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Repurposing of CNS accumulating drugs Gemfibrozil and Doxylamine for enhanced sensitization of glioblastoma cells through modulation of autophagy
Abstract GBM is one of the most aggressive malignancies, having the greatest fatality rate and average life years lost. The current standard medicine, temozolomide (TMZ), is ineffective, requiring the development of new treatments. However, identifying and introducing a novel medicine takes time and money. In this context, repurposing FDA-approved drugs can be a novel yet efficient alternative method. Here, we, therefore, investigated the differential expression signatures of genes of patients suffering from GBM from publicly available GEO datasets and constructed a connectivity map. Functional annotation and KEGG pathway analysis showed dysregulated molecular activities and pathways. Based on their gene ontologies, putative key genes and hub genes linked with the disease were identified, and the C-MAP database was scanned for FDA-approved medicinal compounds that could alter hub gene expression or associated pathways. Our in-silico investigation showed that Gemfibrozil (Gem) and Doxylamine (Doxy) might reverse GBM disease patterns by deregulating GBM-related genes. Evaluation of the GBM inhibitory potential of these drugs through in-vitro and three-dimensional spheroid assay showed promising results. These drugs were more cytotoxic than TMZ; however, they synergised with TMZ as well. Interestingly, the cellular homeostatic process autophagy which has been implicated significantly in GBM pathogenesis and therapy resistance, was found to be inhibited by the drugs Gemfibrozil and Doxylamine, signifying their prospective potential. Therefore, in this study, we, for the first time, identify drugs with the ability to cross the blood brain barrier (BBB), with potential cytotoxic effects beyond TMZ, and with autophagy inhibitory potential, which can be further explored for repurposing against GBM.
Structural basis for the substrate specificity of Helix pomatia AMP deaminase and a chimeric ADGF adenosine deaminase
Correction to Supporting Information for Bardy et al., Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro
Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study
Background Acute kidney injury (AKI) and acute kidney disease (AKD) are frequent complications of hospitalization, resulting in reduced outcomes and increased cost burden. However, these conditions are only sometimes recognized and promptly treated. Leveraging electronic health records (EHR), we explored the potential of artificial intelligence (AI) in diagnosing AKI and AKD during hospitalization. Methods We retrospectively analyzed EHRs collected from all patients admitted in 2022 to our public hospital network. AKI and AKD were defined according to international guidelines. The database was divided into training and validation sets. Machine Learning (ML) algorithms were developed with 10-fold cross-validation, and diagnostic accuracy was evaluated. Findings We analyzed 34,579 hospitalizations (mean age of 60 years, 50% females). Baseline renal function was available in ~50% of cases. AKI and AKD complicated 10% and 1.5% of hospitalizations, respectively. The majority of AKI episodes (77%) occurred within the first three days of hospitalization, and >50% of subjects with AKI were discharged before complete renal function recovery. ML accurately predicted AKI (AUC-ROC 79%) during hospitalization, based on data available before and at hospital admission. Among subjects with AKI on the first day and longer in-hospital observation, the ML accuracy in predicting AKD transition increased (AUC-ROC from 76% to 88%) by integrating EHR accumulated during the hospitalization. The negative predictive value (NPV) progressively increased from 94% to 98% consistently. Shapely additive explanations documented that age, urgent hospital admission, AKI severity, and baseline renal function were associated with AKI. Renal function trajectory during the first days of hospitalization was the most relevant predictor of AKD. Interpretation ML, relying on EHR before and during hospitalization, may accurately predict AKI and AKD. The high NPV also suggests its implementation as a tool to rule out the risk of renal failure, aid in individualizing patient care, and allocate healthcare resources.
A directionally evolved genomic feature in BRSK2 harbors divergent alleles in neurocognitive disorders
Antigenicity analysis of the recombinant outer membrane NlpI protein of Mannheimia haemolytica
Quantification of motor abnormalities with instrumental tasks among adolescents with first-episode schizophrenia and depressive disorders
Recovery of Sphagnum from drought is controlled by species-specific moisture thresholds
Abstract As the largest terrestrial carbon (C) store, peatlands are vital to meeting climate targets. Sphagnum, a genus of ca. 350 species, sustains many peatlands through its high water content and chemistry which inhibits decomposition and vascular plant proliferation. However, many peatlands face increased risk of drought due to climate change, and how Sphagnum will respond and recover from drought is unknown. We measured moisture content, CO2 and methane (CH4) flux, and photosynthetic pigments in two species, S. palustre and S. squarrosum, over increasing drought (1–10 weeks) and recovery (1–10 weeks) periods. We identified biomass moisture thresholds of 12 g g− 1 (S. palustre) and 18 g g− 1 (S. squarrosum) below which irreversible damage occurred to photosynthesis. Due to higher moisture retention, and a lower moisture threshold, S. palustre withstood longer drought than S. squarrosum. These species-specific thresholds provide important insight for modelling peatland C sinks and for sustainable peatland restoration.
Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone
Post-mortem human Alzheimer´s brain metallome depends on Braak stages and brain regions
Health fitness, physical activity, and quality of life in patients undergoing first chemotherapy for lung cancer: a cross-sectional study
Optimizing piezoelectric actuator placement for enhanced vibration control using genetic algorithms
Bacterial genome-encoded ParMs
A test method for selecting suitable cleaning indicators for routine cleaning monitoring on a washer-disinfector in a central sterile supply department
Cleaning indicators are widely used to evaluate the efficacy of cleaning processes in automated washer-disinfectors (AWDs) in healthcare settings. In this study, we systematically analyzed the performance of commercial indicators across multiple simulated cleaning protocols to guide the correct selection of suitable cleaning indicators in Central Sterile Supply Departments (CSSD). Eleven commercially available cleaning indicators were tested in five cleaning simulations, P0 to P4, where P1 represented the standard cleaning process in CSSD, while P2-P4 incorporated induced-error cleaning processes to mimic real-world errors. All indicators were uniformly positioned at the top level of the cleaning rack to ensure comparable exposure. Key parameters, including indicator response dynamics (e.g., wash-off sequence) and final residue results, were documented throughout the cleaning cycles. The final wash-off results given by the indicators under P0, in which no detergent was injected, were much worse than those of the other four processes. Under different simulations, the final results of the indicators and their wash-off sequences changed substantially. In conclusion, an effective indicator must be selected experimentally. The last indicator to be washed off during the normal cleaning process that can simultaneously clearly show the presence of dirt residue under induced error conditions is the optimal indicator for monitoring cleaning processes.
Tailoring clinical management after embryo transfer using β-hCG levels in resource-limited settings
Abstract Cleavage-stage embryo transfers are often the best option for patients with limited oocytes or low fertilization rates due to medical or financial constraints. This study analyzed 424 women undergoing β-hCG testing 14 days after embryo transfer at a tertiary care center in India. Pregnancy outcomes were classified as no live births (biochemical pregnancies, ectopic pregnancies, miscarriages) or live births (single/multiple births). Higher β-hCG levels were associated with greater chances of live birth but also an increased risk of complications like preterm birth and preeclampsia, particularly in multiple pregnancies. A β-hCG threshold of 468 mIU/mL was identified as the optimal predictor of live birth, with 75% sensitivity and 72% specificity. Receiver operating characteristic (ROC) curve analysis confirmed its strong predictive value. By reducing the need for frequent monitoring, this single-test approach helps ease the emotional, financial, and logistical burdens patients face during IVF treatment. The study highlights β-hCG as a simple, cost-effective tool for providing early reassurance, guiding counseling, and personalizing follow-up, particularly in low-resource settings where access to fertility care remains challenging.
Numerical simulation of a borehole sidewall acoustic transmitter for single-well reflection imaging logging用于单井反射成像测井的井眼侧壁声发射器的数值模拟
Moral judgments in online discourse are not biased by gender
Abstract The interaction between social norms and gender roles prescribes gender-specific behaviors that influence moral judgments. While previous work has demonstrated the existence of gender-bias in judgments, these studies are mainly based on controlled experiments that may not reflect real-world decision-making processes. Here, we study how moral judgments are biased by the self-disclosed gender of the protagonist of a story. Using data from , a Reddit community with 17 million members who share first-hand experiences seeking community judgment on their behavior, we employ machine learning techniques to match stories describing similar situations that differ only by the protagonist’s gender. We find no direct causal effect of the protagonist’s self-disclosed gender on the received moral judgments, except for stories about “friendship and relationships”, where male protagonists receive more negative judgments. Our findings complement existing correlational studies and suggest that gender roles may exert greater influence in specific social contexts. These results have implications for understanding sociological constructs and highlight potential biases in data used to train large language models.