Browse Articles

Numerical and experimental study of the optimal specimen geometry for direct tension strength tests in high tensile strength fiber reinforced concrete

Association between urinary volatile organic compound metabolites and sarcopenia in the US general population: a cross-sectional NHANES study from 2011 to 2018

Prevalence, correlates, and treatment behaviors for urolithiasis and renal colic-like pain symptoms at the population level in Poland

Accessibility of basic public health service promotes social integration of elderly migrants in China

Mapping drought characteristics in northern Algerian Basins using the ERA5-Land dataset

Doping effects of indium and copper on ferromagnetism in N-type magnetic semiconductor Ba(Zn,Co)2As2

Trying to use temporal and kinematic parameters for the classification in wheelchair badminton

Introduction This study explores the potential for the temporal and kinematic datas link to propulsion technique and athlete performance collected here to contribute to evidence-based classification for wheelchair badminton athletes. Materials and methods Nineteen experienced wheelchair badminton players underwent propulsion tests with a badminton racket. Wheelchair were equipped with inertial measurement units. The first analysis conducted involved comparing the parameters between class WH1 and WH2. Subsequently, a hierarchical clustering analysis was performed on the parameters with significant differences. Results Regarding propulsion technique parameters, WH1 athletes exhibit a longer braking phase compared to WH2 athletes. Generally, the performance of WH1 athletes is inferior to that of WH2 athletes. Concerning hierarchical clustering analysis, the results reveal the formation of three clusters based on principal components explaining 70% of the variation in the parameters considered in the analysis. Conclusion Thus, the results of this study indicate a longer braking time for WH1 athletes compared to WH2, along with lower overall performance. The clusters results could suggest a potential evolution of the current classification towards three distinct classes of wheelchair badminton players. However, these findings should be interpreted with caution, given that the included performance parameters can be influenced by numerous factors, potentially undermining the robustness of the clustering methodology employed. This study highlights the need to strengthen the current classification process in wheelchair badminton.

Functional and structural analysis of SITTER patients with spinal muscular atrophy

Cytochemical analysis of cerebrospinal fluid in tuberculous meningitis versus other etiologies

Background Meningeal tuberculosis (TBM) is the most severe form of extrapulmonary tuberculosis due to its high mortality and long-term sequelae in survivors. Methods A cross-sectional study of diagnostic tests was carried out in a private clinical laboratory in Lima, Peru. All cerebrospinal fluid (CSF) samples from patients with suspected meningitis were analyzed with cytochemical and biochemical studies, as well as smear microscopy, India ink, the FilmArray Meningitis/Encephalitis panel, Xpert® MTB/RIF or Xpert MTB/RIF Ultra, and culture for common bacterias, fungi or mycobacterial. Results 450 CSF samples were included. The main microorganisms detected were Mycobacterium tuberculosis (8.9%), Cryptococcus neoformans (6.0%), and Streptococcus pneumoniae (2.4%). 97.5% of patients with TBM presented positive Xpert MTB/RIF or Ultra. The median number of red blood cells, leukocytes, and percentage of mononuclear cells, glucose, and proteins in the CSF was 57.5 cells/μl, 91.5 cells/μl, 70%, 22.5 mg/dL and 218.3 mg/dL, respectively. Likewise, patients with TMB had the lowest glucose levels (median: 22.5, IQR: 11 - 35) compared to other etiologies of meningitis. While bacterial meningitis had the highest leukocyte (median: 173 μl; IQR: 17 - 520) and protein levels (median: 289.7; IQR: 92 - 556). Conclusion The characteristics of the cytochemical study of CSF can guide the differential diagnosis by identifying general trends of tuberculous meningitis and other meningitis etiologies. However, it remains necessary to establish methods with greater precision to properly define the etiological agent causing meningitis.

Revisiting eye blink in Parkinson’s disease

TsRNA-49–73-Glu-CTC: A promising serum biomarker in non-small cell lung cancer

Objective Lung cancer has the highest incidence and mortality rates globally, with the majority of cases classified as non-small cell lung cancer (NSCLC). Due to the absence of specific tumor biomarkers, most lung cancer cases are diagnosed at an advanced stage. Therefore, the identification of novel molecular biomarkers with high sensitivity and specificity for early diagnosis is deemed crucial for enhancing the treatment of NSCLC. Transfer RNA-derived small RNA (tsRNA) is closely associated with malignant tumors and holds promise as a potential biomarker for tumor diagnosis. This study aimed to investigate whether serum tsRNA could serve as a biomarker for NSCLC. Methods Differentially expressed tsRNAs were identified through high-throughput sequencing of serum samples obtained from patients with NSCLC and healthy individuals. Additional serum samples were collected for validation using Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR). The diagnostic performance of these tsRNAs was assessed through Receiver Operating Characteristic (ROC) Curve Analysis. Furthermore, preliminary functional exploration was undertaken through cell experiments. Results tsRNA-49-73-Glu-CTC is highly expressed in the serum of patients with NSCLC and demonstrates superior diagnostic value compared to commonly used tumor markers in clinical practice, such as Carcinoembryonic Antigen (CEA), Neuron-Specific Enolase (NSE), and Cytokeratin 19 Fragment (CYFRA). A combined diagnostic approach enhances the accuracy of NSCLC detection. Additionally, tsRNA-49-73-Glu-CTC is highly expressed in A549 cells, and transfection with a tsRNA-49-73-Glu-CTC inhibitor significantly reduces both proliferation and migration capabilities. Conclusions tsRNA-49-73-Glu-CTC has the potential to serve as a novel molecular diagnostic biomarker for NSCLC and plays a significant role in the biological processes associated with NSCLC proliferation and migration.

Application of dual branch and bidirectional feedback feature extraction networks for real time accurate positioning of stents

Gender representation among speakers at the Japanese Society of Anesthesiologists meetings: A retrospective analysis

Purpose This study investigates the gender distribution of speakers at the Japanese Society of Anesthesiologists (JSA) annual and branch meetings of the Japanese Society of Anesthesiologists. Methods We examined the gender of speakers in sessions at both JSA annual and branch meetings. We also verified the speakers’ Japanese medical licensure status and years of qualification. Results We analyzed 383 sessions from JSA annual meetings between 2019 and 2024, which included 827 speaker slots. Of them, 679 (82.1%) were men and 148 (17.9%) were women. Women were significantly underrepresented in sessions with fewer speaker slots (chi-square test, p =  0.006; trend test, p <  0.001). Furthermore, sessions were frequently composed entirely of men: 73.1% of all sessions and 44.3% of panel presentations were solely male participants. Among the subspecialties, female representation was high in obstetric anesthesia (36.8%) and pediatric anesthesia (31.8%) but low in cardiovascular anesthesia (6.3%). Among 508 speakers with confirmed Japanese medical licenses, 425 (83.7%) were men, and 83 (16.3%) were women, with no significant differences in gender distribution based on the year of licensure (Fisher’s exact test, p =  0.968; trend test, p =  0.463). Additionally, we examined 104 sessions from JSA branch meetings between 2019 and 2023, comprising 176 speaker slots. Of them, 147 (83.5%) were men and 29 (16.5%) were women. There was no significant difference in gender distribution among branch meetings across different regions (p =  0.984). Conclusion These findings underscore the need for proactive measures to promote gender diversity in Japan’s anesthesiology field.

Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments

Correction: Cohort profile of a prospective cohort study among middle-aged community-dwellers in rural Vietnam: The Khánh Hòa cardiovascular study

A novel method for estimating functional connectivity from EEG coherence potentials

Explainable machine learning for predictive modeling of blowing snow detection and meteorological feature assessment using XGBoost-SHAP

Accurate forecasting of blowing snow events is vital for improving numerical models of snow processes, yet traditional predictive methods often lack interpretability. This study leverages eXtreme Gradient Boosting (XGBoost) to detect blowing snow events using meteorological and snow flux monitoring data from three weather stations in the Alps. Through 5-fold cross-validation, the model achieved impressive performance metrics, with precision rates exceeding 0.94 for non-blowing snow events and 0.77-0.80 for blowing snow events. The SHAP framework was employed to analyze the relative importance of meteorological factors, revealing that maximum wind speed (WS-MAX), average wind speed (WS-AVG), air temperature (AT), and relative humidity (AH) are the most influential factors. Additionally, Partial dependence plots (PDP) demonstrated a linear correlation between increased WS-MAX and the probability of blowing snow, while WS-AVG showed diminishing returns beyond 10 m/s. Notably, AT below -3°C strongly correlates with blowing snow occurrence, whereas AT above -3°C exhibits a negative relationship. Relative humidity plays a significant role, with values exceeding 60% stabilizing the probability of blowing snow, peaking near 100%. This research contributes to drifting snow event dynamics by integrating explainable artificial intelligence techniques (XAI), thereby improving model interpretability and supporting data-driven decision-making in meteorological applications.

Footwork recognition and trajectory tracking in track and field based on image processing

Bioactivity and Element Composition of Three Endodontic Root Canal Sealers

Development of intelligent hybrid controller for torque ripple minimization in electric drive system with adaptive flux estimator: An experimental case study

In order to ensure optimal performance of permanent magnet synchronous motors (PMSMs) across many technical applications, it is imperative to minimize torque fluctuations and reduce total harmonic distortion (THD) in stator currents. Hence, this study proposes the utilization of an adaptive flux estimator (AFE) in conjunction with an Intelligent Hybrid Controller (IHC) to mitigate the ripples and total harmonic distortion (THD). The IHC system is constructed by integrating PI and fuzzy logic controllers (FLC) in a cascade configuration, alongside a new switching unit that facilitates automatic switching between the two controllers during various operations of the PMSM. AFE estimates accurate flux which is required to achieve ripple free high dynamic performance of the PMSM drive by using a limiter to fix the flux at reference flux value of the drive. The proposed controller with AFE has achieved its originality through the refinement of membership functions located at the center of the universe of discourse (UOD) and the enhancement of the switching function. These improvements have resulted in increased sensitivity in the proximity to the reference speed. The Fuzzy Logic Controller (FLC) demonstrates superior performance when operating in a transient state, whereas the Proportional-Integral (PI) controller of the proposed system exhibits satisfactory performance under steady-state situations. The efficacy of AFE with IHC is substantiated by the simulation and experimental analysis reported in this study. A significant reduction in both total harmonics distortion (THD) and torque ripples are found.