There's a scene that keeps coming back to me since I heard Professor Madalena Patrício, of the University of Lisbon's Faculty of Medicine, speak about artificial intelligence in health professions education. It isn't a scene from her talk, exactly — it's from Pluribus, a series I watched recently that, somehow, found a new way to be read in light of what she said. In the show, a signal from space carries the code of a virus that fuses almost all of humanity into a single consciousness. At first glance, what's frightening is the loss of individuality. But something more unsettling may be happening underneath. The infected don't become less intelligent — quite the opposite. They gain instant access to everything any of them has ever known, thought, or concluded. The answer stops being the end of a search. It starts existing before the question even has time to form.That's what makes the world of the show so strange. There's no longer any need to sit with uncertainty, hold a doubt, test a hypothesis, or change one's mind. Knowledge is simply always available, with no gap in which something has to be worked out by a person. Humanity doesn't lose the capacity to know — it slowly loses the need to think. Carol, the protagonist and one of the few people left immune, notices what everyone else doesn't. What looks like harmony to almost everyone feels to her like a slow death of reasoning: not because people have stopped knowing things, but because they no longer need to discover them. Even as everyone else seems to move in perfect sync, in a flawless community, watching it unfold is deeply unsettling.It's hard not to recognize, on a much smaller scale and without any alien virus, a version of this already showing up in my Uni. A student who opens a language model before even forming their own question isn't necessarily doing anything wrong. But they may be making a quiet trade — handing the machine exactly the part of the process that was supposed to help them build their own thinking. The tool offers speed, content, availability. The risk shows up when that ease starts replacing the work of figuring things out. Maybe the real question isn't whether a student uses AI. It's this: what still happens inside them before they accept an answer? Or, more to the point — what has stopped happening?That shift also moves the teacher's place. For a long time, teaching meant, in large part, transmitting what one already knew. The teacher was where knowledge accumulated; the student was the one meant to receive it. That model already had its own limits, but artificial intelligence introduces a break that's hard to ignore. On the surface, if information is just one prompt away, then simply transmitting that knowledge starts to make the teacher's presence in the room feel unnecessary. To follow that logic without question is to fall into a trap now being dressed up as a reason to embrace AI as a legitimate teaching tool. What gets left out of that reasoning is the very thing that justifies a teacher's existence in the first place: the job of teaching a student to think critically and independently.Madalena Patrício called this epistemological leadership. The phrase feels exact, because it moves the teacher away from simply knowing and toward helping someone else decide how to know — how to doubt, how to recognize the edges of what only seems like knowledge. In Pluribus, that kind of formation becomes pointless, because disagreement disappears entirely. And maybe that's exactly where the story offers its most powerful metaphor. Thinking requires some degree of friction. Evidence can contradict a conviction; a question can unsettle an answer; an experience can force someone to rebuild an entire structure of thought. Jack Mezirow placed that movement at the center of transformative learning: it's the dilemma that doesn't fit how we already understand the world that forces us to rethink it. Discomfort, in that sense, isn't a flaw in learning — it's part of it. If knowledge is a path where what you gather along the way matters as much as where you end up, then skipping that path in the name of efficiency may cost someone not just a particular learning experience, but thinking itself as an essential tool of what makes us human.This is why Carol becomes something far more interesting than the standard rebel. She doesn't reject connection simply because anything new feels threatening — she insists on understanding before accepting. She holds onto her own opinion when the rest of the world no longer sees the point of having one; she keeps questioning when everyone else has forgotten what the verb to ask even means. Carol's resistance isn't a rejection of intelligence. It's a refusal to give up her own consciousness — a way of keeping her humanity intact, even as almost everyone else has already lost theirs.There's something of that same posture in what Patrício proposes to institutions. Rather than turning AI into a matter of allegiance — for it or against it, adopt or resist — she talks instead about readiness. And readiness isn't a statement you make once; it's a practice you build. It means giving clear institutional direction, backing teachers and students, and holding onto ethical principles even when the pressure for productivity points toward shortcuts. An institution can fold AI into every one of its processes and still not be ready for it. Adoption happens fast. Educating a student doesn't. In health education, in particular, there are limits worth treating as non-negotiable. AI can suggest, but it can't answer for a clinical decision. It can organize and summarize evidence, but it can't take responsibility for acting on it. It can help simulate a case, but it can't reproduce an encounter with an actual sick person — their vulnerability, their contradictions, their silences, their history. The machine can take part in a judgment. It can't be the one who carries it. That's not just about AI's technical limits, which will keep shifting and improving — it's something more fundamental. In health training, there are decisions someone has to be accountable for. Teaching that is a different job from teaching someone to use a tool.Our job as teachers is to help students tell the difference between a conviction built from knowledge that's been critically examined, and an answer that arrived ready-made — produced by a mathematical language system that's very good at linking words, patterns, and probabilities, but that can never take on our responsibility for what we do with what it gives us. That's why the kind of training in Unis has to happen on two fronts. Teachers need to learn to use AI with real discernment — which has very little to do with mastering prompting techniques, and a lot to do with building the habit of interrogating an answer before passing it along. Students, meanwhile, need to learn to be suspicious of answers that sound too good: to spot the error, notice the gap, recognize the bias, and register when a text's confidence outruns the evidence behind it.Today, that skill starts to look like a basic form of literacy — not just finding a source, but knowing how to judge what a machine hands you as an answer. Rules can set limits. They can block some kinds of misuse. But they can't teach anyone to think. Only practice does that: testing the AI's answer against what you already know, hunting for what's missing, checking what seems right, sitting with the doubt a little longer. Learning to disagree with the machine may turn out to be one of the most important ways of learning from it. Without that habit, the gap between institutions will only widen. On one side, schools that teach students to think with AI, keeping it firmly in its place as a tool. On the other, schools that simply let it fill whatever space it finds. The inequality won't just be about access to technology — it will be about the quality of thinking each institution manages to cultivate around it. In Pluribus, the fusion erases that gap entirely. There's no longer one mind standing next to another. There's one mind, inside everyone. Maybe that's exactly the line a university needs to hold.Some competencies belong to both knowledge and practice: understanding where AI gets things right and where it doesn't, recognizing its biases, reasoning ethically about how it's used — but also communicating with a patient, deciding alongside them, working with other professionals, and holding space for everything that happens in care that no block of text could ever capture. Technology is going to keep advancing. The question was never how to stop it. The question is figuring out what shouldn't be allowed to advance into the space that belongs to the human being in the room. There's a part of health work that schools have always trained less, precisely because it resists being turned into something you can test: listening to someone, holding their vulnerability, noticing what went unsaid, building a decision together, staying present in the face of suffering. These skills are harder to measure. They are the most important ones.AI can change health and education in deep ways. But what that change ends up meaning still depends entirely on who's using it. Empathy, warmth, compassionate listening aren't humanist decorations bolted onto the technique once the "real work" is done — they are part of the care itself. And they're also exactly what technology, by definition, can't do for us. By the end of the talk, I was left with one simple idea the speaker had planted: a lecture only really matters if it changes something after it's over. What Patrício seemed to be asking of universities wasn't just that they teach knowledge about AI, but that they help form the judgment to deal with it — not just that they hand students a tool, but that they teach them to answer for how that tool gets used.That's when my mind drifted back to Carol. She never got to choose a world without that intelligence in it. She had to learn to live beside something that knew more than she did, without letting what it knew decide what she herself should think. That might be the most precise challenge for anyone teaching today: not to raise people immune to artificial intelligence — that would be impossible, and maybe not even desirable — nor to raise people willing to hand it the work of thinking. Maybe our role now is to shape people who can stand beside it, as questioners. People capable of asking before accepting, doubting when it matters, recognizing when an answer isn't enough. Capable, above all, of keeping some distance between what the machine knows and what we choose to do with what it knows.That distance isn't a flaw in the technology. It's the space where our responsibility lives. We need to start forming more Carols.Disclosure: I am not against AI, in fact, I used it to translate this text from the original one. You can read it below:A resposta que chega antes da perguntaHá uma cena que continua voltando à minha cabeça desde que ouvi a professora Madalena Patrício, da Faculdade de Medicina da Universidade de Lisboa, falar sobre inteligência artificial na formação de profissionais de saúde. Não é exatamente uma cena da palestra. É de Pluribus, série que assisti recentemente e que, de algum modo, encontrou ali uma nova chave de leitura. Na série, um sinal vindo do espaço carrega o código de um vírus capaz de fundir quase toda a humanidade em uma única consciência. À primeira vista, o que assusta é a perda da individualidade. Mas talvez haja algo mais inquietante acontecendo. Os infectados não ficam menos inteligentes. Ao contrário: passam a ter acesso imediato a tudo o que qualquer um deles já soube, pensou ou concluiu. A resposta deixa de ser o fim de uma busca. Ela passa a existir antes mesmo de a pergunta amadurecer.É isso que torna aquele mundo tão estranho. Não há mais necessidade de atravessar a incerteza, sustentar uma dúvida, testar uma hipótese, mudar de ideia. O conhecimento está sempre disponível, sem o intervalo em que alguma coisa precisa ser elaborada por uma pessoa. A humanidade não perde a capacidade de saber; perde, aos poucos, a necessidade de pensar. A protagonista Carol, uma das poucas pessoas imunes, percebe o que os demais não percebem. O que parece harmonia para quase todos lhe parece uma espécie de morte lenta do raciocínio. Não porque as pessoas tenham deixado de saber, mas porque já não precisam descobrir. Mesmo que agora todos pareçam estar agindo em sintonia, numa comunidade perfeita, o efeito da nova realidade causa muito desconforto em quem assiste.É difícil não reconhecer, em escala muito menor e sem qualquer vírus extraterrestre, uma possibilidade que começa a aparecer na sala de aula. O estudante que abre um modelo de linguagem antes de formular a própria pergunta talvez não esteja fazendo nada de errado. Mas pode estar fazendo uma troca silenciosa: entrega à máquina justamente a parte do processo que deveria ajudá-lo a formar seu próprio pensamento. A ferramenta oferece velocidade, amplitude, disponibilidade. O risco aparece quando essa facilidade começa a substituir a elaboração. Talvez a questão, portanto, não seja saber se um estudante usa IA. A questão é outra: o que ainda acontece dentro dele antes de aceitar a resposta? Ou, ainda mais importante, o que está deixando de acontecer?Essa mudança desloca também o lugar do professor. Durante muito tempo, ensinar significou, em grande medida, transmitir aquilo que se sabia. O professor era o repositório onde o conhecimento estava reunido; o estudante, aquele que deveria recebê-lo. Esse modelo já carregava suas próprias limitações, mas a inteligência artificial introduz uma ruptura difícil de ignorar. Em um primeiro momento, se a informação está a apenas um prompt de distância, então a simples transmissão desse conhecimento torna a presença do professor na sala de aula desnecessária. Seguir por este caminho é aceitar sem questionar a armadilha que vem no sendo apresentada como razão para endossar a IA como ferramenta legítima de ensino.  Fica de fora desse raciocínio o mais importante e que justifica em essência a própria existência da figura do professor: a necessidade de ensinar o aluno a pensar de forma critica e autônoma.Madalena Patrício chamou isso de liderança epistemológica. A expressão parece precisa porque desloca o professor da posição de quem simplesmente sabe para a de quem ajuda outra pessoa a decidir como saber, como duvidar e como reconhecer os limites daquilo que parece saber. Em Pluribus, essa formação se torna desnecessária porque a discordância desaparece. E talvez seja justamente aí que a história ofereça sua metáfora mais poderosa. Pensar exige algum grau de atrito. Uma evidência pode contrariar uma convicção; uma pergunta pode desorganizar uma resposta; uma experiência pode obrigar alguém a rever uma estrutura inteira de pensamento. Jack Mezirow colocou esse movimento no centro da aprendizagem transformadora: é o dilema que não cabe no modo como já compreendemos o mundo que nos obriga a reconstruí-lo. O desconforto, nesse caso, não é um defeito do aprendizado. É parte dele. Se o conhecimento é uma caminhada onde o que se colhe pelo caminho é tão ou mais importante do que o destino final, eliminar essa trajetória em nome da eficiência, pode significar também privar alguém não apenas de uma determinada experiência de aprendizado, mas também do próprio “pensar” como ferramenta essencial na constituição da nossa humanidade.É por esta razao que a Carol transcende a simples figura da rebelde para se tornar algo imensamente mais interessante. Ela não rejeita a conexão porque tudo o que é novo lhe parece ameaçador. Ela exige compreender antes de aceitar. Manter uma opinião própria quando o restante do mundo já não vê motivo para ter uma; continuar questionando quando todos os demais se esqueceram do significado do verbo perguntar. A rebeldia da Carol não representa uma negação à inteligência, ela expressa o desejo de não renunciar à própria consciência e assim permanecer com a sua humanidade intacta (mesmo que na série a maior parte das pessoas já tenha perdido).Há algo dessa atitude naquilo que Patrício propõe às instituições de ensino. Em vez de transformar a IA em uma questão de adesão — ser a favor ou contra, adotar ou resistir —, ela fala em prontidão. E prontidão não é simplesmente fazer uma declaração. É uma prática construída. Significa oferecer uma direção institucional clara, apoiando professores e estudantes e sustentando princípios éticos mesmo que a ânsia por produtividade sugira seguir por atalhos. Uma instituição pode incorporar inteligência artificial a todos os seus processos e, ainda assim, não estar preparada para ela. A adoção acontece depressa. A formação do aluno, leva tempo.  Quando falamos em educação em saúde, existem limites que devemos considerar inegociáveis. A IA pode sugerir, mas não pode responder por uma decisão clínica. Pode organizar e resumir evidências, mas não assumir a responsabilidade de quem decide agir a partir delas. Pode ajudar a simular um caso, mas não reproduzir o encontro com uma pessoa doente — com sua vulnerabilidade, suas ambiguidades, seu silêncio, sua história. A máquina pode participar do julgamento. Não pode sustentar o julgamento. Isso não depende apenas das limitações técnicas da IA, que certamente continuarão mudando e evoluindo. Depende de algo mais fundamental: na formação em saúde, existem decisões pelas quais alguém precisa responder. Ensinar isso é diferente de ensinar a usar uma ferramenta.Nosso papel como docentes é o de ensinar a reconhecer a diferença entre uma convicção construída a partir de conhecimento examinado criticamente e uma resposta que chegou pronta, produzida por um sistema de linguagem matemático, capaz de associar palavras, padrões e probabilidades, mas que não pode assumir por nós a responsabilidade pelo que fazemos com elas. Por isso, a formação precisa acontecer em duas frentes. Os professores precisam aprender a usar IA com discernimento, e isso está muito longe de apenas dominar técnicas de prompting. Trata-se de desenvolver o hábito de interrogar a resposta antes de transmiti-la. Os estudantes, por sua vez, precisam aprender a desconfiar de respostas que parecem boas demais: identificar o erro, perceber a lacuna, reconhecer o viés, notar quando a segurança do texto excede a solidez da evidência.Hoje, essa competência se aproxima de uma forma básica de letramento: saber não apenas encontrar uma fonte, mas avaliar aquilo que uma máquina nos entrega como resposta. Regras podem estabelecer limites. Podem impedir alguns usos indevidos. Mas não ensinam ninguém a pensar. Isso só acontece quando existe prática: confrontar a resposta da IA com aquilo que se sabe, procurar o que falta, testar o que parece certo, sustentar a dúvida por mais alguns minutos. Aprender a discordar da máquina talvez se torne uma das formas mais importantes de aprender com ela. Sem esse exercício, a diferença entre instituições tende a se aprofundar. De um lado, estarão aquelas que ensinam estudantes a pensar com a IA, mantendo-a no lugar de ferramenta. De outro, aquelas que simplesmente permitem que ela ocupe os espaços que encontrar. A desigualdade não estará apenas no acesso à tecnologia, mas na qualidade do pensamento que cada instituição terá conseguido formar em torno dela. Em Pluribus, a fusão elimina essa distância. Não existe mais um pensamento ao lado de outro. Existe um pensamento dentro de todos. Talvez seja essa a fronteira que uma universidade precise preservar.Existem competências que pertencem tanto ao conhecimento quanto à prática: compreender onde a IA acerta e erra, reconhecer seus vieses, raciocinar eticamente sobre seu uso; mas também comunicar-se com o paciente, decidir com ele, trabalhar com outros profissionais e sustentar aquilo que acontece no cuidado e que nenhuma resposta textual consegue conter. A tecnologia continuará avançando. A questão nunca foi impedir esse avanço. A questão é saber o que não pode avançar para dentro do lugar que pertence ao humano. Há uma parte do trabalho em saúde que as escolas historicamente treinam menos justamente porque é difícil de transformar em prova: escutar alguém, acolher sua vulnerabilidade, perceber o que não foi dito, construir uma decisão compartilhada, estar presente diante do sofrimento. São competências menos fáceis de medir, mas são as mais importantes.A IA pode mudar profundamente a prática em educação e saúde. O sentido dessa mudança, porém, continua dependendo de quem a utiliza. Empatia, acolhimento, escuta compassiva não são adornos humanistas acrescentados à técnica depois que o essencial foi resolvido. São parte do próprio cuidado. E são também aquilo que a tecnologia, por definição, não pode fazer por nós. Ao final da palestra, fiquei com uma ideia simples provocada pela palestrante: uma palestra só importa quando modifica alguma coisa depois que termina. O que Patrício parecia pedir às universidades não era apenas que ensinassem conhecimento sobre IA, mas que formassem critérios para lidar com ela; não apenas que oferecessem uma ferramenta, mas que ensinassem a responder pelo modo como essa ferramenta é usada.Foi então que meu pensamento voltou novamente em Carol. Ela não teve a possibilidade de escolher um mundo sem aquela inteligência. Precisou aprender a viver ao lado de algo que sabia mais do que ela sem permitir que esse conhecimento determinasse o que ela própria deveria pensar. Talvez seja esse o desafio mais preciso para quem ensina hoje. Não formar pessoas imunes à inteligência artificial — isso seria impossível e talvez nem desejável. Tampouco formar pessoas dispostas a entregar a ela o trabalho de pensar. Talvez o nosso papel agora seja o de formar pessoas capazes de permanecer ao lado dela, como questionadores. Capazes de perguntar antes de aceitar, de duvidar quando necessário, de reconhecer quando uma resposta não basta. Capazes, sobretudo, de conservar alguma distância entre aquilo que a máquina sabe e aquilo que nós decidimos fazer com o que ela sabe.Essa distância não é uma deficiência da tecnologia. É o espaço da nossa responsabilidade. Precisamos formar mais Carols.

This 12th N3 Neonatal Nutrition Network Study Day is a multi-professional study day exploring a range of topics related to the nutritional needs of premature and sick babies. Following talks from expert speakers, participants will have the opportunity to apply their knowledge to a range of case studies in small multi-professional groups.Click here to register!Topics for Talks and Workshops:Parenteral Nutrition: Current Practice and Emerging DevelopmentsAmino Acids, Vitamins and Minerals in Neonatal NutritionAdvances in NEC Prevention and ManagementHuman Milk, Lactation and Breastfeeding Support in Neonatal CareOptimising Growth and Nutrition in Preterm InfantsNutritional Challenges in Surgical and Complex Neonatal ConditionsFeeding Strategies for Preterm and Sick InfantsNutrition Beyond the Neonatal UnitAdvances and Controversies in Neonatal NutritionWho should Attend? Any professional involved in the care of babies in neonatal units Paediatricians and Neonatologists at all levels Paediatric & Neonatal: Gastroenterologists , Surgeons, Pharmacists Nurses Dietitians, Speech & Language Therapists and Occupational Therapists We welcome abstract submissions showcasing your work - or your team’s - in the fields of neonatal nutrition and growth. The closing date for abstract submissions is 12:00 midday on Friday 18th September. For more details contact: uclh.educationevents@nhs.net.For virtual delegates:All lectures will be viewable live but workshops will not be streamed. To participate in the workshops you will need to attend in person.If you have any questions please get in touch at uclh.educationevents@nhs.net

Branching off my other thread about acute limb ischemia in neonates, I would like to focus on using transdermal patches in neonates. In that case we used a nitroglycerin patch to localize vasodilation and facilitate blood flow to the ischemic limb.Sometimes topical administration of drugs may be required (e.g., palliative analgesia with fentanyl patches, local vasodilation with nitroglycerin patches), or treatment with a drug only available as a transdermal patch (scopolamine/hyoscine). Since there are no transdermal patches approved for use in children, as well as neonates, neonatal clinicians should understand how to safely manipulate transdermal patches and adapt them to the neonate based on limited evidence and pharmacological reasoning.Attached hereby is a poster presented a couple of years ago in EAPS 2024:Shaniv and Litvin - Transdermal Patches for Neonates.pdfIt reviews the composition and pharmacology of transdermal patches, and demonstrates our preferred method of patch manipulation using partial covering rather than cutting patches, which is a common but hazardous practice:Welcome to share your experience - do you use transdermal patches in your NICUs? How often? In what way?

Dear colleagues,our colleague, Millie Bennett from Imperial College Healthcare NHS Trust in London, is conducting her PhD research on congenital CMV and has prepared a survey.She is hoping to reach clinicians from as many different institutions as possible, regardless of their level of expertise in congenital CMV and thankful to all of you for this little effort.Please find attached the Participant Information Sheet, and a draft invitation email/newsletter text with the survey link for distribution. The survey can also be accessed here: https://redcap.idhs.ucl.ac.uk/surveys/?s=7FNDDDWMEH4LYEWC, any responses would be much appreciated!If there is any further information, please contact to her amelia.bennett.25@ucl.ac.ukMany thanks again for your support.Annika Tiit-VesingiPediatrician, Tartu University Children's Hospital, NeoIPC

Previous posts described that very preterm infants ex-utero are ‘programmed’ to grow at a similar rate to the fetus before birth, gaining about 2g/kg/day of protein (as well as gaining fat, water, minerals etc. etc.). A 25 week infants increases her birthweight by more than 400% (500g to 2500g) over 3 months of NICU stay. Thankfully, that rate of weight gain isn’t sustained into early infancy and protein needs dramatically reduce as the fetus/baby approaches term.We don’t want adult sized toddlers, right?!Breastmilk alone cannot meet protein needs of very preterm infants on the NICU, although high volume feeds and other supplements may ameliorate some of the growth faltering we commonly observe. We’ll discuss approaches in another post.Energy needs are dramatic. Using different theoretical approaches, most estimates are that total energy requirements in preterm infants are around 120kcal/kg/day on average. Compare this to Colombian cycling legend Egan Bernal who won the Tour de France in 2019. He weighs around 60-70kg and during ‘le Tour’ consumes around 6,000-7,000 Kcal/day. Imagine how wonderful it would be to consume that many calories without getting fat?If you estimate energy requirements you can see that very preterm infants need more energy, than the energy required to cycle up and down mountains. Some of that energy in preterm infants is expended in wriggling about, but that only accounts for a small fraction. A small amount of energy is also expended in heat production - more if you don’t keep them warm with KMC or incubators.Most of the energy is ‘used’ in two ways(1) being converted into energy stored in tissues (fat, protein, glycogen etc.) or,(2) accounted for by the energy consumed by “Basal Metabolic Rate”, or BMR.BMR is effectively the energy required to run all your vital functions and cellular systems at rest, including brain, cardiac and respiratory activity, sodium/potassium pumps in every cell, hepatic production of proteins etc. BMR actually doesn’t vary that much during the NICU stay - it’s around 50kcal/kg/day. Heat production and activity are around 5-10kcal/kg each per day, meaning there’s around 50kcal/kg/day remaining to be stored as protein and fat.BMR doesn’t really alter (except during acute illness when it’s not by definition ‘basal’), nor can you stop babies moving, breathing, heart pumping or trying to keep potassium inside their cells. That all takes around 60-70kcal. Simple maths will show you that giving 100kcal/kg/day, which sounds like a good amount, means you’re only making 30kcal available for tissue deposition. A seemingly small deficit of 20kcal.The problem is that the ‘first’ 70kcal you give has to go to BMR (and movement and heat.) You are then only meeting 60% (30/50) of the energy needed for growth. Growth faltering is pretty common even when you think you’re doing a good job. Which of us could maintain our weight on 60% of predicated needs? Though there are a few who could risk giving it a go.Failing to give enough macronutrients to a preterm infants is also very common and often avoidable; seriously, why would you not give enough? And if body growth is slow, maybe brain growth is also slow, and maybe some of the long-term neuro-cognitive sequelae of preterm birth are more common… It’s difficult to see how any of this can be good, despite a few sceptics telling us not to worry about growth, and the experts who tell us there’s nothing more dangerous than an expert.Human brains are massiveIt is the human brain that distinguishes us from every other mammal including other primates. It is huge and it requires lots of energy to develop and function. Throughout pregnancy neurones formed in one part of the body need to migrate to their final destinations (NEURONAL MIGRATION) ready to perform critical brain functions.At birth babies have all the neurones they will ever need - about 100 billion. But, they are not wired together very well, and that process of SYNAPTOGENESIS takes a huge amount of energy over the next 2 years. In the first few postnatal months, babies form 1,000,000 synapses per second! If we gave a term infant 40% less energy I expect he would have to down-regulate synaptogenesis. If you are not impressed by that stat feel free to unsubscribe and watch England lose on penalties to Germany. Synaptogenesis is especially active in the visual and auditory cortex of course. We can see it coming.We can discuss apoptosis, synaptic pruning and myelination in other posts.Although it is difficult to precisely measure, it seems that about 50-60% of energy expenditure in healthy newborn infants over the first months is the brain. Growth has slowed from fetal life, thankfully, and diets have low protein but energy demands are high. Whilst protein seems to dominate nutrient requirements as a preterm infant on the NICU, after term age ENERGY becomes most important, and all efforts focus on this. Lots of lipids, lactose in milk, and just enough protein to form the cells and support slow growth.As adults, the brain represents about 20-25% of energy expenditure but we do not know what % it is for preterm infants. Synaptogenesis is starting slowly in the second and third trimester, and whilst I used to think the brain might represent 50% of all energy in preterm infants, I suspect the true figure is much less, as proportionately more of the energy is needed for lean mass accretion. Maybe it’s only 20-25%, who knows? Either way, it’s significant, and failing to meet energy needs, or observing growth faltering must cause concern about the adequacy of macronutrient supply for the brain.Brain growth and differentiation is so complex we won’t ever understand it. We would need an even bigger brain to understand it, which would then be even more complex to understand … catch 22. What we do know, is that these processes are under the control of multiple hormones, especially IGF-1 (Insulin like growth factor 1). IGF-1 concentrations ex-utero are pretty dismal compared to in-utero (50-80% lower), and whilst some companies are investing millions into trials of synthetic supplemental IGF-1 the data to date are underwhelming. There is however, some evidence that macronutrient intakes (energy and protein) modulate IGF-1 concentrations providing a potential mechanistic link between diet and brain differentiation and growth.Back to breastmilk.Mother’s own milk, especially fresh (i.e. warm), is life-saving for premature infants and the most cost-effective in all of medicine.Prove me wrong. All medicine, not just neonatal. Donated human milk has lost many of it’s functional benefits through storage, pasteurisation and transport, but is still associated with lower rates of NEC even though it doesn’t seem to reduce surgical NEC or mortality (another post needed there). This might be because although heat permanently alters protein structures (antibodies etc.) it has much less effect on small sugars (HMOs) which may be a key component in reducing NEC risk.Human milk with <1g/100mL will never meet protein requirements in extremely preterm infants. But for energy, the equation differs. Human milk has around 65kcal/100mL albeit with substantial variation between individuals and stage of expression, and usually much lower in DHM. This means that feeding MOM at 180-200ml/kg/day will meet energy needs in most very preterm infants (but won’t meet micronutrient, mineral, vitamins needs etc.) DHM at 200ml/kg/day won’t meet needs.Protein is the problem. Preterm babies need more protein than from MOM alone. If energy was the problem, we could solve it more easily.Where can we get more protein?We’re surrounded by a key element for the diet, nitrogen. Preterm babies breathe more nitrogen in and out than oxygen, and have at their finger tips (well alveoli) the core element for building amino acids. Why don’t they wake up, smell the coffee and use it? Caffeine citrate is, of course, odourless which might be part of the problem.Nitrogen is the core element of amino acids, amino acids make proteins, and proteins make prizes … but humans (like all mammals) are incapable of turning atmospheric nitrogen into amino acids. Nature didn’t think it necessary. Shame. How disappointing to watch a sick preterm infants on a ventilator, being given all the nitrogen it could dream off, being ‘expirated’ 0.3 seconds later before it could be fixed.We need plants or insects which animals can eat, which we can then eat, or consume their milk. Theoretically, we could get extra protein from non-animal sources (seaweed, plants, bioreactor bacterial synthesising proteins) but that won’t be available this decade (but I am predicting that next decade milk-identical protein will come from a bioreactor). As of 2026, the protein preterm babies need has to come from another animal, and whether you like it or not that’s going to be a cow in the global majority. I could though write another post on human milk-protein-derived products if you want?Please subscribe to these posts and the Baby Loss posts from the Butterfly project. Some of the email sign-ups end up in junk mail folders so check there. Feel free to comment and spread the word. I’m open to questions, but can’t promise to answer them.Also posted here on Substack.