Prevalence and determinants of constipation in children in Asia: a systematic review and meta-analysis.

Background: 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. Methods: 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 abstract. 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. Findings: 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. Interpretation: 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. Funding: The National Science and Technology Council, Taiwan.

Effects of xylo-oligosaccharide on gut microbiota, brain protein expression, and lipid profile induced by high-fat diet.

Midlife overweight and obesity are risk factors of cognitive decline and Alzheimer' s disease (AD) in late life. In addition to increasing risk of obesity and cognitive dysfunction, diets rich in fats also contributes to an imbalance of gut microbiota. Xylo-oligosaccharides (XOS) are a kind of prebiotic with several biological advantages, and can selectively promote the growth of beneficial microorganisms in the gut. To explore whether XOS can alleviate cognitive decline induced by high-fat diet (HFD) through improving gut microbiota composition, mice were fed with normal control or 60% HFD for 9 weeks to induce obesity. After that, mice were supplemented with XOS (30 g or 60 g/kg-diet) or without, respectively, for 12 weeks. The results showed that XOS inhibited weight gain, decreased epidydimal fat weight, and improved fasting blood sugar and blood lipids in mice. Additionally, XOS elevated spatial learning and memory function, decreased amyloid plaques accumulation, increased brain-derived neurotrophic factor levels, and improved neuroinflammation status in hippocampus. Changes in glycerolipids metabolism-associated lipid compounds caused by HFD in hippocampus were reversed after XOS intervention. On the other hand, after XOS intervention, increase in immune-mediated bacteria, Faecalibacterium was observed. In conclusion, XOS improved gut dysbiosis and ameliorated spatial learning and memory dysfunction caused by HFD by decreasing cognitive decline-associated biomarkers and changing lipid composition in hippocampus.

Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures.

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.

Protective Potential of ß-Hydroxybutyrate against Glucose-Deprivation-Induced Neurotoxicity Involving the Modulation of Autophagic Flux and the Monomeric Aß Level in Neuro-2a Cells.

Hypoglycemia has been known as a potential contributory factor to neurodegenerative diseases, such as Alzheimer's disease. There may be shared pathogenic mechanisms underlying both conditions, and the ketone body, ß-hydroxybutyrate (BHB), as an alternative substrate for glucose may exert neuroprotection against hypoglycemia-induced injury. To investigate this, Neuro-2a cells were subjected to a 24 h period of glucose deprivation with or without the presence of BHB. Cell viability, reactive oxygen species (ROS) production, apoptosis, autophagy, and adenosine triphosphate (ATP) and beta-amyloid peptide (Aß) levels were evaluated. The results show that Neuro-2a cells deprived of glucose displayed a significant loss of cell survival with a corresponding decrease in ATP levels, suggesting that glucose deprivation was neurotoxic. This effect was likely attributed to the diverse mechanisms including raised ROS, defective autophagic flux and reduced basal Aß levels (particularly monomeric Aß). The presence of BHB could partially protect against the loss of cell survival induced by glucose deprivation. The mechanisms underlying the neuroprotective actions of BHB might be mediated, at least in part, through restoring ATP, and modulating ROS production, autophagy flux efficacy and the monomeric Aß level. Results imply that a possible link between the basal monomeric Aß and glucose deprivation neurotoxicity, and treatments designed for the prevention of energy impairment, such as BHB, may be beneficial for rescuing surviving cells in relation to neurodegeneration.

The Distribution of Autoantibodies by Age Group in Children with Type 1 Diabetes versus Type 2 Diabetes in Southern Vietnam.

Asian children are increasingly being diagnosed with type 1 diabetes (T1D) or type 2 diabetes (T2D), and the presence of coexisting islet autoimmune antibodies complicate diagnosis. Here, we aimed to determine the prevalence of islet cell autoantibodies (ICAs) and glutamic acid decarboxylase 65 autoantibodies (GADAs) in children with T1D versus T2D living in Vietnam. This cross-sectional study included 145 pediatric patients aged 10.3 ± 3.6 years, with 53.1% and 46.9% having T1D and T2D, respectively. ICAs were reported in only 3.9% of pediatric T1Ds, which was not significantly different from the 1.5% of those with T2D. Older children with T1D were positive for either ICAs, or ICAs and GADAs (5-9 and 10-15 years), whereas only a small proportion of children aged 0-4 years were positive for GADAs (18%). Notably, 27.9% of children with T2D aged 10-15 were positive for GADAs, and all were classified as overweight (n = 9) or obese (n = 10). GADAs were more commonly observed in T1D patients younger than four years than ICAs, which were more prevalent in older children (5-15 years). Even though few children with T2D carried ICAs and GADAs, finding a better biomarker or an appropriate time to confirm diabetes type may require further investigation.

Botanical Antcin K Alleviates High-Fat Damage in Palm Acid Oil-Treated Vascular Endothelial Cells and Macrophages.

Lipid metabolism disorder is the most critical risk factor for atherosclerosis, and the process involves lipid deposition in the arterial intima. In Taiwan, antcin K, an active triterpenoid from the fruiting bodies of Antrodia camphorata, has been considered a potential lipid-lowering agent. Despite this, the possible therapeutic mechanisms of antcin K remain unclear. To explore the crucial role of botanical antcin K in reducing atherosclerotic plaque, we used SVEC4-10 vascular endothelial cells and RAW264.7 macrophages with palm acid oil-induced high-fat damage as our cell models. Our results showed through using the DPPH assay that antcin K had excellent free radical scavenging ability. Antcin K treatment can significantly alleviate the high-fat damage and reduce the levels of inflammatory factors of TNF-α and IL-1ß in vascular endothelial cells and macrophages, as shown through MTT assay and ELISA. Furthermore, antcin K treatment can effectively enhance migration ability and clear lipid deposition in macrophages, as shown by using cell migration assay and oil red O staining. When stained with immunofluorescence, antcin K was shown to significantly decrease the expression of adhesion molecules of vascular cell adhesion molecule 1 (VCAM-1) in vascular endothelial cells involved in monocyte migration and inflammation. Antcin K not only reduced the expression of the CD36 scavenger receptor but also augmented the expression of Kruppel-like factor 4 (KLF4) transcription factor in macrophages, which inhibits the transformation of macrophages into foam cells underlying the pathological process of atherosclerosis. Taking our findings into account, we suggested that botanical antcin K could have therapeutic potential for the treatment of atherosclerosis.

On the road to explainable AI in drug-drug interactions prediction: A systematic review.

Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.

Pathological brain lesions in girls with central precocious puberty at initial diagnosis in Southern Vietnam.

PURPOSE: Cranial magnetic resonance imaging (MRI) is recommended to identify intracranial lesions in girls with central precocious puberty (CPP). Yet, the use of routine MRI scans in girls with CPP is still debatable, as pathological findings in girls 6 years of age or older with CPP are limited. Therefore, we aimed to identify the prevalence of brain lessons in CPP patients stratified by age group (0-2, 2-6, and 6-8 years). METHODS: This retrospective cross-sectional study recruited 257 girls diagnosed with CPP for 6 years (2010-2016). MRI was used to detect brain abnormalities. Levels of luteinizing hormone, follicle-stimulating hormone, and sex hormones in blood samples were measured. RESULTS: Most girls had no brain lesions (82.9%, n=213), and of the minor proportion of girls with CPP that exhibited brain lesions (17.1%, n=44), 32 girls had organic CPP. Pathological findings were detected in 33.3% (2 of 6) of girls aged 0-2 years, 15.6% (5 of 32) of girls aged 2-6 years, and 3.6% (8 of 219) of girls aged 6-8 years. Hypothalamic hamartoma and tumors in the pituitary stalk were the most common pathological findings. The likelihood of brain lesions decreased with age. Girls with organic CPP were more likely to be younger (6.1±2.4 vs. 7.3±1.3 years, p<0.01) than girls with idiopathic CPP. CONCLUSION: Older girls appeared to have a lower prevalence of organic CPP. Clinicians should cautiously use cranial MRI for girls aged 6-8 years with CPP.

Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches.

BACKGROUND: A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test which solely focused on LH 30-minute post-stimulation was considered to identify girls with central precocious puberty (CPP). However, it was tested using traditional statistical methods. With advanced computer science, we aimed to develop a machine learning-based diagnostic model that processed baseline CPP-related variables and a brief GnRHa stimulation test for CPP diagnosis. METHODS: We recruited girls suspected of precocious puberty and underwent a GnRHa stimulation test at Children Hospital 2, Vietnam, and Cathay General Hospital, Taiwan. Clinical data, bone age measurement, and 30-min post-stimulation blood test were used to build up the predictive model. The candidate model was developed by different machine learning algorithms that were mainly evaluated by sensitivity, specificity, the area under the receiver operator characteristic curve (AUC), and F1-score in internal and external validation data to classify girls as CPP and non-CPP at different time-points (0-min, 30-min, 60-min, and 120-min post-stimulation). RESULTS: Among the 614 girls diagnosed with PP, 524 (85.3%) had CPP. The random forest algorithm yielded the highest value of F1-score (0.976), specificity (0.893), positive predicted value (0.987), and relatively high value of AUC (0.972) that contributed to high probability to identify CPP. The performance metrics of the 30-min post-stimulation diagnostic model including sensitivity and specificity surpassed those of the 0-minute model (0-min) and were equivalent to those of the model obtained 60-min and 120-min post-stimulation. Hence, our machine learning-based model helps shorten the stimulation test to 30 minutes after GnRHa injection, in general, it requires 120 minutes for a completed GnRHa stimulation test. CONCLUSIONS: We developed a diagnostic model based on clinical features and a single sample 30-minute post-stimulation to identify CPP in girls that can reduce distress for children caused by multiple blood samplings.

Fat-Free Mass and Skeletal Muscle Mass Gain Are Associated with Diabetes Remission after Laparoscopic Sleeve Gastrectomy in Males but Not in Females.

Besides massive body weight loss, laparoscopic sleeve gastrectomy (LSG) causes massive lean mass, including fat-free mass (FFM) and skeletal muscle mass (SM) that present higher metabolic rates in males. This study examines sex differences in FFM and SM changes of type 2 diabetes (T2D) remission at 12 months post-LSG. This cohort study recruited 119 patients (53.7% females) with T2D and obesity (body mass index 42.2 ± 7.0 kg/m2) who underwent LSG. Fat-mass (FM) loss was higher in males than in females (-12.8 ± 6.2% vs. -9.9 ± 5.0%, p = 0.02) after one-year post-operation. Regardless of the weight-loss difference, males had higher FFM and SM gain than did females (12.8 ± 8.0 vs. 9.9 ± 5.0% p = 0.02 and 6.5 ± 4.3% vs. 4.9 ± 6.2%, p = 0.03, respectively). Positive correlations of triglyceride reduction with FM loss (r = 0.47, p = 0.01) and SM gain (r = 0.44, p = 0.02) over 12 months post-operation were observed in males who achieved T2D remission. The T2D remission rate significantly increased 16% and 26% for each additional percentage of FFM and SM gain one year after LSG, which only happened in males. Increased FFM and SM were remarkably associated with T2D remission in males, but evidence lacks for females.

New Insights into Depressive Disorder with Respect to Low-Grade Inflammation and Fish Oil Intake.

Unipolar depression has been recognized as one of the major diseases by the World Health Organization in the 21st century. The etiology of depression is complicated and includes genetic factors, stress, aging, and special physical status (pregnancy, metabolic syndrome, and trauma). Numerous animal and human studies have demonstrated that n-3 polyunsaturated fatty acids (n-3 PUFAs) are highly correlated to cognition and depression. These nutritional antidepressants, including EPA and DHA, have a range of neurobiological activities contributing to their potential antidepressant effects. Our preclinical and clinical studies have indicated that n-3 PUFA supplementation in addition to standard antidepressant medications may provide synergistic neuroprotective and antioxidant/inflammatory effects. To translate our preliminary findings into clinical application, this paper reviews the existing evidence on the antidepressant effects of n-3 PUFAs and the potential underlying mechanisms, which include modulation of chronic lowgrade inflammation and the corresponding changes in peripheral blood immune biomarkers.

Effects of Catalyst Pretreatment on Carbon Nanotube Synthesis from Methane Using Thin Stainless-Steel Foil as Catalyst by Chemical Vapor Deposition Method.

Synthesis of carbon nanotubes (CNTs) was carried out using methane as a carbon source via the chemical vapor deposition (CVD) method. A thin stainless-steel foil was used as catalyst for CNT growth. Our results revealed that pretreatment step of the stainless-steel foil as a catalyst plays an important role in CNT formation. In our experiments, a catalyst pretreatment temperature of 850 °C or 950 °C was found to facilitate the creation of Fe- and Cr-rich particles are active sites on the foil surface, leading to CNT formation. It is noted that the size of metallic particles after pretreatment is closely related to the diameter of the synthesized CNTs. It is interesting that a shorter catalyst pretreatment brings the growth of semiconducting typed CNTs while a longer pretreatment creates metallic CNTs. This finding might lead to a process for improving the quality of CNTs grown on steel foil as catalyst.

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.

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.

Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences.

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.

Nutrient Intake through Childhood and Early Menarche Onset in Girls: Systematic Review and Meta-Analysis.

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 (ß = -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.

Parental satisfaction with quality of neonatal care in different level hospitals: evidence from Vietnam.

BACKGROUND: Most health systems provide the most specialized, and presumably also the highest quality of care at a central level. This study assessed parental satisfaction and its determinants in the context of neonatal care in a provincial as well as a national hospital of Vietnam. METHODS: In this cross-sectional quantitative study, parents of 340 preterm infants admitted to neonatal care units of a national and a provincial hospital in 2018 were interviewed using structured questionnaires. Unadjusted and adjusted linear regression models were used to assess the relationship between parental satisfaction and hospital rank. RESULTS: The mean parental satisfaction score was 3.74 at the provincial, and 3.56 at the national hospital. These satisfaction differences persisted when parent and child characteristics were adjusted for in multivariate analysis. Longer length of stay and worsening infant health status were associated with parents reporting lower levels of satisfaction with the quality of care being provided at the healthcare facility. CONCLUSIONS: This study suggests that parents of preterm infants admitted in a provincial hospital were more satisfied with the quality of care received than those in a specialized national hospital. Length of stay and infant health status were the two most important determinants of level of parental satisfaction.

Fish Oil, but Not Olive Oil, Ameliorates Depressive-Like Behavior and Gut Microbiota Dysbiosis in Rats under Chronic Mild Stress.

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.

mHealth information for migrants: an e-health intervention for internal migrants in Vietnam.

BACKGROUND: Socio-economic development in Vietnam has resulted in increased internal migration particularly among young women seeking employment opportunities in cities. Vietnamese female migrants who enter new environments often encounter the loss or neglect of their right to access sexual and reproductive health services. To address this, a mobile health (mHealth) intervention model was implemented over 12 months (2013-2014) in a factory in the Long Bien industrial zone of Hanoi, Vietnam. METHODS: The intervention provided sexual and reproductive health services for female migrants through text messaging, information booklets accompanied maps, and free counseling via a hotline. To evaluate the impact of the intervention, pre- and post-intervention data were collected to measure changes in women's knowledge and practices related to sexual and reproductive health. Qualitative data in the form of personal interviews were also collected. The sample size for the baseline survey was 411 women, and for the post-intervention survey it was 482 women (the intervention involved an open cohort). The majority of women were unmarried and under the age of 25. RESULTS: Results indicate that there was high uptake of the intervention services and that most women found the services important and useful. In addition, there was evidence that the intervention (1) increased women's knowledge of sexual and reproductive health (e.g., proper use of condoms, identification of high-risk behaviors such as having unprotected sex), and (2) fostered improved practices related to sexual and reproductive health (e.g., increased gynecological check-ups and use of condoms). CONCLUSIONS: The study demonstrated the feasibility of implementing a multi-faceted intervention for migrant women working in an industrial zone in Hanoi, Vietnam as well as its successful uptake and some early positive effects. This can be used to inform future design and implementation of mHealth/eHealth intervention models for migrant and other vulnerable/hard to reach population.

Antiviral Cystine Knot α-Amylase Inhibitors from Alstonia scholaris.

Cystine knot α-amylase inhibitors are cysteine-rich, proline-rich peptides found in the Amaranthaceae and Apocynaceae plant species. They are characterized by a pseudocyclic backbone with two to four prolines and three disulfides arranged in a knotted motif. Similar to other knottins, cystine knot α-amylase inhibitors are highly resistant to degradation by heat and protease treatments. Thus far, only the α-amylase inhibition activity has been described for members of this family. Here, we show that cystine knot α-amylase inhibitors named alstotides discovered from the Alstonia scholaris plant of the Apocynaceae family display antiviral activity. The alstotides (As1-As4) were characterized by both proteomic and genomic methods. All four alsotides are novel, heat-stable and enzyme-stable and contain 30 residues. NMR determination of As1 and As4 structures reveals their conserved structural fold and the presence of one or more cis-proline bonds, characteristics shared by other cystine knot α-amylase inhibitors. Genomic analysis showed that they contain a three-domain precursor, an arrangement common to other knottins. We also showed that alstotides are antiviral and cell-permeable to inhibit the early phase of infectious bronchitis virus and Dengue infection, in addition to their ability to inhibit α-amylase. Taken together, our results expand membership of cystine knot α-amylase inhibitors in the Apocynaceae family and their bioactivity, functional promiscuity that could be exploited as leads in developing therapeutics.

Discovery of a linear cyclotide from the bracelet subfamily and its disulfide mapping by top-down mass spectrometry.

Cyclotides are heat-stable macrocyclic peptides from plants that display a wide range of biological activities. They can be divided into two subfamilies: Möbius or bracelet, based on the presence or absence of a cis-proline residue in loop 5, respectively. Currently, over 150 cyclotides have been discovered, but only four linear variants of the Möbius subfamily have been hitherto isolated. In this study, we report the discovery of two novel cyclotides, hedyotide B1 and hedyotide B2, from the aerial parts of Hedyotis biflora. Hedyotide B1 has a cyclic cystine knot structure typical of cyclotides. Interestingly, hedyotide B2 possesses a linear backbone and is the first linear representative of the bracelet subfamily. Disulfide mapping of hedyotide B2 by a top-down MS/MS approach showed that it shares the same knotted disulfide arrangement as conventional cyclotides. Its unfolding pathway also showed that the penetrating disulfide bond Cys III-VI is the most stable disulfide linkage. Cloning of the gene encoding hedyotide B2 revealed a nonsense mutation that introduces a premature stop codon at the conserved Asn residue position, which is essential for an end-to-end backbone ligation. Biophysical characterization showed that hedyotide B2 was more susceptible to exopeptidase degradation as compared with hedyotide B1. Hedyotide B2 was also inactive against all four tested bacterial strains, whereas hedyotide B1 was bactericidal to Escherichia coli and Streptococcus salivarius at low micromolar concentration. Our results provide a deeper understanding of the structures, functions, and biosynthetic processing of cyclotides and uncyclotides in plants.