Tag: pubmed

Radiol Phys Technol. 2025 Oct 2. doi: 10.1007/s12194-025-00973-4. Online ahead of print.

ABSTRACT

Automatic exposure control (AEC) in digital radiography adjusts exposure time based on the chosen milliamperage (mA) and the patient’s anatomical characteristics, ensuring the delivery of an appropriate radiation dose for optimal image quality. This study aimed to evaluate the reproducibility of AEC systems in general X-ray machines under various conditions. AEC reproducibility was assessed in two general X-ray machines: the SIEMENS Multix Top and the DRGEM GXR-40S. Both systems offer three sensitivity settings (high, medium, and low). A stack of Thai ten-baht coins, consisting of one and five layers, was used as a test object and placed directly over the AEC sensor. Ten exposures were carried out for repeated measurements. Differences in mAs values and coefficients of variation (CV) were calculated, and statistical analysis was performed using the Mann-Whitney U test. Results showed that mAs values changed in response to tube voltage, sensitivity setting, object thickness, and sensor position; however, these variations remained within acceptable limits. A higher mAs value was observed at lower tube voltages (80-81 kVp), a lower sensitivity setting (D or Slow), and a five-layer coin thickness. No significant differences were observed at higher tube voltage (100 kVp) and higher sensitivity (H or Fast; p > 0.05). In conclusion, AEC reproducibility testing showed mean mAs ranges of 0.51-3.25 with a maximum CV of 2.60% for SIEMENS, and 0.37-1.62 with a maximum CV of 3.37% for DRGEM. Both systems met international standard guidelines, with a CV below 5.00%, as recommended by AAPM Report No. 150, confirming consistent mAs values under various conditions.

PMID:41037241 | DOI:10.1007/s12194-025-00973-4

Phys Eng Sci Med. 2025 Oct 2. doi: 10.1007/s13246-025-01648-5. Online ahead of print.

ABSTRACT

Accurate prediction of surface doses is crucial for clinical outcomes in radiotherapy. Surface dose distribution must be predicted accurately by calculation algorithms in the treatment planning system (TPS). This study aims to compare surface dose calculations from the Eclipse TPS with radiochromic film measurements to evaluate the reliability of these calculation algorithms. Measurements with radiochromic films were performed using 6 MV photon beams. Treatment plans for 3D conformal radiotherapy (3DCRT), intensity-modulated radiotherapy (IMRT), and volumetric arc therapy (VMAT) were generated on the TPS and calculated using various algorithms. Treatment plans were irradiated on Gafchromic EBT3 films with a PTW head and neck phantom. EBT3 films were compared to calculation algorithms via FilmQA™ Pro (version 7.0) software with multi-channel analysis. Dosimetric evaluations were statistically analyzed. Commercial calculation algorithms underestimated the surface dose in 3DCRT, IMRT, and VMAT treatment plans. For 3DCRT, the underestimations were 8.0% with the AAA algorithm and 8.7% with AXB. In VMAT, the underestimations were 10.2% with AAA and 12.9% with AXB. For IMRT, the underestimations were 6.6% with AAA and 7.3% with AXB. The AAA algorithm closely matched surface dose measurements among calculation methods. The dosimetric results indicate that both AAA and AXB algorithms, as implemented in the Eclipse™ TPS, tend to underestimate surface dose compared to EBT3 film measurements. Accurate knowledge of the dose in the superficial region is crucial to prevent acute skin reactions or to deliver an effective dose to superficial tumors in clinically significant cases. Therefore, our surface dose measurements offer more accurate evaluations, making Gafchromic EBT3 films suitable for such cases.

PMID:41037238 | DOI:10.1007/s13246-025-01648-5

Biol Trace Elem Res. 2025 Oct 2. doi: 10.1007/s12011-025-04825-5. Online ahead of print.

ABSTRACT

Zinc is essential for testicular development, spermatogenesis, sperm protection, and male fertility maintenance. However, existing research on the relationship between zinc and sperm quality parameters shows inconsistent results. This study aimed to evaluate seminal plasma zinc levels and analyze their correlation with sperm parameters. We collected 791 male semen samples, measuring seminal plasma zinc levels via spectrophotometry and assessing the linear relationship between zinc levels and sperm parameters using RCS curves and threshold effect analysis. The results demonstrated an inverted U-shaped association between seminal plasma zinc concentration and both sperm concentration and total sperm count (p for nonlinear < 0.001). When the concentration of zinc in seminal plasma is < 156.54 mg/L, sperm concentration increases with the increase of zinc concentration in seminal plasma (β = 0.19, 95% CI: 0.12-0.26, p < 0.001), but when it is > 156.54 mg/L, although there is no statistically significant difference (β = -0.09, 95% CI: -0.20-0.02, P =0.097), sperm concentration shows a downward trend (β = -0.09, 95% CI: -0.20-0.02, p = 0.097). When the concentration of zinc in seminal plasma reaches 158.13 mg/L, the total sperm count increases significantly (β = 0.71, 95% CI: 0.45-0.96, p < 0.001), but it decreases when it exceeds this level (β = -0.31, 95% CI: -0.73 – 0.11, p = 0.152). Total zinc content demonstrated a positive linear correlation with both sperm concentration and total sperm count (p < 0.001). The findings revealed a nonlinear association between seminal plasma zinc levels and both sperm concentration and total sperm count. Total zinc content exhibited a linear relationship with these seminal parameters. These results highlight the critical need to establish a safe supplementation threshold for seminal plasma zinc.

PMID:41037237 | DOI:10.1007/s12011-025-04825-5

Curr Environ Health Rep. 2025 Oct 2;12(1):35. doi: 10.1007/s40572-025-00502-w.

ABSTRACT

BACKGROUND: In utero and childhood exposure to toxic metals is associated with poor child growth, a predictor of adverse health outcomes. Most existing research focuses on exposure to single metals; the effects of metal mixtures largely remain understudied. Further, few studies consider how diet/nutrients interact with metal mixtures.

OBJECTIVE: To synthesize research on the relationship between in utero and childhood metal mixture exposures, nutritional status-metal exposure interactions, and child anthropometric outcomes.

METHODS: PubMed and Embase were used to search literature published in 2010-2023. Included studies consisted of at least two in utero or childhood toxic metal exposures and examined anthropometric parameters as their main outcomes. Included articles underwent full-text screenings. Information on exposures, findings, nutritional variables, and statistical methods was extracted.

RESULTS: After deduplication and title and abstract screening, 95 publications were included; 70 on prenatal growth and 25 on postnatal growth. Nutritional status/diet was assessed as an effect modifier in 4.3% studies on prenatal and 12% studies on postnatal growth. Birthweight (91.4%), and height and body mass index (64%) were common indicators of prenatal and postnatal growth, respectively. Finally, 41.4% of studies on prenatal and 20% on postnatal growth included statistical models that tested for mixture effects.

CONCLUSION: Although many studies included multiple metals, their mixture effects largely remain untested. Additionally, inclusion of nutritional status/dietary intakes in statistical models is rare, highlighting the need for further research.

PMID:41037236 | DOI:10.1007/s40572-025-00502-w

Eat Weight Disord. 2025 Oct 2;30(1):79. doi: 10.1007/s40519-025-01753-0.

ABSTRACT

PURPOSE: Currently, there is a growing awareness among individuals about health and nutrition. Therefore, it is important to comprehend the factors that influence eating habits and attitudes. This study aims to investigate the potential mediation effect of eating attitudes in the relationship between social media use and well-being, as well as to explore whether the moderating effect of the level of orthorexia nervosa influences this relationship.

METHODS: The sample consisted of 599 adults (M_age = 29.82, SD = 9.39; 68% female) from Turkey and Northern Cyprus. Participants were recruited via convenience sampling through university networks, reflecting a culturally diverse context rooted in Mediterranean and Middle Eastern dietary norms. The study used the Social Media Usage Purposes, Eating Attitudes Test Short Form, Orthorexia nervosa Questionnaire-11, and the WHO-Five Well-being Index. A cross-sectional design was employed, and data were analysed using Hayes’ Process Macro (Model 58) to test for moderated mediation.

RESULTS: The study found that eating attitudes played a partial mediating role in the relationship between social media use and well-being among adults. Social media use positively predicted eating attitudes (β = .83, p < .001) and well-being (β = 1.05, p < .05), and eating attitudes significantly predicted well-being (β = .94, p < .001). Also, orthorexia nervosa moderated the mediating effect of eating attitudes in the relationship between social media use and well-being. Interestingly, the moderating effect was stronger among individuals with low levels of orthorexia nervosa, contrary to initial expectations.

CONCLUSIONS: The current study suggests that eating attitudes are a key behavioral mechanism linking social media use and well-being, and this pathway is influenced by individuals’ orthorexia nervosa tendencies. These findings could aid in the development of interventions for eating disorders at both clinical and social levels and guide individuals towards healthier lifestyles. Importantly, while orthorexia nervosa moderated the indirect relationship between social media use and well-being, the study did not find a direct association between orthorexia nervosa and social media use.

LEVEL OF EVIDENCE: Level III. Evidence obtained from well-designed cohort or case-control analytic studies.

PMID:41037224 | DOI:10.1007/s40519-025-01753-0

Neurol Sci. 2025 Oct 2. doi: 10.1007/s10072-025-08488-x. Online ahead of print.

ABSTRACT

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) is a severe autoimmune inflammatory disease of the central nervous system characterized by microglial activation and neuroinflammation. Chitotriosidase (CHIT1), a microglial activation biomarker, has been implicated in neurodegenerative and neuroinflammatory diseases, but its role in NMOSD remains unclear.

METHODS: Thirty-four patients with NMOSD, 30 healthy controls (HCs), and 30 patients with other noninflammatory neurological disorders (ONNDs) were included. Cerebrospinal fluid (CSF) and serum CHIT1 levels were measured via enzyme-linked immunosorbent assay. Comprehensive clinical parameters were collected from all participants. Statistical comparisons and receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic performance of CHIT1 for NMOSD.

RESULTS: CSF CHIT1 levels were significantly higher in the NMOSD group than in the ONND group (p < 0.001). In contrast, serum CHIT1 levels did not differ significantly between NMOSD patients and either ONND or HC groups. Subgroup analysis revealed higher CSF CHIT1 concentrations in NMOSD patients with gadolinium-enhancing lesions than in those without such lesions (p = 0.035). ROC analysis demonstrated that CSF CHIT1 could distinguish NMOSD patients from patients with ONNDs, with an area under the curve of 0.730. Additionally, CSF CHIT1 levels correlated positively with the Expanded Disability Status Scale score (r = 0.457, p = 0.007).

CONCLUSION: An elevated CSF CHIT1 level in NMOSD patients is significantly associated with greater disease severity, suggesting its potential as a diagnostic and prognostic biomarker. These findings highlight the role of CHIT1 in the pathogenesis of NMOSD and warrant further investigation into its clinical applicability.

PMID:41037213 | DOI:10.1007/s10072-025-08488-x

J Robot Surg. 2025 Oct 2;19(1):653. doi: 10.1007/s11701-025-02839-8.

ABSTRACT

Gastric cancer requires surgical resection for cure, with robot-assisted minimally invasive gastrectomy (RAMIG) emerging as an alternative to open gastrectomy (OG). Comparative data on postoperative pain and recovery remain limited. This study aimed to compare RAMIG versus OG in patients with resectable gastric cancer, focusing on postoperative opioid consumption, pain intensity, and recovery parameters. In this retrospective cohort study, 138 patients with resectable gastric cancer underwent either RAMIG (n = 39) or OG (n = 99) between May 2021 and August 2023. Primary endpoints were pain intensity (Numerical Rating Scale (NRS)) and opioid consumption. Secondary endpoints comprised intensive/intermediate care (ICU/IMC) and hospital stays, blood loss, severe complications, and operative duration. Statistical analysis used SPSS version 29.0 with Mann-Whitney U and Fisher’s exact tests (p < 0.05). RAMIG showed reduced opioid consumption (p = 0.002) and lower NRS scores during mobilization on days 5 and 7 (p = 0.011; p = 0.002) and at rest on day 7 (p = 0.005). The RAMIG group experienced significantly shortened ICU/IMC stays (p < 0.001), reduced hospitalization duration (p < 0.001), and decreased intraoperative blood loss, although operative duration was prolonged. RAMIG demonstrates favorable outcomes regarding opioid requirements, pain management, ICU/IMC and hospital stays, and blood loss compared to OG, despite longer operative duration. These findings support RAMIG as an effective approach enabling accelerated recovery in patient-centered care, though prospective randomized validation studies are warranted. Trial registration: DRKS00036368, retrospectively registered 11th of March 2025.

PMID:41037210 | DOI:10.1007/s11701-025-02839-8

J Mol Neurosci. 2025 Oct 2;75(4):132. doi: 10.1007/s12031-025-02412-w.

ABSTRACT

This study aims to develop and evaluate deep neural network (DNN) models for accurately predicting the recurrence risk of glioblastoma multiforme (GBM) to enhance individualized treatment strategies and improve patient outcomes. This study implemented DNN architectures optimized using a hybrid differential evolution neural network (HDE-NN) framework to forecast GBM recurrence risk, particularly in patients at advanced disease stages. The models were trained and validated on a multimodal dataset comprising genomic profiles, imaging-derived metrics, and longitudinal clinical records from 780 GBM patients. Data were sourced from The Cancer Genome Atlas (TCGA) and institutional repositories. Performance was benchmarked against conventional machine learning models, including support vector machines (SVM), random forests (RF), and standard DNNs. The models were implemented in Python. The proposed HDE-optimized DNN achieved an accuracy of 94%, precision of 92%, recall of 90%, F1 score of 91%, and an AUC-ROC of 0.96. These metrics significantly outperformed baseline models, with improvements of 6-12% across evaluation criteria. Confidence intervals (95%) were computed via tenfold cross-validation, confirming statistical robustness. This research introduces a high-performance and generalizable deep learning framework for GBM recurrence prediction. By incorporating multi-source clinical and genomic data, the model demonstrates superior predictive capacity over traditional methods. These findings support the integration of AI-driven tools into GBM care workflows to improve prognosis assessment and personalize therapeutic interventions.

PMID:41037206 | DOI:10.1007/s12031-025-02412-w

Brain Inform. 2025 Oct 2;12(1):25. doi: 10.1186/s40708-025-00274-x.

ABSTRACT

Understanding how demographic factors influence visual attention is crucial for the development of adaptive and user-centered web interfaces. This paper presents a gender-aware saliency prediction system based on fine-tuned deep learning models and demographic-specific gaze behavior. We introduce the WIC640 dataset, which includes 640 web page screenshots categorized by content type and country of origin, along with eye-tracking data from 85 participants across four age groups and both genders. To investigate gender-related differences in visual saliency, we fine-tuned TranSalNet, a Transformer-based saliency prediction model, on the WIC640 dataset. Our experiments reveal distinct gaze behavior patterns between male and female users. The female-trained model achieved a correlation coefficient (CC) of 0.7786, normalized scanpath saliency (NSS) of 2.4224, and Kullback-Leibler divergence (KLD) of 0.5447; the male-trained model showed slightly lower performance (CC = 0.7582, NSS = 2.3508, KLD = 0.5986). Interestingly, the general model trained on the complete dataset outperformed both gender-specific models, highlighting the importance of inclusive training data. Statistical analysis revealed significant gender-related differences in 9 out of 12 saliency features and a trend of reduced fixation dispersion with increasing age. While this study does not yet incorporate temporal gaze modeling, the results suggest practical benefits for intelligent systems aiming to personalize user experiences based on demographic features. The WIC640 dataset is publicly available and offers a valuable resource for future research on adaptive AI systems, visual attention modeling, and demographic-aware interface design.

PMID:41037184 | DOI:10.1186/s40708-025-00274-x

J Robot Surg. 2025 Oct 2;19(1):657. doi: 10.1007/s11701-025-02851-y.

ABSTRACT

AIMS: Robotic surgery continues to expand rapidly in elective settings; however, its role in emergency care is limited to date. This study aims to evaluate the safety and feasibility of the adoption of robotic emergency general surgery (EGS) within a high-volume centre.

METHODS: Robotic EGS cases performed between 2020 and 2024 at a large UK university hospital were identified and matched 1:3 to non-robotic cases based on operation type, age, gender, and pathology. Data on demographics, operative details, and operative and clinical outcomes were collected. Groups were compared using appropriate statistical tests.

RESULTS: A total of 369 patients were included, with 95 (25.7%) in the robotic and 274 (74.3%) in the non-robotic (open/laparoscopic) EGS group. There were no differences between groups for demographics, procedures, or pathology. No statistically significant differences were observed in major complications (10.5% vs 9.1%, p = 0.688), conversion to open surgery (1.1% vs 3.9%, p = 0.174), post-operative length of stay (4 vs 3 days, p = 0.814), and 6-month mortality (0.0% vs 2.9%, p = 0.092) between robotic and non-robotic groups. Adjusted analyses showed no association between surgical approach and differences in operative time, major complications, or post-operative stay.

CONCLUSION: The introduction of robotic emergency general surgery is safe and feasible with comparable short-term clinical outcomes to non-robotic approaches. Further research is needed to explore the impact of an established robotic EGS programme on long-term clinical, patient, and surgeon-reported outcomes.

PMID:41037147 | DOI:10.1007/s11701-025-02851-y