Diabetic foot ulcers (DFUs) are among the most serious and common complications of diabetes mellitus. They significantly affect patients’ quality of life and impose a substantial economic burden on healthcare systems worldwide. In Korea, the prevalence of diabetes and related complications, such as DFUs, has been increasing, reflecting a broader global trend. DFUs are associated with severe complications, including infections, neuropathy, and peripheral arterial disease, often leading to amputation. In Korea, diabetic foot complications are a major cause of non-traumatic lower-extremity amputations, with high mortality rates following amputation. DFUs also significantly reduce patients’ quality of life and increase healthcare costs. The management of DFUs requires a multidisciplinary approach that integrates medical, surgical, and advanced therapeutic interventions to prevent severe outcomes, such as amputation. This comprehensive review of DFU management in patients with diabetes was developed in collaboration with the Diabetic Study Group of the Korean Diabetes Association and Korean Society for Diabetic Foot. This review examines the epidemiology, clinical significance, diagnosis, and evidence-based treatment of DFUs.

This study aimed to determine the prevalence of human epidermal growth factor receptor 2 (HER2)–ultralow breast cancer among cases initially classified as HER2 immunohistochemistry (IHC) 0 and assess interobserver variability in interpreting low-level HER2 expression. Methods: In this multicenter retrospective study, all invasive breast cancer cases diagnosed between January and December 2022 across 10 Korean institutions were retrieved. Institutional pathologists reexamined HER2 IHC slides originally reported as IHC 0 according to the 2018 American Society of Clinical Oncology/College of American Pathologists guidelines and reclassified them as HER2-null (0), HER2-ultralow (0+), or HER2-low (1+). Slides from 10% of HER2-null and HER2-ultralow cases were digitized for central review and independently assessed by two pathologists, with discrepancies resolved by consensus. Results: Among 8,026 cases, 2,836 cases (35.5%) were initially reported as IHC 0. Upon re-review, 1,673 (59.0%), 1,139 (40.2%), and 24 (0.8%) cases were reclassified as HER2-null, HER2-ultralow, and HER2-low, respectively. The prevalence of HER2-ultralow breast cancer varied considerably across institutions (23.7%–78.1%). Central review of 268 digitized cases showed concordance in 193 cases (72.0%). Among the 75 discordant cases, 54 tumors (72.0%) were upgraded from HER2-null to HER2-ultralow, and 18 (24.0%) tumors were upgraded from HER2-ultralow to HER2-low. Furthermore, two tumors (2.7%) were downgraded from HER2-ultralow to HER2-null. Conclusions: Approximately 40% of cases initially categorized as IHC 0 were reclassified as HER2-ultralow. The substantial inter-institutional variability observed in interpreting low-level HER2 expression highlights the need for standardized training and quality assurance to ensure accurate identification of patients eligible for HER2-targeted antibody–drug conjugates.

Objective: To evaluate the accuracy and reasoning capabilities of large multimodal language models compared with those of neuroradiology subspecialty-trained radiologists in neuroradiology case interpretation. Materials and Methods: This experimental study used custom-made 401 radiologic quizzes derived from articles published in RadioGraphics covering neuroradiology and head and neck topics (October 2020 to February 2024). We prompted the GPT-4 Turbo with Vision (GPT-4V), GPT-4 Omni, Gemini Flash, and Claude models to provide the top three differential diagnoses with a rationale and describe examination characteristics such as imaging modality, sequence, use of contrast, image plane, and body part. The temperature was adjusted to 0 and 1 (T1). Two neuroradiologists answered the same questions.The accuracies of the large language models (LLMs) and the neuroradiologists were compared using generalized estimating equations. Three neuroradiologists assessed the rationale provided by the LLMs for their differential diagnoses using four-point scales, separately for specific lesion locations and imaging findings, and evaluated the presence of hallucinations and the overall acceptability of the responses. Results: Top-3 accuracy (i.e., correct answers present among top-3 differential diagnoses) of LLMs ranged from 29.9% (120 of 401) to 49.4% (198 of 401, obtained with GPT-4V in the T1 setting), while radiologists achieved 80.3% (322 of 401) and 68.3% (274 of 401), respectively (P < 0.001). Regarding the rationale for differential diagnoses, GPT-4V (T1) accurately identified both the specific lesion location and imaging findings in 30.7% (123 of 401) and 12.9% (16 of 124) of cases without textual clinical history. Hallucinations occurred in 4.5% (18 of 401), and only 29.4% (118 of 401) of the LLM-generated analyses were deemed acceptable. GPT-4V (T1) demonstrated high accuracy in identifying the imaging modality (97.4% [800 of 821]) and scanned body parts (92.2% [756 of 820]). Conclusion LLMs remarkably underperformed compared with neuroradiologists and showed unsatisfactory reasoning for their differential diagnoses, with performance declining further in cases without textual input of clinical history. These findings highlight the limitations of current multimodal LLMs in neuroradiological interpretation and their reliance on text input.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

Objective Exploring genetic differences among geographic strains of Anopheles sinensis in China. Methods Based on the mitochondrial cytochrome oxidase subunit I(COⅠ )and ribosomal the second internal transcribed spacer(ITS2), genetic distance analyses were conducted on An. sinensis samples collected nationwide. Results Genetic differentiation was observed within An. sinensis geographic strains, primarily reflected in the genetic distances of COⅠ sequences, with some distances between samples exceeding those between closely related species. However, ITS2 sequences showed relatively low levels of differentiation within geographic strains. No significant separation was observed, and gene exchange exhibited a random pattern among geographic strains. Conclusions Significant differences in genetic distance were observed among An. sinensis samples, along with evidence of interspecific continuity among sibling species. For the identification of closely related species using wild-collected Anopheles mosquitoes samples, reliance solely on commonly used barcodes such as COⅠ and ITS2 may be insufficient. Additional molecular markers, including COⅡ and D3 gene fragments, should also be considered. Caution should be exercised when using COⅠ genes to identify cryptic species, especially within Anopheles mosquitoes.

Purpose: Accumulating evidence has clarified that gut dysbiosis is involved in lung cancer development and progression. Although the relationship between tumors and gut microbiota has been extensively studied using clinical samples, no studies have examined the association between mutant epidermal growth factor receptor (EGFR)–induced lung carcinogenesis and dysbiosis in gut microbiota. Therefore, we investigated the gut microbiota profiles in stool samples from human lung-specific conditional EGFR-mutant transgenic mice during lung tumor carcinogenesis. Materials and Methods: Stool samples were collected before tamoxifen treatment (V1) and at each time point following mutant EGFR expression in lung tissue (V2) and lung tumor appearance (V3). Fecal 16S rRNA taxonomy was analyzed to assess microbial diversity, composition, and dynamic changes at each time point. Results: We found that microbiota richness and diversity were significantly elevated when tumors developed and grew in the lung. Phylogenetic analysis of the microbial community revealed that Lachnospiraceae, Ruminococcaceae, Porphyromonadaceae, Rhodospirillaceae, Odoribacteraceae, and Desulfovibrionaceae showed a significant increase at the V3 stage compared to the V1 stage at the family level. In contrast, Lactobacillaceae, Bacteroidaceae, Muribaculaceae, Coriobacteriaceae, and Rikenellaceae significantly decreased at the V3 stage compared to the V1 stage. Furthermore, Lactobacillus species, also known as short chain fatty acid-producing bacteria, were relatively abundant at the V1 stage but were depleted with the occurrence of lung tumors at the V3 stage. Conclusion Changes in gut microbiota, such as Lactobacillus species, may be a predictive factor for the emergence and progression of tumors in an animal model of lung adenocarcinoma induced by mutant EGFR.

Thyroid cancer, one of the most common endocrine tumors, generally has a favorable prognosis but remains a significant medical and societal concern due to its high incidence. Early diagnosis and treatment of differentiated thyroid cancer (DTC) significantly affect long-term outcomes, requiring the selection and application of appropriate initial treatments to improve prognosis and quality of life. Recent advances in technology and health information systems have enhanced our understanding of the molecular genetics of thyroid cancer, facilitating the identification of aggressive subgroups and enabling the accumulation of research on risk factors through big data. The Korean Thyroid Association (KTA) has revised the “KTA Guidelines on the Management of Differentiated Thyroid Cancers 2024” to incorporate these advances, which were developed by a multidisciplinary team and underwent extensive review and approval processes by various academic societies. This article summarizes the 2024 KTA guidelines for nuclear medicine imaging in patients with DTC, written by the Nuclear Medicine members of the KTA Guideline Committee, and covers 18 F-FDG PET/CT and radioiodine imaging with SPECT/CT in the management of DTC. 

Thyroid cancer, one of the most common endocrine tumors, generally has a favorable prognosis but remains a significant medical and societal concern due to its high incidence. Early diagnosis and treatment of differentiated thyroid cancer (DTC) significantly affect long-term outcomes, requiring the selection and application of appropriate initial treatments to improve prognosis and quality of life. Recent advances in technology and health information systems have enhanced our understanding of the molecular genetics of thyroid cancer, facilitating the identification of aggressive subgroups and enabling the accumulation of research on risk factors through big data. The Korean Thyroid Association (KTA) has revised the “KTA Guidelines on the Management of Differentiated Thyroid Cancers 2024” to incorporate these advances, which were developed by a multidisciplinary team and underwent extensive review and approval processes by various academic societies.This article summarizes the 2024 KTA guidelines for radioactive iodine (RAI) therapy in patients with DTC, written by the Nuclear Medicine members of the KTA Guideline Committee, and covers RAI therapy as initial management of DTC and RAI therapy in advanced thyroid cancer.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.