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Obesity is associated with chronic low back pain (CLBP), but the association between fat distribution and CLBP is unclear. This cross-sectional study evaluated the relationship using the Korean National Health and Nutrition Examination Survey data. A total of 10,606 adults (average age: 45.4, female: 57.1%) were included. We estimated the regional fat distribution, waist circumference, and body fat proportion, compared the values in people with and without CLBP, and stratified the estimates by sex and obesity status using a multivariable linear model. There were no statistically significant differences in the average waist circumference between the people with and without CLBP (p = 0.731) and the average fat proportion between those with and without CLBP (p = 0.731). The average regional fat distribution was significantly higher in the people with CLBP than in those without CLBP, in the upper limbs (11.4%, 95% confidence interval [CI]: [11.3, 11.5] vs. 11.2%, 95% CI: [11.1, 11.3], p < 0.05) and in the lower limbs (31.9%, 95% CI: [31.6, 32.2] vs. 31.4%, 95% CI: [31.2, 31.6], p < 0.01). More obvious among men, fat distribution in the lower limbs is higher than in people without obesity (p < 0.001). People with CLBP tend to have a higher fat distribution in the limbs than those without it and obese people with CLBP would need to reduce the fat in the lower limbs.

Objectives; The accumulation and usefulness of clinical data have increased with IT development. While using clinical data that needs to be identifiable to obtain meaningful information, it is essential to ensure that data is de-identified and unnecessary clinical information is minimized to protect personal information. This process requires criteria and an appropriate method as there are clear identifiers as well as quasi-identifiers that are not readily identifiable. Methods; To formulate such a method, first, primary quasi-identifiers were selected by classifying information in 20 clinical personal information database tables into Direct-Identifier (DID), Quasi-Identifier (QI), Sensitive Attribute (SA), and Non-Sensitive Attribute (NSA) according to its type. Secondary QIs were then selected by assessing the risk for outliers by measuring uniqueness values of the selected data and scoring re-identification by calculating equivalence class of the influence on other data on QI removal. Third, the risk of re-identification of data users was numeralized and classified. Lastly, the final QI according to user class was determined by comparing the calculated re-identification scores to the threshold values of user classes. Results; Eventually, final QIs ranging from a minimum of 18 to a maximum of 28 were selected by making an assumption about user classes and using it as criteria. Conclusions; The QI selection method presented by the current investigators can be used by researchers at the final checkup stage before they de-identify the selected QIs. Therefore, clinical data users can securely and efficiently use clinical data containing personal information by objectively selecting QIs using the method proposed in the present study.

Optimal transport conditions for the live black rockfish and changes in their characteristics during long-distance transport were investigated. Oxygen consumption by black rockfish was lowe...

Context:Allium senescens Linn. (Liliaceae) (ASL) has been traditionally used in Korea and other Asian countries for improving digestive and liver functions. Objective: The anti-hepatofibrosis effect of ASL ethanol extract in cellular and experimental fibrosis rat model was investigated. Materials and methods:In vitro cell viability, cell cycle and apoptosis in hepatic stellate cells (HSCs) were studied using MTT assay, flow cytometry and Annexin V-FITC/PI staining. Thioacetamide (TAA; 200 mg/kg, i.p.)-induced liver fibrosis model using Sprague Dawley rats (n = 10) was developed in vivo by injecting TAA twice per week for 13 weeks. ASL (25 and 100 mg/kg) and silymarin (50 mg/kg) were administered through oral gavage 2 times per week from 7th to 13th week. Specific fibrotic-related biomarkers such as aspartate transaminase (AST), alanine transaminase (ALT), glutathione and hydroxyproline levels in serum were analyzed by spectrophotometer using commercial kits. Morphological, histopathological and fibrotic-related gene expression such as TGF-β, Col1α1 and α-SMA in liver tissues was estimated by hematoxylin and eosin staining, Picrosirius red stain and quantitative real-time polymerase chain reaction, respectively. Results: ASL (0.1 mg/mL) and silymarin (0.05 mg/mL) treatment induced apoptosis (4.06% and 8.67%) in activated HSC-T6 cells, compared with control group (3.7%). The altered morphology in activated primary HSCs was also restored by ASL (0.1 mg/mL) treatment. Further, ASL (100 and 25 mg/kg) ameliorated the TAA-induced altered fibrotic-related biomarkers, histopathological changes and fibrotic-related gene expression significantly (p

The development of the wireless Internet environment enables smart devices to be interconnected in various environments to provide convenient services to users. Specifically, in connected vehicle and smart home, the infotainment system and home electronics are connected to the Internet to individually provide services to users. The connected vehicle uses the display panel to show users music, video, navigation, and so on desired, or to develop a service that reduces the burden of driving and allows other activities by autonomous vehicles. However, these services are designed and developed independently on the platform and are not suitable for the concept of residential space of the future society. In this article, we propose a smart life platform by combining the concepts of connected vehicle and smart home into one platform. It also provides the SLP-MCAF to provide secure services within the platform. The SLP-MCAF strongly reinforces the authentication of objects and introduces clusters to improve availability so that services are not separated from the automobile or home, and services are continuous. Through simulation, we demonstrate the stability and efficiency of the proposed platform.

A theory of the time-resolved photoemission spectroscopy (TRPES) is developed, which enables to explore the real-time electron dynamics of infinitely periodic crystalline solids. In the strongly correlated electron systems NiO and CuO, the early-stage dynamics of the valence band edge are found to be sharply contrasted between those in the spectra of TRPES. This provides a new dynamical insight to the Zaanen–Sawatzky–Allen (ZSA) classification scheme of correlated insulators and makes us assert that NiO dynamically behaves like the Mott–Hubbard insulator (MHI) and CuO like the charge transfer insulator (CTI). In the two-dimensional carbon layer graphene, the real-time electron dynamics of quantum-phase-dressed excited states, i.e., due to the Berry phase and the pseudospin correlation, are investigated in an unprecedented way through the time-resolved angle-resolved photoemission spectroscopy (TR-ARPES). In particular, the dephasing dynamics of optically doped electrons and holes in the massless Dirac band, accompanying a field-induced gliding of the Dirac cone, are discovered.

BACKGROUND Postoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information. OBJECTIVE The objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach. METHODS In our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type. RESULTS In the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables. CONCLUSIONS A machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery.

The absorption range of Si is extended to essential wavelength range of lidar sensor for self-driving car via strain engineering., Silicon has been widely used in the microelectronics industry. However, its photonic applications are restricted to visible and partial near-infrared spectral range owing to its fundamental optical bandgap (1.12 eV). With recent advances in strain engineering, material properties, including optical bandgap, can be tailored considerably. This paper reports the strain-induced shrinkage in the Si bandgap, providing photosensing well beyond its fundamental absorption limit in Si nanomembrane (NM) photodetectors (PDs). The Si-NM PD pixels were mechanically stretched (biaxially) by a maximum strain of ~3.5% through pneumatic pressure–induced bulging, enhancing photoresponsivity and extending the Si absorption limit up to 1550 nm, which is the essential wavelength range of the lidar sensors for obstacle detection in self-driving vehicles. The development of deformable three-dimensional optoelectronics via gas pressure–induced bulging also facilitated the realization of unique device designs with concave and convex hemispherical architectures, which mimics the electronic prototypes of biological eyes.

Melasma is a common hypermelanosis that occurs in areas exposed to sunlight, especially the face. It occurs rarely before puberty and is commonly observed in women, especially women of childbearing age. It is also observed in 10% of men. New melasma often arises and existing melasma may worsen during pregnancy. Symptoms appear as brown macules with irregular borders and occur symmetrically in the photodistribution of sunlight on the face. They are often fused in a reticular pattern. They are darkened by sunlight, which makes them worse during summer and improves during winter, i.e., there is a seasonal change. Melasma is common among Asian and Hispanic women who live in areas with strong sunlight. It occurs frequently in patients with Fitzpatrick skin types IV, V, and VI [1].

Although there are many side effects that can occur after laser treatment, the most common and problematic side effect in darker skin including Koreans is postinflammatory hyperpigmentation (PIH). Therefore, a full understanding and management of PIH are necessary. In this chapter, we go through PIH in detail.

Many papers and dermatology laser texts usually describe lasers about white skin. In separate articles or chapters laser treatment of “darker skin” or ethnic, nonwhite, pigmented skin is described [1–4]. Here, the term “darker skin” refers to all races except Caucasians, including Chinese, Japanese, Indian, Pakistani, Hispanic, Latino, African, African-Caribbean, and African American. Fitzpatrick skin types are III, IV, V, and VI [5].

Skin rejuvenation is a treatment that targets age-related pigmentation disorders and telangiectasia or improves wrinkles and skin texture [1]. It can be classified by treatment methods; ablative rejuvenation (AR) by CO2 laser and erbium-doped yttrium aluminium garnet (Er:YAG) laser or nonablative rejuvenation (NAR), which induces a selective thermal reaction to the dermis and subcutaneous fat while sparing the epidermis. It can also be classified by treatment targets; type I rejuvenation targets pigmentation and vascular disorders while type II rejuvenation focuses on improving wrinkles and skin texture. For example, AR and NAR are both treatment modalities for type II rejuvenation (Table 9.1). Generally, NAR refers to rejuvenation treatment that uses nonablative methods to improve wrinkles and skin texture (type II rejuvenation).

In “Chap. 1,” we reviewed that the most important theory regarding lasers is the theory of selective photothermolysis. In practice, however, there are cases in which the theory of selective photothermolysis does not apply and sometimes it even deviates from the theory of selective photothermolysis [1]. Next, we will review three advanced theories developed from the selective photothermolysis theory (Table 3.1).

Hair is a cylindrical fiber consisting of tightly adhered, cornified epithelial cells that only mammals have [1]. It is present anywhere on the skin except on palms and soles, distal ends of fingers and toes, boundaries of mucous membranes, and the glans penis.

There are many types of scars. They vary according to the shape of the scar surface, shape of the scar itself, color, and chronology. Scars may be classified by scar surface (flat, elevated, depressed, or irregular) by morphology (atrophic, normorophic, hypertrophic, or keloid) by color (depigmented, hypopigmented, hyperpigmented, or discolored) or by chronology (acute, chronic, immature, or mature) [1].