ALLN-177, oral enzyme therapy for hyperoxaluria

PURPOSE: To evaluate the potential of ALLN-177, an orally administered, oxalate-specific enzyme therapy to reduce urine oxalate (UOx) excretion in patients with secondary hyperoxaluria. METHODS: Sixteen male and female subjects with both hyperoxaluria and a kidney stone history were enrolled in an open-label study. Subjects continued their usual diets and therapies. During a 3-day baseline period, two 24-h (24-h) urines were collected, followed by a 4-day treatment period with ALLN-177 (7,500 units/meal, 3 × day) when three 24-h urines were collected. The primary endpoint was the change in mean 24-h UOx from baseline. Safety assessments and 24-h dietary recalls were performed throughout. RESULTS: The study enrolled 5 subjects with enteric hyperoxaluria and 11 with idiopathic hyperoxaluria. ALLN-177 was well tolerated. Overall mean (SD) UOx decreased from 77.7 (55.9) at baseline to 63.7 (40.1) mg/24 h while on ALLN-177 therapy, with the mean reduction of 14 mg/24 h, (95% CI - 23.71, - 4.13). The calcium oxalate-relative urinary supersaturation ratio in the overall population decreased from a mean of 11.3 (5.7) to 8.8 (3.8) (- 2.8; 95% CI - 4.9, - 0.79). This difference was driven by oxalate reduction alone, but not any other urinary parameters. Mean daily dietary oxalate, calcium, and fluid intake recorded by frequent diet recall did not differ by study periods. CONCLUSION: ALLN-177 reduced 24-h UOx excretion, and was well tolerated. The results of this pilot study provided justification for further investigation of ALLN-177 in patients with secondary hyperoxaluria

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system.Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set.Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC=0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of similar to 18% against AtheroRisk-Conventional ML (AUC=0.68, P<0.0001, 95% CI: 0.64 to 0.72).Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Metastasis of Malignant Melanoma to Urinary Bladder: A Case Report and Review of the Literature

Aims. Metastatic malignant melanoma of the urinary bladder is a rare clinical entity, with only twenty-three published cases to date. We present a case of this rare entity, a thorough review of the literature, and differential diagnosis of melanoma in the bladder. Methods and Results. A 55-year-old woman with a history of malignant melanoma of the right thigh, excised eight years ago, presented with back pain, fatigue, and hematuria. She underwent computed tomography (CT) scan and was found to have metastases within the liver, spleen, lungs, and urinary bladder. She underwent cystoscopy and transurethral resection of three polypoid lesions. Histologic and immunohistochemical examination revealed metastatic malignant melanoma involving bladder mucosa. Conclusions. This case illustrates the importance of including malignant melanoma in the differential diagnosis of high grade neoplasms of bladder, especially in cases where the relevant clinical history is not available

Renal parenchyma thickness: a rapid estimation of renal function on computed tomography

Purpose: To define the relationship between renal parenchyma thickness (RPT) on computed tomography and renal function on nuclear renography in chronically obstructed renal units (ORUs) and to define a minimal thickness ratio associated with adequate function. Materials and Methods: Twenty-eight consecutive patients undergoing both nuclear renography and CT during a six-month period between 2004 and 2006 were included. All patients that had a diagnosis of unilateral obstruction were included for analysis. RPT was measured in the following manner: The parenchyma thickness at three discrete levels of each kidney was measured using calipers on a CT workstation. The mean of these three measurements was defined as RPT. The renal parenchyma thickness ratio of the ORUs and non-obstructed renal unit (NORUs) was calculated and this was compared to the observed function on Mag-3 lasix Renogram. Results: A total of 28 patients were evaluated. Mean parenchyma thickness was 1.82 cm and 2.25 cm in the ORUs and NORUs, respectively. The mean relative renal function of ORUs was 39%. Linear regression analysis comparing renogram function to RPT ratio revealed a correlation coefficient of 0.48 (p < 0.001). The linear regression equation was computed as Renal Function = 0.48 + 0.80 * RPT ratio. A thickness ratio of 0.68 correlated with 20% renal function. Conclusion: RPT on computed tomography appears to be a powerful predictor of relative renal function in ORUs. Assessment of RPT is a useful and readily available clinical tool for surgical decision making (renal salvage therapy versus nephrectomy) in patients with ORUs

Dedicated robotics team reduces pre-surgical preparation time

Context: Robot-Assisted Laparoscopic Radical Prostatectomy (RALRP) requires significant preoperative setup time for the room, staff, and surgical platform. The utilization of a dedicated robotics operating room (OR) staff may facilitate efficiency and decrease costs. Aims: We sought to determine the degree to which preoperative time decreased as experience was gained. Materials and Methods: A total of 476 patients with a mean age of 60.2 years were evaluated (11/2006 to 1/2010). Data was assimilated through an institutional review board approved blinded, prospective database. Utilizing time from patient arrival in the OR to robot docking as preoperative preparation, our experience was evaluated. Age, body mass index (BMI), and American Society of Anesthesiologists risk scores (ASA) were compared. Statistical Analysis Used: Analysis of variance; Two-sample t-test for unequal variances. Results: The first and last 100 cases were found to have similar age (P=0.27), BMI (P=0.11), and ASA (P=0.09). The average preoperative times were 66. 4 and 53.4 min, respectively (P<0.05). The second 100 patients treated were found to have a significantly shorter preoperative time when compared to the first 100 patients (P<0.05). When the first 100 cases were divided into cohorts of 10 cases the mean preoperative time for the first through fourth cohorts were 80.5, 69.3, 78.8, and 64.7 min, respectively. After treatment of our first 30 patients we found a significant drop in preoperative time. This persisted throughout the remainder of our experience. Conclusions: From the time of patient arrival a number of tasks are accomplished by the non-physician operating room staff during RALRP. The use of a consistent staff can decrease preoperative setup times and, therefore, the overall length of surgery