The Great Imitator: A Case of Accidental Kratom Overdose.

Kratom (Mitragyna speciosa) is an herb that is sold over the counter in both pill and liquid forms. It contains opioid and stimulant properties and is used for relaxation as well as for weaning off opioid addictions. While a few adverse effects of kratom have been already reported, mainly with concerns around its toxicity, very little is known about it. We report a case of a female in her 40s presenting with signs of hypoxia reversed with naloxone administration, initially suspected to be a case of opioid overdose. Upon becoming alert and oriented, the patient and her husband reported that she consumed a large amount of kratom bought from the local gas station, and he later noticed that her lips were turning blue and she was becoming increasingly altered. Her urine toxicology was noted to be negative for opioids or any other substance use. The patient survived this accidental overdose due to the quick action of her husband, who rushed her to the emergency department (ED) upon realizing she appeared altered and very ill. It is important for emergency medicine practitioners to be aware of kratom overdose as a possible item on the differential diagnosis. This paper focuses on kratom overdose presentation and treatment.

Cost-Effective Training Models in Point-of-Care Ultrasound for Medical Students in Emergency Medicine: An Evaluation of Current Resources.

Background Ultrasound is becoming more widely utilized in clinical practice; however, its effectiveness is limited by the operator's skills. Simulation models are attractive options for developing skills because they allow inexperienced users to practice without the risk of endangering patients. Objective The purpose of this study was to identify commercially available and homemade ultrasound models to describe them in terms of materials, cost, and whether they are high- or low-fidelity for medical student education. Methods This is an investigational study on cost-effective ultrasound training methods for medical students. Our study was performed using search engines in Google, Google Scholar, and PubMed to search for models for the following five modalities: foreign body identification, intravenous (IV) injection training, abdominal ultrasound, ocular ultrasound, and ultrasound-guided lumbar puncture training. Results Most homemade models for foreign body identification, IV injection training, and ocular ultrasound could be created for less than $20. IV injection training models were the cheapest commercially available models. There are multiple commercially available options for abdominal ultrasound models, but no options were found for homemade construction. The construction cost for lumbar puncture models was larger due to the need to purchase an anatomically accurate set of lumbar vertebrae. Conclusions This study provides initial guidance and suggestions for ultrasound training models that are currently available. Ultrasound models that can be cheaply made or purchased increase accessibility for medical students to gain early exposure in a cost-effective and safe manner.

Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.

BACKGROUND: Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. METHODS: In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. RESULTS: The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. CONCLUSION: With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

Improved Synthesis of Ag/SiO2 Colloidal Nanocomposites and Their Antibacterial Activity Against Ralstonia solanacearum 15.

Ag/SiO2 colloidal nanocomposites (NCs) were prepared through the semi-continuous chemical reduction of silver ions on a silica surface; NaBH4 was used as a primary reducing agent, while carboxymethyl cellulose (CMC) served as a secondary reductant and a stabilizer at low temperature. Silver nanoparticles (AgNPs) of an average diameter of 3.89±0.18 nm were uniformly and densely dispersed on the SiO2 surface, forming 218.6-nm-sized Ag/SiO2 NCs. The zeta potential of the Ag/SiO2 NCs (-92.6 mV) was more negative than that of silica (-24 mV), indicating their high long-term stability. Furthermore, their proposed formation mechanism was confirmed via Fourier transform infrared spectroscopy. Then, the bactericidal effect of the Ag/SiO2 was evaluated based on their minimal inhibitory concentration (MIC) against Ralstonia solanacearum 15 (R. solanacearum 15); it was 62.5 ppm, much lower than that of conventional AgNPs (500 ppm). Therefore, these highly stable Ag/SiO2 colloidal NCs with more effective antibacterial activity than conventional AgNPs are a promising nanopesticide in agriculture.

A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction.

BACKGROUND AND AIM: deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and improves the processing time of the left ventricle detection process by reducing the overfitting and vanishing gradient problem. METHODOLOGY: the proposed system consists of an enhanced deep convolutional neural network with an extra convolutional layer, and dropout layer to solve the problem of overfitting and vanishing gradient. Data augmentation was used for increasing the accuracy of feature extraction for left ventricle detection. RESULTS: four pathological groups of datasets were used for training and evaluation of the model: heart failure without infarction, heart failure with infarction, and hypertrophy, and healthy. The proposed model provided an accuracy of 94% in left ventricle detection for all the groups compared to the other current systems. The results showed that the processing time was reduced from 0.45 s to 0.34 s in an average. CONCLUSION: the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data.