Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Best Practices Regarding Treatment Fidelity in the Family Colorectal Cancer Awareness and Risk Education Randomized Controlled Trial

Treatment fidelity is associated with improvement in research outcomes and increased confidence in significant findings. However, few studies report on recommended areas of treatment fidelity (i.e., study design, training, treatment delivery, treatment receipt, and treatment enactment), leaving a dearth of information about implementation components that contributed to a study’s success. Without such information, it is difficult for researchers to correctly assess previous findings and for practitioners to correctly implement findings into practice. Thus, it is crucial that studies assess both treatment fidelity and applicability of treatment fidelity findings. We report measures of treatment fidelity in a randomized controlled trial of an intervention promoting colonoscopy in at-risk relatives of colorectal cancer (CRC) patients. We describe assessments related to both treatment delivery and treatment receipt. We conducted separate ANCOVAs to model the change in each of the treatment receipt variables, comparing the two intervention arms. Compared with the control group, the intervention group had significantly greater improvements in CRC knowledge (f = 17.46, p < .0001), perceptions about susceptibility (f = 15.08, p = .0002), response efficacy (f = 7.46, p = .0076), self-efficacy (f = 8.16, p = .0053), and reduced decisional uncertainty (f = 19.59, p < .0001) from baseline to 1-month follow-up. Overall, our study adhered to most of the best-practice guidelines for behavioral intervention fidelity. This demonstrates that our intervention was delivered as intended and positively affected the cognitive processes that are purported to be predictive of adherent behavioral outcomes.
Probabilistic factor models for web site recommendation

Due to the prevalence of personalization and information filtering applications, modeling users' interests on the Web has become increasingly important during the past few years. In this paper, aiming at providing accurate personalized Web site recommendations for Web users, we propose a novel probabilistic factor model based on dimensionality reduction techniques. We also extend the proposed method to collective probabilistic factor modeling, which further improves model performance by incorporating heterogeneous data sources. The proposed method is general, and can be applied to not only Web site recommendations, but also a wide range of Web applications, including behavioral targeting, sponsored search, etc. The experimental analysis on Web site recommendation shows that our method outperforms other traditional recommendation approaches. Moreover, the complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations.
Platinum-based chemotherapy in advanced non-small-cell lung cancer: optimal number of treatment cycles

ABSTRACT Platinum-based chemotherapy remains the standard-of-care for most patients affected by advanced non-small cell lung cancer (NSCLC). The platinum compounds currently used in NSCLC are cisplatin and carboplatin. The availability of new generation drugs has led to the adoption of schedules with lower doses of platinum compounds leading to increased tolerability. Several data suggest that third generation cisplatin-based regimens are slightly superior to carboplatin-based chemotherapy, with a different safety profile, and so cisplatin should remain the standard reference for the treatment of selected patients with advanced NSCLC. Recent evidence emphasized that the optimal number of first-line platinum cycles should be four for any NSCLC histology. New platinum compounds and the use of functional genomics to deliver platinum drugs as personalised medicine, are being investigated. Here we review the current status of cisplatin and carboplatin regimens looking to the future role of platinum compounds in advanced NSCLC patients.
Getting Started with Medical Apps: Apps You Should Know About

The rise of mobile technology has brought exponential growth of software for use by physicians, patients, and others in face-to-face and remote interactions. Although most librarians are aware of the growing importance of mobile technology, and in particular mobile applications (apps), figuring out which apps will play an important role in health care is an often overwhelming challenge. The Apple App Store has surpassed 500,000 apps and the Android Market is keeping pace at 400,000 (1, 2). This makes it impossible to keep up with the apps useful in one’s own life, much less predict which ones might be useful to patrons. There is tremendous potential for customized care through mobile devices. An article in The Economist last year points out that although much of the discussion about customized care centers around improvements in our understanding of genetics, mobile health software is likewise rapidly propelling the personalized health care movement forward (3). As we begin to understand just what is possible, from apps for monitoring patient health remotely, to apps to make physicians more effective and to share information more easily with patients, the industry expects astounding growth from both established players such as GE and Hewlett Packard to the plethora of health startups. There has already been astounding growth in the mobile health field and a 2011 report by the market research firm, Parks Associates, predicts digital health technology revenues will surpass $5.7 billion by 2015 (4). With more apps being developed for library resources, not only will there be increased requests for assistance with these apps, but also likely
PROGNOSIS FOR SURVIVAL WITH CYSTIC FIBROSIS: THE EFFECTS OF EARLY DIAGNOSIS AND CYSTIC FIBROSIS CENTER CARE

Warwick, W.J. (Department of Pediatrics, University of Minnesota Hospitals, Minneapolis, Minnesota, 55455, U.S.A.) Prognosis for survival with cystic fibrosis: the effects of early diagnosis and cystic fibrosis center care. Acta Paediatr Scand 1982; suppl 301: 27‐31. — Improved diagnosis, i.e. more cystic fibrosis (CF) patients diagnosed at an early age, is possible but will require a great increase in the sensitivity of physicians, providing care for infants and young children, to the possibility that early symptoms could be CF at a time a baby is “too well to have CF” coupled with a simple fast screening type of sweat test. Prognosis for survival for CF patients treated at CF Centers is improved with early diagnosis but the data concerning newborn screening are still too few. The general care of CF patients around the world in the absence of a CF Center shows expected survival of 50 percent to 4 or 5 years of age; addition of a CF Center and a CF Care Team increases that 50 percent survival to 20 years. Improvement in early diagnosis or newborn screening is needed to reduce the number of CF patients who die undiagnosed. CF Center and CF team care increases the survival age of CF patients by 4 to 6 times. Early diagnosis before pulmonary complications have begun more than double the effectiveness of CF Center care. In Denmark, the Paediatric Department TG provides the leadership and training and clinical care and research needed to maintain progress and to improve upon the prognosis for CF. Data from Denmark obtained through the central Danish CF Registry confirm in Denmark these observations from around the world of the benefits of early diagnosis and center treatment.
Haematological Aspects during Treatment with Anticonvulsant Drugs

The literature comprises many reports on haematological complications during anticonvulsant medication. Systematic haematological surveillance of epileptic patients is considered necessary to prevent these complications. This paper discusses ways and means to ensure maximum efficacy of this surveillance in clinics without a specialized haematological laboratory. A description is also given of methods suitable for supplementing and extending the routine examinations.
Interactive learning for efficiently detecting errors in insurance claims

Many practical data mining systems such as those for fraud detection and surveillance deal with building classifiers that are not autonomous but part of a larger interactive system with an expert in the loop. The goal of these systems is not just to maximize the performance of the classifier but to make the experts more efficient at performing their task, thus maximizing the overall Return on Investment of the system. This paper describes an interactive system for detecting payment errors in insurance claims with claim auditors in the loop. We describe an interactive claims prioritization component that uses an online cost-sensitive learning approach (more-like-this) to make the system efficient. Our interactive prioritization component is built on top of a batch classifier that has been trained to detect payment errors in health insurance claims and optimizes the interaction between the classifier and the domain experts who are consuming the results of this system. The goal is to make these auditors more efficient and effective as well as improving the classification performance of the system. The result is both a reduction in time it takes for the auditors to review and label claims as well as improving the precision of the system in finding payment errors. We show results obtained from applying this system at two major US health insurance companies indicating significant reduction in claim audit costs and potential savings of $20-$26 million/year making the insurance providers more efficient and lowering their operating costs. Our system reduces the money being wasted by providers and insurers dealing with incorrectly processed claims and makes the healthcare system more efficient.
Mining mobility data to minimise travellers' spending on public transport

As the public transport infrastructure of large cities expands, transport operators are diversifying the range and prices of tickets that can be purchased for travel. However, selecting the best fare for each individual traveller's needs is a complex process that is left almost completely unaided. By examining the relation between urban mobility and fare purchasing habits in large datasets from London, England's public transport network, we estimate that travellers in the city cumulatively spend, per year, up to approximately GBP 200 million more than they need to, as a result of purchasing the incorrect fares. We propose to address these incorrect purchases by leveraging the huge volumes of data that travellers create as they move about the city, by providing, to each of them, personalised ticket recommendations based on their estimated future travel patterns. In this work, we explore the viability of building a fare-recommendation system for public transport networks by (a) formalising the problem as two separate prediction problems and (b) evaluating a number of algorithms that aim to match travellers to the best fare. We find that applying data mining techniques to public transport data has the potential to provide travellers with substantial savings.
Biomechanical and Physical Characteristics of Whitewater Kayakers with and without Shoulder Pain

Participation in whitewater kayaking is growing faster than any other outdoor recreational pursuit. With increases in participation, an increase in the number of injuries associated with whitewater kayaking may also become apparent. Overuse injuries are the most prevalent type of injury found in whitewater kayakers. Due the large range of motion and forces that occur through the shoulder while kayaking, the most common injury location is the shoulder. Little scientific inquiry has been performed assessing the kinematics of kayaking and the musculoskeletal attributes of these athletes. Sixteen whitewater kayakers with shoulder pain and sixteen whitewater kayakers without shoulder pain participated in this study. Each subject underwent kinematic and electromyographic analysis of the forward kayak stroke. Additionally, participants underwent clinical examination of shoulder injury, clinical assessment of shoulder and torso range of motion, posterior shoulder tightness assessment, isokinetic strength testing at the shoulder, and a scapular kinematic evaluation during a standardized humeral elevation task.The most common type of injury found was related to overuse. Statistical comparisons occurred between the involved and uninvolved limb in the shoulder pain group and between the involved and uninvolved and matching shoulders in the control group, respectively. Significant differences were found between in involved and uninvolved shoulder for shoulder internal rotation and abduction range of motion. Additional differences were found for these variables between the involved shoulder in the pain group and the matching shoulder in the control group. Kayakers with shoulder pain present with decreased shoulder range of motion on their involved shoulder. Assessment of the specific types of injuries seen in whitewater kayakers should be further evaluated. Additionally, the role of increasing range of motion through injury prevention programs in whitewater kayakers with shoulder pain should be investigated.
DNA hypomethylation in plasma as a cancer biomarker: when less is more?

Evaluation of: Chan KC, Jiang P, Chan CW et al. Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing. Proc. Natl Acad. Sci. U.S.A. 110(47), 18761–18768 (2013). Early cancer detection strategies are required to identify patients at initial stages, when curative-intended treatment is more effective. Assessment of genome-wide hypomethylation might allow for early cancer detection in bodily fluids, but requires high throughput technologies, such as next generation sequencing. In the study under evaluation, performance of a hypomethylation and copy number aberration (CNA) assay for detection of hepatocellular carcinoma (HCC), based on massive parallel bisulfite sequencing of plasma DNA was assessed. Sensitive (92/69%) and specific (88/94%) HCC detection (using ‘OR’/‘AND’ algorithms) was achieved using a mean sequencing depth of 93 million reads in one lane. Using the ‘AND’ or the ‘OR’ algorithms, other cancer types were detected with 60% sensitivity and 94% specificity, or 85% sensitivity and 88% specificity, respectively. Lowering sequencing depth to 10 million reads (decreasing analysis time and cost) did not alter hypomethylation performance but impaired that of CNA.
Using the gini coefficient for bug prediction in eclipse

The Gini coefficient is a prominent measure to quantify the inequality of a distribution. It is often used in the field of economy to describe how goods, e.g., wealth or farmland, are distributed among people. We use the Gini coefficient to measure code ownership by investigating how changes made to source code are distributed among the developer population. The results of our study with data from the Eclipse platform show that less bugs can be expected if a large share of all changes are accumulated, i.e., carried out, by relatively few developers.
Predictive biomarkers investigated in glioblastoma

Glioblastoma is the most aggressive primary brain tumor in adults. Consequently, new therapeutic strategies are needed. Tumor response to cytotoxic chemotherapy is heterogeneous across patients. Interestingly, predictive biomarkers of response to these classic chemotherapeutic agents have been identified in neuro-oncology (i.e., 1p/19q co-deletion, IDH mutation and O6-methylguanine DNA-methyltransferase promoter methylation). The most emblematic biomarker in glioblastoma is O6-methylguanine DNA-methyltransferase promoter methylation that predicts response to temozolomide. In parallel, innovative drugs are emerging. Some of these agents have shown some activity but in a limited number of glioblastoma patients. One of the major challenges is to identify molecular predictors of response to these smart drugs for an efficient personalized medicine. These novel agents have been tested in clinical trials enrolling glioblastoma patients. Although none of them has been validated prospectively in Phase III clinical trials, interesting molecular predictors of response to these drugs have been investigated and are presented in this review, which also reports more advanced biomarkers.
The current state of molecular cytogenetics in cancer diagnosis

Cytogenetics and molecular cytogenetics are and will continue to be indispensable tools in cancer diagnostics. Leukemia and lymphoma diagnostics are still emphases of routine (molecular) cytogenetics and corresponding studies of solid tumors gain more and more prominence. Here, first a historical perspective of molecular tumor cytogenetics is provided, which is followed by the basic principles of the fluorescence in situ hybridization (FISH) approach. Finally the current state of molecular cytogenetics in cancer diagnostics is discussed. Nowadays routine diagnostics includes basic FISH approaches rather than multicolor-FISH. The latter together with modern high-throughput methods have their impact on research to identify new tumor-associated genomic regions.
Recognition of visual memory recall processes using eye movement analysis

Physical activity, location, as well as a person's psychophysiological and affective state are common dimensions for developing context-aware systems in ubiquitous computing. An important yet missing contextual dimension is the cognitive context that comprises all aspects related to mental information processing, such as perception, memory, knowledge, or learning. In this work we investigate the feasibility of recognising visual memory recall. We use a recognition methodology that combines minimum redundancy maximum relevance feature selection (mRMR) with a support vector machine (SVM) classifier. We validate the methodology in a dual user study with a total of fourteen participants looking at familiar and unfamiliar pictures from four picture categories: abstract, landscapes, faces, and buildings. Using person-independent training, we are able to discriminate between familiar and unfamiliar abstract pictures with a top recognition rate of 84.3% (89.3% recall, 21.0% false positive rate) over all participants. We show that eye movement analysis is a promising approach to infer the cognitive context of a person and discuss the key challenges for the real-world implementation of eye-based cognition-aware systems.
Recent progress in protein profiling of clinical tissues for next-generation molecular diagnostics

Differences in protein levels and the activation of signaling pathways have been extensively studied in tumor tissues, but the implementation of protein profiling methods in routine hospital workflows lags far behind that of nucleic acid-based approaches. In this review, major technologies that are currently used for measuring protein abundances in human tissues are highlighted, and for each method several examples are provided. We differentiate between extract-based and section-based methods that are each further divided into targeted- and discovery-based approaches (i.e., when the proteins to be analyzed are known or for finding promising new biomarker candidates, respectively). Current problems in protein profiling are addressed and ways in which protein profiling can successfully be implemented in routine clinical workflows are shown.
Citation pattern matching algorithms for citation-based plagiarism detection: greedy citation tiling, citation chunking and longest common citation sequence

Plagiarism Detection Systems have been developed to locate instances of plagiarism e.g. within scientific papers. Studies have shown that the existing approaches deliver reasonable results in identifying copy&paste plagiarism, but fail to detect more sophisticated forms such as paraphrased plagiarism, translation plagiarism or idea plagiarism. The authors of this paper demonstrated in recent studies that the detection rate can be significantly improved by not only relying on text analysis, but by additionally analyzing the citations of a document. Citations are valuable language independent markers that are similar to a fingerprint. In fact, our examinations of real world cases have shown that the order of citations in a document often remains similar even if the text has been strongly paraphrased or translated in order to disguise plagiarism. This paper introduces three algorithms and discusses their suitability for the purpose of citation-based plagiarism detection. Due to the numerous ways in which plagiarism can occur, these algorithms need to be versatile. They must be capable of detecting transpositions, scaling and combinations in a local and global form. The algorithms are coined Greedy Citation Tiling, Citation Chunking and Longest Common Citation Sequence. The evaluation showed that if these algorithms are combined, common forms of plagiarism can be detected reliably.
KRAS mutation testing in clinical practice

Activating mutation of KRAS plays a significant role in the pathogenesis of common human malignancies and molecular testing of KRAS mutation has emerged as an essential biomarker in the current practice of clinical oncology. The presence of KRAS mutation is generally associated with clinical aggressiveness of the cancer and reduced survival of the patient. Therapeutically, KRAS mutation testing has maximum utility in stratifying metastatic colorectal carcinoma and lung cancer patients for treatment with targeted therapy. Diagnostically, KRAS mutation testing is useful in the workup of pancreaticobiliary and thyroid cancers, particularly using cytological specimens. In the era of precision medicine, the role of KRAS mutation testing is poised to expand, likely in a setting of combinatorial therapeutic strategy and requiring additional mutation testing of its upstream and/or downstream effectors.
Recent advances in the laboratory detection of carbapenemase-producing Enterobacteriaceae

ABSTRACT Carbapenemase-producing Enterobacteriaceae (CPE), mainly Klebsiella pneumoniae and Escherichia coli, have been increasing rapidly on a global scale and are considered to be significant health threats. The most common carbapenemases are KPCs, NDMs, OXA-48-like, IMPs and VIMs but their distribution and prevalence differs between countries. The accurate, simple, cost effective and rapid detection of carbapenemases in clinical laboratories is an important initial step to control the spread of CPE within institutions. The diversity of carbapenemases in general, has challenged a simple approach for the detection of most types of CPE. This article summarizes the current and describes newer techniques available for the detection of carbapenemases among Enterobacteriaceae. The authors also provide a simplified approach for the accurate and rapid detection of CPEs that can easily be implemented in a clinical diagnostic laboratory.