Optimizing Levothyroxine Dose Adjustment After Thyroidectomy With a Decision Tree.

BACKGROUND: After thyroidectomy, patients require Levothyroxine (LT4). It may take years of dose adjustments to achieve euthyroidism. During this time, patients encounter undesirable symptoms associated with hypo- or hyper-thyroidism. Currently, providers adjust LT4 dose by clinical estimation, and no algorithm exists. The objective of this study was to build a decision tree that could estimate LT4 dose adjustments and reduce the time to euthyroidism. METHODS: We performed a retrospective cohort analysis on 320 patients who underwent total or completion thyroidectomy at our institution. All patients required one or more LT4 dose adjustments from their initial postoperative dose before attaining euthyroidism. Using the Classification and Regression Tree algorithm, we built various decision trees from patient characteristics, estimating the dose adjustment to reach euthyroidism. RESULTS: The most accurate decision tree used thyroid-stimulating hormone values at first dose adjustment (mean absolute error = 13.0 µg). In comparison, the expert provider and naïve system had a mean absolute error of 11.7 µg and 17.2 µg, respectively. In the evaluation dataset, the decision tree correctly predicted the dose adjustment within the smallest LT4 dose increment (12.5 µg) 79 of 106 times (75%, confidence interval = 65%-82%). In comparison, expert provider estimation correctly predicted the dose adjustment 76 of 106 times (72%, confidence interval = 62%-80%). CONCLUSIONS: A decision tree predicts the correct LT4 dose adjustment with an accuracy exceeding that of a completely naïve system and comparable to that of an expert provider. It can assist providers inexperienced with LT4 dose adjustment.

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

Characterization of the ribosome biogenesis landscape in E. coli using quantitative mass spectrometry.

The ribosome is an essential and highly complex biological system in all living cells. A large body of literature on the assembly of the ribosome in vitro is available, but a clear picture of this process inside the cell has yet to emerge. Here, we directly characterized in vivo ribosome assembly intermediates and associated assembly factors from wild-type Escherichia coli cells using a general quantitative mass spectrometry (qMS) approach. The presence of distinct populations of ribosome assembly intermediates was verified using an in vivo stable isotope pulse-labeling approach, and their exact ribosomal protein contents were characterized against an isotopically labeled standard. The model-free clustering analysis of the resultant protein levels for the different ribosomal particles produced four 30S assembly groups that correlate very well with previous in vitro assembly studies of the small ribosomal subunit and six 50S assembly groups that clearly define an in vivo assembly landscape for the larger ribosomal subunit. In addition, de novo proteomics identified a total of 21 known and potentially new ribosome assembly factors co-localized with various ribosomal particles. These results represent new in vivo assembly maps of the E. coli 30S and 50S subunits, and the general qMS approach should prove to be a solid platform for future studies of ribosome biogenesis across a host of model organisms.

Measuring the dynamics of E. coli ribosome biogenesis using pulse-labeling and quantitative mass spectrometry.

The ribosome is an essential organelle responsible for cellular protein synthesis. Until recently, the study of ribosome assembly has been largely limited to in vitro assays, with few attempts to reconcile these results with the more complex ribosome biogenesis process inside the living cell. Here, we characterize the ribosome synthesis and assembly pathway for each of the E. coli ribosomal protein (r-protein) in vivo using a stable isotope pulse-labeling timecourse. Isotope incorporation into assembled ribosomes was measured by quantitative mass spectrometry (qMS) and fit using steady-state flux models. Most r-proteins exhibit precursor pools ranging in size from 0% to 7% of completed ribosomes, and the sizes of these individual r-protein pools correlate well with the order of r-protein binding in vitro. Additionally, we observe anomalously large precursor pools for specific r-proteins with known extra-ribosomal functions, as well as three r-proteins that apparently turnover during steady-state growth. Taken together, this highly precise, time-dependent proteomic qMS approach should prove useful in future studies of ribosome biogenesis and could be easily extended to explore other complex biological processes in a cellular context.

Expected prevalence from the differential diagnosis of anterior knee pain in adolescent female athletes during preparticipation screening.

CONTEXT: Anterior knee pain is a common disorder in female athletes with an undefined cause. The relative prevalence of specific patellofemoral disorders associated with anterior knee pain in adolescent females remains undetermined. OBJECTIVE: To determine the prevalence of specific patellofemoral disorders obtained using the differential diagnosis of anterior knee pain in adolescent female athletes during preparticipation screening. DESIGN: Descriptive epidemiology study. SETTING: Preparticipation screening evaluations at a county public school district in Kentucky. PATIENTS OR OTHER PARTICIPANTS: A total of 419 unique middle and high school-aged female athletes. MAIN OUTCOME MEASURE(S): Participants were evaluated by physicians for anterior knee pain over 3 consecutive basketball seasons. Given the longitudinal nature of this study, some participants were tested longitudinally over multiple years. RESULTS: Over the course of 3 basketball seasons, 688 patient evaluations were performed. Of these, 183 (26.6%) were positive for anterior knee pain. A statistically significant difference was noted in the prevalence of anterior knee pain by school level, with 34.4% (n = 67) in high school-aged athletes versus 23.5% (n = 116) in middle school-aged athletes (P < .05). In the 1376 knees evaluated, patellofemoral dysfunction was the most common diagnosis, with an overall prevalence of 7.3% (n = 100). The only diagnosis shown to be statistically different between age levels was Sinding-LarsenJohansson disease or patellar tendinopathy, with 38 cases (9.7%) in high school-aged and 31 (3.1%) in middle school-aged athletes (P < .05). CONCLUSIONS: Anterior knee pain was present in 26.6% of the adolescent female athletes screened over 3 years. Symptoms of anterior knee pain likely persist after middle school-aged onset and reach peak prevalence during the high school years.

Quantitation of the ribosomal protein autoregulatory network using mass spectrometry.

Relative levels of ribosomal proteins were quantified in crude cell lysates using mass spectrometry. A method for quantifying cellular protein levels using macromolecular standards is presented that does not require complex sample separation, identification of high-responding peptides, affinity purification, or postgrowth modifications. Perturbations in ribosomal protein levels by overexpression of individual proteins correlate to known autoregulatory mechanisms and extend the network of ribosomal protein regulation.