Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Differentiating between the diagnoses of mood disorders and other psychiatric disorders, and predicting treatment response in depression has long been a concern for clinicians. Machine learning (ML) is one part of artificial intelligence that focuses on instructing computers to mimic the cognitive abilities of the human brain through training. This study will review the research on the use of ML techniques to differentiate diagnoses and predict treatment responses in mood disorders based on electroencephalography (EEG) data. There have been several attempts to differentiate between the diagnoses of bipolar disorder and major depressive disorder , mood disorders, and other psychiatric disorders using ML techniques found on EEG markers. Previous studies have shown that accuracy varies depending on which EEG markers are used, the sample size, and the ML technique. Also, precise and improved ML approaches can be developed by adapting the various feature selection and validation methods that reflect each disease’s characteristics. Although ML faces some limitations and challenges in solving for consistent and improved accuracy in the diagnosis and treatment of mood disorders, it has a great potential to understand mood disorders better and provide valuable tools to personalize both identification and treatment.

Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) has emerged as an important nosocomial pathogen.The purpose of this study was to determine the effective methods for performing surveillance cultures of CRAB. Methods: Nasal and rectal swabs were obtained concurrently from hospitalized intensive care unit patients colonized with CRAB. All the samples were inoculated in CHROMagar Acinetobacter medium with CR102 (CHROMagar), MacConkey agar medium supplemented with 5 µg/mL imipenem (MCA-IPM), and triptic soy broth medium supplemented with 5 µg/ mL imipenem (TSB-IPM). CRAB detection rates for each sample were compared. Results: The CRAB detection rate in either one of the nasal or rectal swabs from the 37 patients tested were 89.2% (33/37) with the use of CHROMagar, 78.4% (29/37) with the use of MCA-IMP, and 86.5% (32/37) with the use of TSB-IMP. Conclusion We determined that concurrent use of both nasal and rectal swabs and CHROMagar could be an effective method for CRAB surveillance cultures.

PURPOSE: To compare the outcomes in patients with diabetic macular edema (DME) treated with intravitreal dexamethasone implants and intravitreal bevacizumab injections.METHODS: A retrospective cohort study was designed using 66 patients with DME treated with intravitreal dexamethasone (n = 35; 35 eyes) and intravitreal bevacizumab (n = 31; 31 eyes). Post-treatment changes in hyperreflective foci in the inner and outer retina were characterized using optical coherence tomography, central macular thickness, outer limiting membrane, and photoreceptor inner segment-outer segment junctions. Visual acuities were analyzed 4 weeks after bevacizumab injections and 8 weeks after dexamethasone injections.RESULTS: Both groups showed a decrease in the number of hyperreflective foci after treatment: from 10.6 ± 11.8 to 6.3 ± 5.9 (p = 0.005) in the intravitreal dexamethasone implant group and from 11.6 ± 8.5 to 7.7 ± 6.7 (p < 0.001) in the intravitreal bevacizumab injection group. The mean central macular thickness in the dexamethasone group changed significantly from 586.8 µm to 297.7 µm after treatment and the visual acuity improved significantly from 0.33 logMAR to 0.38 logMAR after treatment (p < 0.001 and p = 0.018, respectively). The mean central macular thickness in the bevacizumab group showed a significant decrease from 467.1 µm to 353.2 µm after treatment (p < 0.001), but there was no significant change in the visual acuities: 0.34 logMAR to 0.32 logMAR after treatment (p = 0.464).CONCLUSIONS: Both intravitreal dexamethasone implants and bevacizumab treatments in patients with DME showed improved outcomes including a decrease in hyperreflective foci shown by optical coherence tomography.
Bevacizumab ; Cohort Studies ; Dexamethasone ; Humans ; Intravitreal Injections ; Macular Edema ; Membranes ; Retina ; Retrospective Studies ; Tomography, Optical Coherence ; Visual Acuity

Purpose: To evaluate the ocular adverse event (OAE) and the incidence rate that can occur after the COVID-19 vaccination. Methods: Patients who visited with an ophthalmologic diagnosis within a month of COVID-19 vaccination were retrospectively analyzed. OAEs were categorized as ischemia and inflammation by their presumed pathogenesis and were compared by types of vaccine: messenger RNA (mRNA) and viral vector vaccine. The crude incidence rate was calculated using data from the Korea Disease Control and Prevention Agency. Results: Twenty-four patients with OAEs after COVID-19 vaccination were reviewed: 10 patients after mRNA and 14 after viral vector vaccine. Retinal vein occlusion (nine patients) and paralytic strabismus (four patients) were the leading diagnoses. Ischemic OAE was likely to occur after viral vector vaccines, while inflammatory OAE was closely related to mRNA vaccine (p = 0.017). The overall incidence rate of OAE was 5.8 cases per million doses: 11.5 per million doses in viral vector vaccine and 3.4 per million doses in mRNA vaccine. Conclusions OAEs can be observed shortly after the COVID-19 vaccination, and their category was different based on the types of vaccine. The information and incidence of OAE based on the type of vaccine can help monitor patients who were administered the COVID-19 vaccine.

Background: Automated hematology analyzers report various research parameters. Certain parameters may have clinical implications, whereas others are used to set flags in automated hematology analyzers. In this study, we established sex-specific reference intervals for one reticulocyte parameter and two platelet clump parameters in Korean adults and examined the necessity of separate reference intervals for each sex. Methods: A total of 264 healthy adults (157 men and 107 women) aged between 18 and 65 years who underwent regular health check-ups were enrolled. Complete blood cell count was measured using Advia2120i (Siemens, Germany) within 4 hours of phlebotomy. Reference intervals were established according to the Clinical and Laboratory Standards Institute EP28-A3 guideline, and the necessity of separate sex-specific reference intervals was examined according to the method outlined by Harris and Boyd. Results: The reference intervals for each parameter were as follows:reticulocyte hemoglobin (g/L), 132.8–172.0 (men), 96.7–162.8 (women); the number of platelet clumps (# platelet clumps), 10.8–61.0 (men), －22.5–64.4 (women); and %platelet clumps, 0.20–1.49 (men), －0.57–1.53 (women).Reticulocyte hemoglobin showed a significant difference between men and women, whereas # platelet clumps and %platelet clumps did not; however, all three parameters needed separate sex-specific reference intervals. Conclusions We established sex-specific reference intervals for a reticulocyte parameter and platelet clumps parameters in Korean adults for the first time.The results could provide helpful information in clinical decision-making and instrument setting.

Objective: The prolonged coronavirus disease-2019 (COVID-19) pandemic is likely to cause psychological distress in people. This systematic review aimed to identify the effectiveness of virtual reality (VR)-based psychological intervention among individuals with psychological distress during the COVID-19 crisis. PubMed, Ovid MEDLINE, Cochrane Library, Web of Science, Embase, and PsycINFO databases were searched for articles published until July 2022. Methods: The available citations were deduplicated and screened by two authors using the title and abstract information. Eligibility criteria were constructed according to the PICOT guidelines. Empirical studies of all designs and comparator groups were included if they appraised the impact of an immersive VR intervention on any standardized measure indicative of psychological distress (stress, anxiety, depression, and post-traumatic symptoms) or improvements in quality of life in participants, including COVID-19 patients, medical staff working with COVID-19 patients, and people who had experienced strict social distancing during the COVID-19 pandemic. Results: The results were discussed using a narrative synthesis because of the heterogeneity between studies. Seven of the studies met the inclusion criteria. There were two randomized controlled trials and five uncontrolled studies on VR interventions. Conclusion All studies reported significant improvement in a wide range of psychological distress during COVID-19, ranging from stress, anxiety, depression, and post-traumatic symptoms to quality of life, supporting the efficacy of VR-based psychological intervention. Our results suggest that VR intervention has potential to ameliorate COVID-19-related psychological distress with efficacy and safety.

Epidermal cell adhesion molecule (EpCAM) is a tumor-associated antigen (TAA), which has been considered as a cancer vaccine candidate. The EpCAM protein fused to the fragment crystallizable region of immunoglobulin G (IgG) tagged with KDEL endoplasmic reticulum (ER) retention signal (EpCAM-FcK) has been successfully expressed in transgenic tobacco (Nicotiana tabacum cv. Xanthi) and purified from the plant leaf. In this study, we investigated the ability of the plant-derived EpCAM-FcK (EpCAM-FcKP ) to elicit an immune response in vivo. The animal group injected with the EpCAM-FcKP showed a higher differentiated germinal center (GC) B cell population (~9%) compared with the animal group injected with the recombinant rhEpCAM-Fc chimera (EpCAM-FcM ). The animal group injected with EpCAM-FcKP (~42%) had more differentiated T follicular helper cells (Tfh) than the animal group injected with EpCAM-FcM (~7%). This study demonstrated that the plant-derived EpCAM-FcK fusion antigenic protein induced a humoral immune response in mice.