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Mnemonic factors associated with the tip-of-the-tongue phenomenon
Abstract The tip-of-the-tongue (ToT) phenomenon is a transient semantic memory retrieval failure. Here we examined to what extent different mnemonic factors (i.e., age of acquisition, frequency of retrieval, recency of last retrieval) impact ToTs during the retrieval of famous faces and places. Eighty adults completed a self-paced experiment for both stimuli. This required making judgements on whether they knew the name, were in a ToT state, the image was familiar or the name was unknown, as well as completing follow-up questions examining the mnemonic factors of interest. Results revealed that later acquired names, a lower frequency of retrieval, and less recently encountered names, all predicted an increase in ToT occurrences. These findings followed a similar pattern across faces and places, with places being stronger predictors for each mnemonic factor. By examining these factors simultaneously across these semantic categories, we provide further evidence regarding the variables determining transient retrieval failures.
Platelet-rich plasma and IL-1β antagonist receptor peptide attenuate the inflammatory process of muscle injury in wistar rats
Enhanced chemotaxis and degradation of nonylphenol in Pseudoxanthomonas mexicana via CRISPR-mediated receptor modification
An enhanced CNN with ResNet50 and LSTM deep learning forecasting model for climate change decision making
Abstract Climate change poses a significant challenge to wind energy production. It involves long-term, noticeable changes in key climatic factors such as wind power, temperature, wind speed, and wind patterns. Addressing climate change is essential to safeguarding our environment, societies, and economies. In this context, accurately forecasting temperature and wind power becomes crucial for ensuring the stable operation of wind energy systems and for effective power system planning and management. Numerous approaches to wind change forecasting have been proposed including both traditional forecasting models and deep learning models. Traditional forecasting models have limitations since they cannot describe the complex nonlinear relationship in climatic data, resulting in low forecasting accuracy. Deep learning techniques have promising non-linear processing capabilities in weather forecasting. To further advance the integration of deep learning in climate change forecasting, we have developed a hybrid model called CNN-ResNet50-LSTM, comprising a Convolutional Neural Network (CNN), a Deep Convolutional Network (ResNet50), and a Long Short-Term Memory (LSTM) model to predict two climate change factors: temperature and wind power. The experiment was conducted using three publicly available datasets: Wind Turbine Scada (Scada) Dataset, Saudi Arabia Weather history (SA) dataset, and Wind Power Generation Data for 4 locations (WPG) dataset. The forecasting accuracy is evaluated using several evaluation metrics, including the coefficient of determination ( $$\:{\text{R}}^{2}$$ ), Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE) and Root Mean Squared Error (RMSE). The proposed CNN-ResNet50-LSTM model was also compared to five regression models: Dummy Regressor (DR), Kernel Ridge Regressor (KRR), Decision Tree Regressor (DTR), Extra Trees Regressor (ETR), and Stochastic Gradient Descent Regressor (SGDR). Findings revealed that CNN-ResNet50-LSTM model achieved the best performance, with $$\:{\text{R}}^{2}$$ scores of 98.84% for wind power forecasting in the Scada dataset, 99.01% for temperature forecasting in the SA dataset, 98.58% for temperature forecasting and 98.35% for wind power forecasting in the WPG dataset. The CNN-ResNet50-LSTM model demonstrated promising potential in forecasting both temperature and wind power. Additionally, we applied the CNN-ResNet50-LSTM model to predict climate changes up to 2030 using historical data, providing insights that highlight its potential for future forecasting and decision-making.
A robustly rooted tree of eukaryotes reveals their excavate ancestry
Photocatalytic degradation of reactive black 5 from synthetic and real wastewater under visible light with TiO2 coated PET photocatalysts
Analysis of the correlation between anthropometric indices and levels of selected hormones in relation to problematic internet use: blood parameters in problematic internet use
The role of vmPFC in accessing the temporality of life events for mental time travel
Print, melt, repeat: 3D-printing formula yields sturdy objects time after time
Liver cancer recurrence predicted by immune-cell location and gene expression
Correction: Developing an operational definition of housing instability and homelessness in Veterans Health Administration’s medical records
Sustainable approach for synthesis of new coumarin-linked Schiff bases in DABCO-based ionic liquid and their identification as aldose reductase inhibitors
Abstract Aldehyde reductase (ALR1) is the enzyme that speeds up the reduction of many types of aldehydes into sorbitol and D-glucose. The essential enzyme of the polyol pathway, aldose reductase (ALR2), is responsible for the development of chronic complications associated with diabetes when activated under hyperglycemic conditions. Since it is a crucial mediator for the oxidative and inflammatory signaling pathways, ALR is thought to be a target for various diseases. Many medicines are available for the treatment of ALR-associated issues but due to their long term side-effects they are not effectively used. Coumarin is a naturally occurring compound, and its derivatives are widely used in the treatment of many ailments. Therefore, in the pursuit to find potential alternate candidates as drug leads, we have prepared new coumarin-based Schiff base analogues using DABCO-C 7 -F ionic liquid and compared with conventional method. The sustainable approach making use of DABCO-C 7 -F ionic liquid, not only made the synthesis easier but also it is cost- and time-effective. The synthesized analogues were further examined for their potentials against ALR2 (IC50 = 1.61 to 11.20 µM) as well as checked selectivity via screening against ALR1 enzyme. Moreover, the molecular docking study was performed to elucidate the binding interactions of active compounds. The results showed that the synthesized compounds may have the potential to be further studied as new and selective anti-diabetic agents.
Investigation of multi-drug resistant Candida auris using species-specific molecular markers in immunocompromised patients from a tertiary care hospital in Quetta, Pakistan
Introduction Candida auris is an emerging multidrug-resistant pathogen responsible for nosocomial infections worldwide, characterized by high mortality rates and significant challenges in detection due to frequent misidentification. Classified by the WHO as a pathogen of critical importance since it exhibits resistance to multiple antifungal agents, particularly fluconazole, and is highly transmissible in healthcare settings. Conventional detection methods often lack the accuracy required for effective infection control. This study aimed to conduct inferential and molecular analyses of C. auris and other yeast species infecting immunocompromised patients in the Special and Intensive Care Units (SCU and ICU) of a tertiary care hospital in Quetta, Pakistan. In this region, C. auris remains rarely studied and is frequently misdiagnosed by clinical staff due to limited awareness and diagnostic challenges. Notably, no prior research has been conducted on C. auris in Quetta. The study also sought to develop reliable diagnostic methods suitable for resource-limited settings, addressing a critical gap in healthcare infrastructure. Materials and methods Samples (150 each) from the ear, axilla, groin, and saliva of SCU/ICU patients were collected and processed on yeast malt agar, with preliminary identification using Brilliance Candida Agar (BCA) and CHROMagar Candida Plus (CCP). Advanced techniques, including PCR amplification of ITS regions, DNA sequencing, RFLP with Msp1, MALDI-TOF, Vitek 2, and species-specific primers, were used for identification. Antifungal susceptibility to fluconazole, amphotericin B, and voriconazole were also assessed. Results The culture test revealed that 42.6% samples were positive for yeast infections. In addition to detecting Candida auris in 4 cultures, chromogenic media identified 6 other Candida species: C. albicans, C. dubliniensis, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis. Further validation through advanced techniques, including molecular diagnostics and MALDI-TOF, enabled the identification of additional species: C. famata, C. kefyr, C. lusitaniae, and Meyerozyma (Candida) guilliermondii. Out of all identified yeast species C. dubliniensis was the most common, followed by C. albicans and C. tropicalis, with the highest infection rates observed in saliva samples. Antifungal Susceptibility Tests (AST) revealed that C. auris isolates were resistant to Fluconazole, Amphotericin B, and Voriconazole, highlighting multidrug resistance. This study represents the first report of novel multidrug-resistant C. auris from Quetta, Pakistan, indicating that C. auris is prevalent among ICU and SCU patients. Novel species specific primers targeting phospholipase B, topoisomerase II, CDR and 18s genes were designed in our laboratory and not previously reported in earlier studies, proved highly effective for the rapid identification of Candida species. The established protocol using these primers is recommended for implementation in resource-limited laboratory settings. The statistical analysis demonstrated significant correlations between Candida species infection (dependent variable) and several independent factors (variables) emphasizing the importance of targeted diagnostics and intervention strategies.
Temporal trends in cardiovascular disease risk factors attributed burden in Iran, 1990–2021
Parathyroid near-infrared autofluorescence differently benefits depending on the surgeon’s skill for preventing from hypoparathyroidism after total thyroidectomy: A systematic review and meta-analysis
Objective To evaluate the role of parathyroid near-infrared autofluorescence in reducing the incidence of postoperative hypocalcemia and hypoparathyroidism after total thyroidectomy, and to determine which surgeons benefit most from parathyroid near-infrared autofluorescence use. Methods A literature search was conducted in PubMed, Web of Science, and the Cochrane Library databases for English-language articles published from June 2011 to October 31, 2023. The inclusion criteria were studies conducted on patients who underwent total thyroidectomy for benign or malignant thyroid pathologies, comparing postoperative parathyroid function between parathyroid near-infrared autofluorescence techniques and conventional surgery with data on calcium and/or parathyroid hormone levels. The exclusion criteria included: reviews, letters, meta-analyses, case reports, animal experiments, or basic research. Of the initial 387 articles retrieved, we included 14. A meta-analysis was performed to calculate the pooled odds ratio and weighted mean deviation with a random-effects model. Main outcomes were Calcium and parathyroid hormone levels after total thyroidectomy with or without parathyroid near-infrared autofluorescence use. Results Fourteen studies were included in the meta-analysis. Pooled odds ratios of temporary and permanent hypocalcemia were 0.56 (95% confidence interval 0.43–0.72) and 0.61 (95% confidence interval 0.33–1.13), respectively. Meta-regression analysis revealed that near-infrared autofluorescence benefits surgeons with the high incidence of temporary hypocalcemia by naked eye surgery (≥15%) by reducing temporary hypocalcemia (p = 0.0091) and skillful surgeons by increasing the number of autotransplanted parathyroid glands (p = 0.0225). Conclusions Parathyroid near-infrared autofluorescence has different benefits depending on the skill level of the surgeon.
A ternary encoding network fusing scale awareness and large kernel attention for camouflaged object detection
Quercetin alleviates cerebral ischemia and reperfusion injury in hyperglycemic animals by reducing endoplasmic reticulum stress through activating SIRT1
Hyperglycemia aggravates cerebral ischemic reperfusion injury (CIRI). Neuroprotective drugs that are effective in reducing CIRI in animals with normoglycemic condition are ineffective in ameliorating CIRI under hyperglycemic condition. This study investigated whether quercetin alleviates hyperglycemic CIRI by inhibiting endoplasmic reticulum stress (ERS) through modulating the SIRT1 signaling pathway. A middle cerebral artery occlusion/reperfusion (MCAO/R) model was induced in STZ-injected hyperglycemic rats. High glucose and oxygen glucose deprivation/reoxygenation (OGD/R) models were established in HT22 cells. The results demonstrated that hyperglycemia exacerbated CIRI, and quercetin pretreatment decreased the neurological deficit score and cerebral infarct volume, and alleviated neuron damage in the cortex of the penumbra in hyperglycemic MCAO/R rats, indicating that quercetin could be a candidate for treating hyperglycemic CIRI. Moreover, quercetin pretreatment reduced apoptosis, inhibited the expression of the ERS marker proteins GRP78 and ATF6, and mitigated the expression of the ERS-mediated proapoptotic protein CHOP in hyperglycemic MCAO/R rats, suggesting that quercetin alleviated hyperglycemic CIRI by inhibiting ERS and ERS-mediated apoptosis. Furthermore, quercetin upregulated Sirt1 expression in HG+OGD/R treated HT22 cells and inhibited PERK, p-eIF2α, ATF4, and CHOP expression. In contrast, the SIRT1 selective inhibitor EX-527 blocked the effect of quercetin on protein expression in the SIRT1/PERK pathway and aggravated HT22 cell injury. These findings indicate that quercetin inhibits ERS-mediated apoptosis through modulating the SIRT1 and PERK pathway. In conclusion, quercetin alleviates hyperglycemic CIRI by inhibiting ERS-mediated apoptosis through activating SIRT1 that consequently suppressed ERS signaling.
Author Correction: From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study
Enhancement of YTHDF2 plays a protective role in acute IRI models through downregulation of TUG1 expression
As one of the major causes of acute kidney injury, renal ischemia-reperfusion is a common health problem in a series of clinical situations, including renal transplantation. Although the mechanisms of renal IRI have been widely investigated, effective strategies are still in lacking for its prevention and treatment. In previous study, we found that the down-regulation of taurine upregulated gene 1, a long non-coding RNA (lncRNA TUG1), markedly alleviated renal IRI through mitigating the cell inflammation and apoptosis. At meanwhile, YTHDF2, an RNA methylation reading protein, was identified as a vital player in IRI of distinct organs, however, not reported in kidney. We then conducted the current study on the function of YTHDF2 in renal IRI and its regulatory role to TUG1. Based on renal IRI models in vitro and in vivo, dramatical down-regulation of YTHDF2 was presented. Subsequently, exogenous perturbation of YTHDF2 was conducted and its protective effects on cell apoptosis were demonstrated in acute IRI exogenous. Furthermore, with the same model, it was indicated that YTHDF2 protein negative regulated TUG1 RNA via direction interaction. Since then, YTHDF2 was proved as a potential protector of renal IRI through restraining of TUG1. In further speculation, induction of YTHDF2 in IRI will possibly become a possible strategy to combat the pathological process post renal transplantation or other clinical conditions.