Search Results (2,483)

Cancer is a significant health challenge globally, with millions of people affected every year, resulting in high morbidity and mortality. Although other treatment options are available with limitations, chemotherapy, either standalone or combined with other therapeutic procedures, is the most commonly used practice of treating cancer. In chemotherapy, cancer cells/malignant tumors are targeted; however, due to less target specificity, along with malignant cells, normal cells are also affected, which leads to various off-target effects (side effects) that impact the patient quality of life. Out of all the different types of cancers, breast cancer is the most common type of cancer in humans worldwide. Current anticancer drug discovery research aims to develop therapeutics with higher potency and lower toxicity, which is only possible through target-specific therapy. Antibody–drug conjugates (ADCs) are explicitly designed to target malignant tumors and minimize off-target effects by reducing systemic cytotoxicity. Several ADCs have been approved for clinical use and have shown moderate to good efficacy so far. Considering various aspects, chemotherapy and ADCs are useful in treating cancer. However, ADCs provide a more focused and less toxic approach, which is especially helpful in cases where resistance to chemotherapy (drug resistance) occurs and in the type of malignancies in which specific antigens are overexpressed. Ongoing ADC research aims to develop more target-specific cancer treatments. In short, this study presents a concise overview of ADCs specific to breast cancer treatment. This study provides insight into the classifications, mechanisms of action, structural aspects, and clinical trial phases (current status) of these chemo-biologic drugs (ADCs). Full article

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup. Full article

Background/Objectives: This study aims to evaluate the possible clinical application of interleukin 8 (IL-8) as a single biomarker and its capabilities in combination with carbohydrate antigen (CA19-9), carcinoembryonic antigen (CEA), and carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) as diagnostic and prognostic tools for pancreatic ductal adenocarcinoma (PDAC). Methods: A total of 170 serum samples from patients with PDAC (n = 100), chronic pancreatitis (CP) (n = 39), and healthy individuals (n = 31) were analysed. IL-8 and CEACAM6 were measured by an enzyme-linked immunosorbent assay (ELISA). CA19-9 and CEA were determined by chemiluminescent microparticle immunoassay, and bilirubin was quantified using a diazonium salt reaction. Receiver operating characteristic curve analysis, logistic regression, and Kaplan–Meier analyses were performed to evaluate the properties of a single IL-8 and in combination with other biomarkers. Results: The concentrations of IL-8 were statistically significantly higher in the PDAC group compared to the CP and control groups. Heterogeneous levels of IL-8 correlated with PDAC stages (p = 0.007). IL-8 had good and satisfactory diagnostic efficacy in differentiating PDAC from controls (0.858; p < 0.001) and patients with CP (0.696; p < 0.001), respectively. High and low expressions of IL-8 were not significantly associated with overall survival (OS) or disease-free survival (DFS). A combination of IL-8, CEACAM6, and CA19-9 reached the highest AUC values for differentiating PDAC from the control group. The best classification score between PDAC and the control group with CP patients was obtained by merging IL-8 and CA19-9 (0.894; p < 0.001). Conclusions: These results provide compelling evidence of IL-8 as a promising diagnostic biomarker. Nonetheless, due to the high complexity of PDAC, only the conjunction of IL-8, CA19-9, and CEACAM6 integrates sufficient diagnostic capabilities. Full article

This study leverages a Random Forest model to predict flood hazard in Arizona, New Mexico, Colorado, and Utah, focusing on enhancing sustainability in flood management. Utilizing the National Flood Hazard Layer (NFHL), an intricate flood map of Arizona was generated, with the Random Forest Classification algorithm assessing flood hazard for each grid cell. Weather variable predictions from TerraClimate were integrated with NFHL classifications and Digital Elevation Model (DEM) analyses, providing a comprehensive understanding of flood dynamics. The research highlights the model’s capability to predict flood hazard in areas lacking NFHL classifications, thereby supporting sustainable flood management by elucidating weather’s influence on flood hazard. This approach aligns with sustainable development goals by aiding in resilient infrastructure design and informed urban planning, reducing the impact of floods on communities. Despite recognizing constraints such as input data precision and the model’s potential limitations in capturing complex variable interactions, the methodology offers a robust framework for flood hazard evaluation in other regions. Integrating diverse data sources, this study presents a valuable tool for decision-makers, supporting sustainable practices, and enhancing the resilience of vulnerable regions against flood hazards. This integrated approach underscores the potential of advanced modeling techniques in promoting sustainability in environmental hazard management. Full article

Patients with systemic lupus erythematosus (SLE) are known to frequently suffer from comorbid cardiovascular diseases (CVDs). There are abundant data on cytokine levels and their role in the pathogenesis of SLE, while growth factors have received much less attention. The aim of this study was to analyze growth factor levels in SLE patients and their association with the presence of comorbid CVDs. The serum concentrations for the granulocyte-macrophage colony-stimulating factor (GM-CSF), nerve growth factor β (NGFβ), glial cell line-derived neurotrophic factor (GDNF), and neuregulin-1 β (NRG-1β) were determined in the SLE patients (n = 35) and healthy individuals (n = 38) by a Luminex multiplex assay. The NGFβ and NRG-1β concentrations were shown to be significantly higher in the total group of SLE patients (median [Q1–Q3]: 3.6 [1.3–4.5] and 52.5 [8.5–148], respectively) compared with the healthy individuals (2.9 [1.3–3.4] and 13.7 [4.4–42] ng/mL, respectively). The GM-CSF and GDNF levels did not differ. Interestingly, elevated NRG-1β levels were associated with the presence of CVDs, as SLE patients with CVDs had significantly higher NRG-1β levels (99 [22–242]) compared with the controls (13.7 [4.4–42]) and patients without CVDs (19 [9–80] ng/mL). The model for the binary classification of SLE patients with and without CVDs based on the NRG-1β level had an average predictive ability (AUC = 0.67). Thus, altered levels of growth factors may be associated with comorbid CVDs in SLE patients. Full article

Pancreatic neuroendocrine tumors (NETs) are rare well-differentiated neoplasms with limited therapeutic options and unknown cells of origin. The current classification of pancreatic neuroendocrine tumors is based on proliferative grading, and guides therapeutic strategies, however, tumors within grades exhibit profound heterogeneity in clinical manifestation and outcome. Manifold studies have highlighted intra-patient differences in tumors at the genetic and transcriptomic levels. Molecular classification might become an alternative or complementary basis for treatment decisions and reflect tumor biology, actionable cellular processes. Here, we provide a comprehensive review of genomic, transcriptomic, proteomic and epigenomic studies of pancreatic NETs to elucidate patterns shared between proposed subtypes that could form a foundation for new classification. We denote four NET subtypes with distinct molecular features, which were consistently reproduced using various omics technologies. Full article

Although mesenchymal stem cells (MSCs) insertion has gained recent attention as a joint-preserving procedure, no study has conducted direct intralesional implantation of human umbilical cord-derived MSCs (hUCMSCs) in patients with ONFH. This is a protocol for a phase 1 clinical trial designed to assess the safety and exploratory efficacy of human umbilical cord-derived osteoblasts (hUC-Os), osteogenic differentiation-induced cells from hUCMSCs, in patients with early-stage ONFH. Nine patients with Association Research Circulation Osseous (ARCO) stage 1 or 2 will be assigned to a low-dose (1 × 10⁷ hUC-O cells, n = 3), medium-dose (2 × 10⁷ cells, n = 3), and high-dose group (4 × 10⁷ cells, n = 3) in the order of their arrival at the facility, and, depending on the occurrence of dose-limiting toxicity, up to 18 patients can be enrolled by applying the 3 + 3 escalation method. We will perform hUC-O (CF-M801) transplantation combined with core decompression and follow-up for 12 weeks according to the study protocol. Safety will be determined through adverse event assessment, laboratory tests including a panel reactive antibody test, vital sign assessment, physical examination, and electrocardiogram. Efficacy will be explored through the change in pain visual analog scale, Harris hip score, Western Ontario and McMaster Universities Osteoarthritis Index, ARCO stage, and also size and location of necrotic lesion according to Japanese Investigation Committee classification before and after the procedure. Joint preservation is important, particularly in younger, active patients with ONFH. Confirmation of the safety and efficacy of hUC-Os will lead to a further strategy to preserve joints for those suffering from ONFH and improve our current knowledge of cell therapy. Full article

Phospholipids are the main building components of cell membranes and are also used for cell signaling and as energy storages. Cancer cells alter their lipid metabolism, which ultimately leads to an increase in phospholipids in cancer tissue. Surgical energy instruments use electrical or vibrational energy to heat tissues, which causes intra- and extracellular water to expand rapidly and degrade cell structures, bursting the cells, which causes the formation of a tissue aerosol or smoke depending on the amount of energy used. This gas phase analyte can then be analyzed via gas analysis methods. Differential mobility spectrometry (DMS) is a method that can be used to differentiate malignant tissue from benign tissues in real time via the analysis of surgical smoke produced by energy instruments. Previously, the DMS identification of cancer tissue was based on a ‘black box method’ by differentiating the 2D dispersion plots of samples. This study sets out to find datapoints from the DMS dispersion plots that represent relevant target molecules. We studied the ability of DMS to differentiate three subclasses of phospholipids (phosphatidylcholine, phosphatidylinositol, and phosphatidylethanolamine) from a control sample using a bovine skeletal muscle matrix with a 5 mg addition of each phospholipid subclass to the sample matrix. We trained binary classifiers using linear discriminant analysis (LDA) and support vector machines (SVM) for sample classification. We were able to identify phosphatidylcholine, -inositol, and -ethanolamine with SVM binary classification accuracies of 91%, 73%, and 66% and with LDA binary classification accuracies of 82%, 74%, and 72%, respectively. Phosphatidylcholine was detected with a reliable classification accuracy, but ion separation setups should be adjusted in future studies to reliably detect other relevant phospholipids such as phosphatidylinositol and phosphatidylethanolamine and improve DMS as a microanalysis method and identify other phospholipids relevant to cancer tissue. Full article

Background: In recent years, the healthcare field has experienced significant advancements. New diagnostic techniques, treatments, and insights into the causes of various diseases have emerged. Despite these progressions, cancer remains a major concern. It is a widespread illness affecting individuals of all ages and leads to one out of every six deaths. Lung and colon cancer alone account for nearly two million fatalities. Though it is rare for lung and colon cancers to co-occur, the spread of cancer cells between these two areas—known as metastasis—is notably high. Early detection of cancer greatly increases survival rates. Currently, histopathological image (HI) diagnosis and appropriate treatment are key methods for reducing cancer mortality and enhancing survival rates. Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. Methods: Therefore, this paper proposes a refined DL model that integrates feature fusion for the multi-classification of lung and colon cancers. The proposed model incorporates three DL architectures: ResNet-101V2, NASNetMobile, and EfficientNet-B0. Each model has limitations concerning variations in the shape and texture of input images. To address this, the proposed model utilizes a concatenate layer to merge the pre-trained individual feature vectors from ResNet-101V2, NASNetMobile, and EfficientNet-B0 into a single feature vector, which is then fine-tuned. As a result, the proposed DL model achieves high success in multi-classification by leveraging the strengths of all three models to enhance overall accuracy. This model aims to assist pathologists in the early detection of lung and colon cancer with reduced effort, time, and cost. The proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs. The dataset was pre-processed using resizing and normalization techniques. Results: The model was tested and compared with recent DL models, achieving impressive results: 99.8% for precision, 99.8% for recall, 99.8% for F1-score, 99.96% for specificity, and 99.94% for accuracy. Conclusions: Thus, the proposed DL model demonstrates exceptional performance across all classification categories. Full article

The elasticity of red blood cells (RBCs) is crucial for their ability to fulfill their role in the blood. Decreased RBC deformability is associated with various pathological conditions. This study explores the application of machine learning to predict the elasticity of RBCs using both image data and detailed physical measurements derived from simulations. We simulated RBC behavior in a microfluidic channel. The simulation results provided the basis for generating data on which we applied machine learning techniques. We analyzed the surface-area-to-volume ratio of RBCs as an indicator of elasticity, employing statistical methods to differentiate between healthy and diseased RBCs. The Kolmogorov–Smirnov test confirmed significant differences between healthy and diseased RBCs, though distinctions among different types of diseased RBCs were less clear. We used decision tree models, including random forests and gradient boosting, to classify RBC elasticity based on predictors derived from simulation data. The comparison of the results with our previous work on deep neural networks shows improved classification accuracy in some scenarios. The study highlights the potential of machine learning to automate and enhance the analysis of RBC elasticity, with implications for clinical diagnostics. Full article

Autoimmune polyglandular syndrome (APS) comprises a complex association of autoimmune pathological conditions. APS Type 1 originates from loss-of-function mutations in the autoimmune regulator (AIRE) gene. APS2, APS3 and APS4 are linked to specific HLA alleles within the major histocompatibility complex, with single-nucleotide polymorphisms (SNPs) in non-HLA genes also contributing to disease. In general, variability in the AIRE locus and the presence of heterozygous loss-of-function mutations can impact self-antigen presentation in the thymus. In this study, whole-exome sequencing (WES) was performed on a sixteen-year-old female APS3A/B patient to investigate the genetic basis of her complex phenotype. The analysis identified two variants (p.Arg111Trp and p.Thr101Ile) of the hepatitis A virus cell receptor 2 gene (HAVCR2) encoding for the TIM-3 (T cell immunoglobulin and mucin domain 3) protein. These variants were predicted, through in silico analysis, to impact protein structure and stability, potentially influencing the patient’s autoimmune phenotype. While confocal microscopy analysis revealed no alteration in TIM-3 fluorescence intensity between the PBMCs isolated from the patient and those of a healthy donor, RT-qPCR showed reduced TIM-3 expression in the patient’s unfractionated PBMCs. A screening conducted on a cohort of thirty APS patients indicated that the p.Thr101Ile and p.Arg111Trp mutations were unique to the proband. This study opens the pathway for the search of TIM-3 variants possibly linked to complex autoimmune phenotypes, highlighting the potential of novel variant discovery in contributing to APS classification and diagnosis. Full article

Alzheimer’s disease is a neurodegenerative condition primarily attributed to environmental factors, abnormal protein deposits, immune system dysregulation, and the consequential death of nerve cells in the brain. On the other hand, Parkinson’s disease manifests as a neurological disorder featuring primary motor, secondary motor, and non-motor symptoms, accompanied by the rapid demise of cells in the brain’s dopamine-producing region. Utilizing brain images for accurate diagnosis and treatment is integral to addressing both conditions. This study harnessed the power of artificial intelligence for classification processes, employing state-of-the-art transformer models such as Swin transformer, vision transformer (ViT), and bidirectional encoder representation from image transformers (BEiT). The investigation utilized an open-source dataset comprising 450 images, evenly distributed among healthy, Alzheimer’s, and Parkinson’s classes. The dataset was meticulously divided, with 80% allocated to the training set (390 images) and 20% to the validation set (90 images). Impressively, the classification accuracy surpassed 80%, showcasing the efficacy of transformer-based models in disease detection. Looking ahead, this study recommends delving into hybrid and ensemble models and leveraging the strengths of multiple transformer-based deep learning architectures. Beyond contributing crucial insights at the intersection of artificial intelligence and neurology, this research emphasizes the transformative potential of advanced models for enhancing diagnostic precision and treatment strategies in Alzheimer’s and Parkinson’s diseases. It signifies a significant step towards integrating cutting-edge technology into mainstream medical practices for improved patient outcomes. Full article

Objective: The safety, feasibility, and potential functional improvement following the intravenous infusion of mesenchymal stem cells (MSCs) were investigated in patients with chronic severe spinal cord injury (SCI). Methods: The intravenous infusion of autologous MSCs cultured in auto-serum under Good Manufacturing Practices (GMP) was administered to seven patients with chronic SCI (ranging from 1.3 years to 27 years after the onset of SCI). In addition to evaluating feasibility and safety, neurological function was evaluated using the American Spinal Injury Association Impairment Scale (AIS), International Standards for Neurological Classification of Spinal Cord Injury (ISCSCI-92), and Spinal Cord Independence Measure III (SCIM-III). Results: No serious adverse events occurred. Neither CNS tumors, abnormal cell growth, nor neurological deterioration occurred in any patients. While this initial case series was not blinded, significant functional improvements and increased quality of life (QOL) were observed at 90 and 180 days post-MSC infusion compared to pre-infusion status. One patient who had an AIS grade C improved to grade D within six months after MSC infusion. Conclusions: This case series suggests that the intravenous infusion of autologous MSCs is a safe and feasible therapeutic approach for chronic SCI patients. Furthermore, our data showed significant functional improvements and better QOL after MSC infusion in patients with chronic SCI. A blind large-scale study will be necessary to fully evaluate this possibility. Full article

Information sharing on social media has become a common practice for people around the world. Since it is difficult to check user-generated content on social media, huge amounts of rumors and misinformation are being spread with authentic information. On the one hand, most of the social platforms identify rumors through manual fact-checking, which is very inefficient. On the other hand, with an emerging form of misinformation that contains inconsistent image–text pairs, it would be beneficial if we could compare the meaning of multimodal content within the same post for detecting image–text inconsistency. In this paper, we propose a novel approach to misinformation detection by multimodal feature fusion with transformers and credibility assessment with self-attention-based Bi-RNN networks. Firstly, captions are derived from images using an image captioning module to obtain their semantic descriptions. These are compared with surrounding text by fine-tuning transformers for consistency check in semantics. Then, to further aggregate sentiment features into text representation, we fine-tune a separate transformer for text sentiment classification, where the output is concatenated to augment text embeddings. Finally, Multi-Cell Bi-GRUs with self-attention are used to train the credibility assessment model for misinformation detection. From the experimental results on tweets, the best performance with an accuracy of 0.904 and an F1-score of 0.921 can be obtained when applying feature fusion of augmented embeddings with sentiment classification results. This shows the potential of the innovative way of applying transformers in our proposed approach to misinformation detection. Further investigation is needed to validate the performance on various types of multimodal discrepancies. Full article

Scorpion toxins are relatively short cyclic peptides (<150 amino acids) that can disrupt the opening/closing mechanisms in cell ion channels. These peptides are widely studied for several reasons including their use in drug discovery. Although improvements in RNAseq have greatly expedited the discovery of new scorpion toxins, their annotation remains challenging, mainly due to their small size. Here, we present a new pipeline to annotate toxins from scorpion transcriptomes using a neural network approach. This pipeline implements basic neural networks to sort amino acid sequences to find those that are likely toxins and thereafter predict the type of toxin represented by the sequence. We anticipate that this pipeline will accelerate the classification of scorpion toxins in forthcoming scorpion genome sequencing projects and potentially serve a useful role in identifying targets for drug development. Full article