BioMed Research International最新文献

Migraine is a complex neurological disorder with significant implications for individual well-being and public health. Predicting migraine occurrences after treatment is crucial for evaluating therapeutic efficacy and enabling personalized care, yet remains largely underexplored. This study proposes a robust machine learning framework to predict posttreatment migraine headache occurrences using real-world headache log data collected from 133 patients undergoing biofeedback therapy. The methodology includes rigorous data preprocessing, outlier removal via the interquartile range (IQR) method, and class imbalance correction through the synthetic minority oversampling technique (SMOTE). A total of 10 classical and a hybrid ensemble machine learning models were developed and optimized through GridSearch with fivefold cross-validation. Performance was evaluated using different metrics, with the best-performing hybrid ensemble model achieving an accuracy and F1-score of 81%, with an area under the receiver operating characteristic curve (AUROC) of 0.87. Additionally, permutation feature importance analysis was employed to enhance model interpretability, identifying medication status, duration of treatment, and patient age as critical predictors. These outcomes validate the prospect of explainable AI-driven models in forecasting migraine recurrence posttreatment, providing a step forward toward intelligent clinical decision support systems for migraine management.

[This corrects the article DOI: 10.1155/2021/2899043.].

[This corrects the article DOI: 10.1155/2013/719858.].

The marine environment is a significant origin of bioactive substances like carotenoids. Marine carotenoids are secondary metabolites with mechanism-anchored benefits across redox, immune, and metabolic pathways, comprising antioxidant, anti-inflammatory, antidiabetic, anticancer, and antimicrobial activities. These bioactive compounds have garnered significant interest from the pharmaceutical, nutraceutical, and cosmetic industries, driving the exploration for novel natural reservoirs of carotenoids. However, most of the research has focused on carotenoids found in fruits, vegetables, and other higher plant components. Despite increasing interest, there are few publications on carotenoids found in marine sources such as seaweed, microalgae, and marine animals. This review summarizes chemistry, biosynthesis, extraction methods, bioavailability, and the bioactivities reported for major marine carotenoids (e.g., α- and β-carotene, lycopene, fucoxanthin, astaxanthin, zeaxanthin, canthaxanthin, spirilloxanthin, halocynthiaxanthin, neoxanthin, and peridinin).

There have been some concerns about the potential adverse consequences of proton pump inhibitors (PPIs) on bone health, specifically with respect to bone mineral density (BMD). The objective of the present investigation was to evaluate the impact of PPIs on BMD in the lumbar spine, femoral neck, and total hip regions. On the basis of PRISMA, a systematic review and meta-analysis were performed. PubMed, Scopus, Cochrane Library, and Google Scholar were used to find eligible observational studies published between January 2010 and January 2025. The ROBINS-I tool was employed to evaluate potential for bias, and studies with critical bias were excluded. To synthesize the data, random-effects models were utilized, and the I ² statistic was used to evaluate heterogeneity. The sensitivity analyses and publication bias were also performed. A systematic review and quantitative synthesis used 20 and seven records, respectively, of 170 records screened. The comprehensive pooled analysis revealed that the decrease in BMD with the use of PPIs was modest yet statistically significant (SMD -0.15, 95% CI -0.21 to 0.09) alongside a substantial degree of heterogeneity (I ² = 93.6%). Subgroup analysis demonstrated that there were significant decreases at the femoral neck (SMD -0.27, 95% CI -0.46 to -0.09), but not at the lumbar spine or the total hip. The funnel plot analysis indicated a certain level of asymmetry, and the sensitivity analysis indicated that the results were mostly robust; unless the study that excluded one outlier was used in the analysis, then the lumbar spine results would change. The use of PPIs is related to a significant but relatively small decrease in BMD, most obvious at the femoral neck, although findings across anatomical locations are heterogeneous. The results of this study support the cautious use of PPIs in all people at risk of osteoporosis and the need to conduct a high-quality prospective study to understand site-specific effects in the future.

To develop a telomere-related prognostic signature for esophageal carcinoma (ESCA), we integrated bioinformatics and machine learning approaches. Hub genes were identified from overlapping differentially expressed genes (DEGs). A prognostic model was constructed using LASSO and multivariate Cox regression, validated in independent GEO datasets, and further verified through cytological experiments. We also elucidated the mechanism by which MAPK12 promotes ESCA migration. The model robustly predicted survival of patients with ESCA, supported by both high-throughput data and experimental evidence. Our findings highlight MAPK12 as a promising biomarker and provide a theoretical basis for understanding ESCA pathogenesis and developing targeted therapies.

Background: Considering the role of a healthy diet in preventing atherogenesis, the use of beetroot as a functional food with anti-inflammatory, antioxidant, and anti-dyslipidemic effects due to its high nitrate content and bioactive compounds is one of the interesting approaches in coronary artery disease (CAD), a common worldwide chronic disease.

Methods: This is a secondary analysis of a randomized, double-blind, placebo-controlled clinical trial that was conducted on 90 male (67.8%) and female (32.2%) chronic patients with CAD for 4 weeks. This secondary analysis investigated the effect of beetroot capsules within three groups on fatty acid profile, total antioxidant capacity, total oxidant status, malondialdehyde, and myeloperoxidase.

Results: In subjects with 4 weeks of beetroot consumption, significant changes in the level of SFAs/PUFAs (-130 μg/mL, p = 0.04), PA/OA (-250 μg/mL, p = 0.02), and MPO (-9.60 U/L, p < 0.01) were established. In beetroot-vitamin C group, docosahexaenoic acid (9.3 μg/mL, p < 0.01), omega 3 (31 μg/mL, p < 0.001), EPA + DHA (10 μg/mL, p < 0.01), SFAs/PUFAs (-370 μg/mL, p < 0.001), PA/OA (-360 μg/mL, p < 0.001), TOS (-1.42 μM, p < 0.01), and MPO (-12.42 U/L, p < 0.001) had notable changes.

Conclusion: Beetroot capsule consumption had favorable effects on serum omega 3 fatty acids as well as TOS and MPO, the oxidant and atherogenesis factors.

Objective: This study is aimed at investigating the association between sleep duration and visual impairment, as well as its relationship with major eye diseases, using data from a large-scale population-based survey.

Methods: This cross-sectional study investigated the prevalence of visual impairment and major eye conditions among 5231 individuals aged 40 and older, utilizing data from the NHANES 2005-2008 survey. Sleep duration was categorized as short (< 7 h), normal (7-9 h), and long (> 9 h). Visual impairment was defined as corrected visual acuity < 20/40, and major eye diseases included cataract surgery, diabetic retinopathy, age-related macular degeneration, glaucoma, any retinopathy, any ocular disease, and any objectively determined ocular disease. Univariate and multivariate logistic regression were used to assess associations between sleep duration, visual impairment, and major eye diseases. Stratified analyses were further conducted based on diabetes and hypertension status.

Results: The long sleep group demonstrated a significantly higher prevalence of vision impairment, cataract surgery, glaucoma, as well as any ocular disease and any objectively determined ocular disease compared to the normal and short sleep groups (all p < 0.05). After adjusting for confounders, long sleep duration was significantly associated with visual impairment (OR: 2.44, 95% CI: 1.09-5.49, p = 0.035), glaucoma (OR: 3.38, 95% CI: 1.06-10.8, p = 0.042), and any objectively determined ocular disease (OR: 2.24, 95% CI: 1.08-4.65, p = 0.035). No significant associations were found between short sleep duration and visual impairment or major eye diseases after controlling for confounders. In the nondiabetic population, long sleep was significantly related to glaucoma, any objectively determined ocular disease, and visual impairment. Among hypertensive patients, long sleep was associated with glaucoma.

Conclusion: Long sleep duration is independently associated with visual impairment, glaucoma, and any objectively determined ocular disease. Longitudinal studies are needed to validate current results and explore causal mechanisms.

Background: Listeria aquatica FSL S10-1188 is a freshwater bacterium characterized by distinctive metabolic properties and a genome enriched in hypothetical proteins (HPs). Although it is not considered pathogenic, its genomic features and aquatic habitat raise concerns about potential involvement in horizontal antimicrobial resistance (AMR) gene transfer.

Methods: The genome of L. aquatica FSL S10-1188 (GenBank Accession: CP011539.1) contains 2789 predicted proteins that were initially screened. From these, 919 HPs were prioritized based on sequence length (> 50 amino acids), physicochemical stability assessed using ProtParam, and consistent subcellular localization predictions from CELLO, PSORTb, and PSLpred. Among these, two stable cytoplasmic, HPs EUJ18943.1 and EUJ18676.1, were selected for comprehensive characterization. Functional insights were explored through BLASTp, secondary structure prediction, and 3D structural modeling using AlphaFold3. Model quality was validated using multiple tools including SAVES v6.0 and QMEANDisCo. Virulence potential was predicted via VICMpred, VirulentPred, and DeepVF. Putative ligand-binding sites were identified using PrankWeb, and molecular docking analyses were conducted using ProBiS for ligand prediction and AutoDock to evaluate ligand-binding affinity.

Results: EUJ18943.1 (136 aa) and EUJ18676.1 (206 aa) are stable, hydrophilic proteins with acidic isoelectric points. BLASTp identified EUJ18943.1 as homologous to Imm48 immunity proteins and EUJ18676.1 as a GyrI-like detoxification protein. Both showed alpha-helix-rich secondary structures, with high-confidence AlphaFold3 models (pTM scores: 0.87 and 0.94). Structural validation confirmed the model quality. Virulence prediction tools classified both as potential virulence factors. Active sites were predicted by PrankWeb and ProBiS, identifying kanamycin A (score: 2.32) and streptomycin (score: 2.10) as top ligands. AutoDock v4.5.6 revealed strong binding affinities (Δ G = -7.17 and - 4.30 kcal/mol, respectively).

Conclusion: This study provides the first in silico structural and functional characterization of two HPs from L. aquatica FSL S10-1188, suggesting roles in stress response and virulence. These findings highlight their potential in AMR gene transfer and demonstrate the importance of computational annotation for guiding future experimental validation.

Background: Efficient analysis of patient histories is essential for dental treatment planning. Electronic health records (EHRs) facilitate the immediate identification of risk factors, such as medication allergies and sensitivities to dental materials, thereby reducing the risk of medical errors.

Objective: This study is aimed at comparing the effectiveness of an EHR interface with traditional paper medical records (PMRs) in evaluating the medical histories of dental patients.

Methods: Two hundred patient records were randomly assigned to either the PMR or EHR group. Twenty dental students reviewed the records and reported the planned dental procedure for each patient. Recording and reporting times, as well as the number of oversights, were measured for both groups.

Results: Students using EHRs identified critical systemic conditions more rapidly and with greater accuracy, with no oversights detected. In contrast, students using PMRs required more time and were more likely to overlook important health information. The mean number of oversights in the PMR group was significantly higher (9.3 ± 0.46, p ≤ 0.01).

Conclusion: The use of EHRs in dental practice significantly improves the identification of critical systemic conditions, enhances patient safety, and increases clinical efficiency. Despite the benefits, the high costs of software acquisition, staff training, and technical support may pose challenges to widespread adoption.