Abdullah Alamri

Ovarian endometriosis (OE) is a chronic, inflammatory gynecological disorder associated with sterility and an elevated risk of ovarian cancer. Despite its high prevalence, the complex molecular mechanisms governing OE pathogenesis remain poorly investigated. We conducted a comprehensive histopathological and molecular investigation of OE in a cohort of 188 Saudi women (88 patients with OE and 100 healthy controls) using histopathological, qRT-PCR, immunostaining, and Western blot techniques. Histopathological analysis confirmed significant stromal fibrosis and chronic inflammation in endometriotic lesions. Gene expression profiling revealed a pro-proliferative, anti-apoptotic signature, marked by the upregulation of PTTG1 and the downregulation of TNFRSF10D, CDK4, and CDKN1A. Interestingly, we identified a post-transcriptional regulatory paradox in the inflammatory response: while IL-6 mRNA was significantly upregulated, its corresponding protein level was downregulated, suggesting a novel, tightly controlled mechanism to limit excessive local inflammation. Besides the increased autophagic activity and decreased Ubiquitin mRNA levels, epigenetic dysregulation was prominent, characterized by the upregulation of DNA methyltransferase DNMT3B and the downregulation of the histone variant H3.1. These findings elucidate novel molecular pathways underlying OE pathogenesis as evidenced by a post-transcriptional paradox in IL-6 expression, and uncover key dysregulations spanning cell proliferation, apoptosis, inflammation, autophagy, and epigenetic regulation.

This study aims to estimate the shear wave velocity and identify the depth of the bedrock and the engineering site characterization utilizing the multichannel analysis of surface waves (MASW) method for sustainable urban development in the Al-Madinah Al-Munawarah area. Twenty-seven MASW profiles were carried out using Geode digital seismographs with a 24-geophone array of 4.5 Hz in the urban expansion area of Al-Madinah Al-Munawarah. The methodology entailed rigorous calibration during data collection, processing, and inversion to ensure precise shear velocity measurements. Results reflect subsurface conditions accurately where shear wave velocity (Vs) varies between 180 m/s and 1200 m/s across three main layers: alluvium deposits, which transfer laterally in some areas into vesicular basalt with Vs ranges from 180 to 360 m/s; fractured basalt where Vs varies between 360 and 760 m/s; and weathered basaltic rock with Vs that spans from 760 to 1200 m/s. Moreover, the average shear wave velocity of up to 30 m depth (Vs30) and ranging from 180–480 m/s indicate Site Class D (stiff soil) and Class C (soft rock and dense soil) according to National Earthquake Hazards Reduction Program (NEHRP). Furthermore, the depth of bedrock varies between 18 and 29 mm indicating the great thickness of soil deposits throughout the study area. These results provide civil engineers and urban planners with vital data about soil deposits characterization and geotechnical conditions in the area where alluvium deposits and vesicular basalt represent weak zones that require more attention during urban construction. Results will contribute as well, to a great extent, in achieving the sustainable development plans of Saudi Vision 2030.

The timely and reliable prediction of crop yields on a larger scale is crucial for ensuring a stable food supply and food security. In the last few years, many studies have demonstrated that deep learning can offer reliable solutions for crop yield prediction. However, a key challenge in applying deep-learning models to crop yield prediction is their reliance on extensive training data, which are often lacking in many parts of the world. To address this challenge, this study introduces TrAdaBoost.R2, along with fine-tuning and domain-adversarial neural network deep-transfer-learning strategies, for predicting the winter wheat yield across diverse climatic zones in the USA. All methods used the bidirectional LSTM (BiLSTM) architecture to leverage its sequential feature extraction capabilities. The proposed transfer-learning approaches outperformed the baseline deep-learning model, with mean absolute error reductions ranging from 9% to 28%, demonstrating the effectiveness of these methods. Furthermore, the results demonstrate that the semi-supervised transfer-learning approach using the two-stage version of TrAdaBoost.R2 and fine-tuning achieved a superior performance compared to the domain-adversarial neural network and standard TrAdaBoost.R2. Additionally, the study offers insights for improving the accuracy and generalizability of crop yield prediction models in diverse agricultural landscapes across different regions.

Colon cancer (CC) is a significant cause of death worldwide, particularly in Saudi Arabia. To increase the accuracy of diagnosis and treatment, it is important to discover new specific biomarkers for CC. The main objectives of this research are to identify potential specific biomarkers for the early diagnosis of CC by analyzing the expressions of eight cancer testis (CT) genes, as well as to analyze how epigenetic mechanisms control the expression of these genes in CC cell lines. Tissue samples were collected from 15 male patients with CC tissues and matched NC tissues for gene expression analysis. The expression levels of specific CT genes, including ADAD1, DMRTC2, PRSS54, SYCE1, SYCP1, TEX101, TEX48, and TMPRSS12, were assessed using quantitative techniques. To validate the gene expression patterns, we used publicly available CC statistics. To investigate the effect of inhibition of DNA methylation and histone deacetylation on CT gene expression, in vitro experiments were performed using HCT116 and Caco-2 cell lines. There was no detected expression of the genes neither in the patient samples nor in NC tissues, except for TEX48, which exhibited upregulation in CC samples compared to NC tissues in online datasets. Notably, CT genes showed expression in testis samples. In vitro, experiments demonstrated significant enhancement in mRNA expression levels of ADAD1, DMRTC2, PRSS54, SYCE1, SYCP1, TEX101, TEX48, and TMPRSS12 following treatment with 5-aza-2’-deoxycytidine and trichostatin A in HCT116 and Caco-2 cell lines. Epigenetic treatments modify the expression of CT genes, indicating that these genes can potentially be used as biomarkers for CC. The importance of conducting further research to understand and target epigenetic mechanisms to improve CC treatment cannot be overemphasized.

Adoption of plant-derived compounds for the management of oral cancer is encouraged by the scientific community due to emerging chemoresistance and conventional treatments adverse effects. Considering that very few studies investigated eugenol clinical relevance for gingival carcinoma, we ought to explore its selectivity and performance according to aggressiveness level. For this purpose, non-oncogenic human oral epithelial cells (GMSM-K) were used together with the Tongue (SCC-9) and Gingival (Ca9-22) squamous cell carcinoma lines to assess key tumorigenesis processes. Overall, eugenol inhibited cell proliferation and colony formation while inducing cytotoxicity in cancer cells as compared to normal counterparts. The recorded effect was greater in gingival carcinoma and appears to be mediated through apoptosis induction and promotion of p21/p27/cyclin D1 modulation and subsequent Ca9-22 cell cycle arrest at the G0/G1 phase, in a p53-independent manner. At these levels, distinct genetic profiles were uncovered for both cell lines by QPCR array. Moreover, it seems that our active component limited Ca9-22 and SCC-9 cell migration respectively through MMP1/3 downregulation and stimulation of inactive MMPs complex formation. Finally, Ca9-22 behaviour appears to be mainly modulated by the P38/STAT5/NFkB pathways. In summary, we can disclose that eugenol is cancer selective and that its mediated anti-cancer mechanisms vary according to the cell line with gingival squamous cell carcinoma being more sensitive to this phytotherapy agent.

Cardiovascular disease (CVD) is one of the leading causes of mortality worldwide, and multiple single‑nucleotide polymorphisms of DNA repair genes have been found to be associated with CVD. The aim of the present study was to assess the effects of the genetic variants of RAD51 recombinase (RAD51) and 8‑oxoguanine DNA glycosylase (OGG1) on CVD through genotyping and statistical analysis. Regardless of whether there is a significant association or not, the genotyping data on these two polymorphisms are valuable, because there is limited availability of it in certain populations. A total of 240 blood samples were analyzed and genotyped using TaqMan genotyping; 120 were obtained from cases with a history of CVD, and 120 from cases with no history of CVD. A questionnaire was administered to gather information on age, demographics, sex and clinical features, and confirmation was carried out using medical records. The results of the present study confirmed that the polymorphism rs1052133 in OGG1 had no significant association with CVD. On the other hand, the polymorphism rs1801321 in RAD51 exhibited a significant association with CVD. Collectively, the results of the present study revealed that the polymorphism rs1801321 in RAD51 exhibited a significant association with CVD, however a larger sample size to confirm the present findings, may allow for the early identification of CVD and may aid in the decision‑making process concerning treatments for CVD.