Particulate matter (PM), a major air pollutant, is a complex mixture of solid and liquid particles of various sizes. PM has been demonstrated to cause intracellular inflammation in human ...keratinocytes, and is associated with various skin disorders, including atopic dermatitis, eczema, and skin aging. Resveratrol is a natural polyphenol with strong antioxidant properties, and its beneficial effects against skin changes due to PM remain elusive. Therefore, in the present study, we investigated the effect of resveratrol on PM-induced skin inflammation and attempted to deduce the molecular mechanisms underlying resveratrol's effects. We found that resveratrol inhibited PM-induced aryl hydrocarbon receptor activation and reactive oxygen species formation in keratinocytes. It also suppressed the subsequent cellular inflammatory response by inhibiting mitogen-activated protein kinase activation. Consequentially, resveratrol reduced PM-induced cyclooxygenase-2/prostaglandin E2 and proinflammatory cytokine expression, including that of matrix metalloproteinase (MMP)-1, MMP-9, and interleukin-8, all of which are known to be central mediators of various inflammatory conditions and aging. In conclusion, resveratrol inhibits the PM-induced inflammatory response in human keratinocytes, and we suggest that resveratrol may have potential for preventing air pollution-related skin problems.
The transmission mode of severe acute respiratory syndrome coronavirus 2 is primarily known as droplet transmission. However, a recent argument has emerged about the possibility of airborne ...transmission. On June 17, there was a coronavirus disease 2019 (COVID-19) outbreak in Korea associated with long distance droplet transmission.
The epidemiological investigation was implemented based on personal interviews and data collection on closed-circuit television images, and cell phone location data. The epidemic investigation support system developed by the Korea Disease Control and Prevention Agency was used for contact tracing. At the restaurant considered the site of exposure, air flow direction and velocity, distances between cases, and movement of visitors were investigated.
A total of 3 cases were identified in this outbreak, and maximum air flow velocity of 1.2 m/s was measured between the infector and infectee in a restaurant equipped with ceiling-type air conditioners. The index case was infected at a 6.5 m away from the infector and 5 minutes exposure without any direct or indirect contact.
Droplet transmission can occur at a distance greater than 2 m if there is direct air flow from an infected person. Therefore, updated guidelines involving prevention, contact tracing, and quarantine for COVID-19 are required for control of this highly contagious disease.
The risk of developing tuberculosis (TB) in allogeneic hematopoietic stem cell transplantation (HSCT) recipients is expected to be relatively high in an intermediate TB burden country. This ...single-center retrospective study was conducted to investigate risk factors and the incidence of TB after allogeneic HSCT.
From January 2004 to March 2011, 845 adult patients were enrolled. Starting April 2009, patients were given isoniazid (INH) prophylaxis based on interferon-γ release assay results. The incidence of TB was analyzed before and after April 2009, and compared it with that of the general population in Korea.
TB was diagnosed in 21 (2.49%) of the 845 allogeneic HSCT patients. The median time to the development of TB was 386 days after transplantation (range, 49-886). Compared with the general population, the standardized incidence ratio of TB was 9.10 (95% CI; 5.59-14.79). Extensive chronic graft-versus-host disease (GVHD) was associated with the development of TB (P = 0.003). Acute GVHD, conditioning regimen with total body irradiation and conditioning intensity were not significantly related. INH prophylaxis did not reduce the incidence of TB (P = 0.548). Among 21 TB patients, one patient had INH prophylaxis.
Allogeneic HSCT recipients especially those who suffer from extensive chronic GVHD are at a high risk of developing TB. INH prophylaxis did not statistically change the incidence of TB, however, further well-designed prospective studies are needed.
Although deep neural networks have shown promising results in the diagnosis of skin cancer, a prospective evaluation in a real-world setting could confirm these results. This study aimed to evaluate ...whether an algorithm (http://b2019.modelderm.com) improves the accuracy of nondermatologists in diagnosing skin neoplasms.
A total of 285 cases (random series) with skin neoplasms suspected of malignancy by either physicians or patients were recruited in two tertiary care centers located in South Korea. An artificial intelligence (AI) group (144 cases, mean SD age, 57.0 17.7 years; 62 43.1% men) was diagnosed via routine examination with photographic review and assistance by the algorithm, whereas the control group (141 cases, mean SD age, 61.0 15.3 years; 52 36.9% men) was diagnosed only via routine examination with a photographic review. The accuracy of the nondermatologists before and after the interventions was compared.
Among the AI group, the accuracy of the first impression (Top-1 accuracy; 58.3%) after the assistance of AI was higher than that before the assistance (46.5%, P = .008). The number of differential diagnoses of the participants increased from 1.9 ± 0.5 to 2.2 ± 0.6 after the assistance (P < .001). In the control group, the difference in the Top-1 accuracy between before and after reviewing photographs was not significant (before, 46.1%; after, 51.8%; P = .19), and the number of differential diagnoses did not significantly increase (before, 2.0 ± 0.4; after, 2.1 ± 0.5; P = .57).
In real-world settings, AI augmented the diagnostic accuracy of trainee doctors. The limitation of this study is that the algorithm was tested only for Asians recruited from a single region. Additional international randomized controlled trials involving various ethnicities are required.
The prevalence of age-related neurodegenerative diseases has risen in conjunction with an increase in life expectancy. Although there is emerging evidence that air pollution might accelerate or ...worsen dementia progression, studies on Asian regions remains limited. This study aimed to investigate the relationship between long-term exposure to PM
and the risk of developing Alzheimer's disease and vascular dementia in the elderly population in South Korea.
The baseline population was 1.4 million people aged 65 years and above who participated in at least one national health checkup program from the National Health Insurance Service between 2008 and 2009. A nationwide retrospective cohort study was designed, and patients were followed from the date of cohort entry (January 1, 2008) to the date of dementia occurrence, death, moving residence, or the end of the study period (December 31, 2019), whichever came first. Long-term average PM
exposure variable was constructed from national monitoring data considering time-dependent exposure. Extended Cox proportional hazard models with time-varying exposure were used to estimate hazard ratios (HR) for Alzheimer's disease and vascular dementia.
A total of 1,436,361 participants were selected, of whom 167,988 were newly diagnosed with dementia (134,811 with Alzheimer's disease and 12,215 with vascular dementia). The results show that for every 10 µg/m
increase in PM
, the HR was 0.99 (95% CI 0.98-1.00) for Alzheimer's disease and 1.05 (95% CI 1.02-1.08) for vascular dementia. Stratified analysis according to sex and age group showed that the risk of vascular dementia was higher in men and in those under 75 years of age.
The results found that long-term PM
exposure was significantly associated with the risk of developing vascular dementia but not with Alzheimer's disease. These findings suggest that the mechanism behind the PM
-dementia relationship could be linked to vascular damage.
Detection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can ...potentially lead to false-positive results.
To evaluate whether an algorithm can automatically locate suspected areas and predict the probability of a lesion being malignant.
Region-based convolutional neural network technology was used to create 924 538 possible lesions by extracting nodular benign lesions from 182 348 clinical photographs. After manually or automatically annotating these possible lesions based on image findings, convolutional neural networks were trained with 1 106 886 image crops to locate and diagnose cancer. Validation data sets (2844 images from 673 patients; mean SD age, 58.2 19.9 years; 308 men 45.8%; 185 patients with malignant tumors, 305 with benign tumors, and 183 free of tumor) were obtained from 3 hospitals between January 1, 2010, and September 30, 2018.
The area under the receiver operating characteristic curve, F1 score (mean of precision and recall; range, 0.000-1.000), and Youden index score (sensitivity + specificity -1; 0%-100%) were used to compare the performance of the algorithm with that of the participants.
The algorithm analyzed a mean (SD) of 4.2 (2.4) photographs per patient and reported the malignancy score according to the highest malignancy output. The area under the receiver operating characteristic curve for the validation data set (673 patients) was 0.910. At a high-sensitivity cutoff threshold, the sensitivity and specificity of the model with the 673 patients were 76.8% and 90.6%, respectively. With the test partition (325 images; 80 patients), the performance of the algorithm was compared with the performance of 13 board-certified dermatologists, 34 dermatology residents, 20 nondermatologic physicians, and 52 members of the general public with no medical background. When the disease screening performance was evaluated at high sensitivity areas using the F1 score and Youden index score, the algorithm showed a higher F1 score (0.831 vs 0.653 0.126, P < .001) and Youden index score (0.675 vs 0.417 0.124, P < .001) than that of nondermatologic physicians. The accuracy of the algorithm was comparable with that of dermatologists (F1 score, 0.831 vs 0.835 0.040; Youden index score, 0.675 vs 0.671 0.100).
The results of the study suggest that the algorithm could localize and diagnose skin cancer without preselection of suspicious lesions by dermatologists.
The diagnostic performance of convolutional neural networks (CNNs) for diagnosing several types of skin neoplasms has been demonstrated as comparable with that of dermatologists using clinical ...photography. However, the generalizability should be demonstrated using a large-scale external dataset that includes most types of skin neoplasms. In this study, the performance of a neural network algorithm was compared with that of dermatologists in both real-world practice and experimental settings.
To demonstrate generalizability, the skin cancer detection algorithm (https://rcnn.modelderm.com) developed in our previous study was used without modification. We conducted a retrospective study with all single lesion biopsied cases (43 disorders; 40,331 clinical images from 10,426 cases: 1,222 malignant cases and 9,204 benign cases); mean age (standard deviation SD, 52.1 18.3; 4,701 men 45.1%) were obtained from the Department of Dermatology, Severance Hospital in Seoul, Korea between January 1, 2008 and March 31, 2019. Using the external validation dataset, the predictions of the algorithm were compared with the clinical diagnoses of 65 attending physicians who had recorded the clinical diagnoses with thorough examinations in real-world practice. In addition, the results obtained by the algorithm for the data of randomly selected batches of 30 patients were compared with those obtained by 44 dermatologists in experimental settings; the dermatologists were only provided with multiple images of each lesion, without clinical information. With regard to the determination of malignancy, the area under the curve (AUC) achieved by the algorithm was 0.863 (95% confidence interval CI 0.852-0.875), when unprocessed clinical photographs were used. The sensitivity and specificity of the algorithm at the predefined high-specificity threshold were 62.7% (95% CI 59.9-65.1) and 90.0% (95% CI 89.4-90.6), respectively. Furthermore, the sensitivity and specificity of the first clinical impression of 65 attending physicians were 70.2% and 95.6%, respectively, which were superior to those of the algorithm (McNemar test; p < 0.0001). The positive and negative predictive values of the algorithm were 45.4% (CI 43.7-47.3) and 94.8% (CI 94.4-95.2), respectively, whereas those of the first clinical impression were 68.1% and 96.0%, respectively. In the reader test conducted using images corresponding to batches of 30 patients, the sensitivity and specificity of the algorithm at the predefined threshold were 66.9% (95% CI 57.7-76.0) and 87.4% (95% CI 82.5-92.2), respectively. Furthermore, the sensitivity and specificity derived from the first impression of 44 of the participants were 65.8% (95% CI 55.7-75.9) and 85.7% (95% CI 82.4-88.9), respectively, which are values comparable with those of the algorithm (Wilcoxon signed-rank test; p = 0.607 and 0.097). Limitations of this study include the exclusive use of high-quality clinical photographs taken in hospitals and the lack of ethnic diversity in the study population.
Our algorithm could diagnose skin tumors with nearly the same accuracy as a dermatologist when the diagnosis was performed solely with photographs. However, as a result of limited data relevancy, the performance was inferior to that of actual medical examination. To achieve more accurate predictive diagnoses, clinical information should be integrated with imaging information.
Background
Pigmented contact dermatitis (PCD), a rare variant of non‐eczematous contact dermatitis, is clinically characterized by sudden‐onset brown or grey pigmentation on the face and neck. It is ...hypothesized to be caused by repeated contact with low levels of allergens.
Objectives
This study evaluated the risk of using hair dyes in patients with PCD in Korea.
Methods
A total of 1033 PCD patients and 1366 controls from 31 university hospitals were retrospectively recruited. We collected and analysed the data from the patient group, diagnosed through typical clinical findings of PCD and the control group, which comprised age/sex‐matched patients who visited the participating hospitals with pre‐existing skin diseases other than current allergic disease or PCD.
Results
Melasma and photosensitivity were significantly more common in the control group, and a history of contact dermatitis was more common in the PCD group. There were significantly more Fitzpatrick skin type V participants in the PCD group than in the control group. There was no significant difference in sunscreen use between the groups. Using dermatologic medical history, Fitzpatrick skin type and sunscreen use as covariates, we showed that hair dye use carried a higher PCD risk (odds ratio OR before adjustment: 2.06, confidence interval CI: 1.60–2.65; OR after adjustment: 2.74, CI: 1.88–4.00). Moreover, henna users had a higher risk of PCD (OR before adjustment: 5.51, CI: 4.07–7.47; OR after adjustment: 7.02, CI: 4.59–10.74), indicating a significant increase in the risk of PCD with henna dye use. Contact dermatitis history was more prevalent in henna users than in those using other hair dyes in the PCD group (17.23% vs. 11.55%).
Conclusion
Hair dye use is a risk factor for PCD. The risk significantly increased when henna hair dye was used by those with a history of contact dermatitis.
Objectives
Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection ...of post-biopsy pneumothorax in chest radiographs (CRs), in consecutive cohorts reflecting actual clinical situation.
Methods
We retrospectively included post-biopsy CRs of 1757 consecutive patients (1055 men, 702 women; mean age of 65.1 years) undergoing percutaneous lung biopsies from three institutions. A commercially available deep learning algorithm analyzed each CR to identify pneumothorax. We compared the performance of the algorithm with that of radiology reports made in the actual clinical practice. We also conducted a reader study, in which the performance of the algorithm was compared with those of four radiologists. Performances of the algorithm and radiologists were evaluated by area under receiver operating characteristic curves (AUROCs), sensitivity, and specificity, with reference standards defined by thoracic radiologists.
Results
Pneumothorax occurred in 17.5% (308/1757) of cases, out of which 16.6% (51/308) required catheter drainage. The AUROC, sensitivity, and specificity of the algorithm were 0.937, 70.5%, and 97.7%, respectively, for identification of pneumothorax. The algorithm exhibited higher sensitivity (70.2% vs. 55.5%,
p
< 0.001) and lower specificity (97.7% vs. 99.8%,
p
< 0.001), compared with those of radiology reports. In the reader study, the algorithm exhibited lower sensitivity (77.3% vs. 81.8–97.7%) and higher specificity (97.6% vs. 81.7–96.0%) than the radiologists.
Conclusion
The deep learning algorithm appropriately identified pneumothorax in post-biopsy CRs in consecutive diagnostic cohorts. It may assist in accurate and timely diagnosis of post-biopsy pneumothorax in clinical practice.
Key Points
• A deep learning algorithm can identify chest radiographs with post-biopsy pneumothorax in multicenter consecutive cohorts reflecting actual clinical situation.
• The deep learning algorithm has a potential role as a surveillance tool for accurate and timely diagnosis of post-biopsy pneumothorax.
To retrospectively evaluate the diagnostic performance and complications of C-arm cone-beam computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) in 1108 patients.
This ...retrospective study was approved by the institutional review board with waiver of patient informed consent. From January 2009 to December 2011, 1108 patients (633 male, 475 female; mean age, 62.4 years ± 12.3 standard deviation) with 1116 pulmonary lesions (mean size, 2.7 cm ± 1.7) underwent 1153 cone-beam CT-guided PTNBs. A coaxial system with 18-gauge cutting needles was used. Diagnostic performance, complication rate, influencing factors, and patient radiation exposure were investigated. Variables influencing diagnostic performance and complications were assessed by using uni- and multivariate logistic regression analyses.
Among 1153 PTNBs, pathologic analysis showed 1148 (99.6%) were technically successful (766 malignant 66.4%, 323 benign 28.0%, and 59 5.1% indeterminate). Sensitivity, specificity, and accuracy for diagnosis of malignancy were 95.7% (733 of 766), 100% (323 of 323), and 97.0% (1056 of 1089), respectively. In regard to diagnostic failures (five technical failures, 33 false-negative findings), lesions 1 cm in diameter or smaller and lesions in the lower lobe were significant risk factors (P = .028 and P = .034, respectively). As for complications, pneumothorax and hemoptysis occurred in 196 (17.0%) and 80 (6.9%) procedures, respectively. Multivariate analysis revealed two or more pleural passages and emphysema along the needle pathway were the two most significant risk factors for pneumothorax, and ground-glass nodules were the most significant risk factor for hemoptysis (P < .001 for all). Virtual guidance was a significant protective factor for both pneumothorax and hemoptysis (P < .001 for both). Mean estimated effective radiation dose through cone-beam CT-guided PTNBs was 7.3 mSv ± 4.1.
Cone-beam CT-guided PTNB is a highly accurate and safe technique with which to diagnose pulmonary lesions with reasonable radiation exposure.
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