Optimization of Vitamin B1, B2, and B6 Absorption in Nang Tay Dum Floating Rice Grains.

As reported by the FAO, in 2022, approximately 735 million people experienced undernourishment, underscoring the critical need for effective strategies to address micronutrient deficiencies. Among these strategies, the mass fortification of staple foods, particularly rice-a dietary staple for half of the global population-has emerged as one of the most effective approaches. Conventional milling processes diminish the nutritional content of rice, necessitating the development of fortification methods to enhance its nutrient profile. This study investigates advanced fortification techniques to improve the nutritional value of rice, focusing on vitamins B1, B2, and B6, with guidelines from the US Institute of Medicine's Dietary Reference Intakes. The results indicate that implementing ultrasonic treatments and optimal soaking conditions (60 °C for 60 min) significantly enhances the absorption of these vitamins. Effective parameters included a concentration of 1500 ppm for vitamin B1 and higher levels for vitamins B2 and B6, with a rice-to-vitamin solution ratio of 1:4. These conditions yielded an absorbed vitamin B1 content of 1050 mg/kg, bringing the fortified rice closer to meeting recommended intake levels. Given the global average daily consumption of 100 g of rice per person, this research demonstrates the feasibility of fortifying rice to address nutrient deficiencies effectively and contribute to improved dietary health worldwide. Further enhancement of vitamin B2 and B6 levels remains essential for optimal fortification, highlighting the potential of fortified rice as a sustainable solution for improving global nutrition.

A Holistic Way to Understand the Determinants of Physical Activity in Urban New South Wales, Australia: A Codesigned Systems Mapping Project.

BACKGROUND: To meet the World Health Organization goal of reducing physical inactivity by 15% by 2030, a multisectoral system approach is urgently needed to promote physical activity (PA). We report the process of and findings from a codesigned systems mapping project to present determinants of PA in the context of urban New South Wales, Australia. METHODS: A participatory conceptual mapping workshop was held in May 2023 with 19 participants working in education, transportation, urban planning, community, health, and sport and recreation. Initial maps were developed and refined using online feedback from the participants. Interviews were conducted with 10 additional policymakers from relevant sectors to further refine the maps. RESULTS: Two systems maps were cocreated, identifying over 100 variables influencing PA and their interconnections. Five settings emerged from the adults' map-social and community, policy, built environment and transportation, health care, and workplace-and 4 for the young people's map-family, school, transportation, and community and environment. The maps share similarities, such as regarding potential drivers within the transportation, community, and built environment sectors; however, the young people's map has a specific focus on the school setting and the adults' map on workplace and health care settings. Interviews with policymakers provided further unique insights into understanding and intervening in the PA system. CONCLUSIONS: This codesigned participatory systems mapping process, supplemented by stakeholder interviews, provided a unique opportunity to bring together stakeholders across sectors to understand the complexity within the PA system and begin to identify leverage points for tackling physical inactivity in New South Wales.

Structural Basis for Dimerization and Activation of UvrD-family Helicases.

UvrD-family helicases are superfamily 1A motor proteins that function during DNA replication, recombination, repair, and transcription. UvrD family monomers translocate along single stranded (ss) DNA but need to be activated by dimerization to unwind DNA in the absence of force or accessory factors. However, prior structural studies have only revealed monomeric complexes. Here, we report the first structures of a dimeric UvrD-family helicase, Mycobacterium tuberculosis UvrD1, both free and bound to a DNA junction. In each structure, the dimer interface occurs between the 2B subdomains of each subunit. The apo UvrD1 dimer is observed in symmetric compact and extended forms indicating substantial flexibility. This symmetry is broken in the DNA-bound dimer complex with leading and trailing subunits adopting distinct conformations. Biochemical experiments reveal that the E. coli UvrD dimer shares the same 2B-2B interface. In contrast to the dimeric structures, an inactive, auto-inhibited UvrD1 DNA-bound monomer structure reveals 2B subdomain-DNA contacts that are likely inhibitory. The major re-orientation of the 2B subdomains that occurs upon UvrD1 dimerization prevents these duplex DNA interactions, thus relieving the auto-inhibition. These structures reveal that the 2B subdomain serves a major regulatory role rather than participating directly in DNA unwinding.

The impact of data imputation on air quality prediction problem.

With rising environmental concerns, accurate air quality predictions have become paramount as they help in planning preventive measures and policies for potential health hazards and environmental problems caused by poor air quality. Most of the time, air quality data are time series data. However, due to various reasons, we often encounter missing values in datasets collected during data preparation and aggregation steps. The inability to analyze and handle missing data will significantly hinder the data analysis process. To address this issue, this paper offers an extensive review of air quality prediction and missing data imputation techniques for time series, particularly in relation to environmental challenges. In addition, we empirically assess eight imputation methods, including mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer, to scrutinize their impact on air quality data. The evaluation is conducted using diverse air quality datasets gathered from numerous cities globally. Based on these evaluations, we offer practical recommendations for practitioners dealing with missing data in time series scenarios for environmental data.

Validation studies and multi-omics analysis of Zhx2 as a candidate quantitative trait gene underlying brain oxycodone metabolite (oxymorphone) levels and behavior.

Sensitivity to the subjective reinforcing properties of opioids has a genetic component and can predict addiction liability of opioid compounds. We previously identified Zhx2 as a candidate gene underlying increased brain concentration of the oxycodone (OXY) metabolite oxymorphone (OMOR) in BALB/cJ (J) versus BALB/cByJ (By) females that could increase OXY state-dependent reward. A large structural intronic variant is associated with a robust reduction of Zhx2 expression in J mice, which we hypothesized enhances OMOR levels and OXY addiction-like behaviors. We tested this hypothesis by restoring the Zhx2 loss-of-function in Js (MVKO) and modeling the loss-of-function variant through knocking out the Zhx2 coding exon (E3KO) in Bys and assessing brain OXY metabolite levels and behavior. Consistent with our hypothesis, Zhx2 E3KO females showed an increase in brain OMOR levels and OXY-induced locomotor activity. However, contrary to our hypothesis, state-dependent expression of OXY-CPP was decreased in E3KO females and increased in E3KO males. We also overexpressed Zhx2 in the livers and brains of Js and observed Zhx2 overexpression in select brain regions that was associated with reduced OXY state-dependent learning. Integrative transcriptomic and proteomic analysis of E3KO mice identified astrocyte function, cell adhesion, extracellular matrix properties, and endothelial cell functions as pathways influencing brain OXY metabolite concentration and behavior. These results support Zhx2 as a quantitative trait gene underlying brain OMOR concentration that is associated with changes in OXY behavior and implicate potential quantitative trait mechanisms that together inform our overall understanding of Zhx2 in brain function.

Effectiveness of COVID-19 vaccines against hospitalisation, death and infection over time in Aotearoa New Zealand: a retrospective cohort study.

AIMS: This study aimed to evaluate the effectiveness of COVID-19 vaccines in preventing COVID-19 outcomes when the Omicron variant was predominant in Aotearoa New Zealand. METHODS: We conducted a retrospective cohort study using routinely available data (8 December 2020-28 February 2023). We evaluated the vaccine effectiveness (VE) of COVID-19 vaccines using the Cox proportional-hazards model, adjusting for covariates. RESULTS: The VE against COVID-19 hospitalisation (VEH) for the second booster dose compared to no vaccination was found to be 81.8% (95% confidence interval [95% CI]: 73.6-87.5) after 1 month post-vaccination. After 4 months, VEH was 72.2% (95% CI: 58.5-81.4), and after 6 months VEH was 49.0% (95% CI: 7.9-71.8). Similarly, VEH decreased after the first booster dose (1-month VEH=81.6% [95% CI: 75.6-86.1]; 2 months VEH=74.7% [95% CI: 68.2-79.9]; and 6 months VEH=57.4% [95% CI: 45.8-66.6]). VE against COVID-19 death (VED) was 92.9% (95% CI: 82.1-97.2) 2 months after the first booster vaccination, with VED being sustained until months 5 and 6 (VED=87.2%; 95% CI: 67.4-94.9). The VE after the second dose of the vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (VEI) (real-time polymerase chain reaction [RT-PCR]) was sustained at 5 months post-vaccination (40.6%; 95% CI: 25.6-52.5). CONCLUSION: We provide a comprehensive quantification of both VE and VE waning. These findings can guide policymakers to help evaluate the COVID-19 vaccination programme and minimise the effect of future COVID-19 in Aotearoa New Zealand.

Aryl-Substituted Acridine Donor Derivatives Modulate the Transition Dipole Moment Orientation and Exciton Harvesting Properties of Donor-Acceptor TADF Emitters.

Thermally activated delayed fluorescence (TADF) compounds are highly attractive as sensitizing and emitting materials for organic light-emitting diodes (OLEDs). The efficiency of the OLED depends on multiple parameters, most of which rely on the properties of the emitter including those that govern the internal quantum and outcoupling efficiencies. Herein, we investigate a series of aryl substituted acridine donor derivatives of the donor-acceptor TADF emitter DMAC-TRZ, with the objective of correlating their properties, such as triplet harvesting efficiency and transition dipole moment orientation, with their corresponding device efficiency. The decoration of the DMAC donor with substituted aryl groups not only modifies the molecular weight and length of the emitter but also affects the emission color and the capacity for the emitters to efficiently harvest triplet excitons. The presence of electron-withdrawing 4-cyanophenyl and 4-trifluoromethylphenyl groups in, respectively, CNPh-DMAC-TRZ and CF3Ph-DMAC-TRZ, blue-shifts the emission spectrum but slows down the reverse intersystem crossing rate constant (k RISC), while the opposite occurs in the presence of electron-donating groups in t BuPh-DMAC-TRZ and OMePh-DMAC-TRZ (red-shifted emission spectrum and faster k RISC). In contrast to our expectations, the OLED performance of the five DMAC-TRZ derivatives does not scale with their degree of horizontal emitter orientation but follows the k RISC rates. This, in turn, demonstrates that triplet harvesting (and not horizontal emitter orientation) is the dominant effect for device efficiency using this family of emitters. Nonetheless, highly efficient OLEDs were fabricated with t BuPh-DMAC-TRZ and OMePh-DMAC-TRZ as emitters, with improved EQEmax (∼28%) compared to the reference DMAC-TRZ devices.

Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds.

Recently, various modern experimental screening pipelines and assays have been developed to find promising anticancer drug candidates. However, it is time-consuming and almost infeasible to screen an immense number of compounds for anticancer activity via experimental approaches. To partially address this issue, several computational advances have been proposed. In this study, we present iACP-GCR, a model based on multitask learning on graph convolutional residual neural networks with two types of shortcut connections, to identify multitarget anticancer compounds. In our architecture, the graph convolutional residual neural networks are shared by all the prediction tasks before being separately customized. The NCI-60 data set, one of the most reliable and well-known sources of experimentally verified compounds, was used to develop our model. From that data set, we collected and refined data about compounds screened across nine cancer types (panels), including breast, central nervous system, colon, leukemia, nonsmall cell lung, melanoma, ovarian, prostate, and renal, for model training and evaluation. The model performance evaluated on an independent test set shows that iACP-GCR surpasses the three advanced computational methods for multitask learning. The integration of two shortcut connection types in the shared networks also improves the prediction efficiency. We also deployed the model as a public web server to assist the research community in screening potential anticancer compounds.

Effectiveness of a Smartphone-Based Stress Management Program for Depression in Hospital Nurses During COVID-19 in Vietnam and Thailand: 2-Arm Parallel-Group Randomized Controlled Trial.

BACKGROUND: During the COVID-19 pandemic, health care professionals experienced high levels of depression. However, extant research has not highlighted effective internet-based psychological interventions to improve the mental health in this population during the pandemic. It remains unclear whether self-guided, internet-based cognitive behavioral therapy (iCBT) programs are effective in improving the mental health of health care workers during the COVID-19 pandemic. OBJECTIVE: The aim of this study was to evaluate the effectiveness of a smartphone-based iCBT stress management program for reducing the depression experienced by nurses in Vietnam and Thailand. METHODS: From March to April 2022, a 2-arm, parallel-group randomized controlled trial was implemented. One arm offered a 7-week self-guided iCBT program, and the other offered treatment as usual as a control arm. Full-time nurses were recruited from 6 hospitals: 2 hospitals in Vietnam and 4 hospitals in Thailand. The primary outcome of this program was the severity of depression measured by the Depression Anxiety Stress Scale-21 items. Follow-up surveys were conducted to measure the change in depression severity at 3 months (July-August 2022) and at 6 months (October-November 2022) after baseline. Mixed modeling for repeated measures was used to test the effects of the intervention compared with the control for the follow-up. RESULTS: A total of 1203 nurses were included in this study: 602 in the intervention group and 601 in the control group. The follow-up rate at 3 and 6 months ranged from 85.7% (515/601) to 87.5% (527/602). The completion rate for the program was 68.1% (410/602). The group difference in depression was significant at the 3-month follow-up (coefficient=-0.92, 95% CI -1.66 to -0.18; P=.02) and nonsignificant at the 6-month follow-up (coefficient=-0.33, 95% CI -1.11 to 0.45; P=.41). The estimated effect sizes were -0.15 and -0.06 at the 3- and 6-month follow-ups, respectively. CONCLUSIONS: Our study shows that the smartphone-based iCBT program was effective in reducing depression at the 3-month follow-up among hospital nurses in Vietnam and Thailand during the COVID-19 pandemic. However, the effect size was small, and therefore, these results may not be clinically meaningful. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000044145; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000050128. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.20944/preprints202303.0450.v1.

Cryo-EM confirms a common fibril fold in the heart of four patients with ATTRwt amyloidosis.

ATTR amyloidosis results from the conversion of transthyretin into amyloid fibrils that deposit in tissues causing organ failure and death. This conversion is facilitated by mutations in ATTRv amyloidosis, or aging in ATTRwt amyloidosis. ATTRv amyloidosis exhibits extreme phenotypic variability, whereas ATTRwt amyloidosis presentation is consistent and predictable. Previously, we found unique structural variabilities in cardiac amyloid fibrils from polyneuropathic ATTRv-I84S patients. In contrast, cardiac fibrils from five genotypically different patients with cardiomyopathy or mixed phenotypes are structurally homogeneous. To understand fibril structure's impact on phenotype, it is necessary to study the fibrils from multiple patients sharing genotype and phenotype. Here we show the cryo-electron microscopy structures of fibrils extracted from four cardiomyopathic ATTRwt amyloidosis patients. Our study confirms that they share identical conformations with minimal structural variability, consistent with their homogenous clinical presentation. Our study contributes to the understanding of ATTR amyloidosis biopathology and calls for further studies.

Knowledge and attitudes about dementia among nursing students in Vietnam: a cross sectional study.

Introduction: Dementia is one of the most pressing health concerns in ageing population, posing significant burdens not only on the well-being and independence of people with dementia (PWD), but also on their families and communities. Building capacity for nursing students is essential for effectively enhancing the quality of life for affected people. However, various studies have highlighted knowledge gaps concerning dementia among nursing students worldwide. This study aimed to examine knowledge and attitude preparedness for dementia care among nursing students in Vietnam and associated factors. Methods: We used cross-sectional study design with four medical schools in northern, central and southern Vietnam which were selected based on convenience. Participants included full-time diploma senior nursing students (N = 600). A self-administered questionnaire consisted of 34 true-false questions evaluating students' knowledge about dementia (ADKS) and 20 items using a 5-point Likert scale assessing their attitudes toward dementia (DAS). The tools were piloted and validated with 60 students with good internal reliability. Results: Nursing students exhibited insufficient knowledge of dementia but displayed positive attitudes towards dementia. Engaging four-year diploma (ß = 0.501), being female (ß = 0.827), and achieving greater academic performance (ß = 0.795) statistically influenced nursing students' knowledge of dementia (p < 0.05). Similarly, being female (ß = 2.179), and possessing better knowledge of dementia (ß = 2.740) statistically effected nursing students' attitude toward dementia (p < 0.05). Conclusions: Students with greater academic achievement and females demonstrated better knowledge and attitudes toward dementia. To better nursing students' preparedness for dementia care, education on dementia care, especially practical training should be paid more attention.

Large-Scale Field Trials of an Eimeria Vaccine Induce Positive Effects on the Production Index of Broilers.

Live coccidiosis vaccines have mainly been used to reduce Eimeria species infection, which is considered the most economically important disease in the poultry industry. Evaluation data on vaccine effectiveness through large-scale field experiments are lacking, especially in broilers. Thus, the effectiveness of a commercial coccidiosis vaccine was evaluated in approximately 900,000 chicks reared on three open-broiler farms where coccidiosis is prevalent. The vaccine's effectiveness after vaccination of 1-day-old chicks was monitored using three parameters (lesion score, fecal oocyst shedding, and production index, PI) in nine trials performed three times on each farm. Lesion scores were confirmed in three different areas of the intestine because the vaccine contained four Eimeria species. The average lesion scores were 0.36 to 0.64 in the duodenal region, 0.30 to 0.39 in the jejuno-ileal region, and 0.18 to 0.39 in the cecal region. The average fecal oocyst shedding rate ranged from 19,766 to 100,100 oocysts per gram, showing large variations depending on farms and buildings within the farm. Compared with the PI of the previous 9-10 trials on each farm, the PI increased by 2.45 to 23.55. Because of the potential for perturbation of the fecal microbiota by live coccidiosis vaccines, the fecal microbiota was investigated using 16S rRNA microbial profiling. Although the ß-diversity was significantly different in distribution and relative abundance among farms (PERMANOVA, pseudo-F = 4.863, p = 0.009), a Kyoto Encyclopedia of Genes and Genomes pathway analysis found no significant bacterial invasion of the epithelial cell pathway across farms. This large-scale field trial of a live Eimeria vaccine indicates that coccidiosis vaccines can have meaningful effects on the poultry industry and could be used as an alternative to the prophylactic use of anticoccidial drugs under field conditions.

An efficient hybrid deep learning architecture for predicting short antimicrobial peptides.

Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs.

Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium.

Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Current susceptibility testing approaches limit our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms. Salmonella Typhimurium (S. Typhimurium) is an enteric pathogen responsible for severe gastrointestinal illness and invasive disease. Despite widespread resistance, ciprofloxacin remains a common treatment for Salmonella infections, particularly in lower-resource settings, where the drug is given empirically. Here, we exploit high-content imaging to generate deep phenotyping of S. Typhimurium isolates longitudinally exposed to increasing concentrations of ciprofloxacin. We apply machine learning algorithms to the imaging data and demonstrate that individual isolates display distinct growth and morphological characteristics that cluster by time point and susceptibility to ciprofloxacin, which occur independently of ciprofloxacin exposure. Using a further set of S. Typhimurium clinical isolates, we find that machine learning classifiers can accurately predict ciprofloxacin susceptibility without exposure to it or any prior knowledge of resistance phenotype. These results demonstrate the principle of using high-content imaging with machine learning algorithms to predict drug susceptibility of clinical bacterial isolates. This technique may be an important tool in understanding the morphological impact of antimicrobials on the bacterial cell to identify drugs with new modes of action.

An assessment of uncontrolled human interventions on the contemporary sediment budget and morphological alterations of the Vu Gia Thu Bon River basin, central Vietnam.

The Vu Gia Thu Bon (VGTB) River basin is critical for regional development and prosperity in water resources. However, human interventions (e.g., dam construction and sand mining) have significantly affected this basin's sediment budget and morphological alterations over recent decades. Such humane actions drive an imbalance in water resources in the basin from upstream to downstream. Therefore, this study investigated spatiotemporal changes in sediment budget and morphology alterations using long-term data and bathymetric surveys; from these data, dams and sand mining contributions were quantified and differentiated. Based on field survey data and interviews, we estimated the sand-mining volume by incorporating reported and a newly proposed empirical formula. The results show that the total riverbed incision volume from 2010 to 2021 was 63.30 Mm3, with an incision rate of 0.14 m/yr. The officially reported sand-mining rate was 1.12 Mm3/yr, while the newly proposed empirical formula estimated 4.4 Mm3/yr. According to the developed empirical formula, the percentage reductions in the sediment budget due to sand mining and upstream dams were 69.7 % and 30.3 %, respectively, according to reports, and 17.8 % and 82.2 %. The statistical method was thus likely too conservative compared to the developed empirical formula. We found that the natural sediment supplies sourced from upstream were insufficient to compensate for the mined bed material. Therefore, our combination of different datasets permitted the assessment of future geomorphological developments within the VGTB River basin under the ongoing sediment deficits. The results of this study provide valuable insights into the impacts of human interventions, specifically sand mining, on the sediment budget, morphological alterations, and riverbed incision. The developed assessment forms the foundation for developing and expanding the region's water/sediment resource management strategies.

An Effective Health System Approach to End TB: Implementing the Double X Strategy in Vietnam.

Countries that are high burden for TB must reverse the COVID-19 pandemic's devastating effects to accelerate progress toward ending TB. Vietnam's Double X (2X) strategy uses chest radiography (CXR) and GeneXpert (Xpert) rapid diagnostic testing to improve early detection of TB disease. Household contacts and vulnerable populations (e.g., individuals aged 60 years and older, smokers, diabetics, those with alcohol use disorders, and those previously treated for TB) with and without TB symptoms were screened in community campaigns using CXRs, followed by Xpert for those with a positive screen. In public non-TB district facilities, diabetics, respiratory outpatients, inpatients with lung disease, and other vulnerable populations underwent 2X evaluation. During COVID-19 restrictions in Vietnam, the 2X strategy improved access to TB services by decentralization to commune health stations, the lowest level of the health system, and enabling self-screening using a quick response mobile application. The number needed to screen (NNS) with CXRs to diagnose 1 person with TB disease was calculated for all 2X models and showed the highest yield among self-screeners (11 NNS with CXR), high yield for vulnerable populations in communities (60 NNS) and facilities (19 NNS), and moderately high yield for household contacts in community campaigns (154 NNS). Computer-aided diagnosis for CXRs was incorporated into community and facility implementation and improved physicians' CXR interpretations and Xpert referral decisions. Integration of TB infection and TB disease evaluation increased eligibility for TB preventive treatment among household contacts, a major challenge during implementation. The 2X strategy increased the rational use of Xpert, employing a health system-wide approach that reached vulnerable populations with and without TB symptoms in communities and facilities for early detection of TB disease. This strategy was effectively adapted to different levels of the health system during COVID-19 restrictions and contributed to post-pandemic TB recovery in Vietnam.

A prediction model of abnormal acid reflux in gastroesophageal reflux disease.

BACKGROUND AND AIM: The measurement of esophageal acid exposure time (AET) using combined multichannel intraluminal impedance-pH (MII-pH) tests is the gold standard for diagnosing gastroesophageal reflux disease (GERD). However, this catheter-based 24-h test can cause considerable patient discomfort. Our aim is to identify factors affecting AET and to develop a scoring model for predicting AET abnormalities before conducting the MII-pH test. METHODS: Of the 366 patients who underwent MII-pH test at two facilities in Japan and Vietnam, 255 patients who also had esophagogastroduodenoscopy and high-resolution manometry were included in this study. Logistic regression analysis was conducted using risk factors for AET > 6% identified from a derivation cohort (n = 109). A scoring system predicting AET > 6% was then constructed and externally validated with a separate cohort (n = 146). RESULTS: Three variables were derived from the prediction model: male gender, Hill grades III-IV, and weak mean distal contractile integrals. Based on these scores, patients were classified into low (0 point), intermediate (1-3 points), and high (4 points) risk groups. The probabilities of having an AET > 6% were 6%, 34%, and 100% for these groups, respectively. A score of < 1 excluded patients with abnormal AET, with a negative predictive value of 93.8% in the derivation cohort and 80.0% in the validation cohort. CONCLUSIONS: We derived and externally validated a prediction model for abnormal AET. This system could assist in guiding the appropriate treatment strategies for GERD.

The influence of attributes on community preferences regarding antibiotic treatment: evidence from a discrete choice model.

Antibiotic resistance (AR) rates in Vietnam are among the highest in Asia, and recent infections due to multi-drug resistance in the country have caused thousands of deaths each year. This study investigated a Vietnamese community's preferences for antibiotic treatment and its knowledge and attitudes regarding antibiotics. A discrete choice experiment-based survey was developed and administered to the population of interest. The respondents were given sociodemographic-, knowledge- and attitude-related items and 17 pairs of choice tasks. Two hypothetical options were included in each choice task. Latent class analysis was conducted to determine the differences among the respondents' preferences. Among 1,014 respondents, 805 (79.4%) gave valid questionnaires. A three-latent-class model with four covariates (age, healthcare-related education or career, occupation, and attitude classifications) was used in the analysis. All five attributes significantly influenced the respondents' decisions. The majority, including young employed respondents with non-healthcare-related work or education, found treatment failure more important. Older respondents who had healthcare-related education/careers and/or appropriate antibiotic use- and antibiotics resistance-related attitudes, regarded contribution to antibiotic resistance as an important attribute in selecting antibiotic treatments. Unemployed individuals with correct knowledge identified the cost of antibiotic treatment as the most essential decision-making factor. Findings suggest minimal antibiotic impact on resistance; only 7.83% view it as amajor concern. The respondents exhibited substantial preference heterogeneity, and the general Vietnamese public had poor knowledge of and attitudes toward antibiotic use and antibiotic resistance. This study emphasizes the need for individual responsibility for antibiotic resistance and appropriate antibiotic use.

Identification of Critical Immune Regulators and Potential Interactions of IL-26 in Riemerella anatipestifer-Infected Ducks by Transcriptome Analysis and Profiling.

Riemerella anatipestifer (RA) is an economically important pathogen in the duck industry worldwide that causes high mortality and morbidity in infected birds. We previously found that upregulated IL-17A expression in ducks infected with RA participates in the pathogenesis of the disease, but this mechanism is not linked to IL-23, which primarily promotes Th17 cell differentiation and proliferation. RNA sequencing analysis was used in this study to investigate other mechanisms of IL-17A upregulation in RA infection. A possible interaction of IL-26 and IL-17 was discovered, highlighting the potential of IL-26 as a novel upstream cytokine that can regulate IL-17A during RA infection. Additionally, this process identified several important pathways and genes related to the complex networks and potential regulation of the host immune response in RA-infected ducks. Collectively, these findings not only serve as a roadmap for our understanding of RA infection and the development of new immunotherapeutic approaches for this disease, but they also provide an opportunity to understand the immune system of ducks.