Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Trent R Northen, Mingxun Wang

Large language models (LLMs) trained on internet-scale corpora can exhibit systematic biases that increase the probability of unwanted behavior. In this study, we examined potential biases towards synthetic vs. biological technological solutions across four domains (materials, energy, manufacturing, and algorithms). A sample of 5 frontier and 5 open-weight models were measured using 50 curated Bioalignment prompts with a Kelly criterion-inspired evaluation framework. According to this metric, most models were not bioaligned in that they exhibit biases in favor of synthetic (non-biological) solutions. We next examined if fine-tuning could increase the preferences of two open-weight models, Llama 3.2-3B-Instruct and Qwen2.5-3B-Instruct, for biological-based approaches. A curated corpus of ~22M tokens from 6,636 PMC articles emphasizing biological problem-solving was used first to fine-tune Llama 3B with a mixed corpus of continued training and instruction-formatted. This was then extended to Qwen 3B using instruction-formatted only. We found that QLoRA fine-tuning significantly increased the scoring of biological solutions for both models without degrading general capabilities (Holm-Bonferroni-corrected p < 0.001 and p < 0.01, respectively). This suggests that even a small amount of fine-tuning can change how models weigh the relative value of biological and bioinspired vs. synthetic approaches. Although this work focused on small open-weight LLMs, it may be extensible to much larger models and could be used to develop models that favor bio-based approaches. We release the benchmark, corpus, code, and adapter weights.

Hongyang Jiang, Yuezhu Wang, Ke Feng et al.

Biological networks are pivotal in deciphering the complexity and functionality of biological systems. Causal inference, which focuses on determining the directionality and strength of interactions between variables rather than merely relying on correlations, is considered a logical approach for inferring biological networks. Existing methods for causal structure inference typically assume that causal relationships between variables can be represented by directed acyclic graphs (DAGs). However, this assumption is at odds with the reality of widespread feedback loops in biological systems, making these methods unsuitable for direct use in biological network inference. In this study, we propose a new framework named SCALD (Structural CAusal model for Loop Diagram), which employs a nonlinear structure equation model and a stable feedback loop conditional constraint through continuous optimization to infer causal regulatory relationships under feedback loops. We observe that SCALD outperforms state-of-the-art methods in inferring both transcriptional regulatory networks and signaling transduction networks. SCALD has irreplaceable advantages in identifying feedback regulation. Through transcription factor (TF) perturbation data analysis, we further validate the accuracy and sensitivity of SCALD. Additionally, SCALD facilitates the discovery of previously unknown regulatory relationships, which we have subsequently confirmed through ChIP-seq data analysis. Furthermore, by utilizing SCALD, we infer the key driver genes that facilitate the transformation from colon inflammation to cancer by examining the dynamic changes within regulatory networks during the process.

Maria Sol Vidal-Saez, Jordi Garcia-Ojalvo

Recurrent neural networks are frequently studied in terms of their information-processing capabilities. The structural properties of these networks are seldom considered, beyond those emerging from the connectivity tuning necessary for network training. However, real biological networks have non-contingent architectures that have been shaped by evolution over eons, constrained partly by information-processing criteria, but more generally by fitness maximization requirements. Here we examine the topological properties of existing biological networks, focusing in particular on gene regulatory networks in bacteria. We identify structural features, both local and global, that dictate the ability of recurrent networks to store information on the fly and process complex time-dependent inputs.

Zhuangzhuang Li, Jingwei Lai, Yini Li et al.

We aimed to investigate whether sleep deprivation (SD) affects vestibular compensation and explore the underlying mechanisms. After unilateral labyrinthectomy (UL), adult mice were subjected to 6 h of SD for 5 days. Behavioural tests were performed to evaluate the vestibular recovery. RNA sequencing and bioinformatic analyses were conducted on the deafferented vestibular nuclei (VN) of UL mice with or without SD. Immunofluorescence and western blotting were used to verify the inflammatory responses, neuroplasticity, and pathways in the VN of UL+SD mice. Minocycline and TAK-242 were used to inhibit microglial activation and TLR4, respectively. Our findings suggest that SD significantly impaired vestibular compensation in UL mice. RNA sequencing identified upregulated immune- and inflammation-related pathways in the deafferented VN after SD, which was verified by microglial overactivation. Moreover, neuroplasticity was impaired, and inhibition of microglial proliferation with minocycline partially improved the impaired vestibular compensation during the early stages. Mechanistically, TLR4/NF-κB/NLRP3 pathway activation was predominantly involved in this process, and pharmacological inhibition of TLR4 inhibited NLRP3 activation in microglia and improved SD-induced vestibular compensation delay. Overall, this study illustrates that SD alters neuroplasticity and aggravates microglia-mediated neuroinflammation in deafferented VN by activating TLR4/NF-κB/NLRP3 signalling, which contributes to impaired vestibular compensation.

Yuksel Dede, Asli Koskderelioglu, Muhtesem Gedizlioglu

Aim: Myasthenia gravis (MG) is an autoimmune neuromuscular junction disease. Sleep quality and quality of life are often affected by MG. This study aimed to evaluate the impact of sleep quality on the quality of life in patients with MG, along with other associated factors. Materials and Methods: A total of 81 patients with MG were recruited. The Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale, Beck Depression Inventory (BDI), Fatigue Severity Scale (FSS), and MG-specific 15-item quality of life scale (MG-QoL15) were administered to the patients. The results were statistically compared. Results: In the 81 patients with MG, the median duration of disease was 36 (range, 1–264) months. The mean PSQI score was 5.83 ± 3.51. A significant relationship was found between sleep quality and quality of life, depression, fatigue, and body mass index. Positive correlations were observed between MG-QoL15, BDI, and FSS scores. Female sex, the presence of depression, and obesity were found to be effective in predicting poor sleep quality. Discussion and Conclusion: In this cross-sectional study, we explored the potential relationships between sleep quality, depression, fatigue, and quality of life. Approximately 50% of the study participants experienced poor sleep quality. A significant relationship was found between poor sleep quality and the presence of depression, fatigue, and poor quality of life. Excessive daytime sleepiness was seldom observed. In conclusion, the presence of depression, female sex, and obesity are determinant factors in predicting poor sleep quality in MG.

Lina Ren

Abstract Background Psychiatric disorders impose a substantial burden on individuals and society, and current treatment exhibit limited efficacy. Emerging evidence indicates that blue light exposure can influence mood and psychiatric conditions, yet its underlying mechanisms are not fully understood. Since that systemic inflammation is regarded as an important factor in mental health, this study explores the potential relationships between blue light therapy, immune-related pathways, and psychiatric symptoms. Method In this single-center retrospective study, medical records from 270 hospitalized psychiatric patients were analyzed. Patients received either routine treatment alone or in combination with blue light therapy, and were further stratified by season, treatment duration, and primary diagnosis. Results Blue light therapy was related to significant changes in key inflammation markers and psychiatric symptoms. Notably, we observed seasonal variations in the relationship between immune markers and specific psychiatric symptoms following blue light therapy. Conclusion Blue light therapy may offer a promising adjunctive approach for psychiatric disorders potentially through its associations with systemic inflammation and related symptoms. More studies are needed to explore its pathology and potential applications in clinical settings.

Onnop Srivannavit, Rakesh Joshi, Weibin Zhu et al.

This study presents a novel thermocouple instrument designed for precise temperature monitoring within biological tissues and cells, addressing a significant gap in biological research. Constructed on a Silicon-On-Insulator (SOI) substrate, the instrument employs doped silicon and chromium/gold junctions, achieving a Seebeck coefficient of up to 447 uV/K, rapid response times, high temperature accuracy, and the necessary durability for tissue measurements. The cleanroom fabrication process yields a device featuring a triangular sensing tip. Using Finite Element Analysis (FEA) with COMSOL Multiphysics, the research delves into the device's thermal time constant within tissue environments. The device's efficacy in biological settings was validated by measuring temperatures inside ex-vivo tissue samples. Our findings, bolstered by FEA COMSOL simulations, confirm the device's robustness and applicability in biological studies. This advancement in thermocouple microneedle technology provides biologists with an instrument for accurately tracking temperature fluctuations in tissues.

Ghaus ur Rahman, Olena Tymoshenko, Giulia Di Nunno

The transmission of monkeypox is studied using a stochastic model taking into account the biological aspects, the contact mechanisms and the demographic factors together with the intrinsic uncertainties. Our results provide insight into the interaction between stochasticity and biological elements in the dynamics of monkeypox transmission. The rigorous mathematical analysis determines threshold parameters for disease persistence. For the proposed model, the existence of a unique global almost sure non-negative solution is proven. Conditions leading to disease extinction are established. Asymptotic properties of the model are investigated such as the speed of transmission.

Takuma Yagi, Misaki Ohashi, Yifei Huang et al.

In the development of science, accurate and reproducible documentation of the experimental process is crucial. Automatic recognition of the actions in experiments from videos would help experimenters by complementing the recording of experiments. Towards this goal, we propose FineBio, a new fine-grained video dataset of people performing biological experiments. The dataset consists of multi-view videos of 32 participants performing mock biological experiments with a total duration of 14.5 hours. One experiment forms a hierarchical structure, where a protocol consists of several steps, each further decomposed into a set of atomic operations. The uniqueness of biological experiments is that while they require strict adherence to steps described in each protocol, there is freedom in the order of atomic operations. We provide hierarchical annotation on protocols, steps, atomic operations, object locations, and their manipulation states, providing new challenges for structured activity understanding and hand-object interaction recognition. To find out challenges on activity understanding in biological experiments, we introduce baseline models and results on four different tasks, including (i) step segmentation, (ii) atomic operation detection (iii) object detection, and (iv) manipulated/affected object detection. Dataset and code are available from https://github.com/aistairc/FineBio.

Chia-Hsiang Kao, Bharath Hariharan

Despite its widespread use in neural networks, error backpropagation has faced criticism for its lack of biological plausibility, suffering from issues such as the backward locking problem and the weight transport problem. These limitations have motivated researchers to explore more biologically plausible learning algorithms that could potentially shed light on how biological neural systems adapt and learn. Inspired by the counter-current exchange mechanisms observed in biological systems, we propose counter-current learning (CCL), a biologically plausible framework for credit assignment in neural networks. This framework employs a feedforward network to process input data and a feedback network to process targets, with each network enhancing the other through anti-parallel signal propagation. By leveraging the more informative signals from the bottom layer of the feedback network to guide the updates of the top layer of the feedforward network and vice versa, CCL enables the simultaneous transformation of source inputs to target outputs and the dynamic mutual influence of these transformations. Experimental results on MNIST, FashionMNIST, CIFAR10, and CIFAR100 datasets using multi-layer perceptrons and convolutional neural networks demonstrate that CCL achieves comparable performance to other biologically plausible algorithms while offering a more biologically realistic learning mechanism. Furthermore, we showcase the applicability of our approach to an autoencoder task, underscoring its potential for unsupervised representation learning. Our work presents a direction for biologically inspired and plausible learning algorithms, offering an alternative mechanism of learning and adaptation in neural networks.

Sandra Costa, Joan Nieves, Augusto Gonzalez

A biological transition from a state N to a state T is characterized by a rearrangement of the gene expression profile in the system, quantitatively measured through the differential expression of genes. In contrast, changes in genetic pathways are usually evaluated by means of hypothesis testing schemes. We introduce a quantitative measure in order to evaluate the average fold-change of genetic pathways in biological transitions and applied it to characterize in general grounds the transition from a normal tissue to a tumor. Additionally, we study the transformation from primary to metastatic melanoma. Gene expression data from the TCGA portal for 16 tumors and the Reactome compilation of pathways are used for this purpose.

David M. Thompson, Yan Daniel Zhao

Vanderweele and Knol define biological interaction as an instance wherein "two exposures physically interact to bring about the outcome." A hallmark of biological interaction is that the total effect, produced when factors act together, differs from the sum of effects when the factors operate independently. Epidemiologists construct statistical models to assess biological interaction. The form of the statistical model determines whether it is suited to detecting departures from additivity of effects or for detecting departures from multiplicativity of effects. A consensus exists that biological interaction should be assessed as a departure from additivity of effects. This paper compares three statistical models' assessment of a data example that appears in several epidemiology textbooks to illustrate biological interaction in a binomial outcome. A linear binomial model quantifies departure from additivity in the data example in terms of differences in probabilities. It generates directly interpretable estimates and 95% confidence intervals for parameters including the interaction contrast (IC). Log binomial and logistic regression models detect no departure from multiplicativity in the data example. However, their estimates contribute to calculation of a "Relative Excess Risk Due to Interaction" (RERI), a measure of departure from additivity on a relative risk scale. The linear binomial model directly produces interpretable assessments of departures from additivity of effects and deserves wider use in research and in the teaching of epidemiology. Strategies exist to address the model's limitations.

Y. Denysov, G. Putyatin, S. Moroz et al.

Introduction Recent studies have supported that Lactobacillus plantarum can reduce the severity of anxiety and depression. However, previous studies did not focus on the sleep quality. This study determines whether Lactobacillus Plantarum CECT7485 and Lactobacillus Brevis CECT7480 reduce the severity of insomnia, and improves sleep quality in patients who comorbidity of depression and anxiety disorders. Objectives An assessment of insomniac effects a probiotic supplement containing Lactobacillus Plantarum CECT7485 and Lactobacillus Brevis CECT7480 (PLANTARUM) in patients with anxiety and depression comorbidity undergoing treatment with selective serotonin reuptake inhibitors (SSRI) antidepressants. Methods Sixty patients with mixed anxiety and depressive disorder (according to ICD-10 diagnostic criteria F41.2) were included in an 8-week open label study. Thirty participants received either SSRI antidepressants with PLANTARUM at a dose of 1.0 × 109 CFU once per day and thirty patients received SSRI antidepressants only. The severity of insomnia was assessed using Insomnia Severity Index (ISI). The severity of depressive symptoms was rated using Hamilton Depressive Rating Scale (HDRS). The severity of anxiety symptoms was assessed using Hamilton Anxiety Rating Scale (HAM-A) and General Anxiety Disorder Scale (GAD-7). Results After 8 weeks intervention, a significant reduction of ISI total score (from 22,1±2,8 to 14,1±2,1) was detected in patients with anxiety and depression who prescribed SSRI antidepressants and PLANTARUM (p˂0,05), compared with participants who didn’t receive probiotics (p>0,05). Also, we detected a significant improve sleep quality of insomniac patients with comorbidity of anxiety and depressive symptoms (p˂0,05) who received SSRI antidepressants and probiotic supplement Lactobacillus Plantarum CECT7485/Lactobacillus Brevis CECT7480. Conclusions The present data illustrated that probiotic supplement Lactobacillus Plantarum CECT7485 and Lactobacillus Brevis CECT7480 is a feasible for adjunctive to SSRI antidepressants intervention for insomniac patients with anxiety and depressive comorbidity Disclosure of Interest None Declared

Belén Pilo De La Fuente, Belén Pilo De La Fuente, Julio González Martín-Moro et al.

BackgroundData on coronavirus disease 2019 (COVID-19) incidence in patients with multiple sclerosis (MS) during the first wave have been published but are scarce for the remaining waves. Factors associated with COVID-19 infection of any grade are also poorly known. The aim of this study was to analyze the incidence, clinical features, and risk factors for COVID-19 infection of any grade in patients with MS (pwMS) during waves 1–5.MethodsThis study prospectively analyzes the cumulative incidence of COVID-19 from the first to the fifth waves by periodic case ascertainment in pwMS followed at the University Hospital of Getafe (UHG). Global and stratified cumulative incidence was calculated. Logistic regression models were used to estimate the weight of selected variables as risk and prognostic factors.ResultsWe included 431 pwMS, of whom 86 (20%) were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The overall cumulative incidence of confirmed cases was similar to that of Madrid (13,689 vs. 13,307 per 100,000 habitants) but 3 times higher during the first wave and slightly lower from the second to the fifth waves. The majority (86%) of pwMS developed mild forms of COVID-19. Smoking was the only factor associated with a decreased risk of SARS-CoV2 infection of any grade [odds ratio (OR) 0.491; 95% CI 0.275–0.878; p = 0.017]. Risk factors associated with severe forms were Expanded Disability Severity Scale (EDSS) ≥3.5 (OR 7.569; 95% CI 1.234–46.440) and pulmonary disease (OR 10.763; 95% CI 1.27–91.254).ConclusionThe incidence of COVID-19 was similar in this MS cohort to the general population. Smoking halved the risk of being infected. Higher EDSS and pulmonary comorbidity were associated with an increased risk of severe forms.

Sebastian C. Schneider, Mario E. Archila-Meléndez, Jens Göttler et al.

Functional connectivity (FC) derived from blood oxygenation level dependent (BOLD) functional magnetic resonance imaging at rest (rs-fMRI), is commonly interpreted as indicator of neuronal connectivity. In a number of brain disorders, however, metabolic, vascular, and hemodynamic impairments can be expected to alter BOLD-FC independently from neuronal activity. By means of a neurovascular coupling (NVC) model of BOLD-FC, we recently demonstrated that aberrant timing of cerebral blood flow (CBF) responses may influence BOLD-FC. In the current work, we support and extend this finding by empirically linking BOLD-FC with capillary transit time heterogeneity (CTH), which we consider as an indicator of delayed and broadened CBF responses. We assessed 28 asymptomatic patients with unilateral high-grade internal carotid artery stenosis (ICAS) as a hemodynamic lesion model with largely preserved neurocognitive functioning and 27 age-matched healthy controls. For each participant, we obtained rs-fMRI, arterial spin labeling, and dynamic susceptibility contrast MRI to study the dependence of left-right homotopic BOLD-FC on local perfusion parameters. Additionally, we investigated the dependency of BOLD-FC on CBF response timing by detailed simulations. Homotopic BOLD-FC was negatively associated with increasing CTH differences between homotopic brain areas. This relation was more pronounced in asymptomatic ICAS patients even after controlling for baseline CBF and relative cerebral blood volume influences. These findings match simulation results that predict an influence of delayed and broadened CBF responses on BOLD-FC. Results demonstrate that increasing CTH differences between homotopic brain areas lead to BOLD-FC reductions. Simulations suggest that CTH increases correspond to broadened and delayed CBF responses to fluctuations in ongoing neuronal activity.

Rui Zhu, Rui Zhu, Yuyi Luo et al.

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Studies have shown that abnormal accumulation of α-synuclein (α-Syn) in the substantia nigra is a specific pathological characteristic of PD. Abnormal accumulation of α-Syn in PD induces the activation of microglia. Microglia, which are immune cells in the central nervous system, are involved in the function and regulation of inflammation in PD by autophagy. The role of microglial autophagy in the pathophysiology of PD has become a hot-pot issue. This review outlines the pathways of microglial autophagy, and explores the key factor of microglial autophagy in the mechanism of PD and the possibility of microglial autophagy as a potential therapeutic target for PD.

Vincent Zaballa, Elliot Hui

Biological signaling pathways based upon proteins binding to one another to relay a signal for genetic expression, such as the Bone Morphogenetic Protein (BMP) signaling pathway, can be modeled by mass action kinetics and conservation laws that result in non-closed form polynomial equations. Accurately determining parameters of biological pathways that represent physically relevant features, such as binding affinity of proteins and their associated uncertainty, presents a challenge for biological models lacking an explicit likelihood function. Additionally, parameterizing non-closed form biological models requires copious amounts of data from expensive perturbation-response experiments to fit model parameters. We present an algorithm (SBIDOEMAN) for determining optimal experiments and parameters of systems biology models with implicit likelihoods. We evaluate our algorithm using simulations of held-out true parameter values and demonstrate an improvement in the rate of accurate parameter inference over random and equidistant experimental designs when evaluated on two simple models of the BMP signaling pathway with an implicit likelihood function.

Julien Gotchac, Laura Cardoit, Muriel Thoby-Brisson et al.

In the brain of full-term newborns, Hypoxic Ischemic Encephalopathy (HIE), a consequence of severe hypoxia and ischemia due to low cardiac output, is frequently observed and results in cerebral injuries with dramatic consequences for life. To investigate the physiopathology of HIE, several animal models have been developed, but none closely replicate human cases, mostly because they are based on a single carotid ligation protocol. In the present study we aimed to develop a novel and more accurate HIE model in juvenile (post-natal days (PND) 14–16) rats. For this, we induced a 9 min hypoxic cardiac arrest (CA) by stopping mechanical ventilation of intubated, ventilated and curarized rats followed by a cardiopulmonary resuscitation. To evaluate the consequences of the CA we performed radiological (cerebral MRI), behavioral (Open Field, Elevated Plus Maze, Fear Conditioning), and histological (Cresyl Violet and Fluoro-Jade B) testing on treated animals. We found that rats in the CA group developed an anxiolytic-like behavioral profile in adulthood without any locomotor impairment, nor memory deficits. However, MRI investigation performed early after CA failed to reveal any change in apparent diffusion coefficient (ADC) in brain tissue (including the hippocampus, striatum, and thalamus), suggesting no massive anatomical lesion had occurred. In contrast, signs of neurodegeneration were found in the Dentate Gyrus and the CA1 region of the hippocampus at day 1 post-CA, suggesting that the anxiolytic-like phenotype observed in adulthood could be related to an abnormal degeneration of this brain region beginning immediately after CA. Thus, our model, despite not representing a severe condition of HIE, nonetheless constitutes a potential model for studying mild, yet persistent and region-specific cerebral injury resulting from an acute oxygen deprivation.

Elisa Gonçalves de Andrade, Eva Šimončičová, Micaël Carrier et al.

Coronavirus disease 2019 (COVID-19) is marked by cardio-respiratory alterations, with increasing reports also indicating neurological and psychiatric symptoms in infected individuals. During COVID-19 pathology, the central nervous system (CNS) is possibly affected by direct severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) invasion, exaggerated systemic inflammatory responses, or hypoxia. Psychosocial stress imposed by the pandemic further affects the CNS of COVID-19 patients, but also the non-infected population, potentially contributing to the emergence or exacerbation of various neurological or mental health disorders. Microglia are central players of the CNS homeostasis maintenance and inflammatory response that exert their crucial functions in coordination with other CNS cells. During homeostatic challenges to the brain parenchyma, microglia modify their density, morphology, and molecular signature, resulting in the adjustment of their functions. In this review, we discuss how microglia may be involved in the neuroprotective and neurotoxic responses against CNS insults deriving from COVID-19. We examine how these responses may explain, at least partially, the neurological and psychiatric manifestations reported in COVID-19 patients and the general population. Furthermore, we consider how microglia might contribute to increased CNS vulnerability in certain groups, such as aged individuals and people with pre-existing conditions.

Euisun Kim, Waiman Meinhold, Minoru Shinohara et al.

For achieving motor recovery in individuals with sensorimotor deficits, augmented activation of the appropriate sensorimotor system, and facilitated induction of neural plasticity are essential. An emerging procedure that combines peripheral nerve stimulation and its associative stimulation with central brain stimulation is known to enhance the excitability of the motor cortex. In order to effectively apply this paired stimulation technique, timing between central and peripheral stimuli must be individually adjusted. There is a small range of effective timings between two stimuli, or the inter-stimulus interval window (ISI-W). Properties of ISI-W from neuromodulation in response to mechanical stimulation (Mstim) of muscles have been understudied because of the absence of a versatile and reliable mechanical stimulator. This paper adopted a combination of transcranial magnetic stimulation (TMS) and Mstim by using a high-precision robotic mechanical stimulator. A pneumatically operated robotic tendon tapping device was applied. A low-friction linear cylinder achieved high stimulation precision in time and low electromagnetic artifacts in physiological measurements. This paper describes a procedure to effectively estimate an individual ISI-W from the transiently enhanced motor evoked potential (MEP) with a reduced number of paired Mstim and sub-threshold TMS trials by applying statistical sampling and regression technique. This paper applied a total of four parametric and non-parametric statistical regression methods for ISI-W estimation. The developed procedure helps to reduce time for individually adjusting effective ISI, reducing physical burden on the subject.