Patient activation is critical in hospitalized older adults preparing for discharge as it enhances their ability to self-care at home. Little is known about how person-centred care and demographic predictors could influence activation in Asian patients.
To explore patient activation and its predictors in hospitalized older adults in Singapore.
Multi-centre cross-sectional survey of hospitalized older adults. Multivariable analysis conducted with age, gender, education, socioeconomic status, functional dependency and perception of person-centred care as potential predictors to patient activation.
300 older adults were surveyed, 65% were at the top two levels of activation. Perception of person-centred nursing care was the strongest predictor with the largest effect on patient activation, (β=0.22, b=3.48, 95% CI1.70-5.26, p<0.001). Other predictors were age, education, income and independence in care.
Our study highlights the importance of person-centred nursing care in raising patient activation in hospitalized older adults, enhancing their capacity to self-care.
Our study highlights the importance of person-centred nursing care in raising patient activation in hospitalized older adults, enhancing their capacity to self-care.This qualitative project used conventional content analysis of interview data to examine nurses' experience with and perception of the Elder Veteran Program, an inpatient geriatric consulting service, at a midwestern Veterans' hospital. Nurses were recruited from nursing units utilizing the program and completed individual interviews (N = 10). Participants described the impact of the program within four categories providing comprehensive care to patients, contributing to individual growth of nurses, promoting team-based care, and as a resource. Participants described several barriers and facilitators to implementation of the program on their unit, including workload and time, shifts and availability of program staff, perceived need, inclusion criteria, perception of program staff, education of nurses, communication, and the inpatient environment. This project provides opportunities for further examination of healthcare providers' experience with inpatient geriatric programs, how those experiences may relate to effectiveness of programs, and important areas of support for hospital staff.
Approved first-line treatments for patients with BRAF V600-mutant advanced melanoma include nivolumab (a programmed cell death protein 1 inhibitor) plus ipilimumab (a cytotoxic T lymphocyte antigen-4 inhibitor; NIVO+IPI) and the BRAF/MEK inhibitors dabrafenib plus trametinib (DAB+TRAM), encorafenib plus binimetinib (ENCO+BINI), and vemurafenib plus cobimetinib (VEM+COBI). Results from prospective randomized clinical trials (RCTs) comparing these treatments have not yet been reported. This analysis evaluated the relative efficacy and safety of NIVO+IPI versus DAB+TRAM, ENCO+BINI, and VEM+COBI in patients with BRAF-mutant advanced melanoma using a matching-adjusted indirect comparison (MAIC).
A systematic literature review identified RCTs for DAB+TRAM, ENCO+BINI, and VEM+COBI in patients with BRAF-mutant advanced melanoma. Individual patient-level data for NIVO+IPI were derived from the phase III CheckMate 067 trial (BRAF-mutant cohort) and restricted to match the inclusion/exclusion criteria of the comparaed melanoma treated with NIVO+IPI compared with BRAF/MEK inhibitors, with the greatest benefits noted after 12 months.
Results of this MAIC demonstrated durable OS and PFS benefits for patients with BRAF-mutant advanced melanoma treated with NIVO+IPI compared with BRAF/MEK inhibitors, with the greatest benefits noted after 12 months.
Women represent an increasing proportion of the oncology workforce; however, globally this does not translate into leadership roles, reflecting disparities in career opportunities between men and women. The Spanish Society of Medical Oncology (SEOM) undertook a survey to investigate gender disparity in the Spanish oncology context.
An online survey was made available to SEOM medical oncologists between February and May 2019. It included demographics, professional context and achievements, parenthood and family conciliation issues, workplace gender bias, and approaches to address disparities.
Of the 316 eligible respondents, 71.5% were women, 59.5% were aged 45 or younger, and 66.1% had children. Among women, 12.4% were division or unit heads, compared with 45.5% of men, with most women (74.3%) being attending medical oncologists, compared with 45.5% of men. More males were professors (34.4% versus 14.2% of females), had a PhD (46.7% versus 28.8%), and/or had led clinical research groups (41.1% versus 9. in management and leadership of institutions and professional societies.
There is a clear paucity of equal opportunities for female oncologists in Spain. This can be addressed by encouraging professional development and merit recognition particularly for younger female oncologists, and empowering women to be involved in management and leadership of institutions and professional societies.Rapid and efficient processing of sexual assault evidence will accelerate forensic investigation and decrease casework backlogs. The standardized protocols currently used in forensic laboratories require the continued innovation to handle the increasing number and complexity of samples being submitted to forensic labs. Here, we present a new technique leveraging the integration of a bio-inspired oligosaccharide (i.e., Sialyl-LewisX) with magnetic beads that provides a rapid, inexpensive, and easy-to-use strategy that can potentially be adapted with current differential extraction practice in forensics labs. This platform (i) selectively captures sperm; (ii) is sensitive within the forensic cut-off; (iii) provides a cost effective solution that can be automated with existing laboratory platforms; and (iv) handles small volumes of sample (∼200 μL). This strategy can rapidly isolate sperm within 25 minutes of total processing that will prepare the extracted sample for downstream forensic analysis and ultimately help accelerate forensic investigation and reduce casework backlogs.Mathematical models are useful tools in the study of physiological phenomena. However, due to differences in assumptions and formulations, discrepancy in simulations may occur. Among the models for cardiomyocyte contraction based on Huxley's cross-bridge cycling, those proposed by Negroni and Lascano (NL) and Rice et al. (RWH) are the most frequently used. This study was aimed at developing a computational tool, ForceLAB, which allows implementing different contraction models and modifying several functional parameters. As an application, electrically-stimulated twitches triggered by an equal Ca2+ input and steady-state force x pCa relationship (pCa = -log of the molar free Ca2+ concentration) simulated with the NL and RWH models were compared. The equilibrium Ca2+-troponin C (TnC) dissociation constant (Kd) was modified by changing either the association (kon) or the dissociation (koff) rate constant. With the NL model, raising Kd by either maneuver decreased monotonically twitch amplitude and duration, as expected. With the RWH model, in contrast, the same Kd variation caused increase or decrease of peak force depending on which rate constant was modified. Additionally, force x pCa curves simulated using Ca2+ binding constants estimated in cardiomyocytes bearing wild-type and mutated TnC were compared to curves previously determined in permeabilized fibers. Mutations increased kon and koff, and decreased Kd. Both models produced curves fairly comparable to the experimental ones, although sensitivity to Ca2+ was greater, especially with RWH model. The NL model reproduced slightly better the qualitative changes associated with the mutations. It is expected that this tool can be useful for teaching and investigation.
Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep convolutional neural networks (DCNN) are currently the main tool used for image analysis and classification purposes. There are several DCNN architectures among them AlexNet, GoogleNet, and residual networks (ResNet).
This paper presents a new computer-aided diagnosis (CAD) system based on feature extraction and classification using DL techniques to help radiologists to classify breast cancer lesions in mammograms. This is performed by four different experiments to determine the optimum approach. The first one consists of end-to-end pre-trained fine-tuned DCNN networks. In the second one, the deep features of the DCNNs are extracted and fed to a support vector machine (SVM) classifier with different kernel functions. https://www.selleckchem.com/products/poziotinib-hm781-36b.html The third experiment performs deep features fusion to demonstrate that combining deep features will enhance the accuracy of the SVM classifiers. Finally, in the fourth experiment, principal component analysis (PCA) is introduced to reduce the large feature vector produced in feature fusion and to decrease the computational cost. The experiments are performed on two datasets (1) the curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) and (2) the mammographic image analysis society digital mammogram database (MIAS).
The accuracy achieved using deep features fusion for both datasets proved to be the highest compared to the state-of-the-art CAD systems. Conversely, when applying the PCA on the feature fusion sets, the accuracy did not improve; however, the computational cost decreased as the execution time decreased.
The accuracy achieved using deep features fusion for both datasets proved to be the highest compared to the state-of-the-art CAD systems. Conversely, when applying the PCA on the feature fusion sets, the accuracy did not improve; however, the computational cost decreased as the execution time decreased.Exposure to poisonous plants is hazardous to health; thus, reliable species identification is required to decide the most appropriate treatment. Since ingested plants are too much degraded for visual observation, DNA barcoding can be used as a molecular tool for species identification. Considering the universal primers, PCR and sequencing success rate, and diversity of the poisonous plants, the rbcL DNA marker was selected for molecular identification. A reference DNA barcode library for 100 poisonous plant species was created using rbcL DNA barcodes. For the poisonous plants represented in the library, 100% and 89% species differentiation was observed at the genus and species level, respectively. All the undifferentiated species were congeneric species. Mapping the metabolites of the poisonous plants to the DNA based phylogenetic tree indicated that the phylogenetically related species also had related toxic compounds. Therefore, genus-level identification may be sufficient in the practical application of DNA barcoding in poisoning cases. We conclude that rbcL can be used as a primary marker, and if required, ITS2 or trnH-psbA may be used as a secondary marker to identify the poisonous plants. The present study provides the foundation to develop a reliable molecular method to identify the poisonous species from the vomit samples of poisoning cases.