Diagnostic Indicators of ECG for Coronary Slow Flow Phenomenon; a Systematic Review and Meta-Analysis

Introduction: Currently, epicardial coronary angiography is still the only diagnostic tool for Coronary Slow Flow Phenomenon (CSFP). This study aimed to systematically review studies that compared Electrocardiogram (ECG) findings between patients with and without CSFP. Methods: Using relevant key terms, we systematically searched MEDLINE, Scopus, Embase, and Web of Science to find relevant studies up to February 5th, 2023. Effect sizes in each study were calculated as mean differences and crude odds ratio; then, random-effect models using inverse variance and Mantel-Haenszel methods were used to pool standardized mean differences (SMD) and crude odds ratios, respectively. Results: Thirty-two eligible articles with a total sample size of 3,937 patients (2,069 with CSFP) were included. CSFP patients had higher P-wave maximum (Pmax) (SMD: 1.02 (95% confidence interval (CI): 0.29 - 1.76); p=0.006) and P-dispersion (Pd) (SMD: 1.63 (95% CI: 0.99 - 2.27); p<0.001) compared to the control group. CSFP group also showed significantly longer QT wave maximum duration (SMD: 0.69 (95% CI: 0.33 - 1.06); p<0.001), uncorrected QTd (SMD: 1.89(95% CI: 0.67 - 3.11); p=0.002), and corrected dispersion (QTcd) (SMD: 1.63 (95% CI: 1.09 - 2.17), p<0.001). The frontal QRS-T angle was significantly higher in the CSFP group in comparison with the control group (SMD: 1.18 (95% CI: 0.31 - 2.04; p=0.007). While CSFP patients had a significantly higher T-peak to T-end (Tp-e) (SMD:1.71 (95% CI: 0.91, 2.52), p<0.001), no significant difference was noted between groups in terms of Tp-e to QT (p=0.16) and corrected QT ratios (p=0.07). Conclusion: Our findings suggest several ECG parameters, such as P max, Pd, QT, QTc, QTd, QTcd, Tp-e, and frontal QRS-T angle, may be prolonged in CSFP patients, and they could be employed as diagnostic indicators of CSFP before angiography

Measuring the economic values of natural resources along a freeway: a contingent valuation method

Natural resources are often victims of development. On the one hand, a freeway is under construction, while on the other hand there are unique natural resources along the route of the freeway. Both of them are essential to us. Thus, we cannot ignore either of them. The purpose of this study is to find the monetary equivalent of the natural resources in response to a challenge between environment and development. Dealing with non-use values, we apply the contingent valuation method (CVM). Our results show that the mean of willingness to pay (WTP) is US$1.84 per household for supporting natural resources along the route of the freeway. Total WTP for supporting natural resources along the route of the freeway is US$77 million annually. The findings indicate that Iranian people have a high sensitivity for supporting natural resources along the route of the freeway. Hence, the government should give more attention to natural resources along the freeway and change its route

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.Medicine, Faculty ofScience, Faculty ofNon UBCPhysics and Astronomy, Department ofRadiology, Department ofReviewedFacultyResearche

In-house Optimization Radiolabeling of Recombinant scFv with 99mTc-Tricarbonyl and Stability Studies: Radiolabeling scFv with technetium tricarbonyl

His-tagged scFv fragments of monoclonal antibodies have better pharmacokinetic properties than whole antibodies. Radiolabeled scFvs are considered for targeted imaging and treatment. Technetium tricarbonyl provides radiolabeling of scFvs without losing its biological activity in a fast and easy procedure. Technetium tricabonyl was prepared as follows: A freshly eluted solution of Na99mTcO4 was added to a mixture containing sodium carbonate, sodium potassium tartarate, boranocarbonate, sodium borohydride. The mixture was heated for 30 min at 100°C. Radiochemical purity was determined using radio thin lyer chromatography. Then, technetium tricarbonyl was added to a solution of scFv in PBS buffer and incubated for 2 h at 50°C, purified by PD-10 column and radiochemical purity was determined. Results showed that radiochemical purity of technetium tricarbony was over 98%. The best conditions for radiolabeling of scFv was: scFv concentration &gt;2 mg/mL, PBS buffer, 2 h incubation at 50°C, pH 8-9, and high activity concentration of tricarbonyl. The best radiochemical purity of scFv was 70% before purificarion. Radiolabeled scFv was stable in PBS for 24 h incubation and there was no release of technetium in competition with histidine. In this study, we optimized radiolabeling of a scFv with technetium tricarbonyl using house made boranocarbonates. The results are promising and will be used for future studies. HIGHLIGHTS Radiolabeling of scFv was done directly by 99mTc-tricarbonyl. 99mTc-tricarbonyl was prepared in house from boranocarbonate. 99mTc-Radiolabeled scFv can be used for radioimmunoscintigraphy

Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR

Abstract Background Prostate-specific membrane antigen (PSMA) PET/CT imaging is widely used for quantitative image analysis, especially in radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC). Unknown features influencing PSMA biodistribution can be explored by analyzing segmented organs at risk (OAR) and lesions. Manual segmentation is time-consuming and labor-intensive, so automated segmentation methods are desirable. Training deep-learning segmentation models is challenging due to the scarcity of high-quality annotated images. Addressing this, we developed shifted windows UNEt TRansformers (Swin UNETR) for fully automated segmentation. Within a self-supervised framework, the model’s encoder was pre-trained on unlabeled data. The entire model was fine-tuned, including its decoder, using labeled data. Methods In this work, 752 whole-body [68Ga]Ga-PSMA-11 PET/CT images were collected from two centers. For self-supervised model pre-training, 652 unlabeled images were employed. The remaining 100 images were manually labeled for supervised training. In the supervised training phase, 5-fold cross-validation was used with 64 images for model training and 16 for validation, from one center. For testing, 20 hold-out images, evenly distributed between two centers, were used. Image segmentation and quantification metrics were evaluated on the test set compared to the ground-truth segmentation conducted by a nuclear medicine physician. Results The model generates high-quality OARs and lesion segmentation in lesion-positive cases, including mCRPC. The results show that self-supervised pre-training significantly improved the average dice similarity coefficient (DSC) for all classes by about 3%. Compared to nnU-Net, a well-established model in medical image segmentation, our approach outperformed with a 5% higher DSC. This improvement was attributed to our model’s combined use of self-supervised pre-training and supervised fine-tuning, specifically when applied to PET/CT input. Our best model had the lowest DSC for lesions at 0.68 and the highest for liver at 0.95. Conclusions We developed a state-of-the-art neural network using self-supervised pre-training on whole-body [68Ga]Ga-PSMA-11 PET/CT images, followed by fine-tuning on a limited set of annotated images. The model generates high-quality OARs and lesion segmentation for PSMA image analysis. The generalizable model holds potential for various clinical applications, including enhanced RLT and patient-specific internal dosimetry

Datasheet1_Utility of electrocardiogram to predict the occurrence of the no-reflow phenomenon in patients undergoing primary percutaneous coronary intervention (PPCI): a systematic review and meta-analysis.docx

BackgroundThe no-reflow phenomenon affects about one out of five patients undergoing Primary Percutaneous Coronary Intervention (PPCI). As the prolonged no-reflow phenomenon is linked with unfavorable outcomes, making early recognition is crucial for effective management and improved clinical outcomes in these patients. Our review study aimed to determine whether electrocardiogram (ECG) findings before PCI could serve as predictors for the occurrence of the no-reflow phenomenon.Methods and materialsWe systematically searched MEDLINE, Scopus, and Embase to identify relevant studies. The random-effect model using inverse variance and Mantel-Haenszel methods were used to pool the standardized mean differences (SMD) and odds ratios (OR), respectively.ResultSixteen eligible articles (1,473 cases and 4,264 controls) were included in this study. Based on our meta-analysis of baseline ECG findings, the no-reflow group compared to the control group significantly had a higher frequency of fragmented QRS complexes (fQRS) (OR (95% CI): 1.35 (0.32–2.38), P-value = 0.01), and Q-waves (OR (95% CI): 1.97 (1.01–2.94), P-value ConclusionOur findings suggest that prolonged QRSD, delayed RWPT, higher fQRS prevalence, and the presence of a Q wave on baseline ECG may predict the occurrence of the no-reflow phenomenon in patients undergoing PPCI.</p

Additional file 1 of Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR

Supplementary Material 1: S1. Patient characteristics. S2. Pre-training loss function calculation. S3. Evaluation metric