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من أين تأتي هذه البذرة ؟ : نمو النباتات
يتحدث هذا الكتاب من أين تأتي هذه البذرة ؟ عن نمو النباتات تأليف كيم يوون جيونج حيث عندما يأتي فصل الربيع، تذوب الأرض المجمدة وتنبت من التربة براعم ذات لون أخضر زاه بدءا ببذرة واحدة في الربيع تصنع مئات البذور وتتناثر بحلول نهاية فصل الخريف وخلف بذرة واحدة صلبة صغيرة تختبئ بذور أخر كثيرة وسر حياة النبات.
BOOK
PET imaging of neuroinflammation in neurological disorders
A growing need exists for reliable in-vivo measurement of neuroinflammation to better characterise the inflammatory processes underlying various diseases and to inform the development of novel therapeutics that target deleterious glial activity. PET is well suited to quantify neuroinflammation and has the potential to discriminate components of the neuroimmune response. However, there are several obstacles to the reliable quantification of neuroinflammation by PET imaging. Despite these challenges, PET studies have consistently identified associations between neuroimmune responses and pathophysiology in brain disorders such as Alzheimer's disease. Tissue studies have also begun to clarify the meaning of changes in PET signal in some diseases. Furthermore, although PET imaging of neuroinflammation does not have an established clinical application, novel targets are under investigation and a small but growing number of studies have suggested that this imaging modality could have a role in drug development. Future studies are needed to further improve our knowledge of the cellular mechanisms that underlie changes in PET signal, how immune response contributes to neurological disease, and how it might be therapeutically modified.
Journal Article
Metabolomics profiles associated with diabetic retinopathy in type 2 diabetes patients
Diabetic retinopathy (DR) is a common complication of diabetes, and it is the consequence of microvascular retinal changes due to high glucose levels over a long time. Metabolomics profiling is a rapidly evolving method used to identify the metabolites in biological fluids and investigate disease progression. In this study, we used a targeted metabolomics approach to quantify the serum metabolites in type 2 diabetes (T2D) patients. Diabetes patients were divided into three groups based on the status of their complications: non-DR (NDR, n = 143), non-proliferative DR (NPDR, n = 123), and proliferative DR (PDR, n = 51) groups. Multiple logistic regression analysis and multiple testing corrections were performed to identify the significant differences in the metabolomics profiles of the different analysis groups. The concentrations of 62 metabolites of the NDR versus DR group, 53 metabolites of the NDR versus NPDR group, and 30 metabolites of the NDR versus PDR group were found to be significantly different. Finally, sixteen metabolites were selected as specific metabolites common to NPDR and PDR. Among them, three metabolites including total DMA, tryptophan, and kynurenine were potential makers of DR progression in T2D patients. Additionally, several metabolites such as carnitines, several amino acids, and phosphatidylcholines also showed a marker potential. The metabolite signatures identified in this study will provide insight into the mechanisms underlying DR development and progression in T2D patients in future studies.
Journal Article
Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review
Background
Medical education must adapt to different health care contexts, including digitalized health care systems and a digital generation of students in a hyper-connected world. The aims of this study are to identify and synthesize the values that medical educators need to implement in the curricula and to introduce representative educational programs.
Methods
An integrative review was conducted to combine data from various research designs. We searched for articles on PubMed, Scopus, Web of Science, and EBSCO ERIC between 2011 and 2017. Key search terms were “undergraduate medical education,” “future,” “twenty-first century,” “millennium,” “curriculum,” “teaching,” “learning,” and “assessment.” We screened and extracted them according to inclusion and exclusion criteria from titles and abstracts. All authors read the full texts and discussed them to reach a consensus about the themes and subthemes. Data appraisal was performed using a modified Hawker ‘s evaluation form.
Results
Among the 7616 abstracts initially identified, 28 full-text articles were selected to reflect medical education trends and suggest suitable educational programs. The integrative themes and subthemes of future medical education are as follows: 1) a humanistic approach to patient safety that involves encouraging humanistic doctors and facilitating collaboration; 2) early experience and longitudinal integration by early exposure to patient-oriented integration and longitudinal integrated clerkships; 3) going beyond hospitals toward society by responding to changing community needs and showing respect for diversity; and 4) student-driven learning with advanced technology through active learning with individualization, social interaction, and resource accessibility.
Conclusions
This review integrated the trends in undergraduate medical education in readiness for the anticipated changes in medical environments. The detailed programs introduced in this study could be useful for medical educators in the development of curricula. Further research is required to integrate the educational trends into graduate and continuing medical education, and to investigate the status or effects of innovative educational programs in each medical school or environment.
Journal Article
Impact of coronavirus disease 2019 on respiratory surveillance and explanation of high detection rate of human rhinovirus during the pandemic in the Republic of Korea
Background After the detection of the first case of coronavirus disease 2019 (COVID‐19) in South Korea on January 20, 2019, it has triggered three major outbreaks. To decrease the disease burden of COVID‐19, social distancing and active mask wearing were encouraged, reducing the number of patients with influenza‐like illness and altering the detection rate of influenza and respiratory viruses in the Korea Influenza and Respiratory Viruses Surveillance System (KINRESS). We examined the changes in respiratory viruses due to COVID‐19 in South Korea and virological causes of the high detection rate of human rhinovirus (hRV) in 2020. Methods We collected 52 684 oropharyngeal or nasopharyngeal swab samples from patients with influenza‐like illness in cooperation with KINRESS from 2016 to 2020. Influenza virus and other respiratory viruses were confirmed using real‐time RT‐PCR. The weekly detection rate was used to compare virus detection patterns. Results Non‐enveloped virus (hRV, human bocavirus, and human adenovirus) detection rates during the COVID‐19 pandemic were maintained. The detection rate of hRV significantly increased in 2020 compared with that in 2019 and was negatively correlated with number of COVID‐19‐confirmed cases in 2020. The distribution of strains and genetic characteristics in hRV did not differ between 2019 and 2020. Conclusions The COVID‐19 pandemic impacted the respiratory virus detection rate. The extremely low detection rate of enveloped viruses resulted from efforts to prevent the spread of COVID‐19 in South Korea. The high detection rate of hRV may be related to resistance against environmental conditions as a non‐enveloped virus and the long period of viral shedding from patients.
Journal Article
Detection of Fusarium solani using cutinase antibody and its application in diagnosing fungal keratitis in an animal model
Fungal keratitis (FK) is a sight-threatening infectious disease that can result in blindness if not appropriately treated. Although keratitis associated with filamentous fungi was rarely reported in the past, the incidence of fungal keratitis due to contact lens usage has increased. The clinical manifestations of fungal keratitis often resemble those of other corneal infections, potentially delaying accurate diagnosis and treatment. In this study, we developed a Fusarium -specific polyclonal peptide antibody targeting the cutinase of F. solani and evaluated its diagnostic potential for FK in an animal model. To assess the specificity of the cutinase antibody, we employed enzyme-linked immunosorbent assay (ELISA). The ELISA results demonstrated that the cutinase antibody specifically interacted with the cell lysates and conditioned media of F. solani . Additionally, an immunocytochemistry assay confirmed the specificity of the cutinase antibody for F. solani in samples co-cultured with human corneal epithelial (HCE) cells and other keratitis-causing agents. To validate our in vitro findings, FK animal models were established by infecting the corneas of BALB/c mice with F. solani . The cutinase antibody specifically detected Fusarium antigens in the tear-wash samples and eyeball lysates of FK mice. These results demonstrate that the cutinase antibody is highly specific to F. solani antigens, indicating its potential utility in developing an antibody-based diagnostic method for FK.
Journal Article
Building a Cardiovascular Disease Prediction Model for Smartwatch Users Using Machine Learning: Based on the Korea National Health and Nutrition Examination Survey
Smartwatches have the potential to support health care in everyday life by supporting self-monitoring of health conditions and personal activities. This paper aims to develop a model that predicts the prevalence of cardiovascular disease using health-related data that can be easily measured by smartwatch users. To this end, the data corresponding to the health-related data variables provided by the smartwatch are selected from the Korea National Health and Nutrition Examination Survey. To classify the prevalence of cardiovascular disease with these selected variables, we apply logistic regression, artificial neural network, and support vector machine among machine learning classification techniques, and compare the appropriateness of the algorithm through classification performance indicators. The prediction model using support vector machine showed the highest accuracy. Next, we analyze which structures or parameters of the support vector machine contribute to increasing accuracy and derive the importance of input variables. Since it is very important to diagnose cardiovascular disease early correctly, we expect that this model will be very useful if there is a tool to predict whether cardiovascular disease develops or not.
Journal Article
Anti-necroptotic effects of human Wharton’s jelly-derived mesenchymal stem cells in skeletal muscle cell death model via secretion of GRO-α
Human mesenchymal stem cells (hMSCs) have therapeutic applications and potential for use in regenerative medicine. However, the use of hMSCs in research and clinical medicine is limited by a lack of information pertaining to their donor-specific functional attributes. In this study, we compared the characteristics of same-donor derived placenta (PL) and Wharton’s jelly (WJ)-derived hMSCs, we also compared their mechanism of action in a skeletal muscle disease in vitro model. The same-donor-derived hWJ- and hPL-MSCs exhibited typical hMSC characteristics. However, GRO-α was differentially expressed in hWJ- and hPL-MSCs. hWJ-MSCs, which secreted a high amount of GRO-α, displayed a higher ability to inhibit necroptosis in skeletal muscle cells than hPL-MSCs. This demonstrates the anti-necroptotic therapeutic effect of GRO-α in the skeletal muscle cell death model. Furthermore, GRO-α also exhibited the anti-necroptotic effect in a Duchenne muscular dystrophy (DMD) mouse model. Considering their potential to inhibit necroptosis in skeletal muscle cells, hWJ-MSCs and the derived GRO-α are novel treatment options for skeletal muscle diseases such as DMD.
Journal Article
Superconductivity emerging from a stripe charge order in IrTe2 nanoflakes
Superconductivity in the vicinity of a competing electronic order often manifests itself with a superconducting dome, centered at a presumed quantum critical point in the phase diagram. This common feature, found in many unconventional superconductors, has supported a prevalent scenario in which fluctuations or partial melting of a parent order are essential for inducing or enhancing superconductivity. Here we present a contrary example, found in IrTe
2
nanoflakes of which the superconducting dome is identified well inside the parent stripe charge ordering phase in the thickness-dependent phase diagram. The coexisting stripe charge order in IrTe
2
nanoflakes significantly increases the out-of-plane coherence length and the coupling strength of superconductivity, in contrast to the doped bulk IrTe
2
. These findings clarify that the inherent instabilities of the parent stripe phase are sufficient to induce superconductivity in IrTe
2
without its complete or partial melting. Our study highlights the thickness control as an effective means to unveil intrinsic phase diagrams of correlated van der Waals materials.
Superconductivity often appears due to suppression of competing electronic orders. Here, the authors present a contrary example showing a superconducting dome inside the parent phase with a stripe charge order in IrTe
2
nanoflakes and identify their unusual superconducting properties.
Journal Article
Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke
We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715–0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.
Journal Article