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.

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate diagnosis, often relying on computed tomography (CT). However, the associated ionizing radiation poses long-term risks. Modern artificial intelligence reconstruction algorithms have shown promise in reducing radiation dose while maintaining image quality. Therefore, we aimed to evaluate the dose reduction capabilities of a deep learning-based denoising (DLD) algorithm in traumatic neuroradiological emergency CT scans. MATERIALS AND METHODS: This retrospective single-center study included 100 patients with neuroradiological trauma CT scans. Full-dose (100%) and low-dose (25%) simulated scans were processed using iterative reconstruction (IR2) and DLD. Subjective and objective image quality assessments were performed by four neuroradiologists alongside clinical endpoint analysis. Bayesian sensitivity and specificity were computed with 95% credible intervals. RESULTS: Subjective analysis showed superior scores for 100% DLD compared to 100% IR2 and 25% IR2 (p < 0.001). No significant differences were observed between 25% DLD and 100% IR2. Objective analysis revealed no significant CT value differences but higher noise at 25% dose for DLD and IR2 compared to 100% (p < 0.001). DLD exhibited lower noise than IR2 at both dose levels (p < 0.001). Clinical endpoint analysis indicated equivalence to 100% IR2 in fracture detection for all datasets, with sensitivity losses in hemorrhage detection at 25% IR2. DLD (25% and 100%) maintained comparable sensitivity to 100% IR2. All comparisons demonstrated robust specificity. CONCLUSIONS: The evaluated algorithm enables high-quality, fully diagnostic CT scans at 25% of the initial radiation dose and improves patient care by reducing unnecessary radiation exposure.

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.

Identifying defects and varieties of Malting Barley Kernels.

This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach's advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.

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.

Data quality assessment of interventional trials in public trial databases.

OBJECTIVE: High-quality data entry in clinical trial databases is crucial to the usefulness, validity, and replicability of research findings, as it influences evidence-based medical practice and future research. Our aim is to assess the quality of self-reported data in trial registries and present practical and systematic methods for identifying and evaluating data quality. STUDY DESIGN AND SETTING: We searched ClinicalTrials.Gov (CTG) for interventional total knee arthroplasty (TKA) trials between 2000 and 2015. We extracted required and optional trial information elements and used the CTG's variables' definitions. We performed a literature review on data quality reporting on frameworks, checklists, and overviews of irregularities in healthcare databases. We identified and assessed data quality attributes as follows: consistency, accuracy, completeness, and timeliness. RESULTS: We included 816 interventional TKA trials. Data irregularities varied widely: 0%-100%. Inconsistency ranged from 0% to 36%, and most often nonrandomized labeled allocation was combined with a "single-group" assignment trial design. Inaccuracy ranged from 0% to 100%. Incompleteness ranged from 0% to 61%; 61% of finished TKA trials did not report their outcome. With regard to irregularities in timeliness, 49% of the trials were registered more than 3 months after the start date. CONCLUSION: We found significant variations in the data quality of registered clinical TKA trials. Trial sponsors should be committed to ensuring that the information they provide is reliable, consistent, up-to-date, transparent, and accurate. CTG's users need to be critical when drawing conclusions based on the registered data. We believe this awareness will increase well-informed decisions about published articles and treatment protocols, including replicating and improving trial designs.

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.

Consistency in the Assessment of Dried Blood Spot Specimen Size and Quality in U.K. Newborn Screening Laboratories.

In 2015, U.K. newborn screening (NBS) laboratory guidelines were introduced to standardize dried blood spot (DBS) specimen quality acceptance and specify a minimum acceptable DBS diameter of ≥7 mm. The UK 'acceptable' avoidable repeat rate (AVRR) is ≤2%. To assess inter-laboratory variability in specimen acceptance/rejection, two sets of colored scanned images (n = 40/set) of both good and poor-quality DBS specimens were distributed to all 16 U.K. NBS laboratories for evaluation as part of an external quality assurance (EQA) assessment. The mean (range) number of specimens rejected in the first EQA distribution was 7 (1-16) and in the second EQA distribution was 7 (0-16), demonstrating that adherence to the 2015 guidelines was highly variable. A new minimum standard for DBS size of ≥8 mm (to enable a minimum of six sub-punches from two DBS) was discussed. NBS laboratories undertook a prospective audit and demonstrated that using ≥8 mm as the minimum acceptable DBS diameter would increase the AVRR from 2.1% (range 0.55% to 5.5%) to 7.8% (range 0.55% to 22.7%). A significant inverse association between the number of specimens rejected in the DBS EQA distributions and the predicted AVVR (using ≥8 mm minimum standard) was observed (r = -0.734, p = 0.003). Before implementing more stringent standards, the impact of a standard operating procedure (SOP) designed to enable a standardized approach of visual assessment and using the existing ≥7 mm diameter (to enable a minimum of four sub-punches from two DBS) as the minimum standard was assessed in a retrospective audit. Implementation of the SOP and using the ≥7 mm DBS diameter would increase the AVRR from 2.3% (range 0.63% to 5.3%) to 6.5% (range 4.3% to 20.9%). The results demonstrate that there is inconsistency in applying the acceptance/rejection criteria, and that a low AVVR is not an indication of good-quality specimens being received into laboratories. Further work is underway to introduce and maintain standards without increasing the AVRR to unacceptable levels.

Impact of COVID-19 lockdown on sleep patterns and the related problems among nursing students.

AIM: To evaluate the impact of the COVID-19 lockdown on sleep patterns and quality among nursing students in our college. DESIGN: A cross-sectional study was carried out. METHODS: A total of 302 nursing students aged 18-25 years, representing both genders and various academic levels, participated in this study. A pre-validated, self-administered questionnaire was used to assess sleep quality during COVID-19 and it was distributed through various social media platforms for data collection. RESULTS: Female students comprised the majority (92.1%) of participants. Of 332 nursing students, 302 completed the questionnaire, yielding a 91% response rate. Statistically significant differences were observed before and during the COVID-19 lockdown regarding the need to sleep after waking, feeling refreshed upon waking, satisfaction with individual sleep patterns and experiencing restless and troubled sleep (p = 0.001). Additionally, approximately one-third of nursing students (32.9%) reported poor sleep quality during the COVID-19 pandemic, with minimal impact on the total sleep hours among the studied cohorts. PUBLIC CONTRIBUTION: The COVID-19 pandemic has statistically significant impacted nursing students' sleep quality and levels. Acknowledging these challenges and planning for providing supporting measurements is essential to ensuring that nursing students can maintain their physical and mental health, which is critical for their ability to provide quality healthcare.

Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies Using AI (QUADAS-AI): Protocol for a Qualitative Study.

BACKGROUND: Quality assessment of diagnostic accuracy studies (QUADAS), and more recently QUADAS-2, were developed to aid the evaluation of methodological quality within primary diagnostic accuracy studies. However, its current form, QUADAS-2 does not address the unique considerations raised by artificial intelligence (AI)-centered diagnostic systems. The rapid progression of the AI diagnostics field mandates suitable quality assessment tools to determine the risk of bias and applicability, and subsequently evaluate translational potential for clinical practice. OBJECTIVE: We aim to develop an AI-specific QUADAS (QUADAS-AI) tool that addresses the specific challenges associated with the appraisal of AI diagnostic accuracy studies. This paper describes the processes and methods that will be used to develop QUADAS-AI. METHODS: The development of QUADAS-AI can be distilled into 3 broad stages. Stage 1-a project organization phase had been undertaken, during which a project team and a steering committee were established. The steering committee consists of a panel of international experts representing diverse stakeholder groups. Following this, the scope of the project was finalized. Stage 2-an item generation process will be completed following (1) a mapping review, (2) a meta-research study, (3) a scoping survey of international experts, and (4) a patient and public involvement and engagement exercise. Candidate items will then be put forward to the international Delphi panel to achieve consensus for inclusion in the revised tool. A modified Delphi consensus methodology involving multiple online rounds and a final consensus meeting will be carried out to refine the tool, following which the initial QUADAS-AI tool will be drafted. A piloting phase will be carried out to identify components that are considered to be either ambiguous or missing. Stage 3-once the steering committee has finalized the QUADAS-AI tool, specific dissemination strategies will be aimed toward academic, policy, regulatory, industry, and public stakeholders, respectively. RESULTS: As of July 2024, the project organization phase, as well as the mapping review and meta-research study, have been completed. We aim to complete the item generation, including the Delphi consensus, and finalize the tool by the end of 2024. Therefore, QUADAS-AI will be able to provide a consensus-derived platform upon which stakeholders may systematically appraise the methodological quality associated with AI diagnostic accuracy studies by the beginning of 2025. CONCLUSIONS: AI-driven systems comprise an increasingly significant proportion of research in clinical diagnostics. Through this process, QUADAS-AI will aid the evaluation of studies in this domain in order to identify bias and applicability concerns. As such, QUADAS-AI may form a key part of clinical, governmental, and regulatory evaluation frameworks for AI diagnostic systems globally. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58202.

Assessing the quality of breast cancer-related videos on TikTok: A cross-sectional study.

Purpose: Breast cancer, the most common cancer in women globally, highlights the need for patient education. Despite many breast cancer discussions on TikTok, their scientific evaluation is lacking. Our study seeks to assess the content quality and accuracy of popular TikTok videos on breast cancer, to improve the dissemination of health knowledge. Methods: On August 22, 2023, we collected the top 100 trending videos from TikTok's Chinese version using "breast cancer/breast nodule" as keywords. We noted their length, TikTok duration, likes, comments, favorites, reposts, uploader types, and topics. Four assessment tools were used: Goobie's six questions, the Patient Educational Material Assessment Tool (PEMAT), the Video Information and Quality Index (VIQI), and the Global Quality Score (GQS). These instruments evaluate videos based on content, informational integrity, and overall quality. Results: Among the 100 videos, content quality was low with Goobie's questions mostly scoring 0, except for management at 1.0 (QR 1.0). PEMAT scores were moderate: 54.1 (QR 1.6) for sum, 47.0 (QR 18.8) for PEMAT-A, and 52.3 (QR 11.7) for PEMAT-U. Regarding the quality of information, the VIQI (sum) median was 14.1 (QR 0.2). Additionally, the median GQS score was 3.5 (QR 0.1). Medical professionals' videos focused on breast cancer stages, while patient videos centered on personal experiences. Patient videos had lower content and overall quality compared to those by medical professionals (PEMAT, GQS: P < 0.001, P = 0.004) but received more comments, indicating higher engagement (all P < 0.05). Conclusion: TikTok's breast cancer content shows educational potential, but while informational quality is moderate, content quality needs improvement. Videos by medical professionals are of higher quality. We recommend increased involvement of healthcare professionals on TikTok to enhance content quality. Non-medical users should share verified information, and TikTok should strengthen its content vetting. Users must scrutinize the credibility of health information on social platforms.

Investigating the use of signal detection information in supervised learning-based image denoising with consideration of task-shift.

Purpose: Recently, learning-based denoising methods that incorporate task-relevant information into the training procedure have been developed to enhance the utility of the denoised images. However, this line of research is relatively new and underdeveloped, and some fundamental issues remain unexplored. Our purpose is to yield insights into general issues related to these task-informed methods. This includes understanding the impact of denoising on objective measures of image quality (IQ) when the specified task at inference time is different from that employed for model training, a phenomenon we refer to as "task-shift." Approach: A virtual imaging test bed comprising a stylized computational model of a chest X-ray computed tomography imaging system was employed to enable a controlled and tractable study design. A canonical, fully supervised, convolutional neural network-based denoising method was purposely adopted to understand the underlying issues that may be relevant to a variety of applications and more advanced denoising or image reconstruction methods. Signal detection and signal detection-localization tasks under signal-known-statistically with background-known-statistically conditions were considered, and several distinct types of numerical observers were employed to compute estimates of the task performance. Studies were designed to reveal how a task-informed transfer-learning approach can influence the tradeoff between conventional and task-based measures of image quality within the context of the considered tasks. In addition, the impact of task-shift on these image quality measures was assessed. Results: The results indicated that certain tradeoffs can be achieved such that the resulting AUC value was significantly improved and the degradation of physical IQ measures was statistically insignificant. It was also observed that introducing task-shift degrades the task performance as expected. The degradation was significant when a relatively simple task was considered for network training and observer performance on a more complex one was assessed at inference time. Conclusions: The presented results indicate that the task-informed training method can improve the observer performance while providing control over the tradeoff between traditional and task-based measures of image quality. The behavior of a task-informed model fine-tuning procedure was demonstrated, and the impact of task-shift on task-based image quality measures was investigated.

Navigating electronic health record accuracy by examination of sex incongruent conditions.

OBJECTIVE: The increasing reliance on electronic health records (EHRs) for research and clinical care necessitates robust methods for assessing data quality and identifying inconsistencies. To address this need, we develop and apply the incongruence rate (IR) using sex-specific medical conditions. We also characterized participants with incongruent records to better understand the scope and nature of data discrepancies. MATERIALS AND METHODS: In this cross-sectional study, we used the All of Us Research Program's latest version 7 (v7) EHR data to identify prevalent sex-specific conditions and evaluated the occurrence of incongruent cases, quantified as IR. RESULTS: Among the 92 597 males and 152 551 females with condition occurrence data available from All of Us and sex-conformed gender, we identified 167 prevalent sex-specific conditions. Among the 37 537 biological males and 95 499 biological females with these sex-specific conditions, we detected an overall IR of 0.86%. Attempt to include non-cisgender participants result in inflated overall IR. Additionally, a significant proportion of participants with incongruent conditions also presented with conditions congruent to their biological sex, indicating a mix of accurate and erroneous records. These incongruences were not geographically or temporally isolated, suggesting systematic issues in EHR data integrity. DISCUSSION: Our findings call attention to the existence of systemic data incongruences in sex-specific conditions and the need for robust validation checks. Extending IR evaluation to non-cisgender participants or non-sex-based conditions remain a challenge. CONCLUSION: The sex condition-specific IR, when applied to adult populations, provides a valuable metric for data quality assessment in EHRs.

Automated echocardiography view classification and quality assessment with recognition of unknown views.

Purpose: Interpreting echocardiographic exams requires substantial manual interaction as videos lack scan-plane information and have inconsistent image quality, ranging from clinically relevant to unrecognizable. Thus, a manual prerequisite step for analysis is to select the appropriate views that showcase both the target anatomy and optimal image quality. To automate this selection process, we present a method for automatic classification of routine views, recognition of unknown views, and quality assessment of detected views. Approach: We train a neural network for view classification and employ the logit activations from the neural network for unknown view recognition. Subsequently, we train a linear regression algorithm that uses feature embeddings from the neural network to predict view quality scores. We evaluate the method on a clinical test set of 2466 echocardiography videos with expert-annotated view labels and a subset of 438 videos with expert-rated view quality scores. A second observer annotated a subset of 894 videos, including all quality-rated videos. Results: The proposed method achieved an accuracy of 84.9 % ± 0.67 for the joint objective of routine view classification and unknown view recognition, whereas a second observer reached an accuracy of 87.6%. For view quality assessment, the method achieved a Spearman's rank correlation coefficient of 0.71, whereas a second observer reached a correlation coefficient of 0.62. Conclusion: The proposed method approaches expert-level performance, enabling fully automatic selection of the most appropriate views for manual or automatic downstream analysis.

Use of the BRANT-MERQS scoring table for the quality assessment of type 3 medication review in patients with rheumatoid arthritis and those with type 2 diabetes mellitus.

Background: A type 3 medication review (MR3) is a patient-centred medication service primarily provided by pharmacists and is presently employed routinely in several countries. In this process, pharmacists interview patients and collaborate with the treating physician to optimize the patient's pharmacotherapy, taking into account the patient's medication history and other medical data including laboratory values. The need to maintain the quality of such interventions during and after their initial implementation cannot be overstated. Aim: The objective of this study was to refine and assess a scoring table to evaluate the quality of MR3 conducted in Belgian community pharmacies. Methods: The comprehensive quality of MR3s was assessed by scoring its various components using a previously developed scoring table, called BRANT-MERQS, Brussels Antwerp Medication Review Quality Score. MR3s were analysed from an implementation study with patients suffering from rheumatoid arthritis (RA, subproject 1) and type 2 diabetes mellitus (T2DM, subproject 2). Additional information was obtained during a telephone call with a subset of participating pharmacists of subproject 1 who finalized their first MR3. Results: In subproject 1, a total of 21 MR3s of patients with RA were examined. The assessment showed favourable scores for elements such as a well-organized medication schedule, treatment adherence, and the elaboration of specific interventions. However, certain other quality criteria posed challenges in the evaluation, for example, the use of simple and understandable language. Pharmacists faced time constraints, and elderly general practitioners (GPs) displayed limited enthusiasm, which were notable barriers observed for this subproject. In the context of subproject 2 that investigated 41 MR3s in patients with T2DM, the quality criteria of interaction between pharmacist and GP, and used sources and tools received high scores. However, there was still room for improvement, especially in areas such as accurate dosing, handling kidney function, QT prolongation, correctly associating laboratory values with relevant drugs and medical conditions, and optimisation of medication schedules for patients. Conclusion: This study demonstrated the feasibility of MR3 quality assessment through a scoring system. However, it also unveiled the tool's current imperfections and highlighted the ongoing need for refinement, something expected of a new service in an implementation phase.

Assessment of Eye Care Apps for Children and Adolescents Based on the Mobile App Rating Scale: Content Analysis and Quality Assessment.

BACKGROUND: In China, the current situation of myopia among children and adolescents is very serious. Prevention and control of myopia are inhibited by the lack of medical resources and the low awareness about eye care. Nevertheless, mobile apps provide an effective means to solve these problems. Since the health app market in China is still immature, it has become particularly important to conduct a study to assess the quality of eye-care apps to facilitate the development of better eye-care service strategies. OBJECTIVE: This study aimed to evaluate the quality, functionality, medical evidence, and professional background of eye-care apps targeting children and adolescents in the Chinese app stores. METHODS: A systematic search on iOS and Android app stores was performed to identify eye-care apps for children and adolescents. The general characteristics, development context, and functional features of the apps were described. Quality assessment of the apps was completed by 2 independent researchers using the Mobile App Rating Scale. RESULTS: This study included 29 apps, of which 17 (59%) were developed by commercial organizations and 12 (41%) had a design with relevant scientific basis. The main built-in functions of these apps include self-testing (18/29, 62%), eye exercises (16/29, 55%), and eye-care education (16/29, 55%). The mean overall quality of eye-care apps was 3.49 (SD 0.33), with a score ranging from 2.89 to 4.39. The overall Mobile App Rating Scale score exhibited a significant positive correlation with the subscale scores (r=0.81-0.91; P<.001). In addition, although most apps provided basic eye-care features, there are some deficiencies. For example, only a few apps were developed with the participation of medical organizations or professional ophthalmologists, and most of the apps were updated infrequently, failing to provide the latest eye-care information and technology in a timely manner. CONCLUSIONS: In general, the quality of eye-care apps for children and teenagers in Chinese app stores is good. These apps fulfill users' needs for eye-care services to a certain extent, but they still suffer from insufficient medical background, low user engagement, and untimely updates. In order to further improve the effectiveness of eye-care apps, cooperation with medical institutions and professional ophthalmologists should be strengthened to enhance the scientific and authoritative nature of the apps. At the same time, interactive features and regular updates should be added to enhance user participation and the continuity of the apps. This study provides a reference for future development or improvement of eye-care apps, which can help promote myopia prevention and control.

Can Patients With Urogenital Cancer Rely on Artificial Intelligence Chatbots for Treatment Decisions?

OBJECTIVES: In the era of artificial intelligence, almost half of the patients use the internet to get information about their diseases. Our study aims to demonstrate the reliability of the information provided by artificial intelligence chatbots (AICs) about urogenital cancer treatments. METHODS: The most frequently searched keyword about prostate, bladder, kidney, and testicular cancer treatment via Google Trends was asked to 3 different AICs (ChatGPT, Gemini, Copilot). The answers were evaluated by 5 different examiners in terms of readability, understandability, actionability, reliability, and transparency. RESULTS: The DISCERN score evaluation indicates that ChatGPT and Gemini provided moderate quality information, while Copilot's quality was low. (Total DISCERN scores; 41, 42, 35, respectively). PEMAT-P Understandability scores were low (40%) and PEMAT-P Actionability scores were moderate only for Gemini (60%) and low for the others (40%). Their readability according to the Coleman-Liau index was above the college level (16.9, 17.2, 16, respectively). CONCLUSIONS: In the era of artificial intelligence, patients will inevitably use AICs due to their easy and fast accessibility. However, patients need to recognize that AICs do not provide stage-specific treatment options, but only moderate-quality, low-reliability information about the disease, as well as information that is very difficult to read.

Quality, supply chain, and use of Trypanocidal drugs among camel keepers in north-eastern Kenya: A cross-sectional study.

The non-cyclic trypanosomiasis (surra), caused by Trypanosoma evansi, and mechanically transmitted by biting flies, hinders camel productivity in Kenya. Trypanocides are the most commonly used drugs to control surra. However, emergence of drug resistance by the parasites is a major limitation to control efforts. There is limited information on the quality of trypanocides, the supply chain and drug-use practices among camel keepers potentially contributing to development of drug resistance in Kenya. We sought to fill this gap by conducting a cross-sectional study among camel keepers in Isiolo and Marsabit counties, Kenya. We mapped the trypanocide drugs supply chain through quantitative and qualitative surveys. We administered a semi-structured questionnaire to camel keepers to generate data on trypanocides-use practices, including the types, sources, person who administers treatment, reconstitution, dosage, route and frequency of administration, among others. Additionally, we tested the quality of trypanocidal drugs retailed in the region. We mapped a total of 55 and 49 agro-veterinary outlets and general (ordinary) shops retailing veterinary drugs in the two counties, respectively. These comprised of 29 and 26 agro-veterinary outlets, as well as 24 and 25 general shops in Isiolo and Marsabit counties, respectively. Overall, the respondents experienced 283 surra cases in the three-month recall period, which were treated with trypanocides. The majority of these cases were diagnosed by camel owners (71.7%) and herders (24.1%). A significant proportion of the cases were treated by camel owners (54.8%), herders (35.3%), the owner's son (3.2%) and veterinary personnel (1.1%) (χ2 = 24.99, p = 0.000). Most of the households sourced the drugs from agro-veterinary outlets (59.0%), followed by general shops (19.8%), veterinary personnel (2.1%), and open-air markets (0.4%) (χ2 = 319.24, p = 0.000). Quinapyramine was the most (56.9%) predominantly used trypanocide in treatment of surra, followed by homidium (19.8%), isometamidium (15.9%), diminazene aceturate (6.7%), and ethidium (0.7%) (χ2 = 340.75, p < 0.000). Only a meager proportion of respondents (15.2%) used the drugs correctly as instructed by the manufacturers. We recorded an association between correct drug usage, with the person who administers the treatment (χ2 = 17.7, p = 0.003), and the type of trypanocide used (χ2 = 19.4, p < 0.001). All the drug samples tested had correct concentrations of active ingredient (100.0%), and therefore of good quality. We have demonstrated that whereas the trypanocides retailed in the region by authorized vendors are of good quality, there is widespread incorrect handling and use of the drugs by unqualified individuals, which may contribute to treatment failure and emergence of trypanocide resistance.