Isolation, Culture, and Quality Assessment of Clinical-Grade Corneal Stromal Stem Cells.

The challenge of treating corneal scarring through keratoplasties lies in the limited availability of donor tissue. Various studies have shown the therapeutic use of cultivated corneal stromal stem cells (CSSCs) to mitigate tissue inflammation and suppress fibrosis and scar tissue formation in preclinical corneal wound models. To develop CSSC therapy for clinical trials on patients with corneal scarring, it is necessary to generate clinical-grade CSSCs in compliant to Good Manufacturing Practice (GMP) regulations. This chapter elucidates human CSSC isolation, culture, and cryopreservation under GMP-compliant conditions. It underscores quality assessment encompassing morphological traits, expression of stemness markers, anti-inflammatory activity, and keratocyte differentiation potency.

Decreasing Social Media Use Through Motivational Interviewing: A Pediatric Primary Care Quality Improvement Project.

BACKGROUND: Social media use has been correlated to worsening anxiety and depression in teenagers. LOCAL PROBLEM: In the United Staes, social media is frequently used amongst adolescents, and rates of anxiety and depression in this population have increased over time. METHODS: A convenience sample of adolescents aged 12 to 18 years old were evaluated using a pre-post design. INTERVENTIONS: This quality improvement project introduced screen time assessment and motivational interviewing to decrease social media use and improve mental health among adolescents. RESULTS: PHQ-9 scores remained consistent throughout the project. There was an overall decrease in participant SCARED scores and social media use declined over the 12-week period. CONCLUSIONS: Results from this project suggest an opportunity for pediatric healthcare providers to standardize social media use screening in adolescent care and utilize motivational interviewing to promote adolescent wellbeing.

Data quality assessment of the Dutch Breast Implant Registry by automated data verification using medical billing data.

BACKGROUND: The Dutch Breast Implant Registry (DBIR) provides real-time population-based data to monitor and improve the quality and safety of breast implants and to trace patients in the event of an (inter)national recall. To serve these main goals, the capture rate and percentage of implants that are actually registered should be known and data should be complete. This study aimed to describe an automated verification process for capture rates and data completeness using medical billing data as the external source. METHODS: DBIR-data on reconstructive permanent breast implants and tissue expanders (TEs) from 2019 were compared to medical billing data. At the hospital level, the capture rate of DBIR and accuracy of the selected data points were assessed. RESULTS: In total, 2389 implants (1420 patients) were included from 12 participating hospitals (11% of all healthcare institutions registering in DBIR). DBIR had capture rates of 99% to 114% for inserted permanent implants and TEs and 49% for explanted permanent implants and TEs. Among the 9015 data points analyzed in DBIR, 8861 (98%) matched the medical billing data. Mastopexy had the highest matching percentage (100%) and capsulectomy the lowest (86%). CONCLUSION: This study showed varying capture rates in DBIR depending on the intervention group, indicating that registration of DBIR-data and medical billing codes could be further optimized. Data accuracy was high as only 2% of data points did not correspond to medical billing data. For future data verification, other external data sources could be explored, including sources that enable verification of cosmetic implants.

Ultra-high resolution spectral photon-counting CT outperforms dual layer CT for lung imaging: Results of a phantom study.

PURPOSE: The purpose of this study was to compare lung image quality obtained with ultra-high resolution (UHR) spectral photon-counting CT (SPCCT) with that of dual-layer CT (DLCT), at standard and low dose levels using an image quality phantom and an anthropomorphic lung phantom. METHODS: An image quality phantom was scanned using a clinical SPCCT prototype and an 8 cm collimation DLCT from the same manufacturer at 10 mGy. Additional acquisitions at 6 mGy were performed with SPCCT only. Images were reconstructed with dedicated high-frequency reconstruction kernels, slice thickness between 0.58 and 0.67 mm, and matrix between 5122 and 10242 mm, using a hybrid iterative algorithm at level 6. Noise power spectrum (NPS), task-based transfer function (TTF) for iodine and air inserts, and detectability index (d') were assessed for ground-glass and solid nodules of 2 mm to simulate highly detailed lung lesions. Subjective analysis of an anthropomorphic lung phantom was performed by two radiologists using a five-point quality score. RESULTS: At 10 mGy, noise magnitude was reduced by 29.1 % with SPCCT images compared to DLCT images for all parameters (27.1 ± 11.0 [standard deviation (SD)] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 6 mGy with SPCCT images, noise magnitude was reduced by 8.9 % compared to DLCT images at 10 mGy (34.8 ± 14.1 [SD] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 10 mGy and 6 mGy, average NPS spatial frequency (fav) was greater for SPCCT images (0.75 ± 0.17 [SD] mm-1) compared to DLCT images at 10 mGy (0.55 ± 0.04 [SD] mm-1) while remaining constant from 10 to 6 mGy. At 10 mGy, TTF at 50 % (f50) was greater for SPCCT images (0.92 ± 0.08 [SD] mm-1) compared to DLCT images (0.67 ± 0.06 [SD] mm-1) for both inserts. At 6 mGy, f50 decreased by 1.1 % for SPCCT images, while remaining greater compared to DLCT images at 10 mGy (0.91 ± 0.06 [SD] mm-1 vs. 0.67 ± 0.06 [SD] mm-1, respectively). At both dose levels, d' were greater for SPCCT images compared to DLCT for all clinical tasks. Subjective analysis performed by two radiologists revealed a greater median image quality for SPCCT (5; Q1, 4; Q3, 5) compared to DLCT images (3; Q1, 3; Q3, 3). CONCLUSION: UHR SPCCT outperforms DLCT in terms of image quality for lung imaging. In addition, UHR SPCCT contributes to a 40 % reduction in radiation dose compared to DLCT.

Quality Assessment of Periapical Radiographs Taken by Dental Assistants Using the Recent Faculty of General Dental Practice (FGDP) Guidelines.

BACKGROUND: Periapical radiographs play a pivotal role in dentistry, offering invaluable insights essential for various dental procedures. OBJECTIVE: This study aims to systematically assess the quality of intraoral periapical (IOPA) radiographs evaluating adherence to the recent guidelines established by the Faculty of General Dental Practice (FGDP). METHODS: A cross-sectional study was conducted at the University College of Dentistry (UCD), employing a non-probability consecutive sampling technique to acquire a calculated sample of 300 IOPA radiographs from the operative, oral surgery, and oral radiology departments. Two senior faculty members evaluated the radiographs according to the recent two-tier grading system outlined in the FGDP guidelines. RESULTS: The study revealed that 197 (65.67%) of the assessed radiographs were diagnostically acceptable, while 103 (34.33%) were deemed diagnostically unacceptable. Contrast problems emerged as the most prevalent issue, accounting for 85 (28.3%) of the cases. Other common problems included incorrect film positioning in 66 (22%), incorrect vertical cone angulation in 37 (12.3%), incorrect horizontal cone angulation in 11 (3.7%), and incorrect processing in 15 (5%) of the IOPA radiographs. CONCLUSION: This study revealed that approximately two-thirds of the IOPA radiographs were deemed diagnostically acceptable. However, contrast issues emerged as the predominant concern affecting image quality. These findings highlight the critical importance of continuous quality improvement initiatives in radiographic practices to enhance diagnostic precision and ensure optimal patient care.

Household electricity consumption prediction using database combinations, ensemble and hybrid modeling techniques.

Household electricity consumption (HEC) is changing over time, depends on multiple factors, and leads to effects on the prediction accuracy of the model. The objective of this work is to propose a novel methodology for improving HEC prediction accuracy. This study uses two original datasets, namely questionnaire survey (QS) and monthly consumption (MC), which contain data from 225 consumers from Maharashtra, India. The original datasets are combined to create three additional datasets, namely QS + MC, QS equation (QsEq) + next month's consumptions, and QsEq + MC. Furthermore, the HEC prediction accuracy is boosted by applying different approaches, like correlation methods, feature engineering techniques, data quality assessment, heterogeneous ensemble prediction (HEP), and the hybrid model. Five HEP models are created using dataset combinations and machine learning algorithms. Based on the MC dataset, the random forest provides the best prediction of RMSE (36.18 kWh), MAE (25.73 kWh), and R2 (0.76). Similarly, QsEq + MC dataset adaptive boosting provides a better prediction of RMSE (36.77 kWh), MAE (26.18 kWh), and R2 (0.76). This prediction accuracy is further increased using the proposed hybrid model to RMSE (22.02 kWh), MAE (13.04 kWh), and R2 (0.92). This research work benefits researchers, policymakers, and utility companies in obtaining accurate prediction models and understanding HEC.

What factors affect a patient's subjective perception of MRI examination.

MRI is becoming increasingly available and more common. However, it is a long examination, within a limited space, and making strong demands on the patient for proper co-operation. Using survey data collected by prospective questionnaire, this work examines the influence of patient preparation and type of MRI device on patients' subjective perception of the examination. The work analysed 800 patient questionnaires from 7 radiology centres, 12 MRI machines from 3 manufacturers. It was shown that 20% of patients were not informed at all or only insufficiently about the MRI examination by the referring physician, and this had a statistically significant effect on subjective perception as to the length of the examination. In claustrophobic patients, there was no significant difference in the perception of MRI examination between machine types (open vs. closed) or between bore size. This work demonstrated the influence of technical parameters of MRI devices on some other evaluated aspects in terms of patients' perception of MRI examinations (such as noise perception or peripheral nerves irritation) and that the preparation prior to the examination itself plays also an important role. Sufficient explanation from the referring physician, good workplace time management, and sufficient communication with the patient influence the subjective perception of the examination and thus indirectly its diagnostic benefit.

Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng.

Red ginseng (RG) has been traditionally valued in Northeast Asia for its health-enhancing properties. Recent advancements in hyperspectral imaging (HSI) offer a non-destructive, efficient, and reliable method to assess critical quality indicators of RG, such as reducing sugar content (RSC), water content (WC), and hollow rate (HR). This study developed predictive models using HSI technology to monitor these quality indicators over the spectral range of 400-1700 nm. Image features were enhanced using Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), followed by classification through Spectral Angle Mapping (SAM). The best-performing model for RSC achieved an R2 value of 0.6198 and a root mean square error (RMSE) of 0.013. For WC, the optimal model obtained an R2 value of 0.6555 and an RMSE of 0.014. The spatial distribution of RSC, WC, and HR was effectively visualized, demonstrating the potential of HSI for on-site quality control of RG. This study provides a foundation for real-time, non-invasive monitoring of RG quality, addressing industry needs for rapid and reliable assessment methods.

Current Advances in Patient-Perceived Quality Assessment Within Romanian Healthcare.

The global healthcare landscape is shifting toward patient-centered care, emphasizing the integration of patient feedback into service delivery. Romania, aligning with this trend, has implemented patient-perceived quality assessment tools to enhance healthcare services and better meet patient needs and expectations. This study aims to review comprehensively the implementation and impact of these tools in Romania, focusing on their role in improving healthcare quality. By examining key assessment instruments such as the Patient Satisfaction Questionnaire (PSQ), the Service Quality (SERVQUAL) model, and the Romanian Healthcare Quality Assessment Survey (RHQAS), the research seeks to understand how these tools have been used to identify areas for improvement and drive advancements in patient care. Employing a comprehensive review methodology, the study will conduct a thorough literature search to identify relevant studies, reports, and publications, analyzing the PSQ, SERVQUAL, and RHQAS in detail to understand their measurement domains, psychometric properties, and application within Romania. Additionally, qualitative data from interviews with healthcare providers and patients may be collected to offer further insights into the use and effectiveness of these tools. The study's findings are expected to provide valuable insights into the role of patient-perceived quality assessment tools in enhancing healthcare in Romania, identifying strengths, weaknesses, and opportunities for improvement. The results will highlight the effectiveness of combining international methodologies with localized adaptations to address the specific needs of the Romanian healthcare system, ultimately contributing to the ongoing efforts to improve patient satisfaction and health outcomes by informing the development and refinement of patient-centered care initiatives in Romania.

Automated cardiac coverage assessment in cardiovascular magnetic resonance imaging using an explainable recurrent 3D dual-domain convolutional network.

BACKGROUND: Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment. PURPOSE: This study examines the full cardiac coverage using a 3D dual-domain convolutional model and then improves this model using an innovative explainable salient region detection model and a recurrent architecture. METHODS: Salient regions are extracted from the short-axis cine CMR stacks using a three-step proposed algorithm. Changing the architecture of the 3D dual-domain convolutional model to a recurrent one and taking advantage of the salient region detection model creates a kind of attention mechanism that leads to improved results. RESULTS: The results obtained from the images of over 6200 participants of the UK Biobank population cohort study show the superiority of the proposed model over the previous studies. The dataset is the largest regarding the number of participants to control the cardiac coverage. The accuracies of the proposed model in identifying the presence/absence of basal/apical slices are 96.22% and 95.42%, respectively. CONCLUSION: The proposed recurrent architecture of the 3D dual-domain convolutional model can force the model to focus on the most informative areas of the images using the extracted salient regions, which can help the model improve accuracy. The performance of the proposed fully automated model indicates that it can be used for image quality control in population cohort datasets and real-time post-imaging quality assessments. Codes are available at https://github.com/mohammadhashemii/CMR_Cardiac_Coverage_Control.