Convolutional neural networks (CNNs), a particular type of deep learning architecture, are positioned to become one of the most transformative technologies for medical applications. The aim of the ...current study was to evaluate the efficacy of deep CNN algorithm for the identification and classification of dental implant systems.A total of 5390 panoramic and 5380 periapical radiographic images from 3 types of dental implant systems, with similar shape and internal conical connection, were randomly divided into training and validation dataset (80%) and a test dataset (20%). We performed image preprocessing and transfer learning techniques, based on fine-tuned and pre-trained deep CNN architecture (GoogLeNet Inception-v3). The test dataset was used to assess the accuracy, sensitivity, specificity, receiver operating characteristic curve, area under the receiver operating characteristic curve (AUC), and confusion matrix compared between deep CNN and periodontal specialist.We found that the deep CNN architecture (AUC = 0.971, 95% confidence interval 0.963-0.978) and board-certified periodontist (AUC = 0.925, 95% confidence interval 0.913-0.935) showed reliable classification accuracies.This study demonstrated that deep CNN architecture is useful for the identification and classification of dental implant systems using panoramic and periapical radiographic images.
We report on the capability of polydopamine (PDA), a mimic of mussel adhesion proteins, as an electron gate as well as a versatile adhesive for mimicking natural photosynthesis. This work ...demonstrates that PDA accelerates the rate of photoinduced electron transfer from light‐harvesting molecules through two‐electron and two‐proton redox‐coupling mechanism. The introduction of PDA as a charge separator significantly increased the efficiency of photochemical water oxidation. Furthermore, simple incorporation of PDA ad‐layer on the surface of conducting materials, such as carbon nanotubes, facilitated fast charge separation and oxygen evolution through the synergistic effect of PDA‐mediated proton‐coupled electron transfer and the high conductivity of the substrate. Our work shows that PDA is an excellent electron acceptor as well as a versatile adhesive; thus, PDA constitutes a new electron gate for harvesting photoinduced electrons and designing artificial photosynthetic systems.
Nature as role model: Comparable to quinones that extract electrons from chlorophyll in the natural photosystem II, polydopamine (PDA) accelerates proton‐coupled electron transfer and enables efficient charge separation of Ru(bpy)32+. The introduction of PDA as an electron gate as well as a versatile adhesive significantly increases the efficiency of photochemical water oxidation.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic ...electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta‐analysis was performed. The risk of bias was assessed using the QUADAS‐2 tool. We included 47 studies: focusing on imaging data (n = 20) and non‐imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi‐layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.
Full text
Available for:
CMK, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The Montreal Cognitive Assessment (MoCA) is known to have discriminative power for patients with Mild Cognitive Impairment (MCI). Recently Cognitive Reserve (CR) has been introduced as a factor that ...compensates cognitive decline. We aimed to assess whether the MoCA reflects CR. Furthermore, we assessed whether there were any differences in the efficacy between the MoCA and the Mini-Mental State Examination (MMSE) in reflecting CR.
MoCA, MMSE, and the Cognitive Reserve Index questionnaire (CRIq) were administered to 221 healthy participants. Normative data and associated factors of the MoCA were identified. Correlation and regression analyses of the MoCA, MMSE and CRIq scores were performed, and the MoCA score was compared with the MMSE score to evaluate the degree to which the MoCA reflected CR.
The MoCA reflected total CRIq score (CRI; B = 0.076, P < 0.001), CRI-Education (B = 0.066, P < 0.001), and CRI-Working activity (B = 0.025, P = 0.042), while MMSE reflected total CRI (B = 0.044, P < 0.001) and CRI-Education (B = 0.049, P < 0.001) only. The MoCA differed from the MMSE in the reflection of total CRI (Z = 2.30).
In this study, we show that the MoCA score reflects CR more sensitively than the MMSE score. Therefore, we suggest that MoCA can be used to assess CR and early cognitive decline.
Accumulated clinical and biomedical evidence indicates that the gut microbiota and their metabolites affect brain function and behavior in various central nervous system disorders. This study was ...performed to investigate the changes in brain metabolites and composition of the fecal microbial community following injection of amyloid β (Aβ) and donepezil treatment of Aβ-injected mice using metataxonomics and metabolomics. Aβ treatment caused cognitive dysfunction, while donepezil resulted in the successful recovery of memory impairment. The Aβ + donepezil group showed a significantly higher relative abundance of Verrucomicrobia than the Aβ group. The relative abundance of 12 taxa, including Blautia and Akkermansia, differed significantly between the groups. The Aβ + donepezil group had higher levels of oxalate, glycerol, xylose, and palmitoleate in feces and oxalate, pyroglutamic acid, hypoxanthine, and inosine in brain tissues than the Aβ group. The levels of pyroglutamic acid, glutamic acid, and phenylalanine showed similar changes in vivo and in vitro using HT-22 cells. The major metabolic pathways in the brain tissues and gut microbiota affected by Aβ or donepezil treatment of Aβ-injected mice were related to amino acid pathways and sugar metabolism, respectively. These findings suggest that alterations in the gut microbiota might influence the induction and amelioration of Aβ-induced cognitive dysfunction via the gut–brain axis. This study could provide basic data on the effects of Aβ and donepezil on gut microbiota and metabolites in an Aβ-induced cognitive impairment mouse model.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
A self-calibrating bipartite viscosity sensor 1 for cellular mitochondria, composed of coumarin and boron-dipyrromethene (BODIPY) with a rigid phenyl spacer and a mitochondria-targeting unit, was ...synthesized. The sensor showed a direct linear relationship between the fluorescence intensity ratio of BODIPY to coumarin or the fluorescence lifetime ratio and the media viscosity, which allowed us to determine the average mitochondrial viscosity in living HeLa cells as ca. 62 cP (cp). Upon treatment with an ionophore, monensin, or nystatin, the mitochondrial viscosity was observed to increase to ca. 110 cP.
Full text
Available for:
IJS, KILJ, NUK, PNG, UL, UM
Abstract
We present the spectra of complex organic molecules (COMs) detected in HOPS 373SW with the Atacama Large Millimeter/submillimeter Array (ALMA). HOPS 373SW, which is a component of a ...protostellar binary with a separation of 1500au, has been discovered as a variable protostar by the JCMT transient monitoring survey with a modest (∼30%) brightness increase at submillimeter wavelengths. Our ALMA target-of-opportunity observation at ∼345 GHz for HOPS 373SW revealed extremely young chemical characteristics with strong deuteration of methanol. The dust continuum opacity is very high toward the source center, obscuring line emission from within 0.″03. The other binary component, HOPS 373NE, was detected only in C
17
O in our observation, implying a cold and quiescent environment. We compare the COM abundances relative to CH
3
OH in HOPS 373SW with those of V883 Ori, which is an eruptive disk object, as well as other hot corinos, to demonstrate the chemical evolution from envelope to disk. High abundances of singly, doubly, and triply deuterated methanol (CH
2
DOH, CHD
2
OH, and CD
3
OH) and a low CH
3
CN abundance in HOPS 373SW compared to other hot corinos suggest a very early evolutionary stage of HOPS 373SW in the hot corino phase. Since the COMs detected in HOPS 373SW would have been sublimated very recently from grain surfaces, HOPS 373SW is a promising place to study the surface chemistry of COMs in the cold prestellar phase before sublimation.
Activated macrophages are classified into two different forms: classically activated (M1) or alternatively activated (M2) macrophages. The presence of M1/M2 phenotypic polarization has also been ...suggested for microglia. Here, we report that the secreted protein lipocalin 2 (LCN2) amplifies M1 polarization of activated microglia. LCN2 protein (EC50 1 μg/ml), but not glutathione S‐transferase used as a control, increased the M1‐related gene expression in cultured mouse microglial cells after 8–24 h. LCN2 was secreted from M1‐polarized, but not M2‐polarized, microglia. LCN2 inhibited phosphorylation of STAT6 in IL‐4‐stimulated microglia, suggesting LCN2 suppression of the canonical M2 signaling. In the lipopolysaccharide (LPS)‐induced mouse neuroinflammation model, the expression of LCN2 was notably increased in microglia. Primary microglial cultures derived from LCN2‐deficient mice showed a suppressed M1 response and enhanced M2 response. Mice lacking LCN2 showed a markedly reduced M1‐related gene expression in microglia after LPS injection, which was consistent with the results of histological analysis. Neuroinflammation‐associated impairment in motor behavior and cognitive function was also attenuated in the LCN2‐deficient mice, as determined by the rotarod performance test, fatigue test, open‐field test, and object recognition task. These findings suggest that LCN2 is an M1‐amplifier in brain microglial cells.—Jang, E., Lee, S., Kim, J.‐H., Kim, J.‐H., Seo, J.‐W., Lee, W.‐H., Mori, K., Nakao, K., Suk, K. Secreted protein lipocalin‐2 promotes microglial M1 polarization. FASEB J. 27, 1176–1190 (2013). www.fasebj.org
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of ...this system for the diagnosis and prediction of periodontally compromised teeth (PCT).
Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python.
The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars.
We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.
The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases ...(NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi‐layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK‐3β), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell‐based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long‐term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.
Synopsis
MLnet is a multi‐layered network expansion model that finds proteins with pathophysiological relevance for groups of diseases. Application to four neurodegenerative diseases predicts multiple members of the insulin pathway as common modifiers.
MLnet uses data integration and a multi‐layered network expansion model to identify and prioritize for experimental testing proteins that affect pathophysiology across multiple diseases.
When applied to Alzheimer's disease, Huntington's disease, and spinocerebellar ataxia types 1 and 3, MLnet identifies multiple members of the insulin pathway, proteostasis machinery and microtubule apparatus as common modifiers.
The impact of the identified genes on neurodegenerative disease phenotypes is tested in Drosophila, human cell lines and mouse disease models.
MLnet is available at http://ssbio.cau.ac.kr/software/mlnet and can be used for any group of diseases.
MLnet is a multi‐layered network expansion model that finds proteins with pathophysiological relevance for groups of diseases. Application to four neurodegenerative diseases predicts multiple members of the insulin pathway as common modifiers.
Full text
Available for:
FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
PDF
