Cloning of anti-hbsag single chain variable fragments from hybridoma cells for one-step elisa

Hepatitis B virus (HBV) infection is a worldwide health problem. More than 400 million people are chronic HBV carriers in the world. Infected individuals are at a high risk of developing liver cirrhosis and hepatocellular carcinoma as the main consequences of HBV. The discoveries of fast diagnostic systems and new therapeutic applications are crucial in the fight against viral hepatitis. In this paper we present the generation of a single-chain variable fragment (scFv) from a mouse monoclonal antibody specific to the HBV surface antigen (HBsAg) and demonstrate its expression as a bacterial alkaline phosphatase (AP) fusion protein. In this study, we constructed scFvs from hybridoma cells expressing HBsAg-specific antibody using phage display technology and expressed them in Escherichia coli. The anti-HBsAg scFvs were inserted into pQE-2 vector to produce scFv antibody genetically fused to bacterial AP. Reproducibility of the recombinant HBsAg-scFv fusion protein was tested using Enzyme-linked Immunosorbent Assay (ELISA). Present preliminary findings indicate that the anti-HBsAg-scFv AP conjugate could be further used for the development of one-step ELISA for the detection of HBV

Volume XLIX, Number 13, November 10, 1931

3rd World Heart Failure Congress -- NOV 29-DEC 02, 2012 -- Istanbul, TURKEYWOS: 00031169820000

Inotrope Analysis for Acute and Chronic Reduced-EF Heart Failure Using Fuzzy Multi-Criteria Decision Analysis

Heart failure is a progressive disease that leads to high mortality rates if left untreated, and inotropes are a class of drugs used to treat a type of heart failure where patients have reduced ejection fraction (HFrEF). This study aims to utilize the Fuzzy-Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE), an effectively used multi-criteria decision making (MCDM) technique. To analyze the characteristics of the most often used inotropes for acute HFrEF and chronic HFrEF, we use the same parameters set with distinct importance factors and aims for each property and, therefore, mathematically demonstrate the strengths and weaknesses of each inotrope alternative. As a result, a detailed ranking list for each HFrEF class was obtained, with supplementary information on how each parameter contributed to the ranking of each inotrope. From these results, it was concluded that the F-PROMETHEE method is applicable for evaluating the risks and benefits of various inotropes to determine a starting point for treating an average patient when making a quick decision without complete patient data. As demonstrated in this study, it is possible to easily use the same data set and only change some preference parameters according to individual needs to produce patient-specific results. In this study, we showed that creating a decision-making system that mathematically assists clinical specialists with their decision-making process is feasible

The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review

One of the most challenging and prevalent side effects of LVAD implantation is that of right heart failure (RHF) that may develop afterwards. The purpose of this study is to review and highlight recent advances in the uses of AI in evaluating RHF after LVAD implantation. The available literature was scanned using certain key words (artificial intelligence, machine learning, left ventricular assist device, prediction of right heart failure after LVAD) was scanned within Pubmed, Web of Science, and Google Scholar databases. Conventional risk scoring systems were also summarized, with their pros and cons being included in the results section of this study in order to provide a useful contrast with AI-based models. There are certain interesting and innovative ML approaches towards RHF prediction among the studies reviewed as well as more straightforward approaches that identified certain important predictive clinical parameters. Despite their accomplishments, the resulting AUC scores were far from ideal for these methods to be considered fully sufficient. The reasons for this include the low number of studies, standardized data availability, and lack of prospective studies. Another topic briefly discussed in this study is that relating to the ethical and legal considerations of using AI-based systems in healthcare. In the end, we believe that it would be beneficial for clinicians to not ignore these developments despite the current research indicating more time is needed for AI-based prediction models to achieve a better performance

Chylothorax After Thoracoabdominal Aneurysm Repair: Efficacy of Somatostatin

WOS: 000287189000030PubMed ID: 20926242Chylothorax is a rare but serious complication that presents after thoracoabdominal aortic aneurysm surgery. There are insufficient data to reach a consensus on how to manage it. Some researchers have suggested early reoperation for high output drainage. We present the case of a patient who underwent thoracoabdominal aortic replacement and who subsequently developed postoperative chylothorax. High output (> 1,000 mL per day) chest tube drainage until postoperative day 4 drastically decreased and stopped in a week with the administration of somatostatin and total parenteral nutrition which helped avoid a major re-exploration. Surgery should be reserved as an option only for patients with persistent leaks that do not respond to somatostatin therapy

Education for Healthcare in Disasters: an Imperative for Undergraduate Medical Education

WOS: 000295492900027Disasters, which are defined as "situations or events which overwhelm the capacity of local sources to address needs", have been increasing in terms of frequency, diversity and destructiveness, both in our country and throughout the world. The situation of healthcare systems becomes more complicated in disasters because of the increased healthcare needs of populations and the necessity for multidisciplinary work and collaboration with volunteer teams, besides the direct damages on infrastructure and loss of manpower. One of the main tools for coping with these problems is training healthcare workers for disasters. However, it is difficult to claim that disaster preparedness and education for health professionals for responding efficiently are adequate, both for Turkey and the world. Although there are numerous training programs, many of them are organized without any collaboration of institutions, have severe gaps in the content, are unable to cover target groups, and are unable to support coordination of disaster workers from different sectors. In this study, it was aimed to review the existing situation of disaster education in the health sector, and to define the needs for education. Also, core competency objectives for undergraduate medical education are determined and a program is proposed

Diagnostic AI and Cardiac Diseases

(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key search terms (Cardiac Disease, diagnosis, artificial intelligence, machine learning) across three different databases (Pubmed, European Heart Journal, Science Direct) between 2017–2022 in order to reach relatively more recent developments in the field. Our review was structured in order to clearly differentiate publications according to the disease they aim to diagnose (coronary artery disease, electrophysiological and structural heart diseases); (3) Results: Each study had different levels of success, where declared sensitivity, specificity, precision, accuracy, area under curve and F1 scores were reported for every article reviewed; (4) Conclusions: the number and quality of AI-assisted cardiac disease diagnosis publications will continue to increase through each year. We believe AI-based diagnosis should only be viewed as an additional tool assisting doctors’ own judgement, where the end goal is to provide better quality of healthcare and to make getting medical help more affordable and more accessible, for everyone, everywhere

Effects of Platelet-Rich Fibrin on Hard Tissue Healing: A Histomorphometric Crossover Trial in Sheep

Bone defects lead to aesthetic and functional losses, causing dental rehabilitation to be more difficult. The objective of this work is to histologically assess the hard tissue healing of bone defects filled with platelet-rich fibrin (PRF) alone or as an adjuvant for mixing with and covering anorganic bovine bone (ABB), compared to ABB covered with a resorbable collagen membrane (CM). This study was designed as a crossover animal study. Four 5-mm tibia defects, 5 mm apart from each other, were surgically created on the tibias of 6 sheep. The defects were randomly filled with ABB + CM; PRF alone; ABB+PRF; or were left empty. The animals were euthanized on days 10, 20, and 40 post-operatively. No group showed any signs of bone necrosis. Inflammation was observed in 2 control and 3 test defects with no statistically significant difference between groups at each time point. The ABB + CM and ABB + PRF groups experienced the highest bone regeneration ratios. No differences between the empty-defect and PRF groups were observed in regard to bone regeneration. No statistical difference was observed between the ABB+PRF and ABB + CM groups in regard to bone regeneration and the amount of residual graft material at each time point. The use of PRF should be preferred due to its autogenous origin, low cost, and ease of use

First Successful Experience With The Heartware Assist Device In Child In Turkey

3rd World Heart Failure Congress -- NOV 29-DEC 02, 2012 -- Istanbul, TURKEYWOS: 00031169820006