The application of infrared thermography in evaluation of patients at high risk for lower extremity peripheral arterial disease

ObjectiveWe investigated the usefulness of infrared thermography in evaluating patients at high risk for lower extremity peripheral arterial disease (PAD), including severity, functional capacity, and quality of life.MethodsA total of 51 patients (23 males; age 70 ± 9.8 years) were recruited. They completed three PAD-associated questionnaires, including walking impairment, vascular quality of life, and 7-day physical activity recall questionnaires before a 6-minute walking test (6MWT). Ankle-brachial index (ABI) and segmental pressure were analyzed for PAD diagnosis and stenotic level assessment. The cutaneous temperature at shin and sole were recorded by infrared thermography before and after the walk test. Detailed demographic information and medication list were obtained.ResultsTwenty-eight subjects had abnormal ABI (ABI <1), while PAD was diagnosed in 20. No subjects had non-compressible artery (ABI >1.3). Demographic profiles and clinical parameters in PAD and non-PAD patients were similar, except for age, smoking history, and hyperlipidemia. PAD patients walked shorter distances (356 ± 102 m vs 218 ± 92 m; P < .001). Claudication occurred in 14 patients, while seven failed in completing the 6MWT. The rest temperatures were similar in PAD and non-PAD patients. However, the post-exercise temperature dropped in the lower extremities with arterial stenosis, but was maintained or elevated slightly in the extremities with patent arteries (temperature changes at sole in PAD vs non-PAD patients: −1.25 vs −0.15°C; P < .001). The exercise-induced temperature changes at the sole were not only positively correlated with the 6MWD (Spearman correlation coefficient = 0.31, P = .03), but was also correlated with ABI (Spearman correlation coefficient = 0.48, P < .001) and 7-day physical activity recall scores (Spearman correlation coefficient = 0.30, P = .033).ConclusionBy detecting cutaneous temperature changes in the lower extremities, infrared thermography offers another non-invasive, contrast-free option in PAD evaluation and functional assessment

Cell volume restriction by mercury chloride reduces M1-like inflammatory response of bone marrow-derived macrophages

Dysregulation of macrophages in the pro-inflammatory (M1) and anti-inflammatory (M2) sub-phenotypes is a crucial element in several inflammation-related diseases and injuries. We investigated the role of aquaporin (AQP) in macrophage polarization using AQP pan-inhibitor mercury chloride (HgCl2). Lipopolysaccharides (LPSs) induced the expression of AQP-1 and AQP-9 which increased the cell size of bone marrow-derived macrophages. The inhibition of AQPs by HgCl2 abolished cell size changes and significantly suppressed M1 polarization. HgCl2 significantly reduced the activation of the nuclear factor kappa B (NF-κB) and p38 mitogen-activated protein kinase (MAPK) pathways and inhibited the production of IL-1β. HgCl2 attenuated LPS-induced activation of mitochondria and reactive oxygen species production and autophagy was promoted by HgCl2. The increase in the light chain three II/light chain three I ratio and the reduction in PTEN-induced kinase one expression suggests the recycling of damaged mitochondria and the restoration of mitochondrial activity by HgCl2. In summary, the present study demonstrates a possible mechanism of the AQP inhibitor HgCl2 in macrophage M1 polarization through the restriction of cell volume change, suppression of the p38 MAPK/NFκB pathway, and promotion of autophagy

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0

Campaign Investigation of Ionospheric Plasma Irregularities in Sporadic E Region Using FORMOSAT-3/COSMIC Satellite and Chung-Li 30 MHz Coherent Radar

In this article, we present an electron density profile retrieved from total electron density estimated from the difference in phase path excess between GPS frequencies L1 and L2 measured by the FORMOSAT-3/COSMIC satellite, in which the radio occultation inversion technique is employed for retrieval. Except for a regular F layer peak located at a height of about 290 km and a minor peak centered at a height of 140 km, a pronounced sporadic E layer was observed at a height of about 105 km. This intense electron density layer with thickness of about 10 km has very sharp boundaries on the top and bottom sides with scale lengths of -22 and 13 km, respectively. At the time when COSMIC GPS radio occultation took place in the vicinity of Taiwan, the Chung-Li 30 MHz coherent radar detected strong backscatter from 5-meter plasma irregularities. The peak radar backscatter is situated at a height of about 110 km in the topside of the Es layer with a very steep electron density gradient. Interferometry measurement made by the four separate and independent receiving channels of the Chung-Li 30 MHz radar indicates that the configuration of the large scale plasma structure constituted by 5-meter scale field-aligned irregularities is patch-like, and a 2-minute oscillation in zonal displacement of the plasma structure was found. From the temporal displacement of the echo patterns from the plasma irregularities in the bottom side of the layer, the plasma structure in the bottom side of the Es layer was found to move westward at a trace velocity of about 6.2 ms-1. The exceedingly small drift velocity combined with the relatively large scale length of the electron density gradient seem to suggest that the 5-meter plasma irregularities are very unlikely generated through the non-linear cascade process of the large plasma structure at kilometer scale induced by gradient drift instability. Moreover, in light of the fact that both the observed drift velocity (less than 15 m s-1) of the kilometer-meter scale plasma wave and the measured Doppler velocity (about 50 m s-1) of the 5-m plasma irregularities are much smaller than the 280 m s-1 that is required to directly excite plasma waves through gradient drift instability, it suggests that the 5-m plasma irregularities observed by the Chung-Li 30 MHz radar are very unlikely the result of direct excitation through gradient drift instability

Tau PET With 18F-THK-5351 Taiwan Patients With Familial Alzheimer's Disease With the APP p.D678H Mutation

Background: Brain 18F-AV-45 amyloid positron emission tomography (PET) in Taiwanese patients with familial Alzheimer's disease with the amyloid precursor protein (APP) p.D678H mutation tends to involve occipital and cerebellar cortical areas. However, tau pathology in patients with this specific Taiwan mutation remains unknown. In this study, we aimed to study the Tau PET images in these patients.Methods: Clinical features, brain magnetic resonance imaging/computed tomography (MRI/CT), and brain 18F-THK-5351 PET were recorded in five patients with the APP p.D678H mutation and correlated with brain 18F-AV-45 PET images. We also compared the tau deposition patterns among five patients with familial mild cognitive impairment (fMCI), six patients with sporadic amnestic mild cognitive impairment (sMCI), nine patients with mild to moderate dementia due to Alzheimer's disease (AD), and 12 healthy controls (HCs). All of the subjects also received brain 18F-AV-45 PET.Results: The nine patients with sAD and six patients with sMCI had a positive brain AV-45 PET scans, while the 12 HCs had negative brain AV-45 PET scans. All five patients with fMCI received a tau PET scan with the age at onset ranging from 46 to 53 years, in whom increased standard uptake value ratio (SUVR) of 18F-THK-5351 was noted in all seven brain cortical areas compared with the HCs. In addition, the SUVRs of 18F-THK-5351 were increased in the frontal, medial parietal, lateral parietal, lateral temporal, and occipital areas (P &lt; 0.001) in the patients with sAD compared with the HCs. The patients with fMCI had a significant higher SUVR of 18F-THK-5351 in the cerebellar cortex compared to the patients with sMCI. The correlations between regional SUVR and Mini-Mental State Examination score and between regional SUVR and clinical dementia rating (sum box) scores within volumes of interest of Braak stage were statistically significant.Conclusion: Tau deposition was increased in the patients with fMCI compared to the HCs. Increased regional SUVR in the cerebellar cortical area was a characteristic finding in the patients with fMCI. As compared between amyloid and tau PET, the amyloid deposition is diffuse, but tau deposition is limited to the temporal lobe in the patients with fMCI

Advanced Ionospheric Probe scientific mission onboard FORMOSAT-5 satellite

Advanced Ionospheric Probe (AIP) is a piggyback science payload developed by National Central University for FORMOSAT-5 satellite to explore space weather/climate and seismic precursors associated with strong earthquakes. The AIP is an all-in-one plasma sensor that measures ionospheric plasma concentrations, velocities, and temperatures in a time-sharing way and is capable of measuring ionospheric plasma irregularities at a sample rate up to 8192 Hz over a wide range of spatial scales. Electroformed gold grids used in the AIP in theory construct planar electric potential surfaces better than woven grids. Moreover, a plasma injection test performed in the Space Plasma Simulation Chamber has verified that no significant hysteresis is found in current-voltage curves measured by the AIP. It indicates that the AIP can make an accurate measurement of the ionospheric plasma parameters in space. Finally, Ionospheric Plasma and Electrodynamics Instrument (IPEI) observations onboard the ROCSAT-1 satellite are applied to show that the scientific objectives of ionospheric space weather/climate and seismo-ionospheric precursors (SIPs) of the FORMOSAT-5/AIP can be fulfilled. The observations reveal that ion parameter global distributions are helpful in studying the formation and variation in temperature crests and troughs in the 2200 - 2300 local time sector, as well as SIPs in the density and the velocity over the epicenter area, which are anticipated for the FORMOSAT-5 satellite orbit

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion