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The Asian bush mosquito, Aedes japonicus (Theobald, 1901), is an invasive species and a competent vector for several arboviruses, including chikungunya virus, dengue virus, Japanese encephalitis virus, West Nile virus, and Zika virus. Field studies have also detected La Crosse virus in wild populations, further supporting its potential role in arbovirus transmission. To address the fragmented and incomplete state of knowledge regarding its spread, we have assembled a global dataset of documented presences from 1950 to 2025. This data descriptor presents a curated database of geolocated records, formatted as points, derived primarily from peer-reviewed literature and supplemented with validated national survey data and selectively integrated records from the Global Biodiversity Information Facility (GBIF) following rigorous quality control. We detail the methodology for data acquisition, coordinate assignment, and the rigorous validation steps applied. This first comprehensive repository specifically for Ae. japonicus, containing 4618 validated records, provides a critical resource for spatial mapping and risk assessment of this vector and its associated pathogens.

The mammalian central circadian clock resides in the suprachiasmatic nucleus (SCN) of the hypothalamus in the brain and is responsible for coordinating daily rhythms of biological processes spanning from gene expression to behavior. Light, the primary environmental zeitgeber, entrains the SCN via melanopsin-expressing intrinsically photosensitive retinal ganglion cells that project through the retino-hypothalamic tract. Altered circadian rhythms are common in individuals diagnosed with neurodevelopmental and neurodegenerative disorders, and often, associated with structural alterations of the SCN and impaired retinal input; importantly, these anomalies can be recapitulated in animal models. Here, we describe step-by-step protocols for quantitative histomorphometrical analysis of the SCN and the assessment of retinal-SCN connectivity, previously used in mouse models of neurodevelopmental and neurodegenerative disorders. These include measurement of the SCN area, perimeter, height and width using Nissl- or DAPI-stained coronal sections, as well as densitometric and plot profile analyses of cholera toxin β-subunit-labeled retinal projections using Axiovision or Fiji/ImageJ. The protocols incorporate standardized region-of-interest, measurements by masked observers, and consistent scaling procedures to enhance reproducibility. These methods provide a rigorous framework for detecting structural anomalies and connectivity defects in the circadian system and can be broadly applied to other experimental models of circadian dysfunction. Key features • Histomorphometrical analyses of the SCN can provide anatomical bases to understand altered sleep and circadian rhythms in animal models of disease. • Exploration of retinal-SCN connectivity to facilitate the identification of the underlying causes of deficits in the response to photic cues in animal models of disease. • The protocols described here employ widely used and accessible software and provide rigorous but easy-to-follow instructions. • These analyses do not require expensive staining procedures and can be easily implemented in any laboratory. • Strengths for reproducibility: usage of fixed region-of-interest (ROI), measurements averaged from multiple sections per animal, masked observers thoroughly trained.

Accurate complete blood count (CBC) measurements are fundamental to modern hematology practice, and the introduction of new automated hematology analyzers requires rigorous analytical validation to ensure reliability, comparability, and clinical safety. The Atellica HEMA 580 (Siemens Healthineers AG, Erlangen, Germany) is a high-throughput hematology analyzer for which independent Clinical and Laboratory Standards Institute (CLSI)-guided validation data remain limited. This study aimed to perform a comprehensive analytical validation of the Atellica HEMA 580 and to evaluate its analytical comparability with the Sysmex XN-3100 (Sysmex Corporation, Kobe, Japan). Analytical validation was conducted in accordance with CLSI guidelines. Precision was assessed following CLSI EP05-A3, linearity according to EP06-A, carryover using EP07-A2, and method comparison per EP09-A3. Precision was evaluated using 15 within-run and 15 inter-assay replicates across three quality control levels. Linearity was evaluated using proportional dilution from neat to 1:16. Forty-four paired routine clinical samples were analyzed in parallel on both analyzers using ordinary least squares, Passing-Bablok, and Deming regression, supplemented by Bland-Altman analysis. Within-run analytical imprecision was low, with coefficients of variation of 0.76% for white blood cells, 0.73% for red blood cells, 0.25% for hemoglobin, and 3.59% for platelets, together with stable inter-assay reproducibility. Linearity of red blood cell and hemoglobin measurements demonstrated high proportionality across the evaluated analytical ranges. Platelet method comparison demonstrated a modest negative proportional bias (Deming slope 0.95; 95% confidence interval, 0.91-0.99). High correlation coefficients across major hematological parameters further supported strong analytical agreement between the two analyzers. Bland-Altman analysis showed minimal systematic bias, and carryover remained low across all parameters. Atellica HEMA 580 met CLSI analytical validation criteria and demonstrated analytical agreement with the Sysmex XN platform, supporting its use in routine and high-throughput hematology laboratories.

Gastric cancer (GC) remains a leading cause of cancer-related mortality, and current diagnostic biomarkers lack sufficient sensitivity and specificity. tRNA-derived fragments (tRFs), an emerging class of non-coding RNAs, have garnered attention for their stability and regulatory roles in carcinogenesis. This review adopts a comparative perspective to evaluate the distinctive roles of tRFs in GC relative to other non-coding RNAs, particularly microRNAs (miRNAs). We summarize tRF biogenesis and classification, highlighting their unique mechanistic repertoire that encompasses both AGO2-dependent gene silencing and non-canonical protein interactions. Dysregulated tRF profiles in patient samples reveal promising diagnostic candidates (e.g., tRF-23-Q99P9P9NDD, tRF-17-18VBY9M) that demonstrate superior performance to conventional markers. However, we critically examine translational barriers including functional heterogeneity, detection challenges, and the absence of registered clinical trials. We also dissect sources of contradictory findings and propose standardized frameworks for future investigation. tRFs represent functionally versatile regulators with distinct advantages over miRNAs in diagnostic applications. Their clinical translation requires overcoming methodological standardization gaps and evidence thresholds through interdisciplinary efforts integrating multi-omics profiling, advanced delivery systems, and rigorous clinical validation.

Previous research on brain network topology in Tobacco Use Disorder (TUD) has been inconsistent, likely due to overlooking the heterogeneity of addiction severity. Consequently, how these topological alterations manifest across different smoking severities is not fully understood. Resting-state functional magnetic resonance imaging (fMRI) and clinical data were collected from 102 males (24 heavy smokers, 36 light smokers, 42 healthy controls). Based on the fMRI data, we computed global graph metrics and applied Network-Based Statistics (NBS) analysis to identify abnormal subnetworks, and performed correlation analyses between global graph metrics and clinical scales. A Support Vector Machine (SVM) classifier was constructed using functional connectivity (FC) strength, graph metrics, and multi-scale fusion features to discriminate the severity of smoking at the individual level within a rigorous repeated stratified nested cross-validation framework. Graph-theory analysis revealed that patients with Tobacco Use Disorder (TUD) exhibited significant large-scale topological reorganization, characterized by increased global integration, indexed by higher global efficiency (Eglob), and reduced local segregation, indexed by a lower clustering coefficient (Cp). Network-Based Statistics (NBS) identified two distinct subnetworks showing reduced connectivity in the TUD group, primarily involving the default mode, limbic, and sensorimotor networks. Subgroup analyses further demonstrated a severity-dependent pattern of network alterations. Light smokers showed increased Eglob with relatively preserved Cp, whereas heavy smokers exhibited a significant reduction in Cp accompanied by more extensive disruption of connectivity across distributed higher-order networks. In contrast, no significant subnetworks were detected in the light smoker group. Importantly, correlation analysis revealed a significant negative association between Cp and Fagerström Test for Nicotine Dependence (FTND) scores. Finally, a multi-scale machine learning model integrating network features and evaluated using 10 × 5 repeated stratified nested cross-validation achieved optimal classification of smoking severity (ensemble AUC = 0.8380), outperforming models based on single-modality features. TUD involves a severity-dependent pathophysiological gradient, which may reflect a hypothesized shift from compensatory strengthening of global integration in light smokers to advanced-severity weakening of local network organization in heavy smokers. The severity-dependent reduction in local segregation, together with widespread hypoconnectivity, suggests multi-scale network disruption. These multi-scale functional network features provide preliminary evidence supporting their potential utility for severity stratification in TUD.

G protein-coupled receptors (GPCRs) are major drug targets for neurodegenerative, neurodevelopmental and psychiatric disorders and are targeted by a multitude of marketed drugs. Typically, multiple GPCRs are involved in diseases of this type, making precise modulation of these receptors crucial for beneficial responses in patients. In addition, the regulation of GPCRs by ligands and the concomitant modulation of physiological signaling pathways are highly fine-tuned. Considering these complex roles, the molecular understanding of GPCR biology has advanced considerably in recent years for these disorders. Likewise, recent developments in multiplexed cell-based assays that measure GPCR activities and downstream effects have substantially expanded the tools available for early drug discovery. In this review, we highlight the impact of GPCRs on these complex neurological disorders and review the current state of multiplexed, barcoded assays that can be used to screen for and validate GPCR-modulating compounds in living cells. These multiplexed assays enable rigorous assessment of drug selectivity across on- and off-target profiles, including within closely related GPCR subfamilies, while simultaneously capturing relevant systemic pathway responses. We therefore propose that the widespread use of this technology has the potential to substantially accelerate and de-risk GPCR-targeted drug development.

This article presents the passivity analysis of neural networks with time-varying delays (NNTVDs). The primary challenge stems from nonlinear delay-dependent terms that arise in estimating the derivative of the Lyapunov-Krasovskii functional (LKF). To address this, a linearization variable augmentation method is developed that strategically employs zero equations in conjunction with time-varying free-weighting matrices incorporating the delay derivative. This novel formulation completely eliminates nonlinear delay terms, rendering the passivity condition affine with respect to the delay. Furthermore, an improved time-varying S-procedure is proposed, where the multiplier matrices are constructed as affine functions of the delay, its derivative, and their product, providing greater freedom for bounding the neuron activation functions. These two key innovations together yield novel passivity and stability criteria that are significantly less conservative than existing ones, as rigorously demonstrated by comparative numerical examples and a practical case study.

The treatment efficacy of major depressive disorder is limited by its pronounced etiological and phenotypic heterogeneity and the partially understood neurobiology. Although pharmacotherapy is the first-line treatment, around 30% of depressive patients do not sufficiently benefit from selective serotonin reuptake inhibitors. This narrative review summarizes the candidate biomarkers that can reportedly predict antidepressant treatment outcomes across different care stages, including symptomatic response/remission, relapse/recurrence, and functional recovery and quality of life. It highlights the similarities and differences among antidepressants, constructs a functional division framework, and evaluates their temporal utility during treatment. Based on the pathophysiological assumptions of depression, we divide the biomarkers into eight functional types: neurotrophic factors (e.g., peripheral BDNF and BDNF DNA methylation), neuroendocrine markers (HPA-axis activity, cortisol), immune-inflammatory regulators (e.g., IL-6, IL-1β, TNF-α), neurotransmitter-related components (e.g., HTR2A variants), non-coding RNAs (e.g., miR-1202, miR-4707-3p, tiRNA-Gly-GCC-001), gut microbiota profiles, neuroimaging markers (e.g., hippocampal volume, ACC thickness, PET markers of serotonergic targets), and other molecular indices (e.g., CYP450 polymorphisms, S100B, CREB/pCREB). Evidence suggests that some biomarkers show early associations with treatment outcomes, particularly peripheral BDNF-related measures, inflammatory cytokines, hippocampal structure, selected miRNAs, CREB/pCREB, and S100B. However, the available evidence is mixed, the observed associations are generally weak, and most biomarkers remain investigational rather than suitable for routine clinical use. Thus, harmonized laboratory methods, large multi-ethnic cohorts, rigorous external validation, and integrative multimodal prediction models will be essential for achieving biomarker-guided precision psychiatry, so as to translate the existing findings into clinical practice.

Arts on Prescription is a social prescribing model in which health professionals refer adults to community-based artistic activities led by artists or musicians, with the goal of promoting wellbeing and mental health. This systematic review aimed to investigate whether Arts on Prescription improves wellbeing and reduces symptoms of depression and anxiety. PubMed, PsycINFO and Cochrane CENTRAL were searched in November 2024, following a previously developed search strategy. The selection of studies, quality assessment and data extraction were carried out independently by two authors. Disagreements were resolved by consensus. We included randomised control trials, quasi-experimental studies, observational studies and mixed-methods studies assessing the impact of art programmes prescribed by a health professional on adults' wellbeing and symptoms of depression and anxiety. A narrative synthesis of the results was carried out. Of the 3,561 unique citations obtained, six studies met the inclusion criteria. Analysis of the reference lists of the included studies revealed two additional pertinent studies. Five of the studies were quasi-experimental studies and three were observational studies. The sample size ranged from 12 to 1,297 participants, with an average age ranging from 43 to over 80 years old. In all studies, an improvement in wellbeing was reported following participation in Arts on Prescription programmes. Evidence regarding depression and anxiety was limited to one study, which reported statistically significant but clinically modest reductions in both outcomes. Arts on Prescription programmes were consistently associated with improvements in wellbeing across a range of populations and settings. Preliminary evidence suggests potential benefits for depression and anxiety that warrant investigation in more rigorous study designs. Further studies are needed to overcome the limitations of the analysed studies, such as the lack of control groups, small and non-representative samples, and short follow-up periods. https://www.crd.york.ac.uk/PROSPERO/view/CRD42024572685, identifier CRD42024572685.

This scoping review synthesises existing evidence from systematic reviews on the effectiveness and implementation of digital mental health interventions among community-dwelling older adults. Twenty-one systematic reviews were included. Results showed that a range of digital tools demonstrate potential to improve common mental health and psychosocial symptoms among older adults, with most evidence concentrating on digital tools to improve depressive symptoms. However, reviews' findings were frequently mixed and accompanied with cautions that primary evidence under-reported key elements such as theoretical underpinnings, intervention design process, participant demographics, intervention acceptability and usability, participant retention, adverse events, and long-term outcomes. More rigorous research and reporting are needed to understand the mechanisms underpinning effective digital mental health interventions for older adults and how they might mitigate the age-related digital divide in mental health services.

Lupeol is a naturally occurring lupane-type pentacyclic triterpenoid widely distributed in dietary and medicinal plants and has attracted increasing interest as a potential neuroprotective compound. However, despite growing experimental evidence, its mechanistic and translational relevance has not been comprehensively evaluated. This review critically examines current evidence regarding the molecular, cellular and neurobehavioural effects of lupeol in neurodegenerative and neuropsychiatric disorders. Available preclinical studies indicate that lupeol modulates several interconnected pathological processes implicated in neurological disorders, including oxidative stress, neuroinflammation, mitochondrial dysfunction, apoptosis, excitotoxicity and synaptic impairment. These effects include preservation of neuronal integrity, improved cognitive and behavioural outcomes, and enhanced neurotrophic and synaptic plasticity across multiple experimental models of Alzheimer's disease, Parkinson's disease, cerebral ischemia, traumatic brain injury and neuroinflammation. Current findings suggest that lupeol exerts multi-target neuroprotective effects through coordinated regulation of oxidative, inflammatory and neuronal signalling networks rather than through a single molecular mechanism. Despite these encouraging findings, clinical translation remains limited by poor aqueous solubility, low bioavailability and insufficient pharmacokinetic and human safety data. Overall, lupeol represents a promising candidate for further investigation in neurological disorders, although rigorous translational studies remain necessary to clarify its therapeutic applicability.

Fever is a recognized manifestation of active ulcerative colitis (UC); however, isolated fever in the absence of GI symptoms or objective evidence of active colonic inflammation is exceedingly uncommon. The diagnostic challenge is amplified in immunocompromised patients receiving biologic therapy, in whom occult infection must be rigorously excluded. We present a 68-year-old woman with a history of well-controlled HIV infection and recently diagnosed UC who was initiated on vedolizumab, mesalamine, and a prednisone taper in the outpatient setting. She presented with persistent daily fevers and initial concern for an infectious etiology of pyrexia. Ulcerative colitis appeared to be well treated, with outpatient CT demonstrating resolution of colitis, and the patient denied diarrhea, abdominal pain, hematochezia, or other symptoms suggestive of active disease. An extensive infectious workup, including blood cultures, viral studies, fungal biomarkers, Lyme disease testing, tick-borne testing, and autoimmune serologies, was negative. During the laboratory evaluation, thrombocytopenia, markedly elevated inflammatory markers, and elevated D-dimer levels were noted. Despite a comprehensive multidisciplinary evaluation, no infectious, malignant, thrombotic, or rheumatologic etiology was identified. Throughout her hospital course, the patient developed sequelae of a severe inflammatory response, including atrial fibrillation. The patient's fevers were ultimately attributed to an atypical systemic inflammatory manifestation of UC. This case highlights a rare presentation of UC manifesting predominantly as fever of unknown origin (FUO) despite apparent clinical and radiographic remission. Clinicians should recognize that significant systemic inflammation may occur even in the absence of overt GI disease activity after exclusion of alternative causes.

Traditional indices such as the V &#x307; E ${{\dot{V}}_{\mathrm{E}}}$ - V &#x307; C O 2 ${{\dot{V}}_{{\mathrm{C}}{{{\mathrm{O}}}_2}}}$ slope describe ventilatory efficiency within the submaximal, near-linear domain of exercise but underrepresent the nonlinear ventilatory behaviour emerging beyond the first ventilatory threshold (VT1). We applied a semi-logarithmic model that linearizes the post-VT1 response by relating CO2 output to log-transformed ventilation, extracting an empirical slope (b_emp) and normalizing it to a theoretical upper limit of CO2 clearance anchored to predicted maximal voluntary ventilation (MVV_pred), yielding the bounded ventilatory efficiency index &#x3b7; V &#x307; E $\eta {{\dot{V}}_{\mathrm{E}}}$ . In 1150 rigorously screened healthy adults (52.4% women; median age 49 years), &#x3b7; V &#x307; E $\eta {{\dot{V}}_{\mathrm{E}}}$ exhibited minimal sex-related variation (14.3% vs. 14.7%) and small positive associations with age (&#x3b2;&#xa0;=&#xa0;+0.058 &#xb1; 0.007, P&#xa0;<&#xa0;0.0001) and FEV1_pred (%) (&#x3b2;&#xa0;=&#xa0;+0.032 &#xb1; 0.008, P&#xa0;<&#xa0;0.0001), accounting for &#x223c;8.5% of total variance (R2&#xa0;=&#xa0;0.085). Both empirical (median 3.3 [2.7-4.1] L&#xb7;logL- 1) and theoretical reference slopes (23.1 [19.5-27.3] L&#xa0;logL- 1) declined with age, whereas &#x3b7; V &#x307; E $\eta {{\dot{V}}_{\mathrm{E}}}$ remained stable across the lifespan, as confirmed by deterministic simulations demonstrating proportional coupling between ventilatory performance and theoretical capacity. In a post hoc cohort of individuals without cardiopulmonary disease but with isolated diffusive disturbance, multivariable regression identified &#x3b7; V &#x307; E $\eta {{\dot{V}}_{\mathrm{E}}}$ as the only significant independent predictor of reduced diffusing capacity (P&#xa0;=&#xa0;0.016), while age, height, sex and MVV_pred were non-significant (all P&#xa0;>&#xa0;0.20), indicating physiological, rather than geometric, determinants. By referencing ventilatory performance to a theoretical limit of CO2 removal, &#x3b7; V &#x307; E $\eta {{\dot{V}}_{\mathrm{E}}}$ provides a reproducible, scale-independent descriptor that refines the physiological interpretation of ventilatory efficiency across health, ageing and contrasting ventilatory constraints. KEY POINTS: The V &#x307; E ${{\dot{V}}_{\mathrm{E}}}$ - V &#x307; C O 2 ${{\dot{V}}_{{\mathrm{C}}{{{\mathrm{O}}}_2}}}$ slope and nadir underestimate key ventilatory adjustments during the most decisive phase of the exercise response - from the first ventilatory threshold (VT1) to peak exercise. This study introduces a semi-logarithmic approach that linearizes the decisive post-VT1 segment of the ventilatory response, better capturing its underlying physiological behaviour. The resulting slope, when normalized to a theoretical physiological limit for gas exchange and scaled to the predicted maximal voluntary ventilation, yields a bounded efficiency index ( &#x3b7; V &#x307; E $\eta {{\dot{V}}_{\mathrm{E}}}$ , %). &#x3b7; V &#x307; E $\eta {{\dot{V}}_{\mathrm{E}}}$ remained robustly stable and only weakly associated with age and lung function, while showing no meaningful dependence on sex or height in over 1000 healthy adults, from which valuable normative equations were derived. This framework integrates ventilatory drive, gas exchange and diffusion capacity, offering a unified and easily applicable tool for physiological and clinical evaluation of ventilatory efficiency.

Intranasal (IN) administrations of extracellular vesicles (EVs) derived from human-induced pluripotent stem cell (hiPSC)-derived neural stem cells (hNSCs) have shown promise in reducing chronic neuroinflammation mediated by microglia and astrocytes in 5x familial Alzheimer's disease (5xFAD) mice, a model for early-onset Alzheimer's disease (AD). The current study rigorously investigated whether treatment with hiPSC-NSC-EVs could also alleviate several other neuropathological changes contributing to progressive cognitive decline. Three-month-old male and female 5xFAD mice received IN administrations of either hiPSC-NSC-EVs (~30&#x2009;&#xd7;&#x2009;109/week for 2&#x2009;weeks) or vehicle. Two months later, the hippocampus of both male and female 5xFAD mice treated with the vehicle showed increased levels of markers of oxidative stress and mechanistic target of rapamycin (mTOR) signaling, altered expression of genes and/or proteins linked to mitochondria and autophagy, and diminished neurogenesis. In contrast, treatment with hiPSC-NSC-EVs restored levels of oxidative stress markers and the expression of genes and/or proteins linked to various mitochondrial complexes, mitochondrial biogenesis, fission, fusion, and mitophagy closer to na&#xef;ve control levels, indicating alleviation of mitochondrial impairments. These improvements were accompanied by reduced phosphorylated mTOR levels and multiple autophagy markers matching those in na&#xef;ve controls, suggesting a dampening of mTOR signaling and an enhancement of autophagy. Furthermore, mice treated with hiPSC-NSC-EVs showed increased hippocampal neurogenesis, associated with enhanced brain-derived neurotrophic factor signaling. Overall, the results highlight that IN administrations of hiPSC-NSC-EVs in the early stages of AD can help slow the progression of multiple neuropathological changes associated with cognitive decline in 5xFAD mice and potentially AD.

The INTACT (INtegration of Time varying data from weArable sensors for physiCal acTivity) method proposed by Zhang et al. represents a timely and important contribution to the harmonization of accelerometer data across studies. The authors provide a mathematically rigorous presentation and an open source R package. INTACT has clear utility for large scale data integration initiatives. INTACT draws inspiration from techniques used in domains where latent biological mechanisms are poorly defined. We argue that physical activity research is distinct in that the primary latent constructs, time and intensity, are well understood. Accelerometer summary metrics represent proxies for these constructs, raising questions about whether abstraction through an eigenmodel improves interpretation of the physiological meaning. The post harmonization "activity intensity" metric produced by INTACT lacks clear units, scale consistency, and it is unclear how the harmonized metric would be applied to activity classification methods. The method's reliance on eigenfunctions is both a strength and a limitation. There are challenges for comparability across studies, and it will be important for applied users to conduct sensitivity analyses to evaluate eigenfunction selection. We also highlight the critical assumption of a shared eigenspace across datasets and populations, which may be violated in contexts with different physical activity patterns. Additional methodological development and clear interpretability will play an important role in the adoption of this method by the research community.

This study was aimed to discover, identify, and comprehensively explore the characteristics of the complete mitochondrial DNA genomes of the NZW rabbit (Oryctolagus cuniculus). This study utilized genomic DNA (gDNA) which was extracted from New Zealand White (NZW) rabbit's liver tissue. The extracted gDNA was rigorously evaluated for both quality and quantity to ensure optimal suitability for mitochondrial DNA enrichment. The sequencing process was carried out using whole genome sequencing (WGS) analysis with Nanopore technology, employing the Oxford Nanopore Technologies GridION platform and bioinformatic tools. Data analysis was carried out using the MEGA11 software to uncover potential mutations, assess genetic diversity, genetic distance and visualize the phylogenetic relationships. The NZW rabbit mtDNA genome spans 17,374 bp, with adenine (31.5%) and thymine (28.3%) as the dominant nucleotides. Leucine is the most abundant amino acid (15.82%), while cysteine is the least abundant (0.61%). The s-rRNA and l-rRNA genes are 957 bp and 1,581 bp long, respectively. The 2,000 bp D-loop region contains two repetitive elements: a 20-bp sequence repeated 14 times and a 153-bp sequence repeated 5 times, which differs from that previously reported in other rabbit mtDNA genomes, thus becoming a specific characteristic and a novel finding of this study.

Pediatric adjustable spinal device surgery, while generally low-risk, can pose severe perioperative airway challenges. This case report details the first documented instance of a 6-year-old girl developing acute airway hyperresponsiveness and severe extrinsic tracheal stenosis following sequential spinal rod adjustments. Adhering to CARE guidelines, we describe a multidisciplinary nursing management strategy that successfully addressed this life-threatening dual pathology. Key interventions included protocol-driven management of bronchospasm with deep sedation and bronchodilators, early recognition and imaging of stenosis, a rigorous ventilator-associated pneumonia prevention bundle, meticulous planning for safe transfers, and comprehensive supportive care for nutrition, neurological symptoms, and skin integrity. These measures collectively stabilized the airway, reduced peak inspiratory pressure, and maintained oxygen saturation above 95%. Ventilator-associated pneumonia was prevented, a stage-2 pressure injury healed, and postoperative delirium and seizures resolved, leading to discharge on day 9. This case underscores the critical need for proactive airway surveillance, especially with three-dimensional&#xa0;imaging in high-risk pediatric spinal cases, and the value of integrated, evidence-based nursing protocols within a multidisciplinary framework to manage complex perioperative complications.

Deep learning has become integral to medical imaging, but its tendency to memorize training data poses serious risks for patient privacy. Machine unlearning offers a potential remedy by revoking sensitive information, yet existing approaches face three key limitations: (1) they often achieve only output-level changes while residual feature representations remain; (2) they rely on batch retraining, making real-time removal of individual patient images infeasible; and (3) they lack rigorous metrics to verify forgetting in feature space. We propose AdaptForget, a domain-adaptive feature-level unlearning framework for privacy-preserving medical image analysis. AdaptForget introduces out-of-distribution (OOD) guidance to disentangle forgotten data from retained data in the feature manifold, supported by a theoretical feature-level unlearning bound. To prevent feature collapse, we design an OOD-driven feature-output disentanglement loss that enforces structured removal of forgotten data. To enable timely revocation, we formalize the task of single-entry forgetting, allowing immediate erasure of individual patient records. For objective auditing, we propose the isolation verification distance, a novel metric that quantifies feature separation and provides interpretable evidence of forgetting. Extensive experiments on four medical imaging benchmarks (histopathology, retinal fundus, dermatology, and OCT) as well as complementary healthcare record datasets demonstrate that AdaptForget achieves state-of-the-art privacy protection while preserving model utility. Code is publicly available at https://github.com/wangbrav/AdaptForget.

Xenotransplantation of porcine cells, tissues or organs offers a promising strategy to address the critical shortage of human donor organs. However, cross-species pathogen transmission, instant blood-mediated inflammatory reaction (IBMIR)-related liver injury, chronic over-immunosuppression, and long-term safety remain major challenges in porcine islet xenotransplantation. In this study, we performed a clinical trial aimed to evaluate the biosafety of neonatal porcine islet xenotransplantation in patients with type 1 diabetes. Ten recipients were assigned to two groups receiving non-encapsulated neonatal islet cell clusters via the jugular-hepatic-portal vein at mean doses of 6354&#xa0;&#xb1;&#xa0;835&#xa0;IEQ/kg and 11&#xa0;600&#xa0;&#xb1;&#xa0;1100&#xa0;IEQ/kg, respectively. Donor pigs originated from a highly inbred, PERV-C-negative colony reared in designated pathogen-free (DPF) conditions, with rigorous pathogen and PERV screening. Immunosuppression included mycophenolate mofetil, tacrolimus, belatacept, and autologous regulatory T-cell therapy, combined with tocilizumab. Continuous low-dose heparin was infused via a portal vein catheter was used for 7 days as anticoagulation therapy. Recipients and spouses were monitored for over 5 years, with 7 followed for more than 10 years. No PERV transmission or transplant-related infections were detected in any recipient or contact during long-term follow-up. The procedure was safe and well-tolerated, with only transient liver function and coagulation changes and mild adverse events. Both groups showed reduced exogenous insulin requirements and markedly lower hypoglycemia incidence, with better glucose control in the higher-dose group. This study demonstrates that DPF PERV-C-free neonatal porcine islet xenotransplantation is biologically safe and partially effective, supporting its value as a reliable donor source and foundational platform for advancing clinical islet xenotransplantation. ClinicalTrials.gov identifier: NCT03162237.