Soroti University (SUN), is a public multi-campus university in Uganda. It is one of the nine public universities and degree-awarding institutions in the country.
Background: The burden of cancer is escalating in resource-limited settings like Uganda. However, studies on the unmet needs of family caregivers (FCs) of cancer patients are rare in Africa, despite their importance in guiding supportive care programs and high-quality patient-and-family-centered care outcomes. Objective: The study aimed to explore the unmet needs of FCs of cancer patients in Uganda and its relationship with mental health status and quality-of-life outcomes of FCs. Method: A cross-sectional descriptive design was used to collect data from 170 FCs in Uganda. The data were collected using the Hospital Anxiety and Depression Scale, the Quality of Life Index-Cancer Scale, and the Needs Assessment Family Caregiver-Cancer Scale. Results: FCs exhibited reduced quality of life (mean = 73.55 +/- 21.88), anxiety (37.6%), and depressive (20%) symptoms. The lowest scores were in the quality-of-life domains of positive adaptation (52.9%) and financial concerns (63.5%). FCs had significant medical, psychosocial, and financial unmet needs. The key medical and psychosocial unmet needs were related to symptom management, medical care, and emotional distress. The quality of life was associated with anxiety, depression, daily activity, finance (all p < 0.01), and psychosocial unmet needs (p < 0.05). Conclusion: The unmet needs of FCs of cancer patients in Uganda significantly impact their quality of life and mental health. The nurses and other healthcare providers caring for cancer patients must utilize care plans that integrate supportive care programs and interventions to address the unmet needs of FCs to ensure better healthcare system, patient, and caregiver outcomes.
BackgroundSystematic reviews are essential for evidence-based decision-making, but the screening stage is often labor-intensive and susceptible to human error. Machine learning (ML) approaches, including active learning (AL), have increasingly been used to support title and abstract screening. One such approach is the SAFE procedure, which has been proposed to guide the use of AL-assisted screening in systematic reviews. However, evidence on how well this procedure performs in large, heterogeneous datasets generated by broad search strategies remains limited. This study therefore evaluates the effectiveness and reliability of AL-assisted screening with particular focus on the SAFE procedure. Specifically, it examines the comprehensiveness and necessity of the recommended SAFE procedure, assesses the influence of different labeling strategies, and investigates whether AL-assisted screening can help reduce manual screening errors.MethodsScreening of four large, heterogeneous datasets from medication management systematic reviews was simulated using ASReview. The datasets ranged from 3475 to 16218 records. For these datasets 0.08 to 1% of records were included in the final systematic review. Our simulations systematically varied all parameters defined by the SAFE procedure. Recall versus sampling behavior was analyzed, with a focus on the impact of parameter choices on retrieving records selected for full text inclusions and on reducing the number of records to be screened.ResultsAL-assisted screening can effectively reduce the number of records to screen by almost 90% without increasing the risk of missing relevant records in comparison to manual screening. For three of the four datasets, the best performance was achieved with the SAFE procedure combined with the elas-u4 and elas-h3 models and full-text labeling. Under these conditions, ASReview identified all studies included after full-text review and reduced the screening workload by 89-90%. In practical terms, this means that screening only 10-11% of the original records was sufficient to identify all final included studies in these datasets. This parameter combination identified 87% of the studies ultimately included after full-text review in the remaining dataset (16,218 records; 0.6% included at title/abstract screening and 0.08% included after full-text review). For this dataset, the best performance, identifying all studies included after full-text review while reducing the screening workload by 90%, was achieved when using the SAFE procedure with the simpler Naive Bayes model, the TF-IDF feature extractor, and title/abstract labeling.ConclusionsAL-assisted screening can safely and effectively reduce the workload needed to screen the large, heterogeneous datasets common in medication management systematic reviews. We recommend the modified SAFE procedure using full-text labels and the elas models. If the estimated ratio of full text includes is very low, it may be more appropriate to use the original SAFE procedure with title/abstract labeling.
Artificial intelligence (AI) has been applied in a number of breast screening settings with favourable results. While there are a limited number of studies exploring patient attitudes on the use of AI in breast screening, none to date have examined patient perceptions on the use of AI in the symptomatic setting. Following institutional approval, anonymous questionnaires were given to all patients attending the symptomatic breast clinic imaging department from 08/07/2024 to 04/10/2024. The questionnaire included questions on participant demographics and opinion questions on the use of AI in breast imaging. Multinomial logistic regression was performed to examine the associations between sociodemographic characteristics and patients' views about AI use in breast imaging. One thousand five hundred thirty-four participants completed the questionnaire. Most participants were aged 40–59 years(35.8
Efficient thermal management in microchannel systems is essential for modern cooling devices, biomedical systems, energy units, and compact heat exchangers. In this study, the unsteady heat transfer and flow behavior of a Casson-type ternary hybrid nanofluid in a parallel-plate microchannel are investigated under generalized magnetohydrodynamic (MHD) effects. Water is used as the base fluid, while gold (Au) , copper (Cu) , and silver (Ag) nanoparticles are uniformly suspended to improve the thermal transport capacity of the fluid. The main motivation of this work is to examine how memory effects, magnetic field strength, and enhanced nanoparticle properties influence the velocity and temperature distributions in a non-Newtonian microchannel flow. To describe the memory and hereditary characteristics of the flow, the governing equations are modeled using the Constant Proportional Caputo (CPC) fractional derivative. An implicit finite-difference scheme is also developed to support thus study results numerically. The model is validated by comparing the present profiles with the previous study results, and a close agreement is observed. In addition, a magnitude-based sensitivity analysis is carried out to identify the most influential physical parameters. The results show that the thermal Grashof number has the strongest effect on the velocity response, while the effective Prandtl number is the dominant parameter controlling the temperature field. The CPC fractional model provides smoother and more flexible transient behavior compared with the classical integer-order model. Furthermore, the ternary hybrid nanofluid shows better heat transfer performance than simple nanofluid and hybrid nanofluid cases because of its improved effective thermal conductivity.
Goldenseal (Hydrastis canadensis L., Ranunculaceae) is a traditional North American herbal medicine with a long history of use to treat various illnesses, primarily as a chemotherapeutic agent for microbial infections. Two of its alkaloid constituents, berberine and (-)-beta-hydrastine, have well-defined pharmacological effects. In addition to its presence in goldenseal, berberine (and analogues) are found in other plant families. One such is the barberry family (Berberidaceae), encompassing various medicinal and decorative species, including those of the genus Mahonia (Berberis). This 3-h laboratory practical has been devised to allow undergraduate students to isolate berberine and (-)-beta-hydrastine from a commercially available root sample of goldenseal, and both berberine and the closely related alkaloid, jatrorrhizine, from the bark of Mahonia x media 'Winter sun'. Isolation is carried out on a miniature, sustainable and environmentally friendly scale using flash column chromatography. A gradient mobile phase is utilized for the isolation of (-)-beta-hydrastine and berberine from goldenseal. A notable feature of the isolation of berberine and jatrorrhizine from Mahonia is the utilization of silica gel loaded with 10% w/w sodium carbonate. In this way, jatrorrhizine, an acidic alkaloid, is significantly retained while berberine elutes effortlessly from the column. A follow-on spectral assignment workshop is undertaken using 1- and 2-D NMR and IR spectra of the isolated alkaloids. Both formative and summative assessment of student comprehension takes place during the practical, workshop and end-of-semester college examinations. The experiment has been fully validated in the class setting, having been completed by circa 160 third year pharmacy students.
