Morphine (MORPH) is natural alkaloid and mainly used as a pain reliever. Its monitoring in human body fluids is crucial for modern medicine. In this paper, we have developed an electrochemical sensor for submicromolar detection of MORPH. The sensor is based on modified carbon paste electrode (CPE) by investigating the FexW1-xO4 ratio in iron tungstate (FeWO4), as well as the ratio of this material in CPE. For the first time, the effect of the iron–tungsten ratio in terms of achieving the best possible electrochemical characteristics for the detection of an important molecule for humans was examined. Morphological and electrochemical characteristics of materials were studied. The best results were obtained using Fe1W3 and 7.5% of modifier in CPE. For MORPH detection, square wave voltammetry (SWV) was optimized. Under the optimized conditions, Fe1W3@CPE resulted in limit of detection (LOD) of the method of 0.58 µM and limit of quantification (LOQ) of 1.94 µM. The linear operating range between 5 and 85 µM of MORPH in the Britton–Robinson buffer solution (BRBS) at pH 8 as supporting electrolyte was obtained. The Fe1W3@CPE sensor resulted in good selectivity and excellent repeatability with relative standard deviation (RSD) and was applied in real-world samples of human urine. Application for direct MORPH detection, without tedious sample pretreatment procedures, suggests that developed electrochemical sensor has appeared to be a suitable competitor for efficient, precise, and accurate monitoring of the MORPH in biological fluids.

Purpose Educational interventions have already been shown to positively affect awareness of clinical trials (CTs) among medical students. We aimed to explore basic knowledge and attitudes about CTs among medical students in terms of educational interventions that should be reflected in their further involvement in performing CTs and their role in raising awareness about CTs. Methods This cross-sectional, self-report anonymous online survey involved undergraduate medical students of the Medical Faculty University of Sarajevo enrolled in classes held within the Department of Pharmacology and Toxicology in the academic year 2015–2016. To include all accessible subjects for better representation of the whole population, consecutive sampling was applied. Results Among 142 students who completed questionnaire, 50% of them expressed partial or full agreement with the questionnaire statement that they were satisfied with the available information on CTs. Only 38% said they would participate in a CT, 21% would not, while 41% were not sure. Positive correlations were detected for composite subscale scores of agreement with questionnaire statements conveying the student’s knowledge about ethical and legal aspects of CTs and their perception about reliability/integrity and impact of CTs on medical practice. Conclusion Students have knowledge of the basic design and ethical aspects of CTs. Positive attitudes toward the impact of CTs on medical practice were shown in students of higher years of study, where educational intervention of additional knowledge of CTs was inserted and those students expressed better knowledge of CTs. However, no significant impact was detected between knowledge and willingness to participate in CTs, irrespective of years of study, reflecting the third of students that would participate in CTs. Changes in medical curricula led to the change in students’ knowledge and attitudes regarding CTs as well as their involvement in CTs.

Pharmacogenomics (PGx)-guided drug treatment is one of the cornerstones of personalized medicine. However, the genes involved in drug response are highly complex and known to carry many (rare) variants. Current technologies (short-read sequencing and SNP panels) are limited in their ability to resolve these genes and characterize all variants. Moreover, these technologies cannot always phase variants to their allele of origin. Recent advance in long-read sequencing technologies have shown promise in resolving these problems. Here we present a long-read sequencing panel-based approach for PGx using PacBio HiFi sequencing. A capture based approach was developed using a custom panel of clinically-relevant pharmacogenes including up- and downstream regions. A total of 27 samples were sequenced and panel accuracy was determined using benchmarking variant calls for 3 Genome in a Bottle samples and GeT-RM star(*)-allele calls for 21 samples.. The coverage was uniform for all samples with an average of 94% of bases covered at >30×. When compared to benchmarking results, accuracy was high with an average F1 score of 0.89 for INDELs and 0.98 for SNPs. Phasing was good with an average of 68% the target region phased (compared to ~20% for short-reads) and an average phased haploblock size of 6.6kbp. Using Aldy 4, we compared our variant calls to GeT-RM data for 8 genes (CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, SLCO1B1, TPMT), and observed highly accurate star(*)-allele calling with 98.2% concordance (165/168 calls), with only one discordance in CYP2C9 leading to a different predicted phenotype. We have shown that our long-read panel-based approach results in high accuracy and target phasing for SNVs as well as for clinical star(*)-alleles.

Background Engagement is key to interventions that achieve successful behavior change and improvements in health. There is limited literature on the application of predictive machine learning (ML) models to data from commercially available weight loss programs to predict disengagement. Such data could help participants achieve their goals. Objective This study aimed to use explainable ML to predict the risk of member disengagement week by week over 12 weeks on a commercially available web-based weight loss program. Methods Data were available from 59,686 adults who participated in the weight loss program between October 2014 and September 2019. Data included year of birth, sex, height, weight, motivation to join the program, use statistics (eg, weight entries, entries into the food diary, views of the menu, and program content), program type, and weight loss. Random forest, extreme gradient boosting, and logistic regression with L1 regularization models were developed and validated using a 10-fold cross-validation approach. In addition, temporal validation was performed on a test cohort of 16,947 members who participated in the program between April 2018 and September 2019, and the remaining data were used for model development. Shapley values were used to identify globally relevant features and explain individual predictions. Results The average age of the participants was 49.60 (SD 12.54) years, the average starting BMI was 32.43 (SD 6.19), and 81.46% (39,594/48,604) of the participants were female. The class distributions (active and inactive members) changed from 39,369 and 9235 in week 2 to 31,602 and 17,002 in week 12, respectively. With 10-fold-cross-validation, extreme gradient boosting models had the best predictive performance, which ranged from 0.85 (95% CI 0.84-0.85) to 0.93 (95% CI 0.93-0.93) for area under the receiver operating characteristic curve and from 0.57 (95% CI 0.56-0.58) to 0.95 (95% CI 0.95-0.96) for area under the precision-recall curve (across 12 weeks of the program). They also presented a good calibration. Results obtained with temporal validation ranged from 0.51 to 0.95 for area under a precision-recall curve and 0.84 to 0.93 for area under the receiver operating characteristic curve across the 12 weeks. There was a considerable improvement in area under a precision-recall curve of 20% in week 3 of the program. On the basis of the computed Shapley values, the most important features for predicting disengagement in the following week were those related to the total activity on the platform and entering a weight in the previous weeks. Conclusions This study showed the potential of applying ML predictive algorithms to help predict and understand participants’ disengagement with a web-based weight loss program. Given the association between engagement and health outcomes, these findings can prove valuable in providing better support to individuals to enhance their engagement and potentially achieve greater weight loss.

The shallowest intracrustal layer (extending to 8 ± 2 km depth) beneath the Mars InSight Lander site exhibits low seismic wave velocity, which is likely related to a combination of high porosity and other lithological factors. The SsPp phase, an SV‐ to P‐wave reflection on the receiver side, is naturally suited for constraining the seismic structure of this top crustal layer since its prominent signal makes it observable with a single station without the need for stacking. We have analyzed six broadband and low‐frequency seismic events recorded on Mars and made the first coherent detection of the SsPp phase on the red planet. The timing and amplitude of SsPp confirm the existence of the ∼8 km interface in the crust and the large wave speed (or impedance) contrast across it. With our new constraints from the SsPp phase, we determined that the average P‐wave speed in the top crustal layer is between 2.5 and 3.2 km/s, which is a more precise and robust estimate than the previous range of 2.0–3.5 km/s obtained by receiver function analysis. The low velocity of Layer 1 likely results from the presence of relatively low‐density lithified sedimentary rocks and/or aqueously altered igneous rocks that also have a significant amount of porosity, possibly as much as 22%–30% by volume (assuming an aspect ratio of 0.1 for the pore space). These porosities and average P‐wave speeds are compatible with our current understanding of the upper crustal stratigraphy beneath the InSight Lander site.

Tofacitinib is an oral Janus kinase (JAK) inhibitor and is registered for the treatment of ulcerative colitis (UC). The effectiveness of tofacitinib has been evaluated up to 12 months of treatment.

This paper is effectively a scenario analysis of the energy system of Bosnia and Herzegovina (BiH) from the perspective of the possible future reduction of greenhouse gas (GHG) emissions in the power generation sector, with the aim to become climate neutral by 2050, in compliance with the Green Agenda for the Western Balkan. According to the data from 2016, the share of power generation in the total GHG emissions in BiH was approximately 50%. By using the LEAP (Long-range Energy Alternatives Planning) energy model, two scenarios—the “gradual transition scenario” and the “climate neutral” scenario—have been analyzed for the period 2018–2050, and each scenario included decarbonization measures such as the extensive use of Renewable Energy Sources (RES). Unlike the climate neutral scenario, the gradual transition scenario includes the replacement of certain parts of the old, currently-in-operation Coal-fired Power Plants (CFPPs) with the new CFPP, which is more efficient. In the climate-neutral scenario, that part of the existing CFPPs is replaced by a mix of RESs. The results from the first scenario suggest that the share of CFPPs in electricity generation has gradually decreased from 69.3% to 16.3% in 2050, and CO2 emissions from the power generation sector in 2050 will be 2.2 million tons—roughly 83.5% less than in 2014. According to the second scenario, the emphasis is strongly on the growth and promotion of RESs, which have significantly taken over the roles of major producers of electricity, encouraging the low-carbon development of BiH. Analysis results show that, in 2050, there will be no CO2 emissions from power generation. It can be concluded that specifically designed energy models for the optimization of capacities and CO2 emissions through convergence towards RESs could be an optimistic and promising option for BiH to become climate neutral while meeting increasing energy demands. The results show the required RES capacities needed for achieving climate-neutral power generation by 2050, with the current rate level of power generation. Based on the results, RES investment needs can be estimated. Overall, the results of the scenarios can be used for the strategic planning of the power generation sector in BiH until 2050.

Previous research in human-robot interaction has explored using robots to increase objective and hedonic aspects of well-being and quality of life, but there is no literature on how robots might be used to support eudaimonic aspects of well-being (such as meaning in life). A sense of meaning has been shown to positively affect health and longevity. We frame our study around the Japanese concept of ikigai, which is widely used with Japanese older adults to enhance their everyday lives, and is closely related to the concept of eudaimonic well-being (EWB) known in Western countries. Using a mixed-methods and exploratory approach, including interviews with 17 older adults and the collection of 100 survey responses, we explored how older adults in the US experience a sense of meaning, and if and how a social robot could be used to help foster this sense. We find that meaning for older adults is often obtained by helping others, through family connections, and/or through activities of daily life, and that sources of meaning often differ based on the older adults’ living situation. Assessing how meaning compares to happiness and social connection, we highlight general similarities and differences, and also find that living situation influences older adults’ sources of happiness, desire for social connection, and barriers to well-being., in addition to companionship and happiness having a weaker correlation with meaning for those who live alone than for those who live with others. Additionally, we evaluated initial perceptions of a social robot (QT) meant to enhance ikigai and overall well-being. Finding mostly positive perceptions, though those who live alone also reported being less willing to adopt a social robot into their homes. Using both data collected on older adults’ meaning and the potential use of QT to support meaning, we make several design recommendations with regards to using robots to enhance ikigai, such as by prompting daily reflecting, enhancing family bonds, and suggesting new experiences and volunteer opportunities.

Conversation can play an essential role in forging bonds between humans and social robots, but participants need to feel like they are being listened to, remembered, and cared about in order to effectively build rapport. In this paper, we propose a novel strategy for conducting small talk with a social robot. Our approach is known as the Tiers of Friendship. It is centered around three core design elements: 1) Persuasive content and character is provided through topic modules created by professional creative writers to ensure engaging conversational content and a compelling personality for the social robot. 2) Conversational memory is achieved by allowing topic modules to specify required information that can be learned through conversation or recalled from previous interactions and organizing topic modules into a hierarchy that enforces information requirements between topics. 3) Dynamicity in conversation is promoted through topic navigation that supports fluid transitions to topics of human interest and employs elements of random ordering to create fresh conversation experiences. In this paper, we show how the Tiers of Friendship can be used to generate conversation content for a social robot that encourages the development of rapport. We describe a working implementation of a small talk system for a social robot based on the Tiers of Friendship that combines off-the-shelf ASR and NLU components and custom robot behavior components implemented via behavior trees on ROS. Finally, in order to evaluate our approach's effectiveness, we conduct an elicitation survey that evaluates conversations in terms of perceived engagement, personality traits, and rapport expectation and discuss the implications for social robotics.

Introduction: Pandemics have affected and will continue to affect humankind. Historically the Human Immunodeficiency Virus changed the way dental clinics operate and the COVID-19 pandemic led to an unprecedented closure of dental clinics leading to short- and long-term impact on oral health.  Aim: To assess the impact on oral health and related behaviours related to modern pandemics.  Method: A literature search across eighteen electronic databases was conducted. Three reviewers screened 2029 articles against inclusion criteria and assessed quality. Included articles underwent thematic analysis, followed by narrative synthesis to describe the results.  Results: Forty-eight articles were included that identified themes: (i) oral health related quality of life, (ii) stress and pandemics, (iii) oral health behaviours, (iv) social capital, (v) access to oral healthcare, (vi) fear as a barrier to accessing oral healthcare and (vii) teledentistry.  Conclusion: Pandemics present multiple challenges to both individuals and oral health professionals that impact on oral health and these challenges disproportionately affect the most vulnerable communities. However, with the right support, these impacts can be mitigated through social capital and support to establish healthy routines. The use of digital technologies should be promoted to reach all communities before the next pandemic arrives.