BACKGROUND: Increasing the participation rate in health screenings is a major challenge. In West Virginia, USA, where a statewide, state-funded school-based health screening program has been offered to fifth-grade students and their parents/guardians for nearly 20 years, more than 50% of eligible participants consistently opt-out. Consequently, the purpose of this investigation is to determine a parent/guardian's reasons for deciding whether to participate in a school-based health screening. METHODS: A cross-sectional study design was used and a total of 216 parents/guardians of fourth-grade students from 10 elementary schools in the northeast region of West Virginia participated in the study. The survey, based on the theory of planned behavior (TPB), was used to explore a parent/guardian's intentions when opting in or out of a school-based health screening for their child, and included items that represented direct determinants, indirect determinants, and behavioral intentions. Multiple regression analyses were conducted to measure the questionnaire's potential to predict intentions and identify the predictive strength of each direct determinant. RESULTS: Results show that attitude, subjective norms, and perceived behavioral control in the TPB (43%) provided strong evidence for predicting participation intentions. Specifically, attitude (β=0.73, P<0.001) was the strongest predictor of intention, followed by subjective norms (β=−0.17, P<0.01). CONCLUSION: This study suggests that strategies to facilitate positive attitudes and increase parental awareness of health screening initiatives may influence participation rates within community- and school-based programming.
Child ; Cross-Sectional Studies ; Humans ; Intention ; Mass Screening ; Parents ; West Virginia

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.

Because of location, a mediastinal mass may cause complications such as a major airway obstruction, a superior vena caval obstruction, and cardiac compression during general anesthesia. The patient's condition need to be assessed by several methods to predict the risks associated with general anesthesia. The authors took computed tomographs for a preoperative evaluation of two patients with an anterior mediastinal mass, and the risk of perioperative complications was predicted by measuring the tracheal area. The patients were managed according to the preoperative evaluation but severe ventilation impairments were encountered during anesthesia. In one patient, stable ventilation could not be maintained until spontaneous breathing appeared. The operation was cancelled and the patient was brought into the ICU. In the other patient, a tracheal tube was inserted deeper in an attempt to pass the narrowed tracheal portion due to mediastinal tumor compression resulting in improved ventilation
Airway Obstruction ; Anesthesia* ; Anesthesia, General ; Humans ; Respiration ; Ventilation*

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

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 Pseudomonas aeruginosa (CrPA) is a leading cause of healthcare-associated urinary tract infections (UTIs). Carbapenemase production is an important mechanism that significantly alters the efficacy of frequently used anti-pseudomonal agents. Reporting the current genotypic distribution of carbapenemase-producing P.aeruginosa (CPPA) isolates in relation to antimicrobial susceptibility, UTI risk factors, and mortality is necessary to increase the awareness and control of these strains. Methods: In total, 1,652 non-duplicated P. aeruginosa strains were isolated from hospitalized patients between 2015 and 2020. Antimicrobial susceptibility, carbapenemase genotypes, risk factors for UTI, and associated mortality were analyzed. Results: The prevalence of carbapenem-non-susceptible P. aeruginosa isolates showed a decreasing trend from 2015 to 2018 and then increased in the background of the emergence of New Delhi metallo-β-lactamase (NDM)-type isolates since 2019. The CPPA strains showed 100.0% non-susceptibility to all tested antibiotics, except aztreonam (94.5%) and colistin (5.9%). Carbapenems were identified as a risk and common predisposing factor for UTI (odds ratio [OR] = 1.943) and mortality (OR = 2.766). Intensive care unit (ICU) stay (OR = 2.677) and white blood cell (WBC) count (OR = 1.070) were independently associated with mortality. Conclusions The changing trend and genetic distribution of CPPA isolates emphasize the need for relentless monitoring to control further dissemination. The use of carbapenems, ICU stay, and WBC count should be considered risk factors, and aggressive antibiotic stewardship programs and monitoring may serve to prevent worse outcomes.

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.

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.