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Abstract Sports injuries often arise from improper scheduling of exercise loads, and timely assessment of these loads is essential for minimizing injury risk. This article investigates the validity of using changes in pupillary light reflex (PLR) during dynamic aerobic training as a novel approach to evaluating exercise load. Dynamic aerobic training was conducted on a power bicycle for 15 min. With a 3-minute interval as the demarcation line, PLR measurement was performed before training and heart rate was recorded throughout the process. The Rating of Perceived Exertion (RPE) scale invented by Borg was used for scoring before training and after training. The normal distribution of data was confirmed through the Shapiro-Wilk test. Pearson correlation analysis was used to quantify the correlation between variables. The Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC) analysis were used to determine the indicators of exercise load. The optimal threshold of the indicators was calculated through the Youden index to evaluate sensitivity and specificity. Thirty male second-tier athletes with a mean age of 23.66 ± 2.21 years, a mean height of 175.3 ± 6.5 cm, and a mean weight of 68.99 ± 10.35 kg participated in this study. Based on the RPE scale results, it was confirmed that the 15-minute dynamic aerobic exercise successfully elicited varying levels of perceived exertion among the athletes. The findings of this study indicate significant changes in PLR and heart rate (HR) with increasing exercise duration and external load. There were strong correlations between RPE and maximum constriction velocity (MCV) (|r| = 0.8309, p

Abstract Background Previous studies have shown that the Gaming Disorder Test (GDT) and Gaming Disorder Scale for Adolescents (GADIS-A) have promising validity and reliability when assessing symptoms of gaming disorder among young adults. However, validity and reliability properties of the two scales have not been established among a Vietnamese population. Objective The present study translated the GDT and GADIS-A into Vietnamese and examined their factor structures, measurement invariance, convergent validity, concurrent validity, and divergent validity among university students. Methods A total of 610 young adults (mean age = 21.09 years; 63.4% females) were recruited using convenience sampling and who completed a paper-and-pencil survey between April and June 2023. All participants completed a demographic questionnaire, GDT, GADIS-A, and six standardized scales related to gaming disorder, social media addiction, smartphone addiction, and psychological distress. Confirmatory factor analysis (CFA), internal consistency testing, and Pearson’s correlations were performed. Results CFA showed that the GDT had a one-factor structure and the GADIS-A had a two-factor structure. The internal consistency was excellent for both scales among this population. Moreover, both GDT and GADIS-A showed convergent, concurrent, and divergent validity with other standardized scales. Conclusion The Vietnamese versions of the GDT and GADIS-A have good psychometrics, which may be utilized in future research regarding gaming disorder among Vietnamese populations.

Understanding protein-protein interactions (PPIs) is essential because knowledge regarding PPIs helps in determining the biochemical functions of organisms. Advances made in machine learning techniques, for example, DeepFE-PPI, GcForest-PPI, and DeepPPI, have been applied to enhance PPI prediction performance. However, most methods use either handcrafted or learned features. Improving protein representation quality can significantly impact PPI model performance. This study aims to propose a deep neural network-based model, in which both handcrafted and learned features are fused to improve PPI prediction. In our method, the handcrafted features are computed by sequence descriptors, including Amino Acid Composition (AAC), Local Descriptors, Pseudo-AAC, Amphiphilic Pseudo-AAC, and Quasi-Sequence-Order. The learned features are generated by Word2vec after being trained on a large database of protein sequences. Standard evaluation methods and benchmark datasets are used to assess the proposed model. The results show that our model achieves 95.8% and 99.3% accuracy on the Saccharomyces cerevisiae and Human datasets, respectively. Additionally, the generalizability of our model is evaluated through independent tests, and our findings indicate that the proposed feature combination method correctly classifies experimentally validated interactions in eight datasets, including cross-species and PPI networks. The source code and data for this work are included at https://github.com/npxquynhdhsp/ComFeatPPI.git.

Abstract Background The EQ-5D-Y is a generic preference-weighted measure for children and adolescents which was developed within Europe. Two versions exist, the EQ-5D-Y-3L (Y-3L) and EQ-5D-Y-5L (Y-5L). This study aimed to cross-culturally adapt the Y-3L and Y-5L for use in Singapore and to assess the content validity, specifically, the relevance and comprehensiveness of the EQ-5D-Y descriptive system (DS) in Asia. Methods To culturally adapt the instruments, an expert panel consisting of paediatricians and primary school educators were consulted. Modifications suggested by the expert panel were tested via cognitive debriefing interviews with children aged 8–12 in Singapore. To assess the content validity of the EQ-5D-Y DS, interviews were conducted with both healthy (n = 8) and ill children (n = 6) aged 8–15. In the interviews, children discussed their experience with poor health and commented on the comprehensiveness and relevance of the EQ-5D-Y DS. Results The cross-cultural adaptation process led to minor modifications to the UK English Y-3L and Y-5L versions, including using phrases familiar to the local children and adding examples to facilitate understanding. The five health dimensions in the EQ-5D-Y DS were spontaneously elicited when children discussed their experience with poor health. All health dimensions related to poor health elicited from the interviews fell into three broad categories: physical health (e.g. Appetite, Mobility, and Sleep), mental well-being (e.g. Annoyed/Frustrated and Scared/Worried), and social relationships (e.g. Family and Friends). The EQ-5D-Y DS was generally found to be relevant and comprehensive, although some health dimensions that may be relevant to the local population (Social relationship and Appetite) were not covered. Conclusions The UK English EQ-5D-Y instruments were adapted to produce the Singapore English EQ-5D-Y instrument that were comprehensible to local children as young as 8 years old. The EQ-5D-Y DS was generally relevant and comprehensive to measure poor health of local children. Future studies should ascertain the benefits of adding bolt-on items related to social relationships and appetite to the EQ-5D-Y DS.

Abstract Background Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. Methods We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. Results Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. Conclusions In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

National parks are increasingly recognized as attractive nature-based tourism destinations, playing an important role in tourism development in Vietnam. The study investigates the relationships between nature-based destination attractiveness, tourists’ satisfaction, perceived risk, and their revisit intention for Bach Ma National Park, central Vietnam, particularly in the context of COVID-19. Data were randomly collected from 308 domestic visitors who visited the park from December 2020 to May 2021, and structural equation modeling was performed to examine the relationships. The results of this study show that all six attributes of Bach Ma National Park, including natural, social, and cultural attractions, infrastructure, accessibility, prices, and services, had a significant positive effect on tourist satisfaction. Vietnamese tourists’ perceived risk associated with the pandemic had a significant negative impact on their satisfaction and a positive effect on their revisit intention. The findings from this study make a significant contribution to nature-based tourism literature, particularly towards effective marketing and management of national park-based tourism destination in Vietnam, notably with domestic tourists and in times of shocks such as COVID-19.

Abstract Background In sepsis, the relationship between lymphocyte counts and patient outcomes is complex. Lymphocytopenia and lymphocytosis significantly influence survival, illustrating the dual functionality of lymphocytes in responding to infections. This study investigates this complex interaction, focusing on how variations in lymphocyte counts correlate with all-cause hospital mortality among sepsis patients. Methods This retrospective cohort study analyzed data from two extensive critical care databases: the Medical Information Mart for Intensive Care IV 2.0 (MIMIC-IV 2.0) from Beth Israel Deaconess Medical Center, Boston, Massachusetts, and the eICU Collaborative Research Database (eICU-CRD), which was Multi-center database from over 200 hospitals across the United States conducted by Philips eICU Research Institute. We included adult patients aged 18 years and older who met the Sepsis-3 criteria, characterized by documented or suspected infection and a Sequential Organ Failure Assessment (SOFA) score of 2 or higher. Sepsis patients were categorized into quartiles based on lymphocyte counts. The primary outcome was all-cause mortality in the hospital, with 90 and 60-day all-cause mortality as the secondary outcomes. Univariable and multivariable Cox proportional hazard regressions were utilized to assess lymphocyte counts' impact on hospital mortality. An adjusted restricted cubic spline (RCS) analysis was performed to elucidate this relationship further. Subgroup analyses were also conducted to explore the association across various comorbidity groups among sepsis and septic shock patients. Results Our study included 37,054 patients, with an observed in-hospital mortality rate of 16.6%. Univariable and multivariable Cox proportional hazard regression models showed that lymphocyte counts were independently associated with in-hospital mortality (HR = 1.04, P

Background: Out-of-hospital cardiac arrest (OHCA) is a major public health problem. Acute respiratory distress syndrome (ARDS) is a common condition in OHCA patients. We investigated the relationship between the expression of programmed death-1 (PD-1) related molecules and the development and prognosis of ARDS. Methods: Between January 2021 and December 2023, post-resuscitated patients were screened for eligibility in the study. PD-1 related molecules expression was measured by flow cytometry at 48 h of admission in patients with OHCA. The prognostic variables were the development of ARDS during hospitalization and the 28-day patient mortality rate. We analyzed the relationship between the expression of PD-1-related molecules and the development of secondary ARDS in OHCA patients, and assessed the correlation of this expression with the prognosis of ARDS patients. Results: In total, 107 consecutive OHCA patients were enrolled in this study. The median age of the enrolled patients was 60 years, with an age range of 53 to 67 years, and 71 % were male. Among the cardiac arrest patients, 44.8 % had a cardiac etiology, 30.8 % were witnessed, 17.8 % received bystander CPR, and 66.4 % had an initial rhythm of asystole. Our results showed that only monocyte ligand programmed death ligand-1 (PD-L1) expression was significantly elevated in the ARDS group of OHCA patients (P

Selecting an appropriate formwork system becomes complex as one must take into account various factors and their impacts in order to make informed decisions tailored to each specific project. Thirty-three influencing factors were identified specifically for consideration when choosing the formwork in the Vietnamese context. After analysing data collected through a survey with these persons working in the construction field, the study has identified primary groups by using Exploratory Factor Analysis (EFA). Next, the study creates an Analytic Hierarchy Process (AHP) to compare the alternatives for selecting formwork within the scope of considering the evaluation criteria discovered from the previous factor analysis step. The results of this investigation clarify the primary determinants influencing the choice of formwork systems and concurrently pinpoint the optimal alternative via the formulated model. Additionally, the outcomes of this study aim to enhance managerial awareness concerning formwork techniques and promote the widespread utilization of decision support tools in high-rise construction projects. This initiative has the potential to improve the accuracy of planning operations and enhance the success of construction projects.

Objective: This research aims to investigate the prognosis value using the time-weighted average neutrophil-to-lymphocyte ratio (TWA-NLR) for predicting all-cause hospital mortality among sepsis patients. Data were analyzed through the use of the eICU Collaborative Research Database (eICU-CRD 2.0) as well as Medical Information Mart for Intensive Care IV 2.2 (MIMIC-IV 2.2). Methods: Septic patients from both eICU-CRD 2.0 as well as MIMIC-IV 2.2 databases were included. The neutrophil-to-lymphocyte ratios (NLR) were available for analysis, utilizing complete blood counts obtained on days one, four, and seven following ICU admission. The TWA-NLR was computed at the end of the seven days, and patients were then stratified based on TWA-NLR thresholds. 90-day all-cause mortality during hospitalization was the primary objective, with 60-day all-cause hospital mortality as a secondary objective. The correlation between TWA-NLR and sepsis patients' primary outcome was analyzed using univariable and multivariable Cox proportional hazard regressions. A restricted cubic spline (RCS) analysis was conducted in an attempt to confirm this association further, and subgroup analyses were employed to evaluate the correlation across various comorbidity groups. Results: 3921 patients were included from the eICU-CRD 2.0, and the hospital mortality rate was 20.8 %. Both multivariable as well as univariable Cox proportional hazard regression analyses revealed that TWA-NLR was independently correlated with 90-day all-cause hospital mortality, yielding a hazard ratio (HR) of 1.02 (95 % CI 1.01–1.02, P-value10.5) and lower (≤10.5) TWA-NLR cohorts. A significantly decreased incidence of 90-day all-cause hospital mortality (HR = 0.56, 95 % CI 0.48–0.64, P-value

This study presents a HPLC-DAD method developed for the simultaneous detection of histamine H1 antagonist drugs and analgesic compounds, including paracetamol, cetirizine hydrochloride, cinnarizine, chlorpheniramine maleate, and loratadine, in herbal formulations. This method systematically investigates critical parameters for enhancing separation and quantification using HPLC-DAD. Chemometric techniques were employed to optimize the method for detecting adulterants, with sample preparation involving liquid-phase extraction and a mobile phase for separation using HPLC-DAD. The calibration curves were linear over concentrations ranging from 10 to 40 µg/mL, with correlation coefficients exceeding 0.99. The method exhibits good accuracy, with mean recoveries ranging from 90 to 105 %, and precision (% RSD) for intraday and interday precision: 0.71 to 2.94 %. The limits of detection (LODs) and limits of quantification (LOQs) for each compound are provided. The HPLC-DAD method ensures rigorous quality control, detecting potential adulteration in 32 traditional medicine samples and emphasizing its safety and efficacy in managing allergic conditions. The proposed approach is considered environmentally friendly based on evaluations from commonly used greenness assessment tools, Green analytical chemistry metrics (ComplexGAPI, AGREE, BAGI) confirm the method's sustainability and practicality.

In this investigation, we propose a solution for the author’s gender identification task called AGI-P. This task has several real-world applications across different fields, such as marketing and advertising, forensic linguistics, sociology, recommendation systems, language processing, historical analysis, education, and language learning. We created a new dataset to evaluate our proposed method. The dataset is balanced in terms of gender using a random sampling method and consists of 1944 samples in total. We use accuracy as an evaluation measure and compare the performance of the proposed solution (AGI-P) against state-of-the-art machine learning classifiers and fine-tuned pre-trained multilingual language models such as DistilBERT, mBERT, XLM-RoBERTa, and Multilingual DEBERTa. In this regard, we also propose a customized fine-tuning strategy that improves the accuracy of the pre-trained language models for the author gender identification task. Our extensive experimental studies reveal that our solution (AGI-P) outperforms the well-known machine learning classifiers and fine-tuned pre-trained multilingual language models with an accuracy level of 92.03%. Moreover, the pre-trained multilingual language models, fine-tuned with the proposed customized strategy, outperform the fine-tuned pre-trained language models using an out-of-the-box fine-tuning strategy. The codebase and corpus can be accessed on our GitHub page at: https://github.com/mumairhassan/AGI-P

The yttrium aluminium garnet (YAG) and sapphire materials are commonly used in laser and optical devices. Producing an ultra-precise surface quality is necessary for the application in optical devices. However, YAG and sapphire materials belong to difficult-to-machine materials with high brittleness and hardness. Therefore, ensuring the main criterion of producing a quality surface in the nanometre form with the ability to remove the material when finishing this material is challenging. Eliminating machining residues using chemical–mechanical slurry (CMS) is essential in creating ultra-precise components in optical devices. Based on this feature, this work investigates the efficiency of the CMS polishing process by comparing the surface reaction modes with the ionic and solid reaction modes when polishing YAG and sapphire materials. The study procedures aim to clarify the polishing performance corresponding to these two reaction types. Experimental analyses show the balance between CMS polishing technology’s mechanical and chemical effects with the ionic reaction model that can be generated. Results also show that the ionic surface reaction modes give more uniform material removal than the solid reaction on YAG and sapphire crystal surfaces. Therefore, when polished by CMS technology with ionic surface reaction modes, the surface quality is better than solid reaction. In the CMS polishing with ionic responses, adding more Na2SiO3–5H2O content to the CMS is necessary if the chemical reaction is weak. This process will facilitate the –Si–OH components to be more distributed on the workpiece’s surface, thereby improving reaction speed and correcting the balance between mechanical and chemical in polishing by CMS. This balance action results in an ultra-smooth YAG crystal surface without scratches, with a roughness obtained in the nanometre form (Ra = 1.031 nm) after polishing by CMS-2.

Living standards are rising due to a more developed society, and recreation, particularly tourism, is becoming more critical. Expanding the tourist industry is one of the most significant concerns in economic growth. Tourism revenue has helped increase residents’ income, leading to socio-economic development. In recent years, emerging Vietnamese tourism spots like Hon Son, Sapa, Hue, Phu Quoc in Vietnam, and others have consistently drawn travellers to visit and experience through social networking platforms. Tourism potential is tremendous, but foreign visitors’ information about tourist destinations still needs to be improved. This work proposes an approach to integrating machine learning algorithms into an information system to consult tourism traveling. Machine learning algorithms can classify question topics, predict user intent, and predict conversation scenarios to give appropriate responses. Our method is evaluated on the dataset, including 7319 samples on 11 topics collected from the TWCS dataset, using three algorithms: Bag of Words, BERT, and RoBERTa. BERT achieved the highest performance among the surveyed algorithms with 90 % in accuracy and 90.1 % in F1-Score. From the trained model, the team built a mobile application on Android to deploy the chatbot application with the Flutter framework based on Dart, an object-oriented programming language developed by Google using the concept of containers. The system’s functionality serves two primary user groups: administrators and application users. Administrators can utilize the application’s primary functions to manage content set up, and train a chatbot. Users can access information about locations, read location articles, check hotel prices, and use chatbots to find answers to their location-related questions. Administrators can also train the chatbot model to expand its knowledge.

Innovation management theory has been successfully applied in the management and development of many economic and educational organizations. In Vietnam, researchers have been interested in leadership and innovation management in education such as innovating teaching methods and managing teaching activities. The article focuses on analyzing and researching the application of the innovation management theory in assessing student learning outcomes in primary schools in Pho Yen city, Thai Nguyen province according to the management cycle including planning, organizing implementation, directing implementation, inspection and evaluation the effectiveness of applying innovation management theory. Thereby clearly identifying limitations and causes of limitations as a premise for proposing solutions.

Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure’s abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach.

Abstract Background Intimate partner violence (IPV) is a prevalent social determinant of health. The US Preventive Services Task Force recommends routine IPV screening of women, but uptake remains variable. The Veterans Health Administration (VHA) initiated implementation facilitation (IF) to support integration of IPV screening programs into primary care clinics. An evaluation of IF efforts showed variability in IPV screening rates across sites. The follow-up study presented here used a Matrixed Multiple Case Study (MMCS) approach to examine the multilevel factors impacting IPV screening program implementation across sites with varying levels of implementation success. Methods This mixed methods study is part of a larger cluster randomized stepped wedge Hybrid-II program evaluation. In the larger trial, participating sites received 6 months of IF consisting of an external facilitator from VHA’s Office of Women’s Health working closely with an internal facilitator and key site personnel. Recognizing the heterogeneity in implementation outcomes across sites, the MMCS approach was used to enable interpretation of qualitative and quantitative data within and across sites to help contextualize the primary findings from the larger study. Qualitative data collection was guided by the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework and included interviews with key informants involved in IPV screening implementation at eight sites. Quantitative data on IPV screening uptake was derived from medical records and surveys completed by key personnel at the same eight sites to understand implementation facilitation activities. Results Fifteen factors influencing IPV screening implementation spanning all four i-PARIHS domains were identified and categorized into three distinct categories: (1) factors with enabling influence across all sites, (2) factors deemed important to implementation success, and (3) factors differentiating sites with high/medium versus low implementation success. Conclusions Understanding the influencing factors across multi-level domains contributing to variable success of IPV screening implementation can inform the tailoring of IF efforts to promote spread and quality of screening. Implementation of IPV screening programs in primary care with IF should consider consistent engagement of internal facilitators with clinic staff involved in implementation, the resourcefulness of external facilitators, and appending resources to IPV screening tools to help key personnel address positive screens. Trial registration ClinicalTrials.gov NCT04106193. Registered on September 26, 2019.

Abstract Knowledge about capacity losses related to the solid electrolyte interphase (SEI) in sodium‐ion batteries (SIBs) is still limited. One major challenge in SIBs is that the solubility of SEI species in liquid electrolytes is comparatively higher than the corresponding species formed in Li‐ion batteries. This study sheds new light on the associated capacity losses due to initial SEI formation, SEI dissolution and subsequent SEI reformation, charge leakage via SEI and subsequent SEI growth, and diffusion‐controlled sodium trapping in electrode particles. By using a variety of electrochemical cycling protocols, synchrotron‐based X‐ray photoelectron spectroscopy (XPS), gas chromatography coupled with mass spectrometry (GC‐MS), and proton nuclear magnetic resonance (1H‐NMR) spectroscopy, capacity losses due to changes in the SEI layer during different open circuit pause times are investigated in nine different electrolyte solutions. It is shown that the amount of capacity lost depends on the interplay between the electrolyte chemistry and the thickness and stability of the SEI layer. The highest capacity loss is measured in NaPF6 in ethylene carboante mixed with diethylene carbonate electrolyte (i.e., 5 µAh h−1/2pause or 2.78 mAh g·h−1/2pause) while the lowest value is found in NaTFSI in ethylene carbonate mixed with dimethoxyethance electrolyte (i.e., 1.3 µAh h−1/2pause or 0.72 mAh g·h−1/2pause).

Sand fly transmitted Leishmania species are responsible for severe, wide ranging, visceral and cutaneous leishmaniases. Genetic exchange can occur among natural Leishmania populations and hybrids can now be produced experimentally, with limitations. Feeding Phlebotomus orientalis or Phlebotomus argentipes on two strains of Leishmania donovani yielded hybrid progeny, selected using double drug resistance and fluorescence markers. Fluorescence activated cell sorting of cultured clones derived from these hybrids indicated diploid progeny. Multilocus sequence typing of the clones showed hybridisation and nuclear heterozygosity, although with inheritance of single haplotypes in a kinetoplastid target. Comparative genomics showed diversity of clonal progeny between single chromosomes, and extraordinary heterozygosity across all 36 chromosomes. Diversity between progeny was seen for the HASPB antigen, which has been noted previously as having implications for design of a therapeutic vaccine. Genomic diversity seen among Leishmania strains and hybrid progeny is of great importance in understanding the epidemiology and control of leishmaniasis. As an outcome of this study we strongly recommend that wider biological archives of different Leishmania species from endemic regions should be established and made available for comparative genomics. However, in parallel, performance of genetic crosses and genomic comparisons should give fundamental insight into the specificity, diversity and limitations of candidate diagnostics, vaccines and drugs, for targeted control of leishmaniasis.

Abstract Background Late detection of hepatocellular carcinoma (HCC) results in an overall 5-year survival rate of less than 16%. Liquid biopsy (LB) assays based on detecting circulating tumor DNA (ctDNA) might provide an opportunity to detect HCC early noninvasively. Increasing evidence indicates that ctDNA detection using mutation-based assays is significantly challenged by the abundance of white blood cell-derived mutations, non-tumor tissue-derived somatic mutations in plasma, and the mutational tumor heterogeneity. Methods Here, we employed concurrent analysis of cancer-related mutations, and their fragment length profiles to differentiate mutations from different sources. To distinguish persons with HCC (PwHCC) from healthy participants, we built a classification model using three fragmentomic features of ctDNA through deep sequencing of thirteen genes associated with HCC. Results Our model achieved an area under the curve (AUC) of 0.88, a sensitivity of 89%, and a specificity of 82% in the discovery cohort consisting of 55 PwHCC and 55 healthy participants. In an independent validation cohort of 54 PwHCC and 53 healthy participants, the established model achieved comparable classification performance with an AUC of 0.86 and yielded a sensitivity and specificity of 81%. Conclusions Our study provides a rationale for subsequent clinical evaluation of our assay performance in a large-scale prospective study.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

Abstract Recent large-scale societal disruptions, from the COVID-19 pandemic to intensifying wildfires and weather events, reveal the importance of transforming governance systems so they can address complex, transboundary, and rapidly evolving crises. Yet current knowledge of the decision-making dynamics that yield transformative governance remains scant. Studies typically focus on the aggregate outputs of government decisions, while overlooking their micro-level underpinnings. This is a key oversight because drivers of policy change, such as learning or competition, are prosecuted by people rather than organizations. We respond to this knowledge gap by introducing a new analytical lens for understanding policymaking, aimed at uncovering how characteristics of decision-makers and the structure of their relationships affect their likelihood of effectuating transformative policy responses. This perspective emphasizes the need for a more dynamic and relational view on urban governance in the context of transformation.

Background and Aim: Many studies have reported on the phenomenon of co-infections involving two or more pathogens (bacteria or viruses) over the past few years. However, very few studies on this issue were conducted in Vietnam. Therefore, this study aimed to determine the circulation of single and multiple porcine parvovirus (PPV) (e.g., PPV1, PPV2, PPV3, and PPV4), porcine bocavirus (PBoV), and torque teno virus (TTV) (TTV1 and TTV2) infections in Vietnamese pigs. Materials and Methods: A total of 174 porcine circovirus 2-positive samples from pigs (n = 86 for 2017 and n = 88 for 2021), including from the sera and internal organs, across 11 provinces were examined by polymerase chain reaction. Results: This study demonstrated the wide distribution of DNA viruses among pig farms in Vietnam in 2021, with the detection rate for PPV ranging from 3.4% to 27.3% among PPV1-PPV4. Moreover, the detection rates of TTV genotypes were confirmed to be 14.8% (TTV1) and 63.6% (TTV2), respectively, and the positive rate of PBoV was 65.9%. The most frequent combinations were double and triple infections. Double infection was found in 16/86 (18.6%) in 2017 and 26/88 (29.5%) in 2021, while triple infection was found at 19/86 (22.1%) in 2017 and 26/88 (29.5%) in 2021. The incidence of simultaneous detection of more than three viruses was low. Conclusion: These results provide at least partial information about the occurrence of three viruses, including PPV (including PPV1 to 4), PBoV, and TTV (TTV1 and TTV2), in pigs. Determination of particular viruses in pigs will help to prevent the porcine respiratory disease complex caused by DNA viruses in Vietnamese pigs in the future.

Abstract Background African swine fever (ASF) is a notifiable viral disease of pigs and wild boars that could lead to serious economic losses for the swine industry. Objectives The aim of this study was to identify risk factors in the early phase of ASF outbreaks in Vietnamese swine herds during the first epidemic year. Methods The period of interest for this case–control study was February to July 2019. A questionnaire was administered in northern Vietnam where all early cases of ASF were reported. Producers of herds with reported cases were asked to provide information starting from the day of onset of clinical signs as well as 30 days prior to that day. The period of interest for controls was within the 6 months of the first outbreak in Vietnam (February 2019). Questionnaires included 55 questions; responses were received from 67 cases and 115 controls. Logistic regression analysis was used to identify factors associated with ASF status. Results Thirty‐seven variables of interest (among a total of 55 variables) were associated with ASF status in univariate analysis (p

Steel production is one of the major focuses of Vietnamese government’s industrial development plans in the last few decades and the industry has experienced sustained growth where enterprises have made great strides in terms of scale, output, and technology. The growth and expansion of the steel industry, however, must be carefully evaluated due to the sizeable amount of waste, including wastewater, gas, and solid waste, as well as the significant emission of pollutants. Vietnamese steel companies are confronted with the difficulty of establishing sustainable practices to satisfy the sustainability requirements of stakeholders. Green suppliers evaluation and selection is a complex process where multiple quantitative and qualitative criteria must be considered. In this study, a Multicriteria Decision Making (MCDM) model is developed to support the green suppliers evaluation process in the steel manufacturing industry in Vietnam. The proposed model combines Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Tomada de Decisao Interativa Multicriterio (TODIM) methods to determine the most suitable suppliers based on relevant criteria. The main contribution of this study is to develop a Fuzzy MCDM model to support decision makers in the steel industry in green steel suppliers evaluation proceess. The proposed model is then applied to a case study in Vietnam to demonstrate its application feasibility. The results suggested that among the potential alternatives, Supplier 5 (Viet Uc Steel) is identified as the optimal supplier.

This paper presents a novel decentralized dynamic state estimation (DSE) method for estimating the dynamic states of a transmission line in real-time. The proposed method utilizes the sampled measurements from the local end of a transmission line, and thereafter DSE is performed by employing an unscented Kalman filter. The advantage of the proposed method is that the remote end state variables of a transmission line can be estimated using only the local end variables and, hence, the need for communication infrastructure is eliminated. Furthermore, an exact nonlinear model of the transmission line is utilized for estimation and the DSE of one transmission line is independent of the other lines. These in turn result in reduced complexity, higher accuracy, and easier implementation of the decentralized estimator. The proposed method is applied to a case study with realistic transmission line parameters. The results from the case study affirm that the proposed method accurately estimates the state variables under different operating conditions. Furthermore, robust performance is achieved with different noise variances and types. The proposed DSE method is envisioned to have potential applications in transmission line monitoring, control, and protection.

In this study, we assessed a highly structured, yearlong, case-based course designed for undergraduate pre-health students. We incorporated both content learning assessments and developed a novel method called Multiple Mini Exams for assessing course impact on the development of skills that professional schools often seek in pre-health students, focusing on students' abilities to collaborate with others, display bedside manners, synthesize patient case details, appropriately use scientific and medical language, and effectively attain patients' medical histories. This novel method utilized a rubric based on desired medical student skills to score videotaped behaviors and interactions of students role playing as doctors in a hypothetical patient case study scenario. Overall, our findings demonstrate that a highly structured course, incorporating weekly student performance and presentation of patient cases encompassing history taking, diagnosis, and treatment, can result in content learning, as well as improve desired skills specific for success in medical fields.

Comparing language tests and test preparation materials holds important implications for the latter's validity and reliability. However, not enough studies compare such materials across a wide range of indices. Therefore, this study investigated the text complexity of IELTS academic reading tests (IRT) and IELTS reading practice tests (IRPrT). Fine-grained quantitative analyses were undertaken to delineate measures of lexical, syntactic, and discourse complexity across a corpus of 108 IRT and 108 IRPrT published by Pearson, Macmillan, and Cengage Learning. The results suggest little difference between IRT and IRPrT at the lexical level; however, there were significant differences in some measures of syntactic and discourse level complexity. The findings bear implications for stakeholders including learners as test takers, instructors, material developers, and language testing researchers. We interpret this to mean that while IRPrT materials are lexically conducive to the practice for IRT and can provide a similar experience, the IRPrT do show some differences in the amount of subordination and idea repetition at the discourse level. Therefore, instructors and learners may seek to supplement practice with these structures when preparing for IRT, and the designers of such practice materials should consider aligning these factors in the future.

Although group work activities are commonly employed in communicative second language (L2) classes, there are few studies that investigate how much teachers and students, the two main stakeholders, agree or differ on how group work activities should be conducted. To fill this gap, this study, which was carried out at a public university in Vietnam, examined the perceptions of both teachers and students to uncover matches and mismatches in their perceptions of important issues for effective group work implementation. The study explored the actual experiences of six teachers and ten students, who were chosen by convenience sampling. Thematic analysis of the interviews indicated that the benefits of group work were widely agreed upon, as were the preferences for group size and self-selection of group members. However, there was disagreement over ways of grouping, the role of leadership, and the use of peer assessment. The findings have significant implications for relevant stakeholders, especially in the Asian English as a foreign language (EFL) context, about what would work well and what needs to be addressed to maximize the effectiveness of group work implementation.

The significant drop in estrogen levels during menopause increases the cardiovascular risks, one of which is cerebrovascular atherosclerosis. Research on rheoencephalography (REG) parameters for the early diagnosis of cerebrovascular atherosclerotic lesions is of great interest to scientists because of its ease of implementation, low cost, and non-invasiveness. The objectives of study are to evaluate the vascular tone, cerebral circulation flow in each hemisphere of the brain of menopausal women, and some associated factors through waveform characteristics and parameters in REG. A controlled cross-sectional descriptive study was conducted on a group of patients including 80 menopausal women and a control group of 46 menstruating women. All patients were measured REG in the frontal-occipital leads by VasoScreen 5000 impedance REG meter. In menopausal women, the percentage of sharp waves, the percentage of clear side waves, and the average REG were all lower than in the control group (p

Agriculture is an important link to many issues that challenge society today, including adaptation to and mitigation of climate change, food security, and communicable and non-communicable diseases in animals and humans. Transformation of agriculture and food systems has become a priority for a range of federal agencies and global organizations. It is imperative that food and agricultural researchers effectively harness the global convergence of priorities to overcome research “silos” through deep and sustained systemic change. Herein, we identify intersections in federal and global initiatives encompassing climate adaptation and mitigation; human health and nutrition; animal health and welfare; food safety and security; and equity and inclusion. Many agencies and organizations share these priorities, but efforts to address them remain uncoordinated and opportunities for collaboration untapped. Based on the interconnectedness of the identified priority areas, we present a research framework to catalyze agricultural transformation, beginning with the research enterprise. We propose that transformation in agricultural research should incorporate (1) innovation, (2) integration, (3) implementation, and (4) evaluation. This framework provides approaches for food and agricultural research to contribute to sustainable, flexible, and coordinated transformation in the agricultural sector.

This paper develops a real-time control method based on deep reinforcement learning aimed to determine the optimal control actions to maintain a sufficient secure operating limit. The secure operating limit refers to the limit to the most stressed pre-contingency operating point of an electric power system that can withstand a set of credible contingencies without violating stability criteria. The developed deep reinforcement learning method uses a hybrid control scheme that is capable of simultaneously adjusting both discrete and continuous action variables. The performance is evaluated on a modified version of the Nordic32 test system. The results show that the developed deep reinforcement learning method quickly learns an effective control policy to ensure a sufficient secure operating limit for a range of different system scenarios. The performance is also compared to a control based on a rule-based look-up table and a deep reinforcement learning control adapted for discrete action spaces. The hybrid deep reinforcement learning control managed to achieve significantly better on all of the defined test sets, indicating that the possibility of adjusting both discrete and continuous action variables resulted in a more flexible and efficient control policy.

Early stage chemical development presents numerous challenges, and achieving a functional balance is a major hurdle, with many early compounds not meeting the clinical requirements for advancement benchmarks due to issues like poor oral bioavailability. There is a need to develop strategies for achieving the desired systemic concentration for these compounds. This will enable further evaluation of the biological response upon a compound–target interaction, providing deeper insight into the postulated biological pathways. Our study elucidates alternative drug delivery paradigms by comparing formulation strategies across oral (PO), intraperitoneal (IP), subcutaneous (SC), and intravenous (IV) routes. While each modality boasts its own set of merits and constraints, it is the drug’s formulation that crucially influences its pharmacokinetic (PK) trajectory and the maintenance of its therapeutic levels. Our examination of model compounds G7883 and G6893 highlighted their distinct physio-chemical attributes. By harnessing varied formulation methods, we sought to fine-tune their PK profiles. PK studies showcased G7883′s extended half-life using an SC oil formulation, resulting in a 4.5-fold and 2.5-fold enhancement compared with the IP and PO routes, respectively. In contrast, with G6893, we achieved a prolonged systemic coverage time above the desired target concentration through a different approach using an IV infusion pump. These outcomes underscore the need for tailored formulation strategies, which are dictated by the compound’s innate properties, to reach the optimal in vivo systemic concentrations. Prioritizing formulation and delivery optimization early on is pivotal for effective systemic uptake, thereby facilitating a deeper understanding of biological pathways and expediting the overall clinical drug development timeline.

IntroductionBreast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes.MethodsHere, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects.ResultsWe identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity.DiscussionOur findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

Ultraviolet (UV)-based devices have shown their effectiveness on various germicidal purposes. To serve their design optimisation, the disinfection effectiveness of a vertically cylindrical UV lamp, whose wattage ranges from P = 30 − 100 W, is numerically investigated in this work. The UV radiation is solved by the Finite Volume Method together with the Discrete Ordinates model. Various results for the UV intensity and its bactericidal effects against several popular virus types, i.e., Corona-SARS, Herpes (type 2), and HIV, are reported and analysed in detail. Results show that the UV irradiance is greatly dependent on the lamp power. Additionally, it is indicated that the higher the lamp wattage employed, the larger the bactericidal rate is observed, resulting in the greater effectiveness of the UV disinfection process. Nevertheless, the wattage of P ≤ 100W is determined to be insufficient for an effective disinfection performance in a whole room; higher values of power must hence be considered in case intensive sterilization is required. Furthermore, the germicidal effect gets reduced with the viruses less sensitive to UV rays, e.g, the bactericidal rate against the HIV virus is only ∼8.98% at the surrounding walls.

This study aims to evaluate the factors affecting the level of environmental accounting information disclosure based on the perceived level of hotel managers. The author has relied on previous studies, especially primary data access studies, to assess managers' feelings about the issue of environmental accounting disclosure. Data was collected from managers at 216 companies operating in the hospitality sector. Quantitative research methods are used to explore the influencing factors and assess the degree of influence of each factor on environmental accounting information disclosure. The results show that the disclosure of environmental accounting information is influenced by the following factors: managers' viewpoints, legal regulations, profitability, business size and pressure from society. From there, the authors make recommendations to improve environmental accounting for companies doing business in the hotel sector in Vietnam, increasing the company's competitiveness in the context of global integration.