Narrow Results

The emergence of drug resistance in Type 1 (T1) breast cancer poses a critical challenge to effective treatment and patient outcomes. Our study introduces an innovative framework that integrates Bayesian statistical methods with machine learning (ML) to advance predictive modeling of drug resistance mechanisms in T1 breast cancer. By uniting the strengths of dynamic Bayesian networks (DBNs) and ML, this approach enables the analysis of complex, multi-dimensional clinical data, including genomic, proteomic, and treatment response datasets.

DBNs are employed to model the temporal evolution of resistance mechanisms, capturing time-dependent biological changes. ML algorithms complement this by uncovering intricate patterns and forecasting resistance trajectories under various therapeutic regimens. This synergistic combination not only identifies key biomarkers and resistance pathways but also addresses uncertainty and variability in patient responses, providing a robust predictive tool.

The resulting model offers actionable insights to clinicians, aiding in the optimization of treatment strategies and improving patient outcomes. This work highlights the transformative potential of integrating Bayesian and ML methodologies to unravel complex biological phenomena, paving the way for advancements in precision oncology and personalized medicine.

Various tyrosine kinase inhibitors (TKIs) have been developed to target human epidermal growth factor receptor (EGFR) for cancer therapy. However, many patients treated with first-line TKIs are clinically observed to eventually establish a gatekeeper T790M mutation in the ATP-binding site of the EGFR kinase domain, which is primarily responsible for acquired drug resistance to cancers. Over the past decades, a number of noncovalent, wild-type-sparing and ATP-competitive inhibitors (NWAIs) were reported to selectively target the T790M mutant over wild-type kinase, which are independent of the traditional inhibitor classification system that categorizes EGFR TKIs into four generations. Here, we systematically investigated the intermolecular interaction of wild-type EGFR (EGFR${}^{\text{W}\text{T}})$ and its T790M mutant (EGFR${}^{\text{T}790\text{M}})$ with 15 existing NWAI inhibitors, paying attention to the structural and energetic responses of inhibitor ligands to the gatekeeper mutation. It was revealed that the NWAIs can be typed into three classes I, II and III, which can form S$\cdot \cdot \cdot \pi$ interactions, hydrophobic (van der Waals) contacts and weak hydrogen (halogen) bonding with the side-chain thioether moiety of the mutant Met790 residue, respectively, thus conferring additional affinity and specificity to inhibitor ligands upon the T790M mutation. In addition, we further performed 2D-chemical similarity search to identify new class I NWAIs, from which two Staurosporine analogs (i.e. UCN01 and ZHD0501) were identified to have a good selectivity for EGFR${}^{\text{T}790\text{M}}$ over EGFR${}^{\text{W}\text{T}}$. They can be exploited as promising leading chemical scaffolds to further develop potent, selective, wild-type-sparing NWAI inhibitors of EGFR${}^{\text{T}790\text{M}}$ gatekeeper mutant.

Tuberculosis (TB) is among the 10 top causes of deaths worldwide, and one-quarter of the world population hosts latent TB pathogens. Therefore, avoiding the emergence of drug-resistant strains has become a central issue in TB control. In this work, we propose a nested model for TB transmission and control, wherein both within-host and between-host dynamics are modeled. We use the model to compare the effects of three types of antibiotic treatment protocols and combinations thereof in an in silico population. For a fixed value of antibiotics clearance rate and relative efficacy against resistant strains, the oscillating intermittent protocol, pure or combined, is the most effective against the sensitive strains. However, this protocol also creates a selective advantage for the resistant strains, returning the worst result in comparison to the other protocols. We suggest that nested models should be further developed, since they might be able to inform decision-makers regarding the optimal TB control protocols to be applied under the specific parameters and other epidemiological factors in different populations.

Tamoxifen is one of the most common hormone therapy drug for estrogen receptor (ER)-positive breast cancer. Tumor cells with drug resistance often cause recurrence and metastasis in cancer patients. Luteolin is a natural compound found from various types of vegetables and exhibit anticancer activity in different cancers. This study demonstrated that luteolin inhibits the proliferation and induces apoptosis of tamoxifen-resistant ER-positive breast cancer cells. Luteolin also causes cell cycle arrest at the G2/M phase and decreases mitochondrial membrane potential. Besides, luteolin reduces the levels of activated PI3K/AKT/mTOR signaling pathway. The combination treatment of luteolin and PI3K, AKT, or mTOR inhibitors synergistically increases apoptosis in tamoxifen-resistant ER-positive breast cancer cells. Ras gene family (K-Ras, H-Ras, and N-Ras), an activator of PI3K, was transcriptionally repressed by luteolin via induction of tumor suppressor mixed-lineage leukemia 3 (MLL3) expression. MLL3 increases the level of monomethylation of Histone 3 Lysine 4 on the enhancer and promoter region of Ras genes, thus causes repression of Ras expressions. Our finding implies that luteolin was a promising natural agent against tamoxifen resistance of breast cancer.

Gliomas are tumors of the primary central nervous system associated with poor prognosis and high mortality. The 5-year survival rate of patients with gliomas received surgery combined with chemotherapy or radiotherapy does not exceed 5%. Although temozolomide is commonly used in the treatment of gliomas, the development of resistance limits its use. MicroRNAs are non-coding RNAs involved in numerous processes of glioma cells, such as proliferation, migration and apoptosis. MicroRNAs regulate cell cycle, PI3K/AKT signal pathway, and target apoptosis-related genes (e.g., BCL6), angiogenesis-related genes (e.g., VEGF) and other related genes to suppress gliomas. Evidence illustrates that microRNAs can regulate the sensitivity of gliomas to temozolomide, cisplatin, and carmustine, thereby enhancing the efficacy of these agents. Moreover, traditional Chinese medicine (e.g., tanshinone IIA, xanthohumol, and curcumin) exert antiglioma effects by regulating the expression of microRNAs, and then microRNAs inhibit gliomas through influencing the process of tumors by targeting certain genes. In this paper, the mechanisms through which microRNAs regulate the sensitivity of gliomas to therapeutic drugs are described, and traditional Chinese medicine that can suppress gliomas through microRNAs are discussed. This review aims to provide new insights into the traditional Chinese medicine treatment of gliomas.

This paper studies the dynamics of the hepatitis B virus (HBV) model and the therapy regimens of HBV disease. First, we propose a new mathematical model of HBV with drug resistance, and then analyze its qualitative and dynamical properties. Combining the clinical data and theoretical analysis, we demonstrate that our model is biologically plausible and also computationally viable. Second, we demonstrate that the intermittent antiviral therapy regimen is one of the possible strategies to treat this kind of complex disease. There are two main advantages of this regimen, i.e. it not only may delay the development of drug resistance, but also may reduce the duration of on-treatment time compared with the long-term continuous medication. Moreover, such an intermittent antiviral therapy can reduce the adverse side effects. Our theoretical model and computational results provide qualitative insight into the progression of HBV, and also a possible new therapy for HBV disease.

Generalized language-of-thought arguments, appropriate, in the sense of Dretske, to interacting cognitive modules, permit exploration of how disease states interact with medical treatment, given an embedding context of structured psychosocial stress. The interpenetrating feedback between treatment and response creates a kind of idiotypic hall of mirrors generating a synergistic pattern of efficacy, treatment failure, adverse reactions, and patient noncompliance which, from a Rate Distortion perspective, embodies a distorted image of externally-imposed structured stress. For the US, accelerating spatial and social spread of such stress enmeshes both dominant and subordinate populations in a linked system of pathogenic social hierarchy which will express itself, not only in an increasingly unhealthy society, but in the diffusion of therapeutic failure, including, but not limited to, drug-based treatments.

Diversity of drugs against bacterial infections, and development of resistance to such drugs are increasing. We formulate and analyze a deterministic model for the population dynamics of sensitive and resistant bacteria to multiple bactericidal and bacteriostatic antibiotics, assuming that drug resistance is acquired through mutations and plasmid transmission. Model equilibria are determined from qualitative analysis, and numerical simulations are used to assess temporal dynamics of sensitive and drug-resistant bacteria. The model presents three possibilities: elimination of bacteria, persistence of only resistant bacteria, or coexistence of sensitive and resistant bacteria. Evolution to one of these scenarios depends on thresholds numbers involving sensitive and resistant bacteria.

Bacterial plasmids play a fundamental role in antibiotic resistance. However, a lack of knowledge about their biology is an obstacle in fully understanding the mechanisms and properties of plasmid-mediated resistance. This has motivated investigations of real systems in vitro to analyze the transfer and replication of plasmids. In this work, we address this issue with mathematical modeling. We formulate and perform a qualitative analysis of a nonlinear system of ordinary differential equations describing the competition dynamics between plasmids and sensitive and resistant bacteria. In addition, we estimated parameter values from empirical data. Our model predicts scenarios consistent with biological phenomena. The elimination or spread of infection depends on factors associated with bacterial reproduction and the transfer and replication of plasmids. From the estimated parameters, three bacterial growth experiments were analyzed in vitro. We determined the experiment with the highest bacterial growth rate and the highest rate of plasmid transfer. Moreover, numerical simulations were performed to predict bacterial growth.

Currently, more than 600 million people worldwide are diagnosed with COVID-19, while the implication of Tuberculosis cannot be ignored. The combination of COVID-19 and Tuberculosis exacerbates the catastrophe, dealing a serious blow to the healthcare system. This paper addresses how to develop effective and reasonable programs to combat the spread of COVID-19 and Tuberculosis in the absence of Tuberculosis medical resources, as well as exploring the impact of medical resources on optimal control implementation. Therefore, a co-infection dynamic of COVID-19 and Tuberculosis is constructed and analyzed. In order to investigate approaches to mitigate the disease transmission, a comprehensive and integrated study including sensitivity analysis, optimal control design and cost-effectiveness analysis is then performed. The simulation results illustrate that, the combination of the three control measures effectively achieves a win-win result in economic and epidemiological terms. In addition, the impact of Tuberculosis medical resources is highlighted, and the study shows that an appropriate increase in the medical resource supply for Tuberculosis during optimal control can have a stronger inhibitory effect on co-infection. Finally, based on the actual data, the model is validated by fitting the cumulative confirmed case curves of the two diseases.

A system of differential equations for the control of tumor growth cells in a cycle nonspecific chemotherapy is analyzed. Spontaneously acquired drug resistance is taken into account by means of a mutation rate increasingly dependent on time. For general tumor growth and drug kill rates the optimal treatment consists of maximum allowable drug concentration throughout, supporting the conjecture that variable mutation rate to drug resistance does not basically alter the corresponding results of constant mutation rate.

We study the asymptotic behaviour of an infinite system of differential equations describing the expectations in a branching random walk. The original stochastic formulation was employed to describe the process of evolution of reversible drug resistance in cancer cells. The problem is formulated as an operator exponential function in the space of absolutely summable sequences. Conditions are found for the asymptotic decay of the operator exponential function, using methods of the spectral theory of linear operators. A discussion is provided relating mathematical results to the behaviour of models of gene amplification and evolution of DNA repeats.

AUSTRALIA – Diabetes drug may reduce heart attack risk.

AUSTRALIA – E. coli jabs toxin into gut cells.

AUSTRALIA – Survival of wildlife species depends on its neighbor's genes.

INDONESIA – Indonesia sets a carbon time-bomb.

SINGAPORE – New 3D hair follicle model to accelerate cure for baldness.

SINGAPORE – Patient, heal thyself: Solution to personalized treatment for chronic infections could lie in the patient's own blood.

SINGAPORE – NTU and A*STAR scientists create super biomaterials from squids, mussels and sea snails.

UNITED STATES – Drug erases brain tumor in mice.

UNITED STATES – To treat obesity, consider 100 trillion gut bugs.

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UNITED STATES – Heartbeat protects medical implants from hackers.

UNITED STATES – Team uncovers HIV's secret survival trick.

UNITED STATES – TB genomes yield insights on drug resistance.

AFRICA – Meningitis vaccine cuts cases by 94 per cent in Chad.

LATIN AMERICA – Online guides help poor labs build their own equipment.

INDONESIA – Alleged cure for dengue in Indonesia stirs debate.

JAPAN – Daiichi Sankyo submits SAVAYSATM tablets new drug application to the U.S. FDA.

MALAYSIA – Marine bacterial compound shows antiviral potential.

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SINGAPORE – Start-up market's artificial cell membranes can speed up drug discovery.

SINGAPORE – NCCS scientists discover gene regulation is dependent on protein.

SINGAPORE – Elephant shark genome provides new insights into bone formation and adaptive immunity in humans.

SINGAPORE – Vela Diagnostics launches qualitative test to identify never before documented virus in Western hemisphere.

SINGAPORE – New discovery of biomarker to improve diagnosis, prognosis and treatment of esophageal squamous cell carcinoma.

VIETNAM – Setback for dengue-blocking mosquito trial in Vietnam.

AFRICA – Scientists to create disease-resistant Ethiopian enset.

AFRICA – Viral load tests ‘could transform HIV treatment failure’.

EUROPE – QIAGEN and Exosome Diagnostics to develop first-in-class, non-invasive diagnostics for key genetic biomarkers in lung and other cancers.

EUROPE – Study examines probiotic use in preventing gastrointestinal disorders in infants.

EUROPE – Gecko Biomedical's co-founders demonstrate a ‘bio-inspired’ tissue adhesive that shows promise for minimally invasive heart surgery and vessel repair.

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UNITED STATES – Scripps Florida scientists identify possible key to drug resistance in Crohn's disease.

UNITED STATES – On-demand vaccines possible with engineered nanoparticles.

UNITED STATES – Odor receptors discovered in lungs.

UNITED STATES – New technique targets specific areas of cancer cells with different drugs.

AUSTRALIA – Origins of plague: Scientists reveal the cause of one of the most devastating pandemics in human history.

AUSTRALIA – Admedus releases interim phase I results for Herpes study.

CAMBODIA – Study tags cause of malaria drug resistance in Cambodia.

JAPAN – Discovery of mechanism by which sex hormone regulates aggressive behavior.

SINGAPORE – Singapore's first influenza vaccines demonstrates favorable immunogenicity and tolerability in clinical testing.

SINGAPORE – Scientists from Genome Institute of Singapore and Stanford University show RNA architecture expanding understanding of human genetics.

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SINGAPORE – Researchers make new discovery of protein as a promising target for treatment of anaplastic thyroid carcinoma.

AFRICA – African project aims to stop rats in their tracks.

AFRICA – African monsoon project to benefit crops and healthcare.

CANADA – Cancer researchers discover pre-leukemic stem cell at root of AML relapse.

EUROPE – Understanding heart failure at the cellular level.

INDIA – Africa and India cultivate agricultural research ties.

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UNITED STATES – Mouse study shows gene therapy may be possible cure for Hurler syndrome.

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INDIA – Plastic bricks could protect Indian homes from monsoon.

THE PHILIPPINES – Philippines aims for better basic sanitation practices.

SINGAPORE – A*STAR scientists discover gene critical for proper brain development.

AFRICA – Project to conserve indigenous crops launched in Kenya.

AFRICA – Cellphone voice and SMS tech developed to fight Ebola.

AFRICA – Scientists say Kenya’s GMO ban stalling biotech R&D.

AFRICA – Scientists unveil a plan to fight deadly banana disease.

AFRICA – Drug resistance to kill 10 million a year by 2050.

BANGLADESH – Aflatoxin threat in Nepal and Bangladesh.

BANGLADESH – Daily multivitamin improves pregnancy outcomes in South Asia.

EUROPE – New study describes, for the first time, a fundamental mechanism regulating a protein’s shape.

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UNITED STATES – Canopus BioPharma Inc. achieves positive results from an in vitro live Ebola virus study.

AUSTRALIA – Cynata achieves major stem cell manufacturing milestone.

INDONESIA – Rice crucial to Indonesia's food security.

JAPAN – Japan joins Gates's Grand Challenge on tropical diseases.

JAPAN – On-farm system turns rice plants into biofuel and fodder.

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SINGAPORE – Mundipharma demonstrates efficacy of BETADINE(R) formulations against Ebola virus.

SINGAPORE – Scientists determine mechanical forces that drive epithelial wound healing.

SINGAPORE – A*STAR researchers develop expert systems for identifying treatment targets for cancer and rare diseases.

SINGAPORE – NUS researchers pioneer novel strategy to prevent progression of inflammation-associated cancers.

THE PHILIPPINES – New biotech hub aims to nurture more rice researchers.

AFRICA – NAU researcher works to understand forces of abrupt climate change.

AFRICA – Researchers create new model to aid tech selection.

AFRICA – Combining bednets, vaccines ‘good or bad for malaria’.

AFRICA – Experts call for innovations to aid Africa's healthcare.

EUROPE – Europe's first fully remote diabetes trial approved.

UNITED STATES – Scientists identify genetic variations leading to hearing loss in young cancer patients.

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UNITED STATES – Scripps Research Institute scientists develop anti-HIV agent so powerful it can work in a vaccine.

UNITED STATES – Possible strategy identified to combat major parasitic tropical disease.

SINGAPORE – Singapore Health Sciences Authority Approves First Immuno-Oncology Treatment, Bristol-Myers Squibb's Opdivo^® (nivolumab), for Advanced-Stage Lung Cancer

SINGAPORE – The Award of a $7.5 Million Translational Clinical Research Grant from National Medical Research Council Launches the Flagship Programme in Liver Cancer

SINGAPORE – Scientists Discover New Technology Breakthrough in Fighting Viral Diseases

UNITED STATES – BioTek Instruments and Merck Combine Forces for Long-Term Live Cell Experiments

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UNITED STATES – Widespread Loss of Ocean Oxygen to Become Noticeable in 2030s

UNITED KINGDOM – Axol Bioscience Launches iPSC-Derived Astrocyte Kits

TAIWAN – A Natural Plant Metabolite Derivative Q2-3 Efficiently Prevents Metastasis of Mammary Tumour Cells

VIETNAM – Second VINMEC International Hospital Implements Solution for Patient Safety and Quality Management

Beta lactams comprise the largest and still most effective group of antibiotics, but bacteria can gain resistance through different beta lactamases that can degrade these antibiotics. We developed a user friendly tree building web server that allows users to assign beta lactamase sequences to their respective molecular classes and subclasses. Further clinically relevant information includes if the gene is typically chromosomal or transferable through plasmids as well as listing the antibiotics which the most closely related reference sequences are known to target and cause resistance against. This web server can automatically build three phylogenetic trees: the first tree with closely related sequences from a Tachyon search against the NCBI nr database, the second tree with curated reference beta lactamase sequences, and the third tree built specifically from substrate binding pocket residues of the curated reference beta lactamase sequences. We show that the latter is better suited to recover antibiotic substrate assignments through nearest neighbor annotation transfer. The users can also choose to build a structural model for the query sequence and view the binding pocket residues of their query relative to other beta lactamases in the sequence alignment as well as in the 3D structure relative to bound antibiotics. This web server is freely available at http://blac.bii.a-star.edu.sg/.

Viruses are obligatory minute intra-cellular infectious agents with very simple composition. They are nonliving (not active) macromolecules outside the host cell while turning into living active organisms inside host cells. The genetic material (DNA or RNA) carrying the information is crucial for virus replication and enforces the cell to approve virus replication. Consequently, it is cellular resistance against the virus that determines whether a cell at any site is infected or not. In this study, we are interested in the resistance of cells which may be infected by some disturbance such as a function of $t$ or as a random variable. Antimicrobial resistance (AMR) is the wider word for resistance in various kinds of microorganisms and includes resistance to antibacterial, antiviral, anti-parasitic, and anti-fungal medicines. Here we study the AMR problem and also, the waning vaccination in the Percolation area. Percolation is a purely geometric problem in which clusters of connected sites or bonds are clearly defined static objects. We are studying cellular automata from Domany–Kinzel on the population of AMRs as on the spreading network. Each connection is rewired on a one-dimensional chain and combined with any probability p node. Additionally, the Domany–Kinzel model will be applied for AMR and waning vaccination in two dimensions.