of the occupants’ behaviors and the exact time of event. Applying feasible technology to collect occupant activity frequency and time is necessary for completely analyzing the effects of occupant activities on the hourly PM2.5 concentration in the future.Studies have indicated that other indoor human activities, such as smoking or incense stick burning, are major factors affecting the PMntration ,48,49. O2 > 75%. 2.5 concentrations in different seasons and areas. These correlations were significant, and the R2 values ranged from moderately correlated to highly correlated, which indicates that the prediction model in this study can be used to estimate indoor PM2.5 concentrations in across seasons and areas in Taiwan. However, 2.5 concentration. Our study did not investigate the time point and frequency of window opening/closing behaviors. Thus, the time point or frequency of window opening/closing behaviors investigation was necessary in the near future for reducing the relative error.Studies have completed cross-validation for prediction models ,15,50. I2.5 concentration in households [2.5 concentrations was not a crucial predictor. We speculate that outdoor air does not critically influence indoor air in different temperature regions because of doors and windows closing, particularly in the winter. In Taiwan, the frequency of window-opening behavior is high due to its warm climate [2.5 concentrations. We did not find that heater use was associated with indoor PM2.5 concentration, possibly because only 8% of occupants had heaters; moreover, the heaters were only operated 0.3 h/day during the winter in our previous study [Some studies developed indoor aerosol modeling according to the complicated mechanisms (such as coagulation or secondary formation) ,27,28. Huseholds ,14,15, b climate ; thus, ous study . This in2.5 concentrations and further investigate the impact factor. Our results revealed that outdoor PM2.5 concentrations, difference between indoor and outdoor CO2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacing, and mosquito coil burning were key variables, and the predictor was not different on different climate regions. The prediction model of indoor PM2.5 concentrations can explain 74% of the variation and sufficiently predict indoor PM2.5 concentrations across seasons (55 to 69%) and areas (65 to 70%). PM2.5 concentrations from Taiwan’s EPA air quality monitoring stations can be used to estimate the outdoor PM2.5 concentrations of studied households by using the Kriging model and thereby reduce the time and cost of installing PM2.5 monitoring instruments.This study used the data on household indoor and outdoor pollutants, building characteristics, and human indoor activity from the DBH study in Taiwan to develop a prediction model for hourly indoor PM2.5 concentrations in tropical and subtropical regions, and found that the major predictors were different according to different climate regions. Controlling outdoor PM2.5 pollution emission and changing certain human activities can reduce PM2.5 exposure. Occupants should assess the outdoor air quality when they open or close doors or windows for ventilation rate regulation. We assert that our prediction model can estimate indoor PM2.5 concentrations across seasons and areas in Taiwan and can be used for future epidemiological research on the relationship between indoor PM2.5 exposure and health effects and obtaining rapid data for pollution control.This study developed a prediction model for predicting indoor hourly PM


Patients with type 2 diabetes mellitus (T2DM) are at increased risk of non-alcoholic fatty liver disease (NAFLD) and might eventually progress to advanced fibrosis, cirrhosis and hepatocellular carcinoma (HCC). Recommendations on whether to screen for NAFLD in diabetic patients remains conflicted between major guidelines. Transient elastography using FibroScan with CAP (controlled attenuation parameter) can assess both liver steatosis and fibrosis simultaneously. This paper took a new look at the prevalence of NAFLD and the severity of fibrosis among T2DM patients in Vietnam. The study was conducted using a cross-sectional design in T2DM adults who attended Dai Phuoc Ho Chi Minh Polyclinic and Polyclinic of Pham Ngoc Thach University of Medicine. Liver steatosis and fibrosis was assessed by FibroScan. NAFLD was diagnosed if CAP > 233 dB/m (steatosis > 5%). Data were analyzed using STATA 12 software program. We found that a total of 307 type 2 diabetic patients qualified for the study’s criteria. The prevalence of NAFLD in T2DM patients based on FibroScan was 73.3%. Rates of mild, moderate and severe steatosis were 20.5%, 21.8% and 30.9%, respectively. The prevalence of significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) was 13.0%, 5.9% and 3.6%, respectively. On multivariate analysis, aspartate aminotransferase (AST)  and platelet levels  were independent of risk factors of advanced fibrosis. Thus, our study supports screening for NAFLD and for evaluating the severity of liver fibrosis in T2DM patients. Estimated incidences of non-alcoholic fatty liver disease (NAFLD) worldwide have increased twice in the last two decades, while the incidences of other chronic liver diseases (CLD) have remained unchanged or are on downward trends . TraditiAlthough almost all NAFLD patients have simple steatosis only, 10%–20% of patients represent the active form: non-alcoholic steatohepatits (NASH) . MortaliType 2 diabetes mellitus (T2DM) is the main risk factor of NAFLD. Patients with T2DM are at a greater risk of NAFLD and have a higher rate of death and progression to cirrhosis than non-diabetic individuals . TherefoNAFLD is diagnosed when there is evidence of ≥ 5% hepatic steatosis either by histology or imaging and absence of secondary causes of fatty accumulation ,8. FibroPatients older than 18 years with known T2DM or previously unknown diabetes but a displaying fasting glucose of ≥ 126 mg/dL (7 mmol/L) or HbA1c ≥ 6.5%  and who (1) Excessive daily alcohol intake ≥ 30 g in men and ≥ 20 g in women. (2) Causes of secondary hepatic steatosis: pregnancy, abuse of steatogenic drugs , severe malnutrition. (3) Positive hepatitis B surface antigen or hepatitis C viral antibody. (4) Other chronic liver diseases: autoimmune hepatitis, hemochromatosis, Wilson’s disease, primary biliary cirrhosis, drug-induced hepatitis. (5) Measurement failure or unreliable results on TE  > 30% or success rate < 60%). With a predicted prevalence of NAFLD defined on FibroScan as 72.8% in a previous report , our stuPatients who met all study criteria were enrolled. Participating subjects who had given their written informed consent were checked for medical histories, physical examinations and laboratory tests.Participants were required to fast for at least 3 h before the procedure. TE was performed by a single experienced operator using FibroScan 530 compact  with a standard M probe. Liver stiffness measurement (LSM) and CAP were described by the median of 10 successful measurements. LSM was considered reliable only if IQR/med < 30% and success rate > 60%. CAP score was considered reliable if 10 valid acquisitions were obtained.Significant liver fibrosis (≥F2), advanced fibrosis (≥F3) and cirrhosis (F4) based on TE were defined as LSM ≥ 7 kPa, ≥ 8.7 kPa and ≥ 11.5 kPa, respectively, according to the previous study of LSM using TE in NAFLD patients  with mil2 for adult Asians, as proposed by the World Health Organization (WHO) [Obesity was established as a body mass index (BMI) ≥ 25 kg/mon (WHO) . Waist con (WHO) . Hyperteon (WHO) . The preon (WHO) . Patienton (WHO) : centralon (WHO) . Patienton (WHO) .p-value < 0.05 was assumed to be statistically significant.Data were summarized and examined using STATA 12 software. Continuous variables with normal distribution were expressed as mean and standard deviation (SD), whereas median with IQR (25 th–75 th) were used to illustrate results with asymmetric distribution. In addition, categorical elements were represented as numbers and percentages. Comparison between continuous variables was performed by t-test or Mann–Whitney test where appropriate. Chi-square test was performed to deal with the categorical factors. Logistic regression was used to investigate patients’ characteristics independently associated with advanced fibrosis. Variables proven to be significant on univariate analysis were selected for multivariate analysis. n = 3) and failed examination (n = 1). Data from 165 women and 142 men were analyzed. The average age was 56.8 ± 11.3. The BMI of the sample was normally distributed, with a mean of 24.9 ± 2.9 kg/m2. Approximately one-half of the study population had a BMI greater than 25 kg/m2. The mean waist circumference was significantly higher (p < 0.05) in men (90.4 ± 8.9 cm) compared to women (82 ± 9.1 cm). However, there was no statistical difference in the prevalence of central obesity between men and women, which consisted of 63.5% for both sexes. The patient characteristics are shown in From 7/2018 to 6/2019, a total of 311 patients qualified for the study’s inclusions. Four patients were eliminated due to unreliable FibroScan results  were also found in NAFLD patients. The proportion of at least significant fibrosis, advanced fibrosis and cirrhosis was 13%, 5.9% and 3.6%. Significant fibrosis was found to be increased with increasing BMI . PatientFibroScan revealed that hepatic fibrosis was mostly found in patients with NAFLD. Majority of patients with at least significant fibrosis (≥F2) had NAFLD (38/40) while all of patients with advanced fibrosis (≥F3) and cirrhosis were identified with NAFLD based on FibroScan.Forty-eight-point five percent of diabetic patients were identified with NASH after excluding other causes of CLD. ALT was observed to be elevated in a great number of those patients (93.3%) while 10 individuals had normal ALT but significant fibrosis (≥F2) on FibroScan (6.7%).p = 0.008) and platelets  It can be seen in In our study of 307 T2DM patients who underwent TE, we found a high prevalence of NAFLD of 73.3%, including more than one-third of severe steatosis. The proportion of significant fibrosis, advanced fibrosis and cirrhosis was 13.0%; 5.9% and 3.6%, respectively. Forty-eight-point five percent of patients in our study had NASH, which consisted of more than 65% NAFLD patients. AST and platelets were independent predictive factors of advanced fibrosis.2 versus 28.2 kg/m2 and 26.6 kg/m2 in the study by Kwok and Lee-Lee Lai, respectively. BMI is known to impact CAP measurements [We compared our results with other prior studies. The studies of Kwok (Hongkong) and Lee-Lee Lai  showed that the prevalence of NAFLD in diabetic patients based on FibroScan was 72.8% and 72.4% with the CAP cut-offs of 222 dB/m and 263 dB/m, respectively ,19. Overurements . Furtherurements ,22, whicRegarding the percentage of diabetic patients with NAFLD, the range is between 49%–69.4% . Recentl2 may have been influenced [2 and 97.2 cm) was greater than in ours (24.9 kg/m2 and 85.9 cm). Another possible reason for this is that liver fibrosis related to NAFLD in Asia is not as common as in Europe. Natural development of NAFLD to end-stage liver disease usually progresses slowly, lasting for around 10 to 20 years, and the latter appearance of NAFLD results in a lower proportion of advanced fibrosis or cirrhosis in many Asian surveys compared to those in Europe [FibroScan in our center is not equipped with an XL probe, so the accuracy of LSM in individuals having a BMI greater than 28 kg/mfluenced . As far fluenced . Apparenn Europe .p < 0.05). Vice versa, diabetes is associated with increased risk of advanced fibrosis as well. Another study using FibroScan to evaluate liver fibrosis in both diabetic and non-diabetic patients revealed that advanced fibrosis in diabetic subjects was 26.9% while only 4.5% of non-diabetic individuals had advanced fibrosis [2, 64.6% patients still had NAFLD and 7% had significant fibrosis. In previous studies about NAFLD, such non-obese patients usually have other components of MS [The presence of NAFLD in T2DM patients can lead to substantial risk of liver fibrosis. In our study, the proportion of fibrosis severities in patients with NAFLD were considerably higher than patients without NAFLD (fibrosis . In addits of MS . Obviousts of MS .NAFLD is believed to be the leading reason of ALT elevation in type 2 diabetic patients . Our stu2. Secondly, owning to lack of liver biopsy for diagnostic confirmation of TE findings, we do not have the precise prevalence of NASH. Finally, our findings may not be generalizable to patients with T2DM in all primary care settings as well as diabetic inpatients at hospitals. Future studies should concentrate on enhancing the quality of the current topic.Our work has several limitations. First of all, we do not have an XL probe to perform FibroScan examination on patients with BMI greater than 28 kg/mTo sum up, we found that the prevalence of NAFLD based on transient electrography is high among T2DM patients. Patients with NAFLD are at an increased risk of advanced fibrosis than those without NAFLD. Our study supports screening for NAFLD and evaluating severity of hepatic fibrosis in T2DM patients.


Characterization techniques for the CNDs and PES membranes were transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), Raman spectroscopy, atomic force microscopy (AFM), contact angle and a pure water flux assessment system operated at 300 kPa. TEM results showed that the CNDs were well dispersed with a uniform shape and size (6.7 ± 2.8 nm). Raman spectroscopy revealed that the CNDs were embedded on the PES and the ID/IG ratio slightly increased, showing that the membranes maintained good structural integrity.The CNDs/PES proved to be more hydrophilic than PES. The glassy carbon electrode (GCE) in anodic stripping voltammetry (ASV) technique detected 99.78% Cd2+ removal by 0.5% CNDs/PES at optimum conditions: 30 min. contact time, at pH 5 and 0.5 ppm Cd2+ solution. The 0.5% CNDs/PES removed Cd(II) due to the hydroxyl group (-OH) and carboxyl group (-COO-) on the membrane composite. It was established that Cu2+ and Pb2+ have a significant interfering effect during the analysis of Cd2+ using GCE in ASV technique. The 0.5% CNDs/PES is recyclable because it removed above 95% of cd2+ in four cycles. In a spiked tap water sample, 58.38% of Cd2+ was sensed by GCE of which 95% was in agreement with the value obtained from inductively coupled plasma optical emission spectrometry (ICPOES). Cadmium(II) is a toxic heavy metal in aquatic systems. As a potential solution, green carbon nanodots (CNDs) were synthesized from oats and embedded on polyethersulfone membrane (PES) via phase inversion for the adsorption of Cd Water contamination due to heavy metals has become a worldwide concern since the 1990s and has been a challenge for many environmental scientists . In port2+ can lead to many types of diseases such as nephrotoxicity, kidney disease, renal function hypertension, hepatic injury, lung damage, teratogenetic effects, skeletal deformation (Itai-itai) and cardiovascular diseases −3/−4 redox probe as depicted in The bare GCE was electrochemically characterised using cyclic voltammetry (CV) at a potential range of −0.200 to 0.600 V at a scan rate of 50 mV s+2 in different electrolytes was obtained by using 0.1 M HCl as an electrolyte , GCE electrodes gave an increased current response during the stripping of 10 ppm cadmium at a potential of 0.200 V.2+ from 10 ppm standard solution was done by using five membranes, which were: pure PES membrane, 0.01% CNDs/PES, 0.05% CNDs/PES and 0.5% CNDs/PES. 2+ adsorption because a higher percentage removal of 46.81% was achieved. This proved that 0.5% CNDs embedded on the membrane created more sites for the adsorbate (Cd2+) to bind onto the composite membranes. In most studies, where carbon-based nanomaterials are used, higher loadings (above 0.4% or above 0.5%) affected properties such pore size, hydrophilicity, antifouling properties and roughness of the membrane. This is due to the fact that high loadings cause agglomeration of the nanomaterial −3/−4 redox probe. The Cd2+ was detected by GCE in ASV under optimised parameters such as 0.1 M HCl (electrolyte), 200 s deposition time and −900 mV electrodeposition potential. The batch adsorption experiments displayed that the 0.5% CNDs/PES removed 99.78% of cadmium(II) from synthetic solutions when pH, time and concentration of Cd2+ solution were optimised to 5, 30 min and 0.5 ppm. The interference study showed that Pb2+ and Cu2+ competed with Cd2+ for adsorption on the active sites of the membrane surface, hence lower analyte percentage was detected in their presence. This scenario was also experienced in determination of -Cd2+ in spiked water sample, where 58.38% Cd2+ was detected due to the sample matrix.For the detection of cadmium(II) in water, the GCE was electrochemically characterised using CV at a potential range of −0.200 to 0.600 V and a scan rate of 50 mV sIt established that the 0.5% CNDs/PES membrane is reusable because the same membrane was applied for four cycles to remove above 95% of cadmium(II) in water.


Knowledge and attitude towards organ donation are critical factors influencing organ donation rate. We aimed to assess the knowledge and attitude towards organ donation in adolescents in Austria and Switzerland.A paper-based survey was performed in two secondary schools (age range 11–20 years) in Austria and Switzerland. 354/400 surveys were sufficiently answered and analyzed.Our study found that knowledge on organ donation is scarce in adolescents. Less than 60% of those surveyed thinks that a person is dead when declared brain dead. 84.6% would authorize organ donation after brain death for themselves, but only 69% would authorize organ donation after brain death for a close relative. 93.7% would accept a donor organ if they needed one. Family discussions, rather than school discussions, influenced knowledge on organ donation, the percentage of respondents who have a firm opinion on organ donation and the rate of declaration of this opinion. Age, gender, nationality and religion also influenced knowledge and attitude towards organ donation. Nearly one third of adolescents are of the opinion that selling non-vital organs should be legalized.Since having had family discussions, a potentially modifiable factor, was positively associated with knowledge and attitude towards organ donation, we postulate that educational programs stimulating family discussions on organ donation may be a promising strategy to increase knowledge. The number of organ donations after brain death vary widely across countries, as exemplified by the Eurotransplant figures for 2018, which show an organ donation rate between 29.4/million population in Belgium and 11.3/million population in Germany . KnowledA paper-based questionnaire in German  was used for analysis. Descriptive data are presented as absolute numbers, percentages or by median and 95% confidence interval (CI). First, univariate inter-group data analysis for previous family and school discussions, gender, nationality and religion were performed using independent sample t-test and chi-squared test. In addition, multivariate logistic regression was used to identify which of the factors identified by univariate analysis influenced the formation of a firm opinion towards organ donation. A n = 13, 3.7% of the cohort), it was not feasible to analyze this nationality separately. To account for the internationality of one of the schools, a group of “other” countries was necessary. Table Four hundred questionnaires (200 at LAZ and 200 at BT) were distributed between June and October 2019. Three hundred fifty-nine questionnaires were returned (89.8%), 5 of these were insufficiently answered  and therefore excluded from analysis. 354 (88.5%) questionnaires were included in the analysis  of the respondents stated that they have a firm opinion on organ donation and only 21.3% have officially declared this opinion (e.g. by carrying an organ donation card or by having set up a patient provision). Of those who already officially declared their opinion, 81.3% declared that they authorized organ donation, whereas the remaining 18.7% filed an objection. When asked for their opinion, 84.6% would authorize organ donation after brain death for themselves, but only 69% would authorize organ donation after brain death for a close relative. 93.7% would accept a donor organ in case they would need one. 29% of adolescents had discussed organ donation in their families whereas only 17.3% already had discussions at school.Only 59.8% of the participants of our survey believe that a person is really dead when he/she is declared brain dead. When adolescents were asked which legal options for organ donation they know (multiple answers possible), 87.7 and 80.9% of the responses were correct by stating that volunteer living donation and donation after brain death are legal. However, 11.1 and 8.5% were of the opinion that buying an organ from either a brain dead donor or a living donor is legal. Adolescents were also asked, which legal possibilities to authorize organ donation they know (multiple answers possible). More than half of the respondents (54.7%) named opt-in (e.g. organ donation card), 27.2% authorization by next-of kin and 18.3% opt-out. 37% stated that they do not know which possibilities exist. Only 5.3% of the participants named all 3 correct answers. Interestingly, 29.8% stated that, in their opinion, it should be legal to sell non-vital organs. Knowledge on political discussions to change existing organ donation laws was scarce; 88.7% stated that they have not heard about any of these efforts, 3.7% thought that such efforts take place in Austria (which is not true), 4.5% in Germany and 5.4% in Switzerland (both true).When asked for their personal arguments to donate organs (multiple answers possible), 74.9% named the wish to help, 73.4% the possibility to receive an organ as reasons, 33.1% would take pride in helping somebody else, 23.9% think that organ donation would give death a purpose, and 4.6% would donate for religious reasons. Interestingly, when asked within this context, only 8.9% stated that they would not donate organs at all. When the entire cohort was next asked for their personal arguments against organ donation (multiple answers possible), 40.3% did not want to make a decision yet, 36% were afraid of abuse through organ trafficking, 29.5% were afraid that in case of authorization of organ donation doctors would not do everything to save their lives, 31.5% were afraid that they might not be dead after declaration of brain death, 10.4% generally did not want to donate, 8.8% thought that organ donation disturbs the peace of death or disfigures the body and 5.8% had religious reasons.p = 0.02) and 4.3% versus 11.1% of those without family discussions thought that selling organs from brain dead donors is legal (p = 0.03). Also, significantly more participants who had family discussions knew that opt-out , opt-in  or authorization by the family  are legal options for organ donation. Previous school discussions on organ donation were interestingly associated with a higher percentage of participants thinking (wrongly) that buying organs from brain dead donors is legal  . Similarly, family discussions on organ donation was associated with a higher percentage of official declaration of the opinion , and of authorization of organ donation    and significantly more said that they would donate because they also would like to receive an organ in case they need one . School discussions did not influence personal arguments for or against organ donation . Gender significantly influenced knowledge about organ donation; 84.2% of female adolescents knew that donation after death is legal, compared to 75.3% of the male participants. Fewer females also thought that buying organs from living donors is legal . No female participant stated that they had heard about discussions to change the organ donation law in Austria (which is true), as opposed to 7.6% of male participants (p = 0.001), but fewer female participants stated that they had heard about these discussions in Germany, where such discussions took place . Gender also significantly influenced the attitude towards organ donation; 88.5% of females would allow organ donation after their own brain death whereas only 80.4% of male adolescents would only allow it (p = 0.04). Regarding their personal reasons, more female adolescents feared that they would not be really dead when declared brain dead . Significantly more females would donate organs to help others , and because they also would like to receive an organ in case they need one   versus 15  years, Regarding nationality, participants from 29 nations took part in the survey, due to the internationality of one of the two schools , whereas in the Austrian school BT, people from only 6 nations participated. Therefore, nationality was grouped into Austria, Germany+Switzerland and “other countries” as described above.p = 0.004). Similarly, Austrian participants knew in 87.6% that donation after death is legal, compared to 75.6% in participants from Germany+Switzerland and 64.3% in participants from other countries (p = 0.001). On the other hand, 11.1% of the participants from Germany+Switzerland and 27.1% in participants from other countries thought that selling organs from living donors is legal, compared to 5.2% of Austrian participants (p = 0.001). Results were comparable for selling organs from brain dead donors . Nearly half of the participants from other nations (45.7%) compared to 24.7% of the Austrian and 28.4% of the Germany+Switzerland participants thought that selling organs should be legalized. More participants from Austria (58.8%) and Germany+Switzerland (54.4%) knew that an organ donation card is an option to declare the will regarding organ donation as compared to participants from other nations (40%) (p = 0.026). More participants from Germany+Switzerland (7.8%) and other nations (10%) knew that a change in legislation was discussed in Switzerland compared to Austrian participants (2.6%) (p = 0.03). Fewer Austrian participants stated that they had a firm opinion (38.9%) compared to Germany+Switzerland (52.8%) and other nations (52.2%) (p = 0.03). This resulted in less frequent declaration of their opinion in Austrians (12.4%) compared to Germany+Switzerland (24.7%) and other nations (41.4%) (p = 0.0001). This might be influenced by the fact that Austrian participants were significantly younger (1 year) than participants from Germany+Switzerland and other nations .91.2% of Austrian participants knew that volunteer living donation is legal, compared to 85.6% in participants from Germany+Switzerland and 75.7% in participants from other countries (p = 0.001). Austrians (78.9%) and participants from Germany+Switzerland (73.3%) would significantly more often donate organs to help others compared to other nations (58.6%) (p = 0.004). Significantly more Austrians (80.9%) and participants from Germany+Switzerland (73.3%) would also donate organs because they also would like to receive an organ in case they need one compared to other nations (47.1%) (p = 0.0001). For Austrians (40.7%), being proud to help others was more frequently a positive argument for organ donation compared to Germany+Switzerland (23.3%) and other nations (21.4%) (p = 0.001). Religious reasons were more frequently a positive argument for other nations (11.4%) compared to Austria (2.6%) and Germany+Switzerland (3.3%)  named the fear of not being dead when declared brain dead as an argument against organ donation compared to Germany+Switzerland (23.3%) and other nations (12.9%) (.5% namedp = 0.0001). More Atheists (21.3%) and participants with other religions (23.5%) compared to Christians (8%) thought that selling organs from living donors is legal. Declaration of an opinion regarding organ donation was more common in atheists (35%) versus Christians . Atheists also opposed against organ donation more often (9.8%), compared to 2.9% of Christians and none of the participants with other religions (p = 0.03). 61.1% of Christians and 63.9% of atheists believed that a person is dead after declaration of brain death, whereas only 23.5% of those with other religions believe this (p = 0.007). The argument of not wanting to donate was more common amongst atheists (13.1%) and participants with other religions (23.5%) more frequently than Christians (7.2%) (p = 0.04), whereas Christians (31.5%) more often feared that they would not be really dead when declared brain dead compared to atheists (11.5%) and other religions (27.4%). Religious reasons as an argument against organ donations was more common amongst participants with other religions (23.5%) compared to Christians (5.1%) and atheists (0%) (p < 0.0001). Significantly more Christians (76.4%) would donate an organ because they also would like to receive an organ in case they need one compared to atheists (57.4%) and other religions (58.9%) (p = 0.005). For Christians (36.2%) and other religions (29.4%), being proud to help others was also more frequently a positive argument for organ donation compared to atheists (16.4%) (p = 0.01)  and atheists (80.3%) compared to participants with other religions 58.8%) knew that volunteer living donation is legal  and family discussions  remained significant explanatory variables for having a firm opinion on organ donation. Additionally, we also analyzed factors that influenced if adolescents would allow organ donation for themselves after brain death. We included age and gender in the model, and only gender  remained as significant explanatory variable for allowing organ donation after brain death.To understand which factors influenced participants’ firm opinions, we performed multivariate logistic regression analysis. We included age, nation and family discussions because these variables were significant on univariate analysis. Age  are able to increase organ donation rates; however, the effect of public education campaigns is modest, with only an average 5% gain in organ donation rates. Negative mass media exposure, however, can drastically decrease organ donation rates, possibly for prolonged periods of time . The rolDemographic factors such as age, gender, religion and nationality influenced knowledge and attitude towards organ donation in our study. This was expected from our own data in students and health care professionals , 8 and aAn interesting finding in our survey was that nearly 1/3 of the respondents was of the opinion that selling organs should be legalized. The ethical acceptance of incentives or compensation for organ donation seems to be broad in younger adults , 38, desOur study has some limitations: only two schools were included, which were chosen because of personal connections of the authors, therefore the complete picture on adolescents’ views on organ donation in Austria and Switzerland may not have been captured. However, despite personal connections, no additional educational efforts took place in these schools that would have biased the results. There was some heterogeneity in demographic characteristics, especially regarding the mixture of nationalities between the two schools; therefore, it is difficult to distinguish between effects of nationality and age and effects of the school visited. To strengthen these findings, future studies would include more schools to represent a more diverse population, as well as larger cohorts to distinguish between the effects of the demographics of the population surveyed.In conclusion, our study provides interesting insights into the knowledge and attitude of adolescents towards organ donation in Austria and Switzerland. In our small pilot study, despite scarce knowledge on organ donation, more than 4/5 of adolescents between the ages of 11–20 would donate their own organs. Since we found that family discussions were the most prominent modifiable factor that influences knowledge and attitude towards organ donation in adolescents, our data may suggest that targeted public education programs to stimulate family discussions can be a promising strategy to increase the number of adolescents who have a firm opinion on organ donations. From our pilot study, this seems to be the prerequisite to official declaration of the opinion. Once adolescents have made up or declared their opinion, most have a favorable donation intention. The association between having had family discussions and the willingness to authorize organ donation may be a topic for further research. These results can be of relevance for stake holders involved in developing strategies to improve organ donation rates.Additional file 1. German version of the survey.Additional file 2. English version of the survey.