Introduction: Bacterial skin and soft tissue infections (SSTIs) are a cause of frequent inpatient and outpatient care visits whose causative agents are associated with a high antimicrobial resistance burden. For insig...Introduction: Bacterial skin and soft tissue infections (SSTIs) are a cause of frequent inpatient and outpatient care visits whose causative agents are associated with a high antimicrobial resistance burden. For insights on antimicrobial susceptibilities in a rural setting, we examined specimens from suspected SSTIs from two public health facilities in Kenya. We additionally assessed antibiotic use, appropriateness of empiric therapy and risk factors for SSTI. Methodology: Between 2021 and 2023, 265 patients at Kisii and Nyamira County Referral hospitals were enrolled. Wound swabs/aspirates were collected and processed following standard microbiological procedures. Identification and antimicrobial susceptibility were performed using the VITEK 2 Compact platform. Demographic, clinical, and microbiological data were analyzed with R Statistical software. Results: S. aureus was isolated in 16.2% (43/265) of patients with a methicillin resistance (MRSA) proportion of 14% (6/43). While 13/15 drugs elicited susceptibilities ranging from 84% - 100%, penicillin (16%) and trimethoprim-sulfamethoxazole [TMP-SXT] (23%) yielded the lowest susceptibilities. Escherichia coli (n = 33), Klebsiella pneumoniae (n = 8), Pseudomonas aeruginosa (n = 8), and Citrobacter species (n = 4) were the most commonly isolated gram-negative species. Gram-negative strains showed high susceptibilities to most of the tested drugs (71% - 100%) with the exception of ampicillin (18%), TMP-SXT (33%), and first and second generation cephalosporins. Conclusions: The low MRSA prevalence and generally high antibiotic susceptibilities for S. aureus and gram-negative bacteria present opportunities for antibiotic stewardship in the study setting. Diminished susceptibilities against penicillin/ampicillin and TMP-SXT accord with prevailing local data and add a layer of evidence for their cautious empiric use.展开更多
We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuab...We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.展开更多
文摘Introduction: Bacterial skin and soft tissue infections (SSTIs) are a cause of frequent inpatient and outpatient care visits whose causative agents are associated with a high antimicrobial resistance burden. For insights on antimicrobial susceptibilities in a rural setting, we examined specimens from suspected SSTIs from two public health facilities in Kenya. We additionally assessed antibiotic use, appropriateness of empiric therapy and risk factors for SSTI. Methodology: Between 2021 and 2023, 265 patients at Kisii and Nyamira County Referral hospitals were enrolled. Wound swabs/aspirates were collected and processed following standard microbiological procedures. Identification and antimicrobial susceptibility were performed using the VITEK 2 Compact platform. Demographic, clinical, and microbiological data were analyzed with R Statistical software. Results: S. aureus was isolated in 16.2% (43/265) of patients with a methicillin resistance (MRSA) proportion of 14% (6/43). While 13/15 drugs elicited susceptibilities ranging from 84% - 100%, penicillin (16%) and trimethoprim-sulfamethoxazole [TMP-SXT] (23%) yielded the lowest susceptibilities. Escherichia coli (n = 33), Klebsiella pneumoniae (n = 8), Pseudomonas aeruginosa (n = 8), and Citrobacter species (n = 4) were the most commonly isolated gram-negative species. Gram-negative strains showed high susceptibilities to most of the tested drugs (71% - 100%) with the exception of ampicillin (18%), TMP-SXT (33%), and first and second generation cephalosporins. Conclusions: The low MRSA prevalence and generally high antibiotic susceptibilities for S. aureus and gram-negative bacteria present opportunities for antibiotic stewardship in the study setting. Diminished susceptibilities against penicillin/ampicillin and TMP-SXT accord with prevailing local data and add a layer of evidence for their cautious empiric use.
文摘We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.
