People with obesity exhibit losses in muscle proteostasis that are partly improved by exercise training.

This pilot experiment examines if a loss in muscle proteostasis occurs in people with obesity and whether endurance exercise positively influences either the abundance profile or turnover rate of proteins in this population. Men with (n = 3) or without (n = 4) obesity were recruited and underwent a 14-d measurement protocol of daily deuterium oxide (D2 O) consumption and serial biopsies of vastus lateralis muscle. Men with obesity then completed 10-weeks of high-intensity interval training (HIIT), encompassing 3 sessions per week of cycle ergometer exercise with 1 min intervals at 100% maximum aerobic power interspersed by 1 min recovery periods. The number of intervals per session progressed from 4 to 8, and during weeks 8-10 the 14-d measurement protocol was repeated. Proteomic analysis detected 352 differences (p < 0.05, false discovery rate < 5%) in protein abundance and 19 (p < 0.05) differences in protein turnover, including components of the ubiquitin-proteasome system. HIIT altered the abundance of 53 proteins and increased the turnover rate of 22 proteins (p < 0.05) and tended to benefit proteostasis by increasing muscle protein turnover rates. Obesity and insulin resistance are associated with compromised muscle proteostasis, which may be partially restored by endurance exercise.

Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.

Breast cancer patient characterisation and visualisation using deep learning and fisher information networks.

Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification. However, the added complexity that makes DL models so successful reduces their ability to explain which features are relevant to the model, or whether the model is biased. The main aim of this study is to propose a novel visualisation to help characterise breast cancer patients using Fisher Information Networks on features extracted from mammograms using a DL model. In the proposed visualisation, patients are mapped out according to their similarities and can be used to study new patients as a 'patient-like-me' approach. When applied to the CBIS-DDSM dataset, it was shown that it is a competitive methodology that can (i) facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of the FIN visualisations and 'patient-like-me' analysis, and (ii) help improve diagnostic accuracy and reduce overdiagnosis by identifying the most likely diagnosis based on clinical similarities with neighbouring patients.

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time.

Adaptation of rat fast-twitch muscle to endurance activity is underpinned by changes to protein degradation as well as protein synthesis.

Muscle adaptations to exercise are underpinned by alterations to the abundance of individual proteins, which may occur through a change either to the synthesis or degradation of each protein. We used deuterium oxide (2 H2 O) labeling and chronic low-frequency stimulation (CLFS) in vivo to investigate the synthesis, abundance, and degradation of individual proteins during exercise-induced muscle adaptation. Independent groups of rats received CLFS (10 Hz, 24 h/d) and 2 H2 O for 0, 10, 20, or 30 days. The extensor digitorum longus (EDL) was isolated from stimulated (Stim) and contralateral non-stimulated (Ctrl) legs. Proteomic analysis encompassed 38 myofibrillar and 46 soluble proteins and the rates of change in abundance, synthesis, and degradation were reported in absolute (ng/d) units. Overall, synthesis and degradation made equal contributions to the adaptation of the proteome, including instances where a decrease in protein-specific degradation primarily accounted for the increase in abundance of the protein.

Fractional Synthesis Rates of Individual Proteins in Rat Soleus and Plantaris Muscles.

Differences in the protein composition of fast- and slow-twitch muscle may be maintained by different rates of protein turnover. We investigated protein turnover rates in slow-twitch soleus and fast-twitch plantaris of male Wistar rats (body weight 412 ± 69 g). Animals were assigned to four groups (n = 3, in each), including a control group (0 d) and three groups that received deuterium oxide (D2O) for either 10 days, 20 days or 30 days. D2O administration was initiated by an intraperitoneal injection of 20 µL of 99% D2O-saline per g body weight, and maintained by provision of 4% (v/v) D2O in the drinking water available ad libitum. Soluble proteins from harvested muscles were analysed by liquid chromatography-tandem mass spectrometry and identified against the SwissProt database. The enrichment of D2O and rate constant (k) of protein synthesis was calculated from the abundance of peptide mass isotopomers. The fractional synthesis rate (FSR) of 44 proteins in soleus and 34 proteins in plantaris spanned from 0.58%/day (CO1A1: Collagen alpha-1 chain) to 5.40%/day NDRG2 (N-myc downstream-regulated gene 2 protein). Eight out of 18 proteins identified in both muscles had a different FSR in soleus than in plantaris (p < 0.05).

Stair negotiation behaviour of older individuals: Do step dimensions matter?

Stair falls are a major health problem for older people. Most studies on identification of stair fall risk factors are limited to staircases set in given step dimensions. However, it remains unknown whether the conclusions drawn would still apply if the dimensions had been changed to represent more challenging or easier step dimensions encountered in domestic and public buildings. The purpose was to investigate whether the self-selected biomechanical stepping behaviours are maintained when the dimensions of a staircase are altered. Sixty-eight older adults (>65 years) negotiated a seven-step staircase set in two step dimensions (shallow staircase: rise 15 cm, going 28 cm; steep staircase: rise 20 cm, going 25 cm). Six biomechanical outcome measures indicative of stair fall risk were measured. K-means clustering profiled the overall stair-negotiating behaviour and cluster profiles were calculated. A Cramer's V measured the degree of association in membership between clusters. The cluster profiles revealed that the biomechanically risky and conservative factors that characterized the overall behaviour in the clusters did not differ for the majority of older adults between staircases for ascent and descent. A strong association of membership between the clusters on the shallow staircase and the steep staircase was found for stair ascent (Cramer's V: 0.412, p < 0.001) and descent (Cramer's V: 0.380, p = 0.003). The findings indicate that manipulating the demand of the task would not affect the underpinning mechanism of a potential stair fall. Therefore, for most individuals, detection of stair fall risk might not require testing using a staircase with challenging step dimensions.

Reliability of Protein Abundance and Synthesis Measurements in Human Skeletal Muscle.

The repeatability of dynamic proteome profiling (DPP), which is a novel technique for measuring the relative abundance (ABD) and fractional synthesis rate (FSR) of proteins in humans, is investigated. LC-MS analysis is performed on muscle samples taken from male participants (n = 4) that consumed 4 × 50 mL doses of deuterium oxide (2 H2 O) per day for 14 days. ABD is measured by label-free quantitation and FSR is calculated from time-dependent changes in peptide mass isotopomer abundances. One-hundred one proteins have at least one unique peptide and are used in the assessment of protein ABD. Fifty-four of these proteins meet more stringent criteria and are used in the assessment of FSR data. The median (M), lower-, (Q1 ) and upper-quartile (Q3 ) values for protein FSR (%/d) are M = 1.63, Q1  = 1.07, and Q3  = 3.24, respectively. The technical CV of ABD data has a median value of 3.6% (Q1 1.7% to Q3 6.7%), whereas the median CV of FSR data is 10.1% (Q1 3.5% to Q3 16.5%). These values compare favorably against other assessments of technical repeatability of proteomics data, which often set a CV of 20% as the upper bound of acceptability.

A novel multivariate approach for biomechanical profiling of stair negotiation.

Stair falls, especially during stair descent, are a major problem for older people. Stair fall risk has typically been assessed by quantifying mean differences between subject groups (e.g. older vs. younger individuals) for a number of biomechanical parameters individually indicative of risk, e.g., a reduced foot clearance with respect to the stair edge, which increases the chances of a trip. This approach neglects that individuals within a particular group may also exhibit other concurrent conservative strategies that could reduce the overall risk for a fall, e.g. a decreased variance in foot clearance. The purpose of the present study was to establish a multivariate approach that characterises the overall stepping behaviour of an individual. Twenty-five younger adults (age: 24.5 ±â€¯3.3 y) and 70 older adults (age: 71.1 ±â€¯4.1 y) descended a custom-built instrumented seven-step staircase at their self-selected pace in a step-over-step manner without using the handrails. Measured biomechanical parameters included: 1) Maximal centre of mass angular acceleration, 2) Foot clearance, 3) Proportion of foot length in contact with stair, 4) Required coefficient of friction, 5) Cadence, 6) Variance of these parameters. As a conventional analysis, a one-way ANOVA followed by Bonferroni post-hoc testing was used to identify differences between younger adults, older fallers and non-fallers. To examine differences in overall biomechanical stair descent behaviours between individuals, k-means clustering was used. The conventional grouping approach showed an effect of age and fall history on several single risk factors. The multivariate approach identified four clusters. Three clusters differed from the overall mean by showing both risky and conservative strategies on the biomechanical outcome measures, whereas the fourth cluster did not display any particularly risky or conservative strategies. In contrast to the conventional approach, the multivariate approach showed the stepping behaviours identified did not contain only older adults or previous fallers. This highlights the limited predictive power for stair fall risk of approaches based on single-parameter comparisons between predetermined groups. Establishing the predictive power of the current approach for future stair falls in older people is imperative for its implementation as a falls prevention tool.

A Systematic Review and Meta-Analysis of Proteomics Literature on the Response of Human Skeletal Muscle to Obesity/Type 2 Diabetes Mellitus (T2DM) Versus Exercise Training.

We performed a systematic review and meta-analysis of proteomics literature that reports human skeletal muscle responses in the context of either pathological decline associated with obesity/T2DM and physiological adaptations to exercise training. Literature was collected from PubMed and DOAJ databases following PRISMA guidelines using the search terms 'proteom*', and 'skeletal muscle' combined with either 'obesity, insulin resistance, diabetes, impaired glucose tolerance' or 'exercise, training'. Eleven studies were included in the systematic review, and meta-analysis was performed on a sub-set (four studies) of the reviewed literature that reported the necessary primary data. The majority of proteins (n = 73) more abundant in the muscle of obese/T2DM individuals were unique to this group and not reported to be responsive to exercise training. The main response of skeletal muscle to exercise training was a greater abundance of proteins of the mitochondrial electron transport chain, tricarboxylic acid cycle and mitochondrial respiratory chain complex I assembly. In total, five proteins were less abundant in muscle of obese/T2DM individuals and were also reported to be more abundant in the muscle of endurance-trained individuals, suggesting one of the major mechanisms of exercise-induced protection against the deleterious effects of obesity/T2DM occurs at complex I of the electron transport chain.

The Relationship Between Whole-Body External Loading and Body-Worn Accelerometry During Team-Sport Movements.

PURPOSE: To investigate the relationship between whole-body accelerations and body-worn accelerometry during team-sport movements. METHODS: Twenty male team-sport players performed forward running and anticipated 45° and 90° side-cuts at approach speeds of 2, 3, 4, and 5 m/s. Whole-body center-of-mass (CoM) accelerations were determined from ground-reaction forces collected from 1 foot-ground contact, and segmental accelerations were measured from a commercial GPS accelerometer unit on the upper trunk. Three higher-specification accelerometers were also positioned on the GPS unit, the dorsal aspect of the pelvis, and the shaft of the tibia. Associations between mechanical load variables (peak acceleration, loading rate, and impulse) calculated from both CoM accelerations and segmental accelerations were explored using regression analysis. In addition, 1-dimensional statistical parametric mapping (SPM) was used to explore the relationships between peak segmental accelerations and CoM-acceleration profiles during the whole foot-ground contact. RESULTS: A weak relationship was observed for the investigated mechanical load variables regardless of accelerometer location and task (R2 values across accelerometer locations and tasks: peak acceleration .08-.55, loading rate .27-.59, and impulse .02-.59). Segmental accelerations generally overestimated whole-body mechanical load. SPM analysis showed that peak segmental accelerations were mostly related to CoM accelerations during the first 40-50% of contact phase. CONCLUSIONS: While body-worn accelerometry correlates to whole-body loading in team-sport movements and can reveal useful estimates concerning loading, these correlations are not strong. Body-worn accelerometry should therefore be used with caution to monitor whole-body mechanical loading in the field.

Match Physical Performance of Elite Female Soccer Players During International Competition.

Datson, N, Drust, B, Weston, M, Jarman, IH, Lisboa, P, and Gregson, W. Match physical performance of elite female soccer players during international competition. J Strength Cond Res 31(9): 2379-2387, 2017-The purpose of this study was to provide a detailed analysis of the physical demands of competitive international female soccer match play. A total of 148 individual match observations were undertaken on 107 outfield players competing in competitive international matches during the 2011-2012 and 2012-2013 seasons, using a computerized tracking system (Prozone Sports Ltd., Leeds, England). Total distance and total high-speed running distances were influenced by playing position, with central midfielders completing the highest (10,985 ± 706 m and 2,882 ± 500 m) and central defenders the lowest (9,489 ± 562 m and 1,901 ± 268 m) distances, respectively. Greater total very high-speed running distances were completed when a team was without (399 ± 143 m) compared to with (313 ± 210 m) possession of the ball. Most sprints were over short distances with 76% and 95% being less than 5 and 10 m, respectively. Between half reductions in physical performance were present for all variables, independent of playing position. This study provides novel findings regarding the physical demands of different playing positions in competitive international female match play and provides important insights for physical coaches preparing elite female players for competition.

Wavelet-based gene selection method for survival prediction in diffuse large B-cell lymphomas patients.

Microarray technology allows simultaneous measurements of expression levels for thousands of genes. An important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on wavelet transform for survival-relevant gene selection is presented. Cox proportional hazard model is typically used to build prediction model for patients' survival using the selected genes. The prediction model will be evaluated with the R2, concordance index, likelihood ratio statistic and Akaike information criteria. The results proved that good performance of survival prediction is achieved based on the selected genes. The results suggested the possibility of developing more advanced tools based on wavelets for gene selection from microarray data sets in the context of survival analysis.

Supervised wavelet method to predict patient survival from gene expression data.

In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis.

Conditional independence mapping of DIGE data reveals PDIA3 protein species as key nodes associated with muscle aerobic capacity.

Profiling of protein species is important because gene polymorphisms, splice variations and post-translational modifications may combine and give rise to multiple protein species that have different effects on cellular function. Two-dimensional gel electrophoresis is one of the most robust methods for differential analysis of protein species, but bioinformatic interrogation is challenging because the consequences of changes in the abundance of individual protein species on cell function are unknown and cannot be predicted. We conducted DIGE of soleus muscle from male and female rats artificially selected as either high- or low-capacity runners (HCR and LCR, respectively). In total 696 protein species were resolved and LC-MS/MS identified proteins in 337 spots. Forty protein species were differentially (P<0.05, FDR<10%) expressed between HCR and LCR and conditional independence mapping found distinct networks within these data, which brought insight beyond that achieved by functional annotation. Protein disulphide isomerase A3 emerged as a key node segregating with differences in aerobic capacity and unsupervised bibliometric analysis highlighted further links to signal transducer and activator of transcription 3, which were confirmed by western blotting. Thus, conditional independence mapping is a useful technique for interrogating DIGE data that is capable of highlighting latent features. BIOLOGICAL SIGNIFICANCE: Quantitative proteome profiling revealed that there is little or no sexual dimorphism in the skeletal muscle response to artificial selection on running capacity. Instead we found that noncanonical STAT3 signalling may be associated with low exercise capacity and skeletal muscle insulin resistance. Importantly, this discovery was made using unsupervised multivariate association mapping and bibliometric network analyses. This allowed our interpretation of the findings to be guided by patterns within the data rather than our preconceptions about which proteins or processes are of greatest interest. Moreover, we demonstrate that this novel approach can be applied to 2D gel analysis, which is unsurpassed in its ability to profile protein species but currently has few dedicated bioinformatic tools.

Website design: technical, social and medical issues for self-reporting by elderly patients.

There is growing interest in the use of the Internet for interacting with patients, both in terms of healthcare information provision and information gathering. In this article, we examine the issues in designing healthcare websites for elderly users. In particular, this article uses a year-long case study of the development of a web-based system for self-reporting of symptoms and quality of life with a view to examine the issues relating to website design for elderly users. The issues identified included the technical, social and medical aspects of website design for elderly users. The web-based system developed was based on the European Quality of Life 5-Dimensions health-status questionnaire, a commonly used tool for patient self-reporting of quality of life, and the more specific coronary revascularisation outcome questionnaire. Currently, self-reporting is generally administered in the form of paper-based questionnaires to be completed in the outpatient clinic or at home. There are a variety of issues relating to elderly users, which imply that websites for elderly patients may involve different design considerations to other types of websites.

Effect of a cooked meat meal on serum creatinine and estimated glomerular filtration rate in diabetes-related kidney disease.

OBJECTIVE: Fasting is not routinely recommended for renal function tests, despite the known effects of cooked meat on creatinine. We therefore studied variation in creatinine and estimated glomerular filtration rate (eGFR) after a standardized cooked meat meal in 80 subjects: healthy volunteers and diabetic patients with chronic kidney disease (CKD) stages 1 and 2, 3a, 3b, and 4 (n = 16/group). RESEARCH DESIGN AND METHODS: The interventions were a standardized cooked meat and a nonmeat meal, each providing ∼54 g protein, together with 250 mL water, on separate days. Fasting and postprandial blood samples at 1, 2, and 4 h were drawn for creatinine measurement using a kinetic alkaline picrate assay on an Olympus AU640 analyzer. The modified four-variable Modification of Diet in Renal Disease equation traceable to isotope dilution mass spectrometry creatinine was used to calculate eGFR. RESULTS: Consumption of a standardized cooked meat meal significantly increased serum creatinine and resulted in significant fall in eGFR in all stages of CKD studied; 6 of 16 CKD 3a patients were misclassified as CKD 3b. This effect of cooked meat on serum creatinine disappears after 12 h of fasting in all study participants. CONCLUSIONS: Creatine in meat is converted to creatinine on cooking, which is absorbed, causing significant increases in serum creatinine. This could impact management, as threshold for commencing and withdrawing certain medications and expensive investigations is defined by eGFR. eGFR calculated using fasting serum creatinine would be a better reflection of kidney function in these patients.

A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data.

BACKGROUND: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. METHODOLOGY/PRINCIPAL FINDINGS: Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. CONCLUSIONS/SIGNIFICANCE: We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing.

Transformation in the pharmaceutical industry--a systematic analysis of operational evidence.

Through systematic collection and trending of pharmaceutical data, operational evidence to verify existence of 14 factors affecting the ongoing pharmaceutical transformation has been compiled. These 14 factors are termed transformation triggers. The theoretical evidence in support of these triggers is carried forward from a systematic review of the literature that was conducted previously. Trends in operational evidence and the associated theoretical evidence were compared to identify areas of similarity and contrast. Areas of strong correlation between theoretical evidence and operational evidence included four transformation triggers: a fully integrated pharma network, personalized medicine, translational research, and pervasive computing. Key areas of contrast included three transformation triggers-namely, healthcare management focus, adaptive trials, and regulatory enforcement-for which the operational evidence was stronger than the theoretical evidence. LAY ABSTRACT: The intent of this paper is to provide proof to demonstrate if there is any operational evidence that supports the 14 transformation triggers previously identified during the theoretical part of this research. The theoretical evidence for these triggers was carried forward to this paper for study from an operational perspective. The practical evidence established in this paper was compared with the corresponding theoretical evidence to identify areas of similarity and difference. This resulted in four triggers that had strong relationship between operational and theoretical evidence; they are a fully integrated pharma network, personalized medicine, translational research, and pervasive computing. The areas of difference included three transformation triggers for which the operational evidence was stronger than the theoretical evidence. These were healthcare management focus, adaptive trials, and regulatory enforcement.

Characterization of the evolution of the pharmaceutical regulatory environment.

This paper is part of a research study that is intended to identify pharmaceutical quality risks induced by the ongoing transformation in the industry. This study establishes the current regulatory context by characterizing the development of the pharmaceutical regulatory environment. The regulatory environment is one of the most important external factors that affects a company's organization, processes, and technological strategy. This is especially the case with the pharmaceutical industry, where its products affect the quality of life of the consumers. The quantitative analysis of regulatory events since 1813 and review of the associated literature resulted in identification of six factors influencing the regulatory environment, namely public health protection, public health promotion, crisis management, harmonization, innovation, and modernization. From 1813 to the 1970s the focus of regulators was centered on crisis management and public health protection-a basic mission that has remained consistent over the years. Since the 1980s a gradual move in the regulatory environment towards a greater focus on public health promotion, international harmonization, innovation, and agency modernization may be seen. LAY ABSTRACT: The pharmaceutical industry is currently going through changes that affect the way it performs its research, manufacturing, and regulatory activities. The impact of these changes on the approaches to quality risk management requires more understanding. The authors are engaged in research to identify elements of the changes that influence pharmaceutical quality. As quality requirements are an integral part of the pharmaceutical regulations, a comprehensive understanding of these regulations is seen as the first step. The results of this study show that (i) public health protection, public health promotion, crisis management, harmonization, innovation, and modernization are factors that affect regulations in the pharmaceutical industry; (ii) the regulators' main mission of public health protection has remained a constant feature over the years; and (iii) since the 1970s other factors such as public health promotion, international harmonization, innovation, and agency modernization are playing more important role in regulatory agency thinking and actions.