Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.

Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides. In this study, a k-nearest neighbor model based on multi-Laplacian and kernel risk sensitive loss was proposed, which introduces a kernel risk loss function derived from the K-local hyperplane distance nearest neighbor model as well as combining the Laplacian regularization method to predict therapeutic peptides. The findings indicated that the suggested approach achieved satisfactory results and could effectively predict therapeutic peptide sequences.

Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy.

BACKGROUND: Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection strategy of negative samples significantly affects the performance of contrastive learning methods. RESULTS: In this study, we propose CCL-ASPS, a novel deep learning model that incorporates Collaborative Contrastive Learning (CCL) and Adaptive Self-Paced Sampling strategy (ASPS) for drug-target interaction prediction. CCL-ASPS leverages multiple networks to learn the fused embeddings of drugs and targets, ensuring their consistent representations from individual networks. Furthermore, ASPS dynamically selects more informative negative sample pairs for contrastive learning. Experiment results on the established dataset demonstrate that CCL-ASPS achieves significant improvements compared to current state-of-the-art methods. Moreover, ablation experiments confirm the contributions of the proposed CCL and ASPS strategies. CONCLUSIONS: By integrating Collaborative Contrastive Learning and Adaptive Self-Paced Sampling, the proposed CCL-ASPS effectively addresses the limitations of previous methods. This study demonstrates that CCL-ASPS achieves notable improvements in DTI predictive performance compared to current state-of-the-art approaches. The case study and cold start experiments further illustrate the capability of CCL-ASPS to effectively predict previously unknown DTI, potentially facilitating the identification of new drug-target interactions.

TRIP 13-dependent pathways promote the development of gastric cancer.

TRIP13 is highly expressed in various human tumors and promotes tumorigenesis. We aimed to explore the biological effect of TRIP13 on gastric cancer. The RNA sequence data were retrieved from TCGA to evaluate TRIP13 mRNA expression in gastric cancer. Paired formalin-fixed paraffin-embedded blocks were further analyzed to verify the relationship between TRIP13 expression and carcinogenic status. The functions of TRIP13 on the proliferation of gastric malignancy were investigated by MTT, flow cytometry, colony formation experiment, and nude mouse tumor formation experiment. Finally, microarray analysis of TRIP13-related pathways was performed to identify the potential underlying mechanism of TRIP13 in gastric cancer. TRIP13 was found to have high expression in tumor samples. TRIP13 expression status was significantly subjective to tumor-node-metastasis (TNM) staging and poor survival. The downregulation of TRIP13 promoted apoptosis and inhibited tumor growth. TRIP13-dependent JAK/STAT and NF-κB signaling cascade were found as two key pathways in the carcinogenesis of GC. In conclusion, TRIP13 participates in the carcinogenesis of stomach cancer, and its overexpression in the cancerous tissues dovetail with advanced stage and survival. Moreover, TRIP13 functions as an upstream regulator of the JAK/STAT and p53 signaling pathways, which play critical roles in developing various malignancies.

Mucosal microbial microenvironment in early gastric neoplasia and non-neoplastic gastric disease.

BACKGROUND AND AIM: The biological characterization of microbial environment in early gastric cancer (EGC), other than Helicobacter pylori, is limited. This study aimed to explore the microbial microenvironment in chronic gastritis (CG), fundic gland polyps (FGPs), low-grade intraepithelial neoplasia (LGIN), and EGC. METHODS: 16S-rRNA gene sequencing and bioinformatic analysis were performed on 63 individuals with 252 mucosal biopsies or endoscopic submucosal dissection margin samples from endoscopy. RESULTS: The microbiota in gastric LGIN functions analogously to EGC in terms of functional prediction. Neoplastic lesions showed a significant difference to CG or FGPs in beta diversity of the microbiota. Bacteria genera including Paracoccus, Blautia, Barnesiella, Lactobacillus, Thauera, Collinsella were significantly enriched in gastric neoplastic mucosa (LGIN and EGC) compared with non-neoplastic tissues (CG and FGPs). While Pseudomonas and Kingella were depleted in neoplastic tissues. FGPs showed a distinctive microbial network system that negatively interacted with Helicobacter. CONCLUSIONS: In terms of the mucosal microbial microenvironment, gastric LGIN and EGC showed no significant difference as early neoplastic lesions. We observed a coordinated microbial microenvironment that correlated negatively with Helicobacter.

Impact of negative emotions and insomnia on sepsis: A mediation Mendelian randomization study.

BACKGROUND: Negative emotions and insomnia (NEI) can lead to inflammation, which is a characteristic of sepsis. However, the interaction among NEI and sepsis has not yet been proven. Therefore, Mendelian mediation was used to explore this relationship in this study. METHODS: The genetic correlation NEI and sepsis was assessed by via linkage disequilibrium scores (LDSC). A two-sample Mendelian randomization (MR) study design was performed to examine the causal association between NEI and sepsis using the inverse variance weighted (IVW) method. The reliability of the results was estimated by weighted median and MR-Egger methods, but heterogeneity was evaluated via Radial and Cochran's Q tests. Biases in gene polymorphisms were checked by MR-Egger regression and MR-PRESSO. Mendelian mediation analyses were applied to quantify the intermediary effect and proportional contribution. RESULTS: A genetic link between sepsis and depression was determined via LDSC analysis. The relationship between depression and sepsis was revealed through MR analysis [odds ratio (OR) = 1.21, 95 % confidence interval (CI) = 1.08-1.36, p = 1.07 × 10-3)]. The results were not influenced by heterogeneity or pleiotropy biases. Chitinase 3 Like 1 (CHI3L1) was a mediator with a mediation effect size of 0.12. The ratio of the intermediated effect to total effect was 10.31 %. CONCLUSION: CHI3L1 is a key factor which mediates the interaction between NEI and sepsis.

Are onconeural antibodies a clinical phenomenology in paraneoplastic limbic encephalitis?

Paraneoplastic neurological syndromes (PNSs) occur in patients with cancer and can cause clinical symptoms and signs of dysfunction of the nervous system that are not due to a local effect of the tumor or its metastases. Most of these clinical syndromes in adults are associated with lung cancer, especially small cell lung cancer (SCLC), lymphoma, and gynecological tumors. The finding of highly specific antibodies directed against onconeural antigens has revolutionized the diagnosis and promoted the understanding of these syndromes and led to the current hypothesis of an autoimmune pathophysiology. Accumulating data strongly suggested direct pathogenicity of these antibodies. The field of PNS has expanded rapidly in the past few years with the discovery of limbic encephalitis associated with glutamic acid decarboxylase (GAD) 65, the voltage (VGKC-gated potassium channel) complex, the methyl (N-NMDA-D-aspartate), alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), and gamma aminobutyric acid (GABA) (B) receptors, and so forth. Despite this, the clinical spectrum of these diseases has not yet been fully investigated. The clinical importance of these conditions lies in their frequent response to immunotherapies and, less commonly, their association with distinctive tumors. This review provides an overview on the pathogenesis and diagnosis of PNS, with emphasis on the role of antibodies in limbic encephalitis.

Critical illness polyneuropathy and myopathy: a systematic review.

Critical illness polyneuropathy and critical illness myopathy are frequent complications of severe illness that involve sensorimotor axons and skeletal muscles, respectively. Clinically, they manifest as limb and respiratory muscle weakness. Critical illness polyneuropathy/myopathy in isolation or combination increases intensive care unit morbidity via the inability or difficulty in weaning these patients off mechanical ventilation. Many patients continue to suffer from decreased exercise capacity and compromised quality of life for months to years after the acute event. Substantial progress has been made lately in the understanding of the pathophysiology of critical illness polyneuropathy and myopathy. Clinical and ancillary test results should be carefully interpreted to differentiate critical illness polyneuropathy/myopathy from similar weaknesses in this patient population. The present review is aimed at providing the latest knowledge concerning the pathophysiology of critical illness polyneuropathy/myopathy along with relevant clinical, diagnostic, differentiating, and treatment information for this debilitating neurological disease.