Browse Articles

Mediating role of fall fear and exercise self-efficacy in the nexus between low back pain knowledge and kinesiophobia in pregnancy-related low back pain pregnant women

Integrative study of Anchusa italica Retz flower’s microstructure and chemical composition

Impact of isolated lumbar extension strength training on reducing nonspecific low back pain, disability, and improving function: a systematic review and meta-analysis

A comprehensive investigation of the relationship between propulsion speed and water influx in coal mine TBM inclined shaft projects

TriSpectraKAN: a novel approach for COPD detection via lung sound analysis

Abstract This study aims to create an automated, accessible, and cost-effective diagnostic tool for chronic obstructive pulmonary disease (COPD). Traditional diagnostic methods are expensive, time-consuming, and require specialized equipment. The proposed TriSpectraKAN model leverages audio-based lung sound features to improve early diagnosis. TriSpectraKAN is a hybrid model combining spectral features and the Kolmogorov–Arnold Network (KAN) to analyze lung sounds using Mel-frequency cepstral coefficients (MFCCs), chromagram, and Mel spectrograms. Each sub-model focuses on a different audio feature, capturing unique sonic signatures. These features are merged through a hybrid network for comprehensive analysis. The model, trained on a COPD dataset, was deployed on a Raspberry Pi for real-time use. TriSpectraKAN achieved 93% accuracy, an F1 score of 0.98, precision of 0.97, and recall of 0.98. This multimodal approach captured a broad range of lung sound features, improving diagnosis accuracy compared to traditional methods. The integration of multiple audio features in TriSpectraKAN enhances COPD diagnosis, demonstrating the potential of AI and machine learning to transform respiratory disease diagnosis through accessible tools.

NlpD as a crucial factor in desiccation resistance and biofilm formation in Cronobacter sakazakii

Unexpectedly high rate of unrecognized acute kidney injury and its trend over the past 14 years

Abstract Acute kidney injury (AKI) is a frequent yet often overlooked complication. This study examines the incidence, unrecognized rate, and outcomes of AKI in adults at a large public Chinese hospital from 2010 to 2023. AKI rates were calculated, and outcomes were assessed using follow-up records. Multivariate logistic regression identified risk factors for unrecognized AKI. Among 2,790,540 patients, 5,080 met the AKI criteria, with an overall incidence of 0.18% (0.78% in hospitalizations, 0.05% in outpatients). The unrecognized AKI was 76.3%. In this group, 75% were stage 1, 16.7% stage 2, and 8.3% stage 3. Orthopedics had the highest unrecognized rate (94.5%) and ICUs the lowest (55.77%). Unrecognition of AKI improved from 90.3% in 2010–2011 to 70.2% in 2022–2023. AKI stage progression was linked to poorer survival. Patients with recognized AKI recovered faster than those with unrecognized AKI (8.0 vs. 9.0 days, p < 0.001). The mean follow-up time was 15.8 days, with similar rates at 28 and 90 days post-AKI for both groups. Risk factors for unrecognized AKI included lower AKI stage, baseline creatinine, absence of shock/heart disease/hypertension, and non-nephrology/surgery admissions. Non-nephrology physicians’ unfamiliarity with AKI guidelines may contribute to low awareness. Improved early detection and monitoring in high-risk groups are needed.

Development and characterization of carbon-based conductive pastes with high mechanical integrity under bending stress for room-temperature printable electronics

Abstract Room temperature processing of flexible electronics has become of great interest, as it allows for simpler and cheaper methodologies for high throughput manufacturing of printed electronics. This study focuses on the development and characterization of carbon-based conductive pastes made from a combination of graphite (G) and carbon black (CB), in a polymethyl methacrylate (PMMA) polymer matrix. Raw materials were characterized by Raman Spectroscopy, FTIR, SEM and TEM, showing the structural properties, morphologies and particles size which influenced the characteristics of the pastes. By varying the ratios of G/CB (1 to 4), carbon filler content (11.6–20%), and polymer content (1.5–7%), 48 different formulations were fabricated and further analyzed to determine their electrical conductivity as films. This process identified the optimal formulation for each G/CB ratio. Pastes with higher relative graphite content (G/CB ratios of 3 and 4) yielded the lowest resistivities (as low as 0.078 Ω cm) attributed to the effective formation of conductive networks between G and CB. Best-performing pastes were further characterized by sheet resistance, viscosity, adhesion, and scanning electron microscopy (SEM) analysis to understand the microstructure of the films. Flexible electrodes fabricated on PET substrates withstood 6000 bending cycles, thermal stress at 70 °C, and immersion in water, maintaining electrical conductivity. These results have significant implications for the future development of carbon-based conductive materials for room-temperature applications in flexible and printed electronics.

Extraction of mango seed kernels via super fluid extraction and their anti-H. pylori, anti-ovarian and anti prostate cancer properties

Machine learning based seizure classification and digital biosignal analysis of ECT seizures

Abstract While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy (ECT) remains rather limited. With the advent of digital seizure collection systems, the development of novel ECT seizure quality metrics and treatment guidance systems in particular will require cutting-edge digital seizure analysis. Using artificial intelligence will offer more analytical degrees of freedom and could play a key role in enhancing the precision of currently available procedures. To this end, we developed the first machine learning (ML) framework that can classify ictal and non-ictal EEG segments, accurately identifying seizure endpoints—a critical step in deriving seizure quality parameters—and computing these metrics at least as reliable as existing precomputed scores. The ML model retained in this study effectively discriminated ictal from non-ictal EEG segments with 89% accuracy, precision, and sensitivity. The reproduced ECT quality parameters showed correlations up to ϱ = 0.99 (p < 0.01) with the pre-calculated values from the stimulation device and did not significantly differ from the reference values. Mean seizure duration differences were 0.23 ± 15.59 s compared to the expert rater and 0.28 ± 16.19 s compared to the stimulation device. The study highlights the potential of integrating ML into the field of ECT and emphasizes the critical role of a highly sensitive seizure detection method in reliably determining seizure duration and deriving subsequent quality indices, paving the way for more individualized treatment strategies and novel approaches to determine seizure quality.

Comparative study of third-generation sequencing-based CASMA-trio and STR linkage analysis for identifying SMN1 2 + 0 carriers

An efficient multi-objective framework for wireless sensor network using machine learning

Optimizing wind turbine blade pitch control via input output differential model free adaptive control

Prevalence of erectile dysfunction as long-COVID symptom in hospitalized Japanese patients

Selection and validation of reference genes in alfalfa based on transcriptome sequence data

A multiple correspondence analysis of the fear of falling, sociodemographic, physical and mental health factors in older adults

Low lying excited states quantum entanglement and continuous quantum phase transitions

Detecting the physiological and molecular mechanisms by which abscisic acid (ABA) regulates the consistency of sweet cherry fruit maturity

Aflatoxin B1 (AFB1) biodegradation by a lignolytic phenoloxidase of Trametes hirsuta

Abstract Aflatoxin B1 (AFB1) is a highly potent mycotoxin that poses a serious threat to human and animal health. This study investigated the biodegradation of AFB1 by the supernatant of submerged cultured Trametes hirsuta, with a focus on identifying and characterizing the responsible enzyme(s). The extracellular enzymes of the white-rot mushroom were extracted from the supernatant and pre-separated using anion exchange fast protein liquid chromatography (FPLC). To pinpoint the specific enzyme, the eluted protein fractions exhibiting the highest degradation activity were subjected to detailed biochemical and proteomic analyses. A second purification step, ultrafiltration, yielded an electrophoretically pure enzyme. Sequencing of tryptic peptides using a nano-LC system coupled to a qQTOF mass spectrometer identified the enzyme as a lignolytic phenoloxidase. The enzyme exhibited a molecular mass of 55.6 kDa and achieved an impressive AFB1 degradation rate of 77.9% under optimized experimental conditions. This is the first fungal lignolytic phenoloxidase capable of aflatoxin degradation without requiring hydrogen peroxide as a cofactor, highlighting its unique catalytic mechanism. It may be used in mycotoxin remediation strategies, such as treating the surfaces of contaminated fruits, vegetables, and nuts.

Identification of disorder and effect of annealing on physical properties of half-metallic Fe–Ti–Sn based Heusler alloys

One notable yet typical characteristic of Heusler compounds is their susceptibility to antisite defects and disorders. Concerning this, the Fe2TiSn Heusler compound has been an exciting and disputed candidate, where the ground state properties are highly affected by antisite disorder. Attempts to manipulate the extent of the disorder via doping or substitution are well-documented. This study presents qualitative evidence for significant modification of the disorder, achievable through careful heat treatment of the compositions. Polycrystalline compositions of Fe2TiSn, Fe2Ti0.75Cr0.25Sn, and Fe2TiSn0.5Sb0.5 are investigated for its electrical transport and magnetic properties by separately investigating the samples of each composition in its “as-prepared” state and after post-annealing treatment. Temperature-dependent neutron diffraction has been recorded on the as-prepared compositions to estimate the antisite disorder in these compositions. The structural refinement of the neutron diffraction data confirms the presence of approximately 5% Fe–Ti antisite disorder in these compositions. An additional 10% Fe–Sb disorder is found to exist in Fe2TiSn0.5Sb0.5. The secondary heat treatment of all the compositions leads to a noticeable decrease in antisite disorder, which is seen to drastically affect its electrical resistivity and magnetization. These findings highlight the effectiveness of heat treatment in improving the ordering of the crystal structure of the compositions, with implications for their suitability in spintronics application.