Browse Articles

Assessment of allergy knowledge among the Palestinian community: A cross-sectional study

Introduction Allergy is a form of chronic illness with an increasing prevalence globally. Adequate knowledge among the community about the causes, symptoms, and treatment of allergy is crucial in preventing the associated life-threatening complications. Limited research has been conducted in Palestine regarding this health priority. The current study aimed to assess the Palestinian community’s level of knowledge regarding allergy. Methods An observational cross-sectional study was conducted using an online questionnaire targeting Palestinians aged 18 years and older between 1 June 2024 and 26 January 2025. The questionnaire gathered demographic information about the participants and assessed their knowledge level concerning allergy. Results A total of 1002 participants were included in the study. The mean age of the participants was 30.33 years. 66.1% of the participants were females, 60.2% were unmarried, 60.1% had possessed a bachelor’s degree, 63.1% had a personal history of allergy, and 82% reported knowing of someone with allergies. The mean knowledge score about allergies was 5.4 out of 10, with over half of the participants having an average level of knowledge (4–6). The knowledge score about allergies was significantly different based on the participants’ sex, marital status, place of residence, educational level, and occupation (p-value <0.001). Conclusion The Palestinians’ knowledge of allergy is considered good regarding its common causes and symptoms. However, there is still inadequate knowledge about the treatment of allergy and its less common causes. The community awareness of allergy should be improved through targeted campaigns and brochures aimed at achieving earlier diagnosis and proper management to prevent the development of life-threatening complications.

Retraction: The association between maternal tobacco smoking during pregnancy and the risk of attention-deficit/hyperactivity disorder (ADHD) in offspring: A systematic review and meta-analysis

Sculpting Low-Dimensional Hybrid Lead Halides via Pressure-Induced Polymerization

Using a neural network approach and starspots dependent models to predict effective temperatures and ages of young stars

This study presents a statistical approach to accurately predict the effective temperatures of pre-main sequence stars, which are necessary for determining stellar ages using the isochrone methodology and cutting-age starspots-dependent models. By training a Neural Network model on high-quality spectroscopic temperatures from the Gaia-ESO Survey as the response variable, and using photometric data from Gaia DR3 and 2MASS catalogs as explanatory variables, we implemented a methodology to accurately derive the effective temperatures of much larger populations of stars for which only photometric data are available. The model demonstrated robust performance for low-mass stars with temperatures below 7 000 K, including young stars, the primary focus of this work. Predicted temperatures were employed to construct Hertzsprung-Russell diagrams and to predict stellar ages of different young clusters and star forming regions through isochrone interpolation, achieving excellent agreement with spectroscopic-based ages and literature values derived from model-independent methods like lithium equivalent widths. The inclusion of starspot evolutionary models improved the age predictions, providing a more accurate description of stellar properties. Additionally, the results regarding the effective temperature and age predictions of the young clusters provide evidence for intrinsic age spreads in the youngest clusters, suggesting multiple formation events over time.

Editorial Note: Expression of Concern: Chaperone-mediated autophagy targets IFNAR1 for lysosomal degradation in free fatty acid treated HCV cell culture

The relationship between dietary intake of B vitamins and H. pylori seropositivity

Introduction The association of vitamins with Helicobacter pylori (H. pylori) infection is of great interest yet currently remains controversial. Most studies have focused on serum levels of vitamins, whereas we aimed to detect a correlation between dietary intake of B vitamins and H. pylori infection. Methods We accessed the National Health and Nutrition Examination Survey (NHANES) of U.S. citizens and employed the Full Sample 2-Year mobile examination center (MEC) Examination Weight for data analysis For the 1999–2000 NHANES data period, 3,485 U.S. citizens made up our study.This cross-sectional study analyzed the relationship between dietary B vitamins and H. pylori seropositivity through weighted logistic regression. Variables were used to perform subgroup analyses, and the presence of linear correlation was explored using restricted cubic spline (RCS) plot progression. Results Participants with H. pylori infections displayed lower intakes of vitamins B1, B2, niacin, B6, folate, and B12 compared with those without H. pylori infection. The Odds Ratio values (95% Confidence Interval) of the highest quartile of dietary intake were 0.69 (0.48–0.98), 0.63 (0.49–0.81), 0.69 (0.48–0.97), 0.71 (0.52–0.98), 0.67 (0.51–0.88), and 0.93 (0.66–1.29) for Log Vitamin B1, Log Vitamin B2, Log Niacin, Log Vitamin B6, Log Folate, and Log Vitamin B12, respectively, in comparison to the lowest quartile. Age and ethnicity were found to influence the association between B vitamins consumption and H. pylori infection. Conclusion The dietary intake of vitamins B1, B2, niacin, B6, and folate were inversely correlated with the probability of an H. pylori infection. The mechanisms underlying this association may involve gastrointestinal flora, oxidative stress, as well as immune and mitochondrial regulation.

Development, Scope, and Catalyst Selection in Boron-Catalyzed Iterative Glycosylation

The impact of diet and oral hygiene on dental caries among Turkish children: A cross-sectional study

Background Dental caries remains one of the most prevalent chronic conditions affecting children worldwide, yet they are largely preventable through modifiable factors such as diet and oral hygiene. This study aimed to examine the associations between dietary intake, oral hygiene practices, anthropometric measurements, and dental caries in children. Methods This cross-sectional study was conducted in Bursa, Türkiye, between 1 October 2023 and 20 February 2024 with 210 children aged 5–12 years. Data was collected through a structured questionnaire on sociodemographic factors, oral hygiene, and a food frequency questionnaire related to dental health. Body weight and height were measured, and dental examinations were performed by a trained dentist using DMFT/dmft indices. Results Mean DMFT and dmft scores were 0.9 ± 1.3 and 3.9 ± 2.8, respectively. Children who brushed their teeth had significantly lower caries scores ( p  < 0.001); brushing twice daily was associated with lower dmft scores ( p  < 0.001). Caries scores did not differ significantly by body mass index ( p  > 0.05). Higher DMFT scores were associated with citrus fruits ( β  = 0.322, p  < 0.001), white bread ( β  = 0.423, p  < 0.001), and fruit juice ( β  = 0.050, p  < 0.05) consumption. Higher dmft scores were linked to chocolate ( β  = 0.286), biscuits, cookies, and cakes ( β  = 0.448), chips ( β  = 0.179), and carbonated soft drinks ( β  = 0.789) ( p  < 0.001). Conclusions These results highlight the importance of promoting healthy eating and proper oral hygiene to prevent childhood dental caries and suggest that early oral health status may have broader implications for long-term systemic health, warranting further longitudinal investigation.

Hyperpolarized NMR Reveals Low-Populated Folding Intermediates in DNA

Comparing the physiques of elite Polish female and male swimmers training for short and long distances with their non-training peers – Is swimming a health-promoting sport?

In athletes, anthropometric measures are widely used to prescribe desirable body weight, to optimize competitive performance, and to evaluate the effectiveness of various training regimens. However, it also seems interesting to find out which values of anthropometric indices showing a significant relationship with health risk characterise the top athletes, especially in popular sports. The aim of the study was to characterise the physique of female and male swimmers compared to their non-training peers and to determine to what extent it is influenced by training distance. Somatic measurements were taken in 30 female and 30 male top Polish swimmers. The subjects were divided into four groups, i.e., SDF (n = 24) and SDM (n = 24) groups comprising females and males training for short-distance, and LDF (n = 6) and LDM (n = 6) groups comprising those training for long-distance. The swimmers were compared with their non-training peers, 373 females and 155 males aged 20–30 years. BMI, WHR, BF, BAI and Slenderness index were calculated to describe the athletes’ physiques. Swimmers had significantly lower BMI (19.3 ± 1.4 in SDF and 22.1 ± 0.6 in LDF compared to 23.1 ± 3.7 in non-swimmers, p  < 0.001) and WHR (0.7 ± 0.0 in SDF and 0.8 ± 0.0 in LDF, compared to 0.9 ± 0.1 in non-training peers, p  < 0.001) and BF, but only in SDF group (22.7 ± 1.6, compared to 24.2 ± 5.9 in non-swimmers, p  < 0.01). In contrast, athletes had significantly higher Slenderness index values (44.8 ± 1.1 in SDF, and 42.9 ± 0.5 in LDF, vs. 41.9 ± 3.6 in non-training peers, p  < 0.001). It was also found that the distance trained differentiates especially the physique of female swimmers, and that physique has a greater impact on athletic performance in short-distance swimmers. Monitoring the physique of top swimmers provides an insight into the type of adaptation to the training process, which in turn enables the identification of factors that determine sports success. Such observations also document the health benefits of swimming, allowing this form of physical activity to be promoted to the public.

Structural flexibility of the nicotinamide group of NADH in butanol dehydrogenase YqdH from Fusobacterium nucleatum

Butanol dehydrogenases (BDHs) are NAD(P)H-dependent oxidoreductases that catalyze the reversible conversion of butanol to butyraldehyde. These enzymes play essential roles in microbial butanol fermentation and show significant potential for biofuel synthesis and bioremediation. The crystal structures of BDHs from Fusobacterium nucleatum and Thermotoga maritima have clarified cofactor recognition and proposed reaction mechanisms. However, their distinct cofactor-binding modes and conformational differences in the substrate-binding cleft remain poorly characterized. In this study, we report the crystal structure of Fusobacterium nucleatum butanol dehydrogenase YqdH (FnYqdH) in a partially NADH-bound state. Electron density map analysis showed stable binding of the adenosine and diphosphate groups of NADH to the nucleotide-binding domain of FnYqdH. Conversely, the nicotinamide group was not observed, indicating that it was in an unbound state. Structural comparisons of FnYqdH complexed with either partial ADP or NADH revealed that the adenosine group is stabilized by hydrogen bonds with Thr143, Thr187, and Val184. Nicotinamide group binding induces positional and conformational changes in the diphosphate group of NADH. A comparative analysis of FnYqdH and TmBDH proteins revealed distinct conformational differences between their nucleotide-binding and catalytic domains, including variations in their substrate-binding metal ion sites. In particular, amino acid sequence and structural analyses of the BDH family revealed significant variability in the residues responsible for metal ion binding. Based on the observed flexibility of the nicotinamide group of NADH and the open conformation of FnYqdH, a potential reaction mechanism of FnYqdH is proposed. These findings offer valuable insights into the cofactor and substrate recognition within the BDH protein family.

Empirically calibrated simulations reveal the limits of phenotypic clustering algorithms for biodiversity assessment in data-scarce crops

Clustering algorithms are widely used for phenotypic characterization and germplasm management, particularly in data-scarce crops such as neglected and underutilized species (NUS) that lack genomic resources. However, their performance under biologically realistic conditions remains poorly understood. Standard clustering methods commonly applied in crop research often assume distinct, isotropic, and homogeneous clusters, assumptions rarely satisfied in real-world phenotypic datasets. We developed a flexible and empirically calibrated simulation framework, using phenotypic data from West African fonio ( Digitaria exilis ), to benchmark the performance of eleven clustering algorithms under both idealized and realistic scenarios. Our simulations integrated heterogeneous trait distributions (normal, gamma), strong inter-trait correlations (up to r = –0.84), heteroscedasticity, and moderate population structure (mean Pst = 0.16 ± 0.001, achieved through iterative calibration). Each scenario was replicated 100 times, with clustering accuracy evaluated using external (ARI, NMI) and internal (Silhouette, Davies–Bouldin) validation metrics under standardized conditions. The results revealed consistently poor algorithm performance under realistic conditions (e.g., ARI < 0.07), including for widely used methods in Neglected and Underutilized Species (NUS) research such as K-means, GMM, and PAM. Notably, conventional validation metrics failed to detect biologically meaningful structure revealed by geometric diagnostics, highlighting a critical methodological limitation. Performance markedly improved under idealized conditions, validating our simulation framework. These findings highlight the risk of overinterpreting clustering outputs from weakly structured phenotypic datasets and expose key limitations in current biodiversity analysis practices, particularly those guiding plant genetic resource conservation programs. We provide an open-source R-based diagnostic tool, with parameter specifications to assist practitioners in selecting reproducible and interpretable clustering approaches for germplasm management and biodiversity assessment in data-scarce crops.

Excision in Two Dimensions: Synthesis of 2D Metal–Organic Nanosheets via Clip-off Chemistry

Retraction: Cyclophilin A (CypA) Interacts with NF-κB Subunit, p65/RelA, and Contributes to NF-κB Activation Signaling

Unifying Dearomatization and Rearomatization via Stereoselective Chlorination/Dechlorination: Resolution of Axially Chiral 1-Aryl-2-naphthols

Electrophysiological fingerprints of healthy cervical epithelial and HeLa cells: Membrane potential, zeta potential and passive electrical properties

There is a growing appreciation that cellular electrical mechanisms play an important role both in cell regulation, and in cell dysregulation in diseases such as cancer. These electrical mechanisms are measured using several different methods, which yield characteristics including the membrane potential, capacitance and conductance, the extracellular (ζ) potential and cytoplasm conductivity. However, since these are measured using different techniques, the combination of all of these (the cancer electrome ) has yet to be described. In this paper, we report on the difference between the electromes of cervical cancer cell line HeLa, with clinically-derived primary cervical epithelial cells. These were investigated using dielectrophoresis (DEP) and ζ-potential, with these data then being used to calculate the membrane potential V m . Results indicate significant differences in membrane conductance and capacitance, membrane potential, ζ-potential and cytoplasm conductivity between the two cell types. Furthermore, treatment with the K + blocker Tetraethylammonium caused distinct alterations in electrophysiology of the two lineages, pointing towards different roles for K + in cancer and normal cells. This work presents a novel and cost-effective approach, combining five distinct electrical properties to form a “fingerprint” to characterize and discriminate healthy and malignant cells in a label-free, rapid manner.

Highly Reduced Alkali-Metal Nickelates: Synthesis, Structure, Catalytic Applications, and Alkali-Metal Effects

Predicting extreme thermal degradation of ascorbic acid (Vitamin C) using Bayesian-Inverse Weibull models: Applications in stability analysis and process optimization

Ascorbic acid (Vitamin C) is a thermally sensitive compound extensively used in pharmaceuticals, nutraceuticals, and food industries, where its degradation under high-temperature conditions can compromise product quality and efficacy. Accurate prediction of extreme thermal degradation events is crucial for ensuring stability, optimizing manufacturing processes, and meeting regulatory standards. However, traditional degradation models often fail to capture rare but critical degradation behaviors, resulting in inadequate risk assessments and suboptimal process controls. In this study, we develop a Bayesian-Inverse Weibull modeling framework to predict extreme thermal degradation pathways of ascorbic acid under accelerated stress conditions. The Inverse Weibull distribution, known for its effectiveness in modeling heavy-tailed data, is integrated with a Bayesian hierarchical approach to incorporate prior knowledge, experimental data, and uncertainty quantification. This framework enables precise estimation of degradation thresholds, failure probabilities, and optimal storage and processing conditions. Using experimental thermal degradation data, we validate the model and demonstrate its application in optimizing manufacturing processes to mitigate degradation risks. The results highlight the model’s superior capability in predicting rare degradation events, providing actionable insights for improving product stability, reducing waste, and ensuring regulatory compliance. This approach offers a robust tool for chemometric analysis and process optimization in industries reliant on thermally sensitive compounds like ascorbic acid.

Chirality-Driven Metal–Phenolic Artificial Enzymes Enable Renal Targeting and Antioxidative Therapy Enhancement for Acute Kidney Injury

Estimating weaning duration from incremental dentine δ15N and δ13C using a sequence-based LSTM neural network: A deep learning framework for bioarchaeological applications

The estimation of weaning duration from incremental dentine δ 15 N and δ 13 C values offers insights into health, nutrition, and demography in past populations. In this study, we developed a novel machine learning approach to estimate weaning duration using a sequence-based Long Short-Term Memory (LSTM) neural network model. The model was trained on published serial isotopic data from 279 individuals across three tooth types (M1, dM1, dM2). We enhanced predictive performance by incorporating temporal features (differences, rolling averages, slopes) and encoding tooth type to allow the model to learn type-specific patterns. The model was evaluated with RMSE, MAE, and R 2 metrics using Monte Carlo dropout to quantify predictive uncertainty. As a proof of concept, we compared our model to established methods in weaning estimation (WEAN, ChangeR) using 20 individuals from the site of Thessaloniki. Our model achieved an RMSE of 0.46 years (95% CI: 0.38–0.54), a MAE of 0.36 years (95% CI: 0.29–0.44), and an R 2 of 0.82 (95% CI: 0.73–0.88), indicating reliable predictive accuracy. In contrast to previous approaches that rely primarily on δ 15 N, our method leverages sequential patterns in both δ 15 N and δ 13 C to infer the weaning process. This study introduces the first machine learning framework specifically designed to estimate weaning duration from serial isotopic data. By modelling complex, non-linear developmental trajectories, it offers a scalable and robust tool for reconstructing weaning practices in bioarchaeological and related research studies.