Background: Dengue is endemic in Saudi Arabia, particularly in Jeddah, Makkah and Jizan. Outbreaks in these regions are mainly linked to climate conditions and mass travel during religious pilgrimages. Methods: This retrospective cross-sectional study evaluated the diagnostic performance of the non-structural protein 1 (NS1) antigen test and IgM ELISA, compared with RT-PCR. A total of 2850 samples were collected from patients hospitalised with moderate to severe symptoms across six regions in Saudi Arabia from 2022 to 2024. The samples were tested simultaneously using all three methods and 95% CIs were calculated using the Wilson score method. Results: Out of 3419 samples analysed, 1402 tested negative for dengue and 1448 tested positive. The sensitivity of the NS1 antigen test was 73.5% (95% CI 70.7 to 76.2%). The IgM ELISA test demonstrated a sensitivity of 65.8% (95% CI 62.9 to 68.7%). Combining the results of both tests increased the sensitivity to 92.4% (95% CI 90.6 to 93.9%). This combined diagnostic approach facilitates the detection of both early-phase (NS1-positive) and later-phase (IgM-positive) infections, offering a more comprehensive diagnostic tool. Conclusions: Implementing a combined testing strategy within Saudi Arabia’s diagnostic pipelines could enhance disease surveillance, enable earlier detection and strengthen public health efforts against dengue outbreaks.

Background: Secondary dengue infection (SDI), often associated with increased disease severity, remains a diagnostic and public health challenge in endemic regions. This study evaluates the use of combined IgG and PCR testing in identifying SDI cases and explores the demographic, geographic and clinical characteristics of SDI in Saudi Arabia. Methods: A total of 2850 patient samples were collected from six regions in Saudi Arabia, from January 2022 to March 2024. Laboratory testing included NS1 antigen detection, IgM and IgG ELISA and RT-PCR for viral RNA. SDI was defined as IgG+/PCR+ based on the WHO criteria. Results: Of 1448 PCR-confirmed dengue cases, 493 (34%) were classified as SDIs. The highest proportions of SDIs were observed in Asir (71.4%), Jazan (60.0%) and Makkah (50.4%), with Makkah contributing the largest number of SDI cases overall (243/482). The highest burden was observed in males and individuals aged 21–40 y. Four deaths occurred among IgG+ patients with comorbidities. Conclusions: Combined IgG and PCR profiling enhances the early identification of SDIs. Regional clustering and age-related patterns also underscore the need for targeted clinical monitoring and public health interventions.

Background: Dengue fever is an emerging public health threat in Saudi Arabia. To guide evidence-based vector control, this study aimed to characterise the epidemiological and clinical patterns of infections across key regions in the Kingdom between 2022 and 2024. Methods: A total of 2850 serum samples from suspected dengue patients were collected from hospitals in seven regions in Saudi Arabia. RNA extraction and dengue virus detection were conducted using standardised reverse transcription polymerase chain reaction (RT-PCR) protocols. Statistical analysis was applied to identify the sociodemographic predictors of PCR-confirmed dengue. Results: Among the 2850 individuals tested, 1447 (50.8%) were PCR positive. Most cases (n=1269) occurred in 2023, with Makkah alone recording 801 cases. Males represented 73.5% of PCR-positive cases, while non-Saudi nationals accounted for 47.7%. Multivariable regression showed significantly lower odds of positivity among females (odds ratio [OR] 0.65 [95% confidence interval {CI} 0.55 to 0.76], p<0.001) and higher odds among non-Saudi nationals (OR 1.17 [95% CI 1.00 to 1.38], p=0.050). Despite the relatively low caseloads, Jazan and Asir had higher adjusted odds compared with Madinah. Conclusions: Dengue transmission shows strong geographic, demographic and seasonal variation, with evidence of epidemic activity in 2023. These findings underscore the need for targeted vector control and enhanced surveillance in high-risk regions.

The progression of tuberculosis (TB) from a latent infection to an active disease involves intricate modifications in host immune, inflammatory, oxidative, and metabolic pathways. Ferroptosis represents a distinct form of regulated cell death that requires iron and is associated with excessive lipid peroxidation and has been associated with tissue damage in TB; however, its connection with host transcriptional changes during active TB is not fully understood. This study sought to identify and externally validate immune–inflammatory hub genes among differentially expressed genes (DEGs) in active TB that overlap with a ferroptosis-associated gene set derived from FerrDb, employing an integrated transcriptomic and systems-biology methodology. Differential expression analysis of GSE37250 revealed 1015 DEGs in active TB compared to latent TB, comprising 585 upregulated and 430 downregulated genes, and 93 DEGs in active TB compared to healthy controls, including 65 upregulated and 28 downregulated genes. Intersection analysis identified 94 DEGs common to the active TB versus latent TB comparison and the ferroptosis-associated gene set, and eight DEGs common to the active tuberculosis versus healthy-control comparison and the same gene set, with no genes shared across all three sets. Functional enrichment of the 94 intersection genes underscored immune response, defense response, stress response, Toll-like receptor signaling, NOD-like receptor signaling, IL-17 signaling, TNF signaling, glutathione metabolism, neutrophil degranulation, cytokine signaling, and antimicrobial metal sequestration. Protein–protein interaction analysis followed by cytoHubba prioritization identified 10 hub genes: IL1B, TLR4, CXCL10, MMP9, CYBB, MPO, CD36, LCN2, S100A8, and LTF. Subsequent to outcome-independent probe selection, external validation in GSE28623 demonstrated significant positive differential expression of LCN2, S100A8, and LTF, while GSE62525 showed significant positive differential expression of IL1B, TLR4, MMP9, MPO, LCN2, and LTF. LCN2 and LTF were significantly upregulated in both validation datasets, indicating the strongest cross-dataset reproducibility. These results identify an immune–inflammatory transcriptional network intersecting with ferroptosis-associated genes in active TB. Notably, the transcriptomic findings do not confirm ferroptotic cell death but suggest candidate genes and biological processes for future experimental exploration.