MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation.

MiRNAs are a class of small non-coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time-consuming, a large number of computational models have been developed to effectively predict reliable disease-related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA-disease associations and then utilizes the label propagation algorithm to reliably predict disease-related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA-disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA-disease association prediction.

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods.

Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

Dynamics of nutrients, sensory quality and microbial communities and their interactions during co-fermentation of pineapple by-products and whey protein.

In this study, a new fermented food was developed using pineapple by-products and whey protein (2.6%) as raw materials through the co-fermentation of autochthonous lactic acid bacteria and yeast. To better understand the fermentation mechanism and the impact of microorganisms on the entire fermentation system, we tracked the changes in carbohydrate and amino acid profiles, organoleptic quality and microbial community during the fermentation process. Compared with unfermented samples, dietary fiber and free amino acids increased significantly as fermentation proceeded. The fermented samples were significantly lower in astringency and bitterness and significantly higher in sourness, umami and richness. The fermented products were richer in volatile compounds with floral, cheesy, fruity and other flavors. Relevant analyses showed that the core microbial community was highly correlated with the quality attributes of the fermented products. Microorganisms such as Lactococcus, Weissella, Hanseniaspora, Saccharomyces and Lachancea contributed significantly to the fermented products.

Pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries under topical anesthesia.

Cataract is the main cause of visual impairment and blindness worldwide while the only effective cure for cataract is still surgery. Consecutive phacoemulsification under topical anesthesia has been the routine procedure for cataract surgery. However, patients often grumbled that they felt more painful during the second-eye surgery compared to the first-eye surgery. The intraoperative pain experience has negative influence on satisfaction and willingness for second-eye cataract surgery of patients with bilateral cataracts. Intraoperative ocular pain is a complicated process induced by the nociceptors activation in the peripheral nervous system. Immunological, neuropsychological, and pharmacological factors work together in the enhancement of intraoperative pain. Accumulating published literatures have focused on the pain enhancement during the second-eye phacoemulsification surgeries. In this review, we searched PubMed database for articles associated with pain perception differences between consecutive cataract surgeries published up to Feb. 1, 2024. We summarized the recent research progress in mechanisms and interventions for pain perception enhancement in consecutive second-eye phacoemulsification cataract surgeries. This review aimed to provide novel insights into strategies for improving patients' intraoperative experience in second-eye cataract surgeries.

Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Automated Segmentation and Classification of Vocal Cord Leukoplakia.

Objectives: Vocal cord leukoplakia is clinically described as a white plaque or patch on the vocal cords observed during macroscopic examination, which does not take into account histological features or prognosis. A clinical challenge in managing vocal cord leukoplakia is to assess the potential malignant transformation of the lesion. This study aims to investigate the potential of deep learning (DL) for the simultaneous segmentation and classification of vocal cord leukoplakia using narrow band imaging (NBI) and white light imaging (WLI). The primary objective is to assess the model's accuracy in detecting and classifying lesions, comparing its performance in WLI and NBI. Methods: We applied DL to segment and classify NBI and WLI of vocal cord leukoplakia, and used pathological diagnosis as the gold standard. Results: The DL model autonomously detected lesions with an average intersection-over-union (IoU) >70%. In classification tasks, the model differentiated between lesions in the surgical group with a sensitivity of 93% and a specificity of 94% for WLI, and a sensitivity of 99% and a specificity of 97% for NBI. In addition, the model achieved a mean average precision of 81% in WLI and 92% in NBI, with an IoU threshold >0.5. Conclusions: The model proposed by us is helpful in assisting in accurate diagnosis of vocal cord leukoplakia from NBI and WLI.

Differences in Immunological Landscape between EGFR-Mutated and Wild-Type Lung Adenocarcinoma.

Recent clinical trials of lung adenocarcinoma with immune checkpoint inhibitors revealed that lung adenocarcinoma patients with EGFR mutations have a poor response to immunotherapy. However, the mechanisms have not been addressed. We performed immunohistochemistry analyses of resected lung adenocarcinoma tissues with and without EGFR mutations to investigate and compare the characteristics of the tumor microenvironment (TME). We retrospectively enrolled a total of 323 lung adenocarcinoma patients (164 had EGFR mutations), and their corresponding tissue samples were analyzed by the EGFR mutation test and immunohistochemistry. We selected the markers of the immune checkpoint molecule (PD1, PD-L1, and LAG-3) and immune cell (CD3, CD4, CD8, and Foxp3) as markers of the tumor microenvironment. Our results revealed that patients had a distinct tumor microenvironment between EGFR-mutant and wild-type lung adenocarcinomas; the expression of CD3, CD4, PD-L1, and Foxp3 in EGFR-mutant tumors was significantly higher than that in wild-type tumors, while the expression of LAG3 and PD-1 showed a positive correlation with EGFR-wild-type tumors. In survival analysis, EGFR-wild-type patients had longer disease-free survival (DFS) than EGFR-mutant patients (P = 0.0065). Our research demonstrates significant differences in tumor microenvironment composition between EGFR-mutant and wild-type patients. Our findings provide novel evidence that contributes to understanding the mechanism underlying the poor efficacy of immune checkpoint inhibitors.

Motif discovery using an immune genetic algorithm.

In this paper, a new immune genetic algorithm for motif discovery is proposed. The algorithm adopts concentration regulation mechanism to maintain the population diversity and vaccine mechanism to inhibit degeneracy during evolution. Experimental results have demonstrated the method's capacity to find known motifs in relatively long promoter sequences and multiple motifs within a single run.