Clinical Course and Nutritional Management of Propionic and Methylmalonic Acidemias

Propionic and methylmalonic acidemias result in multiple health problems including increased risk for neurological and intellectual disabilities. Knowledge regarding factors that correlate to poor prognosis and long-term outcomes is still limited. In this study, we aim to provide insight concerning clinical course and long-term complications by identifying possible correlating factors to complications. Results. This is a retrospective review of 20 Egyptian patients diagnosed with PA (n = 10) and MMA (n = 10) in the years 2014–2018. PA patients had lower DQ/IQ and were more liable to hypotonia and developmental delay. The DQ/IQ had a strong negative correlation with length of hospital stay, frequency of PICU admissions, time delay until diagnosis, and the mode ammonia level. However, DQ/IQ did not correlate with age of onset of symptoms or the peak ammonia level at presentation. Both the growth percentiles and albumin levels had a positive correlation with natural protein intake and did not correlate with the total protein intake. Additionally, patients on higher amounts of medical formula did not necessarily show an improvement in the frequency of decompensation episodes. Conclusion. Our findings indicate that implementation of NBS, vigilant and proactive management of decompensation episodes, and pursuing normal ammonia levels during monitoring can help patients achieve a better neurological prognosis. Furthermore, patients can have a better outcome on mainly natural protein; medical formula should only be used in cases where patients do not meet 100–120% of their DRI from natural protein

Multifocal fatty liver disease, insulin resistance and carotid atherosclerosis: exploring the interrelated relationship

Introduction: Multifocal fatty liver infiltrations are not uncommon ultrasonographic finding; they are explained by the presence of aberrant vascular supply independent of the portal circulation or insulin resistance. Aim: To study the significance of this ultrasonographic finding. Methods: A study group (n = 96) with multifocal fatty liver and two control groups: healthy subjects (n = 100) and patients with diffuse fatty liver disease (n = 100) were enrolled. They were tested for fasting blood glucose, lipid profile, transaminases, serum insulin, glycated hemoglobin, Homeostatic Model Assessment of Insulin Resistance, high-sensitivity C-reactive protein and liver stiffness in Fibroscan. Results: Patients with multifocal fatty liver showed a statistically significant higher values of serum transaminases, markers of insulin resistance, high-sensitivity C-reactive protein, and neutrophil lymphocyte ratio (p <0.05). Lipid profile parameters were significantly higher (p <0.05). Mean liver stiffness (9.8 ± 1.2 kPa) and carotid intima media thickness (1.16 ± 0.2 mm) were significantly higher when compared to healthy subjects and patients with diffuse fatty liver disease. Independent predictors of insulin resistance and premature carotid atherosclerosis in patients with multifocal fatty liver disease were: serum gamma-glutamyl transferase (odds ratio 1.69), high-sensitivity C-reactive protein (odds ratio 1.62), uric acid (odds ratio 1.55), very low-density lipoprotein (odds ratio 1.74), total cholesterol/high-density lipoprotein (odds ratio 1.58) and severity of liver stiffness measured by Fibroscan (odds ratio 1.9). Conclusions: Multifocal fatty liver is an aggressive form of nonalcoholic fatty liver disease and should be considered a radiological sign of insulin resistance that needs special attention and management

Diagnostic accuracy of 18-F FDG-PET/CT in evaluation of malignant neuronal involvement in neurologically manifested cancer patients

Aim and objectives: The aim of this study was to assess the role of 18-F FDG-PET/CT in evaluating the peripheral malignant neuronal affection as well as perineural tumoral spread that occurs in patients with cancers. Methodology: 50 patients with clinical symptoms of neurological deficits (34 male and 16 female) were included, their ages ranged from 17 to 74 with a mean of 45 years. PET/CT was done for all patients followed by clinical correlation after anti-inflammatory drugs and chemotherapy. Results: Interpretation of the PET/CT studies and clinical correlation revealed 10 true positive cases with malignant neuronal involvement, 4 false positive cases diagnosed clinically as radiotherapy-induced neuropathy, 34 true negative cases and 2 false negative cases with negative PET/CT study and clinical evidence of nerve affection with sensitivity 83.33%, specificity 89.47%, PPV 71.43%, NPV 94.44% and diagnostic accuracy 88%. P-value > 0.05 was considered statistically significant. Conclusion: PET/CT has a significant role in detection of neuronal involvement by malignancy in cancer patients. Correlation between PET/CT and clinical outcome after chemotherapy improves the accuracy of diagnosis

Evaluation of coronary stents using mult

Background: Recurrent ischemic symptoms after coronary stenting require imaging assessment to rule-out in-stent restenosis or occlusion. Aim: To evaluate role of multi-detector computed tomography in assessment of coronary artery stents. Patients and methods: Twenty-four patients were referred to assess coronary stents. All were subjected to history taking, clinical examination and computed tomography angiography of coronary arteries using 320-row multi-detector computed tomography. Results: There were totally sixty-three coronary artery stents. Only six stents were non-interpretable. Where forty-eight patent, while nine stents showed in-stent restenosis of significant degree (⩾50%), most stents 3.0 mm diameter. Conclusion: Multi-detector computed tomography is considered convenient and reliably non-invasive imaging modality for assessment of suspected coronary stents with large diameter

Towards better delineation of hydrothermal alterations via multi-sensor remote sensing and airborne geophysical data

Abstract Integrating various tools in targeting mineral deposits increases the chance of adequate detection and characterization of mineralization zones. Selecting a convenient dataset is a key for a precise geological and hydrothermal alteration mapping. Remote sensing and airborne geophysical data have proven their efficiency as tools for reliable mineral exploration. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced land imager (ALI), Landsat 8 (L8), and Sentinel 2 data are widely-used data among various types of remote sensing images in resolving lithological and hydrothermal alteration mapping over the last two decades. ASTER is a well-established satellite in geological remote sensing with detailed Short-wave infrared (SWIR) range compared to visible and near-infrared region (VNIR) that controls iron-associated alteration detection. On contrary, ALI has excellent coverage of the VNIR area (6 bands), but does not possess the potentiality of ASTER for the SWIR and thermal regions. Landsat 8 is widely used and highly recommended for lithological and hydrothermal alteration mapping. The higher spatial (up to 10 m) resolution of Sentinel 2 MSI has preserved its role in producing accurate geological mapping. Notwithstanding the foregoing, implementing the four datasets in a single study is time-consuming. Thus, an important question when commencing an exploration project for hydrothermal alterations-related mineralization (orogenic mineral deposits in the current research) is: which dataset should be adopted to fulfill proper and adequate outputs? Here the four widely recommended datasets (ASTER, ALI, L8, and sentinel 2) have been tested by applying the widely-accepted techniques (false color combinations, band ratios, directed principal component analysis, and constrained energy minimization) for geological and hydrothermal alteration mapping of Gabal El Rukham-Gabal Mueilha district, Egypt. The study area is covered mainly by Neoproterozoic heterogeneous collection of ophiolitic components, island arc assemblage, intruded by enormous granitic rocks. Additionally, airborne magnetic and radiometric data were applied and compared with the remote sensing investigations for deciphering the structural and hydrothermal alteration patterns within the study area. The results demonstrated a different extent from one sensor to another, highlighting their varied efficacy in detecting hydrothermal alterations (mainly hydroxyl-bearing alterations and iron oxides). Moreover, the analysis of airborne magnetic and radiometric data showed hydrothermal alteration zones that are consistent with the detected alteration pattern. The coincidence between high magnetic anomalies, high values of the K/eTh ratio, and the resultant alterations confirm the real alteration anomalies. Over and above that, the remote sensing results and airborne geophysical indications were verified with fieldwork and petrographic investigations, and strongly recommend combining ASTER and Sentinel 2 results in further investigations. Based on the outputs of the current research, we expect better hydrothermal alteration delineation by adopting the current findings as they sharply narrow the zones to be further investigated via costly geophysical and geochemical methods in mineral exploration projects

Patient-Driven Network Selection in multi-RAT Health Systems Using Deep Reinforcement Learning

The recent pandemic along with the rapid increase in the number of patients that require continuous remote monitoring imposes several challenges to support the high quality of services (QoS) in remote health applications. Remote-health (r-health) systems typically demand intense data collection from different locations within a strict time constraint to support sustainable health services. On the contrary, the end-users with mobile devices have limited batteries that need to run for a long time, while continuously acquiring and transmitting health-related information. Thus, this paper proposes an adaptive deep reinforcement learning (DRL) framework for network selection over heteroge-neous r-health systems to enable continuous remote monitoring for patients with chronic diseases. The proposed framework allows for selecting the optimal network(s) that maximizes the accumulative reward of the patients while considering the patients' state. Moreover, it adopts an adaptive compression scheme at the patient level to further optimize the energy consumption, cost, and latency. Our results depict that the proposed framework outperforms the state-of-the-art techniques in terms of battery lifetime and reward maximization.This work was made possible by NPRP grant # NPRP12S-0305-190231 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors

Propionic and Methylmalonic Acidemias: Initial Clinical and Biochemical Presentation

PA and MAA have numerous nonspecific presentations, potentially leading to delayed diagnosis or misdiagnosis. In this paper, we present the clinical and biochemical characteristics of MMA and PA patients at initial presentation. Results. This is a retrospective review of 20 patients with PA (n=10) and MMA (n=10). The most observed symptoms were vomiting (85%) and refusing feeding (70%). Ammonia was 108.75±9.3 μmol/l, showing a negative correlation with pH and bicarbonate and positive correlation with lactate and anion gap. Peak ammonia did not correlate with age of onset (r=0.11 and p=0.64) or age at diagnosis (r=0.39 and p=0.089), nor did pH (r=0.01, p=0.96; r=−0.25, p=0.28) or bicarbonate (r=0.07, p=0.76; r=−0.22, p=0.34). There was no correlation between ammonia and C3 : C2 (r=0.1 and p=0.96) or C3 (r=0.23 and p=0.32). The glycine was 386±167.1 μmol/l, and it was higher in PA (p=0.003). There was a positive correlation between glycine and both pH (r=0.56 and p=0.01) and HCO3 (r=0.49 and p=0.026). There was no correlation between glycine and ammonia (r=−0.435 and p=0.055) or lactate (r=0.32 and p=0.160). Conclusion. Clinical presentation of PA and MMA is nonspecific, though vomiting and refusing feeding are potential markers of decompensation. Blood gas, lactate, and ammonia levels are also good predictors of decompensation, though increasing levels of glycine may not indicate metabolic instability

Image1_Editorial: Novel methods and technologies for the evaluation of drug outcomes and policies.jpeg

The full text of this article can be freely accessed on the publisher's website