A comprehensive review on phytochemicals in the treatment and prevention of pancreatic cancer: Focusing on their mechanism of action.

Background and Aims: Pancreatic cancer develops in the normal tissues of the pancreas from malignant cells. The chance of recovery is not good, and the chance of survival 5 years following diagnosis is quite low. Pancreatic cancer treatment strategies such as radiotherapy and chemotherapy had relatively low success rates. Therefore, the present study aims to explore new therapies for treating pancreatic cancer. Methods: The present study searched for information about pancreatic cancer pathophysiology, available treatment options; and their comparative benefits and challenges. Aiming to identify potential alternative therapeutics, this comprehensive review analyzed information from renowned databases such as Scopus, PubMed, and Google Scholar. Results: In recent years, there has been a rise in interest in the possibility that natural compounds could be used as treatments for cancer. Cannabinoids, curcumin, quercetin, resveratrol, and triptolide are some of the anticancer phytochemicals now used to manage pancreatic cancer. The above compounds are utilized by inhibiting or stimulating biological pathways such as apoptosis, autophagy, cell growth inhibition or reduction, oxidative stress, epithelial-mesenchymal transformation, and increased resistance to chemotherapeutic drugs in the management of pancreatic cancer. Conclusion: Right now, surgery is the only therapeutic option for patients with pancreatic cancer. However, most people who get sick have been diagnosed too late to benefit from potentially effective surgery. Alternative medications, like natural compounds and herbal medicines, are promising complementary therapies for pancreatic cancer. Therefore, we recommend large-scale standardized clinical research for the investigation of natural compounds to ensure their consistency and comparability in pancreatic cancer treatment.

Effect of gamma (γ-) radiation on the opto-structural and morphological properties of green synthesized BaO nanoparticles using Moringa Oleifera leaves.

In this current assessment, BaO synthesized from Moringa Oleifera leaves were irradiated using 0-75 kGy gamma radiation and investigated its physical impacts. The x-ray diffraction (XRD) data demonstrated the synthesis of tetragonal BaO, and no phase deviation was observed after irradiation. As doses are increased, the overall crystallite size were decreased due to an increase in defects and disorders. The tetragonal BaO was evident in Fourier transform infrared (FTIR) spectra prior to and following irradiation, while peak intensities and wavenumbers varied considerably. The as-prepared BaO showed a spherical shape morphology, and Field emission scanning electron microscopy (FESEM) indicated no vital deviations in it after irradiation. As irradiation shifts from 0 to 75 kGy, optical bandgap was increased from 4.55 to 4.93 eV, evaluated using Kubelka-Munk (K-M) equation from UV-vis-NIR spectrophotometer. Opto-electronic and photonic devices have challenges in extreme radiation conditions, such as space and nuclear environments. So, these assessments suggested that BaO can withstand high levels of gamma photon and could be a good option for photonic and optoelectronic instruments in an extreme gamma-ray exposed conditions.

Altered IL-3 and lipocalin-2 levels are associated with the pathophysiology of major depressive disorder: a case-control study.

BACKGROUND: Major Depressive Disorder (MDD) is a common mental ailment and is the primary reason for disability. It manifests a severe impact on moods, thoughts, and physical health. At present, this disorder has become a concern in the field of public health. Alteration of neurochemicals is thought to be involved in the pathogenesis of many psychiatric disorders. Therefore, we aimed to evaluate serum IL-3 and lipocalin-2 in MDD patients and healthy controls (HCs). METHOD: We included a total of 376 participants in this study. Among them, 196 were MDD patients, and 180 were age-sex-matched HCs. MDD patients were recruited from the Psychiatry Department of Bangabandhu Sheikh Mujib Medical University (BSMMU), but the controls were from different parts of Dhaka. All study participants were evaluated by a psychiatrist using the DSM-5 criteria. To assess the severity of the depression, we used the Hamilton depression (Ham-D) rating scale. Serum IL-3 and lipocalin-2 levels were measured using commercially available enzyme-linked immune-sorbent assay kits (ELISA kits). RESULTS: According to this study, we observed elevated serum levels of IL-3 (1,024.73 ± 29.84 pg/mL) and reduced levels of serum lipocalin-2 (29.019 ± 2.073 ng/mL) in MDD patients compared to HCs (911.11 ± 20.55 pg/mL and 48.065 ± 3.583 ng/mL, respectively). No associations between serum levels of IL-3 and lipocalin-2 and depression severity were observed in patients. CONCLUSIONS: According to the present findings, alterations of serum IL-3 and lipocalin might be associated with the pathogenesis of MDD. These results support that altered serum neurochemicals can serve as early risk assessment markers for depression. Further interventional studies are recommended for a better understanding of the role of IL-3 and lipocalin-2 in the pathophysiology of depression.

Altered serum TNF-α and MCP-4 levels are associated with the pathophysiology of major depressive disorder: A case-control study results.

BACKGROUND: Major Depressive Disorder (MDD) is a debilitating mental health condition with complex etiology, and recent research has focused on pro-inflammatory cytokines and chemokines as potential contributors to its pathogenesis. However, studies investigating the roles of TNF-α and MCP-4 in MDD within the Bangladeshi population are scarce. This study aimed to assess the association between serum TNF-α and MCP-4 levels and the severity of MDD, exploring their potential as risk indicators for MDD development. METHODS: This case-control study enrolled 58 MDD patients from Bangabandhu Sheikh Mujib Medical University (BSMMU) Hospital, Dhaka, Bangladesh, alongside 30 age, sex, and BMI-matched healthy controls. MDD diagnosis followed DSM-5 criteria and disease severity using the 17-item Hamilton Depression Rating Scale (Ham-D). We measured serum TNF-α and MCP-4 levels using ELISA assays according to the supplied protocols. RESULTS: The study revealed significantly elevated serum TNF-α levels in MDD patients (47±6.6 pg/ml, mean±SEM) compared to controls (28.06±1.07 pg/ml). These increased TNF-α levels positively correlated with Ham-D scores (Pearson's r = 0.300, p = 0.038), suggesting a potential association between peripheral TNF-α levels and MDD pathology. Additionally, MDD patients exhibited significantly higher serum MCP-4 levels (70.49±6.45 pg/ml) than controls (40.21±4.08 pg/ml). However, serum MCP-4 levels showed a significant negative correlation (r = -0.270, P = 0.048) with Ham-D scores in MDD patients, indicating a more complex role for MCP-4 in MDD pathogenesis. CONCLUSION: This study highlights that Bangladeshi MDD patients exhibit heightened inflammatory and immune responses compared to controls, supporting the cytokine hypothesis in MDD pathogenesis. Serum TNF-α, but not MCP-4, shows promise as a potential biomarker for assessing the risk of MDD development, which could aid in early detection. Future investigations involving larger populations and longitudinal studies are essential to confirm the utility of these cytokines as biomarkers for MDD.

A review on brain tumor segmentation based on deep learning methods with federated learning techniques.

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues.

Association of granulocyte macrophage colony-stimulating factor and interleukin-17 levels with obsessive-compulsive disorder: a case-control study findings.

Obsessive-compulsive disorder (OCD) is a mental condition that affects many people and is characterized by recurring obsessions and compulsions. It significantly impacts individuals' ability to function ordinarily daily, affecting people of all ages. This study aimed to investigate whether or not the cytokines granulocyte macrophage colony-stimulating factor (GM-CSF) and interleukin-17 (IL-17) are involved in the pathophysiology of OCD. A case-control study with 50 OCD patients and 38 healthy volunteers served as the controls for this investigation. The levels of GM-CSF and IL-17 in the serum of both groups were measured with enzyme-linked immunosorbent assay (ELISA) kits. In addition, the sociodemographic characteristics of the population under study were studied. Based on the findings of this study, OCD patients had significantly elevated levels of IL-17 than the controls, it appears that there may be a function for IL-17 in the pathophysiology of OCD. It was also discovered that the severity of OCD and IL-17 levels had a significant positive correlation. On the other hand, when comparing the levels of GM-CSF, there was no significant difference between the patients and the controls. This study provides evidence supporting the involvement of cytokine IL-17 in the pathophysiology of OCD. This study suggests IL-17 as a diagnostic biomarker for OCD and adds to our knowledge of the function that the immune system plays in this condition.

Risk Evaluation and Mitigation Strategies for Potential Outbreaks of Adenovirus Infection: Evidence From the Recent Incidences in West Bengal, India.

Recent outbreaks of highly virulent and pathogenic viruses such as COVID-19, monkeypox, and Nipah virus have prompted global concerns. Another threat has emerged in West Bengal, India, in the form of Human Adenovirus (HAdV), particularly affecting children and immunocompromised individuals. The DNA virus HAdV can cause respiratory, liver, renal, and neurological issues. Politically unstable areas with military and medical camps and refugee communities are at risk because they spread in densely populated areas. Due to its rapid mutation and dissemination, the virus represents a global threat. Although scientists have developed vaccines for specific serotypes of HAdV, their primary application is limited to military contexts. Antiviral and immunotherapy research is continuing, but treatment choices are limited. Public awareness programs and hygiene measures are essential to preventing a global pandemic. Governments should invest in healthcare infrastructure and diagnostics, and researchers should focus on developing vaccines and therapies. The West Bengal outbreak is a clear reminder that governments, healthcare professionals, and researchers must work together to control and prevent HAdV. To effectively comprehend and address this rising viral threat, it is imperative to engage in further research and documentation.

Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification.

The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals.

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques.

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

Evaluation of inflammatory cytokines in drug-naïve major depressive disorder: A systematic review and meta-analysis.

Objective: Altered levels of peripheral inflammatory and proinflammatory cytokine markers affect the different clinical stages of major depressive disorder (MDD). A concrete understanding of the causal mechanism of MDD is a prerequisite in developing treatment strategies and preventive plans. Here we aimed to conduct an updated systematic review and meta-analysis of studies assessing the association of C-reactive protein (CRP), INF-γ, MCP-1, and TNF-α in the peripheral fluid of drug-naïve MDD patients and healthy controls (HCs). Methods: We extracted articles from PubMed, ProQuest, PsycINFO, Web of Science, and Scopus databases from inception until 14 February 2021, to find relevant studies. In this meta-analysis, we included a total of 23 eligible studies (1,366 MDD patients and 1,342 controls) in the final meta-analysis. The Cochran's chi-square Q-test and I2-index were applied to measure the heterogeneity and inconsistency of all combined results. We selected a random-effect model during the analysis and measured publication biases using the funnel plot. We performed Bonferroni adjustment for multiple testing. Results: We found a high level of TNF-α in MDD patients than in control subjects Standardized Mean Difference (SMD) with a random-effects model: 1.04, 95% CI: 0.69-1.39, z = 5.84, p < 0.001). The levels of CRP (SMD with a random-effects model: 0.18, 95% CI: -0.85-1.23, z = 0.35, p = 0.73), INF-ɤ (SMD with a random-effects model: -0.05, 95% CI: -2.72-2.62, z = 0.03, p = 0.97), and MCP-1 (SMD with a random-effects model: 0.70, 95% CI: -0.09-1.49, z = 1.73, p = 0.08) were not significantly varies between MDD patients and HCs. Conclusion: The present study findings suggest the upregulated level of peripheral TNF-α but not CRP, INF-γ, and MCP-1 involve in depression. The elevated inflammatory cytokines confirmed the inflammatory state of depression. Therefore, inflammatory cytokines might serve as potential risk assessment markers in MDD.

Evaluation of the Association between FGFR2 Gene Polymorphisms and Breast Cancer Risk in the Bangladeshi Population.

Breast cancer is considered the most frequent cause of mortality from malignancy among females. Fibroblast growth factor receptor 2 (FGFR2) gene polymorphisms are highly related to the risk of breast cancer. However, no investigation has been carried out to determine the association of FGFR2 gene polymorphisms in the Bangladeshi population. Based on polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), this study was performed to evaluate the association of FGFR2 (rs1219648, rs2420946, and rs2981582) variants in 446 Bangladeshi women (226 cases and 220 controls). A significant association of the FGFR2 rs1219648 variant with breast malignancy was reported in additive model 1 (aOR = 2.87, p < 0.0001), additive model 2 (aOR = 5.62, p < 0.0001), the dominant model (aOR = 2.87, p < 0.0001), the recessive model (aOR = 4.04, p < 0.0001), and the allelic model (OR = 2.16, p < 0.0001). This investigation also explored the significant association of the rs2981582 variant with the risk of breast cancer in additive model 2 (aOR = 2. 60, p = 0.010), the recessive model (aOR = 2.47, p = 0.006), and the allelic model (OR = 1.39, p = 0.016). However, the FGFR2 rs2420946 polymorphism showed no association with breast cancer except in the overdominant model (aOR = 0.62, p = 0.048). Furthermore, GTT (p < 0.0001) haplotypes showed a correlation with breast cancer risk, and all variants showed strong linkage disequilibrium. Moreover, in silico gene expression analysis showed that the FGFR2 level was upregulated in BC tissues compared to healthy tissues. This study confirms the association of FGFR2 polymorphisms with breast cancer risk.

Antioxidant and antimicrobial potential of water lily extracts and their effects on the quality of frozen Nile tilapia (Oreochromis niloticus) fillets.

This study was investigated to evaluate the antioxidant and antimicrobial potential of water lily extracts and their effects on the quality of Nile tilapia (Oreochromis niloticus) fillets during frozen storage (-18 ± 1°C). Antioxidant and antimicrobial activities of water lily extracts, and chemical, microbiological, and sensory qualities of fish fillets were assessed. Results showed that the highest total phenolic content (34.07 mg GAE/g) and total flavonoid content (32.67 mg QE/g extract) were found in the ethanolic extract and the lowest in water extract of water lily. The ethanolic extracts of water lily also exhibited the highest antioxidant capacities and antimicrobial activities than other hydroethanolic and water extracts. The water lily extracts-treated fish fillets showed the highest potentiality in lowering the pH, total volatile basic nitrogen, and thiobarbituric acid reactive substances than the untreated fillets throughout the storage period. Moreover, ethanolic extracts of water lily exhibited comparatively higher efficacy in inhibiting bacterial growth in fish fillets than other extracts-treated fillets. The ethanolic extracts-treated fillets also showed better sensory attributes than hydroethanolic and control fillets. Therefore, ethanolic extract of water lily can be used as a natural preservative in enhancing the quality and prolonging the shelf life of Nile tilapia fillets during frozen storage.