Reduce Mortality Rates Research Articles

Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer

Early diagnosis of lung cancer, the leading cause of cancer-related death worldwide, is critical for reducing mortality rate. However, current diagnostic methods are invasive, time-consuming, costly, and may not always provide accurate diagnoses. For early diagnosis, recent research has focused on noninvasive approaches, including the detection of volatile organic compounds (VOCs) in human exhaled breath. Changes in the composition and concentration of VOCs in exhaled breath may indicate lung cancer, and this approach offers several advantages over traditional diagnostic methods. Moreover, the combination of a breath gas sensing system and machine learning algorithms provides a more accurate diagnosis. In this study, for the early diagnosis of lung cancer, a breath analysis system was developed using a gas sensor array and deep learning algorithm. The breath analysis system was designed to detect multiple VOCs in exhaled breath using ten semiconductor metal oxide (SMO), one photoionization detector (PID), nine electrochemical (EC) gas sensors. In total, 181 clinical breath samples (from 74 healthy controls and 107 lung cancer patients) were collected and analyzed using a 1D convolutional neural network (CNN) algorithm. The results showed an overall accuracy of 97.8% in classifying healthy controls and lung cancer patients using a complete clinical dataset. Through a comparison of the single-sensor type data and multimodal sensor data and performance analysis of three different deep learning models (multilayer perceptron, recurrent neural network, and CNN), we validated the potential of the breath analyzer with a multimodal sensor system and a 1D CNN as a lung cancer diagnostic device.

Cancer Guard: Early Detection of Breast Cancer

Breast cancer stands as the most prevalent form of cancer among women, globally contributing to the highest number of cancer-related deaths. Timely detection of abnormalities significantly enhances the prospects of successful treatment and reduces mortality rates. Hence an automatic detection will be very useful for medical practitioner. This research introduces a novel framework for enhancing breast cancer detection and stage classification by integrating image processing techniques such as Gray Level Co-occurrence Matrix (GLCM) and Convolutional Neural Network (CNN) techniques. Initially, mammographic images undergo preprocessing to improve quality, followed by GLCM feature extraction for capturing textural information. With the help of GLCM technique, accuracy of the network can be increased by extracting various features. A CNN model is then employed for automatic feature learning and classification. This framework enhances the accuracy of distinguishing between malignant and benign tissues and extends to stage detection, enabling classification into various stages. Experimental results demonstrate the effectiveness of the proposed approach in achieving high precision and recall rates, suggesting potential for clinical integration to improve patient outcomes and streamline healthcare workflows.

Care needs of adults with spinal trauma in the prehospital and hospital setting from the perspective of patient care team: A qualitative research.

Appropriate care of patients with definite spinal cord injury or at risk of it in the prehospital and hospital stages requires comprehensive planning in the health system. It is also the requirement of any successful program to explain the needs from the perspective of its stakeholders. Thus, this study aimed to discover the care needs of adults with spinal trauma in prehospital and hospital settings from the perspective of the patient care team. This qualitative study was conducted with the participation of urban and rural prehospital emergency personnel and emergency departments of educational and therapeutic hospitals affiliated to Isfahan, Tehran, Shiraz, Kermanshah, Ahvaz, and Yasuj Universities of Medical Sciences, through conducting 36 in-depth semi-structured interviews from September to December 2021. Using purposive sampling method, the participants were selected considering the maximum variation. The data saturation was reached after conducting interviews and group discussions with 36 subjects. Data were analyzed using conventional content analysis approach. Lundman and Graneheim approach were used for the study rigour. Data were simultaneously analyzed using MAXQDA software version 10. During the data analysis, two themes of prehospital care with two main categories (emergency care and management of secondary complications of spinal trauma) and hospital care with two main categories (emergency care and management of secondary complications of spinal trauma) emerged. Emergency care and management of secondary complications of spinal cord injury in the prehospital and hospital stages can affect treatment results, improve quality of life, and reduce mortality rate, secondary injuries, and healthcare costs. Thus, identification of the care needs of the adults with spinal trauma from the perspective of the patient care team can help the authorities to plan appropriate interventions.

Exploring Gender Disparities in Factors Influencing Colorectal Cancer Screening Compliance in Singapore.

The uptake of colorectal cancer (CRC) screening remains suboptimal in Singapore. Existing research highlights gender-related disparities in screening behaviors. This study aims to evaluate the gender differences in factors associated with CRC screening compliance in Singapore, with a specific focus on cancer screening beliefs and knowledge on CRC screening guidelines. Data were collected from an online survey on cancer screening belief, knowledge, and practices in Singapore. Multivariate logistic regression analysis was conducted to identify independent factors associated with compliance in CRC screening. The study included 633 participants aged 50-69 years, eligible for CRC screening. Only 132 participants (20.9%) complied with CRC screening guidelines with no significant gender differences observed in compliance rates. Most participants held positive beliefs on routine screening (86.9%) and perceived screening benefits in improving treatment outcomes (89.6%) and reducing mortality rates (77.6%). However, both genders exhibited limited knowledge regarding CRC screening guidelines. Only 28.3% were aware that CRC screening should commence at age 50 years. While nearly half of participants knew FIT (44.7%) and colonoscopy (52.0%) as CRC screening modalities, only 27.5% and 15.2% recognized the recommended intervals for FIT and colonoscopy screening respectively. Multivariate analysis revealed a positive association between knowledge on CRC screening guidelines and CRC screening compliance for both genders. Gender-specific variations were observed in the factors associated with CRC screening compliance. Specifically, women with a family history of cancer, believing in routine cancer screening, and prior adherence to breast cancer screening and men within the older cohort (55-69 years) were more likely to comply with CRC screening. Low compliance with CRC screening was observed in Singapore. Targeted interventions should address gender-specific factors and highlight CRC screening guidelines as a crucial component of cancer education for entire screening eligible population in order to improve CRC screening compliance.

A systematic review of deep learning methods for the classification and segmentation of prostate cancer on magnetic resonance images

AbstractProstate Cancer (PCa) is a prevalent global threat to male health, contributing significantly to male cancer‐related mortality. Timely detection and management are pivotal for improved outcomes, as successful cure rates are highest in the early stages. Deep learning (DL) methodologies offer a promising avenue to enhance the precision of detection, potentially reducing mortality rates. Magnetic resonance imaging (MRI) stands out as an effective tool for PCa diagnosis, providing comprehensive visuals of the prostate and adjacent tissues. This technology aids in identifying cancerous developments early on, crucial for treatment planning. Utilizing MRI for PCa detection has demonstrated increased accuracy, minimizing unnecessary biopsies, and facilitating personalized treatment decisions. Recent studies showcase the potential of DL methods in identifying and segmenting the prostate in MRI scans. These techniques not only improve diagnostic precision but also assist in treatment planning and monitoring. Incorporating DL in MRI‐based PCa diagnosis holds immense potential for enhancing efficiency and accuracy, promising better treatment outcomes. However, further research is imperative to explore these methods comprehensively, especially in larger and more diverse patient populations. This review evaluates the progress in employing DL techniques, rooted in artificial intelligence, for categorizing and outlining the prostate and lesions in MR images, underscoring the need for continued investigation and validation in varied clinical settings.

Factors Associated with the Nutritional Status of Mechanically Ventilated Patients in Intensive Care Unit: A Cross Sectional Study

Background: Nutritional status significantly impacts the outcomes of critically ill patients in intensive care units (ICU), with malnutrition being a common complication that can exacerbate morbidity and mortality rates. The Nutrition Risk in the Critically ill (NUTRIC) score is a valuable tool for identifying patients at risk of malnutrition and guiding nutritional interventions. Objective: This study aimed to evaluate the factors influencing the nutritional status, as assessed by the NUTRIC score, of mechanically ventilated patients in ICUs across tertiary care hospitals, and to explore the association between nutritional risk and patient outcomes. Methods: A cross-sectional descriptive study was conducted in five tertiary care hospitals in Khyber Pakhtunkhwa, Pakistan, from August to December 2023. The study included patients aged 18 and above, who were mechanically ventilated for more than 48 hours. The NUTRIC score was calculated based on data collected on demographics, clinical characteristics, and laboratory investigations. Multiple linear regression analysis was performed to identify factors affecting the NUTRIC score using SPSS version 25. Results: Out of 335 patients, 75.2% were male, and 51.3% were aged between 50-75 years. A high NUTRIC score (indicating higher risk of malnutrition) was found in 25% of patients, while 74.9% had a low NUTRIC score. Factors significantly affecting the NUTRIC score included ventilation status (invasive/non-invasive), number of comorbidities, age, status of life (alive/deceased), and length of stay in ICU. The mortality rate was 3.9%, with a higher prevalence in older age groups. Conclusion: The study highlights the prevalence of nutritional risk among mechanically ventilated ICU patients and underscores the importance of early nutritional assessment and intervention. Targeted nutritional support, especially for patients with higher risk profiles, is crucial for improving outcomes and reducing mortality rates in this vulnerable population.

Impressive predictive model for Breast Cancer based on Machine Learning

INTRODUCTION: Breast cancer is a major health concern for women all over the world.
 OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade.
 METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei.
 RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature.
 CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.

Exploring the Efficacy of Midodrine for Tapering Off Vasopressors.

Sepsis and septic shock represent critical conditions, often necessitating vasopressor support in the intensive care unit (ICU). Midodrine, an oral vasopressor, has gathered attention as a potential adjunct to vasopressor therapy, aiming to facilitate weaning and improve clinical outcomes. However, the efficacy of midodrine remains questionable, with conflicting evidence from clinical trials and meta-analyses. This article provides a comprehensive review of the literature on midodrine's role in ICU settings by gathering evidence from multicenter trials, retrospective studies, and meta-analyses. While some studies suggest a limited benefit of midodrine in expediting vasopressor weaning and reducing ICU/hospital stays, others report potential advantages, particularly in reducing mortality rates among septic shock patients. Ongoing efforts aim to address knowledge gaps surrounding midodrine's efficacy and safety.

Effect of enrofloxacin on clinical parameters and mucociliary system of broilers challenged with H9N2 avian influenza/infectious bronchitis viruses.

Effect of antibacterials on mucociliary system and clinical outcome of chickens with mixed viral respiratory conditions is not properly addressed. We evaluated enrofloxacin effects on clinical parameters and mucociliary system of broilers challenged with H9N2/IB viruses. Broilers (105), at the age of 25 days, were randomly allocated into three groups: Group 1 (negative control), no treatment; Group 2 (positive control [PC]) challenged by intranasal and intraocular route. Group 3 (antibiotic [AB]-treated) challenged and also received enrofloxacin started after manifestation of clinical signs (day 2 post-challenge [pc]) and continued for 5 days. Administration of AB was not associated with appreciable changes in body weight, feed conversion ratio (FCR) or the severity of clinical signs although it slightly reduced mortality rate as compared to PC group (p>0.05). Virus shedding period and number of virus positive tracheal and caecal tonsil samples were also statistically similar between PC and AB groups. In necropsy, the most profound effect of AB was decreased pleuropneumonia severity score on day 12 pc. Histopathological lesion scores were statistically the same between PC and AB groups. However, the administration of AB increased the number of tracheal goblet cells, with no effect on ciliostasis. We found a weak positive effect of enrofloxacin administration in H9N2/IB-infected chickens. Considering the risks of AB treatment in broiler chickens, the results of this small-scale study do not encourage the benefit of enrofloxacin use in these viral diseases.

Exploring the therapeutic role of early heparin administration in ARDS management: a MIMIC-IV database analysis

BackgroundAcute respiratory distress syndrome (ARDS) is a severe respiratory condition characterized by a high mortality rate, the management of which relies on supportive care and a profound understanding of its pathophysiology. Heparin, with its anticoagulant and potential anti-inflammatory properties, offers a new therapeutic opportunity for the treatment of ARDS.MethodsIn this retrospective cohort study, we examined the MIMIC-IV database for ARDS patients who received prophylactic heparin within the first 72 h of ICU admission. Employing propensity score matching and inverse probability weighting (IPW) analysis, we evaluated the impact of early heparin use on patient outcomes, focusing on mortality rates.ResultsPatients who received prophylactic heparin had a significantly lower in-hospital mortality rate compared to those who did not (13.55% vs 17.93%, HR = 0.71, 95% CI: 0.54–0.93, P = 0.012). This result remained significant after propensity score matching (12.75% vs 17.93%, HR = 0.65, 95% CI 0.47–0.90, P = 0.010). Analysis using five different statistical models indicated that early use of heparin significantly reduced the in-hospital mortality rate, with HR = 0.669 (95% CI 0.487–0.919, P = 0.013) in the doubly robust model without balanced covariates; HR = 0.705 (95% CI 0.515–0.965, P = 0.029) with all covariates considered; HR = 0.660 (95% CI 0.491–0.888, P = 0.006) in the propensity score (IPW) model; HR = 0.650 (95% CI 0.470–0.900, P = 0.010) in the propensity score matching model; and HR = 0.706 (95% CI 0.536–0.930, P = 0.013) in the multivariate Cox regression model. Secondary outcomes indicated that heparin use was also associated with reduced mortality rates at 60 days, and 90 days.ConclusionThis research highlights that early prophylactic administration of heparin may substantially lower mortality in ARDS patients. These findings underscore the potential of heparin as a key component in the management of ARDS, offering a new perspective and novel strategies for clinical treatment.

Daily Activity Lifelogs of People With Heart Failure: Observational Study.

Globally, heart failure (HF) affects more than 64 million people, and attempts to reduce its social and economic burden are a public health priority. Interventions to support people with HF to self-manage have been shown to reduce hospitalizations, improve quality of life, and reduce mortality rates. Understanding how people self-manage is imperative to improve future interventions; however, most approaches to date, have used self-report methods to achieve this. Wearable cameras provide a unique tool to understand the lived experiences of people with HF and the daily activities they undertake, which could lead to more effective interventions. However, their potential for understanding chronic conditions such as HF is unclear. This study aimed to determine the potential utility of wearable cameras to better understand the activities of daily living in people living with HF. The "Seeing is Believing (SIB)" study involved 30 patients with HF who wore wearable cameras for a maximum of 30 days. We used the E-Myscéal web-based lifelog retrieval system to process and analyze the wearable camera image data set. Search terms for 7 daily activities (physical activity, gardening, shopping, screen time, drinking, eating, and medication intake) were developed and used for image retrieval. Sensitivity analysis was conducted to compare the number of images retrieved using different search terms. Temporal patterns in daily activities were examined, and differences before and after hospitalization were assessed. E-Myscéal exhibited sensitivity to specific search terms, leading to significant variations in the number of images retrieved for each activity. The highest number of images returned were related to eating and drinking, with fewer images for physical activity, screen time, and taking medication. The majority of captured activities occurred before midday. Notably, temporal differences in daily activity patterns were observed for participants hospitalized during this study. The number of medication images increased after hospital discharge, while screen time images decreased. Wearable cameras offer valuable insights into daily activities and self-management in people living with HF. E-Myscéal efficiently retrieves relevant images, but search term sensitivity underscores the need for careful selection.

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model.

This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms-Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)-were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868-0.929) and area under the ROC curve (AUC) of 0.940 (0.898-0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.

Trends in Gastric Cancer Mortality in Montenegro, 1990–2018: Joinpoint Regression

Introduction: Gastric cancer (GC) remains a significant global public health problem, despite the decreasing trends in GC mortality rates in the last 5 decades. Our study aimed to examine the pattern of GC mortality in Montenegro between 1990 and 2018 and to contribute to the future by designing a national long-term strategy for the control and prevention of GC. Methods: Gastric cancer mortality data in Montenegro from 1990 to 2018 were collected. Mortality rates were age-standardized to the World Standard Population for estimating both the overall and gender-specific trends. The joinpoint regression model was used to assess GC mortality and identified significant changes in the linear time trend. Linear and Poisson regressions were also applied for additional trend analyses. Results: Joinpoint regression reveals a statistically significant decrease in the age-standardized rate for the overall level, on average by 1.4% per year (AAPC [95% IP] = −1.4 [−2.4 to −0.4]; p = 0.007), which was due to a decrease in the age-standardized rate in men with an average annual change of −1.8% (AAPC [95% IP] = −1.8 [−2.9 to −0.6]; p = 0.003), while in women the rates were stable (p = 0.565). The results for age groups indicate that a decline was registered at the overall level, and among men, as a consequence of the trend of decreasing age-specific rates for the age group 55–64 on average annually by 2% among men (AAPC [95% IP] = −2 [−3.8 to −0.1]; p = 0.035), and for the overall level (AAPC [95% IP] = −2 [−3.7 to −0.3]; p = 0.026). Conclusion: Our findings indicate a noteworthy decline in age-standardized overall GC mortality rates among men in Montenegro, while rates for women have remained constant. National strategies to further reduce mortality rates for GC are necessary.

Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening

Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as “fingerprint”, in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.

The multispecies microbial cluster of Fusobacterium, Parvimonas, Bacteroides and Faecalibacterium as a precision biomarker for colorectal cancer diagnosis.

The incidence of colorectal cancer (CRC) has increased worldwide, and early diagnosis is crucial to reduce mortality rates. Therefore, new noninvasive biomarkers for CRC are required. Recent studies have revealed an imbalance in the oral and gut microbiomes of patients with CRC, as well as impaired gut vascular barrier function. In the present study, the microbiomes of saliva, crevicular fluid, feces, and non-neoplastic and tumor intestinal tissue samples of 93 CRC patients and 30 healthy individuals without digestive disorders (non-CRC) were analyzed by 16S rRNA metabarcoding procedures. The data revealed that Parvimonas, Fusobacterium, and Bacteroides fragilis were significantly over-represented in stool samples of CRC patients, whereas Faecalibacterium and Blautia were significantly over-abundant in the non-CRC group. Moreover, the tumor samples were enriched in well-known periodontal anaerobes, including Fusobacterium, Parvimonas, Peptostreptococcus, Porphyromonas, and Prevotella. Co-occurrence patterns of these oral microorganisms were observed in the subgingival pocket and in the tumor tissues of CRC patients, where they also correlated with other gut microbes, such as Hungatella. This study provides new evidence that oral pathobionts, normally located in subgingival pockets, can migrate to the colon and probably aggregate with aerobic bacteria, forming synergistic consortia. Furthermore, we suggest that the group composed of Fusobacterium, Parvimonas, Bacteroides, and Faecalibacterium could be used to design an excellent noninvasive fecal test for the early diagnosis of CRC. The combination of these four genera would significantly improve the reliability of a discriminatory test with respect to others that use a single species as a unique CRC biomarker.

Introduction of the Danish Lung Nodule Registry: A part of the Danish Lung Cancer Registry

BackgroundThe majority of lung cancer cases are diagnosed late, resulting in poor prognosis and high mortality rates. Early detection and management of lung cancer can improve patient outcomes and reduce mortality rates. Pulmonary nodules are key factors in the early detection of lung cancer, they are common in high-risk populations and require correct classification to determine whether they are benign or malignant. Over the last decade a steep increase in the number of thoracic CT scans has been seen in Denmark, resulting in substantial resources allocated to CT follow-up of incidentally detected pulmonary nodules. The implementation of a nationwide Danish prospective pulmonary nodule registry is to methodically record pulmonary nodules and thereby evaluate the scope of pulmonary nodule follow-up, the nature of the nodules, and the clinical progression of patients with pulmonary nodules. MethodsA prospective pulmonary nodule registry (Danish Lung Nodule Registry) will be a natural appendix to the Danish Lung Cancer Registry. Three new ICD-10 classification codes will be introduced, defining the type of nodule: /DR91.1/ Solid nodule /DR91.2/ Part-solid nodule; /DR91.3/ Non-solid nodule. Furthermore, an additional letter will describe whether the imaging exam is performed on suspicion of lung cancer (A), or the finding is incidental (B). Registration of the nodules will be performed by the departments of respiratory medicine who manage follow-up of pulmonary nodules. It is estimated that around 7000 nodules will be registered annually. DiscussionThe registration of patients in the lung nodule registry complies with current Danish legislation. The registry will be seamlessly integrated with other nationwide Danish registries, including the Danish Lung Cancer Registry, to collect additional patient data and improve the quality and scope of the data acquired. The results from these comprehensive epidemiological studies will be of significant interest and offer valuable research opportunities.

Silymarin: Unveiling its pharmacological spectrum and therapeutic potential in liver diseases-A comprehensive narrative review.

Liver diseases, encompassing conditions such as cirrhosis, present a substantial global health challenge with diverse etiologies, including viral infections, alcohol consumption, and non-alcoholic fatty liver disease (NAFLD). The exploration of natural compounds as therapeutic agents has gained traction, notably the herbal remedy milk thistle (Silybum marianum), with its active extract, silymarin, demonstrating remarkable antioxidant and hepatoprotective properties in extensive preclinical investigations. It can protect healthy liver cells or those that have not yet sustained permanent damage by reducing oxidative stress and mitigating cytotoxicity. Silymarin, a natural compound with antioxidant properties, anti-inflammatory effects, and antifibrotic activity, has shown potential in treating liver damage caused by alcohol, NAFLD, drug-induced toxicity, and viral hepatitis. Legalon® is a top-rated medication with excellent oral bioavailability, effective absorption, and therapeutic effectiveness. Its active component, silymarin, has antioxidant and hepatoprotective properties, Eurosil 85® also, a commercial product, has lipophilic properties enhanced by special formulation processes. Silymarin, during clinical trials, shows potential improvements in liver function, reduced mortality rates, and alleviation of symptoms across various liver disorders, with safety assessments showing low adverse effects. Overall, silymarin emerges as a promising natural compound with multifaceted hepatoprotective properties and therapeutic potential in liver diseases.

Advances in Managing Chytridiomycosis for Australian Frogs: Gradarius Firmus Victoria.

Extensive knowledge gains from research worldwide over the 25 years since the discovery of chytridiomycosis can be used for improved management. Strategies that have saved populations in the short term and/or enabled recovery include captive breeding, translocation into disease refugia, translocation from resistant populations, disease-free exclosures, and preservation of disease refuges with connectivity to previous habitat, while antifungal treatments have reduced mortality rates in the wild. Increasing host resistance is the goal of many strategies under development, including vaccination and targeted genetic interventions. Pathogen-directed strategies may be more challenging but would have broad applicability. While the search for the silver bullet solution continues, we should value targeted local interventions that stop extinction and buy time for evolution of resistance or development of novel solutions. As for most invasive species and infectious diseases, we need to accept that ongoing management is necessary. For species continuing to decline, proactive deployment and assessment of promising interventions are more valid than a hands-off, do-no-harm approach that will likely allow further extinctions.

A wellbeing program to promote mental health in paediatric burn patients: Study protocol.

One of the most traumatic injuries a child can experience is a severe burn. Despite improvements in medical treatments which have led to better physical outcomes and reduced mortality rates for paediatric burns patients, the psychological impact associated with experiencing such a traumatic injury has mostly been overlooked. This is concerning given the high incidence of psychopathology amongst paediatric burn survivors. This project will aim to pilot test and evaluate a co-designed trauma-focused intervention to support resilience and promote positive mental health in children and adolescents who have sustained an acute burn injury. Our first objective is to collect pilot data to evaluate the efficacy of the intervention and to inform the design of future trauma-focussed interventions. Our second objective is to collect pilot data to determine the appropriateness of the developed intervention by investigating the changes in mental health indicators pre- and post-intervention. This will inform the design of future interventions. This pilot intervention study will recruit 40 children aged between 6-17 years who have sustained an acute burn injury and their respective caregivers. These participants will have attended the Stan Perron Centre of Excellence for Childhood Burns at Perth Children's Hospital. Participants will attend a 45-minute weekly or fortnightly session for six weeks that involves building skills around information gathering, managing reactions (behaviours and thoughts), identifying, and bolstering coping skills, problem solving and preventing setbacks. The potential effects and feasibility of our intervention will be assessed through a range of age-appropriate screening measures which will assess social behaviours, personal qualities, mental health and/or resilience. Assessments will be administered at baseline, immediately post-intervention, at 6- and 12-months post-intervention. The results of this study will lay the foundation for an evidence-based, trauma-informed approach to clinical care for paediatric burn survivors and their families in Western Australia. This will have important implications for the design of future support offered to children with and beyond burn injuries, and other medical trauma populations.

Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.

Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung, which are either cancerous or non-cancerous. Accurate segmentation of these nodules is crucial for early detection and diagnosis of lung cancer. However, lung nodules can have various shapes, sizes, and densities, making their accurate segmentation a difficult task. Moreover, they can be easily confused with other structures in the lung, including blood vessels and airways, further complicating the segmentation process. To address this challenge, this paper proposes a novel multi-crop convolutional neural network (multi-crop CNN) model that utilizes different sized cropped regions of CT scan images for accurate segmentation of lung nodules. The model consists of three modules, namely the feature representation module, boundary refinement module, and segmentation module. The feature representation module captures features from the lung CT scan image using cropped regions of different sizes, while the boundary refinement module combines the boundary maps and feature maps to generate a final feature map for the segmentation process. The segmentation module produces a high-resolution segmentation map that shows improved accuracy in segmenting cancerous lung nodules. The proposed multi-crop CNN model is evaluated on two segmentation datasets namely LUNA 16 and LIDC-IDRI with an accuracy of 98.3% and 98.5%, respectively. The performances are measured in terms of accuracy, recall, precision, dice coefficient, specificity, AUC/ROC, Hausdorff distance, Jaccard index, and average Hausdorff. Overall, the proposed multi-crop CNN model demonstrates the potential to enhance the lung nodule segmentation accuracy, which could lead to earlier detection and diagnosis of lung cancer and ultimately reduce mortality rates associated with the disease.