Search Results (841)

Background/Objectives: Long COVID, characterized by persistent symptoms following SARS-CoV-2 infection, poses a significant occupational health concern among healthcare workers (HCWs). This study aimed to evaluate the prevalence of long COVID, symptom patterns, work-related impact, and vaccination status among healthcare personnel in a Romanian pediatric hospital. Methods: A cross-sectional study was conducted in 2024 among 903 hospital employees during routine occupational health assessments. Data were collected using structured questionnaires and medical records, focusing on previous SARS-CoV-2 infection, vaccination status, persistent symptoms, and functional impact. Results: Long COVID was identified in 28.6% of participants, with excessive fatigue (53.5%), musculoskeletal pain, respiratory difficulties, and cognitive complaints being the most common symptoms. Staff with chronic comorbidities or increased exposure risk had significantly higher rates of functional impairment. Fewer reinfections were reported among vaccinated individuals; however, vaccination was not significantly associated with the presence of long COVID symptoms. Older age and comorbidities were correlated with higher risk. Conclusions: The findings underline the need for long-term occupational health strategies and individualized support programs for HCWs affected by long COVID, particularly in high-risk groups. Full article

Frailty is a complex biological process that is associated with adverse outcomes in community-dwelling and hospitalized patients. While clinical evaluation is the current gold standard for screening and diagnosis, such an approach is not without its limitations (such as personnel and resource requirement). In this review, we will discuss prospective biomarkers for frailty. Opportunistic and deliberate radiological testing could provide important information that complements clinical frailty evaluation. Novel biochemical panels may yield additional methods for frailty screening in the future. It is known that early frailty intervention could lead to better outcomes for patients. Integration of electronic medical records, laboratory and radiological results, as well as clinical informatics infrastructure could result in augmented clinical decision-making and more optimized healthcare resources utilization. Full article

Background: This study stems from the perceived need to update skills and training in the process of educating healthcare professionals in light of the needs of individuals and their families. Objectives: This study aimed to assess the prevailing attitudes toward death and hospice care among medical students in China, providing a foundation for implementing hospice care and death education within these institutions. Methods: We conducted an online survey questionnaire with 568 medical students. Results: The results indicate that the overall attitude toward death was more inclined to accepting death neutrally. Gender, place of origin, educational background, willingness to care for terminally ill patients, experience in caring for terminally ill patients, and more will affect the attitude toward death of medical students. Compared to their rural counterparts, medical students in urban areas are more likely to view death as neutral. Instead of reducing fear, death and hospice education made people more likely to avoid situations. All five dimensions of death attitude exhibit a substantial positive connection with attitudes toward hospice care. In comparison to earlier research, medical students exhibit increasingly favorable attitudes regarding hospice care, and their overall perspective on death remains one of natural approval, suggesting that the integration of death and hospice care teaching is progressing effectively in mainland China. Conclusions: Simultaneously, it was discovered that numerous deficiencies required enhancement, including the need for timely feedback and optimization in hospice care instruction and death education, as well as insufficient attention and educational guidance regarding the individual differences and psychological conditions of medical personnel in the future. Full article

Modern, small-diameter endoscopic instruments, such as resectoscopes (e.g., the GUBBINI System) and mini-hysteroscopes, are widely used in clinical practice. These tools allow endoscopic procedures to be conducted without cervical dilation, often in an outpatient setting, and under local anesthesia alone. Background/Objectives: The present retrospective study aimed to analyze the perioperative and delayed complications of hysteroscopy performed under local anesthesia. This study also assessed the pain experienced during hysteroscopy under local anesthesia, depending on the type of procedure performed. Methods: A retrospective analysis was conducted in 1945 patients who underwent hysteroscopy under local anesthesia at the Center for Hysteroscopy, Heliodor Święcicki Gynecological and Obstetrical Clinical Hospital, Karol Marcinkowski Medical University, Poznań, Poland, between January 2021 and December 2023. Hysteroscopic procedures were performed with the GUBBINI Mini Hystero-Resectoscope through a paracervical block using lignocaine. Results: The procedure was discontinued in 46 patients, accounting for 2.36% of all hysteroscopies. The most common reasons for procedure discontinuation were severe pain and uterine perforation, accounting for 52.8% and 13% of discontinued procedures, respectively. The complication rates were low: uterine perforation occurred in 0.3% of cases (n = 6), and late complications requiring readmission occurred in 0.2% (n = 3). The average pain intensity score for all the patients was 2 points (2.8 ± 2.14). Conclusions: Our study confirmed that hysteroscopy performed under local anesthesia is a safe and effective diagnostic and therapeutic method for selected uterine pathologies, noting increased risks in cases such as extensive intrauterine adhesions. The low complication rates in both the perioperative and postoperative stages indicate the high safety profile of this procedure, particularly when performed by experienced personnel using standardized, validated protocols. Full article

Ongoing changes in the distribution and abundance of several tick species of medical relevance in Canada have prompted the development of the eTick platform—an image-based crowd-sourcing public surveillance tool for Canada enabling rapid tick species identification by trained personnel, and public health guidance based on tick species and province of residence of the submitter. Considering that more than 100,000 images from over 73,500 identified records representing 25 tick species have been submitted to eTick since the public launch in 2018, a partial automation of the image processing workflow could save substantial human resources, especially as submission numbers have been steadily increasing since 2021. In this study, we evaluate an end-to-end artificial intelligence (AI) pipeline to support tick identification from eTick user-submitted images, characterized by heterogeneous quality and uncontrolled acquisition conditions. Our framework integrates (i) tick localization using a fine-tuned YOLOv7 object detection model, (ii) resolution enhancement of cropped images via super-resolution Generative Adversarial Networks (RealESRGAN and SwinIR), and (iii) image classification using deep convolutional (ResNet-50) and transformer-based (ViT) architectures across three datasets (12, 6, and 3 classes) of decreasing granularities in terms of taxonomic resolution, tick life stage, and specimen viewing angle. ViT consistently outperformed ResNet-50, especially in complex classification settings. The configuration yielding the best performance—relying on object detection without incorporating super-resolution—achieved a macro-averaged F1-score exceeding 86% in the 3-class model (Dermacentor sp., other species, bad images), with minimal critical misclassifications (0.7% of “other species” misclassified as Dermacentor). Given that Dermacentor ticks represent more than 60% of tick volume submitted on the eTick platform, the integration of a low granularity model in the processing workflow could save significant time while maintaining very high standards of identification accuracy. Our findings highlight the potential of combining modern AI methods to facilitate efficient and accurate tick image processing in community science platforms, while emphasizing the need to adapt model complexity and class resolution to task-specific constraints. Full article

Wound care in military and combat environments poses distinct challenges that set it apart from conventional medical practice in civilian settings. The nature of injuries sustained on the battlefield—often complex, contaminated, and involving extensive tissue damage—combined with limited access to immediate medical intervention, significantly increases the risk of infection, delayed healing, and adverse outcomes. Traditional wound dressings frequently prove inadequate under such extreme conditions, as they have not been designed to address the specific physiological and logistical constraints present during armed conflicts. This review provides a comprehensive overview of recent progress in the development of advanced wound dressings tailored for use in military scenarios. Special attention has been given to multifunctional dressings that go beyond basic wound coverage by incorporating biologically active macromolecules such as collagen, chitosan, thrombin, alginate, therapeutic peptides, and growth factors. These compounds contribute to properties including moisture balance control, exudate absorption, microbial entrapment, and protection against secondary infection. This review highlights the critical role of advanced wound dressings in improving medical outcomes for injured military personnel. The potential of these technologies to reduce complications, enhance healing rates, and ultimately save lives underscores their growing importance in modern battlefield medicine. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

Military personnel deployed to Iraq and Afghanistan were exposed to emissions from open-air burn pits, where plastics, metals, and medical waste were incinerated. These exposures have been linked to deployment-related respiratory diseases (DRRD) and may also impact neurological health via the lung–brain axis. To investigate molecular mechanisms, adult male rats were exposed to filtered air, naphthalene (a representative volatile organic compound), or a combination of naphthalene and carbon black (surrogate for particulate matter; CBN) via whole-body inhalation (six hours/day, three consecutive days). Lung, brain, and plasma samples were collected 24 h after the final exposure. Pro-inflammatory biomarkers were assessed using multiplex electrochemiluminescence and western blot. Differentially expressed genes (DEGs) were identified by RNA sequencing, and elastic net modeling was used to define exposure-predictive gene signatures. CBN exposure altered inflammatory biomarkers across tissues, with activation of nuclear factor kappa B (NF-κB) signaling. In the lung, gene set enrichment revealed activated pathways related to proliferation and inflammation, while epithelial–mesenchymal transition (EMT) and oxidative phosphorylation were suppressed. In the brain, EMT, inflammation, and senescence pathways were activated, while ribosomal function and oxidative metabolism were downregulated. Elastic net modeling identified a lung gene signature predictive of CBN exposure, including Kcnq3, Tgfbr1, and Tm4sf19. These findings demonstrate that inhalation of a surrogate burn pit mixture induces inflammatory and metabolic gene expression changes in both lung and brain tissues, supporting the utility of this animal model for understanding systemic effects of airborne military toxicants and for identifying potential biomarkers relevant to DRRD and Veteran health. Full article

Background: Kidney transplantation from a living donor is considered the most beneficial form of treatment for end-stage renal failure, which, in addition to providing patients with better treatment results, significantly improves their quality of life. Understanding factors that influence the willingness to donate kidneys to strangers is critical in promoting and expanding the living donor pool. When considering the decision to become an altruistic kidney donor, individuals must evaluate multiple factors, including the identity of the recipient and their own perceived level of safety. This study aimed to assess the willingness of dialysis center employees to act as living kidney donors for a stranger. Methods: We conducted a cross-sectional study from February 2023 to June 2024 among dialysis specialists across Poland. The study involved 1093 people (doctors and nurses). The study used our survey questionnaire and standardized tools. Results: Nurses (vs. physicians) and those who advocated the regulation of unspecified living kidney donation in Poland, did not believe in the risk of organ trafficking, and would donate a kidney to a husband/wife or friend and accept kidney transplantation from a husband/wife were more likely to donate a kidney to a stranger. Furthermore, respondents who accepted a loved one’s decision to donate a kidney to a stranger were significantly more willing to donate a kidney to such a person themselves. Perceived self-efficacy was positively associated with the willingness to donate a kidney to a stranger. Conclusions: Less than half of healthcare professionals supported unspecific living organ donation in Poland, and nurses were more willing to donate than physicians. The factors supporting the decision generally included knowledge about organ donation and transplantation, a lack of fear of organ trafficking, and attitudes towards donation. Full article

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article

Background/Objectives: Pathology is often perceived as a technical medical specialty that lacks direct contact with the patient. However, oncological histopathological diagnosis requires a high degree of moral and emotional responsibility. The objective of this study was to investigate how empathy is manifested toward the “invisible” patient, the emotional impact on pathology staff, and potential repercussions in their personal lives. Method: We conducted a descriptive, cross-sectional study with a quantitative component, using an anonymous 22-item questionnaire among Romanian pathologists and medical personnel working in pathology services. The questionnaire was focused on three research directions: professional empathy in the absence of direct patient contact, the emotional impact of oncologic diagnosis on medical personnel in pathology departments, and the carryover of emotions from professional to personal life. A total of 165 respondents were included in the study (physicians, technicians, registrars). Results: Most of the respondents consider that the absence of the patient’s direct contact does not cancel the empathy, this being felt in a cognitive and more natural way. Over 60% of the respondents see oncologic histopathological diagnosis as an emphatic medical act. Over 80% of the respondents experience a sense of emotional responsibility and 70% consider that professional training does not include adequate emotional support. There is a high interest in empathy and psychological support. The professional activity of a pathologist may influence sleep, dreams, and the perception on their own health status. Diagnosing pediatric or young patients is perceived as particularly emotionally challenging. Collegial support is moderate and discussion about professional stress is rare. Conclusions: Empathy is present and relevant in pathology, despite the absence of direct patient interaction. Oncological diagnostics has a significant emotional impact on pathology department personnel, with the need to acknowledge the emotional dimension of the profession and to integrate psychological support mechanisms into pathology practice. Full article

In the last two decades, there has been a considerable surge in the development of artificial intelligence. Imaging is most frequently employed for the diagnostic evaluation of patients, as it is regarded as one of the most precise methods for identifying the presence of a disease. However, a study indicates that approximately 800,000 individuals in the USA die or incur permanent disability because of misdiagnosis. The present study is based on the use of computer-aided diagnosis of colorectal cancer. The objective of this study is to develop a practical, low-cost, AI-based decision-support tool that integrates clinical test data (blood/stool) and, if needed, colonoscopy images to help reduce misdiagnosis and improve early detection of colorectal cancer for clinicians. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) are utilized in conjunction with a graphical user interface (GUI), which caters to individuals lacking programming expertise. The performance of the artificial neural network (ANN) is measured using the mean squared error (MSE) metric, and the obtained performance is 7.38. For CNN, two distinct cases are under consideration: one with two outputs and one with three outputs. The precision of the models is 97.2% for RGB and 96.7% for grayscale, respectively, in the first instance, and 83% for RGB and 82% for grayscale in the second instance. However, using a pretrained network yielded superior performance with 99.5% for 2-output models and 93% for 3-output models. The GUI is composed of two panels, with the best ANN model and the best CNN model being utilized in each. The primary function of the tool is to assist medical personnel in reducing the time required to make decisions and the probability of misdiagnosis. Full article

Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. It aims to uncover latent topical structures in the literature and critically evaluate assumptions to identify gaps and limitations. Methods: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. We tested models ranging from 2 to 10 topics. To guide interpretation and labeling, we evaluated them using log perplexity, topic coherence, and LDAvis visualizations. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) psychological and group-based interventions for PTSD and trauma, (3) peer support and experiences of emergency and military personnel, and (4) mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments. Full article

Background/Objectives: A perineal incision (episiotomy) is a surgical procedure involving the controlled cutting of perineal tissues to widen the vaginal outlet during the second stage of labor. The aim of this study was to assess Polish women’s knowledge regarding perineal incision. Methods: This study was conducted using an unvalidated, anonymous questionnaire created in Google Forms. Results: Women with higher education, those who had undergone childbirth, and those who identified the Internet, medical personnel, medical personnel on social media, and medical journals as sources had significantly higher levels of knowledge. Respondents aged 25 years or younger had significantly lower knowledge levels compared to those aged over 26. Additionally, respondents living in cities with populations of up to 500,000 had significantly lower levels of knowledge compared to women living in larger cities. Conclusions: The level of women’s knowledge about perineal incision varies and is influenced by several factors. Significant determinants of higher levels of knowledge were higher education, having a history of obstetric delivery, being over 25 years old, and using information provided by medical personnel, including those present on social media. Full article

Microbiological contamination of healthcare workers’ gowns represents a critical risk for the transmission of healthcare-associated infections (HAIs). Despite their use as protective equipment, gowns can act as reservoirs of antibiotic-resistant bacteria, favouring the spread of pathogens between healthcare workers and patients. The presence of these resistant bacteria on healthcare workers’ gowns highlights the urgent need to address this risk as part of infection control strategies. The aim of this work was to assess the microbiological risks associated with the contamination of healthcare staff gowns with Gram-negative bacteria, including the ESKAPE group, and their relationship with antimicrobial resistance. An observational, cross-sectional, prospective study was conducted in 321 hospital workers. The imprinting technique was used to quantify the bacterial load on the gowns, followed by bacterial identification by MALDI-TOF mass spectrometry. In addition, antimicrobial resistance profiles were analysed, and tests for carbapenemases and BLEE production were performed. The ERIC-PCR technique was also used for molecular analysis of Pantoea eucrina clones. Several Gram-negative bacteria were identified, including bacteria of the ESKAPE group. The rate of microbiological contamination of the gowns was 61.05% with no association with the sex of the healthcare personnel. It was observed that critical areas of the hospital, such as intensive care units and operating theatres, showed contamination by medically important bacteria. In addition, some strains of P. eucrina showed resistance to carbapenemics and cephalosporins. ERIC-PCR analysis of P. eucrina isolates showed genetic heterogeneity, indicating absence of clonal dissemination. Healthcare personnel gowns are a significant reservoir of pathogenic bacteria, especially in critical areas of Hospital Juárez de México. It is essential to implement infection control strategies that include improving the cleaning and laundering of gowns and ideally eliminating them from clothing to reduce the risk of transmission of nosocomial infections. Full article