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Go back Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Can a human brain be linked to a computer? Last updated: 06/11/24 Scientists in the US have succeeded in mapping the three-dimensional structure of the network of neurons in one cubic millimetre of mouse brain- a feat that would require two petabytes of storage. The human brain contains approximately 100 billion neurons, which is one million times the number of neurons found in a cubic millimetre of mouse brain. The researchers counted over 100,000 neurons and over a billion connections between them within this small cube of brain tissue. To find all the neurons and reconstruct the neural network, researchers had to slice the mouse brain 25,000 times. The issue is that the amount of data to store would kill any single computer. Memory and experiences that would have defined people later would be lost if they tried to store their minds too early. Using a computer too late may result in the accumulation of a mind with dementia, which would not work so well. Human tissue would have to be cut into zillions of thin slices using techniques compatible with dying and cutting. Local electrical changes that travel down dendrites and axons allow neurons to communicate with one another. However, when reconstructing the 3D structure, this may not be possible. After we die, our brains undergo significant chemical and anatomical changes. At the age of 20, they begin to lose 85,000 neurons per day due to apoptosis, or programmed cell death. Many memories that would have shaped a person later would be lost if he or she tried to store their mind too early. There are numerous steps involved in developing a computer capable of storing and processing human-level intelligence. It may be impossible for an artificial intelligence to produce sensations and actions identical to those provided and produced by your biological body. Bots are susceptible to hacking and hardware failure. Connecting sensors to the AI's digital mind would also be difficult. Written by Jeevana Thavarajah Related articles: The evolution of AI / The wonders of the human brain / AI in genetic diagnoses

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link AI: the next step in diagnosis and treatment of genetic diseases 13/12/24, 11:28 AI can process data sets and identify patterns and biomarkers With the development of more intricate Artificial Intelligence (AI) software, which has rapidly grown from the chaotic chatbots to the more well-formed ChatGPT, it is easy to think we are seeing the rise of powerful artificial intelligence that could potentially replace us all. However, there is one problem. Originality does not exist for AI, at least not complete originality. At its most basic, an AI program is trained on a set of data, whether this be an entire search engine’s worth of data, as is the case for ChatGPT, or a few images and phrases gathered from the internet. Therefore, an AI does not know any more than what it can quote or infer from the provided data, which means that a piece of art, a picture of a family, or any short story AI is asked to produce is often a replica of techniques or a chaotic and terrifying mess of images it has been given to use. However, here also lies its strength. AI can take in thousands of images and data sets and notice minor changes and differences the average person could not. It is, therefore, not AI’s ability to create the unique, but instead its ability to recognise the mundane that we can utilise, even in diagnosing and treating genetic disorders. Diagnosis By analysing PET, MRI, fMRI and genetic data, AI can process enormous data sets and identify subtle patterns and biomarkers that often elude human observations, enabling earlier and more precise diagnosis. When looking at examples of the application of AI in the diagnosis of genetic disorders, a good reference is the so-far successful use of AI in diagnosing Huntington’s disease. Huntington’s disease diagnosis using AI Huntington’s disease symptoms present as patients experience involuntary movements and a decline in decision-making processes. Huntington's disease is a genetic disorder, meaning it is caused by a faulty gene, in this case, a fault in the Huntingtin gene (Htt). The Huntington’s disease mutation in Htt results from CAG trinucleotide repeats, a highly polymorphic expansion of Htt consisting of the CAG (cytosine, adenine, guanine) nucleotides (DNA building blocks). Whilst CAG repeats are common and often normal and unharmful, individuals with Huntington’s disease possess an abnormally high number of these CAG repeats (more than 36). When an individual has an abnormally high number of CAG repeats, their Htt proteins do not fold into their proper shape, causing them to bond with other proteins and become toxic to a cell, which ultimately causes cell death in crucial medium spiny neurons (MSN) in the basal ganglia. Basal ganglia are brain structures responsible for the fine-tuning of our motor processes, which they do by essentially allowing neurons to respond in a preferred direction (a target muscle) rather than a null direction using MSNs. So, it is clear how Huntington's disease symptoms occur; mutant Htt leads to cell death in MSNs, leading to the basal ganglia’s inability to control movement, which causes characteristic involuntary behaviours, among other symptoms. Because we identified these changes in Htt and loss of MSN in the basal ganglia, PET, MRI, and fMRI scans are often used in the diagnosis of Huntington’s disease, in addition to genetic and mobility tests. By collecting and extracting clinical and genetic data, certain AI algorithms can analyse the broad range of Huntington’s disease clinical manifestations, identify differences, including even minute changes in the basal ganglia that a doctor may not have, and make an earlier diagnosis. One branch of AI that has proved effective is machine learning. Machine learning models in diagnosis Machine learning uses data and algorithms to imitate the way humans learn. For Huntington's disease diagnosis, this involves the identification of biomarkers and patterns in medical images, gene studies and mobility tests, and detecting subtle changes between data sets, distinguishing Huntington’s disease patients from healthy controls. While machine learning in Huntington’s disease diagnosis comes in many forms, the decision tree model, where the AI uses a decision tree as illustrated in the Project Gallery, has proven very effective. A decision tree model looks at decisions and their possible consequences and breaks them into subsets branching downward, going from decision to effect. Recent research using AI in Huntington’s disease diagnosis has utilised this model to analyse gait dynamics data. This data looks at variation in stride length, how unsteady a person is while walking, and the degree to which one stride interval (the time between strides) differs from any previous and any subsequent strides. For an individual, it is widely accepted that if they have abnormal variations in stride (their walking speed is reduced, their stance is widened), then they are exhibiting symptoms of Huntington’s disease. Therefore, by using this gait data, and having the machine learning model come up with a mean value for stride variation for trial patients, it will be able to discern which patients have stride variation associated with Huntington’s disease (a higher variation in stride) and those that do not. Researchers found that using this method of diagnosis, they were able to accurately identify which gaits belonged to Huntington's disease patients, with an accuracy of up to 100%. Furthermore, researchers also found decision tree models useful when identifying whether a gene links with Huntington’s disease when comparing patients' genetic information with prefrontal cortex samples, with this method’s accuracy being 90.79%. With these results and even more models showing incredible promise, AI is already proving itself useful when it comes to identifying and diagnosing sufferers of genetic disorders, such as those with Huntington’s disease. But this leads us to ask, can AI even help in the treatment of those suffering from genetic disorders? Treatment- current studies in cystic fibrosis While AI models can be applied diagnostically for disorders such as Huntington's disease, they may also be relied upon in disease treatment. The use of AI in tailored treatment is the focus of current research, with one even looking at improving the lives of those suffering from cystic fibrosis. Around 10,800 people are recorded as having cystic fibrosis in the UK, and this debilitating disorder results in a buildup of thick mucus, leading to persistent infections and other organ complications. The most common cause of cystic fibrosis is a mutation in the gene coding for the protein CFTR, resulting from a deletion in its coding gene, causing improper folding in the protein CFTR, as we saw in Huntington’s disease. This misfolding leads to its retention in the wrong place in a cell, so it can no longer maintain a balance of salt and water on body surfaces. Because of the complex symptoms arising from this imbalance, this disease is very difficult to manage, but there is hope, and hope comes as SmartCare. SmartCare involved home monitoring and followed 150 people with cystic fibrosis for six months, having them monitor their lung function, pulse, oxygen saturation and general wellness and upload recorded data to an app. Subsequently, researchers at the University of Cambridge used machine learning to create a predictive algorithm that used this lung, pulse, and oxygen saturation data, identifying patterns that were associated with a decline in a patient's condition, and then predicted this decline much faster than the patient of their doctor could. On average, this model could predict a decline in patient condition 11 days earlier than when the patient would typically start antibiotics, allowing health providers to respond quicker and patients to feel less restricted by their health. This project was, in fact, so successful that the US CF Foundation is now supporting a clinical implementation study, called Breath, which began in 2019 and continues to this day. Although there is a long way to go, using AI, the future can seem brighter. In Huntington’s disease and cystic fibrosis, we can see its effectiveness in both disease diagnosis and treatment. With the usage of AI predicted to increase in the future, there is a great outlook for patients and an opportunity for greater quality of care. This ultimately could ease patient suffering and prevent patient deaths. All this positive research tells us AI is our friend (although science fiction would often persuade us otherwise), and it will guide us through the tricky diagnosis and treatment of our most challenging diseases, even those engrained in our DNA. Written by Faye Boswell Related articles: AI in drug discovery / Can a human brain be linked to a computer? / AI in medicinal chemistry Project Gallery

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Artificial Intelligence in Drug Research and Discovery 13/12/24, 11:30 Using the new technology AI to develop drugs Drug research has been transformed by artificial intelligence (AI), which has become a game-changing technology in several industries. Only a small portion of potential drugs make it to the market after the lengthy and expensive traditional drug discovery process. A drug's discovery and development can take over ten years and cost an average of US$2.8 billion. Even then, nine out of 10 medicinal compounds fall short of passing regulatory approval and Phase II clinical trials. The use of AI in this process, however, has the potential to greatly improve effectiveness, accuracy, and success rates. Given that AI can help with rational drug design, support decision-making, identify the best course of treatment for a patient, including personalised medicines, manage the clinical data generated, and use it for future drug development, it is reasonable to assume that it will play a role in the development of pharmaceutical products from the laboratory bench to bedside table. There are several ways in which AI is currently being used to enhance the drug discovery process. One of the primary applications is virtual screening ( Figure 2 ), which involves using machine learning algorithms to analyse large libraries of chemical compounds and predict which ones are likely to be effective against a specific disease target. This can significantly reduce the time and cost required for drug discovery by narrowing down the number of compounds that need to be tested in the lab. Another way AI is being used in drug discovery is through generative models, which use deep learning algorithms to design molecules that are optimised for specific therapeutic targets. This approach can be used to design molecules that are effective against a specific target while also minimising toxicity or other undesirable properties. Data analysis is another area where AI can be applied in drug discovery. By analysing large datasets of biological and chemical information, AI can help researchers identify patterns and relationships that may be relevant to drug discovery. For example, AI can be used to analyse genomic data to identify potential drug targets or to analyse drug-drug interactions to identify potential safety issues. However, one of the main challenges is the need for high-quality data, as AI models rely on large amounts of data to make accurate predictions. Additionally, there is a risk that AI models may miss important insights or make incorrect predictions if the data used to train them is biased or incomplete. Nevertheless, the continued development of AI and its amazing tools seeks to lessen the difficulties experienced by pharmaceutical firms, impacting both the medication development process and the full lifecycle of the product, which may account for the rise in the number of start-ups in this industry. The importance of automation will increase as a result of using the most up-to-date AI-based technologies, which will not only shorten the time needed for products to reach the market but also enhance product quality, increase overall production process safety, and make better use of available resources while also being cost-effective. In conclusion, the use of AI in drug discovery has the potential to revolutionize the field and significantly improve the success rate of potential drug candidates. Despite the challenges and limitations, the continued research and development of AI in drug discovery will undoubtedly lead to faster, cheaper, and more accurate drug development. Written by Navnidhi Sharma Related articles: A breakthrough procedure in efficient drug discovery / AI in medicinal chemistry / AI advancing genetic disease diagnosis Project Gallery

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link AI in medicinal chemistry 13/12/24, 11:29 How it's used We are always surrounded by medicine, whether this be through, for example, the cabinet in your house containing prescription drugs or walking by a pharmacy during the day. It is no secret that medical drugs are essential - they both mitigate the symptoms of disease and even prevent further future illness. However, whilst ingesting a tablet is easy for most, it seems to be that we can sometimes forget the vigorous amount of scientific research that goes into successfully synthesising a new drug, i.e., the core of medicinal chemistry. This process typically takes up to an astounding 10 years or more, but with new artificial intelligence (AI) emerging it is thought to be that this number will lower. What exactly is artificial intelligence? It can broadly be defined as the ability to produce human intelligence through the use of machinery such as computers or software. Based on this, one may question why AI is needed if we can just simply communicate ideas through writing, speaking and so on. The answer is increased efficiency – one example of man made neurones is discussed on the website Interesting Engineering, which are able to produce impulses up to one billion times per second. Fascinatingly, this is quicker than humans, so it could also be argued that AI is actually better than us! There are many phases of the drug development process, from early pre-clinical research to post-market surveillance. When a drug is administered, the body uses enzymes such as mainly those from the CYP family to break the compound down into smaller structures, through a process known as metabolism. Drug metabolism can create toxic molecules that are able to covalently bind to proteins in the body causing serious illness, but also molecules that can be harmlessly excreted through faeces or urine. Of course, chemists can look for sites of metabolism by studying the angles and positions of atoms, however AI is able to do this much quicker and with higher accuracy. SuperCYPsPred is an example of a free online web application that can predict if a drug may be a CYP enzyme inhibitor in pre-clinical drug discovery, as the software is able to identify five of such inhibitors. Through this, we can understand how a drug’s metabolic pathway may differ and investigate further early on, allowing scientists to make structural changes before proceeding onto the next phase of development. Through this, millions of pounds can be saved from marketing an unsuccessful drug as well as decrease the chances of causing injury to the public. AI is also able to use machine learning (ML) to carry out tasks. ML is when machinery processes a large data set and identifies complex patterns to problem solve. From this then comes deep learning (DL), which allows this ML to be applied in different fields. For example, DeepCE is a “novel deep learning computer model” that helps predict changes in gene expression with certain drugs. It is able to do this by using the following two sources: DrugBank which contains data for 11,000 safely approved drugs and the L1000 dataset that has information on over 1 million perturbed organ tissue gene expressions. From this, researchers were able to obtain 10 drug candidates for the treatment of COVID-19 infection, in which 2 have been successfully marketed. Based on the above, it is clear that AI holds a lot of power in speeding up the drug discovery and development process. With the technology sector advancing in general as well, we are looking at a future where AI will become even more dominant in the pharmaceutical research industry. Whilst AI can predict several drug properties, it is also important to remember that we physically cannot predict every single thing out there – we can only try our best, which AI is aiding. Written by Harsimran Kaur Related articles: AI in drug discovery / A breakthrough procedure in efficient drug discovery / Role of chemistry in medicine Project Gallery

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The Silent Protectors 12/12/24, 16:17 How chemistry ensures nuclear safety standards Nuclear safety is vital in ensuring the correct procedures and policies are in place whilst handling radioactive waste/materials. This article will go into the crucial role that chemistry plays in upholding and enhancing nuclear safety standards, highlighting its multifaceted contributions to the protection of both people and the environment. Chemical Analysis in Radioactive Material Detection: The use of many analytical tools in chemistry allows us to detect radioactive material and quantify these materials. The most popular techniques used are chromatography, spectroscopy and mass spectroscopy in chemically identifying these materials. We can use these techniques to early identify any hazardous implications of the materials and in warning symbols. Radiation Dosimetry and Health Protection: Dosimetry is the scientific radiation dose determined by calculations and multiple measurements. The different techniques used to make these values are different types of chemical dosimeters are used such as solid, aqueous, and gases but the most important among all are aqueous dosimeters. Beyond this, it contributes to the creation of protective materials and gear, safeguarding the health of workers in nuclear environments. These advancements exemplify safety on a personal level. Chemical Processes in Nuclear Fuel Cycles: Chemistry ensures the sustainable use of nuclear energy, maximises fuel efficiency, and reduces nuclear waste. The future of nuclear power could be cleaner and more efficient with the help of innovations in this field. The main stages are uranium mining and processing, enrichment of uranium, nuclear reactor fuel fabrication and innovations in fuel cycle chemistry. Understanding and optimising these chemical processes within the nuclear fuel cycle is paramount for ensuring the sustainability, safety, and efficiency of nuclear energy production. Chemistry continues to be a driving force in advancing these processes, contributing to the responsible harnessing of the atom for the benefit of society. Regulatory Compliance and Standards: International and national standards for nuclear safety are underpinned by chemical principles. Chemistry not only ensures compliance with these standards but also drives initiatives to exceed regulatory requirements, setting new benchmarks for safety in the nuclear industry. The government website has a document full of policies in place to ensure all standards are met universally. In conclusion, the silent protectors remain vigilant, their contributions often unseen but undeniably crucial. Chemistry's enduring commitment to nuclear safety ensures that as we unlock the vast potential of nuclear energy, we do so with a profound sense of responsibility, guided by the silent but unwavering hand of chemical expertise. In this symbiotic relationship, chemistry and nuclear energy coalesce to forge a path towards a safer, cleaner, and more sustainable future. Written by Anam Ahmed Related articles: Nuclear fusion / Nuclear medicine / Advances in mass spectrometry Project Gallery

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link A deep dive into the hallmarks defining Alzheimer’s disease 12/12/24, 12:19 Exploring the distinctive features that define and disrupt the brain The progressive decline in neurocognition, resulting in a detrimental effect on one’s activities of daily living, is referred to as dementia. It typically affects people over the age of 65. Multiple theories have been proposed to explain the pathogenesis of Alzheimer’s disease (AD), including the buildup of amyloid plaques in the brain and the formation of neurofibrillary tangles (NFT) in cells. Understanding the pathophysiology of AD is imperative to the development of therapeutic strategies. Therefore, this article will outline the major hallmarks and mechanisms of AD. Hallmark 1: amyloid plaques One of the most widely accepted hypotheses for AD is the accumulation of amyloid beta protein (Aβ) in the brain. Aβ is a 4.2 kDa peptide consisting of approximately 40–42 amino acids, originating from a precursor molecule called amyloid precursor protein. This process, defined as amyloidosis, is strongly linked to brain aging and neurocognitive decline. How do the amyloid plaques form? See Figure 1 . Reasons for the accumulation of amyloid plaques: Decreased autophagy: Amyloid proteins are abnormally folded proteins. Autophagy in the brain is primarily carried out by neuronal and glial cells, involving key structures known as autophagosomes and lysosomes. When autophagy becomes downregulated, the metabolism of Aβ is impaired, eventually resulting in plaque buildup. Overproduction of acetylcholinesterase (AChE): Acetylcholine (Ach) is the primary neurotransmitter involved in memory, awareness, and learning. Overproduction of ACHE by astrocytes into the synaptic cleft can lead to excessive breakdown of Ach, with detrimental effects on cognition. Reduced brain perfusion: Blood flow delivers necessary nutrients and oxygen for cellular function. Reduced perfusion can lead to “intracerebral starvation”, depriving cells of the energy needed to clear Aβ. Reduced expression of low-density lipoprotein receptor-related protein 1: Low-density lipoprotein receptor-related protein 1 (LRP1) receptors are abundant in the central nervous system under normal conditions. They are involved in speeding up the metabolic pathway of Aβ by binding to its precursor and transporting them from the central nervous system into the blood, thereby reducing buildup. Reduced LRP1 expression can hinder this process, leading to amyloid buildup. Increased expression of the receptor for advanced glycation end products (RAGE): RAGE is expressed on the endothelial cells of the BBB, and its interaction with Aβ facilitates the entry of Aβ into the brain. Hallmark 2: neurofibrillary tangles See Figure 2 Neurofibrillary tangles are excessive accumulations of tau protein. Microtubules typically support neurons by guiding nutrients from the soma (cell body) to the axons. Furthermore, tau proteins stabilise these microtubules. In AD, signalling pathways involving phosphorylation and dephosphorylation cause tau proteins to detach from microtubules and stick to each other, eventually forming tangles. This results in a disruption in synaptic communication of action potentials. However, the exact mechanism remains unclear. Recent studies suggest an interaction between Aβ and tau, where Aβ can cause tau to misfold and aggregate, forming neurofibrillary tangles inside brain cells. Both Aβ and tau can self-propagate, spreading their toxic effects throughout the brain. This creates a vicious cycle, where Aβ promotes tau toxicity, and toxic tau can further exacerbate the harmful effects of Aβ, ultimately causing significant damage to synapses and neurons in AD. Hallmark 3: neuroinflammation Microglia are the primary phagocytes in the central nervous system. They can be activated by dead cells and protein plaques, where they initiate the innate immune response. This involves the release of chemokines to attract other white blood cells and the activation of the complement system which is a group of proteins involved in initiating inflammatory pathways to fight pathogens. In AD, microglia bind to Aβ via various receptors. Due to the substantial accumulation of Aβ, microglia are chronically activated, leading to sustained immune responses and neuroinflammation. Conclusion The contributions of amyloid beta plaques, neurofibrillary tangles and chronic neuroinflammation provide a framework for understanding the pathophysiology of AD. AD is a highly complex condition with unclear mechanisms. This calls for the need of continued research in the area as it is crucial for the development of effective treatments. Written by Blessing Amo-Konadu Related articles: Alzheimer's disease (an overview) / CRISPR-Cas9 to potentially treat AD REFERENCES 2024 Alzheimer’s Disease Facts and Figures. (2024). Alzheimer’s & dementia, 20(5). doi:https://doi.org/10.1002/alz.13809. A, C., Travers, P., Walport, M. and Shlomchik, M.J. (2001). The complement system and innate immunity. [online] Nih.gov. Available at: https://www.ncbi.nlm.nih.gov/books/NBK27100/ . Bloom, G.S. (2014). Amyloid-β and tau: the Trigger and Bullet in Alzheimer Disease Pathogenesis. JAMA neurology, [online] 71(4), pp.505–8. doi:https://doi.org/10.1001/jamaneurol.2013.5847. Braithwaite, S.P., Stock, J.B., Lombroso, P.J. and Nairn, A.C. (2012). Protein Phosphatases and Alzheimer’s Disease. Progress in molecular biology and translational science, [online] 106, pp.343–379. doi:https://doi.org/10.1016/B978-0-12-396456-4.00012-2. Heneka, M.T., Carson, M.J., El Khoury, J., Landreth, G.E., Brosseron, F., Feinstein, D.L., Jacobs, A.H., Wyss-Coray, T., Vitorica, J., Ransohoff, R.M., Herrup, K., Frautschy, S.A., Finsen, B., Brown, G.C., Verkhratsky, A., Yamanaka, K., Koistinaho, J., Latz, E., Halle, A. and Petzold, G.C. (2015). Neuroinflammation in Alzheimer’s disease. The Lancet. Neurology, 14(4), pp.388–405. doi:https://doi.org/10.1016/S1474-4422(15)70016-5. Kempf, S. and Metaxas, A. (2016). Neurofibrillary Tangles in Alzheimer′s disease: Elucidation of the Molecular Mechanism by Immunohistochemistry and Tau Protein phospho- proteomics. Neural Regeneration Research, 11(10), p.1579. doi:https://doi.org/10.4103/1673-5374.193234. Kumar, A., Tsao, J.W., Sidhu, J. and Goyal, A. (2022). Alzheimer disease. [online] National Library of Medicine. Available at: https://www.ncbi.nlm.nih.gov/books/NBK499922/. Ma, C., Hong, F. and Yang, S. (2022). Amyloidosis in Alzheimer’s Disease: Pathogeny, Etiology, and Related Therapeutic Directions. Molecules, 27(4), p.1210. doi:https://doi.org/10.3390/molecules27041210. National Institute on Aging (2024). What Happens to the Brain in Alzheimer’s Disease? [online] National Institute on Aging. Available at: https://www.nia.nih.gov/health/alzheimers-causes-and-risk-factors/what-happens-brain- alzheimers-disease. Stavoe, A.K.H. and Holzbaur, E.L.F. (2019). Autophagy in Neurons. Annual Review of Cell and Developmental Biology, 35(1), pp.477–500. doi: https://doi.org/10.1146/annurev-cellbio-100818-125242 . Project Gallery

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The environmental impact of EVs 12/12/24, 12:25 A chemical perspective Electric vehicles (EVs) are gaining momentum worldwide as a greener alternative to conventional internal combustion engine vehicles (ICEVs). The environmental benefits of EVs extend beyond their efficient use of electricity. In this article, we explore the chemical aspects of EVs and their environmental impact, shedding light on their potential to mitigate climate change and reduce pollution. Greenhouse Gas Emissions Reduction: EVs play a crucial role in addressing climate change by significantly reducing greenhouse gas (GHG) emissions. Unlike ICEVs that rely on fossil fuels, EVs generate zero tailpipe emissions. By utilising electricity as their energy source, EVs minimise the release of carbon dioxide (CO2) and other GHGs responsible for global warming. However, it's essential to consider the environmental implications of electricity generation, emphasising the need for renewable energy sources to maximise the positive impact of EVs. Battery Chemistry and Resource Management: The heart of an EV lies in its rechargeable battery, typically composed of lithium-ion technology. The production and disposal of these batteries present both opportunities and challenges. Raw materials, such as lithium, cobalt, and nickel, are essential components of EV batteries. Responsible mining practices and efficient recycling techniques are vital to minimising the environmental impact of resource extraction and ensuring proper disposal or repurposing of used batteries. Electrochemical Reactions and Energy Storage: Electric vehicles rely on electrochemical reactions within their batteries to store and release energy. These reactions involve the flow of ions, typically lithium ions, between the positive and negative electrodes. Understanding the chemistry behind these processes enables the development of more efficient and durable battery systems. Continued research and innovation in battery chemistry hold the potential to enhance energy storage capabilities, extend EV range, and improve overall performance. Air Quality and Emission Reduction: EVs contribute to improved air quality due to their zero tailpipe emissions. By eliminating the release of pollutants such as nitrogen oxides (NOx), particulate matter (PM), and volatile organic compounds (VOCs), EVs reduce smog formation and respiratory health risks. This is particularly significant in urban areas, where high concentrations of vehicular emissions contribute to air pollution. The adoption of EVs can help combat these issues and create cleaner and healthier environments. Battery Recycling and the Circular Economy: Given the increasing demand for EVs, battery recycling plays a vital role in ensuring a sustainable future. Recycling allows for the recovery of valuable materials and reduces the need for resource extraction. Effective recycling processes can mitigate the environmental impact of battery production, minimise waste generation, and promote a circular economy approach, where materials are reused and recycled to their fullest extent. Future Prospects and Chemical Innovations : Advancements in battery technology and chemical engineering are key to unlocking the full potential of EVs. Research efforts are focused on developing alternative battery chemistries, such as solid-state batteries, which offer improved energy density, safety, and recyclability. Additionally, exploring sustainable materials and manufacturing processes for batteries can further reduce the environmental footprint of EVs. In conclusion, electric vehicles represent a promising solution to combat climate change, reduce pollution, and promote sustainable transportation. From the chemistry behind battery systems to their impact on air quality and resource management, EVs offer a greener alternative to traditional vehicles. Continued research, innovation, and collaboration between the automotive industry, chemical scientists, and policymakers are essential for realising the full potential of EVs and creating a cleaner, more sustainable future. Written by Navnidhi Sharma Related articles: The brain-climate connection / Plastics and their environmental impact Project Gallery

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Genetically-engineered bacteria break down plastic in saltwater 12/12/24, 12:22 Unlocking the potential to tackle plastic pollution in oceans Groundbreaking discovery in the fight against plastic pollution North Carolina State University researchers have made a groundbreaking discovery in the fight against plastic pollution in marine environments. They have successfully genetically engineered a marine microorganism capable of breaking down polyethylene terephthalate (PET), a commonly used plastic found in water bottles and clothing, contributing to the growing problem of ocean microplastic pollution. Introducing foreign enzymes to V. natriegens The modified organism, created by incorporating genes from the bacterium Ideonella sakaiensis into the genome of Vibrio natriegens , can effectively degrade PET in saltwater conditions. This achievement marks the first time foreign enzymes have been successfully expressed on the surface of V. natriegens cells, making it a significant scientific breakthrough. PET microplastics pose a significant challenge in marine ecosystems, and current methods of removing them, such as extracting and disposing of them in landfills, are not sustainable. The researchers behind this study aim to find a more environmentally friendly solution by breaking down PET into reusable products, like thermoformed packaging (takeaway cartons) or textiles (clothing, duvets, pillows, carpeting). The team worked with two bacteria species, V. natriegens and I. sakaiensis . V. natriegens , known for its rapid reproduction in saltwater, served as the host organism, while I. sakaiensis provided the enzymes necessary for PET degradation. The researchers first rinsed the plastics collected from the ocean to remove high-concentration salts before initiating the plastic degradation process. Challenges ahead While this breakthrough is a significant step forward, three key challenges are still ahead. The researchers aim to incorporate the DNA responsible for enzyme production directly into the genome of V. natriegens to enhance stability. Because DNA is the genetic material responsible for the production of enzymes, and enzymes are proteins that are responsible for carrying out various chemical reactions in the body, by incorporating the DNA responsible for enzyme production into the genome of V. natriegens , the researchers can enhance the stability of the enzyme production. Thus, this DNA is essential for producing the enzymes necessary for PET degradation, as it contains the genetic information vital for encoding the proteins needed for PET breakdown. Additionally, the research team plans to modify V. natriegens further to feed on the byproducts generated during PET degradation. Lastly, they seek to engineer V. natriegens to produce a desirable end product from PET, such as a molecule that can be utilised in the chemical industry. Collaboration with industry groups Collaboration with industry groups is also crucial in determining the market demand for the molecules that V. natriegens can produce. The researchers are open to working with industry partners to explore the vast production scale and identify the most desirable molecules for commercial use. By introducing the genes responsible for PET degradation into V. natriegens using a plasmid, the researchers successfully induced the production of enzymes on the surface of the bacterial cells. The modified V. natriegens demonstrated its ability to break down PET microplastics in saltwater, providing a practical and economically feasible solution for addressing plastic pollution in marine environments. This research represents a significant advancement in the field, as it is the first time that V. natriegens has been genetically engineered to express foreign enzymes on its cell surface. This breakthrough opens up possibilities for further modifications, such as incorporating the DNA from I. sakaiensis directly into the genome of V. natriegens to make the production of plastic-degrading enzymes a more stable feature of the organism. The researchers aim to modify V. natriegens to feed on the byproducts produced during the breakdown of PET and create a desirable end product for the chemical industry. The researchers are open to collaborating with industry groups to identify the most desirable molecules to be engineered into V. natriegens for production. This groundbreaking research, published in the AIChE Journal with the support of the National Science Foundation under grant 2029327, paves the way for developing more efficient and sustainable methods for addressing plastic pollution in saltwater environments. Conclusion The research has made a breakthrough in the fight against plastic pollution in marine environments. By incorporating genes from the bacterium I. sakaiensis into the genome of V. natriegens , they created a genetically modified marine microorganism capable of breaking down PET. This achievement provides a practical and economically feasible solution to address plastic pollution in aquatic ecosystems. The researchers are now looking into further modifications to the organism to enable it to feed on byproducts and to produce a desirable end product that can be used in the chemical industry. This research highlights the potential of genetic engineering to create sustainable solutions to the growing problem of plastic pollution. Written by Sara Maria Majernikova Related article: Plastics and their environmental impact Project Gallery

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Reaching new horizons in Alzheimer's research 12/12/24, 12:17 The role of CRISPR-Cas9 technology The complexity of Alzheimer’s Alzheimer's disease (AD) is a formidable foe, marked by its relentless progression and the absence of a definitive cure. As the leading cause of dementia, its prevalence is expected to triple by 2050. Traditional therapies mainly focus on managing symptoms; however, advances in genetics research, specifically CRISPR-Cas9 gene-editing technology, offer newfound hope for understanding and treating this debilitating condition. The disease is characterized by progressive deterioration of cognitive function, with memory loss being its hallmark symptom. Primarily affecting individuals aged 65 and over, age is the most significant risk factor. Although this precise cause remains elusive, scientists believe that a combination of genetic, lifestyle and environmental factors contributes to its development. CRISPR’s role in Alzheimer’s research After the discovery of using CRISPR-Cas9 for gene editing, this technology is receiving interest for its potential ability to manipulate genes contributing to Alzheimer’s. Researchers from the University of Tokyo used a screening technique involving CRISPR-Cas9 to identify calcium, proteins, and integrin-binding protein 1, which is involved in the formation of AD. Furthermore, Canadian researchers have edited genes in brain cells to prevent Alzheimer’s using CRISPR. The team identified a genetic variant called A673T, found to decrease Alzheimer’s likelihood by a factor of four and reduce Alzheimer’s biomarker beta-amyloid (Aβ). Using CRISPR in petri dish studies, they managed to activate this A673T variant in lab-grown brain cells. However, the reliability and validity of this finding are yet to be confirmed by replication in animal studies. One final example of CRISPR application is targeting the amyloid precursor protein (APP) gene. The Swedish mutation in the APP gene is associated with dominantly inherited AD. Scientists were able to specifically target and disrupt the mutant allele of this gene using CRISPR, which decreased pathogenic Aβ peptide. Degenerating neurons are surrounded by Aβ fibrils, the production of Αβ in the brain initiates a series of events which cause the clinical syndrome of dementia. The results of this study were replicated both ex vivo and in vivo and demonstrated this could be a potential treatment strategy in the future. The road ahead While CRISPR technology’s potential in Alzheimer’s research is promising, its therapeutic application is still in its Infancy. Nevertheless, with the aid of cutting-edge tools like CRISPR, deepening our understanding of AD, we are on the cusp of breakthroughs that could transform the landscape of Alzheimer’s disease treatment. Written by Maya El Toukhy Related articles: Alzheimer's disease (an overview) / Hallmarks of Alzheimer's Project Gallery

Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Alzheimer's disease 12/12/24, 12:16 The mechanisms of the disease Introduction to Alzheimer’s disease Alzheimer’s disease is a neurodegenerative disease that results in cognitive decline and dementia with increasing age, environmental and genetic factors contributing to its onset. Scientists believe this is the result of protein biomarkers that build-up in the brain and accumulate within neurones. As of 2020, 55 million people suffer with dementia, with Alzheimer’s being a leading cause. Thus, it is crucial we develop efficacious treatments, with final adverse effects. A new drug called Iecanemab, may be the key to a new era of Alzheimer’s treatment… The disease is most common in people over 65, with 1/14 affected in the UK, thus, there is a huge emphasis on defining the disorder and developing drug treatments. The condition results in difficulty with memory, planning, decision making and can result in co-morbidities such as depression or personality change. This short article will explain the pathology of the disorder and the genetic predispositions for its onset. It will also explore future avenues for treatment, such as the drug I ecanemab that may provide, “a new era for Alzheimer’s disease”. Pathology and molecular aspects The neurodegeneration seen in Alzheimer’s has, as far, been associated protein dispositions in the brain, such as the amyloid precursor protein (APP) and Tau tangles. This has been deduced by PET scans and post-mortem study. APP, located on chromosome 21, is responsible for synapse formation and signalling. It is cleaved to b-amyloid peptides by enzymes called secretases, but overexpression of both these factors can be neurotoxic (figure 1). The result is accumulation of protein aggregates called beta-amyloid plaques in neurons, impairing their survival. This deposition starts in the temporo-basal and front-medial areas of the brain and spreads to the neocortex and sensory-motor cortex. Thus, many pathways are affected, resulting in the characteristic cognitive decline. Tau proteins support nerve cells structurally and can be phosphorylated at various regions, changing the interactions they have with surrounding cellular components. Hyperphosphorylation of these proteins result in the Tau pathology in the form of tau oligomer (short peptides) that is toxic to neurons. These enter the limbic regions and neocortex. It is not clearly defined which protein aggregate proceeds the other, however, the amyloid cascade hypothesis suggests that b-amyloid plaque pathology comes first. It is speculated that b-amyloid accumulation leads to activation of the brain’s immune response, the microglial cells, which then promotes the hyperphosphorylation of Tau. Sometimes, there is a large release of pro-inflammatory cytokines, known as a cytokine storm, that promotes neuroinflammation. This is common amongst older individuals, due to a “worn-out” immune system, which may in part explain Alzheimer’s disease. Genetic component to Alzheimer’s disease There is strong evidence obtained through whole genome-sequencing studies (WGS), that suggests there is a genetic element to the disease. One gene is the Apoliprotein E (APOE) gene, responsible for b-amyloid clearance/metabolism. Some alleles of this gene show association with faulty clearance, leading to the characteristic b-amyloid build-up. In the body, proteins are made consistently depending on need, a dysregulation of the recycling process can be catastrophic for the cells involved. PSEN1 gene that codes for the presenilin 1 protein, part of a secretase enzyme complex. As mentioned, the secretase enzyme is responsible for the cleavage of APP, the precursor for b-amyloid. Variants of this gene have been associated with early onset Alzheimer’s disease, due to APP processing being altered to produce a longer form of the b-amyloid plaque. The genetic aspects to Alzheimer’s disease are not limited to these genes, and in actuality, one gene can have an assortment of mutation that results in a faulty protein. Understanding the genetic aspects, may provide avenue for gene therapy in the future. Treatment Understanding the point in which the “system goes wrong” is crucial for directing treatment. For example, we may use secretase inhibitors to reduce the rate of plaque formation. An example of this is the g- secretase BACE1 inhibitor. There is a need for this drug-type to be more selective to its target, as has been found to produce unwanted adverse effects. A more selective approach may be to target the patient’s immune system with the use of monoclonal antibodies (mAb). This means designing an antibody that recognises a specific component, such as the b-amyloid plaque, so it may bind and then encourage immune cells to target the plaque (figure 3). An example is Aducanumab mAb, which targets b-amyloid as fibrils and oligomers. The Emerge study demonstrated a decrease in amyloid by the end of the 78-week study. As of June 2021, Aducanumab received approval from the FDA for prescription of this drug, but this is controversial as there are claims it brings no clinical benefit to the patient. The future of Alzheimer’s disease Of note, drug development and approval is a slow process, and there must be a funding source in order to carry out plans. Thus, particularly in Alzheimer’s, it is relevant to educate the public and funding bodies to supply the financial support to the process. However, with many hits (potential drug candidates), these often fail at phase III clinical trials. Despite this, another mAb, lecanemab, has recently been approved by the FDA (2023), due to its ability to slow cognitive decline by 27% in early Alzheimer’s disease. The Clarity AD study on Iecanemab, found the drug benefited memory and thinking, but also allowed for better performance of daily tasks. This drug is currently being prescribed on a double-blind basis, meaning a patient may either receive the drug or the placebo. This study shows a hope for those suffering from the disease. Drugs that have targeted the Tau tangles, have as far, not been successful in clinical trials. However, the future of Alzheimer’s treatment may be in the combination therapy directed to both Tau protein and b-amyloid. Washington universities neurology department have launched a trial known as Tau NextGen, in which participants will receive both Iecanemab and tau-reducing antibody. Conclusion This article provides a summary to what we know about Alzheimer’s disease and the potential treatments of the future. Overall, the future of Alzheimer’s treatment lies in the combination therapy to target known biomarkers of the disease. Written by Holly Kitley Related articles: CRISPR-Cas9 as Alzheimer's treatment / Hallmarks of Alzheimer's Project Gallery