Performance optimization – "sometimes" – result creativity

Seen first as an art, " dell'arte della scrittura venezziana " as he called Luke Paciolo accounting became in turn a technique , a science , a language formalized a social game and, more recently , a techno- science. Long preserved and still used the phrase " art of manipulating figures " which is not far from the truth if we consider the professional accountant freedom to choose between two or more accounting policy choice of the best options is dependent a multitude of factors including its ingenuity . Ingenuity , as well as imagination , novelty , originality and creativity are attributes that led the accounting field coverage of a certain reality , as desired and as there are not . Talking about the performance is natural to ask the following questions : is there a dose of fiction in it ? , That the steps in obtaining and menţienerea performance management may be considered constructive practices and practices with negative meanings

Liquid biopsy for early detection of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly prevalent and lethal cancer globally. Over 90% of HCC cases arise in the context of liver cirrhosis, and the severity of the underlying liver disease or advanced tumor stage at diagnosis significantly limits treatment options. Early diagnosis is crucial, and all guidelines stress the importance of screening protocols for HCC early detection as a public health objective. As serum biomarkers are not optimal for early diagnosis, liquid biopsy has emerged as a promising tool for diagnosis, prognostication, and patients’ stratification for personalized therapy in various solid tumors, including HCC. While circulating tumor cells (CTCs) are better suited for personalized therapy and prognosis, cell-free DNA (cfDNA) and extracellular vesicle-based technologies show potential for early diagnosis, HCC screening, and surveillance protocols. Evaluating the added value of liquid biopsy genetic and epigenetic biomarkers for HCC screening is a key goal in translational research. Somatic mutations commonly found in HCC can be investigated in cfDNA and plasma exosomes as genetic biomarkers. Unique methylation patterns in cfDNA or cfDNA fragmentome features have been suggested as innovative tools for early HCC detection. Likewise, extracellular vesicle cargo biomarkers such as miRNAs and long non-coding RNAs may serve as potential biomarkers for early HCC detection. This review will explore recent findings on the utility of liquid biopsy for early HCC diagnosis. Combining liquid biopsy methods with traditional serological biomarkers could improve the overall diagnostic accuracy for early HCC detection

Is measuring serum ammonia helpful in patients with liver cirrhosis?

Background. Ammonia has been traditionally viewed as one of the main culprits for the development of hepatic encephalopathy. In the setting of liver cirrhosis or portal-systemic shunting, hepatocytes fail to metabolize ammonia, and thus excess ammonia reaches the systemic circulation, and from there, the brain. Material and methods. We performed a descriptive study involving 28 adult patients with liver cirrhosis. None of the patients had overt hepatic encephalopathy at the time of assessment, as judged by the West Haven criteria. Severity of liver cirrhosis was measured through the Child-Pugh and MELD scores. Serum ammonia was measured by venous sampling. Results. Mean age of the patients was 50±10 years-old. There were 68% males (n=19). Mean MELD score was 17±5 points. Mean Child-Pugh score was 8±2 points. Mean serum ammonia level was 76±37 μmol/L (range: 34-204 μmol/L). Serum ammonia levels correlated significantly with both scores of liver disease severity, more so with MELD (R=0.61, p=0.0005), than with the Child-Pugh score (R=0.38, p=0.04) Conclusions. We reaffirm the importance of measuring blood ammonia in patients with liver cirrhosis, since it is a helpful biomarker which correlates with liver disease severity and hepatic encephalopathy

Vertebroplasty - minimally invasive treatment for vertebral fractures

Interventional Radiology Department, University Hospital, Bucharest, Romania, “Carol Davila” University, Neurosurgery Clinic, University Hospital, Bucharest, Romania, “Carol Davila” University, Radiology and Medical Imaging Clinic, University Hospital, Bucharest, Romania, Al VIII-lea Congres Naţional de Ortopedie și Traumatologie cu participare internaţională 12-14 octombrie 2016Background and purpose: Vertebral fracture is the most common complication of osteoporosis and sometimes also in osteolytic methastasis, active hemangiomas or multiple myelomas. We present the indications, technique, complications, etc. Methods: Vertebroplasty is the percutaneous placement of polymethylmethacrylate (PMMA) into vertebral compression fractures for relief of pain, performed under fluoroscopic guidance while the exact mechanism of pain relief is unknown, it is believed that the delivery of the ciment into the fracture stabilizes the vertebral body, obtaining an analgesic effect. Results: We present our experience of 14 years in percutaneous vertebroplasty (and kyphoplasty) with common indications, results, complications, new indications, tips and tricks, etc. Conclusions: Vertebroplasty is an alternative to spinal surgery. In experienced centers, percuta-neous vertebroplasty is safe and effective in the treatment of patients with painful vertebral com-pression fractures

Strategies of using organic fertilizers on the permanent grasslands from North-eastern Romania

From the viewpoint of the total natural grassland area, Romania is found on the fifth place in Europe, after France, Great Britain, Spain and Germany. In the present strategy of using organic fertilizers on permanent grasslands, there are economic and ecological concerns, which main aims are resource saving and environment protection, and less important ones, yield increases. The experiment has investigated the influence of organic fertilizers, applied each year or every 2-3 years, at rates of 10-40 Mg ha-1, in a Festuca valesiaca grassland, situated at the height of 107 m, at Ezăreni-Iasi County, and at rates of 10- 30 Mg ha-1, in an Agrostis capillaris+Festuca rubra grassland, situated at the height of 707 m at Pojorîta-Suceava County, on yield and flower composition

Recycling Li-ion batteries in eco-friendly environments

ÖSSZEFOGLALÁS: Jelen tanulmány egy olyan kísérlet eredményeit mutatja be, amelynek célja a színesfémek (pl. Co, Li Cu és Al) kinyerése a már elhasználódott, a mobiltelefon-iparban alkalmazott Li-ion akkumulátorokból. Egy optimális eljárás került kifejlesztésre a LiCoO2 vegyületet tartalmazó aktív paste (elektrolit) elválasztására az alumínium katódtól. Ehhez ultrahangos fürdőt használtunk, amelyben különböző savas oldatok (pl. citromsav, ecetsav, tejsav) szerepeltek oldóanyagként. Az általunk kidolgozott eljárás a következő előnyökkel rendelkezik: alacsony költségigény, nagyfokú hatékonyság (90%), környezetbarát. ABSTRACT: The paper presents the results of a research carried out with the goal of recovering the useful non-ferrous metals (i.e. Co, Li Cu and Al) from spent Li-ion batteries used in the mobile phone industry. An optimal process was developed to separate active paste (containing LiCoO2 compound) from the aluminium cathode. For this purpose, an ultrasonic bath was used, in which different acid solutions (i.e. citric acid, acetic acid, lactic acid) were introduced as a leaching agent. This recovery process presents the following advantages: it has low costs, the process has high recovery efficiency (90%), and is largely ecological

Finite Element Analysis of a Novel Aortic Valve Stent

Worldwide, one of the leading causes of death for patients with cardiovascular disease is aortic valve failure or insufficiency as a result of calcification and cardiovascular disease. The surgical treatment consists of repair or total replacement of the aortic valve. Artificial aortic valve implantation via a percutaneous or endovascular procedure is the minimally invasive alternative to open chest surgery, and the only option for high-risk or older patients. Due to the complex anatomical location between the left ventricle and the aorta, there are still engineering design optimization challenges which influence the long-term durability of the valve. In this study we developed a computer model and performed a numerical analysis of an original self-expanding stent for transcatheter aortic valve in order to optimize its design and materials. The study demonstrates the current valve design could be a good alternative to the existing commercially available valve devices

Deep Learning Algorithm for the Confirmation of Mucosal Healing in Crohn’s Disease, Based on Confocal Laser Endomicroscopy Images

Background and Aims: Mucosal healing (MH) is associated with a stable course of Crohn’s disease (CD) which can be assessed by confocal laser endomicroscopy (CLE). To minimize the operator’s errors and automate assessment of CLE images, we used a deep learning (DL) model for image analysis. We hypothesized that DL combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) can distinguish between normal and inflamed colonic mucosa from CLE images. Methods: The study included 54 patients, 32 with known active CD, and 22 control patients (18 CD patients with MH and four normal mucosa patients with no history of inflammatory bowel diseases). We designed and trained a deep convolutional neural network to detect active CD using 6,205 endomicroscopy images classified as active CD inflammation (3,672 images) and control mucosal healing or no inflammation (2,533 images). CLE imaging was performed on four colorectal areas and the terminal ileum. Gold standard was represented by the histopathological evaluation. The dataset was randomly split in two distinct training and testing datasets: 80% data from each patient were used for training and the remaining 20% for testing. The training dataset consists of 2,892 images with inflammation and 2,189 control images. The testing dataset consists of 780 images with inflammation and 344 control images of the colon. We used a CNN-LSTM model with four convolution layers and one LSTM layer for automatic detection of MH and CD diagnosis from CLE images. Results: CLE investigation reveals normal colonic mucosa with round crypts and inflamed mucosa with irregular crypts and tortuous and dilated blood vessels. Our method obtained a 95.3% test accuracy with a specificity of 92.78% and a sensitivity of 94.6%, with an area under each receiver operating characteristic curves of 0.98. Conclusions: Using machine learning algorithms on CLE images can successfully differentiate between inflammation and normal ileocolonic mucosa and can be used as a computer aided diagnosis for CD. Future clinical studies with a larger patient spectrum will validate our results and improve the CNN-SSTM model

Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model

Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient (grayscale, color Doppler, arterial and venous phase contrast-enhancement and elastography). Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%, [94.5, 98.9] and 98.1%, [96.81, 99.4] for PDAC, 96.5%, [94.1, 98.8], and 99.7%, [99.3, 100] for CPP, and 98.9%, [97.5, 100] and 98.3%, [97.1, 99.4] for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time