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Background This paper provides an update the current state of knowledge on the status of C. trachomatis (CT) in India. CT is one of the most common Sexually Transmitted Infections (STIs) in the world, yet evidence on the burden in developing countries, as India is scarce. Results This paper includes all the studies containing C. trachomatis testing that were identified in the literature search for the literature review on gonorrhea in India. The papers were included from PubMed, EMBASE and Google Scholar. The studies that contained data on CT were considered for further inclusion. Studies were included in this review if they contained C. trachomatis testing data Results This paper provides 31 new studies that were not featured in the previous review. Prevalence of CT in the included studies ranges from 0.4% to 29%. The studies are classified in four patient categories namely: OPD and STI clinic attendees, Commercial sex workers and their clients, Men who have sex with men and Trans genders and population groups. The most commonly reported testing method was PCR. This paper helps to provide a broader picture regarding C. trachomatis in India by providing prevalence levels for some groups of high relevance for STIs, like Commercial Sex Workers (CSW) and Men who have Sex with Men (MSM). This review provides more ample background in the CT burden in India among the most vulnerable and fringe groups. This paper also raises the issue of referencing of research papers on STIs in India to facilitate review and research on the matter

Background: Sexually transmitted infections (STIs), like Chlamydia trachomatis and Neisseria gonorrhoeae (CT and NG, respectively) are linked to an important sexual and reproductive health (SRH) burden worldwide. Behavior is an important predictor for SRH, as it dictates the risk for STIs. Assessing the behavior of a population helps to assess its risk profile. Methods: Study participants were recruited at a gynecology outpatient department (OPD) in the Allahabad district in Uttar Pradesh India, and a questionnaire was used to assess demographics, SRH, and obstetric history. Patients provided three samples (urine, vaginal swab, and whole blood). These samples were used to identify CT and NG using PCR/NAAT and CT IgG ELISA. Results: A total of 296 women were included for testing, mean age was 29 years. No positive cases of CT and NG were observed using PCR/NAAT. A 7% (22/296) positivity rate for CT was observed using IgG ELISA. No positive association was found between serology and symptoms (vaginal discharge, abdominal pain, dysuria, and dyspareunia) or adverse pregnancy outcomes (miscarriage and stillbirth). Positive relations with CT could be observed with consumption of alcohol, illiteracy, and tenesmus (p-value 0.02&ndash, 0.03). Discussion: STI prevalence in this study was low, but a high burden of SRH morbidity was observed, with a high symptomatic load. High rates of miscarriage (31%) and stillbirth (8%) were also observed among study subjects. No associations could be found between these ailments and CT infection. These rates are high even for low- and middle-income country standards. Conclusion: This study puts forward high rates of SRH morbidity, and instances of adverse reproductive health outcomes are highlighted in this study, although no associations with CT infection could be found. This warrants more investigation into the causes leading to these complaints in the Indian scenario and potential biases to NAAT testing, such as consumption of over-the-counter antimicrobials.

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multifaceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine. (C) 2016 Published by Elsevier B.V.

Chlamydia trachomatis is the world's most prevalent bacterial Sexually Transmitted infection (STI). It is associated with a wide range of health consequences and sequelae in both the short and long term. Enhanced control of urogenital C. trachomatis infection is particularly important in low- and middle-income countries such as India, where most of the burden goes unnoticed and where limited systematic data is available to gauge the current situation. The World Health Organization (WHO) recently issued its latest strategy on STIs, which is aligned with the achievement of the Sustainable Development Goals (SDGs). Taking the WHO framework into account; this paper puts forward an integrated care model to strengthen the management and control of C. trachomatis in India. The model is compiled of five key components of STI management (awareness, prevention diagnosis, treatment and follow-up). The model considers barriers to effective C. trachomatis control into account. The barriers are discussed and compiled into different categories. A roadmap for the implementation of other similarmodels to enhance C. trachomatis control in the future is provided.

Background: Personalised medicine (PM) is an innovative way to produce better patient outcomes by using an individualised or stratified approach to disease and treatment rather than a collective treatment approach for patients. Despite its tangible advantages, the complex process to translate PM into the member states and European healthcare systems has delayed its uptake. The aim of this study is to identify relevant barriers represented by an index to summarise challenging areas for the implementation of PM in Europe. Methods: A systematic literature review was conducted, and a gaps-and-needs assessment together with a strengths-weaknesses-opportunities-and-threats analysis were applied to review strategic reports and conduct interviews with key stakeholders. Furthermore, surveys were sent out to representatives of stakeholder groups. The index was constructed based on the priorisation of relevant factors by stakeholders. Results: A need for stakeholder-agreed standards at all levels of implementation of PM exists, from validating biomarkers to definitions of ‘informed consent'. The barriers to implement PM are identified in 7 areas, namely, stakeholder involvement, standardisation, interoperable infrastructure, European-level policy making, funding, data and research, and healthcare systems. Conclusions: Challenges in the above-mentioned areas can and must be successfully tackled if we are to create a healthier Europe through PM. In order to create an environment in which PM can thrive for the patients' best outcomes, there is an urgent need for systematic actions to remove as many barriers as possible.

Chlamydia trachomatis (hereafter CT) is Gram-negative, obligate intracellular pathogen. It causes the world's most common non-viral sexually transmitted disease. India is home to the world's greatest burden of infectious diseases, yet information on prevalence rates of CT is scarce. This article systematically reviews the literature for the prevalence rates and testing methods in India. A total of 27 studies were included. Four main patients groups (symptomatic women, infertile women, pregnant women and asymptomatic population groups) could be identified with varying rates of CT (0.1%–32% using PCR, 2.4%–75% using ELISA serology). Most of the studies originated from urban settings, 11 of them from New Delhi. In-house PCR was the most common diagnostic technique used generating the following ranges in prevalence for the four group studies: symptomatic women 10%–50%, pregnant women 0.1%–2.5% and asymptomatic populations 0.9%–24.5%. The rates among infertile women were 9%–68% based on serology results. The prevalence rates featured in this paper are in line with other locations across the Indian subcontinent. This review highlights the extreme heterogeneity in the limited studies available in India on CT and the need for standardized guidelines for diagnosis and management of CT in India. The availability of resources should be considered in the formulation of recommendations., The first systematic literature review on Chlamydia trachomatis in India.

Background: Recently, there has been a substantial increase in relevant genome-based technologies into market. Compared with its utilization in healthcare systems, we notice a huge gap. In order to address this bottleneck, we previously developed the Learning-Adapting-Leveling (LAL) model. Aim: In this article, we aim to demonstrate the overarching reach of the model for translation to market and implementation into healthcare systems moving towards personalized healthcare. Methods: We use qualitative logical reasoning with the LAL model as a reference. Results: We found that technology transfer, health needs assessment, health technology assessment and health impact assessment are justified for their inclusion. In addition, the public health wheel is justified as a good reference frame along with value of information. Conclusion: We conclude that as the LAL model covers all dimensions and tools for translation and implementation in a defined method; it can therefore be considered as the overarching framework for translation and implementation into healthcare.

We see a backlog in the effective and efficient integration of personalized medicine applications such as genome-based information and technologies into healthcare systems. This article aims to expand on the steps of a published innovative model, which addresses the bottleneck of real-time integration into healthcare. We present a deconstruction of the Learning-Adapting-Leveling model to simplify the steps. We found out that throughout the technology transfer pipeline, contacts, assessments and adaptations/feedback loops are made with health needs assessment, health technology assessment and health impact assessment professionals in the same order by the academic–industrial complex, resulting in early-on involvement of all stakeholders. We conclude that the model steps can be used to resolve the bottleneck of implementation of personalized medicine application into healthcare systems.

Chlamydia trachomatis (CT) infections in women can result in tubal pathology (TP). Worldwide 10-15% of all couples are subfertile, meaning they did not get pregnant after 1 year. Part of the routine subfertility diagnostics is the Chlamydia Antibody Test (CAT) to decide for laparoscopy or not in order to diagnose TP. The CAT positive and negative predictive value is such that many unneeded laparoscopies are done and many TP cases are missed. Addition of host genetic markers related to infection susceptibility and severity could potentially improve the clinical management of couples who suffer from subfertility. In the present study, the potential translational and clinical value of adding diagnostic host genetic marker profiles on the basis of infection and inflammation to the current clinical management of subfertility was investigated. This review provides an overview of the current state of the art of host genetic markers in relation to CT infection, proposes a new clinical diagnostic approach, and investigates how the Learning-Adapting-Leveling model (LAL, a public health genomic (PHG) model) can be of value and provide insight to see whether these host genetic markers can be translated into public health. This review shows that the preliminary basis of adding host genetic marker profiles to the current diagnostic procedures of subfertility is present but has to be further developed before implementation into health care can be achieved. CT infection is an example in the field of PHG with potential diagnostic to be taken up in the future in the field of subfertility diagnosis with a time line for integration to be dependent on enhanced participation of many stakeholders in the field of PHG which could be advanced through the LAL model.

Rapid advances in ‘omics’ sciences and technologies have elevated the relevance of personalized medicine. This article reviews the current advances in the application of personalized medicine, outlines and summarizes the key areas that still need to be addressed and gives recommendations in this direction. Eighteen relevant high-level reports on personalized medicine were reviewed in order to identify the gaps and needs that are present for the implementation of personalized medicine. We identify 12 key areas that represent the main obstacles on the road towards the personalization of medicine and divide these 12 key areas into four domains, namely: scientific research and stakeholder collaboration; translational tools; regulations and systematic early dialog with regulators; and uptake into healthcare systems. All of the evaluated reports agree on the imperative need for intensive collaboration among all stakeholders with early active participation and changes in the current healthcare infrastructure.

Background The success rate of timely translation of genome-based technologies to commercially feasible products/services with applicability in health care systems is significantly low. We identified both industry and scientists neglect health policy aspects when commercializing their technology, more specifically, Public Health Assessment Tools (PHAT) and early on involvement of decision makers through which market authorization and reimbursements are dependent. While Technology Transfer (TT) aims to facilitate translation of ideas into products, Health Technology Assessment, one component of PHAT, for example, facilitates translation of products/processes into healthcare services and eventually comes up with recommendations for decision makers. We aim to propose a new model of valorization to optimize integration of genome-based technologies into the healthcare system. Methods The method used to develop our model is an adapted version of the Fish Trap Model and the Basic Design Cycle. Results We found although different, similarities exist between TT and PHAT. Realizing the potential of being mutually beneficial justified our proposal of their relative parallel initiation. We observed that the Public Health Genomics Wheel should be included in this relative parallel activity to ensure all societal/policy aspects are dealt with preemptively by both stakeholders. On further analysis, we found out this whole process is dependent on the Value of Information. As a result, we present our LAL (Learning Adapting Leveling) model which proposes, based on market demand; TT and PHAT by consultation/bi-lateral communication should advocate for relevant technologies. This can be achieved by public-private partnerships (PPPs). These widely defined PPPs create the innovation network which is a developing, consultative/collaborative-networking platform between TT and PHAT. This network has iterations and requires learning, assimilating and using knowledge developed and is called absorption capacity. We hypothesize that the higher absorption capacity, higher success possibility. Our model however does not address the phasing out of technology although we believe the same model can be used to simultaneously phase out a technology. Conclusions This model proposes to facilitate optimization/decrease the timeframe of integration in healthcare. It also helps industry and researchers to come to a strategic decision at an early stage, about technology being developed thus, saving on resources, hence minimizing failures.