The current evolution of the traditional medical model toward the participatory medicine can be boosted by the Internet of Things (IoT) paradigm involving sensors (environmental, wearable, and implanted) spread inside domestic environments with the purpose to monitor the user's health and activate remote assistance. RF identification (RFID) technology is now mature to provide part of the IoT physical layer for the personal healthcare in smart environments through low-cost, energy-autonomous, and disposable sensors. It is here presented a survey on the state-of-the-art of RFID for application to body centric systems and for gathering information (temperature, humidity, and other gases) about the user's living environment. Many available options are described up to the application level with some examples of RFID systems able to collect and process multichannel data about the human behavior in compliance with the power exposure and sanitary regulations. Open challenges and possible new research trends are finally discussed.
The issue of legal regulation of the pharmaceutical sector is highly relevant today, as it affects not only economic stability of the country, but also public health and safety of its citizens. Following its withdrawal from the EU, the United Kingdom faced the need for a restructuring of the regulatory system, which was previously based largely on European legislation. After Brexit, the country encountered the challenge of developing an autonomous framework that takes into account national interests and international obligations. The aim of the study is to analyse the transformation of the legal regulation of the pharmaceutical industry in the United Kingdom after Brexit and to identify the possibilities of adapting this experience to improve Ukrainian legislation in the field of pharmaceuticals. Materials and methods. The empirical basis includes UK and EU legislation, international standards (ICH, PIC/S), as well as scholarly publications and reports from regulatory authorities (MHRA, EMA) and international organizations. Data were retrieved from eur-lex.europa.eu and legislation.gov.uk using the key terms “Brexit,” “pharmaceutical activity,” and “regulatory activity.” A combination of systemic, comparative, historical, and content analysis enabled the identification of post-Brexit legal changes and the assessment of their impact on the pharmaceutical industry. Results. It is proved that the legal framework governing the pharmaceutical sector in the UK at the beginning of Brexit, which had been formed over many years, was stable and effective. The authors analyse the legal status of the main regulator of the UK pharmaceutical sector – UK Medicines and Healthcare products Regulatory Agency (MHRA). It is emphasised that despite political and economic changes, the MHRA has maintained its independence and efficiency in issuing licences, controlling the circulation of medicines and introducing innovative approaches to regulation. It is thanks to this that British pharmaceutical companies have been able to adapt to the new conditions, maintain their competitiveness and even expand their influence in international markets. At the same time, the process of regulating the industry has not been without problems, on the contrary, numerous difficulties have arisen. Attention is paid to the interaction of the MHRA’s interaction with European regulators, in particular the EMA. Based on the analysis, ways of using the UK’s experience to improve Ukrainian pharmaceutical legislation are proposed. Conclusions. Brexit has had a significant impact on the UK pharmaceutical market, creating both opportunities and challenges for the regulatory system. The UK’s experience in adapting its pharmaceutical legislation after leaving the EU can be applied by Ukraine in some areas. For example, the creation of a more flexible registration system based on international standards will help reduce the bureaucratic burden. Integration with international regulators, such as the FDA and EMA, will facilitate the export of Ukrainian medicines and increase confidence in the national quality control system
Michelangelo Mercogliano, Gloria Spatari, Chiara Noviello
et al.
Abstract Introduction Humanitarian crises exacerbate the vulnerability of already fragile healthcare systems and significantly increase the risk of infectious disease outbreaks in low- and middle-income countries (LMICs). This systematic review aims to evaluate strategies and interventions implemented in LMICs to prevent and manage infectious diseases outbreaks during humanitarian crises from 2018 to 2023. Methods A comprehensive literature search was conducted across Scopus, PubMed, and Web of Science, adhering to the PRISMA guideline and the SPIDER framework to identify relevant studies. The review included studies published between 2018 and 2023 focusing on infectious disease prevention and management in LMICs during humanitarian crises. Study quality was assessed using the Joanna Briggs Institute checklist. Results Eleven studies were identified from 1,415 unique articles. These studies addressed diverse interventions, including vaccination campaigns, epidemiologic surveillance, and integrated health services. Cholera outbreaks in Haiti and Mozambique, triggered by gang violence, internal migration, and Cyclone Kenneth, were addressed through epidemiological surveillance, case management, WASH (Water, Sanitation, and Hygiene) service improvements, and oral vaccination campaigns. Mathematical models guided cholera vaccination in Thailand's refugee camps. In India, surveillance and rapid response measures successfully prevented infectious disease outbreaks during the Kumbh Mela gathering. The Philippines improved response times to climate-related disasters using point-of-care testing and spatial care pathways. Despite challenges in Yemen, evaluating malaria surveillance systems led to recommendations for integrating multiple systems. Uganda developed a national multi-hazard emergency plan incorporating vaccination, communication, and risk management, proving useful during the refugee crisis and Ebola outbreak. In South Sudan, integrating immunisation services into nutrition centres increased vaccination coverage among children. Nigeria experienced a rise in measles cases during armed conflicts despite vaccination efforts, while visual communication strategies improved SARS-CoV-2 vaccination rates. Conclusion These interventions highlight the importance of multimodal, targeted, and collaborative responses to address complex health crises without relying on unsustainable investments. Despite the effectiveness of these interventions, infrastructure limitations, insecurity, and logistical constraints were noted. These findings emphasize the need for adaptable and resilient healthcare systems and international collaboration to safeguard the right to health during complex humanitarian crises.
Medical image registration drives quantitative analysis across organs, modalities, and patient populations. Recent deep learning methods often combine low-level "trend-driven" computational blocks from computer vision, such as large-kernel CNNs, Transformers, and state-space models, with high-level registration-specific designs like motion pyramids, correlation layers, and iterative refinement. Yet, their relative contributions remain unclear and entangled. This raises a central question: should future advances in registration focus on importing generic architectural trends or on refining domain-specific design principles? Through a modular framework spanning brain, lung, cardiac, and abdominal registration, we systematically disentangle the influence of these two paradigms. Our evaluation reveals that low-level "trend-driven" computational blocks offer only marginal or inconsistent gains, while high-level registration-specific designs consistently deliver more accurate, smoother, and more robust deformations. These domain priors significantly elevate the performance of a standard U-Net baseline, far more than variants incorporating "trend-driven" blocks, achieving an average relative improvement of $\sim3\%$. All models and experiments are released within a transparent, modular benchmark that enables plug-and-play comparison for new architectures and registration tasks (https://github.com/BailiangJ/rethink-reg). This dynamic and extensible platform establishes a common ground for reproducible and fair evaluation, inviting the community to isolate genuine methodological contributions from domain priors. Our findings advocate a shift in research emphasis: from following architectural trends to embracing domain-specific design principles as the true drivers of progress in learning-based medical image registration.
This paper introduces Tree-NET, a novel framework for medical image segmentation that leverages bottleneck feature supervision to enhance both segmentation accuracy and computational efficiency. While previous studies have employed bottleneck feature supervision, their applications have largely been limited to the training phase, offering no computational benefits during training or evaluation. To the best of our knowledge, this study is the first to propose a framework that incorporates two additional training phases for segmentation models, utilizing bottleneck features at both input and output stages. This approach significantly improves computational performance by reducing input and output dimensions with a negligible addition to parameter count, without compromising accuracy. Tree-NET features a three-layer architecture comprising Encoder-Net and Decoder-Net, which are autoencoders designed to compress input and label data, respectively, and Bridge-Net, a segmentation framework that supervises the bottleneck features. By focusing on dense, compressed representations, Tree-NET enhances operational efficiency and can be seamlessly integrated into existing segmentation models without altering their internal structures or increasing model size. We evaluate Tree-NET on two critical segmentation tasks -- skin lesion and polyp segmentation -- using various backbone models, including U-NET variants and Polyp-PVT. Experimental results demonstrate that Tree-NET reduces FLOPs by a factor of 4 to 13 and decreases memory usage, while achieving comparable or superior accuracy compared to the original architectures. These findings underscore Tree-NET's potential as a robust and efficient solution for medical image segmentation.
Biocompatibility is a critical factor in the application of nanomaterials in medical fields, as these materials must interact safely and effectively with biological systems to be viable for therapeutic and diagnostic use. This article investigates the biocompatibility of nanomaterials, focusing on their interactions with biological cells, tissues, and the immune system. Key properties such as surface chemistry, size, shape, and material composition are examined, as they significantly influence the biological response. The article explores the role of nanomaterials in medical applications, including drug delivery, diagnostic imaging, and tissue engineering, while discussing the challenges involved in enhancing their biocompatibility. A case study on the CaO-CaP binary system is presented, showcasing the use of calcium oxide (CaO) and calcium phosphate (CaP) nanoparticles in bone tissue engineering. This system is widely investigated for its ability to mimic the mineral content of bone and promote osteogenesis, highlighting both its therapeutic potential and challenges in ensuring safe biocompatibility in clinical settings. The article concludes by reviewing strategies to optimize the biocompatibility of nanomaterials and discussing future directions for research in advancing their applications in medical treatments.
Data augmentation is a central component of joint embedding self-supervised learning (SSL). Approaches that work for natural images may not always be effective in medical imaging tasks. This study systematically investigated the impact of data augmentation and preprocessing strategies in SSL for lung ultrasound. Three data augmentation pipelines were assessed: (1) a baseline pipeline commonly used across imaging domains, (2) a novel semantic-preserving pipeline designed for ultrasound, and (3) a distilled set of the most effective transformations from both pipelines. Pretrained models were evaluated on multiple classification tasks: B-line detection, pleural effusion detection, and COVID-19 classification. Experiments revealed that semantics-preserving data augmentation resulted in the greatest performance for COVID-19 classification - a diagnostic task requiring global image context. Cropping-based methods yielded the greatest performance on the B-line and pleural effusion object classification tasks, which require strong local pattern recognition. Lastly, semantics-preserving ultrasound image preprocessing resulted in increased downstream performance for multiple tasks. Guidance regarding data augmentation and preprocessing strategies was synthesized for practitioners working with SSL in ultrasound.
Majid Purabdollah, Vahid Zamanzadeh, Akram Ghahramanian
et al.
Abstract Background Nurses’ professional competencies play a significant role in providing safe care to patients. Identifying the acquired and expected competencies in nursing education and the gaps between them can be a good guide for nursing education institutions to improve their educational practices. Methods In a descriptive-comparative study, students’ perception of acquired competencies and expected competencies from the perspective of the Iranian nursing faculties were collected with two equivalent questionnaires consisting of 85 items covering 17 competencies across 5 domains. A cluster sampling technique was employed on 721 final-year nursing students and 365 Iranian nursing faculties. The data were analyzed using descriptive statistics and independent t-tests. Results The results of the study showed that the highest scores for students’ acquired competencies and nursing faculties’ expected competencies were work readiness and professional development, with mean of 3.54 (SD = 0.39) and 4.30 (SD = 0.45), respectively. Also, the lowest score for both groups was evidence-based nursing care with mean of 2.74 (SD = 0.55) and 3.74 (SD = 0.57), respectively. The comparison of competencies, as viewed by both groups of the students and the faculties, showed that the difference between the two groups’ mean scores was significant in all 5 core-competencies and 17 sub-core competencies (P < .001). Evidence-based nursing care was the highest mean difference (mean diff = 1) and the professional nursing process with the lowest mean difference (mean diff = 0.70). Conclusion The results of the study highlight concerns about the gap between expected and achieved competencies in Iran. Further research is recommended to identify the reasons for the gap between the two and to plan how to reduce it. This will require greater collaboration between healthcare institutions and nursing schools.
Adolescent idiopathic scoliosis (AIS) is a common and progressive spinal deformity in children that exhibits striking sexual dimorphism, with girls at more than fivefold greater risk of severe disease compared to boys. Despite its medical impact, the molecular mechanisms that drive AIS are largely unknown. We previously defined a female-specific AIS genetic risk locus in an enhancer near the PAX1 gene. Here, we sought to define the roles of PAX1 and newly identified AIS-associated genes in the developmental mechanism of AIS. In a genetic study of 10,519 individuals with AIS and 93,238 unaffected controls, significant association was identified with a variant in COL11A1 encoding collagen (α1) XI (rs3753841; NM_080629.2_c.4004C>T; p.(Pro1335Leu); p=7.07E–11, OR = 1.118). Using CRISPR mutagenesis we generated Pax1 knockout mice (Pax1-/-). In postnatal spines we found that PAX1 and collagen (α1) XI protein both localize within the intervertebral disc-vertebral junction region encompassing the growth plate, with less collagen (α1) XI detected in Pax1-/- spines compared to wild-type. By genetic targeting we found that wild-type Col11a1 expression in costal chondrocytes suppresses expression of Pax1 and of Mmp3, encoding the matrix metalloproteinase 3 enzyme implicated in matrix remodeling. However, the latter suppression was abrogated in the presence of the AIS-associated COL11A1P1335L mutant. Further, we found that either knockdown of the estrogen receptor gene Esr2 or tamoxifen treatment significantly altered Col11a1 and Mmp3 expression in chondrocytes. We propose a new molecular model of AIS pathogenesis wherein genetic variation and estrogen signaling increase disease susceptibility by altering a PAX1-COL11a1-MMP3 signaling axis in spinal chondrocytes.
Background: Lung cancer stands as the foremost cause of cancer-related fatalities globally. The presence of cancer stem cells (CSCs) poses a challenge, rendering current targeted tumor therapies ineffective. This study endeavors to investigate a novel therapeutic approach focusing on ferroptosis and delves into the expression of ferroptosis-related genes within lung CSCs. Methods: We systematically examined RNA-seq datasets derived from lung tumor cells (LTCs) and lung cancer stem cells (LSCs), as previously investigated in our research. Our focus was on analyzing differentially expressed genes (DEGs) related to ferroptosis. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), we conducted functional analysis of these ferroptosis-related DEGs. Additionally, we employed protein‒protein interaction networks to identify hub genes. LC‒MS/MS analysis of LTCs and LSCs was conducted to pinpoint the crucial ferroptosis-related gene–thioredoxin-interacting protein (TXNIP).Further, we delved into the immune cell infiltration landscape of LTCs and LSCs, examining the correlation between TXNIP and lung adenocarcinoma (LUAD) using data from The Cancer Genome Atlas (TCGA) database. To complement these findings, we measured the expression levels of TXNIP, glutathione peroxidase 4(GPX4), nuclear receptor coactivator 4 (NCOA4) in LUAD tissues through immunohistochemistry (IHC) staining. Results: A total of 651 DEGs were identified, with 17 of them being ferroptosis-related DEGs. These seventeen genes were categorized into four groups: driver genes, suppressor genes, unclassified genes, and inducer genes. Enrichment analysis revealed significant associations with oxidative stress, cell differentiation, tissue development, and cell death processes. The RNA-seq analysis demonstrated consistent gene expression patterns with protein expression, as evidenced by mass spectrometry analysis. Among the identified genes, SFN and TXNIP were singled out as hub genes, with TXNIP showing particularly noteworthy expression. The expression of the ferroptosis-related gene TXNIP exhibited correlations with the presence of an immunosuppressive microenvironment, TNM stages, and the degree of histological differentiation.Also, the ferroptosis-markers GPX4 and NCOA4 displayed correlations with LUAD. This comprehensive analysis underscores the significance of TXNIP in the context of ferroptosis-related processes and their potential implications in cancer development and progression. Conclusion: The investigation conducted in this study systematically delved into the role of the ferroptosis-related gene TXNIP in Lung CSCs. The identification of TXNIP as a potentially valuable biomarker in this context could have significant implications for refining prognostic assessments and optimizing therapeutic strategies for advanced lung cancer.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Summary: Esophageal squamous cell carcinoma (ESCC) is a common malignancy, characterized by a multistep pathogenic process regulated spatiotemporally within the esophageal epithelial microenvironment, including vessel normalization and immune infiltration. However, empirical evidence elucidating esophageal vascular remodeling and immune infiltration during ESCC tumorigenesis in situ is lacking. In this study, utilizing a mouse model recapitulating progressive human ESCC stages, we established a tissue clearing workflow for three-dimensional visualization and analysis of esophageal vessels and T cell distribution. Through this workflow, we delineated the spatial dynamics of vascular remodeling, CD3+ T cells, and characteristic T cell aggregates employing high-resolution light-sheet fluorescence microscopy across five ESCC pathogenic stages. Vessel remodeling might be coupled with T cell infiltration, and their interactions predominantly occurred at the inflammatory stage. These findings provided insights into research methodologies of esophageal cancer and spatiotemporal landscapes of vascular and T cell during ESCC initiation and progression.
Objective: This systematic review investigates the long-term effect of cochlear implantation (CI) on clinical outcomes in tinnitus patients with sensorineural hearing loss (SNHL). Database Sources: PubMed, Embase, and the Cochrane Library were searched from inception to 30 April 2024. Manual searches of reference lists supplemented these searches when necessary. Review Methods: Original studies included in the meta-analysis had to contain comparative pre- and postoperative data for SNHL patients who underwent CI. Outcomes measured were the Tinnitus Handicap Inventory (THI), Visual Analog Scale (VAS), and Tinnitus Questionnaire (TQ). Results: A total of 28 studies comprising 853 patients showed significant tinnitus improvement after CI: THI mean difference (MD) −14.02 [95%CI −15.29 to −12.76, <i>p</i> < 0.001], TQ MD −15.85 [95%CI −18.97 to −12.74, <i>p</i> < 0.05], and VAS MD −3.12 [95%CI −3.49 to −2.76, <i>p</i> < 0.05]. Subgroup analysis indicated a significant difference between follow-up periods in THI (<i>p</i> < 0.0001) and VAS loudness (<i>p</i> = 0.02). Conclusions: Cochlear implantation substantially improves tinnitus in patients with hearing loss, though the effect may diminish over time. Further research is needed to confirm these findings.