No abstract available.
Acute Disease ; *Gene Rearrangement ; Genome, Human ; Humans ; Leukemia/*diagnosis/genetics ; Polymerase Chain Reaction/*methods ; Translocation, Genetic

A case of fungemia caused by Cyberlindera fabianii was reported in the September issue of Annals of Clinical Microbiology. The C. fabianii that causes rare invasive infection can easily be misidentified as Candida utilis by Vitek-2 YST ID (bioMerieux, USA) and as Candida pelliculosa by API kit (bioMerieux, USA) with high probability. Recently, we also experienced a case of fungemia caused by C. fabianii that was misidentified as C. pelliculosa using API 20C Aux (bioMerieux, USA). As molecular identification is becoming more widespread, cases of C. fabianii infection are expected to be more frequently identified.
Candida ; Fungemia

No abstract available.
Acupuncture Therapy ; Ampicillin/pharmacology/therapeutic use ; Ankle/ultrasonography ; Anti-Bacterial Agents/pharmacology/therapeutic use ; Cellulitis/complications/diagnosis/drug therapy/*microbiology ; Diabetes Mellitus, Type 2/complications ; Gram-Negative Bacterial Infections/complications/diagnosis/drug therapy/*microbiology ; Humans ; Hypertension/complications ; Magnetic Resonance Imaging ; Male ; Microbial Sensitivity Tests ; Middle Aged ; Prevotella nigrescens/drug effects/*genetics/isolation & purification ; RNA, Ribosomal, 16S/*analysis ; Sulbactam/pharmacology/therapeutic use ; Tomography, X-Ray Computed

No abstract available.
Leukemia, Promyelocytic, Acute ; Translocation, Genetic

This report summarizes the 2019 survey results of the external quality assessment (EQA) scheme for the Transfusion Medicine Program (TMP) in Korea. Proficiency testing materials were prepared at the Asan Medical Center for the biannual distribution to participating laboratories. The average accuracy rates and number of participants (in parenthesis) for ten survey items were as follows: ABO typing, 99.4%–99.9% (N=875); RhD typing, 99.8%–100% (N=864); crossmatching, 90.8%–99.6% (N=760); ABO subtyping, 98.2% and 100% (N=58); Rh CcEe antigen testing, both 100% (N=55); weak D test, 97.9%–100% (N=232); antibody screening, 99.7%– 100% (N=316); direct antiglobulin test (DAT) using a polyspecific reagent, 99.6%–100% (N=273); DAT using an immunoglobulin-G monospecific reagent, both 100.0% (N=67); DAT using a C3d monospecific reagent, 95.6%–98.5% (N=67); antibody identification, 87.9%–99.2% (N=132); and ABO Ab titration, 85.7%–100% (N=134). The number of participants showed an average increase of 14% across the ten survey items, with the ABO antibody titration showing the highest increase at 83.6%. While results were generally excellent, antibody identification and ABO antibody titration results showed room for improvement. The 2019 EQA scheme for TMP has contributed to the improvement and maintenance of the participating laboratories to the program.

Background: Recent studies have successfully implemented next-generation sequencing (NGS) in HLA typing. We performed HLA NGS in a Korean population to estimate HLA-A, -B, -C, and -DRB1 allele and haplotype frequencies up to an 8-digit resolution, which might be useful for an extended application of HLA results. Methods: A total of 128 samples collected from healthy unrelated Korean adults, previously subjected to Sanger sequencing for 6-digit HLA analysis, were used. NGS was performed for HLA-A, -B, -C, and -DRB1 using the AllType NGS kit (One Lambda, West Hills, CA, USA), Ion Torrent S5 platform (Thermo Fisher Scientific, Waltham, MA, USA), and Type Steam Visual NGS analysis software (One Lambda). Results: Eight HLA alleles showed frequencies of ≥ 10% in the Korean population, namely, A*24:02:01:01 (19.5%), A*33:03:01 (15.6%), A*02:01:01:01 (14.5%), A*11:01:01:01 (13.3%), B*15:01:01:01 (10.2%), C*01:02:01 (19.9%), C*03:04:01:02 (11.3%), and DRB1*09:01:02 (10.2%). Nine previous 6-digit HLA alleles were further identified as two or more 8-digit HLA alleles. Of these, eight alleles (A*24:02:01, B*35:01:01, B*40:01:02, B*55:02:01, B*58:01:01, C*03:02:02, C*07:02:01, and DRB1*07:01:01) were identified as two 8-digit HLA alleles, and one allele (B*51:01:01) was identified as three 8-digit HLA alleles. The most frequent four-loci haplotype was HLA-A*33:03:01-B*44:03:01:01-C*14:03-DRB1*13:02:01. Conclusions We identified 8-digit HLA-A, -B, -C, and -DRB1 allele and haplotype frequencies in a healthy Korean population using NGS. These new data can be used as a representative Korean data for further disease-related HLA type analysis.

Large Language Models (LLMs), such as ChatGPT (OpenAI, CA, US), have revolutionized scientific writing and research processes across academic disciplines, providing comprehensive support throughout the entire research lifecycle.Generative artificial intelligence (GAI) tools enhance every aspect of scientific writing, from hypothesis genera‑ tion and methodology design to data analysis and manuscript preparation. This review examines the applications of LLMs in hematological research, with particular emphasis on advanced techniques, including prompt engineering and retrieval augmented generation (RAG) frameworks. Prompt engineering methods, including zero-shot and fewshot learning along with a chain-of-thought approach, enable researchers to generate more precise context-specific content, especially in scientific writing. Integrating RAG frameworks with the current medical literature and clinical guidelines significantly reduces the risk of misinformation while ensuring alignment with contemporary medical standards. Even though these GAI tools offer remarkable potential for streamlining research writing and enhancing documentation quality, the study also addresses the critical importance of maintaining scientific integrity, ethical considerations, and privacy concerns in hematological research.

Large Language Models (LLMs), such as ChatGPT (OpenAI, CA, US), have revolutionized scientific writing and research processes across academic disciplines, providing comprehensive support throughout the entire research lifecycle.Generative artificial intelligence (GAI) tools enhance every aspect of scientific writing, from hypothesis genera‑ tion and methodology design to data analysis and manuscript preparation. This review examines the applications of LLMs in hematological research, with particular emphasis on advanced techniques, including prompt engineering and retrieval augmented generation (RAG) frameworks. Prompt engineering methods, including zero-shot and fewshot learning along with a chain-of-thought approach, enable researchers to generate more precise context-specific content, especially in scientific writing. Integrating RAG frameworks with the current medical literature and clinical guidelines significantly reduces the risk of misinformation while ensuring alignment with contemporary medical standards. Even though these GAI tools offer remarkable potential for streamlining research writing and enhancing documentation quality, the study also addresses the critical importance of maintaining scientific integrity, ethical considerations, and privacy concerns in hematological research.

Large Language Models (LLMs), such as ChatGPT (OpenAI, CA, US), have revolutionized scientific writing and research processes across academic disciplines, providing comprehensive support throughout the entire research lifecycle.Generative artificial intelligence (GAI) tools enhance every aspect of scientific writing, from hypothesis genera‑ tion and methodology design to data analysis and manuscript preparation. This review examines the applications of LLMs in hematological research, with particular emphasis on advanced techniques, including prompt engineering and retrieval augmented generation (RAG) frameworks. Prompt engineering methods, including zero-shot and fewshot learning along with a chain-of-thought approach, enable researchers to generate more precise context-specific content, especially in scientific writing. Integrating RAG frameworks with the current medical literature and clinical guidelines significantly reduces the risk of misinformation while ensuring alignment with contemporary medical standards. Even though these GAI tools offer remarkable potential for streamlining research writing and enhancing documentation quality, the study also addresses the critical importance of maintaining scientific integrity, ethical considerations, and privacy concerns in hematological research.

Human immunodeficiency virus (HIV) infection presents a major global health challenge. According to the World Health Organization, approximately 39.0 million people have HIV, as of 2022, with 1.3 million new infections annually. In Korea, the first HIV cases were reported in 1985, and the number of cases has increased continuously since 1988. In 2022, 1,066 cases were newly diagnosed, increasing the total number of cases to approximately 15,880.Current Concepts: Diagnostics play a crucial role in the management of HIV, for early diagnosis and initiation of antiretroviral therapy. Approval of the first HIV antibody test in 1985 by the US Food and Drug Administration marked a notable advance in the ability to diagnose HIV infection. The screening test for HIV infection typically consists of a two-step approach, in which a fourth-generation combination HIV-1/2 immunoassay is followed by a confirmatory Western blot. Despite its high accuracy for chronic infections, the window period and the early-stage of HIV infections are problematic, and populations with low HIV prevalence show a low positive predictive value. A false positive result can occur from blood transfusions, vaccinations, and autoantibodies. Novel diagnostic tools, such as nucleic acid testing for viral RNA/DNA and antigen testing for p24 antigen have reduced the window period, allowing for earlier detection and differentiation between HIV-1 and HIV-2. Pointof-care tests are beneficial in resource-limited settings although their lower sensitivity and specificity require additional confirmatory follow-up testing.Discussion and Conclusion: In this review, we provide a historical perspective on the evolution of HIV diagnostics (serological and molecular) and describe the roles of conventional and point-of-care testing within the laboratory diagnostic network.