Medical Approach Research Articles

Analysis of the Influence of Various Factors on the Prevalence of Tuberculosis in Ukraine

A medical approach to the analysis of socio-economic, medical and demographic factors affecting the prevalence of tuberculosis in Ukraine is insufficient for timely prognosing the prospects for the development of the tuberculosis epidemic and developing an appropriate plan to address its challenges. Objective — to analyse influence of various factors on tuberculosis prevalence in Ukrainian population. Materials and methods. For the analysis, data were collected over the past sixteen years, covering all regions of Ukraine, including information on the number of specialized hospitals, the number of fluoroscopic examinations per 100,000 people, vaccination data, the number of Mycobacterium tuberculosis excretors, the prevalence among urban and rural residents, and the percentage of different demographic groups (workers, health care workers, students, pupils, retirees, the unemployed, homeless people, released prisoners, private sector workers). Results and discussion. The analysis, conducted through the Stacking model, enables the identification of crucial variables that significantly influence the prevalence of tuberculosis. Evaluating the significance of each element in the model enables a deeper comprehension of morbidity dynamics and the optimization of intervention strategies. The creation and validation of machine learning models such as linear regression, random forests, and adaptive boosting have enabled accurate predictions of tuberculosis prevalence. The use of 5-fold cross-validation increased the reliability of the predictions, ensuring stability and accuracy across different demographic groups. Conclusions. The application of artificial intelligence in analyzing socioeconomic, medical, and demographic data has facilitated the identification of key factors influencing the prevalence of tuberculosis in Ukraine. Specifically, the analysis has verified the substantial effects of the quantity of specialized hospitals, the rate of fluoroscopic examinations, and the frequency of bacterial excretion on the prevalence rates.

Racial Equity in Family Approach for Patients Medically Suitable for Deceased Organ Donation.

To conduct a contemporary analysis of the association between family approach of medically suitable potential organ donors and race/ethnicity. Retrospective review of data collected prospectively by Organ Procurement Organizations (OPOs). Ten OPOs representing eight regions of the Organ Procurement and Transplantation Network and 26% of all deceased donor organs recovered in the United States. All hospitalized patients on mechanical ventilation and referred to OPOs as potential donors from January 1, 2018, to December 31, 2022. None. OPOs provided data on referral year, race, sex, donor registration status, screening determination, donation medical suitability, donation type (brain death, circulatory death), and family approach. We evaluated factors associated with family approach to discuss donation using descriptive statistics and multivariable logistic models. Of 255,429 total cases, 138,622 (54%) were screened-in for further evaluation, with variation by race/ethnicity (50% White, 60% Black, 69% Hispanic, and 60% Asian). Among those screened-in, 31,253 (23%) were medically suitable for donation, with modest variation by race/ethnicity (22% White, 26% Black, 23% Hispanic, and 21% Asian). Family approach rate by OPOs of medically suitable cases was 94% (n = 29,315), which did not vary by race/ethnicity (94% White, 93% Black, 95% Hispanic, and 95% Asian). Family approach by OPOs was lower for circulatory death (95%) vs. brain death (97%) cases but showed minimal differences in approach rate based on race/ethnicity between medically suitable patients with different death pathways. In contrast, donor registration status of medically suitable potential donors was highly variable by race/ethnicity (37% overall; 45% White, 21% Black, 29% Hispanic, and 25% Asian). Multivariable models indicated no significant difference of family approach between White and Black (odds ratio [OR], 1.09; 95% CI, 0.95-1.24) or Asian (OR, 1.23; 95% CI, 0.95-1.60) patients. Findings indicate racial equity in OPO family approach rates among patients who were medically suitable for organ donation.

Traditional Cancer Treatments in West Africa: Historical Practices, Current Approaches, and Future Directions

Cancer treatment in West Africa is shaped by a rich tapestry of traditional practices and modern approaches, reflecting both cultural heritage and contemporary medical advancements. This review explores the historical and current landscape of traditional cancer treatments in the region, emphasizing herbal remedies, spiritual practices, and the integration of traditional and modern medical approaches. Traditional cancer treatments often involve plant-based remedies such as African horned cucumber, baobab tree, and soursop, known for their antioxidant, anti-inflammatory, and potential anti-cancer properties. Spiritual and ritualistic practices, including prayer, energy healing, and sacred rituals, offer holistic support, addressing the psychological and emotional aspects of illness. The review examines the integration of traditional and modern practices, highlighting the potential benefits of a collaborative approach while addressing challenges such as safety, efficacy, and cultural sensitivity. Research efforts are focused on validating traditional treatments through scientific studies, standardizing remedies, and ensuring safety. Socioeconomic and cultural factors significantly influence treatment choices, with traditional medicine often serving as a more accessible and culturally resonant option for many patients. Case studies and community experiences provide valuable insights into the practical application of these treatments and underscore the importance of respecting cultural contexts. This review aims to provide a comprehensive understanding of traditional cancer treatments in West Africa, offering recommendations for future research and practice to enhance cancer care through an integrative and culturally sensitive approach. Keywords: Traditional Cancer Treatments, West Africa: Historical Practices.

Effect of Curing Light with Different Intensities on the Penetration of Silver and Fluoride Ions and Dentin Hardness in Primary Carious Molars Following Silver Diamine Fluoride Application: A Comparative Microscopic Ex Vivo Study.

A paradigm shift from surgical to medical approach for caries management has popularized silver diamine fluoride (SDF) as a preventive and interim caries arrest medicament. Few studies conducted have explored the effect of curing light on SDF's microbial property, its penetration, and effect on dentin. However, there is a research gap regarding the effect of different intensities of curing light on SDF performance. To determine the effect of different curing light intensities on SDF penetration depth and dentin hardness in carious lesions of primary molars. Silver diamine fluoride was applied on 30 extracted carious primary molars. Teeth were randomly allocated into three groups-(1) control group, no light curing after application of SDF; (2) light curing of SDF with low intensity (1000 mW/cm2); and (3) light curing of SDF with high intensity (2500 mW/cm2). A scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) analysis was performed to check ion penetration after 1 week, and a Vickers hardness test was used to assess dentin hardness of both infected and affected dentin layers at 1-week and 1-month intervals. Based on the distribution of data, parametric and nonparametric tests were applied. Statistical Package for the Social Sciences (SPSS) version 26 was used for statistical analysis. The level of significance was set at 5%. Silver diamine fluoride penetrated beyond the carious lesion in all three groups. The mean silver ion precipitation in infected dentin in group III (16.90 ± 0.68) was maximum, whereas it was found to be minimum in group II (7.31 ± 0.63). The mean fluoride ion precipitation in affected dentin in group III (4.06 ± 0.41) was highest and least in group II (3.09 ± 0.58). A considerable increase in mean dentin hardness of infected dentin was observed in all three groups (214.00 ± 89.06, 218.00 ± 75.17, 231.00 ± 98.86, respectively; p < 0.001) after 1 month. Applying SDF to carious lesions using a high-intensity dental curing light induced more silver ion precipitation in infected dentin and increased its hardness. Bhatt R, Patel M, Thakkar A, et al. Effect of Curing Light with Different Intensities on the Penetration of Silver and Fluoride Ions and Dentin Hardness in Primary Carious Molars Following Silver Diamine Fluoride Application: A Comparative Microscopic Ex Vivo Study. Int J Clin Pediatr Dent 2024;17(7):766-772.

Respiratory syncytial virus vaccination strategies for older Canadian adults: a cost-utility analysis.

Respiratory syncytial virus (RSV) vaccines could reduce disease burden and costs in older Canadian adults, but vaccination program cost-effectiveness is unknown. We evaluated the cost-effectiveness of different age cut-offs for RSV adult vaccination programs, with or without a focus on people with higher disease risk due to chronic medical conditions. We developed a static individual-based model of medically attended RSV disease to compare alternative age-, medical risk-, and age-plus medical risk-based vaccination policies. The model followed a multiage population of 100 000 people aged 50 years and older. Vaccine characteristics were based on RSV vaccines authorized in Canada as of May 2024, with vaccine protection assumed to last 2 years (or 3 years in scenario analyses). We calculated sequential incremental cost-effectiveness ratios in 2023 Canadian dollars per quality-adjusted life year (QALY) from the health-system and societal perspectives, discounted at 1.5%. Although all vaccination strategies averted medically attended RSV disease, universal age-based strategies were not an efficient use of resources compared with medical risk-based strategies. Vaccinating adults aged 70 years and older with 1 or more chronic medical condition was the optimal strategy for a cost-effectiveness threshold of $50 000 per QALY. Results were sensitive to assumptions about vaccine price, but medical risk-based approaches remained optimal compared with age-based strategies, even when vaccine prices were low. Findings were robust to a range of alternative assumptions. Vaccination programs for RSV in some groups of older Canadians with underlying medical conditions are likely cost-effective. These findings can inform the design of vaccination programs.

Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection

The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and safeguard numerous lives. Medically, chest radiography stands out as an essential and economically viable medical imaging approach for diagnosing and assessing the severity of diverse Respiratory Disorders. However, their detection in Chest X-Rays is a cumbersome task even for well-trained radiologists owing to low contrast issues, overlapping of the tissue structures, subjective variability, and the presence of noise. To address these issues, a novel analytical model termed Exponential Pixelating Integral is introduced for the automatic detection of infections in Chest X-Rays in this work. Initially, the presented Exponential Pixelating Integral enhances the pixel intensities to overcome the low-contrast issues that are then polar-transformed followed by their representation using the locally invariant Mandelbrot and Julia fractal geometries for effective distinction of structural features. The collated features labeled Exponential Pixelating Integral with dually characterized fractal features are then classified by the non-parametric multivariate adaptive regression splines to establish an ensemble model between each pair of classes for effective diagnosis of diverse diseases. Rigorous analysis of the proposed classification framework on large medical benchmarked datasets showcases its superiority over its peers by registering a higher classification accuracy and F1 scores ranging from 98.46 to 99.45 % and 96.53–98.10 % respectively, making it a precise and interpretable automated system for diagnosing respiratory disorders.

Doing ethics with microbes: toward a queer feminist posthuman framework for bioethics.

The Centre for the Social Study of Microbes (CSSM) at University of Helsinki, Finland is a Wellcome-funded hub for creating new approaches to human-microbial relations. Most urgently, the complex relations between microbes, antimicrobial resistance (in its human-health centered definition, understood as infection and disease now uncurable by antibiotics, threatening human life and wellbeing), animals, environments and climate emergency require new theoretical and methodological approaches. These include a variety of research interest from global fermentation practices and cultures to microbial bioremediation techniques, from aquatic microbes to urban gardens. A key orientation of CSSM is also combining ethnography with artistic and performative practice such as felting, painting, installation, sculpture and audiovisual means, to gain a deeper, more sensory and embodied perspective of our shared lives with microbes. As their agency exists outside textuality, new experimental methodology is needed to engage with it. Thinking with microbes invites many ethical issues that often remain unaddressed in medical and scientific approach. As one of such contributions, we suggest a new framework for bioethics. In a conceptual analysis, a queer feminist posthuman framework aims for radical reorientation of human exceptionalism for more-than-human justice while keeping existing social justice issues between groups of people, such as vulnerabilities cascading around gender and sexual variance, in the same framework. Both a theoretical and practical initiative, seeking to forge solidarity between justice movements, this framework could ground policies relevant to a broader bioethical and philosophical research community, and social scientists studying microbes. This open letter discusses this work at the CSSM.

Multi-scale input reconstruction network and one-stage instance segmentation for enhancing heart defect prediction rate

&lt;p&gt;Artifacts and unpredictable fetal movements can hinder clear fetal heart imaging during ultrasound scans, complicating anatomical identification. This study presents a new medical imaging approach that combines one-stage instance segmentation with ultrasound (US) video enhancement for precise fetal heart defect detection. This innovation allows real-time identification and timely medical intervention. The study acquired 100 fetal heart US videos from an Indonesian Hospital featuring cardiac septal defects, generating 1,000 frames for training, validation, and testing. Utilizing a combination of the multi-scale input reconstruction network (MIRNet) for image enhancement and YOLOv8l-seg for real-time instance segmentation, the method achieved outstanding validation results, boasting a 99.50% mAP for bounding box prediction and 98.40% for mask prediction. It delivered a remarkable real-time processing speed of 68.4 frames per second. In application to new patients, the method yielded a 65.93% mAP for bounding box prediction and 57.66% for mask prediction. This proposed approach offers a promising solution to early fetal heart defect detection using ultrasound, holding substantial potential for enhancing healthcare outcomes.&lt;/p&gt;

UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation.

Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies, compared to the local convolutional-based design. However, the self-attention operation has quadratic complexity which proves to be a computational bottleneck, especially in volumetric medical imaging, where the inputs are 3D with numerous slices. In this paper, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient and has linear complexity with respect to the input. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the complexity. Our extensive evaluations on five benchmarks, Synapse, BTCV, ACDC, BraTS, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy. On Synapse, our UNETR++ sets a new state-of-the-art with a Dice Score of 87.2%, while significantly reducing parameters and FLOPs by over 71%, compared to the best method in the literature. Our code and models are available at: https://tinyurl.com/2p87x5xn.

Utilization of Traditional Healing Practices for Cancer Treatment: Benefits, Risks, and Integration with Modern Medicine

Traditional healing practices in Africa, characterized by the use of locally available plants, minerals, and spiritual rituals, have historically played a significant role in managing diseases, including cancer. Despite the overshadowing effect of Western medicine during the colonial period, traditional practices continue to be integral to many communities, providing culturally relevant and accessible treatments. This review explores the utilization of traditional healing practices in cancer treatment, focusing on the benefits, risks, and integration with modern medicine. Traditional cancer treatments, including herbal remedies, spiritual interventions, and physical therapies, offer potential benefits like improved symptom management and emotional support. However, challenges like lack of standardization, scientific validation, and potential interactions with conventional treatments pose significant risks. The integration of traditional and modern medical approaches is emerging as a trend, aiming to improve treatment outcomes and patient quality of life. This requires careful consideration of safety, efficacy, and standardization. Effective integration involves developing collaborative care models, regulatory frameworks, and interdisciplinary partnerships. Future directions include advancing research to understand traditional remedies’ mechanisms, improving quality control, and exploring hybrid therapies. By fostering collaboration between traditional healers, researchers, and modern healthcare professionals, and addressing ethical and safety concerns, traditional practices can be harnessed while enhancing overall cancer care. Keywords: Traditional Healing, Cancer Treatment, Benefits, Risks, Modern Medicine

A CRITICAL REVIEW OF GRAHANI ROGA IN THE CONTEXT OF MANASIKA BHAVA

The rise of Grahani Roga, a gastrointestinal ailment, can be attributed to factors like stress and poor dietary habits in modern lifestyles. Ayurvedic texts connect emotional states such as fear, anger, and sadness to imbalances in Dosha, underscoring the intricate link between mental and physical well-being. Stress disrupts the brain-gut axis, contributing to conditions like IBD, IBS, and GERD, which share symptoms with Grahani Roga, suggesting a potential psychosomatic origin. This article explores Ayurvedic principles of health, emphasizing the balance of Dosha, Agni, Dhatu, and Mala, as well as the role of a serene mind (Prasanna Atma, Indriya, and Mana) in managing Grahani Roga. It advocates integrating traditional Ayurvedic wisdom with modern medical approaches to tackle contemporary health challenges. Future research should delve into the psychosomatic aspects of these conditions to develop comprehensive diagnostic and treatment strategies that amalgamate Ayurvedic insights with modern medical knowledge

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.

Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur’s entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC’22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO’s superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method’s effectiveness in solving image segmentation problems.

Nurses' perspectives and experiences on medical errors: A qualitative study.

Medical errors are among the most important factors that threaten patient safety. Therefore, nurses' perspectives and experiences about medical errors are important for this manner. The aim of this study was to determine in depth the perspectives and experiences of nurses related to how they define medical error, as well as its causes, management and reporting. This descriptive, exploratory study involved a qualitative design. A total of 15 clinical nurses from eleven provinces were reached by snowball sampling method. In the study, nurses' perspectives on medical errors were obtained through semistructured in-depth online interviews conducted based on phenomenological methods. Descriptive analysis was used in the analysis of the data obtained from the interviews. The research was conducted following the COnsolidated criteria for REporting Qualitative checklist. The resulting 26 codes were categorized under seven themes: "Definition of medical error", "Experience of medical errors", "Frequency of medical errors", "Causes of medical errors", "Reporting of medical errors", "Medical error approach" and "Prevention of medical errors". The results show that it is still necessary to increase the awareness of nurses about medical errors and the importance of error reporting. In addition, it reveals the need for leadership to eliminate the negative approach to medical errors and shows that nurse managers should be empowered accordingly. It is the first step of quality and safe care to reveal nurses' perspectives on medical errors, their thoughts about medical errors and their experiences about errors in their institutions. In this study, nurses' knowledge, opinions and experiences regarding medical errors were revealed in this qualitative study. Therefore, this study offers important clues to nursing services, hospital managers and policy makers for clinical and institutional arrangements.

The practical turn in philosophy: A revival of the ancient art of living through modern philosophical practice

AbstractPhilosophical practice, an art of living rooted in ancient traditions, is enriched by modern techniques such as individual counseling, Socratic group dialogues, and organizational consulting. Philosophical counseling, a key aspect of this practice, employs traditional philosophical frameworks and rational reasoning to address clients' concerns, distinguishing itself from psychotherapy while respecting individual autonomy. The growing Western interest in Asian philosophies also underscores a shared pursuit of wisdom, spirituality, and meaning. This paper examines the development, key features, and leading proponents of philosophical practice, its relationship with academic philosophy, and the tensions between their differing aims and methods. The outlook for philosophical counseling is promising, offering an alternative to medical approaches for existential issues and expanding its scope to personal guidance, conflict resolution, and life‐skill development. Its future trajectory hinges on societal trends toward secularization, the de‐medicalization of mental health, and the increasing recognition of philosophy's intrinsic practical value.

Professor ZHUANG Lixing's experience of integrated acupuncture and medication approach to treat motor complications in Parkinson's disease based on the alternation of "movement and stillness"

This paper introduces Professor ZHUANG Lixing's experience in treating motor complications of Parkinson's disease (PD) with acupuncture combined with medication. Based on the characteristics of the alternation of "movement and stillness" in PD motor complications, Professor ZHUANG divides these complications into three distinct periods: "movement" stage, "stillness" stage and "alternation" stage, and proposes an integrated approach of acupuncture and medication, with staged treatment tailored to each period. The main acupoints include Jin's three needles to regulate spirit (four spirit needles, Shenting [GV 24], Yintang [GV 24+], Shenmen [HT 7], Sanyinjiao [SP 6]), along with hand tremor three needles (Hegu [LI 4], Quchi [LI 11], Dingchan), foot tremor three needles (Yinlingquan [SP 9], Yanglingquan [GB 34], Taichong [LR 3]), and Du's three needles (Dazhui [GV 14], Jinsuo [GV 8], Mingmen [GV 4]). The primary medicinal formulas include Lingjiao Gouteng decoction, Banxia Baizhu Tianma decoction, Bazhen decoction combined with Buzhong Yiqi decoction, Sini decoction combined with Yougui pills, and Xiaochaihu decoction. This integrated approach effectively alleviates the motor symptom fluctuations in PD patients, helping them maintain a stable life.

A study on the impact of open source metaverse immersive teaching method on emergency skills training for medical undergraduate students

BackgroundIn recent years, the traditional simulation-based medical teaching approach has faced challenges in meeting the requirements of practical emergency medicine education. This study utilized open-source tools and software to develop immersive panoramic videos using virtual reality technology for emergency medical teaching. It aims to investigate the efficacy of this novel teaching methodology. This transformation shifted the focus from physical simulation to virtual simulation in medical education, establishing a metaverse for emergency medical teaching.MethodsIn accordance with the curriculum guidelines, the instructors produced panoramic videos demonstrating procedures such as spinal injury management, humeral fracture with abdominal wall intestinal tube prolapse, head and chest composite injuries, cardiopulmonary resuscitation, and tracheal intubation. Using Unity software, a virtual training application for bronchoscopy was developed and integrated into the PICO4 VR all-in-one device to create a metaverse teaching environment. Fourth-year medical undergraduate students were allocated into either an experimental group (n = 26) or a control group (n = 30) based on student IDs. The experimental group received instruction through the metaverse immersive teaching method, while the control group followed the traditional simulation-based medical teaching approach. Both groups participated in theoretical and practical lessons as usual. Subsequently, all students underwent a four-station Objective Structured Clinical Examination (OSCE) to assess the effectiveness of the teaching methods based on their performance. Additionally, students in the experimental group provided subjective evaluations to assess their acceptance of the new teaching approach.ResultsBefore the training commenced, there were no significant statistical differences in the first aid test scores between the experimental and control groups. Following the training, the experimental group outperformed the control group in the four-station OSCE examination, with all P-values being less than 0.05. The satisfaction rate among the experimental group regarding the new teaching method reached 88.46%, reflecting levels of satisfaction and extreme satisfaction.ConclusionThe open-source metaverse immersive teaching method has demonstrated a positive impact on enhancing the emergency skills of medical undergraduate students, with a high level of acceptance among students. In comparison to traditional simulated medical teaching methods, this approach requires less time and space, incurring lower costs, and is deemed worthy of wider adoption.

PSYCHODYNAMIC PSYCHOTHERAPY FOR TIME DISTORTION IN ATTENTION DEFICIT HYPERACTIVITY DISORDER

Psychodynamic psychotherapy does not feature in current treatment guidelines for attention‐deficit hyperactivity disorder (ADHD) in the United Kingdom. However, there is some psychoanalytic understanding of the disorder, which is corroborated in current neuroscientific theory, and supports clinicians in thinking beyond the behaviourally biased diagnosis and treatment models. One of the core features of ADHD is distortions in chronoception. This clinical paper therefore explores the psychoanalytic and neuroscientific theories of time perception as a way of exploring a discrete element of the complex idea of executive functioning more generally. It supports therapists approaching such executive dysfunction and to reconsider the treatment guidelines. A case example is used to explore how a transference‐, affect‐ and relationally‐focussed therapy can effect lasting change for people with ADHD in a way that behavioural and medication approaches do not. It is argued that such an intervention can improve oscillatory attention between subjective‐ and objective‐time and increase the capacity to self‐contain emotions. This brings patients into alignment with reality and restarts their psychic development towards themselves, others, the parental couple and grieving, which stalled in infancy. The internalised schema of the 50‐minute therapy session also provides a frame of reference for future tasks.

Copper-MOF and tannic acid-empowered composite cryogel as a skin substitute for accelerated deep wound healing

The effective management of deep skin wounds remains a significant healthcare challenge that often deteriorates with bacterial infection, oxidative stress, tissue necrosis, and excessive production of wound exudate. Current medical approaches, including traditional wound dressing materials, cannot effectively address these issues. There is a great need to engineer advanced and multifunctional wound dressings to address this multifaceted problem effectively. Herein, a rationally designed composite cryogel composed of a Copper Metal-Organic Framework (Cu-MOF), tannic acid (TA), polyvinyl alcohol (PVA), and zein protein has been developed by freeze-thaw technique. Cryogels display a remarkable swelling capacity attributed to their interconnected microporous morphology. Moreover, dynamic mechanical behaviour with the characteristics of potent antimicrobial, antioxidant, and biodegradation makes it a desirable wound dressing material. It was further confirmed that the material is highly biocompatible and can release TA and copper ions in a controlled manner. In-vivo skin irritation in a rat model demonstrated that composite cryogel did not provoke any irritation/inflammation when applied to the skin of a healthy recipient. In a deep wound model, the composite cryogel significantly accelerates the wound healing rate. These findings highlight the multifunctional nature of composite cryogels and their promising potential for clinical applications as advanced wound dressings.

Adaptive decomposition with guided filtering and Laplacian pyramid-based image fusion method for medical applications

Medical image fusion enhances diagnostic precision and facilitates clinical decision-making by integrating information from multiple medical imaging modalities. However, this field is still challenging as the output integrated image, whether from spatial or transform domain algorithms, may suffer from drawbacks such as low contrast, blurring effect, noise, over smoothness, etc. Also, some existing novel works are restricted to specific image datasets. So, to address such issues, a new multi-modal medical image fusion approach based on the advantageous effects of multiple transforms has been introduced in the present work. For this, we use an adaptive image decomposition tool known as Hilbert vibration decomposition (HVD). HVD decomposes an image into different energy components, and after a proper decomposition of the source images, the desirable features of the decomposed components are then passed through a guided filter (GF) for edge preservation. Then, the Laplacian pyramid integrates these filtered parts using the choose max rule. Since HVD offers better spatial resolution and is independent of fixed cut-off frequencies like other transforms, the subjective outputs from this method for different publicly available medical image datasets are clear and better than the previously 20 state-of-the-art published results. Moreover, the obtained values of different objective evaluation metrics such as information entropy (IE): 7.6943, 5.9737, mean: 110.6453, 54.6346, standard deviation (SD): 85.5376, 61.8129, average gradient (AG): 109.2818, 64.6451, spatial frequency (SF): 0.1475, 0.1100, and edge metric (QHK/S): 0.5400, 0.6511 demonstrate its comparability to others. The algorithm's running period of just 0.161244 s also indicates high computational efficiency.