Search Results (1,546)

In recent years, rapid advancements in glaucoma research have led to the development of more effective treatments of this chronic and irreversible condition. Of these, Kahook Blade Dual (KDB) goniotomy and second-generation trabecular micro-bypass stent (iStent) are two novel biomimetic procedures which have designs inspired by the eye’s natural drainage mechanisms. In this retrospective study, we evaluated the safety and effectiveness of both surgeries by including 176 eyes from 110 patients: 142 eyes in the iStent group and 34 in the KDB group. The primary outcomes of this study were the proportions of patients in each group attaining a 20% reduction in IOP and a post-operative IOP < 19 mmHg. At the last follow-up, a 20% reduction in IOP was achieved by 67% of iStent inject patients and 50% of KDB patients (p = 0.07). The iStent group also showed a higher proportion of patients reaching an IOP of less than 19 mmHg (81% vs. 71% in the KDB group, p = 0.13). The number of medications did not decrease in either group from pre-op to the last follow-up. The KDB group had more failures (29.4% vs. 4.2%) and a significantly higher adverse event rate than the iStent inject group (47.1% vs 12.0%). Full article

A reassessment of the risk-benefit balance of the two lipid nanoparticle (LNP)-based vaccines, Pfizer’s Comirnaty and Moderna’s Spikevax, is currently underway. While the FDA has approved updated products, their administration is recommended only for individuals aged 65 years or older and for those aged 6 months or older who have at least one underlying medical condition associated with an increased risk of severe COVID-19. Among other factors, this change in guidelines reflect an expanded spectrum and increased incidence of adverse events (AEs) and complications relative to other vaccines. Although severe AEs are relatively rare (occurring in < 0.5%) in vaccinated individuals, the sheer scale of global vaccination has resulted in millions of vaccine injuries, rendering post-vaccination syndrome (PVS) both clinically significant and scientifically intriguing. Nevertheless, the cellular and molecular mechanisms of these AEs are poorly understood. To better understand the phenomenon and to identify research needs, this review aims to highlight some theoretically plausible connections between the manifestations of PVS and some unique structural properties of mRNA-LNPs. The latter include (i) ribosomal synthesis of the antigenic spike protein (SP) without natural control over mRNA translation, diversifying antigen processing and presentation; (ii) stabilization of the mRNA by multiple chemical modification, abnormally increasing translation efficiency and frameshift mutation risk; (iii) encoding for SP, a protein with multiple toxic effects; (iv) promotion of innate immune activation and mRNA transfection in off-target tissues by the LNP, leading to systemic inflammation with autoimmune phenomena; (v) short post-reconstitution stability of vaccine nanoparticles contributing to whole-body distribution and mRNA transfection; (vi) immune reactivity and immunogenicity of PEG on the LNP surface increasing the risk of complement activation with LNP disintegration and anaphylaxis; (vii) GC enrichment and double proline modifications stabilize SP mRNA and prefusion SP, respectively; and (viii) contaminations with plasmid DNA and other organic and inorganic elements entailing toxicity with cancer risk. The collateral immune anomalies considered are innate immune activation, T-cell- and antibody-mediated cytotoxicities, dissemination of pseudo virus-like hybrid exosomes, somatic hypermutation, insertion mutagenesis, frameshift mutation, and reverse transcription. Lessons from mRNA-LNP vaccine-associated AEs may guide strategies for the prediction, prevention, and treatment of AEs, while informing the design of safer next-generation mRNA vaccines and therapeutics. Full article

In parallel with technical training on knowledge and skills of task-specific medical or surgical procedures, wide arrays of soft skills training would contribute to obstetric safety in the contemporary healthcare setting. This article, as a service evaluation, explored the effect of a specialty-based Crew Resource Management (CRM) training series that transforms the concept of human factors into sustainable measures in fostering clinical safety culture of the Department of Obstetrics and Gynaecology (O&G) in the Queen Elizabeth Hospital. Within the last decade, a tri-phasic programme has been implemented by an inter-professional workgroup which consists of a consultant anaesthesiologist, medical specialists and departmental operations manager from O&G, a nurse simulation specialist, hospital administrators, and a research psychologist. (1) Phase I identified different patterns of attitudinal changes (in assertiveness, communication, leadership, and situational awareness, also known as “ACLS”) between doctors and nurses and between generic and specialty-based sessions for curriculum planning. (2) Phase II evaluated how these specific behaviours changed over 3 months following CRM training tailored for frontline professionals in O&G. (3) Phase III examined the coping style in conflict management and the level of sustainability in self-efficacy over 3 months following specialty-based CRM training. The findings showed the positive impacts of O&G CRM training on healthcare professionals’ increased attitude and behaviour in “ACLS” by 22.7% at a p < 0.05 level, character strengths in conflict management, and non-inferior or sustained level of self-efficacy under tough conditions in the clinical setting up to 3 months after training. As a way forward, incorporating a scenario-based O&G CRM programme into existing skills-based training is expected to change service framework with an innovative approach. In addition, exploring actual clinical outcomes representing a higher level of organisational impacts can be a strategic direction for further studies on the effect of this practical and educational approach on obstetric safety culture. Full article

Background/Objectives: Ulcerative Colitis (UC) lacks longitudinal visual data, which limits both disease progression modeling and the effectiveness of computer-aided diagnosis systems. These systems are further constrained by sparse intermediate disease stages and the discrete nature of the Mayo Endoscopic Score (MES). Meanwhile, synthetic image generation has made significant advances. In this paper, we propose novel ordinal embedding architectures for conditional diffusion models to generate realistic UC progression sequences from cross-sectional endoscopic images. Methods: By adapting Stable Diffusion v1.4 with two specialized ordinal embeddings (Basic Ordinal Embedder using linear interpolation and Additive Ordinal Embedder modeling cumulative pathological features), our framework converts discrete MES categories into continuous progression representations. Results: The Additive Ordinal Embedder outperforms alternatives, achieving superior distributional alignment (CMMD 0.4137, recall 0.6331) and disease consistency comparable to real data (Quadratic Weighted Kappa 0.8425, UMAP Silhouette Score 0.0571). The generated sequences exhibit smooth transitions between severity levels while maintaining anatomical fidelity. Conclusions: This work establishes a foundation for transforming static medical datasets into dynamic progression models and demonstrates that ordinal-aware embeddings can effectively capture disease severity relationships, enabling synthesis of underrepresented intermediate stages. These advances support applications in medical education, diagnosis, and synthetic data generation. Full article

As the prevalence of respiratory diseases continues to rise, inhalation therapy has emerged as a crucial method for their treatment. The effective transmission of medications within the respiratory tract is vital to achieve therapeutic outcomes. Given that most inhaled particles carry electrostatic charges, understanding the electrostatic effect on particle behavior in bifurcated tubes is of significant importance. This work combined Large Eddy Simulation-Lagrangian particle tracking (LES-LPT) technology to simulate particle behavior with three particle sizes (10, 20, and 50 μm) from G2 to G3 (“G” stands for generation) in bifurcated tubes, either with or without electrostatics, under typical human physiological conditions (Re = 1036). The results indicate that the electrostatic force has a significant effect on particle behavior in bifurcated tubes, which increases with particle size. Within the bifurcated tubes, the electrostatic force enhances particle movement in alignment with the secondary flow as well as intensifies the interaction of particles with local turbulent vortices and promotes particle dispersion rather than agglomeration. On the other hand, the distribution of the electrostatic field is influenced by particle behavior. Higher particle concentration presents stronger electrostatic strength, which increases with particle size. Therefore, it can be concluded that the electrostatic interactions among particles can prevent particles from aggregating and enhance the efficiency of inhalation therapy. Full article

The secondary use of electronic health records is essential for developing artificial intelligence-based clinical decision support systems. However, even after direct identifiers are removed, de-identified electronic health records remain vulnerable to re-identification, membership inference attacks, and model extraction attacks. This study examined the balance between privacy protection and model utility by evaluating de-identification strategies and differentially private learning in large-scale electronic health records. De-identified records from a tertiary medical center were analyzed and compared with three strategies—baseline generalization, enhanced generalization, and enhanced generalization with suppression—together with differentially private stochastic gradient descent. Privacy risks were assessed through k-anonymity distributions, membership inference attacks, and model extraction attacks. Model performance was evaluated using standard predictive metrics, and privacy budgets were estimated for differentially private stochastic gradient descent. Enhanced generalization with suppression consistently improved k-anonymity distributions by reducing small, high-risk classes. Membership inference attacks remained at the chance level under all conditions, indicating that patient participation could not be inferred. Model extraction attacks closely replicated victim model outputs under baseline training but were substantially curtailed once differentially private stochastic gradient descent was applied. Notably, privacy-preserving learning maintained clinically relevant performance while mitigating privacy risks. Combining suppression with differentially private stochastic gradient descent reduced re-identification risk and markedly limited model extraction while sustaining predictive accuracy. These findings provide empirical evidence that a privacy–utility balance is achievable in clinical applications. Full article

Background/Objectives: The internet has unquestionably altered how people acquire health information. Instead of consulting with a medical professional, billions of pages of information can be accessed by anyone with a smartphone. Women’s health issues have been historically and culturally taboo in many cultures globally; therefore, internet searches may be particularly useful when researching these topics. Methods: As an exercise in scientific information evaluation, we chose 12 non-cancer topics specific to women’s health and developed a scoring metric based on quantifiable webpage attributes to answer: What topics generate the highest and lowest scores? Does the quality of information (mean score) vary across topics? Does the variation (score deviation) differ among topics? Data were collected following systematic searches after filtering with advanced features of Google and analyzed in a Bayesian framework. Results: The mean score per topic was significantly correlated with the number of sources cited within an article. There were significant differences in the quality scores across topics; “pregnancy” and “sleep” scored the highest and had more sources cited per page than all other topics. The greatest variation in scores were for “cortisol” and “weight”. Conclusions: A purposeful, systematic internet search of 12 critical women’s health topics suggests that scrutiny is necessary when this information is obtained by a typical internet user. Future work should include review by medical professionals based on their interaction with patients who self-report what they know or think about a condition they present and respect, while educating, patients’ own internet searching. Full article

Fifty-two horses aged >15 years, diagnosed with pituitary pars intermedia dysfunction (PPID), and displaying hypertrichosis were recruited via an online survey of PPID horse owners. From mid-December, group T (n = 29) wore Equilume^® light masks extending photoperiod to 15 h daily, while group C1 (n = 23) remained under natural photoperiod. As 85% (44/52) of recruited study horses received pergolide medication, a second unmedicated PPID research herd (C2; n = 17) was recruited and remained under natural photoperiod. Hair coat samples, shedding and body condition scores were collected monthly by owners for 13 months and analysed by the research team. Data related to management, coat condition and PPID clinical signs were collected using bimonthly questionnaires (BMQ). Time (p < 0.001), group (p = 0.025) and time-by-group interaction (p = 0.005) affected hair length. Group differences were attributable to shorter hair lengths in C2, and no differences in hair length occurred between T and C1 (p > 0.05). Time affected shedding scores (p < 0.001) which was advanced by one month in T (p < 0.05). In group T, BMQ responses showed improved coat condition in April (p = 0.035), decreased fat coverage in April and June (p < 0.05), and increased energy/alertness in February (p = 0.022). Improvements in coat condition (p = 0.043), clinical signs of PPID (p = 0.018), and general quality of life (p = 0.035) were reported in T only in a final study questionnaire. Blue light treatment merits further investigation as a complementary treatment for PPID. Full article

The completeness and integrity of multimodal medical data are critical determinants of surgical success and postoperative recovery. However, because of issues such as poor sensor contact, small vibrations, and device discrepancies during signal acquisition, there are frequent missing values in patients’ medical data. This issue is especially prominent in rare or complex cases, where the inherent complexity and sparsity of multimodal data limit dataset diversity and degrade predictive model performance. As a result, clinicians’ understanding of patient conditions is restricted, and the development of robust algorithms to predict preoperative, intraoperative, and postoperative disease progression is hindered. To address these challenges, we propose Med-Diffusion, a diffusion-based generative framework designed to enhance sensor data by imputing missing multimodal clinical data, including both categorical and numerical variables. The framework integrates one-hot encoding, simulated bit encoding, and feature tokenization to improve adaptability to heterogeneous data types, utilizing conditional diffusion modeling for accurate data completion. Med-Diffusion effectively learns the underlying distributions of multimodal datasets, synthesizing plausible data for incomplete records, and it mitigates the data sparsity caused by poor sensor contact, vibrations, and device discrepancies. Extensive experiments demonstrate that Med-Diffusion accurately reconstructs missing multimodal clinical information and significantly enhances the performance of downstream predictive models. Full article

This review explores the current landscape of neural network-based methods for digital image noise processing. Digital cameras have become ubiquitous in fields like forensics and medical diagnostics, and image noise remains a critical factor for ensuring image quality. Traditional noise suppression techniques are often limited by extensive parameter selection and inefficient handling of complex data. In contrast, neural networks, particularly convolutional neural networks, autoencoders, and generative adversarial networks, have shown significant promise for noise estimation, suppression, and analysis. These networks can handle complex noise patterns, leverage context-specific data, and adapt to evolving conditions with minimal manual intervention. This paper describes the basics of camera and image noise components and existing techniques for their evaluation. Main neural network-based methods for noise estimation are briefly presented. This paper discusses neural network application for noise suppression, classification, image source identification, and the extraction of unique camera fingerprints through photo response non-uniformity. Additionally, it highlights the challenges of generating reliable training datasets and separating image noise from photosensor noise, which remains a fundamental issue. Full article

Burns represent a significant medical challenge, and the development of theoretical models has the potential to contribute to the advancement of new diagnostic tools. This study aimed to perform numerical simulations of the Pennes bioheat transfer equation, incorporating heat generation terms due to the body’s immunological response to thermal injury, as well as changes in skin thermal parameters and blood perfusion for each burn type. We propose the incorporation of specific parameters and boundary conditions related to multilayer perfusion into the Pennes bioheat model. Using the proposed layered skin model, we evaluate temperature differences to establish correlations for determining burn depth. In this investigation, 1D and 3D algorithms based on the finite volume method were applied to capture transient and spatial thermal variations, with the resulting temperature distributions demonstrating the ability of the proposed models to describe the expected thermal variations in healthy and burned tissue. This work demonstrates the potential of the finite volume method to approximate the solution of the Pennes biothermal equation. Overall, this study provides a computational framework for analyzing heat transfer in burn injuries and highlights the relevance of mathematical simulations as a tool for future research on infrared thermography in medicine. Full article

Background and Clinical Significance: Advanced pelvic organ prolapse (POP) associated with perineal herniation of pelvic and abdominal organs is a sporadic occurrence in gynaecological practice. Generally, POP affects up to 50% of multiparous women at some point during their lives. Advanced forms (grade III or IV) represent less than 10% of all cases, with severe grade IV prolapse occurring in fewer than 2% of patients. Case Presentation: We report the case of a 48-year-old nulliparous woman with no prior surgical history and no known medical conditions at presentation. The patient presented with severe grade IV POP (Baden–Walker Classification), characterised by abdominal pain, vaginal bleeding and significant urinary incontinence. A computed tomography scan was performed, revealing an extremely large perineal hernia, containing the uterus, urinary bladder, and small bowel loops—a rare finding with only isolated cases reported in the medical literature. Surgical treatment involved a total intracapsular hysterectomy with right-sided adnexectomy and colpoperineorrhaphy. After the surgery, the overall status of the patient was good. However, less than two months later, she returned, complaining of a recurrence of the initial pathology, and was diagnosed with grade II/III POP recurrence despite having no connective tissue disorders or other classical predisposing factors such as pregnancies, pelvic surgery history or obstetric trauma. The case was further complicated by a femoral neck fracture, stage V chronic kidney disease, COVID-19 pneumonia, and a Clostridium difficile infection. All these complications led to the postponement of the gynaecological reintervention procedure. Conclusions: We emphasise the significant challenges in managing this kind of perineal hernia, under unusual conditions and without common risk factors. A personalised, multidisciplinary approach is required, including careful follow-up to prevent early recurrence. Full article

The medical imaging domain frequently encounters the dual challenges of annotation scarcity and class imbalance. A critical issue lies in effectively extracting information from limited labeled data while mitigating the dominance of head classes. The existing approaches often overlook in-depth modeling of sample relationships in low-dimensional spaces, while rigid or suboptimal dynamic thresholding strategies in pseudo-label generation are susceptible to noisy label interference, leading to cumulative bias amplification during the early training phases. To address these issues, we propose a semi-supervised medical image classification framework combining labeled data-contrastive learning with class-adaptive pseudo-labeling (CLCP-MT), comprising two key components: the semantic discrimination enhancement (SDE) module and the class-adaptive pseudo-label refinement (CAPR) module. The former incorporates supervised contrastive learning on limited labeled data to fully exploit discriminative information in latent structural spaces, thereby significantly amplifying the value of sparse annotations. The latter dynamically calibrates pseudo-label confidence thresholds according to real-time learning progress across different classes, effectively reducing head-class dominance while enhancing tail-class recognition performance. These synergistic modules collectively achieve breakthroughs in both information utilization efficiency and model robustness, demonstrating superior performance in class-imbalanced scenarios. Extensive experiments on the ISIC2018 skin lesion dataset and Chest X-ray14 thoracic disease dataset validate CLCP-MT’s efficacy. With only 20% labeled and 80% unlabeled data, our framework achieves a 10.38% F1-score improvement on ISIC2018 and a 2.64% AUC increase on Chest X-ray14 compared to the baselines, confirming its effectiveness and superiority under annotation-deficient and class-imbalanced conditions. Full article

The burgeoning Internet of Medical Things (IoMT) offers unprecedented opportunities for real-time patient monitoring and predictive diagnostics, yet the current systems struggle with scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence. This paper introduces Med-Q Ledger, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger’s efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments. Full article

Background: The European Doctoral Conference in Nursing Science provides a unique platform for doctoral students in nursing and health sciences to present their research in a supportive environment. Celebrating its 20th anniversary, the 2024 conference embraced the motto “looking back and looking forward,” offering an opportunity to reflect on the development of nursing science and future challenges. Results: Held at the Medical University of Graz, Austria, the conference hosted 90 participants from 13 countries. It featured two keynote lectures, three workshops, 48 presentations, and a science slam. Abstract submissions underwent peer review to ensure the quality of presentations. The presentations highlighted key challenges and opportunities across nursing practice, healthcare work environments, education and digitalization in nursing, and health perspectives. Topics included, for example, workforce retention, artificial intelligence in nursing practice, leadership in error management, and culturally sensitive care. The keynotes emphasized the importance of patient and public involvement in research and the benefits of survey data in nursing science. Workshops imparted knowledge and skills regarding funding acquisition, guideline development, and effective research presentation. A science slam introduced innovative and creative ways to present research. Conclusions: The conference showcased the evolving landscape of nursing science, emphasizing the importance of evidence-based practice, supportive working conditions, and constructive collaboration. It demonstrated the enthusiasm and readiness of a new generation of researchers to advance nursing science in a rapidly changing healthcare environment. Full article