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Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201—an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201’s target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application., Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.

PURPOSE To date, there are limited data on the economic burden of childhood glaucoma, a relatively rare but visually debilitating disease. The purpose of this study is to evaluate the immediate costs of illness associated with childhood glaucoma during the first 4 years after presentation. METHODS Data on age, sex, surgical interventions, visits, examinations under anesthesia, and medications were collected yearly following presentation in patients with childhood glaucoma. Inclusion criteria included diagnosis of primary or secondary childhood glaucoma, age less than 21 years, and follow-up of 4 years. A total of 23 patients (39 eyes) were included. Cost analysis used 2013 hospital-specific charges and US average hourly wage to calculate outcomes of total, direct, and indirect costs (significance level P

Objective: This paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu), an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpretations. Materials and Methods: PMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as the Human Genome Variation Society variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, and detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically. Results: At the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. PMKB’s interpretations have been used in over 1500 AmpliSeq tests and 750 whole-exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API. Discussion: An accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology. Conclusion: The PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitate reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API.

BACKGROUND Online student response systems (OSRSs), driven by the Internet and cell phone technology, provide a free, easily accessible method to increase student engagement, facilitate active learning, and provide learners and teachers with instant feedback about learning progress. PURPOSE This article describes undergraduate nursing students' use of 2 OSRSs and their perceptions of the impact of the tools on class participation and engagement. METHODS Students used their own mobile phones or computers to access 2 types of OSRSs: a classic and a game-based OSRS. RESULTS Students indicated that both systems increased participation and engagement. The game-based OSRS was favored over the classic OSRS. The potential for use of the game-based OSRS for assessing rapid-answer fact-based knowledge and the classic OSRS for assessing more complex learning tasks is discussed. CONCLUSION Nurse educators are encouraged to consider integrating online response system technology into their classroom teaching.

Spam emails have been a chronic issue in computer security. They are very costly economically and extremely dangerous for computers and networks. Despite of the emergence of social networks and other Internet based information exchange venues, dependence on email communication has increased over the years and this dependence has resulted in an urgent need to improve spam filters. Although many spam filters have been created to help prevent these spam emails from entering a user's inbox, there is a lack or research focusing on text modifications. Currently, Naive Bayes is one of the most popular methods of spam classification because of its simplicity and efficiency. Naive Bayes is also very accurate; however, it is unable to correctly classify emails when they contain leetspeak or diacritics. Thus, in this proposes, we implemented a novel algorithm for enhancing the accuracy of the Naive Bayes Spam Filter so that it can detect text modifications and correctly classify the email as spam or ham. Our Python algorithm combines semantic based, keyword based, and machine learning algorithms to increase the accuracy of Naive Bayes compared to Spamassassin by over two hundred percent. Additionally, we have discovered a relationship between the length of the email and the spam score, indicating that Bayesian Poisoning, a controversial topic, is actually a real phenomenon and utilized by spammers.

Drug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

Objective: To describe the current data evaluating the efficacy and safety of memantine for the prevention of primary headache disorders. Data Sources: A literature search using MEDLINE (1966-July 2014) and EMBASE (1973-July 2014) was conducted using the search terms memantine, headache, migraine, glutamate, and NMDA. References of identified articles were reviewed for additional, relevant citations. Study Selection and Data Extraction: All English-language articles dealing with the use of memantine for prevention of primary headache disorders were included. Data Synthesis: Data from several retrospective reports and 2 prospective clinical trials suggest that memantine may be a useful treatment option for the prevention of primary headache disorders. The majority of available literature focuses specifically on chronic migraine prevention in refractory patients who had failed multiple previous prophylactic therapies. In these patients, 10 to 20 mg of memantine daily reduced the frequency and intensity of migraine headaches and was generally well tolerated, with few adverse events. Data regarding the efficacy of memantine for other primary headache disorders such as chronic tension type and cluster headaches are limited. Conclusion: Although further studies evaluating the efficacy of memantine for prevention of primary headache disorders are warranted, memantine may be a reasonable option, used either as monotherapy or adjunctive therapy, in the refractory chronic migraine prophylaxis setting.

ObjectiveThis paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu), an interactive online application for collaborative editing, maintenance and sharing of structured clinical-grade cancer mutations interpretations.Materials and MethodsPMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as Human Genome Variation Society (HGVS) variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically.ResultsAt the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. The PMKB’s interpretations have been used in over 1,500 AmpliSeq tests and 750 whole exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API.DiscussionAn accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology.ConclusionThe PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitates reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API.

Advanced glycation endproducts (AGEs) and their receptors, receptor for advanced glycation endproducts (RAGE), are novel groups of molecules with roles in inflammation, cytokine activation, and promotion of cell growth. Recently, RAGE has been implicated in the progression and metastasis of several epithelial tumors. The expression of RAGE was examined in 38 oral squamous cell carcinoma (OSCC) cases by immunohistochemistry. In the OSCCs, RAGE positivity, interpreted as more than 25% positive cells, was detected in 10 of 10 well-differentiated, 3 of 4 well-to-moderately differentiated, 3 of 9 moderately differentiated, 1 of 7 moderate-to-poorly differentiated, and 0 of 8 poorly differentiated tumors. The staining percentage was significantly higher in well-differentiated tumors compared to moderately (P.05) and poorly differentiated (P.05) tumors. All normal mucosa samples were RAGE-positive. Western blot analysis for RAGE was performed on 2 OSCCs and 2 normal oral mucosa samples. Higher expression was observed in the normal tissues compared to the OSCCs. Our results show that RAGE immunoreactivity correlates with histologic differentiation in OSCC.

Background and Objective:A prototype ultrawide field angiographic system and conventional technology were compared.Patients and Methods:Thirty subjects with diabetic retinopathy were imaged in three centers by conventional (Topcon Inc., Paramus, NJ, or Carl Zeiss Meditec, Dublin, CA) and ultrawide field (Optos P200A; Optos Inc., Dunfermline, Scotland) digital fluorescein angiography units. A calibrated grid was digitally placed on the images to facilitate measurements. Main outcome measures were field of view and the number of grid sectors displaying retinal ischemia and neovascularization. Secondary measures were image quality and patient satisfaction scores.Results:The P200A provided a significantly greater visual field extent of 8.7 ± 1.6 disc diameters (DD) compared to the conventional systems (3.4 ± 0.76 DD) (P < .001). Retinal ischemia was better revealed (16.9 ± 15 vs 3.4 ± 4.26 sectors) with the prototype, but image quality was superior with the conventional system. Patient satisfaction scores did not differ between systems.Conclusion:A prototype ultrawide angle imaging system displayed significantly more diabetic retinal pathology in a single image than conventional systems.

Pancreatic amyloid formation by islet amyloid polypeptide (IAPP) is a hallmark pathological feature of type 2 diabetes. IAPP is stored in the secretory granules of pancreatic beta-cells and co-secreted with insulin to maintain glucose homeostasis. IAPP is innocuous under homeostatic conditions but imbalances in production or processing of IAPP may result in homodimer formation leading to the rapid production of cytotoxic oligomers and amyloid fibrils. The consequence is beta-cell dysfunction and the accumulation of proteinaceous plaques in and around pancreatic islets. Beta-site APP-cleaving enzyme 2, BACE2, is an aspartyl protease commonly associated with BACE1, a related homolog responsible for amyloid processing in the brain and strongly implicated in Alzheimer’s disease. Herein, we identify two distinct sites of the mature human IAPP sequence that are susceptible to BACE2-mediated proteolytic activity. The result of proteolysis is modulation of human IAPP fibrillation and human IAPP protein degradation. These results suggest a potential therapeutic role for BACE2 in type 2 diabetes-associated hyperamylinaemia.

Diabetes is associated with increased prevalence, severity, and progression of periodontal disease. To test the hypothesis that activation of RAGE (Receptor for Advanced Glycation End products) contributes to the pathogenesis of diabetes-associated periodontitis, we treated diabetic mice, infected with the human periodontal pathogen Porphyromonas gingivalis, with soluble RAGE (sRAGE). sRAGE is the extracellular domain of the receptor, which binds ligand and blocks interaction with, and activation of, cell-surface RAGE. Blockade of RAGE diminished alveolar bone loss in a dose-dependent manner. Moreover, we noted decreased generation of the proinflammatory cytokines TNF-alpha and IL-6 in gingival tissue, as well as decreased levels of matrix metalloproteinases. Gingival AGEs were also reduced in mice treated with sRAGE, paralleling the observed suppression in alveolar bone loss. These findings link RAGE and exaggerated inflammatory responses to the pathogenesis of destructive periodontal disease in diabetes.

For natural products and phenotypic screen derived small molecules, one of the greatest bottlenecks is target identification. Since most experimental approaches are failure-prone and require a large investment of time and resources, computational methods have the potential to substantially improve target identification efforts. Recently there has been an explosion of genomic, chemical, clinical, and pharmacological datasets that characterize a small molecule's mechanism of action, but no method has been invented to integrate the multiple, independent pieces of evidence provided by each data type into a cohesive prediction framework To address this problem we developed BANDIT: a Bayesian ANalysis to determine Drug Interaction Targets. BANDIT integrates over 20,000,000 pieces of unique information from seven distinct data types to predict drug targets. By integrating these data types within a Bayesian network we outperformed other target prediction methods and achieved a target prediction accuracy of over 90%. Furthermore, we observed that BANDIT was able to predict the precise targeting mechanism as well as the protein target. We further validated BANDIT's accuracy by using it to reproduce the results of a previously published experimental kinase inhibitor screen without any additional data. We observed that our target predictions for the tested small molecules were significantly more likely to be the targets with the highest levels of inhibition in the experimental screen (P = 3e-06). We next used BANDIT to predict targets and mechanisms for over 50,000 small molecules with no known targets. We identified 24 molecules with varying structures and efficacies that we predicted to inhibit microtubule polymerization. Using immunofluorescence and tubulin binding assays we were experimentally validated microtubules as targets for 18 of these compounds - a success rate much higher than expected by random chance. Moreover, one of the greatest challenges with current microtubule chemotherapy is the development of drug resistance, which is lethal for the patients. Remarkably, we identified a subset of our compounds to be active against tumor cells resistant to Eribulin, a microtubule depolymerizing agent FDA approved for breast cancer, and cross-resistant to Vincristine and Colchicine, known depolymerizing drugs. This demonstrated that BANDIT could not only rapidly identify drug mechanisms and targets, but also determine novel molecules with the potential to act on refractory tumors. Finally we used BANDIT to observe how different drug types could interact with one another. We observed many unexpected and exciting results. For instance we observed a tight clustering of the seemingly unrelated classes of beta-blockers with anti-Parkinson's medications. This interaction potentially indicates a shared protein target interaction and provides support to studies proposing the clinical use of beta-blockers in the treatment of tremors in Parkinson's patients. Examples such as this indicate the validity of BANDIT's clustering approach and reveal how previously unknown shared target interactions could cause the unexplained phenotypic effects. This approach could further be expanded to determine undiscovered relationships between classes of drugs, discover which drugs could be repositioned for new uses, and predict clinical drug synergies. Citation Format: Neel S. Madhukar, Prashant Khade, Linda Huang, Kaitlyn Gayvert, Giuseppe Galletti, Paraskevi Giannakakou, Olivier Elemento. Using a data-driven Bayesian approach to predict the targets of orphan small molecules and ways to overcome drug resistance. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-106.

This study examines intra-group information transfers associated with quarterly earnings announcements. Specifically, the objectives of this paper are to investigate whether there is positive information transfers among group-affiliated firms, and to examine how the affiliate’s operating strategy affect the extent of information transfers. The sample includes 615 quarterly earnings releases by firms listed in Taiwan Stock Exchange between 1988 and 1997. The empirical results provide evidence of positive information transfers between affiliated firms associated with quarterly earnings announcements. In addition, the extent of information transfer is dependent on the diversification strategies of the group affiliates. When industry covariation in firms' returns is removed, directional information transfers remain significant. Keywords: Group-affiliated Firms, Information Transfer, Quarterly Earnings Announcement, Market model, Two-index Model

The late phase of long-term potentiation (LTP) requires activation of the mammalian target of rapamycin (mTOR) pathway and synthesis of new proteins. mTOR regulates protein synthesis via phosphorylation of 4E-binding proteins (4E-BPs) and S6K, and via selective up-regulation of 5′ terminal oligopyrimidine (5′ TOP) mRNAs that encode components of the translational machinery. In this study, we explored the regulation of 5′ TOP mRNAs during late-LTP (L-LTP). Synaptic plasticity was studied at Schaffer collateral – CA1 pyramidal cell synapses in rat organotypic hippocampal slices. Forskolin, an adenylate cyclase activator, induced L-LTP in organotypic slices that was mTOR-dependent. To determine if 5′ TOP mRNAs are specifically up-regulated during L-LTP, we generated a 5′ TOP-myr-dYFP reporter to selectively monitor 5′ TOP translation. Confocal imaging experiments in cultured slices revealed an increase in somatic and dendritic fluorescence after forskolin treatment. This up-regulation was dependent on an intact TOP sequence and was mTOR, extracellular signal-regulated kinase (ERK), and phosphatidylinositol 3-kinase (PI3K)-dependent. Our findings indicate that forskolin induces L-LTP in hippocampal neurons and up-regulates 5′ TOP mRNAs translation via mTOR, suggesting that up-regulation of the translational machinery is a candidate mechanism for the stabilization of LTP.

BACKGROUND AND OBJECTIVE The authors used a drusen detection algorithm to quantitate drusen on digitized images and determined its precision and accuracy. PATIENTS AND METHODS Fundus images from 349 participants in the Age-Related Eye Disease Study trial were digitized and analyzed with the Drusen Analyzer (IRIDEX Corporation, Mountain View, CA). The size, number, and area of the drusen in two macular regions were computed by two readers using an interactive approach. Measurements were compared to data generated by reading center methods. RESULTS For the Analyzer, inter-observer agreement was 0.79 (SE = 0.02) and 0.86 (SE = 0.01) (kappa statistic). Intra-observer precision was 0.835 and 0.880 (Spearman coefficients). Categorical agreement (weighted kappa) between the Analyzer and reading center results for the two regions was (0.76, 0.58) and (0.68, 0.68) for readers A and B. The time required to quantitate an image using the Analyzer was 105 ± 52 and 218 ± 102 seconds for the two regions, respectively. CONCLUSIONS Quantitative detection of drusen can be performed reproducibly and efficiently. Comparisons to more labor intensive reading center techniques suggest that the results are similar but that the algorithm is more sensitive and precise. [Ophthalmic Surg Lasers Imaging 2007;38:126-134.] AUTHORS From the UPMC Eye Center (TRF, LH, MP), Department of Ophthalmology; and the Department of Psychiatry (RB), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Accepted for publication January 12, 2007. Supported in part by the National Eye Institute, National Institutes of Health, Bethesda, Maryland (R03 EY13807-01), Research to Prevent Blindness, Inc., New York, New York, and unrestricted funds from William H. Rea. Address correspondence to Thomas R. Friberg, MS, MD, UPMC Eye Center, 203 Lothrop Street, Suite 824, Pittsburgh, PA 15213.

It typically takes 15 years and 2.6 billion dollars to go from a small molecule in the lab to a clinically useable drug. This cost has only increased over the past decade despite advancements in drug development, with one of the biggest bottlenecks being drug target identification. Current efforts are driven by case specific experimentation - a slow and failure-prone process - and each time a new drug is developed these experiments need to be redone from scratch. Recently there has been an explosion of genomic, chemical, clinical, and pharmacological data but no target prediction method has successfully integrated these multiple pieces of evidence into a cohesive approach. Here we propose BANDIT: a Bayesian ANalysis to find Drug Interaction Targets. BANDIT combines data on drug efficacies, post-treatment transcriptional responses, drug structures, side effects, bioassay results, and known targets within a Bayesian framework to predict the targets of small molecules. When applied to a test set of drugs with known targets, BANDIT achieved a predictive power of 91% at identifying drug pairs that share a target (AUROC = 0.91). The power of BANDIT's classifier steadily increased from 60% to 91% as we increased the number of included data types- demonstrating the strength of BANDIT's Big Data approach. Additionally, we found that BANDIT could reproduce the results of a published experimental kinase inhibitor screen with no additional data or training, indicating its potential to replace otherwise tenuous and costly experiments. We next used BANDIT to predict interaction targets for over 50,000 small molecules with no known targets and specifically identified a set of unstudied small molecules predicted to inhibit microtubule formation. We focused on microtubules because of their relevance as chemotherapy targets. Using immunohistochemical staining and tubulin binding assays, we were able to validate these predictions in vitro, identifying new potential chemotherapeutic options. We also found that by clustering drugs based on their BANDIT output, we were able to correctly predict the mechanism of action, as well as the target, for these new small molecule predictions. When this method was applied to the verified microtubule inhibitors, we were able to accurately separate small molecules based on their method of inhibition - polymerizing vs. depolymerizing. Using BANDIT's output we then created an interaction network of all known drugs characterized by their ATC type and intended use. We observed a definite structure to this “drug universe” network with certain drug classes, such as “Dermatologicals” and “Musculo-skeletal System,” clustering together. This information could help advance future drug development efforts by providing information how certain drug classes are similar and could potentially interact in a biological system. Altogether these results attest to BANDIT's ability to provide a novel, broadly applicable way to identify drug targets. By predicting targets for new and established drugs, without the need for numerous target-specific assays, BANDIT could greatly expedite future drug discovery efforts by reducing cost and time. Additionally, by rapidly screening compounds that have been developed but never fully studied we could efficiently increase our potential anti-cancer therapeutic options. Citation Format: Neel S. Madhukar, Linda Huang, Prashant Khade, Katie Gayvert, Paraskevi Giannakakou, Olivier Elemento. Small molecule target prediction and identification of novel anti-cancer compounds using a data-driven bayesian approach. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B162.

Diabetes is a risk factor for periodontal disease in humans. In hyperglycemia, glycoxidation of proteins and lipids results in the formation of advanced glycation endproducts, or AGEs. The accumulation of AGEs in the plasma and tissues, and their interaction with their cellular receptor for AGE (RAGE), has been implicated in diabetic complications. In order to establish a model with which to delineate the specific host response factors that underlie the development of periodontal disease in diabetes, male C57BL/6J mice were rendered diabetic with streptozotocin. One month after documentation of diabetes or control state, mice were inoculated with the human periodontal pathogen Porphyromonas gingivalis, strain 381 (P. gingivalis) or treated with vehicle. Infection with P. gingivalis was achieved, as demonstrated by infiltration of gingival tissue with granulocytes, presence of DNA specific for P. gingivalis as well as increased serum antibody titer to P. gingivalis. At 2 and 3 months after infection, increased alveolar bone loss was demonstrated in P. gingivalis-inoculated diabetic vs. non-diabetic mice, along with enhanced tissue-destructive capacity, as demonstrated by increased collagenolytic activity in gingival extracts. Consistent with an important role for AGE-RAGE interaction, increased AGE deposition and expression of vascular and monocyte RAGE were demonstrated in diabetic gingiva compared with non-diabetic controls. Taken together, these data indicate that we have established a murine model of enhanced periodontal disease in diabetes. This model will serve to delineate molecular mechanisms which account for the increased susceptibility of diabetic patients to periodontal disease.

Reperfusion injury is a phenomenon complicating microvascular reconstruction. Extensive investigations of free-radical scavengers exist in the literature. The potential beneficial effects of verapamil, deferoxamine, and dimethylsulfoxide were studied in the rabbit forelimb subjected to 3 hr of tourniquet ischemia, followed by a period of reperfusion. Five minutes prior to tourniquet release, rabbits were given single intravenous infusions of normal saline, verapamil (0.2 mg/kg), deferoxamine (50 mg/kg), or dimethylsulfoxide (100 mg/kg). Following reperfusion, neither light microscopy nor laser Doppler flowmetry revealed significant differences between the sham and treated limbs. Muscle function was evaluated by measuring maximal twitch tensions in the flexor digitorum superficialis. Deterioration of muscle contractile function at 0.5, 3, and 24 hr after reperfusion was significantly improved by treatment with verapamil or deferoxamine. Conversely, dimethylsulfoxide worsened muscle function post ischemia. Dimethylsulfoxide does not appear to be beneficial, while verapamil and deferoxamine may be of benefit in mitigating reperfusion injury in microvascular transplantation.

Elevation of rat abdominal skin flaps, followed by ligation and division of the left inferior neurovascular pedicle, resulted in only a 40% survival of the area normally perfused by the ligated artery and vein. Superoxide dismutase (SOD) (EC 1.15.1.1) administered i.v. (20,000 U/kg) 30 min before flap elevation increased survival to 52%. SOD derivatized with polyethylene glycol, which increases circulating half-life, was more effective, increasing survival to 80%. This protective effect resulted from the catalytic activity of the derivatized enzyme because inactivation by treatment with H2O2 eliminated its effect on skin flap survival. An equimolar mixture of Desferal and MnCl2, which catalyzes the dismutation of O2- in vitro, improved survival to 72%. Desferal-Fe3+, which lacks in vitro SOD activity, or Mn2+ alone did not affect the survival of skin flaps, but Desferal alone was nearly as effective as the Desferal-Mn2+ mixture. This effect of Desferal may result from acquisition of and subsequent removal of iron in vivo. These results support the view that the superoxide radical or a product derived from it plays a role in limiting the survival of island skin flaps.

The rabbit antiserum and mouse monoclonal hybridoma antibody against porcine cytosolic aspartate aminotransferase (c-AAT) (or cytosolic glutamic oxaloacetic transaminase (c-GOT)) were produced and compared for the localization of c-AAT in rat liver. An indirect immunocytochemical technique was performed using peroxidase-conjugated goat immunoglobulin (Ig) G anti-rabbit IgG and peroxidase-conjugated rabbit IgG anti-mouse IgG as the second antibody. Rats were perfused with paraformaldehyde-lysine-periodate fixative and the liver fragments were immersed in 4% paraformaldehyde and transferred to 10% dimethyl sulfoxide overnight and subjected to cryostat sectioning. The rabbit IgG antibody, 3 individual monoclonal antibodies, and a mixture of these 3 monoclonal antibodies were applied to the tissue sections, respectively, using the same concentration. Under the same experimental conditions, the c-AAT was localized in each individual hepatocyte by both monoclonal and polyclonal antibodies. However, a mixture of three monoclonal antibodies gave stronger staining than a single monoclonal antibody; although two antibodies yield more intense staining than just one, it was still less intense than for three. The conventional rabbit polyclonal antibody against c-AAT produced more reaction product than the combined three monoclonal antibodies. It is concluded that for immunocytochemical study, the use of a single monoclonal antibody is sensitive enough to localize its tissue antigen under the present experimental condition. To obtain a stronger reaction product, a combination of several monoclonal antibodies, at least three or more, may give better staining.