Search Results (201)

Background: Deep infiltrating endometriosis (DIE) and, in particular, bowel endometriosis stand out for their complexity. While surgery for bowel endometriosis has proven to be effective, there is a lack of standardization concerning the technique used and the reported outcomes. Objectives: The objective is to perform a review aiming to summarize the state of the art of bowel endometriosis and to point out the gaps to be addressed by future research. We also propose a novel classification of surgical procedures to fill these gaps and improve management. Methods: A literature search was performed on PubMed from inception to October 2025. Results: The following three major procedures for the excision of bowel endometriosis have been proposed: the nodule shaving, the discoid excision, and the segmental intestinal resection. One further technique, NOSE (natural orifice specimen extraction), can be applied for the removal of the specimen in cases of discoid or segmental resection. To reduce surgical morbidity, current data support the choice of most conservative surgical options, namely nodule dissection and discoid resection, as well as the use of nerve-sparing techniques in case of segmental resection. Nonetheless, there is little evidence concerning the indication and the most appropriate technique to be used, including their relative risks and benefits in terms of pain control, urinary and gastrointestinal function, risk of future relapse, and fertility outcomes. Conclusions: Significant barriers in comparing surgical outcomes due to unclear definitions, lack of standardization, and incomplete reporting are some of the most relevant issues frequently encountered. To fill these gaps, we propose a new classification system for bowel surgery that describes the dimension and the number of the lesions, as well as the type of surgical technique used, supplemented by the information if vaginal opening was necessary for complete lesion resection. This proposition aims to open a discussion on this topic and boost focused research to evaluate the utility of a new classification in clinical practice. Full article

Chloride diffusivity of concrete is essentially determined by its microstructural parameters. Establishing a reliable and accurate prediction model for chloride diffusion has become a research hotspot. In this study, a database containing 144 sets of macro–micro property parameters of concrete is established to train a Multilayer Perceptron (MLP) model. Taking the original collected data as a benchmark, data are randomly missing to simulate data incompleteness, and the models are trained using data filled by the Lagrange, K-Nearest Neighbor (KNN), and Miceforest methods. Moreover, the original data is expanded by the virtual sample generation (VSG) algorithm, based on a Gaussian mixture model (GMM) that fits the joint probability distribution of the original data to generate virtual samples preserving statistical (mean, standard deviation) and physical (e.g., porosity range, pore size ratio) consistency, thus mitigating the randomness caused by small sample sizes. Results indicate that the MLP model demonstrates excellent predictive performance: among schemes handling missing data, the model preprocessed by normalization with KNN imputation yields the best results with testing R² of 0.78; the baseline model (without missing value filling, normalized) achieves testing R² of 0.83, MAE of 0.572, and MSE of 0.424. VSG-expanded data significantly enhances the MLP model’s prediction accuracy. When expanding to 3000 groups, the testing R² reaches 0.85, a 2.4% increase compared to 1000 groups, with further improvements as the dataset expands, confirming the feasibility of the VSG algorithm for small-sample scenarios. Full article

Brain diseases such as ischaemic stroke, Alzheimer’s disease (AD), and glioma were characterized by high mortality and disability rate, and oxidative stress remains a major obstacle in treatment. Plasma–nanomedicine synergistic treatment technology provides a very attractive treatment strategy based on complementarity. This technology integrates cold atmospheric plasma (CAP) with nanomedicine. CAP produces active substances that regulate oxidative stress, while nanomedicine is specially designed for targeted delivery, controlled release, and microenvironmentally responsive activation of therapeutic agents. This integration generates new therapeutic functions and significantly improves the overall therapeutic effect. Despite the broad prospects of this emerging technology, researchers in the fields of medicine, physics, or pharmacy have not yet paid much attention to it. To fill this research gap, this review describes the physicochemical properties and biological effects of CAP and summarizes the latest advances in plasma nanomedicine strategies in the field of brain disease intervention, and reviews the four major nanomedical categories—metal-based, inorganic non-metallic, polymer-based and hydrogel systems—and their clinical applications in the treatment of brain tumors, strokes and neurodegenerative diseases in conjunction with CAP. Finally, we highlight a number of key challenges—limited resources of special CAP equipment, incomplete understanding of the mechanism, obstacles to transformation application—and put forward the future research direction to promote the development of accurate, safe, and clinical transformation value plasma–nanomedicine therapy for brain diseases. Full article

Cleisthenes herzensteini is a commercially important demersal fish in the Northwest Pacific. However, the resource stock of this species has undergone a drastic decline due to overfishing and habitat degradation. As a representative taxon for benthic adaptation in the order Pleuronectiformes, the molecular mechanisms underlying its specialized phenotypic traits remain poorly elucidated. Furthermore, population-level studies focusing on the mitochondrial genome of Cleisthenes herzensteini are currently scarce. Given that the mitochondrial genome serves as an ideal genetic tool for deciphering species evolution and population genetics, sequencing of its mitogenome will help fill critical gaps in genetic resources and provide essential support for species conservation and phylogenetic research. In this study, we sequenced, assembled, and annotated its complete mitochondrial genome. The circular mitogenome is 17,171 bp in length and exhibits a typical A + T bias (54.04%). Repeat sequence analysis identified 35 dispersed repeats. Codon usage analysis revealed that leucine was the most frequently encoded amino acid, with CUU being the preferred codon. Several protein-coding genes possessed incomplete stop codons (T--/TA-), and a nucleotide preference for A and C was observed at the third codon position. Phylogenetic reconstruction based on mitogenomes from 23 species supported the monophyly of the order Pleuronectiformes. C. herzensteini showed the closest relationship with Dexistes rikuzenius, forming a distinct clade alongside Hippoglossoides dubius and Limanda aspera. These results provide essential genetic resources for understanding the evolution and population genetics of C. herzensteini and related flatfishes. According to the investigation, this study represents the first report on the sequencing and analysis of the complete mitochondrial genome of the Cleisthenes herzensteini. This not only fills the gap in mitochondrial genetic information for this species but also provides a reference for subsequent investigations into the phylogenetic relationships and evolutionary processes within the family Pleuronectidae. Full article

Today, machine learning (ML) is generally considered a potent and efficient tool for addressing studies in various diverse domains, including image processing and event prediction on a timescale. ML represents complex relations between features, and these mappings between such features may be applied in simulations of time-dependent events, such as the behavior of animals. Still, ML inherently strongly depends on extensive and consistent datasets, a fact that reveals both the benefits and drawbacks of ML. In the use of ML, insufficient or skewed data can limit the ability of algorithms to accurately predict or generalize possible states. To overcome this limitation, this work proposes an integrated hybrid approach that combines machine learning with methods from cognitive science, here especially inspired by the ACT-R model to approach cases of missing or unbalanced data. By incorporating cognitive processes such as memory, perception, and attention, the model accounts for the internal mechanisms of decision-making and environmental interaction where traditional ML methods fall short. This approach is particularly useful in representing states that are not directly observable or are underrepresented in the data, such as rare behavioral responses for animals, or adaptive strategies. Experimental results show that the combination of machine learning for data-driven analysis and cognitive ‘rule-based’ frameworks for filling in gaps provides a more comprehensive model of animal behavior. The findings suggest that this hybrid approach to simulation models can offer a more robust and consistent way to study complex, real-world phenomena, especially when data is inherently incomplete or unbalanced. Full article

Existing 3D reconstruction algorithms commonly struggle with modeling specific local objects within large-scale scenes, often resulting in a lack of local detail and incomplete geometric structures. While current mainstream point cloud completion methods can restore these missing structures to some degree, they are fundamentally based on generative in-filling, a process that relies on geometric priors learned from large-scale datasets. Consequently, the physical realism and geometric accuracy of the results cannot be guaranteed. To address these limitations, this paper proposes a novel, data-driven framework for point cloud completion. Our core method involves the high-precision, heterogeneous data registration and seamless fusion of an object-level point cloud—reconstructed with high-fidelity appearance and geometry by our optimized Neural Radiance Fields (NeRF) framework—with our target large-scale scene point cloud. By using high-precision, physically based data as a strong prior for geometric completion, we offer an alternative route to conventional generative completion methods. Concurrently, we employ unsupervised evaluation metrics to assess the intrinsic quality of the final results. This work provides a robust and high-fidelity solution to the problem of completing local objects within large-scale scenes. Evaluated on our self-constructed UAV-Recon dataset, the proposed method achieved a Structural Plausibility ≥ 0.995, Geometric Smoothness ≤ 0.19, and Distribution Uniformity ≈ 1.2, offering a robust solution for the high-fidelity completion of local objects within large-scale scenes. Full article

Background/Objectives: Contrast-induced nephropathy (CIN) is a common iatrogenic or medically induced condition among patients who receive intravenous infusion of iodinated contrast media that can cause renal insufficiency, raise the cost of care, and increase mortality risk. This study evaluated the incidence of CIN and predictors of renal function among cancer patients receiving contrast-enhanced computed tomography (CECT). Methods: A prospective, single-center longitudinal study was conducted at King Abdul-Aziz Medical City’s (Jeddah) medical imaging department from December 2021 to December 2023. Convenience sampling was used to select patients who were exposed to CECT based on data filled in the electronic medical record during the study period. Results: The final sample constituted 80 patients (47.71% attrition, mean age = 55.5 years, 58.75% male). The high attrition rate was associated with participants with incomplete records, those who were lost to follow-up, and those whose follow-up Scr was collected after 72 h from CECT administration. There was no statistically significant change in Scr following contrast exposure (mean increase 0.9 µmol/L; paired t = 1.41, p = 0.162; Wilcoxon p = 0.326). The incidence of CIN was 3.75% (3 of 80 patients; 95% confidence intervals (CI), 1.28–10.39%). Regression analysis showed no statistically significant associations between the percentage change in Scr and age, sex, baseline creatinine, or eGFR category (model R² = 0.07). No clinically meaningful predictors of CIN were identified. Conclusions: The incidence of CIN in this study’s cohort of low-risk cancer patients undergoing CECT was low, and contrast exposure did not produce significant short-term changes in renal function. These findings support the safety of modern contrast agents in oncology imaging, but multi-center studies with larger samples and more robust methods are warranted to refine CIN risk assessment in cancer patients undergoing CECT. Full article

Digital twins (DTs) powered by artificial intelligence (AI) are becoming important transformational tools in healthcare, enabling real-time simulation and personalized decision support at the patient level. The aim of this review is to critically examine the evolution, current applications, and future potential of AI-based DTs in healthcare, with a particular focus on their role in enabling real-time simulation and personalized patient-level decision support. Specifically, the review aims to provide a comprehensive overview of how AI-based DTs are being developed and implemented in various clinical domains, identifying existing scientific and technical gaps and highlighting methodological, regulatory, and ethical issues. Taking a “local to global” perspective, the review aims to explore how individual patient-level models can be scaled and integrated to inform population health strategies, global data networks, and collaborative research ecosystems. This will provide a structured foundation for future research, clinical applications, and policy development in this rapidly evolving field. Locally, DTs allow medical professionals to model individual patient physiology, predict disease progression, and optimize treatment strategies. Hospitals are implementing AI-based DT platforms to simulate workflows, efficiently allocate resources, and improve patient safety. Generative AI further enhances these applications by creating synthetic patient data for training, filling gaps in incomplete records, and enabling privacy-respecting research. On a broader scale, regional health systems can use connected DTs to model population health trends and predict responses to public health interventions. On a national scale, governments and policymakers can use these insights for strategic planning, resource allocation, and increasing resilience to health crises. Internationally and globally, AI-based DTs can integrate diverse datasets across borders to support research collaboration and improve early pandemic detection. Generative AI contributes to global efforts by harmonizing heterogeneous data, creating standardized virtual patient cohorts, and supporting cross-cultural medical education. Combining local precision with global insights highlights DTs’ role as a bridge between personalized and global health. Despite the efforts of medical and technical specialists, ethical, regulatory, and data governance challenges remain crucial to ensuring responsible and equitable implementation worldwide. In conclusion, AI-based DTs represent a transformative paradigm, combining individual patient care with systemic and global health management. These perspectives highlight the potential of AI-based DTs to bridge precision medicine and public health, provided ethical, regulatory, and governance challenges are addressed responsibly. Full article

Lipids are essential for the skin, playing a crucial role in forming plasma membranes and maintaining the skin’s permeability barrier and hydration. Intercellular lipids fill the spaces between corneocytes and contribute to the barrier function. Lipid abnormalities in the skin have been observed in many skin diseases, including atopic dermatitis and psoriasis. However, the specific localization and roles of skin lipids at particular sites remain incompletely elucidated due to the limited methods available for comprehensive lipid analysis. This study aims to precisely determine the localization of skin lipids, especially intercellular lipids, and investigate their roles and metabolism using mass spectrometry imaging (MSI). We conducted high-resolution (spatial resolution: 5 µm) matrix-assisted laser desorption/ionization (MALDI)-MSI on the lower back and buttocks and created overlay images of skin lipids to clarify their precise localizations. Ceramide was localized in the outermost layer among intercellular lipids. Cholesterol and free fatty acids were present in the stratum corneum but were at trace levels in the outermost layer. Cholesterol sulfate was abundant in the granular layer and gradually decreased in the stratum corneum, promoting desquamation. Phospholipids were confined to the viable epidermis (stratum corneum-/epidermis+), which forms the plasma membrane. A significant increase in mass intensity in the stratum corneum was observed for ceramide, sphingoid base, cholesterol, and free fatty acids, along with a decrease in phospholipids compared with those in the viable epidermis, based on region of interest analysis (Mann–Whitney test, p < 0.0005). We clarified the precise localization of skin lipids, particularly intercellular lipids. Our findings supported the reported functions of skin lipids at specific sites. Skin lipids are metabolized to form intercellular lipids in the stratum corneum, which are essential for the skin barrier. Our current lipid localization data serve as a baseline, or healthy control dataset, for future MSI-based lipid biomarker research in disease groups. Full article

Background and Objectives: Speech-in-noise testing is essential for evaluating functional hearing abilities in clinical practice. Although the Quick Speech-in-Noise test (QuickSIN) is widely used, no equivalent tool existed for European Portuguese. This study aimed to develop a Speech-in-Noise Test for European Portuguese (SiN-EP), linguistically adapted and calibrated for native speakers, to support clinical assessment of speech perception in realistic listening environments. Materials and Methods: The SiN-EP was developed through a multi-stage process. Sentences were drafted to reflect natural speech patterns and reviewed by native speakers for clarity and grammatical accuracy. Selected sentences were recorded by a female native speaker in a controlled acoustic environment and mixed with multi-talker babble at signal-to-noise ratios (SNR (dB)) from 25 to 0 SNR (dB). A pre-test in a free-field setting at 65 dB SPL was conducted with fifteen normal-hearing young adults. Participants repeated each sentence, and their responses were analyzed to refine list composition, adjust difficulty, and ensure phonetic balance. Results: Intelligibility decreased systematically as SNR (dB) worsened, with ceiling effects at 25 and 20 SNR (dB). At 5 SNR (dB), high variability was observed, with set 5 showing disproportionate difficulty and set 14 containing an incomplete sentence; both were removed. At 0 SNR (dB), all sets demonstrated expected low intelligibility. The final test comprises thirteen lists of six sentences, each maintaining stable intelligibility, phonetic representativeness, and consistent difficulty across SNRs (dB). Conclusions: The SiN-EP provides a linguistically appropriate, phonetically balanced, and SNR (dB) calibrated instrument for assessing speech-in-noise perception in European Portuguese. The refinement process improved reliability and list equivalence, supporting the test’s clinical and research applicability. The SiN-EP fills a critical gap in assessing speech-in-noise perception in European Portuguese speakers, providing a reliable tool for both clinical and research applications. Full article

Missing values in hourly outdoor air temperature series are common and can bias building energy assessments that rely on uninterrupted temperature profiles. This paper examines how the polynomial degree can be used to reconstruct incomplete temperature data from the duration curve, which affect the energy indicators of an air-source heat pump (ASHP). Using an operational dataset from Opole, Poland (1 September 2019–31 August 2020; 5.1% gaps), global polynomials of degree n = 3…11 were fitted to the sorted hourly temperatures, and the reconstructions were mapped back to time. The reconstructions drive a building–ASHP model evaluated for two supply-water regimes (L_WT, leaving water temperature = 35 °C and 45 °C). Accuracy is assessed with mean absolute error (MAE), root-mean-square error (RMSE), and R² on observed, filled, and full subsets—including cold/hot tails—and propagated to energy metrics: seasonal space-heating demand (Q_season); electricity use (E_el); seasonal coefficient of performance (SCOP); peak electrical power (P_el,max); seasonal minimum coefficient of performance (COP_min); and the share of error due to filled hours (WFE_fill). All degrees satisfy $\left|{RE}_{Q_{season}}\right|\le 2\%$. For L_WT = 35 °C, relative changes span ${RE}_{E_{el}}$ ≈ −2.22…−1.63% and ${RE}_{N_{el,max}}$ ≈ −21.6…−7.7%, with ${ER}_{SCOP}$ ≈ +0.53…+0.80%. For L_WT = 45 °C, ${RE}_{E_{el}}$ remains ≈ −0.43% across degrees. A multi-criterion selection (seasonal bias, stability of energy indicators, tail errors, and WFE_fill) identifies n = 7 as the lowest sufficient degree: increasing n beyond seven yields negligible improvements while raising the overfitting risk. The proposed, data-driven procedure makes degree selection transparent and reproducible for gap-filled temperature inputs in ASHP studies. Full article

High-risk human papillomaviruses (HPVs), particularly types 16 and 18, drive carcinogenesis by rewiring host metabolism and mitochondrial function. The oncoproteins E5, E6, and E7 collectively induce mitochondrial fragmentation, increase reactive oxygen species (ROS), and promote a metabolic shift from oxidative phosphorylation (OXPHOS) to glycolysis (the Warburg effect). A redox-sensitive mitochondrial protein, Reactive Oxygen Species Modulator 1 (ROMO1), has emerged as a key mediator of these processes. ROMO1 contributes to mitochondrial morphology, regulates ROS homeostasis, and interacts with key stress-response pathways. While ROMO1 is overexpressed in many cancers and correlates with poor prognosis, recent data suggest that HPV-associated cervical lesions exhibit a unique biphasic expression pattern, with high ROMO1 levels in early stages and reduced expression in advanced tumors. The underlying molecular mechanisms remain unclear, but may involve HPV genome integration, NF-κB suppression, or epigenetic silencing. Key mechanisms such as how HPV modulates ROMO1 expression and how this contributes to stage-dependent metabolic vulnerability remain incompletely understood. This review highlights the current understanding of how HPV oncoproteins impact mitochondrial structure and function, emphasizes the role of ROMO1 in this context, and compares findings with other cancer types. Although no ROMO1-targeted therapies currently exist, the protein may serve as a redox-sensitive biomarker and potential vulnerability in HPV-driven tumors. We propose that targeting mitochondrial fragmentation, ROS signaling, or metabolic reprogramming may offer new avenues for therapeutic intervention. Further research is needed to clarify ROMO1’s dual role in early vs. late-stage disease and to validate its relevance as a clinical target. Our review fills a gap in the current literature by being the first to systematically explore ROMO1’s contribution to HPV-induced mitochondrial dysfunction and metabolic rewiring, and we outline research priorities for future studies. Full article

Colorectal cancer (CRC) is a common tumor, and its incidence increases with age. Elderly CRC patients constitute a daily challenge for oncologists when deciding if they are worthy of active treatments and of which kind, as data from the literature are incomplete or even lacking in specific subsets. To fill this gap, we conducted a narrative review that included not only studies dedicated to the elderly but also extracted elderly groups from major clinical trials. Results in terms of harm and benefit were dissected according to frailty categories (fit, vulnerable, unfit). As it emerged, the management of elderly CRC patients should be based not on age alone, but rather on their frailty level, as assessed by Comprehensive Geriatric Assessment (CGA), which should be implemented in trials and clinical practice. Elderly patients should be treated similarly to young patients in the first and subsequent lines, without precluding the use of innovative drugs. Vulnerable elderly individuals should receive personalized schedules that focus more on symptoms and quality of life; for frail patients, supportive care alone is often a valid option. Full article

Based on the problem of incomplete mining of Additive Manufacturing (AM) potential caused by the limitations of current Design for Additive Manufacturing (DFAM) methods, this paper proposes to integrate Additive Manufacturing and axiomatic design to obtain the global conceptual design method of products to be manufactured with AM. In response to the lower process dependence of AM technology compared to traditional processes, two integration measures of “influence region division” and “process domain forward” are proposed, and finally, the axiomatic design process for AM is obtained. Taking the assembly-free integrated design of mechanical fingers imitating dexterous hands as an example, the conceptual design method studied was validated. The application of innovative features such as flexible finger joints and lattice-filled finger joints shows that the design method proposed in this paper can deeply tap into the manufacturing potential of AM, achieve lightweight and integrated molding of products, which provides useful references for designers. Full article

LiDAR with direct time-of-flight (dToF) technology based on single-photon avalanche diode detectors (SPADs) has been widely adopted in various applications. However, a comprehensive theoretical understanding of its fundamental ranging performance bounds—particularly the degradation caused by pile-up effects due to system dead time and the potential benefits of photon-number-resolving detectors—remains incomplete and has not been systematically established in prior work. In this work, we present the first theoretical derivation of the Cramér–Rao lower bound (CRLB) for dToF systems explicitly accounting for dead time effects, generalize the analysis to SPADs with photon-number-resolving capabilities, and further validate the results through Monte Carlo simulations and maximum likelihood estimation. Our analysis reveals that pile-up not only reduces the information contained within individual ToF but also introduces a previously overlooked statistical coupling between distance and photon flux rate, further degrading ranging precision. The derived CRLB enables the determination of the optimal optical photon flux, laser pulse width (with FWHM of $\approx 0.56\tau$), and ToF quantization resolution that yield the best achievable ranging precision, showing that an optimal precision of approximately $0.53\tau /\sqrt{N}$ remains theoretically achievable, where $\tau$ is TDC resolution and N is the number of laser pulses. The analysis further quantifies the limited performance improvement enabled by increased photon-number resolution, which exhibits rapidly diminishing returns. Overall, these findings establish a unified theoretical framework for understanding the fundamental limits of SPAD-based dToF LiDAR, filling a gap left by earlier studies and providing concrete design guidelines for the selection of optimal operating points. Full article