Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review

Sentiment analysis (SA) is the automated process of detecting and understanding the emotions conveyed through written text. Over the past decade, SA has gained significant popularity in the field of Natural Language Processing (NLP). With the widespread use of social media and online platforms, SA has become crucial for companies to gather customer feedback and shape their marketing strategies. Additionally, researchers rely on SA to analyze public sentiment on various topics. In this particular research study, a comprehensive survey was conducted to explore the latest trends and techniques in SA. The survey encompassed a wide range of methods, including lexicon-based, graph-based, network-based, machine learning, deep learning, ensemble-based, rule-based, and hybrid techniques. The paper also addresses the challenges and opportunities in SA, such as dealing with sarcasm and irony, analyzing multi-lingual data, and addressing ethical concerns. To provide a practical case study, Twitter was chosen as one of the largest online social media platforms. Furthermore, the researchers shed light on the diverse application areas of SA, including social media, healthcare, marketing, finance, and politics. The paper also presents a comparative and comprehensive analysis of existing trends and techniques, datasets, and evaluation metrics. The ultimate goal is to offer researchers and practitioners a systematic review of SA techniques, identify existing gaps, and suggest possible improvements. This study aims to enhance the efficiency and accuracy of SA processes, leading to smoother and error-free outcomes

The Impact of Diabetes Mellitus in Patients with Chronic Obstructive Pulmonary Disease (COPD) Hospitalization

Background: Chronic obstructive pulmonary disease (COPD) is the leading cause of morbidity and mortality worldwide. Diabetes mellitus (DM) has been shown to have adverse inflammatory effects on lung anatomy and physiology. We investigated the impact of DM on COPD patient outcomes during inpatient hospitalization. Methods: We conducted a retrospective analysis using the Nationwide Inpatient Sample (NIS) over the years 2002-2014. Three groups, COPD without diabetes, COPD with diabetes but no complication, and COPD with DM and complication, were analyzed. Results: A total of 7,498,577 were COPD hospitalization; of those, 1,799,637 had DM without complications, and 483,467 had DM with complications. After adjusting for clinical, demographic, and comorbidities, the odds of increased LOS in the COPD/DM with complication were 1.37 (confidence interval (CI): 1.326-1.368), and those of DM without complication were 1.061 (1.052-1.070) when compared with COPD alone. The odds of pneumonia, respiratory failure, stroke, and acute kidney injury were also higher in COPD hospitalizations with DM. Both DM with complication (odds ratio (OR): 0.751 (CI 0.727-0.777)) and DM without complication (OR: 0.635 (CI: 0.596-0.675)) have lesser odds of mortality during hospitalization than with COPD alone. Conclusions: There is a considerable inpatient burden among COPD patients with DM in the United States

The Effects of Warfarin and Direct Oral Anticoagulants on Systemic Vascular Calcification: A Review

Warfarin has been utilized for decades as an effective anticoagulant in patients with a history of strong risk factors for venous thromboembolism (VTE). Established adverse effects include bleeding, skin necrosis, teratogenicity during pregnancy, cholesterol embolization, and nephropathy. One of the lesser-known long-term side effects of warfarin is an increase in systemic arterial calcification. This is significant due to the association between vascular calcification and cardiovascular morbidity and mortality. Direct oral anticoagulants (DOACs) have gained prominence in recent years, as they require less frequent monitoring and have a superior side effect profile to warfarin, specifically in relation to major bleeding. The cost and lack of data for DOACs in some disease processes have precluded universal use. Within the last four years, retrospective cohort studies, observational studies, and randomized trials have shown, through different imaging modalities, that multiple DOACs are associated with slower progression of vascular calcification than warfarin. This review highlights the pathophysiology and mechanisms behind vascular calcification due to warfarin and compares the effect of warfarin and DOACs on systemic vasculature

Impact of Patient Counseling and Socioeconomic Factors on Initiation of Rehabilitation Program in Spinal Cord Injury Patients Presenting to a Tertiary Spine Unit in India

Study Design Prospective case series. Purpose This study aimed to investigate the impact of education, financial income, occupation, and patient counseling on the timing of enrolment in a spinal cord injury (SCI) rehabilitation program. Overview of Literature A rehabilitation program following SCI is essential to improve functional outcomes. Socioeconomic factors can affect the timing of enrolment to a rehabilitation program. Literature on the effects of socioeconomic factors among patients with SCI in the Indian scenario is limited. Methods A prospective, consecutive analysis of patients with SCI was performed with 1-year follow-up. Assessment of the timing of enrolment to a rehabilitation program was performed using the modified Kuppuswamy socioeconomic scores (MKSS). Patients admitted to the SCI unit (group A), underwent intensive individual, group, and family counseling sessions to encourage early enrolment into a rehabilitation program. Patients presenting directly for rehabilitation (group B) were analyzed for comparison. Results A total of 153 patients were recruited. Group A was composed of 122 patients who started the rehabilitation program after a mean of 28 days, compared with a mean of 149 days for 31 patients in group B. In group A, 104 patients (85%; mean MKSS, 14.02) and 18 patients (15%; mean MKSS, 15.61) enrolled for rehabilitation 0.05). Conclusions Early patient counseling in the acute care unit helps in the early enrolment of patients with poor socioeconomic demographic profile to a rehabilitation program

The Pfam protein families database

Peer reviewe

Malaria mosquito control using edible fish in western Kenya: preliminary findings of a controlled study

<p>Abstract</p> <p>Background</p> <p>Biological control methods are once again being given much research focus for malaria vector control. This is largely due to the emerging threat of strong resistance to pesticides. Larvivorous fish have been used for over 100 years in mosquito control and many species have proved effective. In the western Kenyan highlands the larvivorous fish <it>Oreochromis niloticus </it>L. (Perciformes: Cichlidae) (formerly <it>Tilapia nilotica</it>) is commonly farmed and eaten but has not been previously tested in the field for malaria mosquito control.</p> <p>Methods</p> <p>This fish was introduced into abandoned fishponds at an altitude of 1,880 m and the effect measured over six months on the numbers of mosquito immatures. For comparison an untreated control pond was used. During this time, all ponds were regularly cleared of emergent vegetation and fish re-stocking was not needed. Significant autocorrelation was removed from the time series data, and t-tests were used to investigate within a pond and within a mosquito type any differences before and after the introduction of <it>O. niloticus</it>. Mulla's formula was also used on the raw data to calculate the percentage reduction of the mosquito larvae.</p> <p>Results</p> <p>After <it>O. niloticus </it>introduction, mosquito densities immediately dropped in the treated ponds but increased in the control pond. This increase was apparently due to climatic factors. Mulla's formula was applied which corrects for that natural tendency to increase. The results showed that after 15 weeks the fish caused a more than 94% reduction in both <it>Anopheles gambiae s.l</it>. and <it>Anopheles funestus </it>(Diptera: Culicidae) in the treated ponds, and more than 75% reduction in culicine mosquitoes. There was a highly significantly reduction in <it>A. gambiae s.l</it>. numbers when compared to pre-treatment levels.</p> <p>Conclusion</p> <p>This study reports the first field trial data on <it>O. niloticus </it>for malaria mosquito control and shows that this species, already a popular food fish in western Kenya, is an apparently sustainable mosquito control tool which also offers a source of protein and income to people in rural areas. There should be no problem with acceptance of this malaria control method since the local communities already farm this fish species.</p

Sustainable supply chain management: framework and further research directions

This paper argues for the use of Total Interpretive Structural Modeling (TISM) in sustainable supply chain management (SSCM). The literature has identified antecedents and drivers for the adoption of SSCM. However, there is relatively little research on methodological approaches and techniques that take into account the dynamic nature of SSCM and bridge the existing quantitative/qualitative divide. To address this gap, this paper firstly systematically reviews the literature on SSCM drivers; secondly, it argues for the use of alternative methods research to address questions related to SSCM drivers; and thirdly, it proposes and illustrates the use of TISM and Cross Impact Matrix-multiplication applied to classification (MICMAC) analysis to test a framework that extrapolates SSCM drivers and their relationships. The framework depicts how drivers are distributed in various levels and how a particular driver influences the other through transitive links. The paper concludes with limitations and further research directions

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues