Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNN architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNN-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 patients without PCa. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95 Despite the fact that prostate cancer is the most common form of cancer, if detected in the early stages, the survival rates are high due to slow progression of the disease1. Therefore, effective monitoring and early detection are the key for improved patients’ survival.Prostate cancer is the most common form of cancer among males in the United States. In 2017, it was the third leading cause of death from cancer in men in the United States, with around 161,360 new cases which represented 192. Multiparametric MRI which relies heavily on diffusion-weighted imaging (DWI) has been increasingly becoming the standard of care for prostate cancer diagnosis in radiology settings where the area under the receiver operating characteristic curve (ROC) varies from 0.69 to 0.81 for radiologists detecting PCa3. A standardized approach to image interpretation called PI-RADS v24 has been developed for radiologists, however, there remain issues with inter-observer variability in the use of the PI-RADS scheme5.Currently, accepted clinical methods to diagnose clinically significant prostate cancer (PCa) are a combination of the prostate-specific antigen (PSA) test, digital rectal exam, trans-rectal ultrasound (TRUS), and magnetic resonance imaging (MRI). However, PSA screening leads to over-diagnosis, which leads to unnecessary expensive and painful needle biopsies and potential over-treatment8. In medical imaging field, computer-aided detection and diagnosis (CAD), which is a combination of imaging feature engineering and ML classification, has shown potential in assisting radiologists for accurate diagnosis, decreasing the diagnosis time and the cost of diagnosis. Traditional feature engineering methods are based on extracting quantitative imaging features such as texture, shape, volume, intensity, and various statistical features from imaging data followed by a ML classifier such as Support Vector Machines (SVM), Adaboost, and Decision Trees14.Machine learning (ML) is a branch of artificial intelligence (AI) that is based on the idea of the system learning a pattern from a large scale database by using probabilistic and statistical tools and making decisions or predictions on the new data17. These methods consist of convolution layers that are able to extract different features from low-level local features to high-level global features from input images. A fully connected layer at the end of the convolutional neural layers converts convoluted features into the probabilities of certain labels15. Different types of layers, such as batch normalization layer18, which normalizes the input of a layer with a zero mean and a unit variant, and dropout layer19, which is one of regularization techniques that ignores randomly selected nodes, have been shown to improve the performance of deep learning-based methods. Nevertheless, to achieve convincing performance, an optimal combinations and structures of the layers as well as precise fine-tuning of hyper-parameters are required20. This remains as one of the main challenges of deep learning-based methods when applied to different fields such as medical imaging.Deep learning methods have shown promising results in a variety of computer vision tasks such as segmentation, classification, and object-detection21, the medical imaging research community has shifted their interest toward deep learning-based methods for designing CAD tools for cancer detection. As a widely used approach, most of proposed algorithms require user-drawn regions of interest (ROI) to classify these user-annotated ROIs to PCa lesions and non PCa lesions. Tsehay et al.22 conducted a 3 20 inspired CNN with 196 patients. They fine-tuned their classifier by cross-validation method within the training set with 144 patients and achieved area under ROC curve (AUC) of 0.90 AUC on a separated test set of 52 patients. The result was based on 3 With CNNs’ promising results in computer vision fieldet al.23 conducted two dimensional (2D) ROI classification with combination of fused multimodal Residual Network (ResNet)17 and the traditional handcrafted feature extraction method. They augmented the training dataset and used the test set for fine-tuning and evaluating their classifier. They achieved ROI-level (lesion-level) AUC of 0.91. Liu et al.24 used VGGNet inspired 2D CNN classifier to classify each sample corresponding to a 32 25. They separated the dataset of 341 patients into 3 sets, the training set with 199 patients for training, validation set with 30 patients for fine-tuning, and test set with 107 patients for evaluation, and applied data augmentation to all 3 sets. They used 4 different types of input images which were generated with different combinations of DWI, apparent diffusion coefficient map (ADC), Le et al.26 also used VGGNet inspired 9 convolution layers deep three dimensional (3D) CNN classifier to classify 3D PCa lesions vs. non PCa lesions with 32 25. They separated the data set with 341 patients into training set with 201 patients and test set with 140 patients, and achieved lesion-level performance of 0.80 AUC on their test set. They applied cross-validation method within the augmented training set during training. As it will be discussed in Discussion section, the proposed method in this paper is superior compared to these ROI-based solutions in terms of robustness and applicability in clinical usage since it foregoes the need for manually or automatically generating ROIs.Mehrtash et al.27 performed the slice-level analysis with 316 patients by U-Net28 combined with ResNet. They created non-augmented training, validation, and test sets and achieved AUC of 0.79 on the test set, which included only 17 individual slices. The proposed algorithm in this paper performs slice-level detection as well using a much larger sample size with superior performance compared to that proposed by Ishioka et al.27.Slice-level detection algorithms classify each MRI slice into with or without PCa tumors. Ishioka 27. Wang et al.29 compared the performance of deep learning-based methods to non-deep learning-based methods on the classification of PCa MRI slices vs non PCa MRI slices with 172 patients. They evaluated their VGGNet inspired 7 layers (5 convolution layers and 2 inner product layers) CNN classifier’s performance based on cross-validation. First, they classified each slice of a given patient and then converted the slice-level results into patient-level results by a simple voting strategy and achieved the patient-level AUC of 0.84, positive prediction value (PPV) of 79%, and negative prediction value (NPV) of 77%. In this work, we achieved similar results with an independent test set and larger sample size.Patient-level algorithms classify patients into with and without PCa. It is generally a challenging task to merge ROI-based or slice-level results into patient-level results17 CNNs. We have also proposed a novel approach to convert slice-level classification results into patient level using first-order statistical features extractor, a decision tree-based feature selector, and a Random Forest classifier31. For the robustness of the performance, we divided the dataset into three separate sets, the training, validation, and test sets, and ensured that the test set was never seen by the classifier during training and fine-tuning6. We also included all slices that contain prostate and did not limit the pipeline to slices that have been selected for biopsy. Our proposed pipeline’s performance on the independent test set was superior and more robust compared to similar studies that proposed CAD tools for PCa detection using CNNs.In this paper, we propose an automated pipeline for two levels of PCa classification: slice level and patient level. For slice-level classification, we have proposed a stack of individually trained modified ResNetA cohort of 427 consecutive patients with a PI-RADS score of 3 or higher who underwent biopsy were included. Out of 427 patients, 175 patients had clinically significant prostate cancer and 252 patients did not. A total of 5,832 2D slices of each DWI sequence  which contained prostate gland were used as our dataset. We set the patient with Gleason score higher than or equal to 7  as the patient with a clinically significant prostate cancer and patient with Gleason score lower than or equal to 6 (GG = 1) or with no cancer (GG = 0) as the patient without a clinically significant prostate cancer.The DWI data was acquired between January 2014 to July 2017 using a Philips Achieva 3T whole body unit MR imaging scanner. The transverse plane of DWI sequences was obtained using a single-slot spin-echo echo-planar imaging sequence with four b values , the validation set with 48 patients (654 slices), and the test set with 108 patients  where the training/validation/test ratio was 64%, 11%, 25%. The separation procedure of the dataset was as follows. First, we separated the dataset into two sets, the training/validation set as 75% and the test set as 25% to maintain a reasonable sample size for the test set. Second, we separated the training/validation set into two sets with training set as 85% of training/validation set and the validation set as 15% of training/validation set  are extracted from the probability sets of CNNs outputs, and important features are selected through a decision tree-based feature selector. In the last stage, a Random Forest classifier is used to classify patients into groups with and without PCa using these first order statistical features. The Random Forest classifier was trained and fine-tuned by the features extracted from the validation set with 10 fold cross-validation method. Figure 17, we chose it as our base architecture for this research. Each Residual Block consists of convolutional layers21 and identity shortcut connection17 that skips those layers, and their outcomes are added at the end, as shown in Figure Since ResNet architecture has shown promising performance in multiple computer vision tasks34. In the original ResNet, batch normalization and ReLU activation layers were followed after the convolution layer, but in pre-activation ResNet, batch normalization and ReLU activation layers comes before the convolution layers. The advantage of this structure is that the gradient of a layer does not vanish even when the weights are arbitrarily small34. Instead of 2-layer deep ResNet block, we implemented a 3-layer deep "bottleneck" building block since it significantly reduces training time without sacrificing the performance17 , increasing the number of CNNs, which leads to an increased number of patient-level features (as discussed in the next section), increases the likelihood of overfitting and hence, decreases the model’s robustness38. All the slice-level probabilities generated by the five CNNs were fed into a first-order statistical features extractor to generate one set of features for each patient. In the proposed pipeline, the patient-level performance significantly improved (2-tailed P = 0.048) using five CNNs compared to a single CNN .Due to the randomness in training CNNs , each CNN may be different despite identical set of hyper-parameters and input datasets. This means each CNN may capture different features for the patient-level classification. Stacked generalization39. The final feature set was constructed by combining important features, Let 40, kurtosis40, and range from the minimum to maximum from each probability set. This produced 90 features for each patient (9 features for PCa and 9 features for non PCa class for each CNN). We selected 26 best features using the decision tree-based feature selector39. The decision tree based-feature selector was fine-tuned and trained with 10 fold cross-validation method using the validation set , maximum (only from PCa class), skewness31 was trained using the validation set and tested on the test set for patient-level classification.Once first-order statistical features were extracted for each patient, a Random Forest classifierThe CNNs were trained using one Nvidia Titan X GPU, 8 cores Intel i7 CPU and 32 GB memory. It took 6 hours to train all five CNNs with up to 100 iterations, less than 10 seconds to train the Random Forest classifier, and less than 1 minute to test all 108 patients.The Sunnybrook Health Sciences Centre Research Ethics Boards approved this retrospective single institution study and waived the requirement for informed consent.41 were used to evaluate the performance of the proposed pipeline. A ROC curve is a commonly used method to visualize the performance of a binary classifier by plotting true positive rates and false positive rates with different thresholds, and an AUC summarizes its performance with a single number. The great advantage of AUC is its validity in an unbalanced dataset. Since only a small number of DWI slices have PCa tumor , AUC is the best way to evaluate the performance of the pipeline. In addition, ROC curve allows us to pick desired specificity and/or sensitivity of the classifier through the threshold. This evaluation method is applied to slice-level and patient-level classifications using the test set with 108 patients .The AUC and ROC curveSince the pipeline contains five individually trained CNNs, there are five different test results at slice level. Table The patient-level AUC by our Random Forest classifier with the features extracted through CNNs was 0.84  or automatically generated segmentation of ROI as a part of the pipeline to generate ROI-based dataset. If it is a manually generated segmentation, the application for clinical use is limited because it ultimately relies on the clinician’s review and expertise and hence, it is not fully automated. If, on the other hand, it is an automatically generated segmentation, the result of classification depends on the performance of the segmentation algorithm, and inaccuracies from ROI segmentation algorithm can lead to poor PCa detection performance. Moreover, most of ROI-based methods use sliding windows of pixels as data structures to feed the CNNs, which makes it a challenging task to achieve an acceptable performance on classification of PCa at patient level due to the fact that each patient’s MRI data constitutes several thousands of windows of pixels. Therefore, ROI-based methodset al.24 where 32 et al. approach24 was reported for augmented test set and only for slices with biopsy, our pipeline AUC was superior (AUC of 0.87 vs. 0.84).In this work, instead of feeding ROIs into CNNs, we used automatically center-cropped DWI images with the only user intervention being to indicate the first and last slice that contained prostate gland. This is advantageous because it does not require generation of ROIs by either hand or segmentation algorithms. Thus, the proposed pipeline performance is independent of ROI generation method. In other words, our pipeline is able to perform PCa diagnosis on patients without the aid of expert readers. A similar approach was taken by Liu 29, but our slice-level performance (AUC: 0.87) and the sample size of the test set , were significantly superior to their performance and sample size. For example, Ishioka et al.27 proposed a slice-level algorithm using 316 patient data for training and validation. The test set was only 17 slices withe AUC of 0.79. Furthermore, we used the results generated by CNNs as features for classifying PCa at patient level, which was not the case with these previous works on slice-level algorithms.There are other studies in the literature that proposed slice-based analysis29. However, it is only relevant for optimizing or fine-tuning the model because there is a possibility that cross-validation leads to a model that overfits. Fine-tuning the classifier based on the performance of the test set  makes the test set not independent from the trained and optimized classifier, and hence, the performance achieved is optimistic and not realistic. The fine-tuning and optimization of the model must be done through a validation set, which is separate than both training and test sets as adopted in our work in this paper and those of27. Moreover, the test set should not be augmented  to keep the robustness of the results. Due to test data cross-contamination with training or validation sets via cross-validation or data augmentation, the performance of some of the proposed models in the literature is rather optimistic.Completely isolating test data from training and validation is crucial to measure true performance of a (deep) machine learning-based classifier. Cross-validation is a well-known method to evaluate the performance of the classifier29 or training deep learning classifier without the validation set23. For the studies that used independent test set27, our results are superior. For example, Liu et al.24 conducted 2D ROI slice-level analysis and achieved 0.84 AUC for ROC-based (centered around biopsy location) classification only compared to 0.87 AUC for our proposed pipeline for slice-level classification.In this work, we divided the entire dataset into three different sets, training, validation, and test set. In the slice-level analysis, the training set was used to train the model, and the validation set was used to fine-tune and optimize our CNNs architecture, and the test set was used to evaluate the performance of the CNNs. In the patient-level analysis, cross validation was used within the validation set to fine-tune and optimize our decision tree-based feature selector and Random Forest classifier, and tested on the test set. As a result, our classifier’s results were more robust compared to studies that used cross-validation as a measure of performance27. This is due to the fact that the 3D MRI volume of each patient may have hundreds or thousands of ROIs. Wang et al.29 converted their slice-level result into patient-level by averaging all of the slice-level probabilities for patient and thresholding the average probabilities to classify PCa at patient level. Although this method achieved patient-level performance similar to our proposed pipeline’s results (AUC: 0.84), it is based on cross validation, which makes it an optimistic result. In contrast, the results presented in this paper is based on a test set which is completely separate than the training and validation sets. Moreover, our test data contained 108 patients, which is significantly larger than the dataset with 17 patients for each fold29.Turning ROI-level results or the slice-level results of MRI data into patient-level result has been a major challenge in PCa classification via deep learningThe main limitation of this work is the fact that similar to CAD papers, the data is inherently biased; those patients are sent to MRI who have an indication of prostate cancer . Thus, the dataset is not a true reflection of the population. In addition, the labels for the data are based on biopsy locations, which are determined by radiologists. In other words, slices with no biopsy are assumed to be negative, based on radiology reports. However, the positive slices are based on pathology (biopsy) reports. Finally, an external validation with a dataset from a different institute is required to verify the performance and robustness of the proposed pipeline across scanners and institutions.45  will be used.In this work, we built a two step automated deep learning pipeline for slice-level and patient-level PCa diagnosis using DWI images. Instead of manual ROI annotation, automated center-cropping was used to maintain independence from expert readers’ intervention. A stack of five CNNs were used to produce improved classification results at slice level. First-order statistical features were extracted from slice-level probabilities to compile slice-level classification results into patient level. The pipeline was tested on an independent test set of 108 patients and the results at both slice level and patient level was superior to the state-of-the-art. As future work, other CNN architectures such as 3D CNNs (to feed the 3D DWI) and recurrent neural networks


The use of self-monitoring of blood pressure, with or without telemonitoring, to guide therapy decisions by physicians for patients with hypertension has been recently demonstrated to reduce blood pressure compared with using clinic monitoring . However, both the cost-effectiveness of these strategies compared with usual care, and whether the additional benefit of telemonitoring compared with self-monitoring alone could be considered value for money, are unknown. This study assessed the cost-effectiveness of physician titration of antihypertensive medication using self-monitored blood pressure, with or without telemonitoring, to make hypertension treatment decisions in primary care compared with usual care. A Markov patient-level simulation model was developed taking a UK Health Service/Personal Social Services perspective. The model adopted a lifetime time horizon with 6-month time cycles. At a willingness to pay of £20 000 per quality-adjusted life year, self-monitoring plus telemonitoring was the most cost-effective strategy  compared with usual care or self-monitoring alone (posting the results to the physician). However, deterministic sensitivity analysis showed that self-monitoring alone became the most cost-effective option when changing key assumptions around long-term effectiveness and time horizon. Overall, probabilistic sensitivity analysis suggested that self-monitoring regardless of transmission modality was likely to be cost-effective compared with usual care , with high uncertainty as to whether telemonitoring or self-monitoring alone was the most cost-effective option. Self-monitoring in clinical practice is cost-effective and likely to lead to reduced cardiovascular mortality and morbidity. Eligible patients were hypertensive, aged >35 years, with a clinic BP >140/90 mm Hg and willing to self-monitor their BP. Across 138 GP practices, 1182 patients were randomized (1:1:1) to antihypertensive titration using self-monitoring, telemonitoring, or usual care (clinic BP).Full details of the TASMINH4 trial have been published elsewhere.9 It was not possible to capture primary care workload not recorded in the clinical records.The self-monitoring and telemonitoring interventions are described in detail in the section on model comparators. The primary outcome for the trial was clinic systolic BP measured by a research nurse at 12 months. Cost and resource utilization data were collected during the trial through notes review.A Markov patient-level simulation was undertaken in TreeAge 2018  to model the different strategies. This type of Markov model tracks the costs and consequences of individual patients passing through the model, with characteristics (taken from TASMINH4 patient-level data) free to vary between patients. The model was run over a lifelong  time horizon to capture all relevant long-term costs and consequences.Each patient had characteristics created by randomly sampling the trial patient-level data by means of a uniform distribution. These characteristics affected their probability of subsequent model events . The model was run with a large number of simulated patients (50 000) to account for interpatient variability and to adequately model a representative clinical population.After randomization, trial participants booked a primary care appointment for a BP check and a medication review. GPs were free to make management decisions and drug choice in all 3 randomized groups. For usual care, GP antihypertensive treatment decisions were based on clinic BP readings. All 3 strategies included the cost of baseline medication review, subsequent primary care consultations and antihypertensive prescriptions Table .8 For the self-monitoring and telemonitoring groups, the GP was asked to use the self-monitored BP readings to guide any antihypertensive treatment decisions.Patients in the self-monitoring alone group were trained to self-monitor their own BP by a nurse and asked to post the readings to their practice every month. Additional costs included 15 minutes self-monitoring training, the BP monitor and 2-part copy forms with reply paid envelopes , including initial acute care costs and long-term care, were obtained from previously published work and standard reference costs Table . Costs a12 Patients who suffered a nonfatal cardiovascular event transitioned to a postevent cardiovascular health state and additional clinical events were not modeled. Once a cardiovascular event had occurred, mortality risk was adjusted accordingly. The impact of each intervention in terms of event reduction was applied as a relative risk, taking into account the mean differences in systolic BP observed in the TASMINH4 trial. Compared with usual care, the mean differences  were −2.1 and −3.5 mm Hg for self-monitoring alone and −3.7 and −4.7 mm Hg for telemonitoring.2 Individual relative risks were applied, taking into account age, sex, and baseline clinic systolic BP. In the base-case analysis, it was assumed that the 12-month differences were maintained over the patient lifetime. Model inputs are detailed in Table The patient pathway is shown in Figure 19Half-cycle correction was applied to model costs and outcomes. Future costs and outcomes were discounted at an annual rate of 3.5% as recommended by the National Institute of Health and Care Excellence.27 score at baseline taken from the TASMINH4 trial,8 calculated using UK tariff values.28 Utility values for cardiovascular events were applied multiplicatively to baseline utility scores. Sex-specific life tables were used to determine the probability of death at different ages, with adjustment to avoid double counting of circulatory deaths.14Health-related quality of life outcomes were modeled in quality-adjusted life years (QALYs), taking into account both quality of life and survival. Utility scores for health states are detailed in Table 29 In line with convention, strategies were ordered from least to greatest cost, with each strategy compared against the next more costly strategy, and strategies which were dominated or extendedly dominated subsequently excluded.30 Probabilistic Sensitivity Analysis (PSA) was undertaken to assess parameter uncertainty.31 Where possible, distributions were attached to probabilities, utilities, and costs in the model. Beta distributions were attached to probabilities and utilities, and gamma distributions were attached to costs. Log-normal distributions were used for the relative risks associated with BP reduction from the interventions and relative risks for mortality. The model was run for 10 000 iterations across 1000 patients, and the results are expressed as a Cost-Effectiveness Acceptability Curve.32 The Cost-Effectiveness Acceptability Curve shows graphically the probability of cost-effectiveness for all strategies across a range of cost per QALY thresholds.A cost-utility analysis from an NHS/Personal Social Services perspective was undertaken to estimate Incremental Cost-Effectiveness Ratios (ICERs). An ICER was calculated as the difference in costs divided by the difference in QALYs of 2 strategies, with results presented as cost per QALY gained. The cost-effectiveness of an intervention was considered in relation to the lower National Institute of Health and Care Excellence threshold of £20 000 per QALY gained.31 The following scenarios were explored:The impact of changing model assumptions was undertaken using deterministic sensitivity analysis to assess model robustness.The time horizon was varied from a lifetime time horizon to between 5 and 20 years following the end of the trial.The duration of the impact of BP reduction because of self-monitoring alone and self-monitoring plus telemonitoring was varied from the base-case assumption of lifetime impact.Post hoc, a further 2 sensitivity analyses were undertaken given uncertainty shown by the PSA as to which option was most cost-effective. These analyses were designed to investigate the effect of bringing the cost of the 2 interventions closer to assess the influence of this on ICER given that:Additional administrative work not captured in the clinical record was likely to have been an issue for self-monitoring alone from our linked qualitative work . This is because someone had to deal with the paper self-monitoring records, add the data onto the clinical record system, and work out an average BP. As the exact time taken for this task was unknown, alternative scenarios for self-monitoring were assessed by including the assumed cost of a GP receptionist’s time for data entry in the self-monitoring group using 2 different time durations (5 or 10 minutes per month per patient receptionist time).A national rollout of telemonitoring was considered likely to lead to reductions in the cost of telemonitoring from economies of scale compared with the relatively small scale used in the trial. Alternative cost scenarios for telemonitoring were, therefore, evaluated by reducing the annual cost of telemonitoring by £10 and £20 per year respectively.Making the telemonitoring more expensive or the self-monitoring less expensive were not considered likely enough scenarios to be modeled. Both would have tended to increase the difference between the 2 interventions in cost and, therefore, favored self-monitoring alone.Base-case findings are reported in Table The results of deterministic sensitivity analysis are shown in Table If an additional 5 minutes of administration time per patient per month for managing paper results were added to self-monitoring, when compared with the base-case results, this resulted in a higher ICER for self-monitoring versus usual care (£5484/QALY) and a lower ICER for telemonitoring (£11 670/QALY) versus self-monitoring. When this additional time was increased to 10 minutes per month, telemonitoring demonstrated extended dominance over self-monitoring alone and had an ICER of £7331/QALY compared with usual care. If the cost of telemonitoring was reduced annually by £10 per patient, self-monitoring remained cost-effective at an ICER of £3305/QALY and the ICER for telemonitoring versus self-monitoring reduced to £10 554/QALY compared with the base-case result. A decrease in the annual cost of telemonitoring by £20 per patient resulted in a much lower ICER of £3685/QALY when compared with self-monitoring.This model-based economic evaluation has demonstrated that antihypertensive titration by physicians in primary care using patient self-monitoring of BP data is cost-effective compared with usual care, provided that the BP improvements associated with self-monitoring observed here last at least 3 years after the end of the trial (which lasted 1 year). With longtime horizons and the assumption of ongoing efficacy, telemonitoring was cost-effective over and above self-monitoring alone at the lower National Institute of Health and Care Excellence threshold of £20 000 per QALY. However, this was not the case with shorter durations of action or shorter time horizons, although the results were finally balanced and sensitive to small increases in costs for self-monitoring (5–10 minutes extra work per month per person) or small reductions in costs for telemonitoring (£10–20 per patient per year), both of which seem plausible. PSA reflected these issues and indicated high levels of uncertainty as to whether self-monitoring alone or in addition to telemonitoring was the most cost-effective.33 Furthermore, patients in the study were drawn from a wide range of primary care practices in varied settings across England, and therefore, the results are likely to be generalizable, at least in a UK NHS context. Patients in the trial were recruited on the basis of standardized clinic BP measurements which might have reduced the white coat effect; however, this would tend to suggest that self-monitoring might be even more cost-effective in a population chosen on the basis of normal clinic measurement where the prevalence of white coat hypertension might be higher. Furthermore, although alternative model parameter estimates would be required to produce precise country-specific cost-effectiveness estimates, systematic reviews suggest similar effect sizes in self-monitoring trials with cointerventions in the UK and internationally.34 Therefore, given that self-monitoring reduced health service costs, and these are generally lower in the NHS than in other comparable developed countries, these results retain wide relevance.The model was based on the results of TASMINH4, the only primary care trial of physician antihypertensive titration utilizing self-monitoring and/or telemonitoring BP measurements, with currently recommended targets for self-monitored BP.35 in assessing the costs and consequences of the interventions. This is preferable to the more common Markov cohort model, which is limited by the use of a population with a homogenous set of characteristics, because of the model’s inherent lack of memory.35 The patient-level simulation was able to draw on individual patients’ trial data, with characteristics allowed to vary (using tracker variables to overcome the lack of Markov memory issues), and therefore, made the findings representative of the TASMINH4 trial population. The modeling method was also more efficient by avoiding the construction of a large number of Markov health states.The economic analysis used a patient-level Markov simulation8 and similarly in other self-monitoring trials.37 Undertaking the analysis from an NHS/Personal Social Services perspective, in line with the National Institute of Health and Care Excellence reference case, may mean that broader health service and societal benefits of telemonitoring have been neglected.19 In the future, telemonitoring has the potential to be integrated into electronic health care records, allowing seamless management by primary care nurses and GPs, and also facilitating remote access to a doctor. Furthermore, self-monitoring may be more beneficial to individuals in both working age populations and those requiring a carer, reducing, for example, time off work to attend appointments either for oneself or a relative. Conversely, in the current analysis, any additional time required by GPs to process the results from self-monitoring or telemonitoring was not captured. The sensitivity analyses showed that inclusion of unrecorded costs of processing manual self-monitoring records, or economies of scale resulting in cheaper telemonitoring, would make telemonitoring more cost-effective. Results of the PSA illustrated the considerable uncertainty in the results with similar probabilities of cost-effectiveness for both interventions, in part, driven by the uncertainty in the estimates of BP lowering. Finally, the risk of further events once someone had an initial cardiovascular event was not modeled, and baseline cardiovascular disease (affecting around 5% of participants) was not included in the model, hence potential additional benefits of secondary preventative treatment, which might be expected to favor self-monitoring, were ignored.The base-case model assumed extrapolation of the effectiveness of the interventions beyond the 12 months of the trial to a lifetime effect. However, sensitivity analyses demonstrated that as long as the intervention effects persisted for an additional 3 years, self-monitoring would still be cost-effective. This seems a reasonable assumption, as the BP differences between the 2 intervention groups and the usual care group widened between 6 months and 1 year in the TASMINH4 trial38 However, it was a within-trial analysis reporting results in natural units (cost per 1 mm Hg systolic BP point reduced) and did not undertake any longer-term modeling of costs and consequences (including QALYs) arising from the intervention. Direct comparison of within-trial results at 6 months shows that compared with their published ICER of £25.60/mm Hg, both self-monitoring and telemonitoring in TASMINH4 were much more cost-effective compared with usual care .A previous United Kingdom-based economic evaluation undertook a cost-effectiveness analysis of a trial of 6 months of telemonitoring versus usual care.37 conducted a follow-up analysis of their 18-month long United States-based trial which included telemedicine and home BP monitoring to assess longer-term clinical and economic outcomes. Results showed that the BP improvements in the trial were maintained for at least a further 18 months after trial completion , but that health care costs did not decrease. Similar results have been recently published from a self-monitoring/community pharmacist intervention.36 Therefore, the extrapolation of the TASMINH4 results over a numbers of years seems to be reasonable.Maciejewski et al5 in a similar patient population found that self-management with telemonitoring and self-titration was cost-effective compared with usual care (ICER £1624 per QALY for men and £4923 for women), which is in accordance with the current findings that telemonitoring was cost-effective when compared to usual care.A previous trial by our group (TASMINH2)8 The case for self-monitoring of hypertension (with or without telemonitoring) has been further supported by this cost-effectiveness analysis but, ultimately, the decision whether or not to implement telemonitoring in primary care for the management of hypertension may need to include factors such as practice logistics and patient preferences not included in this modeling exercise, as well as a careful focus on procurement prices.The TASMINH4 trial demonstrated that self-monitoring, with or without telemonitoring, led to significantly lower BP compared with usual care.Antihypertensive titration by physicians in primary care using self-monitoring of BP, with or without telemonitoring, is cost-effective compared with usual care, provided self-monitoring has ongoing effects on BP reduction for at least 3 additional years. The decision as to whether or not to use telemonitoring is finely balanced. However, both digital and manual approaches to implementation of self-monitoring are cost-effective relative to usual care, so availability of both may be appropriate where patient choice determines which is used.We acknowledge the support of the Primary Care Clinical Trials Unit Oxford. Karen Biddle, Siobhan Milner, and Carla Betts provided additional administrative support. David Yeomans and Derek Shaw served as patient representatives for the trial management and steering groups. Dr Oliver Gibson developed a previous system for telemonitoring that was helpful in the development of the current system, and Dr Mauro Santos and Carmelo Valderes provided technical support in the latter part of the trial. Additional members of the trial steering group were Mike Moore (chair), Pip Logan, and Dr Chris Clark. Members of the data monitoring committee were Dr Martyn Lewis (chair), Dr Emma Bray, and Sarah Purdy.This work was funded by the National Institute for Health Research (NIHR) via a Programme Grant (RP-PG-1209–10051). R.J. McManus held an NIHR Professorship and leads the self-management theme of the NIHR Oxford Collaboration for Leadership in Applied Research in Health and Care (CLAHRC). F.D.R. Hobbs acknowledges part support from the NIHR School for Primary Care Research (SPCR), the NIHR Oxford CLARHC, and the NIHR Biomedical Research Centre (BRC), Oxford. The views and opinions expressed are those of the authors and do not necessarily reflect those of the NHS, NIHR, or the Department of Health and Social Care.R.J. McManus received blood pressure monitors from Omron which were used in the TASMINH4 trial. The other authors report no conflicts.


The progression of lung cancer is associated with inactivation of programmed cell death protein 1, abbreviated as PD- 1 which regulatesthe suppression of the body's immune system by suppressing T- cell inflammatory activity and is responsible for preventing cancer cellgrowth. It is of interest to identify inhibitors for PD-L1 dimeric structure through molecular docking and virtual screening. The virtualscreened compound XGIQBUNWFCCMAS-UHFFFAOYSA-N (PubChem CID: 127263272) displays a high affinity with the target protein.ADMET analysis and cytotoxicity studies further add weight to this compound as a potential inhibitor of PD-L1. The establishedcompound BMS-202 still shows the high re-rank score, but the virtual screened drug possesses a better ADMET profile with a higherintestinal absorption value and lower toxicity. There are 8.8 million recorded deaths of malignant cancer everyyear according to WHO data and this number keep on increasingwhich is a clear indication of the threat this disease poses. Studiesin the past decade have confirmed that the immune systemdisplays a variety of mechanisms to combat the growth of cancercells in the body. Hence, in order for the cancer cells to grow anddevelop, the cells have to find ways to repress these immunologicalmechanisms. One such mechanism used is altering the expressionof co-inhibitory and co-stimulatory articulated molecules .Cancer Until now, monoclonal antibodies such as Nivolumab andPembrolizumab (which bind to PD-1) and Avelumab andDurvalumab (which bind to PD- L1) have gained the U.S. Food andDrug Administration acceptance other than several others whichare in various phases of trials. The antibodies approved are directedagainst metastatic non-small-cell lung cancer (NSCLC), melanoma,and renal cell carcinoma. The development of small molecules thatare targeted towards the inhibition of this interaction is lagging ascompared to the development of monoclonal antibodies. Smallmolecules for the same resolution could offer many advances thatmight be complementary and potentially better than largebiological molecules . For exaA series of small molecules targeting the PD-1/PD-L1 interactionhave been reported -6. BristLiterature studies were carried out to search for establishedinhibitors of PD-L1 ligand, which were capable of binding andhence inhibiting the activity of the protein. The total number ofestablished inhibitors was found to be 311, which were chosen forfurther analysis. Out of the 311 small molecule inhibitors, only onewas found to have a PubChem ID  while thThe protein 3D structure or the crystal structure of the targetprotein i.e. the PD-L1 dimer was taken from the Protein Data Bank(PDB) with the PBD ID: 5J8O . This stThe molecular docking investigations were carried out by usingMolegro Virtual Docker (MVD) which unified with high potentialPiece-Wise Linear Potential (PLP) and MolDock scoring function-35. The The compound with PubChem ID of 117951478 which is BMS- 202was used to carry out a similarity search to attain a bettercompound having a greater binding affinity to the dimer structureother than any previously established drugs -57. The The 'unnamed complex' structure file generated in the establisheddrug docking result folder and was opened in Molegro VirtualDocker. All the constraints, cavities and ligands in the structurewere removed to obtain only the protein structure , 40. ThThe admetSAR database available at http://lmmd.ecust.edu.cn:8000/ offers an open and free interface to search for the biologicaland chemical profile of a compound. The properties mentioned inthe ADMET profile such as digestion, absorption, metabolism,toxicity, excretion and so on, provide us with essential informationrelated to the development and discovery of drugs. The admetSARdatabase mostly consists of 22 qualitative classifications and 5quantitative regression models, which aid in providing theoutcome with high precision based on the prediction. Hence theestimation of the properties of the compounds was predicted usingadmetSAR. The properties of the established compound and virtualscreened compound such as the bioactivity properties and toxicitywere predicted by using admetSAR , 40.The 3D chemical structures were retrieved from NCBI's PubChemdatabase in 3DSDF format. Some compounds that lack PubChemID or the 3D structure was unavailable in PubChem were drawnwith the help of MarvinSketch5.6.0.2, . Schrodinger suite was used for the optimization ofligands . The flexibledocking was achievedby taking receptor protein structure and allligand compounds in Molegro Virtual Docker 2010.4.0.0. MolecularVisualization was done with the assistance of Accelrys DiscoveryStudio® Visualizer 3.5.0.12158 .ADMET profiles were obtained and tabulated using admetSAR. Cytotoxicity study was conducted usingCLC-Pred (Way2Drug © 2011 – 2018).In silico studies require methods of phenotypic screening todecrease the time as well as the cost of the experiments that wouldbe conducted in vivo for the screening of anticancer agents throughmillions of natural and synthetic chemical inhibitors. Previouslyestablished PASS (Prediction of Activity Spectra for Substances)algorithm was used to produce and confirm the classification SARmodels for calculating and predicting the cytotoxicity of inhibitorsagainst varying kinds of human cell lines using ChEMBLexperimental data. By utilizing the provided SAR models, a freelyavailable web-service was developed for cell-line cytotoxicityprofile prediction (CLC-Pred: Cell-Line Cytotoxicity Predictor)based on their structural formula. This webservice residesathttp://way2drug.com/Cell-line/. After the input is provided inthe web service, probabilities are given in the form of 'Pa'(probability "to be active") which gives the estimate chance of theinput compound fitting into the sub-class of active compounds, andPi (probability "to be inactive") which gives the estimate chance ofthe input compound belonging to the sub-class of inactivecompounds.The docking results of the pre-established 311 drugs establishedBMS-202 as the compound showing the best interaction .This coPharmacophore mapping helps to provide necessary spatialsystematic topographies of molecular interaction with a particulartarget receptor other than the technique of molecular docking.Pharmacophore studies deliver an accurate query on the optimuminteraction of the drug with its target protein, assisted byannotations and denote the aligned poses of the molecule and aidus to find the high interaction mode between target protein andcompound. As the interaction of the receptor PD-L1 and the drugwith virtual screened drug PubChem CID-127263272, is found to bequite effective, pharmacophore studies are carried out to studyvarious interactions presented in the complex formed. The variousinteractions that were mapped consisted of hydrogen bondinteractions, van der walls (vdW) interaction, ligand interactions.The illustration in A relative ADMET profile comparison was carried out for selectedinhibitors. Predictions were based on parameters such as the Blood-Brain Barrier (BBB), Human Intestinal Absorption (HIA), AMESToxicity, and LD50 rat toxicity. The established inhibitor PD1-PDL1inhibitor, BMS- 202, the virtual screened drugXGIQBUNWFCCMAS-UHFFFAOYSA-N, CID: 127263272 andthree other top compounds BMS- 198, BMS- 200 and PubChem CID126843234 were taken up for comparison according to ADMETstudies. These five inhibitors were graphically represented using Rprogrammingas represented in The results of cytotoxicity studies for the best virtual screenedcompound that is compound with PubChem CID 127263272 aresummarized in The BMS-202 drug, introduced by the Bristol-Myers Squibb (BMS)still binds to the PD-L1 dimeric structure with the highest affinity.We show that the compound XGIQBUNWFCCMASUHFFFAOYSA-N, CID: 127263272 which, even though displays aslightly lower binding affinity, displays some properties thatproject it to be at better when compared to BMS- 202. The virtualscreened drug displays a better ADMET profile when compared toBMS-202. The pharmacophore interactions of the drug portray thatthe drug binds to the dimeric PD-L1 structure with three hydrogenbonds. The presence of numerous van del walls (vdW) interactionsfurther adds to the binding affinity of the drug to the proteinstructure. Cytotoxicity studies confirm that the molecular has apotential of acting as an effective inhibitor pending further in vitroanalysis.The authors declare no conflict of interest,financial or otherwise.


Stil) in DA cell proliferation, neural protection, and regeneration, created a new field for studying DA cells and possible treatment of PD. In DA neurons, Stil functions through the Sonic hedgehog (Shh) pathway by releasing the inhibition of SUFU to GLI1, and thereby enhances Shh-target gene transcription required for neural proliferation, protection, and regeneration. In this review article, we will highlight some of the new findings from researches relate to Stil in DA cells using zebrafish models and cultured mammalian PC12 cells. The findings may provide the proof-of-concept for the development of Stil as a tool for diagnosis and/or treatment of human diseases, particularly those caused by DA neural degeneration.Degeneration of dopaminergic (DA) neurons in the brain is the major cause for Parkinson’s disease (PD). While genetic loci and cellular pathways involved in DA neuron proliferation have been well documented, the genetic and molecular and cellular basis of DA cell survival remains to be elucidated. Recently, studies aimed to uncover the mechanisms of DA neural protection and regeneration have been reported. One of the most recent discoveries, i.e., multi-function of human oncogene SCL/TAL interrupting locus ( Stil is a human oncogene which may also function as ON-OFF switches that regulate the death and survival of neuronsStil is required for neural proliferation and regenerationStil-mediated Shh signaling transduction protects dopaminergic neurons in response to toxic drug treatmentStil mRNA in neurons?How to control the expression of Stil play a role in regulation of neural function?Does Stil be developed as a molecular marker for diagnosis of neural degeneration?Can Stil) is a human oncogene that was originally identified from human T-cell lymphoblastic leukemia, where it functions as a hematopoietic transcription factor1. The sequence of Stil gene is highly conserved in vertebrate species2. In humans, the Stil transcript is comprised of 18 exons which yield a peptide of 1287 amino acids in 143-KD3. While STIL does not share any known structural motifs with other proteins, analyses of the human STIL sequence revealed some similarities between STIL and the C-terminus of cytokine TGF-β4 and the C-terminus of centriole duplication factor Ana25. STIL is expressed in all cell types examined so far10. In prometaphase-synchronized cells and mouse tumor cell lines, STIL functions as a cell cycle checkpoint protein that regulates the transition of mitotic entry during cell proliferation11. In developing human cancer cells, STIL is localized to the pericentriolar region of the centrosomes, where it regulates in spindle pole positioning as well as centriole formation and duplication14.The SCL/TAL interrupting locus 15. In mutants which lack proper Stil expression and/or with defective structure of Shh receptors, the embryos develop abnormally and die at age of E11. Using molecular genetic approaches, Izraeli et al.15 and Kasai et al.16 demonstrated that Stil functions in the Sonic hedgehog (Shh) pathway. Specifically, Stil regulates the transcription of Shh-target gene Gli1. Normally, GLI1 forms heterodimers after binding to cytoplasmic protein SUFU. The heterodimers cannot be translocated to the nucleus thereby the transcription of Gli1 gene is inhibited. When STIL is expressed, STIL binds SUFU, frees GLI1 from SUFU repression. Then, GLI1 can enter the nucleus, and gene transcription can start. If Stil is mutated, the transcription of Gli1 cannot be initiated  neurons18. In humans, DA-mediated neural transduction is required for integration of sensory-motor signals and the control of movement22. The lack of DA cells and/or malfunction of DA neural networks may lead to a variety of neurological diseases, such as PD26. In this article, we will review some of the findings relate to the roles of Stil in DA cells. The data presented in this article are obtained from studies conducted in zebrafish models and cultured mammalian DA cell lines. The results may provide insight for future development of Stil as a potential bio-marker for better understanding the molecular and cellular mechanisms underlying human neurological disorders.Recent studies have shown that Stil were carried out using cultured cancer cell lines and mouse models29, research that characterize Stil mutations that cause dramatic phenotypes  can also be found by using other species, such as zebrafish. Pfaff et al.30 reported that in zebrafish, STIL is highly expressed in proliferating cells. During metaphase, STIL is concentrated in areas near the poles of the mitotic spindles. In cassiopeia  mutants, the Stil gene is mutated, and the organization of spindle pole in proliferating cells is completely disrupted. Often, the mutants lack one or both centrosomes. As a consequence, the embryos develop abnormally and die between 7 and 10 days post-fertilization. In another study, Li and Dowling31 demonstrated that in zebrafish night blindness b  mutants, not only the development but also the function of the CNS are interrupted. In homozygous nbb mutants, massive cell degeneration occurs in the developing brain, and the embryos die before 7 days post-fertilization. In heterozygous nbb mutants, animals are viable but after 9 months of age, abnormalities are found in the CNS, e.g., the number of retinal DA cells is decreases, and the animals become night blind particularly after prolonged dark adaptation. In addition, in nbb mutants the susceptibility of DA cells to neurotoxin is increased17, and the rate of DA cell regeneration is decreased32. Molecular cloning and sequence analysis of the nbb locus revealed a nucleotide substitution at a donor-splicing site in the Stil transcript, which results in a predicted truncated peptide with 301 amino acids17.While most the studies related to Stil is required for maintaining the DA cells , Li et al.17 examined the survival of DA neurons in zebrafish retinas in response to treatment with DA-specific neurotoxins, such as 6-hydroxydopamine (6-OHDA)34. They found that deficiency in Stil expression led to increases in toxic susceptibility in DA neurons. In wild-type zebrafish, for example, treatment with sub-toxic levels of 6-OHDA (0.33 µg/μl) produced no obvious effects on the survival of DA cells , or increase of Shh signaling transduction by knocking-down SUFU expression with Sufu-specific MOs. In zebrafish, treatment with gene-specific MOs can efficiently knock-down their protein expressions36. In wild-type zebrafish, decreases in STIL expression (by Stil-MO) led to increases of the susceptibility of DA cell to sub-toxic levels of 6-OHDA. As a consequence, DA cell degeneration was observed when treated with sub-toxic levels of 6-OHDA  lowered the susceptibility of DA cells to neural toxins and protected the DA cells after drug treatment  examined the proliferation of DA cells in adult zebrafish retinas after 6-OHDA induced neural degeneration. In this study, toxic levels of 6-OHDA (5.0 µg/μl) was applied in order to effectively induce the degeneration of DA cells. After a few days of 6-OHDA treatment, the samples were collected and labeled with antibodies against proliferating cell nuclear antigen (PCNA), a marker protein that only expresses in proliferating cells43. In wild-type animals, after drug-induced cell death of DA cells, robust regeneration of DA cells occurred, e.g., PCNA-positive cells could be readily identified , PC12 precursor cells can be proliferated and differentiated into DA cells50. The overexpression of Stil (by transfection with full-length human Stil sequence) increased the rate of cell proliferation of PC12 cells, by approximately 30% compared to control cells. In contrast, knock-down of Stil function (by Stil-specific shRNA) reduced the rate of PC12 cell proliferation, to a level approximately 45% of controls18.The roles of STIL expression in mammalian DA cell proliferation were evaluated using cultured PC12 cellsShh pathway as a de-repressor for proper signaling transduction in PC12 cells, Sun et al.51 examined the co-expression of STIL and Shh down-stream proteins, such as SUFU and GLI1, in PC12 cells. They found that both SUFU and GLI1 could be detected by anti-STIL antibody pull-down experiments. STIL and SUFU could also be detected by using anti-GLI1 antibody pull-down assays. These results provide evidence for the conserved roles of STIL in the Shh pathway in mammalian DA cells. The roles of Stil in Shh pathway in PC12 cells can also be demonstrated using pharmacological approaches. Carr et al.18 proposed that down-regulation of Shh signaling transduction by cyclopamine would mimic the proliferation defects caused by Stil-shRNA knock-down. They found that treatment with cyclopamine indeed reduced the proliferation rate of PC12 cells. Interestingly, overexpression of Stil was able to maintain a significant higher growth rate in the presence of cyclopamine. This suggests that when the initial Shh signaling is blocked, some of the down-stream signaling transduction could be continued by exogenous expression of STIL. That is, STIL binds SUFU and releases GLI1, then GLI1 enters the nucleus and starts gene transcription.To investigate whether or not STIL also functions in the Shh signaling has a role on PC12 cell proliferation depends on the availability of STIL. This can be tested using pharmacologic approaches with purmorphamine while the expression of Stil is down- or up-regulated. It is known that purmorphamine functions as a SMO receptor agonist in the Shh pathway54. The application of purmorphamine increased the rate of PC12 cell proliferation18. However, when the expression of Stil mRNA was blocked by Stil-shRNA, purmorphamine could not fully rescue the defects in PC12 cell proliferation18. That suggests that activation the up-stream Shh signaling transduction is not able to bring the proliferation rate to control levels if the expression of down-stream genes  is inhibited. Based on these observations, Carr et al.18 concluded that STIL may induce cell proliferation by propagating the intracellular Shh signal for subsequent transcription of Shh-target genes, which are cell cycle specific.By the same token, whether or not the up-regulation of 18 examined the distribution of STIL in PC12 cells. They found that STIL was expressed in both developing and NGF-induced mature cells. In differentiated mature cells, STIL was ubiquitous expressed in the cytoplasm, nucleus, as well as in the neurites. The result is not unexpected, considering the primary roles of Stil in mitotic entry, spindle pole organization, centriole formation and duplication. The expression of STIL in mature cells indicates novel non-proliferative functions of STIL in post-mitotic and differentiate cells, such as neural survival, neurite extension, synthesis or release of neurotransmitters. This supports the notion that Stil is involved in the regulation of microtubule elongation55.Carr et al.Stil is not involved in PC12 cell neural differentiation. After NGF induction, out-growth of neurites was observed in PC12 cells regardless the levels of Stil expression. Also, the rate neurite growth was indistinguishable among the cells in which the expression of Stil was knocked-down (by transfection with Stil-shRNA) or the expression of Stil mRNA was up-regulated (by transfection with plasmids that contained the full-length of human Stil DNA)18.While STIL is required for DA cell prolifaration and growth, the expression of Stil expression on neural protection have been evaluated in cultured PC12 cells56. It appears that, to some extent, STIL may function as a “two-edged blade” that involves in both neural proliferation and degeneration, depends on the status of the cell. In proliferating PC12 cells, knock-down of STIL expression (by shRNA) produced no obvious effect on cells’ drug toxicity, e.g., cell death occured when treated with high doses of 6-OHDA, similar as observed in control cells  increased the susceptibility of PC12 cells to neurotoxins, and when exposed to higher doses of 6-OHDA , excessive cell death was observed  can be predicted . Future researches on Stil shall include but not limit to the characterization of the regulation of Stil expression in cancer cells and neurons, the interplay between Stil products and hormones that regulate cancer growth and neural degeneration, and the mechanisms of Stil-mediated Shh signaling transduction in drug resistance. At some point, one shall be able to control the molecular ON-OFF switch for Stil expression, and this will lead to either cell proliferation  or apoptosis . It is expected that in the future Stil may be developed as a tool for diagnosis and/or treatment of human diseases.In summary, this paper provides evidence that through the


The gingival overgrowth is a common finding in the clinical practice with a diverse etiology. There are no treatment guidelines defined for this oral lesions. These can provoke discomfort to the patient and often, can alter the function of the stomatologic system. This article presents a case report of a bilateral gingival overgrowth in a 68 years old woman wearing a fixed upper-arch implant-supported prosthesis placed five years ago. The clinical exam after removing the prosthesis showed an intense accumulation of plaque around the intermediate abutments associated to a mucosal enlargement with suppuration on touching the buccal area of the implant in position 1.5 and a probing depth of 8mm. The 2.4 and 2.5 implants also showed vestibular mucosal enlargement and a probing depth of 6mm. No changes were observed in the peri-implant bone level measured in the periapical radiographs. An incisional biopsy was made on second quadrant and sent for the histopathological study. The definitive diagnosis was inflammatory fibro-epithelial hyperplasia. No recurrence has been reported after a 6 month follow-up. Key words:Fibro-epithelial hyperplasia, gingival enlargement, gingival overgrowth, full-arch implant-supported prosthesis. Gingival reactive lesions are one of the main pathologies that affects the gingival tissue . Within Nowadays, in the clinical practice, the term “gingival hyperplasia” is used based on the clinical appearance rather than histological evidence. Thus, it would be more appropriate for clinicians to use the clinical term “gingival enlargement” in the absence of histological confirmation .The term inflammatory hyperplasia is used to describe a large range of common occurring nodular growths of the oral mucosa that histologically represent inflamed fibrous and granulation tissue. The size of these reactive hyperplastic masses is variable, depending on the intensity and type of irritant stimulus and besides, on the inflammation degree of the affected tissue.The main etiological factor seems to be the chronic trauma due to poorly fitting dental prostheses, calculus, over-contoured restorations, acute or chronic lesions due to bites or fractured teeth  as well The gingival overgrowth, depending on its extension, could have multiple effects on the stomatognatic system: functional disorders (impaired speech), difficulty in chewing and even aesthetic problems that could cause psychological impairment .The aim of this article is to describe the clinicopathological characteristics of a patient with a fixed upper-arch implant supported prosthesis with bilateral gingival enlargement.The patient was a 68 years old woman suffering from depression, hypothyroidism, arrhythmias and hypercholesterolemia, pharmacologically controlled with clomipramine 25mg 0-0-1), lormetazepan 2mg (0-0-0.5), fluoxetine 20mg (1-0-0), levotiroxin 100mg (1-0-0), bisoprolol 2.5mg (1-0-0) and simvastatin 20mg (0-0-1). She did not have toxic habits neither allergies. The patient attended the dental clinic because of pain on the right side of the upper jaw. She wore a fixed upper-arch implant supported prosthesis placed five years ago and she had not attended the control visits for the last 2 years  for the histopathological exam.The histopathological exam found that the lesion was mainly constituted by fibrocellular collagen connective tissue with scarce cellularity and a diffuse and mild lymphoplasmacytic chronic inflammatory infiltration. The superficial mucosal epithelium was parakeratinized and hyperplastic but without any dysplastic phenomena Fig. A,B. HencThe intimate contact between bone and titanium implants was first demonstrated in 1969, and since then the bone-implant interface has been extensively investigated. However, the study of microflora and peri-implant tissues have almost exclusively been carried out over the last decade .Nowadays, implant-supported restorations constitute a common treatment in dentistry. Nevertheless, short and long-term complications may occur. These can be mechanical, when the damage affects the implant or the restorative components, or biological, when there is a damage on peri-implant tissues ,7.et al. , cyclosporine (immunosuppressant) or calcium channel blocking drugs are some examples .The changes observed on histological sections appear in both the epithelium and lamina propria of the gingival tissue. Epithelial hyperplasia produced by the proliferation of the epithelial basal layer cell associated to acanthosis origins the penetration of epithelial cords in lamina propria. The inflammatory process is characterized by an intensive fibroblasts proliferation that suggests these alterations are provoked by the presence of plaque. The connective tissue have a predominance of lymphoplasmacytic mononuclear cells (specific immune response) and macrophages (non-specific response), which indicates the presence of a chronic inflammation. Besides, epithelial surface might present hyperkeratinized or parakeratinized areas due to a tissue protection process .et al. (It is noteworthy that there are other factors that might induce gingival overgrowth such as allergic reactions ,5. SchouRegardless of its etiology, the treatment of gingival enlargements should firstly consist in a hygienic phase based on professional cleaning and education on oral hygiene, followed by a surgical treatment such as gingivoplasty or gingivectomy since, in many cases, it is not possible to reduce its size despite re-establishing an adequate hygiene. The need to perform the histopathological analysis of the excised tissue in all cases, to confirm the clinical diagnosis, is evident. Although malignant lesions associated to dental implants are rare findings, they can have similar clinical characteristics ,9.Finally, it would be very interesting to follow up a cohort of patients with these lesions, since their long-term behavior is still unknown.


Self-treatment with antimicrobials is common in sub-Saharan Africa. Little is known about the prevalence of this practice where malaria transmission intensity is low, and little is known about the prevalence of self-treatment with other medications such as antihypertensives and antihyperglycemics.A two-stage randomized population-based cluster survey with selection proportional to population size was performed in northern Tanzania. Self-identified healthcare decision-makers from randomly selected households were asked to report instances of self-medication without a prescription in the preceding year. Associations between self-treatment and sociodemographic characteristics were assessed with Pearson’s chi-squared and the Student’s t-test.p=0.005), younger age  and higher socioeconomic status score .A total of 718 participants completed the survey, and 344 (47.9%) reported any household member obtaining medication without a prescription. Of these, 85 (11.8%) obtained an antimicrobial and four (0.6%) obtained an antihypertensive or antihyperglycemic. Of respondents reporting self-treatment, 306 (89.0%) selected the medication themselves. Self-treatment with antimicrobials was associated with post-primary education (OR 1.95, 95% CI 1.22–3.16, Self-treatment with antimicrobials in an area of low malaria transmission intensity was uncommon and self-treatment with antihypertensives and antihyperglycemics was rare. The use of antimicrobials without a prescription has drawn the interest of public health officials primarily due to concerns that such inappropriate use may be contributing to growing prevalence of antibacterial and antimalarial resistance.8 These concerns have resulted in calls for better regulation of pharmaceutical dispensing across the continent. Less is known about the prevalence of antimicrobial self-treatment in Tanzania than in other countries in sub-Saharan Africa. However, preliminary studies have demonstrated high levels of willingness among local pharmacists to dispense antimicrobials without a prescription, despite Tanzanian laws which require prescriptions for all antibacterials and antimalarials.10Self-treatment with antimicrobials obtained without a prescription is common in sub-Saharan Africa. In recent community-based surveys conducted in Cameroon,11 less is known about who specifically decides that antimalarial treatment is indicated without a clinician’s prescription. Knowledge of the recommenders’ identities will help tailor effective interventions to combat inappropriate use of prescription medications in sub-Saharan Africa. For example, different interventions would be warranted if the primary selectors of specific medications were patients vs pharmacists.Beyond characterizing the specific prevalence of self-treatment in Tanzania, additional important questions about the use of non-prescribed pharmaceutical agents in sub-Saharan Africa remain unanswered. First, little research has been done to identify the recommenders of specific pharmaceutical agents. Although the use of antimalarials without a prescription is common in sub-Saharan Africa,11 As malaria prevalence declines across sub-Saharan Africa,12 increasing numbers of people will be residing in areas of low-malaria transmission; but it is unknown how antimicrobial self-treatment behaviors differ in such areas.A second important gap in understanding self-treatment in the region is to understand how self-treatment patterns differ in areas of low-malaria transmission. The majority of studies regarding antimicrobial use have been conducted in areas where prevalence of malaria is high.15 there is likely to be increasing demand for medications for these chronic conditions. Self-treatment for hypertension and diabetes would be concerning given the need for careful consideration of side effects, comorbidities and dose adjustments.There has also been sparse study of self-treatment with non-antimicrobial medications such as antihypertensives and antihyperglycemics. As hypertension and diabetes become increasingly prevalent across sub-Saharan Africa,The objectives of this study were the following: to estimate the prevalence of self-treatment with antimicrobials, antihypertensives and antihyperglycemics in an area of low malaria transmission intensity, to identify risk factors for self-treatment, and to identify recommenders of specific medications. To do so, we conducted a cross-sectional community survey of adults in the Kilimanjaro Region.16 The local malaria prevalence is low; a recent survey found the prevalence of malaria among children under 5 years of age to be between 0.0–0.5%.17 The dominant local tribe is the Chagga tribe.The study was conducted from February–May 2018 in three districts of the Kilimanjaro Region of northern Tanzania: the urban district of Moshi Urban (population 184 289) and two surrounding rural districts, Moshi Rural and Hai .18 Sixty sub-districts in the study districts were chosen randomly in a population-weighted fashion, with urban and rural settings selected proportionally to their population size. The 60 sub-districts selected for study inclusion are depicted in A two-stage randomized population-based cluster survey was performed with selection proportional to population size, following WHO recommendations for vaccination coverage cluster surveys.Surveyors conducted the surveys in Swahili on tablets  using Open Data Kit software . Participants were asked whether or not they or anyone else in the house had self-treated with any medication without a prescription in the past 12 months. They were further asked to estimate the total number of times they had obtained medications without a prescription and to identify what kind of medication was obtained in these instances, if known. Respondents selected the type of medication from a standardized picklist, and surveyors were trained to assist with this question when respondents had trouble identifying the appropriate medication type. Participants were also asked to report where they would seek care if a member of their household were to have a febrile illness. Demographic information including age and gender of the respondent as well as the education level of the head of household was also collected.19 using nine binary variables: post-primary education, presence of electricity in the home, health insurance coverage, home floor material, ownership of a bank account, ownership of a car, ownership of a television, ownership of a refrigerator and presence of a flush toilet in the home. Participant age, gender, religion, tribal affiliation, health insurance ownership and education level were defined by participant self-report. The minimum sample size was computed assuming a proportion  of 0.1, a precision of 10%, a design effect of 1.5 and an inflation factor of 1.2. The sample size calculation resulted in a minimum of 60 sub-districts to be visited with 11 households surveyed from each village. Sub-district population data were taken from the 2012 Tanzania Population and Housing Census.16Summary statistics for categorical variables were reported as proportions, and for continuous variables were reported as medians and ranges, or means and SDs. Pearson’s chi-square was used to analyze the association between self-treatment with antimicrobials and certain categorical respondent characteristics; the t-test was used to compare means between groups. ORs and corresponding CIs were calculated from contingency tables. The t-test was performed in STATA (v15.1); all other analyses were performed in R (v3.3.2). All analyses were performed with a 0.05 cut-off level for statistical significance. Antimicrobial medication was defined as either antibacterial, antimalarial or antihelmintic. Urban residence was defined as residence in Moshi Urban District. ‘Preferring pharmacy for febrile syndrome’ was defined as identifying a pharmacy as the first choice facility for either a male or a female with a fever. A socioeconomic status (SES) score was derived via principal component analysisThis study received ethics approval from the Duke Health Institutional Review Board (protocol Pro00016134), the Kilimanjaro Christian Medical Centre Research Ethics Committee (protocol number 295) and the Tanzania National Institutes for Medical Research Ethics Coordinating Committee (protocol NIMR/HQ/R.8a/Vol. IX/1000).Of 718 individuals participating in the survey, 485 (67.5%) were females. The median (range) age of participants was 48 (17–99) years and 230 (32.0%) households had health insurance. Sociodemographic characteristics of survey respondents are presented in Table Of participants, 344 (47.9%) reported that they or someone in their household had obtained a medication without a prescription in the past 12 months. Of those obtaining pharmaceutical agents without a prescription, the median (range) number of times they reported doing so over the preceding year was 4 . Table Table p=0.005), were more likely to be younger (p=0.007) and were more likely to have higher SES (p=0.023) than those who did not. There was no association between obtaining antimicrobials without a prescription and female gender, urban residence, health insurance ownership, religion or tribal affiliation. Respondents who reported self-treatment with antimicrobials were significantly more likely than those who did not to prefer seeking healthcare directly at pharmacies for febrile illnesses .Table This paper describes the prevalence of self-treatment with both antimicrobial and non-antimicrobial medications in an area of low malaria transmission intensity in northern Tanzania. By self-report, self-treatment with antimicrobials was uncommon and the use of antihypertensives and antihyperglycemics without a prescription was rare. The vast majority of respondents who acknowledged obtaining medications without prescriptions reported that they self-selected the medication. A higher level of education, higher SES and younger age were associated with self-treatment, and those who endorsed self-treatment were also more likely to prefer seeking care at pharmacies for febrile illnesses.20 Other studies performed in other settings with high malaria prevalence in sub-Saharan Africa have found similarly high prevalences of antimicrobial self-treatment.6 The findings of our study suggest that self-treatment with antimicrobials generally, not just antimalarials, may be substantially less common in areas of low-malaria prevalence. Other possible explanations for lower prevalence of self-treatment in northern Tanzania include sociocultural influences, more effective regulation of dispensation of medications, and more abundant access to healthcare in this setting. Further research is needed to determine the prevalence of self-treatment in other settings with low malaria prevalence and to explore factors contributing to low self-medication rates in northern Tanzania.The proportion of study participants reporting self-treatment with an antimicrobial in the preceding year was substantially lower than what has been reported elsewhere in sub-Saharan Africa. For example, in a recent study in Sudan that also relied on respondent self-report, 74% of adults reported use of an antimicrobial without a prescription within the preceding month.Of the antimicrobials, antihelmintics were the most frequently obtained without a prescription in this study. Antihelmintics have not been a focus of previous research regarding self-treatment in sub-Saharan Africa, but the findings presented here suggest the use of antihelmintics without a prescription may be a regional phenomenon worth studying further. Tanzanian pharmaceutical regulations do not require a prescription for most oral antihelmintics. Antihelmintic mass drug administration (MDA) campaigns, which are common in the Kilimanjaro Region and across sub-Saharan Africa, may also have normalized such self-treatment outside of MDA programs. Self-medication with antihypertensives and antihyperglycemics was fortunately extremely rare in this study population, reported by less than 1% of all participants. Further study of the use of these medications without prescriptions is warranted in other settings where self-treatment is more common.20 To maximize effectiveness, educational initiatives to combat inappropriate self-medication with antimicrobials in sub-Saharan Africa should be designed to target the younger, more educated population. Interestingly, self-medication with antimicrobials was associated with higher SES in this study, suggesting that financial concerns about physician and hospital charges may not have been an important driver of self-treatment in this community. Respondents who reported self-treatment with antimicrobials were also significantly more likely to identify a pharmacy as the first place they would seek care for a febrile illness. This link between care-seeking behavior and self-treatment suggests that educational programming regarding the risks of inappropriate self-medication with antimicrobials ought to include information about the appropriate use of pharmacies.The primary risk factors for self-medication with antimicrobials in this northern Tanzanian population were younger age and increased education. This finding is consistent with other studies from sub-Saharan Africa which also found self-treatment to be associated with greater levels of education and younger age.5 Moreover, we suspect that the vast majority of households in our study would have had at least one member who had some kind of illness in the preceding 12 months.Our study had several limitations. First, like other investigations of self-treatment in sub-Saharan Africa, this study relied on respondent self-report, which is subject to social desirability bias. If participants felt embarrassed to admit obtaining medications without prescriptions, this may have resulted in underreporting of the true rate of self-treatment. Similarly, participants’ responses were subject to recall bias. If respondents forgot certain instances of self-treatment or were unaware of self-treatment episodes by other members of the household, this may also have resulted in an underestimation of the self-medication rate. Furthermore, this study also depended on participants’ ability to correctly distinguish between different kinds of medication. Respondents may, for example, have incorrectly identified an antibacterial as a cold medication or an antihelmintic as an antimalarial. However, our study design was similar to those used in other studies of self-medication in sub-Saharan Africa, which were likely subject to the same biases. Finally, it should be noted that this survey was administered to all eligible households without regard to history of illnesses in the preceding year, potentially resulting in the inclusion of households in which no member had any reason to self-medicate in the preceding year. This approach, however, is consistent with the methods of other community surveys of self-medication in sub-Saharan Africa.In conclusion, self-treatment with both antimicrobials and other prescription medicines such as antihypertensives and antihyperglycemics was uncommon in an area of northern Tanzania with low prevalence of malaria. Further research is needed to explore factors that contribute to low prevalence of self-treatment in this area relative to what has been observed elsewhere in sub-Saharan Africa.Supplementary DataClick here for additional data file.