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Understanding what goes wrong in the brain Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Epilepsy 101 29/04/25, 16:10 Last updated: Published: 09/10/24, 11:32 Understanding what goes wrong in the brain Epilepsy is a condition that affects millions of people worldwide, often causing unprovoked seizures due to irregular brain activity. But what exactly happens in the brain when someone has epilepsy? It is important to establish that not everyone with seizures has epilepsy. While epilepsy can start at any age, it often begins in childhood, or in people over the age of 60. Epilepsy can be due to genetic factors - 1 in 3 people with epilepsy have family history- or brain damage from causes like stroke, infection, severe head injury or a brain tumour. However, around half of epilepsy cases have an unknown cause. Now, imagine your brain as a big city with lots of lights. Each light represents a part of your brain that controls things like movement, feelings, and thoughts. Epilepsy is like when the lights in the city start flickering or shut completely. There are three main types of epilepsy, and each affects the lights in different ways: 1) Generalized epilepsy: when all the lights in the city flicker or go out at once, affecting the whole brain. There are two main kinds: Generalized Motor (Grand Mal) Seizures: Imagine the lights in the city going wild, making everything shake. This is like the shaking or jerking movements during myoclonic or tonic-clonic seizures. Generalized Non-Motor (Absence) Seizures: Picture the lights suddenly pausing, making everything freeze. During these seizures, a person might stare into space or make small, repeated movements, like lip-smacking. 2) Focal epilepsy: when only the lights in one part of the city flicker or go out. This means only one part of the brain is affected: Focal Aware Seizures: The lights flicker, but people in that part of the city know what’s happening. The person stays aware during the seizure. Focal Impaired Awareness Seizures: The lights flicker, and people lose track of what’s going on. The person might not remember the seizure. Focal Motor Seizures: Some lights flicker, causing strange movements, like twitching, rubbing hands, or walking around. Focal Non-Motor Seizures: The lights stay on, but everything feels strange, like sudden change in mood or temperature. The person might feel odd sensations without moving in unusual ways. 3) ‘Unknown’ epilepsy: ‘Unknown’ epilepsy is like a power outage where no one knows where it happened because the person was alone or asleep during the seizure. Doctors might later figure out if it's more like generalized or focal epilepsy. Some people can even have both types. But how do doctors find out if someone has epilepsy? A range of tests could be used to look at the brain’s activity and structure, including: Electroencephalogram (EEG): detects abnormal electrical activities in the brain using electrodes. This procedure can be utilised in Stereoelectroencephalography (SEEG), a more invasive method where the electrodes are placed directly on or within the brain to locate the abnormal electrical activities more precisely. Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI): form images of the brain to detect abnormal brain structures such as brain scarring, tumours or damage that may cause seizures. Blood tests: test for genetic or metabolic disorders, or health conditions such as anaemia, infections or diabetes that can trigger seizures. Magnetoencephalogram (MEG): measures magnetic signals generated by nerve cells to identify the specific area where seizures are starting, to diagnose focal epilepsy. Positron emission tomography (PET): detects biochemical changes in the brain, detecting regions of the brain with lower-than-normal metabolism linked to seizures. Single-photon emission computed tomography (SPECT): identifies seizure focus by measuring changes in blood flow in the brain during or between seizures, using a tracer injected into the patient. The seizure focus in this scan is seen by an increase in blood flow to that region. So, how does epilepsy affect the brain? For most people, especially those with infrequent or primarily generalised seizures, cognitive issues are less likely compared to those with focal seizures, particularly in the temporal lobe. The following cognitive functions can be affected: Memory : seizures can disrupt the hippocampus in the temporal lobe, responsible for storing and receiving new information. This can lead to difficulties in remembering words, concepts, names and other information. Language : seizures can affect areas of the brain responsible for speaking, understanding and storing words, which can lead to difficulties in finding familiar words. Executive function: seizures can impact the frontal lobe of the brain which is responsible for planning, decision making and social behaviour, leading to challenges in interacting, organising thoughts and controlling unwanted behaviour. While epilepsy itself cannot be cured, treatments exist to control seizures including: Anti-Epileptic Drugs (AEDs): suppress the brain’s ability of sending abnormal electrical signals - effective in 70% of patients. Diet: ketogenic diets can reduce seizures in some medication- resistant epilepsies and in children as they alter the chemical activity in the brain. Surgery: 1) Resective Surgery: removal of the part of the brain causing the seizures, such as temporal lobe resection, mainly for focal epilepsy. 2) Disconnective Surgery: cutting the connections between the nerves through which the seizure signals travel in the brain, such as in corpus callosotomy, mainly for generalised epilepsy. 3) Neurostimulation device implantation (NDI): insertion of devices in the body to control seizures by stimulating brain regions to control the electrical impulses causing the seizures. This includes vagus nerve stimulation and Deep Brain Stimulation (DBS). Even though epilepsy can be challenging, many people manage it successfully with the right treatment. Continued research offers hope for even better, long lasting treatments in the future. Written by Hanin Salem Related articles: Different types of epilepsy seizures / Alzheimer's disease / Parkinson's disease / Autism REFERENCES D’Arrigo, T. (n.d.). What Are the Types of Epilepsy? [online] WebMD. Available at: https://www.webmd.com/epilepsy/types-epilepsy [Accessed 5 Aug. 2024]. Epilepsy Foundation. (n.d.). Thinking and Memory. [online] Available at: https://www.epilepsy.com/complications-risks/thinking-and-memory [Accessed 10 Aug. 2024]. GOSH Hospital site. (n.d.). Invasive EEG monitoring. [online] Available at: https://www.gosh.nhs.uk/conditions-and-treatments/procedures-and- treatments/invasive-monitoring/ [Accessed 9 Aug. 2024]. My Epilepsy Team.com. (2016). Epilepsy: What People Don’t See (Infographic) | MyEpilepsyTeam. [online] Available at: https://www.myepilepsyteam.com/resources/epilepsy-what-people-dont-see- infographic [Accessed 29 Aug. 2024]. National institute of Neurological Disorders and stroke (2023). Epilepsy and Seizures | National Institute of Neurological Disorders and Stroke. [online] www.ninds.nih.gov . Available at: https://www.ninds.nih.gov/health- information/disorders/epilepsy-and-seizures [Accessed 10 Aug. 2024]. NHS (2020). Epilepsy. [online] NHS. Available at: https://www.nhs.uk/conditions/epilepsy/ [Accessed 10 Aug. 2024]. Project Gallery

How do they work? Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link A concise introduction to Markov chain models 20/03/25, 11:59 Last updated: Published: 09/03/24, 18:16 How do they work? Introduction A Markov chain is a stochastic process that models a system that transitions from one state to another, where the probability of the next state only depends on the current state and not on the previous history. For example, assuming that X 0 is the current state of a system or process, the probability of a state, X 1 , depends only on X 0 which is of course the current state of the system as stated. P ( X 1 ) = f ( P ( X 0 )) It may be hard to think of any real-life processes that follow this behaviour because there is the belief that all events happen in a sequence because of each other. Here are some examples: Games e.g. chess - If your king is in a certain spot on a chess board, there will be a maximum of 4 transition states that can be achieved that all depend on the initial position of chess piece. The parameters for the Markov model will obviously vary depending on your position on the board which is the essence of the Markov process. Genetics - The genetic code of an organism can be modelled as a Markov chain, where each nucleotide (A, C, G, or T) is a state, and the probability of the next nucleotide depends only on the current one. Text generation - Consider the current state as the most recent word. The transition states would be all possible words which could follow on from said word. Next word prediction algorithms can utilize a first-order Markov process to predict the next word in a sentence based on the most recent word. The text generation example is particularly interesting because only considering the previous word when trying to predict the next word sentence would lead to a very random sentence. That is where we can change things up using various mathematical techniques. k-Order Markov Chains (adding more steps) In a first-order Markov chain, we only consider the immediately preceding state to predict the next state. However, in k-order Markov chains, we broaden our perspective. Here’s how it works: Definition: a k-order Markov chain considers the previous states (or steps) when predicting the next state. It’s like looking further back in time to inform our predictions. Example: suppose we’re modelling the weather. In a first-order Markov chain, we’d only look at today’s weather to predict tomorrow’s weather. But in a second-order Markov chain, we’d consider both today’s and yesterday’s weather. Similarly, a third-order Markov chain would involve three days of historical data. By incorporating more context, k-order chains can capture longer-term dependencies and patterns. As k increases, the model becomes more complex, and we need more data to estimate transition probabilities accurately. See diagram below for a definition of higher order Markov chains. Markov chains for Natural Language Processing A Markov chain can generate text by using a dictionary of words as the states, and the frequency of words in a corpus of text as the transition probabilities. Given an input word, such as "How", the Markov chain can generate the next word, such as "to", by sampling from the probability distribution of words that follow "How" in the corpus. Then, the Markov chain can generate the next word, such as "use", by sampling from the probability distribution of words that follow "to" in the corpus. This process can be repeated until a desired length or end of sentence is reached. That is a basic example and for more complex NLP tasks we can employ more complex Markov models such as k-order, variable, n-gram or even hidden Markov models. Limitations of Markov models Markov models for tasks such as text generation will struggle because they are too simplistic to create text that is intelligent and sometimes even coherent. Here are some reasons why: Fixed Transition Probabilities: Markov models assume that transition probabilities are constant throughout. In reality, language is dynamic, and context can change rapidly. Fixed probabilities may not capture these nuances effectively. Local Dependencies: Markov chains have local dependencies, meaning they only consider a limited context (e.g., the previous word). They don’t capture long-range dependencies or global context. Limited Context Window: Markov models have a fixed context window (e.g., first-order, second order, etc.). If the context extends beyond this window, the model won’t capture it. Sparse Data : Markov models rely on observed data (transition frequencies) from the training corpus. If certain word combinations are rare or absent, the model struggles to estimate accurate probabilities. Lack of Learning: Markov models don’t learn from gradients or backpropagation. They’re based solely on observed statistics. Written by Temi Abbass Related articles: Latent space transformation s / Evolution of AI FURTHER READING 1. “Improving the Markov Chain Approach for Generating Text Used for…” : This work focuses on text generation using Markov chains. It highlights the chance based transition process and the representation of temporal patterns determined by probability over sample observations . 2 . “Synthetic Text Generation for Sentiment Analysis” : This paper discusses text generation using latent Dirichlet allocation (LDA) and a text generator based on Markov chain models. It explores approaches for generating synthetic text for sentiment analysis . 3. “A Systematic Review of Hidden Markov Models and Their Applications” : This review paper provides insights into HMMs, a statistical model designed using a Markov process with hidden states. It discusses their applications in various fields, including robotics, finance, social science, and ecological time series data analysis . Project Gallery

Not getting enough sleep can increase the risk of developing Alzheimer’s Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Sleep less…remember less: the hidden link between sleep and memory loss Last updated: 10/07/25, 18:27 Published: 17/04/25, 07:00 Not getting enough sleep can increase the risk of developing Alzheimer’s People often don’t get enough sleep for a variety of reasons, ranging from intentional choices like work or study demands (because who needs sleep when you’ve got deadlines, right?), to the growing concern with screen time (a.k.a. the “I’ll just watch one more episode” syndrome), and of course, procrastination (where your brain convinces you that 3 a.m. is a great time to suddenly get productive). But it’s not all fun and games—serious issues like insomnia, sleep apnoea, family responsibilities, or even shift work can also interfere with rest. Sleep disorders are increasingly common, with around one in three people in the UK affected, and they’re particularly prevalent among the elderly. However, not getting enough sleep can increase the risk of developing Alzheimer’s disease (AD). How do sleep disorders impact Alzheimer’s disease? Insomnia is characterised by difficulty falling asleep or staying asleep, which can lead to prolonged fatigue and memory issues. As shown in Figure 1 , people with insomnia tend to have some similarity in markers as those with Alzheimer’s disease, such as an increased level of Aβ and tau proteins in the brain. This is primarily because a lack of sleep prevents the effective removal of harmful products from the brain – this accumulation increases a person’s risk of AD. A plethora of experimental studies on humans and animals have shown that lack of sleep can lead to increased circulating levels of TNF-α and the gene resulting in more TNF-α secretion. This pro-inflammatory cytokine exacerbates AD pathology because neuroinflammation can lead to dysfunction and cell death, which are key markers of AD. Other pro-inflammatory cytokines, like IL-1, have been found to be relevant in the link between sleep deprivation and AD. Overexpression of IL-1 in the brain leads to abnormal changes in nerve cell structures especially relating to Aβ plaques. This highlights IL-1’s key role in plaque evolution and the synthesis of Amyloid Precursor Protein, which promotes amyloid production that eventually results in AD pathology. What type of sleep can impact one’s risk of Alzheimer’s disease? Studies using more objective measures, like actigraphy (which tracks sleep-wake activity), found that sleep quality (sleep efficiency) is more important than total sleep time. For example, women with less than 70% sleep efficiency were more likely to experience cognitive impairment. Increased wakefulness during the night also moderated the relationship between amyloid deposition (a hallmark of AD) and memory decline. Uncertainties… However, it remains unclear whether poor sleep directly causes AD or if the disease itself leads to sleep disturbances. Some studies suggest a bidirectional relationship. Aging itself leads to poorer sleep quality, including reduced sleep efficiency, less slow-wave sleep (SWS), and more frequent awakenings. Sleep disorders like obstructive sleep apnoea, insomnia, and restless legs syndrome also become more common with age. What are the next steps? The good news is that many sleep disorders, including insomnia, are manageable, and improving sleep quality could be a simple yet powerful way to reduce Alzheimer’s risk. Additionally, early diagnosis and treatment of conditions like sleep apnoea and insomnia may help slow or even prevent neurodegenerative changes. s researchers continue to explore the intricate relationship between sleep and Alzheimer’s, one thing is clear: getting a good night’s sleep isn’t just about feeling refreshed. It is a crucial investment in long-term brain health. Written by Blessing Amo-Konadu Related articles: Overview of Alzheimer's / Hallmarks of Alzheimer's / CRISPR-Cas9 in AD treatment / Memory erasure / Does insomnia run in families? REFERENCES Lucey, B. (2020). It’s complicated: The relationship between sleep and Alzheimer’s disease in humans. Neurobiology of Disease , [online] 144, p.105031. doi: https://doi.org/10.1016/j.nbd.2020.105031 . NHS (2023). Insomnia . [online] www.nhsinform.scot . Available at: https://www.nhsinform.scot/illnesses-and-conditions/mental-health/insomnia/ . Pelc, C. (2023). Not getting enough deep sleep may increase the risk of developing dementia . [online] Medicalnewstoday.com . Available at: https://www.medicalnewstoday.com/articles/not-getting-enough-deep-sleep-may-increase-dementia-risk#Clarifying-the-link-between-sleep-aging-and-dementia-risk [Accessed 22 Dec. 2024]. Sadeghmousavi, S., Eskian, M., Rahmani, F. and Rezaei, N. (2020). The effect of insomnia on development of Alzheimer’s disease. Journal of Neuroinflammation , 17(1). doi: https://doi.org/10.1186/s12974-020-01960-9 . Project Gallery

What they are, uses, and future outlook Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Advancements in Semiconductor Laser Technology 08/07/25, 16:19 Last updated: Published: 23/06/24, 09:39 What they are, uses, and future outlook Lasers have revolutionised many fields starting from the telecommunications, data storage to medical diagnostics and consumer electronics. And among the semiconductor laser technologies, Edge Emitting Lasers (EEL) and Vertical Cavity Surface Emitting Lasers (VCSEL) emerged as critical components due to their unique properties and performance. These lasers generate light through the recombination of electrons and holes in a semiconductor material. EELs are known for their high power and efficiency and they are extensively used in fiber optic communications and laser printing. VCSELs on the other hand are compact and are used for applications like 3D sensing. Traditionally VCSELs have struggled to match the efficiency levels of EELs however a recent breakthrough particularly in multi junction VCSEL, has demonstrated remarkable efficiency improvements which place the VCSELs to surpass EELs in various applications. This article focuses on the basics of these laser technologies and their recent advancements. EELs are a type of laser where light is emitted from the edge of the semiconductor wafer. This design contrasts with the VCSELs which emit light perpendicular to the wafer surface. EELs are known for their high power output and efficiency which makes them particularly suitable for applications that require long-distance light transmission such as fiber optic communications, laser printing and industrial machining. EELs consist of an active region where electron hole recombination occurs to produce light. This region is sandwiched between two mirrors forming a resonant optical cavity. The emitted light travels parallel to the plane of the semiconductor layers and exits from the edge of the device. This design allows EELs to achieve high gain and power output which makes them effective for transmitting light over long distances with minimal loss. VCSELs are a type of semiconductor laser that emits light perpendicular to the surface of the semiconductor wafer unlike the EELs which emit light from the edge. VCSELs have gained popularity due to their lower threshold currents and ability to form high density arrays. VCSELs consist of an active region where electron-hole recombination occurs to produce light. This region is situated between two highly reflective mirrors which forms a vertical resonant optical cavity. The light is emitted perpendicular to the wafer surface which allows for efficient vertical emission and easy integration into arrays. Recent advancements in VCSEL technology marked a significant milestone in the field of semiconductor lasers. And in particular the development of multi junction VCSEL which led to the improvements in power conversion efficiency (PCE) of the laser. Research conducted by Yao Xiao et al. and team has demonstrated the potential of a multi junction VCSELs to achieve efficiency levels which were previously thought unattainable. This research focuses on cascading multiple active regions within a single VCSEL to enhance gain and reduce threshold current which leads to higher overall efficiency. The study employed a multi-junction design where several active regions are stacked vertically within the VCSEL. This design increases the volume of the gain region and lowers the threshold current density resulting in higher efficiency. Experimental results from the study revealed that a 15-junction VCSEL achieved a PCE of 74% at room temperature when driven by nanosecond pulses. This efficiency is the highest ever reported for VCSELs and represents a significant leap forward from previous records. Simulations conducted as part of the study indicated that a 20-junction VCSEL could potentially reach a PCE exceeding 88% at room temperature. This suggests that further optimization and refinement of the multi-junction approach could yield even greater efficiencies. The implications of this research are profound for the future of VCSEL technology. Achieving such high efficiencies places VCSELs as strong competitors to EELs particularly in applications where energy efficiency and power density are critical. The multi junction VCSELs demonstrated in the study shows promise for a wide range of applications and future works may focus on optimizing the fabrication process, reducing thermal management issues and exploring new materials to further enhance performance. Integrating these high-efficiency VCSELs into commercial products could revolutionize industries reliant on laser technology. Written by Arun Sreeraj Related articles: The future of semi-conductor manufacturing / The search for a room-temperature superconductor / Advances in mass spectrometry Project Gallery

Integrins are desirable to target cancer Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Biochemistry of cancer: integrins, the desirable targets 20/03/25, 12:01 Last updated: Published: 24/05/23, 08:39 Integrins are desirable to target cancer Every year, eight million people worldwide pass away from cancer, and this number is expected to rise. Cancer can damage a wide range of organs in people of various ages. It is quite honest to say that Cancer is the most common and severe problem in clinical medicine. Cancer's fundamental problems shed light on the biochemical and genetic processes underlying the unchecked expansion of cancer cells. The extracellular matrix (ECM)'s biochemical and biomechanical properties affect how sensitive cells are. Cell health depends on different reactions, such as proliferation, apoptosis, migration, and differentiation. The tumour microenvironment also largely influences cancer metastasis, medication resistance, and recurrence. Transmembrane glycoproteins called integrins mediate connections between cells and the ECM and connect it to the cytoskeleton. They relay the information from the ECM through downstream signalling pathways and can hence control the properties of the cell. Mammals have so far been found to contain 24 different integrin heterodimers, formed by combining 18 α- and 8 β-subunits. A cell's ability to bind to specific ECM elements depends on the pattern of integrin expression, which also affects how a cell recognises and reacts to its surroundings. These same integrin-mediated pathways are used by tumour cells in the context of cancer to boost invasiveness and oncogenic survival as well as to create a host milieu that supports tumour development and metastatic dissemination ( Figure 1 ). Hence, Integrins are interesting targets for cancer therapy due to their role in tumour progression, and several integrin antagonists, including antibodies and synthetic peptides, have been successfully used in clinics for cancer therapy. Unligated integrins may have a detrimental effect on tumour survival. They are generally unligated in adherent cells, which leads to the cleavage of caspase 8, which in turn causes tumour cells to undergo apoptosis through a process known as integrin-mediated death (IMD) ( Figure 2 ). Integrins' precise chemical signals and the mechanical environment of the ECM control how cancer cells behave. A key role is also played by the ECM's physicochemical environment. Chemically altered substrate surfaces have been used to study this interaction, but topology and functionality control are still difficult to achieve. Modifying a cell's local chemical environment does offer a viable method for selectively controlling the behaviour of cancer cells. Together, targeted external cue presentation has the potential to enhance existing intracellular cancer therapy approaches. When combined with other targeted therapies (tyrosine kinase inhibitors, anti-growth factor antibodies) for anticancer treatment, integrin inhibition may be used as a potential target for drug development. However, it needs to be thoroughly evaluated in the pre-clinical phase, possibly taking into account all of the plausible escape mechanisms by which tumour cells can develop. Written by Navnidhi Sharma Related articles: Why whales don't get cancer / Breast cancer and asbestos / MOFs in cancer drug delivery / Anti-cancer metal compounds REFERENCES Hamidi, H., Pietilä, M., & Ivaska, J. (2016). The complexity of integrins in cancer and new scopes for therapeutic targeting. British Journal of Cancer, 115(9), 1017–1023. https://doi.org/10.1038/bjc.2016.312 Jacob, M., Varghese, J., Murray, R. K., & Weil, P. A. (2016). Cancer: An Overview (V. W. Rodwell, D. A. Bender, K. M. Botham, P. J. Kennelly, & P. A. Weil, Eds.). Access Medicine; McGraw-Hill Education. https://accessmedicine.mhmedical.com/content.aspx?bookid=1366§ionid=73247495 Li, M., Wang, Y., Li, M., Wu, X., Setrerrahmane, S., & Xu, H. (2021). Integrins as attractive targets for cancer therapeutics. Acta Pharmaceutica Sinica B. https://doi.org/10.1016/j.apsb.2021.01.004 Yoshii, T., Geng, Y., Peyton, S., Mercurio, A. M., & Rotello, V. M. (2016). Biochemical and biomechanical drivers of cancer cell metastasis, drug response and nanomedicine. Drug Discovery Today, 21(9), 1489–1494. https://doi.org/10.1016/j.drudis.2016.05.011 Zhao, H., F. Patrick Ross, & Teitelbaum, S. L. (2005). Unoccupied αvβ3Integrin Regulates Osteoclast Apoptosis by Transmitting a Positive Death Signal. Molecular Endocrinology, 19(3), 771–780. https://doi.org/10.1210/me.2004-0161 Project Gallery

Synaptic plasticity is the process of connections within the brain changing to adapt to new information over time. It is of increasing significance in neuroscience, especially in the field of memory. Early research into synaptic plasticity was conducted by many of those Go Back Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Our understanding of how the brain forms connections between things we’ve learnt, and how London taxi drivers fit in Last updated: 18/11/24 Published: 05/01/23 Synaptic plasticity is the process of connections within the brain changing to adapt to new information over time. It is of increasing significance in neuroscience, especially in the field of memory. Early research into synaptic plasticity was conducted by many of those now considered the pioneers of neuroscience. For instance, Terje Lomo (1966) experimented on rabbit hippocampus with repeated, high-frequency stimulation, identifying long term potentiation, the persistent strengthening of synapses leading to enduring increases in signal transmission between neurons. Prior to work by Lomo, Ramon y Cajal (1911) proposed the idea that the strength of synaptic connections had to change to alter existing memories. One key question which is pertinent for both humans and other animals alike is how to keep track of our surroundings - how do our memories encode and store information on the places we visit so we can remember the directions for next time? Seminal work by Maguire et al., (2001) assessed whether physical changes “could be detected in the healthy brain” of London taxi drivers, given the repertoire of spatial experience required to navigate London without aid. Sixteen taxi drivers were studied with fifty controls. Using (structural) magnetic resonance imaging (MRI), it was found that taxi drivers’ posterior hippocampi were larger than that of control subjects, and the more experience the drivers had, the greater the size of their right posterior hippocampi. Such changes in tissue volume take place gradually over time, because of task-related training. Recent work by Spiers et al., (2022) looked at the difference in spatial navigational ability between city-dwellers and those living in rural areas. A subset of ~400,000 participants from 38 countries played a video game to test their skill in spatial navigation, with city-dwellers performing worse than those who grew up outside cities. More specifically, individuals were better at navigating in environments that were topologically like where they grew up. Hence, one interpretation of these results is that the place where a person grows up impacts their ability to accurately navigate new, unfamiliar environments since this is based on the synaptic connections made between existing information in the brain. In conclusion, synaptic plasticity is the change in connections in the brain over time; interest and research in this field, especially spatial navigation, are increasing significantly. Written by Manisha Halkhoree Full article published in Brain Insights- BNA Bulletin (Issue no. 96, Autumn 2022) Related articles: The wonders of the human brain / The brain-climate connection REFERENCES Nicoll, R. A Brief History of Long-Term Potentiation. Neuron. 2017 Jan 18; 93(2): 281-290. Available from: https://www.sciencedirect.com/science/article/pii/S0896627316309576 Maguire E, Gadian D, Johnsrude I, et. al. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Sci U S A. 2000 Apr 11; 97(8): 4398–4403. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC18253/ Coutrot A, Manley E, Goodroe S., et. al. Entropy of city street networks linked to future spatial navigation ability. Nature. 2022 March 30; Nature 604: 104-110. Available from: https://www.nature.com/articles/s41586-022-04486-7

You can have endometriosis and PCOS at the same time Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Are PCOS and endometriosis sisters? 09/07/25, 10:52 Last updated: Published: 30/01/24, 21:33 You can have endometriosis and PCOS at the same time The label of PCOS or endometriosis can have physical and emotional consequences for women. It is important for both male and females to gain a better understanding of such conditions, the symptoms and the challenges they pose. Such knowledge can act as physical and emotional support in times of need. It creates a safe space where the person with PCOS is comfortable discussing their experiences, feelings and concerns knowing they are being heard and supported by the right people. With research fast developing there is a plethora of information out there, so WHAT do you believe in and WHAT do you ignore and WHOM do you believe and WHOM do you ignore? Endometriosis and polycystic ovary syndrome (PCOS) both affect females and can have similar symptoms. However, the causes and some key symptoms are different. Endometriosis is a painful disorder in which tissue that normally lines the inside of your uterus grows outside the uterus. (Read more on Endometriosis breakthrough ). PCOS is an endocrine system disorder where small fluid-filled sacs develop in the ovaries. You can have endometriosis and PCOS at the same time. A 2015 study found that women with PCOS had a higher risk for a diagnosis of endometriosis. Another 2014 study determined that there is a strong link between endometriosis and PCOS with pelvic pain and trouble getting pregnant. What is a normal menstrual cycle? Let’s polish up the basics! The brain, ovaries and uterus work together to prepare the body per month for pregnancy. Follicle-stimulating Hormone (FSH) and Luteinising Hormone (LH) are made by the pituitary gland and progesterone and oestrogen are made in the ovaries. Many females with PCOS do not ovulate regularly and it may take these females longer to become pregnant. Irregular periods results in months where ovulation does not occur. Where the ovaries do not produce progesterone the lining of the uterus becomes thicker but shedding is very irregular which can lead to heavy and prolonged bleeding. PCOS affects 1 in 10 women in the UK. Women with PCOS experience irregular menstrual cycles, acne, excess hair growth, infertility, pregnancy complications and cardiovascular disease. PCOS can be associated with weight gain and obesity in approximately one-half of females. Females with PCOS can also be at increased risk of other problems that can impact quality of life. These include depression and anxiety, sexual dysfunction and eating disorders. Although PCOS is not ‘completely’ reversible there are many ways you can minimise the symptoms. Most females can lead a normal life and are able to conceive without significant complications. A pelvic examination is requested by your GP to assess the ovaries for a diagnosis to be made. Imaging tests for examining the ovaries are pelvic and intravaginal ultrasonography, however, the latter may be extremely uncomfortable if sexually inactive. Please be aware this article acts to capture your attention, encouraging you to delve further into the subject and continue your self-education on this topic and by no means is everything about PCOS. It is essential to consult with a healthcare professional if you suspect you may have symptoms of either PCOS or endometriosis. Proper diagnosis and management can help address specific concerns and improve overall reproductive health. Written by Khushleen Kaur Related articles: Endometriosis breakthrough / Underreporting in endometriosis / Gynaecology REFERENCES R. Hart and D. A. Doherty, Fertility Specialists of Western Australia (R.H.), Bethesda Hospital, 6008. K. J. Holoch, R. F. Savaris, D. A. Forstein, P. B. Miller, H. Lee Higdon, C. E. Likes and B. A. Lessey, https://doi.org/10.5301/je.5000181 , 2014, 6, 79–83. R. J. Norman, D. Dewailly, R. S. Legro and T. E. Hickey, The Lancet, 2007, 370, 685–697. Project Gallery

An analysis of risk and protective factors Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Postpartum depression in adolescent mothers Last updated: 24/06/25, 16:39 Published: 10/07/25, 07:00 An analysis of risk and protective factors Impact and prevalence According to the DSM-5, postpartum depression (PPD), also known as postnatal depression, is characterised by psychological and physical symptoms – including anhedonia, depressed mood and abnormal differences in sleep patterns – with a postpartum onset within one month after childbirth. Long-term effects of PPD, which are the same for adult and adolescent mothers, include weaker attachment between the mother and the child and developmental delays in children. Whilst treatment methods for postnatal depression have been more thoroughly investigated in adult mothers than in teenage mothers, prevalence rates of postpartum depression are found to be higher in adolescent mothers, with teenage mothers being twice as likely to be depressed as adult mothers. Postpartum depression in adolescent mothers is a prominent concern, as studies have found that up to 57% of teenage mothers report moderate to severe symptoms of PPD. Risk and protective factors A definite risk factor for postpartum depression in teenage mothers is a lack of social support. Research shows that adolescent mothers face more challenges but have fewer resources and less social support than adult mothers. This is prominent in Barnet et al.’s (1996) research, which found that adolescent mothers who received emotional support from either their mother or the baby’s father were less likely to exhibit depressive symptoms postpartum. Others support this research and suggest that social support has a direct effect on PPD in teenage mothers. Additionally, a lack of wider social support results in stigma, with a common assumption being that young mothers are incompetent parents and that children should not raise other children. Thus, another aspect of the lack of social support that might lead to PPD is stigma. However, an abundance of social support can also be detrimental, as it might make the young mothers feel incapable or inadequate, also leading to postnatal depression. Therefore, it is vital to determine the appropriate amount of support required for adolescent mothers. Another important risk factor affecting adolescent mothers that leads to postpartum depression is stress, which can be, but does not have to be, caused by a lack of social support. Research shows that higher stress levels are positively associated with depressive symptoms, and teenage mothers who reported higher stress levels displayed higher levels of PPD than adolescent mothers with lower stress levels. Therefore, in order to reduce the rate of postpartum depression in adolescent mothers, interventions should focus on decreasing the mothers’ stress levels. A crucial protective factor for PPD in adolescent mothers is self-esteem. Logsdon et al. (2005) found that lower self-esteem was predictive of postnatal depression in teenage mothers, and Caldwell & Antonucci (1997) found that self-esteem has a strong negative correlation with PPD symptoms in adolescent mothers. Therefore, higher self-esteem can shield young mothers from postpartum depression. Conclusions Overall, adolescent mothers are a particularly vulnerable population due to the additional challenges they face and the common lack of preparation for motherhood amongst teenage mothers. Social support, both a lack thereof or an excess amount, is commonly identified in the literature as a key risk factor for PPD in young mothers, as well as stigma and stress. High self-esteem and confidence in one’s own parenting skills are prominent and promising protective factors. The few interventions that are present demonstrate a promising start towards developing ways to tackle PPD in adolescent mothers. However, there has not been an extensive meta-analysis evaluating existing interventions, a clear limitation and a gap in the literature that should be addressed in future research. Written by Aleksandra Lib Related articles: Depression / Depression in children / Childhood stunting / Gynaecology REFERENCES American Psychiatric Association (APA). (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Barnet, B., Joffe, A., Duggan, A. K., Wilson, M. D., & Repke, J. T. (1996). Depressive symptoms, stress, and social support in pregnant and postpartum adolescents. Archives of pediatrics & adolescent medicine , 150 (1), 64-69. Caldwell, C. H., Antonucci, T. C., Jackson, J. S., Wolford, M. L., & Osofsky, J. D. (1997). Perceptions of parental support and depressive symptomatology among black and white adolescent mothers. Journal of Emotional and Behavioral Disorders , 5 (3), 173-183. Deal, L. W., & Holt, V. L. (1998). Young maternal age and depressive symptoms: Results from the 1988 National Maternal and Infant Health Survey. American Journal of Public Health, 88 , 266–270 Dinwiddie, K. J., Schillerstrom, T. L., & Schillerstrom, J. E. (2017). Postpartum depression in adolescent mothers. Journal of Psychosomatic Obstetrics & Gynecology , 39 (3), 168–175. Field T. (1992). Infants of depressed mothers. Development and Psychopathology, 4 , 49-66. Logsdon, M. C., Birkimer, J. C., Simpson, T., & Looney, S. (2005). Postpartum depression and social support in adolescents. Journal of Obstetric, Gynecologic & Neonatal Nursing , 34 (1), 46-54. Radke-Yarrow, M., Cummings, E. M., Kuczynski, L., & Chapman, M. (1985). Patterns of attachment in two- and three-year-olds in normal families and families with parental depression. Child Development, 56 , 886-893. Schmidt, R. M., Wiemann, C. M., Rickert, V. I., & Smith, E. O. B. (2006). Moderate to severe depressive symptoms among adolescent mothers followed four years postpartum. Journal of Adolescent Health , 38 , 712–718. Project Gallery

Richard Dawkins's work and the Modern Evolutionary Synthesis Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Is the immune system ‘selfish’? – a Dawkins perspective Last updated: 22/09/25, 10:59 Published: 25/09/25, 07:00 Richard Dawkins's work and the Modern Evolutionary Synthesis Evolution and Dawkins’ perspective Charles Darwin introduced the unprecedented theory of evolution by natural selection in his famous work ‘On the Origin of Species’, published in 1859. Gregor Mendel, who explained the concept of Mendelian genetics (the inheritance of genes), was a contemporary of Darwin, but his research was recognised much later on, beyond his time. In the 20 th century, the Modern Evolutionary Synthesis was formed and gave a foundation for how biological life has formed as we see it today. The Modern Evolutionary Synthesis is widely accepted and strongly supported by experimental and observational evidence across an array of life. Human beings have even leveraged these concepts for hundreds of years through artificial selection, imposing our own sometimes superficial selective pressures on organisms to express characteristics that we desire (such as the case of the Belgian Blue cattle, with a mutation in the myostatin gene making it a muscular, lean beef, or perhaps artificial selection in dog breeding). Richard Dawkins’ breakout book, ‘The Selfish Gene’, published in 1976, took him from an unknown voice at the University of Oxford passionate about the works of evolution across all animals, to a lauded voice in the scientific community. His concept of genes being selfish is the idea that natural selection works at the gene level, whereby genes over time become better at replication, with the organism acting as a ‘survival machine’ built to help genes propagate. It is important to note that the term ‘selfish’ is not meant metaphysically or philosophically. Figure 1 explains what ‘selfish’ means. Taking this further, it can be argued that genes helping organisms resist pathogenic attack are more likely to survive and propagate. This means the immune system does not exist to protect the body holistically but rather to protect its genes individually. The immune system evolved through the gene-centric lens As previously mentioned, the immune system has become integral to all complex organisms responding to pathogens as a selective pressure. Those genes that have conferred a greater ability to combat or resist a particular pathogen allow the organism an improved survival chance until reproductive age has been achieved. The window whereby the organism has reached reproductive maturity and is reproducing is what the genes have been selected to get, which is why many genetic pathways end up becoming detrimental to an organism in old age (explained by the antagonistic pleiotropy hypothesis- APT- and the disposable soma theory). This remains especially true for the immune system. One must also understand that only vertebrates are biologically equipped with an adaptive immune system (allowing for memory and effective response to previous pathogens), with Figure 2 explaining this difference. This supports that the immune system is a ‘selfish system’, as while many organisms survive without adaptive immunity, more complex organisms have evolved to include it because of our prolonged individual survival and delay in reproductive maturity (indicating that survivability until our reproductive window is an intense selective pressure). Immune imperfection through the ‘Selfish System’ lens We now understand there is a compelling point to be made that the immune system has evolved with the reproductive window in mind and to allow as much gene propagation in a population as possible. If we accept this point of view, it explains many of the trade-offs and imperfections of the immune system when we look at the potential harm caused by immunity. Allergies are one such example, whereby hypersensitivity causes an immune response to harmless substances, which, through the gene-centric lens, may have evolved to detect pathogens such as parasites. This further supports the ‘selfish system’ idea as reproductive success on a population scale is not impaired by a significant amount by allergies. One such study showed that women with allergies and asthma, despite having systemic inflammation, did not have a reduced fertility rate when analysing the relationship between an increase in allergic diseases in the 20 th century and a decrease in fertility globally. Chronic inflammation through persistent immune activation in old age (a concept termed inflammaging) is another such example. We previously mentioned that past reproductive age natural selection weakens, meaning that our genes are selected for early life immune optimisation, even if that means they cause problems later in old age. Processes such as cellular senescence, inflammasome activation, oxidative stress, immune cell dysregulation and so on begin to occur, leading to an increased risk of age-related diseases such as cardiovascular disease, cancer, dementia, sarcopenia and so on. Immune evolution is therefore a ‘selfish system’ because it seems to care more about gene propagation in the young to middle-aged years in comparison to long-term organism health, as many immune systems rapidly decline and become detrimental. Conclusion This perspective of the immune system as a ‘selfish system’ allows us to understand that it is not a protector of the organism throughout its life span, as we may perceive it to be, but rather that it is a mechanism evolved and optimised to propagate genetic material during the organism’s reproductive window (expanding beyond humans). This analysis of the immune system through Richard Dawkins' lens of the “selfish gene” helps us to understand many of the limitations of the immune system. Working on treatments to preserve and maintain the immune system’s healthy state, which reflects young adult life, appears to be a promising approach for future clinical prevention plans for old age diseases. There are many currently being researched and emerging treatments with this principle in mind, such as senotherapeutics and mTOR inhibitors (such as rapamycin and other rapalogs), making this an interesting field to keep up to date with. Written by Yaseen Ahmad Related article: Darwin and Galápagos Tortoises Project Gallery

Unveiling the paradigm shift in cognitive impairment through machine learning Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Brain metastasis hacks brain activity and jams neuronal communication Last updated: 29/05/25, 10:46 Published: 29/05/25, 07:00 Unveiling the paradigm shift in cognitive impairment through machine learning Understanding the impact of brain metastasis on neuronal communication Introduction Researchers from the Spanish National Research Council (CSIC) and the Spanish National Cancer Research Centre (CNIO) have made a ground-breaking discovery related to brain metastasis and its impact on brain activity and neuronal communication. This finding could potentially explain why half of all patients with brain metastasis experience cognitive impairment. Understanding the influence on neural circuits The research , published in Cancer Cell, aimed to comprehend how brain metastasis affects the functionality of neuronal circuits beyond the physical mass of the tumour. The researchers conducted multidimensional modelling of brain functional analyses in the context of brain metastasis and tested various preclinical models from different primary sources and oncogenic profiles. The study was able to separate the effect on local field potential oscillatory activity from cortical and hippocampal areas. This helped researchers learn more about the different ways that brain metastasis can affect people. The authors highlighted the importance of this comprehensive approach in unravelling the complex dynamics of brain metastasis. Detecting metastases through electrical activity Through the measurement of electrical activity in the brains of mice with and without metastases, the researchers discovered distinct electrophysiological differences between the two groups. The researchers used artificial intelligence to confirm that metastases were indeed to blame for these differences. Using an automatic algorithm trained with numerous electrophysiological recordings, the researchers developed a model that could accurately identify the presence of metastases. Furthermore, the algorithm demonstrated the ability to distinguish metastases originating from different primary tumours, such as skin, lung, and breast cancer. These findings provide clear evidence of the specific impact that metastasis has on the brain's electrical activity. Paradigm shift in understanding brain metastases The study represents a significant paradigm shift in the understanding of brain metastases. Traditionally, neurological dysfunction in patients with brain metastasis was attributed solely to the physical mass effect of the tumour. However, this research indicates that changes in brain activity resulting from tumour-induced biochemical and molecular alterations also contribute to these symptoms. The implications of this paradigm shift are far-reaching and have potential implications for the prevention, early diagnosis, and treatment of brain metastasis. By recognising that neurological symptoms are not solely due to the physical presence of the tumour, medical professionals can explore novel diagnostic and therapeutic strategies. Potential therapeutic targets Looking ahead, the researchers are eager to explore potential therapeutic targets that can protect the brain from cancer-induced disruptions in neuronal circuits. They aim to identify molecules involved in metastasis-induced changes in neuronal communication, intending to evaluate them as possible therapeutic targets. The researchers want to create strategies that might stop or lessen the neurological dysfunction that patients frequently experience by understanding the biochemical and molecular changes brought on by brain metastasis. This could lead to advancements in the prevention, early diagnosis, and treatment of brain metastasis, ultimately improving patient outcomes. Conclusion The groundbreaking studies carried out by the Spanish National Research Council and the Spanish National Cancer Research Centre have shed light on how brain metastasis affects brain activity and neuronal communication. By dissociating the effects of tumour mass from changes in brain activity, the study has revealed the complex dynamics of brain metastasis and its contribution to cognitive impairment in patients. The discovery of distinct electrophysiological differences and the development of an algorithm to detect metastases offer promising opportunities for early diagnosis and personalised treatment. This paradigm shift in understanding brain metastases opens the door for novel diagnostic and therapeutic strategies, as well as the exploration of potential therapeutic targets to protect the brain from cancer-induced disruptions. With further research, it is hopeful that advancements in the prevention, early diagnosis, and treatment of brain metastasis will improve patient outcomes and lead to a better understanding of neurological dysfunction in these patients. Written by Sara Maria Majernikova Related articles: Cancer on the move / Cancer magnets / Latent space transformations / Uploading brain to a computer REFERENCE Sanchez-Aguilera A, Masmudi-Martín M, Navas-Olive A, Baena P, Hernández-Oliver C, Priego N, Cordón-Barris L, Alvaro-Espinosa L, García S, Martínez S et al : Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits . Cancer Cell 2023, 41 (9):1637-1649.e1611. Project Gallery