On the frontiers of Twitter data and sentiment analysis in election prediction: a review.

Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

Assessment of acute, sub-acute, chronic and genotoxicity of polyherbal formulation DCD-684 in mice.

Digas colic drops (DCD-684) a polyherbal formulation containing Carum carvi, Foeniculum vulgare, Mentha arvensis, Mentha piperita and Zingiber officinale is widely used in Pakistan against gastrointestinal ailments including infantile colic. The DCD-684 (0.03-3ml/kg.bw) administered orally in acute (7-days) and sub-acute toxicity (14-days) tests, displayed neither mortality nor toxicological changes in physical, behavioral, biochemical and histopathological parameters. In chronic study (90-days), DCD-684 (0.3-12ml/kg.bw) also revealed no changes. However, at 18 and 36 ml/kg.bw, liver demonstrated mild inflammation correlating with raised aspartate transaminase (AST), alkaline phosphatase (ALP) and alpha fetoprotein (AFP) levels. Increased levels of urea and inflamed renal parenchyma indicated mild nephro-toxicity with high alanine aminotransferase (ALT) at 36ml/kg.bw. The LD50 of DCD-684 in mice was 27.5 ml/kg.bw. In hepatocytes at 36ml/kg.bw, elevated mRNA expression of pro-inflammatory chemokines and cytokines were evident. DCD-684 neither damaged DNA nor induced cytotoxicity in micronucleus assay. In conclusion, polyherbal DCD-684 caused neither hepatic, renal, genotoxicity nor any undesirable effect in mice. Higher doses administered for 90 days showed mild toxic effects with no sign of necrosis, fibrosis or genotoxicity. Thus, in mice DCD-684 demonstrated a wide margin of safety to be used for the relief of infantile colic.

Antispasmodic activity and mechanism of action of polyherbal formulation DCD-684 on rabbit jejunum.

Digas colic drops (DCD-684) is a polyherbal formulation containing decoctions of five medicinal plants namely Carum carvi L., Foeniculum vulgare Mill, Mentha arvensis L., Mentha piperita L. and Zingiber officinale Roscoe. These plants have been extensively used in traditional medicine for the treatment of various gastrointestinal diseases including abdominal colic. This study was conducted to determine the spasmolytic effect of DCD-684 (100% v/v) and its individual plant components on isolated rabbit jejunum (in vitro) and their possible mechanism of action. The effects were evaluated on spontaneous and pre-contracted tissues using KCl (80mM) and other contractile agonists including acetylcholine (0.3µM), carbamylcholine (0.3µM), serotonin (10 µM) and histamine (100µM) in the presence and absence of DCD-684. The various concentrations of DCD-684 (0.1-3% v/v) demonstrated spasmolytic effects on both spontaneous (IC50=0.75%) and KCl-induced contractions (IC50=1.6%), respectively. It also inhibited the contractions induced by acetylcholine (IC50=0.45%), carbamylcholine (IC50=0.95%), serotonin (IC50=0.95%) and histamine (IC50=0.87%). The DCD-684 exhibited synergistic effect due to its five plant components suggesting that spasmolytic cascade is probably governed by muscarinic and/or nicotinic receptors, serotonergic histaminergic, as well as calcium channel blocking mechanisms. Thereby, providing the pharmacological basis of its therapeutic use in the gastrointestinal motility disorders and related inflammatory ailments.

ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection.

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include 'No Referable Diabetic Macular Edema Grade (DME)' and 'Referable DME' while five categories consist of 'Proliferative diabetic retinopathy', 'Severe', 'Moderate', 'Mild', and 'No diabetic retinopathy'. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.

Naturalistic outcome of medication-naïve obsessive compulsive disorder treated with serotonin reuptake inhibitors.

OBJECTIVE: The data on the course of obsessive compulsive disorder (OCD) is mostly derived from studying chronic, severely ill patients with varying degree of treatment resistance. We studied the course and outcome of OCD patients who were medication-naïve at initial assessment compared to those who were medicated. MATERIAL AND METHODS: We analyzed the clinical chart data of all patients with a primary diagnosis of OCD attending a speciality OCD clinic in India during a specified period and compared outcome between medication-naïve (n = 75) and medicated (n = 117) patients. RESULTS: The mean time to remission was shorter in the medication-naïve [18.99 months (95 % CI: 14.61-23.37)] compared to medicated [33.91 months (95 % CI: 27.55-40.28)] patients. The survival distribution of the two groups was significantly different as per the log-rank test (χ2 = 5.76, p = 0.02). In the Cox proportional hazards regression, medication-naïve status predicted time to remission. Overall, the rate of remission was the same in both groups (57 %). CONCLUSIONS: Medication-naïve OCD patients seem to remit faster than the previously treated patients. Future prospective naturalistic studies can compare the outcome of medication naïve OCD patients treated with medications and CBT.

Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection.

Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall (with 2 classes and 3 classes) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets.

Restraint prevalence and perceived coercion among psychiatric inpatients from South India: A prospective study.

BACKGROUND: The Indian Mental Health Care Act 2017 (MHCA -2017) advocates the duty to provide treatment in the least coercive manner. Little data exists on how Indian patients perceive coercion in medical settings. AIMS: To study the prevalence of restraint in a Indian psychiatric inpatient unit, and to examine the level of perceived coercion correlating to various forms of restraint. METHODOLOGY: This is a hospital based prospective observational study. Two hundred patients were recruited through computer generated random number sampling. In eligible subjects, demographic and clinical data, restraints used and assessments related to perceived coercion were completed within 3 days of admission. Perceived coercion was reassessed at the time or within 3 days before discharge. RESULTS: In 66.5% one or more restraint measures were used, physical restraints in 20%, chemical restraints in 58%, seclusion in 18%, and involuntary medication in 32%. ECT is associated with the lowest level of perceived coercion followed by isolation/seclusion, chemical restraint, involuntary medication and physical restraint. Male gender, being married, rural background, low socioeconomic status, having a mood disorder, and alcohol or drug dependence was associated with an increased risk of physical or chemical restraint. Having a mood disorder, being from a rural area and a lower socioeconomic status was associated with being subjected to more than one form of coercion. CONCLUSION: Restraint measures are more prevalent in psychiatric hospital care in India than in Europe. Physical restraint is particularly associted with higher perceived coercion.

Resurgence of catatonia following tapering or stoppage of lorazepam - A case series and implications.

The resurgence of catatonia following tapering of lorazepam is a common clinical phenomenon. However, there is limited evidence on the relationship between tapering method of lorazepam and resurgence of catatonic state. We report seven (0.6%) such patients who were found to have resurgence of catatonia. The mean age is 35.7 years; five of them had schizophrenia and other psychotic spectrum disorders. Five of them had resurgence within one week of stoppage, and three of them had multiple resurgences and required maintenance treatment with lorazepam. So gradual tapering and maintenance treatment with lorazepam might be effective in preventing resurgence of catatonia.