Proliferating trichilemmal tumours are benign but locally aggressive skin neoplasms arising from hair follicles. Rarely, they can become malignant and must be appropriately managed to prevent recurrence and metastasis. One must have a low threshold for diagnosing this rare neoplasm.
Sentiment analysis (SA) is used to extract opinions from a huge amount of data and these opinions are comprised of multiple words. Some words have different semantic meanings in different fields and we call them domain specific (DS) words. A domain is defined as a special area in which a collection of queries about a specific topic are held when user do queries in the data regarding the domain appear. But Single word can be interpreted in many ways based on its context-dependency. Demonstrate each word under its domain is extremely important because their meanings differ from each other so much in different domains that a word meaning from A in one context can change into Z in another context or domain. The purpose of this research is to discover the correct sentiment in the message or comment and evaluate it either it is positive, negative or neutral. We collected tweets dataset from different domains and analyze it to extract words that have a different definition in those specific domains as if they are used in other fields of life they would be defined differently. We analyzed 52115 words for finding their DS meaning in seven different domains. Polarity had been given to words of the dataset according to their domains and based on this polarity they have been recognized as positive negative and neutral and evaluated as domain-specific words. The automatic way is used to extract the words of the domain as we integrated and afterward the comparison to identify that either this word differs from other words as far as domain is concerned. This research contribution is a prototype that processes your data and extracts their domain-specific words automatically. This research improved the knowledge about the context-dependency and found the core-specific meanings of words in multiple fields.
Objectives: To assess appropriate antibiotics use in neonatal sepsis and to highlight the need for developing an Antibiotic stewardship program at local levels. Methods: A clinical audit was conducted in the neonatal ward of the tertiary care hospital of Lahore for one year from May 2019 to May 2020. Reports of blood culture and drugs susceptibility were gathered from the microbiology department, and clinical records were evaluated about the choice of the antimicrobials, dosage, frequency, and clinical prognosis. The statistics were applied using SPSS software. Results: Eighty five neonates with the mean age of five days were treated in tertiary care hospital for septicemia. Every patient received more than one antibiotic empirically. The most prescribed drug combination (90.6%) was Cefotaxime and Amikacin. Optimum antibiotics dose was prescribed in only 70.2% of cases. Blood isolates showed gram-negative bacilli in 69 (81.2%) cases, gram positive cocci in 14 (16.5%) cases, two (2.3%) culture susceptibility reports showed growth of candida. Gram negative organisms were most susceptible to Imipenem (54%), Piperacillin-Tazobactam (48%) and Gentamicin (48%). Gram-positive organisms showed the most susceptibility to Vancomycin (100%), Amikacin (92%), and Co-amoxiclav (85%). Meropenem (39%), Linezolid (28%), and Vancomycin (27%) were the most commonly given alternate antibiotics. All the patients (n=10, 11.8%) whose culture sensitivity reporting showed susceptibility to empirical therapy survived. Conclusion: Due to poor availability of latest data about local antibacterial resistance pattern and lack of knowledge among pediatricians about latest antibiotic prescribing protocols, many inconsistencies were noted in the use of antibiotics in neonatal sepsis which resulted in a poor outcome hence, reflecting in international key health indicators (neonatal mortality rate) of country. Concerning the change in the resistance pattern of microorganisms to antimicrobials, it is high time to collect local data about antibacterial susceptibility and develop an antibiotic stewardship program to stop inappropriate use of antibiotics. doi: https://doi.org/10.12669/pjms.38.6.5073 How to cite this:Usman A, Khan MZ, Khan MZ, Hussain A. Audit to optimize antibiotics use in neonatal septicemia. Pak J Med Sci. 2022;38(6):---------. doi: https://doi.org/10.12669/pjms.38.6.5073 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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