Maintaining fluid balance between intracellular and extracellular compartments is essential for physiological stability, yet disturbances in this equilibrium are common in conditions such as dehydration, sepsis, and perioperative stress. Traditional techniques for quantifying body water distribution, including isotope dilution, are accurate but invasive and unsuitable for repeated or bedside monitoring. Bioelectrical impedance spectroscopy (BIS) offers a non-invasive, rapid, and frequency-dependent approach to estimating total body water (TBW), extracellular water (ECW), and intracellular water (ICW), making it a promising candidate for real-time fluid assessment. This pilot study evaluated the sensitivity of BIS in detecting controlled fluid shifts under three conditions: intravenous infusion of graded volumes of lactated Ringer's solution, incremental gravity-drip infusion, and oral ingestion of water in divided portions. BIS measurements demonstrated reproducible, volume-dependent changes in TBW, ECW, and ICW, with distinct temporal patterns characterized by early rises, redistribution phases, and stabilization plateaus. Moderate fluid loads of 500-1000 mL yielded stable and sustained hydration profiles, whereas smaller volumes induced minimal shifts and larger volumes produced greater variability, reflecting regulatory corrections. These findings support the feasibility of BIS as a practical bedside tool for monitoring hydration dynamics. Larger clinical studies are guaranteed to confirm its role in fluid management for surgical and critically ill populations.

Electrochemical sensors face critical challenges in achieving ultra-high sensitivity and specificity within complex biological matrices. To address these limitations, this study proposes an Integrated Electrochemical Computational Model (IECM), a novel end-to-end framework that couples kinetics, electrochemistry, and signal amplification sub-modules to predict and optimize sensor performance. Unlike previous descriptive reviews, this work validates the IECM through rigorous experimentation using rGO/MXene/AuNP nanocomposite interfaces. The limit of detection (LOD) was determined based on the standard definition (3sigma/slope, where sigma is the standard deviation of the blank signal), yielding exceptionally low values of 0.12 fg/mL for prostate-specific antigen (PSA) and 5.0 × 10⁻¹⁸ M for microRNA-21, with corresponding calibration curve slopes confirming high signal-to-noise ratios. In complex matrices such as serum and saliva, the sensor demonstrated robust anti-interference capabilities, achieving recovery rates of 92.0% ± 3.5% and cross-reactivity rates below 3.5%. Long-term stability tests indicated a signal retention of 68.7% after 8 weeks under moderate storage conditions. Furthermore, double-blind clinical trials against gold-standard assays revealed a high concordance rate (κ = 0.93), confirming the model's predictive accuracy and the sensor's clinical potential. This research establishes a unified computational-experimental paradigm for the rational design of next-generation biosensors.

Vitiligo is a common skin depigmentation disorder; assessing its state is crucial for the treatment outcome. Collecting multimodal data for vitiligo assessment is complex and costly in clinical practices, and the limited data size restricts the performance of deep learning models. Transfer learning can alleviate the shortage of training data in medical image recognition, however, its applications in vitiligo state assessment are constrained by feature differences between natural and medical images and insufficient generalization to different modalities. To address these challenges, this paper introduces a vitiligo state assessment method based on progressive transfer learning and multimodal domain adaptation. The scheme uses a large set of unlabeled medical images as a bridge to reduce the discrepancies between natural and medical images through multi-step fine-tuning. An adaptive parameter unfreezing strategy is then applied for the accurately labeled target data to improve the adaptability and accuracy of the model. In addition, an integrated multimodal domain adaptation approach based on CycleGAN and hue, saturation, value color space transformation is proposed to reduce the impact of modality differences on transfer learning. Experimental results demonstrate that compared to the conventional transfer learning method, the proposed method improves the accuracy by 2.4% and 3.6% on the clinical and Wood's lamp modality datasets, respectively. This accurate vitiligo state assessment method can be applied to a wide range of multimodal dermatological diseases where labeled data is limited.

The cluster dose concept offers an alternative to the radiobiological effectiveness-based model for describing radiation-induced biological effects. This study examines the application of a neural network to predict cluster dose distributions, with the goal of replacing the computationally intensive simulations currently required. Cluster dose distributions are predicted using a U-Net that was initially pretrained on conventional dose distributions. Using transfer learning techniques, the decoder path is adapted for cluster dose estimation. Both the training and pretraining datasets include head and neck regions from multiple patients and carbon ion beams of varying energies and positions. Monte Carlo simulations were used to generate the ground truth cluster dose distributions. The U-Net enables cluster dose estimation for a single pencil beam within milliseconds using a graphics processing unit. The predicted cluster dose distributions deviate from the ground truth by less than 0.35%. This proof-of-principle study demonstrates the feasibility of accurately estimating cluster doses within clinically acceptable computation times using machine learning. By leveraging a pretrained neural network and applying transfer learning techniques, the approach significantly reduces the need for large-scale, computationally expensive training data.

Objective.This study aims to investigate neutron beam characteristics for Boron Neutron Capture Therapy (BNCT) targeting brain tumours and to clarify the physical limitations of single-field irradiation for treatment of deep-seated tumours.Approach.Using Monte Carlo simulations with a voxelated Snyder head phantom, we systematically evaluated the impact of three key characteristics-neutron energy (mono-beam), beam radius, and current-to-flux ratio (J/Φ) -on dose distributions.Results.The analysis identified the 1-10 keV energy range as optimal for depth penetration. Increasing theJ/Φ was found to linearly enhance the tumour dose by improving beam directionality. However, a critical trade-off was observed with beam radius.Significance.These findings demonstrate the fundamental limits of single-field BNCT for deep-seated malignancies and highlight the necessity of multiple-irradiation strategies such as each beam should shape narrow, high-J/Φ epithermal beams. By establishing a database of each beam characteristics provides the essential physical foundation for designing future personalised and multi-beam treatment plans.

Introduction.Total-body PET scanners are advanced technologies in medical imaging, enabling whole-body imaging with high precision and sensitivity. They reduce radioactive dose and shorten imaging time. One of the most advanced systems is the Biograph Vision Quadra by Siemens Healthineers, representing the new generation of this technology. With LSO crystals and an axial field of view of 106 cm, it provides complete body imaging in a single scan, improving efficiency and safety. However, their high cost remains a challenge; prices typically range from 3 to 5 million dollars. To reduce costs and improve performance, alternative scintillation crystals such as LYSO, BGO, and NaI (Tl) have been investigated due to their properties of light sensitivity, decay time, and production cost.Materials and methods.The performance of the Biograph Vision Quadra was evaluated using the GATE Monte Carlo simulation tool. Simulations followed NEMA NU 2-2018 standards to assess parameters including image resolution, sensitivity, count rate, and scatter fraction. Data were reconstructed and analyzed using CASToR and AMIDE software.Results.The simulations closely matched experimental data, showing an error rate of about 8% with clinical data, confirming their accuracy. Among the crystals, BGO proved the most cost-effective, offering 19% higher sensitivity than LSO, LYSO, and NaI (Tl), while maintaining adequate spatial resolution and NECR. Although its light output is lower than LSO, BGO's cost-effectiveness and good performance in sensitivity and count rate make it a suitable choice for reducing scanner costs.Conclusion.Replacing LSO with BGO can significantly improve the cost-effectiveness of the Biograph Vision Quadra without compromising image quality. This study highlights the potential for optimizing PET scanners through alternative scintillation materials, improving accessibility and supporting early disease detection in clinical practice.

In the rapidly emerging field of transferring healthcare from hospitals to patients' homes, it is essential that signs of remote deterioration at home should be detected early to timely upscale care when needed. As patient deterioration is a rare event, datasets have insufficient event data for supervised machine learning. Personalized anomaly detection (AD) may be a good alternative. Therefore, this study aimed to obtain insight into the use of personalized AD models in detecting early signs of patient deterioration at home using wearable sensor data. To address this, Isolation Forest and Local Outlier Factor models were applied to detect signs of deterioration in terms of mortality or unplanned readmission in the 24 preceding hours in two datasets: one of heterogeneous patients, the other of postoperative patients. A pipeline was developed for continuously updating personalized AD models over time and applying them to detection windows twice per day. Results were compared with the Remote Early Warning Score. Isolation Forest (AUROC: 0.69) and Local Outlier Factor (AUROC: 0.67) models were able to find some early signs of deterioration in heterogeneous patients (n = 113). However, in postoperative patients (n = 193), Isolation Forest (AUROC: 0.41) and Local Outlier Factor (AUROC 0.44) performed badly. The Remote Early Warning Score was able to find some early signs of deterioration for both groups (AUROC: 0.63-0.76). Based on these findings, three requirements were formulated that should be fulfilled for a potentially successful application of personalized AD. First, the training set should be normal and representative of non-deteriorating patients. Second, signs of deterioration should exhibit abnormal characteristics. Third, non-deteriorating patients should not exhibit abnormal characteristics.

Objective: This study presents a non-invasive method for estimating the second lactate threshold (LT2) in cyclists by modeling the dynamic heart rate (HR) response to power output (PO) using discrete-time transfer function (TF) techniques.Approach: Eleven trained recreational cyclists completed an incremental step test with simultaneous HR, PO, gas exchange, and blood lactate measurements. Two TF models were developed: a time-invariant (TI) model with constant parameters and a time-variant (TV) model whose parameters adapt over time to reflect physiological changes. LT2 was estimated from deviations in model behavior and validated against laboratory-derived LT2 using the modified Dmax method. Agreement was evaluated using absolute error, Pearson correlation, Spearman rank correlation, and Q-Q plots to assess normality of model residuals.Main results: The TV model provided markedly higher accuracy than the TI model. TV estimates showed a mean absolute error of 4%, with LT2 predicted within 10 W for 9 of 11 participants (Pearson r=0.947; Spearmanρ=0.954). TI estimation resulted in an average error of 11%, with only 5 participants within 10 W (Pearson r=0.759; Spearmanρ=0.756). Q-Q plots revealed deviations from normality in both models' error distributions, particularly for the TI model, supporting the use of rank-based correlation alongside Pearson's r. The TV model captured characteristic changes in HR-PO dynamics more reliably, especially around the transition to heavy-severe intensity.Significance: The proposed TV modeling approach offers an accurate, practical, and fully non-invasive alternative to blood lactate testing, requiring only HR and PO data typically collected by standard cycling devices. Although the method cannot estimate LT1, it holds promise for regular monitoring of LT2 in both laboratory and field settings and may broaden access to metabolic threshold assessment for athletes and coaches.

Traumatic Brain Injury resulting from the impact of the human skull is one of the leading causes of death worldwide. The knowledge of the biomechanics of such injuries is essential for the effective design of protective headgear. This study investigates the biomechanical responses of a human skull subjected to an impact with a rigid wall. Finite element analyses were performed by impacting various skull locations, and the utility concept approach was introduced to rank skull vulnerability. Time history plots of the velocity, acceleration and energy were extracted to determine the dynamic responses of the skull. The nasal bone is the weakest and exhibits the lowest stiffness among all skull bones, and transmits a lower level of the impact force to the brain. Parietal bone experiences the greatest deceleration, resulting in higher force transmission to the brain. The frontal bone is found to be the strongest and stiffest bone of the Human skull. The impact on the zygomatic bone also makes the temporal bone vulnerable, emphasising the need for protection of both bones in sagittal skull impacts. These findings provide critical biomechanical insights for optimising protective head equipment tailored to regional vulnerability.

Human skin is continuously exposed to varying environmental conditions. While it is known that the skin's biophysical properties are influenced by seasonal changes, the impact of these conditions on its electrical characteristics-particularly skin surface susceptance-remains unclear. Therefore, this study aimed to investigate the effects of seasonal variations on skin surface susceptance using a low-frequency electrical technique. The investigation was performed on 46 (23 males and 23 females) apparently healthy volunteers. Readings of electrical skin surface susceptance were taken from the volar forearm in all four seasons between autumn 2024 and June 2025. Seasonal changes had a significant effect on skin surface susceptance, with the highest values recorded in summer and the lowest in winter. In addition, a highly significant (p< 0.05,r= 0.98) positive correlation was established between seasonal temperature and the skin surface susceptance, and a significant (p< 0.05,r= -0.97) negative correlation between seasonal temperature and the skin surface susceptance was obtained. No significant differences were observed between male and female groups in response to seasonal changes, indicating that gender is an unimportant factor in this context. Our results suggest that seasonal variations should be taken into consideration when using the skin electrical technique. In addition, this will be relevant in the applications of skin sensors and dermatology.