Biology subjects

Baldi, P. F.

Publications and source records attributed to Baldi, P. F..

6 recordsLinked to original sources

bioRxiv2026

The Critical Period Microbiota Shape Brain Plasticity

The gut microbiota is increasingly recognized as a regulator of brain function, yet its role in experience-dependent plasticity during postnatal development remains largely unknown. Here, we show that disrupting the gut microbiota with antibiotics during critical periods of visual cortex development impairs ocular dominance plasticity (ODP) in juvenile mice. Antibiotic treatment induces marked changes in microbial community composition and is accompanied by extensive transcriptional remodeling of the visual cortex, including pathways involved in extracellular matrix organization, blood-brain barrier function, and myelination. Remarkably, fecal transplantation of the juvenile microbiota into adult recipients restores ODP. These findings identify the gut microbiota as a previously unrecognized regulator of neurodevelopmental plasticity and support the existence of microbiota-dependent critical periods of brain development. More broadly, our results suggest that early-life microbial perturbations may have lasting consequences for lifelong brain function and reveal that juvenile microbiota-derived signals could be exploited to promote plasticity in the adult brain.

neuroscience↗

bioRxiv2025

Succinate Modulation as a Novel Mechanism Underlying the Effects of Intermittent Fasting on Brain Function and Metabolism in Diet-Induced Obesity

Obesity significantly impacts the central nervous system (CNS), increasing risks of neuropsychiatric disorders and dementia. Intermittent fasting (IF) shows promise for improving peripheral and CNS health, but its mechanisms are unclear. Using a diet-induced obesity mouse model (10 weeks high fat diet (HFD), then 4 weeks intervention), we compared HFD, HFD-IF, ad libitum control chow (CC), and CC-IF groups. Switching to CC or IF reduced body weight, fat mass, and improved glucose tolerance. Notably, CC-IF uniquely enhanced exploration and reduced anxiety-like behavior. Transcriptomics revealed HFD-induced hippocampal neuroinflammation, while metabolomics identified a specific succinate signature in CC-IF mice: plasma concentration decreased while liver and brown adipose tissue (BAT) levels increased. Succinate supplementation mimicked CC-IF metabolic and behavioral benefits and reduced hippocampal inflammation. These findings suggest that regulating plasma succinate and its metabolism in liver and BAT may represent a novel mechanism underlying the metabolic, neuroinflammatory, and behavioral improvements induced by IF.

neuroscience↗

bioRxiv2023

Performance of Large Language Models on Pharmacy Exam: A Comparative Assessment Using the NAPLEX

BackgroundThere has been considerable recent effort in integrating Large Language Models (LLMs) across different fields, including healthcare. However, the possibility of applying LLMs in pharmacy-related sciences is under-explored. ObjectivesThis study aims to evaluate the capabilities and limitations of six LLMs-GPT-3.5, GPT-4, Llama-2-7B, Llama-2-13B, Llama-2-70B, and Mistral-7B, in the field of pharmacy by assessing their reasoning abilities on a sample of the North American Pharmacist Licensure Examination (NAPLEX). Additionally, we explore the potential impacts of LLMs on pharmacy education and practice. MethodsTo evaluate the LLMs, we utilized the sample of the NAPLEX exam comprising 225 multiple-choice questions sourced from the APhA Complete Review for Pharmacy, 13th Edition | Pharmacy Library. These questions were presented to the Large Language Models through either local deployment or the Application programming interface (API), and the answers generated by the LLMs were subsequently compared with the answer key. ResultsThere is a notable disparity in the performance of the LLMs. GPT-4 emerged as the top performer, accurately answering 87.1% of the questions. Among the six LLMs evaluated, GPT-4 was the only model capable of passing the NAPLEX exam. ConclusionWe examined the performance of Large Language Models based on their model size, training methods, and fine-tuning algorithms. Given the continuous evolution of LLMs, it is reasonable to anticipate that future models will effortlessly excel in exams such as the NAPLEX. This highlights the significant potential of LLMs to influence the field of pharmacy. Hence, we must evaluate both the positive and negative implications associated with the integration of LLMs in pharmacy education and practice.

bioinformatics↗

bioRxiv2023

Clinical Knowledge and Reasoning Abilities of AI Large Language Models in Pharmacy: A Comparative Study on the NAPLEX Exam

ObjectiveThis study aims to evaluate the capabilities and limitations of three large language models (LLMs) - GPT-3, GPT-4, and Bard, in the field of pharmaceutical sciences by assessing their pharmaceutical reasoning abilities on a sample North American Pharmacist Licensure Examination (NAPLEX). We also analyze the potential impacts of LLMs on pharmaceutical education and practice. MethodsA sample NAPLEX exam consisting of 137 multiple-choice questions was obtained from an online source. GPT-3, GPT-4, and Bard were used to answer the questions by inputting them into the LLMs user interface. The answers provided by the LLMs were then compared with the answer key. ResultsGPT-4 exhibited superior performance compared to GPT-3 and Bard, answering 78.8% of the questions correctly. This score was 11% higher than Bard and 27.7% higher than GPT-3. However, when considering questions that required multiple selections, the performance of each LLM decreased significantly. GPT-4, GPT-3, and Bard only correctly answered 53.6%, 13.9%, and 21.4% of these questions, respectively. ConclusionAmong the three LLMs evaluated, GPT-4 was the only model capable of passing the NAPLEX exam. Nevertheless, given the continuous evolution of LLMs, it is reasonable to anticipate that future models will effortlessly pass the exam. This highlights the significant potential of LLMs to impact the pharmaceutical field. Hence, we must evaluate both the positive and negative implications associated with the integration of LLMs in pharmaceutical education and practice.

scientific communication and education↗

bioRxiv2020

Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

Semi-supervised machine learning post-processors critically improve peptide identification of shot-gun proteomics data. Such post-processors accept the peptide-spectrum matches (PSMs) and feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSMs using the trained parameters, often yielding significantly more identified peptides across q-value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as support vector machines. In contrast, the powerful training capabilities of deep learning models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep models significantly improve the recalibration of PSMs compared to the most accurate and widely-used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that deep learning is able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed database-search functions.

bioinformatics↗

bioRxiv2019

Hippocampal ensembles represent sequential relationships among discrete nonspatial events

The hippocampus is critical to the temporal organization of our experiences, including the ability to remember past event sequences and predict future ones. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain poorly understood. Here we recorded hippocampal ensemble activity as rats remembered a sequence of nonspatial events (5 odor presentations unfolding over several seconds), using a task with established parallels in humans. Using novel statistical methods and deep learning techniques, we then identified new forms of sequential organization in hippocampal activity linked with task performance. We discovered that sequential firing fields ("time cells") provided temporal information within and across events in the sequence, and that distinct types of task-critical information (stimulus identity, temporal order, and trial outcome) were also sequentially differentiated within event presentations. Finally, as previously only observed with spatial information, we report that the representations of past, present and future events were sequentially activated within individual event presentations, and that these sequential representations could be compressed within an individual theta cycle. These findings strongly suggest that a fundamental function of the hippocampal network is to encode and preserve the sequential order of experiences, and use these representations to generate predictions to inform decision-making.

neuroscience↗