From Evidence-based Medicine to Marketing-based Medicine: Evidence from Internal Industry Documents

While much excitement has been generated surrounding evidence-based medicine, internal documents from the pharmaceutical industry suggest that the publicly available evidence base may not accurately represent the underlying data regarding its products. The industry and its associated medical communication firms state that publications in the medical literature primarily serve marketing interests. Suppression and spinning of negative data and ghostwriting have emerged as tools to help manage medical journal publications to best suit product sales, while disease mongering and market segmentation of physicians are also used to efficiently maximize profits. We propose that while evidence-based medicine is a noble ideal, marketing-based medicine is the current reality.     

Deep Learning and Its Applications in Biomedicine

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.     

Canadian and Landed Immigrant Applicants to Canadian Medical Schools for 1968-69

There were 2337 Canadian and Landed Immigrant applicants for the fall 1968 entering classes at Canadian medical schools. These applicants filed a total of 4579 applications.The results of this study show that there are regional differences in the quantity and quality of the applicant pool for Canadian medical schools. The study also shows that despite the fact that Canadian and landed immigrant applicants are filing more applications than they have in the past two years, there has been no appreciable change in the ratio of applicants to available places. A further point to be noted is that the participation of women both as applicants to and as medical students in the entering class of 1968-69 at Canadian medical schools was higher than in previous years.     

Lectindb: a plant lectin database

Lectins, a class of carbohydrate-binding proteins, are now widely recognized to play a range of crucial roles in many cell-cell recognition events triggering several important cellular processes. They encompass different members that are diverse in their sequences, structures, binding site architectures, quaternary structures, carbohydrate affinities, and specificities as well as their larger biological roles and potential applications. It is not surprising, therefore, that the vast amount of experimental data on lectins available in the literature is so diverse, that it becomes difficult and time consuming, if not impossible to comprehend the advances in various areas and obtain the maximum benefit. To achieve an effective use of all the data toward understanding the function and their possible applications, an organization of these seemingly independent data into a common framework is essential. An integrated knowledge base ( Lectindb, http://nscdb.bic.physics.iisc.ernet.in ) together with appropriate analytical tools has therefore been developed initially for plant lectins by collating and integrating diverse data. The database has been implemented using MySQL on a Linux platform and web-enabled using PERL-CGI and Java tools. Data for each lectin pertain to taxonomic, biochemical, domain architecture, molecular sequence, and structural details as well as carbohydrate and hence blood group specificities. Extensive links have also been provided for relevant bioinformatics resources and analytical tools. Availability of diverse data integrated into a common framework is expected to be of high value not only for basic studies in lectin biology but also for basic studies in pursuing several applications in biotechnology, immunology, and clinical practice, using these molecules.     

微机医学图像库

建立一个微机医学图像库,使（微机医学图像库）能象普通数据库一样便于管理和使用,材料和方法,开发硬件为５８６－９０机和ＥｐｓｏｎｓＥＳ－８００ｃ扫描仪,开发应用软件为Ｗｉｎｄｏｗｓ３．１的ＧＵＩ为开发环境,以Ｆｏｘｐｒｏ２．６为开发语言,Ｐｏｗｅｒｐｏｉｎｔ４．０（以后简称ＰＰＴ）为图像工具。     

An efficient medical database system implementation with its tool and data structure consideration

The development of a new system for medical database application running on minicomputer under MUMPS system is described. Two kinds of data representation in global structure are briefly reviewed. The use of a subject oriented multi-dimensional data structure greatly simplifies database design and facilitates data manipulation, organization, selective retrieval and software development. It is concluded that the program generator approach can provide the flexibility necessary for various applications and future growth of computerized medical record system. The final system has been proven effective in practical operation. The future extension concerns the introduction of multi-microprocessor structure and logic representation is presented.     

对基金项目开展网络同行评议的几点思考

国家自然科学基金委生命科学部六处(畜牧兽医与水产和动物学科)通过对2003年基金项目开展网上评议,使我们真正体会到网上评议的优越性,不仅大大提高工作效率,还使同行专家评审意见全文反馈终于成为可能。但是,若要使网上评议更加科学、公正,除了进一步完善专家库外,国家自然科学基金委还需建设一个“基金项目评议的信息资源链接平台”和“专家评议的反评估网络数据库”,从而完善同行评议网络体系。     

Recent patents involving virus nucleotide sequences; host defense, RNA silencing and expression vector strategies

Improved knowledge of the molecular biology of viruses, including recent gains in virus sequence data analysis, has greatly contributed to recent innovations in medical diagnostics, therapeutics, drug development and other related areas. Virus sequences have been used for the development of vaccines and antiviral agents to block the spread of viral infections, as well as to target and battle chronic diseases such as cancer. Virus sequences are now routinely employed in a wide array of RNA silencing technologies. Viruses can also be engineered into expression vectors which in turn can be used as protein production platforms as well as delivery vehicles for gene therapies. This review article outlines a number of patents that have been recently issued with respect to virus sequence data and describes some of their biotechnological applications.     

The TOPAS tool for particle simulation,a Monte Carlo simulation tool for physics,biology and clinical research

PurposeThis paper covers recent developments and applications of the TOPAS TOol for PArticle Simulation and presents the approaches used to disseminate TOPAS.Materials and methodsFundamental understanding of radiotherapy and imaging is greatly facilitated through accurate and detailed simulation of the passage of ionizing radiation through apparatus and into a patient using Monte Carlo (MC). TOPAS brings Geant4, a reliable, experimentally validated MC tool mainly developed for high energy physics, within easy reach of medical physicists, radiobiologists and clinicians. Requiring no programming knowledge, TOPAS provides all of the flexibility of Geant4.ResultsAfter 5 years of development followed by its initial release, TOPAS was subsequently expanded from its focus on proton therapy physics to incorporate radiobiology modeling. Next, in 2018, the developers expanded their user support and code maintenance as well as the scope of TOPAS towards supporting X-ray and electron therapy and medical imaging. Improvements have been achieved in user enhancement through software engineering and a graphical user interface, calculational efficiency, validation through experimental benchmarks and QA measurements, and either newly available or recently published applications. A large and rapidly increasing user base demonstrates success in our approach to dissemination of this uniquely accessible and flexible MC research tool.ConclusionsThe TOPAS developers continue to make strides in addressing the needs of the medical community in applications of ionizing radiation to medicine, creating the only fully integrated platform for four-dimensional simulation of all forms of radiotherapy and imaging with ionizing radiation, with a design that promotes inter-institutional collaboration.     

Influence measures based on the volume of confidence ellipsoids for GEE

The generalized estimating equations (GEE) derived by Liang and Zeger to analyze longitudinal data have been used in a wide range of medical and biological applications. To make regression a useful and meaningful statistical tool, emphasis should be placed not only on inference or fitting, but also on diagnosing potential data problems. Most of the usual diagnostics for linear regression models have been generalized for GEE. However, global influence measures based on the volume of confidence ellipsoids are not available for GEE analysis. This article presents an extension of these measures that is valid for correlated‐measures regression analysis using GEEs. The proposed measures are illustrated by an analysis of epileptic seizure count data arising from a study of prograbide as an adjuvant therapy for partial seizures and some simulated data sets.     

A new approach to the rapid determination of protein side chain conformations

Two efficient algorithms have been developed which allow amino acid side chain conformations to be optimized rapidly for a given peptide backbone conformation. Both these approaches are based on the assumption that each side chain can be represented by a small number of rotameric states. These states have been obtained by a dynamic cluster analysis of a large data base of known crystallographic structures. Successful applications of these algorithms to the prediction of known protein conformations are presented.     

AI applications to medical images: From machine learning to deep learning

PurposeArtificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context.MethodsA narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections.ResultsWe first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way.ConclusionsBiomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.     

Median regression with censored cost data

Because of the skewness of the distribution of medical costs, we consider modeling the median as well as other quantiles when establishing regression relationships to covariates. In many applications, the medical cost data are also right censored. In this article, we propose semiparametric procedures for estimating the parameters in median regression models based on weighted estimating equations when censoring is present. Numerical studies are conducted to show that our estimators perform well with small samples and the resulting inference is reliable in circumstances of practical importance. The methods are applied to a dataset for medical costs of patients with colorectal cancer.     

Homing endonucleases: from microbial genetic invaders to reagents for targeted DNA modification

Homing endonucleases are microbial DNA-cleaving enzymes that mobilize their own reading frames by generating double strand breaks at specific genomic invasion sites. These proteins display an economy of size, and yet recognize long DNA sequences (typically 20 to 30 base pairs). They exhibit a wide range of fidelity at individual nucleotide positions in a manner that is strongly influenced by host constraints on the coding sequence of the targeted gene. The activity of these proteins leads to site-specific recombination events that can result in the insertion, deletion, mutation, or correction of DNA sequences. Over the past fifteen years, the crystal structures of representatives from several homing endonuclease families have been solved, and methods have been described to create variants of these enzymes that cleave novel DNA targets. Engineered homing endonucleases proteins are now being used to generate targeted genomic modifications for a variety of biotech and medical applications.     

Detection of native-like models for amino acid sequences of unknown three-dimensional structure in a data base of known protein conformations.

We present an approach which can be used to identify native-like folds in a data base of protein conformations in the absence of any sequence homology to proteins in the data base. The method is based on a knowledge-based force field derived from a set of known protein conformations. A given sequence is mounted on all conformations in the data base and the associated energies are calculated. Using several conformations and sequences from the globin family we show that the native conformation is identified correctly. In fact the resolution of the force field is high enough to discriminate between a native fold and several closely related conformations. We then apply the procedure to several globins of known sequence but unknown three dimensional structure. The homology of these sequences to globins of known structures in the data base ranges from 49 to 17%. With one exception we find that for all globin sequences one of the known globin folds is identified as the most favorable conformation. These results are obtained using a force field derived from a data base devoid of globins of known structure. We briefly discuss useful applications in protein structural research and future development of our approach.     

Clinical pharmacology and efficacy of fluoroquinolones in fish

The fluoroquinolones are a relatively new group of man-made antibacterial compounds that are highly effective against a wide variety of gram-negative and gram-positive organisms. They have been evaluated for several years for human and veterinary medical applications and more recently they have been evaluated for use in fish. Studies in fish have included minimum inhibitory concentration (MIC) evaluations of the drugs against bacteria found to be pathogenic to fish, pharmacokinetic studies, laboratory efficacy studies, and field trials. The data collected to date encourage further evaluations of fluoroquinolones in fish.     

Physicochemical, toxicological and hygienic aspects of ethylene oxide application for the sterilization of medical products. III. Setting hygienic norms in sterilized medical products

Some of the drawbacks are discussed associated with hygienic norms for gaseous sterilizing agents in polymeric products for medical applications. A new approach is proposed for regulating ethylene oxide (EO) in such products. To this end, limiting types of biological effect and its pathways have been determined and substantiated as well as the reliability factor for establishing hygienic norms for EO in medical products. Daily threshold EO limit values were calculated for momentary and repeated exposure of humans. The duration of long-term and short-term exposure of the patient to sterilized products was evaluated on the basis of EO extraction kinetics from a variety of materials. Using daily threshold limit values, threshold residual values (TRV) of EO were calculated for different groups of polymeric products for medical applications.     

Teaching acid/base physiology in the laboratory

Acid/base homeostasis is one of the most difficult subdisciplines of physiology for medical students to master. A different approach, where theory and practice are linked, might help students develop a deeper understanding of acid/base homeostasis. We therefore set out to develop a laboratory exercise in acid/base physiology that would provide students with unambiguous and reproducible data that clearly would illustrate the theory in practice. The laboratory exercise was developed to include both metabolic acidosis and respiratory alkalosis. Data were collected from 56 groups of medical students that had participated in this laboratory exercise. The acquired data showed very consistent and solid findings after the development of both metabolic acidosis and respiratory alkalosis. All results were consistent with the appropriate diagnosis of the acid/base disorder. Not one single group failed to obtain data that were compatible with the diagnosis; it was only the degree of acidosis/alkalosis and compensation that varied.     

Medical data base system with an ability of automated diagnosis.

We carried out an experiment on a medical information system in which a clinical data base is combined organically with computer programs for automated diagnosis. In this system, the parameters for automated diagnosis are devised to be renewed as the contents of the data base (patient's information) increase. This system can be regarded as a data base possessing a kind of diagnosing ability which grows up with time. We have named this system "Intelligent Data Base". The algorithm for computer diagnosis used in this study is based on maximum likelihood method, and each likelihood is weighted with a prior probability of each disease. The discrimination efficiency of this method is logically equal to that of the Bayes rule. First 27 cases were learnt by the system and correct diagnosis was obtained in 78% of the cases. When cases for learning increased to 82, the percentage of correct diagnosis was improved to 95%.     

CRISPR-Cas9 base editors and their current role in human therapeutics

BackgroundConsistent progress has been made to create more efficient and useful CRISPR-Cas9-based molecular toolsfor genomic modification.MethodsThis review focuses on recent articles that have employed base editors (BEs) for both clinical and research purposes.ResultsCRISPR-Cas9 BEs are a useful system because of their highefficiency and broad applicability to gene correction and disruption. In addition, base editing has beensuggested as a safer approach than other CRISPR-Cas9-based systems, as it limits double-strand breaksduring multiplex gene knockout and does not require a toxic DNA donor molecule for genetic correction.ConclusionAs such, numerous industry and academic groups are currently developing base editing strategies withclinical applications in cancer immunotherapy and gene therapy, which this review will discuss, with a focuson current and future applications of in vivo BE delivery.