Browsing by Author "Tawfik, Abdel Rahman"

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Awareness of Periodontal Health among Pregnant Females in Government Setting in United Arab Emirates
(2023) Khamis, Amar Hassan; Tawfik, Abdel Rahman; Bain, Crawford; Jamal, Mohamed; Atieh, Momen; Shah, Maanas
Objective: Periodontal disease is one of the most common infectious diseases. Several factors are associated with increased susceptibility of periodontal disease such as hormonal changes during pregnancy. Although pregnancy does not directly cause gingivitis, it can aggravate preexisting periodontal disease. This study aimed to evaluate knowledge of the association between periodontal disease and pregnancy in pregnant females. Materials and Methods: A convenience sample of pregnant females attending two United Arab Emirates government hospitals was recruited for this study. A 23-item questionnaire was developed with four sections, covering sociodemographic details, oral hygiene, oral symptoms during pregnancy, and knowledge of periodontal health during pregnancy. The study was conducted between April and October 2017. All participants consented to the survey. Results: A total of 100 participants with a mean age of 31 years (± 5.9) completed the survey. Most respondents brushed their teeth 2 to 3 times a day (65%), used a manual toothbrush (93%) but only visited the dentist when in pain (62%). Few respondents self-reported any gingival signs and symptoms during pregnancy; 38% had bleeding gums, 27% had no gum swelling, and 34% had bad odor/taste/smell. Only 21% of pregnant females lost a tooth/teeth during pregnancy, 15% believed that pregnancy increased the likelihood of gum disease, and 66% of gynecologists did not advise a visit to the dentist. Housewives were significantly less knowledgeable about periodontal health than students/employed respondents (p = 0.01). Quality of knowledge was not associated with educational attainment (< 0.06). Respondents > 30 years of age were more likely to believe in “a tooth for a baby” than younger participants aged < 30 years (p < 0.05). A logistic regression model showed that educational attainment was not a predictor for the belief in “a tooth for a baby” but age was a significant predictor (odds ratio = 2.0).
Characterizing Circulating microRNA Signatures of Type 2 Diabetes Subtypes.
(2025-01-14) Sulaiman, Fatima; Khyriem, Costerwell; Dsouza, Stafny; Abdul, Fatima; Alkhnbashi, Omer; Faraji, Hanan; Tawfik, Abdel Rahman; Khamis, Amar Hassan; Bayoumi, Riad
Type 2 diabetes (T2D) is a heterogeneous disease influenced by both genetic and environmental factors. Recent studies suggest that T2D subtypes may exhibit distinct gene expression profiles. In this study, we aimed to identify T2D cluster-specific miRNA expression signatures for the previously reported five clinical subtypes that characterize the underlying pathophysiology of long-standing T2D: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and mild early-onset diabetes (MEOD). We analyzed the circulating microRNAs (miRNAs) in 45 subjects representing the five T2D clusters and 7 non-T2D healthy controls by single-end small RNA sequencing. Bioinformatic analyses identified a total of 430 known circulating miRNAs and 13 previously unreported novel miRNAs. Of these, 71 were upregulated and 37 were downregulated in either controls or individual clusters. Each T2D subtype was associated with a specific dysregulated miRNA profile, distinct from that of healthy controls. Specifically, 3 upregulated miRNAs were unique to SIRD, 1 to MARD, 9 to MOD, and 18 to MEOD. Among the downregulated miRNAs, 11 were specific to SIRD, 9 to SIDD, 2 to MARD, and 1 to MEOD. Our study confirms the heterogeneity of T2D, represented by distinguishable subtypes both clinically and epigenetically and highlights the potential of miRNAs as markers for distinguishing the pathophysiology of T2D subtypes.
Characterizing Circulating microRNA Signatures of Type 2 Diabetes Subtypes.
(2025-01-14) Sulaiman, Fatima; Khyriem, Costerwell; Dsouza, Stafny; Abdul, Fatima; Alkhnbashi, Omer; Faraji, Hanan; Tawfik, Abdel Rahman; Khamis, Amar Hassan; Bayoumi, Riad
Type 2 diabetes (T2D) is a heterogeneous disease influenced by both genetic and environmental factors. Recent studies suggest that T2D subtypes may exhibit distinct gene expression profiles. In this study, we aimed to identify T2D cluster-specific miRNA expression signatures for the previously reported five clinical subtypes that characterize the underlying pathophysiology of long-standing T2D: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and mild early-onset diabetes (MEOD). We analyzed the circulating microRNAs (miRNAs) in 45 subjects representing the five T2D clusters and 7 non-T2D healthy controls by single-end small RNA sequencing. Bioinformatic analyses identified a total of 430 known circulating miRNAs and 13 previously unreported novel miRNAs. Of these, 71 were upregulated and 37 were downregulated in either controls or individual clusters. Each T2D subtype was associated with a specific dysregulated miRNA profile, distinct from that of healthy controls. Specifically, 3 upregulated miRNAs were unique to SIRD, 1 to MARD, 9 to MOD, and 18 to MEOD. Among the downregulated miRNAs, 11 were specific to SIRD, 9 to SIDD, 2 to MARD, and 1 to MEOD. Our study confirms the heterogeneity of T2D, represented by distinguishable subtypes both clinically and epigenetically and highlights the potential of miRNAs as markers for distinguishing the pathophysiology of T2D subtypes.
Development and Comparison of Conventional and 3D-Printed Laboratory Models of Maxillary Defects
(2023) Alanezi, Ahmad; Aljanahi, May; Moharamzadeh, Keyvan; Ghoneima, Ahmed; Tawfik, Abdel Rahman; Hassan Khamis, Amar; Abuzayda, Moosa
Background: Recording accurate impressions from maxillary defects is a critical and challenging stage in the prosthetic rehabilitation of patients following maxillectomy surgery. The aim of this study was to develop and optimize conventional and 3D-printed laboratory models of maxillary defects and to compare conventional and digital impression techniques using these models. Methods: Six different types of maxillary defect models were fabricated. A central palatal defect model was used to compare conventional silicon impressions with digital intra-oral scanning in terms of dimensional accuracy and total time taken to record the defect and produce a laboratory analogue. Results: Digital workflow produced different results than the conventional technique in terms of defect size measurements which were statistically significant (p < 0.05). The time taken to record the arch and the defect using an intra-oral scanner was significantly less compared with the traditional impression method. However, there was no statistically significant difference between the two techniques in terms of the total time taken to fabricate a maxillary central defect model (p > 0.05). Conclusions: The laboratory models of different maxillary defects developed in this study have the potential to be used to compare conventional and digital workflow in prosthetic treatment procedures.