Non-business e-commerce in Malaysia: An investigation of key adoption

Problem statement: Non-business EC is a relatively new research niche in the general e-commerce stream. Application of e-commerce by profit oriented organization already become bread and butter but still limited applied in non-business sectors such as academic institutions (as in the present study), non-profit organizations, religious organizations and government agencies. Nowadays e-commerce becomes crucially essential in reducing their expenses and improving their operations.Therefore, application of this new innovation should enhance to no non-business sectors to be livelier.Understanding the key factors of facilitating and adopting the e-commerce in non-business are still need to enrich in particularly within Malaysian context. A field survey was conducted to determine key factors that facilitate the adoption of non-business EC in Malaysian Universities. Approach: One main focus of IT implementation research has been to determine why people accept or reject new technology. The current research will explore why Non-business institutions will accept or reject e- commerce. Since e-commerce adoption decision is a strategic one, a comprehensive list of potential facilitators and non-facilitators for the strategic use of information technology was derived from past research. Thus factors used as the basis for collecting data from 65 schools, centers and units from 5 public universities in Kota Kinabalu and Kuala Lumpur. These data were factor-analyzed to determine the key underlying dimensions of facilitators. On the basis of the resulting 5 dimensions namely, relative advantage, network orientation, information efficiency, innovativeness and competitiveness, regression analysis was done to determine the impact of the 5 dimensions on adoption. Results: They suggest that relative advantage, network orientation and information efficiency are the most important facilitators to the used of e-commerce in non-business sectors. Inhibitors were not estimated eventually, as there were no non-users among the respondents. Conclusion: The results implies the non-business sectors should look into advantages, network orientation and information efficiency as a strategic based for implementing e-commerce in more effective manner to achieve their goals

How to improve drug dosing for patients with renal impairment in primary care - a cluster-randomized controlled trial

Background: Patients with chronic kidney disease (CKD) are at increased risk for inappropriate or potentially harmful prescribing. The aim of this study was to examine whether a multifaceted intervention including the use of a software programme for the estimation of creatinine clearance and recommendation of individual dosage requirements may improve correct dosage adjustment of relevant medications for patients with CKD in primary care. Methods: A cluster-randomized controlled trial was conducted between January and December 2007 in small primary care practices in Germany. Practices were randomly allocated to intervention or control groups. In each practice, we included patients with known CKD and elderly patients (>=70 years) suffering from hypertension. The practices in the intervention group received interactive training and were provided a software programme to assist with individual dose adjustment. The control group performed usual care. Data were collected at baseline and at 6 months. The outcome measures, analyzed across individual patients, included prescriptions exceeding recommended maximum daily doses, with the primary outcome being prescriptions exceeding recommended standard daily doses by 30% or more. Results: Data from 44 general practitioners and 404 patients are included. The intervention was effective in reducing prescriptions exceeding the maximum daily dose per patients, with a trend in reducing prescriptions exceeding the standard daily dose by more than 30%. Conclusions: A multifaceted intervention including the use of a software program effectively reduced inappropriately high doses of renally excreted medications in patients with CKD in the setting of small primary care practices

Predicting kinase inhibitor resistance: Physics-based and data-driven approaches.

Resistance to small molecule drugs often emerges in cancer cells, viruses, and bacteria as a result of the evolutionary pressure exerted by the therapy. Protein mutations that directly impair drug binding are frequently involved in resistance, and the ability to anticipate these mutations would be beneficial in drug development and clinical practice. Here, we evaluate the ability of three distinct computational methods to predict ligand binding affinity changes upon protein mutation for the cancer target Abl kinase. These structure-based approaches rely on first-principle statistical mechanics, mixed physics- and knowledge-based potentials, and machine learning, and were able to estimate binding affinity changes and identify resistant mutations with remarkable accuracy. We expect that these complementary approaches will enable the routine prediction of resistance-causing mutations in a variety of other target proteins

Adverse Effects of Cholinesterase Inhibitors in Dementia, According to the Pharmacovigilance Databases of the United-States and Canada.

This survey analyzes two national pharmacovigilance databases in order to determine the major adverse reactions observed with the use of cholinesterase inhibitors in dementia. We conducted a statistical analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS) and the Canada Vigilance Adverse Reaction Database (CVARD) concerning the side effects of cholinesterase inhibitors. The statistics calculated for each adverse event were the frequency and the reporting odds ratios (ROR). A total of 9877 and 2247 reports were extracted from the FAERS and CVARD databases, respectively. A disproportionately higher frequency of reports of death as an adverse event for rivastigmine, compared to the other acetylcholinesterase inhibiting drugs, was observed in both the FAERS (ROR = 3.42; CI95% = 2.94-3.98; P<0.0001) and CVARD (ROR = 3.67; CI95% = 1.92-7.00; P = 0.001) databases. While cholinesterase inhibitors remain to be an important therapeutic tool against Alzheimer's disease, the disproportionate prevalence of fatal outcomes with rivastigmine compared with alternatives should be taken into consideration

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft