Search Results (9)

Colorectal cancer (CRC) has a high mortality rate worldwide. Oral and intestinal microbiota members may have an effect on gastrointestinal tumors’ pathogenesis, particularly in CRC. Designed as a pilot study, this study’s aim was to investigate the relationship between CRC and oral microbiota and to identify potential biomarkers for CRC diagnosis. Saliva samples were collected from recently diagnosed CRC patients (n = 14) and healthy controls (n = 14) between March 2023 and December 2023. Microbiota (16S rRNA) analyses were conducted on these saliva samples using a next-generation sequencing method. Phylogenetic analyses, including alpha diversity, principal component analysis (PCA), principal coordinate analysis (PCoA), beta diversity, biomarker, and phenotype analyses, were conducted using the Qiime2 (Quantitative Insights Into Microbial Ecology) platform. Alpha diversity indices (Shannon: p = 0.78, Cho1: p = 0.28, Simpson: p = 0.81) showed no significant difference between CRC and control groups. Beta diversity analysis using Bray–Curtis PCoA indicated significant differences in the microbial community between the two groups (p = 0.003). Examination of OTU distributions revealed that the Mycoplasmatota phylum was undetectable in the oral microbiota of healthy controls but was significantly elevated in CRC patients (CRC: 0.13 ± 0.30, Control: 0.00 ± 0.00, p < 0.05). Additionally, Metamycoplasma salivarium, Bacteroides intestinalis, and Pseudoprevotella muciniphila were undetectable in healthy controls but significantly more prevalent in CRC patients (p < 0.05 for all three species). LEfSe analysis identified eight species with an LDA score > 2, Granulicatella adiacens, Streptococcus thermophilus, Streptococcus gwangjuense, Capnocytophaga sp. FDAARGOS_737, Capnocytophaga gingivalis, Granulicatella elegans, Bacteroides intestinalis, and Pseudoprevotella muciniphila, as potential biomarkers. The results of this study contribute critical evidence of the role of oral microbiota in the pathogenesis of colorectal cancer. Alterations in the microbiota suggest potential biomarkers in understanding the biological mechanisms underlying CRC and developing diagnostic and therapeutic strategies. Full article

Urticaria is a skin disorder characterized by erythematous, edematous, and pruritic lesions. Intestinal microorganisms can trigger various immunological responses, and Blastocystis has been suggested to affect gut-associated lymphoid tissue homeostasis and induce allergic reactions. This study aimed to evaluate the effect of Blastocystis on the intestinal microbiota in patients with urticaria. A total of 33 patients diagnosed with urticaria and 34 healthy controls were included. Independent sample t-tests, Welch’s t-tests, or Mann–Whitney U tests were applied to assess differences in the Shannon, Simpson, and Chao-1 indices between groups. Significant differences were observed in Proteobacteria (p = 0.015), Bacteroidetes (p = 0.008), Escherichia (p = 0.005), Phocaelcola (p = 0.043), and Prevotella (p = 0.047) between the urticaria and control groups. Bacteroidetes (p = 0.003) and Phocaelcola (p = 0.032) also differed significantly between samples with and without Blastocystis. Overall microbiota composition showed a significant difference between Blastocystis-positive and -negative samples (p = 0.009). The Firmicutes/Bacteroidetes ratio was 4.1 in healthy controls and 6.4 in urticaria patients. In conclusion, both urticaria and Blastocystis infection significantly influence intestinal microbiota composition, suggesting a potential interaction between Blastocystis colonization and host immune regulation in urticaria. Full article

Background/Objectives: The neonatal and infant periods represent a critical window for brain development, characterized by rapid and heterogeneous processes such as myelination and cortical maturation. Accurate assessment of these changes is essential for understanding normative trajectories and detecting early abnormalities. While conventional MRI provides valuable insights, automated classification remains challenging due to overlapping developmental stages and sex-specific variability. Methods: We propose SEPoolConvNeXt, a novel deep learning framework designed for fine-grained classification of neonatal brain development using T1- and T2-weighted MRI sequences. The dataset comprised 29,516 images organized into four subgroups (T1 Male, T1 Female, T2 Male, T2 Female), each stratified into 14 age-based classes (0–10 days to 12 months). The architecture integrates residual connections, grouped convolutions, and channel attention mechanisms, balancing computational efficiency with discriminative power. Model performance was compared with 19 widely used pre-trained CNNs under identical experimental settings. Results: SEPoolConvNeXt consistently achieved test accuracies above 95%, substantially outperforming pre-trained CNN baselines (average ~70.7%). On the T1 Female dataset, early stages achieved near-perfect recognition, with slight declines at 11–12 months due to intra-class variability. The T1 Male dataset reached >98% overall accuracy, with challenges in intermediate months (2–3 and 8–9). The T2 Female dataset yielded accuracies between 99.47% and 100%, including categories with perfect F1-scores, whereas the T2 Male dataset maintained strong but slightly lower performance (>93%), especially in later infancy. Combined evaluations across T1 + T2 Female and T1 Male + Female datasets confirmed robust generalization, with most subgroups exceeding 98–99% accuracy. The results demonstrate that domain-specific architectural design enables superior sensitivity to subtle developmental transitions compared with generic transfer learning approaches. The lightweight nature of SEPoolConvNeXt (~9.4 M parameters) further supports reproducibility and clinical applicability. Conclusions: SEPoolConvNeXt provides a robust, efficient, and biologically aligned framework for neonatal brain maturation assessment. By integrating sex- and age-specific developmental trajectories, the model establishes a strong foundation for AI-assisted neurodevelopmental evaluation and holds promise for clinical translation, particularly in monitoring high-risk groups such as preterm infants. Full article

Colorectal cancer (CRC) is one of the most common cancers worldwide and a significant global health issue. The human gut microbiota, a complex ecosystem hosting numerous microorganisms such as bacteria, viruses, fungi, and protozoa, plays a crucial role. Increasing evidence indicates that gut microbiota is involved in CRC pathogenesis. In this study, the gut microbiota profiles, short-chain fatty acids, zonulin, and lipopolysaccharide-binding protein levels of newly diagnosed CRC patients were analyzed along with healthy controls to elucidate the relationship between CRC and the gut microbiota. The study included 16 newly diagnosed CRC patients and 16 healthy individuals. For microbiota analysis, DNA isolation from stool samples was performed using the Quick-DNA™ Fecal/Soil Microbe Miniprep Kit followed by sequencing using the MinION device. Data processing was conducted using Guppy software (version 6.5.7) and the Python (3.12) programming language. ELISA kits from Elabscience were utilized for analyzing LBP and zonulin serum levels. Fecal short-chain fatty acids were analyzed using GC-MS/MS equipped with a flame ionization detector and DB-FFAP column. Microbial alpha diversity, assessed using Shannon and Simpson indices, was found to be lower in CRC patients compared to healthy controls (p = 0.045, 0.017). Significant differences in microbial beta diversity were observed between the two groups (p = 0.004). At the phylum level, Bacteroidota was found to be decreased in CRC patients (p = 0.027). Potential biomarker candidates identified included Enterococcus faecium, Ruminococcus bicirculans, Enterococcus gilvus, Enterococcus casseliflavus, Segatella oris, and Akkermansia muciniphila. Serum zonulin levels were higher in CRC patients (CRC = 70.1 ± 26.14, Control = 53.93 ± 17.33, p = 0.048). There is a significant relationship between gut microbiota and CRC. A multifactorial evaluation of this relationship could shed light on potential biomarker identification and the development of new treatment options for CRC. Full article

The growing threat of infectious diseases requires novel therapeutics with different mechanisms of action. Antimicrobial peptides (AMPs), which are crucial for innate immunity, are a promising research area. The medicinal leech (Hirudo medicinalis) is a potential source of bioactive AMPs that are vital while interacting with microorganisms. This study aims to investigate the antimicrobial properties of peptides found in the H. medicinalis genome using a novel high-throughput screening method based on the expression of recombinant AMP genes in Escherichia coli. This approach enables the direct detection of AMP activity within cells, skipping the synthesis and purification steps, while allowing the simultaneous analysis of multiple peptides. The application of this method to the first identified candidate AMPs from H. medicinalis resulted in the discovery of three novel peptides: LBrHM1, NrlHM1 and NrlHM2. These peptides, which belong to the lumbricin and macin families, exhibit significant activity against E. coli. Two fragments of the new LBrHM1 homologue were synthesised and studied: a unique N-terminal fragment (residues 1–23) and a fragment (residues 27–55) coinciding with the active site of lumbricin I. Both fragments exhibited antimicrobial activity in a liquid medium against Bacillus subtilis. Notably, the N-terminal fragment lacks homologues among previously described AMPs. Full article

Background: Renal tumors, encompassing benign, malignant, and normal variants, represent a significant diagnostic challenge in radiology due to their overlapping visual characteristics on computed tomography (CT) scans. Manual interpretation is time consuming and susceptible to inter-observer variability, emphasizing the need for automated, reliable classification systems to support early and accurate diagnosis. Method and Materials: We propose KidneyNeXt, a custom convolutional neural network (CNN) architecture designed for the multi-class classification of renal tumors using CT imaging. The model integrates multi-branch convolutional pathways, grouped convolutions, and hierarchical feature extraction blocks to enhance representational capacity. Transfer learning with ImageNet 1K pretraining and fine tuning was employed to improve generalization across diverse datasets. Performance was evaluated on three CT datasets: a clinically curated retrospective dataset (3199 images), the Kaggle CT KIDNEY dataset (12,446 images), and the KAUH: Jordan dataset (7770 images). All images were preprocessed to 224 × 224 resolution without data augmentation and split into training, validation, and test subsets. Results: Across all datasets, KidneyNeXt demonstrated outstanding classification performance. On the clinical dataset, the model achieved 99.76% accuracy and a macro-averaged F1 score of 99.71%. On the Kaggle CT KIDNEY dataset, it reached 99.96% accuracy and a 99.94% F1 score. Finally, evaluation on the KAUH dataset yielded 99.74% accuracy and a 99.72% F1 score. The model showed strong robustness against class imbalance and inter-class similarity, with minimal misclassification rates and stable learning dynamics throughout training. Conclusions: The KidneyNeXt architecture offers a lightweight yet highly effective solution for the classification of renal tumors from CT images. Its consistently high performance across multiple datasets highlights its potential for real-world clinical deployment as a reliable decision support tool. Future work may explore the integration of clinical metadata and multimodal imaging to further enhance diagnostic precision and interpretability. Additionally, interpretability was addressed using Grad-CAM visualizations, which provided class-specific attention maps to highlight the regions contributing to the model’s predictions. Full article

With the growing severity of antibiotic resistance, antimicrobial peptides demonstrate significant potential for medical applications. Here, we performed genome and transcriptome sequencing of 30 Asian buffalo leech (Hirudinaria manillensis) individuals and integrated data from three other leech species (Whitmania pigra, Hirudo nipponia, and Hirudo medicinalis) to investigate genetic variation and gene expression of H. manillensis macins. Three macins (Hman1, Hman2, and Hman3), along with their encoding genes (Hman1, Hman2, and Hman3), were identified in H. manillensis. Hman1 exhibited the highest similarity (63.5 ± 12.0%) to macins from other leeches, followed by Hman2 (57.8 ± 7.4%) and Hman3 (30.0 ± 3.5%). Both amino acid and codon sequences of Hman1 were conserved within the species, whereas Hman2 and Hman3 exhibited markedly higher variability. All Hman1 sequences were translatable, while four Hman2 and 28 Hman3 sequences had degenerated into pseudogenes. Transcripts per million (TPM) values for Hman1, Hman2, and Hman3 were 2196.63, 242.35, and 1.22, respectively. Total macin expression in H. manillensis was less than 1/20 of that in W. pigra. Based on sequence variation and expression patterns, we propose that Hman1 retains functionality while Hman2 and Hman3 have lost or are losing their antibacterial functions. Full article

Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients’ quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI). Materials and Methods: In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model. Results: We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained. Conclusions: Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model’s general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms. Full article

Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients. Full article