The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people’s lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for “coronavirus” and “vaccines” as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public’s anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.

Background and Objectives: Chronic lower back pain (LBP) is a common clinical disorder. The early identification of patients who will develop chronic LBP would help develop preventive measures and treatment. We aimed to develop machine learning models that can accurately predict the risk of chronic LBP. Materials and Methods: Data from the Sixth Korea National Health and Nutrition Examination Survey conducted in 2014 and 2015 (KNHANES VI-2, 3) were screened for selecting patients with chronic LBP. LBP lasting >30 days in the past 3 months was defined as chronic LBP in the survey. The following classification models with machine learning algorithms were developed and validated to predict chronic LBP: logistic regression (LR), k-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and artificial neural network (ANN). The performance of these models was compared with respect to the area under the receiver operating characteristic curve (AUROC). Results: A total of 6119 patients were analyzed in this study, of which 1394 had LBP. The feature selected data consisted of 13 variables. The LR, KNN, NB, DT, RF, GBM, SVM, and ANN models showed performances (in terms of AUROCs) of 0.656, 0.656, 0.712, 0.671, 0.699, 0.660, 0.707, and 0.716, respectively, with ten-fold cross-validation. Conclusions: In this study, the ANN model was identified as the best machine learning classification model for predicting the occurrence of chronic LBP. Therefore, machine learning could be effectively applied in the identification of populations at high risk of chronic LBP.

Background: The efficacy of dexamethasone plus palonosetron for postoperative nausea and vomiting (PONV) prophylaxis is not firmly established. This randomized, double-blind, controlled study evaluated whether the combination was superior to palonosetron alone in preventing PONV in patients receiving intravenous patient-controlled analgesia (IV-PCA) after upper extremity surgery.

Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.

Pain after anterior cruciate ligament (ACL) reconstruction is usually intense in the early postoperative period, but the efficacy of a multimodal analgesia approach remains controversial. This study aimed to investigate the analgesic efficacy of pregabalin in multimodal analgesia after ACL reconstruction. Patients who underwent ACL reconstruction under spinal anesthesia and agreed to use intravenous patient-controlled analgesia (IV-PCA) were randomly administered placebo (control group, n = 47) or pregabalin 150 mg (pregabalin group, n = 46) 1 h before surgery and 12 h after initial treatment. Pain by verbal numerical rating scale (VNRS) at rest and with passive flexion of knee was assessed at postoperative 12, 24, and 36 h and 2 weeks. IV-PCA consumption, rescue analgesic use, and side effects were also evaluated. Lower scores of VNRS were obtained with passive flexion of knee in the pregabalin group than in the control group at postoperative 24 (7(4–8) vs. 8(6–9), p = 0.043) and 36 h (4(3–7) vs. 5(4–9), p = 0.042), and lower value of VNRS at rest was observed in the pregabalin group [0(0–1)] than in the control group [1(0–2)] at postoperative 2 weeks (p < 0.001). No differences were obtained for IV-PCA consumption, rescue analgesic use, and side effects except for dizziness for postoperative 12 h. Pregabalin as an adjuvant to multimodal analgesic regimen significantly reduced early postoperative pain in patients undergoing ACL reconstruction.