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Long-term preservation of biological material for biomedical or animal conservation purposes currently relies on ultracold temperatures. However, almost complete dehydration of cells in the presence of trehalose could allow cost-effective and flexible storage at ambient temperatures. The study aimed to characterize optimal trehalose-loading methods before passive air-drying and explore different storage options using domestic cat primary fibroblasts as a model. In Experiment 1, cells were loaded with trehalose using optimized electroporation (EP), thermal shock, or cold-responsive nanoparticles (CRNPs) containing trehalose. An evaluation of the cell viability after freezing and thawing was first conducted to compare trehalose protections conveyed by the different loading methods. In Experiment 2a, cells loaded with trehalose were passively air-dried for 2 days (once dry state was confirmed) and then assessed for viability, DNA integrity, or proliferative ability. In Experiment 2b, dried cells were stored for up to 4 weeks either at ambient temperatures, ambient temperatures under vacuum, 4°C, or -20°C while being assessed for viability every 24 hours for the first 5 days of storage and at the 2- or 4-week timepoint. Loading cells with trehalose using EP and thermal shock methods achieved cryoprotective levels, but CRNPs did not enable them to reach that. After reaching the dry state, cells from all trehalose-loading methods and untreated controls had viable cells. Additionally, trehalose-loaded cells from all methods had no increase in DNA damage after drying when compared with fresh controls. Following each method, a small number of cells retained proliferative ability. Storage at 4°C helped prolong viability through the initial 5 days compared with other storage conditions; however, no cells were viable after 2 weeks. Cells maintain viability, DNA integrity, and proliferative ability after reaching the dry state; however, storage conditions beyond 5 days must be improved.

Neurofibromatosis type 1 (NF1) is associated with cognitive impairments affecting attention, executive function, memory, visuospatial abilities, and processing speed, which are well described in children and adolescents and may interfere with daily functioning. In contrast, cognitive functioning in adults with NF1 remains less clearly defined, particularly in individuals without previous neurological or psychological conditions affecting cognitive functioning that may confound neuropsychological performance. This cross-sectional study aimed to characterize cognitive performance across multiple domains in adults with NF1 and to examine the relationship between cognitive performance and psychological and clinical variables. Eighty-seven adults with NF1 and no intellectual disability or previously diagnosed cognitive or psychiatric disorders underwent standardized neuropsychological assessment across seven cognitive domains commonly reported as affected in NF1, particularly in pediatric populations: visual memory, verbal memory, executive function, attention, visuospatial ability, working memory, and visuomotor speed. Participants also completed questionnaires assessing sociodemographic and psychological variables. Cognitive performance was standardized using age-adjusted normative Z-scores. Adults with NF1 performed below the normative mean across several cognitive domains relative to age-adjusted Spanish reference data. Executive function showed clinically significant impairment (z&#x2009;=&#x2009;-2.267), whereas attention showed low-average performance (z&#x2009;=&#x2009;-1.171). Although attention was associated with skin severity and visuospatial ability with depressive symptoms (p&#x2009;<&#x2009;0.05), these associations were not significant after correction for multiple comparisons. These findings suggest a consistent pattern of cognitive underperformance in adults with NF1 without previous neurological or psychological conditions affecting cognitive functioning, supporting the presence of a specific cognitive vulnerability associated with the condition. Although some clinical factors showed associations with specific cognitive domains, these relations were small and did not remain significant after correction for multiple comparisons. These results highlight the importance of routine cognitive assessment and targeted cognitive and psychosocial support strategies, including their integration into genetic counseling, to improve clinical follow-up and patient care.

The integration of self-sensing functionality into 3D-printed cementitious materials presents a promising pathway toward intelligent infrastructure capable of real-time structural health monitoring. This study investigates the influence of nano-titanium dioxide (nano-TiO&#x2082;, NT) on the mechanical, electrical, and piezoresistive behaviour of both mould-cast and extrusion-based 3D-printed cementitious composites. NT was incorporated at 0-30% replacement levels (by binder weight) to evaluate its effects on flowability, compressive strength, flexural strength, electrical resistivity, and fractional change in resistivity (FCR) under cyclic compression. Results indicate that a 10% NT dosage provides the optimal balance between printability, strength enhancement, and piezoresistive sensitivity. Compressive strength increased by approximately 15% compared to the control mixture, while higher dosages (>&#x2009;15%) resulted in strength degradation due to nanoparticle agglomeration and reduced fly ash contribution. Electrical resistivity decreased with increasing NT content up to 15%, indicating an enhanced ability of the cementitious matrix to support interconnected charge-transport pathways through reduced interparticle spacing, improved microstructural packing, and modified ionic conduction. The 3D-printing process further improved compressive strength (up to 34%) and piezoresistive response due to filament alignment and densification effects, although flexural strength decreased because of interlayer anisotropy. The findings demonstrate that NT can serve as a semi-conductive modifier in printable self-sensing cementitious systems, provided that dispersion and interlayer bonding are carefully controlled.

To overcome the poor conductivity of pure MOFs and the structural collapse of MOF-derived carbons, this study constructed an electrochemical sensing interface by integrating high-surface-area UiO-66-NH2 with catalytically active bimetallic CoFe alloy-embedded N-doped carbon nanosheets (CoFe@NC). The composite exhibits uniform morphology, clear crystal structure, and a large specific surface area (227.58 m2&#xb7;g-1), which contribute to enhanced enrichment and detection performance toward CA. This sensor demonstrates a wide linear range (0.001-7 &#x3bc;M) and an ultralow detection limit (0.29 nM), along with excellent anti-interference ability, reproducibility, and stability. By combining density functional theory (DFT) calculations with electrochemical experiments and in-situ Raman characterization, the roles of various components in composite materials during the CA oxidation process, the regulatory mechanism of bimetallic alloys on the electronic structure of the material, the reaction mechanism of CA at the sensing interface, and the synergistic enhancement effect of the bimetallic system were elucidated. When applied to red wine, green tea, blueberries, and apple peel, recoveries of this sensor ranged from 97.2 to 103.8%. The quantitative results were in excellent agreement with those obtained by ultraviolet-visible (UV-vis) spectrophotometry and high performance liquid chromatography (HPLC), confirming the high accuracy and reliability of this sensor. This work not only provides a high-performance sensing platform for trace CA detection in food but also offers a novel strategy for the design and application of electrochemical sensors based on hierarchically structured MOF composites through the deep integration of experimental and theoretical approaches.

In addition to a gap detection threshold, an auditory gap detection task also provides information on response time. This study investigated the association of response time from an adaptive gap detection task with two validated measures of cognitive processing speed as well as scores from a cognitive screener, while accounting for the effects of age, peripheral hearing ability, and HIV status. Participants (age 17-45&#x202f;years) were from a prospective cohort study in Dar es Salaam, Tanzania with normal hearing ability and no reported neurological diseases. The final sample included 283 unique subjects (158 living with HIV, 125 without HIV) matched on age and sex. Multiple linear regression models were employed to assess the relationship between gap response time and cognitive processing speed scores from the Tests of Variables of Attention, Cogstate test battery, and Montreal Cognitive Assessment. Regression analysis showed significant relationships between gap response time and all processing speed scores, except one. Age showed varying degrees of association with different processing speed measures, but peripheral hearing ability did not show any significant relationship with speed measures. This study identifies a link between cognitive processing speed and gap detection response times. With further validation, gap detection response times could emerge as a straightforward yet informative measure of cognitive processing speed and would expand the clinical usefulness of the gap test. In the audiology clinic, this measure may hold promise as a tool for detecting and monitoring cognitive decline.

Forests are vital for regulating climate and sustaining biodiversity, but climate change threatens their ability to do so, especially in the tropics. Our knowledge of how tropical forests and their constituent trees will respond to changes in climate is largely based on functional trait studies; however, few previous studies have investigated trait changes within individual tropical trees across decades, limiting our ability to predict the future of these forests. In this study, we leveraged historical and contemporary botanical specimens collected from the same individual trees in the southern Peruvian Amazon, measured a suite of leaf traits to test for individual-level trait changes over nearly 40&#x2009;years, and then related these changes to concurrent changes in local climate. We hypothesized that trees have acclimated their functional traits in response to increasing air temperatures and drought intensification and that this acclimation should help to maintain stable leaf temperatures through time. In accord with our hypothesis, we found significant decreases in measured leaf traits, including size and shape metrics and stomatal traits, within individuals through time. We used these measured traits to model leaf temperatures through time, which increased faster than would be expected based on changes in air temperature alone. This accelerated warming of leaves was due to decreased stomatal conductance, a potential acclimation of trees to dry season intensification and rising [CO2], thus limiting leaf transpirational cooling. In other words, trees have decreased abilities to cool their leaves, and consequently they may be approaching critical thermal thresholds faster than they would in the absence of water limitation. Our study provides evidence that while individual trees are acclimating to climate change, tropical forests are undergoing increasing thermal stress and that intensifying drought may be elevating this risk.

Gut microbiota-derived extracellular vesicles have emerged as crucial mediators in microbe-host communication, not only facilitating intracellular communication, quorum sensing, and horizontal gene transfer among bacteria but also playing a central role in cross-kingdom dialogue. In recent years, bacterial extracellular vesicles (BEVs) have attracted widespread attention due to their ability to carry a diverse array of bioactive molecules-such as proteins, lipids, and nucleic acids-and deliver them to host cells, thereby precisely regulating host metabolic and immune homeostasis. This review systematically elaborates the entire biological process of BEVs, from their biogenesis to functional interactions with host cells, with a specific emphasis on revealing their roles in the pathogenesis of various metabolic diseases-including obesity, type 2 diabetes (T2DM), metabolic dysfunction-associated steatotic liver disease (MASLD), atherosclerosis, and hypertension-at both molecular and cellular levels. Furthermore, leveraging their inherent stability, biocompatibility, and targeting capabilities, we discuss the translational potential and challenges of BEVs in the diagnosis and treatment of metabolic disorders. Beyond summarizing the latest research advances on BEVs in metabolic disorders, this review provides a critical analysis of current mechanistic insights and clinical translation pathways, aiming to establish a theoretical framework for developing novel microbiome-based metabolic interventions. Deciphering the BEV-mediated microbiota-host interaction network holds promise for pioneering new strategies for the precision prevention and treatment of metabolic disease.

Waterborne diseases are caused by pathogens that are transmitted via ingestion of contaminated water and remain a leading cause of death particularly in young children. Climate change threatens to undermine the progress that has been made in reducing waterborne diseases. In this Review, we explore how meteorological conditions that are influenced by climate change, including temperature, heavy rainfall and flooding, drought, and extreme weather, affect the biology and transmission of waterborne pathogens, with a focus on those that spread via the faecal-oral route. We discuss evidence that these impacts vary across pathogens and consider how such information is used to project disease risks under future climatic conditions, including incorporating social vulnerability and pathogen-specific outcomes to more accurately estimate future disease burden. We also review strategies to blunt climate-related increases in waterborne diseases, including vaccination; water, sanitation and hygiene interventions; and enhanced surveillance. As climate change continues to alter our global environment, understanding its impacts on waterborne diseases can improve our ability to reduce climate harms, identify and protect vulnerable populations, and develop evidence-based approaches to promote population health.

Telomeres play a crucial role in maintaining genomic stability in healthy cells. However, they gradually shorten during the cell cycle, leading to chromosomal instability. Telomere length and telomerase activity are vital factors that counteract cellular degradation in cancer development and tumor persistence. Telomerase, which is activated in most cancer cells due to telomerase catalytic subunit (hTERT) overexpression, serves as a universal biomarker that is essential for cancer cell growth and survival. The upregulation of hTERT, often associated with G&#x2009;>&#x2009;A mutations in its promoter region, is frequently implicated in cancer progression. Consequently, anti-telomerase therapy has been proposed as a potentially more efficacious alternative to conventional treatment. Small-molecule inhibitors have garnered significant attention owing to their selectivity or ability to modulate multiple proteins. However, challenges, such as low response rates, brief response durations, toxicity, and resistance persist. Several strategies have been proposed to target telomerase activity and the telomere structure. These include the utilization of G-quadruplex-stabilizing compounds and telomere-specific oligonucleotide inhibitors of telomerase such as GRN163L and T-oligos. Another therapeutic approach involves the use of biological antisense oligonucleotides that specifically inhibit hTERT and human telomerase RNA component genes, potentially reducing telomerase activity and generating robust DNA signals in cancer cells. Immunotherapy targeting hTERT represents a recent advancement in cancer treatments. This approach leverages the immune system to target cancer cells with high hTERT expression, thereby offering a potentially more reliable treatment strategy. This review provides an overview of current research on telomerase-targeting small-molecule inhibitors, antisense oligonucleotides, and immunotherapy, discussing their mechanisms, clinical applications, and prospects in cancer treatment.

The emphysematous phenotype is an important phenotype in chronic obstructive pulmonary disease (COPD), with substantial morbidity and mortality. The mechanisms underpinning the role of alveolar type II (AT2) cells in alveolar repair within this phenotype remain poorly understood. This study aimed to elucidate the role of PHLDA1, a potential stemness regulator in AT2 cells, on emphysema development. Utilizing mice model, we performed a targeted knockdown of PHLDA1 in AT2 cells and subsequently exposed these mice to tobacco smoke to assess the resultant severity of emphysema and related alveolar damage. We manipulated PHLDA1 expression in AT2 cells line or primary mouse AT2 cells to examine its influence on AT2 stemness-related processes- differentiation, proliferation, and wound closure ability. The specific pathway of PHLDA1 mediated in AT2 cells, as well as its interaction with the GLI1 protein, was further investigated. Mice with reduced PHLDA1 expression developed the emphysema independent of smoking exposure. PHLDA1 knockdown in AT2 cells attenuated their proliferation via the Hedgehog pathway, impairing wound closure ability in the emphysematous phenotype. We also discovered a binding relationship between PHLDA1 and GLI1, where PHLDA1 modulates the nuclear translocation of GLI1, thus regulating the Hedgehog pathway and influencing the stemness and proliferation of AT2 cells. Our study suggests that PHLDA1 is a critical factor in the proliferation process of AT2 cells via modulation of GLI1 nuclear translocation. This regulation is essential to the pathogenesis of the emphysematous phenotype in COPD, signifying potential therapeutic targets for intervention.

This study proposes a hybrid underwater crack image processing method. The first half of the algorithm is based on traditional image processing, which analyzes and judges the color of an image, establishes the image chromaticity factor K, and performs color correction when K is greater than a threshold value. A quadtree analysis method is used to estimate the background light of the image. The latter half of the algorithm is based on deep learning (DL), using convolutional neural networks (CNNs) to learn image features, adopting an edge feature extraction network to extract edge feature information based on the characteristics of underwater crack images, and finally the image features are fused with the edge feature. Depthwise separable convolution and pixel attention mechanisms were used in CNNs to improve feature extraction ability while reducing computational complexity. Ultimately, crack image restoration relies on the underwater image model to achieve enhanced clarity. This hybrid algorithm combined the interpretability of traditional algorithms with the generality of DL algorithms. Compared with various traditional image enhancement and DL algorithms, the proposed algorithm achieved good performance in terms of parameters such as peak signal-to-noise ratio, structural similarity, underwater image quality measure and pixel-based contrast quality Index.

To compare the facial emotion recognition (FER) accuracy and reaction time (RT) of patients with performance-type social anxiety disorder (pSAD) and generalized-type social anxiety disorder (gSAD) with healthy controls (HCs). A total of 56 patients who were diagnosed as having SAD (31 gSAD and, 25 pSAD) according to the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) were included in the study. Forty individuals with no psychiatric disorders were included as the HCs. FER skills were assessed using a task that included Ekman's basic emotions and a neutral face. Additionally, the Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), and the Liebowitz Social Anxiety Scale (LSAS) were administered to all participants. FER performances of patients with pSAD were similar to the HC group. The accuracy rates for emotions other than sadness in patients with gSAD were similar to those in the HC group. The RT to all facial expressions in patients with gSAD was statistically significantly shorter than in the HCs (p&#x2009;<&#x2009;0.005). The RT given to facial emotions other than sadness was shorter in the pSAD patient group compared with the HC group. A negative correlation was found between STAI-state anxiety and neutral face recognition. (r&#x2009;=&#x2009;-0.308, p&#x2009;<&#x2009;0.05). However, except for neutral face recognition, no significant correlation was observed between the BDI, STAI-state, STAI-trait, LSAS-fear, LSAS-avoidance, and FERT subscales. The current study casts doubt on some of the effects reported in the literature on SAD's FER ability. In this study, no significant difference was found in FER ability among SAD subtypes; this may suggest that a common mechanism exists in both subtypes.

Although it had been reported that the translation product of the functional aquaporin gene, aqp1, is localized to the contractile vacuole complex of Paramecium multimicronucleatum, the molecules that pass through this channel protein had not been identified. In the present study, we introduced and expressed this protein in Xenopus oocytes and identified the molecules that pass through this channel protein using a swelling assay. The aqp1 mRNA injected into Xenopus oocytes was found to be sufficiently expressed and functional within the oocytes. As a result, P. multimicronucleatum AQP1 (PmAQP1) allowed water to pass through at a relatively slow rate of approximately 50 &#xd7;&#x2009;10-4cm/s, a rate comparable to that of aquaporins present in the contractile vacuoles of other protists. This water permeability was almost completely inhibited by tetraethylammonium, an orthodox aquaporin inhibitor, and phloretin, an aquaglyceroporin inhibitor. PmAQP1 also had the ability to pass glycerol or urea through, but the rates of these processes could not be accurately measured in this study. These findings demonstrate that PmAQP1 is an atypical multifunctional channel, suggesting that the contractile vacuole of free-living protists plays a crucial role not only in osmoregulation but also in the clearance of metabolic wastes.

To provide an overview of diagnostic tests for Stage B heart failure (SBHF), synthesizing evidence from guidelines and clinical studies. Advances in diagnostic technologies have expanded the ability to identify subclinical myocardial remodelling and early myocardial injury before symptom onset. We highlight the central role of transthoracic echocardiography as the cornerstone diagnostic modality for detecting subclinical myocardial remodelling and dysfunction, including the use of speckle tracking echocardiography. In parallel, circulating biomarkers, especially natriuretic peptides and high-sensitivity cardiac troponins, can play important roles in the detection and risk stratification of SBHF. Additional diagnostic approaches, including electrocardiography, chest X-ray, cardiac magnetic resonance imaging, cardiac computed tomography, nuclear imaging, and exercise stress testing, are reviewed for their adjunctive roles in selected clinical contexts. Emerging applications of artificial intelligence are explored as promising strategies to increase the diagnostic precision, scalability, and early detection of SBHF in clinical practice. SBHF - representing a subclinical phase of HF characterized by structural cardiac abnormalities, functional impairment, or persistently abnormal cardiac biomarkers in individuals - has historically been difficult to recognize in the community. Advances in imaging, biomarkers, and AI may improve the feasibility of detecting this entity, creating a crucial window for intervention, because timely risk stratification and preventive strategies during SBHF may attenuate progression to symptomatic HF and reduce its long-term clinical and economic burden.

Polycystic Ovary Syndrome (PCOS) is the most common reproductive-metabolic disorder in women of reproductive age, yet preclinical models show considerable variability in their ability to capture its multiple diagnostic phenotypes and various non-diagnostic features. Androgen-based rodent models are widely used to mimic clinical hyperandrogenism; however, studies using dehydroepiandrosterone (DHEA), the most abundant circulating androgen in humans, have produced inconsistent findings across metabolic and behavioral domains. Here, we systematically evaluated a DHEA-based model of PCOS in female Long-Evans rats (N&#x202f;=&#x202f;25), assessing reproductive, metabolic, and behavioral outcomes. To model a moderate metabolic challenge, relevant to the high comorbidity of PCOS and obesity, animals were given access to either a 10% fructose solution or water. We hypothesized that DHEA would induce PCOS-relevant phenotypes and that fructose consumption would exacerbate these effects. DHEA treatment increased ovarian testosterone levels and disrupted estrous cyclicity, consistent with key reproductive features of PCOS; however, no significant differences were observed in ovarian cyst count. In contrast, DHEA produced no robust effects across metabolic measures (including glucose tolerance, insulin resistance, and adiposity), anxiety-like behavior, or spatial working memory, and fructose exposure did not meaningfully exacerbate these outcomes. Together, these findings indicate that while DHEA reliably reproduces reproductive features of PCOS, it is insufficient to model broader metabolic and behavioral phenotypes under modest dietary challenge. These results highlight important limitations of this commonly used model and underscore the need for more robust metabolic manipulations when aiming to produce multi-system features of PCOS.

Obstructive sleep apnea (OSA) affects approximately 15% of pregnancies and is associated with adverse maternal and fetal outcomes. Although polysomnography (PSG) is the diagnostic gold standard, increasing clinical demand creates substantial bottlenecks in manual PSG scoring and specialist review. Conventional screening tools often show limited discriminative ability in high-risk referred populations. Therefore, optimized risk stratification models are needed to streamline clinical workflows, prioritize diagnostic resource allocation, and facilitate timely intervention for high-risk pregnant patients. This retrospective observational cohort study recruited pregnant women with suspected OSA who underwent level 2 portable PSG. Six machine learning algorithms, including XGBoost, logistic regression, GBM, neural networks, KNN, and AdaBoost, were constructed based on integrated clinical and oximetry features. Feature importance screening and DeLong's test-based pairwise comparison were performed to determine the optimal feature combination for model construction. The primary outcome was any OSA defined by an apnea-hypopnea index (AHI)&#x2009;&#x2265;&#x2009;5 events/h, while the secondary outcome was moderate-to-severe OSA (AHI&#x2009;&#x2265;&#x2009;15 events/h). All models were optimized using 10-fold cross-validation and externally validated on an independent testing set. Model performance was comprehensively assessed via receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Among the 667 enrolled participants, 305 (45.7%) were diagnosed with OSA. The 3% oxygen desaturation index, lowest SpO2, body mass index, waist circumference, and abdominal circumference were identified as core predictive variables. For the primary screening of any OSA, logistic regression and neural networks achieved robust and comparable discriminative performance; the logistic regression model attained a testing-set AUC of 0.872 with a sensitivity of 78.0%. For moderate-to-severe OSA prediction, AdaBoost and GBM exhibited excellent predictive efficacy, with testing-set AUCs of 0.956 and 0.955, respectively. DCA confirmed that the established models yield favorable clinical net benefit across broad risk threshold ranges, enabling optimized clinical screening and priority referral strategies. This machine learning-based risk stratification framework demonstrates promising diagnostic performance for identifying OSA in symptomatic pregnant women under clinical referral. Leveraging structured medical records incorporating sleep history and physical measurements, this tool serves as an auxiliary triage strategy to assist clinical decision-making. The proposed models may help identify high-risk patients for expedited PSG assessment, which has the potential to optimize diagnostic workflows and improve resource allocation in specialized obstetric sleep medicine services.