Particle therapy (PT) is an advanced type of external beam radiotherapy, using charged particles such as protons or heavy ions, expectedly offering significant clinical advantages in cancer treatment. Adopting innovative technologies, such as PT, in clinical practice, requires economic evidence. Data on the actual cost of standard or innovative radiotherapy interventions are however scarce, and no guidelines exist to consistently perform costing in this field. This systematic literature review evaluates the methods used, and analytical assumptions taken and costs calculated in costing studies of PT, to inform a standardised costing framework. A comprehensive search was conducted across four databases to identify costing studies, published in English between 1/2000 and 5/2024. Data extraction focused on costing methods and analytical assumptions of the selected studies, along with reported costing figures. Studies quality was appraised applying an adapted CHEERS checklist. The selection process resulted in the inclusion of 12 studies. Considerable heterogeneity was observed in costing methods and the underlying assumptions of the analysis, resulting in highly variable cost estimates. Despite this variability, the studies had high scores in quality assessments. Based on the review results, costing recommendations aligned with accounting principles were proposed to inform future costing analyses. The review findings highlight the gap in costing practices in PT. Recommendations to consider for PT costing are proposed, but still further refinement and consensus from the community to be implemented as standardised guidelines. Such standardization is essential to facilitate robust economic evaluations and support evidence-based reimbursement decisions.
Particle-based radiotherapy, including proton therapy, heavy-ion therapy, and boron neutron capture therapy (BNCT), enables conformal dose delivery but introduces model-dependent biological-dose uncertainty. This uncertainty reflects variations in relative biological effectiveness (RBE), linear energy transfer (LET), oxygenation, microdosimetric spectra, and boron distribution, so identical nominal prescriptions may not yield equivalent biological effects. This review summarizes representative clinical, secondary-review, and investigational model families, including fixed and variable-RBE proton models, the local effect model (LEM) and microdosimetric kinetic model (MKM) families for heavy ions, and compound biological effectiveness (CBE), photon-isoeffective-dose, microdosimetric, and nanodosimetric BNCT frameworks. We compare their assumptions, input descriptors, validation status, clinical readiness, and implementation barriers. We further distinguish prescription-embedded clinical systems from secondary-review models and investigational mechanistic frameworks to clarify the current level of clinical evidence supporting each model family. Published experimental and model-comparison studies indicate that proton RBE may increase in high-LET distal-edge regions, that LEM- and MKM-based carbon-ion systems can yield clinically relevant, site- and endpoint-dependent differences in RBE-weighted dose interpretation, and that BNCT biological dose is strongly affected by component-dose and boron-distribution assumptions. We propose an evidence-informed four-layer harmonization perspective, rather than a formal consensus guideline, consisting of minimum reporting, reference mapping, model-sensitivity quality assurance (QA), and registry-based clinical learning.
Psychological research frequently relies on statistical tests targeting single distributional parameters, typically means, despite empirical data often differing in variance, skewness, or overall shape. We introduce the ζ ov test, a permutation-based inferential procedure built on the Overlapping Index, an effect size quantifying similarity between empirical distributions. The proposed approach evaluates global distributional differences without relying on parametric assumptions. Through simulations manipulating mean, variance, skewness, and sample size, we examine the ζ ov test alongside commonly used tests (t, Welch, Wilcoxon-Mann-Whitney, Kolmogorov-Smirnov, and variance tests), while acknowledging that these tests address different null hypotheses. Results indicate that the ζ ov test maintains adequate Type I error control under the simulated scenarios and shows comparatively high sensitivity to distributional differences, particularly when these involve more than a single parameter. An applied example using reaction-time data shows how distributional overlap detects differences missed by mean-based analyses. Rather than replacing traditional tests, the method provides a theoretically aligned global assessment that encourages distribution-aware inference and integration of visualization and descriptive analysis into statistical workflows. The ζ ov framework supports ongoing methodological shifts in the psychological sciences toward robust, assumption-light, and interpretable statistical reasoning.
The aim of this study is to develop a height-dependent analytical-numerical model describing capsule motion in a vertical pneumatic gravity conveyor designed for the transportation of encapsulated solid household waste in multi-story buildings. The proposed formulation combines a force-balance analytical framework with ANSYS-based numerical implementation to evaluate capsule dynamics under varying mass and fall-height conditions. The governing equations are derived under engineering assumptions including rigid-body axial motion, lumped aerodynamic drag representation, hydraulically smooth pipe walls, and steady annular airflow conditions. Capsule motion is analyzed considering gravitational acceleration, aerodynamic resistance, and pressure losses in the confined annular gap between the capsule and the pipe wall. The model predicts non-linear dependence of capsule velocity and pressure loss on capsule mass and fall height. The results demonstrate a nonlinear relationship between capsule velocity, mass, and fall height under the adopted modeling assumptions, including rigid-body axial motion, quasi-steady annular airflow, and lumped drag representation. For the investigated parameter range, capsule velocity varies from approximately 1 to 13 m/s depending on mass (1-16 kg) and fall height (5-50 m), while pressure losses remain within 0-1.2 × 10⁴ Pa. The obtained relationships enable estimation of airflow requirements necessary to safely decelerate capsules at the base of the system. The proposed framework is intended for preliminary engineering design and parametric analysis. In this study, ANSYS is employed strictly as a numerical implementation environment for the analytically derived governing equations rather than as a full CFD flow solver. The model does not account for capsule rotation, eccentric motion, wall roughness evolution, or transient airflow effects, which define the applicability limits of the proposed framework. These aspects are identified as directions for future experimental validation and refinement.
Intelligence has been attributed to a growing number of non-primate species, including birds, cephalopods, and jumping spiders. This review outlines key findings in animal intelligence, finding that there is significant support for the presence of a wide variety of intelligent capacities in these groups. Yet, how intelligence can be achieved with relatively small, non-mammalian brains remains an open question, and neuroscientific efforts have not established the relationship between brains and intelligence. Core to these research programs is the assumption that intelligence sits higher on a scale of complexity than other kinds of cognition, requiring greater neural resources. This review shows that, despite its ubiquity, there is little evidence available for this assumption, and that it does not align well with the goals of contemporary research on animal intelligence. I argue that, like other biological functions, cross-species comparisons of cognition are not usefully framed as a question of as better or worse, simpler or more complex. Cognitive differences between species are the result of differing cognitive styles, rather than gradations on a universal scale of cognition. Intelligence is the human cognitive style, and intelligent animals are those whose cognitive style bears a family resemblance to ours. This approach embraces the anthropocentrism of the concept of intelligence, while rejecting the idea that intelligence is a superior capacity. With this alternative intelligence concept in mind, investigations into animal intelligence and its neural basis could gain traction by shifting away from the search for greater absolute neural capacity in animals, instead approaching these smaller-brained species as models that reveal what is not required for intelligence.
Since the introduction of single-cell RNA sequencing (scRNA-seq), numerous computational approaches have been developed to reconstruct dynamic cellular processes from static transcriptional profiles. These methods order cells along continuous trajectories by assessing their similarity in the gene expression space. However, they rely on several assumptions, such as prior knowledge of the structure and directionality of the expected genealogy. These assumptions can limit their application to complex cellular systems with poorly understood developmental paths. To address this challenge, we introduce FIERCE (Framework for InfERence of veloCity of the Entropy), a novel computational pipeline designed to predict the changes in the differentiation potency of single cells during dynamic processes. Through a fully unsupervised approach, FIERCE enables the inference of cell lineages directly on the differentiation landscape of the biological system, thus eliminating the need for prior specification of developmental parameters. We demonstrate the efficacy of FIERCE by reconstructing three well-known mouse differentiation systems and by quantifying its accuracy on simulated data. FIERCE R package is available on GitHub at  https://github.com/bicciatolab/FIERCE. Supplementary data are available at Bioinformatics online.
Two recently published viewpoint articles by JMIR Publications highlighted the 2 faces of medical informatics research. On the one hand, they spotlight the significant advances digital technologies bring to health management and delivery. Over the last decade, technological advances have transformed health care worldwide. Consumers have much broader access to digital health tools, gathering an unprecedented amount of data that can be used to gain personalized insights. Digital technologies support medical professionals in clinical and administrative work like never before. On the other hand, these articles emphasize the persisting divide between the potential of these technologies and their integration into everyday practice. Substantial barriers remain that often hinder the broader adoption of digital health tools. In this viewpoint article, we offer a pragmatic perspective on a problem often overlooked in medical informatics: the incompatibility of coexisting solutions in complex sociotechnical contexts. It problematizes underlying assumptions in medical informatics-oriented and design-oriented research approaches, such as design science research, more precisely by conceptualizing the "solution trap." The solution trap refers to situations in which designers introduce a new solution without recognizing existing ones, creating tensions among coexisting sociotechnical practices. We emphasize the need for a nuanced understanding of context unevenness and propose solution patchwork as a coordination approach to evade the solution trap. Solution patchwork describes an approach in which designers integrate new artifacts into established practices and institutional logics to ensure compatibility across the broader sociotechnical system. The solution patchwork sensitizes medical informatics researchers and designers to navigate the problem and solution spaces in health systems globally.
Chronic wounds remain a major global health challenge despite substantial advances in biomaterials, regenerative medicine, and wound-care technologies. Current therapeutic strategies are largely based on the assumption that chronic wounds represent impaired or incomplete healing responses and therefore require augmentation of regenerative processes. This paradigm has driven the development of increasingly sophisticated wound dressings incorporating extracellular matrix analogs, growth factors, stem cells, extracellular vesicles, biosensors, and bioelectronic components. However, the clinical impact of these innovations has often fallen short of expectations. In this review, we propose a conceptual framework intended to generate experimentally testable hypotheses rather than provide a definitive mechanistic model. Persistent alterations in immune, stromal, vascular, extracellular matrix, metabolic, mechanical, and microbial networks create interconnected feedback systems that resist transition toward regeneration. From this perspective, successful therapy requires not only stimulation of repair mechanisms but also disruption of the processes that stabilize chronicity. We discuss how advances in systems biology, immunomodulatory biomaterials, bioelectronics, artificial intelligence, and precision medicine support the emergence of adaptive therapeutic interfaces capable of sensing, interpreting, and reprogramming pathological tissue behavior. Unlike previous reviews that primarily summarize emerging wound dressings or regenerative biomaterials, this Review proposes a systems-level conceptual framework in which chronic wounds are interpreted as stable pathological tissue states maintained by multiscale biological memory. This perspective integrates biomaterials, systems biology, artificial intelligence, and tissue-state dynamics into a unified translational model that has not previously been presented in the wound-healing literature. Previous reviews have predominantly focused on the design, biological activity, or clinical performance of individual biomaterials. In contrast, the present Review proposes a systems-level framework that integrates wound biology, biological memory, tissue-state dynamics, artificial intelligence, and adaptive biomaterials into a unified conceptual model for precision wound medicine. This state-based model reframes advanced wound dressings as tools for biological state engineering and provides a translational framework for the future of chronic wound management.
Globally, an estimated 2.2 billion adults and 430 million children and adolescents are affected by overweight or obesity. Interventions based on the transtheoretical model (TTM) have demonstrated efficacy in modifying dietary behaviors, reducing body mass index (BMI), and improving other health outcomes in overweight or obese populations. However, the magnitude of these effects varies across studies and study designs. This study aimed to systematically evaluate the impact of TTM-based interventions on BMI and other physical health indicators among individuals with overweight or obesity, with separate analyses for randomized controlled trials (RCTs) and one-group repeated measures designs (ORMDs). A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching six databases-CNKI, PubMed, Web of Science, Cochrane Library, EBSCO, and Scopus-from their inception through March 30, 2025. Studies were eligible if they employed TTM-based interventions and reported BMI outcomes in overweight or obese populations. Hedges' g served as the effect size measure. For ORMDs, sensitivity analyses were conducted using pre-post correlation coefficients of r = 0.3, 0.5, and 0.7. Random-effects meta-analyses (REML estimator) were performed separately for RCTs and ORMDs. Subgroup analyses and meta-regression were conducted for BMI outcomes to explore sources of heterogeneity. Publication bias was assessed using funnel plots and Egger's test, and sensitivity was evaluated using leave-one-out analyses. A total of 20 studies (1,736 participants) were included, comprising 9 RCTs and 11 ORMDs. Among ORMDs, TTM-based interventions significantly reduced BMI [k = 11, g = -0.467, 95% CI (-0.763, -0.171), p = 0.002, I2  = 86.6%], body weight [k = 5, g = -0.323, 95% CI (-0.563, -0.084), p = 0.008, I2  = 78.9%], and waist circumference [k = 2, g = -0.375, 95% CI (-0.542, -0.209), p < 0.001, I2  = 0%], and significantly increased self-efficacy [k = 5, g = 1.195, 95% CI (0.699, 1.692), p < 0.001, I2  = 82.6%]. ORMD effect sizes were robust across pre-post correlation coefficients (r = 0.3, 0.5, 0.7). Conversely, among RCTs, TTM-based interventions did not produce statistically significant effects on BMI [k = 9, g = -0.070, 95% CI (-0.198, 0.058), p = 0.285, I2  = 0%], body weight [k = 6, g = -0.030, 95% CI (-0.207, 0.147), p = 0.742, I2  = 0%], waist circumference [k = 4, g = -0.090, 95% CI (-0.281, 0.101), p = 0.354, I2  = 31.6%], or self-efficacy [k = 3, g = 0.778, 95% CI (-0.077, 1.633), p = 0.074, I2  = 92.3%]. For ORMD BMI, subgroup analyses identified intervention format (education plus exercise vs. education only, p = 0.0002) and age group (<18 vs. ≥18 years, p = 0.035) as significant moderators. Meta-regression showed that baseline BMI, age, number of sessions, and total intervention hours did not significantly moderate the effects of BMI (all p > 0.05). Publication bias was not detected for most outcomes, except for RCT weight (Egger's p = 0.029). Leave-one-out sensitivity analyses confirmed the robustness of all pooled estimates. TTM-based interventions showed significant improvements in BMI, body weight, waist circumference, and self-efficacy when evaluated using ORMDs, with effects robust to assumptions about pre-post correlation. However, these improvements were not statistically significant under the more conservative between-group comparisons of RCTs. The discrepancy highlights the importance of study design in evaluating behavioral interventions. ORMDs may capture within-person change that RCT between-group contrasts fail to detect, but are also susceptible to expectancy and maturation effects. Future research should prioritize well-controlled RCTs with adequate blinding and intention-to-treat analyses, and interventions combining nutritional education with exercise are recommended for optimal BMI reduction. PROSPERO, CRD42024620040.
Within breast screening programs, digital breast tomosynthesis (DBT) can play a key role in the assessment pathway for screen-recalled abnormalities, where women may undergo multiple or additional imaging views, making accurate characterization of mean glandular dose (MGD) particularly important. Breast dosimetry has traditionally been based on the Dance model, with the more recent AAPM/EFOMP TG282 framework offering a more anatomically realistic alternative. This study aimed to compare MGD estimates derived using the Dance and TG282 methodologies across a large dataset of DBT examinations performed in a breast screening service. In addition, the study compared breast density metrics from the DBT-imaged population with the breast density assumptions of the TG282 model and evaluated the relationship between breast density and MGD estimates. This retrospective study analyzed 7,142 DBT images acquired between 2022 and 2025 from Siemens and Hologic mammography systems installed at assessment sites within the breast screening program in Queensland, Australia. MGD was estimated for each DBT exposure using the Dance and TG282 methodologies and evaluated as a function of compressed breast thickness (CBT). Statistical comparisons were performed using the Wilcoxon signed-rank test. Results were additionally stratified by vendor model to explore potential system-dependent effects. Volumetric breast density (VBD) was estimated using Volpara, and median values were evaluated as a function of CBT. The relationship between VBD and MGD was assessed within a representative CBT range using Spearman correlation analysis. TG282 MGD estimates were additionally recalculated using exposure-specific VBD values and compared with estimates derived using model-defined median VBD. Across the full dataset, TG282 yielded MGD values that were, on average, 8.98% lower than those derived using the Dance methodology for craniocaudal (CC) views and 7.91% lower for mediolateral oblique (MLO) views (p < 0.001). The magnitude of difference between MGD estimates showed dependence on CBT and system-specific beam quality. In addition, the study population exhibited higher median VBD values than those assumed by the TG282 reference breast model. The influence of VBD on MGD estimates varied by system, with stronger dependence observed for systems with softer beam quality. Recalculation of TG282 MGD using exposure-specific VBD resulted in slightly lower estimates, further increasing the relative difference compared with Dance-derived values to approximately -10%. Differences between TG282 and Dance MGD estimates depend on CBT and system beam quality, with TG282 producing lower estimates overall. The higher breast density observed in the DBT assessment population and its system-dependent influence on MGD estimates highlight the importance of accounting for both population breast density characteristics and mammography system mix when comparing DBT MGD estimates across populations.
Early detection of developmental disorders can be aided by analyzing infant craniofacial morphology, but modeling infant faces is challenging due to limited data and frequent spontaneous expressions. We introduce BabyFlow, a generative AI model that disentangles facial identity and expression, enabling independent control over both. Using normalizing flows, BabyFlow learns flexible, probabilistic representations that capture the complex, non-linear variability of expressive infant faces without restrictive linear assumptions. To address scarce and uncontrolled expressive data, we perform cross-age expression transfer, adapting expressions from adult 3D scans to enrich infant datasets with realistic and systematic expressive variants. As a result, BabyFlow improves 3D reconstruction accuracy, particularly in highly expressive regions such as the mouth, eyes, and nose, and supports synthesis and modification of infant expressions while preserving identity. However, the current model is trained on a relatively small cohort of craniofacially unaffected infants, which may limit generalization to pathological morphologies. Additionally, integrating BabyFlow with diffusion models offers a promising direction for data synthesis, as it could serve as a conditioning mechanism for generating expressive and realistic 2D infant images. Further evaluation is needed to assess image realism, 3D-2D geometric consistency, robustness, and potential ethical and bias-related concerns.
The growing environmental concerns associated with the fast fashion industry, along with its significant greenhouse gas (GHG) emissions, have highlighted the need for sustainable practices, such as clothing reuse, to mitigate the impact of textile waste on the environment. This study proposes a GHG emission equation for clothing reuse, taking into account both lifespan extension and the number of reuse cycles. By incorporating lifespan extension and the displacement rate, which has traditionally been a primary focus, the study aims to provide a more accurate estimate of the GHG reduction effects of clothing reuse. It calculates GHG emission factors based on weight and item type, allowing for tailored applications. These factors can be used to develop specific regulations for particular types of clothing, provide consumers with relevant information, and assess the environmental impact of the clothing industry at a national level. The study presents an illustrative scenario in which approximately 3,173 t of clothing reuse could lead to a minimum GHG reduction of 65,344 t-CO2eq/year (N = 1), depending on assumptions regarding displacement rates and lifespan extension. Although the reduction amount is relatively small, the study highlights the role of reuse as an effective GHG reduction strategy. It also emphasizes the importance of minimizing additional emissions from the reuse process, such as washing and transportation, and suggests that recycling be considered after a certain number of reuse cycles for further reductions.
For decades, oncology dose selection has been guided by the maximum tolerated dose (MTD) and plasma pharmacokinetics (PK), reflecting assumptions appropriate for classical cytotoxic chemotherapies. However, the advent of high-affinity, targeted therapies, including kinase inhibitors, epigenetic modulators, and radioligands challenges this paradigm. These agents achieve robust target engagement at doses far below the MTD, and systemic plasma concentrations often fail to reflect pharmacologically relevant exposure at tumor or hematologic sites. Physiologically-based pharmacokinetic (PBPK) modeling, extended to incorporate target-site dynamics, offers a mechanistic framework linking dose, systemic exposure, and local pharmacology. By integrating tissue physiology, drug properties, and target interactions, target-site PBPK provides insights into heterogeneous tumor penetration, intracellular distribution, and variable target occupancy that plasma PK alone cannot capture. Clinical examples, such as PSMA-targeted radioligands and tyrosine kinase inhibitors, illustrate how these models can inform rational dose selection, optimize ligand design, and guide individualized therapy. As oncology moves toward mechanism-driven, biology-aligned development, target-site PBPK represents a pivotal tool for translating preclinical insights into patient-specific dosing strategies and for redefining the standard of precision pharmacology.
FLASH-RT is an irradiation modality using Ultra-High-Dose-Rates, where a healthy tissue sparing effect is observed. Different particle types have distinct characteristics in lateral and distal dose deposition, which can affect the amount of healthy tissue sparing by the FLASH effect. However, few comparative studies investigate this impact. In this Monte Carlo-based study, we compare different irradiation modalities in a simple geometrical model and demonstrate how dose, dose-rate, and particle type influence the volume of healthy tissue benefiting from FLASH. The results indicate that less conformal modalities, such as X-rays and VHEE, benefit more from the effect than hadrons. Despite this, FLASH X-rays and FLASH VHEE still deposit higher dose to healthy tissue than conventional hadrons. Assuming a FLASH-triggering dose threshold and dose-rate threshold from literature, we found that hadrons require a target dose of 20 Gy and target dose-rate of 75 Gy/s to achieve healthy tissue sparing, about twice as high as for VHEE and X-rays. The results imply that highly fractionated treatments cannot be applied under current assumptions of FLASH-triggering dose and dose-rate thresholds. Future studies using clinical dose distributions could explore the use of multiple angles, fractionation possibilities, and relative biological effectiveness (RBE) for more specific clinical cases.
Objective evaluation of quantitative-imaging (QI) methods based on how reliably they measure true values is important for clinical translation. Performing such evaluation with patient data is highly desirable but hindered by the lack of gold standards. To address this challenge, advancing on previous studies, we propose a no-gold-standard evaluation technique, NGSE-Corr, that objectively evaluates QI methods without true values. The technique assumes a linear stochastic relationship between true and measured values, characterized by a slope, bias, and multivariate Gaussian-distributed noise term that models correlated noise across QI methods. We derive a maximum-likelihood approach to estimate these parameters using only measured values. From the estimates, we compute noise-to-slope ratio (NSR) to rank QI methods based on precision. Numerical experiments showed that NGSE-Corr reliably estimated the NSR, accurately rankedmethods, and maintained performance even when assumptions made by the technique were partially violated. We also validated NGSE-Corr in an in silico imaging trial to rank three quantitative SPECT methods for measuring regional activity uptake in patients with bone metastatic castrate-resistant prostate cancer treated with radium-223. NGSE-Corr correctly identified the most precise QI method and ranked the methods for 95% (95% CI, 89%-98%) and 91% (95% CI, 84%-95%) of trials, respectively, with data from 50 patients. Performance further improved with larger cohorts. With 200 patients, NGSE-Corr yielded same rankings as those obtained with true values across all trial instances. These findings demonstrate the ability of NGSE-Corr to accurately rank QI methods without gold standards and motivate clinical validation and broader applications.
Research on sexual dissidence under Francoism has often highlighted the repression of homosexual men and trans women, while the trajectories of lesbians remain underexplored. This absence has typically been explained through the notion of 'structural invisibility': women's same-sex relationships were both easier to conceal and less directly targeted by Francoist legal apparatus. Yet new documentary findings challenge this assumption. This article analyses a previously unknown set of medical and administrative files attributed to the Patronato de Protección a la Mujer, an institution jointly run by the Francoist state and the Catholic Church to discipline 'wayward' women. The documents, originating from the reformatory Nuestra Señora del Pilar in San Fernando de Henares, include psychiatric assessments, behavioral reports, and hospital admission forms explicitly referring to lesbianism. These records reveal how female homosexuality was actively pathologized and punished, often through transfers to psychiatric hospitals. Methodologically, the paper reads bureaucratic and psychiatric language as technologies of control while also attending to the fragmentary glimpses of subjectivity and resistance preserved in the files. Small gestures -laughter during a medical exam, drawings appended to reports, affectionate bonds- emerge as traces of agency within a coercive system designed to suppress them. By situating these materials within broader transnational frameworks of psychiatric and moral regulation, the article contributes to rethinking the place of lesbian experience in Francoist Spain. Ultimately, the corpus operates as a counter-archive, destabilizing narratives of invisibility and opening new avenues for queer historical memory.
Inducible nitric oxide synthase (iNOS) and its product nitric oxide (NO) were historically linked to poor melanoma outcomes, yet recent evidence shows NO supports anti-tumor immunity. This study examines how iNOS shapes anti-PD-1 efficacy, particularly through interferon signaling. B16-D5 melanoma tumors were implanted in wild-type (WT) and iNOS knockout (KO) mice to compare tumor growth and response to anti-PD-1 therapy. Flow cytometry, apoptosis assays, and RNA sequencing assessed NO production, PD-L1 expression, and interferon-related gene activation. In vitro, melanoma cells were treated with NO donors (DETA NONOate, SNAP) to assess proliferation and apoptosis. Peripheral blood mononuclear cells from 27 melanoma patients receiving anti-PD-1 therapy were analyzed with multiparameter flow cytometry to correlate NO-associated immune subsets with progression-free survival (PFS). Tumors grew significantly faster in iNOS KO mice, and anti-PD-1 therapy had no effect, demonstrating that iNOS-derived NO contributes to treatment efficacy. NO donors inhibited melanoma proliferation and induced apoptosis in vitro. Transcriptomic analysis showed anti-PD-1 upregulated interferon pathway genes (STAT1, IRF1, IFNB1) in WT but not iNOS KO mice. In patients, a NO-producing dendritic-cell subset (DAF-FM+CD11c+) was associated with improved PFS (hazard ratio 0.453; 95% CI = 0.270-0.992; p=0.048), indicating a NO-dependent enhancement of interferon-driven immune activity. iNOS-derived NO is necessary for effective anti-PD-1 immunotherapy in melanoma, promoting interferon signaling and immune activation. Loss of iNOS impairs tumor control and immune responsiveness, supporting NO as a potential biomarker and therapeutic adjunct to be explored while challenging assumptions about its deleterious role in melanoma.
There are two main ways in which a mandatory vaccination policy is often defended-by accusing vaccine refusers of 'free riding', or by appealing to the 'harm principle'. In their recent book, Roland Pierik and Marcel Verweij take the latter approach-an attractive option, as they note, due to the wide acceptance of the harm principle. But in arguing that vaccine refusal should indeed be thought to constitute harm, they end up endorsing a very controversial version of the harm principle, which we should not automatically expect to secure widespread support. Steven Smith suggests that this (common) argumentative strategy amounts to free riding on the reputation of the harm principle, and is an inevitable consequence of invoking the principle as the foundation of an argument. In its most basic form, the principle is hard to resist. But in order to use it to justify a policy, you need to make some assumptions about what constitutes harm, which will lose you supporters along the way. In appealing to the wide appeal of the harm principle, Pierik and Verweij do not adequately recognise the controversial nature of their claims, and thus the need for stronger and more elaborate arguments in their support.
Single-level lumbar spinal stenosis (LSS) that does not respond to conservative therapy is now of the standard care given due to minimal invasive decompression, but comparative 12-month outcome data of arthroscope-aided single-portal surgery of the lumbar spine (AUSS) and percutaneous endoscopic lumbar decompression (PELD) are scarce. There are also no individual outcome prediction models of these technique specific populations. To compare perioperative and 12-month patient-reported outcomes between AUSS and PELD and to develop and evaluate exploratory machine-learning (ML) models for predicting 12-month favorable outcome, disability (Oswestry Disability Index [ODI]), and back pain (visual analog scale [VAS]) after surgery. This retrospective comparative cohort study analyzed a de-identified patient-level dataset of 865 adults with single-level LSS and complete 12-month follow-up (AUSS n = 445; PELD n = 420). The dataset was provided as a de-identified patient-level file generated directly from source clinical records and was not reconstructed from published aggregate summary statistics; key assumptions include single-center retrospective treatment allocation, imaging-confirmed single-level LSS eligibility, and complete 12-month follow-up as an inclusion criterion. Clinical, perioperative, and complication variables were compared using appropriate nonparametric and parametric tests. Baseline group imbalance was quantified using standardized mean differences (SMDs). Classification and regression ML models were developed using stratified five-fold cross-validation on an 80% development set and evaluated on an independent 20% holdout set. Class imbalance was addressed with inverse-frequency class weighting. Given the exploratory nature of the analysis and dataset provenance, all ML results are interpreted as internal exploratory findings requiring external prospective validation. Mean age was 65.0 years; 89.6% of patients (775/865) achieved a favorable 12-month modified MacNab outcome. AUSS was associated with shorter total operating time (45.47 ± 3.19 vs 54.39 ± 5.24 min; P < 0.001; SMD - 2.07), shorter intracanal decompression time (21.40 ± 2.31 vs 35.49 ± 3.55 min; P < 0.001; SMD - 4.73), and markedly lower fluoroscopy exposure (7.57 ± 2.35 vs 38.41 ± 7.59 s; P < 0.001; SMD - 5.55). PELD showed a less access-traumatic profile with smaller incisions (7.87 ± 1.14 vs 19.71 ± 2.03 mm; P < 0.001; SMD 7.13), lower blood loss (9.41 ± 1.38 vs 17.57 ± 6.35 mL; P < 0.001; SMD 1.75), and lower cost (17 496 ± 603 vs 21 956 ± 581 CNY; P < 0.001). Baseline age imbalance was substantial (SMD = - 0.64), and all group comparisons should be interpreted in this context. Favorable 12-month outcome rates were 93.7% for AUSS and 85.2% for PELD (P < 0.001). Among ML classifiers, random forest showed the most balanced holdout performance: ROC-AUC 0.596, PR-AUC 0.909, sensitivity 0.922, specificity 0.222, and Brier score 0.143. Calibration was suboptimal (intercept 1.72; slope 0.53). Regression performance was limited: holdout R2 = - 0.000 for 12-month ODI and R2 = 0.053 for 12-month back-pain VAS. High rates of 12-month favourable outcome in both procedures had been attained. Operative efficiency and lesser radiations were enhanced by AUSS; PELD was linked with tissue disturbance minimization, a decrease in the amount of blood loss, and low cost. There was no significant difference in overall complication rates. Exploratory ML models demonstrated little predictive power especially in outcomes related to disability, and in unfavorable minority group, and can only be prospectively validated before any personalised clinical usage. Such findings are hypothesis-generating and they should not be relied on to make clinical judgments alone.
Situs inversus totalis (SIT) is a rare congenital laterality anomaly characterized by complete mirror-image transposition of the thoracic and abdominal viscera. Although SIT is not a proven cancer-predisposition syndrome, cancer in this setting challenges the spatial assumptions that guide diagnosis, staging, surgery, radiotherapy, and surveillance. We conducted a retrospective, single-center observational study of patients with radiologically confirmed SIT treated at our cancer center between July 2017 and December 2025. Patients with histologically confirmed malignancy were included. Clinical, pathological, imaging, treatment, and follow-up data were extracted from medical records. Overall survival (OS) was summarized descriptively; Kaplan-Meier analysis was used only to visualize survival and censoring patterns. Among 11 patients with SIT, 6 had histologically confirmed malignancies. Median age was 66.5 years (range, 47-76), and median follow-up was 47.5 months (range, 30-55). Tumors included hepatocellular carcinoma, lung squamous cell carcinoma, lung adenocarcinoma, cervical adenocarcinoma, poorly differentiated abdominal adenocarcinoma, and follicular lymphoma transformed to high-grade B-cell lymphoma. Treatment included surgery, radiotherapy, chemotherapy, immunotherapy, targeted therapy, transarterial chemoembolization, and autologous stem cell transplantation. 4 patients died, and 2 were alive at last contact. Pooled median OS was 50 months and was interpreted only descriptively. Across cases, outcomes reflected tumor type, stage, and molecular features, whereas SIT mainly affected lesion localization, laterality recognition, nodal or vascular mapping, operative orientation, and treatment planning. Malignancy in SIT is best understood as cancer within a reversed anatomical coordinate system. Standard oncologic care remains feasible but requires explicit anatomical verification and multidisciplinary planning.