Relationships of BRAF V600E Gene Mutation With Some Immunohistochemical Markers and Recurrence Rate in Patients With Thyroid Carcinoma.

Background: The B-type rafkinase (BRAF) V600E gene mutation plays an important role in the pathogenesis, diagnosis, and prognosis of thyroid carcinoma. This study was conducted to investigate the rate of the BRAF V600E mutation, the relationships between the BRAF V600E gene mutation and some immunohistochemical markers, and recurrence rate in patients with differentiated thyroid cancer. Method: The study was conducted by a descriptive and longitudinal follow-up method on 102 thyroid carcinoma patients at 103 Military Hospital, Hanoi, Vietnam. All patients were identified with the BRAF V600E gene mutation by real-time polymerase chain reaction. Results: The rate of BRAF V600E gene mutation in patients with thyroid cancer was 60.8%. Patients with BRAF V600E gene mutation had a significantly higher rate of positive cyclooxygenase 2 (COX-2) and Ki67 markers than those without the mutation (COX-2: odds ratio [OR] = 2.93; 95% confidence interval [CI] = 1.27-6.74, P = .011; Ki67: OR = 3.41; 95% CI = 1.31-8.88, P = .01). A statistically significant relationship was identified between the rate of BRAF V600E mutation and the rate of positive Hector Battifora mesothelial 1 (HBME-1) (B = -1.040; P = .037) and COX-2 (B = -1.123; P = .023) markers. The recurrence rate in patients with BRAF V600E gene mutation was significantly higher than that in those without the mutation (P = .007). The mean of the recurrence time of patients with BRAF V600E mutation was significantly lower than that in those without the mutation (P = .011). Conclusions: A high prevalence of BRAF V600E gene mutation was found in thyroid carcinoma patients. The rates of positive HBME-1, COX-2, and Ki67 markers were significantly correlated to BRAF V600E gene mutation. Patients with BRAF V600E gene mutation showed a significantly higher relapse rate and earlier relapse time than those without the mutation.

Predicting Antibiotic Resistance in ICUs Patients by Applying Machine Learning in Vietnam.

Introduction: Artificial Intelligence (AI) and machine learning (ML) are used extensively in HICs to detect and control antibiotic resistance (AMR) in laboratories and clinical institutions. ML is designed to predict outcome variables using an algorithm to enable "machines" to learn the "rules" from the data. ML is increasingly being applied in intensive care units to identify AMR and to assist empiric antibiotic therapy. This study aimed to evaluate the performance of ML models for predicting AMR bacteria and resistance to antibiotics in two Vietnamese hospitals. Patients and Methods: A cross-sectional study combined with retrospective was conducted from 1st January 2020 to 30th June 2022. Five models were developed to predict antibiotic resistance of bacterial infections of ICU patients. Two datasets were prepared to predict AMR bacteria and antibiotics with ML models. The performance of the prediction models was evaluated by various indicators (sensitivity, specificity, precision, accuracy, F1-score, PRC, AuROC, and NormMCC) to determine the optimal time point for data selection. Python version 3.8 was used for statistical analyses. Results: The accuracy, F1-score, AuROC, and normMMC of LightGBM, XGBoost, and Random Forest models were higher than those of other models in both datasets. In both datasets 1 and 2, accuracy, F1-score, AuROC and normMCC of the XGBoost model were the highest among five models (from 0.890 to 1.000). Only Random Forest models had specificity scores higher than 0.850. High scores of sensitivity, accuracy, precision, F1-score, and normMCC indicated that the models were making accurate predictions for datasets 1 and 2. Conclusion: XGBoost, LightGBM, and Random Forest were the best-performed machine learning models to predict antibiotic resistance of bacterial infections of ICUs patients using the patients' EMRs.

Baseline concentration of microplastics in surface water and sediment of the northern branches of the Mekong River Delta, Vietnam.

The Mekong River Delta in Vietnam, is concerned by numerous microplastic-related issues such as a lack of wastewater treatment facilities and mismanagement of plastic waste released from agriculture, aquaculture and related activities. This study aimed to examine the presence of microplastics in surface water and sediment by collecting samples from six sites along the Tien River and its distributaries in February 2019. The results showed that the average concentration of microplastics over the entire area was 53.8 ± 140.7 items m-3 in surface water and 6.0 ± 2.0 items g-1 dried weight in sediment, with a predominance of microplastic fibres rather than fragments, respectively 85 % and 98 % in surface water and sediment. In the main flow of surface water, the concentration of microplastics was stable; however, in the sediment, microplastic concentration was affected by the high dynamic flow regime rather than the sources where microplastics are released.

Anthropogenic fibres in white clams, Meretrix lyrata, cultivated downstream a developing megacity, Ho Chi Minh City, Viet Nam.

Anthropogenic fibres are an emerging pollutant worldwide. The Can Gio mangrove area is located downstream of the Saigon River, and is characterised by high level of anthropogenic fibres originating from domestic and industrial textile and apparel manufacturing. In this area, biota is thus subjected to a high potential risk of anthropogenic fibre contamination. This study aims to characterise the accumulation of anthropogenic fibres in different tissues, i.e. gills, digestive systems, and remaining tissues, of white clams (Meretrix lyrata) cultivated in the Can Gio beach sand, during a seven-month sampling period. The results showed an average concentration of 3.6 ± 2.1 fibres individual-1 or 2.7 ± 2.4 fibres g-1 ww. Higher fibre accumulation was observed in remaining tissues than in gills and digestive systems, and no temporal variation was observed in all clam tissues. The intake of fibres by humans consuming clams was estimated to be 324 fibres inhabitant-1 yr-1.

Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images.

Coronary artery disease is caused primarily by vessel narrowing. Extraction of the coronary artery area from images is the preferred procedure for diagnosing coronary diseases. In this study, a U-Net-based network architecture, 3D Dense-U-Net, was adopted to perform fully automatic segmentation of the coronary artery. The network was applied to 474 coronary computed tomography (CT) angiography scans performed at Wanfang Hospital, Taiwan. Of these, 10% were used for testing. The CT scans were divided into patches of 16 original high-resolution slices. The slices were overlapped between patches to take advantage of surrounding imaging information. However, an imbalance between the foreground and background presents a challenge in smaller-object segmentation such as with coronary arteries. The network was optimized and achieved a promising result when the focal loss concept was adopted. To evaluate the accuracy of the automatic segmentation approach, the dice similarity coefficient (DSC) was calculated, and an existing clinical tool was used. The subjective ratings of three experienced radiologists were used to compare the two ratings. The results show that the proposed approach can achieve a DSC of 0.9691, which is significantly higher than other studies using a deep learning approach. In the main trunk, the results of automatic segmentation agree with those of the clinical tool; they were significantly better in some small branches. In our study, automatic segmentation tool shows high-performance detection in coronary lumen vessels, thereby providing potential power in assisting clinical diagnosis.

Improved diagnosis-medication association mining to reduce pseudo-associations.

BACKGROUND AND OBJECTIVE: Association rule mining has been adopted to medical fields to discover prescribing patterns or relationships among diseases and/or medications; however, it has generated unreasonable associations among these entities. This study aims to identify the real-world profile of disease-medication (DM) associations using the modified mining algorithm and assess its performance in reducing DM pseudo-associations. METHODS: We retrieved data from outpatient records between January 2011 and December 2015 in claims databases maintained by the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan. The association rule mining's lift (Q-value) was adopted to quantify DM associations, referred to as Q1 for the original algorithm and as Q2 for the modified algorithm. One thousand DM pairs with positive Q1-values (Q1+) and negative or no Q2-values (Q2- or Q2∅) were selected as the validation dataset, in which two pharmacists assessed the DM associations. RESULTS: A total of 3,120,449 unique DM pairs were identified, of which there were 333,347 Q1+Q2- pairs and 429,931 Q1+Q2∅ pairs. Q1+Q2- rates were relatively high in ATC classes C (29.91%) and R (30.24%). Classes L (69.91%) and V (52.52%) demonstrated remarkably high Q1+Q2∅ rates. For the 1000 pairs in the validation, 93.7% of the Q1+Q2- or Q1+Q2∅ DM pairs were assessed as pseudo-associations. However, classes M (5.3%), H (4.5%), and B (4.1%) showed the highest rates of plausible associations falsely given Q2- or Q2∅ by the modified algorithm. CONCLUSIONS: The modified algorithm demonstrated high accuracy to identify pseudo-associations regarded as positive associations by the original algorithm and would potentially be applied to improve secondary databases to facilitate research on real-world prescribing patterns and further enhance drug safety.

Microplastic in atmospheric fallouts of a developing Southeast Asian megacity under tropical climate.

Microplastics in atmospheric fallouts from a developing megacity influenced by a tropical monsoon climate were investigated during a year. Three sites were selected according to the surrounding population density, the land use and occupation. The microplastic deposition fluxes varied between of 71-917 items m-2 d-1, for an observation size range of 300-5000 µm. Fibers predominated while fragments were observed occasionally. Unexpectedly, contrary to available scientific literature, deposition fluxes did not vary temporally with rainfall and wind intensity or direction, showing no effect of the tropical climate. Variations were observed between sites and were attributed to their environmental characteristics: population density and occupation space. The median length of fibers also differed between sites and could be related to in-situ fragmentation processes due to occupation space (solid waste treatment facility). Those first results from tropical climate region are showing interesting insights and are opening new perspectives on the understanding of microplastics fate from atmospheric fallouts.

Baseline assessment of microplastic concentrations in marine and freshwater environments of a developing Southeast Asian country, Viet Nam.

In aquatic environments, assessment of microplastic concentrations is increasing worldwide but environments from developing countries remain under-evaluated. Due to disparities of facilities, financial resources and human resources between countries, protocols of sampling, analysis and observations used in developed countries cannot be fully adapted in developing ones, and required specific adaptations. In Viet Nam, an adapted methodology was developed and commonly adopted by local researchers to implement a microplastic monitoring in sediments and surface waters of 21 environments (rivers, lakes, bays, beaches) of eight cities or provinces. Microplastic concentrations in surface waters varied from 0.35 to 2522 items m-3, with the lowest concentrations recorded in the bays and the highest in the rivers. Fibers dominated over fragments in most environments (from 47% to 97%). The microplastic concentrations were related to the anthropogenic pressure on the environment, pointing out the necessity in a near future to identify the local sources of microplastics.

Tissue factor expression pattern in human non-small cell lung cancer tissues indicate increased blood thrombogenicity and tumor metastasis.

Non-small cell lung cancer (NSCLC) comprises of 75% of all lung cancers. Human full length tissue factor (flHTF), the physiological initiator of blood coagulation, is aberrantly expressed in certain solid tumors. FlHTF and its soluble isoform, alternatively spliced human tissue factor (asHTF), have been shown to contribute to thrombogenicity of the blood of healthy individuals. The aim of this study was to quantify flHTF and asHTF on mRNA and protein levels (using immunohistochemistry, immunoblotting, and ELISA) on a panel of human NSCLC tissue and plasma specimens. The tissue factor (TF) expression of 21 pulmonary adenomatous (AC) and 12 normal healthy tissues was assessed by real-time qRT-PCR. The TF protein concentration was quantified by ELISA in a subset of 11 AC and 9 normal tissue specimens as well as in the plasma of 13 lung cancer patients and 15 healthy controls. We found a significant increase in the ratio of flHTF/HGAPDH mRNA in AC (0.24+/-0.06 vs. 0.07+/-0.01; p=0.02 vs. controls) and in asHTF/HGAPDH mRNA (0.027+/-0.01 vs. 0.004+/-0.001; p=0.03 AC vs. controls). AsHTF mRNA expression was significantly lower in patients with stage IA disease compared to patients with higher grade stages, pointing to TF as being a marker of malignancy and metastases. TF protein of lung tumors was significantly increased in AC (p=0.004 vs. controls). TF in plasma was up-regulated in lung cancer patients (334.9+/-95.4 vs. 124.1+/-14.8 pg/ml; p=0.02 vs. controls). Immunohistochemical and immunoblotting data are in line with the increased TF expression, showing elevated blood thrombogenicity of NSCLC patients. The up-regulation of flHTF and, especially, asHTF in AC suggests not only a raised risk of thrombosis, but also of tumor progression, thereby, indicating a poor prognosis in these patients.

Effect of ionizing radiation on cellular procoagulability and co-ordinated gene alterations.

BACKGROUND AND OBJECTIVES: Ionizing radiation (IR) is associated with thrombotic vascular occlusion predicting a poor clinical outcome. Our study examined whether IR induced tissue factor (TF) expression and procoagulability. We further investigated coordinated gene alterations associated with TF upregulation in the myelomonocytic leukemia THP-1 cells. DESIGN AND METHODS: TF expression was determined by quantitative Reverse Transcriptase (TaqMan) PCR, TF ELISA and TF activity by a two stage chromogenic assay in the time course of days 1, 3, 7, 10, and 17 post IR. To detect IR-induced alterations in gene expression, Affymetrix HG U133 Plus 2.0 microarrays were used. RESULTS IR induced a significant increase in TF/GAPDH mRNA ratios and cellular TF protein on days 3 and 7 post IR (20 Gy [p>or=0.01] and 40 Gy [p or=0.001] vs. control respectively), suggesting IR immediately alters the cellular thrombogenicity. TF upregulation post IR was confirmed in PBMNCs. Gene expression profiling showed IR increased the expression of inflammatory and apoptosis-related pathways known to be involved in the regulation of TF expression. INTERPRETATION AND CONCLUSIONS: TF upregulation together with inflammation and apoptosis may increase the thrombogenicity of tissues. The demonstrated upregulation of TF might play a pivotal role in radiation associated thrombosis.

Antioxidative treatment inhibits the release of thrombogenic tissue factor from irradiation- and cytokine-induced endothelial cells.

OBJECTIVES: The aim of this study was to investigate the effect of the antioxidants pyrrolidine dithiocarbamate (PDTC) and N-acetylcysteine (NAC) on the ionizing radiation (IR)- and tumor necrosis factor-alpha (TNF-alpha) induced tissue factor (TF) expression and its release from human umbilical vein endothelial cells (HUVECs). METHODS: HUVECs were irradiated with a single dose of either 5 Gy or 10 Gy and stimulated with TNF-alpha (10 ng/mL) in the presence or absence of PDTC and NAC, respectively. Quantitative real-time PCR, ELISA, and TF activity measurements were performed, including TF activity in the supernatant. Apoptosis was detected by flow cytometric active caspase-3 measurement and formation of reactive oxygen species (ROS) by chemiluminescence. RESULTS: We demonstrated a thus far uninvestigated persistent induction of TF expression in HUVECs after treatment with IR and TNF-alpha. Combined stimulation with IR and TNF-alpha led to an immense shedding of microparticle-associated TF which was positively correlated with apoptosis and ROS formation. Antioxidative pre-treatment reduced not only apoptosis and ROS formation, but also the release of thrombogenic microparticles. CONCLUSIONS: Antioxidative treatment inhibited apoptosis and shedding of microparticles, thereby reducing thrombogenicity. Thus, antioxidants may help to prevent late thrombosis after antiproliferative treatment when used in combination with anticoagulants.