No abstract available.
Low Back Pain*

No abstract available.
Child* ; Humans ; Hypercalciuria*

Purpose: This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II). Methods: A total of 222 participants were surveyed.Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables–social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)– and three endogenous variables–perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)–were investigated. Results: The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16,p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p= .010), RIT (β = - .12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance. Conclusion This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.

Purpose: This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II). Methods: A total of 222 participants were surveyed.Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables–social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)– and three endogenous variables–perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)–were investigated. Results: The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16,p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p= .010), RIT (β = - .12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance. Conclusion This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.

Purpose: This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II). Methods: A total of 222 participants were surveyed.Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables–social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)– and three endogenous variables–perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)–were investigated. Results: The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16,p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p= .010), RIT (β = - .12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance. Conclusion This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.

Purpose: This study aimed to identify factors that influence the intention to use smart monitor-based mobile health (SBM) technology among middle-aged inpatients, based on the technology acceptance model II (TAM II). Methods: A total of 222 participants were surveyed.Data were analyzed using SPSS Statistics 23.0 and IBM SPSS Amos 23. Seven exogenous variables–social influence (SI), personal self-efficacy, (PSE), environmental self-efficacy (ESE), health literacy, health concerns, resistance to innovative technology (RIT), accessibility (AC)– and three endogenous variables–perceived ease of use (PEOU), perceived usability (PU), and intention to use (ITU)–were investigated. Results: The hypothesized path model demonstrated a good fit for the data. SI (β = .13, p = .042), PU (β = .46, p < .001), and PEOU (β = .16,p = .008) had significant direct effects on the ITU, which explained 39.5% of the variance. Additionally, SI (β = .27, p < .001), ESE (β = .16, p= .010), RIT (β = - .12, p = .026), AC (β = .28, p < .001), and PEOU (β = .20, p = .001) indirectly affected ITU through PU, which explained 50.7% of the variance. Furthermore, PSE (β = .38, p < .001) indirectly influenced ITU via PEOU, which explained 38.4% of the variance. Conclusion This study demonstrates that the TAM II can be used to effectively predict ITU in SBMs among middle-aged inpatients. To expand the intention to use SBMs, it is necessary to develop SBMs that include content and programs that promote PU, SI, and PEOU.

No abstract available.
Animals ; Horns* ; Kidney*

No abstract available.
Ectopia Cordis* ; Hernia, Umbilical*

The purposes of this study were to develop an in vitro co-culture model of epithelial tissue with dermal equivalent, cultured at an air-liquid interface, and to evaluate the effects of extracellular matrix and concentration of calcium and fetal bovine serum in medium to find optimized culture condition. Oral keratinizing epithelial cells in monolayer culture were grown in Mitomycin-treated 3T3 feeder. Primary cultured oral epithelial cells were reconstituted onto the dermal equivalents consisting of 3T3 fibroblast and type I collagen, and co-culture was grown at the air-liquid interface. The histomorphological development of reconstituted oral epithelium in vitro for 21 days revealed 10~12 layered statified epithelium, closely similar to the parakeratinized gingival epithelium. Neither laminin nor type IV collagen was able to induce keratinocyte differentiation. But a mixture of laminin and type IV collagen induced well-polarized keratinizing tissue with anchoring structure of basal cells. When the reconstituted oral epithelium was incubated in 1.0% and 0.5% serum-containing medium, the granular cell layers with orthokeratinization developed. The reconstituted epidermis generated in serum-free keratinocyte growth medium (KGM)-containing pituitary extract showed features of incomplete differentiation. The present study shows that the dermal equivalents containing fibroblasts will support epidermal morphogenesis and differentiation. And these results suggest that extracellular matrix and calcium concentration are important factors during the reconstitution of keratinizing epithelium in vitro.
Calcium ; Coculture Techniques ; Collagen Type I ; Collagen Type IV ; Epidermis ; Epithelial Cells ; Epithelium ; Extracellular Matrix ; Fibroblasts ; Keratinocytes* ; Laminin ; Morphogenesis ; Population Characteristics*

No abstract available.
Neoplasm Metastasis* ; Pancreatic Neoplasms* ; Scalp* ; Thorax*