Clinical profile of patients with inferolateral myocardial infarction: a comparison study

Background: Patient with lateral wall changes in inferior wall myocardial infarction (MI) usually ignored most often. However above lateral MI changes in the clinical setting of inferior wall MI usually impact on final clinical outcome and prognosis of the patients. Our aim of the study is observe diversity in clinical profile of inferolateral MI in comparison to patient with inferior wall without any lateral wall MI changes.Methods: Current study enrolled 405 patients admitted to cardiology emergency with inferior wall MI. Patients divided into three groups on the basis of ECG changes, inferolateral (group A), isolated inferior wall MI (group B) and inferior wall MI with RVMI (group C). Patients with RVMI (group C) excluded from the group. Clinical profile and outcomes compared between group A and B.Results: Around 33.8% pts with inferior wall STEMI have ECG changes of lateral wall AMI. STEMI equivalent in lead V1, V2 which is the most common ECG presentation, seen in 65% inferolateral patients. 45.7% of population of inferolateral MI are above age group of 60 years. 66.2 % of the patients with inferolateral MI having two or more risk factors. Dyspnoea is one of common symptom seen in 29.9% patients. Statistically significant number of patients in inferlateral MI have clinical picture of LVF (crepitation, S3), higher Killip class as compared to inferior wall MI. Incidence of complications like LVF, MR, VT, death is significantly higher in this group.Conclusions: Around 1/3rd patient with lateral MI changes seen in inferior wall MI patients. STEMI equivalent changes in anterior precordial leads are most common ECG presentation. More no patients are older and with multiple risk factors. Complication and death are higher in this group. So meticulous attention should be given to patients with lateral wall MI changes in inferior wall MI.

ISOLATION OF CUCURBITACIN-B FROM CUCUMIS CALLOSUS AND ITS HYPOGLYCEMIC EFFECT IN ISOLATED RAT ENTEROCYTES

Objective: The pericarp of fruits of Cucumis callous (Rottl.) Cogn. (Cucurbitaceae) is traditionally used for curing diabetes, epilepsy, and diarrhea. It has an active compound include Cucurbitacin-B (CuB), which acts as a potent inducer of CYP450 of rat enterocytes. This study was conducted with the aim of elaborating and reconciling our previous finding on the glucose-lowering effect of Cucumis callosus (Rottl.) Cogn. fruits.Methods: In vivo hypoglycemic potential for methanolic pericarp extracts from C callosus (MPCC, 350 mg/kg b.w. p. o), methanolic seed extract of C callosus (MSCC, 250 mg/kg b.w. p. o) and CuB (80 µg/kg b.w. p. o) were studied in streptozotocin (STZ, 55 mg/kg b.w. i. p) induced diabetic rats. Metformin (25 mg/kg b.w. p. o) served as reference drug. Ex vivo model of intestinal tissue preparation of Swiss albino rats named Single Pass Intestinal Perfusion (SPIP) technique was performed for ex vivo hypoglycemic study. The glucose levels in the serosal fluid were determined by commercially available glucose oxidase kit and compared with the standard drug metformin (0.1 mg/kg).Results: In vivo results showed that administration of MPCC (350 mg/kg b.w. p. o) and Cucurbitacin-B (80 µg/kg b.w. p. o) produced the hypoglycemic effect. The MPCC (1.4 mg/kg) and CuB (0.4 µg/kg) produced hypoglycemic effect in ex vivo technique. These effects are due to induction of 0.53 mµmoles of CYP450 proteins with maximum absorption at 454 mµ in rat enterocytes.Conclusion: The present investigation gave evidence that bitter pericarp of C callosus fruit has a hypoglycemic effect due to the presence of Cucurbitacin B as phytoconstituent but seeds did not have such effects

EVALUATION OF ANTICANCER ACTIVITY OF CUCUMIS CALLOSUS AGAINST EHRLICH'S ASCITES CARCINOMA BEARING MICE

Objective: Our previous research isolated Cucurbitacin B (CuB) and ebenone leucopentaacetate (ELP) from methanolic fruit extract of Cucumis callosus (MFCC). The fruits of C. callosus (Rottl.) Cogn. (Family: Cucurbitaceae) plant have been traditionally used for antioxidant, anti-inflammatory, and antidiabetic actions. The objective of this research was to evaluate in vitro and in vivo anticancer effect of MFCC on Ehrlich Ascites Carcinoma (EAC) cell lines.Methods: In vitro anticancer assay of MFCC and standard drug, 5-fluorouracil (5-FU) was evaluated using Trypan blue and 3-(4, 5-dimethylthiazol-yl)-2, 5-diphenyl tetrazolium bromide methods. In vivo anticancer activity of MFCC and 5-FU was also performed after 24h of EAC cells (2×106cells/ mouse) inoculation based on toxicity study for 9 consecutive days. The activity of the extract was assessed by the study of tumor volume, tumor weight, viable and non-viable cell count, hematological parameters, and biochemical estimations.Results: The MFCC showed the direct antitumor effect on EAC cells in a dose-dependent manner with an IG50 value of 0.61 mg/ml. Furthermore, MFCC (350 mg/kg) exhibited significant (p<0.01) decrease in tumor volume, tumor weight, and viable cell count of EAC-treated mice. Hematological profile, biochemical estimation assay significantly (p<0.01) reverted to normal level in MFCC, and 5-FU treated mice.Conclusion: The anticancer activity of fruits of C callosus is may be either due to the presence of CuB or/and ELP as phytoconstituent and the activity is comparable to standard drug 5-FU

Machine Learning-based Linear regression way to deal with making data science model for checking the sufficiency of night curfew in Maharashtra, India

The birthplace of the novel Covid-19 sickness or COVID-19 began its spread around Wuhan city, China. The spread of this novel infection sickness began toward the start of December 2019. The Covid-19 illness spreads from one individual to another through hacking, sniffling, etc. To stop the spreading of the novel Covid-19 infection the distinctive nation has presented diverse strategies. Some regularly utilized methods are lockdown, night curfew, etc. The fundamental intention of the systems was to stop the social events and leaving homes without serious issues. Utilizing a diverse system Covid-19 first stage can address for saving individuals. Presently the second influx of this novel Covid illness has begun its top from the mid of April-May. The second convergence of this novel Covid disorder flooded all through the world and in India too. To stop the spread of this novel Covid sickness India's richest state Maharashtra government constrained the decision of night curfew. In this paper, we are taking as a relevant examination the night curfew on a schedule of Maharashtra. Here, we study that this system may or may not be able to stop the spread of pandemics. We are using the Machine learning(ML) approach to managing regulate study this case. ML has various systems yet among all of those here we use Linear Regression for the current circumstance. The reproduced insight that readies the plan orchestrated to learn with no other person. Linear Regression is the affirmed strategy for looking over the connection between two sections. Between the two segments, one is astute and another is a seen variable

Relativistic coupled-cluster-based linear response theory for ionization potentials of alkali-metal and alkaline-earth-metal atoms

We have developed and applied the relativistic coupled-cluster-based linear response theory (RCCLRT) for computing the principal as well as the shake-up ionization potentials (IP's) of Li, Be, Na, and Mg where the single-particle orbitals are generated by solving the relativistic Hartree-Fock-Roothaan equations using the Gaussian basis functions on a grid. The computed principal and shake-up ionization energies by the RCCLRT approach are in favorable agreement with the experimental results. Since for the (one-valence) IP problem, there is a formal equivalence between the principal IP values as obtained from the CCLRT and those obtained as eigenvalues of the multireference coupled-cluster theory, the computed quantities are fully size extensive. The approach via the RCCLRT has the additional advantage of providing the shake-up IP's as well. These are, however, not fully size extensive, but the error scales as the number of valence excitations (2h-1p), so the inextensivity error is rather small

An enolato-bridged dinuclear Cu(II) complex with a coumarin-assisted precursor: a spectral, magnetic and biological study

Coumarin assisted dinuclear [Cu2(L)2(DMF)2] (1) shows weak ferromagnetic interaction and a geometric distortion occurs while lowering the temperature in EPR

Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019.

The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.Funding/Support: The Institute for Health Metrics and Evaluation received funding from the Bill & Melinda Gates Foundation and the American Lebanese Syrian Associated Charities. Dr Aljunid acknowledges the Department of Health Policy and Management of Kuwait University and the International Centre for Casemix and Clinical Coding, National University of Malaysia for the approval and support to participate in this research project. Dr Bhaskar acknowledges institutional support from the NSW Ministry of Health and NSW Health Pathology. Dr Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, which is funded by the German Federal Ministry of Education and Research. Dr Braithwaite acknowledges funding from the National Institutes of Health/ National Cancer Institute. Dr Conde acknowledges financial support from the European Research Council ERC Starting Grant agreement No 848325. Dr Costa acknowledges her grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia, IP under the Norma Transitória grant DL57/2016/CP1334/CT0006. Dr Ghith acknowledges support from a grant from Novo Nordisk Foundation (NNF16OC0021856). Dr Glasbey is supported by a National Institute of Health Research Doctoral Research Fellowship. Dr Vivek Kumar Gupta acknowledges funding support from National Health and Medical Research Council Australia. Dr Haque thanks Jazan University, Saudi Arabia for providing access to the Saudi Digital Library for this research study. Drs Herteliu, Pana, and Ausloos are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Dr Hugo received support from the Higher Education Improvement Coordination of the Brazilian Ministry of Education for a sabbatical period at the Institute for Health Metrics and Evaluation, between September 2019 and August 2020. Dr Sheikh Mohammed Shariful Islam acknowledges funding by a National Heart Foundation of Australia Fellowship and National Health and Medical Research Council Emerging Leadership Fellowship. Dr Jakovljevic acknowledges support through grant OI 175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Dr Katikireddi acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). Dr Md Nuruzzaman Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Dr Yun Jin Kim was supported by the Research Management Centre, Xiamen University Malaysia (XMUMRF/2020-C6/ITCM/0004). Dr Koulmane Laxminarayana acknowledges institutional support from Manipal Academy of Higher Education. Dr Landires is a member of the Sistema Nacional de Investigación, which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación. Dr Loureiro was supported by national funds through Fundação para a Ciência e Tecnologia under the Scientific Employment Stimulus–Institutional Call (CEECINST/00049/2018). Dr Molokhia is supported by the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’ National Health Service Foundation Trust and King’s College London. Dr Moosavi appreciates NIGEB's support. Dr Pati acknowledges support from the SIAN Institute, Association for Biodiversity Conservation & Research. Dr Rakovac acknowledges a grant from the government of the Russian Federation in the context of World Health Organization Noncommunicable Diseases Office. Dr Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. Dr Sheikh acknowledges support from Health Data Research UK. Drs Adithi Shetty and Unnikrishnan acknowledge support given by Kasturba Medical College, Mangalore, Manipal Academy of Higher Education. Dr Pavanchand H. Shetty acknowledges Manipal Academy of Higher Education for their research support. Dr Diego Augusto Santos Silva was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil Finance Code 001 and is supported in part by CNPq (302028/2018-8). Dr Zhu acknowledges the Cancer Prevention and Research Institute of Texas grant RP210042

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

<span style="font-size:15.0pt;mso-bidi-font-size:12.0pt;mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-GB;mso-fareast-language:EN-US;mso-bidi-language:HI" lang="EN-GB">Pyrene-appended Schiff base as a turn-on fluorescence sensor for Al³⁺ detection and interaction with DNA</span>

1446-1450Pyrene appended Schiff base, 1,3-bis-((E)-pyrene-1-ylmethyleneamino)propan-2-ol (HL) fluoresces at 419 nm and 508 nm in DMF solution upon excitation at 344 nm. The addition of Al3+ results in a significant 100 fold increase of its fluorescent intensity with the emission maximum red shifted from 421 nm to 508 nm with intense green emission. This phenomenon may be attributed to the formation of a 1:2 stoichiometric [Al2Cl5(L)] complex, which has been supported by Jobs, NMR titration and mass spectral data. The complexation inhibits the photo-induced electron transfer process and the cheletropic enhancement of fluorescence mechanism is responsible for the turn-on fluorescence sensitivity. The limit of detection of Al3+ (7.42×10-8 M) is the second lowest in the series of pyrene functionalized chemosensor. Both HL and its Al3+ complex, [Al2Cl5(L)], show DNA stacking interaction (binding constant Kb 9.8×104 M-1 (of HL) and 3.8×104 M-1 (of [Al2Cl5(L)]).</span

Ionization potentials of beryllium-like ions from the relativistic coupled-cluster-based linear response theory

We first test our recently developed relativistic coupled-cluster-based linear response theory (RCCLRT) by computing the principal and the shake-up ionization potentials (IP) of highly stripped atoms. Comparison is made between the IP values obtained from the relativistic and non-relativistic CCLRT calculations. The comparisons for light atoms, where the relativistic contributions are negligible, provide a test of the method, while comparisons for heavy atoms quantify the relativistic effect. For one-valence problems such as ionization processes, there is a formal equivalence between the principal IP values from the CCLRT and multi-reference coupled cluster (MRCC) theory when using the 1h-0p model space. The principal IPs obtained from the CCLRT are size-extensive, but the (2h-1p) shake-up IPs are not fully size-extensive, where the size-inextensivity error scales as the number of valence excitations. The computed valence and shake-up ionization energies via the RCCLRT approach are in excellent agreement with experimental data. The IPs computed using the non-relativistic CCLRT also match favourably well with the experiment only when the atomic number and ionic charge are small