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Abstract Background Vietnam’s primary mechanism of achieving sustainable funding for universal health coverage (UHC) and financial protection has been through its social health insurance (SHI) scheme. Steady progress towards access has been made and by 2020, over 90% of the population were enrolled in SHI. In 2022, as part of a larger transition towards the increased domestic financing of healthcare, tuberculosis (TB) services were integrated into SHI. This change required people with TB to use SHI for treatment at district-level facilities or to pay out of pocket for services. This study was conducted in preparation for this transition. It aimed to understand more about uninsured people with TB, assess the feasibility of enrolling them into SHI, and identify the barriers they faced in this process. Methods A mixed-method case study was conducted using a convergent parallel design between November 2018 and January 2022 in ten districts of Hanoi and Ho Chi Minh City, Vietnam. Quantitative data were collected through a pilot intervention that aimed to facilitate SHI enrollment for uninsured individuals with TB. Descriptive statistics were calculated. Qualitative interviews were conducted with 34 participants, who were purposively sampled for maximum variation. Qualitative data were analyzed through an inductive approach and themes were identified through framework analysis. Quantitative and qualitative data sources were triangulated. Results We attempted to enroll 115 uninsured people with TB into SHI; 76.5% were able to enroll. On average, it took 34.5 days to obtain a SHI card and it cost USD 66 per household. The themes indicated that a lack of knowledge, high costs for annual premiums, and the household-based registration requirement were barriers to SHI enrollment. Participants indicated that alternative enrolment mechanisms and greater procedural flexibility, particularly for undocumented people, is required to achieve full population coverage with SHI in urban centers. Conclusions Significant addressable barriers to SHI enrolment for people affected by TB were identified. A quarter of individuals remained unable to enroll after receiving enhanced support due to lack of required documentation. The experience gained during this health financing transition is relevant for other middle-income countries as they address the provision of financial protection for the treatment of infectious diseases.

Abstract Background In the field of tuberculosis (TB), Community Healthcare Workers (CHWs) have been engaged for advocacy, case detection, and patient support in a wide range of settings. Estimates predict large-scale shortfalls of healthcare workers in low- and middle-income settings by 2030 and strategies are needed to optimize the health workforce to achieve universal availability and accessibility of healthcare. In 2018, the World Health Organization (WHO) published guidelines on best practices for CHW engagement, and identified remaining knowledge gaps. Stop TB Partnership’s TB REACH initiative has supported interventions using CHWs to deliver TB care in over 30 countries, and utilized the same primary indicator to measure project impact at the population-level for all TB active case finding projects, which makes the results comparable across multiple settings. This study compiled 10 years of implementation data from the initiative’s grantee network to begin to address key knowledge gaps in CHW networks. Methods We conducted a cross-sectional study analyzing the TB REACH data repository (n = 123) and primary survey responses (n = 50) of project implementers. We designed a survey based on WHO guidelines to understand projects’ practices on CHW recruitment, training, activities, supervision, compensation, and sustainability. We segmented projects by TB notification impact and fitted linear random-effect regression models to identify practices associated with higher changes in notifications. Results Most projects employed CHWs for advocacy alongside case finding and holding activities. Model characteristics associated with higher project impact included incorporating e-learning in training and having the prospect of CHWs continuing their responsibilities at the close of a project. Factors that trended towards being associated with higher impact were community-based training, differentiated contracts, and non-monetary incentives. Conclusion In line with WHO guidelines, our findings emphasize that successful implementation approaches provide CHWs with comprehensive training, continuous supervision, fair compensation, and are integrated within the existing primary healthcare system. However, we encountered a great degree of heterogeneity in CHW engagement models, resulting in few practices clearly associated with higher notifications.

Active case finding (ACF) is a strategy that aims to identify people with tuberculosis (TB) earlier in their disease. This outreach approach may lead to a reduction in catastrophic cost incurrence (costs exceeding 20% of annual household income), a main target of WHO’s End TB Strategy. Our study assessed the socio-economic impact of ACF by comparing patient costs in actively and passively detected people with TB. Longitudinal patient cost surveys were prospectively fielded for people with drug-sensitive pulmonary TB, with 105 detected through ACF and 107 passively detected. Data were collected in four Vietnamese cities between October 2020 and March 2022. ACF reduced pre-treatment (USD 10 vs. 101, p < 0.001) and treatment costs (USD 888 vs. 1213, p < 0.001) in TB-affected individuals. Furthermore, it reduced the occurrence of job loss (15.2% vs. 35.5%, p = 0.001) and use of coping strategies (28.6% vs. 45.7%, p = 0.004). However, catastrophic cost incurrence was high at 52.8% and did not differ between cohorts. ACF did not significantly decrease indirect costs, the largest contributor to catastrophic costs. ACF reduces costs but cannot sufficiently reduce the risk of catastrophic costs. As income loss is the largest driver of costs during TB treatment, social protection schemes need to be expanded.

Summary: The competency-based undergraduate curriculum reform at the University of Medicine and Pharmacy at Ho Chi Minh City, Faculty of Medicine (UMP-FM) is detailed and reviewed in reference to the instructional and institutional reforms, and enabling actions recommended by the Lancet 2010 Commission for Health Professional Education. Key objectives are to: revise the overall 6-year curriculum to be more integrated and competency-based; reinforce students’ knowledge application, problem-solving, clinical competence, self-directed learning and soft skills; develop a comprehensive and performance-based student assessment programme; and establish a comprehensive quality monitoring programme to facilitate changes and improvements. New features include early introduction to the practice of medicine, family- and community-based medicine, professionalism, interprofessional education, electives experiences, and a scholarly project. Institutional reform introduces a faculty development programme, joint planning mechanism, a “culture of critical inquiry”, and a transparent faculty reward system. Lessons learnt from the curriculum reform at UMP-FM could be helpful to medical schools from low- and middle-income countries considering transitioning from a traditional to a competency-based curriculum. Funding: This work receives no external funding.

Multi-access edge computing (MEC) can improve the users' computational capacity and battery life by moving computing services to the network edge. In addition, data-content caching on a MEC server improves the user quality of experience and decreases the backhaul network congestion. Moreover, non-orthogonal multiple access (NOMA) has recently been implemented to increase network throughput and capacity. Combining these techniques can improve the user performance and benefit the network. This paper investigates a combined computational offloading and data-content caching problem for NOMA-MEC systems. The aim was to achieve the minimum total completion latency of all users by jointly optimizing the offloading decision, caching strategy, computational resource, and power allocation. This satisfies the constraints within the scope of the potential violation for energy consumption, offloading decision, and the computation and storage capacity of the MEC server. The formulated problem is a mixed-integer non-linear programming and a non-convex problem. To solve this challenging problem, a block successive upper-bound minimization method was implemented to obtain efficient solutions. Numerous simulation results were presented to demonstrate the convergence and efficacy of the proposed algorithm. Compared with other schemes of all-offloading, local-only, and equal resources, our proposed algorithm can approximately reduce the total completion latency by 17.68%, 26.02%, and 70.98%.

Ruoyao Zou,1,2 Mingjun Zheng,3 Mingzi Tan,4 Haoya Xu,1,2 Nannan Luan,1 Liancheng Zhu1,2 1Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, People’s Republic of China; 2Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, Liaoning, People’s Republic of China; 3Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Munich, Germany; 4Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning 110042, People’s Republic of ChinaCorrespondence: Liancheng ZhuDepartment of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, People’s Republic of ChinaEmail medecin@126.comPurpose: To identify key pathogenic genes and reveal the potential molecular mechanisms of endometrial cancer (EC) using bioinformatics analysis and immunohistochemistry validation.Materials and Methods: Through weighted gene co-expression network analysis (WGCNA), a co-expression network was constructed based on the top 25% variant genes in the GSE50830 dataset downloaded from gene expression omnibus (GEO). GO and KEGG pathway enrichment analyses were performed using the DAVID online tool. Candidate genes were selected using the cytoHubba plug-in of Cytoscape, mRNA expression levels and prognostic values in EC were analyzed by Oncomine, GEPIA, and Kaplan–Meier Plotter database to determine hub genes. One hub gene was validated by immunohistochemical (IHC) staining of 116 paraffin-embedded endometrial tissues and TCGA-UCEC cohort. Genes co-expressed with this hub gene were identified by LinkedOmics. Finally, its correlation with immune infiltration was evaluated by TIMER.Results: Three co-expression modules and five candidate genes in each module were obtained by WGCNA; four hub genes were identified (LGR5, SST, ZNF558, and PTGDS). The mRNA levels of LGR5 and SST were significantly upregulated in EC, whereas those of ZNF558 and PTGDS were significantly downregulated; the expression of all four genes was associated with EC prognosis. Further validation demonstrated that PTGDS was significantly downregulated in the EC group compared with the atypical hyperplasia and normal endometrial groups, and its low expression was an independent risk factor for worse prognosis of EC. Biological function analysis indicated that PTGDS might be involved in the adaptive immune response, leukocyte migration, as well as in the regulation of cell adhesion molecules and chemokine signaling. Additionally, PTGDS expression was positively correlated with immune infiltration status of B cells, CD4+ T cells and macrophages.Conclusion: LGR5, SST, ZNF558, and PTGDS may participate in the development, progression, and prognosis of EC, in which PTGDS may be a novel biomarker and therapeutic target for EC.Keywords: endometrial cancer, bioinformatics analysis, WGCNA, immunohistochemistry, PTGDS

Pharmacies represent a key health system entry point for people with TB in Viet Nam, but high fragmentation hinders their broader engagement. Professional networking apps may be able to facilitate pharmacy engagement for systematic TB screening and referral. Between September and December 2019, we piloted the use of a social networking app, SwipeRx, to recruit pharmacists for a TB referral scheme across four districts of Ho Chi Minh City, Viet Nam. We measured chest X-ray (CXR) referrals and TB detection yields at participating pharmacies and fielded 100 acceptability surveys, divided into pharmacists who did and did not make a CXR referral. We then fitted mixed-effect odds proportional models to explore acceptability factors that were associated with making a CXR referral. 1,816 push notifications were sent to pharmacists via the SwipeRx app and 78 indicated their interest in participating; however, only one was within the pilot's intervention area. Additional in-person outreach resulted in the recruitment of 146 pharmacists, with 54 (37.0%) making at least one CXR referral. A total of 182 pharmacy customers were referred, resulting in a total of 64 (35.2%) CXR screens and seven people being diagnosed with TB. Compared to pharmacists who did not make any CXR referrals, pharmacists making at least one CXR referral understood the pilot's objectives more clearly (aOR = 2.6, 95% CI: 1.2-5.8) and they believed that TB screening increased customer trust (aOR = 2.7, 95% CI: 1.2-5.8), benefited their business (aOR = 2.8, 95% CI: 1.3-6.2) and constituted a competitive advantage (aOR = 4.4, 95% CI: 1.9-9.9). They were also more confident in using mHealth apps (aOR = 3.1, 95 CI%: 1.4-6.8). Pharmacies can play an important role in early and increased TB case finding. It is critical to highlight the value proposition of TB referral schemes to their business during recruitment. Digital networking platforms, such as SwipeRx, can facilitate referrals for TB screening by pharmacists, but their ability to identify and recruit pharmacists requires optimization, particularly when targeting specific segments of a nation-wide digital network.

Abstract In this study, we originally aimed to characterize the potential role of Argonaute 2 (AGO2) in the nucleus, a key protein of the miRNA machinery. We combined Chromatin Immunoprecipitation (ChIP) with high throughput sequencing (ChIP-seq) and quantitative mass spectrometry (ChIP-MS) using the broadly used AGO2 11A9 antibody to determine interactions with chromatin and nuclear proteins. We found a previously described interaction between AGO2 and SWI/SNF on chromatin with ChIP-MS and observed enrichment at enhancers and transcription start sites using ChIP-seq. However, antibody specificity issues can produce misleading results for ChIP, RNA-seq and Mass spectrometry. Therefore, we developed a CRISPR/Cas9 engineered AGO2−/− HEK293T cell line to validate our findings. ChIP-qPCR and immunoprecipitation combined with MS (IP-MS) showed that the 11A9 antibody associates with chromatin and SWI/SNF in the absence of AGO2. Furthermore, stoichiometry, IP-MS and co-IP analysis suggests a direct interaction of this antibody with SMARCC1, a component of the SWI/SNF complex. For this reason, particular care should be taken in performing and interpreting experiments in which the 11A9 antibody is used to study a nuclear role of AGO2.

Functional characterisation of non-coding risk variants for colorectal cancer is missing. Here the authors conduct a large-scale proteome-wide analysis of disease associated SNPs to characterize allele-specific transcription factor binding landscape in colorectal cancer and identify one functionally significant SNP.

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

A rare germline duplication upstream of the bone morphogenetic protein antagonist GREM1 causes a Mendelian-dominant predisposition to colorectal cancer (CRC). The underlying disease mechanism is strong, ectopic GREM1 overexpression in the intestinal epithelium. Here, we confirm that a common GREM1 polymorphism, rs16969681, is also associated with CRC susceptibility, conferring ∼20% differential risk in the general population. We hypothesized the underlying cause to be moderate differences in GREM1 expression. We showed that rs16969681 lies in a region of active chromatin with allele- and tissue-specific enhancer activity. The CRC high-risk allele was associated with stronger gene expression, and higher Grem1 mRNA levels increased the intestinal tumor burden in ApcMin mice. The intestine-specific transcription factor CDX2 and Wnt effector TCF7L2 bound near rs16969681, with significantly higher affinity for the risk allele, and CDX2 overexpression in CDX2/GREM1-negative cells caused re-expression of GREM1. rs16969681 influences CRC risk through effects on Wnt-driven GREM1 expression in colorectal tumors.

OBJECTIVE: The GALAD score, a serum biomarker-based model, predicts the likelihood of hepatocellular carcinoma (HCC) in patients with chronic liver disease. We evaluated the performance of the GALAD score compared to that of liver ultrasound in detecting HCC. PATIENTS AND METHODS: This study recruited a group of 136 patients with HCC and a control group of 436 patients with cirrhosis or chronic hepatitis B or hepatitis C. The performance of the GALAD score and ultrasound in detecting HCC in these patients was analyzed using the area under the receiver operating characteristic curve (AUC). The sensitivity and specificity of the optimal GALAD score were compared to those of ultrasound. RESULTS: The AUC of the GALAD score for detecting HCC was 0.940 [95% confidence interval (CI) 0.92-0.96], higher than that of ultrasound [0.939 (0.91-0.96), p < 0.001]. At a threshold of 1.24, the GALAD score had a sensitivity of 91.2% and a specificity of 81.9% for detecting HCC. The AUC of the GALAD score for early HCC detection was 0.75 (95% CI 0.71-0.80, p < 0.001; threshold 1.13, sensitivity 87.5%, specificity 67.8%, p < 0.001). The combination of GALAD and ultrasound (GALADUS score) showed further improvement, achieving an AUC of 0.97 (95% CI 0.96-0.99; cut-off point 1.37, sensitivity 95.6%, specificity 89.2%, p < 0.001). CONCLUSIONS: In our study, the GALADUS score showed improved performance compared to the GALAD score. Therefore, we suggest that the performance of the GALAD score should be reconsidered and that it should be evaluated in combination with ultrasound for HCC detection. [ABSTRACT FROM AUTHOR]

Water pollution in the Vam Co River basin is becoming more complicated due to untreated wastewater being directly discharged into rivers and canals from agricultural, industrial, and domestic activities. To assess the water quality in this area, this study conducted monitoring at ten sampling locations (S1-S10) from 2018 to 2022, calculated the Water Quality Index (WQI) for each parameter, and simulated water quality in 2022 using the 1D- MIKE 11 model developed by DHI with two main modules including HD and AD. The findings showed that most parameters did not surpass the allowable limits per QCVN 08-MT:2015/BTNMT on Vietnam National Technical Regulation on Surface Water Quality. However, organic and microbial pollution led to certain parameters, such as BOD5, COD, and Coliform, exceeding the limits. The lowest water quality was recorded in Long An province, especially at sampling locations S3, S4, and S6, with the average WQI for nine water quality parameters from February to July 2022 being 58.4, 67.8, and 21.1, respectively. Additionally, the simulation outcomes of the MIKE 11 model salinity, BOD5, DO, and NH4 aligned with the real measurements taken. It has been observed that the southern area of the Vam Co River Basin possesses poorer water quality than the northern part, with Long An province located downstream of the Vam Co River basin being the primary source of pollution. The development of this hydraulic model signifies a crucial milestone in comprehending and regulating the effects of pollution in monitoring and managing water management systems, controlling saline intrusion, and ensuring water supply for agricultural production and daily use in the Vam Co River basin. [ABSTRACT FROM AUTHOR]