TMG seems to be a safe alternative induction strategy in patients for SF immunosuppression in pediatric renal transplantation. Extended follow-up and greater enrollment are necessary to fully explore the impact of TMG dosing on viral replication posttransplantation.

As a deubiquitination (DUB) enzyme, ubiquitin-specific protease 13 (USP13) is involved in a myriad of cellular processes, such as mitochondrial energy metabolism, autophagy, DNA damage response, and endoplasmic reticulum-associated degradation (ERAD), by regulating the deubiquitination of diverse key substrate proteins. Thus, dysregulation of USP13 can give rise to the occurrence and development of plenty of diseases, in particular malignant tumors. Given its implications in the stabilization of disease-related proteins and oncology targets, considerable efforts have been committed to the discovery of inhibitors targeting USP13. Here, we summarize an overview of the recent advances of the structure, function of USP13, and its relations to diseases, as well as discovery and development of inhibitors, aiming to provide the theoretical basis for investigation of the molecular mechanism of USP13 action and further development of more potent druggable inhibitors.

We simulated epidemic projections of a potential COVID-19 outbreak in a residential university population in the United States under varying combinations of asymptomatic tests (5% to 33% per day), transmission rates (2.5% to 14%), and contact rates (1 to 25), to identify the contact rate threshold that, if exceeded, would lead to exponential growth in infections. Using this, we extracted contact rate thresholds among non-essential workers, population size thresholds in the absence of vaccines, and vaccine coverage thresholds. We further stream-lined our analyses to transmission rates of 5 to 8%, to correspond to the reported levels of face-mask-use/physical-distancing during the 2020 pandemic. Our results suggest that, in the absence of vaccines, testing alone without reducing population size would not be sufficient to control an outbreak. If the population size is lowered to 34% (or 44%) of the actual population size to maintain contact rates at 4 (or 7) among non-essential workers, mass tests at 25% (or 33%) per day would help control an outbreak. With the availability of vaccines, the campus can be kept at full population provided at least 95% are vaccinated. If vaccines are partially available such that the coverage is lower than 95%, keeping at full population would require asymptomatic testing, either mass tests at 25% per day if vaccine coverage is at 63–79%, or mass tests at 33% per day if vaccine coverage is at 53–68%. If vaccine coverage is below 53%, to control an outbreak, in addition to mass tests at 33% per day, it would also require lowering the population size to 90%, 75%, and 60%, if vaccine coverage is at 38–53%, 23–38%, and below 23%, respectively. Threshold estimates from this study, interpolated over the range of transmission rates, can collectively help inform campus level preparedness plans for adoption of face mask/physical-distancing, testing, remote instructions, and personnel scheduling, during non-availability or partial-availability of vaccines, in the event of SARS-Cov2-type disease outbreaks.

We simulated epidemic projections of a potential COVID-19 outbreak in a university population of 38,000 persons, under varying combinations of mass test rate (0% to 10%), contact trace and test rate (0% to 50%), transmission rate (probability of transmission per contact per day), and contact rate (number of contacts per person per day). We simulated four levels of transmission rate, 14% (average baseline), 8% (average for face mask use), 5.4% (average for 3ft distancing), and 2.5% (average for 6ft distancing and face mask use), interpolating results to the full range to understand the impact of uncertainty in effectiveness, feasibility, and adherence of face mask use and physical distancing. We evaluated contact rates between 1 and 25, to identify the threshold that, if exceeded, could lead to several deaths. When transmission rate was 8%, for trace and test at 50%, the contact rate threshold was 8. However, any time delays in trace, test, and isolation quickly raised the number of deaths. Keeping contact rate to 3 or below was more robust to testing delays, keeping deaths below 1 up to a delay of 5 days from the time of infection to diagnosis and isolation. For a contact rate of 3, the number of trace and tests peaked to about 70 per day and relaxed to 25 with the addition of 10% mass test. When transmission rate was 5.4%, for trace and test at 50%, the contact rate threshold was 10. However, keeping contact rate to 4 or below was more robust to delays in testing, keeping deaths below 1 up to a delay of 6 days from the time of infection to diagnosis and isolation. For contact rate of 4, the number of trace and tests peaked at 50 per day and relaxed to 10 per day with the addition of 10% mass test. Threshold estimates can help develop on-campus scheduling and indoor-spacing plans in conjunction with plans for asymptomatic testing for COVID-19. Combination thresholds should be selected specific to the setting based on an assessment of the feasibility and resource 48 availability for testing and quarantine.

Background. Low-and-middle-income countries (LMICs) have higher mortality-to-incidence ratio for breast cancer compared to high-income countries (HICs) because of late-stage diagnosis. Mammography screening is recommended for early diagnosis, however, the infrastructure capacity in LMICs are far below that needed for adopting current screening guidelines. Current guidelines are extrapolations from HICs, as limited data had restricted model development specific to LMICs, and thus, economic analysis of screening schedules specific to infrastructure capacities are unavailable. Methods. We applied a new Markov process method for developing cancer progression models and a Markov decision process model to identify optimal screening schedules under a varying number of lifetime screenings per person, a proxy for infrastructure capacity. We modeled Peru, a middle-income country, as a case study and the United States, an HIC, for validation. Results. Implementing 2, 5, 10, and 15 lifetime screens would require about 55, 135, 280, and 405 mammography machines, respectively, and would save 31, 62, 95, and 112 life-years per 1000 women, respectively. Current guidelines recommend 15 lifetime screens, but Peru has only 55 mammography machines nationally. With this capacity, the best strategy is 2 lifetime screenings at age 50 and 56 years. As infrastructure is scaled up to accommodate 5 and 10 lifetime screens, screening between the ages of 44-61 and 41-64 years, respectively, would have the best impact. Our results for the United States are consistent with other models and current guidelines. Limitations. The scope of our model is limited to analysis of national-level guidelines. We did not model heterogeneity across the country. Conclusions. Country-specific optimal screening schedules under varying infrastructure capacities can systematically guide development of cancer control programs and planning of health investments.