A prospective, randomized trial of tacrolimus/prednisone versus tacrolimus/prednisone/mycophenolate mofetil in renal transplant recipients

Background. Between September 20, 1995 and September 20, 1997, 208 adult patients undergoing renal transplantation were randomized to receive tacrolimus/prednisone (n=106) or tacrolimus/prednisone/mycophenolate mofetil (n=102), with the goal of reducing the incidence of rejection. Methods. The mean recipient age was 50.7±13.7 years. Sixty-three (30.3%) patients were 60 years of age or older at the time of transplantation. The mean donor age was 34.5±21.7 years. The mean cold ischemia time was 30.5±9.2 hr. The mean follow-up is 15±7 months. Results. The overall 1-year actuarial patient survival was 94%; the overall 1-year actuarial graft survival was 87%. When the patient and graft survival data were stratified to recipients under the age of 60 who did not have delayed graft function, the overall 1-year actuarial patient survival was 97%, and the corresponding 1-year actuarial graft survival was 93%. There were no differences between the two groups. The overall incidence of rejection was 36%; in the double-therapy group, it was 44%, whereas in the triple therapy group, it was 27% (P=0.014). The mean serum creatinine was 1.6±0.8 mg/dL A total of 36% of the successfully transplanted patients were taken off prednisone; 32% of the patients were taken off antihypertensive medications. The incidence of delayed graft function was 21%, the incidence of cytomegalovirus was 12.5%, and the initial and final incidences of posttransplant insulin-dependent diabetes mellitus were 7.0% and 2.9%; again, there was no difference between the two groups. Conclusions. This trial suggests that the combination of tacrolimus, steroids, and mycophenolate mofetil is associated with excellent patient and graft survival and a lower incidence of rejection than the combination of tacrolimus and steroids

Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip

The emergence of pathogens resistant to existing antimicrobial drugs is a growing worldwide health crisis that threatens a return to the pre-antibiotic era. To decrease the overuse of antibiotics, molecular diagnostics systems are needed that can rapidly identify pathogens in a clinical sample and determine the presence of mutations that confer drug resistance at the point of care. We developed a fully integrated, miniaturized semiconductor biochip and closed-tube detection chemistry that performs multiplex nucleic acid amplification and sequence analysis. The approach had a high dynamic range of quantification of microbial load and was able to perform comprehensive mutation analysis on up to 1,000 sequences or strands simultaneously in <2 h. We detected and quantified multiple DNA and RNA respiratory viruses in clinical samples with complete concordance to a commercially available test. We also identified 54 drug-resistance-associated mutations that were present in six genes of Mycobacterium tuberculosis, all of which were confirmed by next-generation sequencing

Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip

The emergence of pathogens resistant to existing antimicrobial drugs is a growing worldwide health crisis that threatens a return to the pre-antibiotic era. To decrease the overuse of antibiotics, molecular diagnostics systems are needed that can rapidly identify pathogens in a clinical sample and determine the presence of mutations that confer drug resistance at the point of care. We developed a fully integrated, miniaturized semiconductor biochip and closed-tube detection chemistry that performs multiplex nucleic acid amplification and sequence analysis. The approach had a high dynamic range of quantification of microbial load and was able to perform comprehensive mutation analysis on up to 1,000 sequences or strands simultaneously in <2 h. We detected and quantified multiple DNA and RNA respiratory viruses in clinical samples with complete concordance to a commercially available test. We also identified 54 drug-resistance-associated mutations that were present in six genes of Mycobacterium tuberculosis, all of which were confirmed by next-generation sequencing

ICAR: endoscopic skull‐base surgery

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Throughput optimization in multi-cell CDMA networks

In this paper, we investigate the performance of a multi-cell CDMA network by determining the maximum throughput that the network can achieve for a given gradeof-service requirement, quality-of-service requirement, network topology and call arrival rate profile. Our analysis is restricted to the reverse link and accounts for mobility of users between cells. A constrained nonlinear optimization problem is formulated that maximizes the network throughput subject to upper bounds on the blocking probabilities and a lower bound on the bit energy to interference ratio. The goal is to optimize the usage of network resources, provide consistent grade-of-service for all the cells in the network, and maintain a pre-specified quality-of-service. The solution to the optimization problem yields the maximum network throughput as well as the maximum number of calls that should be admitted in each cell for a given topology and call arrival rate profile. Our optimization algorithm yields significantly higher throughput compared with traditional call admission schemes. © 2005 IEEE

Mobility-based CAC algorithm for arbitrary call-arrival rates in CDMA cellular systems

This paper presents a novel approach for designing a call-admission control (CAC) algorithm for code-division multiple-access (CDMA) networks with arbitrary call-arrival rates. The design of the CAC algorithm uses global information; it incorporates the call-arrival rates and the user mobilities across the network and guarantees the users\u27 quality of service (QoS) as well as prespecified blocking probabilities. On the other hand, its implementation in each cell uses local information; it only requires the number of calls currently active in that cell. We present several cases for a nontrivial network topology where our CAC algorithm guarantees QoS and blocking probabilities while achieving significantly higher throughput than that achieved by traditional techniques. We also calculate the network capacity, i.e., the maximum throughput for the entire network, for prespecified blocking probabilities and QoS requirements. © 2005 IEEE

Cell Placement in a CDMA Network

This presentation discusses research on cell placement in a CDMA network. In order to enable iterative cell placement the authors use a computationally efficient iterative process to calculate the inter-cell and intra-cell interferences as a function of pilot-signal power and base station location

Cell placement in a CDMA network

Traditional design rules, wherein cells are dimensioned in order to get an equal amount of demand in each cell are not directly applicable to CDMA networks where large cells can cause a lot of interference to adjacent small cells. In order to enable iterative cell placement we use a computationally efficient iterative process to calculate the inter-cell and intra-cell interferences as a function of pilot-signal power and base station location. These techniques enable us to improve the placement of cells in a CDMA network so as to enhance network capacity. We show examples of how networks using this design technique will provide higher capacity than ones designed using conventional techniques

Call admission control scheme for arbitrary traffic distribution in CDMA cellular systems

Designing a call admission control (CAC) algorithm that guarantees call blocking probabilities for arbitrary traffic distribution in CDMA networks is difficult. Previous approaches have assumed a uniform traffic distribution or excluded mobility to simPlifY the design complexity. We define a set of feasible call configurations that results in a GAG algorithm that captures the effect of having an arbitrary traffic distribution and whose complexity scales linearly with the number of cells. To study the effect of mobility and to differentiate between the effects of blocking new calls and blocking handoff calls, we define a net revenue function. The net revenue is the sum of the revenue generated by accepting a new call and the cost of a forced termination due to a handoff failure. The net revenue depends implicitly on the GAG algorithm. We calculate the implied costs which are the derivatives of the implicitly defined net revenue function and capture the effect of increases in the number of calls admitted in one cell on the revenue of the entire network. Given a network topology with established traffic levels, the implied costs are used in the calculation of a CAC algorithm that enhances revenue and equalizes call blocking probabilities. Moreover, our algorithm provides guaranteed grade-of-service for all the cells in the network for an arbitrary traffic distribution