Experience in hepatic resection for metastatic colorectal cancer: Analysis of clinical and pathologic risk factors

Background. The selection of patients for resective therapy of hepatic colorectal metastases remains controversial. A number of clinical and pathologic prognostic risk factors have been variably reported to influence survival. Methods. Between January 1981 and December 1991, 204 patients underwent curative hepatic resection for metastatic colorectal cancer. Fourteen clinical and pathologic determinants previously reported to influence outcome were examined retrospectively. This led to a proposed TNM staging system for metastatic colorectal cancer (mTNM). Results. No operative deaths occurred (death within 1 month). Overall 1-, 3-, and 5-year survivals were 91%, 43%, and 32%, respectively. Gender, Dukes' classification, site of primary colorectal cancer, histologic differentiation, size of metastatic tumor, and intraoperative blood transfusion requirement were not statistically significant prognostic factors (p > 0.05). Age of 60 years or more, interval of 24 months or less between colorectal and hepatic resection, four or more gross tumors, bilobar involvement, positive resection margin, lymph node involvement, and direct invasion to adjacent organs were significant poor prognostic factors (p < 0.05). In the absence of nodal disease or direct invasion, patients with unilobar solitary tumor of any size, or unilobar multiple tumors of 2 cm or smaller (stages I and II) had the highest survival rates of 93% at 1 year, 68% at 3 years, and 61% at 5 years. Unilobar disease with multiple lesions greater than 2 cm (stage III) resulted in 1-, 3-, and 5-year survivals of 98%, 45%, and 28%, respectively. Patients with bilobar involvement (multiple tumors, any size, or a single large metastasis) (stage IVA) had survival rates of 88% at 1 year, 28% at 3 years, and 20% at 5 years (p < 0.00001). Patients with nodal involvement or extrahepatic disease (stage IVB) experienced the poorest outcome with 1-, 3- , and 5-year survivals of 80%, 12%, and 0%, respectively (p < 0.00001). Conclusions. The proposed mTNM staging system appears to be useful in predicting the outcomes after hepatic resection of metastatic colorectal tumors

MedSurv: a software application for creating, conducting and managing medical surveys and questionnaires

MedSurv is a system designed for the rapid creation and maintenance of research surveys and questionnaires that does not require programmer intervention. MedSurv is built with medical surveys in mind and utilizes a group-based permission control with additional security features to help ensure compliance with applicable healthcare regulations. MedSurv is designed as a module for DotNetNuke [1], an open source portal and content management system built with ASP.Net technology, and therefore can be deployed and managed as intranet, extranet, and web sites. At the same time, all data is stored at the researcher\u27s institution to guarantee the required data privacy. Thanks to its built-in support for user authentication and user roles, there is no need to create such functionality from scratch. However, a group-based permissions system is added to MedSurv to support sufficient granularity for access control. Although from the data access point of view data storage acts as a relational table, MedSurv uses a solution that we call virtual tables. The premise behind such a solution is that the structure of the tables is itself stored in a set of relational tables within the database, essentially creating a miniature database within the database. This additional layer is transparent to the user and removes the need for any programming or database knowledge. At the same time it gives the user the flexibility of changing the survey at runtime. Unlike a traditional structure that may require database developer\u27s involvement each time a survey is added or changed, with virtual tables there is very low developer and database administration need after launch. MedSurv allows for creating complex medical surveys and is, in particular, used to develop questionnaires for research driven data collection in the Department of Gastroenterology

Unsupervised machine learning of integrated health and social care data from the Macmillan Improving the Cancer Journey service in Glasgow

Background: Improving the Cancer Journey (ICJ) was launched in 2014 by Glasgow City Council and Macmillan Cancer Support. As part of routine service, data is collected on ICJ users including demographic and health information, results from holistic needs assessments and quality of life scores as measured by EQ-5D health status. There is also data on the number and type of referrals made and feedback from users on the overall service. By applying artificial intelligence and interactive visualization technologies to this data, we seek to improve service provision and optimize resource allocation.Method: An unsupervised machine-learning algorithm was deployed to cluster the data. The classical k-means algorithm was extended with the k-modes technique for categorical data, and the gap heuristic automatically identified the number of clusters. The resulting clusters are used to summarize complex data sets and produce three-dimensional visualizations of the data landscape. Furthermore, the traits of new ICJ clients are predicted by approximately matching their details to the nearest existing cluster center.Results: Cross-validation showed the model’s effectiveness over a wide range of traits. For example, the model can predict marital status, employment status and housing type with an accuracy between 2.4 to 4.8 times greater than random selection. One of the most interesting preliminary findings is that area deprivation (measured through Scottish Index of Multiple Deprivation-SIMD) is a better predictor of an ICJ client’s needs than primary diagnosis (cancer type).Conclusion: A key strength of this system is its ability to rapidly ingest new data on its own and derive new predictions from those data. This means the model can guide service provision by forecasting demand based on actual or hypothesized data. The aim is to provide intelligent person-centered recommendations. The machine-learning model described here is part of a prototype software tool currently under development for use by the cancer support community.Disclosure: Funded by Macmillan Cancer Support</p

Electronic-structure-induced deformations of liquid metal clusters

Ab initio molecular dynamics is used to study deformations of sodium clusters at temperatures $500\cdots 1100$ K. Open-shell Na$_{14}$ cluster has two shape isomers, prolate and oblate, in the liquid state. The deformation is stabilized by opening a gap at the Fermi level. The closed-shell Na$_8$ remains magic also at the liquid state.Comment: REVTex, 11 pages, no figures, figures (2) available upon request (e-mail to hakkinen at jyfl.jyu.fi), submitted to Phys. Rev.

Structure and properties of small sodium clusters

We have investigated structure and properties of small metal clusters using all-electron ab initio theoretical methods based on the Hartree-Fock approximation and density functional theory, perturbation theory and compared results of our calculations with the available experimental data and the results of other theoretical works. We have systematically calculated the optimized geometries of neutral and singly charged sodium clusters having up to 20 atoms, their multipole moments (dipole and quadrupole), static polarizabilities, binding energies per atom, ionization potentials and frequencies of normal vibration modes. Our calculations demonstrate the great role of many-electron correlations in the formation of electronic and ionic structure of small metal clusters and form a good basis for further detailed study of their dynamic properties, as well as structure and properties of other atomic cluster systems.Comment: 47 pages, 16 figure

On the velocity-dependent fracture toughness of epoxy resins

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44664/1/10853_2004_Article_BF00754491.pd

International criteria for acute kidney injury: advantages and remaining challenges

• Acute Kidney Injury (AKI) is defined using widely accepted international criteria that are based on changes in serum creatinine concentration and degree of oliguria. • AKI, when defined in this way, has a strong association with poor patient outcomes, including high mortality rates and longer hospital admissions with increased resource utilisation and subsequent chronic kidney disease. • The detection of AKI using current criteria can assist with AKI diagnosis and stratification of individual patient risk. • The diagnosis of AKI requires clinical judgement to integrate the definition of AKI with the clinical situation, to determine underlying cause of AKI, and to take account of factors that may affect performance of current definitions