Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical characteristics do not reliably identify non-adherence in patients with uncontrolled hypertension

Abstract Purpose Chemical adherence testing is a reliable method to assess adherence to antihypertensive drugs. However, it is expensive and has limited availability in clinical practice. To reduce the number and costs of chemical adherence tests, we aimed to develop and validate a clinical screening tool to identify patients with a low probability of non-adherence in patients with uncontrolled hypertension. Materials and Methods In 495 patients with uncontrolled hypertension referred to the University Medical Centre Utrecht (UMCU), the Netherlands, a penalised logistic regression model including seven pre-specified easy-to-measure clinical variables was derived to estimate the probability of non-adherence. Non-adherence was defined as not detecting at least one of the prescribed antihypertensive drugs in plasma or urine. Model performance and test characteristics were evaluated in 240 patients with uncontrolled hypertension referred to the Heartlands Hospital, United Kingdom. Results Prevalence of non-adherence to antihypertensive drugs was 19% in the UMCU and 44% in the Heartlands Hospital population. After recalibration of the model’s intercept, predicted probabilities agreed well with observed frequencies. The c-statistic of the model was 0.63 (95%CI 0.53–0.72). Predicted probability cut-off values of 15%–22.5% prevented testing in 5%–15% of the patients, carrying sensitivities between 97% (64–100) and 90% (80–95), and negative predictive values between 74% (10–99) and 70% (50–85). Conclusion The combination of seven clinical variables is not sufficient to reliably discriminate adherent from non-adherent individuals to safely reduce the number of chemical adherence tests. This emphasises the complex nature of non-adherence behaviour and thus the need for objective chemical adherence tests in patients with uncontrolled hypertension.
Development of Piezoresistive Sensors Based on Graphene Nanoplatelets Screen-Printed on Woven and Knitted Fabrics: Optimisation of Active Layer Formulation and Transversal/Longitudinal Textile Direction

Although the force/pressure applied onto a textile substrate through a uniaxial compression is constant and independent of the yarn direction, it should be noted that such mechanical action causes a geometric change in the substrate, which can be identified by the reduction in its lateral thickness. Therefore, the objective of this study was to investigate the influence of the fabric orientation on both knitted and woven pressure sensors, in order to generate knowledge for a better design process during textile piezoresistive sensor development. For this purpose, these distinct textile structures were doped with different concentrations of graphene nanoplatelets (GNPs), using the screen-printing technique. The chemical and physical properties of these screen-printed fabrics were analysed using Field Emission Scanning Electron Microscopy, Ground State Diffuse Reflectance and Raman Spectroscopy. Samples were subjected to tests determining linear electrical surface resistance and piezoresistive behaviour. In the results, a higher presence of conductive material was found in woven structures. For the doped samples, the electrical resistance varied between 105 Ω and 101 Ω, for the GNPs’ percentage increase. The lowest resistance value was observed for the woven fabric with 15% GNPs (3.67 ± 8.17 × 101 Ω). The samples showed different electrical behaviour according to the fabric orientation. Overall, greater sensitivity in the longitudinal direction and a lower coefficient of variation CV% of the measurement was identified in the transversal direction, coursewise for knitted and weftwise for woven fabrics. The woven fabric doped with 5% GNPs assembled in the weftwise direction was shown to be the most indicated for a piezoresistive sensor, due to its most uniform response and most accurate measure of mechanical stress.
Further Validation of the Menu Task: Functional Cognition Screening for Older Adults

Occupational therapists need a brief tool to identify the potential for functional cognitive deficits leading to impaired occupational performance. The objective is to establish the sensitivity and specificity, concurrent and known-group validity of the Menu Task by comparison with performance on the Weekly Calendar Planning Activity (WCPA). Using a cross-sectional design, we administered the Menu Task and the WCPA to a community-dwelling convenience sample of 287 adults aged from 55 to 93 years. The receiver operating characteristic (ROC) analysis estimated sensitivity and specificity. Concurrent and known-group construct validity was examined by comparing scores on the Menu Task with the WCPA scores. As a result, a new cutoff score of 9 was established for the Menu Task (area under the curve [AUC] = 0.80, sensitivity = 0.89, 95% confidence interval [CI] = [0.73, 0.97]; specificity = 0.58, 95% CI = [0.52, 0.64]). Both concurrent and construct validity were supported. The Menu Task demonstrates sensitivity to functional cognitive impairments in a community sample.
Concrete Made with Partially Substitutions of Copper Slag (CPS): A State Art of Review

Copper slag (CPS) is a large amount of waste material produced during the manufacture of copper. The disposal of this waste material becomes a problem for environmental concerns. Therefore, it is necessary to explore feasible alternate disposal options. They may also be utilized in concrete manufacturing to cut down on the usage of cement and natural aggregates. A lot of researchers focus on utilizing CPS in concrete, either as a cement replacement or as a filler material. This article aims to summarize the literature already carried out on CPS in conventional concrete to identify the influence of CPS on the fresh, hardened and durability performance of cement concrete. Results indicate that CPS improved the strength and durability performance of concrete but simultaneously decreased the slump value of concrete. Furthermore, an increase in the durability performance of concrete was also observed with CPS. However, the higher dose results declined in mechanical and durability aspects owing to a scarcity of flowability. Therefore, it is suggested to use the optimum dose of CPS. However, a different researcher recommends a different optimum dose ranging from 50 to 60% by weight of fine aggregate depending on the source of CPS. The review also recommends future researcher guidelines on CPS in concrete.
Feature-Level Analysis of a Smoking Cessation Smartphone App Based on a Positive Psychology Approach: Prospective Observational Study

Background Smoking cessation smartphone apps have emerged as highly accessible tools to support smoking cessation efforts. It is unknown how specific app features contribute to user engagement over time and relate to smoking outcomes. Objective To provide a feature-level analysis of the Smiling Instead of Smoking app (version 2) and to link feature use to subsequent smoking cessation. Methods Nondaily smokers (N=100) used the app for a period of 49 days (1 week before quitting and 6 weeks after quitting). Participants self-reported 30-day point-prevalence abstinence at the end of this period and at a 6-month follow up (the survey response rate was 94% and 89% at these points, respectively). Self-reported 30-day point prevalence abstinence rates were 40% at the end of treatment and 56% at the 6-month follow up. The app engaged users in both positive psychology content and traditional behavioral smoking cessation content. The app sent push notifications to prompt participants to complete prescribed content (ie, a “happiness exercise” every day and a “behavioral challenge” to use the app’s smoking cessation tools on 15 out of 49 days). Actions that participants took within the app were timestamped and recorded. Results Participants used the app on 24.7 (SD 13.8) days out of the 49 prescribed days, interacting with the happiness content on more days than the smoking content (23.8, SD 13.8 days vs 17.8, SD 10.3 days; t99=9.28 [2-tailed]; P<.001). The prescribed content was frequently completed (45% of happiness exercises; 57% of behavioral challenges) and ad libitum tools were used on ≤7 days. Most participants used each ad libitum smoking cessation tool at least once, with higher use of personalized content (≥92% used “strategies,” “cigarette log,” “smoke alarms,” and “personal reasons”) than purely didactic content (79% viewed “benefits of quitting smoking”). The number of days participants used the app significantly predicted 30-day point-prevalence abstinence at the end of treatment (odds ratio [OR] 1.05, 95% CI 1.02-1.09; P=.002) and at the 6-month follow up (OR 1.04, 95% CI 1.008-1.07; P=.01). The number of days participants engaged with the happiness content significantly predicted smoking abstinence at the end of treatment (OR 1.05, 95% CI 1.02-1.08; P=.002) and at the 6-month follow up (OR 1.04, 95% CI 1.007-1.07; P=.02). This effect was not significant for the number of days participants engaged with the smoking cessation content of the app, either at the end of treatment (OR 1.04, 95% CI 0.996-1.08, P=.08) or at the 6-month follow up (OR 1.02, 95% CI 0.98-1.06; P=.29). Conclusions Greater app usage predicted greater odds of self-reported 30-day point-prevalence abstinence at both the end of treatment and over the long term, suggesting that the app had a therapeutic benefit. Positive psychology content and prescriptive clarity may promote sustained app engagement over time. Trial Registration ClinicalTrials.gov NCT03951766; https://clinicaltrials.gov/ct2/show/NCT03951766
A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients

Introduction Identifying COVID-19 patients that are most likely to progress to a severe infection is crucial for optimizing care management and increasing the likelihood of survival. This study presents a machine learning model that predicts severe cases of COVID-19, defined as the presence of Acute Respiratory Distress Syndrome (ARDS) and highlights the different risk factors that play a significant role in disease progression. Methods A cohort composed of 289,351 patients diagnosed with COVID-19 in April 2020 was created using US administrative claims data from Oct 2015 to Jul 2020. For each patient, information about 817 diagnoses, were collected from the medical history ahead of COVID-19 infection. The primary outcome of the study was the presence of ARDS in the 4 months following COVID-19 infection. The study cohort was randomly split into training set used for model development, test set for model evaluation and validation set for real-world performance estimation. Results We analyzed three machine learning classifiers to predict the presence of ARDS. Among the algorithms considered, a Gradient Boosting Decision Tree had the highest performance with an AUC of 0.695 (95% CI, 0.679–0.709) and an AUPRC of 0.0730 (95% CI, 0.0676 – 0.0823), showing a 40% performance increase in performance against a baseline classifier. A panel of five clinicians was also used to compare the predictive ability of the model to that of clinical experts. The comparison indicated that our model is on par or outperforms predictions made by the clinicians, both in terms of precision and recall. Conclusion This study presents a machine learning model that uses patient claims history to predict ARDS. The risk factors used by the model to perform its predictions have been extensively linked to the severity of the COVID-19 in the specialized literature. The most contributing diagnosis can be easily retrieved in the patient clinical history and can be used for an early screening of infected patients. Overall, the proposed model could be a promising tool to deploy in a healthcare setting to facilitate and optimize the care of COVID-19 patients.
Social Life of Females with Persistent COVID-19 Symptoms: A Qualitative Study

Persistent COVID-19 symptoms (long COVID) may bring challenges to long haulers’ social lives. Females may endure more profound impacts given their special social roles and existing structural inequality. This study explores the effects of long COVID on the social life of female long haulers. We conducted semi-structured interviews via Zoom between April and June 2021 with 15 female long haulers in the United States, purposely recruited from Facebook and Slack groups and organization websites related to long COVID. Interviews were audio-recorded and transcribed verbatim with consent. The interview data were managed using MAXQDA and examined by thematic analysis. Long COVID negatively affected female long haulers’ social lives by causing physical limitations, economic issues, altered social relationships, social roles’ conflicts, and social stigma. Long COVID prevented female long haulers’ recovery process. Physical limitations altered their perceptions on body, and family–work conflicts caused tremendous stress. They also experienced internalized stigma and job insecurities. This study provides insights into challenges that COVID-19 female long haulers could face in their return to normal social life, underscoring the vulnerability of females affected by long COVID due to significant alterations in their social lives. Shifting to new methods of communication, especially social media, diminished the adverse effects of long COVID (e.g., social isolation).
World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for treatment of anxiety, obsessive-compulsive and posttraumatic stress disorders – Version 3. Part II: OCD and PTSD

Abstract Aim This is the third version of the guideline of the World Federation of Societies of Biological Psychiatry (WFSBP) Task Force for the Pharmacological Treatment of Anxiety, Obsessive–Compulsive and Posttraumatic Stress Disorders which was published in 2002 and revised in 2008. Method A consensus panel of 34 international experts representing 22 countries developed recommendations based on efficacy and acceptability of the treatments. In this version, not only medications but also psychotherapies and other non-pharmacological interventions were evaluated, applying the same rigorous methods that are standard for the assessment of medication treatments. Result The present paper (Part II) contains recommendations based on published randomised controlled trials (RCTs) for the treatment of OCD (n = 291) and PTSD (n = 234) in children, adolescents, and adults. The accompanying paper (Part I) contains the recommendations for the treatment of anxiety disorders. For OCD, first-line treatments are selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioural therapy (CBT). Internet-CBT was also superior to active controls. Several second-line medications are available, including clomipramine. For treatment-resistant cases, several options are available, including augmentation of SSRI treatment with antipsychotics and other drugs. Other non-pharmacological treatments, including repetitive transcranial magnetic stimulation (rTMS), deep brain stimulation (DBS) and others were also evaluated. For PTSD, SSRIs and the SNRI venlafaxine are first-line treatments. CBT is the psychotherapy modality with the best body of evidence. For treatment-unresponsive patients, augmentation of SSRI treatment with antipsychotics may be an option. Conclusion OCD and PTSD can be effectively treated with CBT and medications.
Smoking in the workplace: A study of female call center employees in South Korea

Smoking among women is characteristically high among call center employees and is associated with various individual and work-related characteristics, which have been paid little attention so far. This study explored the differences in intrapersonal and interpersonal characteristics and environmental factors among Korean women working in call centers by smoking status, based on an ecological model. In this cross-sectional study, an anonymous online survey was conducted among a sample of female employees from three credit card-based call centers (N = 588). Differences in intrapersonal (social nicotine dependence, smoking attitudes, emotional labor), interpersonal (smoking among family or friends, social support), and environmental factors (smoking cessation education, and perceived and preferred smoking policy at work) were compared according to smoking status (smokers, ex-smokers, and never smokers). Approximately 20% (n = 115) were smokers. Smokers were younger, mostly unmarried, had lower education, and had poorer perceived health status than ex- and never smokers. The mean scores for social nicotine dependence and smoking attitude were the highest among smokers, indicating their tendency to underestimate the negative effects of smoking. They also reported the highest level of emotional labor, with about half (50.4%) and almost all (95.7%) reporting smoking behaviors in their families and friends, respectively. Smokers took a lenient stance on the smoking ban policy. The results indicated the necessity to develop tailored smoking cessation programs to motivate female call center employees to quit smoking. As call centers may have a smoking-friendly environment, comprehensive smoking prevention programs considering multilevel factors are required to support smoking cessation.
Identifying Ultraprocessed Foods in Nutrition Research

It is well established that poor diet quality is associated with an increased risk for dementia.1 We typically measure diet quality regarding the amount of specific nutrients in the diet (e.g., omega-3 fatty acids, added sugar, etc.) or dietary patterns (e.g., the Mediterranean or MIND diet, which recommend high fruit and vegetable consumption, whole grains, etc). Recently, there has been a shift in field of nutrition, with many researchers focused on another component of the diet: the level of “processing” in the foods we eat. However, categorizing foods into unprocessed/minimally processed, processed, and ultraprocessed may be more challenging than the categorization methods of nutrition research that focus on specific nutrients or dietary patterns.
SYSTEMATIC Non-LTE STUDY OF THE −2.6 ≤ [Fe/H] ≤ 0.2 F AND G DWARFS IN THE SOLAR NEIGHBORHOOD. I. STELLAR ATMOSPHERE PARAMETERS

We present atmospheric parameters for 51 nearby F and G dwarf and subgiant stars uniformly distributed over the − 2.60 < [ Fe / H ] < + 0.20 ? > metallicity range that is suitable for the Galactic chemical evolution research. Lines of iron in the two ionization stages, Fe i and Fe ii, were used to derive a homogeneous set of effective temperatures, surface gravities, iron abundances, and microturbulence velocities. Our spectroscopic analyses took advantage of employing high-resolution (R ≥ 60,000) Shane/Hamilton and Canada–France–Hawaii Telescope/ESPaDOnS observed spectra and non-LTE (NLTE) line formation for Fe i and Fe ii in the classical one-dimensional model atmospheres. The spectroscopic method was tested in advance with the 20 benchmark stars, for which there are multiple measurements of the infrared flux method effective temperature and their Hipparcos parallax error is less than 10%. We found NLTE abundances from lines of Fe i and Fe ii to be consistent within 0.06 dex for every benchmark star, when applying a scaling factor of S H ? > = 0.5 to the Drawinian rates of inelastic Fe+H collisions. The obtained atmospheric parameters were checked for each program star by comparing its position in the log g– T eff ? > plane with the theoretical evolutionary track of given metallicity and α-enhancement in the Yi et al. grid. Our final effective temperatures lie exactly in between the T IRFM ? > scales of Alonso et al. and Casagrande et al., with a mean difference of +46 and −51 K, respectively. NLTE leads to higher surface gravity compared with that for LTE. The shift in log g is smaller than 0.1 dex for stars with [Fe/H] ≥ − 0.75 ? > , T eff ? > ≤ 5750 K, or log g ≥ 4.20. NLTE analysis is crucial for the very metal-poor turnoff and subgiant stars, for which the shift in log g between NLTE and LTE can be up to 0.5 dex. The obtained accurate atmospheric parameters will be used in the forthcoming papers to determine NLTE abundances of important astrophysical elements from lithium to europium and to improve observational constraints on the chemodynamical models of the Galaxy evolution.
Integral models of P^1 and analytic distribution algebras for GL_2

In the first part of the paper we show that the ring of global sections of arithmetic differential operators on the formal projective line over Zp is isomorphic to the analytic distribution algebra of the 'wide open' congruence subgroup of level zero of GL(2,Zp). In the second part we study rings of logarithmic differential operators on semistable integral models of the projective line over Zp and we relate these to analytic distribution algebra of 'wide open' congruence subgroups of higher level.
Open subgroups and the centre problem for the fourier algebra

Let A(G) be the Fourier algebra of a locally compact group and UCB(G) the C*-algebra of uniformly continuous linear functionals on A(G). We study how the centre problem for the algebra UCB(G)* (resp. A(G)**) is related to the centre problem for the algebras UCB(G)* (resp. A(H)**) of σ-compact open subgroups H of G. We extend some results of Lau-Losert on the centres of UCB(G)* and A(G)**.
Social determinants of health protocol adherence among adults during COVID-19 pandemic in Yogyakarta, Indonesia.

INTRODUCTION Coronavirus disease (COVID-19) must be controlled by involving all stakeholders, including the community. Community protocol compliance with COVID-19 health guidelines is essential. This study assessed the social determinants of health on community protocol compliance with COVID-19 health guidelines among adults in Yogyakarta, Indonesia. MATERIALS AND METHODS This study was a mixed-method study of 461 adults from February through May 2021 in Yogyakarta Province. We collected data through an online survey, focus group discussions, and in-depth interviews. Logistic regression was used to analyze the results. RESULTS Most respondents (86%) always wore masks, followed social distancing (51.8%), and washed their hands regularly (99%). Subjects older than 45 years, women, and community leaders demonstrated greater compliance with COVID-19 health protocols compared to other people. On the other hand, the occupation has become a healthy lifestyle practice indicator. CONCLUSION Gender, age, educational level, economics, and social status were determinants of health protocol adherence among adults in Yogyakarta. Therefore, health providers need to consider social determinants for health promotion approaches and COVID-19 prevention and control strategies.