The pharmacovigilance program of India (PvPI), after its inception, has been reliably acquiring force in bringing issues to light among the masses, healthcare professionals, the pharma industry, and clinical staff at hospitals. Adverse drug reactions are unintended events that occur after exposure to a drug, biological product, or medical device, and they may result in morbidity and mortality. It is critical to monitor the safety of drugs during the post-marketing phase to find long-term and rare ADRs, as well as ADRs in special populations and patients with co-morbidities that are not usually included during clinical trials. The definitive objective of pharmacovigilance is to collate data and analyze it. Assessing the causality between ADRs and drugs is necessary to decrease the occurrence of ADRs and to reduce the risk of drug-related ADRs. ADRs may lead to increased morbidity, increased hospital stays, and increased cost of treatment, resulting in compromised patient safety. Causality assessment is the evaluation of the likelihood that a particular treatment is the cause of an observed adverse event and establishing a causal association between a drug and a drug reaction is necessary to prevent further recurrences. Numerous methods available for establishing a causal association between the drug and adverse events have been broadly classified into clinical judgment or global introspection, algorithms, and probabilistic methods. These include the Swedish method, World Health Organization-Uppsala Monitoring Centre (WHO-UMC) scale, Naranjo\'s algorithm, Kramer algorithm, Jones algorithm, Karch algorithm, Bégaud algorithm, Adverse Drug Reactions Advisory Committee guidelines, Bayesian Adverse Reaction Diagnostic Instrument, and so on. Despite various methods available, none of the causality assessment tools have been universally accepted as the gold standard. Naranjo\'s algorithm and WHO-UMC scales are, however, the most commonly used. Similarly, for preventability and severity assessment of ADRs, the Schumock and Thornton scale and Hartwig and Siegel\'s scale are most commonly used. Hence, we reviewed different tools and methods available to assess the causality, preventability, and severity of ADRs.

BACKGROUND: In an attempt to further improve surgical outcomes, a variety of outcome prediction and risk-assessment tools have been developed for the clinical setting. Risk scores such as the surgical Apgar score (SAS) hold promise to facilitate the objective assessment of perioperative risk related to comorbidities of the patients or the individual characteristics of the surgical procedure itself. Despite the large number of scoring models in clinical surgery, only very few of these models have ever been utilized in the setting of laboratory animal science. The SAS has been validated in various clinical surgical procedures and shown to be strongly associated with postoperative morbidity. In the present study, we aimed to review the clinical evidence supporting the use of the SAS system and performed a showcase pilot trial in a large animal model as the first implementation of a porcine-adapted SAS (pSAS) in an in vivo laboratory animal science setting.
METHODS: A literature review was performed in the PubMed and Embase databases. Study characteristics and results using the SAS were reported. For the in vivo study, 21 female German landrace pigs have been used either to study bleeding analogy (n = 9) or to apply pSAS after abdominal surgery in a kidney transplant model (n = 12). The SAS was calculated using 3 criteria: (1) estimated blood loss during surgery; (2) lowest mean arterial blood pressure; and (3) lowest heart rate.
RESULTS: The SAS has been verified to be an effective tool in numerous clinical studies of abdominal surgery, regardless of specialization confirming independence on the type of surgical field or the choice of surgery. Thresholds for blood loss assessment were species specifically adjusted to >700 mL = score 0; 700-400 mL = score 1; 400-55 mL score 2; and <55 mL = score 3 resulting in a species-specific pSAS for a more precise classification.
CONCLUSIONS: Our literature review demonstrates the feasibility and excellent performance of the SAS in various clinical settings. Within this pilot study, we could demonstrate the usefulness of the modified SAS (pSAS) in a porcine kidney transplantation model. The SAS has a potential to facilitate early veterinary intervention and drive the perioperative care in large animal models exemplified in a case study using pigs. Further larger studies are warranted to validate our findings.

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients\' check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.