Large mucinous cystadenoma in pregnancy: a rare case report

Presence of ovarian tumors in pregnancy is uncommon. In most cases, they are benign, torsion is the most common complication. Management can be conservative or surgical depending on the size, clinical presentation, gestational age, available resources etc. advances in imaging techniques have made the decision making easier. We present a case of primigravida aged 24 years, with 18 weeks’ pregnancy with pain in abdomen. She had a large mass arising from the pelvis. Full work up was done. Imaging was suggestive of mucinous cystadenoma with bilateral hydronephrosis due to mass effect. Laparotomy was done and a 20 kg tumor was removed, histopathology confirmed a huge cystadenoma. Patient was discharged in a stable condition. The management of ovarian tumors in pregnancy can be challenging. Although the safety of antepartum surgical intervention is accepted, abdominal surgery will carry some risk to the pregnant woman and the unborn fetus. Surgery becomes necessary in the presence of rupture, torsion or malignancy

Deep Learning Based Hate Speech Detection on Twitter

There have been growing worries about the effects of the widespread use of hate speech and harsh language on social media sites like Twitter. Effective strategies for recognising and reducing such dangerous material are necessary for resolving this problem. In this research, we give a detailed analysis of four deep learning models for identifying hate speech and inflammatory language on Twitter: the Long Short-Term Memory (LSTM), the Recurrent Neural Network (RNN), the Bidirectional LSTM (Bi-LSTM), and the Gated Recurrent Unit (GRU). We downloaded a large dataset from Kaggle that was curated for hate speech identification and used it in our experiment. We built each model after preprocessing and tokenization, then tweaked their hyperparameters for maximum efficiency. The models' abilities to detect hate speech were evaluated using standard measures including accuracy, precision, recall, and Fl-score. Our findings show that there is a wide range of effectiveness amongst models in terms of identifying hate speech and inflammatory language on Twitter. In terms of accuracy and Fl-scores, the Bi-LSTM and GRU models were superior to the LSTM and RNN. The results of this study imply that using bidirectional and gated processes may increase the models' capability of understanding the interdependencies and contexts of tweets, and hence, their classification accuracy

Search for flavour-changing neutral tqH interactions with H -> gamma gamma in pp collisions at root s=13 TeV using the ATLAS detector

A search for flavour-changing neutral interactions involving the top quark, the Higgs boson and an up-type quark q ( q = c, u) is presented. The proton-proton collision data set used, with an integrated luminosity of 139 fb(-1), was collected at root s = 13TeV by the ATLAS experiment at the Large Hadron Collider. Both the decay process t -> qH in tt production and the production process pp. tH, with the Higgs boson decaying into two photons, are investigated. No significant excess is observed and upper limits are set on the t. cH and the t. uH branching ratios of 4.3x10(-4) and 3.8x10(-4), respectively, at the 95% confidence level, while the expected limits in the absence of signal are 4.7x10(-4) and 3.9x10(-4). Combining this search with ATLAS searches in the H. t+ t- and H. b b final states yields observed (expected) upper limits on the t -> cH branching ratio of 5.8 x 10(-4) (3.0 x 10(-4)) at the 95% confidence level. The corresponding observed (expected) upper limit on the t -> uH branching ratio is 4.0 x 10(-4) (2.4 x 10(-4))

Combined Measurement of the Higgs Boson Mass from the Formula Presented and Formula Presented Decay Channels with the ATLAS Detector Using Formula Presented, 8, and 13 TeV Formula Presented Collision Data

A measurement of the mass of the Higgs boson combining the Formula Presented and Formula Presented decay channels is presented. The result is based on Formula Presented of proton-proton collision data collected by the ATLAS detector during LHC run 2 at a center-of-mass energy of 13 TeV combined with the run 1 ATLAS mass measurement, performed at center-of-mass energies of 7 and 8 TeV, yielding a Higgs boson mass of Formula Presented. This corresponds to a 0.09% precision achieved on this fundamental parameter of the Standard Model of particle physics

Deep CNN based brain tumor detection in intelligent systems

The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications

Should I share it? Factors influencing fake news-sharing behaviour: A behavioural reasoning theory perspective

Social media has become an integral part of our lives because of its popularity among users. However, the dissemination of fake information has been a significant issue for marketers, as it severely damages brand image. This study examined variables related to intentions (for and against) to share fake news online using behavioral reasoning theory (BRT). We also examined the impact of perceived believability as a mediator and how the mediating effects of perceived believability are moderated by social-status seeking and cognitive fluency. Data were collected from 356 respondents using online questionnaires. The hypotheses were tested using structural equation modeling and PROCESS Macro. The results suggest that the joy of missing out (JOMO) and government regulations negatively impact fake news-sharing intention. Source credibility and information quality positively impact fake news-sharing intention. Perceived believability mediates the association between antecedents and fake news-sharing intention. Mediated-moderation analysis show that social status seeking and cognitive fluency also significantly impact fake news-sharing intention. This study enriches the fake news and social media literature, and has managerial implications for marketers

SARS-CoV-2 Variants of Concern and Variations within Their Genome Architecture: Does Nucleotide Distribution and Mutation Rate Alter the Functionality and Evolution of the Virus?

SARS-CoV-2 virus pathogenicity and transmissibility are correlated with the mutations acquired over time, giving rise to variants of concern (VOCs). Mutations can significantly influence the genetic make-up of the virus. Herein, we analyzed the SARS-CoV-2 genomes and sub-genomic nucleotide composition in relation to the mutation rate. Nucleotide percentage distributions of 1397 in-house-sequenced SARS-CoV-2 genomes were enumerated, and comparative analyses (i) within the VOCs and of (ii) recovered and mortality patients were performed. Fisher’s test was carried out to highlight the significant mutations, followed by RNA secondary structure prediction and protein modeling for their functional impacts. Subsequently, a uniform dinucleotide composition of AT and GC was found across study cohorts. Notably, the N gene was observed to have a high GC percentage coupled with a relatively higher mutation rate. Functional analysis demonstrated the N gene mutations, C29144T and G29332T, to induce structural changes at the RNA level. Protein secondary structure prediction with N gene missense mutations revealed a differential composition of alpha helices, beta sheets, and coils, whereas the tertiary structure displayed no significant changes. Additionally, the N gene CTD region displayed no mutations. The analysis highlighted the importance of N protein in viral evolution with CTD as a possible target for antiviral drugs