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Is atopic comorbidity increased in proctocolitis?
No full textBackground: Allergic march refers to the association of multiple allergic diseases but current understanding primarily focuses on immunoglobulin E (IgE) mediated food allergies (FA) (IgE-FA). The impact of non-IgE-FAs on allergic march remains unclear. Objective: To determine whether food protein-induced allergic proctocolitis (FPIAP), a non-IgE-FA, coexists with other allergic diseases during follow-up and to identify predictive factors. Methods: Eighty-four patients diagnosed with FPIAP who had been followed up for at least 3 years and 89 age- and gender-matched controls were compared for the presence of concomitant allergic conditions. Results: Patients with FPIAP who were followed up regularly for at least 3 years were evaluated for the presence of concurrent allergic diseases at a median (interquartile range [IQR]) age of 50 months (47-54 months), whereas, in the age- and gender-matched control group, the median (IQR) age at evaluation was 51 months (47.5-57.5 months). Asthma, allergic rhinitis (AR), and IgE-FA rates in the FPIAP group were 29.8% (n = 25), 29.8% (n = 25), and 15.5% (n = 13), respectively, compared with 14.6% (n = 13), 13.5% (n = 12), and 3.4% (n = 3), respectively, in the control group. Asthma, AR, and IgE-FA were significantly more frequent in the FPIAP group (p = 0.03, p = 0.02, p = 0.01, respectively). Atopic dermatitis in those under the age of 2 years was more prevalent in the FPIAP group (38.6%, [n = 32]) compared with the controls (10.6% [n = 9]) (p = 0.001). Although vomiting at onset was identified as a predictive factor for asthma, maternal rhinitis and delayed introduction of complementary feeding were associated with an increased risk of developing AR in the FPIAP group. Conclusion: This study demonstrated a higher rate of asthma, AR, and IgE-FA in patients with FPIAP compared with age- and gender-matched controls. These findings emphasize the importance of increasing awareness of the potential coexistence of FPIAP with other allergic diseases
Big Data and Machine Learning Techniques to Parametrize Strategic Shocks for Resilience Measurement
No full textDecision-makers recognize that strategic shocks (threats) cannot be fully avoided, prompting a shift toward understanding resilience—preparing for, absorbing, recovering from, and adapting to such shocks. In 2022, a prototype model was developed to evaluate a country's resilience using system dynamics. During testing, the need for a strategic shock parametrization mechanism based on open-access data was identified. This led to the development of the Joint Operations Area Resilience Model, which integrates Big Data and Machine Learning into system dynamics, improving the previous model. A key feature of the new model is its open-access data mechanism for strategic shock parameters. This paper focuses on the integration of Big Data and Machine Learning into the model and the development of a web-based application that automatically scrapes real-world data on strategic shocks (e.g., blackouts, pandemics, cyber-attacks) from open sources. The application processes this data and provides shock input values (magnitude, time, and duration) for the system dynamics model. Users can verify and validate the model through experiments and conduct real-time what-if analyses based on actual shock scenarios. Although data availability and reliability remain challenges, the model's potential to support resilience decision-making is enhanced by expert validation, timely feedback, and reliable data. The main benefit of the project is its shift from a solo simulation approach to a real-time decision support system powered by open-access data
MXenes in diabetes: diagnostic and therapeutic applications
No full textThis review highlights the promising role of MXenes and their composites in diabetes management, emphasizing their dual utility in diagnostics and therapeutics. MXenes' exceptional electrical conductivity, hydrophilicity, mechanical robustness, and tunable surface chemistry facilitate the design of sensitive and selective biosensors for real-time and non-invasive monitoring of key diabetes biomarkers like glucose and acetone. Therapeutically, MXene-based materials enhance healing of diabetic complications such as foot ulcers by modulating inflammation, scavenging reactive oxygen species, promoting angiogenesis, and supporting tissue regeneration via multifunctional hydrogels, patches, and scaffolds. Despite these advances, challenges remain including environmentally harmful synthesis methods, limited scalability, oxidation-induced instability under physiological conditions, and insufficient biocompatibility data. Future efforts are directed toward developing greener and scalable synthesis routes, improving MXene stability through surface modifications, and integrating MXenes with cutting-edge technologies such as wearable devices, 3D bioprinting, and bioelectronics. Additionally, the review uniquely explores the incorporation of artificial intelligence and machine learning techniques to enable personalized and adaptive diabetes management. By providing a comprehensive synthesis of recent developments, current limitations, and innovative future directions, this review offers novel insights aimed at accelerating the clinical translation of MXene-based platforms to significantly enhance diabetes diagnosis and treatment
Synthesis and evaluation of a novel poly(2-hydroxypropylacrylate-co-1H-tetrazole) copolymer for anhydrous proton transport and dielectric relaxation
No full textIn this study, we report for the first time the synthesis and complete characterization of a novel copolymer, poly(2-hydroxypropyl acrylate-co-1H-tetrazole) [P(2HPA-co-1HTz)] as an alternative proton conductive matrix. The structural analysis was confirmed by FT-IR and 1HNMR spectroscopy, while TGA and DSC analyses demonstrated sufficient thermal stability. SEM micrographs revealed a homogeneous morphology favorable for uniform charge transport. Proton conductivity (σ) measurements under completely anhydrous conditions revealed values of 3.53×10⁻5 S/m at 20 Hz and 1.95×10⁻4 S/m at 1 MHz at 420 K, substantially surpassing dry neat commercial membranes. Broadband dielectric measurements showed strong frequency and temperature dependence in both real (ε′) and imaginary (ε″) parts of dielectric constant. At low frequencies, ε′ values were highest and decreased sharply with frequency, converging above 1 kHz. Similarly, ε″ values increased with temperature, especially at low frequencies, suggesting enhanced segmental mobility and charge carrier response. Loss tangent (tanδ) analysis revealed no relaxation peak below 360 K, whereas clear peaks appeared from 370 K upwards. Relaxation times calculated from these peaks were τ = 3.33×10⁻3 s at 380 K (50 Hz), τ = 2.65×10⁻3 s at 400 K (60 Hz), and τ = 7.96×10⁻4 s at 430 K (200 Hz), indicating thermally activated relaxation behavior. Overall, the combination of structural stability, significant anhydrous ionic conductivity, and well-defined dielectric relaxation makes P(2HPA-co-1HTz) a strong SPE matrix candidate for electrochemical devices operating at elevated temperatures
Quantitative 3D Assessment of Iatrogenic Damage During Class II Cavity Preparations by Dental Students
No full textIntroduction and Aims: The aim of this study was to visually and quantitatively determine the extent of iatrogenic damage on the approximal surface of teeth caused by dental students during Class II cavity preparation, and to develop and apply a 3D modeling workflow for the precise quantitative assessment of such damage. Methods: A total of 101 patients (mean age:34.6) with Class II caries and intact adjacent tooth surfaces participated in the study. Teeth requiring endodontic treatment and adjacent teeth with caries were excluded. Cavity preparations were performed by third-, fourth-, and fifth-year undergraduate dental students under supervisor approval. Alginate impressions were taken, and Type IV dental stone models were produced. Damage assessment was performed using stereomicroscopy by 2 independent calibrated examiners, followed by quantitative 3D analysis using intraoral scanning (3Shape TRIOS 3) and Rhinoceros 8 CAD software. Damage was assessed in terms of morphology, buccolingual and cervico-occlusal width, and surface area. Statistical analysis included Chi-square tests, Mann-Whitney U tests, and Spearman's correlation (P .05). The average buccolingual width of damages was 2.40 +/- 1.42 mm, the cervico-occlusal width was 1.77 +/- 1.03 mm, and surface area was 4.66 +/- 4.35 mm(2) of damages. X morphology (a combination of multiple defects) showed significantly higher than other morphologies (P < .05). Strong positive correlations existed between damage widths and surface area (r = 0.906 and r = 0.0872, both P < .001). Conclusion: This study demonstrates that 3D modeling provides objective quantification of iatrogenic damage during dental training. The high prevalence of damage (90.1%) indicates the need for enhanced protective protocols, improved visual access techniques, and integration of 3D assessment methods in dental curricula. The strong correlation between damage dimensions suggests that width measurements can predict total damage extent, enabling early intervention strategies
The effect of postoperative back massage on pain, sleep outcomes and serum cortisol after open-heart surgery: A randomized controlled trial
No full textBackground: Massage is widely recognized as an effective non-pharmacological intervention for reducing pain and anxiety after cardiac surgery. However, its effects on sleep outcomes and biological stress markers remain underexplored. Aim: To evaluate the impact of back massage on postoperative pain, subjective and objective sleep outcomes, and serum cortisol levels in patients undergoing open-heart surgery. Methods: A prospective randomized controlled trial was conducted with 72 patients scheduled for elective open-heart surgery. Participants were randomized (1:1) to an intervention group (back massage) or a control group (routine care with light touch). The intervention consisted of three standardized sessions (15–20 min each) on the first postoperative day. Outcomes included pain (Numeric Rating Scale-Pain), subjective sleep quality (Richard–Campbell Sleep Scale), objective sleep duration (smartwatch measurement), and serum cortisol levels. Data were analyzed using repeated-measures analysis of variance and Brunner–Langer tests in a per-protocol population (n = 64). Results: Back massage was associated with significantly longer total sleep duration (p = 0.037) and greater reduction in pain scores, with significant group, time, and group × time effects (p = 0.002, p < 0.001, p = 0.048). Cortisol levels decreased over time in both groups (p < 0.001), but without significant between-group differences. Subjective sleep quality improved in both groups, and analgesic use declined, with no significant variation between groups. No adverse events were observed. Conclusion: This randomized controlled trial demonstrates that back massage is a safe and feasible intervention after open-heart surgery, improving objectively measured sleep duration and reducing pain. By incorporating objective sleep measures and a biological stress marker (serum cortisol), this study provides novel insights that extend beyond the traditionally reported outcomes of pain and anxiety, supporting massage as a complementary strategy within multimodal nursing care
Engineering Materials
No full textNanomaterial-based sensors are radically changing real-time monitoring technologies in biomedical applications. Offering higher sensitivity, selectivity, and fast response time compared to traditional sensors, these sensors have great potential for detecting various biomarkers and continuous health monitoring. Nanomaterials such as graphene, fullerenes, and metal oxide nanoparticles play an important role in disease diagnosis, monitoring of treatment processes, and personalized medicine applications by improving the performance of biosensors. These sensors, which are used primarily in detecting blood sugar, cancer markers, infection indicators, and biomolecules associated with neurodegenerative diseases, increase patient comfort by offering non-invasive and minimally invasive methods. Nanomaterial-based sensors, which can be integrated into wearable and portable devices, provide revolutionary innovations in the healthcare sector by expanding the possibilities of remote health monitoring and early diagnosis
Modeling and analysis of leakage inductance in Small-Size AFPMSMs with different core materials for compact biomedical pump systems
No full textThis study presents the modeling and analysis of leakage inductance in small axial flux permanent magnet synchronous machines (AFPMSMs) designed for compact biomedical pump systems, focusing on the influence of core materials under magnetic saturation. Two identical AFPMSMs were manufactured, one using a soft magnetic composite (SMC) stator core and the other with a grain-oriented (GO) steel stator core. Synchronous inductance values were analytically calculated, considering magnetizing, slot leakage, tooth-tip leakage, end-winding leakage, and air–gap harmonic leakage components. These calculations were validated through finite element analysis (FEA) simulations and experimental measurements. Results indicate that the phase inductance was 288.90 µH for the GO steel core and 204.86 µH for the SMC core, with a phase-inductance ratio of 0.709 based on LCR meter measurements. During operation between 2000 and 10,000 rpm and 1–10 mNm, the average inductance ratio was found to be 0.743. While SMC cores demonstrated lower leakage inductance, GO steel cores exhibited higher back-EMF and torque output. This comprehensive analysis highlights the critical role of material properties in AFPMSM performance, providing actionable insights for optimizing machine design in compact biomedical pump systems, such as left ventricular assist devices (LVADs), where axial compactness and thermal suitability are decisive. The study emphasizes the importance of combining analytical, simulation, and experimental approaches to achieve accurate inductance modeling and performance evaluation under saturation conditions