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Purpose: Doctoral student well-being is an important matter that shapes the well-being of academics throughout their careers. Given that well-being has been found to be closely related to employee productivity and efficiency, strategies associated with maintaining well-being during PhD studies might be crucial for higher education, its outcomes and-just as importantly-for a balanced life of PhD students. Method: Based on 17 studies, this literature review critically assesses the literature on doctoral student well-being. Results: Theoretical models, concepts of well-being, and methods applied are discussed, as are the results of the articles. The reviewed studies are then discussed based on a SWOT analysis addressing the strengths and weaknesses of the reviewed research as well as the identified opportunities and threats, which can be used as a basis for future research. Based on the review findings and the SWOT analysis, a multidimensional view of the well-being of doctoral students is proposed. Conclusions: The study proposes a more student-centred approach to meeting doctoral students' needs, and the enhancement of doctoral student well-being in order, as a long-term goal, to improve academics' well-being and productivity.

The death literacy index (DLI) was developed in Australia to measure death literacy, a set of experience-based knowledge needed to understand and act on end-of-life (EOL) care options but has not yet been validated outside its original context. The aim of this study was to develop a culturally adapted Swedish-language version of the DLI, the DLI-S, and assess sources of evidence for its validity in a Swedish context. The study involved a multi-step process of translation and cultural adaptation and two validation phases: examining first content and response process validity through expert review ( n = 10) and cognitive interviews ( n = 10); and second, internal structure validity of DLI-S data collected from an online cross-sectional survey ( n = 503). The psychometric evaluation involved analysis of descriptive statistics on item and scale-level, internal consistency and test-retest reliability, and confirmatory factor analysis. During translation and adaptation, changes were made to adjust items to the Swedish context. Additional adjustments were made following findings from the expert review and cognitive interviews. The content validity index exceeded recommended thresholds (S-CVI Ave = 0.926). The psychometric evaluation provided support for DLI-S’ validity. The hypothesized six-factor model showed good fit (χ 2 = 1107.631 p <0.001, CFI = 0.993, TLI = 0.993, RMSEA = 0.064, SRMR = 0.054). High internal consistency reliability was demonstrated for the overall scale (Cronbach’s α = 0.94) and each sub-scale ( α 0.81–0.92). Test-retest reliability was acceptable, ICC ranging between 0.66–0.85. Through a comprehensive assessment of several sources of evidence, we show that the DLI-S demonstrates satisfactory validity and acceptability to measure death literacy in the Swedish context. There are, however, indications that the sub-scales measuring community capacity perform worse in comparison to other sca and may function differently in Sweden than in the original context. The DLI-S has potential to contribute to research on community-based EOL interventions.

Developments in sequencing technologies and the sequencing of an ever-increasing number of genomes have revolutionised studies of biodiversity and organismal evolution. This accumulation of data has been paralleled by the creation of numerous public biological databases through which the scientific community can mine the sequences and annotations of genomes, transcriptomes, and proteomes of multiple species. However, to find the appropriate databases and bioinformatic tools for respective inquiries and aims can be challenging. Here, we present a compilation of DNA and protein databases, as well as bioinformatic tools for phylogenetic reconstruction and a wide range of studies on molecular evolution. We provide a protocol for information extraction from biological databases and simple phylogenetic reconstruction using probabilistic and distance methods, facilitating the study of biodiversity and evolution at the molecular level for the broad scientific community.

Background During COVID-19 pandemic, physiotherapy lecturers faced the challenge of rapidly shifting from face-to-face to online education. This retrospective case-control study aims to compare students’ satisfaction and performances shown in an online course to a control group of students who underwent the same course delivered face-to-face in the previous five years. Methods Between March and April 2020, a class (n = 46) of entry-level physiotherapy students (University of Verona - Italy), trained by an experienced physiotherapist, had 24-hours online lessons. Students exposed to the same course in the previous five academic years (n = 112), delivered with face-to-face conventional lessons, served as a historical control. The course was organized in 3 sequential phases: (1) PowerPoint presentations were uploaded to the University online platform, (2) asynchronous video recorded lectures were provided on the same platform, and (3) between online lectures, the lecturer and students could communicate through an email chat to promote understanding, dispel any doubts and collect requests for supplementary material (e.g., scientific articles, videos, webinars, podcasts). Outcomes were: (1) satisfaction as routinely measured by University with a national instrument and populated in a database; (2) performance as measured with an oral examination. Results We compared satisfaction with the course, expressed on a 5-point Likert scale, resulting in no differences between online and face-to-face teaching (Kruskal-Wallis 2  = 0.24, df = 1, p  = 0.62). We weighted up students’ results by comparing their mean performances with the mean performances of the same course delivered face-to-face in the previous five years, founding a statistical significance in favour of online teaching (Wilcoxon rank sum test W = 1665, p < 0.001). Conclusions Online teaching in entry-level Physiotherapy seems to be a feasible option to face COVID-19 pandemic, as satisfies students as well as face-to-face courses and leading to a similar performance. Entry-level Bachelors in Physiotherapy may consider moving to eLearning to facilitate access to higher education. Universities will have to train lecturers to help them develop appropriate pedagogical skills, and supply suitable support in terms of economic, organizational, and technological issues, aimed at guaranteeing a high level of education to their students. Trial registration Retrospectively registered.

BackgroundThe 10-m walk test (10MWT) is a widely used measure of gait speed in Parkinson’s disease (PD). However, it is unclear if different standardizations of its conduct impact test results.Aim of the studyWe examined the clinical significance of two aspects of the standardization of the 10MWT in mild PD: static vs. dynamic start, and a single vs. repeated trials. Implications for fall prediction were also explored.Methods151 people with PD (mean age and PD duration, 68 and 4 years, respectively) completed the 10MWT in comfortable gait speed with static and dynamic start (two trials each), and gait speed (m/s) was recorded. Participants then registered all prospective falls for 6 months.ResultsAbsolute mean differences between outcomes from the various test conditions ranged between 0.016 and 0.040 m/s (effect sizes, 0.06–0.14) with high levels of agreement (intra-class correlation coefficients, 0.932–0.987) and small standard errors of measurement (0.032–0.076 m/s). Receiver operating characteristic curves showed similar discriminate abilities for prediction of future falls across conditions (areas under curves, 0.70–0.73). Cut-off points were estimated at 1.1–1.2 m/s.ConclusionsDifferent 10MWT standardizations yield very similar results, suggesting that there is no practical need for an acceleration distance or repeated trials when conducting this test in mild PD.

Abstract Objective To investigate how a behavioral health, artificial intelligence (AI)-powered, digital self-management tool affects the daily functions in adults with chronic back and neck pain. Design Eligible subjects were enrolled in a 12-week prospective, multicenter, single-arm, open-label study and instructed to use the digital coach daily. Primary outcome was a change in Patient-Reported Outcomes Measurement Information Systems (PROMIS) scores for pain interference. Secondary outcomes were changes in PROMIS physical function, anxiety, depression, pain intensity scores and pain catastrophizing scale (PCS) scores. Methods Subjects logged daily activities, using PainDrainerTM, and data analyzed by the AI engine. Questionnaire and web-based data were collected at 6 and 12 weeks and compared to subjects’ baseline. Results Subjects completed the 6- (n = 41) and 12-week (n = 34) questionnaires. A statistically significant Minimal Important Difference (MID) for pain interference was demonstrated in 57.5% of the subjects. Similarly, MID for physical function was demonstrated in 72.5% of the subjects. A pre- to post-intervention improvement in depression score was also statistically significant, observed in 100% of subjects, as was the improvement in anxiety scores, evident in 81.3% of the subjects. PCS mean scores was also significantly decreased at 12 weeks. Conclusion Chronic pain self-management, using an AI-powered, digital coach anchored in behavioral health principles significantly improved subjects’ pain interference, physical function, depression, anxiety, and pain catastrophizing over the 12-week study period.

Background The Rasch model allows for linear measurement based on ordinal item responses from rating scales commonly used to assess health outcomes. Such linear measures may be inconvenient since they are expressed as log-odds units (logits) that differ from scores that users may be familiar with. It can therefore be desirable to transform logits into more user-friendly ranges that preserve their linear properties. In addition to user-defined ranges, three general transformations have been described in the literature: the least measurable difference (LMD), the standard error of measurement (SEM) and the least significant difference (LSD). The LMD represents the smallest possible meaningful unit, SEM relates the transformed scale values to measurement uncertainty (one unit on the transformed scale represents roughly one standard error), and LSD represents a lower bound for how coarse the transformed scale can be without loss of valid information. However, while logit transformations are relatively common in the health sciences, use of LMD, SEM and LSD transformations appear to be uncommon despite their potential role. Methods Logit transformations were empirically illustrated based on 1053 responses to the Epworth Sleepiness Scale. Logit measures were transformed according to the LMD, SEM and LSD, and into 0–10, 0-100, and the original raw score (0–24) ranges. These transformations were conducted using a freely available Excel tool, developed by the authors, that transforms logits into user-defined ranges along with the LMD, SEM and LSD transformations. Results Resulting LMD, SEM and LSD transformations ranged 0-34, 0-17 and 0-12, respectively. When considering these relative to the three user-defined ranges, it is seen that the 0-10 range is narrower than the LSD range (i.e., loss of valid information), and a 0-100 range gives the impression of better precision than there is, since it is considerably wider than the LMD range. However, the 0-24 transformation appears reasonable since it is wider than the LSD, but narrower than the LMD ranges. Conclusions It is suggested that LMD, SEM and LSD transformations are valuable for benchmarking in deciding appropriate ranges when transforming logit measures. This process can be aided by the Excel tool presented and illustrated in this paper.

Double diffusion encoding (DDE) makes diffusion MRI sensitive to a wide range of microstructural features, and the acquired data can be analysed using different approaches. Correlation tensor imaging (CTI) uses DDE to resolve three components of the diffusional kurtosis: isotropic, anisotropic, and microscopic kurtosis. The microscopic kurtosis is estimated from the contrast between single diffusion encoding (SDE) and parallel DDE signals at the same b-value. Another approach is multi-Gaussian exchange (MGE), which employs DDE to measure exchange. Sensitivity to exchange is obtained by contrasting SDE and DDE signals at the same b-value. CTI and MGE exploit the same signal contrast to quantify microscopic kurtosis and exchange, and this study investigates the interplay between these two quantities. We perform Monte Carlo simulations in different geometries with varying levels of exchange and study the behaviour of the parameters from CTI and MGE. We conclude that microscopic kurtosis from CTI is sensitive to the exchange rate and that intercompartmental exchange and the transient kurtosis of individual compartments are distinct sources of microscopic kurtosis. In an attempt to disentangle these two sources, we propose a heuristic signal representation referred to as tMGE (MGE incorporating transient kurtosis) that accounts for both effects by exploiting the distinct signatures of exchange and transient kurtosis with varying mixing time: exchange causes a slow dependence of the signal on mixing time while transient kurtosis arguably has a much faster dependence. We find that applying tMGE to data acquired with multiple mixing times for both parallel and orthogonal DDE may enable estimation of the exchange rate as well as isotropic, anisotropic, and transient kurtosis.

Low-molecular weight chemical compounds have a longstanding history as drugs. Target specificity and binding efficiency represent major obstacles for small molecules to become clinically relevant. Protein kinases are attractive cellular targets; however, they are challenging because they present one of the largest protein families and share structural similarities. Bruton tyrosine kinase (BTK), a cytoplasmic protein tyrosine kinase, has received much attention as a promising target for the treatment of B-cell malignancies and more recently autoimmune and inflammatory diseases. Here we describe the structural properties and binding modes of small-molecule BTK inhibitors, including irreversible and reversible inhibitors. Covalently binding compounds, such as ibrutinib, acalabrutinib and zanubrutinib, are discussed along with non-covalent inhibitors fenebrutinib and RN486. The focus of this review is on structure-function relationships.