A comparison between the effects of Portulaca oleracea seeds extract and valsartan on echocardiographic and hemodynamic parameters in rats with levothyroxine-induced thyrotoxicosis

Objective: The aim of the present study was to compare the effects of Portulaca oleracea (Po) seeds extract and those of valsartan on cardiac function in levothyroxine (T4)-treated rats. Materials and Methods: Forty Wistar rats were divided into four groups (n=10): control, levothyroxine (T4), T4 plus valsartan (T4-Val) and T4 plus hydro-alcoholic extract of the P. oleracea seeds (T4-Po). Control group received normal saline. Levothyroxine (100µg/kg/day, i.p.) was administered to three other groups for 4 weeks. Valsartan (8 mg/kg/day, orally) and Po seeds extract (400 mg/kg/day, orally) were administered during the last two weeks of treatment period. At the end of the experiment, echocardiographic and hemodynamic parameters were measured and serum free T4, T3, and T4 were measured. Results: Administration of T4 for 4 weeks significantly increased serum free T4 levels in T4 group but elevations of free T4 levels in T4–Val group were not significant. Free T4 level decreased in T4–Po (

Testosterone May Hold Therapeutic Promise for the Treatment ofIschemic Stroke in Aging: A Closer Look at Laboratory Findings

Male sex is more prone to cerebrovascular disorders, yet the exact role of androgens in cerebralischemia remains unclear. Here we reviewed current understanding of testosterone (TES)neuroprotective activity against ischemic stroke and mechanisms underlying these effects inaging. TES may exert a neuroprotective effect in aging through pathways including inhibition ofoxidant molecules production, enhancing the enzymatic antioxidant capacity of the brain andmodulation of apoptotic cell death. Given this, a better understanding of the neuroprotectiveroles of TES may propose an effective therapeutic strategy to improve the quality of life anddecrease androgen-related cerebrovascular problems in the aging men

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases

Precision diagnostics in cardiac tumours: Integrating echocardiography and pathology with advanced machine learning on limited data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25 % and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94 % in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

The role of entry screening in case finding of tuberculosis among asylum seekers in Norway

<p>Abstract</p> <p>Background</p> <p>Most new cases of active tuberculosis in Norway are presently caused by imported strains and not transmission within the country. Screening for tuberculosis with a Mantoux test of everybody and a chest X-ray of those above 15 years of age is compulsory on arrival for asylum seekers.</p> <p>We aimed to assess the effectiveness of entry screening of a cohort of asylum seekers. Cases detected by screening were compared with cases detected later. Further we have characterized cases with active tuberculosis.</p> <p>Methods</p> <p>All asylum seekers who arrived at the National Reception Centre between January 2005 - June 2006 with an abnormal chest X-ray or a Mantoux test ≥ 6 mm were included in the study and followed through the health care system. They were matched with the National Tuberculosis Register by the end of May 2008.</p> <p>Cases reported within two months after arrival were defined as being detected by screening.</p> <p>Results</p> <p>Of 4643 eligible asylum seekers, 2237 were included in the study. Altogether 2077 persons had a Mantoux ≥ 6 mm and 314 had an abnormal chest X-ray. Of 28 cases with tuberculosis, 15 were detected by screening, and 13 at 4-27 months after arrival. Abnormal X-rays on arrival were more prevalent among those detected by screening. Female gender and Somalian origin increased the risk for active TB.</p> <p>Conclusion</p> <p>In spite of an imperfect follow-up of screening results, a reasonable number of TB cases was identified by the programme, with a predominance of pulmonary TB.</p

Global trends of hand and wrist trauma : a systematic analysis of fracture and digit amputation using the Global Burden of Disease 2017 Study

Background As global rates of mortality decrease, rates of non-fatal injury have increased, particularly in low Socio-demographic Index (SDI) nations. We hypothesised this global pattern of non-fatal injury would be demonstrated in regard to bony hand and wrist trauma over the 27-year study period. Methods The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 was used to estimate prevalence, age-standardised incidence and years lived with disability for hand trauma in 195 countries from 1990 to 2017. Individual injuries included hand and wrist fractures, thumb amputations and non-thumb digit amputations. Results The global incidence of hand trauma has only modestly decreased since 1990. In 2017, the age-standardised incidence of hand and wrist fractures was 179 per 100 000 (95% uncertainty interval (UI) 146 to 217), whereas the less common injuries of thumb and non-thumb digit amputation were 24 (95% UI 17 to 34) and 56 (95% UI 43 to 74) per 100 000, respectively. Rates of injury vary greatly by region, and improvements have not been equally distributed. The highest burden of hand trauma is currently reported in high SDI countries. However, low-middle and middle SDI countries have increasing rates of hand trauma by as much at 25%. Conclusions Certain regions are noted to have high rates of hand trauma over the study period. Low-middle and middle SDI countries, however, have demonstrated increasing rates of fracture and amputation over the last 27 years. This trend is concerning as access to quality and subspecialised surgical hand care is often limiting in these resource-limited regions.Peer reviewe

Global trends of hand and wrist trauma: a systematic analysis of fracture and digit amputation using the Global Burden of Disease 2017 Study

Background: As global rates of mortality decrease, rates of non-fatal injury have increased, particularly in low Socio-demographic Index (SDI) nations. We hypothesised this global pattern of non-fatal injury would be demonstrated in regard to bony hand and wrist trauma over the 27-year study period. Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 was used to estimate prevalence, age-standardised incidence and years lived with disability for hand trauma in 195 countries from 1990 to 2017. Individual injuries included hand and wrist fractures, thumb amputations and non-thumb digit amputations. Results: The global incidence of hand trauma has only modestly decreased since 1990. In 2017, the age- standardised incidence of hand and wrist fractures was 179 per 100 000 (95% uncertainty interval (UI) 146 to 217), whereas the less common injuries of thumb and non-thumb digit amputation were 24 (95% UI 17 to 34) and 56 (95% UI 43 to 74) per 100 000, respectively. Rates of injury vary greatly by region, and improvements have not been equally distributed. The highest burden of hand trauma is currently reported in high SDI countries. However, low-middle and middle SDI countries have increasing rates of hand trauma by as much at 25%. Conclusions: Certain regions are noted to have high rates of hand trauma over the study period. Low-middle and middle SDI countries, however, have demonstrated increasing rates of fracture and amputation over the last 27 years. This trend is concerning as access to quality and subspecialised surgical hand care is often limiting in these resource-limited regions.publishedVersio