Rapid screening of the novel bioactive peptides with notable α-glucosidase inhibitory activity by UF-LC-MS/MS combined with three-AI-tool from black beans.

This work aimed to propose a rapid method to screen the bioactive peptides with anti-α-glucosidase activity instead of traditional multiple laborious purification and identification procedures. 242 peptides binding to α-glycosidase were quickly screened and identified by bio-affinity ultrafiltration combined with LC-MS/MS from the double enzymatic hydrolysate of black beans. Top three peptides with notable anti-α-glucosidase activity, NNNPFKF, RADLPGVK and FLKEAFGV were further rapidly screened and ranked by the three artificial intelligence tools (three-AI-tool) BIOPEP database, PeptideRanker and molecular docking from the 242 peptides. Their IC50 values were in order as 4.20 ± 0.11 mg/mL, 2.83 ± 0.03 mg/mL, 1.32 ± 0.09 mg/mL, which was opposite to AI ranking, for the hydrophobicity index of the peptides was not included in the screening criteria. According to the kinetics, FT-IR, CD and ITC analyses, the binding of the three peptides to α-glucosidase is a spontaneous and irreversible endothermic reaction that results from hydrogen bonds and hydrophobic interactions, which mainly changes the α-helix structure of α-glucosidase. The peptide-activity can be evaluated vividly by AFM in vitro. In vivo, the screened FLKEAFGV and RADLPGVK can lower blood sugar levels as effectively as acarbose, they are expected to be an alternative to synthetic drugs for the treatment of Type 2 diabetes.

Hydrogen peroxide sensitivity connects the activity of COX5A and NPR3 to the regulation of YAP1 expression.

Reactive oxygen species (ROS) are among the most severe types of cellular stressors with the ability to damage essential cellular biomolecules. Excess levels of ROS are correlated with multiple pathophysiological conditions including neurodegeneration, diabetes, atherosclerosis, and cancer. Failure to regulate the severely imbalanced levels of ROS can ultimately lead to cell death, highlighting the importance of investigating the molecular mechanisms involved in the detoxification procedures that counteract the effects of these compounds in living organisms. One of the most abundant forms of ROS is H2 O2 , mainly produced by the electron transport chain in the mitochondria. Numerous genes have been identified as essential to the process of cellular detoxification. Yeast YAP1, which is homologous to mammalian AP-1 type transcriptional factors, has a key role in oxidative detoxification by upregulating the expression of antioxidant genes in yeast. The current study reveals novel functions for COX5A and NPR3 in H2 O2 -induced stress by demonstrating that their deletions result in a sensitive phenotype. Our follow-up investigations indicate that COX5A and NPR3 regulate the expression of YAP1 through an alternative mode of translation initiation. These novel gene functions expand our understanding of the regulation of gene expression and defense mechanism of yeast against oxidative stress.

Influence of resilience on depression among nurses in clean operating departments: The mediating effect of life satisfaction.

BACKGROUND: A clean operating room is an important part of surgical and critical treatment in hospitals. The workload is substantial, the pace is rapid, and the working environment is intense; therefore, nurses who work in clean operating rooms are constantly challenged, which can lead to anxiety, depression, and other mental health issues. Life satisfaction and resilience are important factors that ensure mental health. Therefore, exploring the mediating role of life satisfaction in the influence of resilience on depression among nurses in clean operating rooms can help improve nursing services and teamwork. AIM: To explore the mediating effect of satisfaction on the influence of resilience on depression among nurses in a clean operating department. METHODS: From April to November 2022, 196 nurses from the Department of Clean Operating at Harbin Medical University Cancer Hospital participated in this study. Participants were selected using convenience sampling. Participants' gender, age, marital status, position, length of service, personal monthly income, daily working hours, employment status, and professional title were collected, and the Connor-Davidson resilience scale, satisfaction with life scale, and self-rating depression scale were used to evaluate resilience, life satisfaction, and depression. The researchers conducted professional training in advance, introduced the research methods to the participants before the investigation, and explained the study's significance and purpose. Surveys were distributed and collected on-site. Each questionnaire took 30 min to complete. RESULTS: The average scores for life satisfaction, resilience, and depression were 3.13 (± 0.28), 4.09 (± 0.78), and 56.21 (± 8.70), respectively. The correlation between resilience and depression was negative (r = -0.829, P < 0.01). Life satisfaction was positively related to resilience (r = 0.855, P < 0.01) and negatively related to depression (r = -0.778, P < 0.01). The relationship between resilience and depression was partially mediated by life satisfaction. The value of the mediating effect was -6.853 (26.68% of the total effect). CONCLUSION: Life satisfaction partially mediates the link between resilience and depression among nurses in clean operating departments.

Pain Reported by Chinese Children During Cancer Treatment: Prevalence, Intensity, Interference, and Management.

BACKGROUND: Pain is a frequently reported and distressing symptoms during cancer treatment. However, there is limited evidence on pain reported by Chinese children with cancer. OBJECTIVES: This study aimed to investigate the prevalence, intensity, interference, and management of pain reported by Chinese children during cancer treatment and explore the predictors of pain interference. METHODS: We conducted a cross-sectional survey to investigate the pain intensity, pain interference, co-occurring symptoms (anger, anxiety, depression, fatigue), and pain management strategies reported by children 8 years and older undergoing active cancer treatment in 4 Chinese hospitals. RESULTS: Data were analyzed for 187 children. The prevalence of moderate to severe pain (≥4/10) was 38.50%, with an average pain interference score of 52.97 out of 100. Approximately 24% of children were prescribed pain medicine. Pain interference and pain intensity were marginally correlated (r = 0.047, P < .01) and were both positively correlated with pain duration and co-occurring symptoms and negatively correlated with perceived pain alleviation (all P < .01). Multiple regression analyses suggested that severe pain intensity (B = 2.028, P = .003) and fatigue (B = 0.440, P < .001) significantly predicted higher levels of pain interference (R2 = 0.547, F = 23.102, P < .001). CONCLUSION: Chinese children with cancer reported a low pain intensity score but a relatively high level of pain interference. According to the children's reports, pain has not been sufficiently addressed through Chinese pediatric oncology supportive care. IMPLICATIONS FOR PRACTICE: There is an urgent requirement for comprehensive pain assessment and standardized, targeted interventions in Chinese pediatric oncology pain management.

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods.

Protein-ligand interactions are necessary for majority protein functions. Adenosine- 5'-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is costineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

Identification and Characteristics of the Three Subgroups of Pain in Chinese Children and Adolescents with Cancer.

PURPOSE: Pain is a distressing symptom for children and adolescents with cancer and is experienced by individuals differently. This study sought to determine subgroups according to their pain experiences, and how demographic, clinical, and quality of life (QOL)-related characteristics might differ across subgroups. DESIGN AND METHODS: This cross-sectional study recruited 187 pediatric patients with cancer aged 8 to 17 years old and asked them to complete measures of pain intensity, pain duration, pain interference and pain control using the Chinese translation of the validated questionnaire from the Pain Squad app, as well as 7 PROMIS measures assessing QOL-related outcomes. Latent profile analysis (LPA) was used to identify latent subgroups. RESULTS: Three subgroups of children were identified: low-pain/low-duration (69.5%), moderate-pain/high-duration (19.8%), and high-pain/moderate-duration (10.7%). Hospitalized children were more likely to be in the moderate-pain/high-duration subgroup. Children in the high-pain/moderate-duration subgroup were more likely to be cared for by unemployed caregivers. Scores on depressive symptoms (p = 0.002), anger (p < 0.001), anxiety (p = 0.045), fatigue (p = 0.044), and mobility (p = 0.008) questionnaire were significantly worse in the high-pain/moderate-duration subgroup than the other two subgroup. PRACTICE IMPLICATIONS: This study provides a scientific foundation for further studies exploring predictive factors related to pain experiences. More targeted treatment strategies targeting the specific characteristics of each subgroup will help improve patients' QOL and use of medical resources. CONCLUSIONS: The 3 identified pain subgroups demonstrate the heterogeneity in pain experiences among pediatric patients with cancer. Knowledge of these subgroups can assist clinicians in better identifying and targeting pain treatment for children with cancer.

Self-Reported Fatigue in Chinese Children and Adolescents During Cancer Treatment.

Background: Fatigue is a prevalent and distressing symptom in children and adolescents with cancer. Objectives: This study aimed to (1) investigate the current fatigue status reported by Chinese children and adolescents with cancer during active cancer treatment and (2) examine whether sociodemographic information, disease and treatment information, co-occurring symptoms, function and related clinical data are significantly associated with fatigue according to the biopsychosocial model. Methods: Participants were children aged 8-17 years, who had undergone treatment for cancer at four hospitals in China. Children completed the Chinese version of the Pediatric Patient-Reported Outcomes Measurement Information System short forms. Results: In total, 187 children (33.16% female, mean age 10.28 years) participated. The mean T-score for child-reported fatigue was 48.52 (34-72). Multiple linear regression analysis showed that fatigue in pediatric active cancer treatment could be significantly predicted by greater child-reported pain interference (ß = 0.391, p < .001), greater depressive symptoms (ß = 0.443, p < .001), and reduced mobility (ß = -0.226, p = .004) (adjusted R2 = 0.613, F = 16.476, p < .001). Conclusions: Children and adolescents with cancer experience multiple, intersecting troubling symptoms during their treatment. There is a need to attend to the biopsychosocial aspects of care for children and adolescents during active cancer treatment. To reduce pediatric oncology patients' fatigue level, clinicians could develop culturally sensitive interventions to alleviate children's pain interference, treat depressive symptoms, and maximize their physical mobility.

Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer deeply affecting human health. Diagnosing early-stage PDAC is the key point to PDAC patients' survival. However, the biomarkers for diagnosing early PDAC are inexact in most cases. Therefore, it is highly desirable to identify an effective PDAC diagnostic biomarker. In the current work, we designed a novel computational approach based on within-sample relative expression orderings (REOs). A feature selection technique called minimum redundancy maximum relevance was used to pick out optimal REOs. We then compared the performances of different classification algorithms for discriminating PDAC and its adjacent normal tissues from non-PDAC tissues. The support vector machine algorithm is the best one for identifying early PDAC diagnostic biomarker. At first, a signature composed of nine gene pairs was acquired from microarray gene expression data sets. These gene pairs could produce satisfactory classification accuracy up to 97.53% in fivefold cross-validation. Subsequently, two types of data from diverse platforms, namely, microarray and RNA-Seq, were used to validate this signature. For microarray data, all (100.00%) of 115 PDAC tissues and all (100.00%) of 31 PDAC adjacent normal tissues were correctly recognized as PDAC. In addition, 88.24% of 17 non-PDAC (normal or pancreatitis) tissues were correctly classified. For the RNA-Seq data, all (100.00%) of 177 PDAC tissues and all (100.00%) of 4 PDAC adjacent normal tissues were correctly recognized as PDAC. Validation results demonstrated that the signature had a good cross-platform effect for early detection of PDAC. This work developed a new robust signature that might be a promising biomarker for early PDAC diagnosis.

Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features.

N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.

Mobile Physical Activity Intervention for Breast Cancer Patients During Chemotherapy.

Breast cancer patients showed low engagement in physical activity (PA) during chemotherapy. Evidence showed regular PA has potential to reduce mortality and risks of cancer recurrence, relieve psychological distress, manage symptoms and improve quality of life. Mobile health intervention displays a great advantage to deliver cancer care timely and remotely. A Mobile Physical Activity Program was constructed in a mobile phone application. The application contained 5 modes: information delivering, disease tracking, events reminders, online interaction, health behavior recording (daily walking steps, sleeping time and body weight) and self-reported assessment. Both applications and web-based administration portal were developed by engineers.