Search Results (7,140)

Background: Diabetic ketoacidosis (DKA) in patients with kidney failure receiving dialysis presents a formidable clinical challenge. Standard DKA protocols, designed for patients with preserved renal function, often fail in this cohort and can be unsafe when applied without modification. Patients are at risk of iatrogenic fluid overload, dyskalaemia, and hypoglycaemia due to altered insulin kinetics, impaired gluconeogenesis, and the absence of osmotic diuresis. Purpose: This narrative review aims to synthesise current understanding of DKA pathophysiology in dialysis patients, delineate distinct clinical phenotypes, and propose individualised management strategies grounded in physiology-based reasoning, comparative guideline insights, and consensus-supported literature. Methods: We searched PubMed/MEDLINE, Embase, and Google Scholar (January 2004–June 2024) for adult dialysis populations, using terms spanning DKA, kidney failure, insulin kinetics, fluid balance, and cerebral oedema. Reviews, observational cohorts, guidelines, consensus statements, and physiology papers were prioritised; case reports were used selectively for illustration. Evidence was weighted by physiological plausibility and practice relevance. Nephrology-led authors aimed for a pragmatic, safety-first synthesis, seeking and integrating contradictory recommendations. Conclusions: Our findings highlight the critical need for a nuanced approach to fluid management, a tailored insulin strategy that accounts for glucose-insulin decoupling and prolonged insulin half-life, and careful consideration of potassium and acidosis correction. We emphasise the importance of recognising specific volume phenotypes (hypovolaemic, euvolaemic, hypervolaemic) to guide fluid therapy, and advocating the judicious use of variable-rate insulin infusions (‘dry insulin’) to mitigate fluid overload. We also show that service-level factors are critical. Dialysis-specific pathways, interdisciplinary training, and quality improvement metrics can reduce iatrogenic harm. By linking physiology with workflow adaptations, this review provides a physiologically sound, bedside-oriented map for navigating this complex emergency safely and effectively. In doing so, it advances an individualised model of DKA care for dialysis-dependent patients. Full article

Collecting crop growth data in field environments is crucial for breeding research. The team’s current autonomous soybean phenotyping system requires manual control to start and stop data collection. To address the aforementioned issues, this study innovatively proposes an elliptical calibration rotating geofencing technique. Preprocess coordinates using Z-scores and mean fitting perform global error calibration via weighted least squares, calculate the inclination angle between the row direction and the relative standard direction by fitting a straight line to the same row of data, and establish a rotation model based on geometric feature alignment. Results show that the system achieves an average response time of 0.115 s for geofence entry, with perfect accuracy and Recall rates of 1, meeting the requirements for starting and stopping geographic fencing in soybean ridge trial plots. This technology provides the critical theoretical foundation for enabling a dynamic, on-demand automatic start–stop functionality in smart data collection devices for soybean field trial zones within precision agriculture. Full article

High-grade gliomas (HGGs) are aggressive brain tumors with limited treatment options and poor survival outcomes. Variants including isocitrate dehydrogenase (IDH)-wildtype, IDH-mutant, and histone 3 lysine to methionine substitution (H3K27M)-mutant subtypes demonstrate considerable tumor heterogeneity at the genetic, cellular, and microenvironmental levels. This presents a major barrier to the development of reliable models that recapitulate tumor heterogeneity, allowing for the development of effective therapies. Glioma tumor organoids (GTOs) have emerged as a promising model, offering a balance between biological relevance and practical scalability for precision medicine. In this study, we present a refined methodology for generating three-dimensional, multiregional, patient-derived GTOs across a spectrum of glioma subtypes (including primary and recurrent tumors) while preserving the transcriptomic and phenotypic heterogeneity of their source tumors. We demonstrate the feasibility of a high-throughput drug-screening platform to nominate multi-drug regimens, finding marked variability in drug response, not only between patients and tumor types, but also across regions within the tumor. These findings underscore the critical impact of spatial heterogeneity on therapeutic sensitivity and suggest that multiregional sampling is critical for adequate glioma model development and drug discovery. Finally, regional differential drug responses suggest that multi-agent drug therapy may provide better comprehensive oncologic control and highlight the potential of multiregional GTOs as a clinically actionable tool for personalized treatment strategies in HGG. Full article

Acorus tatarinowii Schott (A. tatarinowii), a traditional Chinese medicine, has been widely used in the treatment of dementia, particularly AD. α-Asarone is the main active component of A. tatarinowii oil, and its neuroprotective effects and underlying molecular mechanism in AD remain unclear. In this study, we utilized different transgenic Caenorhabditis elegans (C. elegans) AD models to investigate the neuroprotective mechanism of α-asarone in vivo. Our findings revealed that α-asarone significantly ameliorated Aβ- and tau-induced phenotypic abnormalities, including deficits in chemotaxis-related learning, hyposensitivity to exogenous serotonin, and impaired neuronal integrity. Furthermore, the α-asarone treatment effectively reduced Aβ-induced oxidative stress. Mechanistically, α-asarone reduced Aβ accumulation and maintained protein homeostasis by stimulating proteasome degradation and autophagy in an SKN-1/Nrf2-dependent manner. Our study highlights the potential of α-asarone as an SKN-1/Nrf2 activator and its capability to facilitate proteostasis, supporting its therapeutic potential for AD treatment. Full article

Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dynamic changes has remained difficult. We developed a deep learning framework for stain-free monitoring of leukemia cell cultures using automated bright-field microscopy in a semi-automated culture system (AICE3, LABMaiTE, Augsburg, Germany). YOLOv8 models were trained on images from K562, HL-60, and Kasumi-1 cells, using an NVIDIA DGX A100 GPU for training and tested on GPU and CPU environments for real-time performance. Comparative benchmarking with RT-DETR and interpretability analyses using Eigen-CAM and radiomics (RedTell) was performed. YOLOv8 achieved high accuracy (mAP@0.5 > 98%, precision/sensitivity > 97%), with reproducibility confirmed on an independent dataset from a second laboratory and an AICE3 setup. The model distinguished between morphologically similar leukemia lines and reliably classified untreated versus differentiated K562 cells (hemin-induced erythroid and PMA-induced megakaryocytic; >95% accuracy). Incorporation of decitabine-treated cells demonstrated applicability to drug testing, revealing treatment-specific and intermediate phenotypes. Longitudinal monitoring captured culture- and time-dependent drift, enabling separation of temporal from drug-induced changes. Radiomics highlighted interpretable features such as size, elongation, and texture, but with lower accuracy than the deep learning approach. To our knowledge, this is the first demonstration that deep learning resolves subtle, drug-induced, and time-dependent morphological changes in unstained leukemia cells in real time. This approach provides a robust, accessible framework for label-free longitudinal drug testing and establishes a foundation for future autonomous, feedback-driven platforms in precision oncology. Ultimately, this approach may also contribute to more precise and adaptive clinical decision-making, advancing the field of personalized medicine. Full article

PITPNM3 has been identified as a crucial gene associated with various phenotypes of retinal disease in humans; however, detailed mechanisms through which PITPNM3 mutations result in these conditions are not fully understood. In this study, we aimed to generate such a preclinical mouse model and evaluate its relevance to human PITPNM3-related conditions. Heterozygous mice were bred to obtain a homozygous genotype, aiming to mimic the human genetic condition. Subsequent phenotyping and genetic segregation analyses were conducted along with electrophysiological studies and histological examinations. Full-field electroretinogram analysis revealed a reduced cone response although the severity was not as pronounced as observed in humans with PITPNM3-related conditions. Histologically, the retinal structure appeared largely unchanged, indicating a discordance between functional impairment and morphological changes. In our preclinical mouse model, the observed phenotypic changes were not as severe as those found in humans with PITPNM3-related conditions and this discrepancy points to a potentially different disease progression trajectory in the mouse model. These findings highlight the importance of longer follow-up periods in such studies and the need for further research to elucidate the genotype–phenotype relationship in PITPNM3. Full article

Left ventricular hypertrophy (LVH) refers to the pathological thickening of the myocardial wall and is strongly associated with several adverse cardiac outcomes and sudden cardiac death. While the biomechanical drivers of LVH are well established, growing evidence points to a critical role for cardiac and systemic metabolism in modulating hypertrophic remodeling and disease pathogenesis. Despite the efficiency of fatty acid oxidation (FAO), LVH hearts preferentially increase glucose uptake and catabolism to drive glycolysis and oxidative phosphorylation (OXPHOS). The development of therapies to increase and enhance LFCA FAO is underway, with promising results. However, the mechanisms of systemic metabolic states and LCFA dynamics in the context of cardiac hypertrophy remain incompletely understood. Further, it is unknown to what extent cardiac metabolism is influenced by whole-body energy balance and lipid profiles, despite the common occurrence of lipotoxicity in LVH. In this study, we measured whole-body and cellular respiration along with analysis of lipid and glycogen stores in a mouse model of LVH. We found that loss of the cardiac-specific gene, myosin-binding protein C3 (Mybpc3), resulted in depletion of adipose tissue, decreased mitochondrial function in skeletal muscle, increased lipid accumulation in both the heart and liver, and loss of whole-body metabolic flux. We found that supplementation of exogenous LCFAs boosted LVH mitochondrial function and reversed cardiac lipid accumulation but did not fully reverse the hypertrophied heart nor systemic metabolic phenotypes. This study indicates that the LVH phenotype caused systemic metabolic rewiring in Mybpc3^−/− mice and that exogenous LCFA supplementation boosted mitochondrial function in both cardiac and skeletal muscle. Full article

Gastric cancer (GC) often presents a poor prognosis due to its asymptomatic phenotype at early stages. Upper endoscopy, which is the current gold standard to diagnose GC, is invasive with limited sensitivity for detecting gastric preneoplasia. Non-invasive biomarkers, such as blood circulating proteins, offer a promising alternative for the early detection of gastric lesions. In this prospective study, we identified plasma protein biomarkers for gastric preneoplasia and cancer using mass spectrometry-based proteomics in an exploratory cohort (n = 39). Fifteen promising protein candidates emerged to distinguish patient categories and were further confirmed by enzyme-linked immunosorbent assays (ELISA) in plasma samples from a validation cohort of 138 participants. Our predictive models demonstrated high classification performance with a minimal set of biomarkers. A four-protein panel (ARG1, CA2, F13A1, S100A12) achieved 94.1–98.2% AUROC (95% CI) for distinguishing cancer from non-cancer cases, while a five-protein panel (ARG1, CA2, HPT, MAN2A1, LBP) reached 97.3–99.5% AUROC (95% CI) for distinguishing cancer or preneoplasia from healthy or non-atrophic gastritis cases on the full cohort. Leveraging simple blood sampling, this strategy holds promise to detect high-risk gastric lesions, even at asymptomatic stages. Such an approach could significantly improve early detection and clinical management of GC, offering direct benefit for patients. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common chronic liver disorders worldwide and can lead to inflammation, fibrosis, and liver cancer. To better understand the impact of an unbalanced hypercaloric diet on liver phenotype in impaired autophagy, the study compared C57BL/6J wild type (WT) and MAPK15-ERK8 knockout (KO) male mice with C57BL/6J background fed for 17 weeks with “Western-type” (WD) or standard diet (SD). Liver features were monitored in vivo by high-frequency ultrasound (HFUS) using a semi-quantitative and parametric assessment of pathological changes in the parenchyma complemented by computer-aided diagnosis (CAD) methods. Liver histology was considered the reference standard. WD induced liver steatosis in both genotypes, although KO mice showed more pronounced dietary effects than WT mice. Overall, HFUS reliably detected steatosis-related parenchymal changes over time in the two mouse genotypes examined, consistent with histology. Furthermore, this study demonstrated the feasibility of extracting quantitative features from conventional B-mode ultrasound images of the liver in murine models at early clinical stages of MASLD using a computationally efficient and vendor-independent CAD method. This approach may contribute to the non-invasive characterization of genetically engineered mouse models of MASLD according to the principles of replacement, reduction, and refinement (3Rs), with interesting translational implications. Full article

Background/Objectives: Escitalopram, a first-line antidepressant, is primarily metabolized by CYP2C19. Its pharmacokinetics are altered during pregnancy. This study aims to predict maternal and fetal exposure to escitalopram during pregnancy and to propose safe and effective dosing strategies using physiologically based pharmacokinetic (PBPK) modeling. Methods: Predictive PBPK models for escitalopram were developed in nonpregnant women, pregnant women, and the fetoplacental unit using the Simcyp^® simulator. Additional models incorporating CYP2C19 phenotypes were constructed. Model performance was evaluated using visual predictive checks and by comparing predicted-to-observed ratios for the maximum plasma concentration (C_max) and the area under the curve (AUC), within an acceptance criterion of 0.7–1.3. Results: Escitalopram concentrations at doses of 10–20 mg declined with advancing gestation. The cord-to-maternal concentration ratio was approximately 0.70 for both doses. Simulations of maternal and fetoplacental PBPK models across CYP2C19 phenotypes showed that most observed concentrations fell within the 95% confidence intervals of the predictions. Based on the therapeutic range attained and the maintenance of steady-state exposure, a once-daily 20 mg escitalopram dose was predicted to be appropriate during pregnancy. Conclusions: These findings suggest that a once-daily 20 mg dose appears optimal during pregnancy, highlighting the need to consider the gestational stage and CYP2C19 phenotype in dose optimization. Full article

Background: Apolipoprotein E (APOE) is the strongest genetic risk determinant for late-onset Alzheimer’s disease (AD). The APOE3 allele is risk-neutral, the APOE4 allele increases the risk of developing AD, and the APOE2 allele is neuroprotective. Methods: We utilized RNA sequencing of hemi-brains from a mouse model homozygous for each of these humanized APOE alleles to study gene expression profiles between mice aged 12 months of age (MO) and 18 MO, independent of β-amyloid and tau pathology. Results: More than half of the differentially expressed genes (DEGs) within each genotype were shared with at least one other APOE allele, including 1610 DEGs that were shared across the three genotypes. These DEGs represent changes driven by aging rather than APOE genotype. Aging induced DEGs and biological pathways involving metabolism, synaptic function, and protein synthesis, among others. Alterations in these pathways were also identified by DEGs unique to APOE4, suggesting that the APOE4 allele drives the aging phenotype. In contrast, fewer pathways were identified from DEGs unique to APOE2 or APOE3. Conclusions: Transcriptomic results suggest that the most significant impact on brain-level expression changes in humanized APOE mice is aging and that APOE4 exacerbates this process. These in vivo findings within an established model system are consistent with brain aging being the greatest risk factor for AD and suggest that APOE4 expression promotes an aging phenotype in the brain that interacts with, and contributes to, aging-driven AD risk. Results reinforce the impact age and APOE allele contribute to AD and age-related neurodegeneration, and foster greater mechanistic understanding as well as inform therapeutic intervention. Full article

Background/Objectives: Sleep disturbances are recognized as a common feature of Long COVID but detailed investigation into the specific nature of these sleep symptoms remain limited. This study analyzes comprehensive sleep questionnaire data from a Long COVID clinic to better characterize the nature and prevalence of sleep complaints in this population. Methods: We conducted a cross-sectional analysis of 200 adults referred to the Stanford Long COVID Clinic. Patients completed an intake questionnaire including three sleep-related items (unrefreshing sleep, insomnia, daytime sleepiness) rated on a 0–5 Likert scale. Additionally, patients completed the Alliance Sleep Questionnaire (ASQ), incorporating the Insomnia Severity Index, Epworth Sleepiness Scale, reduced Morningness–Eveningness Questionnaire, and modules for parasomnia, restless legs, and breathing symptoms. We calculated the prevalence of six sleep symptom domains. Standardized symptom data were analyzed using principal component analysis (PCA) and K-means clustering (k = 2) to explore latent phenotypes and used logistic regression to assess associations between demographic and clinical variables and each sleep complaint. Results: Sleep-related breathing complaints affected 57.5% of participants, insomnia 42.5%, and excessive daytime sleepiness 28.5%. Parallel analysis supported a nine-factor structure explaining ~90% of variance, with varimax rotation yielding interpretable domains such as insomnia/unrefreshing sleep, fatigue/post-exertional malaise, parasomnias, and respiratory symptoms. Gaussian mixture modeling favored a two-cluster solution (n = 94 and n = 106); one cluster represented a higher-burden phenotype characterized by greater BMI, insomnia, daytime sleepiness, gastrointestinal symptoms, and parasomnias. Logistic models using factor scores predicted insomnia with high accuracy (AUC = 0.90), EDS moderately well (AUC = 0.81), but extreme chronotype poorly (AUC = 0.39). In adjusted models, hospitalization during acute COVID-19 was significantly associated with insomnia (OR 4.41; 95% CI 1.27–15.36). Participants identifying as multiracial had higher odds of insomnia (OR 3.22; 95% CI 1.00–10.34), though this narrowly missed statistical significance. No other predictors were significant. Conclusions: Sleep disturbances are frequent and diverse in Long COVID. Factor analysis showed overlapping domains, while clustering identified a higher-burden phenotype marked by more severe sleep and systemic complaints. Symptom-based screening may help target those at greatest risk. Full article

Generative Adversarial Networks (GANs) have emerged as powerful tools in artificial intelligence (AI) with growing relevance in medical imaging. In dermatology, GANs are revolutionizing image analysis, enabling synthetic image generation, data augmentation, color standardization, and improved diagnostic model training. This narrative review explores the landscape of GAN applications in dermatology, systematically analyzing 27 key studies and identifying 11 main clinical use cases. These range from the synthesis of under-represented skin phenotypes to segmentation, denoising, and super-resolution imaging. The review also examines the commercial implementations of GAN-based solutions relevant to practicing dermatologists. We present a comparative summary of GAN architectures, including DCGAN, cGAN, StyleGAN, CycleGAN, and advanced hybrids. We analyze technical metrics used to evaluate performance—such as Fréchet Inception Distance (FID), SSIM, Inception Score, and Dice Coefficient—and discuss challenges like data imbalance, overfitting, and the lack of clinical validation. Additionally, we review ethical concerns and regulatory limitations. Our findings highlight the transformative potential of GANs in dermatology while emphasizing the need for standardized protocols and rigorous validation. While early results are promising, few models have yet reached real-world clinical integration. The democratization of AI tools and open-access datasets are pivotal to ensure equitable dermatologic care across diverse populations. This review serves as a comprehensive resource for dermatologists, researchers, and developers interested in applying GANs in dermatological practice and research. Future directions include multimodal integration, clinical trials, and explainable GANs to facilitate adoption in daily clinical workflows. Full article

Background/Objectives: The classification of patients with diabetes into phenotypes with distinct risks and therapeutic needs is crucial for individualized care. We recently introduced a clustering model for gestational diabetes mellitus (GDM). This study aims to further characterize the proposed clusters and to identify cluster-specific differences in glucometabolic parameters during early pregnancy in an independent cohort. The metabolic profiles and dietary habits of GDM clusters will be compared with those of a normal glucose-tolerant (NGT) control group. Methods: 1088 women (195 who developed GDM and 893 who remained NGT) underwent a broad risk evaluation at early pregnancy. GDM patients were further categorized into the three proposed GDM subtypes (CL1 to CL3). Results: Among GDM patients, 7.7% were classified as CL1, 35.9% as CL2, and 56.4% as CL3. CL1 showed higher age, pregestational BMI, and increased glucose concentrations both at fasting and during the diagnostic oral glucose tolerance test. CL2 was characterized by elevated BMI and fasting glucose, while CL3 showed higher glucose concentrations after the oral glucose load, with BMI levels comparable to NGT mothers. Women in the CL1 group exhibited impaired insulin sensitivity and β-cell function at early pregnancy and showed elevated lipid levels. Compared to NGT women, a positive family history of diabetes was more prevalent in CL1 and CL3, but not in CL2. Dietary patterns were similar across all groups. Conclusions: Our study showed distinct alterations in glucometabolic parameters already at early pregnancy among GDM subtypes. Patients in CL1 exhibited the most unfavorable risk constellation and could benefit from lifestyle changes and nutrition therapy in early pregnancy, despite showing similar dietary patterns as the NGT group. Full article

The clinical heterogeneity of type 1 Gaucher disease (GD1) underscores the limited correlation between the GBA1 genotype and phenotype. This study examined GD1 patients from the Spanish Gaucher Disease Registry carrying heterozygous GBA1 genotypes distinct from NM_000157: c.[1226A>G](N370S); [1448T>C](L444P). Among 374 patients with GD1, 195 (52.1%) had alternative heterozygous combinations, including variants corresponding to severe (37.9%) or moderate (42.1%) mutation, whereas only 20% patients harbored mild variants—all of them in combination with N370S. Descriptive statistics and predictive models based on logistic regression and decision trees were applied. Patients carrying N370S with a different L444P variant showed significantly higher rates of advanced bone disease (59.9%) compared to those with homozygous N370S (38.3%) or N370S; L444P (41.0%) (p = 0.002). Decision tree analysis identified the bone marrow burden score (S-MRI) as the strongest predictor of osteopenia/osteoporosis at diagnosis. Genotype also emerged as a key discriminator for Parkinson’s disease: patients with non-N370S; L444P genotypes showed a markedly higher likelihood of developing Parkinsonism. Overall, GD1 patients with genotypes other than N370S; L444P present more severe phenotypes, particularly with greater skeletal involvement and neurological complications. These findings highlight the importance of genotype stratification and predictive modeling in improving risk assessment and clinical management in GD1. Full article