02579nas a2200241   4500000000100000008004100001260001200042653001400054653001400068653002600082653001200108653003000120653001400150653002000164100001500184700001300199245008600212856012500298300001400423490000700437520187900444022001402323       2022                            d  c05/202210aCytokines10adiagnosis10adrug resistance tests10aleprosy10apolymerase chain reaction10aPrognosis10aSerologic Tests1 aMalhotra K1 aHusain N00aLaboratory perspectives for Leprosy: Diagnostic, prognostic and predictive tools.  uhttps://ijpmonline.org/article.asp?issn=0377-4929;year=2022;volume=65;issue=5;spage=300;epage=309;aulast=Malhotra;type=3  aS300-S3090 v653 a<p>The diagnosis of leprosy poses several challenges. The bacillary load, serology, and tissue response are determined by the host immune status, which make individual tests unsuitable across the spectrum. The sensitivity of tests for identifying paucibacillary cases remains limited, on the other hand, many tests lack specificity in differentiating contacts from diseased cases. Nonetheless, a plethora of laboratory tests have been added to the armamentarium of the clinicians dealing with leprosy. In the current review, we critically analyze the tests available for diagnosis, prognostication, and prediction of treatment response in leprosy. We discuss in brief the conventional tests available and detail the newer serologic and molecular tests added over the past few years with an attempt to suggest the pros and cons of each, and the tests best fit for each clinical scenario. Slit skin smears and skin or nerve biopsies are primarily performed to exclude clinical mimics, confirm a diagnosis, and immunologically subtype the case. Antibody titres of phenolic glycolipid-1 and its synthetic variants can be measured in serum and saliva and provide noninvasive means to detect leprosy with good specificity. Conventional, quantitative, real-time, and other variants of PCR can detect M. leprae DNA and have been used to effect in blood, tissue, and urine samples. T helper I and II cytokine signatures can be used to differentiate the subtypes of leprosy. Newer machine learning algorithms use combinations of these tests to predict the development of leprosy in contacts. Tests to detect treatment response, antimicrobial drug resistance, and predict the onset of reactions in leprosy can be used to advantage. We compare the characteristics of these tests and suggest an algorithm for leprosy diagnosis optimally utilizing them in various clinical settings.</p>
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