Challenges and possible conservation implications of recolonizing dholes Cuon alpinus in Nepal

The Endangered dhole Cuon alpinus is a medium-sized canid that was historically distributed widely across East, Central, South and Southeast Asia. In Nepal, following heavy persecution during the 1970s and 1980s, the species was locally extirpated across large parts of the country. After decades of near absence, the dhole is reportedly showing signs of recovery in various areas of Nepal. We carried out three surveys using camera traps (resulting in a total of 6,550 camera-trap days), reviewed literature and interviewed herders and conservation practitioners (40 interviews) to determine the historical and current distribution of dholes in the country, and the species’ current status. Our camera traps recorded five images of dholes, and the literature review and interview survey provided further insights into the historical and current presence of dholes in Nepal. The combined findings suggest dholes have recolonized many areas where they had been locally extirpated, such as the Annapurna Conservation Area in central Nepal and the Tinjure–Milke–Jaljale forests in the eastern part of the country. Although these returns are encouraging, challenges remain for dhole recolonization, including conflict with livestock herders, human hunting of wild ungulates affecting the species’ prey base, increasing infrastructure development in forested areas, and diseases.The Rufford Foundation, Bernd Thies Stiftung and Rural Reconstruction Nepal.https://www.cambridge.org/core/journals/oryxhj2024Zoology and EntomologySDG-15:Life on lan

Pericapsular Nerve Group Block: An Excellent Option for Analgesia for Positional Pain in Hip Fractures

Fractures in and around the hip are common presentations in the emergency department. It is commonly seen in the elderly as a result of osteoporotic changes. However, younger age groups are also affected, especially as a result of high velocity trauma. Irrespective of age, hip fractures are extremely painful, and it is difficult to position the patients for anesthesia procedures. Most of these cases are performed under subarachnoid block (SAB) or combined spinal-epidural anesthesia (CSEA), which requires the patient to be in sitting or lateral position. Here, we report a series of ten cases where pericapsular nerve group (PENG) block was administered prior to positioning the patients for SAB or CSEA. This block is a recently described regional anesthesia technique that provides excellent analgesia for hip fractures. It also provides very good analgesia for patient positioning during procedures such as SAB or CSEA

Delayed CSF rhinorrhea presenting as a lethal acute bacterial meningitis 5 years post trauma

Key clinical message Delayed presentation of cerebrospinal fluid rhinorrhea is rare following head trauma. It is frequently complicated by meningitis if not addressed in time. This report highlights the importance of its timely management, the lack of which can lead to a fatal outcome. Abstract A 33‐year‐old man presented with meningitis in septic shock. He had a history of severe traumatic brain injury 5 years back following which he had a history of intermittent nasal discharge for the past 1 year. On investigation, he was found to have Streptococcus pneumoniae meningitis, and CT scan of his head showed defects in the cribriform plate which established the diagnosis of meningoencephalitis secondary to cerebrospinal fluid rhinorrhea. The patient did not survive despite appropriate antibiotics

A Robust Chronic Kidney Disease Classifier Using Machine Learning

Clinical support systems are affected by the issue of high variance in terms of chronic disorder prognosis. This uncertainty is one of the principal causes for the demise of large populations around the world suffering from some fatal diseases such as chronic kidney disease (CKD). Due to this reason, the diagnosis of this disease is of great concern for healthcare systems. In such a case, machine learning can be used as an effective tool to reduce the randomness in clinical decision making. Conventional methods for the detection of chronic kidney disease are not always accurate because of their high degree of dependency on several sets of biological attributes. Machine learning is the process of training a machine using a vast collection of historical data for the purpose of intelligent classification. This work aims at developing a machine-learning model that can use a publicly available data to forecast the occurrence of chronic kidney disease. A set of data preprocessing steps were performed on this dataset in order to construct a generic model. This set of steps includes the appropriate imputation of missing data points, along with the balancing of data using the SMOTE algorithm and the scaling of the features. A statistical technique, namely, the chi-squared test, is used for the extraction of the least-required set of adequate and highly correlated features to the output. For the model training, a stack of supervised-learning techniques is used for the development of a robust machine-learning model. Out of all the applied learning techniques, support vector machine (SVM) and random forest (RF) achieved the lowest false-negative rates and test accuracy, equal to 99.33% and 98.67%, respectively. However, SVM achieved better results than RF did when validated with 10-fold cross-validation

Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study

Cardiovascular diseases (CVDs), principally ischemic heart disease (IHD) and stroke, are the leading cause of global mortality and a major contributor to disability. This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019. GBD, an ongoing multinational collaboration to provide comparable and consistent estimates of population health over time, used all available population-level data sources on incidence, prevalence, case fatality, mortality, and health risks to produce estimates for 204 countries and territories from 1990 to 2019. Prevalent cases of total CVD nearly doubled from 271 million (95% uncertainty interval [UI]: 257 to 285 million) in 1990 to 523 million (95% UI: 497 to 550 million) in 2019, and the number of CVD deaths steadily increased from 12.1 million (95% UI:11.4 to 12.6 million) in 1990, reaching 18.6 million (95% UI: 17.1 to 19.7 million) in 2019. The global trends for disability-adjusted life years (DALYs) and years of life lost also increased significantly, and years lived with disability doubled from 17.7 million (95% UI: 12.9 to 22.5 million) to 34.4 million (95% UI:24.9 to 43.6 million) over that period. The total number of DALYs due to IHD has risen steadily since 1990, reaching 182 million (95% UI: 170 to 194 million) DALYs, 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019, and 197 million (95% UI: 178 to 220 million) prevalent cases of IHD in 2019. The total number of DALYs due to stroke has risen steadily since 1990, reaching 143 million (95% UI: 133 to 153 million) DALYs, 6.55 million (95% UI: 6.00 to 7.02 million) deaths in the year 2019, and 101 million (95% UI: 93.2 to 111 million) prevalent cases of stroke in 2019. Cardiovascular diseases remain the leading cause of disease burden in the world. CVD burden continues its decades-long rise for almost all countries outside high-income countries, and alarmingly, the age-standardized rate of CVD has begun to rise in some locations where it was previously declining in high-income countries. There is an urgent need to focus on implementing existing cost-effective policies and interventions if the world is to meet the targets for Sustainable Development Goal 3 and achieve a 30% reduction in premature mortality due to noncommunicable diseases