15 research outputs found

    Whole genome sequencing of Mycobacterium tuberculosis reveals slow growth and low mutation rates during latent infections in humans

    Get PDF
    Very little is known about the growth and mutation rates of Mycobacterium tuberculosis during latent infection in humans. However, studies in rhesus macaques have suggested that latent infections have mutation rates that are higher than that observed during active tuberculosis disease. Elevated mutation rates are presumed risk factors for the development of drug resistance. Therefore, the investigation of mutation rates during human latency is of high importance. We performed whole genome mutation analysis of M. tuberculosis isolates from a multi-decade tuberculosis outbreak of the New Zealand Rangipo strain. We used epidemiological and phylogenetic analysis to identify four cases of tuberculosis acquired from the same index case. Two of the tuberculosis cases occurred within two years of exposure and were classified as recently transmitted tuberculosis. Two other cases occurred more than 20 years after exposure and were classified as reactivation of latent M. tuberculosis infections. Mutation rates were compared between the two recently transmitted pairs versus the two latent pairs. Mean mutation rates assuming 20 hour generation times were 5.5X10⁻¹⁰ mutations/bp/generation for recently transmitted tuberculosis and 7.3X10⁻¹¹ mutations/bp/generation for latent tuberculosis. Generation time versus mutation rate curves were also significantly higher for recently transmitted tuberculosis across all replication rates (p = 0.006). Assuming identical replication and mutation rates among all isolates in the final two years before disease reactivation, the u20hr mutation rate attributable to the remaining latent period was 1.6×10⁻¹¹ mutations/bp/generation, or approximately 30 fold less than that calculated during the two years immediately before disease. Mutations attributable to oxidative stress as might be caused by bacterial exposure to the host immune system were not increased in latent infections. In conclusion, we did not find any evidence to suggest elevated mutation rates during tuberculosis latency in humans, unlike the situation in rhesus macaques

    Revisiting Expectations in an Era of Precision Oncology

    Full text link
    As we enter an era of precision medicine and targeted therapies in the treatment of metastatic cancer, we face new challenges for patients and providers alike as we establish clear guidelines, regulations, and strategies for implementation. At the crux of this challenge is the fact that patients with advanced cancer may have disproportionate expectations of personal benefit when participating in clinical trials designed to generate generalizable knowledge. Patient and physician goals of treatment may not align, and reconciliation of their disparate perceptions must be addressed. However, it is particularly challenging to manage a patient’s expectations when the goal of precision medicineâ personalized responseâ exacerbates our inability to predict outcomes for any individual patient. The precision medicine informed consent process must therefore directly address this issue. We are challenged to honestly, clearly, and compassionately engage a patient population in an informed consent process that is responsive to their vulnerability, as well as everâ evolving indications and evidence. This era requires a continual reassessment of expectations and goals from both sides of the bed.New challenges are faced in this era of precision medicine and targeted therapies. Clear guidelines, regulations, and strategies for implementation are needed. Patients with advanced cancer may have disproportionate expectations of the personal benefit of participating in clinical trials. The informed consent process must address this issue directly and honestly. This era requires a continual reassessment of both patient and physician expectations and goals.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142968/1/onco12322_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142968/2/onco12322.pd

    Revisiting Expectations in an Era of Precision Oncology.

    No full text

    Osteotomized folded scapular tip free flap for complex midfacial reconstruction

    No full text
    Aim: To describe a novel technique for the reconstruction of geometrically complex defects of the midface using an osteotomized folded scapular tip-free flap.Methods: Five patients underwent maxillectomy with defects disrupting two or more of the following facial axes: orbital, nasofacial, and palatal axes. Patients underwent primary reconstruction using an angular artery-based scapular tip-free flap with an osteotomy to fold the flap. Harvest techniques, including placement of osteotomies, folding and plating, surgical esthetic, and functional outcomes, are presented.Results: Osteotomies placed in the scapular tip-free flap allowed folding of the osseous flap and improved restoration of all three facial axes with a single flap. In one patient, the tip of the scapula was used to reconstruct the nasofacial axis, while the body and lateral border were used to reconstruct the palate. In four patients, the tip of the scapula was used to reconstruct the orbital axis, while the body and lateral border were used to reconstruct the nasofacial axis. Patients had successful oronasal separation, healed wounds withstanding adjuvant therapy, satisfactory orbital positioning and facial projection, preserved masticatory surfaces and opportunity for dental implants.Conclusion: The midface is geometrically complex and is one of the most challenging head and neck sites to reconstruct. Ablative defects in this area can disrupt facial axes resulting in poor esthetic and functional outcomes. This study demonstrates the reconstructive advantages of a novel osteotomized folded scapular tip-free flap

    Epidemiological relationships among 11 New Zealand <i>M. tuberculosis</i> cases.

    No full text
    <p>Chronological representation of different subjects in a New Zealand TB outbreak. Each square represent a subject at the time of the TB diagnosis. Broken lines represent known close direct contact with the initial index case “X” during X's period of infectiousness. Solid lines show assumed connections between a case of presumed reactivation (C1 to C2) and a case of potential child-parent transmission (A to T).</p

    In vivo mutation rates in <i>M. tuberculosis</i> strains for generation times ranging from 18 to 240 hours.

    No full text
    <p>The mutation rate for each of the <i>M. tuberculosis</i> isolates was estimated using equation (1) in Ford <i>et al</i>, and calculated as untis of mutation/bp/generation <b>Panel A</b>: The mutation rate for each <i>M. tuberculosis</i> isolate in the study. Isolate C1 and E are from recent infection while strains O and S from reactivation after a prolonged period of latency. The yellow areas represent 95% confidence intervals. <b>Panel B</b>: Isolates with recent infection (E and C1) and reactivation after prolonged latency (O and S) were combined to obtain overall estimates of mutation rates of recent and latent-reactivated disease. The yellow areas represent 95% confidence intervals.</p

    Mutation versus replication rates during the latency period.

    No full text
    <p>To evaluate if the mutation rate was different in early versus late period of latent-reactivated disease, we estimated the mutation rate (calculated as untis of mutation/bp/generation) for isolates with reactivation of TB for the latent and reactivated years. <b>Panel A</b>: A schematic representation for the evaluation of the rate of mutation in the latency period. The mutation rate of the recent activated TB (strains C1 and E) was subtracted from the late activated TB (strains O and S).<b>Panel B</b>: The mutation rate for the latent period was calculated using the difference between the number of mutations for the early reactivation phase and the late reactivated phase. The result was divided by the genome size multiplied by the number of generations for O and S combined in the presumed latent period. All these calculations implicitly assume a homogeneous mutation rate across the genome and over time and when combining activation groups, across isolates. Analyses were performed using PROC GENMOD in SAS 9.2 (SAS Institute Inc, Cary, NC). Graphics were generated using R 2.12.2 (R Foundation for Statistical Computing).</p
    corecore