Solid Dispersions: A tool for improving the Solubility and Dissolution of Metronidazole

Metronidazole is a broad spectrum antibiotic. It is sparingly soluble in water but has oral bioavailability of 93-95%. So solid dispersions (SDs) containing metronidazole was prepared in different ratios (1:1, 1:2 and 1:5) and using different carriers like dextrose, citric acid, polyethylene glycol (PEG-4000) and polyvinylpyrrolidone (PVP). Fusion or melting method was used for SD containing dextrose and citric acid and Solvent evaporation method was used for SD containing PVP and PEG-4000. The solubility studies revealed that solubility of metronidazole was enhanced to manifolds. Best result was exhibited when drug carrier ratio is in the order of 1:5>1:2>1:1. Among the different carriers, the solubility and dissolution was increased to maximum in case of PVP and PEG and almost 100 % drug released within 1 hour. The development of solid dispersions was further confirmed by DSC and XRD

Solid Dispersions: A tool for improving the Solubility and Dissolution of Metronidazole

Metronidazole is a broad spectrum antibiotic. It is sparingly soluble in water but has oral bioavailability of 93-95%. So solid dispersions (SDs) containing metronidazole was prepared in different ratios (1:1, 1:2 and 1:5) and using different carriers like dextrose, citric acid, polyethylene glycol (PEG-4000) and polyvinylpyrrolidone (PVP). Fusion or melting method was used for SD containing dextrose and citric acid and Solvent evaporation method was used for SD containing PVP and PEG-4000. The solubility studies revealed that solubility of metronidazole was enhanced to manifolds. Best result was exhibited when drug carrier ratio is in the order of 1:5>1:2>1:1. Among the different carriers, the solubility and dissolution was increased to maximum in case of PVP and PEG and almost 100 % drug released within 1 hour. The development of solid dispersions was further confirmed by DSC and XRD

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

Angioleiomyoma of uterus masquerading as malignant ovarian tumor

Uterine angioleiomyoma is rare. A 40-year-old nulliparous woman presented with heavy menstrual bleeding (HMB) for the past 2 years and mass per abdomen with severe dysmenorrhea for three cycles. She had received 8 units of packed cell transfusion outside. Clinical examination revealed a huge 32-week-sized abdominopelvic mass with irregular margins. Ultrasonography (USG) of the abdomen and pelvis showed a large solid cystic mass on the right side of the abdomen with a well-defined hypoechoeic rounded lesion of size 5.7 cm × 5.0 cm, in the right lobe of the liver, with ovaries not being imaged separately. On color Doppler USG, there was moderate vascularity throughout. A provisional diagnosis of malignant ovarian tumor with hepatic metastasis was made. Her hemoglobin was 5.7 g/dl, and she had repeated episodes of HMB upon admission. She was transfused with 5 units of packed cells. Computed tomography (CT) showed a large fundal subserosal uterine fibroid on the right side, with a solid ovarian tumor measuring 5.0 cm × 4.5 cm on the left side, with ascitis, right-sided hydronephrosis, and a well-defined hypoechoeic lesion in the right lobe of the liver, suggestive of hepatic hemangioma. Tumor markers were within normal limits. In view of discrepancy in clinical findings, ultrasound, and CT report, CT-guided biopsy of the huge mass was done which revealed leiomyoma, with no evidence of mitosis, pleomorphism, or malignancy. Laparotomy with total abdominal hysterectomy and bilateral salpingo-oophorectomy was done. Histopathology revealed an angioleiomyoma uterus. At 1-year follow-up, she was asymptomatic, and the liver mass was stable

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Not AvailableThe aim of the study is to develop a new canopy temperature based index named plant stress index (PSI) as an irrigation schedule tool and to develop a relationship between soil water depletion and PSI at different threshold values for wheat crop in humid subtropical climate. The experiments were conducted in two growing seasons 2008–2009 and 2009–2010 in Roorkee, India. Irrigations were scheduled as and when PSI reached to 0.25(T2), 0.5 (T3) and 0.75 (T4). Two treatments, one fully irrigated (T1) and one fully stressed (T5) were maintained to measure the reference minimum and maximum canopy temperatures. Canopy temperatures were measured with infrared radiometers. Soil moisture contents were measured with soil moisture sensors buried at 20 cm interval up to 100 cm. An automatic weather station was installed to measure the net radiation, wind speed, relative humidity and air temperature etc. concurrently at half an hour interval on the study field. To maintain PSI below corresponding threshold values, a total irrigation amount of 280, 252, 224, 214 and 50 mm during season 2008–2009 and 275, 240, 224, 201 and 50 mm during season 2009–2010 were applied in T1,T2,T 3T4and T5 treatments, respectively. The experimental results gave an exponential relationship between percentage depletion of available soil moisture and threshold values of PSI. The results suggested that PSI may be maintained at 0.5 for getting optimum production of wheat. Corresponding to 0.5 PSI, the percentage depletion of available soil moisture was found to be approximately equal to 32 %.Not Availabl

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Not AvailableAfield experiment with four irrigation and four nitrogen treatments was carried out with wheat ( HD 2932) for two seasons, 2009-10 and 2010-11 at ICAR-Indian Agricultural Research Institute, New Delhi to quantify the water and nitrogen use efficiencies under differential water and nitrogen levels. Responses of water-use efficiency (WUE), nitrogen-use efficiency (NUE) and wheat yield to variable water and nitrogen levels were investigated. Yield and NUE are significantly affected ( = 0.05) by the different water and nitrogen levels. Under low water level condition along with no nitrogen stress resulted in only 5% decrease in yield compared to the highest yield but it had increased NUE from 34 to 38%. Therefore, low water stress can be allowed which can result in higher NUE and non-significant decrease in yield. The study revealed that the lower water stress resulted in higher NUE, however, higher water stress resulted in decline in NUE.Not Availabl

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Not AvailableField experiments were conducted with four levels of irrigation and nitrogen on wheat for 2 years (2009–2010 and 2010–2011) to quantify and predict the crop water status using hyperspectral remote sensing. Hyperspectral reflectance in 350–2500 nm range was recorded at five growth stages. Based on highest correlation between relative leaf water content (RLWC) and reflectance in five water bands, the booting stage was identified as the most suitable stage for water stress evaluation. Ten hyperspectral water indices were calculated using the first year booting stage reflectance data and prediction models for RLWC and equivalent water thickness (EWT) based on these ten indices were developed. The prediction models for RLWC based on moisture stress index (MSI), normalized difference infrared index (NDII), normalized difference water index 1640 (NDWI) and normalized multiband drought index (NMDI) were identified as the most precise and accurate models as indicated by different validation statistics. The models developed for EWT based on water band index1640 (WBI), MSI, NDWI1640and NMDI were found to be most suitable and accurate. These indices were found to be insensitive to N stress treatments indicating their ability to detect water deficiency as the cause of plant stress. Thus, the study identified four hyperspectral water indices to assess the wheat crop water status at booting stage and developed their respective predictive models.Not Availabl