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On 15 August 2025, a fatal train crash happened in Southern Denmark following a collision with a truck at an unsecured railway crossing, resulting in derailment and one overturned carriage. One person was killed, and 29 people were injured. This case report aims to give a detailed description of the response of emergency medical services to evaluate cross-border and inter-authority cooperation as well as adherence to major incident guidelines and established communication pathways. Initial calls indicated several critical patients and numerous school children on the train. Nineteen Danish and German units, including four helicopters, were dispatched to the scene. Access to the site was restricted to a single narrow countryside road, requiring careful organization of staging areas, access/egress routes, and casualty clearing stations. A Joint Incident Command was formed, and patients were triaged, treated, and registered using an electronic prehospital system allowing for continuous coordination with the dispatch center for hospital allocation. Two critically injured patients were transported by helicopter to two trauma centers in Denmark and Germany, and the remaining patients were distributed across three regional hospitals. The response to this incident largely aligned with national guidelines and cross-border cooperation agreements. However, delays in incident management were identified, and responders criticized infrequent training opportunities for major incidents. The rate of unauthorized radio shifts was lower than previously described. This report describes the response to a major incident, characterized by effective inter-authority cooperation and successful cross-border collaboration between Danish and German emergency services. Routine collaboration during everyday responses proved crucial to ensure efficient collaboration during crises. While adherence to major incident protocols was generally high, challenges remained regarding the site organization and timely establishment of command structures. The observed delays may reflect limited experience with major incident management among regular responders, prompting the need for comprehensive incident command training of prehospital physicians. Compared to previous incidents, communication consistency was notably improved, likely due to dedicated training efforts after prior communication failure. The incident underscored the importance of joint inter-authority response concepts and the central coordinating role of the dispatch center. Enhanced training accessibility and continued evaluation of real-life incidents are recommended to further improve preparedness for future incidents.

We present the Pharmacon toolkit, a pure Python command-line software package for analyzing molecular dynamics (MD) simulations of biomacromolecules. Pharmacon supports trajectories generated by widely used MD simulation engines, such as Amber, Gromacs, CHARMM, NAMD, and OpenMM. Pharmacon is written entirely in Python and uses popular third-party libraries, including MDAnalysis for trajectory handling and NumPy for data processing. The toolkit provides a streamlined, automated workflow that simplifies the routine analysis and postprocessing of MD simulation results of biomacromolecular complexes, making them more accessible and reproducible by aggregating many tedious and time-consuming tasks into a single command-line workflow. As a proof of concept, we applied Pharmacon to analyze, in comparison with other toolkits, results from MD simulations, including intermolecular interactions and geometric measures, across different test protein complexes, including three membrane proteins and one soluble protein complex.

The field of implantable Brain-Computer Interfaces (iBCIs) is rapidly advancing, with individuals with amyotrophic lateral sclerosis (ALS) as key beneficiaries. However, ALS-related cortical degeneration may impair iBCI effectiveness. This study investigated whether structural magnetic resonance imaging (MRI) and functional MRI (fMRI) metrics are associated with the quality of electrocorticography (ECoG) signals critical for iBCI use. Six late-stage ALS participants and 76 controls underwent T1-weighted structural MRI and task-based fMRI during right-hand movement or attempts thereof. ECoG data of ALS participants was benchmarked using ECoG data acquired in epilepsy patients. Grey matter thickness in the sensorimotor cortex and fMRI activation in the motor-hand area were measured. Four ALS participants showed >0.4 mm thinning in the precentral gyrus, while the postcentral gyrus was spared. ECoG signal quality was significantly associated with precentral grey matter thickness, but not with fMRI activity. These findings suggest that presurgical assessment of precentral grey matter thickness could potentially prove useful for iBCI candidate selection in advanced ALS. People with amyotrophic lateral sclerosis (ALS) can lose the ability to move and speak, but their thinking often remains intact. Implantable brain-computer interfaces (iBCIs) can help by translating brain signals into commands for communication devices. However, ALS damages the motor cortex, which may reduce the quality of these signals. In this study, we examined brain scans and electrical recordings from six people with advanced ALS. We found that thinning of the motor cortex was linked to weaker brain signals needed for iBCI control, while functional MRI activity was less predictive. This suggests that measuring motor cortex thickness before surgery could help identify who will benefit most from an iBCI, improving treatment decisions and future clinical trials. We examine presurgical MRI/fMRI and ECoG recordings from people with advanced ALS receiving implanted brain-computer interfaces. Motor cortex thinning is associated with poorer ECoG signal quality, suggesting cortical thickness may help identify candidates likely to benefit.

The design of robotic graspers that can safely interact with deformable, damage-prone materials such as fruits, vegetables, and biological tissues remains an ongoing challenge in robotics. Conventional robotic graspers made of mostly rigid materials have limited compliance and tactile sensing, reducing their applicability to contact-rich manipulation of soft objects. In contrast, humans and animals can interact with their environments safely and intelligently through their bodies' structural properties and nervous systems' computational capabilities. In this article, we present the design and control of a soft grasper inspired by the sea slug, Aplysia californica, and compare its performance with rigid graspers. The soft jaws and actuators allow the grasper to mimic Aplysia's force sensing capability and its ability to conform to complex food as it grasps. Combining Synthetic Nervous Systems (SNSs), an artificial neural network model inspired by computational neuroscience, and network architectures inspired by Aplysia's feeding control circuitry, we designed distributed and interpretable pick-and-place controllers for the soft grasper and its rigid counterparts. During grasping, these controllers either command a fixed closure radius (feedforward position control) or cap the contact force at a predefined level (force feedback control). We first validated our approach in simulation, demonstrating that the controllers can perform pick-and-place behavior that is robust to sensor noise. We then extended the validation to the physical platform to quantitatively compare how much deformation these graspers induced on soft objects. Fruits such as strawberries, tomatoes, and avocados showed little deformation after they were handled by the soft grasper, suggesting that this approach might have significant agricultural uses. The experimental data suggest the value of the bioinspired soft grasper for soft object manipulation.

Surface-Induced Dissociation native Mass Spectrometry (SID-nMS) is a tandem MS activation method that yields information on the connectivity and stoichiometry of protein complexes. While insufficient for direct structure elucidation, the data derived from SID-nMS has considerable potential to inform multimeric protein structure prediction. We hypothesized that incorporating this data into a machine-learning framework could improve multimer prediction accuracy beyond that of existing deep-learning methods. To this end, we developed SIDFold, a novel AlphaFold-based deep-learning network. SIDFold is the first AlphaFold-like network to leverage experimental data during protein complex prediction, and the first deep-learning network to utilize nMS data for structure prediction. We benchmarked SIDFold on the BETA protein set, and observed an improvement in RMSD in 138 of 227 cases including 27 targets in which the predicted structure attained near-native accuracy. We then evaluated the network on 20 proteins with experimental SID-nMS data, yielding an improved RMSD in 18 cases, with five of these cases improving to a high-accuracy complex. Finally, we tested SIDFold against a previously published SID-guided Rosetta docking method, where we saw improvement in 13 of 16 proteins. SIDFold is freely available on GitHub, with example files and commands available in the Supplementary Information.

A prevailing approach for learning visuomotor policies is to employ reinforcement learning to map high-dimensional visual observations directly to action commands. However, the combination of high-dimensional visual inputs and agile maneuver outputs leads to long-standing challenges, including low sample efficiency and significant sim-to-real gaps. To address these issues, we propose Oracle-Guided Masked Contrastive Reinforcement Learning (OMC-RL), a novel framework designed to improve the sample efficiency and asymptotic performance of visuomotor policy learning. OMC-RL explicitly decouples the learning process into two stages: an upstream representation learning stage and a downstream policy learning stage. In the upstream stage, a masked Transformer module is trained with temporal modeling and contrastive learning to extract temporally-aware and task-relevant representations from sequential visual inputs. After training, the learned encoder is frozen and used to extract visual representations from consecutive frames, while the Transformer module is discarded. In the downstream stage, an oracle teacher policy with privileged access to global state information supervises the agent during early training to provide informative guidance and accelerate early policy learning. This guidance is gradually reduced to allow independent exploration as training progresses. Extensive experiments in simulated and real-world environments demonstrate that OMC-RL achieves superior sample efficiency and asymptotic policy performance, while also improving generalization across diverse and perceptually complex scenarios.

Solvent accessible surface area (SASA) is widely used to describe protein stability, ligand binding, mutation effects, and protein-protein interfaces. As structural biology workloads expand to predicted-structure col-lections, trajectories, and large assemblies, SASA tools must combine reproducible calculation with high throughput, low memory use, and workflow-friendly input handling. We present zsasa, a Zig-based SASA engine with command-line and Python interfaces. zsasa implements the established Shrake-Rupley and Lee-Richards algorithms, provides exact f64/f32 modes and an optional bitmask approximation, and supports batch and trajectory workflows, compressed structure inputs, and configurable atom classification including Chemical Component Dictionary (CCD)-based radii for non-standard components. In matched Shrake-Rupley validation on 4,370 Escherichia coli AlphaFold Database structures, exact double-precision zsasa reproduced FreeSASA total SASA values to near numerical identity. In 10-thread batch benchmarks on the E. coli and 23,586-structure human AlphaFold collections, zsasa achieved a 2.94-fold speedup over a FreeSASA batch wrapper in exact f64 mode. In bitmask mode, zsasa reached up to a 9.70-fold speedup, using roughly 12.5% to 25% of the comparator peak memory. Trajectory benchmarks exceeded 1,000 frames/s at tens of megabytes of peak memory, and a 4.5-million-atom PDB stress-test file completed in less than 5 s. These results support zsasa as a practical tool for reproducible, low-memory generation of surface-derived structural features at large scale. zsasa is available under the MIT License at https://github.com/N283T/zsasa .

While general anaesthesia typically induces unconsciousness, some patients retain the capacity to respond behaviourally to noxious stimulation. Reduced frontal alpha (8-12 Hz) power has been proposed as a marker of arousal, but its clinical reliability remains inconsistent. This exploratory study aimed to identify multichannel EEG spectral and connectivity markers associated with intraoperative behavioural responsiveness around noxious stimulation. Sixty-four-channel EEG was recorded intraoperatively from seven patients undergoing microlaryngoscopy under propofol anaesthesia with analgesia. Responsiveness was assessed via reactions to verbal commands. Alpha-band spectral features of responders and non-responders were compared pre- and post-noxious stimulation using descriptive statistics. Linear mixed models evaluated the relationship between channel-wise spectral power, and the factors of noxious stimulation and responsiveness. Event-related synchronization/desynchronization and connectivity via weighted Symbolic Mutual Information (wSMI) were analysed across frequency bands and response categories, with correction for multiple comparisons. Alpha power and global coherence showed minimal changes following noxious stimulation across patients. In contrast, volitional responses to noxious stimulation were associated with increased high-frequency power in sensory-motor and auditory cortices. These responses showed event-related synchronisation in left-central channels, whereas incoherent movements were marked by desynchronisation in the same areas. Theta-band activity further differentiated response types: cognitive responses showed suppression, while incoherent movements showed enhancement, particularly over the same regions. Cognitive responses were associated with increased global whole-brain integration, especially in the theta-band linking motor, auditory, and premotor cortices. Incoherent movements, by contrast, were associated with reductions in global brain connectivity. None of these patients reported postoperative awareness with recall. These preliminary findings suggest that localised high-frequency power increases and enhanced theta-band connectivity may reflect "connected consciousness," in which patients retain the capacity to process sensory input despite anaesthesia. As this state may precede awareness with recall, particularly during noxious stimulation, its detection remains a key clinical challenge, underscoring the potential of these multichannel EEG features as markers. Despite limitations in sample size and variability in anaesthetic and patient factors, the surgical setting enhances the clinical relevance of these findings. However, further validation is required before clinical application for intraoperative monitoring and optimisation of analgesia prior to noxious stimulation.

The liver-brain axis (LBA) represents a bidirectional communication network between the liver and the central nervous system (CNS), governed by integrated neural, humoral, and immune signaling pathways. Emerging evidence indicates that the liver functions not merely as a passive metabolic organ subordinate to central commands, but rather as a dynamic hub that actively senses and modulates peripheral neuroimmune responses. We here first delineate the fundamental communication mechanisms and the transport kinetics of signaling molecules governing LBA interactions. We then examine the profound species-specific disparities between humans and mice-particularly regarding signaling mediators, blood-brain barrier (BBB) architecture, and the hepatic immune microenvironment. From a pathophysiological perspective, we establish that LBA dysfunction serves as a core driver of obesity, diabetes, and their multisystemic sequelae, including cardiovascular diseases and psychiatric disorders such as anxiety and depression. Finally, we highlight recent therapeutic advances targeting the LBA for the management of metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), atherosclerosis, and associated psychiatric conditions, thereby underscoring the immense clinical potential of LBA-targeted interventions.

The selective and consecutive cleavage of C(sp3)─C(sp3) bonds while simultaneously governing chemo-, regio-, and stereoselectivity constitutes a long-standing unresolved challenge in synthetic chemistry. Here, we present a visible light-driven copper catalysis platform that surmounts this obstacle. A chiral bisoxazoline copper complex acts as a multifunctional catalyst, and a tailored sodium sulfoxylate additive modulates the metal coordination sphere, altogether eliminating the requirement for an external photosensitizer. This streamlined system promotes twofold C(sp3)─C(sp3) fragmentation of cyclobutanols and subsequent coupling with imines to construct quaternary carbon stereocenters, releasing ethylene as the sole byproduct. Integrated experimental and computational investigations delineate an unprecedented cascade mechanism: photoinduced ligand-to-metal charge transfer (LMCT) initiates strain-release ring opening, a copper-mediated β-cleavage executes the second fragmentation, and a highly enantioselective radical addition to the imine completes the stereodefined assembly. The sodium sulfoxylate additive proves indispensable for rerouting the pathway toward the second cleavage over premature radical trapping and elevating enantioselectivity to as high as 99% ee. By demonstrating rigorous kinetic command over multiple C(sp3)─C(sp3) cleavages within a single cascade, this work establishes a new paradigm for photochemical asymmetric synthesis.

Children, neonates, and pregnant women are particularly vulnerable during disasters. Fragmentation between specialized pediatric-perinatal systems and general disaster response frameworks can hinder coordinated care. Following lessons from the 2011 Great East Japan Earthquake, Japan established the Disaster Liaison for Pediatric and Perinatal Medicine (DLPPM) to embed specialists within disaster command structures. However, large-scale activation under prolonged infrastructure disruption has not been systematically evaluated. We conducted a structured retrospective descriptive analysis of DLPPM operational records during the first month after the 2024 Noto Peninsula Earthquake. Activities were reviewed across five pre-specified domains to examine how the liaison framework functioned during the acute and subacute phases. DLPPM was integrated into the prefectural disaster headquarters and consolidated maternal-child health information, enabling centralized identification of 83 pregnant women, estimated to represent most pregnant women in the severely affected region. Twenty-one obstetric transfers were coordinated. Pediatric transfers and evacuation of medically dependent children were facilitated through established networks. During the subacute phase, DLPPM initiated maternal-child support measures, including a "Children's Conference" and a support website. These findings suggest that DLPPM functioned as a centralized coordination hub linking specialized clinical networks with disaster governance, although real-time identification of vulnerable families in shelters remained limited. Embedding pediatric and perinatal specialists within disaster headquarters can support structured medical coordination for vulnerable populations. Earlier and more systematic integration with public health and welfare systems is essential to extend this hub function beyond hospital-centered care.

The data presented in this article describe dynamic input-output responses of an automated self-inflating bag resuscitator operating under varying simulated respiratory mechanics. The dataset was acquired from a custom-built motor-driven resuscitator platform developed at the Key Laboratory of Digital Control and System Engineering (DCSELab), Ho Chi Minh City University of Technology, Vietnam. Excitation signals were applied to a direct-current (DC) motor to mechanically compress a standard adult self-inflating bag, while airway pressure, airflow rate, and tidal volume were measured using integrated pressure and flow sensors at a sampling frequency of 1000 Hz. Respiratory mechanics were emulated using a dual adult test lung with adjustable compliance and airway resistance, representing four clinically relevant scenarios: normal lung, stiff lung, obstructed airway, and an extreme combined condition. The repository contains 12 CSV files organized into 4 folders, each corresponding to a different lung configuration. Each folder includes three datasets generated under distinct geometric reference trajectories (cosine-step, parabolic-step, and ramp-step) tracked via a closed-loop control loop. This closed-loop excitation strategy ensures that the resulting commanded pulse-width modulation (PWM) signal possesses sufficient spectral richness and strictly satisfies the persistency of excitation criteria for robust system identification. Each file provides synchronized time-series measurements of gripper position, PWM duty cycle, airway pressure, airflow rate, and derived tidal volume. All data are stored in raw format with clearly labelled columns and physical units to facilitate reproducibility. This dataset provides empirical dynamic data for modelling the nonlinear and time-varying behaviour of automated self-inflating bag resuscitators. It can be reused for developing and benchmarking system identification algorithms, constructing digital twins of low-cost automated self-inflating bag resuscitators, and designing advanced control strategies under diverse simulated respiratory conditions.

Hemorrhagic shock is a leading cause of preventable trauma death, and early risk stratification is needed before laboratory or imaging results are available. We conducted a secondary analysis of CRASH-2 to evaluate whether a START-derived count of reconstructed Red criteria at emergency department arrival provides prognostic discrimination. Criteria were respiratory rate&#x2009;<&#x2009;10 or &#x2265;&#x2009;30/min, capillary refill time&#x2009;&#x2265;&#x2009;2&#xa0;s, and inability to follow commands (Glasgow Coma Scale motor score&#x2009;<&#x2009;6). Because ambulation status was unavailable, formal START color categories were not reconstructed. Criteria were counted from 0 to 3, and associations with 28-day mortality and transfusion requirements were examined. Among 18,756 patients, 2,830 died. Mortality increased stepwise with the count. In adjusted Cox models, mortality risk increased progressively, with an adjusted hazard ratio of 12.45 (95% CI 8.33-18.61) for 3 versus 0 criteria. Discrimination was moderate (AUC 0.725, 95% CI 0.716-0.734) and similar after excluding capillary refill time (AUC 0.721), whereas capillary refill time alone performed less well (AUC 0.600). Transfusion requirements also increased with higher counts. In this high-risk cohort, the reconstructed count may provide an early physiologic risk signal before resource-dependent trauma assessments become available.

Multidrug-resistant bacterial pathogens continue to rise globally, yet scalable methods are needed to infer how resistance determinants co-occur across pathogen populations and to quantify conditional dependencies underlying co-occurrence and shared genetic context. We present ReGAIN (Resistance Gene Association and Inference Network), an open-source framework that applies Bayesian network structure learning to infer probabilistic, conditional dependency relationships among antibiotic resistance, heavy metal tolerance, stress response, and virulence determinants in bacteria. In contrast to pairwise co-occurrence analyses, ReGAIN reports conditional probabilities, relative risks, and absolute risk differences with confidence intervals to prioritize candidate relationships for downstream prioritization. Applied across ESKAPEE pathogens, ReGAIN recapitulated established resistance gene relationships and identified additional candidate patterns consistent with co-selection and shared genetic context. Together, these results support scalable, reproducible population-wide analysis of resistance networks for surveillance, comparative genomics and epidemiology. ReGAIN analyses are performed using Python v3.11.5 and R v4.4.1 and is available as open-source software through Bioconda at {{https://anaconda.org/bioconda/regain-cli}}. Source code and documentation can be found at {{https://github.com/ERBringHorvath/regain_CLI}}. All genomes used in this publication were downloaded from the National Center for Biotechnology Information database. Large supplementary tables and results data from the ESKAPEE pathogen example network analyses can be downloaded from https://figshare.com/articles/dataset/ReGAIN_command_line_software_and_supplemental_figures_/28959431. Supplementary data are available at Bioinformatics online.

Prone positioning for spine surgery can present with life-threatening pulmonary complications like post-operative pneumothorax, due to barotrauma. A 51-year-old male with chronic instability type mid back pain and radiological features of thoracolumbar spondylodiscitis, underwent posterior stabilization, debridement and fusion under general anesthesia in prone position, without any intraoperative event. Immediately post-extubation, he developed acute dyspnea and pulmonary desaturation, for which he was treated with supportive care and was able to maintain satisfactory saturation and observed in the intensive care unit with monitoring. Persistent pulmonary desaturation and requirement of high flow oxygen led to diagnostic intervention with high-resolution computed tomography chest, which showed a small left apical pneumothorax without pleural breach, rib fracture or hemothorax. The patient did not require any invasive intervention and was managed by multidisciplinary team with oxygen therapy, non-invasive ventilation and pulmonary rehabilitation, with an uneventful recovery of pulmonary function and complete resolution of the pneumothorax by 3 weeks post-operative. He was administered appropriate medical management for spondylodiscitis. Post-operative pneumothorax following prone spine surgery, likely from barotrauma, is a rare but potentially catastrophic complication that warrants high clinical vigilance, possible detection of asymptomatic bullae in pre-operative chest imaging. Early recognition, prompt imaging and coordinated multidisciplinary management are essential for optimal outcomes.

Over 39.9 million individuals are living with HIV worldwide. There is a need to develop novel therapeutics to improve treatment or cure HIV. In this study, we evaluated HIV reservoir-targeting chimeric antigen receptor (CAR)/CXCR5 natural killer (NK) cells armored with IL-15 and PD-1 knockout as well as control activated NK cells for their safety and efficacy by treating antiretroviral therapy (ART)-suppressed, HIV-infected humanized DRAGA mice following ART interruption. There were no adverse health outcomes associated with cell infusions. At 56 days post-treatment (DPT), 62.5% of CAR NK-treated and 50% of control NK-treated groups had viral loads below the detection limit compared with 0% of the saline control group. CAR NK-treated animals had, on average, 1.88 times higher peak NK cell levels than control NK-treated animals, and elevated levels of NK cells persisted up to 28 DPT in treated animals. Importantly, animals that had undetectable viral loads at 56 DPT had earlier viral rebound post-ART interruption that coincided with high levels of NK cells, suggesting that timing of treatment with viral recrudescence may play a role in efficacy. This is the first study evaluating NK cell therapies in hDRAGA mice and demonstrates the promise of NK cell therapies for curing HIV.

Plasma cell percentage on CD138 immunohistochemistry of bone marrow trephine biopsies is fundamental to the diagnosis and risk stratification of plasma cell neoplasms, yet manual visual estimation is subject to inter-observer variability. We report a single-center pilot validation of an AI-assisted whole-section workflow using the open-source platform QuPath v0.6 against conventional manual methods in an Indian tertiary hematopathology setting. Fifty consecutive CD138-immunostained trephine biopsies were independently assessed by (A) overview estimation by three hematopathologists, (B) digital whole-section analysis with QuPath v0.6 on whole-slide images acquired with the OptraScan OS-Ultra scanner, and (C) systematic cell-by-cell manual count by the principal investigator (reference standard). Concordance was assessed by Spearman &#x3c1;, intraclass correlation coefficient (ICC), Lin's concordance correlation coefficient (CCC), Bland-Altman analysis, Passing-Bablok regression, and weighted Cohen's &#x3ba;. Diagnostic performance of Method B was evaluated at the International Myeloma Working Group (IMWG) thresholds of 10%, 30%, and 60%. Method B versus Method C showed ICC = 0.995 (95% CI, 0.97-1.00), Lin's &#x3c1;c = 0.995, Spearman &#x3c1; = 0.989, Bland-Altman bias +2.4 percentage points (95% limits of agreement -3.0 to +7.8). Passing-Bablok regression gave slope 1.025 (95% CI, 1.00-1.06) and intercept +1.54, indicating absence of proportional bias. Diagnostic performance at IMWG thresholds was excellent (area under the curve (AUC) 0.995-1.000; sensitivity 100% at every threshold). Weighted Cohen's &#x3ba; for three-grade burden classification was 0.91 with no extreme misclassifications. AI-assisted QuPath-based whole-section plasma cell quantification using CD138 immunohistochemistry achieves excellent concordance with manual reference counting and strong diagnostic performance across all clinically decisive IMWG thresholds.