Soroti University

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.

OBJECTIVE:To assess the diagnostic performance of interleukin-6 (IL-6) versus in-use inflammatory markers (C-reactive protein [CRP], procalcitonin [PCT], neutrophil-lymphocyte ratio [NLR]) in maternal sepsis. METHODS:This was a retrospective cohort study in a single Dublin maternity hospital. All maternity patients (conception to 6-weeks post-partum) in whom IL-6 was measured to investigate suspected sepsis in an 11-month period were included. Cases were categorized twice into physiological (normal, systemic inflammatory response syndrome, sepsis, septic shock) and etiological (bacterial, viral or no infection) categories. Biomarker performance was assessed by area under the receiver operating characteristic curves (AUC) and measures of diagnostic accuracy at optimal cut-offs. Serial sampling evaluated biomarker kinetics. RESULTS:Seventy-two patients were included, 31.9 % had sepsis/septic shock, and 70.9 % had bacterial infection. Significant differences between categories were seen for both physiological and etiological axes for IL-6, for physiological categories alone for NLR, and for neither for PCT and CRP. IL-6 had an AUC of 0.78 for the primary physiological endpoint, significantly higher than CRP and PCT (p = 0.02), but not NLR (AUC 0.72, p = 0.47). The IL-6 AUC for the primary etiological endpoint was 0.94, higher than all other biomarkers (p < 0.001). IL-6 was highly sensitive (91.4 %) but poorly specific (55.1 %) for the physiological endpoint, while both were high in the diagnosis of bacterial infection (88.2 % and 90.5 %). On serial sampling, IL-6 fell rapidly between first and second sampling, while others initially rose further. CONCLUSION:IL-6 exhibited significantly higher sensitivity compared to conventional sepsis biomarkers in detecting both bacterial infection and maternal sepsis.

Background and Objectives: Adverse medication events significantly contribute to emergency department visits, unplanned hospitalizations, and in-hospital morbidity and mortality, particularly in older adults. Identifying potentially inappropriate prescribing is essential to improve safety, care quality, and medication management. The objective of this study was to identify and codify the Screening Tool of Older People's Prescriptions (STOPP)-Screening Tool to Alert to Right Treatment (START) and Beers criteria for large, population-level health databases to detect potentially inappropriate prescribing at the population level. Methods: A subset of the 2014 STOPP/START and 2015 Beers criteria applicable to health administrative data was codified using diagnostic (ICD; International Classification of Diseases) and medication (DIN; Drug Identification Number) codes using provincial health administrative databases in Ontario, Canada, which comprise individual-level linked information on medication dispensation, physician services use, emergency room visits, hospitalizations, mortality, and sociodemographic data. Results: Overall, 103 of 177 (58.2%) criteria were codable (76.5% of 81 STOPP, 23.5% of 34 START, and 53.2% of 62 Beers). Some criteria could not be coded because the population health data used were missing information necessary to the operationalization of these criteria. This included laboratory values (renal function) and diagnostic or clinical information (such as blood pressure, body mass index) that could not be indirectly derived from the population health data. Conclusions: Applying a large subset of codified and well-established clinical criteria to health administrative data offers a promising and potentially cost-effective approach to detect potentially inappropriate prescribing at the population level. The present study contributes the required coding which constitutes the core of this approach. (c) 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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.

We performed a pilot study in which Illumina (ILMN) and nanopore (ONT) whole genome sequencing (WGS) was generated for samples obtained from a 37-year-old patient with locally advanced rectal cancer who did not respond to neoadjuvant chemoradiotherapy (nCRT). Pre-treatment and post-treatment tumour biopsy, adjacent normal tissue, and matched pre-treatment blood samples were sequenced to identify somatic alterations. We used DNA methylation from ONT WGS to detect changes in the tumour epigenomes compared to adjacent normal tissue. We employed established pipelines for the identification of somatic alterations to assess the concordance of SNV, short indel, copy number and structural variation from the short-read and long-read tumour genome data. Overall, 69.4% and 30.1% of SNVs were concordant between technologies in the pre-and post-treatment tumours, respectively. A multinomial logistic regression was performed to further understand discordant SNV calls, highlighting tumour purity, presence in a repetitive region, strand bias, and germline variant allele frequency as factors contributing to discordance. Indel concordance was poor (26.8% and 9.2% in the pre-and post-treatment tumour, respectively). Copy number alteration profiles were highly similar while minimal concordance was reported for structural variants (2.4% and 0.3% for pre-and post-treatment tumours, respectively). Finally, we used the ONT data to detect DNA modifications (5-methylcytosine and 5-hydroxymethylcytosine) between tumour and matched adjacent normal tissue. Based on genome-wide average CpG modification rates, global hypomethylation was observed in tumours compared to adjacent normal tissues. Biologically relevant epigenomic changes related to epithelial-mesenchymal transition were detected in the post-treatment tumour, as well as potential radiation-induced changes in the post-treatment adjacent normal tissue.