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The effect on sleep on nutrition (nutrition timing) Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The interaction between circadian rhythms and nutrition Last updated: 27/04/25, 11:20 Published: 01/05/25, 07:00 The effect on sleep on nutrition (nutrition timing) The circadian system regulates numerous biological processes with roughly a 24-hour cycle, helping the organism adapt to the day-night rhythm. Among others, circadian rhythms regulate metabolism, energy expenditure, and sleep, for which meal timing is an excellent inducer. Evidence has shown that meal timing has a profound impact on health, gene expression, and lifespan. Proper timed feeding in accordance with the natural circadian rhythms of the body might improve metabolic health and reduce chronic disease risk. Circadian rhythms Circadian rhythms are controlled by the central clock of the brain, which coordinates biological functions with the light-dark cycle. Along with meal timing, circadian rhythms influence key elements of metabolism such as insulin sensitivity, fat storage, and glucose metabolism. When meal timing is not synchronised with the body's natural rhythm, it can cause circadian misalignment, disrupting metabolic processes and contributing to obesity, diabetes, and cardiovascular diseases. Literature has indicated that one should eat best during the daytime, particularly synchronised with the active phase of the body. Eating late at night or in the evening when the circadian rhythm of the body is directed towards sleep could impair metabolic function and lead to weight gain, insulin resistance, and numerous other diseases. Also, having larger meals in the morning and smaller meals later in the evening has been linked to improved metabolic health, sleep quality, and even lifespan. A time-restricted eating window, in which individuals eat all meals within a approximately 10–12 hour window, holds promise for improving human health outcomes like glucose metabolism, inflammation, harmful gene expression, and weight loss ( Figure 1 ). It is necessary to consider the impact of meal timing on gene expression. Our genes react to a number of stimuli, including environmental cues like food and light exposure. Gene expression of the body's metabolic, immune, and DNA repair processes are regulated by the body's circadian clock. Disturbances in meal timing influence the expression of these genes, which may result in greater susceptibility to diseases and reduced lifespan. Certain nutrients, such as melatonin in cherries and grapes, and magnesium in leafy greens and nuts, can improve sleep quality and circadian entrainment. Omega-3 fatty acids in fatty fish and flax seeds also have been shown to regulate circadian genes and improve metabolic functions. Other species Meal timing is quite varied among species, and animals have adapted such that food-seeking behavior is entrained into circadian rhythm and environmental time cues. There are nocturnal animals which eat at night, when they are active ( Figure 2 ). These nocturnal animals have evolved to align their meal time with their period of activity to maximise metabolic efficiency and lifespan. Meal timing is optimised in these animals for night activity and digestion. Humans, and most other animals, are diurnal and consume food during the day. In these animals, consuming most of their calories during the day is conducive to metabolic processes like glucose homeostasis and fat storage. These species tend to have better metabolic health when they are on a feeding regimen that is synchronized with the natural light-dark cycle. Conclusion Meal timing is important in human health, genetics, and life expectancy. Synchronising meal times with the body's circadian rhythms optimises metabolic function, reduces chronic disease incidence, and potentially increases longevity by reducing inflammatory genes and upregulating protective ones. This altered gene expression affects the way food is metabolised and metabolic signals are acted upon by the body. Humans naturally gravitate towards eating during daytime hours, while other creatures have feeding habits that are adaptively suited to their own distinct environmental needs. It is important to consider this science and incorporate it into our schedules to receive the best outcome from an activity that we do not normally think about. Written by B. Esfandyare Related article: The chronotypes REFERENCES Meléndez-Fernández, O.H., Liu, J.A. and Nelson, R.J. (2023). Circadian Rhythms Disrupted by Light at Night and Mistimed Food Intake Alter Hormonal Rhythms and Metabolism. International Journal of Molecular Sciences , [online] 24(4), p.3392. doi: https://doi.org/10.3390/ijms24043392 . Paoli, A., Tinsley, G., Bianco, A. and Moro, T. (2019). The Influence of Meal Frequency and Timing on Health in Humans: The Role of Fasting. Nutrients , [online] 11(4), p.719. Available at: https://www.ncbi.nlm.nih.gov/pubmed/30925707 . Potter, G.D.M., Cade, J.E., Grant, P.J. and Hardie, L.J. (2016). Nutrition and the circadian system. British Journal of Nutrition , [online] 116(3), pp.434–442. doi: https://doi.org/10.1017/s0007114516002117 . St-Onge MP, Ard J, Baskin ML, et al. Meal timing and frequency: implications for obesity prevention. Am J Lifestyle Med. 2017;11(1):7-16. Patterson RE, Sears DD. Metabolic effects of intermittent fasting. Annu Rev Nutr. 2017;37:371-393. Zhdanova IV, Wurtman RJ. Melatonin treatment for age-related insomnia. Endocrine. 2012;42(3):1-12. Prabhat, A., Batra, T. and Kumar, V. (2020). Effects of timed food availability on reproduction and metabolism in zebra finches: Molecular insights into homeostatic adaptation to food-restriction in diurnal vertebrates.Hormones and Behavior, 125, p.104820. Project Gallery

Powering life and causing death Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The Dual Role of Mitochondria 11/07/25, 09:57 Last updated: Published: 13/05/24, 13:38 Powering life and causing death Mitochondria as mechanisms of apoptosis Mitochondria are famous for being the “powerhouse of cells” and producing ATP for respiration by being the site for the Krebs cycle, the electron transport chain and the location of electron carriers. However, one thing mitochondria are not known for is mediating programmed cell death, or apoptosis. This is a tightly controlled process within a cell to prevent the growth of cancer cells. One way apoptosis occurs is through the mitochondria initiating protein activation in the cytosol (a part of the cytoplasm). Proteins such as cytochrome c activate caspases by binding to them, causing cell death. Caspases are enzymes that degrade cellular components so they can be removed by phagocytes. Mitochondrial apoptosis is also controlled by the B cell lymphoma 2 (BCL-2) family of proteins. They are split into pro-apoptotic and pro-survival proteins, so the correct balance of these two types of BCL-2 proteins is important in cellular life and death. Regulation and initiation of mitochondrial apoptosis Mitochondrial apoptosis can be regulated by the BCL-2 family of proteins. They can be activated due to things such as transcriptional upregulation or post-translational modification. Transcriptional upregulation is when the production of RNA from a gene is increased. Post-translational modification is when chemical groups (such as acetyl groups and methyl groups) are added to proteins after they have been translated from RNA. This can change the structure and interactions of proteins. After one of these processes, BAX and BAK (some examples of pro-apoptotic BCL-2 proteins) are activated. They form pores in the mitochondrial outer membrane in a process called mitochondrial outer membrane permeabilisation (MOMP). This allows pro-apoptotic proteins to be released into the cytosol, leading to apoptosis. Therapeutic uses of mitochondria Dysregulation of mitochondrial apoptosis can lead to many neurological and infectious diseases, such as neurodegenerative diseases and autoimmune disorders, as well as cancer. Therefore, mitochondria can act as important drug targets, providing therapeutic opportunities. Some peptides and proteins are known as mitochondriotoxins or mitocans, and they are able to trigger apoptosis. Their use has been investigated for cancer treatment. One example of a mitochondriotoxin is melittin, the main component in bee venom. This compound works by incorporating into plasma membranes and interfering with the organisation of the bilayer by forming pores, which stops membrane proteins from functioning. Drugs consisting of melittin have been used as treatments for conditions such as rheumatoid arthritis and multiple sclerosis. It has also been investigated as a potential treatment for cancer, and it induced apoptosis in certain types of leukaemia cells. This resulted in the downregulation of BCL-2 proteins, meaning there was decreased expression and activity.The result of the melittin-induced apoptosis is a preclinical finding, and more research is needed for clinical applications. This shows that mechanisms of mitochondrial apoptosis can be harnessed to create novel therapeutics for diseases such as cancer. It is evident that mitochondria are essential for respiration but also involved in apoptosis. Moreover, mitochondria are regulated by the activation of proteins like BCL-2, BAX and BAK. With further research, scientists can develop more targeted and effective drugs to treat various diseases associated with mitochondria. Written by Naoshin Haque Project Gallery

MIT scientists found reward sensitivity varies by socioeconomic status Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Motivating the Mind 08/02/25, 13:24 Last updated: Published: 22/04/24, 10:41 MIT scientists found reward sensitivity varies by socioeconomic status Behaviour is believed by many, including the famous psychologist B.F. Skinner, to be reinforced by rewards and the degree to which an individual is motivated by rewards is called reward sensitivity. Another common view is that behaviour is influenced by the environment, nowadays including socioeconomic status (SES). People with low SES encounter fewer rewards in their environment, which could affect their behaviour toward pursuing rewards due to their scarcity- Farah 2017. Thus, a study by Decker (2024) investigates the effect of low SES on reward sensitivity in adolescents through a gambling task, using fMRI technology to measure response times, choices and activity in the striatum – the reward centre of the brain. The researchers hypothesised that response times to immediate rewards, average reward rates and striatal activity would differ for participants from high compared to low SES backgrounds. See Figure 1 . The study involved 114 adolescents whose SES was measured using parental education and income. The participants partook in a gambling task involving guessing if numbers were higher or lower than 5, the outcomes of which were pre-determined to create blocks with reward abundance and reward scarcity. Low and high SES background teenagers gave faster responses and switched guesses when the rewards were given more often. Also, immediate rewards made the participants repeat prior choices and slowed response times. In line with the hypothesis, fewer adolescents with lower SES slowed down after rare rewards. Moreover, it was found that lower SES is linked with fewer differences between reward and loss activation in the striatum, indicating experience-based plasticity in the brain. See Figure 2 . Therefore, the research by Decker (2024) has numerous implications for the real world. As adolescents with lower SES displayed reduced behavioural and neural responses to rewards and, according to behaviourism, rewards are essential to learning, attention and motivation, it can be assumed that SES plays a role in the inequality in many cognitive abilities. This critically impacts the understanding of socioeconomic differences in academic achievement, decision-making and emotional well-being, especially if we consider that differences in SES contribute to prejudice based on ingroups and outgroups. Interventions to enhance motivation and engagement with rewarding activities could help buffer against the detrimental impacts of low SES environments on cognitive and mental health outcomes. Overall, this research highlights the need to address systemic inequities that limit exposure to enriching experiences and opportunities during formative developmental periods. Written by Aleksandra Lib Related article: A perspective on well-being REFERENCES Decker, A. L., Meisler, S. L., Hubbard, N. A., Bauer, C. C., Leonard, J., Grotzinger, H., Giebler M. A., Torres Y C., Imhof A., Romeo R. & Gabrieli, J. D. (2024). Striatal and Behavioral Responses to Reward Vary by Socioeconomic Status in Adolescents. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience, 44(11). Farah, M. J. (2017). The neuroscience of socioeconomic status: Correlates, causes, and consequences. Neuron, 96(1), 56-71. Project Gallery

Pre-diabetes is a period before the diagnosis of diabetes mellitus. When level of blood sugar rise above the normal level but it is not high enough to considered as a diabetes. The blood sugar level range between 100-125mg/dl is considered as a pre-diabetes. Causes of pre-diabetes: Obesity Family Go Back Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Pre-diabetes Last updated: 14/11/24 Published: 14/06/23 Pre-diabetes is the period before the diagnosis of diabetes mellitus; when the level of blood sugar rises above the normal level but it is not high enough to considered as diabetes. The blood sugar level ranges between 100 and 125mg/dl in pre-diabetes. Causes of pre-diabetes: Obesity Family history Genetic history Lack of physical activity High calories diet Sign and symptoms: Pre-diabetes does not have any sign and symptoms. Though some of these symptoms may appear: Increase thirst Frequent urination Increased appetite Fatigue Frequent infections Prevention: In medical science, ‘prevention is better than cure’. So, pre-diabetes is one of the most preventable diseases. There are several ways to prevent diabetes such as dietary intervention, physical activities and lifestyle modifications. A low carbohydrate diet focuses on protein and non-starchy food. Low carbohydrate diets help in reducing weight; if patients have diabetes already, then it will help to lower medication dose and reducing morbidity overall. APPLICATION OF LOW CARBOHYDRATE DIET FOR PRE-DIABETES: Low carbohydrate diets are sometimes recommended to individuals who are being treated for diabetes. These diets can be safe and effective in helping people with type 2 diabetes to manage their weight, blood glucose level, and risk of heart disease in the short term . A healthy, balanced meal. Overall, medium-low carbohydrate diets (30%) are effective and sustainable in the long term for most people. As well as reducing your overall carbohydrate intake, replace refined carbohydrate (e.g. white bread and white rice) with high fibre, and complex carbohydrates (e.g. oats and sweet potato) where possible. Reducing your intake of ultra-processed foods (e.g. biscuits and cakes) will also help you avoid refined carbohydrates and reduce sweet cravings. When adapting to a new way of eating, it can be tricky to know how your plate should look. Above is a plate which is an example of how your plate might look, depending on whether you are including complex carbohydrates. Altogether, low carbohydrate diets are helpful for prediabetic or diabetic individuals to maintain their sugar level and ultimately reduce the incidence rate of diabetes globally. Written by Chhaya Dhedi Related articles: Diabetes to become an epidemic? / Diabetes drug to treat Parkinson's

The closest planet to the Sun Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Exploring the solar system: Mercury 09/07/25, 14:08 Last updated: Published: 27/06/23, 15:46 The closest planet to the Sun Mercury, the closest planet to the Sun, holds a significant place in our understanding of the solar system and serves as our first stepping stone in the exploration of the cosmos. Its intriguing history dates back to ancient times when it was studied and recorded by the Babylonians in their celestial charts. Around 350 BC the ancient Greeks, recognized that the celestial body known as the evening and morning star was, in fact, a single entity. Impressed by its swift movement, they named it Hermes, after the swift messenger of their mythology. As time passed, the Roman Empire adopted the Greek discovery and bestowed upon it the name of their equivalent messenger god, Mercury, a name by which the planet is known today. This ancient recognition of Mercury's uniqueness paved the way for our continued exploration and study of this fascinating planet. Mercury's evolution As Mercury formed from the primordial cloud of gas and dust known as the solar nebula, it went through a process called accretion. Small particles collided and gradually merged together, forming larger bodies called planetesimals. Over time, these planetesimals grew in size through further collisions and gravitational attraction, eventually forming the protoplanet that would become Mercury. However, the proximity to the Sun presented unique challenges for Mercury's formation. The Sun emitted intense heat and powerful solar winds that swept away much of the planet's initial atmosphere and surface materials. This process, known as solar stripping or solar ablation, left behind a relatively thin and tenuous atmosphere compared to other planets in the solar system. The intense heat also played a crucial role in shaping Mercury's surface. The planet's surface rocks melted and differentiated, with denser materials sinking towards the core while lighter materials rose to the surface. This process created a large iron-rich core, accounting for about 70% of the planet's radius. Mercury's lack of significant geological activity, such as plate tectonics, has allowed its surface to retain ancient features and provide insights into the early history of our solar system. The planet's surface is dominated by impact craters, much like the Moon. These craters are the result of countless collisions with asteroids and comets over billions of years. The largest and most prominent impact feature on Mercury is the Caloris Basin, a vast impact crater approximately 1,525 kilometres in diameter. The impact of such large celestial bodies created shockwaves and volcanic activity, leaving behind a scarred and rugged terrain. Scientists estimate that the period known as the Late Heavy Bombardment, which occurred around 3.8 to 4.1 billion years ago, was particularly tumultuous for Mercury. During this time, the inner planets of our solar system experienced a high frequency of cosmic collisions. These impacts not only shaped Mercury's surface but also influenced the evolution of other rocky planets like Earth and Mars. Studying Mercury's geology and surface features provides valuable insights into the early stages of planetary formation and the impact history of our solar system. Exploration history Our understanding of Mercury has greatly benefited from a series of pioneering missions that ventured close to the planet and provided valuable insights into its characteristics. Let's delve into the details of these key exploratory endeavours: Mariner 10 (1974-1975): Launched by NASA, Mariner 10 was the first spacecraft to conduct a close-up exploration of Mercury. It embarked on a series of three flybys, passing by the planet in 1974 and 1975. Mariner 10 captured images of approximately 45% of Mercury's surface, revealing its heavily cratered terrain. The spacecraft's observations provided crucial information about the planet's rotation period, which was found to be approximately 59 Earth days. Mariner 10 also discovered that Mercury possessed a magnetic field, albeit weaker than Earth's. MESSENGER (2004-2015): The MESSENGER mission, short for Mercury Surface, Space Environment, Geochemistry, and Ranging, was launched by NASA in 2004. It became the first spacecraft to enter into orbit around Mercury in 2011, marking a significant milestone in the exploration of the planet. Over the course of more than four years, MESSENGER conducted an extensive study of Mercury's surface and environment. It captured detailed images of previously unseen regions, revealing the planet's diverse geological features, including vast volcanic plains and cliffs. MESSENGER's data also indicated the presence of water ice in permanently shadowed craters near Mercury's poles, surprising scientists. Furthermore, the mission discovered that Mercury possessed a global magnetic field, challenging previous assumptions about the planet's magnetism. MESSENGER's observations greatly expanded our knowledge of Mercury's geology, composition, and magnetic properties. BepiColombo (2018-Present): The BepiColombo mission, a joint endeavour between the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA), aims to further enhance our understanding of Mercury. The mission consists of two separate orbiters: the Mercury Planetary Orbiter (MPO) developed by ESA and the Mercury Magnetospheric Orbiter (MMO) developed by JAXA. Launched in 2018, BepiColombo is currently on its journey to Mercury, with an expected arrival in 2025. Once there, the mission will study various aspects of the planet, including its magnetic field, interior structure, and surface composition. The comprehensive data collected by BepiColombo's orbiters will contribute significantly to our knowledge of Mercury and help answer remaining questions about its formation and evolution. These missions have played pivotal roles in advancing our understanding of Mercury. They have provided unprecedented insights into the planet's surface features, composition, magnetic field, and geological history. As exploration efforts continue, we can anticipate further revelations and a deeper understanding of this intriguing world. Future exploration While significant advancements have been made in understanding Mercury, there is still much more to learn. Scientists hope to explore areas of the planet that have not yet been observed up close, such as the north pole and regions where water ice may be present. They also aim to study Mercury's thin atmosphere, which consists of atoms blasted off the surface by the solar wind. Moreover, the advancement of technology may lead to the development of innovative missions to Mercury. Concepts such as landing missions and even manned exploration have been proposed, although the challenges associated with the planet's extreme environment and proximity to the Sun make such endeavours highly demanding. Nevertheless, the quest to unravel Mercury's mysteries continues, driven by the desire to deepen our knowledge of planetary formation, evolution, and the unique conditions that shaped this enigmatic world. Exploring the uncharted areas of Mercury, particularly the north pole, holds great scientific potential. The presence of water ice in permanently shadowed regions has been suggested by previous observations, and investigating these areas up close could provide valuable insights into the planet's volatile history and the potential for water resources. Additionally, studying Mercury's thin atmosphere is of significant interest. Comprised mostly of atoms blasted off the surface by the intense solar wind, understanding the composition and dynamics of this atmosphere could shed light on the processes that shape Mercury's exosphere. In conclusion, while significant progress has been made in unravelling the mysteries of Mercury, there is still much to explore and discover. Scientists aspire to investigate untouched regions, study the planet's thin atmosphere, and employ innovative mission concepts. The future may hold ambitious missions, including landing missions and potentially even manned exploration. As our knowledge and capabilities expand, Mercury continues to beckon us with its fascinating secrets, urging us to push the boundaries of exploration and expand our understanding of the wonders of the solar system. And with that we finish our journey into the history and exploration of Mercury and will move to Venus in the next article. Written by Zari Syed Related articles: Fuel for the colonisation of Mars / Nuclear fusion Project Gallery

The natural body clock and the involvement of genetics Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The chronotypes 10/07/25, 18:28 Last updated: Published: 27/11/24, 11:47 The natural body clock and the involvement of genetics Feeling like heading to bed at 9 pm and waking up at the crack of dawn? These tendencies define your chronotype, backed up by changes within your body. A generally overlooked topic, chronotypes affect our everyday behaviour. Many people innately associate themselves with a certain chronotype, but what do we know about how these physiological differences are caused at a molecular level? The word ‘chronotype’ was first coined in the 1970s, combining the Greek words chrono (time) and type (kind or form). While the term is relatively modern, the concept emerged in the 18th century. Researchers in the 1960s and 1970s, like Jürgen Aschoff, explored how internal biological clocks influence our sleep-wake cycles, leading to the classification of people into morning or evening types based on their activity patterns. The first evidence of body clocks was found in plants rather than humans, thus leading to the invention of flower clocks, which were used to tell the time of the day. Before delving into the details, let us be introduced to the general categories of chronotypes, which describe a person’s inclination to wake up and sleep while also affecting productivity periods. We know of the following three categories: The morning type (also referred to as larks): they are inclined to wake up and go to bed early because they feel most alert and productive in the mornings. The evening type (also called the owls): they feel most alert and productive in the evenings and onwards, so they are inclined to wake up and go to bed later. The intermediate types (also referred to as the doves): they fall in the middle of this range. Let’s explore what we know about the genetics that prove that chronotypes are a natural phenomenon. Genetics of chronotypes The main determining factor in our chronotypes is the circadian period. This is the body’s 24 hour cycle of changes that manifest into feelings of productivity and energy or tiredness. The length of this is crucial in determining our chronotypes. More importantly, specific physiological changes that cause these effects are melatonin and core body temperature. One study suggested that the morning types might have circadian periods shorter than 24 hours, whereas evening chronotypes might have circadian periods longer than 24 hours. A major clock gene is PER, which includes a collection of genes known as PER1, PER2 and PER3, which are thought to regulate circadian period factors. Specifically, it has been observed that a delay in the expression of the PER1 gene in humans causes an increased circadian period. Possible causes for this delay may be rendered to a variation within the negative feedback loop that PER1 operates in, including hereditary differences, environmental causes, changes to hormonal signals and age. This process may describe the mechanism behind the evening chronotype. Molecular polymorphs in the PER3 gene are thought to cause shorter circadian rhythms and the manifestation of the morning types. Similarly, a polymorph in the PER3 gene can be caused by a plethora of causes, as described for PER1. These nuances cause differences in the periodic release and stop of hormones which control the circadian rhythm, such as melatonin and body temperature. This is important in its power to control our energy levels, windows of productivity, and sleep cycles. The consensus remains that chronotypes are attributable to genetic premeditation by 50%, however, it has also been observed that chronotypes are prone to change with advancing age. Increased age is associated with an inclination towards an earlier phase chronotype. Age-related variation has been observed to be higher in men. There also exists an association between geographical locations and phase preference; increasing latitude (travelling North or South) from the earth's equator is associated with later chronotypes. Of course, many variations and factors come into play to affect these findings, such as ethnic genetics, climate, work culture and even population density. The effect on core body temperature and melatonin Polymorphisms in the PER3 cause a much earlier peak in body temperature and melatonin in the morning than in the evening and intermediate types. These manifest as the need to sleep much earlier in the morning and a decreased feeling of productivity later in the day. In contrast, the evening types experience a later release of melatonin and a drop in core body temperature, causing a later onset of tiredness and lack of energy. It can then be inferred that the intermediate types are affected by the expression of these genes in a way that falls in the middle of this spectrum. Conclusion Understanding differences in circadian rhythms and sleep-wake preferences offers valuable insights into human behaviour and health. Chronotypes influence various aspects of daily life, including sleep patterns and quality, cognitive performance and susceptibility to specific health conditions, including sleep-wake conditions. An extreme deviation in circadian rhythms and sleep cycles may lead to such conditions as Advanced sleep-wake phase Disorder (ASPD) and Delayed sleep-wake phase Disorder (DSPD). Recognising these variations is also helpful in optimising work schedules and aligning to jet lags, improving mental and physical health by tailoring our biological rhythms to our environments. Many individuals opt to do a sleep study at an institution to gain insights into their circadian rhythms. A healthcare professional may also prescribe this if they suspect you have a circadian disturbance such as insomnia. The Morning-Eveningness Questionnaire (MEQ) The MEQ is a self-reported questionnaire you may complete to gain more insight into your chronotype category. Clinical psychologist Micheal Breus created it and uses different animals to categorise the chronotypes further. The framework suggests that the Bear represents individuals whose energy patterns are entrained to the rising and the sun's setting and are the most common types in the general population. The Lions describe the early risers, and Wolves roughly equate to the evening types. Recently, a fourth chronotype has been proposed: the Dolphin, whose responses to the questionnaire suggest that they switch between modes. Whether you're a Bear, Lion, Wolf, or Dolphin, understanding your chronotype can be a game-changer in optimising your daily routine. So, what’s your chronotype—and how can you start working with your body’s natural rhythms to unlock your full potential? A sleep study ? The MEQ ? Maybe keeping a tracker. Written by B. Esfandyare Related articles: Circadian rhythms and nutrition / Does insomnia run in families? REFERENCES Emens JS, Yuhas K, Rough J, Kochar N, Peters D, Lewy AJ. Phase Angle of Entrainment in Morning‐ and Evening‐Types under Naturalistic Conditions. Chronobiology International. 2009 Jan;26(3):474–93. Lee, J.H., Kim, I.S., Kim, S.J., Wang, W. and Duffy, J.F. (2011). Change in Individual Chronotype Over a Lifetime: A Retrospective Study. Sleep Medicine Research , 2(2), pp.48–53. doi: https://doi.org/10.17241/smr.2011.2.2.48 . Ujma, P.P. and Kirkegaard, E.O.W. (2021). The overlapping geography of cognitive ability and chronotype. PsyCh Journal , 10(5), pp.834–846. doi: https://doi.org/10.1002/pchj.477 . Shearman LP, Jin X, Lee C, Reppert SM, Weaver DR. Targeted Disruption of the mPer3 Gene: Subtle Effects on Circadian Clock Function. Molecular and Cellular Biology. 2000 Sep 1;20(17):6269–75. Viola AU, Archer SN, James Lynette M, Groeger JA, Lo JCY, Skene DJ, et al. PER3 Polymorphism Predicts Sleep Structure and Waking Performance. Current Biology. 2007 Apr;17(7):613–8. Project Gallery

AI can process data sets and identify patterns and biomarkers Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link AI: the next step in diagnosis and treatment of genetic diseases 08/07/25, 16:19 Last updated: Published: 23/03/24, 17:59 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

The specific research that was recognised for a Nobel Prize in Physics was the discovery of radioactivity. Radioactivity is the spontaneous emission of energy, in the form of radiation, a term that Curie herself coined. Marie Curie researched whether uranium, a weakly radioactive element, was found in other materials. She then analysed pitchblende, Go Back Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The Women who have won the Nobel Prize in Physics Last updated: 13/11/24 Published: 01/03/23 March is International Women’s month, so it seems like the perfect time to celebrate the women who have been awarded Nobel Prizes in Physics. There have only been a total of four women to receive this prestigious award, namely Marie Curie, Maria Goeppert Mayer, Donna Strickland, and Andrea Ghez. This article will detail the research each woman did to achieve the Nobel Prize, as well as the context of their discoveries. Marie Curie (1903) Arguably the most famous of these Nobel Prize winners, Marie Curie won her award for research on radioactive phenomena. Curie received half the Nobel Prize for Physics, shared with her husband, but at first, the committee had only intended to award it to him. This was the first Nobel Prize for Physics ever awarded to a woman. The specific research that was recognised for a Nobel Prize in Physics was the discovery of radioactivity. Radioactivity is the spontaneous emission of energy, in the form of radiation, a term that Curie herself coined. Marie Curie researched whether uranium, a weakly radioactive element, was found in other materials. She then analysed pitchblende, a mineral made partially of uranium but had a higher amount of radiation. Curie investigated other elements that pitchblende could be made up of and, as a result of this, discovered new elements: polonium and radium. Following this, she had ambitions of obtaining pure radium, and following this achievement, she was awarded the Nobel Prize in Physics in 1903. Maria Goeppert Mayer (1963) 60 years after Marie Curie was awarded her Nobel Prize for Physics, Maria Goeppert Mayer became the second female recipient. She received the Prize for her work in 1963 on the nuclear shell model of the atomic nucleus. Goeppert Mayer shared her award with two other physicists who came to the same conclusion as her. The nuclear shell model describes the exact makeup of the atomic nucleus, through the exact numbers of protons and neutrons. Maria Goeppert Mayer’s mathematical work on this model described why there are certain amounts of neutrons and protons in stable atoms. She beautifully described the model in terms of waltzers dancing and spinning in circles. Donna Strickland (2018) The next female Nobel Prize in Physics award winner wouldn’t be until another half-century later, with Donna Strickland. Strickland was awarded the Prize for her work on chirped pulse amplification and its applications. Although the research itself was published in 1985, she didn’t receive the award until 2018. Chirped pulse amplification (CPA) is a technique that takes a very short laser pulse (a light flash) and makes it brighter. The technique is useful for making extremely precise cuts, so is used for many laser-related applications, such as laser eye surgery. The wide range of uses CPA has in medicine makes this an important discovery for physics which led to Strickland being awarded the Nobel Prize award. Andrea Ghez (2020) The result of the work of Andrea Ghez, the fourth female Nobel Prize in Physics recipient, may be the most exciting conclusion of the research described in this article. Ghez won the award for her role in discovering a black hole in the centre of our galaxy. A black hole is a very dense, compact object with gravity so strong that not even light can escape it. Until recently, physicists have not been able to visually observe black holes but instead can detect them by looking at how other objects, such as stars, behave around one. Andrea Ghez and her team used the movement of Sagittarius A* to prove that there was a black hole in the centre of the Milky Way. Written by Madeleine Hales Related articles: Female Nobel prize winners in chemistry / African-American women in cancer research

New research suggests that the cause extends far beyond the nervous system Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Can what we eat, breathe, and do for a living affect our Parkinson’s risk? Last updated: 21/03/25, 11:59 Published: 10/04/25, 07:00 New research suggests that the cause extends far beyond the nervous system Introduction Parkinson’s disease (PD) is the most prevalent movement disorder and the second most common neurodegenerative disorder worldwide. PD is best known for causing tremors and stiffness, but it’s much more than a movement disorder. It also affects mood and speech. While PD is caused by the loss of dopamine-producing neurons in the brain’s substantia nigra, new research suggests that its roots may extend far beyond the nervous system. Surprisingly, the gut microbiome – trillions of bacteria living in our digestive tract – may play a key role in both the development and prevention of PD. These microbes help regulate inflammation and support brain health by influencing microglia, the brain’s immune cells. Diet also seems to matter: a Mediterranean-style diet rich in fruits, vegetables, and healthy fats appears to lower PD risk, while smoking – despite its well-known dangers – has been linked to a puzzling protective effect, possibly due to nicotine’s impact on the brain. Meanwhile, specific jobs, like farming, may increase PD risk due to pesticide exposure, which has been associated with neurodegeneration. The idea that what we eat, breathe, and do for a living could shape our brain health is intriguing. As research continues to uncover these surprising links, it raises an important question: could simple lifestyle changes help protect against neurodegenerative diseases? Gut-Brain Axis The gut-brain axis (GBA) is a two-way communication network between the enteric nervous system of the gastrointestinal (GI) tract and the central nervous system, connecting emotions and cognition with the intestines’ functions. This involves the brain sending signals to the gut and vice versa, which happens through the vagus nerve, gut hormones and the gut microbiome, which can produce chemicals to impact brain activity. This usually explains why stress signals from the brain can influence the digestion of food, causing symptoms such as stomach pain, bloating or changes in bowel movements. Alternatively, signals travelling from the gut to the brain can be seen when we eat something that makes us feel sick – we naturally avoid that food and the place where we ate it. Gut dysbiosis can be triggered by multiple factors, including diet, antibiotic use, infection, inflammation, and chronic stress. Dysbiosis is the imbalance in the composition and activity of the microbiota (microorganisms present in the gut). It is considered a risk factor for PD, but is not a direct cause of it. Changes in the microbiota can induce metabolic changes, which can result in increased local and systemic inflammation in addition to increased permeability of the intestines, making the gut ‘leaky’. Additionally, this can cause increased harmful gut bacteria (such as E. coli or Salmonella ) as they leak through the intestinal lining, producing amyloid proteins which can travel to the brain and cause the accumulation of α-synuclein – a protein linked to neurodegenerative diseases such as PD. There is also a reduction in healthy gut bacteria – which usually produce short-chain fatty acids (SCFAs) such as butyrate – which reduce inflammation and protect the brain cells. Less SCFAs cause an increase in inflammation and loss of the neuroprotective effects of SCFAs. Increased inflammation can eventually cause the weakening of the gut lining and a cycle of worsening dysbiosis, increased inflammation and increased α-synuclein accumulation, which spreads to the brain. Furthermore, gut dysbiosis can decrease the efficacy of dopaminergic treatments, which may be used to treat PD. In gut dysbiosis, harmful bacteria can produce an enzyme called dopa-decarboxylase – which converts Levodopa (a drug used to treat PD) into dopamine within the intestines. Hence, less Levodopa reaches the bloodstream and the brain, where it primarily acts and is converted to dopamine. This results in less Levodopa being converted to dopamine within the brain, reducing the effectiveness of the treatment. Consequently, this leads to motor symptoms and impairments such as tremors, which is a characteristic symptom of PD. Can food protect the brain? Could your diet be influencing your brain health in ways you never imagined? Research suggests that what you eat might play a critical role in either protecting your brain from PD or increasing your risk. People who follow a Mediterranean diet (MD) – rich in olive oil, fish, fruits, vegetables, whole grains, and nuts – may have up to a 25% lower risk of developing PD. Interestingly, this protective effect appears stronger in younger individuals and those in the early stages of PD. So.. what makes the MD so powerful? Gut microbiome boost: the MD promotes beneficial gut bacteria while reducing harmful microbes, supporting overall brain health. Anti-inflammatory effects: fibre from plant-based foods fuels the gut microbiome, leading to the production of SCFAs, which reduce inflammation and may slow PD progression. Mitochondrial protection: compounds in the MD, such as polyphenols in olive oil and omega-3 fatty acids in fish, help repair and protect mitochondria – the powerhouses of our cells. This helps prevent brain cell damage and maintain dopamine function. Neural growth & repair: walnuts and omega-3s may support neuronal growth and reduce protein clumping, a hallmark of PD. On the other hand, a Western diet – high in processed foods, saturated fats, refined sugars, and excess salt – may increase the risk of developing and worsening PD symptoms. Foods commonly associated with faster PD progression include canned fruits and vegetables, soda, fried foods, beef, ice cream, and cheese. Why does this happen? Microbiome disruption: the Western diet fosters an imbalance in gut bacteria, leading to inflammation and potential brain damage. Gut leakiness and neuroinflammation: a diet high in unhealthy fats and low in fibre can damage the gut lining, allowing harmful substances to enter the bloodstream and trigger brain inflammation. Hormonal imbalance: key gut-derived hormones (GLP-1, GIP, and IGN) that help protect neurons are disrupted by poor diet but can be restored through healthier food choices. While diet alone cannot cure PD, growing evidence suggests it can modify the disease course. A diet rich in fibre, healthy fats, and plant-based foods supports gut health, reduces inflammation, and may protect neurons from degeneration. Understanding these diet-microbiome-brain interactions could open new doors to PD prevention and treatment – proving once again that food truly is medicine. The smoking paradox One of the most intriguing findings in PD research is that smokers appear to have a lower risk of developing the disease. Epidemiological studies consistently show that people who smoke are less likely to be diagnosed with PD compared to non-smokers. But why? Scientists believe that nicotine, a key compound in tobacco, may play a neuroprotective role by affecting dopamine-producing neurons – the same cells that are progressively lost in PD disease. Nicotine interacts with receptors in the brain that influence dopamine release, which could help protect these neurons from degeneration. However, clinical trials testing nicotine as a treatment for PD have not shown significant benefits, suggesting that other compounds in tobacco or alternative mechanisms might be involved. Some researchers propose that additional chemicals in cigarette smoke, such as monoamine oxidase inhibitors, antioxidants, or even carbon monoxide at low levels, might contribute to this protective effect. Others suggest that genetic factors or lifestyle differences between smokers and non-smokers could also explain the association. Despite this fascinating link, smoking is not a recommended strategy for preventing PD. The well-documented risks – including cancer, cardiovascular disease, and lung damage – far outweigh any potential benefit. Instead, scientists are investigating whether specific compounds found in tobacco could be harnessed for new treatments without the harmful effects of smoking itself. What about my job? Can your job affect your risk of developing PD? Some studies suggest that certain occupations – like farming – might increase the risk, while others find no clear connection. So, what’s the truth? Let’s break it down. Some research suggests that farmers are more likely to develop PD, possibly due to exposure to pesticides like paraquat and rotenone, which have been linked to brain cell damage. Additionally, heavy metals found in agricultural environments – such as lead and manganese – may contribute to brain inflammation and oxidative stress, both of which play a role in PD. Furthermore, certain metals, including iron, mercury, copper, and manganese, can build up in the brain over time. Scientists believe that long-term exposure could damage the neurons that produce dopamine. However, the exact link isn’t fully understood, and not everyone exposed to these metals develops PD. That said, not all studies agree. Some large-scale research has found no significant link between farming, pesticide exposure, heavy metals and PD risk. This means that while environmental factors might play a role, other things – like genetics, lifestyle, or how long and intensely someone is exposed – could be just as important. So.. should you worry? If you work in farming or are regularly exposed to pesticides and heavy metals, it might be a good idea to take precautions, like using protective equipment and following safety guidelines. However, more research is needed to fully understand how these exposures contribute to PD. For now, staying informed and taking steps to reduce unnecessary exposure to harmful chemicals is a smart approach. What can you do? While there’s no guaranteed way to prevent PD, research suggests that certain lifestyle choices may help reduce the risk. Here are some science-backed steps you can take: 1. Adopt a Mediterranean-style diet: eating a diet rich in whole, plant-based foods, healthy fats (like olive oil and nuts), and lean proteins has been linked to a lower risk of PD. The Mediterranean diet is packed with antioxidants and anti-inflammatory compounds that may help protect brain cells. 2. Stay active: regular exercise isn’t just good for your muscles and heart – it may also help maintain gut health and protect neurons. Activities like walking, swimming, or strength training have been associated with a reduced risk of PD and other neurodegenerative diseases. 3. Limit pesticide exposure: for those in agricultural or industrial settings, protective measures, such as wearing gloves and masks and following safety guidelines, can help reduce exposure to potentially harmful chemicals linked to PD. 4. Monitor gut health: emerging research suggests that the gut microbiome may play a key role in PD. While scientists are still exploring microbiome-targeted therapies, maintaining good gut health by eating fibre-rich foods, fermented foods (like yogurt and kimchi), and staying hydrated may support overall well-being. Conclusion The connection between diet, gut health, lifestyle, and PD is an exciting area of research. While we don’t yet have all the answers, it’s clear that healthy habits – such as eating well, staying active, and minimising harmful exposures – can support both brain and overall health. As science continues to uncover new insights, making informed choices today can help protect your well-being in the long run! Written by Joecelyn Kirani Tan, Hanin Salem, Devikka Sivashanmuganathan & Barayturk Aydin Related articles: TDP43 and Parkinsonism / Diabetes drug to treat Parkinson's REFERENCES Berthouzoz E, Lazarevic V, Zekeridou A, Castro M, Debove I, Aybek S, Schrenzel J, Burkhard PR, Fleury V. Oral and intestinal dysbiosis in Parkinson's disease. Rev Neurol (Paris). 2023 Nov;179(9):937-946. doi: 10.1016/j.neurol.2022.12.010. Epub 2023 Mar 16. PMID: 36934020. Bisaglia M. Mediterranean Diet and Parkinson's Disease. Int J Mol Sci. 2022 Dec 20;24(1):42. doi: 10.3390/ijms24010042. PMID: 36613486; PMCID: PMC9820428. Firestone JA, Lundin JI, Powers KM, Smith-Weller T, Franklin GM, Swanson PD, Longstreth WT Jr, Checkoway H. Occupational factors and risk of Parkinson's disease: A population-based case-control study. Am J Ind Med. 2010 Mar;53(3):217-23. doi: 10.1002/ajim.20788. PMID: 20025075; PMCID: PMC3299410. Gorell JM, Johnson CC, Rybicki BA, Peterson EL, Richardson RJ. The risk of Parkinson's disease with exposure to pesticides, farming, well water, and rural living. Neurology. 1998 May;50(5):1346-50. doi: 10.1212/wnl.50.5.1346. PMID: 9595985. hms.harvard.edu . (2017). The Gut and the Brain. [online] Available at: https://hms.harvard.edu/news-events/publications-archive/brain/gut-brain . Hrncir, T. (2022). Gut Microbiota Dysbiosis: Triggers, Consequences, Diagnostic and Therapeutic Options. Microorganisms, [online] 10(3), p.578. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC8954387/#:~:text=Dysbiosis%20can%20be%20caused%20by,food%20additives)%2C%20and%20hygiene .. Jackson A, Forsyth CB, Shaikh M, Voigt RM, Engen PA, Ramirez V, Keshavarzian A. Diet in Parkinson's Disease: Critical Role for the Microbiome. Front Neurol. 2019 Dec 10;10:1245. doi: 10.3389/fneur.2019.01245. PMID: 31920905; PMCID: PMC6915094. Johns Hopkins Medicine (2025). Can Environmental Toxins Cause Parkinson’s Disease? https://www.hopkinsmedicine.org/health/conditions-and-diseases/parkinsons-disease/can-environmental-toxins-cause-parkinson-disease Kwon, D. et al. (2024) ‘Diet and the gut microbiome in patients with parkinson’s disease’, npj Parkinson’s Disease , 10(1). doi:10.1038/s41531-024-00681-7. Physiopedia. (n.d.). Gut Brain Axis (GBA). [online] Available at: https://www.physio-pedia.com/Gut_Brain_Axis_(GBA) . Project Gallery

The field of neuroscience is rapidly expanding day by day. Study dopamine in the mesolimbic and nigrostriatal pathways; explore shattered brains in traumatic brain injuries; and delve into the mechanics of motion. Neuroscience Articles The field of neuroscience is rapidly expanding day by day. Study dopamine in the mesolimbic and nigrostriatal pathways; explore shattered brains in traumatic brain injuries; and delve into the mechanics of motion. You may also like: Biology , Immunology , Medicine Dopamine in the movement and reward pathways Aka the mesolimbic and nigrostriatal pathways Pseudo-Angelman syndrome A rare neurological disease that causes intellectual deficits. Article #10 in a series on Rare diseases. What does depression do to your brain? The biological explanation of Major Depressive Disorder (MDD). Article #1 in a series on psychiatric disorders and the brain. Neuroimaging and spatial resolution Which type of brain scan has it all? Beyond the bump A breakdown on traumatic brain injuries How does physical health affect mental health? The effects of exercise on the nervous system Mastering motion Looking at reflex, rhythmic and complex movements The brain of a bully The neurological basis of bullying Inside out: the chemistry of depression The role of neurotransmitters. Article #2 in a series on psychiatric disorders and the brain. Vertigo Physiology, causes, relevance Previous