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Clostridioides difficile infection (CDI) is a major cause of antibiotic-associated diarrhea and colitis. It is recognized as one of the most significant hospital-acquired infections. Although CDI can develop severe complications and spores of Clostridioides difficile can be transmitted by the fecal-oral route, CDI is occasionally overlooked in clinical settings. Thus, it is necessary to monitor high CDI risk groups, particularly those undergoing antibiotic treatment, to prevent complications and spread. We developed and validated a deep learning-based model to predict the occurrence of CDI within 28 days after starting antibiotic treatment using longitudinal electronic health records. For each patient, timelines of vital signs and laboratory tests with a 35-day monitoring period and a patient information vector consisting of age, sex, comorbidities, and medications were constructed. Our model achieved the prediction performance with an area under the receiver operating characteristic curve of 0.952 (95% CI: 0.932–0.973) in internal validation and 0.972 (95% CI: 0.968–0.975) in external validation. Platelet count and body temperature emerged as the most important features. The risk score, the output value of the model, exhibited a consistent increase in the CDI group, while the risk score in the non-CDI group either maintained its initial value or decreased. Using our CDI prediction model, high-risk patients requiring symptom monitoring can be identified. This could help reduce the underdiagnosis of CDI, thereby decreasing transmission and preventing complications. [ABSTRACT FROM AUTHOR]

Hydrofluoric acid (HF), the inorganic acid of elemental fluorine, is a highly dangerous substance and death can result from a very small exposure. In addition to local toxicity, HF can trigger fatal systemic reactions by its high affinity for calcium and magnesium. The authors report the autopsy case of a male worker who was exposed to 50% HF while repairing the leakage from an HF tank valve in a semi-conductor washing factory. His colleagues found blisters on his neck after 6 h of work and he was sent to the hospital. However, he expired from cardiac arrest despite an immediate calcium gluconate injection. At autopsy, burns with eschar covering less than 5% of the total body surface were identified on the neck and around both ears, and microscopic examination of the affected skin revealed extensive necrosis of the epidermis and dermis with pustule formation. In chemical analysis, no fluoride ions were detected in blood, vitreous humor, urine, pleural fluid, bile, or skin tissue from the neck. Considering the chemical burns on the neck and the circumstantial information, the cause of death was determined to be HF poisoning. This article presents the clinical manifestations of local and systemic toxicity after the accidental exposure to a high concentration of HF, with histologic demonstrations of chemical burns. [ABSTRACT FROM AUTHOR]

Purpose: Risk of second primary malignancy (SPM) after radioiodine (RAI) therapy has been continuously debated. The aim of this study is to identify the risk of SPM in thyroid cancer (TC) patients with RAI compared with TC patients without RAI from matched cohort. Methods: Retrospective propensity-matched cohorts were constructed across 4 hospitals in South Korea via the Observational Health Data Science and Informatics (OHDSI), and electrical health records were converted to data of common data model. TC patients who received RAI therapy constituted the target group, whereas TC patients without RAI therapy constituted the comparative group with 1:1 propensity score matching. Hazard ratio (HR) by Cox proportional hazard model was used to estimate the risk of SPM, and meta-analysis was performed to pool the HRs. Results: Among a total of 24,318 patients, 5,374 patients from each group were analyzed (mean age 48.9 and 49.2, women 79.4% and 79.5% for target and comparative group, respectively). All hazard ratios of SPM in TC patients with RAI therapy were ≤ 1 based on 95% confidence interval(CI) from full or subgroup analyses according to thyroid cancer stage, time-at-risk period, SPM subtype (hematologic or non-hematologic), and initial age (< 30 years or ≥ 30 years). The HR within the target group was not significantly higher (< 1) in patients who received over 3.7 GBq of I-131 compared with patients who received less than 3.7 GBq of I-131 based on 95% CI. Conclusion: There was no significant difference of the SPM risk between TC patients treated with I-131 and propensity-matched TC patients without I-131 therapy. [ABSTRACT FROM AUTHOR]

Purpose: Mexiletine is a well-known class IB antiarrhythmic agent that is primarily used for the treatment of ventricular arrhythmias. As a major route of metabolism, mexiletine is hydroxylated by cytochrome P450 2D6 (CYP2D6), forming metabolites, which have no significant antiarrhythmic activity. However, few cases of mexiletine overdose with genetic polymorphism have been reported in the literature. Methods: We report a fatal mexiletine intoxication case of a 25-year-old Korean male. After the toxicological screening, mexiletine was initially identified by gas chromatography–mass spectrometry and subsequently confirmed by ultrahigh-performance liquid chromatography–tandem mass spectrometry. Results: The toxicological results described the mexiletine levels in femoral and heart blood as 14.3 (range 13.6–14.6) and 22.4 (range 21.5–23.8) ng/mL, respectively. Clopidogrel was below the limit of quantification (< 0.002 ng/mL) from blood. Eight alleles were genotyped within the CYP2D6, which is the most frequently mutated gene in East Asians, including Koreans. However, no genetic mutant of CYP2D6 was observed in the case. Conclusions: The current result was determined to be a fatal overdose of mexiletine in a young Korean man with CYP2D6 homozygous for the wild allele. A genotyping of CYP2D6 may predict the fatal dose of mexiletine in individuals. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and a leading cause of cancer-related death worldwide. We propose a fully automated deep learning model to detect HCC using hepatobiliary phase magnetic resonance images from 549 patients who underwent surgical resection. Our model used a fine-tuned convolutional neural network and achieved 87% sensitivity and 93% specificity for the detection of HCCs with an external validation data set (54 patients). We also confirmed whether the lesion detected by our deep learning model is a true lesion using a class activation map. [ABSTRACT FROM AUTHOR]

This paper presents a scalable method of near duplicate image detection based on Gist-PCA (principal component analysis) hashing. While most of transform coding methods have been interested in nearest neighbor search with applications to similar image search, we solve a range search problems found in near duplicate detection problems. At first, we argue that the PCA hashing of the Gist descriptor is adequate for near duplicate image detection. Then, we decompose the Gist-PCA binary code into a hash key and a residual binary code for scalability into large-scale datasets. In addition, a multi-block approach is incorporated into the method to deal with strong variations, such as image cropping and border framing. Experimental results show that the proposed method is more accurate and faster than the real-valued Gist descriptor and other nearest neighbor search methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for recognizing facial images based on the relative distances between an input image and example images. Example facial images can be easily collected online, and a large example database can span new possible facial variations not sufficiently learned during the learning phase. We first extract facial features using a baseline classifier that has a certain degree of accuracy. To achieve a better performance of the proposed method, we divide the collected examples into groups using a clustering method (e.g., k-means), where each clustered group contains examples with similar characteristics. We then hierarchically partition a group formed in the previous level into other groups to analyze more specific facial characteristics, which represent an example pyramid. To describe the characteristics of a group using the clustered examples, we divide the example group into a number of sub-groups. We calculate the averages of the sub-groups and select an example most similar to the average in each sub-group because we assume that the averages of the sub-groups can directly represent their characteristics. Using the selected examples, we build example code words for a novel feature extraction. The example code words are used to measure the distances to an input image and serve as anchors to analyze a facial image in the example domain. The distance values are normalized for each group at all pyramid levels, and are concatenated to form novel features for face recognition. We verified the effectiveness of the proposed example pyramid framework using well-known proposed features, including LBP, HOG, Gabor, and the deep learning method, on the LFW database, and showed that it can yield significant improvements in recognition performance. [ABSTRACT FROM AUTHOR]

Technological trajectory is an attractive concept, but is hard to be operationalized. Challenging this issue, we suggest an extended technological trajectory method. It identifies not only the single main path, but derivative paths emanating from technology junctures on the main path. Convergence and divergence of technologies occur on junctures that are identified by using three indicators of technology generality, path-specific generality and path-specific novelty. Dynamic technology tree allows researchers to identify product component—patent linkages in a structured way, deepening our understanding about effects of technological developments on a product. In practice, our approach can help researchers better identify technologically related opportunities, thereby triggering the process from well-focused technology portfolio to technological advantages. High voltage direct current transmission technology is exemplified. [ABSTRACT FROM AUTHOR]

A clothing-type wearable display can be utilized in fashion, bio-healthcare, and safety industries as well as smart textiles for the internet of things (IoTs) and wearable devices. In response to this trend, we demonstrate a textile display that can endure the active movements of a human body. It can be applied to any kind of textile, and is durable against conditions such as rain, sweat, and washing. As a key technology for realizing the multi-directional wrinkle-able textile display, we fabricated a stress-lowering textile platform with an ultrathin planarization layer replicated from the flat surface of glass. An elastomeric strain buffer for reducing mechanical stress is also inserted into the textile platform. Here, organic light-emitting diodes (OLEDs) with red, green and blue color, thin film transistors (TFTs) fabricated at a low temperature below 150 °C, and a washable encapsulation layer blocking both gas and liquid were demonstrated on the textile platform. [ABSTRACT FROM AUTHOR]