Person: Kanjilal, Sanjat
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Kanjilal
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Sanjat
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Kanjilal, Sanjat
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Publication Trends in Antibiotic Susceptibility in Staphylococcus aureus in Boston, Massachusetts, from 2000 to 2014(American Society for Microbiology, 2017) Kanjilal, Sanjat; Sater, Mohamad R. Abdul; Thayer, Maile; Lagoudas, Georgia K.; Kim, Soohong; Blainey, Paul C.; Grad, YonatanABSTRACT The rate of infection by methicillin-resistant Staphylococcus aureus (MRSA) has declined over the past decade, but it is unclear whether this represents a decline in S. aureus infections overall. To evaluate the trends in the annual rates of infection by S. aureus subtypes and mean antibiotic resistance, we conducted a 15-year retrospective observational study at two tertiary care institutions in Boston, MA, of 31,753 adult inpatients with S. aureus isolated from clinical specimens. We inferred the gain and loss of methicillin resistance through genome sequencing of 180 isolates from 2016. The annual rates of infection by S. aureus declined from 2003 to 2014 by 4.2% (2.7% to 5.6%), attributable to an annual decline in MRSA of 10.9% (9.3% to 12.6%). Penicillin-susceptible S. aureus (PSSA) increased by 6.1% (4.2% to 8.1%) annually, and rates of methicillin-susceptible penicillin-resistant S. aureus (MSSA) did not change. Resistance in S. aureus decreased from 2000 to 2014 by 0.8 antibiotics (0.7 to 0.8). Within common MRSA clonal complexes, 3/14 MSSA and 2/21 PSSA isolates arose from the loss of resistance-conferring genes. Overall, in two tertiary care institutions in Boston, MA, a decline in S. aureus infections has been accompanied by a shift toward increased antibiotic susceptibility. The rise in PSSA makes penicillin an increasingly viable treatment option.Publication Rapid Detection of Powassan Virus in a Patient With Encephalitis by Metagenomic Sequencing(Oxford University Press, 2017) Piantadosi, Anne; Kanjilal, Sanjat; Ganesh, Vijay; Khanna, Arjun; Hyle, Emily; Rosand, Jonathan; Bold, Tyler; Metsky, Hayden C; Lemieux, Jacob; Leone, Michael J; Freimark, Lisa; Matranga, Christian B; Adams, Gordon; McGrath, Graham; Zamirpour, Siavash; Telford, Sam; Rosenberg, Eric; Cho, Tracey Alexander; Frosch, Matthew; Goldberg, Marcia; Mukerji, Shibani; Sabeti, PardisAbstract We describe a patient with severe and progressive encephalitis of unknown etiology. We performed rapid metagenomic sequencing from cerebrospinal fluid and identified Powassan virus, an emerging tick-borne flavivirus that has been increasingly detected in the United States.Publication A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection(American Association for the Advancement of Science (AAAS), 2020-11-04) Kanjilal, Sanjat; Oberst, Michael; Boominathan, Sooraj; Zhou, Helen; Hooper, David C.; Sontag, DavidAntibiotic resistance is a major cause of treatment failure and leads to increased use of broad-spectrum agents, which begets further resistance. This vicious cycle is epitomized by uncomplicated urinary tract infection (UTI), which affects one in two women during their life and is associated with increasing antibiotic resistance and high rates of prescription for broad-spectrum second-line agents. To address this, we developed machine learning models to predict antibiotic susceptibility using electronic health record data and built a decision algorithm for recommending the narrowest possible antibiotic to which a specimen is susceptible. When applied to a test cohort of 3629 patients presenting between 2014 and 2016, the algorithm achieved a 67% reduction in the use of second-line antibiotics relative to clinicians. At the same time, it reduced inappropriate antibiotic therapy, defined as the choice of a treatment to which a specimen is resistant, by 18% relative to clinicians. For specimens where clinicians chose a second-line drug but the algorithm chose a first-line drug, 92% (1066 of 1157) of decisions ended up being susceptible to the first-line drug. When clinicians chose an inappropriate first-line drug, the algorithm chose an appropriate first-line drug 47% (183 of 392) of the time. Our machine learning decision algorithm provides antibiotic stewardship for a common infectious syndrome by maximizing reductions in broad-spectrum antibiotic use while maintaining optimal treatment outcomes. Further work is necessary to improve generalizability by training models in more diverse populations.Publication Structural Basis for Continued Antibody Evasion by the SARS-CoV-2 Receptor-Binding Domain(2021-12-02) Nabel, Katherine G.; Clark, Sarah A.; Shankar, Sundaresh; Pan, Junhua; Clark, Lars; Yang, Pan; Coscia, Adrian; McKay, Lindsay G.A.; Varnum, Haley; Brusic, Vesna; Tolan, Nicole V.; Zhou, Guohai; Desjardins, Michaël; Turbett, Sarah E.; Kanjilal, Sanjat; Sherman, Amy; Dighe, Anand; LaRocque, Regina C.; Ryan, Edward; Tylek, Casey; Cohen-Solal, Joel F.; Darcy, Andhao T.; Tavella, Davide; Clabbers, Anca; Fan, Yao; Griffiths, Anthony; Correia, Ivan R.; Seagal, Jane; Baden, Lindsey; Charles, Richelle; Abraham, JonathanMany studies have examined the impact of SARS-CoV-2 variants on neutralizing antibody activity after they have become dominant strains. Here, we evaluate the consequences of further viral evolution. We demonstrate mechanisms through which the SARS-CoV-2 receptor-binding domain (RBD) can tolerate large numbers of simultaneous antibody escape mutations and show that pseudotypes containing up to seven mutations, as opposed to the one to three found in previously studied variants of concern, are more resistant to neutralization by therapeutic antibodies and serum from vaccine recipients. We identify an antibody that binds the RBD core to neutralize pseudotypes for all tested variants but show that the RBD can acquire an N-linked glycan to escape neutralization. Our findings portend continued emergence of escape variants as SARS-CoV-2 adapts to humans.Publication Estimating epidemiologic dynamics from single cross-sectional viral load distributions(2020-09-26) Hay, James; Kennedy-Shaffer, Lee; Kanjilal, Sanjat; Lipsitch, Marc; Mina, MichaelVirologic testing for SARS-CoV-2 has been central to the COVID-19 pandemic response, but interpreting changes in incidence and fraction of positive tests towards understanding the epidemic trajectory is confounded by changes in testing practices. Here, we show that the distribution of viral loads, in the form of Cycle thresholds (Ct), from positive surveillance samples at a single point in time can provide accurate estimation of an epidemic’s trajectory, subverting the need for repeated case count measurements which are frequently obscured by changes in testing capacity. We identify a relationship between the population-level cross-sectional distribution of Ct values and the growth rate of the epidemic, demonstrating how the skewness and median of detectable Ct values change purely as a mathematical epidemiologic rule without any change in individual level viral load kinetics or testing. Although at the individual level measurement variation can complicate interpretation of Ct values for clinical use, we show that population-level properties reflect underlying epidemic dynamics. In support of these theoretical findings, we observe a strong relationship between the time-varying effective reproductive number, R(t), and the distribution of Cts among positive surveillance specimens, including median and skewness, measured in Massachusetts over time. We use the observed relationships to derive a novel method that allows accurate inference of epidemic growth rate using the distribution of Ct values observed at a single cross-section in time, which, unlike estimates based on case counts, is less susceptible to biases from delays in test results and from changing testing practices. Our findings suggest that instead of discarding individual Ct values from positive specimens, incorporation of viral loads into public health data streams offers a new approach for real-time resource allocation and assessment of outbreak mitigation strategies, even where repeat incidence data is not available. Ct values or similar viral load data should be regularly reported to public health officials by testing centers and incorporated into monitoring programs.