Acinetobacter species are non-fermentative Gram-negative coccobacilli and they have emerged as important nosocomial pathogens which are associated with the significant multidrug resistance in recent years. Carbapenem-resistant A. baumannii (CRAB) and pandrug-resistant A. baumannii (PDRAB) were reported in 1991 and 1998, respectively. Fiftyeight isolates of Acinetobacter species recovered from a university hospital between August 2004 and March 2005 were investigated for the existence of CRAB, PDRAB, extended-spectrum beta-lactamase (ESBL)-producing Acinetobacter and examined for their phenotypic and genotypic characteristics. Genomospecies of Acinetobacter species were determined by amplified rDNA restriction analysis (ARDRA) and antimicrobial susceptibility test was performed with 13 kinds of antimicrobial agents. Metallo-beta-lactamase (MBL) producers were screened by modified hodge test and confirmed by imipenem-EDTA disk synergy test. Detection of blaIMP-1, blaVIM-2, blaTEM, and blaPER-1 was performed by PCR. Genomic DNAs were analyzed by pulsed-field gel electrophoresis (PFGE). Among 58 isolates of Acinteobacter species, 40 isolates were identified as genospecies 2 (A. baumannii), 9 were 13TU, 5 were A. phenon 6/ct, and 4 were Acinetobacter genospecies 3 by ARDRA. Thirteen isolates were confirmed as MBL-producers and blaIMP-1 and blaVIM-2 were carried by 5 and 8 isolates of them, respectively. MBL-producers were mostly 13TU, A. phenon 6/ct 13TU, and Acinetobacter genospecies 3 and they were susceptible to ciprofloxacin and ampicillin-sulbactam. BlaPER-1 was carried by thirteen isolates and 12 isolates of them were PDRAB showing resistance to all antimicrobial agents tested, including ceftazidime, cefepime, aztreonam, ciprofloxacin, amikacin, gentamicin, ampicillin-sulbactam, and imipenem. In conclusion, most MBL-producers belonged to 13TU, A. phenon 6/ct 13TU, and Acinetobacter genospecies 3 which were susceptible to ciprofloxacin and ampicillin-sulbactam, whereas 12 of 13 PER-1-producers were PDRAB originated from the same clone.
Acinetobacter* ; Amikacin ; Anti-Infective Agents ; Aztreonam ; beta-Lactamases ; Ceftazidime ; Ciprofloxacin ; Clone Cells ; DNA ; DNA, Ribosomal ; Drug Resistance, Multiple ; Electrophoresis, Gel, Pulsed-Field ; Gentamicins ; Imipenem ; Polymerase Chain Reaction

Acinetobacter species are non-fermentative Gram-negative coccobacilli and they have emerged as important nosocomial pathogens which are associated with the significant multidrug resistance in recent years. Carbapenem-resistant A. baumannii (CRAB) and pandrug-resistant A. baumannii (PDRAB) were reported in 1991 and 1998, respectively. Fiftyeight isolates of Acinetobacter species recovered from a university hospital between August 2004 and March 2005 were investigated for the existence of CRAB, PDRAB, extended-spectrum beta-lactamase (ESBL)-producing Acinetobacter and examined for their phenotypic and genotypic characteristics. Genomospecies of Acinetobacter species were determined by amplified rDNA restriction analysis (ARDRA) and antimicrobial susceptibility test was performed with 13 kinds of antimicrobial agents. Metallo-beta-lactamase (MBL) producers were screened by modified hodge test and confirmed by imipenem-EDTA disk synergy test. Detection of blaIMP-1, blaVIM-2, blaTEM, and blaPER-1 was performed by PCR. Genomic DNAs were analyzed by pulsed-field gel electrophoresis (PFGE). Among 58 isolates of Acinteobacter species, 40 isolates were identified as genospecies 2 (A. baumannii), 9 were 13TU, 5 were A. phenon 6/ct, and 4 were Acinetobacter genospecies 3 by ARDRA. Thirteen isolates were confirmed as MBL-producers and blaIMP-1 and blaVIM-2 were carried by 5 and 8 isolates of them, respectively. MBL-producers were mostly 13TU, A. phenon 6/ct 13TU, and Acinetobacter genospecies 3 and they were susceptible to ciprofloxacin and ampicillin-sulbactam. BlaPER-1 was carried by thirteen isolates and 12 isolates of them were PDRAB showing resistance to all antimicrobial agents tested, including ceftazidime, cefepime, aztreonam, ciprofloxacin, amikacin, gentamicin, ampicillin-sulbactam, and imipenem. In conclusion, most MBL-producers belonged to 13TU, A. phenon 6/ct 13TU, and Acinetobacter genospecies 3 which were susceptible to ciprofloxacin and ampicillin-sulbactam, whereas 12 of 13 PER-1-producers were PDRAB originated from the same clone.
Acinetobacter* ; Amikacin ; Anti-Infective Agents ; Aztreonam ; beta-Lactamases ; Ceftazidime ; Ciprofloxacin ; Clone Cells ; DNA ; DNA, Ribosomal ; Drug Resistance, Multiple ; Electrophoresis, Gel, Pulsed-Field ; Gentamicins ; Imipenem ; Polymerase Chain Reaction

OBJECTIVE: Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. METHODS: Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. RESULTS: Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. CONCLUSION: Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.
Bays ; Forests ; Health Care Sector ; Internet ; Learning ; Machine Learning ; Mental Health ; Residence Characteristics ; Sample Size ; Support Vector Machine

Purpose: In February 2020, the first outbreak of coronavirus disease 2019 (COVID-19) occurred in Daegu, South Korea, and confirmed cases increased sharply, sparking intense anxiety among residents. This study analyzed the data of a mental health survey on students enrolled at a medical school located in Daegu in 2020. Methods: An online survey was administered to 654 medical school students (pre-medical course: 220 students, medical course: 434 students) from August to October 2020, with 61.16% (n=400) valid responses. The questionnaire included items about COVID-19-related experiences, stress, stress resilience, anxiety, and depression. Results: Of the survey participants, 15.5% had experienced unbearable stress, with the most significant stress factors (in descending order) being limited leisure activities, unusual experiences related to COVID-19, and limited social activities. Approximately 28.8% reported psychological distress, and their most experienced negative emotions were helplessness, depression, and anxiety (in descending order). The mean Beck Anxiety Inventory and Beck Depression Inventory-II scores were 2.44 and 6.08, respectively, both within normal ranges. Approximately 8.3% had mild or greater anxiety, and 15% had mild or greater depression. For students under psychological distress, the experience of unbearable stress before COVID-19 affected anxiety (odds ratio [OR], 0.198; p<0.05), and having an underlying condition affected depression (OR, 0.190; p<0.05). With respect to their psychological distress during August–October 2020 compared with that during February–March 2020 (2 months from the initial outbreak), anxiety stayed the same while depression increased and resilience decreased at a statistically significant level. Conclusion It was found that some medical students were suffering from psychological difficulties related to COVID-19, and there were several risk factors for them. This finding suggests that medical schools need to not only develop academic management systems but also provide programs that can help students manage their mental health and emotions in preparation for an infectious disease pandemic.

Background/Aims: Epicardial adipose tissue (EAT) shares pathophysiological properties with other visceral fats and potentially triggers local inflammation. However, the association of EAT with cardiovascular disease (CVD) is still debatable. The study aimed to observe the changes and associations in EAT and risk factors over time, as well as to investigate whether EAT was associated with CVD. Methods: A total of 762 participants from Seoul National University Hospital (SNUH) and SNUH Gangnam Center were included in this study. EAT was measured using coronary computed tomography angiography. Results: Baseline EAT level was positively associated with body mass index (BMI), calcium score, atherosclerotic cardiovascular disease (ASCVD) 10-year risk score, glucose, triglycerides (TG)/high-density lipoprotein (HDL), but not with total cholesterol, low-density lipoprotein (LDL). At follow-up, EAT levels increased in all groups, with low EAT groups demonstrating a significant increase in EAT per year. Change in EAT was associated with a change in BMI, TG/HDL, and glucose, while changes in LDL, calcium score, and ASCVD 10-year risk score were not associated. Although calcium score and ASCVD 10-year risk score were associated with CVD events, baseline information of EAT, baseline EAT/body surface area, or EAT change was not available. Conclusions Metabolic risks, e.g., BMI, TG/HDL, and glucose, were associated with EAT change per year, whereas classical CVD risks, e.g., LDL, calcium score, and ASCVD 10-year risk score, were not. The actual CVD event was not associated with EAT volume, warranting future studies combining qualitative assessments with quantitative ones.

PURPOSE: Vascular access failure is the most common reason for hospitalization among hemodialysis (HD) patients. Cilostazol, which has antiplatelet action and vasodialtory effects, significantly reduces the risk of restenosis after percutaneous coronary intervention in many patients. We conducted this study to evaluate the relationship between the use of antiplatelet agents, especially cilostazol, and arteriovenous fistula (AVF) patency in HD patients. METHODS: A total of 241 patients underwent native AVF creation from January 2001 to December 2008. Among these patients, we selected 86 patients excluding 38 patients (15.8%) with primary technical failure, 49 patients without complete data and 68 patients used cilostazol less than 1 month. Demographic characteristics, medication history and fistula failure rate were collected and analyzed to elucidate the effect of cilostazol to native AVF. RESULTS: From all groups, AVF failure occurred in 24 patients (27.9%). 28 patients received cilostazol (62.3 %) and mean duration of cilostazol therapy was 229.5+/-115.7 days. All patients were classified into two groups according to cilostazol (Cilostazol [n=28, 32.6%] vs. non-Cilostazol [n=58, 67.4%]. There was no statisticallly significant difference in failure rate between the two groups (32.1% vs. 25.9%, p=0.543). In diabetes group, patients who received statin have much lower AVF failure rate (0% vs. 32.4%, p=0.024). Logistic regression analysis showed that female was independent risk factor for access failure (HR 5.549, CI 1.104-27.877, p=0.037). CONCLUSION: Cilostazol and other antiplatelet agent had a no significant association with AVF patency. Female was an independent risk factor for access failure.
Arteriovenous Fistula ; Female ; Fistula ; Hospitalization ; Humans ; Hydroxymethylglutaryl-CoA Reductase Inhibitors ; Logistic Models ; Percutaneous Coronary Intervention ; Platelet Aggregation Inhibitors ; Renal Dialysis ; Risk Factors ; Tetrazoles ; Vascular Patency

Background and Objectives: Non-linear frequency compression (NLFC) technology compresses and shifts higher frequencies into a lower frequency area that has better residual hearing. Because consonants are uttered in the high-frequency area, NLFC could provide better speech understanding. The aim of this study was to investigate the clinical effectiveness of NLFC technology on the perception of speech and music in patients with high-frequency hearing loss. Subjects and Methods: Twelve participants with high-frequency hearing loss were tested in a counter-balanced order, and had two weeks of daily experience with NLFC set on/off prior to testing. Performance was repeatedly evaluated with consonant tests in quiet and noise environments, speech perception in noise, music perception and acceptableness of sound quality rating tasks. Additionally, two questionnaires (the Abbreviated Profile of Hearing Aid Benefit and the Korean version of the International Outcome Inventory-Hearing Aids) were administered. Results: Consonant and speech perception improved with hearing aids (NLFC on/off conditions), but there was no significant difference between NLFC on and off states. Music perception performances revealed no notable difference among unaided and NLFC on and off states. The benefits and satisfaction ratings between NLFC on and off conditions were also not significantly different, based on questionnaires, however great individual variability preferences were noted. Conclusions Speech perception as well as music perception both in quiet and noise environments was similar between NLFC on and off states, indicating that real world benefits from NLFC technology may be limited in Korean adult hearing aid users.

Background: Iliotibial band friction syndrome (ITBFS) is a common disorder of the lateral knee. Previous research has reported that the iliotibial band (ITB) thickness (ITBT) is correlated with ITBFS, and ITBT has been considered to be a key morphologic parameter of ITBFS. However, the thickness is different from inflammatory hypertrophy. Thus, we made the ITB cross-sectional area (ITBCSA) a new morphological parameter to assess ITBFS. Methods: Forty-three patients with ITBFS group and from 43 normal group who underwent T1W magnetic resonance imaging were enrolled. The ITBCSA was measured as the cross-sectional area of the ITB that was most hypertrophied in the magnetic resonance axial images. The ITBT was measured as the thickest site of ITB. Results: The mean ITBCSA was 25.24 ± 6.59 mm 2 in the normal group and 38.75 ± 9.11 mm 2 in the ITBFS group. The mean ITBT was 1.94 ± 0.41 mm in the normal group and 2.62 ± 0.46 mm in the ITBFS group. Patients in ITBFS group had significantly higher ITBCSA (P < 0.001) and ITBT (P < 0.001) than the normal group. A receiver operator characteristic curve analysis demonstrated that the best cut-off value of the ITBT was 2.29 mm, with 76.7% sensitivity, 79.1% specificity, and area under the curve (AUC) 0.88. The optimal cut-off score of the ITBCSA was 30.66 mm 2 , with 79.1% sensitivity, 79.1% specificity, and AUC 0.87. Conclusions ITBCSA is a new and sensitive morphological parameter for diagnosing ITBFS, and may even be more accurate than ITBT.

Background: Iliotibial band friction syndrome (ITBFS) is a common disorder of the lateral knee. Previous research has reported that the iliotibial band (ITB) thickness (ITBT) is correlated with ITBFS, and ITBT has been considered to be a key morphologic parameter of ITBFS. However, the thickness is different from inflammatory hypertrophy. Thus, we made the ITB cross-sectional area (ITBCSA) a new morphological parameter to assess ITBFS. Methods: Forty-three patients with ITBFS group and from 43 normal group who underwent T1W magnetic resonance imaging were enrolled. The ITBCSA was measured as the cross-sectional area of the ITB that was most hypertrophied in the magnetic resonance axial images. The ITBT was measured as the thickest site of ITB. Results: The mean ITBCSA was 25.24 ± 6.59 mm 2 in the normal group and 38.75 ± 9.11 mm 2 in the ITBFS group. The mean ITBT was 1.94 ± 0.41 mm in the normal group and 2.62 ± 0.46 mm in the ITBFS group. Patients in ITBFS group had significantly higher ITBCSA (P < 0.001) and ITBT (P < 0.001) than the normal group. A receiver operator characteristic curve analysis demonstrated that the best cut-off value of the ITBT was 2.29 mm, with 76.7% sensitivity, 79.1% specificity, and area under the curve (AUC) 0.88. The optimal cut-off score of the ITBCSA was 30.66 mm 2 , with 79.1% sensitivity, 79.1% specificity, and AUC 0.87. Conclusions ITBCSA is a new and sensitive morphological parameter for diagnosing ITBFS, and may even be more accurate than ITBT.