Purpose: The duties of paramedics and emergency medical technicians (P&EMTs) are continuously changing due to developments in medical systems. This study presents evaluation goals for P&EMTs by analyzing their work, especially the tasks that new P&EMTs (with less than 3 years’ experience) find difficult, to foster the training of P&EMTs who could adapt to emergency situations after graduation. Methods: A questionnaire was created based on prior job analyses of P&EMTs. The survey questions were reviewed through focus group interviews, from which 253 task elements were derived. A survey was conducted from July 10, 2023 to October 13, 2023 on the frequency, importance, and difficulty of the 6 occupations in which P&EMTs were employed. Results: The P&EMTs’ most common tasks involved obtaining patients’ medical histories and measuring vital signs, whereas the most important task was cardiopulmonary resuscitation (CPR). The task elements that the P&EMTs found most difficult were newborn delivery and infant CPR. New paramedics reported that treating patients with fractures, poisoning, and childhood fever was difficult, while new EMTs reported that they had difficulty keeping diaries, managing ambulances, and controlling infection. Conclusion Communication was the most important item for P&EMTs, whereas CPR was the most important skill. It is important for P&EMTs to have knowledge of all tasks; however, they also need to master frequently performed tasks and those that pose difficulties in the field. By deriving goals for evaluating P&EMTs, changes could be made to their education, thereby making it possible to train more capable P&EMTs.

Background/Aims: Using a nationwide cohort, we investigated the cancer risk in Korean patients with gout. Methods: Data were obtained from the Korean National Health Insurance Service Database. Patients with gout were defined as those aged ≥ 20 years who were diagnosed with gout and received anti-gout medication (allopurinol, colchicine, and benzbromarone) between 2008 and 2010. Patients with nail disorders were randomly assigned to a control group (1:1 ratio) after frequency matching for age and sex. Cancer incidence was then investigated between 2012 and 2018. Cox proportional hazard regression analysis was used to investigate the association between gout and cancer after adjusting for concomitant diseases. Results: This study included 179,930 patients with gout and an equal number of matched controls. The incidence of overall cancer was higher in patients with gout than in controls (incidence rate ratio, 1.08). Cox proportional hazards regression analysis showed that gout was associated with a hazard ratio of 1.053 (95% confidence interval ,1.031 to 1.077) after adjusting for concomitant diseases. Conclusions Gout was associated with a significantly high risk of cancer, especially esophageal, stomach, colon, liver, pancreatic, lung, ovarian, renal, and bladder cancers.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machinelearning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machinelearning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

Objectives: This study determines the effects of comorbidity of mood disorder and alcohol use disorder on suicide behavior. Methods: We converted data from the electronic medical records of one university hospital into a common data model and utilized it in our analysis. We selected 9551 patients with diagnosis codes of mood disorders or alcohol use disorders and divided them into three groups: mood disorder (MD) only, alcohol use disorder (AUD) only, and comorbidity of mood disorder and alcohol use disorder (MD+AUD). The mood disorder group was also subgrouped with depressive (DD) or bipolar affective disorder (BD) groups, and the comorbidity group was classified in the same way. Then, we applied logistic regression analysis to assess the risk of suicide attempts between the diagnostic groups. Subgroup analysis according to age also was conducted. Results: The MD+AUD group had 2.7 (odd ratio [OR]=2.70, 95% confidence intervals [CI]=1.91– 3.81, p<0.0001) and the DD+AUD group had 2.78 (OR=2.78, 95% CI=1.95–3.98, p<0.0001) times higher risk of suicide attempts than the MD only and DD only group, respectively. Furthermore, according to the age subgroup, the risk of suicide attempts was the highest (OR=5.17, 95% CI=2.35–11.40, p<0.0001) in the DD+AUD group for those aged 40–59. There were no significant results in BD. Conclusion The results showed that the comorbidity of mood disorder and alcohol use disorder could increase suicide risk. This study suggested that alcohol use behavior needs to be assessed as well as mood symptoms for suicide prevention.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machinelearning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machinelearning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

Objectives: This study determines the effects of comorbidity of mood disorder and alcohol use disorder on suicide behavior. Methods: We converted data from the electronic medical records of one university hospital into a common data model and utilized it in our analysis. We selected 9551 patients with diagnosis codes of mood disorders or alcohol use disorders and divided them into three groups: mood disorder (MD) only, alcohol use disorder (AUD) only, and comorbidity of mood disorder and alcohol use disorder (MD+AUD). The mood disorder group was also subgrouped with depressive (DD) or bipolar affective disorder (BD) groups, and the comorbidity group was classified in the same way. Then, we applied logistic regression analysis to assess the risk of suicide attempts between the diagnostic groups. Subgroup analysis according to age also was conducted. Results: The MD+AUD group had 2.7 (odd ratio [OR]=2.70, 95% confidence intervals [CI]=1.91– 3.81, p<0.0001) and the DD+AUD group had 2.78 (OR=2.78, 95% CI=1.95–3.98, p<0.0001) times higher risk of suicide attempts than the MD only and DD only group, respectively. Furthermore, according to the age subgroup, the risk of suicide attempts was the highest (OR=5.17, 95% CI=2.35–11.40, p<0.0001) in the DD+AUD group for those aged 40–59. There were no significant results in BD. Conclusion The results showed that the comorbidity of mood disorder and alcohol use disorder could increase suicide risk. This study suggested that alcohol use behavior needs to be assessed as well as mood symptoms for suicide prevention.

Hepatitis B virus X (HBx) protein has been reported as a key protein regulating the pathogenesis of HBV-induced hepatocellular carcinoma (HCC). Recent evidence has shown that HBx is implicated in the activation of autophagy in hepatic cells. Nevertheless, the precise molecular and cellular mechanism by which HBx induces autophagy is still controversial.Herein, we investigated the molecular and cellular mechanism by which HBx is involved in the TRAF6-BECN1-Bcl-2 signaling for the regulation of autophagy in response to TLR4 stimulation, therefore influencing the HCC progression. HBx interacts with BECN1 (Beclin 1) and inhibits the association of the BECN1-Bcl-2 complex, which is known to prevent the assembly of the pre-autophagosomal structure. Furthermore, HBx enhances the interaction between VPS34 and TRAF6-BECN1 complex, increases the ubiquitination of BECN1, and subsequently enhances autophagy induction in response to LPS stimulation. To verify the functional role of HBx in liver cancer progression, we utilized different HCC cell lines, HepG2, SK-Hep-1, and SNU-761. HBx-expressing HepG2 cells exhibited enhanced cell migration, invasion, and cell mobility in response to LPS stimulation compared to those of control HepG2 cells. These results were consistently observed in HBx-expressed SK-Hep-1 and HBx-expressed SNU-761 cells. Taken together, our findings suggest that HBx positively regulates the induction of autophagy through the inhibition of the BECN1-Bcl-2 complex and enhancement of the TRAF6-BECN1-VPS34 complex, leading to enhance liver cancer migration and invasion.

Ipsilateral hemiparesis is a rare and challenging sign in clinical neurological practice.Although the etiology of this manifestation is poorly understood, recent studies have attempted to probe the pathomechanism of this sign with advanced radiological techniques.Additional knowledge about the lesion and unraveling the pathomechanisms causing neurological impairments are important to predict the prognosis and clinical course and to aid in rehabilitation. Therefore, we present a case of a patient with a traumatic subdural hematoma on the left hemisphere and left spastic hemiparesis. Using diffusion tensor imaging (DTI), we concluded that the right corticospinal tract injury caused by compression of the cerebral peduncle accounted for the ipsilateral hemiparesis, also known as Kernohan's notch phenomenon. Thus, this case report highlights the usefulness of the newer radiological techniques, such as DTI, to identify the pathomechanisms of neurological presentations.

BACKGROUND: Healthcare workers (HCWs) have a high risk of tuberculosis (TB) infection. Since August 2017, Korea has mandated the testing of latent TB infection (LTBI) and recommended treatment from HCWs at medical institutions. However, the acceptance/completion rate and adverse events of LTBI treatment have not been analyzed. MATERIALS AND METHODS: From February to August 2017, we conducted a retrospective study at a referral university hospital in Korea, to screen the interferon-gamma release assay (IGRA) tests conducted for all HCWs for detecting and treating LTBI. HCWs diagnosed with LTBI were offered a 9-month isoniazid (9H), 3-month isoniazid/rifampin (3HR), or 4-month rifampin regimen. We investigated the acceptance/completion rate, adverse events, and causes of discontinuation or change in LTBI medication. A major adverse event was one wherein a patient had any adverse event ≥grade 3 causing LTBI treatment interruption. RESULTS: Of the 1,538 HCWs, 1,379 underwent IGRA testing for LTBI. Among them, 13.6% (187/1,379) tested positive and 73.3% (137/187) received treatment. The overall completion rate was 97.8% (134/137). HCWs were significantly more likely to complete first-line therapy with 3HR than with 9H (91.4% vs. 76.7%, P = 0.02). The most common major adverse event was hepatotoxicity (n = 7), followed by thrombocytopenia (n = 1) and anaphylactic shock (n = 1). Hepatotoxicity and hepatotoxicity (≥ grade 2) were more frequent in 9H than in 3HR (39.5% vs. 17.2%, P = 0.006 and 18.6% vs. 3.7%, P = 0.005, respectively). The median time to hepatotoxicity was 96 days (interquartile range, 20 – 103 days). CONCLUSION Completion of first-line therapy for LTBI is more likely with 3HR than with 9H. This might be related to the development of hepatotoxicity after around 3 months of treatment. Anaphylactic shock and platelet count should be carefully monitored in those receiving rifampin-containing regimens.

PURPOSE: OncoHepa test is a multigene expression profile test developed for assessment of hepatocellular carcinoma (HCC) prognosis. Multiplication of α-FP, des-γ-carboxy prothrombin (DCP) and tumor volume (TV) gives the α-FP-DCP-volume (ADV) score, which is also developed for assessment of HCC prognosis. METHODS: The predictive powers of OncoHepa test and ADV score were validated in 35 patients who underwent curative hepatic resection for naïve solitary HCCs ≤5 cm. RESULTS: Median tumor diameter was 3.0 cm. Tumor recurrence and patient survival rates were 28.6% and 100% at 1 year, 48.6% and 82.9% at 3 years, and 54.3% and 71.4% at 5 years, respectively. The site of first tumor recurrence was the remnant liver in 18, lung in 1, and the peritoneum in 1. All patients with HCC recurrence received locoregional treatment. OncoHepa test showed marginal prognostic significance for tumor recurrence and patient survival. ADV score at 4log also showed marginal prognostic difference with respect to tumor recurrence and patient survival. Combination of these 2 tests resulted in greater prognostic significance for both tumor recurrence (P = 0.046) and patient survival (P = 0.048). CONCLUSION: Both OncoHepa test and ADV score have considerably strong prognostic power, thus individual and combined findings of OncoHepa test and ADV score will be helpful to guide postresection surveillance in patients with solitary HCCs ≤5 cm.
Carcinoma, Hepatocellular* ; Humans ; Liver ; Lung ; Peritoneum ; Prognosis ; Prothrombin ; Recurrence ; Survival Rate ; Tumor Burden