How Do Medical Researchers Make Causal Inferences?

Conventional chemotherapeutics lack the specificity and controllability, thus may poison healthy cells while attempting to kill cancerous ones. Newly developed nano-drug delivery systems have shown promise in delivering anti-tumor agents with enhanced stability, durability and overall performance; especially when used along with targeting and triggering techniques. This work traces back the history of chemotherapy, addressing the main challenges that have encouraged the medical researchers to seek a sanctuary in nanotechnological-based drug delivery systems that are grafted with appropriate targeting techniques and drug release mechanisms. A special focus will be directed to acoustically triggered liposomes encapsulating doxorubicin.

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Using increased trust in medical researchers to increase minority recruitment: The RECRUIT cluster randomized clinical trial

Contemporary Clinical Trials ◽

10.1016/j.cct.2021.106519 ◽

2021 ◽

pp. 106519

Author(s):

Barbara C. Tilley ◽

Arch G. Mainous ◽

Rossybelle P. Amorrortu ◽

M. Diane McKee ◽

Daniel W. Smith ◽

...

Keyword(s):

Clinical Trial ◽

Randomized Clinical Trial ◽

Minority Recruitment ◽

Medical Researchers ◽

Cluster Randomized

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Scientists in pyjamas: characterising the working arrangements and productivity of Australian medical researchers during the COVID‐19 pandemic

The Medical Journal of Australia ◽

10.5694/mja2.50860 ◽

2020 ◽

Vol 213 (11) ◽

pp. 516-520 ◽

Cited By ~ 1

Author(s):

David G Chapman ◽

Cindy Thamrin

Keyword(s):

Medical Researchers

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Reactivity in social scientific experiments: what is it and how is it different (and worse) than a Placebo effect?

European Journal for Philosophy of Science ◽

10.1007/s13194-021-00350-z ◽

2021 ◽

Vol 11 (2) ◽

Author(s):

María Jiménez-Buedo

Keyword(s):

Experimental Data ◽

Conceptual Framework ◽

Placebo Effect ◽

Placebo Effects ◽

Causal Inferences ◽

Scientific Experiments ◽

Social Scientific ◽

The Many ◽

Definition Of ◽

In Virtue Of

AbstractReactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework for reactivity that draws on an interventionist approach to causality. The framework allows us to offer an unambiguous definition of reactivity and distinguishes it from placebo effects. Further, it allows us to distinguish between benign and malignant forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data.

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TREASURE: Text Mining Algorithm Based On Affinity Analysis and Set Intersection to Find the Action of Tuberculosis Drugs against Other Pathogens

Applied Sciences ◽

10.3390/app11156834 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6834

Author(s):

Pradeepa Sampath ◽

Nithya Shree Sridhar ◽

Vimal Shanmuganathan ◽

Yangsun Lee

Keyword(s):

Text Mining ◽

Causes Of Death ◽

Intersection Property ◽

Research Papers ◽

Bacterial Diseases ◽

Mining Algorithm ◽

Set Intersection ◽

The World ◽

Medical Researchers

Tuberculosis (TB) is one of the top causes of death in the world. Though TB is known as the world’s most infectious killer, it can be treated with a combination of TB drugs. Some of these drugs can be active against other infective agents, in addition to TB. We propose a framework called TREASURE (Text mining algoRithm basEd on Affinity analysis and Set intersection to find the action of tUberculosis dRugs against other pathogEns), which particularly focuses on the extraction of various drug–pathogen relationships in eight different TB drugs, namely pyrazinamide, moxifloxacin, ethambutol, isoniazid, rifampicin, linezolid, streptomycin and amikacin. More than 1500 research papers from PubMed are collected for each drug. The data collected for this purpose are first preprocessed, and various relation records are generated for each drug using affinity analysis. These records are then filtered based on the maximum co-occurrence value and set intersection property to obtain the required inferences. The inferences produced by this framework can help the medical researchers in finding cures for other bacterial diseases. Additionally, the analysis presented in this model can be utilized by the medical experts in their disease and drug experiments.

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