Asia, which holds 60% of the world’s population, comprises some developing countries which are in economic transition. This paper reviews the epidemiology of stroke in South, East and South-East Asia. Data on the epidemiology of stroke in South, East, and South-East Asia were derived from the Global Burden of Disease study (mortality, disability-adjusted life-years [DALYs] lost because of stroke), World Health Organization (vascular risk factors in the community), and publications in PubMed (incidence, prevalence, subtypes, vascular risk factors among hospitalized stroke patients). Age- and sex-standardized mortality is the lowest in Japan, and highest in Mongolia. Community-based incidence data of only a few countries are available, with the lowest rates being observed in Malaysia, and the highest in Japan and Taiwan. The availability of prevalence data is higher than incidence data, but different study methods were used for case-finding, with different age bands. For DALYs, Japan has the lowest rates, and Mongolia the highest. For community, a high prevalence of hypertension is seen in Mongolia and Pakistan; diabetes mellitus in Papua New Guinea, Pakistan, and Mongolia; hypercholesterolemia in Japan, Singapore, and Brunei; inactivity in Malaysia; obesity in Brunei, Papua New Guinea, and Mongolia; tobacco smoking in Indonesia. Hypertension is the most frequent risk factor, followed by diabetes mellitus and smoking. Ischemic stroke occurs more frequently than hemorrhagic stroke, and subarachnoid hemorrhages are uncommon. There are variations in the stroke epidemiology between countries in South, East, and South-East Asia. Further research on stroke burden is required.
Asia* ; Brunei ; Cerebrovascular Disorders ; Developing Countries ; Diabetes Mellitus ; Epidemiology* ; Hypercholesterolemia ; Hypertension ; Incidence ; Indonesia ; Japan ; Malaysia ; Mongolia ; Mortality ; Obesity ; Pakistan ; Papua New Guinea ; Prevalence ; Risk Factors ; Singapore ; Smoke ; Smoking ; Stroke* ; Subarachnoid Hemorrhage ; Taiwan ; World Health Organization

On page 287, “The lowest rates are observed in Japan (43.4/1,000,000 person-years and Singapore (47.9/100,000 person-years), followed by Bangladesh, Papua New Guinea, and Bhutan.” sentence should be corrected.

This study was designed to evaluate the capacity of fibrin sealant to mediate the repair of critical size defects of rat calvaria. Twenty-four rats were randomized into four groups(n=6) and an 8-mm circular calvarial defect was made in each rat. In Group I and II, as control groups, defects were left untreated and evaluated at postoperative 6th and 12th week, respectively. In Group III and IV, defects were treated with fibrin sealant implantation and evaluated at the same point of time as in Group I and II, respectively. Results were examined by gross and histologic findings, simple radiographic study, and radiodensitometric analysis for bony density quantitatively. By gross and simple radiographic findings, animals with fibrin sealant implantation generally showed thick coverage of defects and focal radioopacity within defect area, but non-treated animals showed only fibrous healing. Histologically, small amount of new appositional bone growth was only seen at the edge of the defect at 6th week in Group III, but in Group IV, there are substantial amounts of new immature bone with well-organized patterns within the defect at 12th week postoperatively. In the radiodensitometric analysis, there was no statistically significant difference between Group I and Group III at 6th week. Compared with Group II, however, Group IV showed significant bony healing (p=0.0225) at 12th week. In conclusion, the fibrin sealant appeared to be an effective mediator in bone regeneration of this critical-sized calvarial defect model, but long-term implantation period should be needed for optimal results.
Animals ; Bone Development ; Bone Regeneration ; Fibrin Tissue Adhesive* ; Fibrin* ; Rats* ; Skull*

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.

The trial of external quality assessment for inborn error of metabolism was performed in 2003. A total 10 specimens for neonatal screening tests were distributed to 43 laboratories with a response rate of 83%. All the control materials were sent as a filter paper form. Each laboratory replied the test result as the screening items they were doing as a rountine test at the reception of the specimen among PKU screening, neonatal TSH, neonatal T4(total/free), galactosemia screen, homocytinuria screen and histidinemia screen. The mean, SD, and CV were analyzed.
Galactosemias ; Infant, Newborn ; Korea* ; Mass Screening ; Metabolism* ; Neonatal Screening

The cannabinoid (CB2) receptor type 2 has been proposed to prevent the degeneration of dopamine neurons in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated mice. However, the mechanisms underlying CB2 receptor-mediated neuroprotection in MPTP mice have not been elucidated. The mechanisms underlying CB2 receptor-mediated neuroprotection of dopamine neurons in the substantia nigra (SN) were evaluated in the MPTP mouse model of Parkinson's disease (PD) by immunohistochemical staining (tyrosine hydroxylase, macrophage Ag complex-1, glial fibrillary acidic protein, myeloperoxidase (MPO), and CD3 and CD68), real-time PCR and a fluorescein isothiocyanate-labeled albumin assay. Treatment with the selective CB2 receptor agonist JWH-133 (10 μg kg⁻¹, intraperitoneal (i.p.)) prevented MPTP-induced degeneration of dopamine neurons in the SN and of their fibers in the striatum. This JWH-133-mediated neuroprotection was associated with the suppression of blood-brain barrier (BBB) damage, astroglial MPO expression, infiltration of peripheral immune cells and production of inducible nitric oxide synthase, proinflammatory cytokines and chemokines by activated microglia. The effects of JWH-133 were mimicked by the non-selective cannabinoid receptor WIN55,212 (10 μg kg⁻¹, i.p.). The observed neuroprotection and inhibition of glial-mediated neurotoxic events were reversed upon treatment with the selective CB2 receptor antagonist AM630, confirming the involvement of the CB2 receptor. Our results suggest that targeting the cannabinoid system may be beneficial for the treatment of neurodegenerative diseases, such as PD, that are associated with glial activation, BBB disruption and peripheral immune cell infiltration.
1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine* ; Animals ; Blood-Brain Barrier ; Chemokines ; Cytokines ; Dopamine* ; Dopaminergic Neurons* ; Fluorescein ; Glial Fibrillary Acidic Protein ; Macrophages ; Mice ; Microglia ; Neurodegenerative Diseases ; Neuroprotection ; Nitric Oxide Synthase Type II ; Parkinson Disease* ; Peroxidase ; Real-Time Polymerase Chain Reaction ; Receptor, Cannabinoid, CB2* ; Receptors, Cannabinoid ; Substantia Nigra