Eurasian Journal of Chemical, Medicinal and Petroleum Research

Abstract Cosmetic facial filler injections have become a cornerstone in aesthetic dermatology; however, associated complications, ranging from mild erythema to severe vascular compromise, necessitate effective and timely intervention. This study proposes a treatment algorithm grounded in the biological stages of wound healing: hemostasis, inflammation, proliferation, and remodeling. By aligning therapeutic strategies with these stages, clinicians can address adverse events more systematically and promote tissue recovery. Early-stage complications such as edema or erythema can often be managed with conservative measures, whereas mid- to late-stage events like granuloma formation or tissue necrosis require more invasive approaches, including hyaluronidase administration, corticosteroids, or surgical debridement. The algorithm emphasizes the importance of early diagnosis, risk stratification, and individualized care, considering patient history, filler type, and injection technique. Additionally, adjunctive therapies—such as hyperbaric oxygen, laser treatment, or platelet-rich plasma—may enhance healing outcomes. This framework aims to reduce long-term morbidity and provide practitioners with a clear, biologically informed decision-making pathway to manage filler-related complications effectively.

Abstract Facial filler injections are widely used in aesthetic dermatology and plastic surgery for rejuvenation and volume restoration. Despite their popularity and generally favorable safety profile, rare but severe complications, including cerebral embolism, have emerged in the literature. This review aims to explore the existing evidence surrounding cerebral embolism resulting from facial filler injections, with a focus on incidence, anatomical risk factors, pathophysiological mechanisms, clinical manifestations, diagnostic challenges, management strategies, and outcomes. An extensive literature search was conducted using PubMed, Scopus, Embase, and Web of Science for studies published from 2015 to 2024. A total of 37 cases were identified, with the glabella, nasal dorsum, and forehead being the most common sites associated with complications. The pathogenesis involves retrograde arterial embolization with filler materials entering cerebral circulation via branches of the ophthalmic artery. Common clinical signs include sudden vision loss, headache, hemiplegia, aphasia, and altered consciousness. Prompt recognition and early management are crucial but often fail to reverse neurological deficits. Preventive strategies, such as anatomical knowledge, appropriate injection technique, and use of blunt cannulas, are essential. This review highlights the need for greater awareness among practitioners and recommends standardized emergency protocols to reduce the burden of these rare but catastrophic events.

Abstract Oil pollution is one of the most persistent environmental threats affecting terrestrial and marine ecosystems worldwide. Bioremediation, an eco-friendly and cost-effective approach, utilizes microorganisms to degrade petroleum hydrocarbons into non-toxic end products. This review highlights the microbial diversity involved in oil degradation, including key bacterial and fungal genera such as Pseudomonas, Acinetobacter, Rhodococcus, Aspergillus, and Candida. It further discusses the enzymatic mechanisms underlying hydrocarbon degradation, both aerobic and anaerobic, and the environmental factors influencing microbial activity, such as temperature, pH, nutrient availability, and oxygen levels. Special attention is given to the role of biosurfactants in increasing hydrocarbon bioavailability and the advancement of modern technologies such as microbial electrochemical systems and genetic engineering for enhanced degradation. This paper also presents successful case studies of microbial oil bioremediation and compares it to other remediation strategies, emphasizing the potential and challenges of microbial approaches in large-scale applications. The comprehensive understanding provided here aims to guide future research and industrial applications for sustainable environmental restoration.

Abstract Introduction:

This study aimed to compare the outcomes and complications of MIE versus OE in patients with distal esophageal cancer.

Materials and Methods:

This retrospective cohort study included 196 patients with distal esophageal cancer treated between 2015 and 2021 at two tertiary hospitals in Tehran. Patients were equally divided into MIE (n=98) and OE (n=98) groups. Preoperative, intraoperative, and postoperative variables including surgical duration, pain intensity, intraoperative blood loss, transfusion requirements, hospital stay, and complication rates were collected and analyzed using SPSS version 23. Appropriate statistical tests were applied with a significance level set at p<0.05.

Results:

The mean duration of surgery was significantly shorter in the MIE group compared to the OE group (280.71 ± 44.83 vs. 425.36 ± 51.69 minutes, p<0.001). MIE patients experienced significantly less intraoperative blood loss (297.14 ± 89.29 vs. 503.57 ± 122.02 mL, p<0.001), required fewer blood transfusions (2.14 ± 0.52 vs. 2.64 ± 0.90 units, p<0.001), and had a shorter hospital stay (14.14 ± 3.27 vs. 18.64 ± 4.31 days, p<0.001). Postoperative pain was also lower in the MIE group (2.86 ± 1.36 vs. 3.43 ± 2.48, p=0.048). However, the number of lymph nodes dissected was significantly higher in the OE group (18.50 ± 3.48 vs. 9.57 ± 3.79, p<0.001), and the incidence of chylothorax was greater in the MIE group (7 vs. 1 cases, p=0.030). There was no statistically significant difference in tracheal injury between the groups (p=0.054).

Conclusion:

Minimally invasive esophagectomy offers several clinical benefits over open surgery, including reduced blood loss, lower postoperative pain, shorter operative time, and decreased hospital stay. However, it is associated with a higher incidence of chylothorax and a lower lymph node yield, possibly due to the learning curve and technical nuances.

Abstract Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC), has emerged as a significant public health challenge, affecting millions of individuals globally. It is characterized by persistent or new symptoms lasting weeks to months beyond the acute phase of COVID-19, including fatigue, dyspnea, cognitive impairment, autonomic dysfunction, and musculoskeletal pain. The underlying pathophysiology is multifactorial and remains under investigation. Current evidence suggests that persistent viral reservoirs in tissues, chronic immune activation, autoimmunity, endothelial dysfunction, microvascular injury, and mitochondrial impairment play major roles. Neurological and autonomic disturbances, along with psychosocial and epigenetic influences, further contribute to the complexity of the syndrome. Management strategies are primarily supportive and multidisciplinary, focusing on symptom relief, rehabilitation, and psychosocial care. Approaches include pulmonary and cardiovascular rehabilitation, pacing and energy conservation for fatigue, pharmacological interventions for dysautonomia, and cognitive therapy for neurocognitive symptoms. Emerging treatments such as immunomodulators, antiviral agents, anticoagulants, and mitochondrial support therapies are under investigation. Despite progress, challenges remain in defining diagnostic criteria, identifying biomarkers, and tailoring personalized interventions. Long COVID represents not only a biomedical issue but also a socioeconomic burden, requiring coordinated healthcare strategies and global research efforts. Understanding its mechanisms and developing effective management approaches are essential to mitigate its long-term impact on individuals and healthcare systems.

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 Methods based on atomic behavior, such as molecular dynamics (MD) simulations, are highly accurate for modeling single-layer graphene sheets. However, their high computational cost limits their use to only small-sized systems. This research addresses this limitation by developing a new atomic-scale finite element method (AFEM) based on the Tersoff-Brenner potential to analyze the mechanical properties of graphene. The proposed AFEM method's efficiency and accuracy were demonstrated through a numerical example of a graphene sheet. When compared to MD simulation results, the new method showed very high accuracy. Additionally, its simulation speed was found to be approximately 100 times faster than that of the MD method. This study also investigated the influence of effective factors on simulation speed, such as the initial non-equilibrium bond length and the number of atoms. The AFEM was further developed to incorporate periodic boundary conditions, as these had not been previously considered for nanostructures using this method. The results showed that AFEM modeling without periodic boundary conditions produced results that were very different from those of MD simulations.

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.