Urinary tract infection (UTI) is common in home care but not easily captured with standard assessment. This study aimed to examine the value of nursing notes in detecting UTI signs and symptoms in home care.
 
  The study developed a natural language processing (NLP) algorithm to automatically identify UTI-related information in nursing notes.
 
  Home care visit notes (n= 1,149,586) and care coordination notes (n=1,461,171) for 89,459 patients treated in the largest nonprofit home care agency in the United States during2014.
 
  We generated 6 categories of UTI-related information from literature and used the Unified Medical Language System (UMLS) to identify a preliminary list of terms. The NLP algorithm was tested on a gold standard set of 300 clinical notes annotated by clinical experts. We used structured Outcome and Assessment Information Set data to extract the frequency of UTI-related emergency department (ED) visits or hospitalizations and explored time-patterns in documentation of UTI-related informatioonsider using NLP to extract clinical data from nursing notes to improve early detection and treatment, which may lead to quality improvement and cost reduction.
 Information in nursing notes is often overlooked by stakeholders and not integrated into predictive modeling for decision-making support, but our findings highlight their value in early risk identification and care guidance. Health care administrators should consider using NLP to extract clinical data from nursing notes to improve early detection and treatment, which may lead to quality improvement and cost reduction.
  This study compared quality indicators across linguistic groups and sought to determine whether disparities are influenced by resident-facility language discordance in long-term care.
 
  Population-based retrospective cohort study using linked databases.
 
  Retrospective cohort of newly admitted residents of long-term care facilities in Ontario, Canada, between 2010 and 2016 (N=47,727). Individual residents' information was obtained from the Resident Assessment Instrument Minimum Data Set (RAI-MDS) to determine resident's primary language, clinical characteristics, and health care indicators.
 
  Main covariates of interest were primary language of the resident and predominant language of the long-term care facility, which was determined using the French designation status as defined in the French Language Services Act. Primary outcomes were a set of quality and safety indicators related to long-term care worsening of depression, falls, moderate-severe pain, use of antipsychotic medication, and physical restraijusting for individual- and facility-level characteristics, suggesting that the disparities observed at the population level cannot be attributed to linguistic factors alone.
 For francophones, quality indicators tended to be worse in the presence of resident-facility language discordance. However, these findings did not persist after adjusting for individual- and facility-level characteristics, suggesting that the disparities observed at the population level cannot be attributed to linguistic factors alone.
  Transforming growth factor beta 1 (TGF-β1) plays an important role in bone mineralization and has been reported to promote osteoblast proliferation and differentiation. However, there is no report about the effects of TGF-β1 on human cementoblasts. The purpose of this study was to clarify the effect of TGF-β1 on the proliferation and differentiation of the human cementoblast cell line (HCEM) invitro.
 
  HCEM cells were stimulated with TGF-β1 at concentrations of 0.05, 0.5, 5, and 10 ng/mL. A proliferation assay was performed from 24-72 hours. The effect of TGF-β1 on mineralization was analyzed by quantifying the area stained with alizarin red on days 7 and 14. Real-time polymerase chain reaction was used to assess the effect of TGF-β1 on the mineralization-related genes alkaline phosphatase, bone sialoprotein, and type I collagen on days 3, 7, and14.
 
  TGF-β1 did not affect cell proliferation. TGF-β1 together with the mineralization medium (consisting of ascorbic acid, dexamethasone, and β-glycerophosphate) increased the alizarin red-stained area on days 7 and 14. Real-time polymerase chain reaction revealed that alkaline phosphatase messenger RNA expression was increased in TGF-β1-stimulated HCEM cells in mineralization medium on days 3 and 7, whereas bone sialoprotein and type I collagen messenger RNA expression was increased on day7.
 
  Although TGF-β1 does not affect cell proliferation, it does promote cell differentiation and mineralization of HCEM cells.
 Although TGF-β1 does not affect cell proliferation, it does promote cell differentiation and mineralization of HCEM cells.
  Tooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achieved by merely relying on CBCT intensity variations. This study aimed to develop and validate an artificial intelligence (AI)-driven tool for automated tooth segmentation on CBCT imaging.
 
  A total of 433 Digital Imaging and Communications in Medicine images of single- and double-rooted teeth randomly selected from 314 anonymized CBCT scans were imported and manually segmented.  https://www.selleckchem.com/products/cc-122.html  An AI-driven tooth segmentation algorithm based on a feature pyramid network was developed to automatically detect and segment teeth, replacing manual user contour placement. The AI-driven tool was evaluated based on volume comparison, intersection over union, the Dice score coefficient, morphologic surface deviation, and total segmentation time.
 
  Overall, AI-driven and clinical reference segmentationsging. These results may open doors for AI-driven applications in surgical and treatment planning in oral health care.
  The purpose of this study was to assess the optimal amplitude and weight of the newly developed contra-angle handpiece. The handpiece uses piston movement without using an endodontic motor and enables a safe, quick, and reliable canal preparation.
 
  A prototype handpiece was designed. Instrumentation was performed on root canal resin blocks by 20 operators in 3 groups the prototype handpiece with an H file (a stainless steel #25 manual H file, the piston group), a manually standardized technique with a K file (stainless steel #15-25 K files, the manual group), and a nickel-titanium (NiTi) reciprocating file with an endodontic motor (Reciproc Blue R25 [VDW, Munich, Germany], the NiTi group). Transportation of the canal center line and the time required for preparation were measured and statistically analyzed.
 
  The optimal condition was an amplitude of 1.35 mm and a weight of 61.0 g. Transportation of the canal center was observed in all groups. A statistically significant difference was found at 2.0-3.0 mm from the apical foramen between the piston or NiTi group and the manual group, but no significant difference was found between the piston and NiTi groups.