Nevin ManimalaScience. 2023 Jan 20;379(6629):228-233. doi: 10.1126/science.adg6746. Epub 2023 Jan 19.
ABSTRACT
Richard Gill is fighting the shoddy statistics that put nurses in prison for serial murder.
PMID:36656931 | DOI:10.1126/science.adg6746
Science. 2023 Jan 20;379(6629):232. doi: 10.1126/science.adg6747. Epub 2023 Jan 19.
NO ABSTRACT
PMID:36656930 | DOI:10.1126/science.adg6747
Science. 2023 Jan 20;379(6629):278-283. doi: 10.1126/science.ade7651. Epub 2023 Jan 19.
ABSTRACT
Synthesizing many-body quantum systems with various ranges of interactions facilitates the study of quantum chaotic dynamics. Such extended interaction range can be enabled by using nonlocal degrees of freedom such as photonic modes in an otherwise locally connected structure. Here, we present a superconducting quantum simulator in which qubits are connected through an extensible photonic-bandgap metamaterial, thus realizing a one-dimensional Bose-Hubbard model with tunable hopping range and on-site interaction. Using individual site control and readout, we characterize the statistics of measurement outcomes from many-body quench dynamics, which enables in situ Hamiltonian learning. Further, the outcome statistics reveal the effect of increased hopping range, showing the predicted crossover from integrability to ergodicity. Our work enables the study of emergent randomness from chaotic many-body evolution and, more broadly, expands the accessible Hamiltonians for quantum simulation using superconducting circuits.
PMID:36656924 | DOI:10.1126/science.ade7651
PLoS Genet. 2023 Jan 19;19(1):e1010054. doi: 10.1371/journal.pgen.1010054. Online ahead of print.
ABSTRACT
We introduce a fast, new algorithm for inferring from allele count data the FST parameters describing genetic distances among a set of populations and/or unrelated diploid individuals, and a tree with branch lengths corresponding to FST values. The tree can reflect historical processes of splitting and divergence, but seeks to represent the actual genetic variance as accurately as possible with a tree structure. We generalise two major approaches to defining FST, via correlations and mismatch probabilities of sampled allele pairs, which measure shared and non-shared components of genetic variance. A diploid individual can be treated as a population of two gametes, which allows inference of coancestry coefficients for individuals as well as for populations, or a combination of the two. A simulation study illustrates that our fast method-of-moments estimation of FST values, simultaneously for multiple populations/individuals, gains statistical efficiency over pairwise approaches when the population structure is close to tree-like. We apply our approach to genome-wide genotypes from the 26 worldwide human populations of the 1000 Genomes Project. We first analyse at the population level, then a subset of individuals and in a final analysis we pool individuals from the more homogeneous populations. This flexible analysis approach gives advantages over traditional approaches to population structure/coancestry, including visual and quantitative assessments of long-standing questions about the relative magnitudes of within- and between-population genetic differences.
PMID:36656906 | DOI:10.1371/journal.pgen.1010054
Clin Exp Dermatol. 2023 Jan 19:llad028. doi: 10.1093/ced/llad028. Online ahead of print.
ABSTRACT
BACKGROUND: The prevalence of vitiligo shows regional variance. Recently, an association between vitiligo and extracutaneous conditions, including other autoimmune, metabolic, and dermatologic disorders, has been suggested. Despite its increasing incidence, the epidemiological trends and comorbidities of vitiligo have rarely been quantified in Asia.
OBJECTIVE: To determine the prevalence, incidence, and associated disorders of vitiligo using the National Health Insurance Service (NHIS) database.
METHODS: We included all vitiligo patients, classified by the International Statistical Classification of Disease, 10th revision (ICD-10) code of L80, with ≥3 documented visits from 2003 to 2019. Incidence and prevalence of vitiligo were estimated along the study period. Age, sex-, insurance type-, and income level-matched controls (ratio 1:5) were selected to compare comorbidities. The odds ratios between comorbidities and vitiligo were calculated through conditional logistic regression.
RESULTS: The incidence and annual prevalence of vitiligo in Korea increased from 2003 and 2019, with incidence peaking in summer. Age-specific incidence showed a bimodal distribution, with the steepest increase in the group aged < 20 years. Many comorbidities, including alopecia areata, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, Crohn’s disease, ulcerative colitis, Sjogren’s syndrome, diabetes mellitus, dyslipidemia, chronic hepatitis, anxiety disorder, and mood disorder, showed higher odds ratios in vitiligo patients than controls.
CONCLUSION: The incidence and prevalence of vitiligo are increasing, particularly among younger patients in Korea. In addition, various comorbidities are associated with vitiligo. Therefore, if vitiligo patients present extracutaneous symptoms, physicians should consider the possibility of other comorbid diseases.
PMID:36656897 | DOI:10.1093/ced/llad028
PLoS Negl Trop Dis. 2023 Jan 19;17(1):e0011065. doi: 10.1371/journal.pntd.0011065. Online ahead of print.
ABSTRACT
La Crosse virus (LACV) is a mosquito-borne pathogen that causes more pediatric neuroinvasive disease than any other arbovirus in the United States. The geographic focus of reported LACV neuroinvasive disease (LACV-ND) expanded from the Midwest into Appalachia in the 1990s, and most cases have been reported from a few high-risk foci since then. Here, we used publicly available human disease data to investigate changes in the distribution of geographic LACV-ND clusters between 2003 and 2021 and to investigate socioeconomic and demographic predictors of county-level disease risk in states with persistent clusters. We used spatial scan statistics to identify high-risk clusters from 2003-2021 and a generalized linear mixed model to identify socioeconomic and demographic predictors of disease risk. The distribution of LACV-ND clusters was consistent during the study period, with an intermittent cluster in the upper Midwest and three persistent clusters in Appalachia that included counties in east Tennessee / western North Carolina, West Virginia, and Ohio. In those states, county-level cumulative incidence was higher when more of the population was white and when median household income was lower. Public health officials should target efforts to reduce LACV-ND incidence in areas with consistent high risks.
PMID:36656896 | DOI:10.1371/journal.pntd.0011065
Fam Med. 2023 Jan;55(1):51-55. doi: 10.22454/FamMed.55.284433.
ABSTRACT
BACKGROUND AND OBJECTIVES: Obstetric care is a core element in family medicine education. New interns typically learn the sterile cervical exam on the job by examining women in labor. This can be uncomfortable for patients and may increase the risk of infection. Simulated training could minimize these challenges, but manufactured models are expensive and not widely available in residency programs. We sought to evaluate a simple, homemade sewn model using stretchy fabric and pipe cleaners that could improve teaching and acquisition of cervical examination skills and common obstetrical procedures.
METHODS: We used the model to teach cervical examination skills to students and new interns and assessed participant satisfaction. We evaluated examination accuracy by grading practice exams on the model before and after a workshop teaching obstetrical procedures including the sterile vaginal exam. We calculated satisfaction using summary statistics. We evaluated pre- and postscores for exam accuracy using paired t tests.
RESULTS: Interns demonstrated a significant improvement in cervical exam skills using the model, and participants reported very high satisfaction with the workshop utilizing the model.
CONCLUSIONS: We developed a simple, low-cost cervical exam model that was shown to be well-regarded by trainees and could be duplicated by other residency programs. This approach provides a unique and accessible way to offer hands-on simulation during obstetrical training. The model may improve trainees’ understanding of the procedures which would lead to better experiences for obstetrical patients.
PMID:36656888 | DOI:10.22454/FamMed.55.284433
JMIR Aging. 2023 Jan 19;6:e36807. doi: 10.2196/36807.
ABSTRACT
BACKGROUND: The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services.
OBJECTIVE: This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device.
METHODS: Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants’ intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device.
RESULTS: The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants’ age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%.
CONCLUSIONS: This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.
PMID:36656636 | DOI:10.2196/36807
JMIR Form Res. 2023 Jan 19;7:e40135. doi: 10.2196/40135.
ABSTRACT
BACKGROUND: Mindfulness and meditation have a rich historical tradition, and a growing scientific base of evidence supports their use in creating positive psychological and neuroplastic changes for practitioners. Although meditation can be taught in various ways, the scientific community has yet to systematically study the impact of different types of meditation on neuropsychological outcomes, especially as it pertains to digital implementation. Therefore, it is critical that the instruction of mindfulness be evidence based because meditation is being used in both scientific and clinical settings.
OBJECTIVE: This study investigated the use of teacher cueing and the integration of neuroscience education into a meditation program. Compassion cueing was chosen as the element of experimental manipulation because traditional lineages of Buddhist meditation teach compassion for self and others as one of the primary outcomes of meditation. We hypothesized that participants receiving compassion cueing would have enhanced neuropsychological outcomes compared with those receiving functional cueing and that gains in neuroscience knowledge would relate to positive neuropsychological outcomes.
METHODS: Participants (n=89) were randomized to receive either functional cueing (control group) or compassion cueing (experimental group) and engaged with five 10-minute meditation sessions a week for 4 weeks. All intervention sessions were administered through digital presentation. All participants completed ecological momentary assessments before and after the daily intervention, as well as pre- and postintervention questionnaires.
RESULTS: Participants demonstrated significant benefits over time, including increased mindfulness and self-compassion, decreased depression, and gains in neuroscience content (all P<.001); however, no significant between-group differences were found. Daily scores from each day of the intervention showed a statistically significant shift from active toward settled. Importantly, long-term increases in mindfulness were positively correlated to changes in compassion (r=0.326; P=.009) and self-compassion (r=0.424; P<.001) and negatively correlated to changes in anxiety (r=-0.266; P=.03) and depression (r=-0.271; P=.03). Finally, the acute effects of meditation were significantly correlated to the longitudinal outcomes (with a small-to-medium effect size), especially those relevant to mindfulness.
CONCLUSIONS: We developed a novel neuroscience-based education-meditation program that enhanced self-regulation as evidenced by improved mindfulness, self-compassion, and mood state. Our findings demonstrate the behavioral importance of engaging with mindfulness meditation and reinforce the idea that the benefits of meditation are independent of teacher cueing behavior. Future studies will need to investigate the brain-based changes underlying these meditation-induced outcomes.
PMID:36656631 | DOI:10.2196/40135
J Med Internet Res. 2023 Jan 19;25:e42672. doi: 10.2196/42672.
ABSTRACT
BACKGROUND: Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services.
OBJECTIVE: This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.
METHODS: We searched 8 electronic databases (MEDLINE, PsycINFO, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) and included studies that met the inclusion criteria. Then, we checked the studies that cited the included studies and screened studies that were cited by the included studies. The study selection and data extraction were carried out by 2 reviewers independently. The extracted data were aggregated and summarized using narrative synthesis.
RESULTS: Of the 1203 studies identified, 69 (5.74%) were included in this review. Approximately, two-thirds of the studies used wearable AI for depression, whereas the remaining studies used it for anxiety. The most frequent application of wearable AI was in diagnosing anxiety and depression; however, none of the studies used it for treatment purposes. Most studies targeted individuals aged between 18 and 65 years. The most common wearable device used in the studies was Actiwatch AW4 (Cambridge Neurotechnology Ltd). Wrist-worn devices were the most common type of wearable device in the studies. The most commonly used category of data for model development was physical activity data, followed by sleep data and heart rate data. The most frequently used data set from open sources was Depresjon. The most commonly used algorithm was random forest, followed by support vector machine.
CONCLUSIONS: Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals for the prescreening assessment of anxiety and depression. Further reviews are needed to statistically synthesize the studies’ results related to the performance and effectiveness of wearable AI. Given its potential, technology companies should invest more in wearable AI for the treatment of anxiety and depression.
PMID:36656625 | DOI:10.2196/42672