Ahmed Samir Abdelhafiz

Background: Chronic obstructive pulmonary disease (COPD) represents a global health challenge, with acute exacerbations contributing to increased morbidity and mortality. Inflammation is a hallmark of COPD. This study employed machine learning techniques to assess the potential of serum levels of Clara cell secretory protein (CCSP-16), interleukin-8 (IL-8), and interleukin-6 (IL-6) for evaluating disease severity and acute exacerbations in COPD patients. Methods: A cross-sectional study included 80 male COPD patients and 60 matched controls. Data collected included demographic, clinical, and spirometry results, along with serum biomarker levels measured via ELISA. Analyses combined conventional statistical methods with machine learning-based feature ranking to identify predictors of severe airway obstruction and exacerbation. Results: COPD patients had significantly lower levels of CCSP-16 and IL-8 compared to healthy controls, while IL-6 levels were markedly higher in COPD patients. Using conventional statistical analyses, elevated CCSP-16 levels were significantly associated with severe airway obstruction (OR 1.87, p = 0.04) and exacerbations (OR 1.72, p = 0.01). Combining all three biomarkers improved the discrimination for severe airway obstruction (AUC = 69%, p = 0.03) and exacerbation events (AUC = 73%, p < 0.001). Feature ranking using multiple machine learning models further highlighted the integration of clinical factors with biomarker data. This approach achieved cross-validated ROC-AUC values of 0.94 and 1.0 for association with severe airway obstruction and exacerbations, respectively. Conclusions: This study demonstrates the potential of CCSP-16, IL-6, and IL-8 as promising non-invasive biomarkers for COPD, as shown by both conventional statistical analyses and feature ranking using machine learning models. The integration of these biomarkers with clinical factors enhanced the predictive accuracy for severe airway obstruction and exacerbation risks. These findings highlight the critical role of combining clinical and inflammatory markers with advanced analytical techniques in improving the management of COPD.

Background: Acute lymphoblastic leukemia (ALL) is the most common cancer during childhood. The goal of this study is to evaluate the value of different peripheral blood parameters in the prediction of induction remission in these patients. Methods: Our study included 100 children with ALL who were admitted to Shefa Al-Orman Children's Cancer Hospital between January 2022 and January 2024. Data about complete blood counts (CBC) and bone marrow parameters at days 0, 8, 19, and 42 of induction chemotherapy were collected and analyzed. Results: B-ALL cases represented 75% of cases. Ninety-four patients achieved complete remission (CR) by the end of induction. A significant decrease in both platelets (PLT) and red cell distribution width (RDW) was found to increase the likelihood of induction failure (p = 0.03 and 0.05, respectively). The ROC curve revealed that only PLT had significant discrimination ability between successful and failed induction, with an AUC of 0.64 (p = 0.03). Conclusion: We concluded that a decrease in platelet count and RDW was associated with induction failure. Long-term follow-up is needed to evaluate the effect of these parameters on disease-free survival and overall survival

Introduction and objectives: Sepsis is a critical, life-threatening condition marked by organ dysfunction. Patients undergoing surgery for cancer are especially susceptible, facing a significantly increased risk compared to the general population. Prompt diagnosis is essential for optimal treatment outcomes. This study investigates the expression levels of IL-17 and CD4/CD25 in postoperative surgical oncology patients diagnosed with sepsis and examines their association with various clinicopathological parameters, including mortality. Methods: Peripheral blood samples were collected 48 h following admission to the surgical ICU. Flow cytometry was employed to measure the expression of IL-17 and CD4/CD25 in both septic patients and non-septic controls. Results: The study included 30 septic patients and an equal number of controls. The expression of IL-17 and CD4/CD25 was significantly higher in septic patients compared to non-septic patients, demonstrating high diagnostic performance. Additionally, elevated IL-17 expression was strongly associated with an increased risk of mortality. Conclusion: Our study provides insight into immune dysregulation in postoperative cancer patients with sepsis, highlighting the dual arms of active inflammation and immunoparalysis. Elevated IL-17 levels, coupled with Treg activation, were linked to poorer outcomes. Therapeutic strategies that target IL-17 signaling and modulate Treg activity may help restore immune balance and improve prognosis in immunocompromised individuals. Furthermore, developing predictive models using machine learning to classify sepsis severity based on immune markers could enhance diagnosis and prognosis.

Background: ChatGPT, a chatbot launched by OpenAI in November 2022, has generated both excitement and concern within the healthcare education, research, and practice communities. This study aimed to explore the knowledge, perceptions, attitudes, and practices of undergraduate medical students regarding the use of ChatGPT and similar chatbots in their academic work. Methods: An anonymous, structured questionnaire was developed using Google Forms and administered to medical students as part of a cross-sectional study. The survey targeted undergraduate medical students from four governorates in Egypt. The questionnaire link was distributed through social media platforms, including Facebook and WhatsApp. The survey comprised four sections: socio-demographic characteristics, perceptions, attitudes, and practices. Results: The survey achieved a response rate of 96%, with 614 out of 640 approached students participating. Prior to the study, most respondents (78.5%) had personal experience using it. Overall, respondents demonstrated positive perceptions, attitudes, and practices toward ChatGPT, with mean scores exceeding 3 for all three variables: 3.99 ± 0.60 for perceptions, 3.01 ± 0.46 for attitudes, and 3.55 ± 0.55 for practices. In general, the students exhibited a high degree of trust in the model, with approximately half trusting the accuracy and reliability of the information provided by ChatGPT. However, more than two-thirds expressed apprehension about its potential misuse in medical education, and around 60% were concerned about the accuracy of information ChatGPT might generate on complex medical topics. Conclusions: Medical students show strong interest and trust in using ChatGPT and similar chatbots for academic purposes but have concerns about the reliability of the information and potential misuse in medical education. The use of AI tools should follow ethical guidelines set by academic institutions, with regular updates to keep pace with technological progress. Future research should focus on the impact of AI on education and personal development, especially among young people.

To enhance efforts against plagiarism, a deeper understanding of its various aspects is essential. This cross-sectional questionnaire study assessed the knowledge, attitudes, subjective norms, and self-reported plagiarism practices among health researchers in Egypt and Lebanon. A validated questionnaire was distributed to postgraduate students and faculty members across 15 universities through personal emails and health researcher-specific social media platforms. Out of 283 respondents, the mean knowledge score was 14.8 ± 3.7 out of a possible 27.0, indicating moderate knowledge of plagiarism. Attitudes toward plagiarism were moderately permissive, with an average score of 62.2 ± 10.9, out of a maximum of 90.0. Subjective norms received a moderate score of 33.6 ± 6.7, where a favorable score should be low (10–23) suggesting that participants perceive society as disapproving of plagiarism and believe its prevalence is low. The mean self-reported Plagiarism Severity Score (PSS) was 4.1 ± 3.6, reflecting a low level of self-reported plagiarism, with a maximum possible score of 18. Multivariable analysis revealed a negative correlation between subjective norms and the PSS (β = −0.194, CI [−0.196: −0.010]). This finding is counterintuitive, as one would typically expect that a lower subjective norms score (indicating stronger disapproval of plagiarism), would correlate with fewer acts of plagiarism. One possible explanation for this result is the presence of negative perceptions of organizational justice—such as feelings of being unfairly treated—which might lead to higher rates of self-reported research misconduct. We recommend that universities in the Arab Middle East region develop research ethics training programs that include responsible conduct in research. However, it is essential for academic leaders to assess and address the research integrity climate within their institutions, as organizational injustice could undermine the effectiveness of these educational efforts.

Introduction: ChatGPT, a recently released chatbot from OpenAI, has found applications in various aspects of life, including academic research. This study investigated the knowledge, perceptions, and attitudes of researchers towards using ChatGPT and other chatbots in academic research. Methods: A pre-designed, self-administered survey using Google Forms was employed to conduct the study. The questionnaire assessed participants’ knowledge of ChatGPT and other chatbots, their awareness of current chatbot and artificial intelligence (AI) applications, and their attitudes towards ChatGPT and its potential research uses. Results: Two hundred researchers participated in the survey. A majority were female (57.5%), and over two-thirds belonged to the medical field (68%). While 67% had heard of ChatGPT, only 11.5% had employed it in their research, primarily for rephrasing paragraphs and finding references. Interestingly, over one-third supported the notion of listing ChatGPT as an author in scientific publications. Concerns emerged regarding AI’s potential to automate researcher tasks, particularly in language editing, statistics, and data analysis. Additionally, roughly half expressed ethical concerns about using AI applications in scientific research. Conclusion: The increasing use of chatbots in academic research necessitates thoughtful regulation that balances potential benefits with inherent limitations and potential risks. Chatbots should not be considered authors of scientific publications but rather assistants to researchers during manuscript preparation and review. Researchers should be equipped with proper training to utilize chatbots and other AI tools effectively and ethically.

We aimed to evaluate the expression profiles of five circulating lncRNAs (HOTAIR, MALAT-1, XIST, SNHG15, and H19) in DLBCL patients and explore potential associations between their expression and different clinicopathological features. Diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin lymphoma (NHL), exhibits marked genetic and clinical heterogeneity, emphasizing the need for improved tools for risk stratification. Long non-coding RNAs (lncRNAs) emerged as regulators in different cellular processes and have been linked to cancer pathogenesis. Real-time quantitative PCR (qRT-PCR) was used to evaluate lncRNA expression in the plasma of 65 newly diagnosed adult DLBCL patients and 30 age-matched controls. HOTAIR expression was significantly elevated in DLBCL patients, while SNHG15 was significantly downregulated. Interestingly, both HOTAIR and SNHG15 demonstrated robust discriminatory power between DLBCL and healthy individuals, achieving area under the curve (AUC) values of 69% and 71%, respectively. H19 expression displayed a significant association with early-stage (stage I) DLBCL. While upregulated HOTAIR was a significant independent predictor of poor prognosis, high SNHG15 expression appeared to have a protective effect on mortality rates. Our findings suggest that circulating lncRNA expression patterns are promising tools as non-invasive biomarkers for diagnosis of DLBCL. Specific lncRNAs, such as HOTAIR, SNHG15, and H19, could offer potential for disease staging and patient prognosis. Long-term follow-up studies are recommended to further elucidate the interplay between these lncRNAs and survival rates, as well as their interactions with other genetic and pathological features of DLBCL.

Objectives: Human endogenous retroviruses (HERVs) are integral components of the human genome, and their reactivation has been implicated in the pathogenesis of some malignancies. External viral co-infections are suspected to play a role in HERV transactivation. This study aimed to investigate the expression of HERV-K np9 elements and HERV-R env gene in pediatric acute lymphoblastic leukemia (ALL) patients. Additionally, we explored potential correlations between HERV expression and common viral infections prevalent in this group of patients. Methods: Blood samples were collected from 43 pediatric ALL patients and 48 age- and sex-matched healthy controls. Quantitative real-time PCR (qRT-PCR) was used to assess the expression of HERV-K np9 and HERV-R env, along with herpes simplex virus (HSV), parvovirus B19, and polyomavirus BK. Results: HERV-K np9 and HERV-R env showed significantly higher expression in the peripheral blood of ALL patients compared to healthy controls ( p < .001 and p = .003, respectively). HSV positivity was associated with significantly increased HERV-K np9 expression. No significant correlations were observed between other investigated viruses and HERV gene expression. Conclusion: The overexpression of HERV-K np9 and HERV-R env in pediatric ALL patients suggest their potential role in leukemogenesis. Our findings also suggest a possible link between HSV infection and HERV reactivation in this population. Future investigations are needed to understand the precise roles of these genes and viral infections in the development of ALL.

Breast cancer, the most prevalent cancer among women, has posed a significant challenge in identifying biomarkers for early diagnosis and prognosis. This study aimed to elucidate the gene expression profile of Estrogen Receptor-1 (ESR-1), long non-coding RNA HOTAIR, and microRNA-130a in the serum of Egyptian breast cancer patients, evaluating the potential of HOTAIR and miR-130a as biomarkers for predicting pathological parameters in BC. The study involved 45 patients with primary BC, with serum samples collected preoperatively and postoperatively twice. The expression levels of ESR-1, HOTAIR, and miR-130a were quantified using real-time PCR and analyzed for correlations with each other and with the clinical and pathological parameters of the patients. Serum HOTAIR levels exhibited a strong positive association with metastasis and demonstrated a significant increase after 6 months in all patients with locally advanced and stage IV BC. Conversely, tumors with advanced stages and metastatic lesions showed significantly lower expression levels of miR-130a. Notably, a significant positive correlation was observed between preoperative ESR-1 expression and both HOTAIR and miR-130a levels. Serum HOTAIR and miR-130a levels have emerged as promising non-invasive biomarkers with the potential to predict the pathological features of BC patients. HOTAIR, an oncogenic long non-coding RNA (lncRNA), and miR-130a, a tumor suppressor miRNA, play crucial roles in tumor progression. Further investigations are warranted to elucidate the intricate interplay between HOTAIR and miR-130a and to fully comprehend the contribution of HOTAIR to BC recurrence and its potential utility in early relapse prediction.