Modeling Studies Research Articles

Molecular modeling studies, in vitro antioxidant and antimicrobial assay and BSA affinity of novel benzyl-amine derived scaffolds as CYP51B inhibitors.

New scaffolds derived from benzylamine were prepared, characterized, and tested for their antimicrobial, antioxidant activities and binding interactions with BSA. Structure-activity relationship analysis revealed that compounds incorporating both benzylamine and quinoline or pyridine moieties (specifically 3a and 3d) demonstrated potent antifungal activity, surpassing that of the standard drug Ketoconazole against Penicillium italicum. Molecular docking studies confirmed significant inhibitory activity against the CYP51B enzyme-an essential component of fungal cell walls. In addition, compounds 3h, 3d and 3b displayed promising DPPH radical scavenging activity, indicating its strong potential as an antioxidant source. Thermodynamic parameters of standard antiradical mechanisms confirmed antiradical capacity expressed via formal hydrogen atom transfer (FHT). The results of spectrofluorometric assays and molecular docking studies on the affinity of the tested compounds for the BSA enzyme confirmed that all compounds show significant binding affinity for active site III, with compound 3d demonstrating the highest binding affinity. Key pharmacokinetic parameters were assessed using ADMET analysis, ensuring the viability of these compounds for potential therapeutic applications.

Prooxidant state in anticancer drugs and prospect use of mitochondrial cofactors and anti-inflammatory agents in cancer prevention.

An extensive body of literature has associated cancer with redox imbalance and inflammatory conditions. Thus, several studies and current clinical practice have relied on the use of anticancer drugs known to be associated with prooxidant state. On the other hand, a number of studies have reported on the effects of several antioxidants, anti-inflammatory agents and of mitochondrial cofactors (also termed mitochondrial nutrients, MNs) in counteracting or slowing carcinogenesis, or in controlling cancer growth. In the available literature, a body of evidence points on the roles of anti-inflammatory agents and of individual MNs against carcinogenesis or in controlling cancer cell proliferation, but only a few reports on the combined use of two or the effect of three MNs. These combinations are proposed as potentially successful tools to counteract carcinogenesis in prospective animal model studies or in adjuvant cancer treatment strategies. A "triad" of MNs are suggested to restore redox balance, mitigate side effects of prooxidative anticancer drugs, or aid in cancer prevention and/or adjuvant therapy. By elucidating their mechanistic underpinnings and appraising their clinical efficacy, we aim to contribute with a comprehensive understanding of these therapeutic modalities.

Improvement of the Performance of An Industrial Wringer

Objective: To maximize the machine's availability through periodic monitoring and propose design improvements to prevent future issues. Context: Overview of the recycling process and the role of the wringer in filtering recycled plastic. Problem Statement: Description of mechanical defects affecting the wringer, including imbalance due to bearing and rotor shaft deformation. Theoretical Analysis: Root Cause Identification, Impact of Imbalance, System Modelling and Simulation Experimental Studies and Strategy: - Practical experiments validate the theoretical and numerical findings and enhance the vibration analysis approach.- Effective scheduling of interventions and repairs is based on the machine's condition, ensuring continuous monitoring through vibration analysis devices. Results and Discussion: Explanation of the proposed modification, including the specific angle adjustment, Discussion on how the adjustment would reduce rotational speed and maintain high wringing performance.

Molecular Modeling Studies of β-aminoacyl containing Homopiperazine derivatives as DPP4 Inhibitors

Dipeptidyl peptidase IV (DPP4) is a promising target for developing novel anti-diabetic and anti-obesity drugs. Pharmacophore based three dimensional quantitative structure activity relationship studies (3D-QSAR) and molecular docking were performed on a series of 56 β-aminoacyl-containing homopiperazine derivatives of DPP4 inhibitors to find out the structural relationship with the activity. The best predictive 3D-QSAR model with pharmacophore based alignment resulted in R2 (training set) value of 0.9407, Q2 (internal test set) value of 0.9053, Pearson-R value of 0.9517 and root mean square error (RMSE) 0.2838. The information rendered by pharmacophore based 3D-QSAR models and docking studies may afford valuable clues to optimize the lead and design of potent, selective inhibitors.

Methods of nivolumab administration in advanced melanoma: A comparison of patients' clinical outcomes treated with flat dose or weight-adjusted dose, a multicenter observational study.

Nivolumab obtained approval in advanced melanoma (AM) with weight-adjusted dose (WAD) administration (3 mg/kg/2 weeks). In 2018, the dosage regimen was changed to flat dose (FD) administration (240 mg/2 weeks or 480 mg/4 weeks) based on a modeling study, without clinical data. AM patients have been prospectively included in the French national multicenter MelBase database since 2013. First-line patients treated with nivolumab monotherapy were included in the WAD or FD groups of this study. The primary end point was the incidence of grade ≥3 immune-related adverse events (irAEs). Secondary end points were incidence of any grade irAEs, and overall survival (OS) and progression-free survival (PFS). Inverse probability of treatment weighting was used to balance groups on their baseline characteristics. Between 2015 and 2022, 348 patients were included: 160 in the WAD and 188 in the FD groups. In the FD group, 45% and 27% of patients weighed <75 kg and >85 kg, respectively. Grade ≥3 and any grade irAEs rates were 13.1% versus 11.7% (p=.8) and 63.1% versus 67.0% (p=.5) in the WAD and FD groups, respectively. After weighting, median PFS was 3.1 and 3.7 months (hazard ratio [HR], 0.84; 95% confidence interval [CI], 0.65-1.07), and median OS was 24.8 and 37.0 months (HR, 0.74; 95% CI, 0.54-1.01) in the WAD and FD groups, respectively. There was no difference in the incidence of severe irAEs and in median PFS between AM patients treated by WAD or FD nivolumab. The median OS between patient groups did not reach statistical significance.

Comparative Study of Different Machine Learning Models for Heat Transfer Performance Prediction of Evaporators in Modular Refrigerated Display Cabinets

This study explores the potential of machine learning models to predict evaporator heat transfer performance in Modular Refrigerated Display Cases (MRDCs). Six experimental datasets from MRDC systems were analyzed to compare the efficacy of six machine learning models: Linear Regression, Decision Tree Regression, Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM). The findings indicate that the ensemble tree-based models, LightGBM and RF, are particularly effective in predicting evaporator heat transfer performance. These models demonstrate high accuracy and robustness, effectively capturing the nonlinear relationship between the evaporator temperature and heat transfer coefficient. Moreover, LightGBM and RF exhibit notable stability and adaptability in scenarios of limited data availability and elevated noise levels. Their consistent predictive accuracy across different experimental conditions highlights their suitability for complex refrigeration systems. This research provides essential insights for optimizing MRDC evaporator performance, establishing a theoretical and data-driven foundation for energy-efficient enhancements and intelligent management within cold chain systems.

Quantitative Structure Activity Relationship Analysis of Benzimidazole Derivatives as Aldose Reductase Inhibitors

Hyperglycemia is involved in the pathogenesis of diabetic neuropathy, retinopathy, nephropathy, and macro-vascular disease via multiple mechanisms, of which increased aldose reductase activity. Aldose reductase inhibitors, when administered from the onset of hyperglycemia, prevent the progression of polyol accumulation–linked complication. In this work, we report quantitative structure activity relationship analysis performed on the Triazino[4,3-α]benzimidazole acetic acid derivatives as aldose reductase inhibitors. The molecular modeling study was performed by using the software BioMed CAChe 6.0 and the regression analysis by SYSTAT 10.2. Various physicochemical parameters belonging to different classes viz. hydrophobic, steric and electronic etc. were calculated. QSAR models were generated using 17 compounds. The model showed a good correlative and predictive ability having convention coefficient (r2) of 0.846 and cross-validated correlation coefficient (q2) of 0.6866 by LOO external validation method. Based on the developed QSAR model, it may be concluded that aldose reductase inhibition activity of benzimidazole acetic acid derivatives is strongly influenced by the thermodynamic and electronic nature of the substituents. QSAR model shows that LOGP, LUMO energy, and steric energy are negatively affecting the biological activity. For a molecule to have higher aldose reductase activity, compound should be less lipophilic and should have more electronegative groups or atoms than electropositive groups or atoms present in the compound.

Study of the Shear Strength Model of Unsaturated Soil in the Benggang Area of Southern China

Benggangs are a unique type of soil erosion commonly found in southern China, with the gully wall being the most dynamic component of the Benggang system and is crucial for assessing its overall progression. The unsaturated shear strength of soil in Benggang areas is a key factor influencing the stability of the gully wall. However, quantitative analyses of the unsaturated shear strength in the gully walls of Benggangs remain limited. In this study, the soil–water characteristic curves (SWCC) and shear strengths of undisturbed soil samples from four different soil layers in the gully wall of Benggang were measured using a pressure membrane meter and a quadruple direct shear apparatus. The results revealed that the water holding capacity of the soil decreased gradually with increasing matrix suction, and the order of the water holding capacity was the sandy soil layer &gt; transition layer &gt; laterite layer &gt; clastic layer. With an increasing soil water content (SWC), the shear strength, cohesion (c), and internal friction angle (φ) of the four soil layers decreased significantly, and the φ showed a power function decreasing curve (p &lt; 0.05), whereas c in the laterite layer and transition layer exhibited a power function decreasing curve (p &lt; 0.01). The c of the sandy soil layer and clastic layer decreased linearly and logarithmically (p &lt; 0.01) with increasing SWC, respectively. The unsaturated shear strength model for the four soil layers was developed based on the Vanapalli model. The root mean square error (RMSE) of the simulated and measured values was less than 29.349, while the Nash–Sutcliffe efficiency (NSE) and R2 values were greater than 0.638 and 0.788, respectively. The model can be used to analyze and predict the unsaturated shear strength in different layers of Benggang gully walls, providing a theoretical foundation for studying the erosion mechanisms of Benggangs.

Major facilitator family transporters specifically enhance caffeyl alcohol uptake during C-lignin biosynthesis.

The mode of transport of lignin monomers to the sites of polymerization in the apoplast remains controversial. C-Lignin is a recently discovered form of lignin found in some seed coats that is composed exclusively of units derived from caffeyl alcohol. RNA-seq and proteome analyses identified a number of transporters co-expressed with C-lignin deposition in the seed coat of Cleome hassleriana. Cloning and influx/efflux analysis assays in yeast identified two low-affinity transporters, ChPLT3 and ChSUC1, that were active with caffeyl alcohol but not with the classical monolignols p-coumaryl, coniferyl, and sinapyl alcohols, consistent with molecular modeling and docking studies. Expression of ChPLT3 in Arabidopsis seedlings enhanced root growth in the presence of caffeyl alcohol, and expression of ChPLT3 and ChSUC1 correlated with lignin C-unit content in hairy roots of Medicago truncatula. We present a model, consistent with phylogenetic and evolutionary considerations, whereby passive caffeyl alcohol transport may be supplemented by hitchhiking on secondary active transporters to ensure the synthesis of C-lignin, and inhibition of synthesis of G-lignin, in the apoplast.

On crop yield modelling, predicting, and forecasting and addressing the common issues in published studies

There has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), as well as the abundance of machine learning modelling approaches. However, there are several common issues in published studies in the field of precision agriculture (PA) that must be addressed. This includes the terminology used in relation to crop yield modelling, predicting, forecasting, and interpolating, as well as the way that models are calibrated and validated. As a typical example, many studies will take a crop yield map or several plots within a field from a single season, build a model with satellite or Unmanned Aerial Vehicle (UAV) imagery, validate using data-splitting or some kind of cross-validation (e.g. k-fold), and say that it is a ‘prediction’ or ‘forecast’ of crop yield. However, this poses a problem as the approach is not testing the forecasting ability of the model, as it is built on the same season that it is then validating with, thus giving a substantial overestimation of the value for decision-making, such as an application of fertiliser in-season. This is an all-too-common flaw in the logic construct of many published studies. Moving forward, it is essential that clear definitions and guidelines for data-driven yield modelling and validation are outlined so that there is a greater connection between the goal of the study, and the actual study outputs/outcomes. To demonstrate this, the current study uses a case study dataset from a collection of large neighbouring farms in New South Wales, Australia. The dataset includes 160 yield maps of winter wheat (Triticum aestivum) covering 26,400 hectares over a 10-year period (2014–2023). Machine learning crop yield models are built at 30 m spatial resolution with a suite of predictor data layers that relate to crop yield. This includes datasets that represent soil variation, terrain, weather, and satellite imagery of the crop. Predictions are made at both the within-field (30 m), and field resolution. Crop yield predictions are useful for an array of applications, so four different experiments were set up to reflect different scenarios. This included Experiment 1: forecasting yield mid-season (e.g. for mid-season fertilisation), Experiment 2: forecasting yield late-season (e.g. for late-season logistics/forward selling), Experiment 3: predicting yield in a previous season for a field with no yield data in a season, and Experiment 4: predicting yield in a previous season for a field with some yield data (e.g. two combine harvesters, but only one was fitted with a yield monitor). This study showcases how different model calibration and validation approaches clearly impact prediction quality, and therefore how they should be interpreted in data-driven crop yield modelling studies. This is key for ensuring that the wealth of data-driven crop yield modelling studies not only contribute to the science, but also deliver actual value to growers, industry, and governments.

Recovery from HPV vaccination deficits caused by the COVID-19 pandemic in Germany: a modeling study of catch-up HPV vaccination among adolescent girls

Health care disruptions associated with the COVID-19 pandemic have caused persistent decreases in human papillomavirus (HPV) vaccination uptake in Germany. The objective of this study was to estimate the cumulative deficit in first doses of the HPV vaccine administered to girls in Germany since the beginning of the pandemic and the projected time to recover from this deficit at different catch-up vaccination uptake levels, focusing on girls 9–14 years of age. This study used a published HPV vaccination modeling tool. Retrospective vaccination data from 2019 were used to calculate the baseline vaccination rate, while data from 1 January 2020 to 31 December 2023 were used to estimate the size and duration of the HPV vaccination deficit accrued since the beginning of the COVID-19 pandemic. We modeled scenarios in which the rate of monthly vaccinations increased from the 2023 average to 5%, 10%, or 15% above the baseline 2019 level. The average monthly number of first doses of HPV vaccine administered to girls 9–14 years of age was 7.4% below the 2019 baseline level in 2020, 21.2% below baseline in 2021, 21.0% below baseline in 2022, and 19.5% below baseline in 2023. At a catch-up vaccination uptake of 5–15% above the 2019 baseline, there would be an estimated accumulated deficit of 267,052–285,798 first doses by 31 December 2024 that would clear between October 2029–April 2040. A catch-up vaccination uptake of 12.3% (11.9–12.7%) every month above baseline would be needed to clear the accumulated deficit by the end of 2030. Conclusions: Significant HPV vaccination deficits have accrued in Germany since the beginning of the COVID-19 pandemic. Sustained efforts to vaccinate the affected cohorts are now needed to mitigate the long-term impacts of the vaccination deficit.What is known:• Health care disruptions associated with the COVID-19 pandemic have caused persistent decreases in human papillomavirus (HPV) vaccination uptake in Germany.What is new:• Our model projected that the HPV vaccination deficit would be cleared between October 2029 (15% above the 2019 baseline level) and April 2040 (5% above the 2019 baseline level).• A catch-up rate of 12.3% (11.9–12.7%) above the 2019 baseline level would be needed to clear the HPV vaccination deficit by 31 December 2030.• Sustained efforts to vaccinate the affected cohorts are now needed to mitigate the long-term impacts of the accrued vaccination deficit on mortality, morbidity, and health care costs from HPV-associated diseases.

A comparative study of machine learning models for sentiment analysis of transboundary rivers news media articles

A comparative study of machine learning models for sentiment analysis of transboundary rivers news media articles

An in vivo evaluation of the safety and efficacy of using decellularized bovine parietal peritoneum membranes as dural substitutes

PurposeThe reconstruction of dura matter is a challenging problem for neurosurgeons. A number of materials for dural reconstruction have recently been developed, but some of them have poor biocompatibility, poor mechanical properties, and adverse effects. Bovine parietal peritoneum is a promising natural material for regenerative medicine and reconstructive surgery. In this study, we conducted an in vivo evaluation of the safety and efficacy of using decellularized bovine peritoneum membranes (BPMs) as natural dural substitutes in a rabbit model.MethodsThe dural defects in mature New Zealand rabbits were studied. A BPM was sutured on the dural defect area of each animal. Autologous periosteum and collagen membranes (Lyoplant®) were used to facilitate a comparison with the BPMs. ELISA, histomorphological analysis, and hematological analysis were carried out to examine the safety and efficacy of using BPMs as dural substitutes.ResultsOur results showed that the BPMs demonstrated a deterioration rate that is suitable for gathering newly formed meningothelial tissue. The thickness and density of BPM fibers prevents resorption in the first few days after use as a plastic material, and the regeneration of the dura mater does not occur at an accelerated pace, meaning that the gradual formation of fibrous tissue prevents adhesion to the brain surface. It was observed that the BPM can integrate with the adjacent tissue to repair dural defects. Moreover, the transplantation of BPMs did not cause significant adverse effects or immunological responses, indicating the safety and good biocompatibility of the BPM.ConclusionThus, our in vivo study in a rabbit model showed that decellularized BPMs may represent a biocompatible natural material that can be used in cases requiring dura matter repair without significant adverse effects.

A brief history of the development of transcranial tissue Doppler ultrasound.

This article documents the early development of the first transcranial Doppler (TCD)-based ultrasound system for continuous monitoring of brain tissue pulsations (BTPs). Transcranial tissue Doppler (TCTD) uses a lightweight, wearable single-element ultrasound probe to track tissue motion perpendicular to the skin's surface, providing tissue displacement estimates along a single beam line. Feasibility tests using an adapted TCD system confirmed that brain tissue motion data can be obtained from existing TCD hardware. Brain Tissue Velocimetry (Brain TV), a TCTD data acquisition system, was then developed to provide a lightweight and portable means of continuously recording TCTD data in real-time. Brain TV measurements are synchronized to a 3-lead electrocardiogram and can be recorded alongside other physiological measurements, such as blood pressure, heart rate and end-tidal carbon dioxide. We have shown that Brain TV is able to record BTPs from sample depths ranging from 22 to 80 mm below the probe's surface and from multiple positions on the head. Studies in healthy volunteers, stroke patients and ultrasound phantom brain models demonstrate how TCTD might provide insights into the relationships between physiological measurements and brain tissue motion and show promise for rapid clinical assessment and continuous monitoring of BTPs.

Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review

BackgroundTraditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission.ObjectivesTo better understand the use of agent-based models (ABMs) in this context, we conducted a systematic review with two main objectives: (1) to examine how ABMs have been employed to model the intricate heterogeneity of M.tb transmission, and (2) to identify the challenges and opportunities associated with implementing ABMs for M.tb.Search methodsWe conducted a systematic search following PRISMA guidelines across four databases (MEDLINE, EMBASE, Global Health, and Scopus), limiting our review to peer-reviewed articles published in English up to December 2022. Data were extracted by two investigators using a standardized extraction tool. Prospero registration: CRD42022380580.Selection criteriaOur review included peer-reviewed articles in English that implement agent-based, individual-based, or microsimulation models of M.tb transmission. Models focusing solely on in-vitro or within-host dynamics were excluded. Data extraction targeted the methodological, epidemiological, and computational characteristics of ABMs used for TB transmission. A risk of bias assessment was not conducted as the review synthesized modelling studies without pooling epidemiological data.ResultsOur search initially identified 5,077 studies, from which we ultimately included 26 in our final review after exclusions. These studies varied in population settings, time horizons, and model complexity. While many incorporated population heterogeneity and household structures, some lacked essential features like spatial structures or economic evaluations. Only eight studies provided publicly accessible code, highlighting the need for improved transparency.Authors’ conclusionsABMs are a versatile approach for representing complex disease dynamics, particularly in cases like TB, where they address heterogeneous mixing and household transmission often overlooked by traditional models. However, their advanced capabilities come with challenges, including those arising from their stochastic nature, such as parameter tuning and high computational expense. To improve transparency and reproducibility, open-source code sharing, and standardised reporting are recommended to enhance ABM reliability in studying epidemiologically complex diseases like TB.

Discovery of MT-7117 (Dersimelagon Phosphoric Acid): A Novel, Potent, Selective, and Nonpeptidic Orally Available Melanocortin 1 Receptor Agonist.

Activation of the melanocortin 1 receptor (MC1R) mediates melanogenesis in melanocytes, anti-inflammatory effects in inflammatory cells, and antifibrotic effects in fibroblasts. Thus, MC1R agonists are expected to be beneficial for treating skin, autoimmune, inflammatory, and fibrotic diseases. Afamelanotide, an α-melanocyte-stimulating hormone (α-MSH) analogue MC1R agonist, is used clinically for treating erythropoietic protoporphyria (EPP) as a subcutaneous implant formulation. We explored nonpeptidic small-molecule MC1R agonists with the aim of identifying more convenient oral drugs. By exploring the structure of previously reported compound 5, we discovered compound 11 (MT-7117: dersimelagon phosphoric acid). This compound exhibited strong MC1R agonistic activity, good pharmacokinetic properties, and excellent safety profiles. Furthermore, compound 11 was effective in animal pigmentation evaluation and skin fibrosis model studies. Compound 11 is currently in clinical trials for the treatment of EPP, X-linked protoporphyria (XLP), and systemic sclerosis (SSc). Proof of concept was obtained in phase 2 clinical studies on EPP and XLP.

The neural basis of neuropsychiatric symptoms in Alzheimer’s disease

Neuropsychiatric symptoms (NPS) such as depression, anxiety, apathy and aggression affect up to 90% of Alzheimer’s disease (AD) patients. These symptoms significantly increase caregiver stress and institutionalization rates, and more importantly they are correlated with faster cognitive decline. However, the neuronal basis of NPS in AD remains largely unknown. Here, we review current understanding of NPS and related pathology in studies of AD patients and AD mouse models. Clinical studies indicate that NPS prevalence and severity vary across different AD stages and types. Neuroimaging and postmortem studies have suggested that pathological changes in the anterior cingulate cortex, hippocampus, prefrontal cortex, and amygdala are linked to NPS, although the precise mechanisms remain unclear. Studies of AD mouse models have indicated that amyloid-beta and tau-related neurodegeneration in the hippocampus, prefrontal cortex, and anterior cingulate cortex are correlated with NPS-like behavioral deficits. A better understanding of the NPS phenotypes and related pathological changes will pave the way for developing a better management strategy for NPS in AD patients.

Cardiovascular health and economic outcomes under improved air quality in China: a modelling study

IntroductionChina faces the dual challenge of high air pollution and an increasing burden of cardiovascular disease (CVD). We aimed to estimate the healthcare costs associated with CVD and the quality-adjusted...

Trends and drivers of hypoxic thickness and volume in the northern Gulf of Mexico: 1985-2018.

Hypoxia is a major environmental issue plaguing the commercially and ecologically important coastal waters of the northern Gulf of Mexico. Several modeling studies have explored this phenomenon, but primarily focus on the areal extent of the mid-summer hypoxic zone. Research into the variability and drivers of hypoxic volume and thickness is also important in evaluating the seasonal progression of hypoxia and its impact on coastal resources. In this study, we compile data from multiple monitoring programs and develop a geospatial model capable of estimating hypoxic thickness and volume across the summer season. We adopt a space-time geostatistical framework and introduce a rank-based inverse normal transformation to simulate more realistic distributions of hypoxic layer thickness. Our findings indicate that, on average, there is a seasonal lag in peak hypoxic volume and thickness compared to hypoxic area. We assess long-term trends in different hypoxia metrics (area, thickness, and volume), and while most metrics did not exhibit significant trends, mid-summer hypoxic thickness is found to have increased at a rate of 5.9 cm/year (p<0.05) over the past three decades. In addition, spring nitrogen load is found to be the major driver of all hypoxia metrics, when considered along with other riverine inputs and meteorological factors in multiple regression models. Hypoxic volume, which was also often influenced by east-west wind velocities, was found to be more predictable than hypoxic thickness.

Cost-effectiveness of human papillomavirus (HPV) vaccination in Tunisia: a modelling study

ObjectivesThe objective of this study is to assess the cost utility of the implementation of the human papillomavirus (HPV) vaccination programme in Tunisia in addition to the current cytology screening...