Eurasian Journal of Chemical, Medicinal and Petroleum Research

Abstract Introduction: Oral squamous cell carcinoma is a biologically aggressive malignancy in which molecular alterations may extend beyond visible tumor tissue into surgical margins. Long non-coding RNAs, particularly LNC-NORAD, may influence genomic stability, stress responses, and tumor progression. This study aimed to assess LNC-NORAD expression changes in tumor and surgical margin samples of OSCC.

Material and methods: This descriptive cross-sectional study was conducted at Tabriz University of Medical Sciences in 2024. Using convenience sampling, 100 specimens comprising 50 oral squamous cell carcinoma tissues and 50 matched healthy surgical margins were collected from 50 patients. LNC-NORAD expression was quantified by real-time PCR and evaluated alongside age, sex, smoking status, tumor site and size, histological grade, invasion characteristics, TNM stage, lymph node involvement, and margin status.

Results: LNC-NORAD expression was significantly higher in OSCC tumor tissues than in matched healthy surgical margins (3.18 ± 1.07 vs. 1.24 ± 0.43; P<0.001). Increased tumoral expression was associated with smoking, larger tumor size, poorer histological differentiation, deeper invasion, perineural invasion, advanced TNM stage, and lymph node metastasis. By contrast, no significant associations were observed with sex, anatomical tumor site, or lymphovascular invasion.

Conclusion: LNC-NORAD is markedly upregulated in OSCC tissues compared with matched surgical margins and is closely associated with several indicators of tumor aggressiveness. These findings suggest that this long non-coding RNA may serve as a useful molecular marker for identifying biologically advanced and clinically high-risk oral squamous cell carcinoma.

Abstract Introduction: Early infection after femoral implant placement is a serious complication influenced by host immunity, wound healing capacity, and nutritional-inflammatory status. Preoperative serum albumin is a practical biomarker that may reflect vulnerability to postoperative infection, but its predictive accuracy remains uncertain in femoral implant surgery. This study aimed to evaluate the sensitivity and specificity of preoperative serum albumin in predicting early infection after femoral implant placement.

Material and methods: This retrospective descriptive cross-sectional study was conducted at Shahid Madani Hospital, Tabriz, on 125 patients selected by convenience sampling. The sample size was estimated using Cochran’s formula. Data were extracted from medical records to evaluate preoperative serum albumin, demographic and clinical variables, operative characteristics, and early postoperative infection after femoral implant placement, with the aim of assessing the diagnostic performance of albumin in predicting early infection.

Results: Among 125 patients, early postoperative infection occurred in 15.2% (19/125). Infected patients had lower preoperative albumin (3.12 ± 0.46 vs 3.66 ± 0.51 g/dL; P < 0.001), a higher rate of hypoalbuminemia (63.2% vs 27.4%; P = 0.002), lower hemoglobin (P = 0.011), and higher inflammatory markers (WBC, P = 0.018; CRP, P = 0.006). Serum albumin predicted infection with good accuracy (cutoff 3.35 g/dL; sensitivity 78.9%, specificity 73.6%, AUC 0.812, P < 0.001).

Conclusion: Preoperative serum albumin appears to be a clinically useful marker for identifying patients at increased risk of early infection after femoral implant placement. Lower albumin levels were consistently associated with infection and showed acceptable diagnostic performance. These findings support incorporating serum albumin into routine preoperative assessment to improve risk stratification and guide perioperative optimization in this surgical population.

Abstract Introduction: Femoral fracture surgery in elderly patients is frequently followed by substantial acute postoperative pain, which may be influenced not only by surgical trauma but also by systemic inflammatory and immune responses. Because the Systemic Immune-Inflammation Index may reflect this biological susceptibility, the present study aimed to pathologically assess its association with the severity of acute postoperative pain after femoral fracture surgery.

Material and methods: This descriptive cross-sectional study was conducted at Shahid Madani Hospital, Tabriz, on 93 elderly patients selected by convenience sampling, with sample size estimated using Cochran’s formula. Demographic, clinical, laboratory, surgical, and postoperative pain data were collected. The Systemic Immune-Inflammation Index was calculated from preoperative platelet, neutrophil, and lymphocyte counts, and its association with acute postoperative pain severity was statistically evaluated.

Results: Patients with severe postoperative pain had markedly higher preoperative SII than those with moderate or mild pain (1801.2±789.6 vs 1280.5±542.7 and 850.2±386.4; P<0.001), along with higher inflammatory markers, lower hemoglobin and albumin, longer operations, greater blood loss, more opioid use, delayed mobilization, and longer hospitalization (all P<0.05). SII correlated with pain intensity (highest 24-hour NRS: r=0.574, P<0.001) and independently predicted severe pain (OR=2.86, 95% CI 1.54-5.31), with good accuracy (AUC=0.812).

Conclusion: Preoperative SII appears to be a clinically useful biomarker for identifying patients at increased risk of severe acute postoperative pain. Its independent association with pain severity and acceptable diagnostic performance suggest that SII may support early perioperative risk stratification and help guide individualized analgesic planning, particularly in patients with an elevated inflammatory burden before surgery.

Abstract Introduction: Liver transplantation is a life-saving treatment for end-stage liver disease, but its success depends heavily on accurate pathologic assessment before, during, and after surgery. Pathology helps define native liver disease, assess donor organ quality, and identify causes of graft dysfunction. The aim of this study was to evaluate the pathologic findings in patients undergoing liver transplantation.

Material and methods: This retrospective descriptive cross-sectional study was conducted at Imam Reza Hospital, Tabriz, on 73 liver transplant patients selected by convenience sampling based on the Cochran formula. Data were extracted from archived medical records and pathology reports, and included demographic characteristics, transplant indications, explant pathology, and available post-transplant histopathologic findings to evaluate the spectrum of pathologic changes in this patient population.

Results: Among 73 liver transplant recipients, cirrhosis was the leading transplant indication (32.9%) and the dominant explant finding (71.2%), while advanced fibrosis/cirrhosis was present in 75.3%. Steatosis was identified in 31.5%, cholestasis in 23.3%, and hepatocellular carcinoma in 19.2%. Hepatocellular carcinoma was significantly associated with older age (P = 0.041), viral hepatitis (P = 0.018), pre-transplant cirrhosis (P = 0.047), dysplasia (P = 0.006), vascular abnormalities (P = 0.039), and microvascular invasion (P < 0.001).

Conclusion: These findings indicate that end-stage cirrhotic liver disease constitutes the principal pathologic burden in liver transplant recipients, while hepatocellular carcinoma represents a clinically important subset linked to adverse pathologic features. Careful explant pathologic evaluation is therefore essential not only for confirming the underlying disease spectrum but also for identifying malignant and high-risk microscopic characteristics with prognostic relevance.

Abstract Introduction: Breast cancer remains a major global health burden, and lymph node involvement is a key pathologic indicator of tumor spread, prognosis, and postoperative treatment planning in patients undergoing mastectomy. Accurate nodal assessment also improves disease staging and therapeutic decision-making. This study aims to evaluate the pathologic status of lymph node involvement in patients undergoing mastectomy.

Material and methods: This retrospective descriptive cross-sectional study was conducted at Shahid Madani Hospital in Tabriz on 250 patients selected by convenience sampling, with sample size estimated using Cochran’s formula. Data were extracted from archived medical and pathology records, and clinicopathologic variables, particularly lymph node status and related breast tumor characteristics, were systematically collected and analyzed to evaluate the pattern of nodal involvement in mastectomy patients.

Results: Among 250 mastectomy patients, invasive ductal carcinoma was the predominant subtype (79.2%), most tumors were grade II (57.2%), and mean tumor size was 3.41 ± 1.67 cm. Lymph node involvement was present in 58.4%, with macrometastasis in 51.2% and extranodal extension in 15.6%. Nodal positivity was significantly associated with larger tumor size (3.98 ± 1.78 vs. 2.61 ± 1.14 cm, P < 0.001), higher grade (P = 0.002), and lymphovascular invasion (60.3% vs. 23.1%, P < 0.001).

Conclusion: These findings indicate that lymph node metastasis is common in mastectomy specimens and is closely linked to adverse pathologic features. Larger tumors, higher histologic grade, and lymphovascular invasion appear to be the strongest correlates of nodal spread, underscoring the importance of careful pathologic lymph node assessment for accurate staging, prognostic stratification, and postoperative treatment planning in breast cancer patients.

Abstract Context: Cardiac arrest remains one of the leading causes of death worldwide, with survival rates remaining low despite advances in treatment protocols. Nurses play a critical role as first responders in cardiopulmonary-cerebral resuscitation (CPCR). Therefore, ensuring adequate CPCR training for nursing staff is essential to improve patient outcomes.

Objective: This systematic review aimed to provide an overview of CPCR training methods for nurses in Iran and to evaluate their effectiveness in improving nurses'' knowledge, skills, and performance.

Methods: A systematic search conducted in international databases including PubMed, Scopus, CINAHL, Google Scholar, and Persian databases including SID, IranMedex, and Magiran for articles published between 2010 and 2020. English and Persian keywords including "education," "training," "cardiopulmonary-cerebral resuscitation," "nurses," "knowledge," "skill," and "performance" were used. 21 articles were identified; after removing duplicates and screening for relevance, 10 studies were included in the final review. Quality assessment performed using appropriate critical appraisal tools.

Results: The review identified three main categories of CPCR training methods: conventional (lectures, pamphlets, and mannequin practice), modern (simulation, workshops, video-based, and flipped classroom), and integrated approaches. Conventional methods primarily improved knowledge but showed limited effects on skills retention. Modern and integrated methods demonstrated superior outcomes in enhancing both knowledge and practical skills. Simulation-based and workshop approaches yielded the highest skill acquisition and satisfaction levels among nurses.

Conclusions: Integrated training methods combining conventional and modern approaches are the most effective for improving nurses'' CPCR competence. Healthcare policymakers and educational managers should implement standardized, multifaceted training programs with regular refresher courses to maintain CPCR skills.

Abstract Introduction: Early postoperative inflammation after lumpectomy may be influenced by tumor biology, particularly estrogen receptor expression, which modulates immune signaling and cytokine activity. However, its relationship with surgical inflammation remains unclear. This study aimed to evaluate the association between estrogen receptor expression and inflammatory responses during the first 24-hour postoperative period.

Material and methods: This descriptive cross-sectional study was conducted at Tabriz University of Medical Sciences in 2026 on 45 women undergoing lumpectomy, selected by convenience sampling. Estrogen receptor expression was assessed immunohistochemically in surgical specimens, while early postoperative inflammation was evaluated by 24-hour serum C-reactive protein measurement. Additional variables included age, body mass index, menopausal status, tumor characteristics, operative duration, white blood cell count, temperature, drainage volume, and pain score.

Results: Our study demonstrated that ER-negative patients exhibited a more pronounced inflammatory response post-lumpectomy. The ER-negative group showed significantly higher 24-hour CRP (16.83 vs. 12.46 mg/L; p=0.001) and WBC counts (10.08 vs. 8.71 × 10³/µL; p=0.017) than ER-positive patients. Regression analysis confirmed an independent inverse association between ER expression and postoperative CRP levels (adjusted β=3.92; p=0.003), underscoring a distinct link between molecular phenotype and acute systemic inflammatory intensity.

Conclusion: ER-negative status in breast cancer patients significantly exacerbates the systemic inflammatory response during the first 24 hours after lumpectomy. This association suggests that ER expression serves as a critical molecular biomarker for early postoperative recovery. Consequently, patients with ER-negative tumors may require heightened clinical surveillance and targeted anti-inflammatory interventions to manage the observed surge in acute inflammatory markers following surgical resection.

Abstract Introduction: Laryngeal squamous cell carcinoma remains a major clinical challenge, and conventional margin assessment may overlook molecular alterations associated with residual disease and field cancerization. DUSP1, a regulator of MAPK signaling, may contribute to tumor behavior. This study aimed to assess DUSP1 expression levels in tumor and surgical margin samples of LSCC.

Material and methods: This descriptive cross-sectional study was conducted in 2024 at Tabriz University of Medical Sciences on 50 patients with laryngeal squamous cell carcinoma, selected through convenience sampling based on the Cochran formula for single-group studies. DUSP1 expression was measured in paired tumor and surgical margin samples, alongside demographic, clinical, and pathological variables, including age, sex, smoking status, tumor site, grade, TNM stage, lymph node involvement, and margin status.

Results: In this study of 50 patients with laryngeal squamous cell carcinoma, mean DUSP1 expression was significantly higher in tumor tissue than in paired surgical margins (2.73 ± 0.68 vs. 1.41 ± 0.37; P=0.001). Tumoral DUSP1 expression was also greater in smokers, poorly differentiated tumors, advanced-stage disease, node-positive cases, and positive surgical margins, indicating a consistent association between elevated DUSP1 levels and more aggressive clinicopathological characteristics.

Conclusion: DUSP1 was significantly overexpressed in LSCC tumor tissue compared with surgical margins and was associated with adverse clinicopathological features. These findings suggest that DUSP1 may serve as a promising molecular indicator of tumor aggressiveness and local disease extension.

Abstract Introduction: This study compares intensive care unit outcomes between obese and non-obese postmenopausal women undergoing gastric surgery, a population vulnerable to distinct metabolic and respiratory risks. Ultimately, we aim to evaluate how obesity status influences postoperative ICU length of stay, complication rates, and resource utilization in these patients.

Material and methods: This descriptive cross-sectional study was conducted at Tabriz University of Medical Sciences during 2023 and included 215 postmenopausal women selected through convenience sampling. Sample size was estimated using the Cochran formula for a single-population proportion. The study assessed demographic, clinical, surgical, and ICU-related variables, including obesity status, comorbidities, operative characteristics, inflammatory markers, postoperative complications, and key intensive care outcomes.

Results: Obese postmenopausal women experienced significantly longer ICU stays (54.82 ± 14.63 vs. 38.15 ± 9.47 hours; p < 0.001) and prolonged mechanical ventilation (8.42 vs. 4.15 hours). Higher rates of respiratory distress (29.81% vs. 9.91%; p < 0.001) and wound infections (12.50% vs. 3.60%; p = 0.014) were observed. Consequently, obesity extended total hospitalization to 8.74 days compared to 6.82 days (p < 0.001), reflecting a more complex and resource-intensive postoperative recovery trajectory.

Conclusion: Obesity in postmenopausal women significantly impairs recovery after gastric surgery, leading to extended ICU stays and increased respiratory and infectious complications. These findings suggest that obese patients require more intensive perioperative management and specialized monitoring. Clinicians should prioritize early mobilization and targeted pulmonary interventions to mitigate the heightened resource utilization and clinical risks associated with elevated body mass in this population.

Abstract Skin cancer, particularly melanoma, poses significant health risks globally. Early detection is crucial for effective treatment and improved patient outcomes. Dermoscopy, a non-invasive imaging technique, has enhanced dermatologists' ability to examine skin lesions. Recent advancements in artificial intelligence (AI), especially deep learning, have shown promising results in automating the analysis of dermoscopic images for skin cancer detection. AI models, particularly convolutional neural networks (CNNs), have been trained on large datasets of dermoscopic images, achieving diagnostic accuracies comparable to or surpassing those of experienced dermatologists. These AI systems can assist in identifying malignant lesions, thereby aiding in early diagnosis and reducing the workload on healthcare professionals. However, challenges remain, including the need for diverse and representative datasets, addressing biases in AI models, and ensuring the clinical applicability of these technologies. This paper reviews the current state of AI applications in dermoscopic image analysis for skin cancer detection, discusses the methodologies employed, evaluates the performance of various AI models, and examines the potential impact on clinical practice. The integration of AI into dermatology holds the promise of enhancing diagnostic accuracy, improving patient outcomes, and optimizing healthcare resources.

Abstract Breast cancer remains one of the leading causes of cancer-related mortality among women globally, highlighting the critical need for early detection and accurate diagnosis. Recent advances in artificial intelligence (AI), encompassing both machine learning (ML) and deep learning (DL) approaches, have demonstrated significant potential in enhancing diagnostic accuracy, reducing human error, and supporting clinical decision-making. This systematic review critically analyzes existing studies that employ AI for breast cancer detection, focusing on methodological approaches, dataset characteristics, model performance, and interpretability. ML-based techniques, including support vector machines, random forests, and gradient boosting, show promising results in structured datasets, particularly where dataset sizes are limited, and interpretability is essential. In contrast, DL approaches, primarily convolutional neural networks and their variants, outperform ML in raw image analysis, multi-modal imaging, and complex feature extraction, achieving higher accuracy and sensitivity. Hybrid models integrating ML and DL, often augmented with radiomics features, offer a balanced framework, combining high predictive performance with improved interpretability. Additionally, explainable AI (XAI) techniques are increasingly applied to DL models, mitigating the “black-box” problem and fostering clinical trust. Despite these advancements, challenges remain, including the need for large, high-quality, multi-institutional datasets, computational resource demands, and generalizability across diverse populations. Low-resource and portable AI solutions offer potential for broader accessibility, though with modest reductions in predictive performance. Overall, AI demonstrates transformative potential in early breast cancer detection, particularly when combined with hybrid and explainable frameworks. Future research should prioritize multi-modal integration, rigorous cross-center validation, and deployment strategies that balance accuracy, interpretability, and accessibility, ultimately facilitating clinical adoption and improving patient outcomes.