ObjectiveTo investigate the mechanisms by which the combination of berberine （BBR） and baicalin （BAI） ameliorates type 2 diabetes mellitus （T2DM） due to internal accumulation of dampness-heat from the perspectives of gut microbiota and metabolomics. MethodsAntibiotics were used to induce pseudo-sterile mice. Thirty pseudo-sterile mice were randomized into a normal fecal microbiota transplantation group （n=10） and a T2DM （syndrome of internal accumulation of dampness-heat） fecal microbiota transplantation group （n=20）. The mice were then administrated with suspensions of fecal microbiota from healthy volunteers and a patient with T2DM due to internal accumulation of dampness-heat by gavage， respectively. Each mouse received 200 µL suspension every other day for a total of 15 times to reshape the gut microbiota. The T2DM model mice were then assigned into a model group （n=8） and a BBR-BAI group （n=11）. BBR was administrated at a dose of 200 mg·kg^-1， and BAI was administrated in a ratio of BBR-BAI 10∶1 based on preliminary research findings. The administration lasted for 8 consecutive weeks. Fasting blood glucose （FBG）， glycated hemoglobin （HbA1c）， insulin （INS）， triglycerides （TG）， total cholesterol （CHOL）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate aminotransferase （AST）， and alanine aminotransferase （ALT） levels were measured to evaluate the effects of the BBR-BAI combination on glucose and lipid metabolism and liver function in T2DM mice. Hematoxylin-eosin staining was employed to observe pathological changes in the colon tissue. The expression of claudin-1， zonula occludens-1 （ZO-1）， and occludin in the colon tissue was determined by Western blot. Real-time quantitative polymerase chain reaction（Real-time PCR） was employed to assess the levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） in the colon tissue. The fecal microbiota composition and differential metabolites were analyzed by 16S rRNA sequencing and ultra-high performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry （UPLC-Q-TOF-MS）， respectively. ResultsThe BBR-BAI combination lowered the FBG， HbA1c， and INS levels （P<0.05， P<0.01） and alleviated insulin resistance （P<0.01） in T2DM mice. Additionally， BBR-BAI elevated the levels of ZO-1， occludin， and claudin-1 （P<0.05， P<0.01） and down-regulated the expression levels of TNF-α， IL-1β， and IL-6 in the colon （P<0.05， P<0.01）. The results of 16S rRNA sequencing showed that BBR-BAI increased the relative abundance of Ligilactobacillus， Phascolarctobacterium， and Akkermansia （P<0.05）， while significantly decreasing the relative abundance of Alistipes， Odoribacter， and Colidextribacter （P<0.05）. UPLC-Q-TOF-MS identified 28 differential metabolites， which were primarily involved in arachidonic acid metabolism and α-linolenic acid metabolism. ConclusionBBR-BAI can ameliorate T2DM due to internal accumulation of dampness-heat by modulating the relative abundance of various bacterial genera in the gut microbiota and the expression of fecal metabolites.

The crossed occlusion is unstable, and for the treatment of such occlusal conditions, it is recommended to form a balance of occlusal force between the arch in contact with occlusal contact, to secure stable vertical support by symmetrically placing the implants in the posterior area, and it is necessary to improve the distorted occlusal plane, to have a regular check-up and to check whether the occlusal conditions are maintained stably after treatment. In this case, in order to resolve unstable occlusal condition in patient with crossed occlusion, the existing occlusal vertical dimension was maintained at the state of wearing the dentures, and the implant placement positions were planned based on the diagnostic waxup, and then the implants were placed using a surgical guides, and after the temporary prostheses stage, the final fixed prostheses were fabricated by incorporating the temporary prostheses form adapted by the patient to develop a stable vertical support, and finally the anterior maxillary region was restored with implant-supported removable partial denture. In this case, the patient adapted well without discomfort during the four-month follow-up and satisfactory aesthetic and functional clinical results were obtained, leading to this report.

Reliable preclinical models for assessing drug-induced cardiotoxicity are essential to reduce the high rate of drug withdrawals during development. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as a promising platform for such assessments due to their expression of cardiacspecific ion channels and electrophysiological properties. In this study, we investigated the effects of eight arrhythmogenic drugs—E4031, nifedipine, mexiletine, JNJ303, flecainide, moxifloxacin, quinidine, and ranolazine—on hiPSC-CMs derived from both healthy individuals and a long QT syndrome (LQTS) patient using multielectrode array systems. The results demonstrated dose-dependent changes in field potential duration and arrhythmogenic risk, with LQTS-derived hiPSC-CMs showing increased sensitivity to hERG channel blockers such as E4031. Furthermore, the study highlights the potential of hiPSC-CMs to model disease-specific cardiac responses, providing insights into genetic predispositions and personalized drug responses.Despite challenges related to the immaturity of hiPSC-CMs, their ability to recapitulate human cardiac electrophysiology makes them a valuable tool for preclinical cardiotoxicity assessments. This study underscores the utility of integrating patientderived hiPSC-CMs with advanced analytical platforms, such as multi-electrode array systems, to evaluate drug-induced electrophysiological changes. These findings reinforce the role of hiPSC-CMs in drug development, facilitating safer and more efficient screening methods while supporting precision medicine applications.

Purpose: Identifying and managing risk factors for lower urinary tract symptoms (LUTS) is crucial because it impacts the quality of life of elderly individuals. Lifestyle factors, including physical activity (PA), and their relationship with LUTS have not been well studied. This objective of this study was to investigate the association between PA and LUTS. Materials and Methods: A total of 7,296 men were included in this cross-sectional study. PA was quantified in metabolic equivalent (MET)-hours per week, and LUTS severity was assessed using the international prostate symptom score. Logistic regression was used to analyze the association between PA and LUTS, including voiding and storage symptoms. Results: The average age of the participants was 57.8 years, and the prevalence of LUTS was 41.3%. After adjusting for potential confounders, PA was inversely associated with the prevalence and severity of moderate-to-severe LUTS, showing a dose-response pattern (all p for trend <0.01). Compared to the minimal activity group, which engaged in <5 MET-hours per week of PA, the odds ratios for moderate to severe LUTS were 0.83 (95% confidence interval [CI]: 0.72–0.97) for men engaging in 15–30 MET-hours per week, 0.82 (95% CI: 0.71–0.95) for 30–60 MET-hours per week, and 0.72 (95% CI: 0.62–0.84) for ≥60 MET-hours per week. The possible protective effect of PA was still observed in the additional analysis for voiding and storage symptoms showing the same dose-response pattern (all p for trend <0.01). Conclusions A higher PA level was associated with a lower prevalence and severity of total, voiding, and storage LUTS in a dose-dependent manner in Korean men.

Background: Respiratory infections play a major role in acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study assessed the prevalence of bacterial and viral pathogens and their clinical impact on patients with AECOPD. Methods: This retrospective study included 1,186 patients diagnosed with AECOPD at 28 hospitals in South Korea between 2015 and 2018. We evaluated the identification rates of pathogens, basic patient characteristics, clinical features, and the factors associated with infections by potentially drug-resistant (PDR) pathogens using various microbiological tests. Results: Bacteria, viruses, and both were detected in 262 (22.1%), 265 (22.5%), and 129 (10.9%) of patients, respectively. The most common pathogens included Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). Notably, a history of pulmonary tuberculosis (odds ratio [OR], 1.66; p=0.046), bronchiectasis (OR, 1.99; p=0.032), and the use of a triple inhaler regimen within the past 6 months (OR, 2.04; p=0.005) were identified as significant factors associated with infection by PDR pathogens. Moreover, patients infected with PDR pathogens exhibited extended hospital stays (15.9 days vs. 12.4 days, p=0.018) and higher intensive care unit admission rates (15.9% vs. 9.5%, p=0.030). Conclusion This study demonstrates that a variety of pathogens are involved in episodes of AECOPD. Nevertheless, additional research is required to confirm their role in the onset and progression of AECOPD.

Purpose: This study assessed the reproducibility and clinical value of the vascular index (VI), derived from superb microvascular imaging (SMI) using Doppler ultrasonography, for evaluating renal function in transplanted kidneys. Methods: This retrospective study included 63 renal transplant patients who underwent grayscale and Doppler ultrasonography with SMI from January 2022 to February 2023. The VI of the transplanted kidneys was measured using three methods (VI_box, VI_F1, VI_F2). The VI was compared across chronic kidney disease (CKD) groups categorized by estimated glomerular filtration rate (eGFR) and Kidney Disease: Improving Global Outcomes (KDIGO) CKD risk groups based on eGFR and albuminuria. The correlation between VI and renal function was evaluated. Univariate and multivariate linear regression analyses were used to identify predictors of eGFR. Results: Significant differences in VI were observed among CKD groups based on eGFR (VI_box, P=0.001; VI_F1, P<0.001; VI_F2, P<0.001) and KDIGO CKD groups based on eGFR and albuminuria (VI_box, P=0.039; VI_F1, P=0.001; VI_F2, P<0.001). VI_F1 and VI_F2 demonstrated moderate/high correlations with eGFR (r=0.627, P<0.001 and r=0.657, P<0.001, respectively) and serum creatinine (r=-0.626, P<0.001 and r=-0.649, P<0.001, respectively). VI_box moderately correlated with eGFR (r=0.445, P<0.001). Multivariate regression identified the urine albumincreatinine ratio (ACR) (adjusted odds ratio [aOR], 1.122; 95% confidence interval [CI], -0.007 to, 0.000; P=0.030) and VI_F2 (aOR, 1.114; 95% CI, 0.466 to 1.235; P<0.001) were independently associated with eGFR. Conclusion The VI measured by drawing a region of interest along the border of the transplanted kidney in SMI (VI_F2) is highly reproducible and correlates well with eGFR. Both VI_F2 and ACR independently predict eGFR.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Essential medical care is a concept referring to indispensable medical care. World Health Organization has defined essential healthcare services as the evidence-based technologies needed to cost-effectively solve health problems. However, most countries have no such term that describes essential healthcare services. Rather, it has been used to efficiently allocate medical resources in countries with limited resources. Therefore, a clear definition of essential healthcare services must be accompanied by a specific purpose and a clear direction for the need. The target to whom medical services are to be provided, the region and institution to be provided, the content of medical services, and the purpose of provision should be distinguished. Recently, as public interest in essential healthcare services has increased, the government announced “the Essential Healthcare Policy Package” and “the 1st Healthcare Reform Implementation Plan” to specifically promote it. But concerns of feasibility and financial estimates are being raised. So, here, I analyze the government policy package and its 1st implantation plan, and suggest policy proposals for them.