Publications by authors named "Jae-Hyun Shin"

Microbial communities play a significant role in maintaining ecosystems in a healthy homeostasis. Presently, in the human gastrointestinal tract, there are certain taxonomic groups of importance, though there is no single species that plays a keystone role. Bacteroides spp.

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Introduction: is the leading cause of healthcare-associated infections in the USA, with an estimated 1 billion dollars in excess cost to the healthcare system annually. infection (CDI) has high recurrence rate, up to 25% after first episode and up to 60% for succeeding episodes. Preliminary in vitro and in vivo studies indicate that alanyl-glutamine (AQ) may be beneficial in treating CDI by its effect on restoring intestinal integrity in the epithelial barrier, ameliorating inflammation and decreasing relapse.

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A millimeter-wave substrate-integrated waveguide (SIW) was firstly demonstrated using the micromachining of photoetchable glass (PEG) for 5G applications. A PEG substrate was used as a dielectric material of the SIW, and its photoetchable properties were used to fabricate through glass via (TGV) holes. Instead of the conventional metallic through glass via (TGV) array structures that are typically used for the SIW, two continuous empty TGV holes with metallized sidewalls connecting the top metal layer to the bottom ground plane were used as waveguide walls.

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Purpose: Although renal failure is a major healthcare burden globally and the cornerstone for preventing its irreversible progression is an early diagnosis, an adequate and noninvasive tool to screen renal impairment (RI) reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance.

Methods: This retrospective cohort study included two hospitals.

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Article Synopsis
  • - A deep learning model (DLM) was developed to screen for sepsis using electrocardiography (ECG) data from over 46,000 patients, with robust validation efforts involving multiple hospitals and ECG recordings.
  • - The DLM demonstrated strong accuracy, achieving an area under the curve (AUC) of up to 0.901 for detecting sepsis and 0.906 for septic shock using 12-lead ECGs, indicating its potential effectiveness in clinical settings.
  • - Results highlighted that specific ECG features, like the QRS complex and T waves, were critical for identifying sepsis, and the model was also able to predict in-hospital mortality in infected patients with a notable AUC of
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Background: is a serious problem for the aging population. Aged mouse model of infection (CDI) has emerged as a valuable tool to evaluate the mechanism of aging in CDI.

Methods: We reviewed five published studies utilizing aged mice (7-28 months) for CDI model for findings that may advance our understanding of how aging influences outcome from CDI.

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Background: Options for infection (CDI) refractory to conventional therapy are limited. Fecal microbiota transplant (FMT) is considered safe and effective treatment for recurrent CDI and could be a treatment option for refractory CDI. We investigated the efficacy and safety of FMT in hospitalized patients who were not responding to standard treatments for CDI.

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Article Synopsis
  • Early detection and intervention of arrhythmia is vital for effective treatment and reducing complications, prompting the development of an explainable deep learning model (XDM) for classification.
  • Utilizing a large dataset of 86,802 electrocardiograms (ECGs), the XDM was validated against external data from 36,961 ECGs to ensure its accuracy and explainability.
  • The XDM demonstrated high performance with area under the curve (AUC) scores of 0.976 and 0.966 during internal and external validation, respectively, indicating it could classify arrhythmias effectively while providing explanations for its classifications, enhancing its usefulness in clinical settings.
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Introduction: The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study.

Methods And Results: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study.

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Aims: Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study.

Methods And Results: This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist.

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Purpose Of Review: Clostridioides difficile infection (CDI) is a significant burden on the health system, especially due to high recurrence rates. Since the beginning of the CDI epidemic in early 2000s, many strategies for combatting recurrence have been explored, with moderate success so far. This review will focus on the most recent developments in recurrent CDI prevention and treatment.

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Fecal microbiota transplantation (FMT) has been shown to be an effective treatment for recurrent infections (rCDIs). We assessed the benefits of a multidisciplinary clinic for screening FMT eligibility in patients with rCDI. Patients seen at the University of Virginia Complicated Clinic (CCDC) underwent comprehensive evaluation for possible FMT.

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Background: Clostridium difficile infection (CDI) is a serious threat for an aging population. Using an aged mouse model, we evaluated the effect of age and the roles of innate immunity and intestinal microbiota.

Methods: Aged (18 months) and young (8 weeks) mice were infected with C difficile, and disease severity, immune response, and intestinal microbiome were compared.

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Clostridium difficile infection (CDI) is one of the most common causes of healthcare-associated infections but an even bigger problem for the aging population. Advanced age leads to higher incidence, higher mortality, and higher recurrences. In our study, recently published in the Journal of Infectious Diseases, we investigated the effect of aging on CDI using a mouse model.

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There are few studies of mobile-Health (mHealth) device application with schizophrenic patients. We aimed to quantitatively assess patient's activity and the relationship between their physical activity and the severity of their psychopathologies. Then we attempted to identify the patients who required intervention and evaluated the feasibility of using the mHealth device.

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Clostridium difficile is an anaerobic, Gram-positive, spore-forming, toxin-secreting bacillus that has long been recognized to be the most common etiologic pathogen of antibiotic-associated diarrhea. C. difficile infection (CDI) is now the most common cause of health care-associated infections in the United States and accounts for 12% of these infections (Magill SS et al.

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Background: The elderly host is highly susceptible to severe disease and treatment failure in Clostridium difficile infection (CDI). We investigated how treatment with vancomycin in the aged host influences systemic and intestinal humoral responses and select intestinal microbiota.

Methods: Young (age, 2 months) and aged (age, 18 months) C57BL/6 mice were infected with VPI 10463 after exposure to broad-spectrum antibiotics.

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Objectives: Clostridium difficile infection (CDI) is a primary cause of antibiotic-associated diarrhoeal illness. Current therapies are insufficient as relapse rates following antibiotic treatment range from 25% for initial treatment to 60% for treatment of recurrence. In this study, we looked at the efficacy of SQ641 in a murine model of CDI.

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Clostridium difficile infection (CDI) is the most common cause of antibiotic-associated diarrhea and a significant burden on the health care system. Aging has been identified in the literature as a risk factor for CDI as well as adverse outcome from CDI. Although this effect of advanced age on CDI could be partially explained by clinical factors associated with aging, biologic factors are important.

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Background: The incidence and clinical impact of coronavirus (CoV) infection in elderly persons and those with underlying cardiopulmonary disease over a long duration is not well described. We determined the incidence and clinical impact of 229E and OC43 CoV in this population during 4 consecutive winters, and compared illnesses to influenza A, respiratory syncytial virus, and human metapneumovirus.

Methods: CoV 229E and OC43 were detected by reverse transcription polymerase chain reaction and serology in 4 adult populations under surveillance for acute respiratory illness during the winters of 1999-2003.

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Synopsis of recent research by authors named "Jae-Hyun Shin"

  • - Jae-Hyun Shin's research primarily focuses on the interplay between gut microbiota, particularly Bacteroides species, and gastrointestinal health, highlighting their roles as key components in maintaining microbial balance and addressing conditions like Clostridioides difficile infection (CDI).
  • - His studies also explore innovative therapeutic approaches such as fecal microbiota transplants for treating refractory CDI and the potential benefits of alanyl-glutamine supplementation in restoring intestinal integrity.
  • - Additionally, Shin investigates the application of artificial intelligence and deep learning models using electrocardiography for early detection of various health issues, including renal impairment and arrhythmia, demonstrating a multidisciplinary approach that integrates microbiology and advanced technology.