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We conducted an updated systematic review and meta-analysis to investigate the effect of colchicine treatment on clinical outcomes in patients with COVID-19.
Systematic review and meta-analysis.
We searched PubMed, Embase, the Cochrane Library, medRxiv and ClinicalTrials.gov from inception to January 2023.
All randomised controlled trials (RCTs) that investigated the efficacy of colchicine treatment in patients with COVID-19 as compared with placebo or standard of care were included. There were no language restrictions. Studies that used colchicine prophylactically were excluded.
We extracted all information relating to the study characteristics, such as author names, location, study population, details of intervention and comparator groups, and our outcomes of interest. We conducted our meta-analysis by using RevMan V.5.4 with risk ratio (RR) and mean difference as the effect measures.
We included 23 RCTs (28 249 participants) in this systematic review. Colchicine did not decrease the risk of mortality (RR 0.99; 95% CI 0.93 to 1.05; I2=0%; 20 RCTs, 25 824 participants), with the results being consistent among both hospitalised and non-hospitalised patients. There were no significant differences between the colchicine and control groups in other relevant clinical outcomes, including the incidence of mechanical ventilation (RR 0.75; 95% CI 0.48 to 1.18; p=0.22; I2=40%; 8 RCTs, 13 262 participants), intensive care unit admission (RR 0.77; 95% CI 0.49 to 1.22; p=0.27; I2=0%; 6 RCTs, 961 participants) and hospital admission (RR 0.74; 95% CI 0.48 to 1.16; p=0.19; I2=70%; 3 RCTs, 8572 participants).
The results of this meta-analysis do not support the use of colchicine as a treatment for reducing the risk of mortality or improving other relevant clinical outcomes in patients with COVID-19. However, RCTs investigating early treatment with colchicine (within 5 days of symptom onset or in patients with early-stage disease) are needed to fully elucidate the potential benefits of colchicine in this patient population.
CRD42022369850.
This study conducted in Dena County is a population-based cohort study as part of the Prospective Epidemiological Research Studies in Iran (PERSIAN). The specific objectives of this study were to estimate the prevalence of region-specific modifiable risk factors and their associations with the incidence of major non-communicable diseases (NCDs).
This PERSIAN Dena Cohort Study (PDCS) was conducted on 1561 men and 2069 women aged 35–70 years from October 2016 in Dena County, Kohgiluyeh and Boyer-Ahmad Province, Southwest Iran. The overall participation rate was 82.7%.
Out of 3630 participants, the mean age was 50.16 years, 2069 (56.9%) were women and 2092 (57.6%) were rural residents. Females exhibited higher prevalence rates of diabetes, hypertension, fatty liver, psychiatric disorders, thyroiditis, kidney stones, gallstones, rheumatic disease, chronic lung disease, depression and osteoporosis compared with males (p126 mg/dL, low-density lipoprotein >100 mg/dL and haematuria, respectively; most of them were female and urban people (p
PDCS will be planned to re-evaluate NCD-related incidence, all-cause and cause-specific mortality every 5 years, along with annual follow-up for 15 years. Some examples of additional planned studies are evaluation of genetic, environmental risk, spirometry and ECG tests.
To synthesise evidence from the literature on hospital nurses' perceived challenges and opportunities in the care of people with dementia.
People with dementia often have longer lengths of hospital stay and poorer health outcomes compared to those without dementia. Nurses play a pivotal role in the care of people with dementia. However, there is a scarcity of systematic reviews that synthesise the challenges and opportunities they perceive.
A mixed-methods systematic review was conducted with a database search covering Ageline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Emcare, Embase, Medline, PsycINFO, ProQuest, Scopus and Web of Science in April 2022. In total, 27 articles that met the selection criteria were critically reviewed and included in this systematic review. Data from the selected articles were extracted and synthesised using a convergent segregated approach.
Three main themes and eight subthemes were identified. Theme 1 described nurse-related factors consisting of the lack of capability in dementia care, experiencing multiple sources of stress and opportunities for nurses to improve dementia care. Theme 2 revealed people living with dementia-related factors including complex care needs and the need to engage family carers in care. Theme 3 explained organisation-related factors comprising the lack of organisational support for nurses and people with dementia and opportunities for quality dementia care.
Hospital nurses experience multidimensional challenges in the care of people with dementia. Opportunities to overcome those challenges include organisational support for nurses to develop dementia care capability, reduce their stress and partner with the family caregivers.
Hospitals will need to build an enabling environment for nurses to develop their capabilities in the care of people with dementia. Further research in empowering nurses and facilitating quality dementia care in acute care hospitals is needed.
The review followed the PRISMA 2020 checklist.
No.
Nurses’ innovative behaviours play a crucial role in addressing the challenges including adapting to emerging technologies, resource limitations and social realities such as population ageing that are intricately tied to today’s healthcare landscape. Innovative behaviours improve healthcare quality, patient safety and satisfaction. Organisational factors and individual attributes influence nurses’ inclination to innovate. With the rise of artificial intelligence and novel technology, healthcare institutions are actively engaged in the pursuit of identifying nurses who demonstrate innovative qualities. Developing a comprehensive protocol to elucidate the various dimensions of nurses’ innovative behaviours and constructing a valid measuring instrument, rooted in this protocol represents a significant step in operationalising this concept.
The study encompasses two phases: a qualitative study combined with a literature review, followed by the design and psychometric evaluation of the instrument. To ensure diversity, a maximum variation purposive sampling method will be used during the qualitative phase to select clinical nurses. In-depth semistructured interviews will be conducted and analysed using conventional content analysis. Additionally, a comprehensive literature review will supplement any missing features not captured in the qualitative phase, ensuring their inclusion in the primary tool. The subsequent quantitative phase will focus on evaluating the questionnaire’s psychometric properties, including face, content and construct validity through exploratory factor analyses (including at least 300 samples) and confirmatory factor analyses (including at least 200 samples). Internal consistency (Cronbach’s alpha), reliability (test–retest), responsiveness, interpretability and scoring will also be assessed.
This study originates from a doctoral dissertation in nursing. Permission and ethical approval from Semnan University of Medical Sciences has been obtained with reference code IR.SEMUMS.1401.226. The study’s findings will ultimately be submitted as a research paper to a peer-reviewed journal.
by Mo Ahamad Khan, Ismail Celik, Haris M. Khan, Mohammad Shahid, Anwar Shahzad, Sachin Kumar, Bilal Ahmed
The quorum sensing mechanism relies on the detection and response to chemical signals, termed autoinducers, which regulate the synthesis of virulence factors including toxins, enzymes, and biofilms. Emerging therapeutic strategies for infection control encompass approaches that attenuate quorum-sensing systems. In this study, we evaluated the antibacterial, anti-quorum sensing, and anti-biofilm activities of Psidium guajava L. methanolic leaf extracts (PGME). Minimum Inhibitory Concentrations (MICs) of PGME were determined as 500 μg/ml for C. violaceum and 1000 μg/ml for P. aeruginosa PAO1. Significantly, even at sub-MIC concentrations, PGME exhibited noteworthy anti-quorum sensing properties, as evidenced by concentration-dependent inhibition of pigment production in C. violaceum 12742. Furthermore, PGME effectively suppressed quorum-sensing controlled virulence factors in P. aeruginosa PAO1, including biofilm formation, pyoverdin, pyocyanin, and rhamnolipid production, with concentration-dependent inhibitory effects. Phytochemical analysis utilizing GC-MS revealed the presence of compounds such as alpha-copaene, caryophyllene, and nerolidol. In-silico docking studies indicated a plausible mechanism for the observed anti-quorum sensing activity, involving favorable binding and interactions with QS-receptors, including RhlR, CviR’, LasI, and LasR proteins. These interactions were found to potentially disrupt QS pathways through suppression of AHL production and receptor protein blockade. Collectively, our findings propose PGME as a promising candidate for the treatment of bacterial infections. Its attributes that mitigate biofilm development and impede quorum-sensing mechanisms highlight its potential therapeutic value.by Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Saurav Mallik, Zhongming Zhao
Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.
FreshRSS 