BACKGROUNDModified Radical Mastectomy (MRM) is the most common surgical procedure for operable breast malignancies. Postoperative pain has been severe in MRM cases and demands for pain relief are high. The technique of performing pectoral nerve block [PECS] using ultrasound guidance is increasing in popularity. We compared the postoperative analgesic profile of ultrasound-guided serratus plane block (SPB) and landmark-guided paravertebral block (PVB). MATERIALS AND METHODSThis study was done as a double blind, randomised, controlled trial among 60 subjects with breast cancer posted for elective MRM, of which 30 subjects were put in PECS block group and 30 subjects in paravertebral block group. PVB was performed under complete aseptic precaution with low resistance technique. PECS block was performed in supine position with the U/S probe directly above 1 st rib where pectoralis major and pectoralis minor muscles are located. Anaesthesia was maintained with sevoflurane 1% and O2/N20 mixture with a fraction of 50% inspired O2. Postoperative pain was recorded using the Visual Analogue Scale (VAS) every one-hour up to 4 hours post-surgery. RESULTSThe mean VAS scores were significantly lesser in the PECS group compared to the PVB group (p<0.05). The mean heart rates in PECS group were significantly lower than PVB group throughout the procedure. There was no significant difference in the intra-OP MAP between both the groups. CONCLUSIONUltrasound-guided PECS block is an alternative analgesic technique to thoracic PVB for postop pain relief in MRM patients. It gives superior analgesia and has fewer complications. BACKGROUND Breast cancer has emerged as the most common cancer in India, and 2 nd most common even in the rural area. Breast cancer accounts for 25% to 32% of all female cancers in all these cities. This implies, practically, one fourth of all female cancer cases are breast cancers. [1] Of the several therapeutic options for breast cancer, Modified Radical Mastectomy (MRM) is the most common surgical procedure for operable breast malignancies. Postoperative pain has been severe in MRM cases and demands for pain relief are high. KEYWORDS
Customer satisfaction and their positive sentiments are some of the various goals for successful companies. However, analyzing customer reviews to predict accurate sentiments have been proven to be challenging and time-consuming due to high volumes of collected data from various sources. Several researchers approach this with algorithms, methods, and models. These include machine learning and deep learning (DL) methods, unigram and skip-gram based algorithms, as well as the Artificial Neural Network (ANN) and bag-of-word (BOW) regression model. Studies and research have revealed incoherence in polarity, model overfitting and performance issues, as well as high cost in data processing. This experiment was conducted to solve these revealing issues, by building a high performance yet cost-effective model for predicting accurate sentiments from large datasets containing customer reviews. This model uses the fastText library from Facebook’s AI research (FAIR) Lab, as well as the traditional Linear Support Vector Machine (LSVM) to classify text and word embedding. Comparisons of this model were also done with the author’s a custom multi-layer Sentiment Analysis (SA) Bi-directional Long Short-Term Memory (SA-BLSTM) model. The proposed fastText model, based on results, obtains a higher accuracy of 90.71% as well as 20% in performance compared to LSVM and SA-BLSTM models.
The Bidirectional Encoder Representations from Transformers (BERT) is a state-of-the-art language model used for multiple natural language processing tasks and sequential modeling applications. The accuracy of predictions from contextbased sentiment and analysis of customer review data from various social media platforms are challenging and timeconsuming tasks due to the high volumes of unstructured data. In recent years, more research has been conducted based on the recurrent neural network algorithm, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) as well as hybrid, neutral, and traditional text classification algorithms. This paper presents our experimental research work to overcome these known challenges of the sentiment analysis models, such as its performance, accuracy, and context-based predictions. We've proposed a fine-tuned BERT model to predict customer sentiments through the utilization of customer reviews from Twitter, IMDB Movie Reviews, Yelp, Amazon. In addition, we compared the results of the proposed model with our custom Linear Support Vector Machine (LSVM), fastText, BiLSTM and hybrid fastText-BiLSTM models, as well as presented a comparative analysis dashboard report. This experiment result shows that the proposed model performs better than other models with respect to various performance measures.
The present study investigated the antitumor effect and antioxidant role of the methanol extract of Oxystelma esculentum R. Br. (Asclepiadaceae) (MEOE) on tumor growth and the host survival time with mice. The antitumor and antioxidant potential of Oxystelma esculentum were studied against Ehrlich's ascites carcinoma cell line (EAC) treated mice. MEOE was administered at doses of 200 and 400 mg/kg body weight (bw) once a day for 9 days after 24 h of tumor inoculation. Among the treated animals, six animals were sacrificed for biochemical and tumor analysis, and the remaining four groups were kept to study lifespan. On day 10, the parameters of tumor volume, packed cell volume, viable, and non-viable cell count were studied. Hematological and liver biochemical parameters, and antioxidant enzymes such as lipid peroxidation (LPO), glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), etc. were estimated. Decreases in tumor volume, packed cell volume, and viable cell count were observed in MEOEtreated mice when compared to EAC-treated mice. Treatment with MEOE at doses of 200 and 400 mg/kg increased the mean survival time to 29.66 ± 0.71 and 34.33 ± 2.34 days, compared with EAC-treated mice at 19.16 ± 1.13 days. The extract also decreased the body weight of the EAC-bearing mice. Hematological profiles indicated a decrease in white blood cells (WBC), an increase in red blood cells (RBC), and, thereby, Hemoglobin (Hb). MEOE restored all the parameters of hematological profiles to approximately normal. Treatment with MEOE decreased the levels of LPO and increased the levels of GSH, SOD, and CAT. These data indicate the methanol extract of Oxystelma esculentum exhibits significant antitumor activity, which might be due to the antioxidant effects on EAC bearing hosts.
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