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BACKGROUND: Patients with cognitive impairment no dementia (CIND) are at an increased risk of progression to Alzheimer's disease (AD). Whether subtle impairments in functional or social abilities at the CIND stage can predict progression to AD is not yet fully determined. We evaluated whether impairments on the Disability Assessment for Dementia (DAD) and Functional Rating Scale (FRS) can predict progression to AD. METHODS: We examined 70 patients with CIND from the ACCORD cohort having complete DAD and FRS baseline and 2-year follow-up data. MANCOVA adjusted for age, sex, education and baseline MMSE score compared the baseline and 2-year change in DAD and FRS scores in CIND patients who progressed to AD versus non-progressors. RESULTS: There were no significant differences between CIND progressors and non-progressors in baseline total DAD or FRS scores. FRS domain analysis revealed that greater impairment in social/occupational functioning significantly predicted progression, while there were no predictive DAD domains. In progressors, both DAD and FRS scores significantly declined over time with the largest changes in instrumental activities of daily living (IADL). CONCLUSION: While changes in IADL characterize the progression from CIND to AD, impairment in complex social-cognitive competency significantly predicts risk of progression and may mark early AD. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Previous research has shown that cognitively-impaired-not-demented (CIND) individuals with at least one neuropsychiatric symptom (NPS) have more functional disability than individuals without any NPSs. The objectives of the present study were to determine whether there are consistent clusters of NPS in CIND individuals and whether certain NPS clusters are more strongly associated with measures of functional disability than other NPS clusters in this population. METHODS: This was a cross-sectional baseline study of NPS using the Neuropsychiatric Inventory (NPI) in a national clinic-based observational cohort study (the Canadian Cohort Study of Cognitive Impairment and Related Dementias study). The present investigation focuses on a subset of CIND subjects (73%) whose informant endorsed the presence of at least one NPI item. RESULTS: A hierarchical cluster analysis identified two NPS clusters. One consisted of mood factors (i.e., depression, anxiety, apathy, irritability, and problems with sleep) and the other cluster captured frontal symptoms (i.e., aberrant motor behavior, disinhibition, agitation, and problems with appetite). NPSs grouped within the mood cluster were more common than the frontal cluster (95% of subjects had at least one NPS within the mood cluster versus 53% in the frontal cluster). However, the frontal cluster was more strongly associated with functional disability measures even after controlling for cognitive status (i.e., the Mini-Mental State Exam) and the mood cluster score. CONCLUSION: The frontal cluster of NPSs was more strongly associated with functional disability than the mood cluster. [ABSTRACT FROM AUTHOR]

BACKGROUND: With the aging of the population, dementia has become an important health concern in most countries. There is a growing body of literature on the importance of cardiovascular risk factors in the development of Alzheimer's disease (AD), vascular dementia, and mixed dementia (AD with cerebrovascular disease). OBJECTIVE: This article reviews the role of major risk factors in dementia between both sexes. METHODS: The MEDLINE, PubMed, and HealthSTAR databases were searched between 1966 and January 2007 for English-language articles on the risk factors for dementia. RESULTS: The distribution and prevalence of major risk factors between the sexes and age groups are varied. Female sex has been associated with increased risk of the development of AD. In women aged >75 years, rates of hypertension, hyperlipidemia, and diabetes are higher than in similarly aged men. Apolipoprotein E epsilon 4 genotype status appears to have a greater deleterious effect on gross hippocampal pathology and memory performance in women compared with men. Midlife hypertension and hypercholesterolemia in both sexes predict a higher risk of developing AD in later life. Diabetes is increasing in frequency to a greater extent in women than in men, and is associated with a substantial risk for cognitive impairment. Dementia in women (probably) and in men (possibly) is influenced by obesity in the middle of life. CONCLUSIONS: It remains critical that large prospective clinical trials be designed to assess the effect of optimum management of vascular risk factors on cognitive functioning and dementia as the primary outcome, and include women and men in numbers adequate for assessment of gender effects. [ABSTRACT FROM AUTHOR]

Benner's model of skill acquisition is currently receiving considerable interest from nurse educationalists, and promises to form the basis for some curricula offered by colleagues of nurse education. This paper debates the 'novice to expert' model and seeks to explain exactly what an 'expert' is. The Benner model proposes that one component of expertise is working from an intuitive base. This claims is disputed and the definition of intuition is contested. Alternative explanations to account for the intuitive responses of Benner's subjects are suggested. [ABSTRACT FROM AUTHOR]

Background and Objectives: Forequarter and hindquarter amputations have traditionally been closed with local tissues, but the technique is plagued by a high rate of complication such as marginal necrosis, seroma, infection, and dehiscence. Filet of limb flaps have been used when local tissues are insufficient for closure, and despite their use in more extensive and complex wounds, outcomes seem to be better in these cases. Recognizing that filet of limb flaps not only serve to cover the wound, but also eliminate dead space, supplement at-risk and/or radiated tissue, pad underlying hard structures, and facilitate neuroma prevention with target muscle reinnervation, we have change our practice to utilize buried filet of limb flaps even when local tissues are technically "sufficient" to close the wound. The purpose of this article to organize and describe the ways in which buried filet-of-limb flaps can be used to achieve important and discrete surgical objectives in forequarter and hindquarter amputation, and to facilitate increased recognition of collaborative interdisciplinary opportunities for spare-part reconstruction., Methods: Retrospective data from the medical records of seven patients, collected between 2010 to 2023 at our single tertiary referral center, were reviewed. This included all patients for whom a buried (or partially buried) filet of limb flap was attempted for forequarter or hindquarter amputation reconstruction., Results: Five males and two females ranging 55 to 75 years of age, met the inclusion criteria. Three cases of forequarter amputation and four cases of hindquarter amputation were included. Six flaps were successfully transferred without major flap-related complications. The mean follow-up period was eight and a half months., Conclusion: Even when local tissues are technically "sufficient" to close forequarter and hindquarter amputation wounds, we have found buried filet of limb flaps to be useful in several ways. These include occupying dead space, providing double-layer coverage, padding hard structures, preventing neuromas, and reconstructing sacro- and spino-pelvic continuity. Our approach emphasizes interdisciplinary collaboration and highlights the potential advantages of buried filet of limb flaps in optimizing patient outcomes for complex limb amputations., (© 2024. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)

Introduction: Minimal research on integrated primary care (IPC) or integrated behavioral health (IBH) has examined clinics in rural communities. The relationships between provider burnout, job satisfaction, and IBH/IPC practices remain understudied, particularly in rural settings., Method: We employed an online survey of 147 medical and behavioral health care providers in primary care settings throughout Montana. Respondents self-identified as predominantly White/European American (89.4%) and female (76.7%). We tested whether degree of adherence to IBH/IPC practices concurrently predicted providers' reports of emotional exhaustion (EE), a dimension of burnout, and job satisfaction. Data were collected during the COVID-19 pandemic, in 2020., Results: In multiple linear regression analyses, providers' reports of IBH/IPC practices significantly predicted EE ( B = -0.036, p < .01) and job satisfaction ( B = 0.123, p < .05), suggesting that higher levels of integration were linked to less EE and greater job satisfaction., Discussion: Our findings contribute to the evidence base regarding the potential usefulness of IBH/IPC models. Specifically, because existing research links provider burnout and low job satisfaction with provider retention difficulties and diminished health, poor patient satisfaction and outcomes, and cost inefficiencies, our findings have potential to inform policy-level discussions regarding the use of IBH/IPC models in rural states like Montana. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

While most adults confronted with the death of a loved one manage to grieve, about 10-20% of individuals develop complicated grief, characterized by persistent distress and impaired social skills, or pathological grief, defined by the onset or decompensation of a psychiatric disorder. Little is known about the biological causes of these grief complications. Recent work suggests that oxytocin, a major neuroendocrine hormone regulating many neurocognitive mechanisms, may be involved in this process. Oxytocin is widely studied and well known for its impact on the mother-child bond and hormonal and brain systems related to attachment and social interactions. In this article, we propose a neurocognitive model of grief complications based on existing data on the role of oxytocin in interpersonal attachment and its impact on brain activity. We suggest that complicated grief is associated with dysfunctional cerebral oxytocinergic signaling and persistent hyperactivation of the nucleus accumbens. This mechanism is involved in limiting the reduction of interpersonal attachment to the deceased during acute phases and in searching for new interpersonal relationships during the recovery phase. We show how the exploration of cerebral oxytocinergic signaling would improve the understanding of physiological grief mechanisms in the general population and could allow the development of new therapeutic perspectives against the complications of grief., (Copyright © 2023 L'Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.)

Background: With rising incidence of skin cancer and relatively increased mortality rates, an improved diagnosis of such a potentially fatal disease is of vital importance. Although frequently curable, it nevertheless places a considerable burden upon healthcare systems. Among the various types of skin cancers, non-melanoma skin cancer is most prevalent. Despite such prevalence and its associated cost, scant proof concerning the diagnostic accuracy via Artificial Intelligence (AI) for non-melanoma skin cancer exists. This study meta-analyzes the diagnostic test accuracy of AI used to diagnose non-melanoma forms of skin cancer, and it identifies potential covariates that account for heterogeneity between extant studies., Methods: Various electronic databases (Scopus, PubMed, ScienceDirect, SpringerLink, and Dimensions) were examined to discern eligible studies beginning from March 2022. Those AI studies predictive of non-melanoma skin cancer were included. Summary estimates of sensitivity, specificity, and area under receiver operating characteristic curves were used to evaluate diagnostic accuracy. The revised Quality Assessment of Diagnostic Studies served to assess any risk of bias., Results: A literature search produced 39 eligible articles for meta-analysis. The summary sensitivity, specificity, and area under receiver operating characteristic curve of AI for diagnosing non-melanoma skin cancer was 0.78, 0.98, & 0.97, respectively. Skin cancer typology, data sources, cross validation, ensemble models, types of techniques, pre-trained models, and image augmentation became significant covariates accounting for heterogeneity in terms of both sensitivity and/or specificity., Conclusions: Meta-analysis results revealed that AI is predictive of non-melanoma with an acceptable performance, but sensitivity may become improved. Further, ensemble models and pre-trained models are employable to improve true positive rating., (© 2023. The Author(s).)

Orthodontic treatment has popularized in Taiwan. Healthcare institutions can be responsive in their coping strategies and determine whether third-party intervention should take place involving medical disputes related to orthodontics in order to repair patient trust. This study draws on orthodontic treatment to explore the effect of various trust repair strategies employed by healthcare institutions and third-party involvement positively affecting outcomes related to trust repair. Patients were recruited among those who have undergone orthodontic treatments, and 353 valid scenario-based questionnaires were collected through an online survey. Results revealed that: (1) the affective and informational repair strategies positively impacted trust repair while the functional repair strategy did not; (2) trust repair positively impacted patient satisfaction/word-of-mouth and mediated between repair strategies and satisfaction/word-of-mouth; and (3) third-party involvement moderated the relationship between trust repair and word-of-mouth. The findings suggest that rather than receiving monetary compensation, patients usually prefer that healthcare institutions acknowledge their fault, offer apologies, and engage in active communications to clarify the causes of medical dispute. Further, an objective third party should be involved to mediate the medical disputes to afford satisfaction all around.

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML., Competing Interests: Declaration of interests C.E.T. receives research support from Notable Labs and serves as a scientific liaison for AstraZeneca. J.E.M. receives research funding from Gilead Pharmaceutical and serves on a scientific advisory board for Ionis Pharmaceuticals. M.W.D. serves on the advisory boards and/or as a consultant for Novartis, Incyte, and BMS and receives research funding from BMS and Gilead. C.S.H. receives research funding from Sellas. T.L.L. consults for Jazz Pharmaceuticals and receives research funding from Tolero, Gilead, Prescient, Ono, Bio-Path, Mateon, Genentech/Roche, Trovagene, AbbVie, Pfizer, Celgene, Imago, Astellas, Karyopharm, Seattle Genetics, and Incyte. D.A.P. receives research funding from Pfizer and Agios and served on advisory boards for Pfizer, Celyad, Agios, Celgene, AbbVie, Argenx, Takeda, and Servier. B.J.D. serves on the advisory boards for Aileron Therapeutics, Aptose, Blueprint Medicines, Cepheid, EnLiven Therapeutics, Gilead, GRAIL, Iterion Therapeutics, Nemucore Medical Innovations, the Novartis CML Molecular Monitoring Steering Committee, Recludix Pharma, the RUNX1 Research Program, ALLCRON Pharma, VB Therapeutics, Vincerx Pharma, and the Board of Directors for Amgen, and receives research funding from EnLiven and Recludix. B.J.D. is principal investigator or co-investigator on Novartis, BMS, and Pfizer clinical trials. His institution, OHSU, has contracts with these companies to pay for patient costs, nurse and data manager salaries, and institutional overhead, but he does not derive salary, nor does his laboratory receive funds, from these contracts. J.W.T. has received research support from Acerta, Agios, Aptose, Array, AstraZeneca, Constellation, Genentech, Gilead, Incyte, Janssen, Kronos, Meryx, Petra, Schrodinger, Seattle Genetics, Syros, Takeda, and Tolero and serves on the advisory board for Recludix Pharma. The authors certify that all compounds tested in this study were chosen without input from any of our industry partners. A subset of findings from this manuscript have been included in a pending patent application., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: An injurious fall is one of the main indicators of care quality in healthcare facilities. Despite several fall screen tools being widely used to evaluate a patient's fall risk, they are frequently unable to reveal the severity level of patient falls. The purpose of this study is to build a practical system useful to predict the severity level of in-hospital falls. This practice is done in order to better allocate limited healthcare resources and to improve overall patient safety., Methods: Four hundred and forty-six patients who experienced fall events at a large Taiwanese hospital were referenced. Eight predictors were used to ascertain the severity of patient falls solely based on the above study population. Multinomial logistic regression, Naïve Bayes, random forest, support vector machine, eXtreme gradient boosting, deep learning, and ensemble learning were adopted to establish predictive models. Accuracy, F1 score, precision, and recall were utilized to assess the models' performance., Results: Compared to other learners, random forest exhibited satisfying predictive performance in terms of all metrics (accuracy: 0.844, F1 score: 0.850, precision: 0.839, and recall: 0.875 for the test dataset), and it was adopted as the base learner for a severity-level predictive system which is web-based. Furthermore, age, ability of independent activity, patient sources, use of assistive devices, and fall history within the past 12 months were deemed the top five important risk factors for evaluating fall severity., Conclusions: The application of machine learning techniques for predicting the severity level of patient falls may result in some benefits to monitor fall severity and to better allocate limited healthcare resources., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Hsi-Hao Wang et al.)

Objective: COVID-19 is a novel, severely contagious disease with enormous negative impact on humanity as well as the world economy. An expeditious, feasible tool for detecting COVID-19 remains yet elusive. Recently, there has been a surge of interest in applying machine learning techniques to predict COVID-19 using non-image data. We have therefore undertaken a meta-analysis to quantify the diagnostic performance of machine learning models facilitating the prediction of COVID-19., Materials and Methods: A comprehensive electronic database search for the period between January 1st, 2021 and December 3rd, 2021 was undertaken in order to identify eligible studies relevant to this meta-analysis. Summary sensitivity, specificity, and the area under receiver operating characteristic curves were used to assess potential diagnostic accuracy. Risk of bias was assessed by means of a revised Quality Assessment of Diagnostic Studies., Results: A total of 30 studies, including 34 models, met all of the inclusion criteria. Summary sensitivity, specificity, and area under receiver operating characteristic curves were 0.86, 0.86, and 0.91, respectively. The purpose of machine learning models, class imbalance, and feature selection are significant covariates useful in explaining the between-study heterogeneity, in terms of both sensitivity and specificity., Conclusions: Our study findings show that non-image data can be used to predict COVID-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of COVID-19, thus improving further diagnostic performance., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Introduction: The Veterans Health Administration (VA) Primary Care-Mental Health Integration (PC-MHI) initiative targets depression (MDD), anxiety/posttraumatic stress disorder (PTSD) and alcohol misuse (AM) for care improvement. In primary care, case finding often relies on depression screening. Whereas clinical practice guidelines solely inform management of depression, minimal information exists to guide treatment when psychiatric symptom clusters coexist. We provide descriptive clinical information for care planners about VA PC patients with depression alone, depression plus alcohol misuse, and depression with complex psychiatric comorbidities (PTSD and/or probable bipolar disorder)., Method: We examined data from a VA study that used a visit-based sampling procedure to screen 10,929 VA PC patients for depression; 761 patients with probable major depression completed baseline measures of health and care engagement. Follow-up assessments were completed at 7 months., Results: At baseline, 53% of patients evidenced mental health conditions in addition to depression; 10% had concurrent AM, and 43% had psychiatrically complex depression (either with or without AM). Compared with patients with depression alone or depression with AM, those with psychiatrically complex depression evinced longer standing and more severe mood disturbance, higher likelihood of suicidal ideation, higher unemployment, and higher levels of polypharmacy. Baseline depression complexity predicted worse mental health status and functioning at follow-up., Discussion: A substantial proportion of VA primary care patients with depression presented with high medical multimorbidity and elevated safety concerns. Psychiatrically complex depression predicted lower treatment effectiveness, suggesting that PC-MHI interventions should co-ordinate and individualize care for these patients. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Effectively improving the medication adherence of patients is crucial. Past studies focused on treatment-related factors, but little attention has been paid to factors concerning human beliefs such as trust or self-efficacy. The purpose of this study is to explore the following aspects of patients with chronic diseases: (1) The relationship between emotional support, informational support, self-efficacy, and trust; (2) the relationship between self-efficacy, trust, and medication adherence; and, (3) whether chronic patients' participation in different types of online communities brings about significant statistical differences in the relationships between the abovementioned variables. A questionnaire survey was conducted in this study, with 452 valid questionnaires collected from chronic patients previously participating in online community activities. Partial Least Squares-Structural Equation Modeling analysis showed that emotional support and informational support positively predict self-efficacy and trust, respectively, and consequently, self-efficacy and trust positively predict medication adherence. In addition, three relationships including the influence of emotional support on trust, the influence of trust on medication adherence, and the influence of self-efficacy on medication adherence, the types of online communities result in significant statistical differences. Based on the findings, this research suggests healthcare professionals can enhance patients' self-efficacy in self-care by providing necessary health information via face-to-face or online communities, and assuring patients of demonstrable support. As such, patients' levels of trust in healthcare professionals can be established, which in turn improves their medication adherence.

Background: Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided., Methods: This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance., Results: The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning., Competing Interests: The authors declare that they have no competing interests., (© 2020 Kuo et al.)

The present study investigated factors associated with health literacy in community-dwelling Taiwanese women, particularly focusing on those associated with prevalent unhealthy behaviors. This cross-sectional study recruited 353 community-dwelling women aged 39-89 years from February to October 2015 in urban, suburban, and rural areas. Variables investigated included physical activity, community activity, tobacco usage, alcohol consumption, and betel-nut chewing. Degree of health literacy was evaluated using the Chinese-language version of the European Health Literacy Survey Questionnaire. Most respondents had inadequate (17.6%), or problematic (49.3%), general health literacy. Multiple logistic regression analyses showed that low educational attainment was closely associated with inadequate or problematic general health literacy. Women who did not engage in regular physical activity or direct community activity were more likely to have inadequate and problematic general health literacy, respectively. Selected unhealthy behaviors (tobacco usage, alcohol consumption, betel-nut chewing) were not associated with health literacy. Low health literacy was prevalent among participants. Lower educational attainment and a lack of physical or community activity were associated with low health literacy. Health literacy should be considered during the process of delivering health information, and health education programs must enhance health literacy tailored to address individuals' lifestyles.

Background: This study explored the possible antecedents that will motivate hospital employees' compliance with privacy policy related to electronic medical records (EMR) from a deterrence perspective. Further, we also investigated the moderating effect of computer monitoring on relationships among the antecedents and the level of hospital employees' compliance intention., Methods: Data was collected from a large Taiwanese medical center using survey methodology. A total of 303 responses was analyzed via hierarchical regression analysis., Results: The results revealed that sanction severity and sanction certainty significantly predict hospital employees' compliance intention, respectively. Further, our study found external computer monitoring significantly moderates the relationship between sanction certainty and compliance intention., Conclusions: Based on our findings, the study suggests that healthcare facilities should take proactive countermeasures, such as computer monitoring, to better protect the privacy of EMR in addition to stated privacy policy. However, the extent of computer monitoring should be kept to minimum requirements as stated by relevant regulations.

Objective: Recognizing frailty, also known as clinical geriatric syndrome in the elderly that is characterized by high vulnerability and low resilience, and its extensive influence in clinical practice is challenging. This study aims to develop a social frailty prediction system based on machine learning approaches in order to identify the social frailty status of the elders in order to advance appropriate social services provision., Materials and Methods: This cross-sectional study enrolled and collected information from 595 community-dwelling seniors aged 65+. Fourteen predictors established from questionnaires and electronic medical records were used to predict the social frailty of participants. Bagged classification and regression trees, model average neural network, random forest, C5.0, eXtreme gradient boosting, and stochastic gradient boosting were used to build the predictive model in use. Performance was compared using accuracy, kappa, area under receiver operating characteristic curve, sensitivity, and specificity. The frailty predictive system was web-based and built upon representational state transfer application program interfaces., Results: C5.0 achieved the best overall performance than remaining learners, and was adopted as the base learner for the social frailty prediction system. In terms of the area under receiver operating characteristic curve (AUC), health literacy (AUC = 0.68) was found to be the most important variable for predicting one's social frailty, followed by comorbidity (AUC = 0.67), religious participation (AUC = 0.67), physical activity (AUC = 0.66), and geriatric depression score (AUC = 0.62)., Conclusions: Results suggest that a combination of such data that is both available and unavailable from electronic medical records is predictive of the social frailty of an elderly population., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Introduction: Whereas universal depression screening has potential to increase identification of mental health concerns among college students, the prevalence of universal screening in college health centers is unknown. This study provides an estimate of the prevalence of universal depression screening at public, 4-year universities in the United States. It also documents systems-level facilitators and correlates of universal screening, as well as reasons for not screening., Method: Online cross-sectional survey of college health center directors and student health personnel of public 4-year universities., Results: There were 131 participants who responded to the survey (25% response): 64.2% (54-74%; 95% confidence interval [CI]) of respondents reported that their clinics use universal depression screening. Characteristics associated with universal depression screening use clustered around systems-level resources, including larger student populations and health care staff, greater perceived financial resources, and shorter estimates of time required for screening. Universal screening use was also associated with respondents' awareness of the U.S. Preventive Services Task Force (USPSTF) recommendation, agreement with the evidence base supporting universal depression screening, and beliefs that codified standards support effective care. Leading reasons for not screening included: lack of mental health professionals, provider reluctance, liability concerns, and lack of clinic space., Discussion: Universal depression screening is reasonably common among college health centers. Care planner discussions regarding depression screening initiatives should address resource concerns, awareness of the USPSTF recommendation and evidence base, and information about the average time it takes to screen. Future research should examine functional outcomes and implementation experiences of college health centers that have adopted universal depression screening. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophrenic patients by adopting machine learning techniques., Methods: Data related to a total of 185 schizophrenic in-patients at a Taiwanese district mental hospital diagnosed with pneumonia between 2013 ~ 2018 were gathered. Eleven predictors, including gender, age, clozapine use, drug-drug interaction, dosage, duration of medication, coughing, change of leukocyte count, change of neutrophil count, change of blood sugar level, change of body weight, were used to predict the onset of pneumonia. Seven machine learning algorithms, including classification and regression tree, decision tree, k-nearest neighbors, naïve Bayes, random forest, support vector machine, and logistic regression were utilized to build predictive models used in this study. Accuracy, area under receiver operating characteristic curve, sensitivity, specificity, and kappa were used to measure overall model performance., Results: Among the seven adopted machine learning algorithms, random forest and decision tree exhibited the optimal predictive accuracy versus the remaining algorithms. Further, six most important risk factors, including, dosage, clozapine use, duration of medication, change of neutrophil count, change of leukocyte count, and drug-drug interaction, were also identified., Conclusions: Although schizophrenic patients remain susceptible to the threat of pneumonia whenever treated with anti-psychotic drugs, our predictive model may serve as a useful support tool for physicians treating such patients.

Protective signaling from the leukemia microenvironment leads to leukemia cell persistence, development of resistance, and disease relapse. Here, we demonstrate that fibroblast growth factor 2 (FGF2) from bone marrow stromal cells is secreted in exosomes, which are subsequently endocytosed by leukemia cells, and protect leukemia cells from tyrosine kinase inhibitors (TKIs). Expression of FGF2 and its receptor, FGFR1, are both increased in a subset of stromal cell lines and primary AML stroma; and increased FGF2/FGFR1 signaling is associated with increased exosome secretion. FGFR inhibition (or gene silencing) interrupts stromal autocrine growth and significantly decreases secretion of FGF2-containing exosomes, resulting in less stromal protection of leukemia cells. Likewise, Fgf2 -/- mice transplanted with retroviral BCR-ABL leukemia survive significantly longer than their +/+ counterparts when treated with TKI. Thus, inhibition of FGFR can modulate stromal function, reduce exosome secretion, and may be a therapeutic option to overcome resistance to TKIs., Editorial Note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter)., Competing Interests: NJ, JM, IE, SJ, RS, DE, AA, CL, DJ, JT, ET No competing interests declared, BD Is currently principal investigator or co-investigator on Novartis clinical trials. His institution, OHSU, has contracts with these companies to pay for patient costs, nurse and data manager salaries, and institutional overhead. He does not derive salary, nor does his lab receive funds from these contracts., (© 2019, Javidi-Sharifi et al.)

Many fish generate thrust by undulating one or multiple elongated fins while keeping their body straight. This propulsion mechanism has stimulated interest in both biology and bio-inspired marine propulsion because its maneuverability and efficiency at low speed. Analytical studies have found that a fin attached to a rigid flat body can produce substantially higher thrust compared to a fin without a body, three- to four-fold for natural swimmers. However, this momentum enhancement has not been confirmed experimentally. In this work, a robotic ribbon fin model with an adjustable-height body was used to test the momentum enhancement for gymontiform swimmers where the undulating fin runs along the ventral side of the body. In a series of experiments, the force generated by the robotic device was measured as the body height of the robot, the undulating fin frequency and the flow speed were changed. It was found that the thrust generated by the ribbon fin is not affected by the presence of a body, thereby resulting in no momentum enhancement due to the fin-body interaction. These results suggest that if there is a benefit at a specific fin-body height ratio of the fishes, the momentum enhancement is not the reason. This result has broader implications in understanding the evolutionary adaption of undulatory fin propulsion and underwater vehicles designs.

Background: Hospitals have increasingly realized that wholesale adoption of electronic medical records (EMR) may introduce differential tangible/intangible benefits to them, including improved quality-of-care, reduced medical errors, reduced costs, and allowable instant access to relevant patient information by healthcare professionals without the limitations of time/space. However, an increased reliance on EMR has also led to a corresponding increase in the negative impact exerted via EMR breaches possibly leading to unexpected damage for both hospitals and patients. This study investigated the possible antecedents that will influence hospital employees' continuance compliance with privacy policy of Electronic Medical Records (EMR). This is done from both motivational and habitual perspectives; specifically, we investigated the mediating role of habit between motivation and continuance compliance intention with EMR privacy policy., Methods: Data was collected from a large Taiwanese medical center by means of survey methodology. A total of 312 responses comprised of various groups of healthcare professionals was collected and analyzed via structural equation modeling., Results: The results demonstrated that self-efficacy, perceived usefulness, and facilitating conditions may significantly predict hospital employees' compliance habit formation, whereas habit may significantly predict hospital employees' intention to continuance adherence to EMR privacy policy. Further, habit partially mediates the relationships between self-efficacy, perceived usefulness, facilitating conditions and continuance adherence intention., Conclusions: Based on our findings, the study suggests that healthcare facilities should take measures to promote their employees' habitualization with continuous efforts to protect EMR privacy parameters. Plausible strategies include improving employees' levels of self-efficacy, publicizing the effectiveness of on-going privacy policy, and creating a positive habit-conducive environment leading to continued compliance behaviors.

The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset-accessible through the Beat AML data viewer (Vizome)-that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML.

The purpose of our study aimed to identify attributes capable of improving physicians' satisfaction levels with the use of a hospital information system (HIS). A model inclusive of system quality, information quality, and service quality related to an HIS is used to form antecedents of user satisfaction. Survey methodology was used to collect an attributive set representing the system quality, information quality, and service quality made available from 150 physicians at a large health-care system in southern Taiwan. Responses were segmented into low and high satisfaction and analyzed with partial least squares and importance-performance analysis. The results reveal that system quality, information quality, and service quality may be used to significantly predict physicians' satisfaction. Two system quality attributes (reliability and response time) were identified as the highest priorities for intervention by low- and high-satisfaction users. Low-satisfaction users further expect improvement of the HIS service quality to take place. The subject health-care system should produce coping interventions for those high priorities to enhance the satisfaction of physicians.

Background: A wide variety of braces are commercially available designed for the adolescent idiopathic scoliosis (AIS), but very few braces for infantile scoliosis (IS) or juvenile scoliosis (JS). The goals of this study were: 1) to briefly introduce an elongation bending derotation brace (EBDB) in the treatment of IS or JS; 2) to investigate changes of Cobb angles in the AP view of X-ray between in and out of the EBDB at 0, 3, 6, 9, and 12 months; 3) to compare differences of Cobb angles (out of brace) in 3, 6, 9, and12 month with the baseline; 4) to investigate changes (out of brace) in JS and IS groups separately., Methods: Thirty-eight patients with IS or JS were recruited retrospectively for this study. Spinal manipulation was performed using a stockinet. This was done simultaneously with a surface topography scan. The procedure was done in the operating room for IS, or in a clinical setting for JS. The brace was edited and fabricated using CAD/CAM method. Radiographs were recorded in and out of bracing approximately every 3 months from baseline to 12 months. A linear mixed effects model was used to compare in and out of bracing, and out of brace Cobb angle change over the 12 month period., Results: Overall, 37.5% of curves are corrected and 37.5% stabilized after 12 months (Thoracic curves 48% correction, 19% stabilization; thoracolumbar curves 33% correction, 56% stabilization and lumbar curves 29% correction, 50% stabilization). The juvenile group had 25.7% correction and 42.9% stabilization, while the infantile group had 50% correction and 32.1% stabilization. There was a significant Cobb angle in-brace reduction in the thoracic (11°), thoracolumbar (12°), and lumbar (12°) ( p  < 0.001). There was no statistically significant change in out of brace Cobb angle from baseline to month 12 ( p  > 0.05). No patients required surgery within the 12 month span., Conclusions: This study describes a new clinical protocol in the development of the EBDB. Short-term results show brace is effective in preventing IS or JS curve progression over a 12 month span., Competing Interests: This study was approved by the Institutional Review Board at Children’s Hospital of Wisconsin (Ref# 782405–3).Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.