● Perfluorooctanesulfonic acid and perfluorooctanoic acid highest in human milk. ● All other perfluoroalkane substances had median values of zero (101 samples). ● Branched PFOS recommended to be ...analyzed separately from linear isomer. ● PFOS and PFOA showed differentiated regional and income distribution. ● Human health risk assessment values not yet available at global level.
Within the global monitoring plan (GMP) established by article 16 of the Stockholm Convention on Persistent Organic Pollutants, perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorohexane sulfonic acid (PFHxS) are recommended for analysis in core matrices to assess occurrence and changes geographically and with time. In 101 samples consisting of 86 national pools and 15 pools from States in Brazil obtained between 2008 and 2019, PFHxS was detected in 17% of the national pools and none in Brazil. PFOA and PFOS had a detection frequency of 100% and 92%, respectively. Other perfluoroalkane substances (PFAS) had either low detection frequencies and median values of zero (carboxylic acids C 4-C 11; except PFOA) or could not be quantified in any sample (sulfonic acids, C 4-C 10, and long-chain carboxylic acids, C 12-C 14). Correlation between PFOA and PFOS was moderately ( r = 0.58). Whereas median values were almost identical (18.9 pg/g f.w. for PFOS; 18.6 pg/g f.w. for PFOA), PFOS showed larger ranges (< 6.2 pg/g f.w.-212 pg/g f.w.) than PFOA (< 6.2 pg/g f.w.-63.4 pg/g f.w.). It was shown that wealthier countries had higher PFOA concentrations than poorer countries. No difference in concentrations was found for samples collected in countries having or not having ratified the Stockholm Convention amendments to list PFOS or PFOA. The goal to achieve 50% decrease in concentrations within ten years was met by Antigua and Barbuda, Kenya, and Nigeria for PFOS and by Antigua and Barbuda for PFOA. In a few cases, increases were observed; one country for PFOS, four countries for PFOA.
The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host's lifetime in response to various ...factors. The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25-50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla. Regarding prediction, the most important features classifying subjects into clusters were measures of obesity (waist to hip ratio, BMI, and visceral fat index), total body water, blood pressure, energy intake, total fat, olive oil intake, total fiber intake, and water intake. In conclusion, the SVM model was shown as a valuable tool to classify healthy individuals based on their gut microbiota composition.
Background. Chronic obstructive pulmonary disease and lung cancer are diseases associated with smoking tobacco cigarettes. Smokers find cessation difficult.Objectives. To determine whether smoking ...the Twisp electronic cigarette (e-cigarette), containing nicotine in a vegetable-based glycerine substance, would reduce carboxyhaemoglobin (COHb) levels in regular cigarette smokers by (i) comparing arterial and venous COHb levels before and after smoking the Twisp e-cigarette for 2 weeks; and (ii) evaluating changes in participants’ perception of their health and lifestyle following the use of Twisp e-cigarettes.Methods. A single group within-subject design was used where tobacco cigarette smokers converted to Twisp e-cigarettes for 2 weeks. Prior to using the Twisp e-cigarette and after using this device for 2 weeks, arterial COHb, venous COHb and venous cotinine levels were determined. Additionally, the participants were asked to complete a questionnaire outlining their perceptions on health and lifestyle.Results. Thirteen participants of median age 38 years (range 23 - 46) with a smoking median of 20 cigarettes/day (range 12 - 30) completed the study. COHb levels (%) were significantly reduced after smoking Twisp e-cigarettes for 2 weeks (mean ± standard deviation (SD) arterial COHb before 4.66±1.99 v. after 2.46±1.35; p=0.014 and mean ±SD venous COHb before 4.37±2.1 v. after 2.50±1.23; p=0.018). There was excellent agreement between arterial and venous COHb levels (intraclass correlation coefficient 0.916). A decrease in cotinine levels (p=0.001) and an increase in oxygen saturation (p=0.002) were also observed. The majority of participants perceived improvements in their health and lifestyle parameters.Conclusion. Smoking the Twisp e-cigarette may be a healthier and more acceptable alternative to smoking tobacco cigarettes.
Lung cancer is the most dangerous disease. Lung cancer has a huge impact on mortality rates worldwide. It is the top causes of cancer-related fatalities worldwide. Lung cancer development, ...prevention, and lifestyle are all linked. Smoking, occupational hazards, air pollution, an unbalanced diet, and other lifestyle factors are major contributors to lung cancer. The use of lifestyle indicators can aid in the early detection of lung cancer. In this study, a model is constructed based on lifestyle data to predict lung cancer, and the model is then extended to predict the level of lung cancer as low, medium, or high. The basic lifestyle parameters are examined first, and if the model forecasts the potential of lung cancer, the second component of the model analyses each parameter further and predicts the level of cancer. The first portion of the model employs logistic regression with k-fold validation and Support Vector Machine with k-fold cross-validation to predict lung cancer. The Support Vector Machine predicts with 90% accuracy, while the logistic regression model predicts with 92% accuracy. The second component of the model used SVM and Random Forest models to estimate the amount of malignancy, with Random Forest providing 96% accuracy and SVM providing 98.42% accuracy for cancer prediction. The goal of this study is to predict lung cancer early using data from lifestyle parameters.
Abstract MicroAbstract The influence of body mass index (BMI) as a predictive factor for response to neoadjuvant chemotherapy, measured by pathologic complete response (pCR), was examined in 324 ...breast cancer (BC) patients. Mutivariable regression analysis did not reveal an association between pCR and continuous as well as categorical BMI. Additional subgroup and meta-analysis showed comparable results. Therefore BMI is not a relevant clinical factor for pCR in BC patients. Introduction There is only limited from clinical routine on the relevance of body mass index (BMI) on pathologic complete response (pCR) following neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. Patients and Methods The impact of BMI on pCR and survival outcome was examined in 324 primary non-metastatic BC patients. An additional meta-analysis was performed on the current data and relevant previously published studies in clinical routine. Results Multivariable regression analysis identified lymph vascular invasion (OR=0.05; CI: 0.01-0.18; p=0.0000), grading 3 (OR=3.12; CI: 1.59-6.12; p=0.0009) and HER2/neu-status (OR= 4.76; CI: 1.86-12.18; p=0.011) as independent factors for pCR after NAC. There was no association between pCR and continuous as well as categorical BMI . Various additional subgroup analyses of molecular BC subtype (triple-negative, luminal-like, HER2-luminal, HER2-like) and BMI also showed no association. These findings were confirmed by the meta-analysis. Except for one subgroup analysis in which overweight and obese patients were combined as one group, no association between BMI and pCR as well as survival outcome was found. Conclusions BMI was not established as a relevant clinical factor. Only lymph vascular invasion, grading 3, luminal-like and HER2/like BC subtype showed predictive and prognostic impact in BC patients receiving NAC.
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