Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries.

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Understanding the role of biomolecular coronas in human exposure to nanomaterials.

Nanomaterials (NMs) are increasingly used in medical treatments, electronics, and food additives. However, nanosafety-the possible adverse effects of NMs on human health-is an area of active research. This review provides an overview of the influence of biomolecular coronas on NM transformation following various exposure routes. We discuss potential exposure pathways, including inhalation and ingestion, describing the physiology of exposure routes and emphasising the relevance of coronas in these environments. Additionally, we review other routes to NM exposure, such as synovial fluid, blood (translocation and injection), dermal and ocular exposure, as well as the dose and medium impact on NM interactions. We emphasize the need for an in-depth characterisation of coronas in different biological media, highlighting the need and opportunity to study lung and gastric fluids to understand NM behaviour and potential toxicity. Future research aims to predict better in vivo outcomes and address the complexities of NM interactions with biological systems.

Longitudinal changes in DNA methylation associated with clozapine use in treatment-resistant schizophrenia from two international cohorts.

The second-generation antipsychotic clozapine is used as a medication for treatment-resistant schizophrenia. It has previously been associated with epigenetic changes in pre-clinical rodent models and cross-sectional studies of treatment-resistant schizophrenia. Cross-sectional studies are susceptible to confounding, however, and cannot disentangle the effects of diagnosis and medication. We therefore profiled DNA methylation in sequential blood samples (n = 126) from two independent cohorts of patients (n = 38) with treatment-resistant schizophrenia spectrum disorders who commenced clozapine after study enrolment and were followed up for up to six months. We identified significant non-linear changes in cell-type proportion estimates derived from DNA methylation data - specifically B-cells - associated with time on clozapine. Mixed effects regression models were used to identify changes in DNA methylation at specific sites associated with time on clozapine, identifying 37 differentially methylated positions (DMPs) (p < 5 × 10-5) in a linear model and 90 DMPs in a non-linear quadratic model. We compared these results to data from our previous epigenome-wide association study (EWAS) meta-analysis of psychosis, finding evidence that many previously identified DMPs associated with schizophrenia and treatment-resistant schizophrenia might reflect exposure to clozapine. In conclusion, our results indicate that clozapine exposure is associated with changes in DNA methylation and cellular composition. Our study shows that medication effects might confound many case-control studies of neuropsychiatric disorders performed in blood.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

A millimetre-scale capacitive biosensing and biophysical stimulation system for emerging bioelectronic bone implants.

Bioelectronic bone implants are being widely recognized as a promising technology for highly personalized bone/implant interface sensing and biophysical therapeutic stimulation. Such bioelectronic devices are based on an innovative concept with the ability to be applied to a wide range of implants, including in fixation and prosthetic systems. Recently, biointerface sensing using capacitive patterns was proposed to overcome the limitations of standard imaging technologies and other non-imaging technologies; moreover, electric stimulation using capacitive patterns was proposed to overcome the limitations of non-instrumented implants. We here provide an innovative low-power miniaturized electronic system with ability to provide both therapeutic stimulation and bone/implant interface monitoring using network-architectured capacitive interdigitated patterns. It comprises five modules: sensing, electric stimulation, processing, communication and power management. This technology was validated using in vitro tests: concerning the sensing system, its ability to detect biointerface changes ranging from tiny to severe bone-implant interface changes in target regions was validated; concerning the stimulation system, its ability to significantly enhance bone cells' full differentiation, including matrix maturation and mineralization, was also confirmed. This work provides an impactful contribution and paves the way for the development of the new generation of orthopaedic biodevices.

Patient-specific surrogate model to predict pelvic floor dynamics during vaginal delivery.

Childbirth is a challenging event that can lead to long-term consequences such as prolapse or incontinence. While computational models are widely used to mimic vaginal delivery, their integration into clinical practice is hindered by time constraints. The primary goal of this study is to introduce an artificial intelligence pipeline that leverages patient-specific surrogate modeling to predict pelvic floor injuries during vaginal delivery. A finite element-based machine learning approach was implemented to generate a dataset with information from finite element simulations. Thousands of childbirth simulations were conducted, varying the dimensions of the pelvic floor muscles and the mechanical properties used for their characterization. Additionally, a mesh morphing algorithm was developed to obtain patient-specific models. Machine learning models, specifically tree-based algorithms such as Random Forest (RF) and Extreme Gradient Boosting, as well as Artificial Neural Networks, were trained to predict the nodal coordinates of nodes within the pelvic floor, aiming to predict the muscle stretch during a critical interval. The results indicate that the RF model performs best, with a mean absolute error (MAE) of 0.086 mm and a mean absolute percentage error of 0.38%. Overall, more than 80% of the nodes have an error smaller than 0.1 mm. The MAE for the calculated stretch is equal to 0.0011. The implemented pipeline allows loading the trained model and making predictions in less than 11 s. This work demonstrates the feasibility of implementing a machine learning framework in clinical practice to predict potential maternal injuries and assist in medical-decision making.

Protocols for isolation and characterization of nanoparticle biomolecular corona complexes.

Engineered nanoparticles (NPs) pose a broad spectrum of interesting properties that make them useful for many applications. However, continuous exposure to NPs requires the need to deeply understand the outcomes when these NPs interact with different biological environments. After exposure within (to) these environments, the pristine surfaces of NPs strongly interact with the molecules from the surrounding medium, including metabolites, lipids, glycan, and proteins, forming the so-called protein corona (PC). It is well established that the NP-PC strongly influences the biological fate of various NPs types, including cellular uptake, toxicity, and biodistribution. Thus, for a proper assessment of potential hazards associated with engineered NPs, it is mandatory to study and evaluate the PC that forms around NPs. Herein, we describe protocols in detail for the isolation and characterization of NP-PC complexes and cover the following aspects: 1) isolation protocols for different nanomaterials in a range of exposing media, including magnetic isolation methods for superparamagnetic NPs, 2) NP physico-chemical characterization using advanced and standard techniques available in regular laboratories, and 3) NP- PC characterization of the protein and glycan components.

Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management.

PROBLEM: Therapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice. OBJECTIVE: This study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm. DESIGN: Here, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models). RESULTS: Our results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics. CONCLUSIONS: Our findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.

Temporal evolution of living donor liver transplantation survival-A United Network for Organ Sharing registry study.

Living donor liver transplantation (LDLT) is a curative treatment for various liver diseases, reducing waitlist times and associated mortality. We aimed to assess the overall survival (OS), identify predictors for mortality, and analyze differences in risk factors over time. Adult patients undergoing LDLT were selected from the United Network for Organ Sharing database from inception (1987) to 2023. The Kaplan-Meier method was used for analysis, and multivariable Cox proportional hazard models were conducted. In total, 7257 LDLT recipients with a median age of 54 years (interquartile range [IQR]: 45-61 years), 54% male, 80% non-Hispanic White, body mass index of 26.3 kg/m2 (IQR: 23.2-30.0 kg/m2), and model for end-stage liver disease score of 15 (IQR: 11-19) were included. The median cold ischemic time was 1.6 hours (IQR: 1.0-2.3 hours) with 88% right lobe grafts. The follow-up was 4.0 years (IQR: 1.0-9.2 years). The contemporary reached median OS was 17.0 years (95% CI: 16.1, 18.1 years), with the following OS estimates: 1 year 95%; 3 years 89%; 5 years 84%; 10 years 72%; 15 years 56%; and 20 years 43%. Nine independent factors associated with mortality were identified, with an independent improved OS in the recent time era (adjusted hazards ratio: 0.53; 95% CI: 0.39, 0.71). The median center-caseload per year was 5 (IQR: 2-10), with observed center-specific improvement of OS. LDLT is a safe procedure with excellent OS. Its efficacy has improved despite an increase of risk parameters, suggesting its limits are yet to be met.

Living Donor Availability Improves Patient Survival in a North American Center: An Intention-to-treat Analysis.

OBJECTIVE: Assess the impact of having a living donor on waitlist outcomes and overall survival through an intention-to-treat analysis. BACKGROUND: Living-donor liver transplantation (LDLT) offers an alternative to deceased donation in the face of organ shortage. An as-treated analysis revealed that undergoing LDLT, compared to staying on the waiting list, is associated with improved survival, even at Model for End-stage Liver Disease-sodium (MELD-Na) score of 11. METHODS: Liver transplant candidates listed at the Ajmera Transplant Centre (2000-2021) were categorized as pLDLT (having a potential living donor) or pDDLT (without a living donor). Employing Cox proportional-hazard regression with time-dependent covariates, we evaluated pLDLT's impact on waitlist dropout and overall survival through a risk-adjusted analysis. RESULTS: Of 4,124 candidates, 984 (24%) had potential living donors. The pLDLT group experienced significantly lower overall waitlist dropouts (5.2%vs. 34.4%, P<0.001) and mortality (3.8%vs. 24.4%, P<0.001) compared to the pDDLT group. Possessing a living donor correlated with a 26% decline in the risk of waitlist dropout (adjusted hazard ratio 0.74, 95%CI 0.55-0.99, P=0.042). The pLDLT group also demonstrated superior survival outcomes at 1- (84.9%vs. 80.1%), 5- (77.6%vs. 61.7%), and 10-year (65.6%vs.52.9%) from listing (log-rank P<0.001) with a 35% reduced risk of death (adjusted hazard ratio 0.65, 95%CI 0.56-0.76, P<0.001). Moreover, the predicted hazard ratios consistently remained below 1 across the MELD-Na range 11-26. CONCLUSIONS: Having a potential living donor significantly improves survival in end-stage liver disease patients, even with MELD-Na scores as low as 11. This emphasizes the need to promote awareness and adoption of LDLT in liver transplant programs worldwide.

A (RP)UHPLC/UV analytical method to quantify dsRNA during the mRNA vaccine manufacturing process.

dsRNA is a product related impurity produced during the mRNA manufacturing process. The established immuno-based detection methods lack the flexibility and speed required to be applied throughout the manufacturing process. The RP-HPLC method developed outperforms these in terms of precision, broader detection range, LOD and LOQ, as well as in output variance. Using this method, dsRNA can be quantified in under 30 min for a single sample.

Self-adaptive rotational electromagnetic energy generation as an alternative to triboelectric and piezoelectric transductions.

Triboelectric and piezoelectric energy harvesters can hardly power most microelectronic systems. Rotational electromagnetic harvesters are very promising alternatives, but their performance is highly dependent on the varying mechanical sources. This study presents an innovative approach to significantly increase the performance of rotational harvesters, based on dynamic coil switching strategies for optimization of the coil connection architecture during energy generation. Both analytical and experimental validations of the concept of self-adaptive rotational harvester were carried out. The adaptive harvester was able to provide an average power increase of 63.3% and 79.5% when compared to a non-adaptive 16-coil harvester for harmonic translation and harmonic swaying excitations, respectively, and 83.5% and 87.2% when compared to a non-adaptive 8-coil harvester. The estimated energy conversion efficiency was also enhanced from ~80% to 90%. This study unravels an emerging technological approach to power a wide range of applications that cannot be powered by other vibrationally driven harvesters.

Investigating the impact of poverty on mental illness in the UK Biobank using Mendelian randomization.

It is unclear whether poverty and mental illness are causally related. Using UK Biobank and Psychiatric Genomic Consortium data, we examined evidence of causal links between poverty and nine mental illnesses (attention deficit and hyperactivity disorder (ADHD), anorexia nervosa, anxiety disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, post-traumatic stress disorder and schizophrenia). We applied genomic structural equation modelling to derive a poverty common factor from household income, occupational income and social deprivation. Then, using Mendelian randomization, we found evidence that schizophrenia and ADHD causally contribute to poverty, while poverty contributes to major depressive disorder and schizophrenia but decreases the risk of anorexia nervosa. Poverty may also contribute to ADHD, albeit with uncertainty due to unbalanced pleiotropy. The effects of poverty were reduced by approximately 30% when we adjusted for cognitive ability. Further investigations of the bidirectional relationships between poverty and mental illness are warranted, as they may inform efforts to improve mental health for all.

A safe, effective and adaptable live-attenuated SARS-CoV-2 vaccine to reduce disease and transmission using one-to-stop genome modifications.

Approved vaccines are effective against severe COVID-19, but broader immunity is needed against new variants and transmission. Therefore, we developed genome-modified live-attenuated vaccines (LAV) by recoding the SARS-CoV-2 genome, including 'one-to-stop' (OTS) codons, disabling Nsp1 translational repression and removing ORF6, 7ab and 8 to boost host immune responses, as well as the spike polybasic cleavage site to optimize the safety profile. The resulting OTS-modified SARS-CoV-2 LAVs, designated as OTS-206 and OTS-228, are genetically stable and can be intranasally administered, while being adjustable and sustainable regarding the level of attenuation. OTS-228 exhibits an optimal safety profile in preclinical animal models, with no side effects or detectable transmission. A single-dose vaccination induces a sterilizing immunity in vivo against homologous WT SARS-CoV-2 challenge infection and a broad protection against Omicron BA.2, BA.5 and XBB.1.5, with reduced transmission. Finally, this promising LAV approach could be applicable to other emerging viruses.

Appendage-resident epithelial cells expedite wound healing response in adult zebrafish.

Adult zebrafish are able to heal large-sized cutaneous wounds in hours with little to no scarring. This rapid re-epithelialization is crucial for preventing infection and jumpstarting the subsequent regeneration of damaged tissues. Despite significant progress in understanding this process, it remains unclear how vast numbers of epithelial cells are orchestrated on an organismic scale to ensure the timely closure of millimeter-sized wounds. Here, we report an unexpected role of adult zebrafish appendages (fins) in accelerating the re-epithelialization process. Through whole-body monitoring of single-cell dynamics in live animals, we found that fin-resident epithelial cells (FECs) are highly mobile and migrate to cover wounds in nearby body regions. Upon injury, FECs readily undergo organ-level mobilization, allowing for coverage of body surfaces of up to 4.78 mm2 in less than 8 h. Intriguingly, long-term fate-tracking experiments revealed that the migratory FECs are not short-lived at the wound site; instead, the cells can persist on the body surface for more than a year. Our experiments on "fin-less" and "fin-gaining" individuals demonstrated that the fin structures are not only capable of promoting rapid re-epithelialization but are also necessary for the process. We further found that fin-enriched extracellular matrix laminins promote the active migration of FECs by facilitating lamellipodia formation. These findings lead us to conclude that appendage structures in regenerative vertebrates, such as fins, may possess a previously unrecognized function beyond serving as locomotor organs. The appendages may also act as a massive reservoir of healing cells, which speed up wound closure and tissue repair.

Facing infant cuteness: How nurturing care motivation and oxytocin system gene methylation are associated with responses to baby schema features.

Baby schema features are a specific set of physical features-including chubby cheeks, large, low-set eyes, and a large, round head-that have evolutionary adaptive value in their ability to trigger nurturant care. In this study among nulliparous women (N = 81; M age = 23.60, SD = 0.44), we examined how sensitivity to these baby schema features differs based on individual variations in nurturant care motivation and oxytocin system gene methylation. We integrated subjective ratings with measures of facial expressions and electroencephalography (EEG) in response to infant faces that were manipulated to contain more or less pronounced baby schema features. Linear mixed effects analyses demonstrated that infants with more pronounced baby schema features were rated as cuter and participants indicated greater motivation to take care of them. Furthermore, infants with more pronounced baby schema features elicited stronger smiling responses and enhanced P2 and LPP amplitudes compared to infants with less pronounced baby schema features. Importantly, individual differences significantly predicted baby schema effects. Specifically, women with low OXTR methylation and high nurturance motivation showed enhanced differentiation in automatic neurophysiological responses to infants with high and low levels of baby schema features. These findings highlight the importance of considering individual differences in continued research to further understand the complexities of sensitivity to child cues, including facial features, which will improve our understanding of the intricate neurobiological system that forms the basis of caregiving behavior.

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

Background: The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. Methods: As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. Results: We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. Conclusions: This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.

[Mycoplasma pneumoniaeoutbreak in the Netherlands 2023-2024]. / Mycoplasma pneumoniae-uitbraak in Nederland 2023-2024.

Currently, there is a nationwide outbreak of Mycoplasma pneumoniae infections. M. pneumoniae is a bacterium that can cause atypical pneumonia, especially in children and young adults, and does not respond to the standard antibiotics prescribed for pneumonia. In addition, the bacterium regularly causes extra-pulmonary symptoms. In our hospitals, we have admitted 100 patients (including 20 children) with M. pneumoniae since the fall of 2023, many of which were young and had severe clinical symptoms. It is important to recognize the clinical picture to start effective antibiotic treatment. In this clinical lesson, we will provide two examples of recently admitted patients and discuss the characteristics of all inpatients who have presented to our hospitals during this epidemic. Finally, we pay attention to antibiotic policy and antibiotic resistance.

Universe-inspired algorithms for control engineering: A review.

Control algorithms have been proposed based on knowledge related to nature-inspired mechanisms, including those based on the behavior of living beings. This paper presents a review focused on major breakthroughs carried out in the scope of applied control inspired by the gravitational attraction between bodies. A control approach focused on Artificial Potential Fields was identified, as well as four optimization metaheuristics: Gravitational Search Algorithm, Black-Hole algorithm, Multi-Verse Optimizer, and Galactic Swarm Optimization. A thorough analysis of ninety-one relevant papers was carried out to highlight their performance and to identify the gravitational and attraction foundations, as well as the universe laws supporting them. Included are their standard formulations, as well as their improved, modified, hybrid, cascade, fuzzy, chaotic and adaptive versions. Moreover, this review also deeply delves into the impact of universe-inspired algorithms on control problems of dynamic systems, providing an extensive list of control-related applications, and their inherent advantages and limitations. Strong evidence suggests that gravitation-inspired and black-hole dynamic-driven algorithms can outperform other well-known algorithms in control engineering, even though they have not been designed according to realistic astrophysical phenomena and formulated according to astrophysics laws. Even so, they support future research directions towards the development of high-sophisticated control laws inspired by Newtonian/Einsteinian physics, such that effective control-astrophysics bridges can be established and applied in a wide range of applications.