METHODS: We conducted a systematic review in accordance with PRISMA 2020 guidelines, guided by the SPIDER framework. PubMed, Scopus, ScienceDirect, and IEEE Xplore were searched from inception to October 2025 for primary studies evaluating computational or clinical decision support interventions aimed at reducing DDIs or inappropriate prescribing. Eligible studies included deterministic rule-based systems, ontological frameworks, and artificial intelligence-driven predictive models. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool, extended with artificial intelligence-specific considerations (PROBAST+AI). Due to heterogeneity in study designs and outcome measures, findings were synthesized narratively.
RESULTS: Ten studies met the inclusion criteria. Earlier interventions predominantly employed deterministic approaches focused on workflow optimization, alert management, and policy enforcement, demonstrating modest improvements in prescribing processes but inconsistent links to patient-level outcomes. More recent studies applied stochastic and generative models using high-dimensional clinical datasets to predict DDIs, reporting strong internal performance metrics. However, PROBAST+AI assessment identified a consistently high risk of bias in the analysis domain for AI-driven studies, primarily due to limited external validation, insufficient calibration reporting, and unclear handling of overfitting and data leakage.
CONCLUSIONS: While stochastic and generative models offer enhanced predictive capacity for DDI detection, current evidence does not demonstrate a proportional improvement in clinically reliable decision support. Deterministic systems provide transparency and safety constraints but lack adaptability to patient-specific contexts. Future interventions must prioritize hybrid architectures that integrate explainable rule-based guardrails with rigorously validated stochastic models to ensure that methodological complexity yields reproducible gains in patient safety.
METHODS: The dengue infection in mouse model was established by inoculation of non-mouse adapted New Guinea C strain dengue virus (DEN-2) in AG129 mice. The freeze-dried CPLJ compounds were identified by Ultra-High Performance Liquid Chromatography High Resolution Accurate Mass Spectrometry analysis. The infected AG129 mice were orally treated with 500 mg/kg/day and 1000 mg/kg/day of freeze-dried CPLJ, starting on day 1 post infection for 3 consecutive days. The blood samples were collected from submandibular vein for plasma NS1 assay and quantitation of viral RNA level by quantitative reverse transcription PCR.
RESULTS: The AG129 mice infected with dengue virus showed marked increase in the production of plasma NS1, which was detectable on day 1 post infection, peaked on day 3 post-infection and started to decline from day 5 post infection. The infection also caused splenomegaly. Twenty-four compounds were identified in the freeze-dried CPLJ. Oral treatment with 500 mg/kg/day and 1000 mg/kg/day of freeze-dried CPLJ did not affect the plasma NS1 and dengue viral RNA levels. However, the morbidity level of infected AG129 mice were slightly decreased when treated with freeze-dried CPLJ.
CONCLUSION: Oral treatment of 500 mg/kg/day and 1000 mg/kg/day of freeze-dried CPLJ at the onset of viremia did not affect the plasma NS1 and viral RNA levels in AG129 mice infected with non-mouse adapted New Guinea C strain dengue virus.
OBJECTIVE: To identify and evaluate the pattern and associated factors to COVID-19 knowledge, attitude, and practice among individuals with comorbidities.
METHODOLOGY: The systematic review followed the PRISMA guidelines. Relevant studies assessing the KAP of comorbid patients were retrieved by carefully searching the PubMed and Google Scholar databases. The appraisal tool for cross-sectional studies was used to determine the quality of the included studies and the risk of biases.
RESULTS: Eighteen studies met the inclusion criteria and were included in the review. The pooled sample size of the included studies was 9,104. Different comorbidities reported in the studies include hypertension, diabetes, psychological disorders, and cancer. Pooled analysis showed that 65% of patients showed good knowledge, 57% of patients showed a positive attitude and 51% of patients followed good practices to manage the COVID-19 in presence of their comorbid condition. Significant factors impacting knowledge, attitude and practice in COVID-19 comorbid patients were ethnicity OR 1.78 [95% CI 1.35-2.32]; educational status 3.2 [2.79-3.58]; urban residence 2.43 [1.65-3.02]; employment Status 1.67[1.34-2.12]; financial Status 4.02[3.66-4.38]; occupation 3.65[3.31-4.25]; information Source 2.64[2.19-3.26]; comorbidity 3.28[2.78-3.61]; and duration of chronic illness 1.59[1.31-2.04].
CONCLUSION: Comorbid COVID-19 patients showed good knowledge, positive attitude and good practice towards the management of the disease.
METHODS: The locomotor activity, learning, and memory were assessed by using open field test and water T-maze test. This study also examined changes in neuronal cell morphology using cresyl violet and apoptosis staining. We also performed immunohistochemical study to analyse the expression of the glutamate AMPA receptor (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) GluA1 subunit and the GABA receptor (γ-Aminobutyric Acid) subtype GABAA α1 subunit in the hippocampus of the same animals.
RESULTS: We found no significant changes in locomotor activity (p > 0.05). The water T-maze data showed that 30 mg/kg dose significantly (p 0.05). Histological data revealed no neuronal morphological changes. Immunohistochemical analysis revealed increased expression of the AMPA GluA1 receptor subunit but there was no effect on GABAA receptor α1 subunit expression in the CA1 and CA2 subregions of the hippocampus.
CONCLUSIONS: The C. asiatica extract therefore improved hippocampus-dependent spatial learning and memory in a dose-dependent manner in rats through the GluA1-containing AMPA receptor in the CA1 and CA2 sub regions of the hippocampus.