Factors associated with 90-day mortality in Vietnamese stroke patients: Prospective findings compared with explainable machine learning, multicenter study.

The prevalence and predictors of mortality following an ischemic stroke or intracerebral hemorrhage have not been well established among patients in Vietnam. 2885 consecutive diagnosed patients with ischemic stroke and intracerebral hemorrhage at ten stroke centres across Vietnam were involved in this prospective study. Posthoc analyses were performed in 2209 subjects (age was 65.4 ± 13.7 years, with 61.4% being male) to explore the clinical characteristics and prognostic factors associated with 90-day mortality following treatment. An explainable machine learning model using extreme gradient boosting and SHapley Additive exPlanations revealed the correlation between original clinical research and advanced machine learning methods in stroke care. In the 90 days following treatment, the mortality rate for ischemic stroke was 8.2%, while for intracerebral hemorrhage, it was higher at 20.5%. Atrial fibrillation was an elevated risk of 90-day mortality in the ischemic stroke patient (OR 3.09; 95% CI 1.90-5.02, p<0.001). Among patients with intracerebral hemorrhage, there was no statistical significance in those with hypertension compared to their counterparts without hypertension (OR 0.65, 95% CI 0.41-1.03, p > 0.05). The baseline NIHSS score was a significant predictor of 90-day mortality in both patient groups. The machine learning model can predict a 0.91 accuracy prediction of death rate after 90 days. Age and NIHSS score were in the top high risks with other features, such as consciousness, heart rate, and white blood cells. Stroke severity, as measured by the NIHSS, was identified as a predictor of mortality at discharge and the 90-day mark in both patient groups.

The experience sampling methodology as a digital clinical tool for more person-centered mental health care: an implementation research agenda.

This position paper by the international IMMERSE consortium reviews the evidence of a digital mental health solution based on Experience Sampling Methodology (ESM) for advancing person-centered mental health care and outlines a research agenda for implementing innovative digital mental health tools into routine clinical practice. ESM is a structured diary technique recording real-time self-report data about the current mental state using a mobile application. We will review how ESM may contribute to (1) service user engagement and empowerment, (2) self-management and recovery, (3) goal direction in clinical assessment and management of care, and (4) shared decision-making. However, despite the evidence demonstrating the value of ESM-based approaches in enhancing person-centered mental health care, it is hardly integrated into clinical practice. Therefore, we propose a global research agenda for implementing ESM in routine mental health care addressing six key challenges: (1) the motivation and ability of service users to adhere to the ESM monitoring, reporting and feedback, (2) the motivation and competence of clinicians in routine healthcare delivery settings to integrate ESM in the workflow, (3) the technical requirements and (4) governance requirements for integrating these data in the clinical workflow, (5) the financial and competence related resources related to IT-infrastructure and clinician time, and (6) implementation studies that build the evidence-base. While focused on ESM, the research agenda holds broader implications for implementing digital innovations in mental health. This paper calls for a shift in focus from developing new digital interventions to overcoming implementation barriers, essential for achieving a true transformation toward person-centered care in mental health.

Handling Missing Data in COVID-19 Incidence Estimation: Secondary Data Analysis.

Background: The COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts. Objective: We aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations. Methods: This study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-19. We selected 7 analytical methods to assess the effects of handling missing data and calculated statistical and epidemiological indices to measure the effectiveness of each method. Results: Our study examined missing data imputation performance across 3 study time periods: zero COVID-19 (n=3149), transition (n=1290), and new normal (n=9288). Imputation analyses showed that K-nearest neighbor (KNN) had the lowest mean absolute percentage change (APC) in CIR across the range (5% to 30%) of missing data. For instance, with 15% missing data, KNN resulted in 10.6%, 10.6%, and 9.7% average bias across the zero COVID-19, transition, and new normal periods, compared to 39.9%, 51.9%, and 289.7% with the maximum likelihood method. The autoregressive integrated moving average model showed the greatest mean APC in the mean number of confirmed cases of COVID-19 during each COVID-19 containment cycle (CCC) when we imputed the missing data in the zero COVID-19 period, rising from 226.3% at the 5% missing level to 6955.7% at the 30% missing level. Imputing missing data with median imputation methods had the lowest bias in the average number of confirmed cases in each CCC at all levels of missing data. In detail, in the 20% missing scenario, while median imputation had an average bias of 16.3% for confirmed cases in each CCC, which was lower than the KNN figure, maximum likelihood imputation showed a bias on average of 92.4% for confirmed cases in each CCC, which was the highest figure. During the new normal period in the 25% and 30% missing data scenarios, KNN imputation had average biases for CIR and confirmed cases in each CCC ranging from 21% to 32% for both, while maximum likelihood and moving average imputation showed biases on average above 250% for both CIR and confirmed cases in each CCC. Conclusions: Our study emphasizes the importance of understanding that the specific imputation method used by investigators should be tailored to the specific epidemiological context and data collection environment to ensure reliable estimates of the CIR.

Applying machine learning in screening for Down Syndrome in both trimesters for diverse healthcare scenarios.

Background: This paper describes the development of low-cost, effective, non-invasive machine learning-based prediction models for Down Syndrome in the first two trimesters of pregnancy in Vietnam. These models are adaptable to different situations with limited screening capacities at community-based healthcare facilities. Method: Ultrasound and biochemical testing alone and in combination, from both trimesters were employed to build prediction models based on k-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting algorithms. Results: A total of 7,076 pregnant women from a single site in Northern Vietnam were included, and 1,035 had a fetus with Down Syndrome. Combined ultrasound and biochemical testing were required to achieve the highest accuracy in trimester 2, while models based only on biochemical testing performed as well as models based on combined testing during trimester 1. In trimester 1, Extreme Gradient Boosting produced the best model with 94% accuracy and 88% AUC, while Support Vector Machine produced the best model in trimester 2 with 89% accuracy and 84% AUC. Conclusions: This study explored a range of machine learning models under different testing scenarios. Findings point to the potential feasibility of national screening, especially in settings without enough equipment and specialists, after additional model validation and fine tuning is performed.

Deep learning-based predictive models for forex market trends: Practical implementation and performance evaluation.

In recent years, there has been growing interest in the prediction of financial market trends, due to its potential applications in the real world. Unlike traditional investment avenues such as the stock market, the foreign exchange (Forex) market revolves around two primary types of orders that correspond with the market's direction: upward and downward. Consequently, forecasting the behaviour of the Forex behaviour market can be simplified into a binary classification problem to streamline its complexity. Despite the significant enhancements and improvements in performance seen in recent proposed predictive models for the forex market, driven by the advancement of deep learning in various domains, it remains imperative to approach these models with careful consideration of best practices and real-world applications. Currently, only a limited number of papers have been dedicated to this area. This article aims to bridge this gap by proposing a practical implementation of deep learning-based predictive models that perform well for real-world trading activities. These predictive mechanisms can help traders in minimising budget losses and anticipate future risks. Furthermore, the paper emphasises the importance of focussing on return profit as the evaluation metric, rather than accuracy. Extensive experimental studies conducted on realistic Yahoo Finance data sets validate the effectiveness of our implemented prediction mechanisms. Furthermore, empirical evidence suggests that employing the use of three-value labels yields superior accuracy performance compared to traditional two-value labels, as it helps reduce the number of orders placed.

How do free healthcare policies impact on utilization of maternal and child health services in fragile settings? Evidence from a controlled interrupted time-series analysis in Burkina Faso.

Burkina Faso has implemented a nationwide free healthcare policy (gratuité) for pregnant and lactating women and children under five since April 2016. Studies have shown that free healthcare policies can increase healthcare service use. However, the emerging COVID-19 pandemic, escalating insecurity, and the political situation in recent years might have affected the implementation of such policies. No studies have looked at whether the gratuité maintained high service use under such changing circumstances. Our study aimed to assess the effects of gratuité on the utilization of facility-based delivery and curative care of children under five in light of this changing context. We employed a controlled interrupted time series analysis using data from the Health Management Information System and annual statistical reports of 2,560 primary health facilities from January 2013 to December 2021. We focused on facility-based deliveries and curative care for children under five, with antenatal care and curative care for children over five as non-equivalent controls. We employed segmented regression with the generalized least square model, accounting for autocorrelation and monthly seasonality. The monthly utilization rate among children under five compared to those above five (controls) immediately increased by 111.19 visits per 1,000 children (95% CI: 91.12; 131.26) due to the gratuité. This immediate effect declined afterwards with a monthly change of 0.93 per 1,000 children (95% CI: -1.57, -0.29). We found no significant effects, both immediate and long-term, on the use of maternal care services attributable to the gratuité. Our findings suggest that free healthcare policies can be instrumental in improving healthcare, yet more comprehensive strategies are needed to maintain healthcare utilization. Our findings reflect the overall situation in the country, while localised research is needed to understand the effect of insecurity and the pandemic at the local level, and the effects of gratuité across geographies and socio-economic statuses.

New records of birds from Central Vietnam.

Background: There has been a series of bird surveys conducted in Vietnam over the last 20 years. However, most of these studies and surveys have focused on sites in Tonkin, the Red River Delta, Cochinchina (Mekong Delta), the Central Highlands and mountainous areas of Central Annam (Central region of Vietnam). The central coastal plain as well as the mountainous region of North Annam have rarely been comprehensively investigated. New information: As a result of our field surveys in 2023 and 2024, a total of 15 species of birds are recorded for the first time from North, Central and South Annam, comprising one Frigate-Bird species (Fregatidae), one ibis (Threskiornithidae), one reed-warbler (Acrocephalidae), one Treecreeper (Certhiidae), two buntings (Emberizidae), one chat (Muscicapidae), one yuhina (Zosteropidae), two nuthatches (Sittidae), two members of the laughingthrush family (Leiothrichidae) and three bulbuls (Pycnonotidae). In addition to photographs confirming the new records, we provide information on the distribution and conservation status of these newly-recorded bird species from Central Vietnam.

A Non-Hemadsorbing Live-Attenuated Virus Vaccine Candidate Protects Pigs against the Contemporary Pandemic Genotype II African Swine Fever Virus.

African swine fever (ASF) is a highly contagious and severe hemorrhagic transboundary swine viral disease with up to a 100% mortality rate, which leads to a tremendous socio-economic loss worldwide. The lack of safe and efficacious ASF vaccines is the greatest challenge in the prevention and control of ASF. In this study, we generated a safe and effective live-attenuated virus (LAV) vaccine candidate VNUA-ASFV-LAVL3 by serially passaging a virulent genotype II strain (VNUA-ASFV-L2) in an immortalized porcine alveolar macrophage cell line (3D4/21, 50 passages). VNUA-ASFV-LAVL3 lost its hemadsorption ability but maintained comparable growth kinetics in 3D4/21 cells to that of the parental strain. Notably, it exhibited significant attenuation of virulence in pigs across different doses (103, 104, and 105 TCID50). All vaccinated pigs remained healthy with no clinical signs of African swine fever virus (ASFV) infection throughout the 28-day observation period of immunization. VNUA-ASFV-LAVL3 was efficiently cleared from the blood at 14-17 days post-infection, even at the highest dose (105 TCID50). Importantly, the attenuation observed in vivo did not compromise the ability of VNUA-ASFV-LAVL3 to induce protective immunity. Vaccination with VNUA-ASFV-LAVL3 elicited robust humoral and cellular immune responses in pigs, achieving 100% protection against a lethal wild-type ASFV (genotype II) challenge at all tested doses (103, 104, and 105 TCID50). Furthermore, a single vaccination (104 TCID50) provided protection for up to 2 months. These findings suggest that VNUA-ASFV-LAVL3 can be utilized as a promising safe and efficacious LAV candidate against the contemporary pandemic genotype II ASFV.

A Model-Informed Drug Development Approach Supporting the Approval of an Unstudied Valbenazine Dose for Patients With Tardive Dyskinesia.

Valbenazine is a highly potent and selective inhibitor of synaptic vesicular monoamine transporter 2. The current therapeutic doses of valbenazine for tardive dyskinesia (TD) are 40, 60, or 80 mg capsules, given orally, once daily (QD). While 40 and 80 mg were investigated in phase 3 KINECT® 3 trial and initially approved, the approval of valbenazine 60 mg was based on the analysis utilizing the Model-informed drug development (MIDD) approach, facilitated through the US Food and Drug Administration's MIDD Pilot Program. This study aimed to demonstrate the efficacy of 60 mg QD dose through model simulations using an established exposure-response (E-R) relationship between valbenazine active metabolite [+]-α-dihydrotetrabenazine exposure and the change from baseline in Abnormal Involuntary Movement Scale total score (AIMS-CFB). A longitudinal E-R model was constructed based on the 40 and 80 mg data from the KINECT 3 trial. The final Emax model adequately predicted dose-dependent improvement in the primary endpoint and was used to interpolate AIMS-CFB for 60 mg at week 6. The efficacy of the unstudied 60 mg dose regimen is expected to be within the range of doses studied clinically with predicted mean AIMS-CFB (95% confidence interval) of -2.69 (-3.30, -2.13) between observed mean AIMS-CFB for 40 mg of -1.92 and 80 mg of -3.39. Results from this analysis provided the key evidence to establish efficacy of 60 mg QD without the need for an additional clinical trial. The availability of valbenazine 60 mg dose fills an existing medical need for patients with TD who could benefit from this third effective dose.

Conquering hypertension in Vietnam: 12- month follow up results from a cluster-randomised controlled trial.

Background: Approximately 20% of adults in Vietnam have hypertension, and management of this chronic condition remains challenging. This study aimed to assess the effectiveness of a multicomponent intervention in reducing blood pressure (BP) in adults with uncontrolled hypertension. Methods: This cluster randomised controlled trial was conducted in sixteen communities (8 intervention and 8 comparison) in a rural setting in Vietnam (2017-2022). Consenting adults (aged ≥ 18 years) with uncontrolled hypertension were enrolled. Sixteen communities were equally randomised to an intervention or comparison group stratified by district. Consenting adults were assigned to study groups by community. The comparison arm received training sessions about hypertension prevention and management for health workers, and patient education materials. The intervention arm received training sessions about hypertension prevention and management for health workers, and patient education materials and three enhancement components including a storytelling intervention, home BP self-monitoring, and expanded community health worker services. The primary outcome was the difference in changes in patient's levels of systolic BP between the study groups over a 12-month follow-up period. Patients and outcome assessors were masked. Findings: A total of 671 patients (340: intervention, 331: comparison) were enrolled in the trial. The mean age was 66 years and 45% were men. At the 12-month follow-up, the mean systolic BP declined by 18.4 mmHg in the intervention group and 3.7 mmHg in the comparison group (differential decline of 14.7 mmHg [95% CI: 11.8-17.6]). The intervention group also achieved better BP control and medication adherence than the comparison group. There were no serious adverse events related to study participation. Interpretation: The results of this trial demonstrate that a multicomponent intervention can effectively reduce elevated BP in individuals with uncontrolled hypertension in Vietnam. Trial registration: This trial was registered at ClinicalTrials.gov, NCT03590691. Funding: National Heart, Lung, and Blood Institute.

Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings.

Individuals with ultrarare disorders pose a structural challenge for healthcare systems since expert clinical knowledge is required to establish diagnoses. In TRANSLATE NAMSE, a 3-year prospective study, we evaluated a novel diagnostic concept based on multidisciplinary expertise in Germany. Here we present the systematic investigation of the phenotypic and molecular genetic data of 1,577 patients who had undergone exome sequencing and were partially analyzed with next-generation phenotyping approaches. Molecular genetic diagnoses were established in 32% of the patients totaling 370 distinct molecular genetic causes, most with prevalence below 1:50,000. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were identified, mainly in individuals with neurodevelopmental disorders. Sequencing data of the subcohort that consented to computer-assisted analysis of their facial images with GestaltMatcher could be prioritized more efficiently compared with approaches based solely on clinical features and molecular scores. Our study demonstrates the synergy of using next-generation sequencing and phenotyping for diagnosing ultrarare diseases in routine healthcare and discovering novel etiologies by multidisciplinary teams.

Evaluation of the diagnostic sensitivity and specificity of two pen-side tests for detecting African swine fever virus in experimentally infected pigs.

African swine fever virus (ASFV) has spread through many countries and regions worldwide, causing significant losses. Timely detection of ASFV-infected pigs is crucial for disease control. In this study, we assessed the performance of two pen-side tests: a portable real-time PCR (qPCR) test for detecting viral genomic DNA and a lateral flow immunoassay (LFIA) for detecting viral antigens. To determine the time from infection to the earliest detection, 10 ASFV-seronegative pigs were inoculated intramuscularly with 104.0 hemadsorption dose 50 of a highly virulent ASFV strain. Whole blood and oral swab samples were alternately collected from each group of five pigs daily until all succumbed to the infection. Samples were promptly subjected to the two pen-side tests upon collection, and a subset was transported to a veterinary diagnostic laboratory for analysis using a reference qPCR assay. Viral genomic DNA was consistently detected by the reference qPCR assay in all blood samples from 2 days postinfection (dpi), preceding the onset of clinical signs, and in oral swabs from 4 dpi onwards. The portable qPCR test demonstrated comparable performance to the reference qPCR assay for both whole blood and oral swab samples. The LFIA exhibited 100% specificity when testing with whole blood samples but showed reduced sensitivity, particularly for blood samples collected early or late after infection. The antigen test did not perform well with oral swabs.

Strategies, processes, outcomes, and costs of implementing experience sampling-based monitoring in routine mental health care in four European countries: study protocol for the IMMERSE effectiveness-implementation study.

BACKGROUND: Recent years have seen a growing interest in the use of digital tools for delivering person-centred mental health care. Experience Sampling Methodology (ESM), a structured diary technique for capturing moment-to-moment variation in experience and behaviour in service users' daily life, reflects a particularly promising avenue for implementing a person-centred approach. While there is evidence on the effectiveness of ESM-based monitoring, uptake in routine mental health care remains limited. The overarching aim of this hybrid effectiveness-implementation study is to investigate, in detail, reach, effectiveness, adoption, implementation, and maintenance as well as contextual factors, processes, and costs of implementing ESM-based monitoring, reporting, and feedback into routine mental health care in four European countries (i.e., Belgium, Germany, Scotland, Slovakia). METHODS: In this hybrid effectiveness-implementation study, a parallel-group, assessor-blind, multi-centre cluster randomized controlled trial (cRCT) will be conducted, combined with a process and economic evaluation. In the cRCT, 24 clinical units (as the cluster and unit of randomization) at eight sites in four European countries will be randomly allocated using an unbalanced 2:1 ratio to one of two conditions: (a) the experimental condition, in which participants receive a Digital Mobile Mental Health intervention (DMMH) and other implementation strategies in addition to treatment as usual (TAU) or (b) the control condition, in which service users are provided with TAU. Outcome data in service users and clinicians will be collected at four time points: at baseline (t0), 2-month post-baseline (t1), 6-month post-baseline (t2), and 12-month post-baseline (t3). The primary outcome will be patient-reported service engagement assessed with the service attachment questionnaire at 2-month post-baseline. The process and economic evaluation will provide in-depth insights into in-vivo context-mechanism-outcome configurations and economic costs of the DMMH and other implementation strategies in routine care, respectively. DISCUSSION: If this trial provides evidence on reach, effectiveness, adoption, implementation and maintenance of implementing ESM-based monitoring, reporting, and feedback, it will form the basis for establishing its public health impact and has significant potential to bridge the research-to-practice gap and contribute to swifter ecological translation of digital innovations to real-world delivery in routine mental health care. TRIAL REGISTRATION: ISRCTN15109760 (ISRCTN registry, date: 03/08/2022).

Lifestyle practices and associated factors among adults with hypertension: Conquering Hypertension in Vietnam-solutions at the grassroots level study.

BACKGROUND: Vietnam is experiencing an increasing prevalence of hypertension in its adult population. In addition to medical therapy, modifying adverse lifestyle practices is important for effective blood pressure control. There are limited data on unhealthy lifestyle practices in patients with chronic diseases, however, particularly among hypertensive patients living in rural Vietnam. Our study objectives were to examine the prevalence of unhealthy lifestyle practices and associated factors among rural Vietnamese adults with uncontrolled hypertension. METHODS: Data from the baseline survey of a cluster randomized trial among hypertensive Vietnamese adults (2017-2022) were utilized. Information on unhealthy lifestyle practices including smoking, excessive alcohol consumption, physical inactivity, and inadequate fruit and vegetable intake was collected from study participants. The primary study outcome was having ≥2 unhealthy lifestyle practices. A multivariable logistic regression model was used to examine factors associated with the primary study outcome. RESULTS: The mean age of the 671 patients was 67 years and 45.0% were men. Nearly three out of every four participants had one or fewer unhealthy practices, 24.0% had two, and 3.3% had three or all four unhealthy lifestyle practices. Men, individuals who did unpaid work or were unemployed, and individuals with hypertension level III were more likely to have ≥2 unhealthy lifestyle practices, whereas individuals with higher education were less likely to have ≥2 unhealthy lifestyle practices compared with respective comparison groups. CONCLUSIONS: We observed a high prevalence of unhealthy lifestyle practices among rural Vietnamese patients with uncontrolled hypertension. Several demographic factors were associated with a greater number of unhealthy lifestyle practices. Newer interventions and educational programs encouraging lifestyle modification practices are needed to control hypertension among adults living in rural settings of Vietnam.

A novel approach for APT attack detection based on feature intelligent extraction and representation learning.

Advanced Persistent Threat (APT) attacks are causing a lot of damage to critical organizations and institutions. Therefore, early detection and warning of APT attack campaigns are very necessary today. In this paper, we propose a new approach for APT attack detection based on the combination of Feature Intelligent Extraction (FIE) and Representation Learning (RL) techniques. In particular, the proposed FIE technique is a combination of the Bidirectional Long Short-Term Memory (BiLSTM) deep learning network and the Attention network. The FIE combined model has the function of aggregating and extracting unusual behaviors of APT IPs in network traffic. The RL method proposed in this study aims to optimize classifying APT IPs and normal IPs based on two main techniques: rebalancing data and contrastive learning. Specifically, the rebalancing data method supports the training process by rebalancing the experimental dataset. And the contrastive learning method learns APT IP's important features based on finding and pulling similar features together as well as pushing contrasting data points away. The combination of FIE and RL (abbreviated as the FIERL model) is a novel proposal and innovation and has not been proposed and published by any research. The experimental results in the paper have proved that the proposed method in the paper is correct and reasonable when it has shown superior efficiency compared to some other studies and approaches over 5% on all measurements.

Effective sludge management: Reuse of biowaste and sewer sediments for fired bricks.

This study partially replaced the clay with sewer sludge (SS) and rice husk (RH-SS) to make fired bricks. The brick samples were examed in terms of shrinkage, water absorption, and compressive strength. Besides, they were analyzed via XRD and metal extraction to determine the heavy metal residuals in the products. The results showed that it was possible to fabricate fired bricks using sewer sludge or rice husk-blended sludge with up to 30% by weight. These brick samples complied with the technical standard for clay brick production, in which the compressive strength was more than 7.5 MPa, water absorption was from 11-16%, and the linear shrinkage was all less than 5%. The rice husk addition helped mitigate the heavy metal residuals in the bricks and leaching liquid, in which all the values were lower than the US-EPA maximum concentration of contaminants for toxicity characteristics.Implications: Previous studies have proved the possibility of mixing sewage sludge from different origins (sewage sludge, river sediment, canal sediment, sewer sediment, etc.) with clay and some wastes to make bricks. In which, mostof the studies used sewage sludge from wastewater treatment plants, very fewdealt with lake/river or sewer sediment. This study shall be the first to study the possibility of employing sewer sediments with the addition of rice husk powder to achieve two targets, including (1) the reuse of biowaste and sludge for brick fabrication and (2) the reduction of heavy metals in final calcined bricks. Different ratios of the rice-husk blended sewer sludge (RH-SS) - clay mixture shall be tested to find the optimized compositions. The results showed that it was possible to fabricate fired bricks using sewer sludge or rice husk-blended sludge with up to 30% by weight, which meant reduce 30% of clay in the brick production. The final products were proved to meet the quality standard in terms of compressive strength (more than 10 MPa), water absorption(from 11-16%), and the linear shrinkage (less than 5%). Larger scale of this study can be an evident to recommend for policy change in the waste reuse in construction field.

Optical Genome Mapping Reveals Disruption of the RASGRF2 Gene in a Patient with Developmental Delay Carrying a De Novo Balanced Reciprocal Translocation.

While balanced reciprocal translocations are relatively common, they often remain clinically silent unless they lead to the disruption of functional genes. In this study, we present the case of a boy exhibiting developmental delay and mild intellectual disability. Initial karyotyping revealed a translocation t(5;6)(q13;q23) between chromosomes 5 and 6 with limited resolution. Optical genome mapping (OGM) enabled a more precise depiction of the breakpoint regions involved in the reciprocal translocation. While the breakpoint region on chromosome 6 did not encompass any known gene, OGM revealed the disruption of the RASGRF2 (Ras protein-specific guanine nucleotide releasing factor 2) gene on chromosome 5, implicating RASGRF2 as a potential candidate gene contributing to the observed developmental delay in the patient. Variations in RASGRF2 have so far not been reported in developmental delay, but research on the RASGRF2 gene underscores its significance in various aspects of neurodevelopment, including synaptic plasticity, signaling pathways, and behavioral responses. This study highlights the utility of OGM in identifying breakpoint regions, providing possible insights into the understanding of neurodevelopmental disorders. It also helps affected individuals in gaining more knowledge about potential causes of their conditions.

The prescription of beta-blockers in older patients with heart failure with reduced ejection fraction: an observational study in Vietnam.

This study in older hospitalized patients with heart failure with reduced ejection fraction (HFrEF) aimed to examine the prevalence of beta-blocker prescription and its associated factors. A total of 190 participants were recruited from July 2019 to July 2020. The inclusion criteria included: (1) aged ≥ 60 years, (2) having a diagnosis of chronic HFrEF in the medical records, (3) hospitalized for at least 48 h. The participants had a mean age of 75.5 ± 9.1, and 46.8% were female. Of these, 55.3% were prescribed beta-blockers during admission. To explore the factors associated with beta-blocker prescription, multivariable logistic regression analysis was applied and the results were presented as odds ratios (OR) and 95% confidence intervals (CI). On multivariate logistic regression models, higher NYHA classes (OR 0.49, 95%CI 0.26-0.94), chronic obstructive pulmonary disease (OR 0.17, 95% CI 0.04-0.85), chronic kidney disease (OR 0.40, 95% CI 0.19-0.83), and heart rate under 65 (OR 0.34, 95% CI 0.12-0.98) were associated with a reduced likelihood of prescription. In this study, we found a low rate of beta-blocker prescriptions, with only around half of the participants being prescribed beta-blockers. Further studies are needed to examine the reasons for the under-prescription of beta-blockers, and to evaluate the long-term benefits of beta-blockers in elderly patients with HFrEF in this population.

Crosstalk between genomic variants and DNA methylation in FLT3 mutant acute myeloid leukemia.

Acute myeloid leukemia (AML) is a type of blood cancer with diverse genetic variations and DNA methylation alterations. By studying the interaction of gene mutations, expression, and DNA methylation, we aimed to gain valuable insights into the processes that lead to block differentiation in AML. We analyzed TCGA-LAML data (173 samples) with RNA sequencing and DNA methylation arrays, comparing FLT3 mutant (48) and wild-type (125) cases. We conducted differential gene expression analysis using cBioPortal, identified DNA methylation differences with ChAMP tool, and correlated them with gene expression changes. Gene set enrichment analysis (g:Profiler) revealed significant biological processes and pathways. ShinyGo and GeneCards were used to find potential transcription factors and their binding sites among significant genes. We found significant differentially expressed genes (DEGs) negatively correlated with their most significant methylation probes (Pearson correlation coefficient of -0.49, P-value <0.001) between FLT3 mutant and wild-type groups. Moreover, our exploration of 450 k CpG sites uncovered a global hypo-methylated status in 168 DEGs. Notably, these methylation changes were enriched in the promoter regions of Homebox superfamily gene, which are crucial in transcriptional-regulating pathways in blood cancer. Furthermore, in FLT3 mutant AML patient samples, we observed overexpress of WT1, a transcription factor known to bind homeobox gene family. This finding suggests a potential mechanism by which WT1 recruits TET2 to demethylate specific genomic regions. Integrating gene expression and DNA methylation analyses shed light on the impact of FLT3 mutations on cancer cell development and differentiation, supporting a two-hit model in AML. This research advances understanding of AML and fosters targeted therapeutic strategy development.

A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema.

Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20 pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300 mg every 2 weeks (Q2W) to every 4 weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.