BACKGROUND/AIMS: Despite numerous studies on the relation of genetic polymorphisms with irritable bowel syndrome (IBS), the results still remain inconclusive. The aim of this study was to assess the possible association between SLC6A4 serotonin transporter gene linked polymorphic region (5-HTTLPR), ADRA2A -1291C>G, GNB3 825C>T, CCK1R intron 779T>C and TRPV1 945G>C polymorphisms and IBS based on Rome III criteria in Korea. METHODS: Study subjects were prospectively recruited from visitors to Seoul National University Bundang Hospital between July 2009 and January 2014. Ninety-nine IBS patients and 171 healthy controls were enrolled. Polymorphisms of above-mentioned 5 genes were genotyped. Serum serotonin from 101 participants was measured by ELISA and compared according to SLC6A4 5-HTTLPR polymorphisms and IBS subtypes. RESULTS: Regarding SLC6A4 5-HTTLPR polymorphism, L/L genotype was significantly associated with the total IBS, constipation predominant IBS (IBS-C) and mixture of diarrhea and constipation IBS (IBS-M) (adjusted OR: 4.35, 95% CI: 1.04-16.67; adjusted OR: 11.11, 95% CI: 1.69-50.00 and adjusted OR: 5.56, 95% CI: 1.05-33.33, respectively). Carrying ADRA2A -1291G allele was significantly associated with total IBS and diarrhea predominant IBS (adjusted OR: 3.37, 95% CI: 1.16-9.77 and adjusted OR: 5.64, 95% CI: 1.18-27.01, respectively). IBS-C patients showed reduced level of serum serotonin compared to controls and patients with diarrhea predominant IBS (50.2 ng/mL vs. 69.0 ng/mL and 92.9 ng/mL, P = 0.017 and P = 0.001, respectively). CONCLUSIONS: Genetic polymorphisms of SLC6A4 5-HTTLPR and ADRA2A -1291C>G could be one of the pathophysiological factors of IBS in Korea. Reduced serum serotonin shown in the IBS-C group suggested a role of serotonin in IBS, but large study is needed for confirming genotypic difference in serum serotonin level.
Alleles ; Constipation ; Diarrhea ; Enzyme-Linked Immunosorbent Assay ; Genotype ; Humans ; Introns ; Irritable Bowel Syndrome* ; Korea ; Polymorphism, Genetic ; Polymorphism, Single Nucleotide ; Prospective Studies ; Receptors, Adrenergic ; Seoul ; Serotonin ; Serotonin Plasma Membrane Transport Proteins*

BACKGROUND: In most cases of a foreign body in the esophagus, an ENT specialist is consulted, which may be time consuming if not evaluated in a timely fashion. The patients are admitted to the hospital and sent to the operating room, where they are placed under anesthesia and the object is removed by using an esophagoscope. METHODS: A prospective randomized trial was conducted during the period from January 1998 to June 2000. All patients presenting to emergency department with blunt objects in the esophagus were included. In one group, with fluoroscopic guidance, a Foley catheter was placed to remove the blunt foreign bodies. And in the other group, we removed them by using the esophagoscpe. We used the t-test for statistical analysis in this study. RESULTS: Total number of patients enrolled in this study was 38. 22 patients were enrolled in the Foley Catheter removal group, and the remaining 16 were enrolled in the esophagoscope removal group. The success rate for the Foley catheter was 21/22(95.5%), and that for the esophagoscope was 15/16(93.8%). The average time of removal for the Foley catheter group was 0.70+/-0.28 hours while that for the esophagoscope group was 5.96+/-2.22 hours. One side effect, nonfatal hypoxia, was noted in the Foley catheter removal group. The average cost for the Foley catheter group were 78,800 won(approximately 60 US dollars) while that for the esophagoscope group took 722,800 won(approximately 600 US dollars). CONCLUSION: In our study, we found that the success rate for removing blunt foreign bodies from the esophagus by using a simple Foley catheter was high, also the Foley catheter was a time saving and cost effective procedure with an excellent safety profile.
Anesthesia ; Anoxia ; Catheters* ; Emergencies* ; Emergency Service, Hospital* ; Esophagoscopes ; Esophagus ; Foreign Bodies* ; Humans ; Operating Rooms ; Prospective Studies ; Specialization

Acute myeloid leukemia (AML) is a fatal hematological malignancy which is resistant to a variety of chemotherapy drugs. Extracellular signal-regulated kinase 5 (ERK5) plays a novel role in chemoresistance in some cancer cells and this pathway is a central mediator of cell survival and apoptotic regulation. The aim of this study was to investigate the effect of ERK5 inhibitor, XMD8-92, on proliferation and apoptosis in AML cell lines. Findings showed that XMD8-92 inhibited the activation of ERK5 by G-CSF and decreased the expression of c-Myc and Cyclin D1. The treatment of XMD8-92 reduced the phosphorylation of ERK5 leading to a distinct inhibition of cell proliferation and increased apoptosis in Kasumi-1 and HL-60 cells. Taken together, our study suggests that the inhibition of ERK5 by XMD8-92 can trigger apoptosis and inhibit proliferation in AMLs. Therefore, the inhibition of ERK5 may be an effective adjuvant in AML chemotherapy.
Apoptosis ; Cell Cycle ; Cell Line ; Cell Proliferation ; Cell Survival ; Cyclin D1 ; Drug Therapy ; Granulocyte Colony-Stimulating Factor ; Hematologic Neoplasms ; HL-60 Cells ; Humans ; Leukemia, Myeloid, Acute ; Mitogen-Activated Protein Kinase 7 ; Phosphorylation

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

Cerebral hyperperfusion syndrome (CHS) is a rare, serious complication of carotid revascularization either after carotid endarterectomy or carotid stent placement. Although extensive effort has been devoted to reducing the incidence of CHS, little is known about the prevention. Postprocedural hypertension is very rare due to autoregulation of carotid baroreceptors but may occur if presented with autonomic dysfunction. We present two cases of CHS after cerebral revascularization that presented autonomic dysfunction.
Angioplasty ; Cerebral Revascularization ; Endarterectomy, Carotid ; Homeostasis ; Humans ; Hypertension ; Incidence ; Pressoreceptors ; Stents

All-trans retinoic acid (ATRA) is a highly effective treatment for acute promyelocytic leukemia (APL), a cytogenetically distinct subtype of acute myeloid leukemia (AML). However, ATRA-based treatment is not effective in other subtypes of AML. In non-APL AML, ATRA signaling pathway is impaired or downmodulated, and consequently fails to respond to pharmacological doses of ATRA. Therefore, complementary treatment strategies are needed to improve ATRA responsiveness in non-APL AML. In this study, we investigated the combined effect of ATRA and bromodomain inhibitor JQ1, proven to have potent anti-cancer activity mainly through inhibition of c-Myc. We showed that the combination of ATRA with JQ1 synergistically inhibited proliferation of AML cells. The synergistic growth inhibition was resulted from differentiation or apoptosis depending on the kind of AML cells. Concomitantly, the combined treatment of ATRA and JQ1 caused greater depletion of c-Myc and hTERT expression than each agent alone in AML cells. Taken together, these findings support the rationale for the use of the combination of ATRA and JQ1 as a therapeutic strategy for the treatment of AML.
Apoptosis ; Leukemia ; Leukemia, Myeloid, Acute ; Leukemia, Promyelocytic, Acute ; Tretinoin