Possible drug–food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug–drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament’s effect, the withdrawals of various medications, and harmful impacts on the patients’ health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug–food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.

Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state-of-the-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general.

Antioxidant proteins are involved importantly in many aspects of cellular life activities. They protect the cell and DNA from oxidative substances (such as peroxide, nitric oxide, oxygen-free radicals, etc.) which are known as reactive oxygen species (ROS). Free radical generation and antioxidant defenses are opposing factors in the human body and the balance between them is necessary to maintain a healthy body. An unhealthy routine or the degeneration of age can break the balance, leading to more ROS than antioxidants, causing damage to health. In general, the antioxidant mechanism is the combination of antioxidant molecules and ROS in a one-electron reaction. Creating computational models to promptly identify antioxidant candidates is essential in supporting antioxidant detection experiments in the laboratory. In this study, we proposed a machine learning-based model for this prediction purpose from a benchmark set of sequencing data. The experiments were conducted by using 10-fold cross-validation on the training process and validated by three different independent datasets. Different machine learning and deep learning algorithms have been evaluated on an optimal set of sequence features. Among them, Random Forest has been identified as the best model to identify antioxidant proteins with the highest performance. Our optimal model achieved high accuracy of 84.6%, as well as a balance in sensitivity (81.5%) and specificity (85.1%) for antioxidant protein identification on the training dataset. The performance results from different independent datasets also showed the significance in our model compared to previously published works on antioxidant protein identification.

Among the genetic and environmental risk factors, nutrition plays a crucial role in determining the timing of puberty. Early menarche onset (EMO) is defined as when girls reach menarche onset at an age which is earlier than the mean/median age of menarche, between 12 and 13 years of age, according to individual ethnicity. The present study examined the association between nutrient intake in childhood and EMO risk in healthy girls by performing a systematic review and meta-analysis of prospective studies. We screened EMBASE, Cochrane Library, PubMed/MEDLINE, and Web of Science databases for 16 eligible studies with all medium-to-high quality scores ranging from 3 to 5 of 6 possible points with 10,884 subjects. Higher intakes of energy (risk ratio (RR) = 3.32, 95% confidence interval (CI) = 1.74–6.34, I² = 97%), and protein (RR = 3.15, 95% CI = 2.87–3.44, I² = 0%) were associated with EMO risk. For each additional 1 g/day animal protein intake in childhood, the age at menarche was approximately two months earlier (β = −0.13, I² = 55%), and high iron intake was associated with EMO (RR = 1.20, 95% CI = 1.03–1.40, I² = 0%). Polyunsaturated fatty acid (PUFAs) intake was associated with EMO risk with a dose-response effect (RR = 1.25, 95% CI = 1.05–1.49, I² = 44%). Girls with a high intake of fiber and monosaturated fatty acids (MUFAs) in childhood experienced later menarche onset (RR = 0.83, 95% CI = 0.69–1.00, I² = 31%; RR = 0.66, 95% CI = 0.50–0.86, I² = 0%, respectively). Thus, adherence to a high intake of animal proteins-, iron- and PUFA-rich food diet makes girls more likely to have EMO, while a high intake of fiber- and MUFA-rich foods may protect girls from EMO. Further studies are expected to investigate the role of specific types of PUFAs and MUFAs on EMO to promote healthy sexual maturity in girls.

Background: This study investigated the effects of fish oil and olive oil in improving dysbiosis and depressive-like symptoms. Methods and results: Male rats were fed normal, fish oil-rich or olive oil-rich diets for 14 weeks. Chronic mild stress (CMS) was administered from week 2. The sucrose preference test (SPT) and forced swimming test (FST) were used to determine depressive-like behavior. The SPT results revealed that the CMS, CMS with imipramine (CMS+P) treatment, and CMS with olive oil diet (CMS+O) groups exhibited significantly reduced sucrose intake from week 8, whereas the fish oil diet (CMS+F) group exhibited significantly reduced sucrose intake from week 10. The FST results showed that the immobile time of the CMS+F group was significantly less than that of the CMS-only group. Next generation sequencing (NGS) results showed CMS significantly reduced the abundance of Lactobacillus and increased that of Marvinbryantia and Ruminiclostridium_6. However, the CMS+F group showed an increase in the abundance of Eisenbergiella, Ruminococcaceae_UCG_009, and Holdemania, whereas the CMS+O group showed an increase in the abundance of Akkermansia. Conclusions: CMS stimuli altered the gut microbiome in depressed rats. Fish oil and olive oil exerted part of a prebiotic-like effect to ameliorate dysbiosis induced by CMS. However, only fish oil ameliorated depressive-like symptoms.

Constipation is prevalent worldwide, significantly increasing healthcare costs and diminishing the quality of life in children affected. Current studies have yielded mixed results regarding the factors associated with constipation, and mainly focusing on patients outside of Asia. Moreover, most of these studies lack focus on the paediatric population. This study aimed to identify the prevalence and associated factors of constipation among children in Asia. In this systematic review and meta-analysis, we systematically searched PubMed, Scopus, and Cochrane for cohort and cross-sectional studies published from database inception up to October 12, 2022, and continued with manual searching until September 2, 2023. Eligible studies were those that included children in Asia aged 0-18 years old suffering from idiopathic constipation, with prevalence value provided in the English The analysis included clinical and general population. Children with organic constipation, who had undergone gastrointestinal surgery, or with congenital defects were excluded, as these factors affect the incidence of constipation. Data included in the analysis were extracted from published reports only. The extracted data were pooled using random-effects model to analyse the prevalence of constipation in children in Asia. This study is registered with PROSPERO, CRD42022367122. Out of 4410 systematically searched studies and 36 manually searched ones, a total of 50 studies were included in the final analysis, encompassing data from 311,660 children residing in Asia. The pooled prevalence of constipation was 12.0% (95% CI 9.3-14.6%, I² = 99.8%). There was no significant difference in constipation prevalence observed by sex and geographical location. Nonetheless, adolescents and children aged 1-9 years exhibited a significantly higher prevalence constipation compared to infants (p < 0.0001) Additionally, significant differences in constipation rates were observed across various diagnostic methods, population sources, and mental health conditions. Despite the high heterogeneity resulting from varying diagnostic tools or definitions used among studies, our review adds to the literature on constipation among children in Asia. It reveals a notably high prevalence of constipation in this demographic. Diagnostic methods, age, and compromised mental health emerged as significant influencers of constipation among children in Asia, highlighting potential strategies to mitigate constipation prevalence in children in Asia. The National Science and Technology Council, Taiwan.