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Eine Sporttherapie ist als unterstutzende Masnahme in der onkologischen Behandlung zu sehen, ist jedoch leider immer noch kein etablierter integraler Bestandteil fur alle Patienten. Tumorpatienten profitieren von einer Sporttherapie, weil sie die Belastbarkeit, eine Fatigue-Symptomatik und die Lebensqualitat verbessert und einer Sarkopenie entgegenwirkt. Beobachtungsstudien der letzten Jahre zeigen zudem eine Verbesserung der Prognose fur sportlich aktive Patienten mit kolorektalem Karzinom im Vergleich zu inaktiven Patienten. Randomisierte Studien fehlen bisher. Fur andere Entitaten aus dem gastroenterologisch-onkologischen Bereich sind diese positiven Auswirkungen von korperlicher Aktivitat bislang nicht bekannt. Eine Sporttherapie sollte aufgrund der vielfaltigen gunstigen Effekte fur alle Patienten ein integraler Bestandteil der Therapie sein. Die allgemeinen Empfehlungen fur Tumorpatienten zu 150 min moderater aerober Aktivitat pro Woche und 2‑maligem allgemeinem Krafttraining konnen von den meisten Patienten nach Diagnosestellung und wahrend der Therapie nicht umgesetzt werden. Individuelle Vorgaben mussen in Abhangigkeit der Belastbarkeit des Patienten gegeben und individuelle Ziele fur jeden Patienten definiert werden. Gerade altere und leistungsschwache Patienten profitieren von einem uberwachten Setting durch fachspezialisiertes Personal in Physiotherapieeinrichtungen und Krebssportgruppen. Eine Sporttherapie kann sowohl unter laufender Therapie als auch danach als sicher eingestuft werden.

BACKGROUND Gum infection, known as gingivitis, is a global issue. Gingivitis does not cause pain; however, if left untreated, it can worsen, leading to bad breath, bleeding gums, and even tooth loss, as the problem spreads to the underlying structures anchoring the teeth in the jaws. The asymptomatic nature of gingivitis leads people to postpone dental appointments until clinical signs are obvious or pain is evident. The COVID-19 pandemic has necessitated social distancing, which has caused many people to postpone dental visits and neglect gingival health. iGAM is a dental mobile health (mHealth) app that remotely monitors gum health, and an observational study demonstrated the ability of iGAM to reduce gingivitis. We found that a weekly dental selfie using the iGAM app reduced the signs of gingivitis and promoted oral health in a home-based setting. OBJECTIVE The aim of this mixed methods study is to assess perceptions, attitudes, willingness to pay, and willingness to use an mHealth app. METHODS The first qualitative phase of the study included eight semistructured interviews, and the second quantitative phase included data collected from responses to 121 questionnaires. RESULTS There was a consensus among all interviewees that apps dealing with health-related issues (mHealth apps) can improve health. Three themes emerged from the interviews: the iGAM app is capable of improving health, the lack of use of medical apps, and a contradiction between the objective state of health and the self-definition of being healthy. Participants were grouped according to how they responded to the question about whether they believed that mHealth apps could improve their health. Participants who believed that mHealth apps can enhance health (mean 1.96, SD 1.01) had a higher willingness to pay for the service (depending on price) than those who did not believe in app efficacy (mean 1.31, SD 0.87; t119=−2417; P=.02). A significant positive correlation was found between the amount a participant was willing to pay and the benefits offered by the app (rs=0.185; P=.04). CONCLUSIONS Potential mHealth users will be willing to pay for app use depending on their perception of the app’s ability to help them personally, provided they define themselves as currently unhealthy. CLINICALTRIAL

BACKGROUND Gingivitis is a nonpainful, inflammatory condition that can be managed at home. Left untreated, gingivitis can lead to tooth loss. Periodic dental examinations are important for early diagnosis and treatment of gum diseases. To contain the spread of the coronavirus, governments, including in Israel, have restricted movements of their citizens which might have caused routine dental checkups to be postponed. OBJECTIVE This study aimed to examine the ability of a mobile health app, iGAM, to reduce gingivitis, and to determine the most effective interval between photograph submissions. METHODS A prospective observational cohort study with 160 unpaid participants divided into 2 equal groups using the iGAM app was performed. The intervention group photographed their gums weekly for 8 weeks. The wait-list control group photographed their gums at the time of recruitment and 8 weeks later. After photo submission, the participants received the same message “we recommended that you read the information in the app regarding oral hygiene habits.” A single-blinded researcher examined all the images and scored them according to the Modified Gingival Index (MGI). RESULTS The average age of the intervention group was 26.77 (SD 7.43) and 28.53 (SD 10.44) for the wait-list control group. Most participants were male (intervention group: 56/75,74.7%; wait-list control group: 34/51, 66.7%) and described themselves as “secular”; most were “single” non-smokers (intervention group: 56/75, 74.7%; wait-list control group: 40/51, 78.4%), and did not take medications (intervention group: 64/75, 85.3%; wait-list control group: 40/51, 78.4%). A total of 126 subjects completed the study. A statistically significant difference (PP=.04). No association was found between knowledge and behavior. Most participants were familiar with the recommendations for maintaining oral health, yet they only performed some of them. CONCLUSIONS A dental selfie taken once a week using an mobile health app (iGAM) reduced the signs of gingivitis and promoted oral health. Selfies taken less frequently yielded poorer results. During the current pandemic, where social distancing recommendations may be causing people to avoid dental clinics, this app can remotely promote gum health.

BACKGROUND Dental visits are unpleasant; sometimes, patients only seek treatment when they are in intolerable pain. Recently, the novel coronavirus (COVID-19) pandemic has highlighted the need for remote communication when patients and dentists cannot meet in person. Gingivitis is very common and characterized by red, swollen, bleeding gums. Gingivitis heals within 10 days of professional care and with daily, thorough oral hygiene practices. If left untreated, however, its progress may lead to teeth becoming mobile or lost. Of the many medical apps currently available, none monitor gingivitis. OBJECTIVE This study aimed to present a characterization and development model of a mobile health (mHealth) app called iGAM, which focuses on periodontal health and improves the information flow between dentists and patients. METHODS A focus group discussed the potential of an app to monitor gingivitis, and 3 semistructured in-depth interviews were conducted on the use of apps for monitoring gum infections. We used a qualitative design process based on the Agile approach, which incorporated the following 5 steps: (1) user story, (2) use cases, (3) functional requirements, (4) nonfunctional requirements, and (5) Agile software development cycles. In a pilot study with 18 participants aged 18-45 years and with different levels of health literacy, participants were given a toothbrush, toothpaste, mouthwash, toothpicks, and dental floss. After installing iGAM, they were asked to photograph their gums weekly for 4 weeks. RESULTS All participants in the focus group believed in the potential of a mobile app to monitor gingivitis and reduce its severity. Concerns about security and privacy issues were discussed. From the interviews, 2 themes were derived: (1) “what's in it for me?” and (2) the need for a take-home message. The 5 cycles of development highlighted the importance of communication between dentists, app developers, and the pilot group. Qualitative analysis of the data from the pilot study showed difficulty with: (1) the camera, which was alleviated with the provision of mouth openers, and (2) the operation of the phone, which was alleviated by changing the app to be fully automated, with a weekly reminder and an instructions document. Final interviews showed satisfaction. CONCLUSIONS iGAM is the first mHealth app for monitoring gingivitis using self-photography. iGAM facilitates the information flow between dentists and patients between checkups and may be useful when face-to-face consultations are not possible (such as during the COVID-19 pandemic).

Background Gum infection, known as gingivitis, is a global issue. Gingivitis does not cause pain; however, if left untreated, it can worsen, leading to bad breath, bleeding gums, and even tooth loss, as the problem spreads to the underlying structures anchoring the teeth in the jaws. The asymptomatic nature of gingivitis leads people to postpone dental appointments until clinical signs are obvious or pain is evident. The COVID-19 pandemic has necessitated social distancing, which has caused many people to postpone dental visits and neglect gingival health. iGAM is a dental mobile health (mHealth) app that remotely monitors gum health, and an observational study demonstrated the ability of iGAM to reduce gingivitis. We found that a weekly dental selfie using the iGAM app reduced the signs of gingivitis and promoted oral health in a home-based setting. Objective The aim of this mixed methods study is to assess perceptions, attitudes, willingness to pay, and willingness to use an mHealth app. Methods The first qualitative phase of the study included eight semistructured interviews, and the second quantitative phase included data collected from responses to 121 questionnaires. Results There was a consensus among all interviewees that apps dealing with health-related issues (mHealth apps) can improve health. Three themes emerged from the interviews: the iGAM app is capable of improving health, the lack of use of medical apps, and a contradiction between the objective state of health and the self-definition of being healthy. Participants were grouped according to how they responded to the question about whether they believed that mHealth apps could improve their health. Participants who believed that mHealth apps can enhance health (mean 1.96, SD 1.01) had a higher willingness to pay for the service (depending on price) than those who did not believe in app efficacy (mean 1.31, SD 0.87; t119=−2417; P=.02). A significant positive correlation was found between the amount a participant was willing to pay and the benefits offered by the app (rs=0.185; P=.04). Conclusions Potential mHealth users will be willing to pay for app use depending on their perception of the app’s ability to help them personally, provided they define themselves as currently unhealthy.

Real time object detection and classification is essential for outdoor surveillance. Current state of the art real time object detection CNNs are trained on natural image datasets. However, outdoor surveillance images have very different characteristics: objects tend to be small and difficult to distinguish (averaging only 3% of image size). In addition, images come in different modalities, for example, nighttime surveillance images are grayscale thermal images representing heat emission not light reflection. Our dataset of images acquired from surveillance videos is comprised of ˜ 640 Daytime (DAY) color images and ˜ 360 nighttime grayscale THERMAL images. The dataset included three object categories: animals, people and vehicles. Because of the lack of large datasets for these scenarios, we evaluated using the much larger VOC dataset to augment our datasets. We conducted a study to determine the best combination of images to include in the training dataset, and how different types of images (i.e. DAY, THERMAL and VOC) affect each-others performance. We examined state of the art object detection and classification CNN architectures, focusing on accuracy and real time performance. By combining different images types THERMAL, DAY and 1200 VOC images in one dataset, the best results were obtained using transfer learning on YOLO-V3 with SPP, achieving 89.5 mAP for DAY images, and 79.53 mAP for THERMAL images, running at 35 fps. This setup provides a robust solution for many surveillance scenarios: night and daytime; far, small objects, as well as zoomed-in, large objects.

The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision-making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems. We present a new method for incorporating textual radiological findings from medical case reports in M-CBIR. The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy–Pathology List. It uses a new case relatedness metric $${\textit{relCase}}$$ that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case. An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods. The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.

Background Dental visits are unpleasant; sometimes, patients only seek treatment when they are in intolerable pain. Recently, the novel coronavirus (COVID-19) pandemic has highlighted the need for remote communication when patients and dentists cannot meet in person. Gingivitis is very common and characterized by red, swollen, bleeding gums. Gingivitis heals within 10 days of professional care and with daily, thorough oral hygiene practices. If left untreated, however, its progress may lead to teeth becoming mobile or lost. Of the many medical apps currently available, none monitor gingivitis. Objective This study aimed to present a characterization and development model of a mobile health (mHealth) app called iGAM, which focuses on periodontal health and improves the information flow between dentists and patients. Methods A focus group discussed the potential of an app to monitor gingivitis, and 3 semistructured in-depth interviews were conducted on the use of apps for monitoring gum infections. We used a qualitative design process based on the Agile approach, which incorporated the following 5 steps: (1) user story, (2) use cases, (3) functional requirements, (4) nonfunctional requirements, and (5) Agile software development cycles. In a pilot study with 18 participants aged 18-45 years and with different levels of health literacy, participants were given a toothbrush, toothpaste, mouthwash, toothpicks, and dental floss. After installing iGAM, they were asked to photograph their gums weekly for 4 weeks. Results All participants in the focus group believed in the potential of a mobile app to monitor gingivitis and reduce its severity. Concerns about security and privacy issues were discussed. From the interviews, 2 themes were derived: (1) “what's in it for me?” and (2) the need for a take-home message. The 5 cycles of development highlighted the importance of communication between dentists, app developers, and the pilot group. Qualitative analysis of the data from the pilot study showed difficulty with: (1) the camera, which was alleviated with the provision of mouth openers, and (2) the operation of the phone, which was alleviated by changing the app to be fully automated, with a weekly reminder and an instructions document. Final interviews showed satisfaction. Conclusions iGAM is the first mHealth app for monitoring gingivitis using self-photography. iGAM facilitates the information flow between dentists and patients between checkups and may be useful when face-to-face consultations are not possible (such as during the COVID-19 pandemic).

Automatic segmentation of anatomical structures in CT scans is an essential step in the analysis of radiological patient data and is a prerequisite for large-scale content-based image retrieval (CBIR). Many existing segmentation methods are tailored to a single structure and/or require an atlas, which entails multistructure deformable registration and is time-consuming. We present a fully automatic atlas-free segmentation of multiple organs of the ventral cavity in contrast-enhanced CT scans of the whole trunk (CECT). Our method uses a pipeline approach based on the rules that determine the order in which the organs are isolated and how they are segmented. Each organ is individually segmented with a generic four-step procedure. Our method is unique in that it does not require any predefined atlas or a costly registration step and in that it uses the same generic segmentation approach for all organs. Experimental results on the segmentation of seven organs—liver, left and right kidneys, left and right lungs, trachea, and spleen—on 20 CECT scans of the VISCERAL Anatomy training dataset and 10 CECT scans of the test dataset yield an average DICE volume overlap similarity score of 90.95 and 88.50%, respectively.

Acute Physiology and Chronic Health Evaluation (APACHE II score) [19]. (PDF 100Â kb)

Ethical bodies that approved the trial. (PDF 185Â kb)

Flowchart study protocol APEC trial. (PDF 6Â kb)

Definitions of the primary endpoint. (DOC 35Â kb)

Modified Glasgow score. [20]. (PDF 16Â kb)

Definition of Chronic Pancreatitis. (PDF 233Â kb)

ERC related complications. (PDF 139Â kb)

Flowchart APEC trial according to CONSORT [43]. (PDF 6Â kb)

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community. QC 20170104

Composite indicators are increasingly used by statistical offices and national or international organizations to convey and compare information on the status of countries, states and provinces. In specific, within the field of aging and older persons, over the last two decades, several international indices were developed in the field of well-being of older persons. In 2017, Doron and Spanier presented for the first time a conceptual framework for a new index of human rights of older persons (the IOPHRI). However, this new index has not been tested or applied in order to examine its ability to contribute to the actual measurement and assessment of the human rights of older persons. In this presentation, the comparative international findings of the implementation of the IOPHRI to six countries will be presented: USA, Australia, Chile, Israel, India, and Ireland. The findings contribute to the further development of measuring the human rights of older persons in two aspects: First, on the methodological level, the findings reveal the challenges involved in comparing different legal systems and “weighing” human rights. Second, on the substantive level, the findings reveal how forma legal human rights of older persons are more connected to legal cultures and histories than to the actual real life experiences of older persons.

Shifting demographics, rising costs of operations, a changing competitive landscape, reductions in state appropriations, pressures for accountability, and a widespread economic decline characterize the environment in which today’s colleges and universities operate. This article examines some of the current responses to these challenges and outlines opportunities for advancing the mission of higher education in the 21st century. The topics covered include student centeredness, interdisciplinary research, access and affordability, technology, globalizing our universities, and service.

Candecomp/Parafac Decomposition (CPD) has emerged as a framework for modeling N-way arrays (higher-order matrices). CPD is naturally well suited for the analysis of data sets comprised of observations of a function of multiple discrete indices. In this study we evaluate the prospects of using CPD for modeling MRI brain properties (i.e. brain volume and gray-level) for schizophrenia diagnosis. Taking into account that 3D imaging data consists of millions of pixels per patient, the diagnosis of a schizophrenia patient based on pixel analysis constitutes a methodological challenge (e.g. multiple comparison problem). We show that the CPD could potentially be used as a dimensionality redaction method and as a discriminator between schizophrenia patients and match control, using the gradient of pre- and post Gd-T1-weighted MRI data, which is strongly correlated with cerebral blood perfusion. Our approach was tested on 68 MRI scans: 40 first-episode schizophrenia patients and 28 matched controls. The CPD subject’s scores exhibit statistically significant result (P < 0.001). In the context of diagnosing schizophrenia with MRI, the results suggest that the CPD could potentially be used to discriminate between schizophrenia patients and matched control. In addition, the CPD model suggests for brain regions that might exhibit abnormalities in schizophrenia patients for future research.

We describe a new method for the automatic segmentation of multiple organs of the ventral cavity in CT scans. The method is based on a set of rules that determine the order in which the organs are isolated and segmented. First, the air-containing organs are segmented: the trachea and the lungs. Then, the organs with high blood content: the spleen, the kidneys and the liver, are segmented. Each organ is individually segmented with a generic four-step pipeline procedure. Our method is unique in that it uses the same generic segmentation approach for all organs and in that it relies on the segmentation difficulty of organs to guide the segmentation process. Experimental results on 20 CT scans of the VISCERAL Anatomy2 Challenge training datasets yield an average Dice volume overlap similarity score of 90.95. For the 10 CT scans test datasets, the average Dice scores is 88.5.

The challenges to higher education in the twenty-first century include developing an increased sensitivity to individual differences among students and encouraging diverse approaches to instruction. Promoting new ways for students to learn involves getting to know students better, experimenting with various methods for transmitting content, and, ultimately, in humanizing the educational environment.

Although endotoxin has been implicated as an important contributor to the systemic inflammatory response (SIR) during cardiopulmonary bypass (CPB), its source remains unclear. While gut translocation has traditionally been perceived as the primary source of endotoxemia, accumulation of endotoxin in pooled pericardial blood may represent an additional source of endotoxin that is continually reinfused into the CPB circuit. Eighteen patients undergoing primary coronary revascularization procedures were prospectively evaluated. Shed blood pooled in the pericardial space was returned to the CPB circuit through cardiotomy suction catheters at 45 min after placement of the aortic crossclamp. Simultaneous samples of pooled pericardial and peripheral arterial blood were obtained and analyzed by a limulus amebocyte lysate assay for the determination of endotoxin concentration, and an enzyme-linked immonosorbert assay for tumor necrosis factor (TNF-α) levels. Significant elevations in endotoxin were demonstrated in pooled pericardial blood samples compared with arterial blood (3.5 ± 0.5 vs 0.8 ± 0.2 pg/ml; p < 0.05). TNF-α levels were below the limits of detection in both samples. These data implicate pooled pericardial blood as an important primary source of endotoxin that, when continually reinfused throughout CPB, may contribute to the overall SIR. Because endotoxemia has been identified as an important predictor of adverse outcomes following cardiac surgery, removal of endotoxin antigen in shed pericardial blood, prior to its reinfusion into the CPB circuit, may provide a directed means to improve perioperative outcome without compromising established blood conservation techniques.

Background . Heparin resistance is an important clinical problem traditionally treated with additional heparin or fresh frozen plasma. We undertook a randomized clinical trial to determine if treatment with antithrombin (AT) concentrate is effective for treating this condition. Methods . Patients requiring cardiopulmonary bypass who were considered to be heparin resistant (activated clotting time 450 IU/kg heparin) were randomized to receive either 1000 U AT or additional heparin. Results . AT concentrate was effective in 42 of 44 patients (96%) for immediately obtaining a therapeutic activated clotting time. This compared favorably to 28 of 41 patients (68%) treated with additional heparin ( p = 0.001). All patients who failed heparin therapy were successfully treated with AT. The patients receiving AT required less time to obtain an adequate ACT but there was no difference in clinical outcomes among the groups. Study patients had deficient AT activity at baseline (56% ± 25%), which improved in those given AT concentrate (75% ± 31% versus 50% ± 23%, p Conclusions . Heparin resistance is frequently associated with AT deficiency. Treating this deficiency with AT concentrate is more effective and faster for obtaining adequate anticoagulation than using additional heparin.

In keeping with their historic role in promoting human, economic, and cultural development, American land-grant universities have unprecedented opportunities to respond to changes taking place in society. Penn State University is responding with a model that emphasizes integration of teaching, research and service, interdisciplinary initiatives, and active collaboration with communities to enhance the quality of life. The substantial challenges of children, youth, and families are one of the priorities of Penn State in advancing this model. The Children, Youth, and Families Consortium, recently established by the university, supports interdisciplinary and collaborative teaching, research, and service to develop and implement prevention and intervention models that promote positive outcomes for children, youth, and families.

Cardiac transplantation is a limited option for end-stage heart failure because of the shortage of donor organs. Left-ventricular assist devices (LVADs) are currently under investigation as permanent therapy for end-stage heart failure, but long-term successful device implantation is limited because of a high rate of serious infections. To examine the relation between LVAD-related infection and host immunity, we investigated immune responses in LVAD recipients.We compared the rate of candidal infection in 78 patients with New York Heart Association class IV heart failure who received either an LVAD (n=40) or medical management (controls, n=38). Fluorochrome-labelled monoclonal antibodies were used in analyses of T-cell phenotype. Analysis of T-cell function included intradermal responses to recall antigens and proliferative responses after stimulation by phytohaemagglutinin, monoclonal antibodies to CD3, and mixed lymphocyte culture. We measured T-cell apoptosis in vivo by annexin V binding, and confirmed the result by assessment of DNA fragmentation. Activation-induced T-cell death was measured after T-cell stimulation with antibodies to CD3. All immunological tests were done at least 1 month after LVAD implantation. Between-group comparisons were by Kaplan-Meier actuarial analysis and Student's t test.By 3 months after implantation of LVAD, the risk of developing candidal infection was 28% in LVAD recipients, compared with 3% in controls (p=0.003). LVAD recipients had cutaneous anergy to recall antigens and lower (70%) T-cell proliferative responses than controls after activation via the T-cell receptor complex (p0.001). T cells from LVAD recipients had higher surface expression of CD95 (Fas) (p0.001) and a higher rate of spontaneous apoptosis (p0.001) than controls. Moreover, after stimulation with antibodies to CD3, CD4 T-cell death increased by 3.2-fold in LVAD recipients compared with only 1.2-fold in controls (p0.05).LVAD implantation results in an aberrant state of T-cell activation, heightened susceptibility of CD4 T cells to activation-induced cell death, progressive defects in cellular immunity, and increased risk of opportunistic infection.

Early experience with aprotinin in deep hypothermic circulatory arrest (DHCA) raised alarm about hazards associated with its use. Based on what little is known about possible mechanistic interactions between hypothermia, stasis, and aprotinin, there is no evidence that aprotinin becomes unusually hazardous in DHCA. Excessive mortality and complication rates have only been reported in clinical series in which the adequacy of heparinization is questionable. Benefits associated with use of aprotinin in DHCA have been inconsistently demonstrated. The only prospective, randomized series showed significant reduction in blood loss and transfusion requirements. Use of aprotinin in DHCA should be based on the same considerations applied in other cardiothoracic procedures.

Background: Multiple stimuli converge in cardiopulmonary bypass to create a tremendous prothrombotic stimulus. The ideal anticoagulant for cardiopulmonary bypass should selectively target only the intravascular stimuli, thereby eliminating pathologic clotting in the bypass circuit while preserving hemostasis in the thoracic cavity. We propose the inhibition of factor IX as such a targeted anticoagulant strategy. Methods: We prepared an inhibitor of activated factor IX and applied it to a primate model of cardiopulmonary bypass to confirm the anticoagulant efficacy of activated factor IX in this setting and to assess more subtle markers of thrombin generation, macrophage procoagulant activity, and cellular tissue factor expression. Seven baboons that received activated factor IX (460 μg/kg) and 7 that received heparin (300 IU/kg) and protamine underwent cardiopulmonary bypass for 90 minutes and were followed after the operation for 3 hours. Results: Analysis of plasma factor IX activity demonstrated adequate inhibition (