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In several European countries, racing without shoes, i.e., barefoot, is a common strategy in trotting races to improve the speed of the horse. Why some trotters can race barefoot without damage to the hoof from excessive wear and others cannot, has been shown to partly be explained by differences in hoof composition. In particular, durable hind hooves are believed to be important to sustain racing without shoes. Also, variation in the proportion of the races the horse races with barefoot hind hooves, has been shown to be affected by genetic differences in Swedish Standardbred trotters (SB) and Swedish-Norwegian Coldblooded trotters (CB). When racing barefoot, the protective properties of the shoe, which prevent the hoof from excessive wear and tear, are absent. If the hoof cannot withstand wear and tear, the damage of the hoof may pose a risk to animal welfare. The question of how barefoot racing should be regulated, especially for young horses, is under discussion in Sweden and elsewhere in Europe. Regulations regarding barefoot racing in young horses differ between countries, and there is a lack of published studies to base regulations on. Therefore, this study aimed to analyze the effect of racing with barefoot hind hooves in young SB and CB on career length. Datasets including shoeing information from 3-year-old SB, and 3- and 4-year-old CB were analyzed, including up to 12,161 horses. Cox proportional hazard models were fitted to analyze the effect of proportion of barefoot races at a young age on the hazard of ending the career at a given time point. Other effects such as sex, year of birth, started as a 2-year-old or not (SB only), early earnings, best racing time, and number of early starts were also included in the models. For SB, horses that raced more than 30% barefoot had an14% higher hazard of ending their career at a given time point compared to the reference group that raced 5% or less barefoot. In CB, the hazard of ending the career at a given time point was 67% higher in the group with the highest proportion of barefoot races compared with the reference group. Although for SB, the reduction of the career length in days was minor. For CB, the effect was larger and verified the negative effect in SB but estimated based on fewer observations. Further studies of voluntary as well as involuntary reasons why Swedish trotters end their careers, are needed to better understand the possible impact of racing barefoot as a young horse. In harness racing, removing the shoes to race barefoot is commonly seen as it helps the horse to run faster. However, not all horses have hooves that can stand the increased wear and tear from racing barefoot, and the hind hooves have been shown to be extra vulnerable. Thus, racing barefoot could possibly violate animal welfare, and the debate continues over whether young trotters should be allowed to race barefoot. Therefore, the aim of this study was to analyze the effect of racing barefoot on hind hooves in young Swedish trotters on their career length. The career length was measured in days from the first race as a 3-year-old for Swedish Standardbred trotters and Swedish-Norwegian Coldblooded trotters. The results showed that the risk of ending the career was higher for horses racing a higher proportion of barefoot races as 3-year-olds compared to a reference group consisting of horses that rarely raced barefoot. In Standardbred trotters, the median career length was reduced by 123 days and 85 days in males and females, respectively, for horses racing more than 50% barefoot, compared with the reference group that never or rarely raced barefoot. Similar results were found for Coldblooded trotters.

Ambulatory blood pressure monitoring (ABPM) plays an irreplaceable role in the diagnosis and management of hypertension. However, more than 100 million of the 1.4 billion people with hypertension worldwide cannot tolerate nighttime ABPM due to noise and arm compression. Previous prediction methods relying on demographic factors and home blood pressure measurements are time-consuming and burdensome while exhibiting limited accuracy for nocturnal hypertension. There is a need for a more accurate, low-burden approach to identify high-risk patients intolerant to nighttime ABPM monitoring. We collected 2,874 ABPM records at a regional medical center to conduct a retrospective cohort study. Kernel density estimation based preprocessing was applied to stabilize data fluctuations. A variational autoencoder based deep learning model was developed using daytime blood pressure and heart rate combined with full-day activity and posture states to predict nocturnal hypertension. Here we show that the ABPM-VAE model achieves an AUC of 0.82 (95% CI 0.77-0.88) on the test set, outperforming the ablation model (AUC 0.67; 95% CI 0.61-0.74; p&#xa0;<&#xa0;0.001) and prior methods based on demographic and home blood pressure data (AUC 0.69). For nocturnal hypertension prediction, the model yields a PPV of 92.12%, NPV of 55.20%, sensitivity of 0.73, and specificity of 0.84. The entropy reduction preprocessing-enhanced deep learning model predicts nocturnal hypertension risk from ABPM without adding burden to patients or physicians. It serves as an effective screening tool to identify high-risk individuals intolerant to nighttime monitoring, serving as a valuable complement to conventional ABPM. ABPM remains the only validated method for diagnosing nocturnal hypertension. However, among the more than 1.4 billion people with hypertension, over 100 million cannot tolerate nighttime ABPM due to noise from cuff inflation and arm compression. Previous prediction approaches relying on demographic factors and home blood pressure measurements are time-consuming, burdensome, and offer limited accuracy. Using existing recorded ABPM data to predict nocturnal hypertension therefore represents a cost-effective and efficient alternative. By applying data smoothing to mitigate short-term fluctuations, we developed a deep learning method that improves the prediction of nocturnal hypertension compared with prior approaches. This method serves as a valuable complement to ABPM and enables effective screening of high-risk patients who are intolerant to nighttime ABPM.

To describe the implementation of the Manual of Good Practices in Humanization in Pediatric Intensive Care Units through an Audit and Feedback strategy and examine observed changes in compliance in a high-complexity unit. A comparative longitudinal observational study (pre-post) was conducted from January to December 2025 in a high-complexity pediatric intensive care unit in Spain. The implementation process followed a 12-month Audit and Feedback cycle structured in four phases: baseline audit, participatory prioritization, decentralized protocol development, and final evaluation. Compliance with 127 evaluable good practices was measured, and 95% confidence intervals were calculated using the Wilson score method. Baseline compliance was 69.3% (88/127; 95% CI: 60.8-76.6). At final evaluation, overall compliance reached 72.4% (92/127; 95% CI: 64.1-79.5), representing an absolute increase of 3.1 percentage points (95% CI: -7.9 to +14.2). The prioritized strategic lines of Communication and Patient well-being showed gains of 14.3 and 8.3 percentage points, respectively; however, overlapping confidence intervals indicate that these changes cannot be distinguished from sampling variability. Among unimplemented practices, 87.2% required organizational and training actions, while 12.8% required direct financial investment. This implementation-focused quality improvement study suggests that Audit and Feedback may be feasible for structuring the monitoring, prioritization, and adoption of humanization practices in a high-complexity pediatric intensive care unit. Modest quantitative changes and organizational outputs were observed, but these findings cannot be considered evidence of intervention effectiveness. Future cycles should assess sustainability and incorporate patient- and family-level outcomes.

A widely accepted approach to setting Analytical Performance Specifications (APS) in laboratory medicine is the Milan models. Milan model 1b is an approach where APS are developed based on patient classification, or clinical decision-making, noting that these factors link to the probability of patient outcomes. This paper describes limitations that should be considered for any APS derived using model 1b, based on any available information from model 2 (biological variation), and model 3 (state of the art). It is also proposed that "state of the art", when defined as being tests which are routinely available, influences studies based on clinical surveys as this the background used by doctors to develop their clinical experience, as well as the ability for the APS to be put into routine use. Additionally, when "state of the art" is defined as the method used in an outcome study, this becomes a reference where such studies cannot support a smaller assay precision, as the assay performance is only one aspect of the variation in the links between laboratory results and clinical findings. Model 1b studies remain a key approach to assigning clinical meaning to assay performance, however outcomes of these studies need to be taken with awareness of relevant limitations.

Breast lesions classified as B3 according to UK diagnostic guidelines [1, 2] pose significant management challenges due to their uncertain malignant potential. Within the B3 category, fibroepithelial lesions (FEL) include fibroadenomas (FA), where a phyllodes tumour (PT) cannot be excluded, and benign phyllodes tumours (BPT). To evaluate the upgrade and downgrade rates of B3 classified FA and BPT within a B3 lesion cohort, identify correlations with patient demographics and lesion characteristics, and assess the impact of other variables on diagnostic outcomes. Of a total of 332 B3 lesions, a study was conducted on 73 patients with B3-classified FA or BPT diagnosed on core needle biopsy between January 2010 and January 2025. Data on demographics, histological diagnosis, lesion size, imaging findings, procedural data, and subsequent histopathological diagnoses (upgrade or downgrade) were collected. Statistical analyses included multivariate logistic regression. All patients were followed for a median of 8&#x2009;years. Of 73 FEL cases studied (36 B3-FA, 37 BPT), the B3-FEL downgrade rate was 37% (27/73), and the FEL upgrade rate was 15% (11/73). Downgraded lesions were associated with a significantly lower mean age (32.17&#x2009;years vs. 40.65&#x2009;years in non-downgraded lesions, p&#x2009;=&#x2009;0.027). Multivariate logistic regression identified lower age as the only predictor for downgrade status. The overall long-term recurrence rate was 2.7%. Our findings suggest that B3-FELs have high downgrade rates. Younger age is associated with downgrading, while lesion size and ultrasound score are not significant predictors of downgrade. A low long-term recurrence rate of 2.7% was observed. Given the substantially higher downgrade rates in younger patients and the low long-term recurrence rates for B3-FELs, multidisciplinary teams could refine risk stratification and clinical decision-making.

Phage therapy offers a promising alternative to antibiotics for treating multidrug-resistant infections. Plasmid-dependent phages (PDPs) are particularly attractive as therapeutics because they can both kill targeted pathogens and prevent the further spread of antibiotic resistance genes encoded by plasmids. However, the evolutionary trajectories of multidrug-resistance (MDR) plasmids under the selective pressure of PDPs remain poorly understood, particularly in eco-evolutionary contexts that remain permissive to plasmid conjugation. We experimentally evolved populations of Escherichia coli carrying the MDR plasmid RP4 in the presence of the plasmid-dependent phage PRD1 under conditions where the benefits of conjugation were either strong or weak. When opportunities for conjugation were rare, PRD1 only transiently suppressed the conjugative plasmid population due to the rapid evolution of phage-resistant plasmids lacking conjugative ability. Increasing ecological opportunities for conjugation enhanced plasmid suppression and delayed the evolution of phage-resistant plasmids. PRD1 resistance was associated with plasmid loss and reduced conjugative ability, although this trade-off was complex because resistance mutations had heterogeneous effects on pilus production and conjugation. Mutations and IS-mediated inactivation in conjugation genes generated a spectrum of resistance phenotypes, from partial resistance (trbB, trbL) to complete resistance (virB4/trbE). Bioinformatic analysis of publicly available IncP plasmids revealed frequent truncations of the VirB4/TrbE protein, suggesting that plasmid-dependent phages may represent an important selective pressure shaping plasmid evolution in natural populations. Our results demonstrate an evolutionary trade-off between conjugative ability and phage resistance that cannot be easily circumvented by plasmids. Targeting multidrug-resistance plasmids with PDPs is likely to drive loss of conjugation, limiting the transfer of antibiotic resistance genes in microbial communities.

The Fatu Hiva monarch (Pomarea whitneyi) is a Critically Endangered (IUCN) passerine species restricted to a single island in French Polynesia, with fewer than 20 individuals remaining. In the Pacific region, avian malaria caused by Plasmodium relictum lineage GRW4 is a major conservation concern because it has been linked to severe population declines and extinctions in Hawai'i and poses a documented threat to endemic birds in New Zealand. On 17 June 2022, a recently fledged juvenile Fatu Hiva Monarch (Pomarea whitneyi) was found dead in its natural habitat on Fatu Hiva. A necropsy was performed on site in humid tropical conditions, with real-time online guidance from an expert based in Switzerland. Gross examination revealed severe emaciation, an empty gastrointestinal tract, diffuse pallor with scant intravascular blood and hepatosplenomegaly with dark discolouration. Despite advanced autolysis having occurred, cytologic tissue imprint and blood-smear assessment revealed high parasitaemia with intraerythrocytic developmental stages and abundant birefringent malarial pigment. Histopathology confirmed a hemozoin-producing haemosporidian infection due to the presence of widespread polarisation-positive and Prussian blue-negative intracytoplasmic pigment in erythrocytes and macrophages. Molecular testing was performed on dried blood pressed onto household filter paper, that had been stored and shipped at ambient temperature for several weeks. Nested cytochrome b polymerase chain reaction and sequencing confirmed a single infection with the GRW4 lineage of Plasmodium relictum. This first confirmed fatal case of avian malaria in this critically depleted island endemic species highlights the urgent need to integrate disease surveillance and vector management into conservation planning. The results suggest that acute severe P. relictum (GRW4) infection was the proximate cause of death, likely occurring against a background of prolonged negative energy balance and chronic stress exposure. Although the direction of causality cannot be fully determined post-mortem, aggressive malaria prevention measures remain justified to avoid further demographic decline in the critically low Fatu Hiva monarch population. To effectively reduce risk, vector control measures must be combined with measures to enhance host resilience and continuous surveillance to monitor transmission and guide adaptive management. Furthermore, this case study shows that a scientifically defensible, lineage-level diagnosis can be achieved using simple, low-cost samples and techniques adapted for remote tropical fieldwork, even when carcass quality is compromised by warm, humid conditions.

Large Arctic rivers transport substantial amounts of freshwater, carbon (C), and nutrients from land to the Arctic Ocean and play an important role in continental-ocean biogeochemical coupling. Although these systems have been intensively studied with respect to C cycling, greenhouse-gas emissions, and hydrochemistry, the biodiversity and environmental controls of their plankton communities remain less well resolved, particularly along large latitudinal gradients sensitive to permafrost thaw and climate warming. To address this issue, we quantified zooplankton abundance, taxonomic structure, phytoplankton biomass, and nutrient concentrations during summer (July) along a >2000-km main-stem transect of the Ob River, the largest river of western Siberia, extending from the Novosibirsk reach to the southern margin of the permafrost zone. Across the full transect, we identified 118 zooplankton species, with rotifers dominating both richness and numerical abundance. Zooplankton assemblages displayed a discrete-continuous longitudinal pattern shaped jointly by basin-scale landscape heterogeneity and local physicochemical conditions. Community structure and total abundance were strongly influenced by the response traits of dominant rotifers to phytoplankton biomass, pH, and related environmental variables. A central finding is that zooplankton responses to nutrients, temperature, and trophic conditions operated with a predictable ecological lag corresponding to one to two generations (approximately 7-10&#xa0;days), equivalent to a 300-500&#xa0;km downstream shift during summer flow. Thus, environmental controls on zooplankton cannot be inferred from instantaneous local measurements alone, but must be interpreted in the context of water travel time and the reproductive dynamics of key taxa. Together, these results provide the first basin-scale mechanistic understanding of zooplankton dynamics in the Ob River and show that large Arctic rivers can serve as sensitive indicators of climate-driven ecological change across northern watersheds.

Interindividual variability in drug efficacy and toxicity remains a major challenge in clinical pharmacotherapy. Although pharmacogenomics has substantially advanced personalized medicine, host genetic variation alone cannot fully explain differences in drug disposition, response, and adverse effects. Increasing evidence identifies the human gut microbiotaas an additional, functionally relevant metabolic layer that complements host drug-metabolizing enzymes, giving rise to the field of pharmacomicrobiomics. This discipline examines bidirectional interactions between drugs and microbial communities that influence absorption, metabolism, enterohepatic circulation, and pharmacodynamic outcomes. The gut microbiota can directly biotransform or sequester drugs through diverse enzymatic reactions, including deconjugation, reduction, and decarboxylation, thereby modifying systemic drug exposure and toxicity. In parallel, microbially derived metabolites and bile acid-mediated signaling pathways regulate host drug-metabolizing enzymes and transporters, including cytochrome P450 enzymes and ATP-binding cassette transporters. Conversely, many commonly used medications-such as antibiotics, chemotherapeutic agents, targeted therapies, immunotherapies, psychotropic drugs, and proton pump inhibitors-can substantially reshape microbial composition and function, resulting in dysbiosis that feeds back onto drug metabolism and therapeutic outcomes. This review summarizes the mechanistic basis and clinical relevance of microbiota-drug interactions across key therapeutic areas, including oncology (chemotherapy and immunotherapy), neuropsychiatric disorders, and metabolic diseases. Well-established examples, including microbial &#x3b2;-glucuronidase-mediated reactivation of irinotecan, microbiota-dependent modulation of levodopa and antidepressant pharmacokinetics, and microbiota-driven variability in immune checkpoint inhibitor efficacy, are discussed to illustrate causality. Emerging microbiome-informed strategies-such as selective inhibition of microbial enzymes, microbiota modulation, and microbial biomarker-based patient stratification-are highlighted. Finally, we examine integration of pharmacomicrobiomics with pharmacogenomics within multi-omic and systems pharmacology frameworks, emphasizing implications for predictive modeling and precision drug metabolism.

Reprogramming of cellular energy metabolism is a defining feature of malignancy, characterized by sustained glucose conversion to lactate under aerobic conditions. Once viewed as metabolic waste or a consequence of mitochondrial dysfunction, lactate is now recognized as a multifunctional metabolite that actively drives malignant progression. This review advances the concept of lactate as a spearhead of malignancy that coordinates metabolic adaptation with microenvironmental conditioning, immune suppression, epigenetic regulation, and metastatic dissemination. Beyond its role in metabolism, lactate functions through receptor-mediated signaling and histone lactylation, linking metabolic state to transcriptional programs associated with epithelial-mesenchymal transition, stemness, immune modulation, and therapeutic resistance. Lactate also reshapes the tumor microenvironment by promoting angiogenesis, stromal activation, immune suppression, and invasive behavior, including effects that cannot be explained solely by extracellular acidosis. Together, these observations support lactate as a critical effector of metastatic competence and a strategic target for disrupting multiple cancer hallmarks.

Accurately predicting respiratory signals from electromyography envelopes is crucial for non-invasively assessing respiratory muscle effort and fatigue. Unlike traditional forecasting, such tasks present the unique challenge of Cross-Sequence Time Series Forecasting (CSTSF), which predicts a target sequence from different source sequences. However, CSTSF is under-researched compared to traditional intra-sequence forecasting, and conventional forecasting models are often inapplicable to CSTSF tasks: they often cannot handle the differing input/output variable counts inherent to such tasks due to their architectural design. To address these limitations, we propose a novel decoupled framework that decomposes the CSTSF task into two sequential stages: intra-sequence forecasting and cross-sequence mapping, which are jointly optimized via a hybrid loss function. This design allows any conventional time series forecasting model to be readily adapted for CSTSF tasks by simply integrating a modality transition module. Within this framework, we further introduce the Multi-Scale Patch Transformer (MSPFormer), which integrates an advanced multi-scale patching backbone to capture multi-period features with our Attention-based Modality Transition (AMT) module to efficiently perform the crucial cross-sequence mapping. Extensive experiments on the private EMG-Respiration dataset and the public Traffic and Electricity dataset for CSTSF demonstrate our model's improved results over state-of-the-art forecasting methods. This work offers a robust CSTSF solution, with significant potential for applications like non-invasive physiological signal estimation.

Astaxanthin is a high-value ketocarotenoid of growing industrial importance; however, its scalable biomanufacturing is constrained by inherent biological complexity. Production performance is governed by hierarchical constraints, including molecular instability, pathway-level flux competition, and system-level trade-offs between cellular growth and stress-induced synthesis. These tightly coupled constraints lead to nonlinear responses and persistent optimization plateaus. These challenges cannot be effectively resolved through localized genetic or process interventions.To address this challenge, this review reconceptualizes astaxanthin biosynthesis as a problem of hierarchical constraint management and proposes a hierarchy-aligned, AI-assisted framework. Within this framework, artificial intelligence operates through distinct pathways across biological scales. At the molecular level, protein language models and sequence-structure-aware approaches enable efficient exploration of enzyme sequence space, prioritizing variants that improve catalytic performance and stability. At the pathway level, graph-based learning models capture network topology, flux coupling, and branch competition, allowing identification of distributed metabolic bottlenecks and coordinated intervention targets. At the system level, convolutional neural networks and multimodal learning approaches quantify phenotypic states and integrate multi-omics data to characterize growth-production trade-offs and cellular state transitions. Large language models further function as an integrative layer, linking model outputs, experimental knowledge, and design constraints to support decision-making and hypothesis generation.By explicitly aligning computational abstractions with biological hierarchy, this framework transforms AI from a generic predictive tool into a structured methodology .It links phenotypic observation, pathway-level reasoning, and molecular design. This approach improves the organization of complex design spaces, reduces reliance on empirical trial-and-error, and supports rational, multiscale optimization. The conceptual framework is extendable to other secondary metabolites whose biosynthesis is conditional, resource-intensive, and tightly coupled to cellular physiology.

Diagnosing anogenital dermatological conditions often requires specialist expertise that is unavailable in many clinical settings. Large language models (LLMs) are increasingly accessible to clinicians, but their diagnostic accuracy for anogenital dermatology has not been evaluated. We evaluated the diagnostic accuracy of three LLMs (Gemini 2.5 Pro, Claude Opus 4.1, and ChatGPT 5 Thinking). This study was conducted between September and November 2025, using de-identified clinical images of anogenital conditions from the STI Atlas (stiatlas.org (https://stiatlas.org/)) and other publicly available sources. Primary outcomes were correct classification of images identified as sexually transmitted infections (STIs) vs non-STIs and the inclusion of the correct diagnosis among the LLMs' top-ranked (top-1), top-3, or top-5 differential diagnoses. Among 218 images, Gemini achieved the highest accuracy for STI binary classification (76.2% [95% CI, 70.5% - 81.9%]) and differential diagnosis (top-1, 39.0% [95% CI, 32.7% - 45.7%]; top-3, 54.6% [95% CI, 47.9% - 61.1%]; top-5, 60.6% [95% CI, 53.9% - 66.9%]), followed by ChatGPT and Claude. In subgroup analysis, all LLMs showed substantially reduced accuracy for diagnostically challenging images (top-5 accuracy range, 29.2% - 40.0%). Gemini consistently outperformed Claude across most subgroups (P < 0.05). None of the LLMs could identify any mpox correctly. LLMs showed limited accuracy for diagnosing anogenital conditions, particularly for challenging images. The best-performing model achieved only 39.0% for top-1 diagnosis, indicating that current LLMs cannot reliably diagnose anogenital conditions. These tools may support supervised clinical triage but need further validation before routine clinical use.

We address the problem of predicting high-detail RNA structure geometry from the information available in low-detail experimental maps. Here, low-detail refers to resolutions &#x2248; 2.5-3.5&#xc5;, where the location of the phosphate groups and the glycosidic bonds can be determined from experimental maps but all other backbone atom positions cannot. In contrast, higher-resolution maps allow high-detail determinations of all backbone atomic positions. To this end, we first create a gold standard dataset of highly curated, experimentally supported RNA suites. Second, we develop and employ a modified version of the previously devised algorithm MINT-AGE to learn clusters that are in high correspondence with the gold standard's conformational classes of suites based on 3D RNA structure. Since some of the gold standard classes are of very small size, a new modified version of MINT-AGE is able to also identify very small clusters. Third, we create a new conformer prediction algorithm, RNAprecis, which assigns low-detail structures to newly designed 3D shape coordinates. Our improvements include: (i) learned classes augmented to cover also very low sample sizes and (ii) replacing distances from clusters by Bayesian posterior probabilities. On test data containing suites modeled as conformational outliers, RNAprecis shows good results suggesting that our learning method generalizes well. In particular, we show that the modified MINT-AGE clustering can more finely delineate between previously unseen suite conformer separations. For example, the 0a conformer has been separated into two clusters seen in different structural contexts. Such new distinctions can have implications for biochemical interpretation of RNA structure.

Lung transplantation (LTx) is the solid organ transplantation with the comparatively worst prognosis. Therefore, improving candidate selection and risk stratification for these patients has become a high-priority research focus. Incorporating objective markers of frailty such as sarcopenia in the pre-transplant evaluation may help achieve this goal. We evaluated the association between CT-measured psoas muscle sarcopenia and early LTx outcomes. We performed a retrospective study including patients who underwent LTx from 2014 to 2018 at our institution with available chest and abdominal CT scans in the year prior to LTx. Psoas cross-sectional area was measured, and previously established sex-specific cutoffs were used to define sarcopenia. We compared sarcopenic and non-sarcopenic LTx recipients regarding 1-year mortality and perioperative outcomes. A total of 140 patients were included, who received LTx primarily for interstitial lung disease (ILD) (n&#x2009;=&#x2009;75, 53%) or chronic obstructive pulmonary disease (n&#x2009;=&#x2009;54, 39%). Forty-six (33%) were sarcopenic. We found no association between psoas sarcopenia and 1-year all-cause mortality (multivariable p=0.13) nor perioperative complications or FEV1 at 1-year post-LTx. Exploratory subgroup analysis revealed an association between psoas sarcopenia and 1-yer all-cause mortality in patients with ILD (multivariable p&#x2009;=&#x2009;0.042). Pre-established sex-specific cutoffs for psoas sarcopenia did not reach statistical significance for 1-year mortality or perioperative outcomes in the overall cohort; given limited statistical power, a clinically meaningful association cannot be excluded. An exploratory signal in ILD patients warrants further investigation. Future studies may benefit from disease-specific threshold validation and the integration of muscle strength and physical performance alongside muscle mass assessment.

Vitreous humour (VH) is a unique, avascular ocular structure whose biochemical composition may reflect retinal pathophysiology. Although VH has been studied using metabolomics, it remains less explored compared to other biological matrices, and existing sample preparation protocols show considerable variation. In fact, most available reports are application-oriented studies that describe the sample preparation conditions used, without providing a rationale for why these particular parameters were chosen, consideration of alternative options, or offering a critical evaluation or systematic comparison of methodologies. Therefore, in this study, we systematically compared and optimised procedures for VH preparation prior to untargeted LC-MS analysis. Key steps, homogenisation, extraction, and preconcentration, were evaluated based on feature coverage, signal intensity, reproducibility, and practical aspects such as throughput and cost. The optimal protocol involved sample disruption using liquid nitrogen combined with ultrasound, protein precipitation and metabolite extraction with acetone, followed by evaporation and resuspension in methanol/water (2:1). This protocol was subsequently applied to VH samples from patients with epiretinal membrane (ERM, n = 14) and macular hole (MH, n = 13), conditions with different clinical manifestation but often combined into a single control group in ophthalmic studies. Univariate and multivariate analyses revealed no statistically robust metabolic differences between ERM and MH samples in this cohort. Simulation-based power analysis indicated that, under the present sample size and multiple-testing correction, the study was primarily powered to detect only large effects; therefore, subtle differences cannot be excluded. These findings support the use of ERM and MH as a pragmatic combined comparator group in LC-MS-based vitreous metabolomics, while warranting validation in larger cohorts. The optimised preparation method provides a practical framework for future metabolomic investigations of VH in ophthalmic research.

Lateral lumbar interbody fusion (LLIF) is a well-established and efficient technique for treating various thoracolumbar spine pathologies. However, in some scenarios, the amount of segmental lordosis achieved is insufficient. Two main strategies have been used to address this: disruption of the anterior longitudinal ligament (ALL) via anterior column realignment and, more recently, the use of expandable interbody cages that allow intraoperative adjustment of segmental lordosis without requiring ALL release. Twelve expandable lateral interbody devices were placed in seven fresh-frozen cadaveric specimens in the prone position. Segmental lordosis (degrees), anterior disc height, and posterior disc height (both in arbitrary units normalized to the L4 vertebral body height) were assessed before and after cage expansion. Because of the small number of clusters, generalized estimating equations (GEE) with Mancl-DeRouen small-sample correction were used as the primary analysis; disc height ratios were modeled on the log scale and the lordosis angle on a Gaussian scale. Seven specimens were included in the study. Two patients underwent three-level instrumentation, one underwent two-level instrumentation, and four underwent one-level instrumentation, totaling 12 instrumented levels. The mean segmental lordosis increased from 4.5&#xb0; (&#xb1;&#x2009;4.3&#xb0;) pre-expansion to 11.8&#xb0; (&#xb1;&#x2009;4.2&#xb0;) post-expansion. Small-sample-corrected GEE models confirmed significant postoperative improvements across all three outcomes: segmental lordosis increased by a mean of 7.25&#xb0; (post-pre; 95% CI, 1.6-12.9; p&#x2009;=&#x2009;0.020), posterior disc height by 61% (post/pre ratio, 1.61; 95% CI, 1.30-1.92; p&#x2009;<&#x2009;0.0001), and anterior disc height by 122% (post/pre ratio, 2.22; 95% CI, 1.31-3.12; p&#x2009;=&#x2009;0.001). Complete ALL rupture occurred in two of the 12 levels (17%). Within the limitations of a small cadaveric feasibility study, the placement of an expandable lateral interbody device in the prone position was associated with immediate increases in segmental lordosis and both anterior and posterior disc heights. These findings should be considered exploratory and hypothesis-generating in nature. Because the study design cannot separate the effect of cage expansion from that of prone positioning and because no static cage control was included, adequately powered prospective clinical studies are required before any claim of superiority can be made.

Clinical trials for rare diseases face a fundamental mathematical challenge that conventional randomized controlled trial (RCT) designs cannot overcome. With approximately 95% of the estimated 10,000-16,000 rare diseases lacking approved therapies, and drug development programs failing at rates exceeding 75% in non-oncology indications, the field confronts a stark reality: Traditional trial designs demand patient numbers that simply do not exist. This perspective article examines the critical mismatch between the statistical requirements of different trial designs (the "demand") and the actual patient populations available for study (the "supply"). We demonstrate mathematically that alternative trial designs-particularly patient-as-own-control and natural history comparator models-can reduce required sample sizes by 5- to 20-fold while maintaining statistical rigor. We further point out that a substantial proportion of rare disease trial failures stem not from therapeutic inefficacy but from recruitment and retention challenges inherent to underpowered RCT designs-challenges that are directly addressable through appropriately matched trial design. Given that most rare disease development programs receive only one opportunity to demonstrate efficacy, the continued application of inappropriate statistical models represents both a scientific failure and an ethical and economic challenge to the rare disease community. We propose that regulatory agencies formalize acceptance of alternative trial designs for rare diseases, supported by explicit mathematical frameworks that transparently account for genetic heterogeneity, pediatric populations, and the statistical efficiency gains achieved through within-subject correlation.