Haematology profile of dogs with primary uterine inertia

Primary uterine inertia is the inherent inability of the uterus to contract and expel a fully grown foetus after the end of the gestation period, through a normal birth canal, in the absence of obstructive dystocia.There are conflicting reports regarding the blood parameters in parturient animals, and animals with dystocia and its role in uterine inertia are not well documented. Haematology analysis was performed in dogs with Complete Primary Uterine Inertia (CPUI, n=9) and Partial Primary Uterine Inertia (PPUI, n=6), as well as control animals with the Foetal Cause of Dystocia (FCD, n=7).Blood samples were collected from the study population and total leucocyte count (TLC), total erythrocyte count (TEC), differential leucocyte count (DLC), erythrocytic indices, haemoglobin and haematocrit values were estimated in an autoanalyzer. Haematology analysis revealed no significant difference between the groups. Differential leucocyte counts exhibited lymphocytosis across the groups suggestive of the active immune response related to CL lysis, a characteristic associated with the termination of the pregnancy in canines. Erythrocyte count, haemoglobin concentration and haematocrit revealed anaemia across the group. The study characterised the haematology profile of prepartum canines, which indicated anaemia associated with haemodilution and lymphocytosis associated with active immune status. It also proved that the haematology profile doesnot have any significant role in the pathogenesis of canine uterine inertia

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. Funding: Bill & Melinda Gates Foundation

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. Funding: Bill & Melinda Gates Foundation

Stochastic rate-dependent elasticity and failure of soft fibrous networks

10.1039/c2sm25450fSoft Matter8267004-7016SMOA

Bioleaching - An Alternate Uranium Ore Processing Technology for India

Meeting the feed supply of uranium fuel in the present and planned nuclear reactors calls for huge demand of uranium, which at the current rate of production, shows a mismatch. The processing methods at UCIL (DAE) needs to be modified/ changed or re-looked into because of its very suitability in near future for low-index raw materials which are either unmined or stacked around if mined. There is practically no way to process tailings with still some values. Efforts were made to utilize such resources (low-index ore of Turamdih mines, containing 0.03% U3O8) by NML in association with UCIL as a national endeavor. In this area, the R&D work showed the successful development of a bioleaching process from bench scale to lab scale columns and then finally to the India’s first ever large scale column, from the view point of harnessing such a processing technology as an alternative for the uranium industry and nuclear sector in the country. The efforts culminated into the successful operation of large scale trials at the 100kg and 2ton level column uranium bioleaching that was carried out at the site of UCIL, Jaduguda yielding a maximum recovery of 69% in 60 days. This achievement is expected to pave the way for scaling up the activity to a 100ton or even more heap bioleaching trials for realization of this technology, which needs to be carried out with the support of the nuclear sector in the country keeping in mind the national interest

Creating a Sustainable Smart Water Leakage Detection System using IoT

Water pipe spillage, especially in drinking water frameworks, could be a noteworthy sustainable issue universally, driving to the wastage of valuable assets. Recognizing the significance of water for all shapes of life, it gets to be pivotal to create sustainable arrangements to address this issue. The aim of this venture is to form an Online of Things (IoT) based framework for water spillage discovery utilizing water stream sensors and an Arduino board. The concept behind this extend is to degree the sum of water provided from the source and compare it with the amount of water gotten at the destination. In case these two measured values are rise to, it shows that there’s no water spillage within the supply pipes. Be that as it may, in the event that there’s a error between the supplied and gotten water volumes, it recommends that there’s a water spillage some place within the pipeline framework. Begin by getting the specified materials, such as water stream sensors, an Arduino board (such as Arduino Uno), jumper wires, a control supply, and other supporting elements. Interface the water stream sensors to the Arduino board utilizing jumper wires. Guarantee appropriate wiring and network to precisely the degree the water flow. Create the computer program: Utilize the Arduino IDE (Coordinates Advancement Environment) or a appropriate programming environment to type in the code for your model. The code ought to empower the Arduino board to perused sustainable information from the water stream sensors and perform the fundamental calculations to compare the provided and gotten water volumes. Conduct calibration tests to guarantee exact estimations. You'll be able do this by comparing the readings from the prototype system with known water amounts to set up a reference point. Screen the estimations and watch if any disparities between provided and gotten water volumes show potential leakage. Implement real-time monitoring and cautions (discretionary): To improve the system’s functionality, we'll be able to consolidate real-time observing and cautions. It is worth noticing that this model show serves as a confirmation of concept. Executing such a framework on a bigger scale would require contemplations such as arrange network, information transmission, and integration with a centralized observing framework

Brain Tumors Classification System Using Convolutional Recurrent Neural Network

The brain is a body organ that controls exercise of the relative multitude of parts of the body. Conceding robotized mind tumors in MRI (Magnetic Reverberation Imaging) is a confounded assignment given size and area variety. This strategy decides a wide range of malignancies in the body. Past techniques devour additional time with less accuracy. A manual assessment can be mistaken because of the degree of intricacies engaged with cerebrum tumors and their properties. However, the above proposition isn’t appropriate for mind tumors because of colossal varieties in size and shape. Our proposed strategy to magnify arrangement performance. First, the expanded tumor district using picture enlargement is utilized to return for capital invested rather than the unique tumor area since it can give hints for tumor types. Second, expanded tumor locale split into progressively refined ring structure subregions. With three-component extraction approaches, employing photographs for information augmentation and rotating photographs at various angles, evaluate the performance of the suggested strategy on a large dataset. Utilizing Convolutional Recurrent Neural Network (CRNN), grouping of the tumor into three categories and thus give a virtual portrayal of exact value

Secure and Sustainable Decentralized Cloud Using IPFS

Our paper focuses on the implementation of a decentralized cloud storage system using the IPFS (InterPlanetary File System) technology. The project aims to provide an alternative to centralized cloud storage by decentralization, enhanced security, and user control over data which is a more sustainable solution than the existing system. In a centralized cloud storage system, data is stored on a single server controlled by a service provider. This centralized approach poses potential risks such as single points of failure, data breaches, and reliance on the trustworthiness of the provider. In contrast, our decentralized cloud storage system distributes data across a network of interconnected nodes. Each node stores a portion of the data, ensuring redundancy and availability. By leveraging IPFS, which utilizes a distributed hash table and content- addressed storage, our system ensures data integrity, fault tolerance, and censorship resistance. Users of our system have greater control over their data. They can participate in the network by contributing their storage resources and accessing their files securely. The project implementation includes features such as file upload, download, retrieval based on IPFS hashes, and user authentication. It also provides functionalities like displaying connected peers, managing file metadata, and enabling file deletion. Overall, our project showcases the potential of decentralized cloud storage, exemplifying its benefits and illustrating how it can be implemented using IPFS technology

Sustainable IoT-Enabled Coma Patient Health Monitoring System

While the patient is asleep and unable to reply, vital bodily processes like breathing and blood circulation are still largely functioning. Comas can emerge as a result of injuries like head trauma or as a side effect of underlying disorders. When the patient’s recovery will occur is not predictable. These individuals require the best treatment and constant monitoring for sustainable care. In this work, patient data is continuously monitored and recorded without human intervention, contributing to a sustainable healthcare approach. Unexpected variations in the patient’s usual range of physical parameters are detected, whether they are awake and blink their eyelids or experience abrupt changes in body temperature. Additionally, the system has the capability to instantly alert a medical expert, ensuring timely intervention. The inclusion of a tilt sensor allows for the detection of even subtle movements, further enhancing the ability to provide sustained monitoring. A medical professional can continuously monitor the patient’s condition as it is displayed on an LCD screen, promoting sustainable and effective healthcare management