Search Results (1,208)

Shared quantum-cloud runtimes expose service outcomes shaped by compilation, backend conditions, and scheduling. We study whether workload-dependent information remains in tenant-visible outputs after scheduler mediation, while separating such evidence from provider-side boundary traces and internal diagnostics. Experiments use a provider-inspired prototype with synthetic IBM-style backend abstractions rather than IBM’s proprietary scheduler. Across 16 independent workload seeds, leave-one-seed-out evaluation using provider-reported waiting time yields a mean accuracy of 0.6010 and a classification-based signed advantage of 0.1010 over the majority-class baseline. Monotone padding and delayed release reduce the estimated advantage to 0.0324 and 0.0418, with mean added delays of 0.185 and 0.131 in normalized simulator-time units. Controlled workload pairs and compilation- and backend-level diagnostics show configuration-dependent, nonmonotonic variation across internal observability channels, but do not attribute the external signal to a uniquely quantum mechanism. A six-run experiment on one public backend provides only weak and variable in-sample timing separability. These results demonstrate preliminary scheduler-boundary separability in the controlled prototype, but do not establish a practical public-cloud attack. Full article

Improving emergency vehicle mobility in congested urban environments is a critical challenge for transportation systems. Although roadway capacity expansions, such as widening roads, are often deployed to reduce congestion, their impact on emergency response performance is not always guaranteed, especially when delays concentrate at critical intersections. This study investigates how roadway capacity expansion affects emergency vehicle performance by using a microscopic traffic simulation framework. The study was applied to a real urban corridor in Mohammedia, Morocco, to provide a solid base for simulations with real-world conditions. A SUMO model was calibrated to represent two roadway configurations: a baseline two-lane layout and a three-lane post-widening scenario. Traffic volumes from 1056 to 3520 vehicles per hour were simulated, and performance was assessed using three emergency-specific indicators: Emergency Response Time (ERT), Delay Ratio (DR), and Priority Mobility Index (PMI). An initial single-run comparison suggested a substantial ERT reduction under moderate demand (343.40 s to 270.90 s, 21.11%); however, a 30-seed replication with paired Wilcoxon signed-rank tests shows that this and nearly all other widening effects are not statistically distinguishable from stochastic simulation noise. Only one of 12 emergency vehicle comparisons (Priority Mobility Index at 18:00) reached significance, and it favored the baseline configuration; none of 12 general traffic comparisons improved significantly, and general traffic was significantly slower under the widened configuration at 22:00 (p < 0.01). A supplementary sensitivity analysis (±20% emergency vehicle demand share) further shows that Delay Ratio conclusions are considerably more sensitive to this assumption (up to 34% relative change) than ERT or PMI (under 8%). These findings indicate that, in this network, roadway capacity expansion alone does not deliver a statistically robust improvement in either emergency vehicle or general mobility, and that a persistent signalized-intersection bottleneck remains the dominant constraint irrespective of lane geometry. The study provides a replicable, statistically validated simulation framework for assessing roadway capacity expansion effectiveness and cautions against single-run comparisons, which can substantially overstate the causal effect of infrastructure interventions in microscopic traffic simulation studies. Full article

We investigate the potential of technology-based attention training (AT), particularly virtual reality (VR), as a stress-management tool. Mental stress is rising globally, and researchers increasingly use immersive technologies, wearable sensors, and machine learning (ML) for its detection and control. This feasibility study examines the impact of fully immersive VR-based AT on mental stress using electroencephalogram (EEG) signals and automated classification. We designed virtual exercises targeting different attention types and analyzed EEG responses with an ML framework; the resulting dataset, collected at the Arab Academy for Science and Technology (Alexandria, Egypt), is publicly available. For an unbiased estimate, we adopt a leakage-free evaluation in which the train/test split is performed by time, before segmentation into overlapping windows, so neighboring windows cannot appear in both sets. Subject-specific tree-based classifiers detected stress with a mean accuracy of about 97%, whereas leave-one-subject-out (LOSO) validation yielded about 67%, indicating strongly individual stress signatures and motivating a subject-specific strategy. Using these models, we compared the number of classifier-predicted stress segments before and after AT and visualized the feature space with T-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). Under subject-specific models the number of stress-predicted segments decreased after AT (Wilcoxon signed-rank $p<0.05$); however, because this reduction was corroborated neither by a non-circular (LOSO) detector nor by a centroid-separation measure, we interpret it as an exploratory, classifier-predicted effect rather than independently validated stress reduction. The results highlight the promise—and the current limits—of integrating immersive VR with EEG-guided analytics for mental-health support. Full article

Background/Objectives: The inflammatory burden index (IBI), calculated as C-reactive protein (CRP) multiplied by the neutrophil-to-lymphocyte ratio (NLR), has demonstrated prognostic value across several solid tumors. Its role in breast cancer, however, has not been investigated. This study evaluated whether pretreatment or post-treatment IBI could predict pathological complete response (pCR) in patients with HER2-positive or triple-negative breast cancer (TNBC) receiving neoadjuvant chemotherapy (NAC). Methods: This single-center retrospective study included 61 patients who completed NAC followed by surgery between 2019 and 2025. IBI was calculated before and after NAC, and the treatment-related change (ΔIBI) was assessed. Conventional inflammatory indices, including NLR, platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), C-reactive protein-to-albumin ratio (CAR), and absolute lymphocyte count (ALC), were evaluated for comparison. Analyses included Mann–Whitney U tests, paired Wilcoxon signed-rank tests, receiver operating characteristic (ROC) curve analysis, and multivariable logistic regression. Results: Twenty-nine patients (47.5%) achieved pCR. No pretreatment or post-treatment inflammatory index was significantly associated with pCR. In paired within-patient analysis, IBI increased significantly during treatment only in patients achieving pCR (p = 0.036), while remaining unchanged in the non-pCR group (p = 0.627). CAR showed an identical pattern, increasing exclusively in the pCR group (p = 0.013). This selective rise was not observed for any index lacking a CRP component and was independent of molecular subtype, anti-HER2 therapy, and chemotherapy regimen. On ROC analysis, ΔCAR yielded the highest area under the curve (AUC) among all inflammatory indices (0.637; p = 0.067), followed by ΔIBI (0.606; p = 0.157); neither reached statistical significance. Ki-67 was the only independent predictor of pCR (AUC 0.724; p = 0.003; optimal cutoff ≥25%). Conclusions: This is the first study to evaluate IBI in HER2-positive and TNBC receiving NAC. Static IBI values did not predict pCR. The selective rise in IBI and CAR during treatment in patients achieving pCR—two independently formulated CRP-based indices showing an identical pattern—suggests that the CRP component carries the biologically relevant signal. This hypothesis-generating observation warrants prospective validation in larger cohorts. Full article

This study presents a configurable speech signal preprocessing and feature-extraction workflow for identifying candidate acoustic biomarkers in acute heart failure. The workflow was evaluated in 12 patients hospitalized with acute heart failure. Short recordings of repeated vowels /a/, /i/, and /o/ were acquired shortly after admission and again before discharge following treatment and clinical stabilization. The pipeline included active-RMS normalization, automatic segmentation of repeated vowels, optional edge trimming, alternative pitch-estimation variants, and extraction of three feature families: phonatory and temporal measures, spectral-shape descriptors, and MFCC-based cepstral features. Within-patient admission-to-discharge differences were evaluated using two-sided Wilcoxon signed-rank tests, with nominal p-values interpreted as exploratory. Phonatory and temporal measures produced the most consistent exploratory findings. The pause-duration trend for /a/ decreased between admission and discharge and was the most configuration-stable individual candidate. CPP maximum and CPP range for /o/ increased consistently across the evaluated phonatory configurations, indicating systematic changes in cepstral prominence. Shimmer-related measures provided additional exploratory findings. MFCC measures showed complementary changes, particularly in MFCC11 variability for /o/, whereas spectral-shape effects were generally weaker and less consistent. Because the study involved a small, single-center cohort without a control group or external validation, the findings should be regarded as hypothesis-generating candidate acoustic measures rather than clinically validated biomarkers. Full article

Background/Objectives: Skin aging involves interconnected inflammatory, oxidative, hormonal, extracellular matrix (ECM), and cellular senescence-related mechanisms, supporting the need for multitarget approaches. This study aimed to evaluate the effects of a multicomponent natural product-based injectable formulation developed in Korean medicine practice, Dong-An Pharmacopuncture (DAP), on skin aging-related pathways. Methods: A network pharmacology approach was used to identify the active compounds, predicted molecular targets, and signaling pathways associated with DAP. Sixty-two active compounds from 11 constituent materials were screened, and 70 final targets were identified using STITCH-based prediction, intersection with GeneCards-derived skin aging-related targets, and quality filtering. Results: Herb-compound-target network analysis yielded 225 compound–target interactions across 292 edges. Protein-protein interaction analysis identified a highly connected network with 1,334 edges, and hub analysis converged on 10 core targets: TNF, IL6, ESR1, TP53, AKT1, PPARG, EGFR, PTGS2, CASP3, and PPARA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses identified four major mechanistic axes: inflammatory and oxidative stress regulation, hormonal skin homeostasis, tissue repair and ECM remodeling, and cellular senescence-related regulation. Because DAP is administered by injection, a Good Laboratory Practice-compliant single-dose subcutaneous toxicity study was additionally conducted in Sprague–Dawley rats, which showed no mortality, abnormal clinical signs, or histopathological findings attributable to DAP at 1.0 mL/head. Conclusions: The findings in this study provide a systems-level framework for the predicted multitarget mechanisms of DAP in skin aging-related pathways and support the need for further experimental validation of its predicted mechanisms and repeated-dose safety. Full article

To investigate the feasibility of reconstructing electrocardiogram (ECG) waveforms from non-contact millimeter-wave radar measurements, a radar-based ECG reconstruction method using a CNN-BiLSTM network is presented. A synchronous acquisition platform integrating a millimeter-wave radar and a BIOPAC physiological signal acquisition system was established to collect chest-wall vibration signals and reference ECG signals. A multi-channel cross-correlation-based channel selection and temporal alignment procedure was employed to construct paired radar–ECG samples. The radar chest-wall vibration signals were filtered using an 8–30 Hz band-pass filter and then fed into the CNN-BiLSTM model, while a joint time–frequency loss function was introduced to constrain ECG reconstruction. On the self-built vital sign dataset, the reconstructed ECG achieved a correlation coefficient of 0.5631 with the reference ECG, while the mean absolute errors of heart rate and R–R interval were 1.00 BPM and 10.02 ms, respectively. These results suggest that the reconstructed signals preserve basic heartbeat timing and overall rhythm-related information, although the waveform-level agreement varies among samples and does not yet demonstrate consistent recovery of fine-grained ECG morphology. Evaluation on a public dataset further showed condition-dependent reconstruction performance under Resting, Apnea, and Valsalva conditions. Published MultiRes-LinkNet values were included only as contextual numerical references because the baseline was not reimplemented within the same experimental pipeline. Overall, the results provide preliminary evidence for the feasibility of contactless ECG reconstruction from millimeter-wave radar signals and suggest its potential value for radar-based vital sign monitoring. Full article

Background: Hantavirus pulmonary syndrome (HPS), also designated hantavirus cardiopulmonary syndrome, is caused by New World hantaviruses, principally Sin Nombre virus in North America and Andes virus in South America. The syndrome is characterized by rapidly progressive noncardiogenic pulmonary edema and myocardial depression, with case fatality rates of 25% to 40%. A 2026 outbreak aboard an expedition cruise ship in the South Atlantic, comprising 13 cases and three deaths, confirmed that Andes virus can be transmitted between humans in a confined setting remote from the rodent reservoir. Methods: Virological, pathophysiological, clinical, and therapeutic aspects of HPS were reviewed, with particular emphasis on cardiopulmonary mechanisms. Sources were identified through PubMed, Scopus, and Google Scholar, with priority given to original research articles, clinical series, and controlled trials published through 2025. Literature published in English and Spanish was included. Results: Pathogenic hantaviruses enter endothelial cells and platelets via αvβ3 integrins, disrupting the VEGF-VEGFR2 signaling axis and rendering endothelial cells hypersensitive to physiological VEGF concentrations. Expansion of CD8+ T cells and activated macrophages releases TNF-alpha, IFN-gamma, and nitric oxide, amplifying microvascular permeability and contributing to myocardial depression. Autopsy studies demonstrate direct hantaviral myocarditis with viral antigen in cardiac endothelium and interstitial macrophages. Transpulmonary thermodilution confirms simultaneous hypovolemia, reduced global ejection fraction, and elevated extravascular lung water. Because the incubation period is long and the cardiopulmonary phase is substantially immune-mediated, seroconversion precedes rather than follows clinical deterioration, which preserves the diagnostic utility of IgM serology in a disease that can kill within 48 h. VA-ECMO initiated at the first signs of cardiopulmonary decompensation has reported survival rates approaching 80% in selected experienced centers. No antiviral has demonstrated efficacy in controlled trials during the cardiopulmonary phase, and no licensed vaccine exists. Conclusions: HPS produces a mixed shock state through increased microvascular permeability, T cell-mediated immunopathology, and direct myocarditis. Management follows a stepwise algorithm: suspected HPS triggers immediate complete blood count with peripheral blood smear and concurrent hantavirus IgM serology and RT-PCR, followed by ICU admission, conservative fluid resuscitation guided by transpulmonary thermodilution, and early contact with an ECMO-capable center at the first sign of rising lactate, falling cardiac index, refractory shock, arrhythmia, or rapid oxygenation failure. Full article

Cross-organization cyber defense must reconcile collaborative learning with privacy and adversarial robustness, yet standard federated learning ships full gradient tensors, leaking sensitive posture and inviting Byzantine manipulation. We present FedMARL-LTI, a federated multi-agent reinforcement learning framework whose architecture answers both pressures with a single decision: each organization’s threat intelligence is shared only as a differentially private 768-dimensional semantic embedding, never as raw data. In the evaluated system, a Weight-DP-protected model-weight delta is also exchanged through the federated aggregator (the semantic abstraction embedding is a parallel channel); the privacy guarantee below is stated for the semantic abstraction channel, and an embeddings-only architecture—which the guarantee enables—is the design this points toward. The contribution is fourfold. (1) Semantic Abstraction (SA) channel: per organization, each round, the local gradient is summarized by an LLM, projected to a 768-dim embedding, L2-clipped, and Gaussian-noised before any numeric quantity leaves the host. The bottleneck reduces the aggregate noise magnitude—the expected L2 norm of the DP noise vector—from $O\left(\sqrt{d_{\mathrm{model}}}\right)$ to $O\left(\sqrt{m}\right)$ with $m=768\ll d_{\mathrm{model}}\approx 3\times {10}^5$. (2) Formal privacy analysis: the SA + DP cascade satisfies $(\epsilon ,\delta )$-DP and bounds per-round mutual information leakage by $\min \{T_{\mathrm{tok}}{\log }_{2}V,m/2{\log }_2(1+C^2/\left(m{\sigma }^2\right)\left)\right\}$, with Rényi composition over $T$ federation rounds. Scope of the guarantee: this bound certifies (i) the semantic-abstraction channel. It does not by itself cover (ii) the weight-aggregation channel, whose Weight-DP protection is analyzed separately, nor (iii) the whole deployed system, which is the composition of the two. We therefore state the ≈1.4-bit/MI bound as a per-round guarantee on information leaving the organization through the SA channel not over every byte the system emits; an embeddings-only configuration—which this bound enables—closes the gap to a whole-system guarantee. (3) Byzantine-resilient ClippedClustering aggregator combining L2 clipping with cosine-similarity clustering. (4) Hierarchical MARL policy with threat-profile-aware LLM-IRR reward shaping, wired end-to-end and disclosed honestly (the evaluated system uses a deterministic Johnson–Lindenstrauss projection in place of the LLM call for reproducibility; the architecture is thus LLM-compatible rather than dependent on a specific model, and a full LLM deployment is the planned extension). We evaluate on CybORG CAGE-4 with $n=5$ organizations, 30 federation rounds × 5 episodes × 100 steps per round. Releasing the SA channel in parallel shows no statistically detectable reward cost at N = 5 vs. the no-privacy baseline; this is measured at reward-shaping coefficient β = 0, so it establishes that the private semantic release does not disturb weight-channel training rather than that semantic sharing improves defense: SA-only $\Delta$reward = $+4.58$ ($t=+1.37$, NS), dual SA + Weight-DP $\Delta$reward = $+4.31$ ($t=+1.30$, NS), all $N=5$ seeds, all $\left|t\right|<1.4$. A controlled signal/noise probe confirms a $19.58\times$ improvement of SA over Weight-DP at a fixed DP budget—matching the predicted $\sqrt{d/m}\approx 19.8$. Under Byzantine sign_flip at 30% ($N=15$), ClippedClustering is directionally strongest ($F_1=0.025$ vs. FedAvg $0.020$, Krum $0.016$) but the edge is not statistically significant (CC vs. Krum $t=+1.59$, $p=0.15$, $d=+0.58$; the earlier $N=5$ “$3.4\times$” gap was small-sample optimism); its Byzantine behavior is on the harsher random_noise attack. Under a corrected implementation, the undefended baselines do not diverge or collapse; the earlier reading (Krum $0.002$, ClippedClustering $0.020$) was a noise-injection artifact and is withdrawn; ClippedClustering is now directionally best on F1 but not significantly, and trails Krum on reward (superseded Cohen’s $d=+3.77$). The cooperative-PPO family (MAPPO, IPPO) outperforms value/actor-critic (QMIX, MADDPG) by $\approx 20$ reward units, $p<0.001$. All host-level $F_1$ values stay below $0.05$ at the 15K-step training horizon used here; the relative claims of the paper (no detectable privacy reward cost, ClippedClustering’s competitive (not decisive) Byzantine behavior on the harsher attacks, cooperative-PPO dominance) are unaffected by this scope. A 200K-step long-horizon replication lifts $F_1$ above the 15K plateau (to $\approx 0.044$, $N=5$)—confirming that horizon, not the privacy/Byzantine machinery, gates absolute accuracy—but a finer 60-checkpoint run shows the climb is volatile and non-monotonic and does not reach deployment-grade, an honest stability-not-compute limitation. FedMARL-LTI is therefore presented as a proof-of-concept for the relative privacy and robustness trade-offs it isolates, not as an operationally deployable cyber defense system. We release all 141 raw run JSON outputs (Phases 1–3, the L4 backend comparison, and the algorithm/aggregator baselines), the figures, and analysis scripts for replication. Full article

Abnormal ship behavior detection is important for maritime traffic surveillance, navigation safety, and risk prevention. However, existing methods often depend on handcrafted features or a single reconstruction or prediction signal, which limits their ability to detect both sustained trajectory abnormalities and abrupt vessel movement changes. This paper proposes a Dual-Error Fusion LSTM–Transformer framework, referred to as DEFLT, for AIS-based abnormal ship behavior detection. A motion-aware vessel representation was first constructed by combining the geographical position, speed over ground, course over ground, and their temporal variations. An LSTM autoencoder reconstructs historical trajectory windows, while a Transformer prediction module estimates subsequent vessel states. The standardized reconstruction and prediction errors are fused into a unified anomaly score to capture complementary evidence from historical trajectory inconsistency and unexpected future motion. Experiments were conducted using real-world AIS data collected during September 2019 from four representative Danish waters. The study considers four abnormal behaviors: speed anomalies, course anomalies, loitering, and route deviations. Compared with KNN, LOF, Isolation Forest, Random Forest, the LSTM-AE, and the Transformer, DEFLT achieves F1-scores of 0.96, 0.97, 0.88, and 0.93 across the four study areas. For type-specific detection, the Macro-F1 values range from 0.61 to 0.86, while Macro-Recall remains between 0.88 and 0.96. Friedman and post hoc Wilcoxon signed-rank tests further demonstrate that DEFLT provides a significant and consistent improvement over all baseline methods. These results verify the effectiveness of dual-error fusion for detecting heterogeneous abnormal ship behaviors from AIS trajectories. In operational settings, DEFLT can serve as an alert-prioritization tool for vessel traffic services and port authorities by directing attention to atypical trajectories that require timely review, thereby supporting safer and more resource-efficient maritime traffic coordination. Full article

Though a number of Eimeria species have been described in geese, the host responses to these parasites at the molecular level have yet to be explored. In the present study, fresh fecal samples were collected for single-oocyst isolation. The recovered oocysts were identified based on molecular analysis using PCR and sequencing. Subsequently, we examined the transcriptional response of the Landes goose cecum following infection with the isolated Eimeria strain. Molecular analysis showed that the isolated Eimeria strain was Eimeria stigmosa, which was named the E. stigmosa SX-01 strain. Transcriptomic profiling identified 3806 differentially expressed genes (DEGs), including 2238 genes with increased expression and 1568 genes with decreased expression. The results obtained from the quantitative reverse transcription PCR (qRT-PCR) analysis confirmed that the RNA sequencing (RNA-seq) data were reliable. According to pathway enrichment analysis for the obtained DEGs, 24 pathways were significantly affected following infection with the E. stigmosa SX-01 strain, such as cytokine–cytokine receptor interaction, intestinal immune network for IgA production, cell adhesion molecules, gap junction, arachidonic acid metabolism, alpha-Linolenic acid metabolism, retinol metabolism, and PPAR signaling pathway. These transcriptional alterations were observed in E. stigmosa-infected geese without obvious clinical signs, indicating that E. stigmosa infection may trigger a strong subclinical host response related to metabolism, immune and inflammatory responses, and intercellular junctional complex-associated processes. Collectively, these findings have implications for better understanding the E. stigmosa–goose interactions at the molecular level and provide a foundation for future functional and comparative studies to dissect the pathogenic mechanisms and molecular markers associated with disease resistance, which are expected to inform the design of effective control strategies. Full article

Diffusion-weighted imaging (DWI) has evolved from a niche research sequence into an increasingly valuable adjunct to conventional magnetic resonance imaging (MRI) in musculoskeletal (MSK) radiology. By providing qualitative and quantitative information on tissue microstructure through assessment of water diffusion and apparent diffusion coefficient (ADC) mapping, DWI offers functional insights beyond conventional morphological imaging. We aim to present the current evidence for DWI in MSK imaging organised around established applications and emerging applications, with particular emphasis on composition-related interpretive pitfalls relevant to differentiating tumours and other pathologies, and to review the technique’s evolving role in routine practice. This narrative review synthesises the current literature on the clinical utility of DWI in MSK imaging. It is structured in four parts: foundations and the tissue composition signal framework, including the basis of qualitative and quantitative assessment; established applications; emerging applications; and assessment of tissue composition-related interpretive as well as technical pitfalls, including those arising due to myxoid matrix, chondroid matrix, blood degradation products, organising thrombus, crystalline or mineralised material, keratinaceous debris, purulent content, cellular haematopoietic marrow, by using original cases from the authors’ institution, which have been confirmed either histologically or surgically. Applications are stratified by strength of evidence. Established applications of DWI include soft tissue abscess detection, differentiation of malignant from benign soft tissue tumours, differentiation of malignant from benign vertebral compression fractures, and myeloma staging and response assessment, as well as treatment response in soft tissue and bone sarcomas. Whole-body MRI with DWI for staging and response assessment in multiple myeloma is guideline-endorsed and supported by prospective multicentre data. Soft tissue abscess detection, soft tissue and bone tumour characterisation, and characterisation of vertebral compression fractures are supported by consistent evidence from multiple independent cohorts, although no universally transferable ADC threshold exists. The emerging applications, which are promising adjuncts supported by small, single-centre or heterogeneous studies with thresholds that have not been externally validated, include ADC ghost sign in osteomyelitis (high specificity but sensitivity of only 20%), peripheral nerve sheath tumour characterisation and surveillance in NF1 patients, peripheral neuropathy and plexopathy, predisposing conditions such as Li Fraumeni syndrome in paediatric cancers, inflammatory myopathy, and postsurgical assessment of residual disease, as well as opportunistic detection of venous thrombosis. Radiomics and machine learning approaches remain experimental. Recent technical advances, including reduced field-of-view imaging, multi-shot acquisition and improved fat suppression, have mitigated but not eliminated historical limitations of susceptibility artefacts and limited spatial resolution. DWI has become an important functional imaging technique that complements conventional MRI across a broad range of musculoskeletal disorders. Understanding the relationship between tissue composition and the diffusion signal is central to both interpreting DWI correctly and avoiding its characteristic pitfalls. DWI is best regarded not as a stand-alone technique but as one component of a multiparametric assessment, in which its functional information is integrated with conventional morphological imaging. Ongoing technical improvement and expanding clinical evidence are expected to further support its integration into routine MSK imaging and its development as a quantitative biomarker for diagnosis, prognostication, and treatment monitoring. Full article

Background/Objectives: Pseudorabies virus (PRV) variant strains have caused widespread outbreaks in China since 2011, and currently available vaccines provide suboptimal protection. Glycoprotein D (gD), the principal target of virus-neutralizing antibodies, represents a promising antigen for subunit vaccine development. However, CHO cell-based production systems suitable for large-scale manufacturing remain insufficiently explored. This study aimed to develop a potentially scalable CHO cell-derived PRV gD subunit vaccine and evaluate its immunogenicity and protective efficacy in mice. Methods: A stable Chinese hamster ovary (CHO) suspension cell line secreting the extracellular domain of PRV gD was established through signal peptide optimization and stepwise serum-free adaptation. The recombinant gD protein was purified using Ni²⁺- Sepharose High-Performance affinity chromatography and subsequently formulated with MONTANIDE ISA 206 adjuvant. Immunogenicity and protective efficacy were assessed in BALB/c mice through serological analysis, neutralization assays, lethal challenge experiments, and quantitative PCR. Results: The gD subunit vaccine induced rapid seroconversion of gD-specific IgG antibodies as early as 7 days post immunization and exhibited a strong booster effect, maintaining high antibody levels. Neutralizing antibodies were first detected at 14 days and increased significantly after booster immunization, with titers markedly exceeding those induced by a commercial inactivated PRV vaccine at 42 days (p = 0.001). Following lethal challenge with 10⁴ TCID₅₀ of the highly virulent PRV-TJ variant strain, vaccinated mice achieved 100% survival without clinical signs. Viral genome copy numbers in the brain and spinal cord were reduced by approximately 3.3 to 4.4 log₁₀ relative to the PBS control group. Conclusions: The CHO cell-derived PRV gD subunit vaccine elicits robust humoral immune responses and provides complete protection against lethal PRV variant challenge in mice. These findings support its further evaluation in the natural swine host toward the development of a safe and scalable subunit vaccine for pseudorabies control. Full article

Software startups operate in environments characterized by rapid change, high uncertainty, and limited resources, resulting in high failure rates and challenges such as premature scaling and cash flow mismanagement. Prior research on pre-MVP validation has largely measured activity by volume rather than by the strength of evidence produced, leaving open whether evidence type, rather than quantity, is associated with startup performance. This study addresses that gap by investigating how early-stage software startups validate their initial idea before building their first Minimum Viable Product (MVP). Through 29 semi-structured interviews with founders from 16 software startups, pre-MVP validation activities were extracted and inductively coded into a six-level Validation Canvas spanning three validation stages identified in the literature: problem validation, problem-solution fit, and product-market fit. Startup performance was assessed through a composite ranking across funding, revenue, profitability, and runway indicators, and validation activities were analyzed thematically to derive the six evidence levels. No clear relationship was observed between the number of validation events and startup performance. Instead, stronger-performing startups tended to reach higher levels of evidence—particularly securing contingent investment commitments (Level 5) or paying customers (Level 6) before full MVP development. Level 6—paying customers before the full product exists—is identified as the strongest form of pre-MVP market evidence, as it directly validates willingness-to-pay without relying on investor confidence. In this study, product-market fit is operationalised as demonstrated commercial viability through external financial commitments rather than interest signals or free sign-ups alone. Based on these exploratory findings, the study proposes the Hierarchy of Validation: a staged, bidirectional process model in which bottom-up traversal from informal interest signals (L1) toward paying customers (L6) emerged as the primary pattern among stronger-performing startups. A top-down direction, in which experienced founders begin at higher evidence levels and work downward, is proposed as a hypothesis for future research. To our knowledge, this is among the first accounts of pre-MVP validation that differentiates strength of evidence rather than volume of activity, contributing the Hierarchy of Validation as an original, exploratory framework for early-stage software startups. These findings remain exploratory and require validation in larger and more diverse samples. Full article

Background: One of the key hallmarks of aging is the age-related decline in immune system function, accompanied by a chronic low-grade inflammation, or “inflammaging”. Simultaneously, a reduced capacity of immune cells to recognize and eliminate pathogens, along with immune exhaustion, is also defined as a sign of aging. Cynomolgus macaques (Macaca fascicularis) belong to a group of non-human primates evolutionarily close to humans and are often used for preclinical research. Methods: In this study, we performed mRNA sequencing of bone marrow and peripheral blood samples from young (5 years old) and old (over 19–21 years old) cynomolgus macaques to identify key markers of immunosenescence. Results: Although an increase in p16 expression was detected, we did not observe the increase in the senescence-associated secretory phenotype (SASP) cytokines reported in previous studies. Instead, we observed a transcriptional profile characterized by increased lymphocyte cytotoxic activity combined with a decrease in proinflammatory signaling, reduced markers of myeloid cells, and lowered sensitivity to pathogen-associated patterns. Similar changes were detected in both blood and bone marrow: decreased expression of naive T-cell markers (CCR7, LEF1, SELL, and FOXO1), reduced markers of the myeloid lineage—neutrophils and monocytes (CD177, CD14, CD163, FPR1, FPR2, and CXCR1)—and downregulation of genes belonging to different pattern-recognition receptor families (TLR1, TLR2, TLR4, TLR5, TLR6, TLR8, TLR10, IFIH1, CLEC4E, NOD2, NLRC4, NLRP12, NLRX1, and NAIP). In contrast, the group of old animals showed increased expression of markers associated with terminally differentiated cytotoxic lymphocytes (CD8+ T cells and NK cells): GZMB, PRF1, KLRK1, FASLG, TBX21, CCR5, and GNLY. Conclusions: Our findings offer new perspectives on the molecular mechanisms of age-associated immune dysregulation in non-human primates, serving as a baseline for selecting key candidate genes in subsequent functional investigations. Full article