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.

Physician–scientists play a pivotal role in bridging clinical practice and biomedical research, advancing medical science, and tackling complex healthcare challenges. In South Korea, the declining number of medical doctors engaging in basic medical sciences has prompted the implementation of various training initiatives since the 2000s. Notable initiatives, such as the Integrated Physician–Scientist Training Program (2019) and the Global Physician–Scientist Training Program (2024), aim to cultivate multidisciplinary physician–scientists capable of addressing unmet medical needs. This study offers a comprehensive overview of the current training systems, funding mechanisms, and strategic approaches for physician–scientists in South Korea, compares them with international best practices, and proposes actionable policy recommendations to enhance their effectiveness and long-term sustainability.

Physician–scientists play a pivotal role in bridging clinical practice and biomedical research, advancing medical science, and tackling complex healthcare challenges. In South Korea, the declining number of medical doctors engaging in basic medical sciences has prompted the implementation of various training initiatives since the 2000s. Notable initiatives, such as the Integrated Physician–Scientist Training Program (2019) and the Global Physician–Scientist Training Program (2024), aim to cultivate multidisciplinary physician–scientists capable of addressing unmet medical needs. This study offers a comprehensive overview of the current training systems, funding mechanisms, and strategic approaches for physician–scientists in South Korea, compares them with international best practices, and proposes actionable policy recommendations to enhance their effectiveness and long-term sustainability.

Physician–scientists play a pivotal role in bridging clinical practice and biomedical research, advancing medical science, and tackling complex healthcare challenges. In South Korea, the declining number of medical doctors engaging in basic medical sciences has prompted the implementation of various training initiatives since the 2000s. Notable initiatives, such as the Integrated Physician–Scientist Training Program (2019) and the Global Physician–Scientist Training Program (2024), aim to cultivate multidisciplinary physician–scientists capable of addressing unmet medical needs. This study offers a comprehensive overview of the current training systems, funding mechanisms, and strategic approaches for physician–scientists in South Korea, compares them with international best practices, and proposes actionable policy recommendations to enhance their effectiveness and long-term sustainability.

Physician–scientists play a pivotal role in bridging clinical practice and biomedical research, advancing medical science, and tackling complex healthcare challenges. In South Korea, the declining number of medical doctors engaging in basic medical sciences has prompted the implementation of various training initiatives since the 2000s. Notable initiatives, such as the Integrated Physician–Scientist Training Program (2019) and the Global Physician–Scientist Training Program (2024), aim to cultivate multidisciplinary physician–scientists capable of addressing unmet medical needs. This study offers a comprehensive overview of the current training systems, funding mechanisms, and strategic approaches for physician–scientists in South Korea, compares them with international best practices, and proposes actionable policy recommendations to enhance their effectiveness and long-term sustainability.

BACKGROUND/OBJECTIVES: The prevalence of hypertension is increasing, and noodles have a high sodium content, so noodle consumption might be associated with a higher prevalence of hypertension. This study aimed to analyze the association between total and types of noodle intake and the risk of hypertension among Korean adults. Subjects aged 40–69 years were selected for this study. SUBJECTS/METHODS: This study included 56,580 participants (18,246 men and 38,334 women) aged 40–69 years old from the Health Examinees study. Noodle and nutrient intakes were assessed using the food frequency questionnaire. Hypertension was diagnosed as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg or antihypertensive medication use. Cox proportional hazard models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for hypertension. RESULTS: Higher noodle consumption was associated with increased hypertension among men (HR, 1.273; 95% CI, 1.166–1.390) and women (HR, 1.116; 95% CI, 1.038–1.199). CONCLUSION We recommended reducing noodle intake and consuming vegetables and fruits to increase potassium intake, which can prevent vascular diseases.