Inhibition of Urban Particulate Matter-Induced Airway Inflammation by RIPK3 through the Regulation of Tight Junction Protein Production.

Urban particulate matter (UPM) is a high-hazard cause of various diseases in humans, including in the respiratory tract, skin, heart, and even brain. Unfortunately, there is no established treatment for the damage caused by UPM in the respiratory epithelium. In addition, although RIPK3 is known to induce necroptosis, its intracellular role as a negative regulator in human lungs and bronchial epithelia remains unclear. Here, the endogenous expression of RIPK3 was significantly decreased 6 h after exposure to UPM. In RIPK3-ovexpressed cells, RIPK3 was not moved to the cytoplasm from the nucleus. Interestingly, the overexpression of RIPK3 dramatically decreased TEER and F-actin formation. Its overexpression also decreased the expression of genes for pro-inflammatory cytokines (IL-6 and IL-8) and tight junctions (ZO-1, -2, -3, E-cadherin, and claudin) during UPM-induced airway inflammation. Importantly, overexpression of RIPK3 inhibited the UPM-induced ROS production by inhibiting the activation of iNOS and eNOS and by regulating mitochondrial fission processing. In addition, UPM-induced activation of the iκB and NF-κB signaling pathways was dramatically decreased by RIPK3, and the expression of pro-inflammatory cytokines was decreased by inhibiting the iκB signaling pathway. Our data indicated that RIPK3 is essential for the UPM-induced inflammatory microenvironment to maintain homeostasis. Therefore, we suggest that RIPK3 is a potential therapeutic candidate for UPM-induced pulmonary inflammation.

Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis.

Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature's relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio-based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy.

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning.

This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related information, making traditional acoustic emission features, such as count and peaks, less effective. To obtain leak-related features, first, acoustic images were obtained from the time series acoustic emission signals using continuous wavelet transform. The acoustic images (AE images) were the wavelet scalograms that represent the time-frequency scales of the acoustic emission signal in the form of an image. The acoustic images carried enough information about the leak, as the leak-related information had a high-energy representation in the scalogram compared to the noise. To extract leak-related discriminant features from the acoustic images, they were provided as input into the convolutional autoencoder and convolutional neural network. The convolutional autoencoder extracts global features, while the convolutional neural network extracts local features. The local features represent changes in the energy at a finer level, whereas the global features are the overall characteristics of the acoustic signal in the acoustic image. The global and local features were merged into a single feature vector. To identify the pipeline leak state, the feature vector was fed into a shallow artificial neural network. The proposed method was validated by utilizing a data set obtained from the industrial pipeline testbed. The proposed algorithm yielded a high classification accuracy in detecting leaks under different leak sizes and fluid pressures.

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory.

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection.

This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.

Down-regulation of diesel particulate matter-induced airway inflammation by the PDZ motif peptide of ZO-1.

Although diesel airborne particulate matter (PM2.5) has been known to play a role in many human diseases, there is no direct evidence that therapeutic drugs or proteins can diminish PM2.5-induced diseases. Nevertheless, studies examining the negative control mechanisms of PM2.5-induced diseases are critical to develop novel therapeutic medications. In this study, the consensus PDZ peptide of ZO-1 inhibited PM2.5-induced inflammatory cell infiltration, pro-inflammatory cytokine gene expression, and TEER in bronchoalveolar lavage (BAL) fluid and AM cells. Our data indicated that the PDZ domain in ZO-1 is critical for regulation of the PM2.5-induced inflammatory microenvironment. Therefore, the PDZ peptide may be a potential therapeutic candidate during PM-induced respiratory diseases.

Mortality and Prognostic Prediction in Very Elderly Patients With Severe Pneumonia.

INTRODUCTION: Although prognostic prediction scores for pneumonia such as CURB-65 score or pneumonia severity index (PSI) are widely used, there were a few studies in very elderly patients. The aim of the study was to validate prognostic prediction scores for severe pneumonia and investigate risk factors associated with in-hospital mortality of severe pneumonia in very elderly patients. METHODS: During the 6-year study period (from October 2012 to May 2018), 160 patients aged 80 or older admitted to medical intensive unit were analyzed retrospectively. Pneumonia severity was evaluated using CURB-65 score, PSI, Sequential Organ Failure Assessment (SOFA) scores, A-DROP, I-ROAD, UBMo index, SOAR score, and lactate. The outcome was in-hospital mortality. RESULTS: The median age was 85 years (interquartile range: 82-88). Nursing home residents accounted for 71 (44.4%) and in-hospital mortality was 40 (25.0%). Logistic regression showed that chronic lung, mechanical ventilation, hemodialysis, and albumin were associated with in-hospital mortality of pneumonia. Using the receiver operating characteristics curve for predicting mortality, the area under the curve in pneumonia was 0.65 for the SOFA score, 0.61 for the CURB-65 score, 0.52 for the PSI, 0.58 for the A-DROP, 0.52 for the I-ROAD, 0.54 for UBMo index, 0.59 for SOAR score, and 0.65 for lactate. CONCLUSION: The performances of the CURB-65 and PSI are not excellent in very elderly patients with pneumonia. Further studies are needed to improve the performance of prognostic prediction scores in elderly patients.

Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-Based Deep Neural Network.

Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis models that have been developed for classifying various fault types in gearboxes. However, the classification results of the conventional fault classification models degrade when they are applied to gearbox systems with multi-level tooth cut gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in discriminating the gear fault types. Due to the improved computational capabilities of modern systems, the application of deep neural networks (DNNs) is getting popular in a variety of research fields, such as image and natural language processing. DNNs are capable of improving the classification results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this research, an adaptive noise control (ANC) and a stacked sparse autoencoder-based deep neural network (SSA-DNN) are used to construct a sensitive fault diagnosis model that can diagnose a gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicatedness. An ANC is applied to gear vibration characteristics to remove a significant level of noise along the frequency spectrum of vibration signals to fix the most fault-informative components of each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault-informative components to separate the fault types considered in this study. Furthermore, the implementation of the SSA-DNN is substituted for feature extraction, feature selection, and the classification processes in traditional fault diagnosis schemes by high-performance unity. The experimental results show that the proposed model outperforms conventional methodologies with higher classification accuracy.

Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems.

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time-frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

Activation of the Nrf2/HO-1 signaling pathway contributes to the protective effects of baicalein against oxidative stress-induced DNA damage and apoptosis in HEI193 Schwann cells.

Baicalein, a flavonoid extracted from the roots of Scutellaria baicalensis Georgi., has various pharmacological effects due to its high antioxidant activity. However, no study has yet been conducted on the protective efficacy of baicalein against oxidative stress in Schwann cells. In this study, we evaluated the protective effect of baicalein on DNA damage and apoptosis induced by hydrogen peroxide (H2O2) in HEI193 Schwann cells. For this purpose, HEI193 cells exposed to H2O2 in the presence or absence of baicalein were applied to cell viability assay, immunoblotting, Nrf2-specific small interfering RNA (siRNA) transfection, comet assay, and flow cytometry analyses. Our results showed that baicalein effectively inhibited H2O2-induced cytotoxicity and DNA damage associated with the inhibition of reactive oxygen species (ROS) accumulation. Baicalein also weakened H2O2-induced mitochondrial dysfunction, increased the Bax/Bcl-2 ratio, activated caspase-9 and -3, and degraded poly(ADP-ribose) polymerase. In addition, baicalein increased not only the expression but also the phosphorylation of nuclear factor-erythroid 2 related factor 2 (Nrf2) and promoted the expression of heme oxygenase-1 (HO-1), a critical target enzyme of Nrf2, although the expression of kelch-like ECH-associated protein-1 was decreased. However, the inhibition of Nrf2 expression by transfection with Nrf2-siRNA transfection abolished the expression of HO-1 and antioxidant potential of baicalein. These results demonstrate that baicalein attenuated H2O2-induced apoptosis through the conservation of mitochondrial function while eliminating ROS in HEI193 Schwann cells, and the antioxidant efficacy of baicalein implies at least a Nrf2/HO-1 signaling pathway-dependent mechanism. Therefore, it is suggested that baicalein may have a beneficial effect on the prevention and treatment of peripheral neuropathy induced by oxidative stress.

The Effect of Hidden Female Smoking on the Association between Smoking and Asthma.

BACKGROUND: The association between smoking and asthma remains controversial. In our previous study, we were not able to show an association between asthma and smoking in Korean adults. One Korean study demonstrated underreporting of smoking history by female adults. The aim of the present study was to investigate the effects of hidden female smoking on the association between asthma and smoking in Korean adults. METHODS: Data were acquired from 23,483 men and women aged ≥19 years who participated in the Korean National Health and Nutrition Examination Surveys (KNHANES). RESULTS: The prevalence of asthma was 1.5%, the self-reported smoking prevalence was 32.4%, and the cotinine-verified smoking prevalence was 30.3%. Of the male cotinine-verified smokers, 6.4% were self-reported nonsmokers (2.7% never smokers and 3.7% ex-smokers), whereas 48.8% of the female cotinine-verified smokers were self-reported nonsmokers (43.2% never smokers and 5.6% ex-smokers). In males, the adjusted odds ratio of the association between smoking and asthma was 0.738 (confidence interval, CI: 0.393-1.173) in self-reported and 0.767 (CI: 0.515-1.143) in cotinine-verified smoking; in females, the adjusted odds ratio was 1.945 (CI: 1.348-2.805) in self-reported and 1.531 (CI: 1.076-2.179) in cotinine-verified smoking. CONCLUSIONS: Our study showed smoking was associated with asthma in females but not in males.

Association of maternal depression and allergic diseases in Korean children.

PURPOSE: An association between childhood allergic disease and parental psychological disorders has been reported. However, little is known about the relationship between childhood allergic disease and maternal depression. This study was performed to determine if there is an association between childhood allergic disease and maternal depression. METHODS: Data were acquired from 13,782 subjects (8091 mothers) ages < 18 years who participated in the Korea National Health and Nutrition Examination Surveys, which was conducted from 2007 to 2014. The presence of allergic disease (asthma or atopic dermatitis) and depression was based on self-reported physician diagnosis in the health interview surveys. RESULTS: Logistic regression analyses showed maternal depression was significantly associated with single marital status (separated or divorced, adjusted odds ratio [aOR] 2.204 [95% confidence interval {CI},1.256-3.867]), less than middle school graduated level (aOR 2.959 [95% CI, 1.514-5.781]), middle school graduated educational level (aOR 2.420 [95% CI, 1.355-4.319]), being a current smoker (aOR 2.257 [95% CI, 1.481-3.438]), being an ex-smoker (aOR 2.533 [95% CI, 1.506-4.260]), maternal asthma (aOR 2.786 [95% CI, 1.453-5.344]), and maternal hypertension (aOR 1.942 [95% CI, 1.106-3.408]). Maternal depression was significantly associated with children's asthma (aOR 2.028 [95% CI, 1.234-3.335]) and children's atopic dermatitis (aOR 1.755 [95% CI, 1.183-2.602]). CONCLUSION: The present study showed maternal depression might be a risk factor for the clinical diagnosis of asthma and atopic dermatitis in Korean children.

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

Immunoglobulin G Subclass Deficiencies in Adult Patients with Chronic Airway Diseases.

Immunoglobulin G subclass deficiency (IgGSCD) is a relatively common primary immunodeficiency disease (PI) in adults. The biological significance of IgGSCD in patients with chronic airway diseases is controversial. We conducted a retrospective study to characterize the clinical features of IgGSCD in this population. This study examined the medical charts from 59 adult patients with IgGSCD who had bronchial asthma or chronic obstructive pulmonary disease (COPD) from January 2007 to December 2012. Subjects were classified according to the 10 warning signs developed by the Jeffrey Modell Foundation (JMF) and divided into two patient groups: group I (n = 17) met ≥ two JMF criteria, whereas group II (n = 42) met none. IgG3 deficiency was the most common subclass deficiency (88.1%), followed by IgG4 (15.3%). The most common infectious complication was pneumonia, followed by recurrent bronchitis, and rhinosinusitis. The numbers of infections, hospitalizations, and exacerbations of asthma or COPD per year were significantly higher in group I than in group II (P < 0.001, P = 0.012, and P < 0.001, respectively). The follow-up mean forced expiratory volume (FEV1) level in group I was significantly lower than it was at baseline despite treatment of asthma or COPD (P = 0.036). In conclusion, IgGSCD is an important PI in the subset of patients with chronic airway diseases who had recurrent upper and lower respiratory infections as they presented with exacerbation-prone phenotypes, decline in lung function, and subsequently poor prognosis.

Clinical characteristics of pediatric patients infected with SARS-CoV-2 versus common human coronaviruses: a national multicenter study.

BACKGROUND: Human coronaviruses (HCoV) cause mild upper respiratory infections; however, in 2019, a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged, causing an acute respiratory disease pandemic. Coronaviruses exhibit marked epidemiological and clinical differences. PURPOSE: This study compared the clinical, laboratory, and radiographic findings of children infected with SARS-CoV-2 versus HCoV. METHODS: SARS-CoV-2 data were obtained from the Korea Disease Control and Prevention Agency (KDCA) registry and 4 dedicated coronavirus disease 2019 (COVID-19) hospitals. Medical records of children admitted with a single HCoV infection from January 2015 to March 2020 were collected from 10 secondary/tertiary hospitals. Clinical data included age, sex, underlying disease, symptoms, test results, imaging findings, treatment, and length of hospital stay. RESULTS: We compared the clinical characteristics of children infected with HCoV (n=475) to those of children infected with SARS-CoV-2 (272 from KDCA, 218 from COVID-19 hospitals). HCoV patients were younger than KDCA patients (older than 9 years:3.6% vs. 75.7%; P<0.001) and patients at COVID-19 hospitals (2.0±2.9 vs 11.3±5.3; P<0.001). Patients with SARS-CoV-2 infection had a lower rate of fever (26.6% vs. 66.7%; P<0.001) and fewer respiratory symptoms than those with HCoV infection. Clinical severity, as determined by oxygen therapy and medication usage, was worse in children with HCoV infection. Children and adolescents with SARS-CoV-2 had less severe symptoms. CONCLUSION: Children and adolescents with COVID-19 had a milder clinical course and less severe disease than those with HCoV in terms of symptoms at admission, examination findings, and laboratory and radiology results.

De novo transcriptome sequencing of Momordica cochinchinensis to identify genes involved in the carotenoid biosynthesis.

The ripe fruit of Momordica cochinchinensis Spreng, known as gac, is featured by very high carotenoid content. Although this plant might be a good resource for carotenoid metabolic engineering, so far, the genes involved in the carotenoid metabolic pathways in gac were unidentified due to lack of genomic information in the public database. In order to expedite the process of gene discovery, we have undertaken Illumina deep sequencing of mRNA prepared from aril of gac fruit. From 51,446,670 high-quality reads, we obtained 81,404 assembled unigenes with average length of 388 base pairs. At the protein level, gac aril transcripts showed about 81.5% similarity with cucumber proteomes. In addition 17,104 unigenes have been assigned to specific metabolic pathways in Kyoto Encyclopedia of Genes and Genomes, and all of known enzymes involved in terpenoid backbones biosynthetic and carotenoid biosynthetic pathways were also identified in our library. To analyze the relationship between putative carotenoid biosynthesis genes and alteration of carotenoid content during fruit ripening, digital gene expression analysis was performed on three different ripening stages of aril. This study has revealed putative phytoene synthase, 15-cis-phytone desaturase, zeta-carotene desaturase, carotenoid isomerase and lycopene epsilon cyclase might be key factors for controlling carotenoid contents during aril ripening. Taken together, this study has also made availability of a large gene database. This unique information for gac gene discovery would be helpful to facilitate functional studies for improving carotenoid quantities.

A hybrid technique for medical image segmentation.

Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.

HPV-18 is a poor prognostic factor, unlike the HPV viral load, in patients with stage IB-IIA cervical cancer undergoing radical hysterectomy.

OBJECTIVES: This study was conducted to determine the prognostic significance of the human papillomavirus (HPV) genotype using the HPV DNA chip (HDC) test and the HPV viral load by the hybrid capture II assay (HC2) in FIGO stage IB-IIA cervical cancer undergoing radical hysterectomy. METHODS: Between January 2001 and December 2005, 204 consecutive patients who underwent radical hysterectomy with pelvic lymphadenectomy for International Federation of Gynecology and Obstetrics (FIGO) stage IB1-IIA cervical cancer were retrospectively reviewed. The Cox proportional hazard models adjusted for covariates were used for analyses and a receiver operating characteristic (ROC) curve was used to determine the HPV viral load in predicting disease progression. RESULTS: Of the 204 cases, the HDC was positive in 195 (95.6%) and the HC2 was positive in 192 (94.1%). The 5-year progression-free survival (PFS) was 78.4%. On multivariate analysis, HPV-18 positivity was an independent prognostic factor predictive for disease progression. The risk of recurrence was higher for HPV-18 positivity (hazard ratio=2.664; 95% confidence interval [CI], 1.437-4.938; P=0.003). The 5-year PFS rate for patients who were HPV-18-negative was 83.8%, which was higher than the 5-year PFS for patients who were HPV-18-positive (54.1%; P<0.001). The area under the ROC curve for the HPV viral load was 0.550 (P=0.314; 95% CI, 0.455-0.644). CONCLUSIONS: The HPV-18 genotype is a reliable prognostic factor of early-stage cervical cancer; however, the HPV viral load may not be helpful in predicting disease prognosis.

Shoulder Pain Treated by Manual Acupuncture and Pharmacopuncture Following Origin/Insertion Technique of Applied Kinesiology: A Case Series of Two Patients.

The aim of this study was to report on the improvement of shoulder pain resulting from disorders of the rotator cuff such as impingement syndrome and adhesive capsulitis, by manual acupuncture (MA) and pharmacopuncture (PA) following origin/insertion technique (OIT) of applied kinesiology (AK). Two patients were treated with MA and PA after OIT on shoulder muscles. The Numerical Rating Scale and the assessment of the Japanese Orthopedic Association scores were used to assess the pain, and ultrasound images were taken to compare treatment outcome. This study showed that MA and PA following OIT may be an effective treatment for impingement syndrome and adhesive capsulitis.

The effect of hidden female smoking on the relationship between smoking and cardiovascular disease.

BACKGROUND: Smoking is a known risk factor for cardiovascular morbidity and mortality, but several Korean studies have shown differing results on the association of current smoking status and the risk of cardiovascular disease (CVD). The aim of the present study was to investigate the association between smoking status and CVD (myocardial infarction and stroke) using national representative populationbased samples. The aim was also to investigate the effects of hidden smokers on the association between CVD and smoking. METHODS: Data were acquired from 28,620 participants (12,875 men and 15,745 women), age 19 years or older, who participated in the Korea National Health and Nutrition Examination Survey (KNHANES) conducted from 2008 to 2016. RESULTS: The multivariable logistic regression analysis showed that ex-smoking status was correlated with CVD when self-reported (odds ratio [OR]: 1.62; 95% confidence interval [CI]: 1.20-2.19) and for survey-cotinine verified-smoking status (OR: 1.57; 95% CI: 1.20-2.19). Interestingly, the present study showed current smoking was not significantly associated with CVD. For the effect of sex on smoking and CVD, self-reported and survey-cotinine-verified ex-smoking status were correlated with CVD in males (OR: 1.45; 95% CI: 1.04-2.04 and OR: 1.43; 95% CI: 1.02-2.02) and in females (OR: 2.74; 95% CI: 1.59-4.71 and OR: 2.92; 95% CI: 1.64-5.18). The ratios of cotinine-verified to self-reported smoking rates were 1.95 for women and 1.08 for men. CONCLUSIONS: In the current study, while ex-smoking status was significantly associated with CVD, current smoking status was not. Female ex-smoking status had a higher adjusted odds ratio for CVD than males compared to non-smoking status. An effect of hidden female smoking was also found on the association between smoking status and CVD in Korean adults.