In the era of precision medicine, pharmacogenetics has substantial potential for addressing inter-individual variability in drug responses. Although pharmacogenetics has been a research focus for many years, resulting in the establishment of several formal guidelines, its clinical implementation remains limited to several gene–drug combinations in most countries, including Korea. The main causes of delayed implementation are technical challenges in genotyping and knowledge gaps among healthcare providers; therefore, clinical laboratories play a critical role in the timely implementation of pharmacogenetics. This paper presents an update of the Clinical Pharmacogenetic Testing and Application guidelines issued by the Korean Society for Laboratory Medicine and aims to provide the necessary information for clinical laboratories planning to implement or expand their pharmacogenetic testing. Current knowledge regarding nomenclature, gene–drug relationships, genotyping technologies, testing strategies, methods for clinically relevant information delivery, QC, and reimbursements has been curated and described in this guideline.

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

BACKGROUND

Students’ perceptions of their educational environment have been determined to influence their educational performance, behavior, and well-being. Assessing the students’ educational environment enables stakeholders to recognize limitations and opportunities for ensuring optimal learning experiences. However, no studies have yet explored the perceived educational environment of occupational therapy (OT) students in the Philippines.

Using the Dundee Ready Education Environment Measure (DREEM) instrument, this study assesses how occupational therapy students perceive their educational environment. Additionally, this study aims to identify strengths and weaknesses in the current educational environment.

This descriptive cross-sectional study utilized a records review of anonymized secondary responses from the Student Life Survey 2021. The DREEM questionnaire was employed to evaluate the perspectives of occupational therapy students in five domains, namely: students’ perception of learning, students’ perceptions of academic self-perceptions, students’ perceptions of atmosphere, and students’ social self-perceptions. Individual item and subscale scores were summarized as mean and standard deviation (SD).

With a total mean score of 124.12 ± 26.79 in the five domains, the OT students positively perceive their educational environment. Data analysis revealed the scores for the DREEM domains of Students' Perception of Teachers (30.50 ± 6.08), Students' Perception of Learning (31.11 ± 5.94), Students' Academic Self-Perceptions (20.37 ± 4.26), Students’ Social Self-Perceptions (15.58 ± 4.00), and Students’ Perceptions of Atmosphere (26.56 ± 6.51). Interpretation for each domain indicates that the educational environment is moving in the right direction, has a more positive approach, is feeling more on the positive side, is not too bad, and has a more positive atmosphere, respectively.

The study revealed a positive perception of the educational environment among OT students. However, there needed improvement in the areas of authoritarian teachers, the demanding program environment, and the overemphasis on factual learning. To guarantee that OT education in the country is of high quality, these findings can be utilized to inform the institution’s stakeholders regarding policy development, curriculum review, and future OT program development and implementation.

Background: Lawsonia intracellularis is the causative agent of proliferative enteropathy and is associated with several outbreaks, causing substantial economic loss to the porcine industry. Objectives: In this study, we focused on demonstrating the protective effect in the mouse model through the immunological bases of two vaccine strains against porcine proliferative enteritis. Methods: We used live-attenuated Salmonella Typhimurium (ST) secreting two selected immunogenic LI antigens (Lawsonia autotransporter A epitopes and flagellin [FliC]-peptidoglycan-associated lipoprotein-FliC) as the vaccine carrier. The constructs were cloned into a Salmonella expression vector (pJHL65) and transformed into the ST strain (JOL912). The expression of immunogenic proteins within Salmonella was evaluated via immunoblotting. Results: Immunizing BALB/c mice orally and subcutaneously induced high levels of LI-specific systemic immunoglobulin G and mucosal secretory immunoglobulin A. In immunized mice, there was significant upregulation of interferon-γ and interleukin-4 cytokine mRNA and an increase in the subpopulations of cluster of differentiation (CD) 4+ and CD 8+ T lymphocytes upon splenocytes re-stimulation with LI antigens. We observed significant protection in C57BL/6 mice against challenge with 106.9 times the median tissue culture infectious dose of LI or 2 × 109 colony-forming units of the virulent ST strain. Immunizing mice with either individual vaccine strains or co-mixture inhibited bacterial proliferation, with a marked reduction in the percentage of mice shedding Lawsonia in their feces. Conclusions Salmonella-mediated LI gene delivery induces robust humoral and cellular immune reactions, leading to significant protection against LI and salmonellosis.