Search

Abstract Background To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient. Results This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism. Conclusions DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA .

Abstract Background When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. Results This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. Conclusions oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM .

Abstract Background Dengue is a common viral illness and severe disease results in life-threatening complications. Healthcare services in low- and middle-income countries treat the majority of dengue cases worldwide. However, the clinical decision-making processes which result in effective treatment are poorly characterised within this setting. In order to improve clinical care through interventions relating to digital clinical decision-support systems (CDSS), we set out to establish a framework for clinical decision-making in dengue management to inform implementation. Methods We utilised process mapping and task analysis methods to characterise existing dengue management at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. This is a tertiary referral hospital which manages approximately 30,000 patients with dengue each year, accepting referrals from Ho Chi Minh city and the surrounding catchment area. Initial findings were expanded through semi-structured interviews with clinicians in order to understand clinical reasoning and cognitive factors in detail. A grounded theory was used for coding and emergent themes were developed through iterative discussions with clinician-researchers. Results Key clinical decision-making points were identified: (i) at the initial patient evaluation for dengue diagnosis to decide on hospital admission and the provision of fluid/blood product therapy, (ii) in those patients who develop severe disease or other complications, (iii) at the point of recurrent shock in balancing the need for fluid therapy with complications of volume overload. From interviews the following themes were identified: prioritising clinical diagnosis and evaluation over existing diagnostics, the role of dengue guidelines published by the Ministry of Health, the impact of seasonality and caseload on decision-making strategies, and the potential role of digital decision-support and disease scoring tools. Conclusions The study highlights the contemporary priorities in delivering clinical care to patients with dengue in an endemic setting. Key decision-making processes and the sources of information that were of the greatest utility were identified. These findings serve as a foundation for future clinical interventions and improvements in healthcare. Understanding the decision-making process in greater detail also allows for development and implementation of CDSS which are suited to the local context.

Abstract Background African swine fever (ASF), caused by the ASF virus (ASFV), was first reported in Vietnam in 2019 and spread rapidly thereafter. Better insights into ASFV characteristics and early detection by surveillance could help control its spread. However, the pathogenicity and methods for early detection of ASFV isolates from Vietnam have not been established. Therefore, we investigated the pathogenicity of ASFV and explored alternative sampling methods for early detection. Results Ten pigs were intramuscularly inoculated with an ASFV strain from Vietnam (titer, 103.5 HAD50/mL), and their temperature, clinical signs, and virus excretion patterns were recorded. In addition, herd and environmental samples were collected daily. The pigs died 5–8 days-post-inoculation (dpi), and the incubation period was 3.7 ± 0.5 dpi. ASFV genome was first detected in the blood (2.2 ± 0.8) and then in rectal (3.1 ± 0.7), nasal (3.2 ± 0.4), and oral (3.6 ± 0.7 dpi) swab samples. ASFV was detected in oral fluid samples collected using a chewed rope from 3 dpi. The liver showed the highest viral loads, and ear tissue also exhibited high viral loads among 11 tissues obtained from dead pigs. Overall, ASFV from Vietnam was classified as peracute to acute form. The rope-based oral fluid collection method could be useful for early ASFV detection and allows successful ASF surveillance in large pig farms. Furthermore, ear tissue samples might be a simple alternative specimen for diagnosing ASF infection in dead pigs. Conclusions Our data provide valuable insights into the characteristics of a typical ASFV strain isolated in Vietnam and suggest an alternative, non-invasive specimen collection strategy for early detection.

Abstract Background Pneumoperitoneum and Trendelenburg position in laparoscopic surgeries could contribute to postoperative pulmonary dysfunction. In recent years, intraoperative lung-protective mechanical ventilation (LPV) has been reportedly able to attenuate ventilator-induced lung injuries (VILI). Our objectives were to test the hypothesis that LPV could improve intraoperative oxygenation function, pulmonary mechanics and early postoperative atelectasis in laparoscopic surgeries. Methods In this randomized controlled clinical trial, 62 patients indicated for elective abdominal laparoscopic surgeries with an expected duration of greater than 2 h were randomly assigned to receive either lung-protective ventilation (LPV) with a tidal volume (Vt) of 7 ml kg− 1 ideal body weight (IBW), 10 cmH2O positive end-expiratory pressure (PEEP) combined with regular recruitment maneuvers (RMs) or conventional ventilation (CV) with a Vt of 10 ml kg− 1 IBW, 0 cmH2O in PEEP and no RMs. The primary endpoints were the changes in the ratio of PaO2 to FiO2 (P/F). The secondary endpoints were the differences between the two groups in PaO2, alveolar-arterial oxygen gradient (A-aO2), intraoperative pulmonary mechanics and the incidence of atelectasis detected on chest x-ray on the first postoperative day. Results In comparison to CV group, the intraoperative P/F and PaO2 in LPV group were significantly higher while the intraoperative A-aO2 was clearly lower. Cdyn and Cstat at all the intraoperative time points in LPV group were significantly higher compared to CV group (p

Abstract Background In Lao People’s Democratic Republic (PDR), tuberculosis (TB) prevalence was estimated at 540/100,000 in 2011. Nevertheless, little is known about the genetic characteristics and anti-TB drug resistance of the Mycobacterium tuberculosis population. The main objective of this work was to study the genetic characteristics and drug resistance of M. tuberculosis population collected during the first National TB Prevalence Survey (TBPS) of Lao PDR (2010–2011). Methods Two hundred and twenty two isolates collected during TBPS (2010–2011) were analyzed with the GenoType MTBDRplus test for M. tuberculosis identification and drug resistance detection. Then, 206 of the 222 isolates were characterized by spoligotyping and MIRU-VNTR typing. Results Among the 222 M. tuberculosis isolates, 11 were mono-resistant to isoniazid and 2 were resistant to isoniazid and rifampicin (MDR-TB), using the GenoType MTBDRplus test. Among the 202 genetically characterized isolates, the East African-Indian (EAI) family was predominant (76.7%) followed by the Beijing (14.4%) and T (5.5%) families. EAI isolates came from all the country provinces, whereas Beijing isolates were found mainly in the northern and central provinces. A higher proportion of Beijing isolates was observed in people younger than 35 years compared to EAI. Moreover, the percentage of drug resistance was higher among Beijing (17.2%) than EAI (5.2%) isolates, and the two MDR-TB isolates belonged to the Beijing family. Combined analysis of the MIRU-VNTR and spoligotyping results (n = 202 isolates) revealed an estimated clustering rate of 11% and the occurrence of mini-outbreaks of drug-resistant TB caused by Beijing genotypes. Conclusions The EAI family, the ancient and endemic family in Asia, is predominant in Lao PDR whereas the prevalence of Beijing, the most harmful M. tuberculosis family for humans, is still low, differently from neighboring countries. However, its association with drug resistance, its presence in young patients and its potential association with recent transmission suggest that the Beijing family could change TB epidemiological pattern in Lao PDR. Therefore, efficient TB control and surveillance systems must be maintained and reinforced to prevent the emergence of highly transmissible and drug-resistant strains in Lao PDR, as observed in neighboring countries.