Background: Extended-spectrum β-lactamase (ESBL)-producing Escherichia coli is among the most important multidrug-resistant pathogens causing bloodstream infections (BSIs).Cefotaximase (CTX-M) enzymes are the most common and highly diverse ESBL family in E.coli. CTX-M-15 in group CTX-M-1 and CTX-M-14 in group CTX-M-9 are the most extensively disseminated enzymes. Multidrug-resistant E. coli strains complicate empirical therapy and increase healthcare burden globally and in Korea. We investigated the molecular epidemiology, sequence types (STs), and ESBL genotypes of E. coli bloodstream isolates in Korea and identified clinical risk factors for cefotaxime resistance. Methods: We collected all non-duplicated isolates of E. coli and related clinical information from patients with BSIs at eight sentinel hospitals in the Korean Global Antimicrobial Resistance Surveillance System (Kor-GLASS) collection network during 2017–2021. Duplicate isolates were removed to ensure representativeness of the data. Antimicrobial susceptibility was tested using disk diffusion tests, and multilocus sequence typing and betalactamase genotyping were performed. Results: Among 9,232 E. coli blood isolates, resistance rates to cefotaxime and ceftazidime were 36.4% and 11.4%, respectively. Among the clinical factors, age > 65 yrs (adjusted odds ratio [aOR], 1.36), hospital-origin infection (aOR, 2.55), and admission type (intensive care unit [ICU] vs. general ward; aOR, 1.34) were significant cefotaxime resistance risk factors. ST131 was the most prevalent among cefotaxime-resistant E. coli (64.8%, 2,180/3,363), followed by ST1193 (5.3%, N = 177), and ST69 (5.1%, N = 170).ST131, ST648, ST405, and ST410 cefotaxime-resistant E. coli isolates frequently harbored blaCTX-M-15, whereas ST1193 and ST68 showed a high proportion of blaCTX-M-27 carriers, and most ST457 and ST5150 isolates carried blaCTX-M-55. Conclusions Continuous monitoring of ESBL-producing E. coli is required to prevent further dissemination, guide empirical therapy, inform infection control policies, and ensure early detection of multidrug-resistant clones with the potential for widespread transmission.

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

In recent years, next-generation sequencing (NGS)–based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

Background: Blepharitis, a chronic inflammatory condition of the eyelid margin, can cause significant discomfort and impair the quality of life. While traditional treatments like eyelid hygiene and medications are often insufficient, non-invasive therapies such as micro-vibration devices are gaining popularity despite minimal empirical validation. This study evaluated the efficacy and safety of a commercially available microvibration device in a rat model of blepharitis. Methods: Blepharitis was induced in rats using complete Freund’s adjuvant injections. The treatment groups included micro-vibration (180 Hz), micro-vibration with thermal enhancement, and pharmacological interventions with cyclosporine or hyaluronic acid. Ocular signs, including tear break-up time (TBUT), fluorescein staining, eyelid swelling, and telangiectasia, were assessed weekly. Immunohistochemical analysis quantified inflammatory cytokines (interleukin [IL]-1β, IL-6, and tumor necrosis factor-α) in ocular tissues. Results: The micro-vibration therapy significantly improved TBUT, reduced eyelid swelling, and lowered inflammatory cytokine levels. Combining thermal therapy with micro-vibration yielded enhanced antiinflammatory effects, though uncontrolled heating caused tissue damage. Pharmacological treatments were effective but less comprehensive compared to micro-vibration therapies. Conclusion Micro-vibration therapy at 180 Hz demonstrated substantial efficacy in alleviating symptoms of blepharitis and reducing inflammation. However, the uncontrolled thermal function of the device posed safety concerns, emphasizing the need for stringent regulation and parameter control in commercially available devices. This study highlights the potential of micro-vibration therapy as a non-invasive treatment for blepharitis while underscoring the importance of device safety.

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

Background and Objectives: Self-monitoring of blood pressure (SMBP) is a reliable method used to assess BP accurately. However, patients do not often know how to respond to the measured BP value. We developed a mobile application-based feed-back algorithm (SMBPApp) for tailored recommendations. In this study, we aim to evaluate whether SMBP-App is superior to SMBP alone in terms of BP reduction and drug adherence improvement in patients with hypertension. Methods: Self-Monitoring of blood pressure and Feed-back using APP in TReatment of UnconTrolled Hypertension (SMART-BP) is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App compared with SMBP alone. Patients with uncomplicated essential hypertension will be randomly assigned to the SMBP-App (90 patients) and SMBP alone (90 patients) groups. In the SMBP group, the patients will perform home BP measurement and receive the standard care, whereas in the SMBP-App group, the patients will receive additional recommendations from the application in response to the obtained BP value. Follow-up visits will be scheduled at 12 and 24 weeks after randomization. The primary endpoint of the study is the mean home systolic BP. The secondary endpoints include the drug adherence, the home diastolic BP, home and office BP. Conclusions SMART-BP is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App. If we can confirm its efficacy, SMBP-App may be scaled-up to improve the treatment of hypertension.Trial Registration: ClinicalTrials.gov Identifier: NCT04470284

Background: As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. Results: In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. Conclusions This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

PURPOSE: Discovery of models predicting the exact prognosis of epithelial ovarian cancer (EOC) is necessary as the first step of implementation of individualized treatment. This study aimed to develop nomograms predicting treatment response and prognosis in EOC. MATERIALS AND METHODS: We comprehensively reviewed medical records of 866 patients diagnosed with and treated for EOC at two tertiary institutional hospitals between 2007 and 2016. Patients’ clinico-pathologic characteristics, details of primary treatment, intra-operative surgical findings, and survival outcomes were collected. To construct predictive nomograms for platinum sensitivity, 3-year progression-free survival (PFS), and 5-year overall survival (OS), we performed stepwise variable selection by measuring the area under the receiver operating characteristic curve (AUC) with leave-one-out cross-validation. For model validation, 10-fold cross-validation was applied. RESULTS: The median length of observation was 42.4 months (interquartile range, 25.7 to 69.9 months), during which 441 patients (50.9%) experienced disease recurrence. The median value of PFS was 32.6 months and 3-year PFS rate was 47.8% while 5-year OS rate was 68.4%. The AUCs of the newly developed nomograms predicting platinum sensitivity, 3-year PFS, and 5-year OS were 0.758, 0.841, and 0.805, respectively. We also developed predictive nomograms confined to the patients who underwent primary debulking surgery. The AUCs for platinum sensitivity, 3-year PFS, and 5-year OS were 0.713, 0.839, and 0.803, respectively. CONCLUSION: We successfully developed nomograms predicting treatment response and prognosis of patients with EOC. These nomograms are expected to be useful in clinical practice and designing clinical trials.
Area Under Curve ; Disease-Free Survival ; Humans ; Medical Records ; Nomograms ; Ovarian Neoplasms ; Platinum ; Prognosis ; Recurrence ; ROC Curve