Evaluation of short-term toxicity of ammonia and nitrite on the survival of whiteleg shrimp, Litopenaeus vannamei juveniles

The effects of short-term toxicity of total ammonia nitrogen (TAN) and nitrite were estimated in juveniles of Litopenaeus vannamei under laboratory conditions. In the first experiment, L. vannamei juveniles were exposed to different concentrations of ammonia (0, 5, 10, 15, 20, 30, and 40 mg of TAN L-1) or nitrite (0, 5, 10, 20, 30, 40, and 50 mg of NO2--N L-1), using the static renewal method at a salinity of 20 ppt and pH 8.2. The survival rates of juveniles significantly decreased when exposed to increased concentrations of ammonia or nitrite during the 96 h bioassays. The 24, 48, 72, and 96 h LC50 values of TAN in juveniles were 45.5, 30.1, 13.8, and 6.3 mg L-1, respectively, while the LC50 values of NO2--N at 24, 48, 72, and 96 h were 37.6, 16.7, 8.8, and 4.8 mg L-1, respectively. Experiment 2 evaluated the tolerance of L. vannamei juveniles at various salinities (5, 10, 15, and 20 ppt) under a high concentration of ammonia or nitrite (5 mg L-1). Results showed that the survival rates of L. vannamei at 5 ppt and 10 ppt were significantly lower than those at 20 ppt after 72 h and 96 h of exposure

POLLUTION OF GROUNDWATER BY LEACHATE FROM DONG THANH LANDFILL DISPOSAL SITE

Joint Research on Environmental Science and Technology for the Eart

A randomized controlled trial of a pharmacist-led intervention to enhance knowledge of Vietnamese patients with type 2 diabetes mellitus

OBJECTIVES: We aimed to assess whether a pharmacist-led intervention enhances knowledge, medication adherence and glycemic control in patients with type 2 diabetes mellitus (T2DM). METHODS: We conducted a single-blinded randomized controlled trial in Vietnam. Individuals with T2DM were recruited from a general hospital and randomly allocated to intervention and routine care. The intervention group received routine care plus counselling intervention by a pharmacist, including providing drug information and answering individual patients' queries relating to T2DM and medications, which had not been done in routine care. We assessed the outcomes: knowledge score as measured by the Diabetes Knowledge Questionnaire, self-reported adherence and fasting blood glucose (FBG) at the 1-month follow-up. KEY FINDINGS: A total of 165 patients (83 intervention, 82 control) completed the study; their mean age was 63.33 years, and 49.1% were males. The baseline characteristics of the patients were similar between the groups. At 1-month follow-up, the pharmacist's intervention resulted in an improvement in all three outcomes: knowledge score [B = 5.527; 95% confidence intervals (CI): 3.982 to 7.072; P < 0.001], adherence [odds ratio (OR) = 9.813; 95% CI: 2.456 to 39.205; P = 0.001] and attainment of target FBG (OR = 1.979; 95% CI: 1.029 to 3.806; P = 0.041). CONCLUSIONS: The pharmacist-led intervention enhanced disease knowledge, medication adherence and glycemic control in patients with T2DM. This study provides evidence of the benefits of pharmacist counselling in addition to routine care for T2DM outpatients in a Vietnam population

Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts: (i) the active-transmit message transmitted by the transmitter using conventional RF signals and (ii) the backscatter message transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios

Drug-Related Problems in Prescribing for Pediatric Outpatients in Vietnam

BACKGROUND: Our study was conducted to determine the prevalence of drug-related problems (DRPs) in outpatient prescriptions, the impact of DRPs on treatment efficacy, safety, and cost, and the determinants of DRPs in prescribing for pediatric outpatients in Vietnam. METHODS: A retrospective cross-sectional study was conducted on pediatric outpatients at a pediatric hospital in Can Tho, Vietnam. DRPs were classified according to the Pharmaceutical Care Network Europe classification (PCNE) of 2020. The study determined prevalence of DRPs and their impacts on efficacy, safety, and cost. Multivariate regression was used to identify the determinants of DRPs. RESULTS: The study included 4339 patients (mean age 4.3, 55.8% male), with a total of 3994 DRPs, averaging 0.92 DRP/prescription. The proportion of prescriptions with at least one DRP was 65.7%. DRPs included inappropriate drug selection (35.6%), wrong time of dosing relative to meals (35.6%), inappropriate dosage form (9.3%), inappropriate indication (7.1%), and drug-drug interactions (0.3%). The consensus of experts was average when evaluating each aspect of efficiency reduction, safety reduction, and treatment cost increase, with Fleiss' coefficients of 0.558, 0.511, and 0.541, respectively (p < 0.001). Regarding prescriptions, 50.1% were assessed as reducing safety. The figures for increased costs and decreased treatment effectiveness were 29.0% and 23.9%, respectively. Patients who were ≤2 years old were more likely to have DRPs than patients aged 2 to 6 years old (OR = 0.696; 95% CI = 0.599-0.809) and patients aged over 6 years old (OR = 0.801; 95% CI = 0.672-0.955). Patients who had respiratory system disease were more likely to have DRPs than patients suffering from other diseases (OR = 0.715; 95% CI = 0.607-0.843). Patients with comorbidities were less likely to have DRPs than patients with no comorbidities (OR = 1.421; 95% CI = 1.219-1.655). Patients prescribed ≥5 drugs were more likely to have DRPs than patients who took fewer drugs (OR = 3.677; 95% CI = 2.907-4.650). CONCLUSION: The proportion of prescriptions in at least one DRP was quite high. Further studies should evaluate clinical significance and appropriate interventions, such as providing drug information and consulting doctors about DRPs

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images. To bridge this gap, we introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets, covering a large number of organs and modalities such as CT, MRI, X-ray, and Ultrasound. We benchmark several state-of-the-art self-supervised algorithms on this dataset and propose a novel self-supervised contrastive learning algorithm using a graph-matching formulation. The proposed approach makes three contributions: (i) it integrates prior pair-wise image similarity metrics based on local and global information; (ii) it captures the structural constraints of feature embeddings through a loss function constructed via a combinatorial graph-matching objective; and (iii) it can be trained efficiently end-to-end using modern gradient-estimation techniques for black-box solvers. We thoroughly evaluate the proposed LVM-Med on 15 downstream medical tasks ranging from segmentation and classification to object detection, and both for the in and out-of-distribution settings. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models. For challenging tasks such as Brain Tumor Classification or Diabetic Retinopathy Grading, LVM-Med improves previous vision-language models trained on 1 billion masks by 6-7% while using only a ResNet-50.Comment: Update Appendi