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
目的探讨中性粒细胞膜碱性磷酸酶(alkaline phosphatase on the surface membrane of neutrophils,mNAP)对革兰阴性菌(GNB)和革兰阳性菌(GPB)血流感染(bloodstream infection,BSI)的临床诊断价值。方法收集2022年1月至2023年12月东海县...目的探讨中性粒细胞膜碱性磷酸酶(alkaline phosphatase on the surface membrane of neutrophils,mNAP)对革兰阴性菌(GNB)和革兰阳性菌(GPB)血流感染(bloodstream infection,BSI)的临床诊断价值。方法收集2022年1月至2023年12月东海县人民医院诊断为BSI的患者418例作为研究对象,根据血培养阳性细菌革兰染色结果,进一步分为GNB感染患者329例和GPB感染患者89例。另选择同期住院的全身炎症反应综合征患者(SIRS)35例作为疾病对照组。收集3组患者的临床资料及常规实验室检查结果,并采集3组患者的静脉血标本,采用流式细胞仪检测各组mNAP的表达水平。采用ROC曲线评估mNAP对GNB和GPB发生BSI的鉴别诊断效能。结果GPB、GNB感染组和SIRS组mNAP的表达水平分别为9588(5677,11343)AB/C,16616(11853,22035)AB/C和5738(2613,9178)AB/C,3组间差异有统计学意义(H=43.95,P<0.0001);进一步行组间两两比较发现,GNB感染组显著高于GPB感染组(U=203.0,P<0.0001)和SIRS组(U=445.0,P<0.0001),且GPB感染组显著高于SIRS组(U=583.0,P<0.0001)。ROC曲线评估mNAP对GNB发生BSI的AUC ROC为0.91(95%CI:0.85~0.96),当cut-off值为10820 AB/C时,其敏感性为80.00%,特异性为88.57%;ROC曲线评估mNAP对GPB发生BSI的AUC ROC为0.69(95%CI:0.55~0.83),当cut-off值为10859 AB/C时,其敏感性为33.00%,特异性为88.13%。结论mNAP对GNB发生BSI的鉴别诊断效能明显高于GPB,或可成为区分GNB和GPB发生BSI的新型生物学标志物。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.
文摘目的探讨中性粒细胞膜碱性磷酸酶(alkaline phosphatase on the surface membrane of neutrophils,mNAP)对革兰阴性菌(GNB)和革兰阳性菌(GPB)血流感染(bloodstream infection,BSI)的临床诊断价值。方法收集2022年1月至2023年12月东海县人民医院诊断为BSI的患者418例作为研究对象,根据血培养阳性细菌革兰染色结果,进一步分为GNB感染患者329例和GPB感染患者89例。另选择同期住院的全身炎症反应综合征患者(SIRS)35例作为疾病对照组。收集3组患者的临床资料及常规实验室检查结果,并采集3组患者的静脉血标本,采用流式细胞仪检测各组mNAP的表达水平。采用ROC曲线评估mNAP对GNB和GPB发生BSI的鉴别诊断效能。结果GPB、GNB感染组和SIRS组mNAP的表达水平分别为9588(5677,11343)AB/C,16616(11853,22035)AB/C和5738(2613,9178)AB/C,3组间差异有统计学意义(H=43.95,P<0.0001);进一步行组间两两比较发现,GNB感染组显著高于GPB感染组(U=203.0,P<0.0001)和SIRS组(U=445.0,P<0.0001),且GPB感染组显著高于SIRS组(U=583.0,P<0.0001)。ROC曲线评估mNAP对GNB发生BSI的AUC ROC为0.91(95%CI:0.85~0.96),当cut-off值为10820 AB/C时,其敏感性为80.00%,特异性为88.57%;ROC曲线评估mNAP对GPB发生BSI的AUC ROC为0.69(95%CI:0.55~0.83),当cut-off值为10859 AB/C时,其敏感性为33.00%,特异性为88.13%。结论mNAP对GNB发生BSI的鉴别诊断效能明显高于GPB,或可成为区分GNB和GPB发生BSI的新型生物学标志物。
