Introduction: Alzheimer’s disease is a progressive neurodegenerative disorder challenging early diagnosis and treatment. Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy and predicting disease progression.Method: This narrative review synthesizes current literature on deep learning applications in Alzheimer’s disease diagnosis using multimodal neuroimaging. The review process involved a comprehensive search of relevant databases (PubMed, Embase, Google Scholar and ClinicalTrials.gov), selection of pertinent studies, and critical analysis of findings. We employed a best-evidence approach, prioritizing high-quality studies and identifying consistent patterns across the literature.Results: Deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models, have shown remarkable potential in analyzing multimodal neuroimaging data. These models can effectively process structural and functional imaging modalities, extracting relevant features and patterns associated with Alzheimer’s pathology. Integration of multiple imaging modalities has demonstrated improved diagnostic accuracy compared to single-modality approaches. Deep learning models have also shown promise in predictive modeling, identifying potential biomarkers and forecasting disease progression.Discussion: While deep learning approaches show great potential, several challenges remain. Data heterogeneity, small sample sizes, and limited generalizability across diverse populations are significant hurdles. The clinical translation of these models requires careful consideration of interpretability, transparency, and ethical implications. The future of AI in neurodiagnostics for Alzheimer’s disease looks promising, with potential applications in personalized treatment strategies.

The coronavirus disease 2019 (COVID-19) pandemic has been a major challenge worldwide for the past years with high morbidity and mortality rates. While vaccination was the cornerstone to control the pandemic and disease spread, concerns regarding safety and adverse events (AEs) have been raised lately. A cross-sectional study was conducted between January 1st and January 22nd, 2022, in six Arabic countries namely Saudi Arabia, Egypt, Syria, Libya, Iraq, and Algeria. We utilized a self-administered questionnaire validated in Arabic which encompassed two main parts. The first was regarding sociodemographic data while the second was about COVID-19 vaccination history, types, doses, and experienced AEs. A multistage sampling was employed in each country, involving the random selection of three governorates from each country, followed by the selection of one urban area and one rural area from each governorate. We included the responses of 1564 participants. The most common AEs after the first and second doses were local AEs (67.9% and 46.6%, respectively) followed by bone pain and myalgia (37.6% and 31.8%, respectively). After the third dose, the most common AEs were local AEs (45.7%) and fever (32.4%). Johnson and Johnson, Sputnik Light, and Moderna vaccines showed the highest frequency of AEs. Factors associated with AEs after the first dose included an increase in age (aOR of 61–75 years compared to the 12–18 years group: 2.60, 95% CI: 1.59–4.25, p = 0.001) and male gender (OR: 0.72, 95% CI: 0.63–0.82, p < 0.001). The cumulative post-vaccination COVID-19 disease was reported with Sinovac (16.1%), Sinopharm (15.8%), and Johnson and Johnson (14.9) vaccines. History of pre-vaccination SARS-CoV-2 infection significantly increases the risk of post-vaccination COVID-19 after the first, second, and booster doses (OR: 3.09, CI: 1.9–5.07, p < 0.0001; OR: 2.56, CI: 1.89–3.47, p < 0.0001; and OR: 2.94, CI: 1.6–5.39, p = 0.0005 respectively). In conclusion, AEs were common among our participants, especially local AEs. Further extensive studies are needed to generate more generalizable data regarding the safety of different vaccines.

Iridoids belong to a family of monoterpenoids comprising the cyclopentan[c]-pyran system; this class of compounds offers a wide range of biological effects, namely antileishmanial, anticancer, antiplasmodial, and anti-inflammatory potency. To date, a large number of biologically active iridoid derivatives have been reported from various plant families, including Rubiaceae, Plantaginaceae, Scrophulariaceae, and Verbenaceae. Furthermore, iridoids have the potential to form conjugates with other anticancer, antidiabetic, antileishmanial, and antimalarial drugs which synergistically have the potential to increase their effects. Additionally, future research should focus on the synthesis of halo analogs as well as preparing homo dimers or heterodimers of iridoids, since these might quite conceivably possess an increased bioactivity.

Background: The adherence pattern of antiepileptic drugs (AEDs) among patients with epilepsy is relatively lower in the United States and different European countries. However, adherence and cost analysis of AEDs in Asian countries have not been thoroughly studied. Therefore, the present study aimed to analyze the cost and adherence of AEDs and its associated factors in patients followed in Pakistan. Methods: Data from prescriptions collected from patients with epilepsy who have visited the Outpatient Department (OPD) of different tertiary care hospitals at the cosmopolitan city of Karachi, Pakistan from December 2015 to November 2019. The mean follow-up period for each participant was about 22 months. Pairwise comparisons from Cox regression/hazard ratios were used to assess the predictors of adherence. Direct costs of AEDs were calculated and presented as the annual cost of drugs. Results: A total of 11,490 patients were included in this study, 51.2 % were male and 48.8 % were female with a mean age of 45.2 ± 15.8 y. Levetiracetam was found as the most prescribing AED in all study participants (32.9 %). Of them, 49.1 % of patients continued their initial recommended treatment. However, 31.3 % of patients have discontinued the therapy, while, 19.6 % were switched to other AED. Adherence with initial treatment was more profound in male (57.4 %) patients, compared to female with a mean age of 44.2 years. Lamotrigine users (60.6 %) showed a higher tendency to retain on initially prescribed drugs. The total cost of epilepsy treatment in the entire study cohort was 153280.5 PKR ($941.9). By applying the Cox regression analysis, it can be observed that the patients with increasing age (OR, 2.04), migraine (OR, 2.21), psychiatric disorders (OR, 4.28), other comorbidities (OR, 1.52) and users of other than top five prescribing AEDs (2.35) were at higher risk of treatment discontinuation. However, levetiracetam (OR, 0.69), valproic acid (OR, 0.52), carbamazepine (OR, 0.81), lamotrigine (OR, 0.80) or lacosamide (OR, 0.65) users have more chances to continue their initial therapy. Conclusions: Similar to western countries, the majority of patients with epilepsy exhibited low adherence with AEDs. Various associated factors for improving adherence were identified in this study.