Polycystic ovary syndrome (PCOS) is a common, complex genetic disorder affecting up to 15% of reproductive-age women worldwide, depending on the diagnostic criteria applied. These diagnostic criteria ...are based on expert opinion and have been the subject of considerable controversy. The phenotypic variation observed in PCOS is suggestive of an underlying genetic heterogeneity, but a recent meta-analysis of European ancestry PCOS cases found that the genetic architecture of PCOS defined by different diagnostic criteria was generally similar, suggesting that the criteria do not identify biologically distinct disease subtypes. We performed this study to test the hypothesis that there are biologically relevant subtypes of PCOS.
Using biochemical and genotype data from a previously published PCOS genome-wide association study (GWAS), we investigated whether there were reproducible phenotypic subtypes of PCOS with subtype-specific genetic associations. Unsupervised hierarchical cluster analysis was performed on quantitative anthropometric, reproductive, and metabolic traits in a genotyped cohort of 893 PCOS cases (median and interquartile range IQR: age = 28 25-32, body mass index BMI = 35.4 28.2-41.5). The clusters were replicated in an independent, ungenotyped cohort of 263 PCOS cases (median and IQR: age = 28 24-33, BMI = 35.7 28.4-42.3). The clustering revealed 2 distinct PCOS subtypes: a "reproductive" group (21%-23%), characterized by higher luteinizing hormone (LH) and sex hormone binding globulin (SHBG) levels with relatively low BMI and insulin levels, and a "metabolic" group (37%-39%), characterized by higher BMI, glucose, and insulin levels with lower SHBG and LH levels. We performed a GWAS on the genotyped cohort, limiting the cases to either the reproductive or metabolic subtypes. We identified alleles in 4 loci that were associated with the reproductive subtype at genome-wide significance (PRDM2/KAZN, P = 2.2 × 10-10; IQCA1, P = 2.8 × 10-9; BMPR1B/UNC5C, P = 9.7 × 10-9; CDH10, P = 1.2 × 10-8) and one locus that was significantly associated with the metabolic subtype (KCNH7/FIGN, P = 1.0 × 10-8). We developed a predictive model to classify a separate, family-based cohort of 73 women with PCOS (median and IQR: age = 28 25-33, BMI = 34.3 27.8-42.3) and found that the subtypes tended to cluster in families and that carriers of previously reported rare variants in DENND1A, a gene that regulates androgen biosynthesis, were significantly more likely to have the reproductive subtype of PCOS. Limitations of our study were that only PCOS cases of European ancestry diagnosed by National Institutes of Health (NIH) criteria were included, the sample sizes for the subtype GWAS were small, and the GWAS findings were not replicated.
In conclusion, we have found reproducible reproductive and metabolic subtypes of PCOS. Furthermore, these subtypes were associated with novel, to our knowledge, susceptibility loci. Our results suggest that these subtypes are biologically relevant because they appear to have distinct genetic architecture. This study demonstrates how phenotypic subtyping can be used to gain additional insights from GWAS data.
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► Biochars effectively removed metal ions from aqueous solutions. ► Pyrolysis at 400°C seems optimal producing biochar as the effective metal ions sorbent. ► The maximum removal was ...obtained in the pH range 5–6. ► The adsorption kinetic was described well by the pseudo second order model. ► The intraparticle diffusion was not the only rate controlling step of the adsorption process.
Kinetic and adsorption studies on the removal of metal ions such as Cu(II), Zn(II), Cd(II) and Pb(II) ions in the biochar (BC) samples have been carried out. The effects of several experimental parameters have been investigated using the batch adsorption technique at different temperature. The effectiveness of Cu(II), Zn(II), Cd(II) and Pb(II) ions removal increases with the increasing initial concentration of biochar and metal ion, pH as well as phase contact time. The maximum adsorption was found in the pH range 5.0–6.0. The kinetics of adsorption was found to be pseudo second order with intraparticle diffusion as one of the rate determining steps. Adsorption studies were also performed at different temperatures and modelled with the Langmuir and Freundlich adsorption isotherms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
We provide new evidence on the relation between option-based compensation and risk-taking behavior by exploiting the change in the accounting treatment of stock options following the adoption of FAS ...123R in 2005. The implementation of FAS 123R represents an exogenous change in the accounting benefits of stock options that has no effect on the economic costs and benefits of options for providing managerial incentives. Our results do not support the view that the convexity inherent in option-based compensation is used to reduce risk-related agency problems between managers and shareholders. We show that all firms dramatically reduce their usage of stock options (convexity) after the adoption of FAS 123R and that the decline in option use is strongly associated with a proxy for accounting costs. Little evidence exists that the decline in option usage following the accounting change results in less risky investment and financial policies.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Mineral criticality is a subjective concept that has evolved throughout history. An abundance of literature on this topic has been published over the last decade, encompassing a variety of criteria ...and methodologies. To our knowledge, this work is the first large-scale effort to organize and analyze recent comprehensive criticality studies in order to determine if a consensus exists within the global community as to which elements are critical. Here, we set aside methodological differences and analyze the results of 32 comprehensive nonfuel mineral criticality studies that evaluate at least 10 elements. Of the 56 elements or elemental groups evaluated, the three most commonly identified as critical in these studies are the rare-earth elements (REE), the platinum-group metals (PGM), and indium. Most of the studies also identify tungsten, germanium, cobalt, niobium, tantalum, gallium, and antimony as critical. These results are consistent with the 11 most recent studies, published post-2014, which also identify bismuth as critical. Furthermore, the same elements identified in the complete dataset, except antimony, were designated as critical when normalized by geographic region. Magnesium was also deemed critical. Elements may be identified consistently as critical for several reasons; similarities in methodologies, which embody evolving perceptions of risk, or changing national and institutional priorities. This work compiles a large number of recent criticality studies in an effort to define a consensus of currently critical materials, essentially defining the modern criticality paradigm, which is valuable when interpreting an individual perspective in more global context.
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•Comparison of results from 32 comprehensive criticality studies (broadly defined).•Goal: outline current criticality paradigm and identify common critical elements.•Similar study outcomes observed directly and using percent criticality metric.•Criticality study results were normalized by geographic region and compared.•Common elements may be fundamentally critical or identified due to similar models.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed ...for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer.
In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings.
The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE's sentence transformer only marginally improved the performance of machine learning models.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Diagnostic errors contribute to as many as 70% of medical errors. Prevention of diagnostic errors is more complex than building safety checks into health care systems; it requires an understanding of ...critical thinking, of clinical reasoning, and of the cognitive processes through which diagnoses are made. When a diagnostic error is recognized, it is imperative to identify where and how the mistake in clinical reasoning occurred. Cognitive biases may contribute to errors in clinical reasoning. By understanding how physicians make clinical decisions, and examining how errors due to cognitive biases occur, cognitive bias awareness training and debiasing strategies may be developed to decrease diagnostic errors and patient harm. Studies of the impact of teaching critical thinking skills have mixed results but are limited by methodological problems.This Perspective explores the role of clinical reasoning and cognitive bias in diagnostic error, as well as the effect of instruction in metacognitive skills on improvement of diagnostic accuracy for both learners and practitioners. Recent literature questioning whether teaching critical thinking skills increases diagnostic accuracy is critically examined, as are studies suggesting that metacognitive practices result in better patient care and outcomes. Instruction in metacognition, reflective practice, and cognitive bias awareness may help learners move toward adaptive expertise and help clinicians improve diagnostic accuracy. The authors argue that explicit instruction in metacognition in medical education, including awareness of cognitive biases, has the potential to reduce diagnostic errors and thus improve patient safety.
In recent years, neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have played a significant role in elucidating the neural ...underpinnings of posttraumatic stress disorder (PTSD). However, a detailed understanding of the neural regions implicated in the disorder remains incomplete because of considerable variability in findings across studies. The aim of this meta-analysis was to identify consistent patterns of neural activity across neuroimaging study designs in PTSD to improve understanding of the neurocircuitry of PTSD.
We conducted a literature search for PET and fMRI studies of PTSD that were published before February 2011. The article search resulted in 79 functional neuroimaging PTSD studies. Data from 26 PTSD peer-reviewed neuroimaging articles reporting results from 342 adult patients and 342 adult controls were included. Peak activation coordinates from selected articles were used to generate activation likelihood estimate maps separately for symptom provocation and cognitive-emotional studies of PTSD. A separate meta-analysis examined the coupling between ventromedial prefrontal cortex and amygdala activity in patients.
Results demonstrated that the regions most consistently hyperactivated in PTSD patients included mid- and dorsal anterior cingulate cortex, and when ROI studies were included, bilateral amygdala. By contrast, widespread hypoactivity was observed in PTSD including the ventromedial prefrontal cortex and the inferior frontal gyrus. Furthermore, decreased ventromedial prefrontal cortex activity was associated with increased amygdala activity.
These results provide evidence for a neurocircuitry model of PTSD that emphasizes alteration in neural networks important for salience detection and emotion regulation.
Analytical data and quantitative near infrared (NIR) spectroscopy models for various lignocellulosic components (including Klason lignin and the constituent sugars glucose, xylose, mannose, ...arabinose, galactose, and rhamnose), ash, and ethanol-soluble extractives were obtained for 53 samples of paper and cardboard. These samples were mostly the type of materials typically found in domestic wastes (
e.g.
newspapers, printing paper, glossy papers, food packaging). A number of the samples (48) were obtained by separating a sample, after milling, into two particle size fractions. It was found that the fractions containing the smaller particles typically had higher ash and Klason lignin contents and lower glucose and xylose contents than the larger particle size fractions. Nevertheless, all of the sample types had attractive total sugars contents (>50%), indicating that these could be suitable feedstocks for the production of biofuels and chemicals in hydrolysis-based biorefining technologies. NIR models of a high predictive accuracy (
R
2
of >0.9 for the independent validation set) were obtained for total sugars, glucose, xylose, Klason lignin, and ash, with values for the Root Mean Square Error of Prediction (RMSEP) of 2.36%, 2.64%, 0.56%, 1.98%, and 4.87%, respectively. Good NIR models (
R
2
of >0.8) were also obtained for mannose, arabinose, and galactose. These results suggest that NIR spectroscopy is a suitable method for the rapid, low-cost, analysis of the major lignocellulosic components of waste paper/cardboard samples.
Summary The leading parameters that define treatment recommendations in early breast cancer are oestrogen-receptor, progesterone-receptor, and human epidermal growth-factor status. Although some ...pathologists report Ki67 in addition to other biological markers, the existing guidelines of the American Society of Clinical Oncology do not include Ki67 in the list of required routine biological markers. The advent of new genetic tests has emphasised the role of proliferative genes, including Ki67, as prognostic and predictive markers. Additionally, randomised studies have retrospectively reviewed data and reported on the role of Ki67 in breast cancer. In light of new data, we have re-assessed evidence that could change guidelines to include Ki67 in the standard pathological assessment of early breast cancers. This review provides an update on the current knowledge on Ki67 and of the evidence in the published work about the prognostic and predictive role of this marker, and provides information on the laboratory techniques used to determine Ki67.
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