Quantification of Methylisothiazolinone and Methylchloroisothiazolinone Preservatives by High-Performance Liquid Chromatography.

Isothiazolinone preservatives (methylisothiazolinone (MIT) and methylchloroisothiazolinone (CMIT) are commonly used in cosmetics, industrial and household products. However, these isothiazolinone derivatives are known to cause allergic contact dermatitis. Hence, a sensitive, accurate, and reliable method for the detection of these compounds is thus warranted. The study aims to analyze concentrations of MIT and CMIT by high performance liquid chromatography. The analytical method used for quantification of MIT and CMIT in cosmetic products (leave-on-baby wet wipes) complies with the validation acceptance criteria (international standards ISO 5725, EU25 European Union for cosmetic regulations). MIT and CMIT were extracted and analyzed in leave-on baby wet-wipes collected from different stores in Riyadh city. Extraction was performed by ultrasonication of the samples, solid-phase extraction, and liquid-liquid extraction. Ten (10) µL of the sample was injected into the HPLC system and samples were analyzed with a mixture of acetic acid and methanol (80:20 v/v) in an isocratic mode. The flow rate was maintained at 1 mL/min. UV detection was performed at 274 nm. The results demonstrated recoveries between 90 and 106%, measurement uncertainty of C +/- 0.4% for methylisothiazolinone and C +/- 0.03% for methylchloroisothiazolinone, repeatability limit (r = 0.2%) and intermediate precision limit; R = 2% and R2 of 0.9996.

Recurrent attention U-Net for segmentation and quantification of breast arterial calcifications on synthesized 2D mammograms.

Breast arterial calcifications (BAC) are a type of calcification commonly observed on mammograms and are generally considered benign and not associated with breast cancer. However, there is accumulating observational evidence of an association between BAC and cardiovascular disease, the leading cause of death in women. We present a deep learning method that could assist radiologists in detecting and quantifying BAC in synthesized 2D mammograms. We present a recurrent attention U-Net model consisting of encoder and decoder modules that include multiple blocks that each use a recurrent mechanism, a recurrent mechanism, and an attention module between them. The model also includes a skip connection between the encoder and the decoder, similar to a U-shaped network. The attention module was used to enhance the capture of long-range dependencies and enable the network to effectively classify BAC from the background, whereas the recurrent blocks ensured better feature representation. The model was evaluated using a dataset containing 2,000 synthesized 2D mammogram images. We obtained 99.8861% overall accuracy, 69.6107% sensitivity, 66.5758% F-1 score, and 59.5498% Jaccard coefficient, respectively. The presented model achieved promising performance compared with related models.

Subthreshold Current Suppression in ReS2 Nanosheet-Based Field-Effect Transistors at High Temperatures.

Two-dimensional rhenium disulfide (ReS2), a member of the transition-metal dichalcogenide family, has received significant attention due to its potential applications in field-effect transistors (FETs), photodetectors, and memories. In this work, we investigate the suppression of the subthreshold current during the forward voltage gate sweep, leading to an inversion of the hysteresis in the transfer characteristics of ReS2 nanosheet-based FETs from clockwise to anticlockwise. We explore the impact of temperature, sweeping gate voltage, and pressure on this behavior. Notably, the suppression in current within the subthreshold region coincides with a peak in gate current, which increases beyond a specific temperature but remains unaffected by pressure. We attribute both the suppression in drain current and the presence of peak in gate current to the charge/discharge process of gate oxide traps by thermal-assisted tunnelling. The suppression of the subthreshold current at high temperatures not only reduces power consumption but also extends the operational temperature range of ReS2 nanosheet-based FETs.

Validity and reliability of the WHOQOL-BREF in the measurement of the quality of life of Sickle disease patients in Bahrain.

Background: Limited attention is devoted to the improvement of the quality of life of patients suffering from the negative consequences of Sickle cell disease (SCD). Our study focuses on the evaluation of the performance of the WHOQOL-BREF as a tool to measure the quality of life of SCD Patients in Bahrain. Methods: We conducted a cross-sectional study that enrolled 273 SCD patients selected using a simple random sampling technique from primary health-care centers in Bahrain in 2019. A designed questionnaire including the WHOQOL-BREF was filled by the patients in the health centers. The reliability of the WHOQOL-BREF was assessed by standardized Cronbach's alpha coefficient, and the validity was measured by convergent validity, principal component analysis and confirmatory factor analysis. Results: The WHOQOL-BREF had good internal consistency as Cronbach's alpha coefficient for the overall scale was 0.91. The convergent validity results indicated that the correlation coefficients values for all scale domains are significantly correlated at α < 0.01. Confirmatory factor analysis found that the four-domain structure produced a robust fit to the data. Conclusion: The WHOQOL-BREF tool has high internal consistency and validity in assessing the quality of life of Sickle Disease patients in Bahrain.

Towards a better understanding of annotation tools for medical imaging: a survey.

Medical imaging refers to several different technologies that are used to view the human body to diagnose, monitor, or treat medical conditions. It requires significant expertise to efficiently and correctly interpret the images generated by each of these technologies, which among others include radiography, ultrasound, and magnetic resonance imaging. Deep learning and machine learning techniques provide different solutions for medical image interpretation including those associated with detection and diagnosis. Despite the huge success of deep learning algorithms in image analysis, training algorithms to reach human-level performance in these tasks depends on the availability of large amounts of high-quality training data, including high-quality annotations to serve as ground-truth. Different annotation tools have been developed to assist with the annotation process. In this survey, we present the currently available annotation tools for medical imaging, including descriptions of graphical user interfaces (GUI) and supporting instruments. The main contribution of this study is to provide an intensive review of the popular annotation tools and show their successful usage in annotating medical imaging dataset to guide researchers in this area.

DV-DCNN: Dual-view deep convolutional neural network for matching detected masses in mammograms.

BACKGROUND AND OBJECTIVE: Mammography is an X-ray imaging technique used for breast cancer screening. Each breast is usually screened at two different angles generating two views known as mediolateral oblique (MLO) and craniocaudal (CC), which are clinically used by radiologists to detect suspicious masses and diagnose breast cancer. Previous studies applied deep learning models to process each view separately and concatenate the features from the two views to detect and classifying masses. However, direct concatenation is not enough to uncover the relationship between the masses that appear in the two views because they can substantially vary in terms of shape, size, and texture. The relationship between the two views should be established by matching correspondence between their extracted masses. This paper presents a dual-view deep convolutional neural network (DV-DCNN) model for matching masses detected from the two views by establishing correspondence between their extracted patches, which leads to more robust mass detection. METHODS: Given a pair of patches as input, the presented model determines whether these patches represent the same mass or not. The network contains two parts: a feature extraction part using tied dense blocks, and a neighborhood patch matching part with three consecutive layers, i.e., a cross-input neighborhood differences layer to find the relationship between the two patches, a patch summary features layer to define a summary of the neighborhood differences and an across-patch features layer to learn a higher-level representation across neighborhood differences. RESULTS: To evaluate the model's performance in diverse cases, several experimental scenarios were followed for training and testing using two public datasets, i.e., CBIS-DDSM and INbreast. We also evaluate the contribution of our mass-matching model within a mass detection framework. Experiments show that DV-DCNN outperforms other related deep learning models and demonstrate that the detection results improve when using our model. CONCLUSIONS: Matching potential masses between two different views of the same breast leads to more robust mass detection. Experimental results demonstrate the efficacy of a dual-view deep learning model in matching masses, which helps in increasing the accuracy of mass detection and decreasing the false positive rates.

Prevalence of sleep disturbance and anxiety due to the COVID-19 pandemic in Saudi Arabia.

INTRODUCTION: In the COVID-19 pandemic, physical and psychological health are of immense concern for the governing bodies and health policymakers in the period of lockdown and self-isolation. An in-depth analysis is required to recognize the changes in mental health among the public of different geographical areas. OBJECTIVE: The study aimed to investigate the sleep quality and anxiety among the population in Saudi Arabia during the lockdown period from March to June 2020. MATERIAL AND METHODS: We conducted a cross-sectional study and surveyed the population in Saudi Arabia during the lockdown. We analyzed the anxiety and sleep quality in a population with variable socio-demographic profiles. We assessed anxiety using the self-rating anxiety scale (SAS) questionnaire and tested the sleep quality by using the Pittsburgh sleep quality index (PSQI) questionnaire. We analysed the questionnaire responses to determine the relationships between anxiety, stress, sleep disturbances by using SPSS, and considered the p-value<0.05 statistically significant. RESULTS: We collected 397 questionnaires from the participants. The respondents were mostly of youth age (19-24 years), 66.5% of respondents were male, while there were 33.5% females. Most of the participants did not contact any COVID-19 patients (75.1%, n=298). The finding revealed that most people did not consider associated anxiety as a worrying disorder as the vast majority were normal with only 12.6% had mild-to-moderate anxiety and almost 1% had moderate-to-severe anxiety. While correlating the sleep patterns disturbance in individuals who had contact with COVID-19 patients, there was a significant sleep disturbance. The disturbance of sleep was in having problems falling sleep (p-value=0.024), having bad/horrible dreams (p-value=0.017), feeling cold at sleeping (p-value=0.038), and difficulty staying active during eating or driving (p-value=0.012). There was a significant correlation with anxiety related to the COVID-19 contact and problems affecting the routine work (p-value=0.001). CONCLUSION: There is a significant correlation with anxiety related to the contact with COVID-19 positive patients and problems affecting the routine work among operational professionals. The sleep quality is poor due to the stay-at-home order, having a disorganized working schedule, and deprived lifestyle. The awareness regarding the publics' mental health related to the pandemic needs to be implemented and psychological guidelines ought to be available for the public. Health measures through the promotion of lifestyle modifications, mindful body practices, meditation, and home exercise can reduce stress and improve the quality of sleep.

Antioxidant status in relation to heavy metals induced oxidative stress in patients with polycystic ovarian syndrome (PCOS).

Polycystic ovary syndrome (PCOS) is a global health concern for women of reproductive age, as 6.5% of women worldwide are affected by this syndrome. PCOS is marked by hyperandrogenism, anovulation, menstrual abnormalities, and polycystic ovaries. Metals such as arsenic, cadmium, lead and mercury are considered to be systemic toxicants/human carcinogens and seem to have devastating effects on humans, even at minimal exposures. One of the probable aetiological factors for PCOS has been identified as oxidative stress. In view of the probable associations among oxidative stress, metal toxicity and PCOS, the present study examined the role of heavy metals in the generation of oxidative stress among females. This prospective study included 106 women (56 women diagnosed with PCOS and 50 women who were not diagnosed with PCOS as control women). There were no significant differences in the sociodemographic characteristics between the two groups except for the irregularity of menses and the presence of acne. The serum As, Cd, Pb, and Hg levels increased and the serum glutathione (GSH) and superoxide dismutase (SOD) levels diminished significantly in the PCOS group compared to the control group at P < 0.001. The SOD levels were negatively correlated with the As and Pb levels at P < 0.05. Additionally, the PCOS group exhibited a strong negative correlation between the GSH and As levels (P < 0.01), GSH and Pb levels (P < 0.05) and GSH and Hg levels (P < 0.01). Furthermore, the As levels were positively correlated with increased levels of Cd, Pb and Hg among PCOS women. Significant positive correlations were observed between Pb and Cd and between Cd and Hg at P < 0.001. The outcome of the study provides clear insight into the role of metal-induced oxidative stress, which plays a vital role in the pathophysiology underlying PCOS and suggests the use of these markers as prognostic tools to reduce the consequences of high-risk exposure to these metals among females.

DU-Net: Convolutional Network for the Detection of Arterial Calcifications in Mammograms.

Breast arterial calcifications (BACs) are part of several benign findings present on some mammograms. Previous studies have indicated that BAC may provide evidence of general atherosclerotic vascular disease, and potentially be a useful marker of cardiovascular disease (CVD). Currently, there is no technique in use for the automatic detection of BAC in mammograms. Since a majority of women over the age of 40 already undergo breast cancer screening with mammography, detecting BAC may offer a method to screen women for CVD in a way that is effective, efficient, and broad reaching, at no additional cost or radiation. In this paper, we present a deep learning approach for detecting BACs in mammograms. Inspired by the promising results achieved using the U-Net model in many biomedical segmentation problems and the DenseNet in semantic segmentation, we extend the U-Net model with dense connectivity to automatically detect BACs in mammograms. The presented model helps to facilitate the reuse of computation and improve the flow of gradients, leading to better accuracy and easier training of the model. We evaluate the performance using a set of full-field digital mammograms collected and prepared for this task from a publicly available dataset. Experimental results demonstrate that the presented model outperforms human experts as well as the other related deep learning models. This confirms the effectiveness of our model in the BACs detection task, which is a promising step in providing a cost-effective risk assessment tool for CVD.

Psycho-social impacts, experiences and perspectives of patients with Cutaneous Leishmaniasis regarding treatment options and case management: An exploratory qualitative study in Tunisia.

Although non-fatal and mostly self-healing in the case of Leishmania (L.) major, cutaneous leishmaniasis (CL) is mainly treated to reduce lesion healing time. Less attention is paid to the improvement of scars, especially in aesthetically relevant areas of the body, which can dramatically affect patients' wellbeing. We explored patients' perspectives about treatment options and the social and psychological burden of disease (lesion and scar). Individual in-depth interviews were conducted with ten confirmed CL patients at two L. major endemic sites in Southern Tunisia (Sidi Bouzid and Gafsa). Participants were selected using a sampling approach along a spectrum covering e.g. age, sex, and clinical presentation. Patients' experiences, opinions and preferences were explored, and their detailed accounts gave an insight on the impact of CL on their everyday lives. The impact of CL was found to be considerable. Most patients were not satisfied with treatment performance and case management. They expected a shorter healing time and better accessibility of the health system. Tolerance of the burden of disease was variable and ranged from acceptance of hidden scars to suicidal thoughts resulting from the fear to become handicapped, and the stress caused by close relatives. Some believed CL to be a form of skin cancer. Unexpectedly, this finding shows the big gap between the perspectives of patients and assumptions of health professionals regarding this disease. This study provided valuable information for better case management emphasizing the importance of improving communication with patients, and accessibility to treatment. It generated context-specific knowledge to policy makers in Tunisia to implement effective case management in a country where access to treatment remains a challenge due to socio-economic and geographic barriers despite a long tradition in CL control.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

Development and Validation of a Simple Diagnostic Method to Detect Gain and Loss of Function Defects in Fibroblast Growth Factor-23.

BACKGROUND: Fibroblast growth factor-23 (FGF23) is a bone-derived hormone that regulates the homeostasis of phosphate and vitamin D. Three substitutions in the hormone are reported to cause autosomal dominant hypophosphatemic rickets and seven substitutions to cause autosomal recessive hyperphosphatemic familial tumoral calcinosis (HFTC). Both disorders are rare in the general population and occur most often in the Eastern Mediterranean region and Africa. None of the mutations could be identified using standard restriction fragment length polymorphism. The only technique currently available to confirm the clinical diagnosis is DNA sequencing. METHODS: Using a tri-primer ARMS-PCR, in vitro site-directed mutagenesis and DNA sequencing, we developed, verified and validated a rapid and reliable diagnostic test for the ten mutations in FGF23. RESULTS: We generated a test for all ten mutations and confirmed each test by DNA sequencing. We increased the specificity of the test by introducing a mismatch at position -2 in the 3'-terminus of the reverse primer of the normal and the mutant sequences. Finally, using DNA sequencing, we validated the technique for FGF23/S129F substitution by testing samples from 80 individuals from two unrelated Arab families harboring HFTC. CONCLUSIONS: This inexpensive and specific method could be adopted where DNA sequencing is not available or affordable.

FGF23-S129F mutant bypasses ER/Golgi to the circulation of hyperphosphatemic familial tumoral calcinosis patients.

FGF23 is essential for the homeostasis of phosphate, and vitamin D. Loss-of-function mutations in this hormone cause hyperphosphatemic familial tumoral calcinosis (HFTC). Earlier reports suggested that intact FGF23 from loss of function mutants such as FGF23/S129F (iFGF23/S129F) is retained intracellularly while the carboxy-terminal fragment is secreted. We sought to investigate the fate of iFGF23/S129F mutant hormone in vivo and in vitro. Five patients clinically diagnosed with HFTC and confirmed by DNA sequencing to carry the c.386 C>T; p.S129F mutation in the homozygous state were studied. Healthy and heterozygous individuals were used as controls in the study. Using ELISA assays, we showed that iFGF23/S129F was 2-5 folds higher in patients' plasma, compared to heterozygous or healthy controls. Importantly, the mutant hormone could not be detected in the patients' sera. However, using proteinase inhibition profiling, we found that a serum metalloproteinase degraded the iFGF23/S129F explaining our failure to detect it in sera. The serum metalloproteinase degrades the WT and the mutant at different rates. Also, confocal microscopy imaging using wild-type (WT) FGF23 or FGF23/S129F mutant in transiently transfected HEK293 and HeLa cells showed weak staining of the Golgi complex with some vesicular staining resembling the ER. Additionally, FGF23 variants (FGF23/WT, FGF23/S129F, FGF23/S71G, and FGF23/R176Q) from stably transfected HEK293 cells secreted high levels into a serum-free medium that can be detected by ELISA and Western blot. Our results suggest that iFGF23/S129F mutant bypasses the ER/Golgi quality control system to the circulation of HFTC patients by an unknown pathway. Finally, we hypothesize that either the mutant hormone is unable to bind α-Klotho-FGFR1c, or it binds the dyad receptor with low affinity and, therefore, incapable of initiating maximal intracellular signaling. Our findings raise the potential use of the WT hormone in therapies of some HFTC patients.

A Systematic Review of Mobile Health Technology Use in Developing Countries.

In developing countries, patients are now more informed about their healthcare options as a result of their use of mobile health (mHealth) technologies. The purpose of this paper is to describe the opportunities and challenges in using mHealth technologies for developing countries. In April 2015, Google Scholar and PubMed were searched to identify articles discussing the types, advantages and disadvantages, effectiveness, evaluation of mHealth technologies, and examples of mHealth implementation in developing countries. A total number of 3,803 articles were retrieved from both databases. Articles reporting the benefits and risks, effectiveness, and evaluation of mHealth were included. Articles that were written in English and from developing countries were also included. We excluded papers that were published before 2005, not written in English, and that were technical in nature. After screening the articles using the inclusion and exclusion criteria, 27 articles were selected for inclusion in the study. Of the 27 papers included in the review, eight described opportunities and challenges relating to mHealth, four focused on smoking cessation, three focused on weight loss, and four papers focused on chronic diseases. We also identified four articles discussing mHealth evaluation and four discussing the use of mHealth as a health promotion tool. We conclude that mHealth can improve healthcare delivery for developing countries. Some of the advantages of mHealth include: patient education, health promotion, disease self-management, decrease in healthcare costs, and remote monitoring of patients. However, there are several limitations in using mHealth technologies for developing countries, which include: interoperability, lack of evaluation standards, and lack of a technology infrastructure.