BackgroundChronic subdural hematoma (CSDH) has high postoperative recurrence rates. This study investigates the effects of hyperbaric oxygen therapy (HBOT) combined with Medical-Psychosocial-Nursing Functional Support (MPNFS) on functional recovery and recurrence prevention in CSDH patients, and establishes a recurrence prediction model.MethodsA total of 184 CSDH patients undergoing burr hole drainage were randomized into a control group and an observation group (HBOT + MPNFS). Neurological (NIHSS), motor (Fugl-Meyer Assessment), and quality-of-life (SF-36) outcomes were assessed preoperatively and at 1-month postoperatively. Complications and 6-month recurrence rates were recorded. Univariate/multivariate logistic regression identified recurrence risk factors, with ROC analysis evaluating predictive accuracy.ResultsThe observation group showed superior 1-month outcomes: lower NIHSS scores (t = 4.94, p < 0.001), higher FMA and SF-36 scores (p < 0.01). Complication and recurrence rates were significantly reduced (p < 0.05). Independent recurrence predictors included brain atrophy (OR = 2.877), poor brain reexpansion (OR = 3.165), preoperative hematoma width ≥ 20 mm (OR = 2.782), and absence of combined intervention (OR = 2.842). The multifactorial model achieved an AUC of 0.7862, indicating robust predictive efficacy.ConclusionHyperbaric oxygen therapy combined with MPNFS enhances neurological/motor recovery, improves quality of life, and reduces complications/recurrence in postoperative CSDH patients.
Turing patterns play a fundamental role in morphogenesis and population dynamics, encoding key information about the underlying biological mechanisms. Yet, traditional inverse problems have largely relied on non-biological data such as boundary measurements, neglecting the rich information embedded in the patterns themselves. Here we introduce a new research direction that directly leverages physical observables from nature--the amplitude of Turing patterns--to achieve complete parameter identification. We present a framework that uses the spatial amplitude profile of a single pattern to simultaneously recover all system parameters, including wavelength, diffusion constants, and the full nonlinear forms of chemotactic and kinetic coefficient functions. Demonstrated on models of chemotactic bacteria, this amplitude-based approach establishes a biologically grounded, mathematically rigorous paradigm for reverse-engineering pattern formation mechanisms across diverse biological systems.
Tianxiang Ma, Valeriia Grudtsyna, Robin V. Bölsterli
et al.
Understanding how biomechanical reorganization governs key biological processes, such as morphogenesis and development, requires predictive insights into stress distributions and cellular behavior. While traditional approaches focused on cell motion as a response to stress, we demonstrate that Lagrangian coherent structures (LCSs) -- robust attractors and repellers in cellular flows -- precede and drive long-term intercellular stress reorganization, physically governed by the mechanical properties of intercellular junctions. We show that this hidden flow skeleton correlates strongly with biomechanical metrics, bridging microscopic cell motion with mesoscopic biomechanics. Specifically, attractors and repellers mark hotspots of compressive and tensile stress enrichment (exceeding tenfold), alongside heterogeneities in cell packing. Notably, these connections remain robust across varying strengths of cell-cell and cell-substrate force transmission. Finally, by linking the attracting regions in the flow skeleton to future cell extrusion spots, we establish a direct link between cell motion and biologically significant outcomes. Together, these findings establish a framework for using cell motion to independently infer biomechanical metrics and bridge the scale mismatch between cell motion and biomechanics, potentially offering a new route to interpret mechanosensitive biological processes directly from cell trajectories.
Siavash Monfared, Aleksandra Ardaševa, Amin Doostmohammadi
The development of complex multicellular organisms from a single parent cell is a highly orchestrated process that cells conduct collectively without central guidance, creating intricate dynamic patterns essential for development and regeneration. Despite significant advances in imaging spatiotemporal dynamics of cell collectives and mechanical characterization techniques, the role of physical forces in biological functions remains poorly understood. Physics-based models are crucial in complementing experiments, providing high-resolution spatiotemporal fields in three dimensions. This review focuses on dense, soft multicellular systems, such as tissues, where mechanical deformation of one cell necessitates the re-organization of neighboring cells. The multi-phase-field model offers a rich physics-based framework to advance our understanding of biological systems and provides a robust playground for non-equilibrium physics of active matter. We discuss the foundational aspects of the multi-phase-field model and their applications in understanding physics of active matter. We also explore the integration of biological physics with experimental data, covering cell migration, heterogeneous cell populations, and confined systems. Finally, we highlight current trends, the importance of multi-phase-field models in biological and physics research, and future challenges.
Introduction
Sexual dysfunction (SD) is common in psychotic illness including schizophrenia, occurring in 30-82% of patients. It negatively impacts wellbeing and antipsychotic compliance, resulting in higher risk of relapse and hospitalisation. Due to over-reliance on spontaneous reports from patients, SD is typically under-identified which prevents investigation and treatment.
Objectives
To establish whether SD is under-identified in patients with psychosis in a general adult community mental health team; to elicit whether the Arizona Sexual Experience Scale (ASEX) improves identification; to investigate and manage identified cases of SD; to make recommendations about identification and monitoring of SD in this patient population.
Methods
A 12-month retrospective audit of patients with psychosis prescribed a long-acting injectable (LAI) antipsychotic (n=36) to identify sexual symptoms was completed. The ASEX was subsequently issued to screen for SD.
Results
Audit: 3/36 (8%) patients had documented sexual symptoms. Of the 18/36 patients that completed the ASEX: 10 (56%) exhibited SD. 4 consented to further investigation. 5 patients experienced significant difficulties with the language used in the ASEX. At the end of the project we revised the ASEX with simpler, colloquial language.
Conclusions
Implementation of the ASEX results in clear improvements in identification and monitoring of SD. Maudsley Practice Guidelines can inform investigation and management of SD. We suggest a review of NICE guidance to incorporate the above into clinical practice. Further work is needed to establish whether the revised ASEX can be developed and validated.
Disclosure of Interest
None Declared
ObjectivesThis study aimed to elucidate the influences of 1p/19q co-deletion on structural connectivity alterations in patients with dominant hemisphere insular diffuse gliomas.MethodsWe incorporated 32 cases of left insular gliomas and 20 healthy controls for this study. Using diffusion MRI, we applied correlational tractography, differential tractography, and graph theoretical analysis to explore the potential connectivity associated with 1p/19q co-deletion.ResultsThe study revealed that the quantitative anisotropy (QA) of key deep medial fiber tracts, including the anterior thalamic radiation, superior thalamic radiation, fornix, and cingulum, had significant negative associations with 1p/19q co-deletion (FDR = 4.72 × 10–5). These tracts are crucial in maintaining the integrity of brain networks. Differential analysis further supported these findings (FWER-corrected p < 0.05). The 1p/19q non-co-deletion group exhibited significantly higher clustering coefficients (FDR-corrected p < 0.05) and reduced betweenness centrality (FDR-corrected p < 0.05) in regions around the tumor compared to HC group. Graph theoretical analysis indicated that non-co-deletion patients had increased local clustering and decreased betweenness centrality in peritumoral brain regions compared to co-deletion patients and healthy controls (FDR-corrected p < 0.05). Additionally, despite not being significant through correction, patients with 1p/19q co-deletion exhibited lower trends in weighted average clustering coefficient, transitivity, small worldness, and global efficiency, while showing higher tendencies in weighted path length compared to patients without the co-deletion.ConclusionThe findings of this study underline the significant role of 1p/19q co-deletion in altering structural connectivity in insular glioma patients. These alterations in brain networks could have profound implications for the neural functionality in patients with dominant hemisphere insular gliomas.
Albert Alonso, Lars Erik J. Skjegstad, Julius B. Kirkegaard
Biological transport networks are highly optimized structures that ensure power-efficient distribution of fluids across various domains, including animal vasculature and plant venation. Theoretically, these networks can be described as space-embedded graphs, and rich structures that align well with observations emerge from optimizing their hydrodynamic energy dissipation. Studies on these models typically use regular grids and focus solely on edge width optimization. Here, we present a generalization of the hydrodynamic graph model which permits additional optimization of node positioning. We achieve this by defining sink regions, accounting for the energy dissipation of delivery within these areas, and optimizing by means of differentiable physics. In the context of leaf venation patterns, our method results in organic networks that adapt to irregularities of boundaries and node misalignment, as well as overall improved efficiency. We study the dependency of the emergent network structures on the capillary delivery conductivity and identify a phase transition in which the network collapses below a critical threshold. Our findings provide insights into the early formation of biological systems and the efficient construction of transport networks.
Despite being optimized, the information processing of biological organisms exhibits significant variability in its complexity and capability. One potential source of this diversity is the limitation of resources required for information processing. However, we lack a theoretical framework that comprehends the relationship between biological information processing and resource limitations and integrates it with decision-making conduced downstream of the information processing. In this paper, we propose a novel optimal estimation and control theory that accounts for the resource limitations inherent in biological systems. This theory explicitly formulates the memory that organisms can store and operate and obtains optimal memory dynamics using optimal control theory. This approach takes account of various resource limitations, such as memory capacity, intrinsic noise, and energy cost, and unifies state estimation and control. We apply this theory to minimal models of biological information processing and decision-making under resource limitations and find that such limitations induce discontinuous and non-monotonic phase transitions between memory-less and memory-based strategies. Therefore, this theory establishes a comprehensive framework for addressing biological information processing and decision-making under resource limitations, revealing the rich and complex behaviors that arise from resource limitations.
This work demonstrates biological detection of a low concentration of O3 by measuring electrochemical impedances of tissues in tobacco and tomato plants located indoor and outdoor. The lower range of generated ozone in the O3-air mix is about 30 ug/m3 over the atmospheric level, which allows phytosensors to be considered as biodetectors of environmental pollutants. The ozone stress affects stomatal regulation that in turn influences the hydrodynamics of fluid transport system in plants. Sensors utilize electrochemical impedance spectroscopy (EIS) to measure ionic fluid content at several positions on the plant stem and calculate a variation of fluid distribution in control and experimental cases indoors. Outdoor setup uses the same methodology and sensors but different analysis due to uncontrolled nature of ozone pollution and the overlap of various stressors. The measurement results indicate a qualitative and quantitative reaction of hydrodynamic system to changes in O3 concentration in the upper part of stem with a delay of 10-20 minutes between the onset of exposure and biological response. The probability of false-negative responses from a single plant is about 0.15 +/-0.06. Pooling data from at least three plants allows for 92% confidence in detecting excess O3. Measurements on days with low and high ozone levels of 80 ug/m3 to 130 ug/m3 result in a 2.33-fold difference in sensor values and thus demonstrate biological detection of high O3 also outdoors. Statistically significant data include 948 sensor-plant attempts during 51 days with 9 plants and about 10E7 samples collected in automated experiments. Long-term measurements have demonstrated the high reliability of electrochemical sensors, especially in harsh outdoor conditions with rain, heat and UV/IR radiations. The described approach has applications in environmental monitoring, biological pollution detection and biosensing.
Introduction
The increasing global suicide rates pose a considerable strain on healthcare professionals. Subsequently, their attitudes toward suicide prevention may influence suicide risk and management, affecting the quality of care.
Objectives
To investigate the attitudes of Pakistani medical students toward suicide and its comparison with different sociodemographic factors.
Methods
A total of 1392 undergraduate medical students belonging to all five years took part in the cross-sectional study conducted in September 2022. In addition to socio-demographic factors, participants were asked about their attitudes toward suicide on a 5-point Likert scale using the ATTS (Attitudes towards suicide) questionnaire. Questions explored competence, religion, experience, and views on suicidal behavior and its treatment. Data were analyzed by using SPSS 26.
Results
The majority of respondents had no prior experience of looking after patients with suicide attempts (88.9%), the experience of having known someone who died by suicide (67.1%), or participation in suicide workshops (94.3%). Statistically significant items showed that males believed more strongly that suicide could be used to end suffering and would consider the possibility of doing it, revenge is the major driving factor, talking about suicide lessens its incidence, and people should have the right to take their own lives. Females more strongly believed that loneliness is the major driving factor, and that suicide is preventable. Preclinical students more strongly believed thought suicide was less justified, especially among young people, not a solution to end incurable illnesses, and that people should not have the right to take their own lives. 996 (71.6%) of respondents expressed their willingness to participate in workshops regarding suicide.
Conclusions
Our study suggests that medical students have little experience in handling suicidal patients and vastly differ in their attitudes. There is a need for suicide management training and further study data to support these findings.
Disclosure of Interest
None Declared
Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances. In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the in vitro experimental results of Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers.
The possibility of establishing a metric of individual genetic risk for a particular disease or trait has sparked the interest of the clinical and research communities, with many groups developing and validating genomic profiling methodologies for their potential application in clinical care. Current approaches for calculating genetic risk to specific psychiatric conditions consist of aggregating genome-wide association studies–derived estimates into polygenic risk scores, which broadly represent the number of inherited risk alleles for an individual. While the traditional approach for polygenic risk score calculation aggregates estimates of gene-disease associations, novel alternative approaches have started to consider functional molecular phenotypes that are closer to genetic variation and are less penalized by the multiple testing required in genome-wide association studies. Moving the focus from genotype-disease to genotype–gene regulation frameworks, these novel approaches incorporate prior knowledge regarding biological processes involved in disease and aggregate estimates for the association of genotypes and phenotypes using multi-omics data modalities. In this review, we discuss and list different functional genomics tools that can be used and integrated to inform researchers and clinicians for a better understanding and diagnosis of psychopathology. We suggest that these novel approaches can help generate biologically driven hypotheses for polygenic signals that can ultimately serve the clinical community as potential biomarkers of psychiatric disease susceptibility.
Introduction
The BFI-2-S assesses the domain level of the Big Five with three prototypical facets of each domain capturing approximately 91% of the total variance in the full BFI-2 domain scales and approximately 89% of the predictive power of the BFI-2 facets in German adaptations and their original American versions.
Objectives
The study aims to investigate the psychometric properties of the Arabic adaptation of the BFI-2 short form.
Methods
The Arabic version of the BFI-2-S a 30-item with 15 and NEO Five-Factor Inventory (NEO–PI-R) were administered to 1560 (576 males, 984 females) Kuwait University undergraduates with a mean age = 22.75 ± 3.81. The internal consistency reliability, factor structure, and convergent validity of the BFI-2-S with NEO–PI-R were assessed.
Results
Cronbach’s alpha was satisfactory for N (0.79), E (0.73), O (0.73), A (0.76) and C (0.77). Results revealed significant gender differences in O, C & E with a favor for males and in N a favor with females. PCA showed that BFI-2-S five factors explains 64.38% of the total variance. However, the high mean correlations between the BFI-2-S and NEO–PI-R scales, with coefficients of (0.67) for the N, (0.66) for the E, (0.56) for the C, (0.61) for the A, and (0.58) for the C. The convergence between each BFI-2-S domain correlated substantially with the relevant NEO-PI-R domain scales, with the average correlation being .62.
Conclusions
The findings support the psychometric properties of the Arabic adaptations of the BFI-2-S as useful instruments for assessing the Big Five.
Disclosure
No significant relationships.
At the basis of most risk assessments aimed at determining the long-term trends in changes in the activity of radionuclides in the environment and thus exposure to ionizing radiation, various concepts of half-lives are used, particularly: biological, environmental (ecological) and effective one. There is a clear lack of consensus on the terminological level and, more importantly, on the methodological level regarding the determination of these half-lives. This manifests by a divergent methodology and the existence of two main, but contradictory concepts. In the first, empirically determined half-life is referred to as environmental or biological. The effective half-life is extrapolated at a later stage after taking into account the physical decay. In the second concept, the effective half-life is the one empirically determined and the remaining half-lives can be extrapolated after correction for physical decay. As both concepts seems to be incompatible with each other, the aim of this work was to thoroughly analyze their theoretical assumptions, conditions, strengths and weaknesses and to define the conditions that must be met in order to enable comparison of the results obtained with the use of two separate concepts. In case of the environmental components the suggested approach is to empirically determine the effective half-life, and subsequently, to extrapolate the environmental and/or biological half-lives.
Division of Psychology, Nanyang Technological University, Singapore, Singapore, Department of Psychology, University of Cambridge, Cambridge, United Kingdom, Mila Quebec Artificial Intelligence Institute, Montreal, QC, Canada, 4 Faculty of Medicine, Montreal Neurological Institute, McGill University, Montreal, QC, Canada, 5 Social Neuroscience Lab at the Translational Psychiatry Unit, Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck, Germany, Department of Neurology, University of Virginia, Charlottesville, VA, United States, Department of Psychology, University of Maryland, College Park, MD, United States, Medical Faculty, Ludwig Maximilians University, Munich, Germany
The study presented here aims at bringing a global perspective to the phenomenon of unequal representation of females in science by offering empirical data of female representation in neuroscience/schizophrenia academic or clinical departments in several institutions around the world. We took advantage of a budding network of scientists and colleagues from different countries to bring the data together. The data presented are related to sex, that is the biological distinction between males and females, based on genetics and reproductive anatomy, while gender, considered a cultural concept was harder to determine. We report data from two clinical/academic departments in Nigeria, Africa; 2 clinical/academic departments from Sudan, Africa; 1 clinical/academic department from South Africa, Africa; 3 academic institutions from Ireland, Europe; 1 clinical/academic institution from Spain, Europe; 2 academic institutions from Buenos Aires University, Argentina; and the Psychiatry Departments at Harvard Medical School, Boston, USA.
Maria Grazia Puxeddu, Maria Grazia Puxeddu, Manuela Petti
et al.
Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions.