Persistent esophageal changes after histologic remission in eosinophilic esophagitis.

BACKGROUND: Eosinophilic esophagitis (EoE) is characterized by persistent or relapsing allergic inflammation, and both clinical and histologic features of esophageal inflammation persist over time in most individuals. Mechanisms contributing to EoE relapse are not understood, and chronic EoE-directed therapy is therefore required to prevent long-term sequelae. OBJECTIVE: We investigated whether EoE patients in histologic remission have persistent dysregulation of esophageal gene expression. METHODS: Esophageal biopsy samples from 51 pediatric and 52 adult subjects with EoE in histopathologic remission (<15 eosinophils per high-power field [eos/hpf]) and control (48 pediatric and 167 adult) subjects from multiple institutions were subjected to molecular profiling by the EoE diagnostic panel, which comprises a set of 94 esophageal transcripts differentially expressed in active EoE. RESULTS: Defining remission as <15 eos/hpf, we identified 51 and 32 differentially expressed genes in pediatric and adult EoE patients compared to control individuals, respectively (false discovery rate < 0.05). Using the stringent definition of remission (0 eos/hpf), the adult and pediatric cohorts continued to have 18 and 25 differentially expressed genes (false discovery rate < 0.05). Among 6 shared genes between adults and children, CDH26 was upregulated in both children and adults; immunohistochemistry demonstrated increased cadherin 26 staining in the epithelium of EoE patients in remission compared to non-EoE controls. In the adult cohort, POSTN expression correlated with the endoscopic reference system score (Spearman r = 0.35, P = .011), specifically correlating with the rings' endoscopic reference system subscore (r = 0.53, P = .004). CONCLUSION: We have identified persistent EoE-associated esophageal gene expression in patients with disease in deep remission. These data suggest potential inflammation-induced epigenetic mechanisms may influence gene expression during remission in EoE and provide insight into possible mechanisms that underlie relapse in EoE.

Improved pathogenicity prediction for rare human missense variants.

The success of personalized genomic medicine depends on our ability to assess the pathogenicity of rare human variants, including the important class of missense variation. There are many challenges in training accurate computational systems, e.g., in finding the balance between quantity, quality, and bias in the variant sets used as training examples and avoiding predictive features that can accentuate the effects of bias. Here, we describe VARITY, which judiciously exploits a larger reservoir of training examples with uncertain accuracy and representativity. To limit circularity and bias, VARITY excludes features informed by variant annotation and protein identity. To provide a rationale for each prediction, we quantified the contribution of features and feature combinations to the pathogenicity inference of each variant. VARITY outperformed all previous computational methods evaluated, identifying at least 10% more pathogenic variants at thresholds achieving high (90% precision) stringency.

Predictive, preventive, and personalized medicine in breast cancer: targeting the PI3K pathway.

Breast cancer (BC) is a multifaceted disease characterized by distinct molecular subtypes and varying responses to treatment. In BC, the phosphatidylinositol 3-kinase (PI3K) pathway has emerged as a crucial contributor to the development, advancement, and resistance to treatment. This review article explores the implications of the PI3K pathway in predictive, preventive, and personalized medicine for BC. It emphasizes the identification of predictive biomarkers, such as PIK3CA mutations, and the utility of molecular profiling in guiding treatment decisions. The review also discusses the potential of targeting the PI3K pathway for preventive strategies and the customization of therapy based on tumor stage, molecular subtypes, and genetic alterations. Overcoming resistance to PI3K inhibitors and exploring combination therapies are addressed as important considerations. While this field holds promise in improving patient outcomes, further research and clinical trials are needed to validate these approaches and translate them into clinical practice.

Application of machine learning models on predicting the length of hospital stay in fragility fracture patients.

BACKGROUND: The rate of geriatric hip fracture in Hong Kong is increasing steadily and associated mortality in fragility fracture is high. Moreover, fragility fracture patients increase the pressure on hospital bed demand. Hence, this study aims to develop a predictive model on the length of hospital stay (LOS) of geriatric fragility fracture patients using machine learning (ML) techniques. METHODS: In this study, we use the basic information, such as gender, age, residence type, etc., and medical parameters of patients, such as the modified functional ambulation classification score (MFAC), elderly mobility scale (EMS), modified Barthel index (MBI) etc, to predict whether the length of stay would exceed 21 days or not. RESULTS: Our results are promising despite the relatively small sample size of 8000 data. We develop various models with three approaches, namely (1) regularizing gradient boosting frameworks, (2) custom-built artificial neural network and (3) Google's Wide & Deep Learning technique. Our best results resulted from our Wide & Deep model with an accuracy of 0.79, with a precision of 0.73, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.84. Feature importance analysis indicates (1) the type of hospital the patient is admitted to, (2) the mental state of the patient and (3) the length of stay at the acute hospital all have a relatively strong impact on the length of stay at palliative care. CONCLUSIONS: Applying ML techniques to improve the quality and efficiency in the healthcare sector is becoming popular in Hong Kong and around the globe, but there has not yet been research related to fragility fracture. The integration of machine learning may be useful for health-care professionals to better identify fragility fracture patients at risk of prolonged hospital stays. These findings underline the usefulness of machine learning techniques in optimizing resource allocation by identifying high risk individuals and providing appropriate management to improve treatment outcome.

A CT-based predictive model for stent-induced vessel damage: application to type B aortic dissection.

OBJECTIVES: The distal stent-induced new entry (distal SINE) is a life-threatening device-related complication after thoracic endovascular aortic repair (TEVAR). However, risk factors for distal SINE are not fully determined, and prediction models are lacking. This study aimed to establish a predictive model for distal SINE based on the preoperative dataset. METHODS: Two hundred and six patients with Stanford type B aortic dissection (TBAD) that experienced TEVAR were involved in this study. Among them, thirty patients developed distal SINE. Pre-TEVAR morphological parameters were measured based on the CT-reconstructed configurations. Virtual post-TEVAR morphological and mechanical parameters were computed via the virtual stenting algorithm (VSA). Two predictive models (PM-1 and PM-2) were developed and presented as nomograms to help risk evaluation of distal SINE. The performance of the proposed predictive models was evaluated and internal validation was conducted. RESULTS: Machine-selected variables for PM-1 included key pre-TEVAR parameters, and those for PM-2 included key virtual post-TEVAR parameters. Both models showed good calibration in both development and validation subsamples, while PM-2 outperformed PM-1. The discrimination of PM-2 was better than PM-1 in the development subsample, with an optimism-corrected area under the curve (AUC) of 0.95 and 0.77, respectively. Application of PM-2 in the validation subsample presented good discrimination with an AUC of 0.9727. The decision curve demonstrated that PM-2 was clinically useful. CONCLUSION: This study proposed a predictive model for distal SINE incorporating the CT-based VSA. This predictive model could efficiently predict the risk of distal SINE and thus might contribute to personalized intervention planning. CLINICAL RELEVANCE STATEMENT: This study established a predictive model to evaluate the risk of distal SINE based on the pre-stenting CT dataset and planned device information. With an accurate VSA tool, the predictive model could help to improve the safety of the endovascular repair procedure. KEY POINTS: • Clinically useful prediction models for distal stent-induced new entry are still lacking, and the safety of the stent implantation is hard to guarantee. • Our proposed predictive tool based on a virtual stenting algorithm supports different stenting planning rehearsals and real-time risk evaluation, guiding clinicians to optimize the presurgical plan when necessary. • The established prediction model provides accurate risk evaluation for vessel damage, improving the safety of the intervention procedure.

Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training.

BACKGROUND: Currently, the diagnosis of invasive pulmonary aspergillosis (IPA) mainly depends on the integration of clinical, radiological and microbiological data. Artificial intelligence (AI) has shown great advantages in dealing with data-rich biological and medical challenges, but the literature on IPA diagnosis is rare. OBJECTIVE: This study aimed to provide a non-invasive, objective and easy-to-use AI approach for the early diagnosis of IPA. METHODS: We generated a prototype diagnostic deep learning model (IPA-NET) comprising three interrelated computation modules for the automatic diagnosis of IPA. First, IPA-NET was subjected to transfer learning using 300,000 CT images of non-fungal pneumonia from an online database. Second, training and internal test sets, including clinical features and chest CT images of patients with IPA and non-fungal pneumonia in the early stage of the disease, were independently constructed for model training and internal verification. Third, the model was further validated using an external test set. RESULTS: IPA-NET showed a marked diagnostic performance for IPA as verified by the internal test set, with an accuracy of 96.8%, a sensitivity of 0.98, a specificity of 0.96 and an area under the curve (AUC) of 0.99. When further validated using the external test set, IPA-NET showed an accuracy of 89.7%, a sensitivity of 0.88, a specificity of 0.91 and an AUC of 0.95. CONCLUSION: This novel deep learning model provides a non-invasive, objective and reliable method for the early diagnosis of IPA.

Diagnostic merits of the Eosinophilic Esophagitis Diagnostic Panel from a single esophageal biopsy.

BACKGROUND: Eosinophilic esophagitis (EoE) is a histologically "patchy" disease with uneven eosinophil distribution along the esophagus, posing a dilemma for histologically analyzing endoscopic biopsy samples, especially when biopsy samples are limited to only the distal esophagus. OBJECTIVE: We investigated whether molecular mRNA profiling of a distal esophageal biopsy sample predicts eosinophilia in the proximal esophagus. METHODS: Esophageal biopsy samples (n = 507) from subjects with EoE were collected from multiple institutions, spanning adults and pediatric patients. Subjects were grouped on the basis of distinct distal (D) and proximal (P) eosinophil counts (D+P+, D+P-, D-P+, and D-P-, with + and - defined as ≥15 or <15 eosinophils/hpf, respectively). Molecular profiles were assessed by using the EoE Diagnostic Panel (EDP), a set of 96 esophageal transcripts used to derive the EDP score. RESULTS: The distal EDP score was correlated with proximal eosinophil levels (r = -0.73; P < .0001). EDP analysis of a histologically negative distal biopsy sample predicted the presence of proximal esophagitis with high sensitivity (85%). In a 2-year follow-up focusing on the cases with discordant histologic and EDP results, histologically negative patients (D-P-) had higher rates of EoE relapse when the EDP was positive than when the EDP was negative (odds ratio = 11; P = .003), indicating predictive medicine capacity. CONCLUSION: EDP analysis of a single distal esophageal biopsy sample predicts remote inflammation in patients with spatially heterogeneous eosinophilia and disease relapse in patients with histologic remission, providing diagnostic merit and predictive medicine capacity for molecular diagnosis of EoE.

A Familial Novel Putative-Pathogenic Mutation Identified in Plaque-Psoriasis by a Multigene Panel Analysis.

Psoriasis is a chronic multifactorial skin disorder with an immune basis. It is characterized by patches of skin that are usually red, flaky and crusty, and that often release silvery scales. The patches appear predominantly on the elbows, knees, scalp and lower back, although they may also appear on other body areas and severity may be variable. The majority of patients (about 90%) present small patches known as "plaque psoriasis". The roles of environmental triggers such as stress, mechanical trauma and streptococcal infections are well described in psoriasis onset, but much effort is still needed to unravel the genetic component. The principal aim of this study was to use a next-generation sequencing technologies-based approach together with a 96 customized multigene panel in the attempt to determine if there are germline alterations that can explain the onset of the disease, and thus to find associations between genotypes and phenotypes. To this aim, we analyzed a family in which the mother showed mild psoriasis, and her 31-year-old daughter had suffered from psoriasis for several years, whereas an unaffected sister served as a negative control. We found variants already associated directly to psoriasis in the TRAF3IP2 gene, and interestingly we found a missense variant in the NAT9 gene. The use of multigene panels in such a complex pathology such as psoriasis can be of great help in identifying new susceptibility genes, and in being able to make early diagnoses especially in families with affected subjects.

Mathematical modeling of the lower urinary tract: A review.

AIMS: Understand what progress has been made toward a functionally predictive lower urinary tract (LUT) model, identify knowledge gaps, and develop from them a path forward. METHODS: We surveyed prominent mathematical models of the basic LUT components (bladder, urethra, and their neural control) and categorized the common modeling strategies and theoretical assumptions associated with each component. Given that LUT function emerges from the interaction of these components, we emphasized attempts to model their connections, and highlighted unmodeled aspects of LUT function. RESULTS: There is currently no satisfactory model of the LUT in its entirety that can predict its function in response to disease, treatment, or other perturbations. In particular, there is a lack of physiologically based mathematical descriptions of the neural control of the LUT. CONCLUSIONS: Based on our survey of the work to date, a potential path to a predictive LUT model is a modular effort in which models are initially built of individual tissue-level components using methods that are extensible and interoperable, allowing them to be connected and tested in a common framework. A modular approach will allow the larger goal of a comprehensive LUT model to be in sight while keeping individual efforts manageable, ensure new models can straightforwardly build on prior research, respect potential interactions between components, and incentivize efforts to model absent components. Using a modular framework and developing models based on physiological principles, to create a functionally predictive model is a challenge that the field is ready to undertake.

Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy.

PURPOSE: The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT). MATERIALS AND METHODS: We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.5 T T2w MR images. The predictive performance of each feature was quantified in terms of Wilcoxon-Mann-Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value. The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one). 1896 radiomic feature were extracted, 91 of which showed significance (p < 0.05) at the univariate analysis. The radiomic model showing the highest predictive value combined the features calculated starting from the gray level co-occurrence-based features. This model achieved an AUC of 0.73 in the training set and 0.91 in the validation set. CONCLUSIONS: The proposed radiomic model showed promising performances in predicting 2-year overall survival before NACRT. Nevertheless, the observed results should be tested in larger studies with consistent external validation cohorts, to confirm their potential clinical use.

'The King of Diseases': An Essay on the Special Attention Paid to Cancer Patients and How It Came About.

The history of cancer during the twentieth century demonstrates that various factors have contributed to the perception of cancer as the 'Emperor of All Maladies', although this has never been true from an epidemiological perspective. Depending on the geographical area, infectious diseases such as tuberculosis, malaria or cardiovascular disease still head the list of the most common illnesses. Within the group of chronic-degenerative diseases, however, cancer has outdistanced the widespread classic infectious diseases as a result of the epidemiologic transition around 1900, at least in the more developed countries. Under the Nazi dictatorship from 1933 to 1945, the perception of cancer in Germany was particularly promoted for propaganda purposes. In the atomic era, cancer began to attract strong public interest as a worthwhile object of research in radiation therapies using large-scale facilities (electron accelerators, 'electron guns'). A further upsurge of interest in cancer was then registered in the context of the debate about the pathogenic role of environmental factors. The remarkable thing is that this increased perception of cancer has not yet been significantly associated with any overarching success in cancer treatment, but it has been associated with ideologies, hopes and advances in technology.

Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage.

OBJECTIVES: To test radiomics-based features extracted from noncontrast CT of patients with spontaneous intracerebral haemorrhage for prediction of haematoma expansion and poor functional outcome and compare them with radiological signs and clinical factors. MATERIALS AND METHODS: Seven hundred fifty-four radiomics-based features were extracted from 1732 scans derived from the TICH-2 multicentre clinical trial. Features were harmonised and a correlation-based feature selection was applied. Different elastic-net parameterisations were tested to assess the predictive performance of the selected radiomics-based features using grid optimisation. For comparison, the same procedure was run using radiological signs and clinical factors separately. Models trained with radiomics-based features combined with radiological signs or clinical factors were tested. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) score. RESULTS: The optimal radiomics-based model showed an AUC of 0.693 for haematoma expansion and an AUC of 0.783 for poor functional outcome. Models with radiological signs alone yielded substantial reductions in sensitivity. Combining radiomics-based features and radiological signs did not provide any improvement over radiomics-based features alone. Models with clinical factors had similar performance compared to using radiomics-based features, albeit with low sensitivity for haematoma expansion. Performance of radiomics-based features was boosted by incorporating clinical factors, with time from onset to scan and age being the most important contributors for haematoma expansion and poor functional outcome prediction, respectively. CONCLUSION: Radiomics-based features perform better than radiological signs and similarly to clinical factors on the prediction of haematoma expansion and poor functional outcome. Moreover, combining radiomics-based features with clinical factors improves their performance. KEY POINTS: • Linear models based on CT radiomics-based features perform better than radiological signs on the prediction of haematoma expansion and poor functional outcome in the context of intracerebral haemorrhage. • Linear models based on CT radiomics-based features perform similarly to clinical factors known to be good predictors. However, combining these clinical factors with radiomics-based features increases their predictive performance.

Accelerating diagnosis of Parkinson's disease through risk prediction.

BACKGROUND: Characterization of prediagnostic Parkinson's Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. METHODS: We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. RESULTS: We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. CONCLUSIONS: Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Stigmatization of Not-Knowing as a Public Health Tool.

Predictive interventions and practices are becoming a defining feature of medicine. The author points out that according to the inner logic and external supporters (i.e., state, industry, and media) of modern medicine, participating in healthcare increasingly means participating in knowing, sharing, and using of predictive information. At the same time, the author addresses the issue that predictive information may also have problematic side effects like overdiagnosis, health-related anxiety, and worry as well as impacts on personal life plans. The question is raised: Should we resort to stigmatization if doing so would increase participation in predictive interventions, and thereby save healthcare costs and reduce morbidity and premature death? The paper concludes that even if such a strategy cannot be ruled out in some forms and contexts, we ought to be very cautious about the dangers of shame and stigmatization.

The shift of the paradigm between ageing and diseases.

In the area of the Medical Sciences, the chronological age has always been, and still is, an indicator by which we try to understand the health status of an individual. However, besides considering people born with an already expressed disease, each human genome has sequence alterations called predisposing mutations; carriers of such genetic alterations have an increased risk of contracting diseases during their life. In addition, the exposome, i.e. the totality of environmental noxae ("hits") to which our body is exposed throughout life (through ingestion, breathing, body surface hits, and psychosociological stress agents, etc.) contributes to increase gradually but inexorably the frailty of an organism, and this process is usually referred to as "physiological ageing". This position paper proposes that we invert our visual angle and view the passage-of-time not as the cause of diseases, but consider the genome alterations present at birth and the noxae received during our life as the real major causes of ageing. The Biomedical Sciences are now increasingly unraveling the etiopathogenesis of most chronic degenerative diseases; thus, it will be possible to monitor and treat those that most contribute to the increased frailty of each person, which is now referred to with the misnomer "physiological ageing". These concepts are not banal; indeed, they imply that we must try to avoid the causes of alterations that result later in chronic degenerative diseases. Thus, we should shift our attention from the cure to the prevention of alterations/diseases also to improve both the length and quality of our life. Moreover, this approach involves real personalized or individualized medicine, thus conferring a more direct benefit to each of us by finalizing either the cure or the monitoring of diseases.

Assessing the Heterogeneity of Complaints Related to Tinnitus and Hyperacusis from an Unsupervised Machine Learning Approach: An Exploratory Study.

INTRODUCTION: Subjective tinnitus (ST) and hyperacusis (HA) are common auditory symptoms that may become incapacitating in a subgroup of patients who thereby seek medical advice. Both conditions can result from many different mechanisms, and as a consequence, patients may report a vast repertoire of associated symptoms and comorbidities that can reduce dramatically the quality of life and even lead to suicide attempts in the most severe cases. The present exploratory study is aimed at investigating patients' symptoms and complaints using an in-depth statistical analysis of patients' natural narratives in a real-life environment in which, thanks to the anonymization of contributions and the peer-to-peer interaction, it is supposed that the wording used is totally free of any self-limitation and self-censorship. METHODS: We applied a purely statistical, non-supervised machine learning approach to the analysis of patients' verbatim exchanged on an Internet forum. After automated data extraction, the dataset has been preprocessed in order to make it suitable for statistical analysis. We used a variant of the Latent Dirichlet Allocation (LDA) algorithm to reveal clusters of symptoms and complaints of HA patients (topics). The probability of distribution of words within a topic uniquely characterizes it. The convergence of the log-likelihood of the LDA-model has been reached after 2,000 iterations. Several statistical parameters have been tested for topic modeling and word relevance factor within each topic. RESULTS: Despite a rather small dataset, this exploratory study demonstrates that patients' free speeches available on the Internet constitute a valuable material for machine learning and statistical analysis aimed at categorizing ST/HA complaints. The LDA model with K = 15 topics seems to be the most relevant in terms of relative weights and correlations with the capability to individualizing subgroups of patients displaying specific characteristics. The study of the relevance factor may be useful to unveil weak but important signals that are present in patients' narratives. DISCUSSION/CONCLUSION: We claim that the LDA non-supervised approach would permit to gain knowledge on the patterns of ST- and HA-related complaints and on patients' centered domains of interest. The merits and limitations of the LDA algorithms are compared with other natural language processing methods and with more conventional methods of qualitative analysis of patients' output. Future directions and research topics emerging from this innovative algorithmic analysis are proposed.

Informative combination of CLU rs11136000, serum HDL levels, diabetes, and age as a new piece of puzzle-picture of predictive medicine for cognitive disorders.

Clusterin (CLU) is the third most important associated risk gene in cognitive disorders. Regarding the controversy about the association of CLU rs11136000 with mild cognitive impairment (MCI), the aim of this study was to investigate a putative association of CLU rs11136000 with MCI as well as the serum biological factors with a special attention to the age as a main dimension of a multifactorial elderly disease in an Iranian elderly cohort in which the mentioned association was not previously investigated. The study also checked the association between diabetes and MCI in this population. A population of 418 individuals containing 236 MCI and 192 control subjects was recruited from the Amirkola health and aging population cohort. Serum biological indexes were assessed by biochemical and enzyme-linked immunosorbent assay, and rs11136000 genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism. Bioinformatics analyses were used to identify the putative effect of rs11136000 on the secondary structure of RNA and chromatin location in different cell lines and tissues. Type 2 diabetes was present with a higher proportion in the MCI group in comparison with the control group (P = 0.041). The frequency of the C allele of CLU rs11136000 was significantly different between cases and controls and was associated with MCI risk (OR 1.79, P = 0.019). Under a dominant genetic model, the CC genotype showed a predisposing effect in individuals aged ≥ 75 years (OR 3.33, P = 0.0004). Interestingly, under an over-dominant model, the CT genotype had a protective effect in this population (OR 4.52, P = < 0.0001). We also found a significant association between the genotypes and high-density lipoprotein (HDL) levels in MCI patients (P = 0.0004). Bioinformatics analysis showed that rs11136000 is located in the transcribed region without any regulatory features such as being enhancer or insulator. Also, the T>C transition of CLU rs11136000 could not cause significant mRNA folding (P = 0.950). Contrary to other studies on Asian populations, this study demonstrated an association between rs11136000 and MCI in an elderly Iranian population. This study also suggests that an age-dependent approach to the previous studies may be performed in order to revise the previous belief in this geographical area. The rs11136000 genotypes in combination with HDL levels and knowledge about diabetes background may be used as a predictive medicine tool for cognitive disorders.

[Big Data: Technical improvement, degradation or transformation of the solidarity model?] / Big Data : amélioration technique, dégradation ou transformation du modèle de solidarité ?

Big Data, the production of a massive amount of heterogeneous data, is often presented as a means to ensure the economic survival and sustainability of health systems. According to this perspective, Big Data could help save the spirit of our welfare states based on the principles of risks-sharing and equal access to care for all. According to a second perspective, opposed to the first, Big Data would fuel a process of demutualization, transferring to individuals a growing share of responsibility for managing their health. This article proposes to develop a third approach: Big Data does not induce a loss of solidarity but a transformation of the European model of welfare states. These are the data that are now the objects of the pooling. Individual and collective responsibilities are thus redistributed. However, this model, as new as it is, remains liberal in its inspiration; it basically allows the continuation of political liberalism by other means.

[Oral cavity microbiocenosis assessment on the basis of bacterial endotoxin and plasmalogens in a saliva by method GAS-liquid chromatography-mass spectrometry.]

The concentration of plasmalogen bacterial and endotoxin levels in the saliva of patients with different severity of periodontal disease, injury prosthetic bed and with various degrees of the oral cavity microbiocenosis violations was studied. Determination of the presence of the pathological process was carried out clinically, according to the condition of periodontal tissues. The degree of microbiological disorders was assessed by the quantitative ratio of the types of microorganisms isolated from the smear taken from the gingival groove. It was found that the concentration of plasmalogen for normal microbiocenosis is not less than 0.7 µg/g. For the intermediate type of microbiocenosis, the concentration of 1.82 µg/g was determined; for dysbiosis - 5.64 µg/g, and for the expressed violation of the microbial composition accompanied by inflammatory processes - 6.54 µg/g. An increase in the concentration of bacterial endotoxin (be) more than 6.25 nanomole/g indicates the pronounced inflammatory process, regardless of the determined intensity of contamination of opportunistic gram-negative microflora.

Dietary phytochemicals in breast cancer research: anticancer effects and potential utility for effective chemoprevention.

Cancerous tissue transformation developing usually over years or even decades of life is a highly complex process involving strong stressors damaging DNA, chronic inflammation, comprehensive interaction between relevant molecular pathways, and cellular cross-talk within the neighboring tissues. Only the minor part of all cancer cases are caused by inborn predisposition; the absolute majority carry a sporadic character based on modifiable risk factors which play a central role in cancer prevention. Amongst most promising candidates for dietary supplements are bioactive phytochemicals demonstrating strong anticancer effects. Abundant evidence has been collected for beneficial effects of flavonoids, carotenoids, phenolic acids, and organosulfur compounds affecting a number of cancer-related pathways. Phytochemicals may positively affect processes of cell signaling, cell cycle regulation, oxidative stress response, and inflammation. They can modulate non-coding RNAs, upregulate tumor suppressive miRNAs, and downregulate oncogenic miRNAs that synergically inhibits cancer cell growth and cancer stem cell self-renewal. Potential clinical utility of the phytochemicals is discussed providing examples for chemoprevention against and therapy for human breast cancer. Expert recommendations are provided in the context of preventive medicine.