DGAT2 inhibition blocks SREBP-1 cleavage and improves hepatic steatosis by increasing phosphatidylethanolamine in the ER.

Diacylglycerol acyltransferase 2 (DGAT2) catalyzes the final step of triglyceride (TG) synthesis. DGAT2 deletion in mice lowers liver TGs, and DGAT2 inhibitors are under investigation for the treatment of fatty liver disease. Here, we show that DGAT2 inhibition also suppressed SREBP-1 cleavage, reduced fatty acid synthesis, and lowered TG accumulation and secretion from liver. DGAT2 inhibition increased phosphatidylethanolamine (PE) levels in the endoplasmic reticulum (ER) and inhibited SREBP-1 cleavage, while DGAT2 overexpression lowered ER PE concentrations and increased SREBP-1 cleavage in vivo. ER enrichment with PE blocked SREBP-1 cleavage independent of Insigs, which are ER proteins that normally retain SREBPs in the ER. Thus, inhibition of DGAT2 shunted diacylglycerol into phospholipid synthesis, increasing the PE content of the ER, resulting in reduced SREBP-1 cleavage and less hepatic steatosis. This study reveals a new mechanism that regulates SREBP-1 activation and lipogenesis that is independent of sterols and SREBP-2 in liver.

A cholesterol-binding bacterial toxin provides a strategy for identifying a specific Scap inhibitor that blocks lipid synthesis in animal cells.

Lipid synthesis is regulated by the actions of Scap, a polytopic membrane protein that binds cholesterol in membranes of the endoplasmic reticulum (ER). When ER cholesterol levels are low, Scap activates SREBPs, transcription factors that upregulate genes for synthesis of cholesterol, fatty acids, and triglycerides. When ER cholesterol levels rise, the sterol binds to Scap, triggering conformational changes that prevent activation of SREBPs and halting synthesis of lipids. To achieve a molecular understanding of how cholesterol regulates the Scap/SREBP machine and to identify therapeutics for dysregulated lipid metabolism, cholesterol-mimetic compounds that specifically bind and inhibit Scap are needed. To accomplish this goal, we focused on Anthrolysin O (ALO), a pore-forming bacterial toxin that binds cholesterol with a specificity and sensitivity that is uncannily similar to Scap. We reasoned that a small molecule that would bind and inhibit ALO might also inhibit Scap. High-throughput screening of a ~300,000-compound library for ALO-binding unearthed one molecule, termed UT-59, which binds to Scap's cholesterol-binding site. Upon binding, UT-59 triggers the same conformation changes in Scap as those induced by cholesterol and blocks activation of SREBPs and lipogenesis in cultured cells. UT-59 also inhibits SREBP activation in the mouse liver. Unlike five previously reported inhibitors of SREBP activation, UT-59 is the only one that acts specifically by binding to Scap's cholesterol-binding site. Our approach to identify specific Scap inhibitors such as UT-59 holds great promise in developing therapeutic leads for human diseases stemming from elevated SREBP activation, such as fatty liver and certain cancers.

A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition.

Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. At first, the input data is pre-processed. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. The important features are then post-processed using the scatter plot matrix method. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. From these experiments, the results can be obtained and showed the efficacy of the proposed model.

Implementation of explainable artificial intelligence in commercial communication systems using micro systems.

The developments in the field of artificial intelligence (AI) and decision making systems are identified as virtuous models for banking and finance sector (BFS) applications. Even though AI provides great advantage in application changes it is essential to remodel the system using explainable artificial intelligence (XAI) design system. Also the standard sensing models provides appropriate monitoring values but huge size of sensors is considered as a major drawback. Thus two diverse problems are addressed in this research work where XAI has been integrated with micro electro-mechanical systems (MEMS) for solving the problems related to BFS applications. Further the data security has been enhanced as XAI is implemented with conviction managements and real time experiments are carried out by developing a unique application by integrating new mathematical designs. To validate the effectiveness of BFS application the developed model is tested with five scenarios which includes multiple parametric arrangements with interpretability process. The tested and compared outcomes with existing models indicates that XAI and MEMS provides inordinate improvements in terms of data impairments thus increasing the transparency of the projected system to an average 96%.

Epigenetic perspectives associated with COVID-19 infection and related cytokine storm: an updated review.

PURPOSE: The COVID-19 pandemic caused by the novel Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) has put the world in a medical crisis for the past three years; nearly 6.3 million lives have been diminished due to the virus outbreak. This review aims to update the recent findings on COVID-19 infections from an epigenetic scenario and develop future perspectives of epi-drugs to treat the disease. METHODS: Original research articles and review studies related to COVID-19 were searched and analyzed from the Google Scholar/PubMed/Medline databases mainly between 2019 and 2022 to brief the recent work. RESULTS: Numerous in-depth studies of the mechanisms used by SARS-CoV-2 have been going on to minimize the consequences of the viral outburst. Angiotensin-Converting Enzyme 2 receptors and Transmembrane serine protease 2 facilitate viral entry to the host cells. Upon internalization, it uses the host machinery to replicate viral copies and alter the downstream regulation of the normal cells, causing infection-related morbidities and mortalities. In addition, several epigenetic regulations such as DNA methylation, acetylation, histone modifications, microRNA, and other factors (age, sex, etc.) are responsible for the regulations of viral entry, its immune evasion, and cytokine responses also play a major modulatory role in COVID-19 severity, which has been discussed in detail in this review. CONCLUSION: Findings of epigenetic regulation of viral pathogenicity open a new window for epi-drugs as a possible therapeutical approach against COVID-19.

A concerted mechanism involving ACAT and SREBPs by which oxysterols deplete accessible cholesterol to restrict microbial infection.

Most of the cholesterol in the plasma membranes (PMs) of animal cells is sequestered through interactions with phospholipids and transmembrane domains of proteins. However, as cholesterol concentration rises above the PM's sequestration capacity, a new pool of cholesterol, called accessible cholesterol, emerges. The transport of accessible cholesterol between the PM and the endoplasmic reticulum (ER) is critical to maintain cholesterol homeostasis. This pathway has also been implicated in the suppression of both bacterial and viral pathogens by immunomodulatory oxysterols. Here, we describe a mechanism of depletion of accessible cholesterol from PMs by the oxysterol 25-hydroxycholesterol (25HC). We show that 25HC-mediated activation of acyl coenzyme A: cholesterol acyltransferase (ACAT) in the ER creates an imbalance in the equilibrium distribution of accessible cholesterol between the ER and PM. This imbalance triggers the rapid internalization of accessible cholesterol from the PM, and this depletion is sustained for long periods of time through 25HC-mediated suppression of SREBPs and continued activation of ACAT. In support of a physiological role for this mechanism, 25HC failed to suppress Zika virus and human coronavirus infection in ACAT-deficient cells, and Listeria monocytogenes infection in ACAT-deficient cells and mice. We propose that selective depletion of accessible PM cholesterol triggered by ACAT activation and sustained through SREBP suppression underpins the immunological activities of 25HC and a functionally related class of oxysterols.

Bioactive food components and their inhibitory actions in multiple platelet pathways.

In addition to hemostasis and thrombosis, blood platelets are involved in various processes such as inflammation, infection, immunobiology, cancer metastasis, wound repair and angiogenesis. Platelets' hemostatic and non-hemostatic functions are mediated by the expression of various membrane receptors and the release of proteins, ions and other mediators. Therefore, specific activities of platelets responsible for the non-hemostatic disease are to be inhibited while leaving the platelet's hemostatic function unaffected. Platelets' anti-aggregatory property has been used as a primary criterion for antiplatelet drugs/bioactives; however, their non-hemostatic activities are not well known. This review describes the hemostatic and non-hemostatic function of human blood platelets and the modulatory effects of bioactive food components. PRACTICAL APPLICATIONS: In this review, we have discussed the antiplatelet effects of several food components. These bioactive compounds inhibit both hemostatic and non-hemostatic pathways involving blood platelet. Platelets have emerged as critical biological factors of normal and pathologic vascular healing and other diseases such as cancers and inflammatory and immune disorders. The challenge for therapeutic intervention in these disorders will be to find drugs and bioactive compounds that preferentially block specific sites implicated in emerging roles of platelets' complicated contribution to inflammation, tumour growth, or other disorders while leaving at least some of their hemostatic function intact.

Optimizing pediatric asthma education using virtual platforms during the COVID-19 pandemic.

BACKGROUND: We compared patient and caregiver knowledge and confidence for managing asthma, and participant experiences when comprehensive asthma education was delivered in person versus in the virtual setting. METHODS: We performed a multi-methods study using structured surveys and qualitative interviews to solicit feedback from patients and caregivers following participation in a comprehensive asthma education session between April 2018 and October 2021. We compared participant knowledge and confidence for managing asthma as well as user experience when the education was attended in-person or virtually. Quantitative responses were summarized descriptively, and qualitative feedback was analyzed for major themes. RESULTS: Of 100 caregivers/patients who completed post education satisfaction surveys and interviews, 52 attended in person and 48 virtually, with the mean age of patients being 6.7 years (range: 1.2-17.0). Participant reported gains in knowledge and confidence for asthma management were not different between groups and 65.2% preferred attending virtual asthma education. The majority of participants described virtual education as a safer modality that was more convenient and accessible. CONCLUSIONS: We demonstrated the successful implementation of a novel, virtual asthma education program for patients and caregivers of children with asthma. Both virtual and in-person delivered asthma education were equally effective for improving perceived knowledge and confidence for asthma self-management and virtual education was considered safer, more convenient and accessible. Virtual asthma education offers an attractive and effective option for improving the reach of quality asthma education programs and may allow more children/patients to benefit.

Multiwalled Carbon Nanotubes as Nanomaterial Tool in the Management of Prostate Cancer: A Possible Nanoformulation Approach.

Prostate cancer (PCa) is one of the leading diseases in men all over the world caused due to over-expression of prostate-specific membrane antigen (PSMA). Currently, the detection and targeting of PCa is one of the major challenges in the prostate gland. Therefore, Bruton tyrosine kinase inhibitor molecules like ibrutinib (Ibr) loaded with nanomaterials like multi-walled carbon nanotubes (MWCNTs), which has good physico-chemical properties may be the best regimen to treat PCa. In this strategy, the chemically modified MWCNTs have excellent 'Biosensing' properties makes it easy for detecting PCa without fluorescent agent and thus targets particular site of PCa. In the present study, Ibr/MWCNTs conjugated with T30 oligonucleotide may selectively target and inhibit PSMA thereby reduce the over-expression in PCa. Hence, the proposed formulation design can extensively reduce the dosage regimen without any toxic effect. Additionally, the present hypothesis also revealed the binding mode of Ibr in the catalytic pocket of PSMA by in silico method. Therefore, we presume that if this hypothesis proves correct, it becomes an additional novel tool and one of the conceivable therapeutic options in treating PCa.

Can plant-derived anti-HIV compounds be used in COVID-19 cases?

People living with HIV are more exposed to the adverse health effects of the worldwide COVID-19 pandemic. The pandemic's health and social repercussions may promote drug abuse and inadequate HIV management among this demographic. The coronavirus pandemic of 2019 (COVID-19) has caused unprecedented disruption worldwide in people's lives and health care. When the COVID-19 epidemic was identified, people with HIV faced significant obstacles and hurdles to achieving optimal care results. The viral spike protein (S-Protein) and the cognate host cell receptor angiotensin-converting enzyme 2 (ACE2) are both realistic and appropriate intervention targets. Calanolides A, Holy Basil, Kuwanon-L, and Patentiflorin have anti-HIV effects. Our computational biology study investigated that these compounds all had interaction binding scores related to S protein of coronavirus of -9.0 kcal /mol, -7.1 kcal /mol, -9.1 kcal /mol, and -10.3 kcal/mol/mol, respectively. A combination of plant-derived anti-HIV compounds like protease inhibitors and nucleoside analogs, which are commonly used to treat HIV infection, might be explored in clinical trials for the treatment of COVID-19.

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment.

The Industrial Internet of Things (IIoT) has received significant attention from several leading industries like agriculture, mining, transport, energy, and healthcare. IIoT acts as a vital part of Industry 4.0 that mainly employs machine learning (ML) to investigate the interconnection and massive quantity of the IIoT data. As the data are generally saved at the cloud server, security and privacy of the collected data from numerous distributed and heterogeneous devices remain a challenging issue. This article develops a novel multi-agent system (MAS) with deep learning-based privacy preserving data transmission (BDL-PPDT) scheme for clustered IIoT environment. The goal of the BDL-PPDT technique is to accomplish secure data transmission in clustered IIoT environment. The BDL-PPDT technique involves a two-stage process. Initially, an enhanced moth swarm algorithm-based clustering (EMSA-C) technique is derived to choose a proper set of clusters in the IIoT system and construct clusters. Besides, multi-agent system is used to enable secure inter-cluster communication. Moreover, multi-head attention with bidirectional long short-term memory (MHA-BLSTM) model is applied for intrusion detection process. Furthermore, the hyperparameter tuning process of the MHA-BLSTM model can be carried out by the stochastic gradient descent with momentum (SGDM) model to improve the detection rate. For examining the promising performance of the BDL-PPDT technique, an extensive comparison study takes place and the results are assessed under varying measures. A significant amount of capital is required. It goes without saying that one of the most obvious industrial IoT concerns is the high cost of adoption. Secure data storage and management connectivity failures are common among IoT devices due to the massive amount of data they create. The simulation results demonstrate the enhanced outcomes of the BDL-PPDT technique over the recent methods. Despite the fact that the offered BDL-PPDT technique has an accuracy of just 98.15 percent, it produces the best feasible outcome. Because of the data analysis conducted as detailed above, it was determined that the BDL-PPDT technique outperformed the other current techniques on a range of different criteria and was thus recommended.

A Concise Review on the Role of Natural and Synthetically Derived Peptides in Colorectal Cancer.

Colorectal cancer being the second leading cause of cancer-associated deaths has become a significant health concern around the globe. Though there are various cancer treatment approaches, many of them show adverse effects and some compromise the health of cancer patients. Hence, significant efforts are being made for the evolution of a novel biological therapeutic approach with better efficacy and minimal side effects. Current research suggests that the application of peptides in colorectal cancer therapeutics holds the possibility of the emergence of an anticancer reagent. The primary beneficial factors of peptides are their comparatively rapid and easy process of synthesis and the enormous potential for chemical alterations that can be evaluated for designing novel peptides and enhancing the delivery capacity of peptides. Peptides might be utilized as agents with cytotoxic activities or as a carrier of a specific drug or as cytotoxic agents that can efficiently target the tumor cells. Further, peptides can also be used as a tool for diagnostic purposes. The recent analysis aims at developing peptides that have the potential to efficiently target the tumor moieties without harming the nearby normal cells. Additionally, decreasing the adverse effects, and unfolding the other therapeutic properties of potential peptides, are also the subject matter of in-depth analysis. This review provides a concise summary of the function of both natural and synthetically derived peptides in colorectal cancer therapeutics that are recently being evaluated and their potent applications in the clinical field.

A Review on an Artificial Intelligence Based Ophthalmic Application.

Artificial intelligence is the leading branch of technology and innovation. The utility of artificial intelligence in the field of medicine is also remarkable. From drug discovery and development to introducing products to the market, artificial intelligence can play its role. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help minimize the risk of vision loss and provide a quality life. With the help of artificial intelligence, the workload of humans and manmade errors can be reduced to an extent. The need for artificial intelligence in the area of ophthalmic is also significant. In this review, we elaborated on the use of artificial intelligence in the field of pharmaceutical product development, mainly with its application in ophthalmic care. AI in the future has a high potential to increase the success rate in the drug discovery phase has already been established. The application of artificial intelligence for drug development, diagnosis, and treatment is also reported with the scientific evidence in this paper.

Review on nutraceuticals: phase transition from preventive to protective care.

Nutraceuticals are essential for healthcare which is an alternative medicine that has gained popularity in recent years. Nutraceuticals consist of nutrients, herbals, and dietary supplements, which make them useful in preserving and promoting health, fighting illness, and improving overall quality of life. Its success or failure will be determined by its rapid expansion, research advances, lack of standards, marketing enthusiasm, quality assurance, and regulations. Nutraceuticals have been used in different regions under different names/categories. however, globally there are no stringent pharmaceutical standards for nutraceutical health products till date, but slowly regulators are paying attention on it. Nutraceuticals can be broadly classified according to it clinical significance, source and therapeutic effects. Nutraceuticals and functional foods have grown to be a multibillion-dollar business worldwide in recent years and personalization is the emerging approach to deliver the best therapeutic effect in future. This review carries extensive information about nutraceutical history, classification, regulatory aspects and industrial perspective.

Peptidylarginine deiminase-4: Medico-formulative strategy towards management of rheumatoid arthritis.

Peptidylarginine deiminase-4 (PAD4) is a calcium-dependent enzyme that catalyzes the conversion of arginine into citrulline of macromolecules in the body. It governs several processes including apoptosis, innate immunity (Netosis), and pluripotency. Dysregulated PAD4 plays a vital role in the occurrence and development of Rheumatoid arthritis (RA). Therefore, PAD4 is considered a promising target for diagnosing and treating RA. Over the last few years research has been carried out on PAD4 inhibitors. When administered it circulates to the entire body and inhibits PAD4 causing immunosuppression which may lead to infection. A growing number of studies have demonstrated infiltration and differentiation of monocytes and macrophages into the inflamed synovium, inducing overexpression of PAD4 levels in the inflamed joints. To overcome the above-mentioned critical issues, the targeted drug delivery systems inhibit PAD4 at the inflamed site. This review provides an update on the PAD4 inhibitors and emerging advanced drug delivery systems for the treatment of RA. Finally, we concluded that active targeting of PAD4 inhibitors to inflamed joints via hybrid nanocarriers provided an improved therapeutic efficacy, minimized extra synovial toxicity, and prevent the occurrence of inflammation in RA.

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts.

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy.

Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model.

Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep learning process aid the designer to abridge the circuitry and also to increase the computation speed at the cost of the accuracy of results. High computational complexity and low-power requirement of portable devices in the dark silicon era sought suitable alternate for Complementary Metal Oxide Semiconductor (CMOS) technology. Gate Diffusion Input (GDI) logic is one of the prompting alternatives to CMOS logic to reduce transistors and low-power design. In this work, a novel energy and area efficient 1-bit GDI-based full swing Energy and Area efficient Full Adder (EAFA) with minimum error distance is proposed. The proposed architecture was constructed to mitigate the cascaded effect problem in GDI-based circuits. It is proved by extending the proposed 1-bit GDI-based adder for different 16-bit Energy and Area Efficient High-Speed Error-Tolerant Adders (EAHSETA) segmented as accurate and inaccurate adder circuits. The proposed adder's design metrics in terms of delay, area, and power dissipation are verified through simulation using the Cadence tool. The proposed logic is deployed to accelerate the convolution process in the Low-Weight Digit Detector neural network for real-time handwritten digit classification application as a case study in the Intel Cyclone IV Field Programmable Gate Array (FPGA). The results confirm that our proposed EAHSETA occupies fewer logic elements and improves operation speed with the speed-up factor of 1.29 than other similar techniques while producing 95% of classification accuracy.

Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models.

Accurate prediction of cardiovascular disease is necessary and considered to be a difficult attempt to treat a patient effectively before a heart attack occurs. According to recent studies, heart disease is said to be one of the leading origins of death worldwide. Early identification of CHD can assist to reduce death rates. When it comes to prediction using traditional methodologies, the difficulty arises in the intricacy of the data and relationships. This research is aimed at applying recent machine learning technology to identify heart disease from past medical data to uncover correlations in data that can greatly improve the accuracy of prediction rates using various machine learning models. Models have been implemented using naive Bayes, random forest algorithms, and the combinations of two models such as naive Bayes and random forest methods. These methods offer numerous attributes associated with heart disease. This proposed system foresees the chance of rising heart disease. The proposed system uses 14 parameters such as age, sex, quick blood sugar, chest discomfort, and other medical parameters which are used in the proposed system. Our proposed systems find the probability of developing heart disease in percentages as well as the accuracy level (accuracy of 93%). Finally, this proposed method will support the doctors to analyze the heart patients competently.

Measuring and Manipulating Membrane Cholesterol for the Study of Hedgehog Signaling.

Cholesterol is an abundant lipid in mammalian plasma membranes that regulates the reception of the Hedgehog (Hh) signal in target cells. In vertebrates, cell-surface organelles called primary cilia function as compartments for the propagation of Hh signals. Recent structural, biochemical, and cell-biological studies have led to the model that Patched-1 (PTCH1), the receptor for Hh ligands, uses its transporter-like activity to lower cholesterol accessibility in the membrane surrounding primary cilia. Cholesterol restriction at cilia may represent the long-sought-after mechanism by which PTCH1 inhibits Smoothened (SMO), a cholesterol-responsive transmembrane protein of the G protein-coupled receptor superfamily that transmits the Hh signal across the membrane.Protein probes based on microbial cholesterol-binding proteins revealed that PTCH1 controls only a subset of the total cholesterol molecules, a biochemically defined fraction called accessible cholesterol. The accessible cholesterol pool coexists (and exchanges) with a pool of sequestered cholesterol, which is bound to phospholipids like sphingomyelin. In this chapter, we describe how to measure the accessible and sequestered cholesterol pools in live cells with protein-based probes. We discuss how to purify and fluorescently label these probes for use in flow cytometry and microscopy-based measurements of the cholesterol pools. Additionally, we describe how to modulate accessible cholesterol levels to determine if this pool regulates Hh signaling (or any other cellular process of interest).

Towards next-generation personalization of tacrolimus treatment: a review on advanced diagnostic and therapeutic approaches.

The benefit of personalized medicine is that it allows the customization of drug therapy - maximizing efficacy while avoiding side effects. Genetic polymorphisms are one of the major contributors to interindividual variability. Currently, the only gold standard for applying personalized medicine is dose titration. Because of technological advancements, converting genotypic data into an optimum dose has become easier than in earlier years. However, for many medications, determining a personalized dose may be difficult, leading to a trial-and-error method. On the other hand, the technologically oriented pharmaceutical industry has a plethora of smart drug delivery methods that are underutilized in customized medicine. This article elaborates the genetic polymorphisms of tacrolimus as case study, and extensively covers the diagnostic and therapeutic technologies which aid in the delivery of personalized tacrolimus treatment for better clinical outcomes, thereby providing a new strategy for implementing personalized medicine.