artificial intelligence as a tool to enhance decision-making in personalized medicine and drug screening

Objective This research aimed to describe several areas in which AI could play a role in the development of Personalized Medicine and Drug Screening, and the transformations it has created in the field of biology and therapy. It also addressed the limitations faced by the application of artificial intelligence techniques and make suggestions for further research. Methods We have conducted a comprehensive review of research and papers related to the role of AI in personalized medicine and drug screening, and filtered the list of works for those relevant to this review. Results Artificial Intelligence can play an important role in the development of personalized medicines and drug screening at all clinical phases related to development and implementation of new customized health products, starting with finding the appropriate medicines to testing their usefulness. In addition, expertise in the use of artificial intelligence techniques can play a special role in this regard. Discussion The capacity of AI to enhance decision-making in personalized medicine and drug screening will largely depend on the accuracy of the relevant tests and the ways in which the data produced is stored, aggregated, accessed, and ultimately integrated. Conclusion The review of the relevant literature has revealed that AI techniques can enhance the decision-making process in the field of personalized medicine and drug screening by improving the ways in which produced data is aggregated, accessed, and ultimately integrated. One of the major obstacles in this field is that most hospitals and healthcare centers do not employ AI solutions, due to healthcare professionals lacking the expertise to build successful models using AI techniques and integrating them with clinical workflows.

NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations

Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is currently unknown if such models are robust in face of negation, which is pervasive across language varieties. In this paper we evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: a pipeline approach, relying on a specific component for negation detection; an augmentation of an ADE extraction dataset to artificially create negated samples and further train the models. We show that both strategies bring significant increases in performance, lowering the number of spurious entities predicted by the models. Our dataset and code will be publicly released to encourage research on the topic.

BERT based Adverse Drug Effect Tweet Classification

This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERTweet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries.

Text Augmentation Techniques in Drug Adverse Effect Detection Task

The paper researches the problem of drug adverse effect detection in texts of social media. We describe the development of such classification system for Russian tweets. To increase the train dataset we apply a couple of augmentation techniques and analyze their effect in comparison with similar systems presented at 2021 years' SMM4H Workshop.

View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data

We present an algorithm based on multi-layer transformers for identifying Adverse Drug Reactions (ADR) in social media data. Our model relies on the properties of the problem and the characteristics of contextual word embeddings to extract two views from documents. Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view. Finally, the initialized classifier in each view is further trained using the initial training examples. We evaluated our model in the largest publicly available ADR dataset. The experiments testify that our model significantly outperforms the transformer-based models pretrained on domain-specific data.

Skin and oral challenge tests performed on 20 patients with suspected beta-lactam allergy in Tishreen University Hospitals – Lattakia

Betalactams are the most commonly prescribed antibiotics Antibiotics worldwide. Suspected allergic reactions to Betalactams are often reported in patients, but only a minority of these reactions results from real immunological mechanism. Here we present our experience in allergological investigation of Betalactam reactions in Tishreen University hospital in Lattakia. To confirm or rule out the suspected diagnosis, Skin tests (pricks and Intradermal tests with PPL (Penicilloyl-polylysine), MDM (minor determinants) and soluble Betalactams) were performed in 20 patients reporting one reaction or more to Betalactam antibiotics. Oral provocation tests (OPT) were performed in patients with negative skin tests. Our results showed that Skin tests to PPL, MDM and Betalactams were positive in only two patients. The OPT confirmed our results as they were positive in those two positive skin tests patients(allergy to Clavulanic acid).Moreover, these results suggest that the diagnostic and predictivevalue of skin teststo Betalactam antibiotics is good, as no positive OPT was observed in patients with negative skin tests. In conclusion, suspected allergic reactions to Betalactam antibioticsremain a relevantproblem due to the need ofthese wide variety of drugs. This is the first study in Syria to date on immediate allergic reactions to Betalactam antibiotics and it showed the necessity to performed skin tests in suspected patients.

Structure-Based Rational Design of Novel Analogues of Nimesulide

Nowadays, the usage of NSAIDs has been increased dramatically for relieving pain and the treatment of divers inflammatory conditions, therefore, it is necessary to develop this class of drug by increasing their activity and decreasing their side effects. Their most common side effect is the gastrointestinal disorders, especially, gastric bleeding resulting from the inhibition of prostaglandins synthesis.

Study of Intestinal Absorption of Telmisartan and Drug Interactions in Rats

Telmisartan is an antihypertensive drug that inhibits angiotensin II receptors type AT1. Studies showed drug interactions with different potentials. This study was intended to investigate the role of some active ingredients acting on influx, efflux and metabolizing enzymes in intestinal absorption of telmisartan prior to hepatic-pass effect. Intestinal perfusion with venous sampling technique was applied in rats by perfusing telmisartan in intestinal lumen, and measuring its concentration and amount versus time.

Rational Design Based Molecular Modeling for Novel Lumiracoxib Analogues: Enhancing The Affinity Toward Cyclooxygenase Type 2 Over Cyclooxygenase Type 1

Compounds, showing selectivity towards COX-2, are promising agents as selective non-steroidal anti-inflammatory drugs (NSAIDs) with lower side effects, especially, gastrointestinal ones. However, recent reports reveal the cardiovascular side effects associated with the selective COX-2 inhibitors. Therefore, attempts have been done to develop the second generation of selective COX-2 inheritors. They have safer profile compared to the first generation. Lumiracoxib belongs to this class of compounds. In this study, the molecular structure of the target enzymes were prepared. Library of rationally designed lumiracoxib analogues were docked.

Effect of different doses of anticoccidial drug (Sulphaquinoxaline) on some blood parameters of broiler chickens

This study aimed to determine the effect of anticoccidial drug (Sulphaquinoxaline), that deployed in poultry farms in The Syrian Arab Republic, on some blood parameters, of broiler chickens for the commercial type(Ross), that available in Syria. We used drug being tested by two doses(therapeutic dose and a double therapeutic dose), as the following: (125, 250)ppm, respectively .The experience birds that braved /120/ chick at aged one day were divided to three equal distribution groups (Control, group(1), Sulphaquinoxaline, group(2) at concentration(125)ppm, Salafaquinoxaline, group(3) at concentration(250)ppm). The experience period continued for(45) days. The anticoccidial drug were presented continuously with fodder, and the blood samples were collected three times during this period. The blood samples were taken from broilers at the age(15 - 30 - 45) days. 10 samples were taken of each group from indentified three groups, then lab tests were done on the blood picture. These Tests included determination of the values of total protein, albumin and Globulin in blood serum, and also it included determination the values of some mineral elements in blood serum. These elements contained[Ca- P- Mg ] in serum.