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Background/aim: The dysregulation of matrix metalloproteinase (MMP) proteins has been reported to be involved in the etiology of pterygium. However, studies about the role of matrix metalloproteinase-11 (MMP-11) are lacking. This study is the first to examine the genomic role of MMP-11 in pterygium., Materials and Methods: The genotypes of MMP-11 rs738791, rs2267029, rs738792, and rs28382575 were determined in 140 pterygium cases and 280 non-pterygium controls by utilizing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and direct sequencing., Results: The genotypic frequencies of MMP-11 rs738792 TT, CT and CC were 40.0%, 50.7%, and 9.3% in the pterygium group, significantly different from those in the non-pterygium group (55.0%, 37.1%, and 7.9%, respectively; p for trend=0.0139). Specifically, individuals carrying the variants CT and CC had a 1.88- and 1.63-fold odds ratio of pterygium risk [95% confidence interval (CI)=1.22-2.89 and 0.77-3.44, p=0.0054 and 0.2834, respectively]. In the dominant model, individuals carrying CT+CC had significantly higher pterygium risk (odds ratio=1.83, 95%CI=1.21-2.77, p=0.0052). No association was found for other MMP-11 polymorphisms. Allelic analysis showed that MMP-11 rs738792 C allele was significantly associated with pterygium risk (odds ratio=1.48, 95%CI=1.08-2.01, p=0.0169). for the variant alleles of other MMP-11 polymorphisms were not associated with pterygium risk., Conclusion: The MMP-11 rs738792 genotypes can serve as a predictive marker for pterygium risk in Taiwanese. Additionally, elucidating the role of MMP-11 in the pathogenesis of pterygium could inform targeted therapies based on MMP-11 modulation., (Copyright © 2024 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Prostate cancer (PCa) is a multifactorial disease influenced by genetic, environmental, and immunological factors. Genetic polymorphisms in the interleukin-10 ( IL-10 ) gene have been implicated in PCa susceptibility, development, and progression. This study aims to assess the contributions of three IL-10 promoter single nucleotide polymorphisms (SNPs), A-1082G (rs1800896), T-819C (rs3021097), and A-592C (rs1800872), to the risk of PCa in Taiwan. The three IL-10 genotypes were determined using PCR-RFLP methodology and were evaluated for their contributions to PCa risk among 218 PCa patients and 436 non-PCa controls. None of the three IL-10 SNPs were significantly associated with the risks of PCa ( p all > 0.05) in the overall analyses. However, the GG at rs1800896 combined with smoking behavior was found to significantly increase the risk of PCa by 3.90-fold (95% confidence interval [95% CI] = 1.28-11.89, p = 0.0231). In addition, the rs1800896 AG and GGs were found to be correlated with the late stages of PCa (odds ratio [OR] = 1.90 and 6.42, 95% CI = 1.05-3.45 and 2.30-17.89, p = 0.0452 and 0.0003, respectively). The IL-10 promoter SNP, A-1082G (rs1800896), might be a risk factor for PCa development among smokers and those at late stages of the disease. These findings should be validated in larger and more diverse populations.

This study aims explore the feasibility of using neural network (NNs) and deep learning to diagnose three common respiratory diseases with few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all three diseases. They revealed 90% accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients' understanding of their conditions via a remote diagnosis. Unlike previous studies that have focused on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-processing.

Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low-dose computed tomography (LDCT) is widely employed on the chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs creates an onerous burden for radiologists. Some computer-aided diagnostic (CAD) tools can provide insight to the use of medical images for diagnosis and can augment diagnostic speed. However, due to the variation in the parameter settings across different patients, substantial discrepancies in image voxels persist. We found that different voxel sizes can create a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel sizes while making isotropic. We examined the ability of our method to differentiate between benign and malignant nodules. Using 11 features, a support vector machine (SVM) was trained on LDCT images using an isotropic voxel with a side length of 1.5 mm for 225 patients in-house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition, to furnish CAD tools for clinical application, future research including LDCT images from multi-centers is encouraged.

The use of deep learning methods for the automatic detection and quantification of bone metastases in bone scan images holds significant clinical value. A fast and accurate automated system for segmenting bone metastatic lesions can assist clinical physicians in diagnosis. In this study, a small internal dataset comprising 100 breast cancer patients (90 cases of bone metastasis and 10 cases of non-metastasis) and 100 prostate cancer patients (50 cases of bone metastasis and 50 cases of non-metastasis) was used for model training. Initially, all image labels were binary. We used the Otsu thresholding method or negative mining to generate a non-metastasis mask, thereby transforming the image labels into three classes. We adopted the Double U-Net as the baseline model and made modifications to its output activation function. We changed the activation function to SoftMax to accommodate multi-class segmentation. Several methods were used to enhance model performance, including background pre-processing to remove background information, adding negative samples to improve model precision, and using transfer learning to leverage shared features between two datasets, which enhances the model's performance. The performance was investigated via 10-fold cross-validation and computed on a pixel-level scale. The best model we achieved had a precision of 69.96%, a sensitivity of 63.55%, and an F1-score of 66.60%. Compared to the baseline model, this represents an 8.40% improvement in precision, a 0.56% improvement in sensitivity, and a 4.33% improvement in the F1-score. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Bone Scan Index (BSI) is an image biomarker for quantifying bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related research focus on binary classification in bone scintigraphy: having metastasis or none. Rare studies focus on pixel-wise segmentation. This study compares three advanced convolutional neural network (CNN) based models to explore bone segmentation on a dataset in-house. The best model is Mask R-CNN, which reaches the precision, sensitivity, and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, and 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability of clinical use on bone segmentation.

The five-year overall survival rate of patients without neck lymph node recurrence is over 50% higher than those with lymph node metastasis. This study aims to investigate the prognostic impact of computed tomogram (CT)-based radiomics on the outcome of metastatic neck lymph nodes in patients with head and neck cancer (HNC) receiving definitive radiotherapy or chemoradiotherapy for organ preservation. The pretreatment 18F-FDG PET/CT of 79 HNC patients was retrospectively analyzed with radiomics extractors. The imbalanced data was processed using two techniques: over-sampling and under-sampling, after which the prediction model was established with a machine learning model using the XGBoost algorithm. The imbalanced dataset strategies slightly decreased the specificity but greatly improved the sensitivity. To have a higher chance of predicting neck cancer recurrence, however, clinical data combined with CT-based radiomics provides the best prediction effect. The original dataset performed was as follows: accuracy = 0.76 ± 0.07, sensitivity = 0.44 ± 0.22, specificity = 0.88 ± 0.06. After we used the over-sampling technique, the accuracy, sensitivity, and specificity values were 0.80 ± 0.05, 0.67 ± 0.11, and 0.84 ± 0.05, respectively. Furthermore, after using the under-sampling technique, the accuracy, sensitivity, and specificity values were 0.71 ± 0.09, 0.73 ± 0.13, and 0.70 ± 0.13, respectively. The outcome of metastatic neck lymph nodes in patients with HNC receiving radiotherapy for organ preservation can be predicted based on the results of machine learning. This way, patients can be treated alternatively. A further external validation study is required to verify our findings.

The kinetics and the conversion features of two 3-component systems (A/B/N), based on the proposed new kinetic schemes of Mokbel and Mau et al, in which a visible LED is used to excite a copper complex to its excited triplet state (G*). The coupling of G* with iodonium salt and ethyl 4-(dimethylamino)benzoate (EDB) produces both free radical polymerization (FRP) of acrylates and the free radical promoted cationic polymerization (CP) of epoxides using various new copper complex as the initiator. Higher FRP and CP conversion can be achieved by co-additive of [B] and N, via the dual function of (i) regeneration [A], and (ii) generation of extra radicals. The interpenetrated polymer network (IPN) capable of initiating both FRP and CP in a blend of TMPTA and EPOX. The synergic effects due to CP include: (i) CP can increase viscosity limiting the diffusional oxygen replenishment; (ii) the cation also acts as a diluting agent for the IPN network, and (iii) the exothermic property of the CP. The catalytic cycle, synergic effects, and the oxygen inhibition are theoretically confirmed to support the experimental hypothesis. The measured results of Mokbel and Mau et al are well analyzed and matching the predicted features of our modeling., Competing Interests: The authors have read the journal’s policy and have the following competing interests: Jui-Teng Lin is the CEO of New Photon Corp. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

Background: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability., Methods: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient's survival 5 years after a cardiac event, i.e., two classes, either yes or no., Results: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%., Conclusions: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients' 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs.

This article presents, for the first time, the efficacy and curing depth analysis of photo-thermal dual polymerization in metal (Fe) polymer composites for 3D printing of a three-component (A/B/M) system based on the proposed mechanism of our group, in which the co initiators A and B are Irgacure-369 and charge-transfer complexes (CTC), respectively, and the monomer M is filled by Fe. Our formulas show the depth of curing (Zc) is an increasing function of the light intensity, but a decreasing function of the Fe and photoinitiator concentrations. Zc is enhanced by the additive [B], which produces extra thermal radical for polymerization under high temperature. The heat (or temperature) increase in the system has two components: (i) due to the light absorption of Fe filler and (ii) heat released from the exothermic photopolymerization of the monomer. The heat is transported to the additive (or co-initiator) [B] to produce extra radicals and enhance the monomer conversion function (CF). The Fe filler leads to a temperature increase but also limits the light penetration, leading to lower CF and Zc, which could be overcome by the additive initiator [B] in thick polymers. Optimal Fe for maximal CF and Zc are explored theoretically. Measured data are analyzed based on our derived formulas.

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians' final decisions.

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.

Kinetic equations for a modeling system with type-I radical-mediated and type-II oxygen-mediated pathways are derived and numerically solved for the photopolymerization efficacy and curing depth, under the quasi-steady state assumption, and bimolecular termination. We show that photopolymerization efficacy is an increasing function of photosensitizer (PS) concentration ( C 0 ) and the light dose at transient state, but it is a decreasing function of the light intensity, scaled by [ C 0 / I 0 ] 0.5 at steady state. The curing (or cross-link) depth is an increasing function of C 0 and light dose (time × intensity), but it is a decreasing function of the oxygen concentration, viscosity effect, and oxygen external supply rate. Higher intensity results in a faster depletion of PS and oxygen. For optically thick polymers (>100 um), light intensity is an increasing function of time due to PS depletion, which cannot be neglected. With oxygen inhibition effect, the efficacy temporal profile has an induction time defined by the oxygen depletion rate. Efficacy is also an increasing function of the effective rate constant, K = k '/ k T 0 . 5 , defined by the radical producing rate ( k ') and the bimolecular termination rate ( k T ). In conclusion, the curing depth has a non-linear dependence on the PS concentration, light intensity, and dose and a decreasing function of the oxygen inhibition effect. Efficacy is scaled by [ C 0 / I 0 ] 0.5 at steady state. Analytic formulas for the efficacy and curing depth are derived, for the first time, and utilized to analyze the measured pillar height in microfabrication. Finally, various strategies for improved efficacy and curing depth are discussed., (Copyright © 2019 Lin, Liu, Chen and Cheng.)

The kinetics and modeling of dual-wavelength (UV and blue) controlled photopolymerization confinement (PC) are presented and measured data are analyzed by analytic formulas and numerical data. The UV-light initiated inhibition effect is strongly monomer-dependent due to different C=C bond rate constants and conversion efficacies. Without the UV-light, for a given blue-light intensity, higher initiator concentration (C 10 ) and rate constant (k') lead to higher conversion, as also predicted by analytic formulas, in which the total conversion rate ( R T ) is an increasing function of C 1 and k'R, which is proportional to k'[gB 1 C 1 ] 0.5 . However, the coupling factor B 1 plays a different role that higher B 1 leads to higher conversion only in the transient regime; whereas higher B 1 leads to lower steady-state conversion. For a fixed initiator concentration C 10 , higher inhibitor concentration ( C 20 ) leads to lower conversion due to a stronger inhibition effect. However, same conversion reduction was found for the same H-factor defined by H 0 = [ b 1 C 10 - b 2 C 20 ]. Conversion of blue-only are much higher than that of UV-only and UV-blue combined, in which high C 20 results a strong reduction of blue-only-conversion, such that the UV-light serves as the turn-off (trigger) mechanism for the purpose of spatial confirmation within the overlap area of UV and blue light. For example, UV-light controlled methacrylate conversion of a glycidyl dimethacrylate resin is formulated with a tertiary amine co-initiator, and butyl nitrite. The system is subject to a continuous exposure of a blue light, but an on-off exposure of a UV-light. Finally, we developed a theoretical new finding for the criterion of a good material/candidate governed by a double ratio of light-intensity and concentration, [ I 20 C 20 ]/[ I 10 C 10 ].

Kinetics and analytical formulas for radical-mediated thiol-ene photopolymerization were developed in this paper. The conversion efficacy of thiol-ene systems was studied for various propagation to chain transfer kinetic rate-ratio (R K ), and thiol-ene concentration molar-ratio (R C ). Numerical data were analyzed using analytical formulas and compared with the experimental data. We demonstrated that our model for a thiol-acrylate system with homopolymerization effects, and for a thiol-norbornene system with viscosity effects, fit much better with the measured data than a previous model excluding these effects. The general features for the roles of R K and R C on the conversion efficacy of thiol (C T ) and ene (C V ) are: (i) for R K = 1, C V and C T have the same temporal profiles, but have a reversed dependence on R C ; (ii) for R K >> 1, C T are almost independent of R C ; (iii) for R K << 1, C V and C T have the same profiles and both are decreasing functions of the homopolymerization effects defined by k CV ; (iv) viscosity does not affect the efficacy in the case of R K >> 1, but reduces the efficacy of C V for other values of R K . For a fixed light dose, higher light intensity has a higher transient efficacy but a lower steady-state conversion, resulting from a bimolecular termination. In contrast, in type II unimolecular termination, the conversion is mainly governed by the light dose rather than its intensity. For optically thick polymers, the light intensity increases with time due to photoinitiator depletion, and thus the assumption of constant photoinitiator concentration (as in most previous models) suffers an error of 5% to 20% (underestimated) of the crosslink depth and the efficacy. Scaling law for the overall reaction order, defined by [A] m [B] n and governed by the types of ene and the rate ratio is discussed herein. The dual ratio (R K and R C ) for various binary functional groups (thiol-vinyl, thiol-acrylate, and thiol-norbornene) may be tailored to minimize side effects for maximal monomer conversion or tunable degree of crosslinking.

Optimal conditions for maximum efficacy of photoinitiated polymerization are theoretically presented. Analytic formulas are shown for the crosslink time, crosslink depth, and efficacy function. The roles of photoinitiator (PI) concentration, diffusion depth, and light intensity on the polymerization spatial and temporal profiles are presented for both uniform and non-uniform cases. For the type I mechanism, higher intensity may accelerate the polymer action process, but it suffers a lower steady-state efficacy. This may be overcome by a controlled re-supply of PI concentration during the light exposure. In challenging the conventional Beer⁻Lambert law (BLL), a generalized, time-dependent BLL (a Lin-law) is derived. This study, for the first time, presents analytic formulas for curing depth and crosslink time without the assumption of thin-film or spatial average. Various optimal conditions are developed for maximum efficacy based on a numerically-fit A-factor. Experimental data are analyzed for the role of PI concentration and light intensity on the gelation (crosslink) time and efficacy.

Background: Spontaneous intracranial hypotension (SIH) is often misdiagnosed, and can lead to severe complications. Conventional MR sequences show a limited ability to aid in this diagnosis. MR-based intracranial pressure (MR-ICP) may be able to detect changes of intracranial elastance and pressure., Purpose: To determine whether MR-ICP is able to differentiate SIH patients from normal subjects, improve diagnostic sensitivity, and provide an insight into the pathophysiology., Study Type: Prospective., Subjects: Twenty-eight SIH cases with orthostatic headache and 20 healthy volunteers., Field Strength/sequence: Cine phase-contrast MRI on a 1.5T scanner., Assessment: Intracranial elastance (IE) was derived from the ratio of the peak-to-peak cerebrospinal fluid (CSF) pressure gradient (PG csf-pp ) and intracranial volume change, obtained by summing all flows before each sequential cardiac frame., Statistical Tests: Student's t-test was used to compare the MR-ICP indexes and flow parameters between SIH patients and healthy volunteers (P < 0.01)., Results: The SIH patients with cervical epidural venous dilatation (EVD) had an IE of 0.121 ± 0.027 mmHg/cm/ml, significantly higher than that of the normal volunteers (0.085 ± 0.027 mmHg/cm/ml; P = 0.002). In contradistinction, the EVD-negative SIH patients, including four with no sign of CSF leaks, had significantly lower IE (0.055 ± 0.012 mmHg/cm/ml) compared with the normal volunteers and the EVD-positive group (P = 0.001, P < 0.001). The EVD-negative patients had significantly lower PG csf-pp (0.024 ± 0.007 mmHg/cm) compared with the normal volunteers and the EVD-positive group (0.035 ± 0.011 mmHg/cm, 0.040 ± 0.010 mmHg/cm; P = 0.003, P < 0.001). Additionally, the MRI flow study showed a significant decrease in transcranial inflow and outflow of SIH patients (P < 0.01)., Data Conclusion: We found that the MR-ICP method is potentially more sensitive than morphological MRI in the early diagnosis of SIH. Also, contrary to common belief, our results suggest that an abnormal craniospinal elastance might be the cause of SIH, instead of CSF leak., Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1255-1263., (© 2018 International Society for Magnetic Resonance in Medicine.)

Cardiac stroke volume (SV) is an essential hemodynamic indicator that can be used to assess whether the pump function of the heart is normal. Non-invasive SV measurement is currently performed using the impedance cardiography (ICG). In this technology, left ventricular ejection time (LVET) is an important parameter which can be determined from the ICG signals. However, the ICG signals are inherently susceptible to artificial noise interference, which leads to an inaccurate LVET measurement and then yields an error in the calculation of SV. Therefore, the goal of the study was to measure LVETs using both the transmission and reflection photoplethysmography (PPG), and to assess whether the measured LVET was more accurate by the PPG signal than the ICG signal. The LVET measured by the phonocardiography (PCG) was used as the standard for comparing with those by the ICG and PPG. The study recruited ten subjects whose LVETs were simultaneously measured by the ICG using four electrodes, the reflection PPG using neck sensors (PPG neck ) and the transmission PPG using finger sensors (PPG finger ). In each subject, ten LVETs were obtained from ten heartbeats selected properly from one-minute recording. The differences of the measured LVETs between the PCG and one of the ICG, PPG neck and PPG finger were -68.2 ± 148.6 ms, 4.8 ± 86.5 ms and -7.0 ± 107.5 ms, respectively. As compared with the PCG, both the ICG and PPG finger underestimated but the PPG neck overestimated the LVETs. Furthermore, the measured LVET by the PPG neck was the closest to that by the PCG. Therefore, the PPG neck may be employed to improve the LVET measurement in applying the ICG for continuous monitoring of SV in clinical settings.

Background: A dentist always checks a patient by using panoramic radiography (PR) initially. The measurement of the minimal distance (MD) between the alveolar crest and the mandibular canal (MC) superior border is critically important before the dental implant surgery, extraction of 3rd molar teeth or any surgery in the posterior area of minimal distance (MD). However, the image of MC is not always clear to identify., Objective: A software is needed for training dentists as well as a tool of demonstration to patients in clinics precisely and quickly. Moreover, it should be able to calculate the MD between the alveolar crest and the MC superior border before dental implant., Methods: A computer-aided software system to semi-automatically detect the MC and mental foramen (MF) in the PR with minimal human interference is proposed., Results: The result shows that the averaged relative error (RE) is 1.83% with a standard deviation of 2.31%., Conclusion: The results show that the proposed algorithm is able to detect the MC superior and inferior borders. This system has the potential to train young clinicians and to replace the manual work in measuring the MD between the alveolar crest and the MC superior border with a minimal human intervention.

Lung cancer has a very high prevalence of brain metastasis, which results in a poor clinical outcome. Up-regulation of a disintegrin and metalloproteinase 9 (ADAM9) in lung cancer cells is correlated with metastasis to the brain. However, the molecular mechanism underlying this correlation remains to be elucidated. Since angiogenesis is an essential step for brain metastasis, microarray experiments were used to explore ADAM9-regulated genes that function in vascular remodeling. The results showed that the expression levels of vascular endothelial growth factor A (VEGFA), angiopoietin-2 (ANGPT2), and tissue plasminogen activator (PLAT) were suppressed in ADAM9-silenced cells, which in turn leads to decreases in angiogenesis, vascular remodeling, and tumor growth in vivo. Furthermore, simultaneous high expression of ADAM9 and VEGFA or of ADAM9 and ANGPT2 was correlated with poor prognosis in a clinical dataset. These findings suggest that ADAM9 promotes tumorigenesis through vascular remodeling, particularly by increasing the function of VEGFA, ANGPT2, and PLAT.

In the last decade, cuffless blood pressure measurement technology has been widely studied because it could be applied to a wearable apparatus. Electrocardiography (ECG), photo-plethysmography (PPG), and phonocardiography are always used to detect the pulse transit time (PTT) because the changed tendencies of the PTT and blood pressure have a negative relationship. In this study, the PPG signal was replaced by the impedance plethysmography (IPG) signal and was used to detect the PTT. The placement and direction of the electrode array for the IPG measurement were discussed. Then, we designed an IPG ring that could measure an accurate IPG signal. Twenty healthy subjects participated in this study. The changes in blood pressure after exercise were evaluated through the changes of the PTT. The results showed that the change of the systolic pressure had a better relationship with the change of the PTT IPG than that of the PTT PPG ( r = 0.700 vs. r = 0.450). Moreover, the IPG ring with spot electrodes would be more suitable to develop with the wearable cuffless blood pressure monitor than the PPG sensor., Competing Interests: The authors declare no conflict of interest. The founding sponsor had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Objective: Analysis of the crosslink time, depth and efficacy profiles of UV-light-activated corneal collagen crosslinking (CXL)., Methods: A modeling system described by a coupled dynamic equations are numerically solved and analytic formulas are derived for the crosslinking time (T*) and crosslinking depth (z*). The z-dependence of the CXL efficacy is numerically produced to show the factors characterizing the profiles., Results: Optimal crosslink depth (z*) and maximal CXL efficacy (Ceff) have opposite trend with respective to the UV light intensity and RF concentration, where z* is a decreasing function of the riboflavin concentration (C0). In comparison, Ceff is an increasing function of C0 and the UV exposure time (for a fixed UV dose), but it is a decreasing function of the UV light intensity. CXL efficacy is a nonlinear increasing function of [C0/I0]-0.5 and more accurate than that of the linear theory of Bunsen Roscoe law. Depending on the UV exposure time and depth, the optimal intensity ranges from 3 to 30 mW/cm2 for maximal CXL efficacy. For steady state (with long exposure time), low intensity always achieves high efficacy than that of high intensity, when same dose is applied on the cornea., Conclusions: The crosslinking depth (z*) and the crosslinking time (T*) have nonlinear dependence on the UV light dose and the efficacy of corneal collagen crosslinking should be characterized by both z* and the efficacy profiles. A nonlinear scaling law is needed for more accurate protocol.

In clinics an accurate vessel segmentation method is important to quantize the vessel volume change with respect to time for artery elasticity measurement. This study proposes a modified version on 3D-expanded dynamic programming to find an optimal surface in a 3D matrix. The aim of this study is to discover the robustness against noises in measuring the cross-sectional area of the femoral artery on MRI datasets of ultra-endurance runners as accurately as possible. To do this, we use phantom images with different added noises and different image contrasts to find out the optimal parameters using grid search. The contrast between the vessel lumen and its background in phantom study is changed to simulate the real MRI dataset. We also add a plaque in phantom images to test the accuracy of the proposed algorithm in dealing pathologic cases. The phantom studies and grid search on selecting optimal parameters can offer an alternative way on parameter selection. In application to MRI, the accuracy is performed via comparisons between the manual tracings of experts and automated results. The mean relative error is 2.1 % ± 2.1 % on testing 11 MRI datasets (total 550 images). The phantom studies and grid search on selecting optimal parameters can offer an alternative way on parameter selection.

Background: The endothelial function has been proven to be an important factor in the pathogenesis of atherosclerosis, hypertension and heart failure. The flow-mediated vasodilation (FMD) of the peripheral artery is an endothelium-dependent function. Brachial-artery ultrasound scanning is the popular method for evaluating FMD. However, good technical training on ultrasonography is required for the user to obtain high-quality data. Therefore, the goal of this study was to propose a new method which only used a sphygmomanometer cuff to occlude the blood flow and record the vascular volume waveform (Vwave)., Results: We used this method to assess the FMD in the menstrual cycle for 26 volunteer females. All female subjects were evaluated two times (M: menstrual phase; F: luteal phase) in one menstrual cycle and for two cycles. In the first cycle, the FMD volume ratio in M was 101.9 ± 45.5 % and was higher in L, at 137.5 ± 62.1 % (p = 0.0032 versus M). In the second cycle, the FMD volume ratios in M and L were 91.4 ± 37.0 % and 124.0 ± 56.4 %, respectively (p < 0.001 vs. M)., Conclusions: Our results have confirmed those results in the study of Hametner et al. Blood pressure measurement and FMD assessment all used the same mechanic of digital blood pressure monitor, which makes our method suitable using at home.

Goal: The goal of this study is to present a modified pulse contour method to estimate the stroke volume (SV) based on an oscillometric sphygmomanometer., Methods: The pulse contour was extracted from the pulse signal of the cuff pressure. The characteristics of the brachial artery, as well as the compliance ( C(artery)) and time constant τ of the Windkessel model, could be determined and used to estimate the SV once the apparatus finished the blood pressure measurement., Results: Assessments of the SV by echocardiography and our method were carried out in 55 subjects. The change in the brachial arterial volume obtained by our method was significantly correlated with that of the two-dimensional ultrasound method (r(v) = 0.871). The estimated SV values by our method for male and female groups, SV(estimate), were also significantly correlated with the echocardiography results, SV(ref) (r(male) = 0.680, r(female) = 0.706 ). The Bland-Altman plot showed agreement between SV(ref) and SV(estimate), with all data points contained within the limits of agreement (± 2 SD). The mean difference and standard deviation (mean ± SD) were 0.101 ± 14.880 ml and 0.650 ± 11.990 ml for the male and female groups, respectively., Conclusion: The blood pressure, SV, and cardiac output were measured simultaneously, making our method well suited for home use., Significance: Our method was embedded in an oscillometric sphygmomanometer.

Long-term endurance exercise could increase activity of parasympathetic nervous and decrease activity of sympathetic nervous at rest. However, previous studies all focused on the effect of endurance training on heart rate variability (HRV) for athletes or sedentary subjects. In Taiwan, elementary school teachers teaching and processing the children's and administrative problems always stand and walk. They will sit down only when they review and correct the students' home work. Thus, the goal of this study was to elucidate the beneficial effect of moderate intensity exercise on relieving mental load of elementary school teachers. There were 20 participants in the exercise group and another 20 participants in the nonexercise group. The exercising teachers performed 12 weeks of moderate intensity exercise training for an average of 30 minutes per day, 3 times per week. HRV was measured before and after the 4th, 6th, and 12th weeks. The time and frequency domain parameters of HRV all had significant increases between the beginning and after 12 weeks of training. However, the time and frequency domain parameters of HRV in the nonexercise group had significant decreases between the beginning and after 12 weeks of training. The long-term moderate exercises can relieve mental load of elementary school teachers. Moreover, age was the considerable factor affecting HRV in this study.

Muscle fatigue is often monitored via the median frequency derived from the surface electromyography (sEMG) power spectrum during isometric contractions. The power spectrum of sEMG shifting toward lower frequencies can be used to quantify the electromanifestation of muscle fatigue. The dynamic sEMG belongs to a nonstationary signal, which will be affected by the electrode moving, the shift of the muscle, and the change of innervation zone. The goal of this study is to find a more sensitive and stable method in order to sense the progression of muscle fatigue in the local muscle during exercise in healthy people. Five male and five female volunteers participated. Each subject was asked to run on a multifunctional pedaled elliptical trainer for about 30 min, twice a week, and was recorded a total of six times. Three decomposed methods, discrete wavelet transform (DWT), empirical mode decomposition (EMD), and ensemble EMD (EEMD), were used to sense the progression of muscle fatigue. They compared with each other. Although the highest frequency components of sEMG by DWT, EMD, and EEMD have the better performance to sense the progression of muscle fatigue than the raw sEMG, the EEMD has the best performance to reduce nonstationary characteristics and noise of the dynamic sEMG.

We propose a fully automated algorithm that is able to select a discriminative feature set from a training database via sequential forward selection (SFS), sequential backward selection (SBS), and F-score methods. We applied this scheme to microcalcifications cluster (MCC) detection in digital mammograms for early breast cancer detection. The system was able to select features fully automatically, regardless of the input training mammograms used. We tested the proposed scheme using a database of 111 clinical mammograms containing 1,050 microcalcifications (MCs). The accuracy of the system was examined via a free response receiver operating characteristic (fROC) curve of the test dataset. The system performance for MC identifications was Az = 0.9897, the sensitivity was 92%, and 0.65 false positives (FPs) were generated per image for MCC detection.

An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.

Surface electromyography (sEMG) is an important measurement for monitoring exercise and fitness. A wireless Bluetooth transmission sEMG measurement system with a sampling frequency of 2 KHz is developed. Traditional muscle fatigue is detected from the median frequency of the sEMG power spectrum. The regression slope of the linear regression of median frequency is an important muscle fatigue index. As fatigue increases, the power spectrum of the sEMG shifts toward lower frequencies. The goal of this study is to evaluate the sensitivity of empirical mode decomposition (EMD) quantifying the electrical manifestations of the local muscle fatigue during exercising in health people. We also compared this method with the raw data and discrete wavelet transform (DWT). Five male and five female volunteers participated. Each subject was asked to run on a multifunctional pedaled elliptical trainer for about 30 minutes, twice a week, and there were a total of six recording times for each subject with a wireless EMG recording system. The results show that sensitivity of the highest frequency component of EMD is better than the highest frequency component of DWT, and raw data.

In this paper, an automated method to localise the right superficial femoral artery (SFA) and identify its boundary on magnetic resonance imaging (MRI) sequences without contrast medium injection is proposed. Some anatomical knowledge combined with the mathematical morphology is used to distinguish SFA from other vessels. Afterwards, the directional gradient, continuity and the local contrast are applied as features to identify the artery's boundary using dynamic programming. The accuracy analysis shows that the system has average unsigned errors 3.1 ± 3.1% on five sequences compared to experts' manual tracings.

This study proposes a fast 3D dynamic programming expansion to find a shortest surface in a 3D matrix. This algorithm can detect boundaries in an image sequence. Using phantom image studies with added uniform distributed noise from different SNRs, the unsigned error of this proposed method is investigated. Comparing the automated results to the gold standard, the best averaged relative unsigned error of the proposed method is 0.77% (SNR = 20 dB), and its corresponding parameter values are reported. We further apply this method to detect the boundary of the real superficial femoral artery (SFA) in MRI sequences without a contrast injection. The manual tracings on the SFA boundaries are performed by well-trained experts to be the gold standard. The comparisons between the manual tracings and automated results are made on 16 MRI sequences (800 total images). The average unsigned error rate is 2.4% (SD = 2.0%). The results demonstrate that the proposed method can perform qualitatively better than the 2D dynamic programming for vessel boundary detection on MRI sequences.

Background: Systematic aerobe training has positive effects on the compliance of dedicated arterial walls. The adaptations of the arterial structure and function are associated with the blood flow-induced changes of the wall shear stress which induced vascular remodelling via nitric oxide delivered from the endothelial cell. In order to assess functional changes of the common carotid artery over time in these processes, a precise measurement technique is necessary. Before this study, a reliable, precise, and quick method to perform this work is not present., Methods: We propose a fully automated algorithm to analyze the cross-sectional area of the carotid artery in MR image sequences. It contains two phases: (1) position detection of the carotid artery, (2) accurate boundary identification of the carotid artery. In the first phase, we use intensity, area size and shape as features to discriminate the carotid artery from other tissues and vessels. In the second phase, the directional gradient, Hough transform, and circle model guided dynamic programming are used to identify the boundary accurately., Results: We test the system stability using contrast degraded images (contrast resolutions range from 50% to 90%). The unsigned error ranges from 2.86% ± 2.24% to 3.03% ± 2.40%. The test of noise degraded images (SNRs range from 16 to 20 dB) shows the unsigned error ranging from 2.63% ± 2.06% to 3.12% ± 2.11%. The test of raw images has an unsigned error 2.56% ± 2.10% compared to the manual tracings., Conclusions: We have proposed an automated system which is able to detect carotid artery cross sectional boundary in MRI sequences during heart cycles. The accuracy reaches 2.56% ± 2.10% compared to the manual tracings. The system is stable, reliable and results are reproducible.

In this paper we propose a novel scheme able to automatically detect the intima and adventitia of both near and far walls of the common carotid artery in dynamic B-mode RF (radiofrequency) image sequences, with and without plaques. Via this automated system the lumen diameter changes along the heart cycle can be detected. Three image sequences have been tested and all results are compared to manual tracings made by two professional experts. The average errors for near and far wall detection are 0.058 mm and 0.067 mm, respectively. This system is able to analyze arterial plaques dynamically which is impossible to do manually due to the tremendous human workload involved.

The oscillometric method has been widely used to measure arterial systolic and diastolic blood pressures, but its potential for arterial blood flow measurements still remains to be explored. The aim of this study was to non-invasively determine arterial blood flow using an oscillometric blood flow measurement system. The system consists of a pneumatic elastic cuff, an air-pumping motor, a releaser valve, a pressure transducer, and an airflow meter. To build a non-linear cuff model, we measured airflow pumped into the pneumatic cuff and cuff pressure using an airflow meter and pressure transducer during the inflation period, respectively. During the deflation period, only the pressure transducer was used to record cuff pressure. Based on the cuff model, the oscillometric blood flow waveform was obtained by integrating the oscillometric pressure waveform. We compared arterial blood flow derived from the maximum amplitude of the oscillometric blood flow waveform with Doppler-measured blood flow calculated with the diameters and blood velocities of the brachial arteries in 32 subjects who underwent diagnostic evaluations for peripheral arterial embolism. A linear correlation coefficient of r = 0.716 was found between the oscillometry- and Doppler-based blood flow measurements in the 32 subjects. These results suggest that blood flow passing through the brachial artery can be quantified non-invasively using the oscillometric approach after appropriate calibration.

We propose a novel dual dynamic programming (DDP) technique for detecting intimal and adventitial layers of the common carotid artery of the B-mode sonographic images. This method embeds the anatomic knowledge into its structure so that the robustness against the speckles is increased. Moreover, it inherits the property of getting the optimal solution as the traditional dynamic programming (TDP). Our experimental study shows that the DDP technique achieves a detection performance comparable to manual tracing achieved by physicians. The results demonstrate that it has the potential to perform qualitatively better than applying TDP twice in intimal and adventitial layer detection on sonographic B-mode images.

Unlabelled: Atherosclerotic changes of the common carotid artery (CCA) may appear like irregularities of the intima-media thickness (abbreviated as IM roughness) in B-mode ultrasound. The purposes of the present study were to investigate whether the CCA IM roughness changes with age and differs between individuals presumed to be healthy and patients with coronary artery disease (CAD)., Methods and Results: Based on a new automatic identification (AI) program, the segmental intima-media thickness (IMT) and IM roughness of the CCA far wall were measured in B-mode images of 15 younger and 22 older volunteers presumed to be healthy, and 46 patients with known CAD. The mean and maximum CCA IMT were lowest in the young group (0.55+/-0.05; 0.65+/-0.06 mm), higher in the older group (0.77+/-0.16; 0.87+/-0.18 mm) and highest in CAD patients (0.88+/-0.18; 1.01+/-0.27 mm) (P<0.01 among all groups). The CCA IM roughness was greatest by far in the CAD patients (0.075+/-0.02 mm; P<0.01 vs. both groups) and increased only slightly with age (younger: 0.035+/-0.008 mm; older: 0.040+/-0.014 mm; P<0.05). Areas under the curve (AUC) of receiver-operating characteristic curves (ROC) predicting patients with CAD were higher for the CCA IM roughness (0.80) than for the mean (0.66) and maximum IMT (0.71)., Conclusions: It is suggested that the CCA IM roughness increases with age, and helps to discriminate between individuals presumed to be healthy and patients with manifest CAD better than the CCA mean and maximum IMT.

Objective: The process of atherosclerosis has been assessed increasingly by sonography in B-mode images of the carotid arteries. Carotid artery atherosclerosis is thought to be associated with major atherosclerotic diseases in other regions of the arterial bed, especially the coronary arteries. In addition to the intima-media thickening as an established surrogate parameter of the atherosclerotic wall process, changes of the sonographic morphologic pattern of the intima-media layer might reflect this process., Methods: After automatic identification of the intimal and adventitial boundaries of the distal common carotid artery wall, the gray level inhomogeneity of the circumscribed intima-media area was quantified. Older subjects without cardiovascular disease (mean +/- SD, 66.2 +/- 3.8 years; n = 43) were compared with young subjects without cardiovascular disease (24.4 +/- 5.3 years; n = 34) and patients with manifest coronary artery disease (65.0 +/- 9.2 years; n = 69)., Results: Patients with coronary artery disease had a significantly higher intima-media thickness (0.92 +/- 0.21 mm; P < .05) and wall inhomogeneity (4.56 +/- 0.97; P < .001) than the old group (0.86 +/- 0.15 mm and 3.83 +/- 0.62). Both parameters were lowest in the young group (0.56 +/- 0.05 mm and 2.53 +/- 0.76; P < .05 for both parameters and groups)., Conclusions: Measurement of gray level wall inhomogeneity of the intima-media layer of the common carotid artery may help discriminate between subjects with high and low atherosclerotic burden and may show age-related changes of the intima-media layer. This new quantitative parameter is thought to reflect the atherosclerotic wall process in addition to the intima-media thickness measurement.

This study presents an innovative automatic system for detecting the intima-media complex of the far wall of the common carotid artery by applying the snake techniques. Cohen's snake was modified and some criteria were added for our applications. In addition, the oscillating problem of using snakes was solved by properly choosing the time step from analysis of the frequency response of the filters. A time-diminishing gravity window, external forces, and a cost function assist the snake in selecting the optimal shape of intimal and adventitia layers. We compared the proposed snake and ziplock snake with respect to the manual extraction contour. The results show that the system can automatically detect the intimal and adventitial layers without any manual correction.