BACKGROUND Tongue abscess(TA)is a very rare clinical condition and its treatment is very important.Surgical drainage is at the forefront in the treatment.Our study includes patients with tongue and tongue base abscess...BACKGROUND Tongue abscess(TA)is a very rare clinical condition and its treatment is very important.Surgical drainage is at the forefront in the treatment.Our study includes patients with tongue and tongue base abscesses.AIM To discuss the clinical and laboratory findings of these patients emphasizing the underlying causes and treatment options with the largest patient series in the English literature.METHODS We included patients with isolated TA who applied to our clinic between January 1,2020 and January 1,2023.Those who lack the recorded data,those who are not between the ages of 18-66,those who have not undergone surgery-interventional procedure,and those who have infection and/or abscess in another place were excluded from the study.RESULTS There were two female(18%)and nine male(82%)patients in our series consisting of 11 patients.Their ages ranged from 18 to 66,and the mean±SD was 48.63±16.3.Considering the localization of the abscess,three anterior abscesses(27%),two lateral abscesses(18%),and six abscesses at the base of the tongue(54%)were detected.CONCLUSION Tongue abscesses can cause acute upper airway obstruction and respiratory collapse.It may be necessary to act quickly for the tracheotomy procedure and this procedure can usually be performed under local anesthesia as intubation cannot be achieved.When we encounter an abscess in an unexpected organ,difficulties may be encountered in the management of the patient.展开更多
We introduce an analytical framework for analyzing tweets to(1)identify and categorize fine-grained details about a disaster such as affected individuals,damaged infrastructure and disrupted services;(2)distinguish im...We introduce an analytical framework for analyzing tweets to(1)identify and categorize fine-grained details about a disaster such as affected individuals,damaged infrastructure and disrupted services;(2)distinguish impact areas and time periods,and relative prominence of each category of disaster-related information across space and time.We first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of disasterrelatedness.We employ LSTM(Long Short-Term Memory)networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature dependencies.Second,we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation(LDA),and identify latent categories of tweets such as affected individuals and disrupted services.Third,we employ spatiallyadaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category,respectively.Using Hurricane Irma as a case study,we analyze over 500 million keyword-based and geo-located collection of tweets before,during and after the disaster.Our results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.展开更多
文摘BACKGROUND Tongue abscess(TA)is a very rare clinical condition and its treatment is very important.Surgical drainage is at the forefront in the treatment.Our study includes patients with tongue and tongue base abscesses.AIM To discuss the clinical and laboratory findings of these patients emphasizing the underlying causes and treatment options with the largest patient series in the English literature.METHODS We included patients with isolated TA who applied to our clinic between January 1,2020 and January 1,2023.Those who lack the recorded data,those who are not between the ages of 18-66,those who have not undergone surgery-interventional procedure,and those who have infection and/or abscess in another place were excluded from the study.RESULTS There were two female(18%)and nine male(82%)patients in our series consisting of 11 patients.Their ages ranged from 18 to 66,and the mean±SD was 48.63±16.3.Considering the localization of the abscess,three anterior abscesses(27%),two lateral abscesses(18%),and six abscesses at the base of the tongue(54%)were detected.CONCLUSION Tongue abscesses can cause acute upper airway obstruction and respiratory collapse.It may be necessary to act quickly for the tracheotomy procedure and this procedure can usually be performed under local anesthesia as intubation cannot be achieved.When we encounter an abscess in an unexpected organ,difficulties may be encountered in the management of the patient.
文摘We introduce an analytical framework for analyzing tweets to(1)identify and categorize fine-grained details about a disaster such as affected individuals,damaged infrastructure and disrupted services;(2)distinguish impact areas and time periods,and relative prominence of each category of disaster-related information across space and time.We first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of disasterrelatedness.We employ LSTM(Long Short-Term Memory)networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature dependencies.Second,we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation(LDA),and identify latent categories of tweets such as affected individuals and disrupted services.Third,we employ spatiallyadaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category,respectively.Using Hurricane Irma as a case study,we analyze over 500 million keyword-based and geo-located collection of tweets before,during and after the disaster.Our results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.
