Mahmoud Nazih

Background: Metabolically dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disease worldwide and is increasing in parallel with metabolic syndrome and obesity. In this exploratory, cross-sectional study, we analysed metabolic changes in the blood and urine of patients with early-stage MASLD (fibrosis grades 0, 1, and 2) to identify metabolites associated with disease presence and the prevalent fibrosis stage and to develop machine learning models for case discrimination and fibrosis-stage stratification. Methods: Fifty-one metabolites, including17 urinary organic acids, 14 blood amino acids, 19 blood acylcarnitines/free carnitine, and glucose, were quantified in 232 participants (100 controls and 132 patients with MASLD: 68 F0, 34 F1, and 30 F2) using gas chromatography–mass spectrometry (GC/MS) and tandem mass spectrometry (MS/MS). MASLD-associated metabolites were visualised using volcano plots, cluster heatmaps, and a metabolic network diagram. Three machine learning approaches, namely, orthogonal partial least squares discriminant analysis (OPLS-DA), random forest (RF), and support vector machines (SVM), were implemented within a strictly leakage-free pipeline (feature selection and preprocessing performed within training folds only), with performance evaluated on independent test sets and validated by permutation testing and repeated cross-validation. Results: A case-discrimination panel comprised β -hydroxy butyrate, adipic acid, acylcarnitines (C0, C2, C3, C10:1, C14:1, and C18:1), formiminoglutamate, glucose, glycine, citric and lactic acids, Leu/Ile, pyroglutamate, sebacic and suberic acids, tiglylglycine, and valine. A stage-stratification panel comprising valine, ethylmalonate, glycine, acylcarnitines (C0, C8:1, C2, C5, C16, C10, C18, and C18:1), Leu/Ile, alanine, glutamine, pyroglutamate, 3-hydroxybutyrate, succinate, vanillylmandelate, and citrulline was associated with MASLD severity. Metabolite-based models discriminated disease status more effectively than FIB-4 (AUC 0.74) and APRI (AUC 0.70) in this cohort; permutation testing (OPLS-DA permutation p ≤ 0.001; random-forest empirical p ≈ 0.01) confirmed the models captured genuine biological structure rather than random patterns. Conclusion: Machine learning applied to targeted blood and urinary metabolomics identified candidate metabolite signatures associated with early-stage MASLD and fibrosis stage. These findings are exploratory and hypothesis-generating; prospective, externally validated studies with metabolically matched comparators are required before clinical application.

Crohn’s disease and ulcerative colitis (UC) are among the intestinal conditions that make up the category known as inflammatory bowel disease. Globally, UC prevalence and incidence are currently rising. There was substantial evidence that many pathways were involved in the pathophysiology of UC. Of these pathways, interleukin 6 (IL-6)/signal transducer and activator of transcription 3 (STAT3), mammalian target of rapamycin and AMP-activated protein kinase (AMPK), Sphingosine kinase (SPHK)/ Sphingosine-1-phosphate, nuclear factor erythroid-2 related factor 2 (Nrf2)/heme oxygenase (HO-1). While small-molecule pharmaceuticals and biologics are available to treat patients with UC, approximately one-third of people receiving treatment do not get better. To find an effective remedy for UC patients, new therapy and medication repurposing have therefore been thoroughly researched. Several medications, including rosiglitazone, amlodipine, felodipine, atorvastatin, metformin, pentoxifylline, nitazoxanide, nifuroxazide, carbocisteine, levetiracetam, topiramate, nicodamid, and vildagliptin, have been shown to have positive effects on multiple organs through their anti-inflammatory properties. Furthermore, data on gut barrier integrity, oxidative stress, and inflammatory pathways showed that these medications had a major favorable impact on these parameters in both cellular and clinical models of UC. Using the findings of in vitro, in vivo, and clinical investigations, the positive effects of these medications on UC are thoroughly outlined and examined in the present research. Having a better knowledge of these protective benefits and the basic mechanisms may make it possible for UC patients to take these medications effectively.

Background/Objectives: Hepatocellular carcinoma (HCC) presents limited therapeutic options for advanced disease, and sorafenib therapy is hampered by significant interpatient heterogeneity in response. This necessitates biomarker-guided strategies to personalize treatment. This study investigated the long noncoding RNAs UCA1 and MALAT1 as pharmacogenomic biomarkers for personalizing sorafenib therapy in advanced HCC. Methods: In a prospective cohort of 154 HCC patients receiving first-line sorafenib (400 mg twice daily), serum lncRNA levels were quantified by RT-qPCR at baseline, Week 4, and Week 12. Expression levels were correlated with treatment response (mRECIST), time-to-progression (TTP), and overall survival (OS). Statistical analyses included Kaplan–Meier estimates, Cox proportional hazards models, and ROC curve analysis. Results: High baseline expression of UCA1 (77.9% of patients) and MALAT1 (73.4%) was associated with aggressive disease. High UCA1 correlated with reduced 12-month survival (60.8% vs. 73.5%, p = 0.026) and shorter median Time-to-Progression (TTP) (18.0 vs. 21.9 weeks, p = 0.002). High MALAT1 was associated with significantly shorter median TTP (18.0 vs. 25.2 weeks, p = 0.003). In multivariable analysis, both lncRNAs were independent prognostic factors for shorter TTP (UCA1: HR = 1.52, p = 0.014; MALAT1: HR = 1.61, p = 0.006). Serial monitoring revealed that a ≥10% rise in either lncRNA by Week 4 predicted a five-fold higher progression risk by Week 12 (52% vs. 10%, p < 0.001), providing a median lead time of 7.0 weeks before radiological confirmation of progression. Conclusions: These findings demonstrate that UCA1 and MALAT1 enable early identification of sorafenib resistance. Baseline stratification and serial monitoring can provide early detection of treatment resistance, informing clinical decision-making and supporting their potential utility for personalizing therapy in advanced HCC.

Background: Hepatocellular carcinoma (HCC) represents 90% of primary liver cancers and is the fourth leading cause of cancer-related death worldwide. Sorafenib remains the standard first-line systemic therapy for advanced HCC, though real-world outcomes vary from clinical trial results. We aimed to evaluate survival outcomes and identify prognostic factors in HCC patients receiving sorafenib in routine clinical practice over an 18-month follow-up period in a real-world clinical setting. Methods: An ambispective cohort study (integrating retrospective and prospective data) was conducted involving 347 patients with confirmed primary HCC treated with Sorafenib between June 2023 and January 2025, of whom 292 had complete 18-month follow-up and were included in the survival analysis. Clinical data, laboratory markers, imaging Response Evaluation Criteria In Solid Tumors (RECIST 1.1), adverse effects, and treatment modifications were analyzed. Kaplan–Meier analysis and Cox regression models were used to determine overall survival (OS), progression-free survival (PFS), and prognostic predictors. Results: The observed OS rates at 6, 12, and 18 months were 69.3%, 45.2%, and 31.1%, respectively. Corresponding PFS rates were 58.6%, 36.9%, and 24.8%. In multivariate analysis, Eastern Cooperative Oncology Group (ECOG) performance status ≥ 1, AFP > 400 ng/mL, Albumin-Bilirubin (ALBI) grade 3, and vascular invasion were significantly associated with reduced OS (p < 0.05). Patients who achieved partial response or stable disease per RECIST were more likely to continue Sorafenib beyond 6 months. Toxicity was generally tolerable, with a low rate of permanent treatment discontinuation. Conclusion: Sorafenib continues to demonstrate efficacy in real-world practice as first-line therapy for advanced HCC, with clear clinical and biochemical indicators, including ALBI grade, AFP, ECOG, and early radiological response, serving as key prognostic factors of treatment success. Early response assessment with identification of poor prognostic factors is critical for optimizing outcomes, and proactive toxicity management can improve outcomes in real-world settings.

Sepsis-associated acute kidney injury (S-AKI) represents a significant health problem associated with adverse outcomes. Our study aimed to assess the value of serum cystatin-C (sCysC) and TNF-α (rs361525) in combination for diagnosing S-AKI patients and predicting their adverse outcomes. The study included 100 critically ill neonates and 100 controls. Patients were categorized into an S-AKI group and a non-AKI group. TNF-α (−238, rs361525) genotyping was performed using RT-PCR, and sCysC was assessed using ELISA. Our study showed a fundamental difference in the genotype frequencies of TNF-α (−238, rs361525) and SNP between S-AKI and non-AKI patients. Furthermore, there was a significant relationship between cystatin C and TNF-α (−238, rs361525), where cystatin C was higher in patients with AA alleles than in patients with GA and GG alleles. Combining GA + AA genotypes with elevated serum cystatin-C levels can serve as a potential diagnostic and prognostic biomarker for AKI development in this population. The GA/AA genotypes independently predicted S-AKI risk (OR = 6.64, p < 0.001). At the same time, elevated sCysC (>9.4 mg/L) emerged as a sensitive biomarker (AUC = 0.848) and independent predictor of adverse outcomes. Collectively, these findings contribute to the growing field of personalized medicine and represent a strategic advantage, enabling prevention-focused care rather than the treatment of established disease.

Background: This study examines colistin resistance in Gram-negative bacteria in Egypt, analyzing prevalence, trends, geographic variations, colistin-carbapenem resistance correlation, and mcr-mediated plasmid resistance. Methods: We conducted a systematic search of articles published between 2014 and 2024 that reported on colistin or mcr-mediated resistance in Gram-negative bacteria isolated from human infections in Egypt, with clearly defined susceptibility testing methods. A random-effects meta-analysis was conducted to estimate colistin resistance prevalence based on broth microdilution (BMD) findings, the gold standard method. To explore the influence of study-level factors—including alternative susceptibility testing methods—a multivariate meta-regression analysis was performed. The results of the meta-regression are reported as regression coefficients (β), representing the difference in colistin resistance, expressed in percentage points. All statistical analyses were conducted using R software. Results: This analysis included 55 studies. Based on BMD susceptibility testing, colistin resistance was observed in 9% of all recovered Gram-negative isolates (95% CI: 6–14%) and was significantly higher among carbapenem-resistant isolates (31%, 95% CI: 25–38%), with p < 0.001. Multivariate meta-regression analysis further confirmed that colistin resistance was significantly higher in carbapenem-resistant isolates compared to the total recovered isolates (β = 9.8% points, p = 0.001). Additionally, colistin resistance has significantly increased over time, with a β = 1.8% points per year (p = 0.001). The use of the VITEK 2 system was associated with lower detected colistin resistance compared to BMD (β = -7.0, p = 0.02). Geographically, resistance rates were higher in Upper Egypt (β = 9.3, p = 0.04). Regarding mcr plasmid-mediated resistance, mcr-1 was the most prevalent resistance gene, particularly in E. coli. In contrast, mcr-2 was rare, detected sporadically in K. pneumoniae and P. aeruginosa. Conclusion: In Egypt, BMD testing identified colistin resistance in 9% of Gram-negative bacteria, increasing to 31% in carbapenem-resistant isolates. This higher resistance in carbapenem-resistant strains suggests stronger selective pressure from frequent colistin use. Additionally, colistin resistance has shown a rising trend over time, likely driven by increased usage and the spread of plasmid-mediated resistance. These findings underscore the urgent need for strict antimicrobial stewardship and alternative therapies to curb resistance evolution.

This study investigated the diagnostic, prognostic, and therapeutic significance of Fc receptor-like 1 (FCRL1) and B-cell activating factor (BAFF) mRNA expression in Egyptian patients with diffuse large B-cell lymphoma (DLBCL) undergoing the standard R-CHOP regimen (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) using quantitative real-time PCR (RT-qPCR). The results demonstrated that FCRL1 and BAFF mRNA expression were significantly elevated in DLBCL patients compared to healthy controls. A strong positive correlation existed between BAFF and FCRL1 expression levels. Diagnostic performance assessed through combined ROC curve analysis revealed that BAFF, FCRL1, and lactate dehydrogenase (LDH) achieved perfect diagnostic accuracy (AUC = 1.0), demonstrating 100% sensitivity, specificity, and predictive values. Further prognostic analysis using COX regression identified elevated FCRL1 expression as the most significant predictor of poor clinical outcomes. Kaplan–Meier survival analysis reinforced this finding, with high FCRL1 expression showing significant associations with reduced overall survival (OS, p = 0.031) and progression-free survival (PFS, p = 0.038). The study underscores the potential utility of BAFF and FCRL1 mRNA as diagnostic markers for DLBCL, with FCRL1 emerging as a promising prognostic marker and potential therapeutic target enabling more tailored treatment approaches for DLBCL, the most common type of B-cell non-Hodgkin lymphoma, and patients receiving R-CHOP therapy.

As of the 7^th of July 2024, 775,754,322 confirmed cases of COVID-19, including 7,053,902 deaths worldwide, had been reported to the WHO (World Health Organization). Nevertheless, untill the 15^th of July 2024, a total of 13,578,710,228 vaccine doses had been administered, with almost no country spared from COVID-19 attacks. The pathophysiology of this virus is complicated, and several symptoms require a deep understanding of the actual mechanisms. It is unclear why some patients develop severe symptoms while others do not, although literature suggests a role for vitamin D. Vitamin D plays a crucial role in the infection or in ameliorating the severity of symptoms. The mechanism of action of vitamin D and vitamin D deficiency (VDD) is well understood. VDD is associated with increased hospitalization of severely ill patients and increased levels of COVID-19-caused mortality. Recent studies suggest that vitamin D levels and genetic variations in the vitamin D receptor (VDR) gene significantly impact the severity and outcomes of COVID-19, especially in the infections caused by Delta and Omicron variants. Furthermore, VDD causes immune system dysregulation upon infection with SARS-CoV-2, indicating that vitamin D sufficiency is crucial in fighting against COVID-19 infection. The therapeutic effect of vitamin D raises interest in its potential role as a prophylactic and treatment adjunct. We evaluate the immunomodulatory effects of vitamin D and its ability to enhance the efficacy of new antiviral drugs like molnupiravir and paxlovid against SARS-CoV-2. This review discusses the role of vitamin D sufficiency and VDD in COVID-19 initiation and progression, emphasizing the molecular mechanisms by which vitamin D exerts its actions as a proactive step for the next pandemic. However, there is still no clear evidence of vitamin D’s impact on prevention and treatment, leading to contradictory findings. Therefore, large-scale randomized trials are required to reach a definitive conclusion. A bibliometric analysis of publications related to vitamin D, immunity, and COVID-19 revealed a significant increase in research activity in this area, particularly in 2020–2024, underscoring the growing recognition of vitamin D’s potential role in the context of the pandemic.