Incheon Medical Center

Atopic dermatitis (AD), allergic rhinitis (AR), and asthma are typical allergic diseases, with a common pathogenesis involving increased type 2 immunity. Despite numerous studies, the association between these diseases and metabolic syndrome (MetS) remains controversial. This study aimed to explore relationships between AD, AR, and asthma both to MetS and to each of its components. Population-based, cross-sectional data were obtained from the Korean National Health and Nutrition Examination Survey from 2019 to 2020. Multiple logistic regression analyses were conducted to evaluate the association between allergic diseases and MetS. The results were compared with those of previous studies of the association between MetS and allergic diseases, identified in a literature review following a search of PubMed and the Cochrane Library. Based on assessments of 1,329, 4,824, and 1,172 patients with AD, AR, and asthma, respectively, negative associations between both AD and AR and MetS were determined, after adjusting for confounding factors. By contrast, asthma was positively associated with MetS (odds ratio [OR] 0.58, 95% confidence interval [CI] 0.50-0.67; OR 0.73, 95% CI 0.67-0.78; OR 1.70, 95% CI 1.51-1.92, respectively). The heterogeneous findings highlight the need for further investigation of associations between the studied allergic diseases and MetS as well as each of its components.

Objective: This study aimed to identify caregivers’ perceptions, preferences, and intentions regarding the use of digital articulation therapy applications for children with DLD. Methods: A cross-sectional online survey was conducted between August and September 2025 among 197 caregivers of children diagnosed with DLD. A 43-item questionnaire was structured into five domains addressing demographics, therapy experience, digital-therapy exposure, and preferences for application-based articulation therapy. Constructs from the Unified Theory of Acceptance and Use of Technology 2, including performance expectancy (PE), social influence (SI), price value (PV), facilitating conditions (FC), and behavioral intention, were analyzed. Results: Most respondents were mothers (96%), and 78.7% of them resided in urban areas. Among them, 84.3% had prior speech therapy experience. Only 15.7% had used educational or therapeutic applications; the main dissatisfaction factors were lack of fun (51.6%) and feedback (19.3%). Caregivers preferred short, engaging sessions (20–40 min per day), video-based (75%) or game-based (64%) content, and feedback every 2 months, with a reasonable monthly cost (20,000–30,000 KRW). Regression analysis revealed that sex (β = −0.451, p = 0.013), PE (β = 0.381, p < 0.001), and PV (β = 0.212, p = 0.034) significantly associated with behavioral intention to use an articulation-therapy application, whereas SI and FC were not significant. Conclusions: Caregivers of children with DLD demonstrated strong willingness to adopt digital articulation therapy applications, particularly when these tools provide meaningful therapeutic outcomes at moderate cost and include motivating, interactive content. Future application design should prioritize treatment functionality, user engagement, and accessibility to enhance adoption and continuity of digital speech-language interventions.

Background: Preventing and managing hospital readmissions is critical in heart failure (HF) care, yet conventional monitoring tools have limitations. While artificial intelligence-enabled electrocardiograms (AI-ECGs) show promise in detecting cardiac dysfunction, little is known about their post-discharge score dynamics in HF patients. Hypothesize: We hypothesized that AI-enabled ECG scores would show distinct temporal patterns after hospital discharge in patients with HF, and that these patterns would differ between patients who experienced rehospitalization and those who did not. Method: This single-center retrospective study analyzed ECG data from patients hospitalized for HF between March 2017 and January 2025 in South Korea. Post-discharge, ECGs were processed using AI-ECG models for left ventricular systolic dysfunction (LVSD), diastolic dysfunction (LVDD), and myocardial infarction (MI). We compared AI-ECG patterns in patients readmitted within six months vs. those not (Figure 1). Temporal trends in AI-ECG scores were assessed using a mixed-effects linear regression model with group and time as fixed effects, and patient as a random effect. Result: Among 1,007 patients, 1,539 hospitalization events were identified. A total of 1,674 ECGs from 269 rehospitalized and 4,066 ECGs from 917 non-rehospitalized patients were collected from 180 days before to 60 days after the index readmission or follow-up end. The mean age was 65.2 years, and 63.1% were male. Diabetes mellitus and chronic kidney disease were significantly more prevalent in the rehospitalization group, whereas other comorbidities were comparable. Significant differences in ECG intervals and axes were also observed, with no notable difference in heart rate. In the LVSD model, rehospitalized patients showed higher scores overall (β = 7.96, 95% CI: 3.18–12.75, p = 0.001) (Figure 2). Time since discharge was associated with decreasing scores (β = –0.096/day, 95% CI: –0.104 to –0.087, p&lt;0.001), but this decline was attenuated in the rehospitalization group (interaction β = 0.092, 95% CI: 0.069–0.115, p&lt;0.001). The LVDD model demonstrated a similar trend, while the MI model exhibited no statistically significant differences in scores (Figure 3). Conclusion: AI-ECG models show potential as dynamic biomarkers for detecting early physiological deterioration and predicting readmission risk in HF patients. These findings support their use in future patient monitoring strategies.

Background and Clinical Significance: Psoriasis, a chronic immune-mediated inflammatory disease, can affect musculoskeletal structures, including the Achilles tendon. Achilles pain in psoriasis patients may arise from tendinitis or neuropathic pain due to peripheral nerve dysfunction, such as sural nerve (SN) involvement, a condition frequently misdiagnosed due to limitations in conventional diagnostics. Fascial tissues are critical in nerve compression syndromes. This case explores the application of a novel quantitative Sonoguide Digital Palpation (SDP) protocol and ultrasound (US)-guided hydrodissection (HD) for SN dysfunction mimicking Achilles tendinopathy in a psoriasis patient. Case Presentation: A 41-year-old male with psoriasis presented with acute onset of right heel stiffness and paresthesia. Physical examination, radiographs, and ultrasound were performed. SDP, employing a validated four-criterion diagnostic framework (including fascial mobility quantification and concordant pain provocation), identified crural fascia restriction affecting SN and reproduced patient’s concordant Achilles pain. High-resolution ultrasonography provided key morphological evidence, revealing a 2.6-fold enlargement of the sural nerve’s cross-sectional area (CSA) on the affected side (13 mm2) compared to the asymptomatic side (5 mm2). Notably, a positive Tinel’s sign was elicited over the psoriatic plaque. US-guided HD was performed using 50 cc of 5% dextrose in water (D5W) without local anesthetic below the psoriatic lesion. Post-HD, the patient reported immediate and significant pain relief (Numeric Pain Rating Scale (NPRS) score reduction from 8 to 2), confirming the prompt correction of a clinically important fascial restriction, associated with improved SN mobility, objectively verified by a post-procedure SDP assessment. At 24-month follow-up, sustained symptom relief and complete functional recovery were reported. Conclusions: This case highlights SDP’s ability to objectively visualize and confirm fascial restriction as a cause of nerve dysfunction by quantitatively reproducing concordant pain. The objective finding of nerve swelling provides sonographic substantiation for the functional diagnosis of nerve dysfunction. This integrated diagnostic approach, combining dynamic functional assessment with morphological confirmation, offers a novel paradigm for evaluating peripheral nerve disorders. US-guided HD of the SN with D5W without local anesthetic shows promise as both a diagnostic confirmatory tool and therapeutic intervention for neuropathic Achilles pain in psoriasis patients with SN involvement, aligning with its efficacy in other peripheral neuropathies. The significant nerve swelling (13 mm2) provides robust morphological corroboration of the functional impairment diagnosed by SDP, offering a more comprehensive diagnostic paradigm.

Abstract Background Monitoring and surveillance of antimicrobial resistance (AMR) is important procedure in clinically patient management and epidemiologically public health. Conventionally such culture based tools as disk diffusion method or broth dilution method for antibiotic susceptibility test has been used. While recently such culture independent approaches as PICRUSt2, Tax4Fun, or MicFunPred have been tried based on predictive functional profiling using 16S rRNA marker gene, evaluation of the tools regarding AMR is little. Methods A total of 20 E. coli strains (Carbapenem resistant (CRE) positive: 10, CRE negative: 10) were used. The AMR phenotype was based on Vitek2 (biomerieux). DNA was extracted from the 20 strains and 16S rRNA (V3-V4 region) and shotgun sequencing were done. The bioinformatic pieplines were QIIM2 for 16S rRNA and MetaPhlAn4 for shotgun. Functional prediction tools were PICRUSt2, Tax4Fun, and MicFunPred for 16S rRNA and AMRFinderPlus for shotgun. The presence/absence of 25 KEGG numbers regarding AMR in PICRUSt2, Tax4Fun, and MicFunPred were compared to shotgun AMR profiles. The F1 scores were calculated according to each 16S marker gene based prediction tools using confusion matrix. Results A total of 14 class of antibiotics including carbapenem were analyzed. The F1 scores of 16S predictive functional profilers regarding AMR were 0.22 for Tax4Fun, 0.12 for PICRUSt2, and 0.08 for MicFunPred. Conclusion While Tax4Fun showed the highest F1 score among three 16S predictive functional profilers, the F1 scores were generally low. The strength of Tax4Fun is the incorporation of user-defined data and the enhanced accuracy of the predictions is expected through incorporation of AMR-specific data in engine training.