METHODS: Data from National Eye Database (NED), involving all patients who have undergone cataract surgery from January 2012 until December 2020 were analyzed.
RESULTS: Total number of patients who had undergone cataract surgery between the year 2012 till 2020 were 231,281 patients (267,781 eyes). Incidence of POE in this population was 0.08%. Patient aged 70 and above (p-value 0.047), Malay ethnicity (p-value: 0.009), presence of ischemic heart disease, renal failure, diabetic retinopathy, and poorer preoperative vision were shown to have a higher risk of POE. Cataract surgeries done in KK-KKM, duration more than 45 minutes, use of general anaesthesia, and no IOL or ACIOL implantation were significantly more in POE patients. Multivariate analysis revealed Malay ethnicity, presence of ocular comorbidity, poor preoperative vision, ACIOL, and presence of intra-operative complication were predictive factors for POE.
CONCLUSIONS: Incidence of POE is low in the Malaysian population. Surgeons have to be aware that Malay ethnicity, presence of ocular comorbidity, poor preoperative visual acuity, placement of IOL and complicated cataract operation have higher risk of POE.
OBJECTIVE: In this research, we propose a novel method for forecasting vector-borne disease risk using Radial Basis Function Networks (RBFNs) and the Darts Game Optimizer (DGO) algorithm.
METHODS: The proposed approach entails training the RBFNs with historical disease data and enhancing their parameters with the DGO algorithm. To prepare the RBFNs, we used a massive dataset of vector-borne disease incidences, climate variables, and geographical data. The DGO algorithm proficiently searches the RBFN parameter space, fine-tuning the model's architecture to increase forecast accuracy.
RESULTS: RBFN-DGO provides a potential method for predicting vector-borne disease risk. This study advances predictive demonstrating in public health by shedding light on effectively controlling vector-borne diseases to protect human populations. We conducted extensive testing to evaluate the performance of the proposed method to standard optimization methods and alternative forecasting methods.
CONCLUSION: According to the findings, the RBFN-DGO model beats others in terms of accuracy and robustness in predicting the likelihood of vector-borne illness occurrences.
METHODS: Adhering rigorously to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a comprehensive scoping review protocol was developed. Collaborating with a research librarian, a systematic search strategy targeted peer-reviewed literature from databases such as PubMed, Embase, Scopus, and Web of Science, complemented by a thorough grey literature search. Titles and abstracts will be screened, followed by extracting bibliographic details and outcome information using a standardized framework. Subsequently, the results will be systematically summarized and presented in a structured tabular format (S1 Checklist).
DISCUSSION: This scoping review promises an in-depth understanding of current research regarding the impact of community knowledge in malaria programmes. The identification of knowledge gaps and intervention needs serves as a valuable resource for malaria-affected countries. The profound implications of community knowledge underscore its pivotal role in enhancing the effectiveness of prevention, control, and elimination efforts. Insights from this review will assist policymakers, empowering implementers and community leaders in designing effective interventions. This concerted effort aims to adeptly leverage community knowledge, thereby propelling progress toward the achievement of malaria elimination goals.
METHODS AND RESULTS: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).
CONCLUSIONS: Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual's risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.
METHODS: In this geospatial modelling analysis, we developed an integrated database containing information on the distribution of Nipah virus infections in humans and animals from 1998 to 2021. We conducted phylodynamic analysis to examine the evolution and migration pathways of the virus and meta-analyses to estimate the adjusted case-fatality rate. We used two boosted regression tree models to identify the potential ecological drivers of Nipah virus occurrences in spillover events and endemic areas, and mapped potential risk areas for Nipah virus endemicity.
FINDINGS: 749 people and eight bat species across nine countries were documented as being infected with Nipah virus. On the basis of 66 complete genomes of the virus, we identified two clades-the Bangladesh clade and the Malaysia clade-with the time of the most recent common ancestor estimated to be 1863. Adjusted case-fatality rates varied widely between countries and were higher for the Bangladesh clade than for the Malaysia clade. Multivariable meta-regression analysis revealed significant relationships between case-fatality rate estimates and viral clade (p=0·0021), source country (p=0·016), proportion of male patients (p=0·036), and travel time to health-care facilities (p=0·036). Temperature-related bioclimate variables and the probability of occurrence of Pteropus medius were important contributors to both the spillover and the endemic infection models.
INTERPRETATION: The suitable niches for Nipah virus are more extensive than previously reported. Future surveillance efforts should focus on high-risk areas informed by updated projections. Specifically, intensifying zoonotic surveillance efforts, enhancing laboratory testing capacity, and implementing public health education in projected high-risk areas where no human cases have been reported to date will be crucial. Additionally, strengthening wildlife surveillance and investigating potential modes of transmission in regions with documented human cases is needed.
FUNDING: The Key Research and Development Program of China.