Effectiveness of mHealth intervention on safe abortion knowledge and perceived barriers to safe abortion services among female sex workers in Vietnam.

Background: Mobile health (mHealth) has been used to promote sexual and reproductive health (SRH) education and services; however, little is known about the use of mHealth to improve safe abortion knowledge and access to safe abortion services among female sex workers (FSWs). This study evaluated the feasibility and effectiveness of iConnect intervention through changes in knowledge on safe abortion and changes in perceived barriers to safe abortion services among FSWs in Vietnam. Methods: iConnect mobile app was developed as an interactive platform to deliver safe abortion education and referral to safe abortion services through short messaging services (SMS) enhanced by tele-counseling for 512 FSWs in Hanoi, Vietnam. A pretest-posttest evaluation was conducted using questionnaire-based phone interviews administered to 251 participants at baseline and 3 months following the intervention. Non-parametric tests evaluated the change in abortion knowledge, behaviors, and perceived barriers to safe abortion. Results: There were significant improvements in the knowledge on safe abortion among the study participants. Specifically, FSWs' knowledge of correct gestational ages (≤22 weeks) for medical abortion increased from 78.9% at baseline to 96.8% (P=0.001). Knowledge of correct gestational ages for medical abortion at the private clinic increased from 45.3% to 63.1% (P=0.001). Knowledge on the consequences of unsafe abortion increased from 75.2% to 92.1% (P=0.001). In addition, perceived stigma and discrimination when seeking safe abortion decreased from 36.5% to 27.8% (P=0.036) and worry about the lack of confidentiality decreased from 23.3% to 15.5% (P=0.035). Conclusions: The evaluation results showed the initial effectiveness of a mobile app-based intervention in improving access to safe abortion information and services among FSWs. A future study is needed to establish the efficacy of the intervention for scaling up in Vietnam and elsewhere.

A genomic perspective on the potential of termite-associated Cellulosimicrobium cellulans MP1 as producer of plant biomass-acting enzymes and exopolysaccharides.

BACKGROUND: Lignocellulose is a renewable and enormous biomass resource, which can be degraded efficiently by a range of cocktails of carbohydrate-active enzymes secreted by termite gut symbiotic bacteria. There is an urgent need to find enzymes with novel characteristics for improving the conversion processes in the production of lignocellulosic-based products. Although various studies dedicated to the genus Cellulosimicrobium as gut symbiont, genetic potential related to plant biomass-acting enzymes and exopolysaccharides production has been fully untapped to date. METHODS: The cellulolytic bacterial strain MP1 was isolated from termite guts and identified to the species level by phenotypic, phylogenetic, and genomic analysis. To further explore genes related to cellulose and hemicellulose degradation, the draft genome of strain MP1 was obtained by using whole-genome sequencing, assembly, and annotation through the Illumina platform. Lignocellulose degrading enzymes and levan production in the liquid medium were also examined to shed light on bacterial activities. RESULTS: Among 65 isolates obtained, the strain MP1 was the most efficient cellulase producer with cellulase activity of 0.65 ± 0.02 IU/ml. The whole genome analysis depicted that strain MP1 consists of a circular chromosome that contained 4,580,223 bp with an average GC content of 73.9%. The genome comprises 23 contigs including 67 rRNA genes, three tRNA genes, a single tmRNA gene, and 4,046 protein-coding sequences. In support of the phenotypic identification, the 16S rRNA gene sequence, average nucleotide identity, and whole-genome-based taxonomic analysis demonstrated that the strain MP1 belongs to the species Cellulosimicrobium cellulans. A total of 30 genes related to the degradation of cellulases and hemicellulases were identified in the C. cellulans MP1 genome. Of note, the presence of sacC1-levB-sacC2-ls operon responsible for levan and levan-type fructooligosaccharides biosynthesis was detected in strain MP1 genome, but not with closely related C. cellulans strains, proving this strain to be a potential candidate for further studies. Endoglucanases, exoglucanases, and xylanase were achieved by using cheaply available agro-residues such as rice bran and sugar cane bagasse. The maximum levan production by C. cellulans MP1 was 14.8 ± 1.2 g/l after 20 h of cultivation in media containing 200 g/l sucrose. To the best of our knowledge, the present study is the first genome-based analysis of a Cellulosimicrobium species which focuses on lignocellulosic enzymes and levan biosynthesis, illustrating that the C. cellulans MP1 has a great potential to be an efficient platform for basic research and industrial exploitation.

Characteristics and biomarkers of patients with central nervous system infection admitted to a referral hospital in Northern Vietnam.

BACKGROUND: Laboratory facilities for etiological diagnosis of central nervous system (CNS) infection are limited in developing countries; therefore, patients are treated empirically, and the epidemiology of the pathogens is not well-known. Tubercular meningitis is one of the common causes of meningitis, which has high morbidity and mortality, but lacks sensitive diagnostic assays. The objectives of this study were to determine the causes of meningitis in adult patients by using molecular assays, to assess the risk factors associated with them, and to explore whether biomarkers can differentiate tubercular meningitis from bacterial meningitis. METHODS: We conducted a cross-sectional study in the Department of Infectious Diseases, Bach Mai Hospital, Hanoi, Vietnam, from June 2012 to May 2014. All patients who were ≥ 16 years old and who had meningoencephalitis suggested by abnormal cerebrospinal fluid (CSF) findings (CSF total cell >5/mm3 or CSF protein ≥40 mg/dL) were included in the study. In addition to culture, CSF samples were tested for common bacterial and viral pathogens by polymerase chain reaction (PCR) and for biomarkers: C-reactive protein and adenosine deaminase (ADA). RESULTS: Total number of patients admitted to the department was 7506; among them, 679 were suspected to have CNS infection, and they underwent lumbar puncture. Five hundred eighty-three patients had abnormal CSF findings (meningoencephalitis); median age was 45 (IQR 31-58), 62.6% were male, and 60.9% were tested for HIV infection. Among 408 CSF samples tested by PCR, out of them, 358 were also tested by culture; an etiology was identified in 27.5% (n=112). S. suis (8.8%), N. meningitis (3.2%), and S. pneumoniae (2.7%) were common bacterial and HSV (2.2%), Echovirus 6 (0.7%), and Echovirus 30 (0.7%) were common viral pathogens detected. M. tuberculosis was found in 3.2%. Mixed pathogens were detected in 1.8% of the CSF samples. Rural residence (aOR 4.1, 95% CI 1.2-14.4) and raised CSF ADA (≥10 IU/L) (aOR 25.5, 95% CI 3.1-212) were associated with bacterial meningitis when compared with viral meningitis; similarly, raised CSF ADA (≥10 IU/L) (aOR 42.2, 95% CI 2.0-882) was associated with tubercular meningitis. CONCLUSIONS: Addition of molecular method to the conventional culture had enhanced the identification of etiologies of CNS infection. Raised CSF ADA (≥10 IU/L) was strongly associated with bacterial and tubercular meningitis. This biomarker might be helpful to diagnose tubercular meningitis once bacterial meningitis is ruled out by other methods.

The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients.

The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.

Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes.

This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia.

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia.

Automated grading of acne vulgaris by deep learning with convolutional neural networks.

BACKGROUND: The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter- and intra-observer variability. OBJECTIVE: To develop and validate an algorithm for the automated calculation of the Investigator's Global Assessment (IGA) scale, to standardize acne severity and outcome measurements. MATERIALS AND METHODS: A total of 472 photographs (retrieved 01/01/2004-04/08/2017) in the frontal view from 416 acne patients were used for training and testing. Photographs were labeled according to the IGA scale in three groups of IGA clear/almost clear (0-1), IGA mild (2), and IGA moderate to severe (3-4). The classification model used a convolutional neural network, and models were separately trained on three image sizes. The photographs were then subjected to analysis by the algorithm, and the generated automated IGA scores were compared to clinical scoring. The prediction accuracy of each IGA grade label and the agreement (Pearson correlation) of the two scores were computed. RESULTS: The best classification accuracy was 67%. Pearson correlation between machine-predicted score and human labels (clinical scoring and researcher scoring) for each model and various image input sizes was 0.77. Correlation of predictions with clinical scores was highest when using Inception v4 on the largest image size of 1200 × 1600. Two sets of human labels showed a high correlation of 0.77, verifying the repeatability of the ground truth labels. Confusion matrices show that the models performed sub-optimally on the IGA 2 label. CONCLUSION: Deep learning techniques harnessing high-resolution images and large datasets will continue to improve, demonstrating growing potential for automated clinical image analysis and grading.

Nocturnal Hypoglycemia Detection using Optimal Bayesian Algorithm in an EEG Spectral Moments Based System.

This paper presents a hypoglycemia detection system using electroencephalogram (EEG) spectral moments from 8 patients with type 1 diabetes (T1D) at night time. Four channels (C3, C4, O1, and O2) associated with glycemic episodes were analyzed. Spectral moments were applied to EEG signal and its corresponding speed and acceleration. During hypoglycemia, theta moments increased significantly (P<; 0.001) and alpha moments decreased significantly (P<; 0.001). The system used an optimal Bayesian neural network for detecting hypoglycemic episodes. Based on the optimal network architecture with the highest log evidence, the final classification results for the test set were 79% and 51% in sensitivity and specificity, respectively. Essentially, the estimated blood glucose profiles correlated significantly to actual values in the test set (P<; 0.0001). Using error grid analysis, 93% of the estimated values were clinically acceptable.

Nocturnal Hypoglycemia Detection using EEG Spectral Moments under Natural Occurrence Conditions.

This paper is concerned with a study of hypoglycemia under natural occurrence conditions at night time. Five adolescents with type 1 diabetes (T1D) participated in the experiments. Patients' blood glucose profiles were interpolated to estimate the intermediate values. The proposed system used spectral moments of electroencephalogram (EEG) signals from central and occipital areas as features for detecting hypoglycemia. We found that hypoglycemia could be detected non-invasively using EEG spectral moments. During hypoglycemic episodes, theta moments increased significantly (P<; 0.005) whereas beta moments decreased significantly (P<; 0.001). Based on the optimal network architecture associated with the highest log evidence, the proposed optimal Bayesian neural network resulted in a sensitivity of 82% and a specificity of 52%. In addition, the estimated blood glucose profiles showed a significant correlation (P<; 1e-6) with interpolated blood glucose values in the test set.

Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia.

Nocturnal hypoglycemia is dangerous that threatens patients because of its unclear symptoms during sleep. This paper is a study of hypoglycemia from 8 patients with type 1 diabetes (T1D) at night. O1 and O2 EEG data of the occipital lobe associated with glycemic episodes were analyzed. Frequency features were computed from Power Spectral Density using Welch's method. Centroid alpha frequency reduced significantly ($\mathrm{P}\lt 0.0001$) while centroid theta increased considerably ($\mathrm{P}\lt 0.01$). Spectral entropy of the unified theta-alpha band rose significantly ($\mathrm{P}\lt 0.005$). These occipital features acted as the input of a Bayesian regularized neural network for detecting hypoglycemic episodes. The classification results were 73% and 60% of sensitivity and specificity, respectively.

Need for a marginal methodology in assessing natural gas system methane emissions in response to incremental consumption.

Accurate quantification of methane emissions from the natural gas system is important for establishing greenhouse gas inventories and understanding cause and effect for reducing emissions. Current carbon intensity methods generally assume methane emissions are proportional to gas throughput so that increases in gas consumption yield linear increases in emitted methane. However, emissions sources are diverse and many are not proportional to throughput. Insights into the causal drivers of system methane emissions, and how system-wide changes affect such drivers are required. The development of a novel cause-based methodology to assess marginal methane emissions per unit of fuel consumed is introduced. Implications: The carbon intensities of technologies consuming natural gas are critical metrics currently used in policy decisions for reaching environmental goals. For example, the low-carbon fuel standard in California uses carbon intensity to determine incentives provided. Current methods generally assume methane emissions from the natural gas system are completely proportional to throughput. The proposed cause-based marginal emissions method will provide a better understanding of the actual drivers of emissions to support development of more effective mitigation measures. Additionally, increasing the accuracy of carbon intensity calculations supports the development of policies that can maximize the environmental benefits of alternative fuels, including reducing greenhouse gas emissions.

Serological evidence of infection with rodent-borne hepatitis E virus HEV-C1 or antigenically related virus in humans.

Zoonotic potential of a rat-derived hepatitis E virus (HEV), designated as HEV-C1, remains unknown. To evaluate the risk for HEV-C1 infection in humans, paired sera of 208 hospitalized febrile patients collected from 2001 to 2003 in Hanoi, Vietnam, were examined for IgG antibodies to HEV-C1 and genotype 1 HEV (HEV-1), which is common in humans. IgG antibodies to virus-like particles (VLPs) of HEV-C1 and/or HEV-1 were detected from 99 of the 208 convalescent sera in enzyme-linked immunosorbent assay (ELISA). IgG antibody titers to HEV-C1 antigen in 3 of the 99 sera were more than 8-fold higher than those to HEV-1 antigen. IgM antibodies to HEV-C1 antigen were detected in acute sera from 2 of the 3 patients in ELISA and Western blotting. However, no HEV genome was detected. Clinical information was available for 1 of the 2 patients. Hepatic enzymes, aspartate aminotransferase and alanine aminotransferase, were mildly elevated (156 IU/l and 68 IU/l, respectively), and hepatomegaly was detected by ultrasonography. The patient recovered from the illness after 17 days. These results indicated that HEV-C1 or its variants infect humans in Vietnam and may cause acute febrile illness with mild liver dysfunction.