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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 Globally, most people with multidrug-resistant tuberculosis (MDR-TB) and their households experience catastrophic costs of illness, diagnosis, and care. However, the factors associated with experiencing catastrophic costs are poorly understood. This study aimed to identify risk factors associated with catastrophic costs incurrence among MDR-TB-affected households in Ho Chi Minh City (HCMC), Viet Nam. Methods Between October 2020 and April 2022, data were collected using a locally-adapted, longitudinal WHO TB Patient Cost Survey in ten districts of HCMC. Ninety-four people with MDR-TB being treated with a nine-month TB regimen were surveyed at three time points: after two weeks of treatment initiation, completion of the intensive phase and the end of the treatment (approximately five and 10 months post-treatment initiation respectively). The catastrophic costs threshold was defined as total TB-related costs exceeding 20% of annual pre-TB household income. Logistic regression was used to identify variables associated with experiencing catastrophic costs. A sensitivity analysis examined the prevalence of catastrophic costs using alternative thresholds and cost estimation approaches. Results Most participants (81/93 [87%]) experienced catastrophic costs despite the majority 86/93 (93%) receiving economic support through existing social protection schemes. Among participant households experiencing and not experiencing catastrophic costs, median household income was similar before MDR-TB treatment. However, by the end of MDR-TB treatment, median household income was lower (258 [IQR: 0–516] USD vs. 656 [IQR: 462–989] USD; p = 0.003), and median income loss was higher (2838 [IQR: 1548–5418] USD vs. 301 [IQR: 0–824] USD; p

In the original publication [...]

Land use transformation in the rapidly growing megacity of Bengaluru in South India has greatly influenced its lake ecosystems. To gain insights into the dynamics of lake ecosystems along an urban-rural gradient, we analyzed lake-cover changes (along with a 300 m buffer zone) of six lakes from 2002 to 2022. The lakes studied were Bellandur and Dodda Bommasandra (urban), Attur and Puttenhalli (peri‑urban), and Chudahalli and Vrishabhavathi (rural). Supervised maximum likelihood Land Use/Land Cover (LULC) classifications were conducted on 162 freely available, RGB Google Earth (GE) images to distinguish between macrophytes, algae, water, and dried land inside the lakes, and built-up and non built-up area in the buffer zone. Antagonistic relationships between macrophytes and algae were observed at all locations. Rainfall was positively correlated with the wet surface area (that is macrophytes, algae, and water) in comparatively dry lakes. Similarly, air temperature was negatively correlated with the wet surface area except for the Vrishabhavathi Lake. However, the built-up area in the buffer zone did not show a consistent correlation with the wet surface area, most likely because of sewage connections of certain lakes with distant urban areas.

Abstract The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by understanding virus taxonomy. Alignment-based methods are commonly used for this task, but are computationally expensive and time-consuming, especially when dealing with large datasets or when detecting new virus variants is time sensitive. An alternative approach, the encoded method, has been developed that does not require prior sequence alignment and provides faster results. However, each encoded method has its own claimed accuracy. Therefore, careful evaluation and comparison of the performance of different encoded methods are essential to identify the most accurate and reliable approach for virus taxonomy classification. This study aims to address this issue by providing a comprehensive and comparative analysis of the potential of encoded methods for virus classification and phylogenetics. We compared the vectors generated for each encoded method using distance metrics to determine their similarity to alignment-based methods. The results and their validation show that K-merNV followed by CgrDft encoded methods, perform similarly to state-of-the-art multi-sequence alignment methods. This is the first study to incorporate and compare encoded methods that will facilitate future research in making more informed decisions regarding selection of a suitable method for virus taxonomy.

This research study aims to determine the factors affecting the academic staff’s research performance in Vietnam’s higher education context. An online survey was conducted, achieving responses from 207 respondents who were lecturers of different scientific fields in Vietnamese universities. The SAS system was used to process both descriptive and inferential statistics. The study’s results indicated that research competency was the main factor affecting Vietnamese university teachers’ domestic and international research achievements. Meanwhile, English proficiency only had a significant impact at the international level. Additionally, social science academics were less skillful in researching and less productive in international research than other sciences. As a result, some implications for improving academic research performance are discussed in this paper.

Abstract Background Insufficient knowledge of the genetic and phenotypic diversity in the local Beninese goat population combined with the lack of understanding of its adaptive capacity to ongoing environmental and societal changes hampers the development of strategies for better management and genetic improvement. The objective of this study was to establish the current geographical distribution of goats in Benin based on their morphology and model the potential habitat suitability of the three known main goat phenotypes (i.e., Djallonké goat or Type I, Sahelian goat or Type II, and their Crossbreeds or Type III) under climate change scenarios. Ten qualitative and 26 linear body measurements were taken on 2114 adult female goats sampled across the three vegetation zones of the country. Fifteen ratios were generated from the quantitative variables. The data were analyzed using generalized linear model procedures followed by multiple comparisons of least-squares means and multivariate analytical methods, including canonical discrimination analysis and hierarchical ascendant classification. Each goat was then assigned to one of the three aforementioned main goat phenotypes following its morphological characteristics and according to the a priori cluster membership defined in the previous step. The Maximum Entropy algorithm was used to model the current and future distribution of the three goat phenotypes under climate change scenario using the Representative Conservation Pathways 4.5 and 8.5. Results All linear body measurements varied among vegetation zones. In the discriminant function analysis, 71% of the measured individuals were correctly classified in their vegetation zone of origin by seven measured variables and three ratios. The cluster procedure analysis revealed two groups of goats subdivided into the three main phenotypes. The modeling results showed that the currently highly favorable habitats were distributed in the South for Type I, in the North for Type II, and both South and North for Type III. However, under climate change scenarios, the favorable habitats for Type I decreased while those of Types II and III increased. Conclusions The results of this study confirm the spatial variation of the goat population in Benin. The habitat suitability model can be used to support decision-making toward better management of goat genetic diversity in Benin.

Objective: Primary liver cancer is one of the main causes of cancer mortality globally, with hepatocellular carcinoma (HCC) being the most frequent type. Chronic hepatitis B virus (HBV) infection is leading cause of HCC. This study aimed to identify significant genes for predicting prognosis in HBV-associated HCC. Methods: The GSE121248 gene expression profile was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) for HBV-associated HCC were identified by analyzing this expression profile. Enrichment analyses were performed to discover the role of DEGs in biological processes, cell components, molecular functions, and pathways. Then, protein-protein interaction (PPI) was constructed and 5 hub genes were identified. Finally, survival analysis was conducted to validate the prognostic value of these genes. Results: A total of 20188 official gene symbols were found, and 119 DEGs were identified between HBV-associated HCC and normal liver tissues. The PPI network identified CCNB1, CDK1, TOP2A, RACGAP1, and ASPM as hub genes. Kaplan-Meier curves showed that the high expression of the hub genes had significantly lower survival. Conclusion: CCNB1, CDK1, TOP2A, RACGAP1, and ASPM could be potential prognostic biomarkers and therapeutic targets for HBV-associated HCC.

Abstract Understanding and governing human settlement patterns is a major challenge of the urban age. While rural settlements emerge as parts of agricultural landscapes, cities typically evolve in economically strategic locations, and over time form hierarchical systems of cities. Purposeful planning and the collective, self-organized behavior of the inhabitants interact in the development of regional settlement patterns. Since self-organizing systems often produce fractal patterns in nature, this study combines approaches of land use science, city ranking, and urban planning under a fractal theory framework, to analyze the settlement system of the Indian Punjab. Scaling levels were defined by discontinuities in the size distribution of built-up areas (Global Urban Footprint), which correlated to population-based classifications (r = 0.9591). Self-similarity across scales was supported by geo-statistical similarity (p

To achieve the Sustainable Development Goal's targets of universal health coverage (UHC) and poverty reduction, interventions are required that strengthen and harmonize both UHC and social protection. Vietnam is committed to achieving financial protection and over 90% of the general population has enrolled in its social health insurance (SHI) scheme. However, an estimated 63% of tuberculosis (TB)-affected households in Vietnam still face catastrophic costs and little is known about the optimal strategies to mitigate the costs of TB care for vulnerable families. This study assessed the acceptability of a social protection package containing cash transfers and SHI using individual interviews (n = 19) and focus group discussions (n = 3 groups). Interviews were analyzed through framework analysis. The study's main finding indicated that both conditional and unconditional cash transfers paired with SHI were acceptable, across six dimensions of acceptability. Cash transfers were considered beneficial for mitigating out-of-pocket expenditure, increasing TB treatment adherence, and improving mental health and general well-being, but the value provided was inadequate to fully alleviate the economic burden of the illness. The conditionality of the cash transfers was not viewed by participants as inappropriate, but it increased the workload of the TB program, which brought into question the feasibility of scale-up. SHI was viewed as a necessity by almost all participants, but people with TB questioned the quality of care received when utilizing it for auxiliary TB services. Access to multiple sources of social protection was deemed necessary to fully offset the costs of TB care. Additional research is needed to assess the impact of cash transfer interventions on health and economic outcomes in order to create an enabling policy environment for scale-up.

Little is known about the long-distance telecoupling effects of urban food demands on land use changes (LUCs) in remote oases of the Southern Sahara. Using the example of two typical oasis settlements on Mont Bagzam in the southern Aϊr Mountains of Niger which are linked to regional and global markets by an unpaved road since 2015, this study aimed at analyzing time trajectories of LUCs and related changing agricultural production patterns. LUCs were quantified for 1955 to 2022 using GIS-based mapping of agriculture and natural vegetation based on historical aerial photographs, CORONA and multi-spectral satellite images, and high resolution drone-based surveys. The results show a major increment in actively used agricultural land in the 850 ha watershed of the two oases from 11 ha in 1955 to 13 ha in 2003 and 68 ha in 2022 as well as the addition of 92 irrigation wells to 16 existing ones between 2003 and 2022. LUCs and evapotranspiration calculated from climatic data of a local weather station allowed to estimate changes of irrigation water needs in the selected watershed. While annual precipitation averages only 214 mm, local reference evapotranspiration may reach 1,476 mm year-1. Therefore, the additional annual irrigation water needs for the newly established fields between 2003 and 2022 cultivated to cash crops rose by 696 million l. To detect LUC effects on soil quality, soil samples of onion and garlic fields of different ages were collected employing a false-time-series approach. Results reveal increasing soil pH and salt concentrations and falling ground water tables, which reflects a negative water balance and ground water extraction above recharge levels. Our study provides evidence that the newly established telecoupled production systems on Mont Bagzam threaten the sustainability of existing local agricultural production and related livelihoods of agro-pastoralists.

Worldwide urbanization drives rural-urban transformation (RUT) which has major consequences in many countries of the Global South where there is an urgent need to better understand and manage the underlying processes and consequences for ecosystem services. To fill existing knowledge gaps on the extent and time course of RUT in Morocco, this study focused on (i) analyzing the spatial patterns of rural-urban transformation in the Rabat-Sale-Kenitra (RSK) region from 1972 to 2020, (ii) identifying key mechanisms of change, and (iii) defining the main driving forces behind the spatial transformation patterns. To this end, we processed data of the Landsat free archive, historical grayscale Corona images, and nighttime lights datasets on Google Earth Engine (GEE) using machine learning classifiers and LandTrendr spectral-temporal segmentation algorithms. With an overall accuracy (OA) ranging from 88–95%, the results revealed that during the study period the RSK region experienced a 473% growth of horizontal built-up reflected in an area increase from 63.4 km2 to 299.9 km2. The main changes occurred along the Kenitra-Rabat-Temara axis and in central cities connected to the main road network. The horizontal expansion of large and medium-sized cities led to the formation of a Rural-Urban Interface (RUI) on the outskirts. The urban sprawl of some cities has affected the surrounding rural lands within the RUI. Environmental, social, economic, and political forces have interacted in shaping the changes in rural-urban landscapes.

Origin of the COVID-19 virus (SARS-CoV-2) has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the hosts for SARS-CoV-2 based on comparative studies using its genomic sequences. This paper investigates the SARS-CoV-2 origin by using artificial intelligence (AI)-based unsupervised learning algorithms and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined SARS-CoV-2 genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidence strongly supporting scientific hypotheses that bats and pangolins are probable hosts for SARS-CoV-2. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

Taking inspiration from the brain, spiking neural networks (SNNs) have been proposed to understand and diminish the gap between machine learning and neuromorphic computing. Supervised learning is the most commonly used learning algorithm in traditional ANNs. However, directly training SNNs with backpropagation-based supervised learning methods is challenging due to the discontinuous and non-differentiable nature of the spiking neuron. To overcome these problems, this paper proposes a novel metaheuristic-based supervised learning method for SNNs by adapting the temporal error function. We investigated seven well-known metaheuristic algorithms called Harmony Search (HS), Cuckoo Search (CS), Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Grammatical Evolution (GE) as search methods for carrying out network training. Relative target firing times were used instead of fixed and predetermined ones, making the computation of the error function simpler. The performance of our proposed approach was evaluated using five benchmark databases collected in the UCI Machine Learning Repository. The experimental results showed that the proposed algorithm had a competitive advantage in solving the four classification benchmark datasets compared to the other experimental algorithms, with accuracy levels of 0.9858, 0.9768, 0.7752, and 0.6871 for iris, cancer, diabetes, and liver datasets, respectively. Among the seven metaheuristic algorithms, CS reported the best performance.

Kyunsu township comprises coastal regions and a multitude of small islands covered by vast tropical evergreen and mangrove forests, and a large water body in the Adman Sea of Myanmar. Due to population growth, residents have increasingly expanded their agricultural land areas into natural tropical evergreen and mangrove forests, leading to deforestation. Understanding the processes and consequences of landscape transformation for surrounding ecosystems is crucial for local policy making and for fostering sustainable crop production in this area. Landsat datasets from 1978, 1989, 2000, 2011, and 2020 were used in a time-series post-classification approach to investigate land use land cover (LULC) changes in the Kyunsu township of Southern Myanmar across the last 40 years. Our study also attempted to assess the effects of the transformation of LULC on carbon stocks. Between 1978 and 2020, major LULC changes occurred with the expansion of Paddy Fields (+90%), Plantations (+11%), Open Forests (+81%), Settlement Areas (+115%), Aquaculture Areas (+1594%), and Others (+188%) while the area covered with Closed Forests shrunk by 44% and with Mangrove Forests by 9%. Water Bodies expanded by 0.13%. Our analyses show that between 1978 and 2020 2453 ha of Paddy Fields expanded into Plantations, 1857 ha to Open Forests, and 1146 ha to Mangrove Forests. Additionally, 12,135 ha of Open Forests, 8474 ha of Closed Forests, and 2317 ha of Mangrove Forests became Plantations. Across the 40 year study period, a total of 40,523 ha of Closed Forests were transformed to Open Forests. Our findings show that transformation of agricultural landscapes in the study area significantly affected deforestation and forest degradation of tropical evergreen rain forests and mangrove forests which are vital sources of ecosystem services. These transformations led to estimated losses of carbon stocks between 1978 and 2020 ranged from 89,260–5,106,820 Mg (average of 1,723,250 Mg) in our study area. Our findings call for sustainable resource intensification to increase production efficiency in existing cultivated areas rather than crop land expansion into natural forests. In addition, our data highlight the need for rigorous policies to conserve and protect tropical natural evergreen and mangrove forest, as key local resources providing multiple ecosystem services.

Since the 1990s, Morocco’s agriculture has been characterized by the co-existence and transformation of both modern and traditional smallholder systems. In the Atlas Mountains, the effects of rural–urban transformation have led to intensified irrigated agriculture in some agricultural areas, while others were abandoned. To better understand these effects, this study aimed at (1) analyzing the land use and land cover (LULC) changes, (2) assessing the structure and dynamics of vegetation, and (3) comparing a Support Vector Machine (SVM) classification approach with a seasonal rules-based approach. We, therefore, employed a semi-automatic supervised classification of LULC using Landsat data from the 1990s to the 2020s to distinguish between Open Canopy Vegetation, Bareland, Forest, and Water. Overall accuracies achieved ranged from 88% to 90% in 1990, 2000, 2010, and 2020. SVM results indicated the share of Bareland as >80% of the landscape in all periods. With the seasonal rules-based approach, 10% less Bareland was detected than with the SVM approach. Our findings indicate the limitation of detecting vegetation reflectance in semi-arid mountainous regions such as that prevailing in Morocco using a single machine learning method.

Automatic modulation classification (AMC), which aims to blindly identify the modulation type of an incoming signal at the receiver in wireless communication systems, is a fundamental signal processing technique in the physical layer to improve the spectrum utilization efficiency. Motivated by deep learning (DL) high-impact success in many informatics domains, including radio signal processing for communications, numerous recent AMC methods exploiting deep networks have been proposed to overcome the existing drawbacks of traditional approaches. DL is capable of learning the underlying characteristics of radio signals effectively for modulation pattern recognition, which in turn improves the modulation classification performance under the presence of channel impairments. In this work, we first provide the fundamental concepts of various architectures, such as neural networks, recurrent neural networks, long short-term memory, and convolutional neural networks as the necessary background. We then convey a comprehensive study of DL for AMC in wireless communications, where technical analysis is deliberated in the perspective of state-of-the-art deep architectures. Remarkably, several sophisticated structures and advanced designs of convolutional neural networks are investigated for different data types of sequential radio signals, spectrum images, and constellation images to deal with various channel impairments. Finally, we discuss some primary research challenges and potential future directions in the area of DL for modulation classification.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Abstract Lichens are symbiotic organisms that produce various secondary metabolites. Here, different lichen extracts were examined to identify secondary metabolites with anti-migratory activity against human lung cancer cells. Everniastrum vexans had the most potent inhibitory activity, and atranorin was identified as an active subcomponent of this extract. Atranorin suppressed β-catenin-mediated TOPFLASH activity by inhibiting the nuclear import of β-catenin and downregulating β-catenin/LEF and c-jun/AP-1 downstream target genes such as CD44, cyclin-D1 and c-myc. Atranorin decreased KAI1 C-terminal interacting tetraspanin (KITENIN)-mediated AP-1 activity and the activity of the KITENIN 3′-untranslated region. The nuclear distribution of the AP-1 transcriptional factor, including c-jun and c-fos, was suppressed in atranorin-treated cells, and atranorin inhibited the activity of Rho GTPases including Rac1, Cdc42, and RhoA, whereas it had no effect on epithelial-mesenchymal transition markers. STAT-luciferase activity and nuclear STAT levels were decreased, whereas total STAT levels were moderately reduced. The human cell motility and lung cancer RT² Profiler PCR Arrays identified additional atranorin target genes. Atranorin significantly inhibited tumorigenesis in vitro and in vivo. Taken together, our results indicated that E. vexans and its subcomponent atranorin may inhibit lung cancer cell motility and tumorigenesis by affecting AP-1, Wnt, and STAT signaling and suppressing RhoGTPase activity.

The present study revealed how local socioecological knowledge elucidated during participatory rural appraisals and historical remote sensing data can be combined for analyzing land use change patterns from 1954 to 2007 in northwestern Vietnam. The developed approach integrated farmer decision rules on cropping preferences and location, visual and supervised classification methods, and qualitative information obtained during various forms of participatory appraisals. The integration of historical remote sensing data (aerial photo, Landsat, LISS III) with farmer decision rules showed the feasibility of the proposed method to explain crop distribution patterns for the assessment period of 53 years. Our approach is beneficial for data-limited environments, which is a prevalent situation for many developing regions. The derived land use and crop type dataset was used for understanding how anthropogenic activities altered the study area of the Chieng Khoi commune during the assessment period of five decades, and what potential impact this can have on the natural resource base. The newly developed approach offers a methodological pathway that can be easily transferred to local government authorities for a better understanding of cropping transitions and agricultural expansion trends in data-limited rural landscapes. The detected land use change patterns and upland cropping expansion of more than two hundred percent in 53 years not only revealed the consequences of the interactions and feedback between farmers and their land, but further highlighted the urgent need for implementing sustainable land management practices in the case study watershed of the Chieng Khoi commune and northwestern Vietnam in general.

Lichens are symbiotic organisms that produce various unique chemicals that can be used for pharmaceutical purposes. With the aim of screening new anti-cancer agents that inhibit cancer cell motility, we tested the inhibitory activity of seven lichen species collected from the Romanian Carpathian Mountains against migration and invasion of human lung cancer cells and further investigated the molecular mechanisms underlying their anti-metastatic activity. Among them, Alectoria samentosa, Flavocetraria nivalis, Alectoria ochroleuca, and Usnea florida showed significant inhibitory activity against motility of human lung cancer cells. HPLC results showed that usnic acid is the main compound in these lichens, and (+)-usnic acid showed similar inhibitory activity that crude extract have. Mechanistically, β-catenin-mediated TOPFLASH activity and KITENIN-mediated AP-1 activity were decreased by (+)-usnic acid treatment in a dose-dependent manner. The quantitative real-time PCR data showed that (+)-usnic acid decreased the mRNA level of CD44, Cyclin D1 and c-myc, which are the downstream target genes of both β-catenin/LEF and c-jun/AP-1. Also, Rac1 and RhoA activities were decreased by treatment with (+)-usnic acid. Interestingly, higher inhibitory activity for cell invasion was observed when cells were treated with (+)-usnic acid and cetuximab. These results implied that (+)-usnic acid might have potential activity in inhibition of cancer cell metastasis, and (+)-usnic acid could be used for anti-cancer therapy with a distinct mechanisms of action.

Residents of the Red River Delta region of northern Vietnam have a long tradition of eating raw fish. Fish-borne zoonotic trematodes (FZTs) are estimated to infect ≈1 million persons in Vietnam. It remains uncertain at what stages in the aquaculture production cycle fish become infected with FZTs. Newly hatched fish (fry) from 8 hatcheries and juveniles from 27 nurseries were therefore examined for FZT infection. No FZTs were found in fry from hatcheries. In nurseries, FZT prevalence in juveniles was 14.1%, 48.6%, and 57.8% after 1 week, 4 weeks, and when overwintered in ponds, respectively. FZT prevalence was higher in grass carp (p

Lichens produce various unique chemicals that can be used for pharmaceutical purposes. To screen for novel lichen secondary metabolites showing inhibitory activity against lung cancer cell motility, we tested acetone extracts of 13 lichen samples collected in Chile. Physciosporin, isolated from Pseudocyphellaria coriacea (Hook f. & Taylor) D.J. Galloway & P. James, was identified as an effective compound and showed significant inhibitory activity in migration and invasion assays against human lung cancer cells. Physciosporin treatment reduced both protein and mRNA levels of N-cadherin with concomitant decreases in the levels of epithelial-mesenchymal transition markers such as snail and twist. Physciosporin also suppressed KITENIN (KAI1 C-terminal interacting tetraspanin)-mediated AP-1 activity in both the absence and presence of epidermal growth factor stimulation. Quantitative real-time PCR analysis showed that the expression of the metastasis suppressor gene, KAI1, was increased while that of the metastasis enhancer gene, KITENIN, was dramatically decreased by physciosporin. Particularly, the activity of 3'-untranslated region of KITENIN was decreased by physciosporin. Moreover, Cdc42 and Rac1 activities were decreased by physciosporin. These results demonstrated that the lichen secondary metabolite, physciosporin, inhibits lung cancer cell motility through novel mechanisms of action.

Bromopropane (BP) compounds, including 1-bromopropane, 2-bromopropane, and 1,2-dibromopropane, are used in industry for various purposes, and their deleterious effects on human health are becoming known. In this study, we examined the effects of BP compounds on the stemness of colorectal cancer cells. At low, non-cytotoxic concentrations, BP compounds significantly increased spheroid formation in CSC221, DLD1, Caco2, and HT29 cells. In addition, the levels of cancer stem cell markers, such as aldehyde dehydrogenase-1, cluster of differentiation 133 (CD133), CD44, Lgr5, Musashi-1, Ephrin receptor, and Bmi-1 increased after exposure to BP compounds. BP compounds increased the transcriptional activity of the TOPflash and glioma-associated oncogene homolog zinc finger protein (Gli) promoters in reporter assays and increased the expression of Gli-1, Gli-2, Smoothened (SMO), and β-catenin by RT-PCR. These results demonstrate for the first time that BP compounds have the potential to promote cancer stemness.

Lichens are symbiotic organisms which produce distinct secondary metabolic products. In the present study, we tested the cytotoxic activity of 17 lichen species against several human cancer cells and further investigated the molecular mechanisms underlying their anti-cancer activity. We found that among 17 lichens species, F. cucullata exhibited the most potent cytotoxicity in several human cancer cells. High performance liquid chromatography analysis revealed that the acetone extract of F. cucullata contains usnic acid, salazinic acid, Squamatic acid, Baeomycesic acid, d-protolichesterinic acid, and lichesterinic acid as subcomponents. MTT assay showed that cancer cell lines were more vulnerable to the cytotoxic effects of the extract than non-cancer cell lines. Furthermore, among the identified subcomponents, usnic acid treatment had a similar cytotoxic effect on cancer cell lines but with lower potency than the extract. At a lethal dose, treatment with the extract or with usnic acid greatly increased the apoptotic cell population and specifically activated the apoptotic signaling pathway; however, using sub-lethal doses, extract and usnic acid treatment decreased cancer cell motility and inhibited in vitro and in vivo tumorigenic potentials. In these cells, we observed significantly reduced levels of epithelial-mesenchymal transition (EMT) markers and phosphor-Akt, while phosphor-c-Jun and phosphor-ERK1/2 levels were only marginally affected. Overall, the anti-cancer activity of the extract is more potent than that of usnic acid alone. Taken together, F. cucullata and its subcomponent, usnic acid together with additional component, exert anti-cancer effects on human cancer cells through the induction of apoptosis and the inhibition of EMT.

A total of 151 patients with 151 operations were observed and retrospectively recorded. All medical staff (including doctors, nurses, and midwives) participating in the preparation of patients were invited to participate in the study. A total of 38 health workers were observed and evaluated. Seven in-depth interviews and focus group discussions were performed. Compliance preparation for preoperative preparation among medical staff is low, with only 45.7% adherence to the process, while 76.8% comply with physical preparation. The overall compliance rate is 67.5%. The study also reveals incomplete surgical review systems, inadequate human resources, and insufficient supervision. Type of surgery and surgical department are factors associated with poor preoperative preparation. Study results highlighted that the level of compliance with the pre-operative patient preparation process of the private hospital in this study was still low. Appropriate strategies are needed to improve human resource barriers and monitor and improve administrative systems, thereby improving the ability of medical staff to comply with the process at the hospital. [ABSTRACT FROM AUTHOR]