Oral submucous fibrosis a disease with malignant potential: report of two Cases

Oral submucous fibrosis (OSF) is a high risk precancerous condition characterized by changes in the connective tissue fibers of the lamina propria and deeper parts leading to stiffness of the mucosa and restricted mouth opening. Patients with severe cases have distinct difficulties in chewing, swallowing and speaking. It predominantly occurs in Indians and other population of the Indian subcontinent with certain oral habits. In patients with submucous fibrosis, the oral epithelium becomes atrophic and thereby becomes more vulnerable to carcinogens. It is now accepted that chewing areca is the most important aetiological factor for developing OSF. The atrophic epithelium shows first an intercellular edema and later epithelial atypia associated with moderate epithelial hyperplasia. From then on, carcinoma may develop any time. It is suggested that submucous fibrosis should be regarded as a condition that causes predisposition to the development of oral cancer. Here we are presenting two cases of oral submucous fibrosis showing malignant potential and development of oral squamous cell carcinoma

Prediction of Machining Conditions Using Machine Learning

The new blast of Machine Learning (ML) and Artificial Intelligence (AI) shows extraordinary expectations in the forward leap of additive manufacturing (AM) process displaying, which is an important step toward determining the cycle structure-property relationship. The advancement of standard AI apparatuses in information science was primarily attributed to the extraordinarily huge amount of named informational collections, that may be obtained throughout the trials or first-rate reenactments. To completely take advantage of the force of AI in AM metal while lightening the reliance on "enormous information", everybody set an Improved Neural Network (INN) structure if the wires the two information and first actual standards include the preservation laws of energy, mass, and energies, towards the NN to illuminate the growing experiences. We suggest compressed-type strategies in the Dirichlet limit regulation in light of a Heaviside capability, that may precisely uphold the BCs and speed up the growing experience. The hotel structure was applied to two agent metal assembling issues, that includes the NIST AM-Benchmark series test. The examinations show that the Motel, owing to the extra actual information, may precisely foresee the temperature and also liquefy pool elements throughout the AM processes in metal along a moderate measure of named informational collections

Fungal infections involving maxillary sinus: a difficult diagnostic task

Fungal infections of the paranasal sinus are increasingly recognized entity both in normal and immunocompromised individuals. Aspergillosis and Mucormycoses being the commonest of all the fungal infections involving maxillary sinus manifests as two distinct entities, a non-invasive and invasive infection. It is important to distinguish the invasive disease from the non-invasive as the treatment and prognosis are different in each. These infections present a diagnostic and therapeutic challenge to the physicians. Early diagnosis is essential in order to avoid high morbidity and mortality associated with the destructive disease and to instigate treatment before irreversible condition arise. The purpose of this paper is to add a few more cases of fungal infections involving maxillary sinus to the literature in both immunocompetent and immunocompromised patients with an emphasis on the fact that early diagnosis is vital in these infections, because delay in initiation of treatment can be life threatening due to propensity of fungi to invade adjacent blood vessels and embolize to distant organs

New Oral Anticoagulants: An Overview

Oral anticoagulant therapy is used in the prevention and treatment of venous thromboembolism (VTE), prevention of stroke and other systemic emboli in patients with atrial fibrillation (AF) and artificial heart valves

Multifactorial control of gonadotropin release for induction of oocyte maturation: Influence of gonadotropin-releasing hormone, gonadotropin release-inhibiting factor and dopamine receptors in the catfish, Heteropneustes fossilis

Several external and internal factors contribute to the reproductive success of teleosts, which makes the reproductive process complex and unique. In the Indian freshwater catfish, Heteropneustes fossilis, monsoon plays a crucial role as it fine tunes the neuroendocrine axis, culminating in oocyte maturation. Therefore, induction of oocyte maturation requires the coordinated interaction among hypothalamic, hypophyseal, and peripheral hormones.  In the present investigation, dual neuroendocrine control of oocyte maturation has been demonstrated in the catfish, H. fossilis. The maturational response in gravid catfish is inhibited in the presence of dopamine but GnRH evokes the oocyte maturation and ovulation. GnRH upregulates the expression of lhb gene as well as increases plasma levels of LH significantly within 30 minutes of its administration. Destruction of the preoptic region in gravid catfish by electrolytic or chemical lesions also causes oocyte maturation and ovulation. But this response is inhibited if dopamine is injected into the nucleus preopticus periventricularis-lesioned fishes. These observations support the role of dopamine as an inhibitory factor, therefore specific receptors of dopamine have been characterized in catfish and their expression in the brain has been quantified. Dopamine receptors are upregulated in dopamine-treated fishes and downregulated if a dopamine antagonist (pimozide) is injected. The present study suggests the presence of inhibitory mechanism for LH secretion in gravid catfish. Abolition of this inhibition is necessary to release LH surge, which in turn stimulates resumption of meiosis and ovulation. Thus peptidergic as well as aminergic systems regulate oocyte maturation in H. fossilis. Neuroendocrine regulation of oocyte maturation and ovulation has major implications for inducing spawning in aquaculture

Vaginal delivery in a patient with asymptomatic severe aortic stenosis: a case report

Heart disease complicates about 1-4% of all pregnancies of which valvular heart disease is the commonest cause. In developing countries, congenital heart diseases are commonly first detected during pregnancy. Most women do well during pregnancy but severe mitral stenosis or severe aortic stenosis are high-risk conditions that can cause significant morbidity and mortality. Unlike asymptomatic severe mitral stenosis, asymptomatic severe aortic stenosis is mWHO category 3. There is no consensus on the mode of delivery in patients with asymptomatic severe aortic stenosis. Here we describe a case of successful vaginal delivery in a woman with severe aortic stenosis. As the patient was asymptomatic and ejection fraction was preserved, a joint decision for vaginal delivery was taken along with the cardiology team. The patient was induced and delivered with operative vaginal delivery. This case shows that vaginal delivery could be a viable option in patients with asymptomatic severe aortic stenosis given continuous haemodynamic monitoring can be provided

Learning Representations on Logs for AIOps

AI for IT Operations (AIOps) is a powerful platform that Site Reliability Engineers (SREs) use to automate and streamline operational workflows with minimal human intervention. Automated log analysis is a critical task in AIOps as it provides key insights for SREs to identify and address ongoing faults. Tasks such as log format detection, log classification, and log parsing are key components of automated log analysis. Most of these tasks require supervised learning; however, there are multiple challenges due to limited labelled log data and the diverse nature of log data. Large Language Models (LLMs) such as BERT and GPT3 are trained using self-supervision on a vast amount of unlabeled data. These models provide generalized representations that can be effectively used for various downstream tasks with limited labelled data. Motivated by the success of LLMs in specific domains like science and biology, this paper introduces a LLM for log data which is trained on public and proprietary log data. The results of our experiments demonstrate that the proposed LLM outperforms existing models on multiple downstream tasks. In summary, AIOps powered by LLMs offers an efficient and effective solution for automating log analysis tasks and enabling SREs to focus on higher-level tasks. Our proposed LLM, trained on public and proprietary log data, offers superior performance on multiple downstream tasks, making it a valuable addition to the AIOps platform.Comment: 11 pages, 2023 IEEE 16th International Conference on Cloud Computing (CLOUD

Synthesis of some new 2-(pyrazol-l-yl)-4-(pyrimidin-5-yl)thiazoles

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