Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm.

Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span. Nevertheless, most currently used techniques ignore certain features that have particular significance and relevance to the classification problem in favor of extracting and choosing deep significance features. One important area of research is the deep learning-based categorization of brain tumors using brain magnetic resonance imaging (MRI). This paper proposes an automated deep learning model and an optimal information fusion framework for classifying brain tumor from MRI images. The dataset used in this work was imbalanced, a key challenge for training selected networks. This imbalance in the training dataset impacts the performance of deep learning models because it causes the classifier performance to become biased in favor of the majority class. We designed a sparse autoencoder network to generate new images that resolve the problem of imbalance. After that, two pretrained neural networks were modified and the hyperparameters were initialized using Bayesian optimization, which was later utilized for the training process. After that, deep features were extracted from the global average pooling layer. The extracted features contain few irrelevant information; therefore, we proposed an improved Quantum Theory-based Marine Predator Optimization algorithm (QTbMPA). The proposed QTbMPA selects both networks' best features and finally fuses using a serial-based approach. The fused feature set is passed to neural network classifiers for the final classification. The proposed framework tested on an augmented Figshare dataset and an improved accuracy of 99.80%, a sensitivity rate of 99.83%, a false negative rate of 17%, and a precision rate of 99.83% is obtained. Comparison and ablation study show the improvement in the accuracy of this work.

Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel.

Objective: Cervical cancer stands as a leading cause of mortality among women in developing nations. To ensure the reduction of its adverse consequences, the primary protocols to be adhered to involve early detection and treatment under the guidance of expert medical professionals. An effective approach for identifying this form of malignancy involves the examination of Pap smear images. However, in the context of automating cervical cancer detection, many of the existing datasets frequently exhibit missing data points, a factor that can substantially impact the effectiveness of machine learning models. Methods: In response to these hurdles, this research introduces an automated system designed to predict cervical cancer with a dual focus: adeptly managing missing data while attaining remarkable accuracy. The system's core is built upon a stacked ensemble voting classifier model, which amalgamates three distinct machine learning models, all harmoniously integrated with the KNN Imputer to address the issue of missing values. Results: The model put forth attains an accuracy of 99.41%, precision of 97.63%, recall of 95.96%, and an F1 score of 96.76% when incorporating the KNN imputation method. The investigation conducts a comparative analysis, contrasting the performance of this model with seven alternative machine learning algorithms in two scenarios: one where missing values are eliminated, and another employing KNN imputation. This study offers validation of the effectiveness of the proposed model in comparison to current state-of-the-art methodologies. Conclusions: This research delves into the challenge of handling missing data in the dataset utilized for cervical cancer detection. The findings have the potential to assist healthcare professionals in achieving early detection and enhancing the quality of care provided to individuals affected by cervical cancer.

Trimeric heptad repeat synthetic peptides HR1 and HR2 efficiently inhibit HIV-1 entry.

The trimeric heptad repeat domains HR1 and HR2 of the human immunodeficiency virus 1 (HIV-1) gp41 play a key role in HIV-1-entry by membrane fusion. To develop efficient inhibitors against this step, the corresponding trimeric-N36 and C34 peptides were designed and synthesized. Analysis by circular dichroism of monomeric and trimeric N36 and C34 peptides showed their capacities to adopt α-helical structures and to establish physical interactions. At the virological level, while trimeric-C34 conserves the same high anti-fusion activity as monomeric-C34, trimerization of N36-peptide induced a significant increase, reaching 500-times higher in anti-fusion activity, against R5-tropic virus-mediated fusion. This result was associated with increased stability of the N36 trimer peptide with respect to the monomeric form, as demonstrated by the comparative kinetics of their antiviral activities during 6-day incubation in a physiological medium. Collectively, our findings demonstrate that while the trimerization of C34 peptide had no beneficial effect on its stability and antiviral activity, the trimerization of N36 peptide strengthened both stability and antiviral activity. This approach, promotes trimers as new promising HIV-1 inhibitors and point to future development aimed toward innovative peptide fusion inhibitors, microbicides or as immunogens.

Overview of external reference pricing systems in Europe.

BACKGROUND AND OBJECTIVES: External reference pricing (ERP) is a price regulation tool widely used by policy makers in the European Union (EU) Member States (MS) to contain drug cost, although in theory, it may contribute to modulate prices up and down. The objective of this article was to summarise and discuss the main findings of part of a large project conducted for the European Commission ('External reference pricing of medicinal products: simulation-based considerations for cross-country coordination'; see www.ec.europa.eu/health/healthcare/docs/erp_reimbursement_medicinal_products_en.pdf) that aimed to provide an overview of ERP systems, both on processes and potential issues in 31 European countries (28 EU MS, Iceland, Norway, and Switzerland). METHODS: A systematic structured literature review was conducted to identify and characterise the use of ERP in the selected countries, to describe its impact on the prices of pharmaceuticals, and to discuss the possible cross-country coordination issues in EU MS. This research was complemented with a consultation of competent authorities' and international organisations' representatives to address the main issues or uncertainties identified through the literature review. RESULTS: All selected countries applied ERP, except the United Kingdom and Sweden. Twenty-three countries used ERP as the main systematic criterion for pricing. In the majority of European countries, ERP was based on legislated pricing rules with different levels of accuracy. ERP was applied either for all marketed drugs or for specific categories of medicines; it was mainly used for publicly reimbursed medicines. The number of reference countries included in the basket varied from 1 to 31. There was a great variation in the calculation methods used to compute the price; 15 countries used the average price, 7 countries used the lowest price, and 7 countries used other calculation methods. Reported limitations of ERP application included the lack of reliable sources of price information, price heterogeneity, exchange rate volatility, and hidden discounts. Spill-over effect and downward price convergence have often been mentioned as ERP's consequences leading to pricing strategies from pharmaceutical companies. CONCLUSION: While ERP is widely used in Europe, processes and availability of price information vary from one country to another, thus limiting ERP implementation. Furthermore, ERP spill-over effect is a major concern of pharmaceutical firms leading to implementation of the so-called 'launch sequence strategies'.

Pharmaceutical expenditure forecast model to support health policy decision making.

BACKGROUND AND OBJECTIVE: With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project 'European Union (EU) Pharmaceutical expenditure forecast' - http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). METHODS: A model was built to assess policy scenarios' impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). RESULTS: Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. CONCLUSIONS: Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of generics had a major impact on savings. However, very aggressive pricing of generic and biosimilar products might make this market unattractive and can be counterproductive. Worth noting, delaying time to access innovative products was also identified as an effective leverage to increase savings but might not be a desirable policy for breakthrough products. Increasing patient financial contributions, either directly or indirectly via their private insurances, is a more likely scenario rather than expanding the national pharmaceutical expenditure coverage.

EU pharmaceutical expenditure forecast.

BACKGROUND AND OBJECTIVES: With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States' pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). METHOD: In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012-2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. RESULTS: According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (-€9,367 million), France (-€5,589 million), and, far behind them, Germany (-€831 million), Greece (-€808 million), Portugal (-€243 million), and Hungary (-€84 million). The main source of savings came from the cardiovascular, central nervous system, and respiratory areas and from biosimilar entries. Oncology, immunology, and inflammation, in contrast, lead to additional expenditure. The model was particularly sensitive to the time to market of branded products, generic prices, generic penetration, and the distribution of biosimilars. CONCLUSIONS: The results of this forecast suggested a decrease in pharmaceutical expenditure in the studied period. The model was sensitive to pharmaceutical policy decisions.

Novel methodology for pharmaceutical expenditure forecast.

BACKGROUND AND OBJECTIVE: The value appreciation of new drugs across countries today features a disruption that is making the historical data that are used for forecasting pharmaceutical expenditure poorly reliable. Forecasting methods rarely addressed uncertainty. The objective of this project was to propose a methodology to perform pharmaceutical expenditure forecasting that integrates expected policy changes and uncertainty (developed for the European Commission as the 'EU Pharmaceutical expenditure forecast'; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). METHODS: 1) Identification of all pharmaceuticals going off-patent and new branded medicinal products over a 5-year forecasting period in seven European Union (EU) Member States. 2) Development of a model to estimate direct and indirect impacts (based on health policies and clinical experts) on savings of generics and biosimilars. Inputs were originator sales value, patent expiry date, time to launch after marketing authorization, price discount, penetration rate, time to peak sales, and impact on brand price. 3) Development of a model for new drugs, which estimated sales progression in a competitive environment. Clinical expected benefits as well as commercial potential were assessed for each product by clinical experts. Inputs were development phase, marketing authorization dates, orphan condition, market size, and competitors. 4) Separate analysis of the budget impact of products going off-patent and new drugs according to several perspectives, distribution chains, and outcomes. 5) Addressing uncertainty surrounding estimations via deterministic and probabilistic sensitivity analysis. RESULTS: This methodology has proven to be effective by 1) identifying the main parameters impacting the variations in pharmaceutical expenditure forecasting across countries: generics discounts and penetration, brand price after patent loss, reimbursement rate, the penetration of biosimilars and discount price, distribution chains, and the time to reach peak sales for new drugs; 2) estimating the statistical distribution of the budget impact; and 3) testing different pricing and reimbursement policy decisions on health expenditures. CONCLUSIONS: This methodology was independent of historical data and appeared to be highly flexible and adapted to test robustness and provide probabilistic analysis to support policy decision making.

Protein kinase C-delta regulates HIV-1 replication at an early post-entry step in macrophages.

BACKGROUND: Macrophages, which are CD4 and CCR5 positive, can sustain HIV-1 replication for long periods of time. Thus, these cells play critical roles in the transmission, dissemination and persistence of viral infection. Of note, current antiviral therapies do not target macrophages efficiently. Previously, it was demonstrated that interactions between CCR5 and gp120 stimulate PKC. However, the PKC isozymes involved were not identified. RESULTS: In this study, we identified PKC-delta as a major cellular cofactor for HIV-1 replication in macrophages. Indeed, PKC-delta was stimulated following the interaction between the virus and its target cell. Moreover, inhibition of PKC-delta blocked the replication of R5-tropic viruses in primary human macrophages. However, this inhibition did not have significant effects on receptor and co-receptor expression or fusion. Additionally, it did not affect the formation of the early reverse transcription product containing R/U5 sequences, but did inhibit the synthesis of subsequent cDNAs. Importantly, the inhibition of PKC-delta altered the redistribution of actin, a cellular cofactor whose requirement for the completion of reverse transcription was previously established. It also prevented the association of the reverse transcription complex with the cytoskeleton. CONCLUSION: This work highlights the importance of PKC-delta during early steps of the replicative cycle of HIV-1 in human macrophages.

Cationic nanoglycolipidic particles as vector and adjuvant for the study of the immunogenicity of SIV Nef protein.

The objective of this study was to test the immunogenicity of SIV Nef protein formulated in cationic nanoglycolipidic particles of 100nm of diameter. In parallel, the adjuvant effect of these nanoglycolipidic particles was compared in similar experiments using GST-Nef in association with the commonly strongest used complete Freund's adjuvant (CFA) or incomplete Freund's adjuvant in association with MDP or MDP alone. Our results showed that these particles do not alter the integrity of our immunogen GST-Nef, which remains stable for more than three months at 4°C. We demonstrated that in the presence of nanoglycolipidic particles antibodies against Nef were produced since the first injection and remained stable after the third injection with high titers for long lasting periods as observed with CFA and IFA/MDP adjuvant. The analysis of immunoglobulin isotype profiles of antibodies generated by the different protocols of immunization showed the preponderance of IgG1 isotypes suggesting the predominance of Th2-type immune response.

Fusion intermediates of HIV-1 gp41 as targets for antibody production: design, synthesis, and HR1-HR2 complex purification and characterization of generated antibodies.

The objective of this project was to study the interaction between HR1 and HR2, the stability of the complex formed, and to characterize the antibodies produced against monomeric HR1 and HR2 peptides as well as the HR1-HR2 complex. In this work, HR1 was mimicked by peptide N36, and HR2 was mimicked by peptide C34L and its analogues C34M2, C34M3, and C34D. Whereas C34M2 and C34M3 are partially composed of D-amino acids, C34D has same sequence as C34L, but is assembled entirely of D-amino acids. Using CD analysis, SPR assays, and gel filtration chromatography, we demonstrate the physical interaction between N36 and C34L and its analogues C34M2 and C34M3, but not C34D. We show that the HR1-HR2 complex is formed rapidly (<1 min) and remains stable, as demonstrated by its inability, in contrast to each free peptide, to inhibit the formation of syncytia. To generate antibodies with predetermined specificity against the transiently exposed intermediate that corresponds to the six-helix bundle structure, purified preformed HR1-HR2 complex was used, in parallel with monomeric HR1 and HR2 peptides, as immunogens in mice. Although the produced antibodies recognize total HIV-1 envelope glycoproteins in ELISA, they are unable to neutralize HIV-1-mediated fusion at 37 °C. However, if the incubation with these antibodies is carried out at 27 °C, a temperature that allows stabilization of the transient intermediate complex, anti-peptide antibodies are able to bind their corresponding domains in HeLa cells expressing HIV-1 gp41 in co-culture with HeLa CD4-CCR5/CXCR4 during the dynamic mechanism of membrane fusion. In agreement with the latter results, these antibodies, if previously incubated for 2 h at 27 °C, are able to strongly neutralize HIV-1 entry by membrane fusion, as shown by their ability to block the formation of syncytia.