Browse Articles
Discover research articles across all indexed journals
Decellularized rat brain extracellular matrix effectively induces the dopaminergic differentiation of human adipose-derived stem cells
The extracellular matrix (ECM) plays essential roles in regulating various aspects of nervous system development. The ECM can be obtained through decellularization techniques, which preserve the native structure of tissue while removing cells and genetic material. Despite recent advancements in decellularization methods, removing cells from brain tissue remains challenging due to its delicate mechanical structure. Moreover, previous studies have not specifically evaluated the impacts of decellularized brain ECM on dopaminergic specification of stem cells. Here, we decellularized rat brain sections using a combination of chemical and enzymatic factors. Successful decellularization of sections was confirmed by DAPI, Haematoxylin and Eosin and Masson’s trichrome staining, laminin immunostaining, DNA content analysis, and scanning electron microscopy. The sections were then recellularized with human adipose tissue-derived stem cells (hADSCs) and subjected to dopaminergic differentiation using a combination of growth factors. Some ADSCs were also differentiated in gelatin-coated tissue culture plates, employing a conventional two-dimensional culture method. After 12 days, the differentiated cells in both conditions expressed certain neuronal markers, especially those related to dopaminergic differentiation. However, GLI1, VMAT2, GIRK2, and TH genes, as well as NEFL, FOXA2, LMX1A, and TH proteins were upregulated in the ADSCs differentiated on decellularized sections. Furthermore, DDC and CALB were exclusively expressed by the ADSCs on decellularized brain sections. Overall, our findings indicated the significance of decellularized brain ECM to serve as an effective bioscaffold for dopaminergic differentiation of hADSCs. This highlights the importance of decellularization techniques for the advancement of midbrain tissue engineering and regenerative medicine for Parkinson’s disease in the future.
The role of kindling mechanism: A validation study of the Hungarian version of the Prediction of Alcohol Withdrawal Severity Scale
Background Early recognition of the complicated form of alcohol withdrawal syndrome (c-AWS) is critical. The Prediction of Alcohol Withdrawal Severity Scale (PAWSS) was developed for the risk analysis of the development of c-AWS. According to the kindling mechanism, the history of previous c-AWS has a pivotal role in the development of current c-AWS. The aims of this study were to reveal (1) the psychometric characteristics of the PAWSS among patients with alcohol withdrawal syndrome (AWS) and alcohol dependence syndrome (ADS) and (2) the role of kindling mechanism in the development of c-AWS by using the PAWSS. Methods This study enrolled 70 inpatients with ADS and AWS. The severity of dependence was measured using the Alcohol Use Disorder Identification Test and the Severity of Alcohol Dependence Questionnaire. Statistical analyses were performed using receiver operating characteristic (ROC) analysis, binary logistic regressions, and for the inter-rater reliability analysis Cohen’s Kappa coefficient was calculated. Results ROC analysis showed that > 6 is the optimal cutoff point for the Hungarian version of the PAWSS. In the case of predictive validity, higher PAWSS score (p < 0.001) predicted current c-AWS. Furthermore, the history of c-AWS (p < 0.001) was a significant variable for current c-AWS. The Cohen’s Kappa coefficient resulted in being 1. Conclusions The probability of current c-AWS was 12 times higher among patients with PAWSS scores of 6 or higher. The chance of current c-AWS was almost 7 times higher in the case of history of c-AWS. These findings suggest that the Hungarian version of PAWSS is a valid and reliable clinical tool for assessing the risk of c-AWS, and highlight the importance of the kindling mechanism in the background of c-AWS.
Evaluating the effectiveness of prenatal exercise promotion strategies on the Xiaohongshu platform: Health beliefs, information quality, and source credibility
Although exercising during pregnancy offers numerous advantages, its prevalence in China remains relatively low. This is primarily attributed to the traditional Chinese belief that pregnancy is a period for rest and recuperation. To alter this perception, numerous individuals have promoted the benefits of prenatal exercise on Xiaohongshu, one of China’s most popular social media platforms. This study utilized the frameworks of the Health Belief Model (HBM) and the Heuristic - Systematic Model (HSM) to explore which strategies are effective in these promotional efforts. A total of 5,016 posts promoting prenatal exercise were identified. From these, 500 samples were randomly selected for coding. Negative binomial regression analysis was conducted to assess the influence of the constructs of HBM and HSM on public engagement. The Kruskal-Wallis test was used to compare various information sources’ differential effects. The results indicated that emphasizing the benefits, self-efficacy, and barriers to exercise significantly impact audience engagement in the context of social media information regarding exercise during pregnancy. Healthcare professionals and pregnant and postpartum women are the most influential information sources in attracting audience engagement. Moreover, source credibility significantly impacts public engagement, and information completeness positively increases the likelihood of favorites. These findings are valuable for optimizing the design of pregnancy exercise promotion information on social media, obtaining social support for prenatal exercise, and contributing to women’s health and well-being.
Risk factor analysis and establishment of a predictive model for epilepsy comorbid with depression
Objective This study aims to utilize our hospital’s existing Stereo Electroencephalography (SEEG) examination results combined with other clinical data to systematically analyze the risk factors for epilepsy comorbid with depression, and to establish a model for predicting the risk of developing depression in epilepsy patients. Clinically, this model can be used to predict the risk of comorbid depression in epilepsy patients, thereby enhancing the identification of this condition and providing a theoretical basis for proactive intervention in depressive symptoms among epilepsy patients. Methods A retrospective analysis was conducted on the clinical data of patients diagnosed with epilepsy in the Department of Neurosurgery at Tongde Hospital Of Zhejiang Province from 01/01/2020–31/12/2024, all of whom underwent Electroencephalography (EEG) examinations. According to the C-NDDI-E scores and clinical manifestations, the epilepsy patients were divided into an epilepsy with comorbid depression group (study group) and epilepsy without depression group (control group). Univariate analysis was performed using SPSS 26.0 software to screen for potential factors contributing to depression comorbid with epilepsy. Variables with a univariate P ≤ 0.05 were entered into a linear Lasso regression analysis. Those with statistical significance were then used to construct a nomogram model for predicting the risk of depression comorbid with epilepsy using R software. Results A total of 152 epilepsy patients were enrolled, including 43 in the study group and 109 in the control group. Univariate analysis showed statistically significant (P < 0.05) differences between the groups in terms of age, employment status, marital status, age of onset, frequency of epileptic seizures, type of drug treatment, scalp EEG-determined epileptogenic zone, SEEG-determined epileptogenic zone, and Activities of Daily Living (ADL) score. Lasso regression analysis revealed that marital status (p = 0.0008), Enrollment age (OR = 0.9152, P = 0.0003, 95% CI: 0.8673–0.9562), frequency of epileptic seizures (OR =5.9946, P = 0.0030, 95% CI: 1.8952–20.6541), type of drug treatment (OR = 44.4062, P = 0.0157, 95% CI: 1.3629–15.6702), SEEG results indicating the epileptogenic zone (hippocampal onset: OR = 12.3489, P = 0.0026, 95% CI: 2.5902–70.9811), and ADL score (OR = 0.9358, P = 0.0314, 95% CI: 0.8785–0.9930) were independent risk factors for depression comorbid with epilepsy. The area under the ROC curve (AUC) was 0.895, indicating strong discriminative ability and high predictive accuracy. Conclusion Independent risk factors for depression comorbid with epilepsy include: hippocampal origin of epilepsy as identified by SEEG, unstable marital status, younger age at the time of enrollment, higher frequency of epileptic seizures (>4 times/month), use of specific anti-seizure medications (such as topiramate, phenobarbital, levetiracetam, and perampanel), and lower activities of daily living (ADL) scores. The nomogram model established based on these factors performs well in relatively accurately predicting the risk of depression comorbid with epilepsy. This facilitates early identification of high-risk patients in clinical practice, enabling timely interventions to prevent the severe consequences of depressive episodes, improving patient adherence to epilepsy treatment, and emphasizing the link between psychological and neuroscientific aspects in epilepsy management to foster interdisciplinary collaboration for more comprehensive patient care.
An information-theoretic foreshadowing of mathematicians’ sudden insights
The “eureka” insights that drive progress in science and mathematics remain shrouded in mystery. Sudden, unexpected, appearing like “flashes of lightning”, these insights have the hallmarks of critical transitions in complex systems. Here, zooming in on mathematicians working on proofs in their own departments, we show that sudden insights are anticipated by a system-agnostic, information-theoretic early warning signal. Using dense behavioral recordings of mathematicians’ moment-to-moment activity, we find that their blackboard interactions (e.g., writing, gesturing; N = 4 , 653 ) became increasingly unpredictable before an insight, analogous to the critical fluctuations that anticipate transitions in physical and ecological systems. We explore analytically when this early warning signal applies to varied systems with discrete, symbolic dynamics. While bibliometric analyses offer a zoomed-out perspective on innovation, publications are a coarse-grained record of individuals’ insights. Explaining the sudden insights of innovators, from scientists to sculptors, requires attending to the local, distributed systems of their intellectual activity.
Gonadotropin-releasing hormone regulates transcription of the inhibin B co-receptor, TGFBR3L, via early growth response one
Lead-free perovskite KCsSnI1.7Cl1.3 material exhibiting superior photocatalytic antimicrobial activity
Abstract The development of environmentally friendly and highly efficient materials is critical for next-generation antibacterial and optoelectronic applications. In this study, we present the successful synthesis of a novel lead-free perovskite, KCsSnI1.7Cl1.3, via a rapid and scalable chemical bath deposition method at 150 °C for just 5 min. The resulting film features well-defined orthorhombic, pyramid-like crystals with uniform grain sizes (800–1000 nm) and a compact, pinhole-free morphology. Remarkably, the material exhibits strong optical absorption up to 800 nm, positioning it as a promising candidate for hot electron generation and light-driven applications. KCsSnI1.7Cl1.3 also demonstrated outstanding antibacterial performance, showing broad-spectrum activity against multiple bacterial strains. The highest inhibition was recorded against Staphylococcus aureus, with inhibition zones increasing from 17 mm in the dark to 35 mm under UV illumination—highlighting its efficient photocatalytic response. The antibacterial effect followed the order: S. aureus > B. subtilis > E. coli > Salmonella sp., and was significantly enhanced with increasing concentrations (100–400 ppm). No inhibition was observed in control wells, confirming the selective activity of the material. With its lead-free composition, strong light-harvesting ability, and exceptional antibacterial properties, KCsSnI1.7Cl1.3 emerges as a highly promising material for future applications in antibacterial coatings, water purification systems, and advanced optoelectronic devices. Its green synthesis, scalability, and multifunctionality offer a sustainable pathway toward real-world deployment in environmental and biomedical fields.
Identification and characterization of nanobodies specific for the human ubiquitin–like ISG15 protein
Profiling plasma protease activity with charge-changing peptides enables detection and classification of gastrointestinal cancers
Abstract Early detection of gastrointestinal (GI) cancers—including colorectal cancer (CRC), gastric cancer (GC), and esophagogastric junction cancer (EGJC)—is essential for improving patient outcomes. However, current diagnostic methods such as endoscopy and colonoscopy are invasive, costly, and not widely accessible. Proteases are elevated in many cancers and are detectable in peripheral blood, making them promising candidates for noninvasive diagnostic strategies. We employed a six-probe charge-changing peptide (CCP) panel to profile cancer-associated protease activity in human plasma. Each CCP undergoes a charge shift upon cleavage by a specific protease, enabling detection via gel electrophoresis. Plasma samples from GI cancer patients (CRC, GC, EGJC; N = 68) and healthy controls (HC; N = 31) were analyzed. Protease activity profiles were analyzed using statistical tests, principal component analysis, and binary logistic regression (LR) models trained on the most informative probes. Model performance was evaluated through repeated cross-validation. Distinct protease activity profiles were observed among CRC, upper GI cancers (UGIC; GC + EGJC), and HC groups. Probe designed to be cleaved by cathepsin B showed the strongest discrimination between cancer and control samples, while probes designed to be cleaved by ubiquitin-specific peptidase 15 and plasmin were identified as the most informative subtype-specific markers for UGIC and CRC, respectively. LR models built on these single probes demonstrated excellent diagnostic performance, with AUCs exceeding 0.95, and both sensitivity and specificity greater than 90%. Our findings highlight CCP-based protease profiling as a minimally invasive, accurate, and scalable method for GI cancer detection and classification. This platform holds strong potential for clinical application in cancer screening, pending further validation in larger, independent cohorts.