Mohamed Yousif Ahmed’s research while affiliated with King Fahad Central Hospital, Gizan and other places

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Publications (1)


Longitudinal pattern of disease cases and clinical parameters of febrile diagnosed patients during the acute phase of disease. (a) Percentage of positively diagnosed cases between 2014 and 2017 (n = 401) collected in Jazan, Saudi Arabia. Data were expressed as percentage of disease‐specific [(number of disease‐specific cases per month/total number of disease‐specific cases between 2014 and 2017) × 100%] and presented in a heatmap format, with white and red colours representing low and high percentage of cases, respectively. Grey colour indicates no disease cases. (b) Clinical parameters reported during the acute phase of disease (within 7 days post‐illness onset) were analysed and presented in a heatmap of normalised scores. In the heatmap, patients were grouped according to the collection time (year) and the clinical parameters were scaled between 0 in green (minimum) and 100 in red (maximum) for each recorded clinical parameter of the respective disease group. Grey colour indicates no reports of clinical symptoms.
Laboratory parameter profiles of febrile diagnosed patients during the acute phase of the disease. Blood and serum samples of healthy controls (n = 31) and patients infected with DENV (n = 170), CHIKV (n = 34), DENV‐CHIKV co‐infection (n = 15), Plasmodium (n = 53), Brucella (n = 8), Neisseria meningitidis (n = 52) or GAS (n = 67) at acute phase (within 7 days post‐illness onset) were tested for (a–c) levels of blood cells and platelets, (d, e) haematological parameters and (f–h) liver inflammation markers. Results are depicted as dot plots with mean ± SD, and statistical analysis between disease groups and healthy controls was conducted with Kruskal–Wallis test with Dunn's post hoc tests (*P < 0.05, **P < 0.01, ***P < 0.001).
Immune signature for differential diagnosis of febrile infections during the acute phase of disease. Serum samples of healthy controls (n = 31) and patients infected with DENV (n = 100), Plasmodium (PLAS; n = 30), Neisseria meningitidis (N.MEN; n = 21), group A streptococcus (GAS; n = 26) or febrile unknown (n = 30) at acute phase (within 7 days post‐illness onset) were subjected to multiplex microbead‐based immunoassay. (a) Levels of immune mediators were analysed and presented in a heatmap of normalised scores. The concentrations of each immune mediator were scaled between 0 and 1, and the average scaled value was computed for each group. (b) Selected immune mediators with disease‐specific profiles are depicted as Tukey box plots with the results of post hoc t‐tests shown as asterisks. One‐way ANOVAs were conducted on the logarithmically transformed concentration with post hoc t‐tests corrected using the method of Bonferroni. ANOVA results were corrected for multiple testing using the method of Benjamini and Hochberg (*P < 0.05, **P < 0.01, ***P < 0.001). Data in b and c are of one independent experiment. (c) Immune mediator signature profiles of febrile patients in the acute phase of disease. Venn diagram shows the generic febrile and virus‐specific immune mediators for each infection compared to healthy controls.
Multivariate analysis of immune mediators of acute febrile patients with DENV, Plasmodium, bacteria (Neisseria meningitidis and GAS) and unknown infections. (a) Conditional inference tree analysis on serum concentrations of eight disease‐specific cytokines from infected patients of DENV (n = 100), Plasmodium (PLAS; n = 30), N. meningitidis (N.MEN; n = 21), group A streptococcus (GAS; n = 26) and patients with febrile unknown diseases (FUD; n = 30) resulted in an optimum immune signature comprising six cytokines (MIP‐1α, TNF‐α, GRO‐α, RANTES, SDF‐1α and PIGF‐1) with overall classification accuracy of 68.6%. The cytokines are displayed in square nodes. Numbers between nodes indicate Log10 concentration of cytokine in pg mL⁻¹ for each split. Each node in conditional inference tree represents a decision to go down one branch or the other depending upon the cut‐off values depicted along the line connecting the successive nodes. Finally, each sample ends up in one of the terminal nodes. Terminal nodes display the relative proportion of samples from DENV (light green), Plasmodium (teal), bacterial (red) and febrile unknown (orange) infections. (b) Radar chart summarising the signature of six immune mediators from conditional inference tree. Serum concentrations for each immune mediator were graphed on separate axes, with all axes being scaled equally from 0% to 100%.
Receiver operating characteristic (ROC) analysis of biomarkers
Systematic analysis of disease‐specific immunological signatures in patients with febrile illness from Saudi Arabia
  • Article
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August 2020

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74 Reads

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27 Citations

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Mohamed Yousif Ahmed

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Lisa FP Ng

Objectives Little is known about the prevalence of febrile illness in the Arabian region as clinical, laboratory and immunological profiling remains largely uncharacterised. Methods A total of 2018 febrile patients from Jazan, Saudi Arabia, were recruited between 2014 and 2017. Patients were screened for dengue and chikungunya virus, Plasmodium, Brucella, Neisseria meningitidis, group A streptococcus and Leptospira. Clinical history and biochemical parameters from blood tests were collected. Patient sera of selected disease‐confirmed infections were quantified for immune mediators by multiplex microbead‐based immunoassays. Results Approximately 20% of febrile patients were tested positive for one of the pathogens, and they presented overlapping clinical and laboratory parameters. Nonetheless, eight disease‐specific immune mediators were identified as potential biomarkers for dengue (MIP‐1α, MCP‐1), malaria (TNF‐α), streptococcal and meningococcal (eotaxin, GRO‐α, RANTES, SDF‐1α and PIGF‐1) infections, with high specificity and sensitivity profiles. Notably, based on the conditional inference model, six of these mediators (MIP‐1α, TNF‐α, GRO‐α, RANTES, SDF‐1α and PIGF‐1) were revealed to be 68.4% accurate in diagnosing different febrile infections, including those of unknown diseases. Conclusions This study is the first extensive characterisation of the clinical analysis and immune biomarkers of several clinically important febrile infections in Saudi Arabia. Importantly, an immune signature with robust accuracy, specificity and sensitivity in differentiating several febrile infections was identified, providing useful insights into patient disease management in the Arabian Peninsula.

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Citations (1)


... For instance, like in humans, it was observed in mice that the number of Brucella-positive cultures rapidly declined after the first weeks of treatment, with the accompanying decrease of the critical cytokine levels and resolution of organ focal infections (48,52,53). Likewise, mice's hematological values and kidney and liver functions are compatible with those reported in humans after antibiotic therapy (54). Likewise, the level of anti-Brucella antibodies in antibiotic-treated infected mice dropped (48,55). ...

Reference:

Reactivation of hidden-latent Brucella infection after doxycycline and streptomycin treatment in mice
Systematic analysis of disease‐specific immunological signatures in patients with febrile illness from Saudi Arabia