Objective: This study explores whether cancer patients’ dysfunctional self-focus is a significant contributor to their fear of progression. In addition, we investigated whether their psychiatric symptoms such as depression, anxiety, and dysfunctional beliefs about sleep may mediate the relationship between these factors. Methods: We conducted a retrospective medical records review of 196 cancer patients who visited the Stress Management Clinic for the first time from March to September 2022. Their demographic information and responses to rating scales such as the Fear of Progression Questionnaire-Short Form (FoP-Q-SF), Dysfunctional Self-focus Attribution Scale (DSAS), Patient Health Questionnaire-9 Items (PHQ-9), State subcategory of the State and Trait of Anxiety Inventory (STAI-S), Insomnia Severity Index (ISI), Cancer-related Dysfunctional Beliefs about Sleep scale (C-DBS), and numeric rating scales of pain and fatigue were collected. Results: A high FoP-Q-SF score was significantly correlated with high PHQ-9 (r=0.60), STAI-S (r=0.38), ISI (r=0.34), C-DBS (r=0.47), pain (r=0.24), fatigue (r=0.37), and DSAS (r=0.58, all p<0.001). A linear regression analysis showed that the FoP-Q-SF score was significantly predicted by younger age (β=-0.13, p=0.011), PHQ-9 (β=0.36, p<0.001), STAI-S (β=0.18, p=0.001), C-DBS (β=0.22, p<0.001), and DSAS (β=0.25, p<0.001). A mediation analysis showed that dysfunctional self-focus directly influenced patients’ fear of progression. In addition, cancer patients’ depression, anxiety, and cancer-related dysfunctional beliefs about sleep mediated this relationship. Conclusion We observed that dysfunctional self-focus may influence cancer patients’ fear of progression, mediated by depression, anxiety, and cancer-related dysfunctional beliefs about sleep.

Objective: The discrepancy between desired time in bed and desired total sleep time (DBST index) is correlated with the severity of insomnia among the general population. This study aimed to explore whether the change in DBST index is associated with changes in insomnia severity. Methods: The study was conducted as a single source tracking online survey among the general population. The first survey (T1) was completed by all 399 participants, and the second survey (T2) was completed by 233 participants 5–6 weeks after the T1 survey with a simple instruction of reducing the DBST index. Participants’ age, sex, marital status, past psychiatric history, and sleep patterns were collected. In addition to the DBST index, the Glasgow Sleep Effort Scale (GSES), Dysfunctional Beliefs about Sleep-2 items (DBS-2), and Insomnia Severity Index (ISI) were rated. Results: The change in the ISI (T1–T2) was significantly correlated with the changes in the GSES (r=0.24, p<0.001), DBS-2 (r=0.22, p<0.001), and DBST index (r=0.15, p=0.020). The change in insomnia severity was expected with change in the GSES (β=0.23, p<0.001), DBS-2 (β=0.20, p=0.002), and DBST index (β=0.13, p=0.037). Mediation analysis showed that change in DBST index directly influenced change in insomnia severity and change in GSES or DBS-2 did not mediate the relationship. Conclusion Changing the DBST index can be a simple way to reduce insomnia severity among the general population.

Objective: This study assessed the reliability and validity of the Stress and Anxiety to Viral Epidemics–9 items (SAVE-9) and Stress and Anxiety to Viral Epidemics–6 items (SAVE-6) scales for measuring viral anxiety among firefighters during the coronavirus disease-2019 pandemic. Methods: An online survey was conducted among 304 firefighters assigned in Gyeonggi-do. The SAVE-9 scale, initially developed for healthcare workers, was adapted for firefighters. We compared it with the SAVE-6 scale designed for the general population among the firefighters sample. The confirmatory factor analysis (CFA) was conducted to explore the factor structure of both scales. Internal consistency reliability was checked using Cronbach’s alpha and McDonald’s omega. Convergent validity was assessed in accordance with the Patient Health Questionnaire–9 and Generalized Anxiety Disorder–7 scales. Results: The SAVE-9 scale demonstrated a Cronbach alpha of 0.880, while the SAVE-6 scale yielded an alpha of 0.874. CFA indicated good model fits for both SAVE-9 and SAVE-6 scales among firefighters sample. The SAVE-9 and SAVE-6 comparably measures viral anxiety of firefighters. Conclusion Both of the SAVE-9 and SAVE-6 scales are reliable and valid instruments for assessing viral anxiety among firefighters during the pandemic.

Objective: This study aimed to explore the mediating effects of cancer-related dysfunctional beliefs regarding sleep and intolerance of uncertainty on the effect of depression, insomnia, and anxiety on fear of progression (FoP). Methods: We retrospectively reviewed medical records of patients with cancer who visited the Sleep Clinic for cancer patients in Asan Medical Center for the first time between December 2021 and March 2022. Data collected included age, sex, types of cancer, staging, current treatment modalities, and history of surgical procedures. In addition, psychological symptoms were rated using the Insomnia Severity Scale (ISI), Patient Health Questionnaire–9 items (PHQ-9), State subcategory of the State and Trait of Anxiety Inventory (STAI-S), Short form of Fear of Progression Questionnaire, Cancer-related Dysfunctional Beliefs about Sleep scale (C-DBS), single item of pain and fatigue, Connor Davidson Resilience Scale 2-item (CD-RISC2), and Intolerance of Uncertainty–12 (IUS-12). The predictive variables for FoP were determined by linear regression analysis. Results: The FoP was significantly correlated with age (r=-0.289), ISI (r=0.178), PHQ-9 (r=0.703), STAI-S (r=0.377), fatigue (r=0.452), CD-RISC2 (r=-0.270), IUS-12 (r=0.585), and C-DBS (r=0.427, all p<0.01). A mediation analysis showed that intolerance of uncertainty and dysfunctional beliefs about sleep mediated the relationship of FoP with insomnia, depression, or anxiety. Conclusion Psychological support for intolerance of uncertainty and cancer-related dysfunctional beliefs about sleep in patients with cancer may be beneficial to reduce their FoP.

Background: Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts. Methods: We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs). Results: Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application’s output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss’ kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss’ kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians’ consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients’ sex and age (P < 0.001 for both). Conclusion Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.

Abnormal aggregation of α-synuclein is a key element in the pathogenesis of several neurodegenerative diseases, including Parkinson’s disease (PD), dementia with Lewy bodies, and multiple system atrophy. α-synuclein aggregation spreads through various brain regions during the course of disease progression, a propagation that is thought to be mediated by the secretion and subsequent uptake of extracellular α-synuclein aggregates between neuronal cells. Thus, aggregated forms of this protein have emerged as promising targets for disease-modifying therapy for PD and related diseases. Here, we generated and characterized conformation-specific antibodies that preferentially recognize aggregated forms of α-synuclein. These antibodies promoted phagocytosis of extracellular α-synuclein aggregates by microglial cells and interfered with cell-to-cell propagation of α-synuclein. In an α-synuclein transgenic model, passive immunization with aggregate-specific antibodies significantly ameliorated pathological phenotypes, reducing α-synuclein aggregation, gliosis, inflammation, and neuronal loss. These results suggest that conformation-specific antibodies targeting α-synuclein aggregates are promising therapeutic agents for PD and related synucleinopathies.

Background: This study explores whether the intolerance of uncertainty among healthcare workers prompts viral anxiety, and whether this association is mediated by their reassuranceseeking behavior and preoccupation with the coronavirus disease 2019 (COVID-19) in Korea. Methods: An online survey was conducted among healthcare workers in Asan Medical Center, on November 29, 2021. Demographic characteristics and responses to items from rating scales were collected, including Stress and Anxiety to Viral Epidemics-9, Coronavirus Reassurance-Seeking Behaviors Scale (CRBS), Obsession with COVID-19 Scale (OCS), Patient Health Questionnaire-9, Insomnia Severity Scale, and Intolerance of Uncertainty-12 (IUS-12). Results: Among the 329 participants, viral anxiety of healthcare workers was predicted by being female (β = 0.14, P = 0.002), CRBS (β = 0.30, P < 0.001), OCS (β = 0.32, P < 0.001), and IUS-12 (β = 0.15, P = 0.002) scores (adjusted R2 = 0.43, F = 31.1, P < 0.001). Mediation analysis showed that the intolerance of uncertainty directly influenced viral anxiety, and reassuranceseeking behavior and obsession with COVID-19 partially mediated the association. Conclusion The intolerance of uncertainty among healthcare workers directly influenced their viral anxiety, and reassurance-seeking behavior and obsession with COVID-19 mediated this association in this era of “living with coronavirus” in Korea.

Objective: We aimed to examined the reliability and validity of Korean version of Social Distancing Phobia scale, and whether intolerance of uncertainty mediates the association of the general population’s social distancing phobia with viral anxiety and depression. Methods: Through this anonymous online survey, we collected responses from 400 individuals in the general Korean population. Participants’ demographic information and rating scales scores, including the Social Distancing Phobia scale, Stress and Anxiety to Viral Epidemics-6 items, Patient Health Questionnaire-9, and Intolerance of Uncertainty-12 items. Results: Confirmatory factor analysis showed a good model fit, and the Korean version of Social Distancing Phobia scale showed good internal consistency. Social distancing phobia was significantly correlated with age (r=0.213, p<0.001), viral anxiety (r=0.390, p<0.001), depression (r=0.244, p<0.001), and intolerance of uncertainty (r=0.323, p<0.001). A linear regression analysis showed that age (β=0.235, p<0.001), viral anxiety (β=0.281, p<0.001), depression (β=0.121, p=0.009), and intolerance of uncertainty (β=0.200, p<0.001; adjusted R2=0.246, F=33.6, p<0.001) predicted social distancing phobia. Mediation analysis revealed that viral anxiety directly influenced social distancing phobia (z=6.48, p<0.001), and intolerance of uncertainty partially mediated this association (z=2.92, p=0.003). Conclusion Social distancing phobia may cause psychological stress but may also increase adherence to physical distancing measures and prevent the spread of viruses.

Background: This study aimed to explore clinical correlates of fear of progression (FoP) among patients with cancer during the coronavirus disease 2019 (COVID-19) pandemic and examine the mediation effect of cancer-related dysfunctional beliefs about sleep (C-DBS). Methods: Medical charts of patients with cancer who visited a psycho-oncology clinic between July and November 2021 were reviewed. Baseline socio-demographic and cancer-related variables were collected. Patients’ self-report questionnaires, regarding FoP, depression (Patient Health Questionnaire-9 items; PHQ-9), viral anxiety (Stress and Anxiety to Viral Epidemics-6 items; SAVE-6), C-DBS, and other distress, were investigated. Pearson’s correlation and linear regression were performed to examine the risk factors of FoP. Mediation effect analysis with the bootstrap method with 2,000 resamples was implemented. Results: A total of 231 patients were included in the analysis. Linear regression revealed that FoP was predicted by age (β = −0.14, P = 0.003), PHQ-9 (β = 0.48, P < 0.001), SAVE-6 (β = 0.34, P < 0.001), and C-DBS (β = 0.15, P = 0.005). FoP was directly influenced by SAVE-6 and mediated by C-DBS, while it was directly influenced by PHQ-9 with no mediation effect. Conclusion During the COVID-19 pandemic, the FoP of patients with cancer was associated with younger age, depression, viral anxiety, and C-DBS. Depression and viral anxiety directly influenced FoP, while C-DBS mediated the association between viral anxiety and FoP.Therefore, oncology healthcare professionals are recommended to assess C-DBS of their patients when they are highly distressed from FoP.

Objective: To determine the incidence of anconeus epitrochlearis (AE) muscle and evaluate the correlation between AE muscle and ulnar motor nerve conduction velocity (NCV). Method: Forty healthy volunteers (80 arms) were evaluated. Ulnar motor nerve conduction study was performed. NCVs at the forearm and across the elbow were calculated. Ultrasonography (US) was used to obtain a transverse scan view of the cubital tunnel and confirm the presence of the AE muscle. Cross-sectional areas (CSAs) of following structures were obtained: ulnar nerve (UNCSA), cubital tunnel (CTCSA), and AE (AECSA). AECSA was divided by CTCSA to obtain the AE/CT ratio. Pearson correlation coefficient (PCC) was calculated to evaluate the relationship between NCV across elbow and each variable obtained by US. Results: Sixty-three (78.75%) of eighty arms showed the presence of AE muscle. NCV had no significant correlations with sonographic variables. However, when 17 arms with a relatively low NCV value (≤ 60 m/s) were analyzed, NCV showed significant correlations with AECSA (PCC: r = −0.674, p=0.003) and AE/CT ratio (PCC: r = −0.516, p=0.034). Conclusion When all 63 cases with AE muscle were analyzed, ulnar NCV showed no significant correlations with sonographic variables. However, when those with NCV value of 60 m/s or less were analyzed, NCV showed significant negative correlations with AECSA and AE/CT ratio.