Tattoo artists use many compounds to create tattoo pigment and several allergic reactions can occur as a result of these additives. The compositions of many inks have been identified; however, as new mixtures are created it becomes difficult to identify the specific ingredients in a particular ink. Allergic reactions to a particular pigment can manifest in several ways including allergic contact dermatitis and photoallergic dermatitis. Subsequently, tattoo ink or pigment allergy reactions can be classified as acute inflammatory reactions, allergic hypersensitivities, and granulomatous, lichenoid, and pseudolymphomatous types of reactions. This paper will review the clinical manifestations and the most common compounds associated with cutaneous reactions to tattoo ink.

The spectrum of hemolytic disease of the newborn has changed over the last few decades. With the implementation of Rhesus D immunoprophylaxis, hemolytic disease due to ABO incompatibility and other alloantibodies has now emerged as major causes of this condition. Though in developing countries, anti D is still a common antibody in pregnant women, many Asian countries have identified alloantibodies other than anti D as a cause of moderate-severe hemolytic disease. The most concerned fact is that, some of these have been described in Rh D positive women. It appears that universal antenatal screening in all pregnant women needs to be initiated, since Rh D positive women are just as likely as D negative women to form alloantibodies. Many developed nations have national screening programs for pregnant women. This is necessary to ensure timely availability of antigen negative blood and reduce effects on the newborn. Although universal screening seems justified, the cost and infrastructure required would be immense. Developing countries and under resourced nations need to consider universal antenatal screening and frame guidelines accordingly.

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It is imperative to recognize discrepant results and resolve them. Correct blood typing and labeling of an individual are essential to prevent ABO incompatibility.

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Blood and blood components are considered drugs because they are used in the treatment of diseases. As with any drug, adverse effects may occur, necessitating careful consideration of therapy. Like any other therapeutic decision, the need for transfusion should be considered on the basis of risks and benefits and alternative treatments available to avoid over- and under-transfusion. This review is focused on the blood transfusion protocol in trauma patients with hemorrhagic shock. Besides, issues related to emergency and massive transfusion have also been elaborated. We conducted a comprehensive MEDLINE search and reviewed the relevant literature, with particular reference to emergency medical care in trauma.

Objectives The objectives of this study are to construct the high definition phenotype (HDP), a novel time-series data structure composed of both primary and derived parameters, using heterogeneous clinical sources and to determine whether different predictive models can utilize the HDP in the neonatal intensive care unit (NICU) to improve neonatal mortality prediction in clinical settings. Materials and Methods A total of 49 primary data parameters were collected from July 2018 to May 2020 from eight level-III NICUs. From a total of 1546 patients, 757 patients were found to contain sufficient fixed, intermittent, and continuous data to create HDPs. Two different predictive models utilizing the HDP, one a logistic regression model (LRM) and the other a deep learning long–short-term memory (LSTM) model, were constructed to predict neonatal mortality at multiple time points during the patient hospitalization. The results were compared with previous illness severity scores, including SNAPPE, SNAPPE-II, CRIB, and CRIB-II. Results A HDP matrix, including 12 221 536 minutes of patient stay in NICU, was constructed. The LRM model and the LSTM model performed better than existing neonatal illness severity scores in predicting mortality using the area under the receiver operating characteristic curve (AUC) metric. An ablation study showed that utilizing continuous parameters alone results in an AUC score of >80% for both LRM and LSTM, but combining fixed, intermittent, and continuous parameters in the HDP results in scores >85%. The probability of mortality predictive score has recall and precision of 0.88 and 0.77 for the LRM and 0.97 and 0.85 for the LSTM. Conclusions and Relevance The HDP data structure supports multiple analytic techniques, including the statistical LRM approach and the machine learning LSTM approach used in this study. LRM and LSTM predictive models of neonatal mortality utilizing the HDP performed better than existing neonatal illness severity scores. Further research is necessary to create HDP–based clinical decision tools to detect the early onset of neonatal morbidities.

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