Antilymphoid antibody preconditioning and tacrolimus monotherapy for pediatric kidney transplantation

Objective: Heavy post-transplant immunosuppression may contribute to long-term immunosuppression dependence by subverting tolerogenic mechanisms; thus, we sought to determine if this undesirable consequence could be mitigated by pretransplant lymphoid depletion and minimalistic post-transplant monotherapy. Study design: Lymphoid depletion in 17 unselected pediatric recipients of live (n = 14) or deceased donor kidneys (n = 3) was accomplished with antithymocyte globulin (ATG) (n = 8) or alemtuzumab (n = 9). Tacrolimus was begun post-transplantation with subsequent lengthening of intervals between doses (spaced weaning). Maintenance immunosuppression, morbidity, graft function, and patient/graft survival were collated. Results: Steroids were added temporarily to treat rejection in two patients (both ATG subgroup) or to treat hemolytic anemia in two others. After 16 to 31 months (mean 22), patient and graft survival was 100% and 94%, respectively. The only graft loss was in a nonweaned noncompliant recipient. In the other 16, serum creatinine was 0.85 ± 0.35 mg/dL and creatinine clearance was 90.8 ± 22.1 mL/1.73 m2. All 16 patients are on monotherapy (15 tacrolimus, one sirolimus), and 14 receive every other day or 3 times per week doses. There were no wound or other infections. Two patients developed insulin-dependent diabetes. Conclusion: The strategy of lymphoid depletion and minimum post-transplant immunosuppression appears safe and effective for pediatric kidney recipients. © 2006 Elsevier Inc. All rights reserved

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Semi-supervised discovery of differential genes

BACKGROUND: Various statistical scores have been proposed for evaluating the significance of genes that may exhibit differential expression between two or more controlled conditions. However, in many clinical studies to detect clinical marker genes for example, the conditions have not necessarily been controlled well, thus condition labels are sometimes hard to obtain due to physical, financial, and time costs. In such a situation, we can consider an unsupervised case where labels are not available or a semi-supervised case where labels are available for a part of the whole sample set, rather than a well-studied supervised case where all samples have their labels. RESULTS: We assume a latent variable model for the expression of active genes and apply the optimal discovery procedure (ODP) proposed by Storey (2005) to the model. Our latent variable model allows gene significance scores to be applied to unsupervised and semi-supervised cases. The ODP framework improves detectability by sharing the estimated parameters of null and alternative models of multiple tests over multiple genes. A theoretical consideration leads to two different interpretations of the latent variable, i.e., it only implicitly affects the alternative model through the model parameters, or it is explicitly included in the alternative model, so that the interpretations correspond to two different implementations of ODP. By comparing the two implementations through experiments with simulation data, we have found that sharing the latent variable estimation is effective for increasing the detectability of truly active genes. We also show that the unsupervised and semi-supervised rating of genes, which takes into account the samples without condition labels, can improve detection of active genes in real gene discovery problems. CONCLUSION: The experimental results indicate that the ODP framework is effective for hypotheses including latent variables and is further improved by sharing the estimations of hidden variables over multiple tests

A 14-year experience with kidney transplantation.

Between November, 1962 and August, 1975, 668 kidney transplants were done in 556 consecutive patients at Denver, Colorado. The Denver experience has been divided into 7 periods of time, according to the conditions of care during each period. The results in related transplantation have changed little during the decade beginning in 1966. The results in unrelated transplantation have not materially changed since 1968. The long-term patient survival after related transplantation has been better than after cadaver transplantation. The results of transplantation in 57 children ages 3 to 18 years have been slightly better than the results of adult transplantation. The outcome of kidney transplantation and the feasibility of improving this therapy with present techniques are limited by our inability to accurately match each patient with the immunologically best donor and by our inability to precisely control the immune system of the recipient. Rejection is still the main reason for graft loss, and sepsis remains the main cause of patient mortality. More specific and less toxic means of achieving graft acceptance are needed before a higher level of patient service can be realized. However, even with the tools now available, thousands of recipients throughout the world have been returned to useful lives

Functional characterization of the YmcB and YqeV tRNA methylthiotransferases of Bacillus subtilis

Methylthiotransferases (MTTases) are a closely related family of proteins that perform both radical-S-adenosylmethionine (SAM) mediated sulfur insertion and SAM-dependent methylation to modify nucleic acid or protein targets with a methyl thioether group (–SCH3). Members of two of the four known subgroups of MTTases have been characterized, typified by MiaB, which modifies N6-isopentenyladenosine (i6A) to 2-methylthio-N6-isopentenyladenosine (ms2i6A) in tRNA, and RimO, which modifies a specific aspartate residue in ribosomal protein S12. In this work, we have characterized the two MTTases encoded by Bacillus subtilis 168 and find that, consistent with bioinformatic predictions, ymcB is required for ms2i6A formation (MiaB activity), and yqeV is required for modification of N6-threonylcarbamoyladenosine (t6A) to 2-methylthio-N6-threonylcarbamoyladenosine (ms2t6A) in tRNA. The enzyme responsible for the latter activity belongs to a third MTTase subgroup, no member of which has previously been characterized. We performed domain-swapping experiments between YmcB and YqeV to narrow down the protein domain(s) responsible for distinguishing i6A from t6A and found that the C-terminal TRAM domain, putatively involved with RNA binding, is likely not involved with this discrimination. Finally, we performed a computational analysis to identify candidate residues outside the TRAM domain that may be involved with substrate recognition. These residues represent interesting targets for further analysis

Functional characterization of the YmcB and YqeV tRNA methylthiotransferases of Bacillus subtilis

Methylthiotransferases (MTTases) are a closely related family of proteins that perform both radical-S-adenosylmethionine (SAM) mediated sulfur insertion and SAM-dependent methylation to modify nucleic acid or protein targets with a methyl thioether group (–SCH3). Members of two of the four known subgroups of MTTases have been characterized, typified by MiaB, which modifies N6-isopentenyladenosine (i6A) to 2-methylthio-N6-isopentenyladenosine (ms2i6A) in tRNA, and RimO, which modifies a specific aspartate residue in ribosomal protein S12. In this work, we have characterized the two MTTases encoded by Bacillus subtilis 168 and find that, consistent with bioinformatic predictions, ymcB is required for ms2i6A formation (MiaB activity), and yqeV is required for modification of N6-threonylcarbamoyladenosine (t6A) to 2-methylthio-N6-threonylcarbamoyladenosine (ms2t6A) in tRNA. The enzyme responsible for the latter activity belongs to a third MTTase subgroup, no member of which has previously been characterized. We performed domain-swapping experiments between YmcB and YqeV to narrow down the protein domain(s) responsible for distinguishing i6A from t6A and found that the C-terminal TRAM domain, putatively involved with RNA binding, is likely not involved with this discrimination. Finally, we performed a computational analysis to identify candidate residues outside the TRAM domain that may be involved with substrate recognition. These residues represent interesting targets for further analysis