Manar G. Shalabi

Background: Colorectal cancer (CRC) is a major global health concern and a leading cause of cancer-related mortality. In Saudi Arabia, it is the most common cancer among men and the third most common among women. The disease affects predominantly older adults, with an increasing number of cases reported in younger populations. Emerging evidence suggests a potential association between Vitamin D deficiency and CRC risk and progression. Aim: This study aimed to investigate the relationship between serum Vitamin D levels and colorectal cancer, and to evaluate its association with clinicopathological characteristics. Methodology: A retrospective case–control study was conducted on newly diagnosed CRC patients between January 2021 and August 2024 at King Abdul-Aziz Specialist Hospital, Prince Muteb Hospital, and the Oncology Center in Al Jouf, Saudi Arabia. A total of 100 CRC cases and 50 healthy controls were included. Serum 25-hydroxyvitamin D levels were measured and categorized as deficient (t-test, one-way ANOVA, and chi-square tests. Results: During the four-year period, 5399 gastrointestinal specimens were analyzed, of which 2111 (39.1%) were colorectal specimens. CRC was diagnosed in 107 cases (5.1%), and 100 patients met the inclusion criteria. The mean age of patients was 53.07 ± 13.3 years, and 69% were older than 50 years. Males represented 58% of cases (male-to-female ratio 1.4:1). Invasive adenocarcinoma was the predominant histological subtype (81%), with the sigmoid colon being the most common tumor site (39%). Vitamin D deficiency was significantly more prevalent in CRC patients (59%) compared to controls (22%). The mean serum Vitamin D level was significantly lower in cases (18.7 ± 11.3 ng/mL) than in controls (34.9 ± 15.6 ng/mL) (p < 0.001). No significant difference in Vitamin D levels was observed between males and females. Lower Vitamin D levels were significantly associated with advanced tumor grade (p = 0.004), lymphovascular invasion (p < 0.001), lymph node involvement (p = 0.001), and distant metastasis (p < 0.001). Representative histopathological images confirmed invasive moderately differentiated adenocarcinoma with characteristic malignant glandular architecture. Conclusions: Vitamin D deficiency was highly prevalent among colorectal cancer patients and was significantly associated with advanced tumor characteristics, including higher grade and metastatic features. These findings suggest a strong inverse relationship between serum Vitamin D levels and CRC development and progression. Further large-scale prospective and interventional studies are warranted to clarify the causal role of Vitamin D and its potential therapeutic implications in colorectal cancer prevention and management.

This study examined the impact of Ramadan fasting on physical activity and sleep patterns among Saudis in Najran, Saudi Arabia. Seventy-three male and female volunteers completed a self-reported questionnaire over five days at Najran University Hospital. Data collected included demographics, physical activity levels, sedentary behaviors, sleep duration, social activities, and mood changes. Results showed that only 29 participants (39.9%) considered themselves physically active, while 44 (60.3%) were sedentary. The mean age of the active group was 41.60±8.21 years. Among females, only 7 (31.8%) were physically active, while 15 (68.2%) were inactive. Factors such as night work, mood changes, and indoor social activities significantly influenced physical activity levels (p<0.005). Regarding sleep, the physically active group had a mean daily sleep duration of 7.43±1.52 hours, while the sedentary group slept an average of 10.20±2.11 hours, showing a statistically significant difference. These findings suggest that Ramadan fasting influences both physical activity and sleep patterns, potentially affecting overall health and well-being.

Cancer presents significant challenges owing to its complex molecular pathways and resistance to therapy. Natural metabolites have significant medicinal potential by regulating the sensing and signaling pathways associated with cancer development. Recognizing their interactions within the tumor microenvironment may unveil innovative techniques for inhibiting malignant activities and improve therapy success. This article highlights studies regarding ovarian cancer metabolism, signaling mechanisms, and therapeutic natural substances. This study summarizes clinical and experimental results to emphasise the synergistic effects of alkaloids, flavonoids, and terpenoids in improving therapeutic effectiveness and alleviating drug resistance. Bioactive compounds are essential in regulating ovarian cancer metabolism and signaling pathways, affecting glycolysis, lipid metabolism, and the survival of tumor cells. This review examines metabolic programming and essential pathways, including glycolysis, TCA cycle, lipid metabolism, PI3K/AKT/mTOR, AMPK, and MAPK, emphasizing their therapeutic significance. The integration of metabolic treatments with medicines based on natural compounds has significant potential for enhancing treatment effectiveness and mitigating therapeutic resistance. Ovarian cancer needs an integrated strategy that includes metabolic reprogramming, signaling modulation, and drugs derived from natural products. Natural chemicals provide intriguing approaches to address chemotherapy resistance and improve treatment efficacy. Further research is required to enhance these methodologies and evaluate their practical applicability for improved patient outcomes.

Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. While timely reperfusion therapies such as percutaneous coronary intervention (PCI) and thrombolysis are essential for salvaging ischemic myocardium, they can paradoxically exacerbate tissue injury through a process known as myocardial infarction reperfusion injury (MIRI). MIRI can contribute to up to 50% of the final infarct size, significantly diminishing the benefits of revascularization and leading to worsened cardiac outcomes. The pathophysiology of MIRI involves complex, interrelated mechanisms including oxidative stress, calcium overload, mitochondrial dysfunction, inflammatory responses, apoptosis, and dysregulated autophagy. Post-reperfusion recovery is further complicated by structural and functional abnormalities such as microvascular obstruction, endothelial dysfunction, and myocardial stunning. Clinically, distinguishing reperfusion injury from ischemic damage is challenging and often requires the use of sensitive biomarkers, such as cardiac troponins, alongside advanced imaging modalities. Although a range of pharmacological (e.g., antioxidants, calcium channel blockers, mitochondrial stabilizers, anti-inflammatory agents) and non-pharmacological (e.g., hypothermia, gene therapy, stem cell-based therapies) interventions have shown promise in preclinical studies, their clinical translation remains limited. This is largely due to the multifactorial and dynamic nature of MIRI. In this context, network pharmacology offers a systems-level approach to understanding the complex biological interactions involved in MIRI, facilitating the identification of multi-target therapeutic strategies. Integrating network pharmacology with omics technologies and precision medicine holds potential for advancing cardioprotective therapies. This review provides a comprehensive analysis of the molecular mechanisms underlying MIRI, examines the current clinical challenges, and explores emerging therapeutic strategies. Emphasis is placed on bridging the translational gap through validated, multi-target approaches and large-scale, multicenter clinical trials. Ultimately, this work aims to support the development of innovative and effective interventions for improving outcomes in patients with myocardial infarction.

Liver cirrhosis continues to be a major global health issue, contributing to high morbidity and mortality due to its progressive nature and associated complications. This review explores recent advancements in the diagnosis and treatment of liver cirrhosis and its related syndromes. Non-invasive diagnostic tools, such as elastography and serum biomarkers, have significantly improved early detection, reducing the need for liver biopsies. Advanced imaging techniques, including MRI and CT, further enhance diagnostic accuracy. In parallel, molecular and genomic research is providing new insights into the pathogenesis of the disease, paving the way for precision medicine. On the treatment front, pharmacological innovations, such as antifibrotic agents and targeted therapies, show promise in slowing disease progression. Endoscopic interventions like variceal banding are improving the management of complications, while advancements in liver transplantation and artificial liver support systems offer life-saving alternatives. Regenerative medicine, particularly stem cell therapy and tissue engineering, is emerging as a promising strategy for liver repair. Managing cirrhosis-related syndromes, including portal hypertension, ascites, hepatic encephalopathy, and hepatorenal syndrome, now involves evolving therapeutic approaches such as transjugular intrahepatic portosystemic shunt (TIPS) and novel pharmacotherapies. Prognostic scoring systems like the MELD and Child–Pugh are being refined with new biomarkers for better risk stratification. The future of cirrhosis care will likely involve the integration of artificial intelligence and machine learning for early diagnosis and personalized treatments, alongside emerging therapies currently under investigation. Despite these advancements, challenges such as costs, accessibility, and healthcare disparities remain barriers to widespread adoption. This review highlights the importance of incorporating innovative diagnostic and therapeutic strategies into clinical practice to improve the outcomes for patients with liver cirrhosis and its complications.

The integration of artificial intelligence and personalized medicine is transforming HIV management by enhancing diagnostics, treatment optimization, and disease monitoring. Advances in machine learning, deep neural networks, and multi-omics data analysis enable precise prognostication, tailored antiretroviral therapy, and early detection of drug resistance. AI-driven models analyze vast genomic, proteomic, and clinical datasets to refine treatment strategies, predict disease progression, and pre-empt therapy failures. Additionally, AI-powered diagnostic tools, including deep learning imaging and natural language processing, improve screening accuracy, particularly in resource-limited settings. Despite these innovations, challenges such as data privacy, algorithmic bias, and the need for clinical validation remain. Successful integration of AI into HIV care requires robust regulatory frameworks, interdisciplinary collaboration, and equitable technology access. This review explores both the potential and limitations of AI in HIV management, emphasizing the need for ethical implementation and expanded research to maximize its impact. AI-driven approaches hold great promise for a more personalized, efficient, and effective future in HIV treatment and care.

Colorectal cancer (CRC) is one of the most common and deadly types of cancer globally. According to the International Agency for Research on Cancer (IARC), CRC is a significant and unavoidable health risk, ranking as the second leading cause of cancer-related deaths worldwide, accounting for nearly 10% of all new cancer cases. While most CRC cases are sporadic, 20–30% are familial. The high mortality rate associated with CRC is largely preventable, but delayed or ineffective treatment can significantly worsen outcomes. Early detection, diagnosis, and treatment are crucial in improving survival rates, yet only about half of cases are identified before the disease advances. Routine screening plays a vital role in catching the disease in its early stages, increasing the chances of successful treatment. This section will explore various CRC screening and early detection methods, including endoscopy, colonoscopy, sigmoidoscopy, blood and stool testing, as well as advanced techniques involving nanoparticles, biomarkers, biosensors, and artificial intelligence. These approaches are essential in enhancing the accuracy and timeliness of CRC detection, ultimately improving patient outcomes.

Cancer remains a global health issue, with early detection, correct diagnosis, and exact prognosis playing critical roles in improving patient outcomes. Recent developments in image processing and pattern recognition have significantly enhanced cancer detection, prediction, diagnosis, and prognosis, bridging the gap between conventional radiological methods and state-of-the-art artificial intelligence (AI)-based analytics. The abstract provides an in-depth introduction of the concepts behind, developing methods, and practical applications of image processing and pattern recognition in oncology. Image preprocessing algorithms, tumor segmentation algorithms, feature extraction, and pattern recognition methods based on artificial intelligence such as machine learning and deep learning models (e.g., CNNs, RNNs, and transformers) are all significant areas. We study cancer prediction using radiomics and radio genomics, artificial intelligence-based histopathological diagnosis, and fusion of multimodal data from imaging, genomics, and clinical history for personalized prognosis. The impact of explainable AI, 3D/4D imaging, nanotechnology-facilitated imaging, and cloud-based AI solutions is also discussed, including precision oncology and remote diagnosis. Even with these developments, significant clinical use limitations remain, such as poor annotated datasets, lack of interpretability, ethical considerations, and regulatory issues. The abstract proceeds with focusing on emerging trends, including federated learning, quantum computing, and real-time AI applications, that can potentially revolutionize cancer imaging and management. Image processing and pattern recognition, if integrated with interdisciplinary research, have the potential to develop more precise, equitable, and egalitarian cancer care in the future.

Human Immunodeficiency Virus (HIV) remains a major public health challenge globally. Recent innovations in diagnostic technology have opened new pathways for early detection, ongoing monitoring, and more individualized patient care, yet significant barriers persist in translating these advancements into clinical settings. This review highlights the cutting-edge diagnostic methods emerging from basic science research, including molecular assays, biosensors, and next-generation sequencing, and discusses the practical and logistical challenges involved in their implementation. By analyzing current trends in diagnostic techniques and management strategies, we identify critical gaps and propose integrative approaches to bridge the divide between laboratory innovation and effective clinical application. This work emphasizes the need for comprehensive education, supportive infrastructure, and multi-disciplinary collaborations to enhance the utility of these diagnostic innovations in improving outcomes in patients with HIV.

Medical imaging and digital image analysis are essential tools in diagnosing and detecting various diseases. One key application is the examination of blood smears, where specific cell types, such as those indicative of megaloblastic anemia, can be identified. A critical component of this process involves analyzing and studying relevant images, as well as conducting experiments to evaluate the effectiveness of different methods and approaches in addressing this diagnostic challenge. As a result of the comparative analysis, it was found that the most effective method for the purpose of isolating the edge with megaloblastic anemia cells is the approach based on the wavelet ideology. This approach has the best indicators of assessing the quality of the resulting images in comparison with other edge detection methods. In some cases, the value of such indicators exceeds similar values for other methods by more than 2 times. In some cases, the indicators for images after contrasting are higher than without contrasting. This is also typical for other approaches to edge detection in images with megaloblastic anemia cells. First, this is typical for images with a uniform background and the absence of multiple peaks in the histogram of the input image brightness distribution. In general, the issue of contrasting the original image for subsequent processing in order to detect edge remains open. At the same time, this study provides an answer to the most effective method for edge detection for images with megaloblastic anemia cells, using the original images contrasting procedure.