Clinical Research Research Articles

Review and analysis of history and utilization of panretinal photocoagulation and ranibizumab after publication of protocol S.

We describe the history, utilization, and series results of panretinal photocoagulation (PRP) and ranibizumab and provide an analysis of PRP and ranibizumab usage before versus after the publication of the 2-year and 5-year results of the Diabetic Retinopathy Clinical Research Network (DRCR.net) Protocol S trial. Number of ranibizumabs performed began to increase and number of PRPs performed began to decrease in 2016. After publication of the 2-year results, there was significant negative trend in PRP services and significant positive trend in ranibizumab services (both P < 0.001). After publication of the 5-year results, there was significant negative trend in PRP services (P = 0.003). There were significant negative trends (all P < 0.001) in reimbursement factors for PRP from 2013 to 2020: average work RVU (wRVU), nonfacility physical expense RVU, facility PE RVU, malpractice RVU (MP RVU). Both PRP and ranibizumab have undergone numerous trials comparing their efficacy to other treatment options or no treatment at all. The publication of the 2-year results of Protocol S was associated with an increase in utilization of ranibizumab and decrease in utilization of PRP, with continued decrease after the publication of the 5-year results.

Integration of basic and clinical researches to develop the biomarker of ‍depression

Because of absence of the objective biomarker for major depressive disorder (MDD) or depressive state, psychiatrists depend on subjective examinations in order to properly diagnose their patients. We recently identified the candidates of the objective biomarker of depressive state of late-onset MDD by profiling gene expressions in white blood cells of patients and model mice. We also investigated whether gene expression profiling of white blood cells was useful to elucidate the biological alterations in the brain. Furthermore, we newly developed transgenic mice which will be useful for elucidating the neurological mechanisms of emotional abnormalities in psychiatric disorder. In this review, I introduce our recent research to help for understanding of translational approaches to develop the biomarker of depression.

Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with a high incidence and mortality rate in intensive care unit (ICU) admissions. Early identification of patients at high risk for developing ARDS is crucial for timely intervention and improved clinical outcomes. However, the complex pathophysiology of ARDS makes early prediction challenging. This study aimed to develop an artificial intelligence (AI) model for automated lung lesion segmentation and early prediction of ARDS to facilitate timely intervention in the intensive care unit. A total of 928 ICU patients with chest computed tomography (CT) scans were included from November 2018 to November 2021at three centers in China. Patients were divided into a retrospective cohort for model development and internal validation, and three independent cohorts for external validation. A deep learning-based framework using the UNet Transformer (UNETR) model was developed to perform the segmentation of lung lesions and early prediction of ARDS. We employed various data augmentation techniques using the Medical Open Network for AI (MONAI) framework, enhancing the training sample diversity and improving the model's generalization capabilities. The performance of the deep learning-based framework was compared with a Densenet-based image classification network and evaluated in external and prospective validation cohorts. The segmentation performance was assessed using the Dice coefficient (DC), and the prediction performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The contributions of different features to ARDS prediction were visualized using Shapley Explanation Plots. This study was registered with the China Clinical Trial Registration Centre (ChiCTR2200058700). The segmentation task using the deep learning framework achieved a DC of 0.734±0.137 in the validation set. For the prediction task, the deep learning-based framework achieved AUCs of 0.916 [0.858-0.961], 0.865 [0.774-0.945], 0.901 [0.835-0.955], and 0.876 [0.804-0.936] in the internal validation cohort, external validation cohort I, external validation cohort II, and prospective validation cohort, respectively. It outperformed the Densenet-based image classification network in terms of prediction accuracy. Moreover, the ARDS prediction model identified lung lesion features and clinical parameters such as C-reactive protein, albumin, bilirubin, platelet count, and age as significant contributors to ARDS prediction. The deep learning-based framework using the UNETR model demonstrated high accuracy and robustness in lung lesion segmentation and early ARDS prediction, and had good generalization ability and clinical applicability. This study was supported by grants from the Shanghai Renji Hospital Clinical Research Innovation and Cultivation Fund (RJPY-DZX-008) and Shanghai Science and Technology Development Funds (22YF1423300).

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

The ADNI4 Digital Study: A novel approach to recruitment, screening, and assessment of participants for AD clinical research.

We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimer's Disease Neuroimaging Initiative (ADNI4). Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs. Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level. Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges. A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate>50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.

Clinical Research Abstracts of the British Equine Veterinary Association Congress 2024.