Browse Articles

Author Correction: Clinical and radiographic comparison of primary molar pulpotomy using formocresol, portland cement, and NeoMTA plus: a randomized controlled clinical trial

Isolation, identification, and characterisation of the malachite green detoxifying bacterial strain Bacillus pacificus ROC1 and the azoreductase AzrC

Association between changes in genital immune markers and vaginal microbiome transitions in bacterial vaginosis

Abstract Bacterial vaginosis (BV), characterized by an imbalance in the vaginal microbiota, is a prevalent condition among women of reproductive age and a risk factor for human immunodeficiency virus, sexually transmitted infections, and preterm birth. BV is generally considered to induce mucosal inflammation, but the specific pathways and cell types involved are not well characterized. This prospective study aimed to assess associations between microbial changes and mucosal immune responses in BV patients. Therefore, samples from 20 premenopausal women with BV and treated with metronidazole were analyzed. Vaginal swabs, menstrual cup, and endocervical cytobrush samples were collected before treatment, weekly for four weeks, and at 2, 4, and 6 months for Nugent scoring, immune cell populations and cytokine analysis. Of 105 study intervals, 27 (25.7%) showed improvement in Nugent category, 61 (58.1%) remained unchanged, and 17 (16.2%) worsened. Improvement correlated with decreased monocytes (p = 0.005), while worsening was linked to increased monocytes (p < 0.001) and dendritic cells (p = 0.02). B cells (p = 0.02) and IFN-γ-induced chemokines - IP-10 (p = 0.007), MIG (p = 0.049), and ITAC (p = 0.005) - were associated with improvement. In conclusion, although the T-cell-associated chemokines IP-10, ITAC, and MIG were strongly associated with improvements in Nugent category, our findings indicate that antigen-presenting cells, particularly monocytes, show the most dynamic response to shifts in the vaginal microbiota in patients with BV.

A fractal–fractional order modeling approach to understanding stem cell-chemotherapy combinations for cancer

Enhanced photocatalytic degradation of Rhodamine B using polyaniline-coated XTiO3(X = Co, Ni) nanocomposites

Abstract In this study, novel polyaniline-coated perovskite nanocomposites (PANI@CoTiO3 and PANI@NiTiO3) were synthesized using an in situ oxidative polymerization method and evaluated for the photocatalytic degradation of Rhodamine B (RhB) a persistent organic pollutant. The nanocomposites displayed significantly enhanced photocatalytic efficiency compared to pure perovskites. The 1%wt PANI@NiTiO3 achieved an impressive 94% degradation of RhB under visible light after 180 min, while 1wt.% PANI@CoTiO3 reached 87% degradation under UV light in the same duration. X-ray diffraction (XRD) confirmed that the crystalline structures of CoTiO3 and NiTiO3 remained intact post-polymerization. At the same time, Fourier transform infrared spectroscopy (FTIR) verified the successful deposition of PANI through characteristic functional group vibrations. Diffuse reflectance spectroscopy (DRS) revealed reduced band gaps of 2.63 eV for 1wt.% PANI@NiTiO3 and 2.46 eV for 1wt.% PANI@CoTiO3, enhancing light absorption across UV and visible ranges. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) analysis demonstrated the uniform distribution of PANI, ensuring consistent surface activity and efficient charge transfer. The photocatalytic test confirmed a pseudo-first-order degradation mechanism. The study elucidates the degradation mechanism through intermediate identification via HPLC-MS analysis, highlighting N-de-ethylation, aromatic ring cleavage and eventual mineralization into CO2 and H2O as critical pathways. Furthermore, the 1wt.%PANI@NiTiO3 nanocomposite demonstrated excellent stability and recyclability, maintaining its degradation efficiency over four consecutive cycles with minimal change. These findings highlight the potential of PANI@XTiO3 nanocomposites for sustainable and efficient wastewater treatment, addressing diverse environmental challenges by tailoring photocatalysts to specific light sources.

Can a single coastal tide gauge help predict internal solitary wave activity offshore

A cross sectional investigation of the development of rhythmic preferences with motor and perceptual tests

Abstract The preferred period hypothesis posits a slowing down of motor and perceptual rhythmic preferences with age, both reflecting an increase in the common internal oscillation period. This study further investigates the preferred period hypothesis by improving the measurement of perceptual rhythmic preferences through two tasks, tempo adjustment and tempo judgment, conducted in auditory and visual modalities. The study was conducted with three groups of children (5–6, 8–9, and 11–12 years old), and a group of young adults (21 to 30 years old) during the same time of the day. Correlational analyses show that the relationship between motor and perceptual rhythmic preferences strengthens between childhood and adulthood. Multilevel analyses reveal a general slowing down of motor and perceptual rhythmic preferences, contingent on the task and modality, with distinct trajectories for males and females. Our study indicate that the range of preferred rhythms narrows with age, becoming less variable in young adulthood, at the expense of a single and consistent, default rhythm. In brief, the preferred period appears only in young adults, with no support for the idea of a slowing down of an internal oscillation that would be reflected by both motor and perceptual rhythmic preferences.

Utilizing hydrophobic sand to construct an air permeable aquiclude to enhance rice yields

Abstract The Chinese government attaches great importance to the ecological restoration of abandoned open-pit mines, increasing the area of cultivated land, and ensuring food security. Soil reconstruction is a crucial step in ecological restoration of abandoned open-pit mines. This study investigated the utilization of hydrophobic sand to create an Air-Permeable Aquiclude (APAC) under the plant root zones, thereby minimizing water infiltration and enhancing soil aeration. Field plot experiments for 2 years have been conducted, with control groups, Clay Aquitard (CAT), and Plastic Aquiclude (PAC), to evaluate the effects of APAC on rice yield, nitrogen utilization, and water efficiency. The findings revealed that utilizing APAC resulted in a significant rise in rice yield, ranging from 8.09 t/hm² to 9.27 t/hm², which were 7.67–27.16% higher than the control groups. Moreover, the APAC led to a remarkable reduction in irrigation water usage by 37.08%, alongside a substantial boost in Irrigation Water Productivity (IWP) efficiency by 28.64–71.12%. Notably, Nitrogen Partial Factor Productivity (NPFP) exhibited a substantial increase of 7.69–27.06%. These outcomes underscore the APAC’s positive role in water and nutrient conservation and enhanced yields.

Experimental study and model prediction of the influence of different factors on the mechanical properties of saline clay

Abstract visual reasoning based on algebraic methods

Beta oscillations predict the envelope sharpness in a rhythmic beat sequence

Abstract Periodic sensory inputs entrain oscillatory brain activity, reflecting a neural mechanism that might be fundamental to temporal prediction and perception. Most environmental rhythms and patterns in human behavior, such as walking, dancing, and speech do not, however, display strict isochrony but are instead quasi-periodic. Research has shown that neural tracking of speech is driven by modulations of the amplitude envelope, especially via sharp acoustic edges, which serve as prominent temporal landmarks. In the same vein, research on rhythm processing in music supports the notion that perceptual timing precision varies systematically with the sharpness of acoustic onset edges, conceptualized in the beat bin hypothesis. Increased envelope sharpness induces increased precision in localizing a sound in time. Despite this tight relationship between envelope shape and temporal processing, it is currently unknown how the brain uses predictive information about envelope features to optimize temporal perception. With the current EEG study, we show that the predicted sharpness of the amplitude envelope is encoded by pre-target neural activity in the beta band (15–25 Hz), and has an impact on the temporal perception of target sounds. We used probabilistic sound cues in a timing judgment task to inform participants about the sharpness of the amplitude envelope of an upcoming target sound embedded in a beat sequence. The predictive information about the envelope shape modulated task performance and pre-target beta power. Interestingly, these conditional beta-power modulations correlated positively with behavioral performance in the timing judgment task and with perceptual temporal precision in a click-alignment task. This study provides new insight into the neural processes underlying prediction of the sharpness of the amplitude envelope during beat perception, which modulate the temporal perception of sounds. This finding could reflect a process that is involved in temporal prediction, exerting top-down control on neural entrainment via the prediction of acoustic edges in the auditory stream.

Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images

Digital quantum simulation of cosmological particle creation with IBM quantum computers

Novel brain SPECT imaging unravels abnormal cerebral perfusion in patients with postural orthostatic tachycardia syndrome and cognitive dysfunction

Abstract Cognitive dysfunction is frequently reported in individuals with postural orthostatic tachycardia syndrome (POTS), possibly resulting from reduced cerebral blood flow (CBF). We used brain SPECT, an accessible imaging modality that has not been systematically evaluated in this patient group. Retrospective review of participants from our registry was undertaken to identify those who had a brain SPECT performed for investigation of cognitive dysfunction. Abnormal CBF was taken as z-score > 2 standard deviations of healthy control reference values. Patient reported outcome measures (PROMs) such as autonomic, gastric and quality of life symptom scores were analyzed. From a total of 56 participants (mean 34.8 ± 10.7 years, 88% females), PROMs indicate: moderate to severe autonomic dysfunction in 75%; at least mild to moderate gastroparesis in 23%; low global health rating and utility scores. Abnormal CBF was seen in 61% but did not differ by POTS triggers. The regions with the lowest mean z-scores were the lateral prefrontal and sensorimotor cortices. Hierarchal regression analyses found number of brain regions with abnormal CBF, autonomic and gastric symptoms to account for 51% of variances in health utility. Cerebral hypoperfusion is prevalent in those with POTS and cognitive dysfunction even whilst supine, contributing to reduced quality of life.

A pediatric emergency prediction model using natural language process in the pediatric emergency department

Abstract This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012–2021) using electronic medical records. Various NLP models, including four machine learning (ML) models with Term Frequency-Inverse Document Frequency (TF-IDF) and two DL models based on the KM-BERT framework, were trained to differentiate emergency cases using clinician transcripts. Gradient Boosting, among the ML models, performed best with an AUROC of 0.715, AUPRC of 0.778, and F1-score of 0.677. DL models, especially the fine-tuned KM-BERT model, showed superior performance, achieving an AUROC of 0.839, AUPRC of 0.879, and F1-score of 0.773. Shapley-based explanations provided insights into model predictions, underlining the potential of these technologies in medical decision-making. This study demonstrates the potential of advanced DL techniques for NLP in emergency medical settings, offering a more precise and efficient approach to managing healthcare resources and improving patient outcomes.

Retraction Note: Serum melatonin levels and in a sample of Iranian patients with migraine

Prediction and control for the transmission of brucellosis in inner Mongolia, China

Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models

Author Correction: IoT-based automated system for water-related disease prediction

Machine learning and AVO class II workflow for hydrocarbon prospectivity in the Messinian offshore Nile Delta Egypt

Abstract This study presents a comprehensive workflow to detect low seismic amplitude gas fields in hydrocarbon exploration projects, focusing on the West Delta Deep Marine (WDDM) concession, offshore Egypt. The workflow integrates seismic spectral decomposition and machine learning algorithms to identify subtle anomalies, including low seismic amplitude gas sand and background amplitude water sand. Spectral decomposition helps delineate the fairway boundaries and structural features, while Amplitude Versus Offset (AVO) analysis is used to validate gas sand anomalies. The entire seismic volume is classified into facies domains using machine learning, which isolates target features from seismic background data. The study area, covering 1850 km 2 , includes major structures such as the Rosetta fault and Nile Delta offshore anticline, with reservoirs consisting of layered sandstones and mudstones. Over 90 wells, including exploration and development wells, have been drilled in the area. Seismic amplitude data, including full and partial offset stacked, were analyzed to classify gas, water, and shale zones. The workflow’s performance is demonstrated through the successful identification of the low-amplitude Swan-E Messinian anomaly, characterized as a high-risk gas prospect. Machine learning techniques, specifically neural network models, were trained to differentiate seismic features such as low-amplitude gas sand from background-amplitude water sand and shale. By iterating over multiple attributes and validating the models on blind test sets and on a blind section, which excluded a known shallow gas field, the workflow significantly improved the ability to detect potential hydrocarbon reservoirs characterized by low seismic amplitude. The results show that this integrated approach reduces exploration risk, quantifies the chance of success, and enhances decision-making in well placement and hydrocarbon exploration. This method is particularly useful for identifying low seismic amplitude anomalies, which are often challenging to detect with conventional seismic analysis. (1) This study developed a workflow to detect low seismic amplitude gas fields in near-field exploration. (2) It uses a machine learning algorithm to classify and explore low-seismic-amplitude gas sand reservoirs. (3) This approach helps estimate the likelihood of success and reduces the risk associated with hydrocarbon exploration wells.