Quantification of Aortic Valve Fibrotic and Calcific Tissue from CTA: Prospective Comparison with Histology.

Background Quantifying the fibrotic and calcific composition of the aortic valve at CT angiography (CTA) can be useful for assessing disease severity and outcomes of patients with aortic stenosis (AS); however, it has not yet been validated against quantitative histologic findings. Purpose To compare quantification of aortic valve fibrotic and calcific tissue composition at CTA versus histologic examination. Materials and Methods This prospective study included patients who underwent CTA before either surgical aortic valve replacement for AS or orthotopic heart transplant (controls) at two centers between January 2022 and April 2023. At CTA, fibrotic and calcific tissue composition were quantified using automated Gaussian mixture modeling applied to the density of aortic valve tissue components, calculated as [(volume/total tissue volume) × 100]. For histologic evaluation, explanted valve cusps were stained with Movat pentachrome as well as hematoxylin and eosin. For each cusp, three 5-µm slices were obtained. Fibrotic and calcific tissue composition were quantified using a validated artificial intelligence tool and averaged across the aortic valve. Correlations were assessed using the Spearman rank correlation coefficient. Intermodality and interobserver variability were measured using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Results Twenty-nine participants (mean age, 63 years ± 10 [SD]; 23 male) were evaluated: 19 with severe AS, five with moderate AS, and five controls. Fibrocalcific tissue composition strongly correlated with histologic findings (r = 0.92; P < .001). The agreement between CTA and histologic findings for fibrocalcific tissue quantification was excellent (ICC, 0.94; P = .001), with underestimation of fibrotic composition at CTA (bias, -4.9%; 95% limits of agreement [LoA]: -18.5%, 8.7%). Finally, there was excellent interobserver repeatability for fibrotic (ICC, 0.99) and calcific (ICC, 0.99) aortic valve tissue volume measurements, with no evidence of a difference in measurements between readers (bias, -0.04 cm3 [95% LoA: -0.27 cm3, 0.19 cm3] and 0.02 cm3 [95% LoA: -0.14 cm3, 0.19 cm3], respectively). Conclusion In a direct comparison, standardized quantitative aortic valve tissue characterization at CTA showed excellent concordance with histologic findings and demonstrated interobserver reproducibility. Clinical trial registration no. NCT06136689 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Almeida in this issue.

High mortality in free-ranging wild boars associated with African swine fever virus in India.

The present study focuses on the pathological and molecular characterization of African swine fever virus (ASFV) associated with an outbreak in wild boars in two national parks in southern India in 2022-2023. Significant mortality was observed among free-ranging wild boars at Bandipur National Park, Karnataka, and Mudumalai National Park, Tamil Nadu. Extensive combing operations were undertaken in both national parks, spanning an area of around 100 km2, originating from the reported epicenter, to estimate the mortality rate. Recovered carcasses were pathologically examined, and ASFV isolates was genetically characterized. Our findings suggested spillover infection of ASFV from nearby domestic pigs, and the virus was equally pathogenic in wild boars and domestic pigs. ASFV intrusion was reported in the Northeastern region of the country, which borders China and Myanmar, whereas the current outbreak is very distantly located, in southern India. Molecular data will help in tracing the spread of the virus in the country.

MRI-based radiomic features for identifying recurrent prostate cancer after proton radiation therapy.

PURPOSE: Magnetic Resonance Imaging (MRI) evaluation of recurrent prostate cancer (PCa) following proton beam therapy is challenging due to radiation-induced tissue changes. This study aimed to evaluate MRI-based radiomic features so as to identify the recurrent PCa after proton therapy. METHODS: We retrospectively studied 12 patients with biochemical recurrence (BCR) following proton therapy. Two experienced radiologists identified prostate lesions from multi-parametric MRI (mpMRI) images post-proton therapy and marked control regions of interest (ROIs) on the contralateral side of the prostate gland. A total of 210 radiomic features were extracted from lesions and control regions on the T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) image series. Recursive Feature Elimination with Cross-Validation method (RFE-CV) was used for feature selection. A Multilayer Perceptron (MLP) neural network was developed to classify three classes: cancerous, benign, and healthy tissue. The 12-core biopsy results were used as the gold standard for the segmentations. The classifier performance was measured using specificity, sensitivity, the area under receiver operating characteristic curve (AUC), and other statistical indicators. RESULTS: Based on biopsy results, 10 lesions were identified as PCa recurrence while eight lesions were confirmed to be benign. Ten radiomic features (10/210) were selected to build the multi-class classifier. The radiomics classifier gave an accuracy of 0.83 in identifying cancerous, benign, and healthy tissue with a sensitivity of 0.80 and specificity of 0.85. The model yielded an AUC of 0.87, 95% CI [0.72-1.00] in differentiating cancer from the benign and healthy tissues. CONCLUSIONS: Our proof-of-concept study demonstrates the potential of using radiomic features as part of the differential diagnosis of PCa on mpMRI following proton therapy. The results need to be validated in a larger cohort.

A Novel Method of Intraoperative Calculation in Follicular Unit Transplantation: 'The Sequential Strip and FUE Method'.

Hair loss, in particular androgenetic alopecia, has troubled humans since the dawn of history. Treatment options for hair restoration have undergone massive transformation from punch grafting to follicular unit transplantation. Current surgical treatment options in hair restoration fall broadly under two categories, follicular unit transplantation most commonly known as strip method and follicular unit extraction (FUE). The strip method though widely used initially is not so common now due to its fair share of disadvantages ranging from linear donor scar, scar widening to strip overharvesting and wastage of grafts. Follicular unit excision (FUE) was introduced as an alternative method for extraction of grafts to combat the donor linear scar produced by strip method but the disadvantages of FUE include the number of grafts harvested in a single session, moth eaten appearance of donor area caused by over extraction of grafts and harvesting from outside the safe zone. Newer developments like extraction of axillary hair, body hair and pubic hair have been sought to overcome the limitations of number of grafts harvested in a single session of FUE. With more patients now affected by alopecia in their early 20s, there is an ever-increasing demand from the patients for the youthful hairline and hence the focus has shifted towards mega and giga sessions of hair transplantation which pose danger of over extraction of grafts leading to depletion of available donor sites. This article elaborates the combined sequential strip and FUE method along with an intraoperative calculation model to overcome the limitations of over extraction and wastage of grafts. (1) Combination of techniques Strip method with FUE. (2) An intraoperative calculation model that aids in limiting over extraction and wastage of grafts. (3) It is a real time model which can be applied in practice with ease.Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Genetic mutations in smoking-associated prostate cancer.

OBJECTIVES: Tobacco smoking is known to cause cancers potentially predisposed by genetic risks. We compared the frequency of gene mutations using a next generation sequencing database of smokers and nonsmokers with prostate cancer (PCa) to identify subsets of patients with potential genetic risks. MATERIALS AND METHODS: Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE) registry was analyzed. The GENIE registry contains clinically annotated sequenced tumor samples. We included 1832 men with PCa in our cohort, categorized as smokers and nonsmokers, and compared the frequency of mutations (point mutations, copy number variations, and structural variants) of 47 genes with more than 5% mutation rate between the two categories and correlated with overall survival using logistic regression analysis. RESULTS: Overall, 1007 (55%) patients were nonsmokers, and 825 (45%) were smokers. The mutation frequency was significantly higher in smokers compared to nonsmokers, 47.6% and 41.3%, respectively (p = 0.02). The median tumor mutational burden was also significantly higher in the samples from smokers (3.59 mut/MB) compared to nonsmokers (1.87 mut/MB) (p < 0.001). Patients with a smoking history had a significantly higher frequency of PREX2, PTEN, AGO2, KMT2C, and a lower frequency of adenomatous polyposis coli (APC) and KMT2A mutations than compared to nonsmokers. The overall mortality rate (28.5% vs. 22.8%) was significantly higher among smokers (p = 0.006). On a multivariate logistic regression analysis, the presence of metastatic disease at the time of diagnosis (OR: 2.26, 95% CI: 1.78-2.89, p < 0.001), smoking history (OR: 1.32, 95% CI: 1.05-1.65, p = 0.02), and higher frequency of PTEN somatic gene mutation (OR: 1.89, 95% CI: 1.46-2.45, p < 0.001) were independent predictors of increased overall mortality among patients with PCa. Patients with PTEN mutation had poorer overall survival compared to men without PTEN mutations: 96.00 (95% CI: 65.36-113.98) and 120.00 (95% CI: 115.05-160.00) months, respectively (p < 0.001) irrespective of smoking history although the G129R PTEN mutation was characteristically detected in smokers. CONCLUSIONS: PCa patients with a tobacco smoking history demonstrated a significantly higher frequency of somatic genetic mutations. Whereas mutations of PREX2, KMT2C, AGO2, and PTEN genes were higher in smokers, the APC and KMT2A mutations were higher in nonsmokers. The PTEN somatic gene mutation was associated with increased overall mortality among patients with PCa irrespective of smoking history. We found that G129R PTEN mutation known to reduce the PTEN phosphatase activity and K267Rfs*9 a frameshift deletion mutation in the C2 domain of PTEN associated with membrane binding exclusively detected in smokers and nonsmokers, respectively. These findings may be used to further our understanding of PCa associated with smoking.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179). RESULTS: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). CONCLUSION: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Development and evaluation of recombinant gD protein based ELISA for sero-surveillance of BoHV-1 in India.

Bovine herpes virus-1 (BoHV-1) is responsible for production losses through decreased milk yields, abortions, infertility, and trade restrictions in the bovine population. The disease is endemic in many countries including India. As the virus harbors a unique feature of latency animals once infected with the virus remain sero-positive for lifetime and can re-excrete the virus when exposed to stressful conditions. Hence, identification and culling of infected animals is only the means to minimize infection-associated losses. In this study, an economical indigenous assay for the detection of BoHV-1 specific antibodies was developed to cater to the huge bovine population of the country. The viral structural gD protein, expressed in the prokaryotic system was used for optimization of an indirect ELISA for bovines followed by statistical validation of the assay. The diagnostic sensitivity and specificity of the indirect ELISA were 82.9% and 91.3% respectively. Systematically collected serum samples representing organized, unorganized and breeding farms of India were tested with the indigenously developed assay for further validation.

A Rare Case of Rosai-Dorfman Disease Affecting the Brachial Plexus: A Case Report.

Sinus histiocytosis with massive lymphadenopathy or Rosai-Dorfman disease is a rare but well-established entity, commonly affecting the lymph nodes of the head and neck. Extranodal presentation, affecting the central nervous system, eye orbit, kidneys, testis, bones, upper respiratory tract, lungs, thyroid, small intestine, and peritoneum, has been reported. We present a case of a rare presentation of Rosai-Dorfman disease affecting the brachial plexus.

The Geriatric Nutritional Risk Index Predicts Postoperative Outcomes in Bladder Cancer: A Propensity Score-Matched Analysis.

PURPOSE: The Geriatric Nutritional Risk Index (GNRI) is a simple screening tool to predict nutrition-related risk of morbidity and mortality in older patients. We assessed whether preoperative GNRI was associated with 30-day complications after radical cystectomy (RC). MATERIALS AND METHODS: Using the American College of Surgeons National Surgical Quality Improvement Program database, we identified patients 65 years or older who underwent RC for the treatment of bladder cancer between 2007 and 2019. Patients were dichotomized into at-risk (GNRI ≤98) or no-risk (GNRI >98) groups. Using propensity score matching, the 2 groups were compared for baseline differences and 30-day outcomes. We evaluated GNRI as an independent predictor of postoperative complications using multivariable logistic regression analysis. RESULTS: We identified 2,926 patients eligible for analysis. After propensity score matching, patients in the at-risk GNRI group had higher rates of any complication (p=0.017), blood transfusion (p=0.002), extended length of stay (p=0.004) and nonhome discharge (p <0.001). Multivariable logistic regression analysis revealed that a decreasing GNRI is an independent prognostic factor for mortality (OR 1.05, 95% CI 1.01-1.08, p=0.009), blood transfusion (OR 1.03, 95% CI 1.02-1.04, p <0.001), pneumonia (OR 1.04, 95% CI 1.01-1.07, p=0.013), extended length of stay (OR 1.03, 95% CI 1.02-1.05, p <0.001) and nonhome discharge (OR 1.04, 95% CI 1.03-1.06, p <0.001). CONCLUSIONS: We demonstrate that nutritional status evaluated by GNRI predicts 30-day complications after RC.

Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT.

BACKGROUND: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD). METHODS AND RESULTS: Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1309/2079 (63%) patients. MLS had higher area under the receiver operator characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, P < .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P < .01) and high-risk CAD, defined as stenosis of the left main, proximal left anterior descending, or triple-vessel CAD (sensitivity 96% vs 89% vs 90%, P < .01). CONCLUSIONS: The MLS is highly sensitive for prediction of both obstructive and high-risk CAD from stress-only MPI and can be applied to a stress-first protocol for automatic cancellation of unnecessary rest imaging.

Machine learning algorithm for feature space clustering of mixed data with missing information based on molecule similarity.

Clustering Algorithms have just fascinated significant devotion in machine learning applications owing to their great competence. Nevertheless, the existing algorithms quite have approximately disputes that need to be further deciphered. For example, most existing algorithms transform one type of feature into another type, which disregards the explicit possessions of information. In addition, most of them deliberate whole features, which may lead to difficulty in calculation and effect in sub-optimal presentation. To address the above difficulties, this paper proposes a novel technique for clustering categorical and numerical features based on feature space clustering of mixed data with missing information (FSCMMI). The procedure involves three stages. Initially, FSCMMI divides the given dataset depending on missing information in instances and features types. The second stage uses the decision-tree procedure to identify the association between instances. Finally, the third stage is used for computing the closeness measure for numerical features and categorical features. Meanwhile, we propose a new training algorithm to cluster mixed datasets. Extensive experimental results on benchmark datasets show that the proposed FSCMMI outperforms several state-of-art clustering methods in terms of accuracy and efficiency.

Combination therapy in metastatic castration sensitive prostate cancer: A Systematic review and network meta-analysis.

Introduction: Studies directly comparing the different combination therapies offered to men with metastatic castration sensitive prostate cancer (mCSPC), are not available yet. This study was designed using the network meta-analysis (NMA) framework to provide a comparison of the different available options for the treatment of men with mCSPC. Methods: A systematic search was performed and the prospective randomized controlled trials reporting the overall survival (OS) or failure-free survival (FFS) were selected for review. A total of 14 studies were included in the NMA. Results: The addition of abiraterone, apalutamide, docetaxel, and docetaxel with zoledronic acid to the androgen deprivation therapy (ADT) demonstrated a significant improvement in the OS. In indirect comparison, abiraterone had a higher impact on the OS as compared to docetaxel (hazard ratio [HR]: 1.21, 95% confidence interval [CI]: 1.0-1.46) and docetaxel with zoledronic acid (HR: 1.31, 95% CI: 1.05-1.63) but not apalutamide. Furthermore, apalutamide was not different than docetaxel or docetaxel with zoledronic acid. There was a significant improvement in the FFS with the combination of abiraterone, apalutamide, docetaxel (HR: 0.61, 95% CI: 0.46-0.81), docetaxel with zoledronic acid (HR: 0.62, 95% CI: 0.43-0.9), and enzalutamide (HR: 0.39, 95% CI: 0.25-0.61) as compared to the ADT alone. Similar to the indirect comparison of OS, abiraterone outperformed docetaxel (HR: 1.66, 95% CI: 1.12-2.47), docetaxel with zoledronic acid (HR: 1.69, 95% CI: 1.06-2.68), and enzalutamide (HR: 1.06, 95% CI: 0.63-1.80), but not apalutamide in terms of impact on the FFS. Conclusion: Overall, abiraterone demonstrated better OS and FFS outcomes as compared to all the other combination strategies in this NMA.

Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study.

BACKGROUND: We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term risk of major adverse cardiovascular events (MACE) in asymptomatic individuals. METHODS: This was a post-hoc analysis of the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) study of participants who underwent baseline coronary artery calcium (CAC) scoring CT and 14-year follow-up for MACE (myocardial infarction, late revascularization, or cardiac death). EAT volume (cm3) and attenuation (Hounsfield units [HU]) were quantified from CT using fully automated deep learning software (< 30 s per case). NAFLD was defined as liver-to-spleen attenuation ratio < 1.0 and/or average liver attenuation < 40 HU. RESULTS: In the final population of 2068 participants (59% males, 56 ± 9 years), those with MetS (n = 280;13.5%) had a greater prevalence of NAFLD (26.0% vs. 9.9%), higher EAT volume (114.1 cm3 vs. 73.7 cm3), and lower EAT attenuation (-76.9 HU vs. -73.4 HU; all p < 0.001) compared to those without MetS. At 14 ± 3 years, MACE occurred in 223 (10.8%) participants. In multivariable Cox regression, MetS was associated with increased risk of MACE (HR 1.58 [95% CI 1.10-2.27], p = 0.01) independently of CAC score; however, not after adjustment for EAT measures (p = 0.27). In a separate Cox analysis, NAFLD predicted MACE (HR 1.78 [95% CI 1.21-2.61], p = 0.003) independently of MetS, CAC score, and EAT measures. Addition of EAT volume to current risk assessment tools resulted in significant net reclassification improvement for MACE (22% over ASCVD risk score; 17% over ASCVD risk score plus CAC score). CONCLUSIONS: MetS, NAFLD, and artificial intelligence-based EAT measures predict long-term MACE risk in asymptomatic individuals. Imaging biomarkers of cardiometabolic disease have the potential for integration into routine reporting of CAC scoring CT to enhance cardiovascular risk stratification. Trial registration NCT00927693.

Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score.

OBJECTIVES: The machine learning ischemia risk score (ML-IRS) is a machine learning-based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA. METHODS: This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male). The primary outcome was revascularization by either percutaneous coronary revascularization or coronary artery bypass grafting. Blinded readers performed semi-automated quantitative coronary plaque analysis. The ML-IRS was automatically computed. Relationships between clinical risk factors, coronary plaque features, and ML-IRS with revascularization were examined. RESULTS: The study cohort consisted of 352 subjects with 1056 analyzable vessels. The ML-IRS ranged between 0 and 81% with a median of 18.7% (6.4-34.8). Revascularization was performed in 26% of vessels. Vessels receiving revascularization had higher ML-IRS (33.6% (21.1-55.0) versus 13.0% (4.5-29.1), p < 0.0001), as well as higher contrast density difference, and total, non-calcified, calcified, and low-density plaque burden. ML-IRS, when added to a traditional risk model based on clinical data and stenosis to predict revascularization, resulted in increased area under the curve from 0.69 (95% CI: 0.65-0.72) to 0.78 (95% CI: 0.75-0.81) (p < 0.0001), with an overall continuous net reclassification improvement of 0.636 (95% CI: 0.503-0.769; p < 0.0001). CONCLUSIONS: ML-IRS from quantitative coronary CT angiography improved the prediction of future revascularization and can potentially identify patients likely to receive revascularization if referred to cardiac catheterization. KEY POINTS: • Machine learning ischemia risk from quantitative coronary CT angiography was significantly higher in patients who received revascularization versus those who did not receive revascularization. • The machine learning ischemia risk score was significantly higher in patients with invasive fractional flow ≤ 0.8 versus those with > 0.8. • The machine learning ischemia risk score improved the prediction of future revascularization significantly when added to a standard prediction model including stenosis.

Age influences the thermal suitability of Plasmodium falciparum transmission in the Asian malaria vector Anopheles stephensi.

Models predicting disease transmission are vital tools for long-term planning of malaria reduction efforts, particularly for mitigating impacts of climate change. We compared temperature-dependent malaria transmission models when mosquito life-history traits were estimated from a truncated portion of the lifespan (a common practice) versus traits measured across the full lifespan. We conducted an experiment on adult female Anopheles stephensi, the Asian urban malaria mosquito, to generate daily per capita values for mortality, egg production and biting rate at six constant temperatures. Both temperature and age significantly affected trait values. Further, we found quantitative and qualitative differences between temperature-trait relationships estimated from truncated data versus observed lifetime values. Incorporating these temperature-trait relationships into an expression governing the thermal suitability of transmission, relative R0(T), resulted in minor differences in the breadth of suitable temperatures for Plasmodium falciparum transmission between the two models constructed from only An. stephensi trait data. However, we found a substantial increase in thermal niche breadth compared with a previously published model consisting of trait data from multiple Anopheles mosquito species. Overall, this work highlights the importance of considering how mosquito trait values vary with mosquito age and mosquito species when generating temperature-based suitability predictions of transmission.

Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT).

BACKGROUND: We aim to establish a multicenter registry collecting clinical, imaging, and follow-up data for patients who undergo myocardial perfusion imaging (MPI) with the latest generation SPECT scanners. METHODS: REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) uses a collaborative design with multicenter contribution of clinical data and images into a comprehensive clinical-imaging database. All images are processed by quantitative software. Over 290 individual imaging variables are automatically extracted from each image dataset and merged with clinical variables. In the prognostic cohort, patient follow-up is performed for major adverse cardiac events. In the diagnostic cohort (patients with correlating invasive angiography), angiography and revascularization results within 6 months are obtained. RESULTS: To date, collected prognostic data include scans from 20,418 patients in 5 centers (57% male, 64.0 ± 12.1 years) who underwent exercise (48%) or pharmacologic stress (52%). Diagnostic data include 2079 patients in 9 centers (67% male, 64.7 ± 11.2 years) who underwent exercise (39%) or pharmacologic stress (61%). CONCLUSION: The REFINE SPECT registry will provide a resource for collaborative projects related to the latest generation SPECT-MPI. It will aid in the development of new artificial intelligence tools for automated diagnosis and prediction of prognostic outcomes.

Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry.

BACKGROUND: Upper reference limits for transient ischemic dilation (TID) have not been rigorously established for cadmium-zinc-telluride (CZT) camera systems. We aimed to derive TID limits for common myocardial perfusion imaging protocols utilizing a large, multicenter registry (REFINE SPECT). METHODS: One thousand six hundred and seventy-two patients with low likelihood of coronary artery disease with normal perfusion findings were identified. Images were processed with Quantitative Perfusion SPECT software (Cedars-Sinai Medical Center, Los Angeles, CA). Non-attenuation-corrected, camera-, radiotracer-, and stress protocol-specific TID limits in supine position were derived from 97.5th percentile and mean + 2 standard deviations (SD). Reference limits were compared for different solid-state cameras (D-SPECT vs. Discovery), radiotracers (technetium-99m-sestamibi vs. tetrofosmin), different types of stress (exercise vs. four different vasodilator-based protocols), and different vasodilator-based protocols. RESULTS: TID measurements did not follow Gaussian distribution in six out of eight subgroups. TID limits ranged from 1.18 to 1.52 (97.5th percentile) and 1.18 to 1.39 (mean + 2SD). No difference was noted between D-SPECT and Discovery cameras (P = 0.71) while differences between exercise and vasodilator-based protocols (adenosine, regadenoson, or regadenoson-walk) were noted (all P < 0.05). CONCLUSIONS: We used a multicenter registry to establish camera-, radiotracer-, and protocol-specific upper reference limits of TID for supine position on CZT camera systems. Reference limits did not differ between D-SPECT and Discovery camera.

Parapharyngeal Alveolar Soft Part Sarcoma in a 5-Year-Old Child.

Alveolar soft part sarcoma (ASPS) is a rare malignancy of head and neck region; orbit and tongue being most common subsites affected by this rare tumor. A 5-year-old female presented with computed tomography scan evidence of right-sided prestyloid parapharyngeal mass. Complete excision was performed through trans-cervical approach. Histopathology, special stain, and immunohistochemistry of the resected specimen confirmed the diagnosis of ASPS. Three-year follow-up did not reveal any local recurrence or distant metastases. Review of the English-language scientific literature did not reveal any documented case of parapharyngeal space ASPS in pediatric age. Only 1 clinical report of ASPS in a 42-year-old female has been documented.

Protein kinase D1 induces G1-phase cell-cycle arrest independent of Checkpoint kinases by phosphorylating Cell Division Cycle Phosphatase 25.

Protein Kinase D1 (PrKD1) functions as a tumor and metastasis suppressor in several human cancers by influencing cell-cycle progression. However, the exact mechanism of cell-cycle regulation by PrKD1 is unclear. Overexpression and ectopic expression of PrKD1 induces G1 arrest in cancer cell lines. Because checkpoint kinases (CHEKs) are known to play a role in progression through the G1 phase, we downregulated CHEK1, which did not overcome the G1 arrest induced by PrKD1. Using in vitro phosphorylation and Western blot assays, we showed that PrKD1 phosphorylates all CDC25 isoforms (known substrates of CHEK kinases), independent from CHEK kinases, suggesting that direct phosphorylation of CDC25 by PrKD1 may be an alternate mechanism of G1 arrest. The study has identified a molecular mechanism for the influence of PrKD1 in cell-cycle progression.

Antitumor effects of chitin specific lectin from Praecitrullus fistulosus by targeting angiogenesis and apoptosis.

In the present study, chitin specific lectin was purified from fruit exudates of Praecitrullus fistulosus. The lectin was purified and analyzed using affinity chromatography, RP-HPLC and electrophoretic studies. Furthermore, protein was identified by MALDI-MS/MS and peptide mass fingerprinting. Purified lectin (PfL) effectively agglutinates RBC, lymphocytes and displayed strong cytotoxicity against colon cancer (line HT29) cells among screened cells. PfL induced apoptosis by altering the expression of apoptotic proteins via caspadse-3 dependent pathway. In vivo studies using EAC mice model proves the efficacy of PfL by activating apoptosis and inhibiting the tumor neovasculature by targeting the MVD, VEGF and MMP's secretion. More importantly, the PfL treatment leads to effective inhibition of tumor growth and a ∼2.71 fold increase in the lifespan of EAC mice. Collectively, our study provides comprehensive evidence that the role of dietary lectins with significant cytotoxic potential by targeting tumor angiogenesis and activating apoptosis in cancer study models.