BioMed Research International最新文献

Background: Cell-in-cell structures (CICs), a novel biomarker for complex cellular interactions, have garnered increasing attention for their potential in predicting cancer patient prognosis. However, the prognostic significance of CICs in tumor outcomes remains inconclusive. To address this, we conducted a meta-analysis to assess the prognostic value of CICs in solid tumors, adhering to the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) guidelines.

Methods: PubMed, Web of Science, and Cochrane Library databases were searched up to October 2024 for the retrieval of full articles. Studies related to the prognosis of cell-in-cell and solid tumors were considered eligible for analysis. The quality of the included studies was assessed according to the National Institute for Health and Clinical Excellence (NICE) Quality assessment tool.

Results: We included 1836 patients with solid tumors to evaluate the association between overall cell-in-cell structures (oCICs) and prognosis, and 429 patients to evaluate the association between four subtypes of CICs (tumor-in-tumor [TiT], tumor-in-macrophage [TiM], macrophage-in-tumor [MiT], and lymphocyte-in-tumor [LiT]) and prognosis. We present the hazard ratio (HR) for overall survival (OS) for the number of CICs for each solid tumor. The combined HR for OS of oCICs was 1.64 (95% CI, 1.18-2.28; p = 0.003), and for LiT, it was 1.43 (95% CI, 1.12-1.83; p = 0.005), indicating that both oCICs and LiT are reliable prognostic factors for solid tumors. However, the combined HRs for OS of TiT, TiM, and MiT were 0.72 (95% CI, 0.35-1.48; p = 0.37), 1.28 (95% CI, 0.67-2.45; p = 0.46), and 1.54 (95% CI, 0.93-2.56; p = 0.09), respectively, suggesting that these subtypes may not be reliable prognostic factors due to the limited number of studies.

Conclusion: The presence of higher numbers of oCICs and LiT is an adverse prognostic factor for patients and affects OS.

Introduction: Bloodstream infections (BSIs) are the presence of circulating microorganisms in the bloodstream. Globally, the distribution and factors that influence BSIs are changing, which is an alarming sign to investigate. In addition, prospective data are limited in Ethiopia. For these reasons, it is necessary to assess the BSI and associated factors.

Objectives: This study was aimed at assessing BSI and associated factors at the University of Gondar Comprehensive Specialized Hospital (UOGCSH), 2023.

Methods: A prospective cross-sectional study was conducted from August 2023 to December 2023 among 252 patients. The data was collected using consecutive sampling techniques, coded, and analyzed using SPSS Version 27. Multivariable binary logistic regression was used for variables with a p value of < 0.2 on bivariable binary logistic regression. Adjusted odds ratio (AOR) with 95% CI was used to report the strength of the association, and p value < 0.05 was used to declare a statistically significant association. The Hosmer and Lemeshow tests were used to confirm the goodness of fit of the model (p value, 0.734).

Results: A total of 228 participants were included in this study, with a mean age of 41 (±18) years. Overall, bacterial growth was detected on 41 (18%) of blood cultures. Of these, 15 (6.6%, 95% CI: 3.5-9.6) were true BSI, while the remaining 26 (11.4%) were contaminants. Klebsiella pneumoniae was the most commonly detected bacterium. Blood volume, stroke, and neutrophil-to-lymphocyte count ratio (NLCR) are significantly associated with BSI, whereas poor venipuncture antiseptic techniques and being febrile are significantly associated with contaminants.

Conclusion: Prevalence of true BSI is low, and collected blood volume, stroke, and high NLCR were associated with BSIs at UOGCSH. Training on blood sample collection, quality checks, and testing anaerobic bacteria and fungi is recommended.

Purpose: The purpose of this study is to analyze the academic production of ultra-processed foods and their relationship with neuropsychiatric disorders, assessing trends, collaboration patterns, and emerging thematic areas between 2019 and 2024.

Methods: The RAMIBS guidelines for scientometric studies were followed. The search was performed in Scopus using terms related to ultra-processed foods and neuropsychiatric disorders. Studies published between January 2019 and December 2024 were included, excluding letters to the editor and conference proceedings. Data were analyzed with SciVal and R Studio, using Bibliometrix to explore metrics such as Scholarly Output, SNIP 2023, CiteScore 2023, h-index, and international collaboration. Bradford's Law was applied to identify key journals.

Results: We identified 53 documents from 50 sources, with an annual growth of 24.57%. The average age of the documents was 2.98 years, with an average of 10.49 citations per publication. Brazil and the United States led in production with six articles each, while the international collaboration reached 18.87%. The most outstanding journals were "Nutrients" (SNIP 1.31, CiteScore 9.2) and "Preventive Medicine" (SNIP 1.37, CiteScore 7.7). Keyword analysis revealed a focus on the relationship between diet, obesity, and mental health. According to Bradford's Law, "Nutrients" led as the most relevant source.

Conclusions: The bibliometric data show a sustained growth in research on ultra-processed foods and neuropsychiatric disorders, with Brazil and the United States as the main contributors. The journal Nutrients played a key role as a source of dissemination. Although high thematic diversity was evident, international collaboration was limited, reflecting opportunities to strengthen global networks.

Purpose: Some challenges with current wound dressings include limited availability of advanced options and antimicrobial effectiveness. Polyurethane (PU) foams could be suitable choices as wound dressings once their disadvantages are resolved. PU wound dressings with antimicrobial and therapeutic properties are emerging as valuable options to prevent wound infection and improve the healing process. This study is aimed at developing a hydrophilic antimicrobial PU foam incorporated with N. sativa oil (NSO) for potential use as a wound dressing material.

Methods: In the formulation of PU foam, polyethylene glycol (PEG) and glycerine ethoxylate were utilized to improve the hydrophilicity. PU foams were subjected to detailed analysis using electron microscopy, FTIR, mechanical properties, liquid absorption, porosity measurement, cytocompatibility, in vitro antibacterial assay, and animal evaluation.

Results: FTIR confirmed the linkages of polyols and isocyanate in PU as well as the presence of NSO in the foam structure. The prepared foams had high porosity with homogeneous and interconnected pores. The addition of NSO to hydrophilic PU foam caused increases in tensile strength, elongation at break, and water absorption. The results of the storage experiment showed that NSO-PU foam remained stable under high humidity and temperature conditions. NSO-PU exhibited no significant toxicity on human dermal fibroblasts in the MTT assay. The presence of NSO also gave the foam antibacterial activity against Escherichia coli and Staphylococcus aureus. Histological study showed enhanced wound healing capability of NSO-PU. In NSO-PU, a thin epidermis composed of keratinocytes was observed at the wound site and the collagen deposited around the wounds treated with NSO-PU was organized and close to normal skin tissue.

Conclusion: These results indicate that this material can be used as a hydrophilic antibacterial wound dressing.

In this article, the researcher explores an automated approach for detecting a brain tumor using MRI scans of the brain. In underdeveloped countries, many people are dying due to the slow detection process and other negligence of radiologists. People suffer from these diseases due to the slow process of recognition. Since the number of patients is greater than that of radiologists, there is the possibility of human error, which can cause serious damage. The detection of tumors from magnetic resonance imaging (MRI) data is an important manual task, specifically in terms of the time that the radiologist performs. In this study, the researchers sought to study state-of-the-art techniques to detect normal brain and brain tumors from MRI using machine learning techniques. The main objective of this study is to develop a novel automated technique for brain tumor detection. Through the worldwide consideration of practical literature, it is clear that traditional approaches are insufficient to resolve all uncertainties and problems. Therefore, a novel approach to examining MRI must be adapted. This study proposes two different novel techniques: one that uses ensemble classification and the other that makes use of the deep learning model of YOLOv3. In ensemble classification, two classification algorithms are used which are support vector machine (SVM) and K-nearest neighbors (KNNs). The YOLOv3 model is used to detect and outline tumor locations in the images. This study used an open-source dataset and data collected from hospitals in Lahore, Pakistan. The ensemble classifier achieved an overall accuracy of 80.50%, while the YOLOv3 model achieved higher performance with 97.80% accuracy, 97.40% precision, 98.18% recall, and a mean intersection over union (IoU) score of 0.65. These results confirm that YOLOv3 is a useful technique for identifying brain tumors.

Introduction: This study aims to investigate computed tomography (CT) radiomic features and dosimetric-clinical biomarkers to predict vocal cord dysfunction (VCD) in nonlaryngeal head and neck cancer (HNC) patients treated with chemoradiation therapy (CRT), using machine learning (ML) models.

Methods: Sixty-five HNC patients who underwent CRT were recruited to assess radiation-induced VCD 6 months posttreatment. For each patient, CT radiomic features of the laryngeal region, clinical, and dose-volume histogram (DVH) metrics were collected to develop ML models. Nine classifiers were trained using selected features obtained from three feature selection algorithms: least absolute shrinkage and selection operator (LASSO), extra trees, and elastic net. The models were built using imaging features alone (radiomics model) and in combination with clinical and dosimetric features (combined model). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC).

Results: Of the 65 patients, 31 developed VCD. Among radiomics models, the AdaBoost and random forest (RF) classifiers performed best, with AUCs of 0.74 and 0.84, respectively. For the combined models, nine classifiers achieved an AUC greater than 0.95 using LASSO and elastic net algorithms. In contrast, only one classifier surpassed an AUC of 0.95 when using the extra trees algorithm.

Conclusion: Our findings demonstrate that pretreatment CT radiomic features are predictive biomarkers for radiation-induced toxicities, including VCD. Furthermore, combining radiomic features with clinical and dosimetric data can improve the predictive modeling of radiotherapy outcomes.

Soil-transmitted helminths (STHs) and schistosomiasis are widespread parasitic diseases in tropical regions, particularly in Africa, with substantial health and socioeconomic burdens. Early diagnosis and treatment are critical for mitigating these impacts. Conventional microscopy-based diagnosis was time-consuming and labor-intensive, posing challenges in resource-limited settings such as Ethiopia. This study developed an innovative system that combined machine learning (ML) and deep learning to analyze microscope images of parasite eggs, improving diagnostic speed and accuracy compared to traditional CNN-only approaches. We compared a hybrid CNN-ML approach with standalone deep learning models and vision transformers (ViTs) for classifying five categories: Ascaris, hookworm, schistosomiasis, Trichuris, and negative samples. The dataset comprised 1490 images from the Ethiopian Public Health Institute, processed with resizing, normalization, and augmentation. CNN architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, and EfficientNetB0) and ViT served as feature extractors, with ML classifiers (SVM, XGBoost, KNN, RF, and DT) performing the predictions. The hybrid CNN-ML model outperformed standalone models, with VGG16-SVM and VGG16-XGBoost achieving the highest test accuracy of 99.31% and 99.35%, respectively. In contrast, standalone CNNs showed lower accuracy (VGG16: 79.98%; DenseNet121: 84.12%). Negative samples were classified with high accuracy across models, while parasite classes exhibited varying performance depending on the architecture. This system enhances diagnostic utility in low-resource settings by enabling real-time analysis. However, limitations include a small, long-stored dataset with limited diversity and potential degradation, which may affect model generalizability.

Background: Infections remain a significant concern in kidney transplant recipients, affecting both graft and patient survival. Understanding the immune cell responses to various pathogens is essential for developing effective prevention and treatment strategies.

Objective: The objective was to analyze research trends in kidney transplantation infection literature and characterize differential immune cell responses to common posttransplant infections.

Methods: A comprehensive analysis of 4277 English language articles on kidney transplantation and infection from the Web of Science Core Collection was conducted. Research output, international collaboration, and keyword trends were analyzed. Immune cell responses to various infections in kidney transplant recipients were systematically evaluated.

Results: The United States (3845 articles), France (1819 articles), and China (1342 articles) were the leading contributors to research in this field. Key research clusters included immunosuppression management, viral infections, and treatment strategies. Most significantly, analysis of immune cell populations revealed distinct patterns of response to different infections. Cytomegalovirus infection increased CD3 + CD8 + midCD56+ NK-T cells and CD3 + CD8+ T cells, while BK polyomavirus reactivation decreased CD4+ and CD8+ T cells. Under immunosuppressive conditions, NK cell numbers were reduced. Kidney transplant infections directly caused decreases in CD4 + CD25+/CD4+ T cells, CD8 + CD25+/CD8+ T cells, and HLA-DR+ monocytes, reflecting differential immune modulation based on infection type.

Conclusion: Different pathogens elicit distinct immune cell responses in kidney transplant recipients, with some infections enhancing specific immune cell populations while others suppress them. These differential patterns of immune modulation reflect the complex interplay between immunosuppressive therapy and infectious agents. Understanding these specific immune responses provides valuable insights for developing targeted infection management strategies and improving monitoring protocols in transplant recipients.

Despite advances in cancer therapy, head and neck squamous cell carcinoma (HNSCC) remains a challenging malignancy with limited treatment options, prompting this investigation into curcumin's antitumor mechanisms through integrated network pharmacology, molecular docking, and in vitro experiments. Our comprehensive analysis identified 34 potential targets, with AKT1, EGFR, and STAT3 emerging as core targets primarily involved in regulating proliferation, apoptosis, and migration via the EGFR/STAT3 pathway. Experimental validation demonstrated curcumin's dose-dependent inhibition of viability, invasion, and migration in FaDu and CAL 27 cells, while promoting apoptosis and downregulating EGFR/STAT3 expression at both mRNA and protein levels-effects that were synergistically enhanced when combined with AG490 inhibitor. RNA-seq analysis further confirmed STAT pathway suppression as a key anticancer mechanism, collectively establishing curcumin's therapeutic potential through EGFR/STAT3 axis modulation. Overall, these preliminary network pharmacology and in vitro experimental results suggest that curcumin is a potential therapeutic agent for HNSCC and is worthy of further study. This study provides a certain theoretical basis for future clinical exploration.